The Minnesota Integrated
Services Project:
Final Report on an Initiative to
Improve Outcomes for
Hard-to-Employ Welfare Recipients
Karin Martinson
K ARIN M ARTINSON
C AROLINE R ATCLIFFE
K ATIE V INOPAL
J OANNA P ARNES
The Urban Institute
Center on Labor, Human
Services, and Population
January 2009
The Minnesota Integrated Services Project:
Final Report on an Initiative to Improve Outcomes for
Hard-to-Employ Welfare Recipients
Karin Martinson
Karin Martinson
Caroline Ratcliffe
Katie Vinopal
Joanna Parnes
The Urban Institute
January 2009
The Urban Institute
2100 M Street, NW
Washington, DC 20037
This report was prepared for the Minnesota Department of Human Services under
Contract Number A72785. The views expressed are those of the authors and should not
be attributed to Minnesota DHS or to the Urban Institute, its trustees, or its funders.
Acknowledgements
The evaluation of the Minnesota Integrated Services Project (ISP) was conducted by the
Urban Institute under contract to the Minnesota Department of Human Services (DHS).
The evaluation is funded by the McKnight Foundation and DHS. The authors are
especially grateful to the many people involved with the Minnesota ISP who took time to
meet with us and help us learn about their programs. We also would like thank Leslie
Crichton, Jane Delage, Marti Fischbach, Mark Kleczewski, and Vania Meyer from the
Minnesota Department of Human Services for reviewing earlier drafts and providing
comments.
Table of Contents
Acknowledgements
Executive Summary................................................................................................. ES-1
Chapter I: Introduction...........................................................................................1
Policy and Program Context.......................................................................... 1
Defining Service Integration.......................................................................... 3
Project Goals and Sites.................................................................................. 4
The ISP Evaluation and Data Sources........................................................... 9
Chapter II: The ISP Programs: Service Strategies and Participant
Characteristics......................................................................................................... 12
Overview of ISP Program Models: What Type of Service Integration Was
Achieved?...................................................................................................... 12
Description of the ISP Programs....................................................................15
Anoka County: Partners for Family Success..................................... 15
Chisago: Chisago County Integrated Services Project.......................23
Crow Wing County: Crow Wing County
Integrated Services Project. .................. ...................................26
Hennepin County: Gateway to Success............................................. 30
Ramsey County: Integrated Services Project.....................................34
Red Lake: Mino Aanokii................................................................... 38
St. Louis County: Project Hope......................................................... 40
Washington County: Integrated Services Project.............................. 44
Chapter III: Change in ISP Participants’ Employment, Earnings, Government
Benefits Receipt, and Employability Measure Scores.......................................... 48
Employment and Earnings............................................................................. 48
MFIP Receipt and Benefit Levels.................................................................. 58
Supplemental Security Income Receipt......................................................... 65
Summary of Patterns of Employment, Earnings, MFIP, and SSI.................. 67
ISP Participant Scores on the Employability Measure.................................. 70
Chapter IV: Summary of ISP Participants Outcomes for Each Site.................. 76
Chapter V: Discussion of Key Findings and Policy Implications...................... 80
References................................................................................................................. 83
Appendix A: Employability Measure.................................................................... A-1
Appendix B: Supplemental Tables........................................................................ B-1
Appendix C: Regression Approach........................................................................C-1
Exhibits and Tables
Executive Summary
Exhibit ES-1: Minnesota Integrated Services Projects.............................................. ES-6
Chapter I
Exhibit 1-1: Location of the Minnesota Integrated Services Projects....................... 6
Table 1-1: Economic and Demographic Profile of the Minnesota
Counties in which the Integrated Services Project Sites are Located............ 7
Chapter II
Exhibit 2-1: Minnesota Integrated Services Projects................................................. 13
Table 2-1: Characteristics of ISP Participants at Enrollment, by Site....................... 16
Table 2-2: Types of Services and Assistance Received by ISP Participants Who
Completed Initial Assessment within a Six-Month
Follow-Up Period, by Site........................... ........................... ......................18
Chapter III
Exhibit 3-1: Average Quarterly Employment Rate and Earnings
Amount in the Two Years Before and After Enrollment............................... 50
Table 3-1: Changes in Employment, Earnings, and MFIP Receipt Over Time........ 56
Exhibit 3-2: Average Monthly MFIP Benefit Receipt and Benefit
Amount in the Two Years Before and After Enrollment............................... 59
Table 3-2: ISP Participants' Supplemental Security Income..................................... 66
Table 3-3: Changes in Employability Measure Scores in the 6 and 12
Month Follow-Up Periods............................................. ................................72
Appendix B
Table B-1: Demographic Characteristics of ISP Participants
at Enrollment, by Site…………………........................................................ B-2
Table B-2: Educational Attainment and Prevalence of Learning
Disabilities for ISP Participants at Enrollment, by Site................................. B-3
Table B-3: Prevalence of Physical and Mental Health, Substance
Abuse, and Domestic Violence Barriers for ISP Participants
at Enrollment, by Site.................................................................................... B-4
Table B-4: Criminal History, Housing Situations, Modes of
Transportation, and Prevalence of Multiple Barriers for
ISP Participants at Enrollment, by Site.......................................................... B-5
Table B-5: Changes in Employment, Earnings, and MFIP Receipt
Over Time in Anoka...................................................................................... B-6
Table B-6: ISP Participants' Supplemental Security Income in Anoka.................... B-7
Appendix C
Table C-1: Logit Regression of ISP Participants'
Employment-Regression Coefficients……………………………………... C-5
Table C-2: Logit Regression of ISP Participants’ Employment-Odds Ratios…….. C-7
Table C-3: OLS Regression of ISP Participants' Earnings………………………... C-9
Table C-4: Logit Regression of ISP Participants' MFIP
Receipt-Regression Coefficients……………………………………………C-11
Table C-5: Logit Regression of ISP Participants’ MFIP Receipt-Odds Ratios…… C-15
Table C-6: OLS Regression of ISP Participants' MFIP Benefit Amount…………. C-19
EXECUTIVE SUMMARY
This report presents the final results for the evaluation of the Minnesota Integrated Services
Projects (ISP) operating in eight sites across the state. Conceived by the Minnesota Department
of Human Services (DHS) and enacted in 2005, the ISP seeks to address the needs of long-term
cash assistance recipients in the Minnesota Family Investment Program (MFIP), many of whom
are in danger of reaching their time limit on cash assistance benefits. The project was designed to
address the serious and complicated barriers these families face by requiring a more coordinated
response from the human service system. To this end, DHS provided grants to eight sites to
address the multiple needs of long-term MFIP recipients. ISP aims to improve both economic
and family-related outcomes for this population by increasing access to more comprehensive
services that address multiple needs, coordinating services provided by multiple service systems,
and focusing on the needs of both adults and children in the household.
The primary goals of this report are to (1) describe the changes in the economic outcomes and
family-related outcomes of ISP participants in each site over a two-year follow-up period, (2)
provide estimates of the relationship between ISP participation and participants’ employment
and MFIP outcomes, controlling for county economic conditions and participant characteristics,
and (3) provide conclusions and policy recommendations based on the experiences of the ISPs.
This report also summarizes findings from the implementation study on the design and operation
of the ISP models from previous evaluation reports.
The Evaluation of the Minnesota Integrated Services Projects
MFIP is Minnesota’s Temporary Assistance to Needy Families (TANF) program and, like most
TANF programs across the country, it requires all cash assistance recipients to work or
participate in employment-related services or risk financial penalties (known as sanctions). MFIP
also establishes a lifetime limit on the receipt of cash benefits of 60 months, with some
extensions allowed, and permits recipients to keep some of their benefits when they go to work,
until their adjusted income reaches 115 percent of the federal poverty level. While the MFIP
program has experienced considerable success in moving some individuals off welfare and into
work, concerns have grown over how to meet the needs of those who remain on cash assistance
for long periods of time, many of whom face significant barriers to employment. Relatively few
strategies targeted at hard-to-employ welfare recipients have been evaluated, with families
continuing to fare poorly over time even in the most successful efforts.
Policy and Program Context
The Minnesota Integrated Services Project is designed to address the multi-faceted needs of
long-term MFIP recipients. In developing the project, state administrators recognized that a
significant portion of this population receive diagnosis or services through other public systems,
including mental health, chemical dependency, disability, child protection, domestic violence,
and services for children with special needs. This reflects that the current delivery systems
providing support to those with significant challenges are generally organized around singleissue expertise, with limited communication or coordination across systems. Moreover, many of
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these barriers were not diagnosed or identified until individuals were close to meeting their time
limits on cash assistance and little time was available to provide needed assistance.
To encourage a more coordinated response from the human services system to address the
serious and complicated barriers, DHS provided grants to address the multiple needs of longterm MFIP recipients. DHS established four primary goals of the ISPs: (1) identify employment
barriers earlier in the family’s time on cash assistance; (2) work with both adults and children in
each family; (3) fundamentally change the way services are delivered so they are provided in an
accessible, integrated, and cost-effective manner; and (4) identify policy and system issues that
interfere with the delivery of services to the adults and children in these families.
Eight sites representing diverse locations across the state were selected for the ISP: Anoka
County, Chisago County, Crow Wing County, Hennepin County, Ramsey County, the Red Lake
Band of Chippewa Indians, St. Louis County, and Washington County. The Chisago and St.
Louis projects are regional in nature and include several surrounding counties. Each site received
funding to operate their program for three years, although in the last year of the project resources
were provided to extend the ISPs an additional year.
Defining Service Integration
To understand the nature of the service integration that occurred in the Minnesota ISPs, it is
useful to discuss the meaning of “service integration.” The desire to simplify and streamline
client processes through service integration is often cited as a solution to the wide range of
uncoordinated programs that exist at the local level. Over the years, the terms “integration” and
“coordination” as well as “collaboration” and “linkages” have often been used interchangeably
and with varying connotations and meanings. It is generally recognized that there is no single
definition of service integration. While the coordination of service delivery systems usually takes
place at the local level, studies have shown that a initiative to coordinate may either be locally
developed (“bottom-up” coordination) or may be encouraged or imposed by federal or state
officials (“top-down” coordination). Studies also recognize a distinction between administrative
service integration strategies with more ambitious goals focused on reforming the delivery
system and more modest operational strategies focused on linking clients to existing services,
without altering the program funding process, service agency responsibility, or organizational
structures.
When developing the ISPs, DHS did not provide a specific definition of “service integration.”
While certain partners were mandated (county human services agency, a managed health care
plan, and a community-based health clinic), ISP sites were given significant discretion in
determining how to structure and operate their service integration models. This approach builds
on the county–administered welfare system in Minnesota, where counties are given significant
latitude in designing a range of programs.
Methodology
The ISP evaluation, funded by the McKnight Foundation and DHS, is a multicomponent study
employing a range of research strategies and data sources. The evaluation includes an
implementation study; a study of participants’ employment, welfare, and family-related
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outcomes based on administrative data; and a regression analysis examining the relationship
between the interventions and participants’ employment, earnings, and welfare receipt.
The report studies individuals who enrolled in the program April 2005 through June 2006, a total
of 987 participants across all sites. For each of the eight sites, with administrative data provided
by DHS, we analyze individual’s monthly MFIP benefits and their quarterly earnings in jobs
covered by the Minnesota unemployment insurance (UI) program over a two-year preenrollment and a two-year post-enrollment period.
The study also examines data from the Employability Measure, an instrument developed by DHS
for program staff to assess and track participant status in 11 areas related to economic success:
child behavior, dependent care, education, financial, health, housing, legal, personal skills, safe
living environment, social support, and transportation. This Employability Measure instrument
was designed to be administered by program staff at ISP enrollment as well as every six months
following enrollment, so that changes over time could be measured. However, the follow-up
Employability Measures were not consistently administered across the sites.
Other data sources include participants’ self-reported responses at ISP enrollment to a number of
questions regarding specific barriers, including mental health, chemical dependency, learning
disabilities, and criminal history. The implementation study is based on site visits and phone
interviews by Urban Institute staff with program staff, a review of a sample of ISP case files in
each site to document the types and levels of services received by ISP participants within a 6 to
18 month follow-up period, and focus groups with ISP participants in four sites (Anoka,
Hennepin, Ramsey, and Washington).
Implementation of the Integrated Services Projects: Target Group,
Program Design, and Services
This section summarizes key findings from the ISP implementation study describing each of the
ISPs, target group and participant characteristics, and program design and services. See
Martinson et al. (2007) for more detail on participant characteristics and program design and
services.
Target Group and Participant Characteristics
•
Most of the ISPs established a broad target group of individuals on MFIP who have
significant barriers to employment without specifically defining the type, number,
or severity of barriers. A few sites target a more narrow population.
Because of the nature of the eligibility criteria, there is significant discretion in determining who
is eligible for and referred to the ISP. The most common method the sites use to identify
appropriate families for ISP is direct referrals from MFIP, with MFIP staff making referrals
within relatively broad parameters. Some ISPs also receive referrals from other agencies,
although this is less common. The program in Ramsey is unique in that it specifically targets
individuals with mental illness. In addition to serving a broader range of families, Anoka targets
ES-3
individuals who are likely to be eligible for the Supplemental Security Income (SSI) program, a
federal income supplement program for the disabled.
•
ISP participants have a wide range of employment-related barriers, with mental
health barriers the most prominent in most sites. Anoka and Ramsey counties serve
populations with higher incidence of barriers, particularly mental and physical
health problems.
At the time of ISP enrollment, more than one-third of ISP participants in most sites reported that
they had a mental health condition that made it hard to work. A smaller but still substantial
segment of ISP participants reported barriers related to physical health, substance abuse,
learning disabilities, and domestic violence issues. The ISP population also exhibited a high
prevalence of multiple barriers to employment, with over 50 percent reporting two or more
barriers in most sites.
There are differences across the sites in terms of the types of barriers faced by ISP participants,
with those in Anoka and Ramsey facing the most significant barriers. Over 60 percent of ISP
participants in Anoka, and over 80 percent in Ramsey, reported a mental health condition that
made it difficult to work and an even greater proportion said they had ever been diagnosed with
depression. In these two sites, approximately 40 percent of the ISP participants had a physical
condition that made it difficult to work and over 70 percent had two or more barriers to
employment.
Program Design
•
The ISPs are relatively small and in most sites served those who volunteered to
participate in the program.
Programs in the ISP are small by design, with target enrollments of 100 to 200 participants, and
a small staff, ranging from 5 to 13 individuals. Once individuals are referred to the program,
they are contacted by ISP staff who inform them of services available through the ISP. Referred
individuals do not become participants in the program until they agree to enroll and complete an
initial assessment documenting the circumstances and barriers the individual and their family
face. With the exception of Crow Wing, the ISPs complement but are separate from the standard
MFIP program in each county. For the most part, individuals volunteered to participate and
were not sanctioned for non-compliance with ISP, except in Crow Wing where ISP was not
separate from the MFIP program and in Washington, where individuals were mandated to
participate once they volunteered.
•
All programs established partnerships with other organizations to serve ISP
participants, although they varied in terms of the closeness of the collaboration.
Most do not have connections with a wide number of partners from other service
delivery systems. Linkages with experts in mental health, child protection, substance
abuse, and public health are most common.
A brief description of each ISP is provided in Exhibit ES-1. County social service agencies play an
important role in several programs and serve as the lead operational agency in two sites. However,
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in five programs (Chisago, Hennepin, Ramsey, St. Louis, and Washington), the lead organization is
a nonprofit community-based organization. These organizations bring a range of expertise to the
program, and some have experience as MFIP employment service providers. Reflecting the need to
address the prevalence of mental health barriers, Anoka, Hennepin, Ramsey, and Washington
established partnerships that provide expertise in this area. Anoka, Crow Wing, and Washington
involve partners to assist with child protection services; Anoka, Crow Wing, and Hennepin include a
public health component; and Crow Wing and Hennepin have partnerships to provide expertise in
chemical dependency issues. While all the ISPs partner with a managed health care plan as required,
in most programs, these organizations did not play a significant role.
Overall, the ISPs generally limited the number of partners from other service delivery systems that
they established formal connections with and focused on establishing linkages with a few key
organizations. With some exceptions, most programs did not expand the number of partners involved
in their programs as they matured. Hennepin is notable for making more significant changes to its
initial program design by adding institutional partners with expertise in specific substantive areas. In
the other sites, several partnerships did not work out as intended, with some partners less involved in
the ISP than originally planned. Of all the sites, the program in Red Lake had the most trouble getting
off the ground and implementing its model as intended. Primarily because of changes in leadership
and shifting priorities, key partnerships were never established, and the level of services provided to
participants is relatively low compared to the other sites.
•
Three sites use a team approach that involves bringing staff with expertise in
different areas to provide services to ISP participants, with all staff housed at the
same physical location.
Anoka, Crow Wing, and Hennepin use a team-based approach where participants may work
with different staff or more than one staff person depending on their needs and the issues they
are facing. However, within this single approach, there were different models. Anoka brought
together a multidisciplinary team with staff from different divisions within the county human
services division to provide expertise in a range of areas including juvenile and criminal justice,
employment and vocational rehabilitation, public health, child protection, mental health,
chemical dependency, and housing. The program also includes an emphasis on applying for SSI,
with about half of the participants working exclusively on this issue with a dedicated staff
person.
Hennepin County contracts with other organizations and uses staff within the organization to
provide a team of colocated staff with expertise in employment, mental health, substance abuse,
domestic violence, public health, and youth issues. This program is also located at a community
health clinic that provides many resources, including a food bank and an on-site medical, dental,
and mental health clinic. Finally, the program in Crow Wing enhances the current MFIP
program by incorporating joint supervision of program staff from the child protection division
and the inclusion of a chemical dependency supervisor for case consultation, from within the
Department of Human Services, and a public health nurse.
ES-5
Exhibit ES-1
Minnesota Integrated Services Projects
Team-based approach
Anoka. This, project, housed in the Anoka County Human Services Division, features a multidisciplinary service team that provides
intensive case management and service coordination for participants. Staff specialize in specific areas, including juvenile and
criminal justice, employment and vocational rehabilitation, public health, child protection, mental health, chemical dependency, and
housing, and participants are assigned to a case manager based on the barriers they are facing. A staff disability advocate assists
participants with the SSI application process. Whenever possible, services for the family are provided in-house by the project team,
though team members also connect with other professionals involved with the family.
Crow Wing. Operated by Crow Wing County Social Services (CWCSS), this project builds on a segment of the county’s existing
MFIP program that targets hard-to-employ MFIP recipients, the Tier 3 program. MFIP recipients who have not found a job through
the county’s standard MFIP program are transferred to a MFIP outreach specialist at CWCSS who provides case management
services and referrals to appropriate community resources. Supervisors from Child Protection Services (CPS) and Chemical
Dependency divisions at CWCSS provide guidance and enhance coordination of services. An ISP specialist works with chemically
dependent mothers, and a nurse from the Crow Wing Public Health Agency is also available to provide services as needed and
participate in monthly staff meetings. The program also features an 8-week support group.
Hennepin. Sponsored by NorthPoint Health and Wellness Center, Inc., a community-based health and human services agency, the
core service of this program is one-on-one case management provided by Family Facilitators employed by NorthPoint as well as
several MFIP employment service providers. Family Facilitators connect participants and their families with services in the
community that address employment and other barriers. Staff from African American Family Services and Turning Point, two
community-based social service agencies, assist participants with chemical dependency and domestic violence issues, and an onsite
psychologist provides mental health assessments and counseling. Monthly support groups are a primary program activity.
Service brokering approach
Chisago. This program operates in a five-county region and is managed by Communities Investing in Families, a nonprofit
organization with experience working with low-income families. Family advocates work one on one with participants to address
barriers, refer them to additional resources in the community, and coordinate with MFIP employment counselors and other service
providers who work with their clients.
Red Lake. This project is operated by the Tribal Council of the Red Lake Band of Chippewa Indians. Through multidisciplinary
case management, community workers link hard-to-employ MFIP recipients with appropriate services and programs on the
reservation, such as GED courses. The program also provides transportation assistance and instruction in traditional work activities
for clients, such as wreath-making, beading, and gardening.
St. Louis. This project is operated in four counties by a set of community action agencies and the Minnesota Chippewa Tribe.
Family employment advocates assess the needs of families and work with them one on one to help connect them with appropriate
community resources to address a range of issues, including transportation, housing, substance abuse, child care, child support,
probation, education, mental health, physical health, and domestic violence. Circles of Support, where participants are matched
with “community allies” to provide support, is a key program component.
Washington. Operated by HIRED, a nonprofit organization that provides MFIP employment services, this project aims to reduce
the likelihood that residents will relocate and assist those who have relocated to reestablish services in Washington County. Its
larger goal is to facilitate case coordination across systems for families involved with multiple service providers. Integrated
Services coordinators complete an in-depth assessment, make individualized referrals to a wide range of services, and communicate
with other professionals involved with the family to better coordinate services. The program has established a working relationship
with the county’s community mental health clinic to provide access to psychological evaluations and mental health services for
clients.
Single service approach
Ramsey. This initiative integrates mental health rehabilitation expertise into the county MFIP employment services program, while
accessing new funding outside the regular MFIP allocation. The ISP provides financial support to several providers to meet
capacity and certification standards to provide services under Adult Rehabilitative Mental Health Services (ARMHS). ARMHS aim
to help individuals with serious mental illness improve functionality, and services are billed directly to Medical Assistance
(Minnesota’s Medicaid program). Each agency has flexibility to provide ARMHS services or partner with an agency that provides
ARMHS services.
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•
The other ISPs primarily rely on the efforts of case managers in bringing
services together for participants.
Known as a service brokering approach, under this model, program staff are responsible
for referring participants to other agencies in the community and coordinating these
services based on the individual needs of participants. While used to some extent by sites
using the team approach, this was the primary method for coordinating services in
Chisago, Red Lake, St. Louis, and Washington. Under this approach, primarily through
the efforts of line staff, the ISPs coordinate a wide range of services that address the
multiple needs of individuals on their caseload including physical or mental health,
substance abuse, housing, special needs of children and domestic violence. ISP staff may
draw on the expertise of organizational partners, but participants are referred to these
organizations to receive assistance rather than having on-site staff person provide the
services.
A single service approach, unique to Ramsey, focuses on providing in-depth assistance
in one service area—mental health. This program systemically brings rehabilitation
expertise in mental health, with a focus on improving daily life skills and functionality,
into the county MFIP but by design does not generally address the other service needs of
participants.
ISP Program Services
•
ISP staff maintain low caseloads, which allow them to maintain frequent
contact ISP participants.
An important aspect of the ISP model in all sites is that staff have very low caseloads,
ranging between about 10 to 40 cases, with most caseloads around 20. Although
frequency varies across sites and among individual staff, staff consistently report very
frequent contact with participants, with many maintaining weekly contact with clients
over long periods of time. In addition to working with participants, many staff strive to
make connections with staff from other service delivery systems that may be providing
services to participants.
Results from the focus groups conducted in four sites indicate that participants were
generally positive and most had developed a very strong bond with their ISP worker.
Focus group participants reported frequent contact with their ISP worker, either by
phone or in face-to-face meetings, and generally described feeling that their worker was
highly accessible and willing to help.
•
Each ISP provides a high level of service in multiple areas, with assistance
with mental health, employment, and child-related issues most common.
A case-file review for a sample of cases examined the type of assistance received by ISP
participants in specific areas in each site. This analysis shows high rates of assistance in
multiple areas in most sites, reflecting both the voluntary nature of the program, where
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those who are enrolled have an interest in receiving services, and the intensive nature of
the services provided by program staff. Providing insights into the breadth of services
provided, we found that across sites participants received services in six assistance areas
on average within a 6-to-18 month period. Our review also reveals substantial
differences across sites in the types of services provided, demonstrating the differing
program goals and design of each of the sites’ ISPs as well as differences in the needs of
their participants. Important patterns of service receipt are:
•
Assistance with mental health issues is widely provided across all sites, reflecting
the high incidence of mental health barriers among ISP participants. Services
typically include assistance in diagnosing mental health problems and referrals to
professionals who provide counseling, therapy, or drug treatment.
•
Assistance in applying for SSI is a major focus of the program in Anoka County,
where 90 percent of ISP participants received assistance in this area, and Ramsey,
where 50 percent did so. This focus reflects the very high level of mental and
physical health barriers among the ISP participants in these two sites.
•
Employment is emphasized, although to varying degrees, in the ISPs. This
emphasis was particularly strong in Crow Wing, St. Louis, and Washington,
where over 80 percent of participants received employment-related services
(primarily individualized job search), and weakest in Anoka and Ramsey where
about one-third of the participants received assistance in this area.
•
Another key goal of the ISP is to more effectively address the needs of the entire
family, and most programs show high levels of assistance in addressing the needs
of participants’ children.
These results are consistent with findings from the focus groups, where ISP participants
described receiving intensive services in multiple areas of assistance and, in general, felt
they would not have received these services were it not for ISP.
•
Compared to the service brokering approach, sites using the team approach
generally provide participants easier access to specialized services and have
more opportunities to bring together staff with different expertise to discuss
specific cases.
Under the team-based approach, staff with expertise in a range of areas are easily
accessible by ISP participants, with no additional referrals or scheduling needed to
receive assistance. Because team members are colocated and under the same
management structure, staff discuss how to best coordinate service needs in different
substantive areas for individual cases through regular, formal meetings, as well as
informal discussions. Sites relying on a service brokering approach are generally not as
successful in bringing staff from different service systems together to provide more
coordinated services, although they sometimes have phone contact with other individual
workers involved with the family, typically on an as-needed basis. In addition, ISP staff
in programs with this model sometimes faced capacity or budgetary constraints at
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organizations where they made referrals, which affected the timeliness and nature of
services received by ISP participants. Overall, sites using a team-based approach were
able to provide more integrated services than those using a service brokering approach.
•
While the Minnesota ISPs provide a comprehensive set of services to their
participants, for the most part, they did not successfully reduce the number
of systems individuals are involved in or coordinate the actions of larger
service delivery systems.
The ISPs focus on identifying a wide range of services that are appropriate for
individuals and assist individuals in accessing these services, sometimes through
working with individual staff in other service delivery systems. To achieve a more
systemic type of service integration, stronger mandates or guidance may be needed from
state or county officials. The ISPs found it difficult to achieve systemic change without
the leverage provided by this type of “top-down” approach. The ISPs were also very
small and designed to operate for a limited period, which made it difficult to affect
broader, long-run changes in the service delivery system. In addition, at initial
implementation, focusing on a single geographic area may also be more productive than
developing pilots covering a wider region as was done in the Chisago and St. Louis sites.
Changes in Employment, Earnings, MFIP and SSI Receipt, and
Employability Measure Scores by ISP Participants
This analysis examines the trends in ISP participants’ employment, earnings, and benefit
receipt two years before they enrolled in the ISP program and two years after enrollment.
We also calculate differences in these outcomes before and after individuals enroll in the
ISP. This analysis also examines changes in participants’ SSI receipt and Employability
Measure scores, which provide information on barriers related to family stability.
To ensure our comparison of pre- and post-enrollment earnings is based on participants
more permanent and steady level of earnings, we define the pre- and post-enrollment
periods to exclude the period immediately before and after enrollment. For our analysis
of employment and earnings which is based on quarterly data, we follow the recent
literature and define the pre-enrollment period to include the fifth through eighth quarter
before enrollment and the post-enrollment period to include the fifth through eighth
quarter after enrollment. For our analysis of MFIP receipt and MFIP benefit levels, which
is based on monthly data, we define the pre-enrollment period to include the 13th through
24th month before enrollment and the post-enrollment period to include the 13th through
24th month after enrollment.
Our examination of participants' employment, earnings, and MFIP receipt uses both
descriptive and multivariate regression analyses. The descriptive analyses show patterns
and changes in the outcomes over time. The multivariate regression analysis provides
estimates of the relationship between ISP participation and participants’ employment and
MFIP outcomes, controlling for county economic conditions (unemployment rate) and
participant characteristics (gender, age, race, and educational attainment measured at
ES-9
enrollment). A key benefit of the regression analysis over the descriptive analysis is that
it takes account of the effects of the economy, as measured by the unemployment rate, on
outcomes. It is important to note that the regressions cannot rule out that unmeasured
factors aside from the program services may be responsible for some of the estimated
relationships. These possible factors could include variables that have been shown to be
related to MFIP outcomes, such as having a second parent in the household, mobility,
serious mental health and chemical dependency diagnoses, marital status, number of
children, whether the person is a U.S. citizen or speaks English, and county poverty rate.
Also, small sample sizes make it more difficult to detect statistically significant
relationships, particularly gains that are small in magnitude.
Key findings from the analysis are as follows:
•
Employment rates and average earnings tend to fall in the quarters leading
up to ISP enrollment and increase in the quarters following enrollment.
Conversely, MFIP receipt rates and average MFIP benefits tend to rise prior
to enrollment and decline after enrollment.
An analysis of ISP participants’ employment and earnings in the two years before and
after ISP enrollment shows that employment and earnings generally decline prior to ISP
enrollment and increase after enrollment. In Anoka, for example, about 40 percent of
ISP participants were employed two years before enrollment, but only 15 percent were
employed in the quarter of enrollment. During the two subsequent years, the
employment rate fluctuated between 20 percent and 30 percent. Average earnings show
a similar pattern. Patterns of MFIP receipt and MFIP benefit levels are inversely related
to trends in employment and earnings. Across the sites, MFIP receipt tends to increase in
the two years before ISP enrollment and fall in the two years after enrollment.
•
The descriptive analysis shows that ISP participants in two sites—Hennepin
and St. Louis—experienced statistically significant increases in employment
and earnings between the pre- and post-enrollment periods. However, the
regression-adjusted differences show statistically significant increases in
employment and earnings for ISP participants in Hennepin only.
The descriptive results show an increase in the employment and earnings of ISP
participants in Hennepin and St. Louis between the pre- and post-enrollment periods.
Hennepin’s employment rate increased by 20.4 percentage points (from 31.7 percent to
52.1 percent) and average quarterly earnings increased by $907 (from $974 to $1,881).
St. Louis’s increases are more modest than Hennepin’s, but still substantial. The
employment rate increased by 9.7 percentage points between the pre- and post-enrollment
periods (from 41.5 percent to 51.2 percent) and average quarterly earnings increased by
$327 (from $911 to $1,237).
Results from the regression analysis, which control for the county unemployment rate
and selected participant characteristics, suggest that only Hennepin’s program may be
associated with increases in both employment and earnings. Hennepin’s regressionadjusted employment rate increased by 11.3 percentage points and average quarterly
ES-10
earnings increased by $465, or $1,860 annually. Although Hennepin ISP participants’
employment and earnings increased, their MFIP receipt and benefit levels did not
significantly decline. This may be due to the income disregard which allows participants
to keep some of their MFIP benefits when earnings are below a specified level.
•
The descriptive analysis shows that ISP participants in four sites experienced
statistically significant decreases in the MFIP receipt rate or in the average
monthly MFIP benefit amount between the pre- and post-enrollment
periods—Anoka, Chisago, St. Louis, and Washington. Three of these sites—
Chisago, St. Louis, and Washington—also had regression-adjusted declines.
The descriptive results show that ISP participants in Chisago, St. Louis, and Washington
experienced statistically significant declines in both their MFIP receipt rate and average
MFIP benefit amount, while ISP participants in Anoka experienced statistically
significant declines in only the MFIP benefit amount.
The regression-adjusted differences show statistically significant declines in only three
sites—Chisago, St. Louis, and Washington. In St. Louis, ISP participants’ experience a
decline in the amount of MFIP benefit received (but not a decline in the receipt rate).
Specifically, average monthly MFIP benefits fell by $87. The regression-adjusted
differences in MFIP receipt are similar in Chisago and Washington—the MFIP receipt
rate declined by 17.0 and 20.7 percentage points and the average monthly MFIP benefits
among declined by $130 and $138, respectively.
These MFIP declines occurred without a coinciding increase in employment or earnings.
The reasons for these MFIP declines in the absence of employment and earnings gains
are not clear. An analysis of ISP participants who reached their time limit during the
study period shows that this was not a reason for the MFIP declines, with few individuals
reaching their time limit and little variation across the sites. St. Louis ISP participants had
increases in employment and earnings that are not statistically significant. 1 The
magnitudes of these increases are not small, and the higher earnings could potentially
account for the decline in MFIP benefits in this site. Washington is one of two sites
where participation in ISP is mandatory and non-compliance could result in a MFIP
sanction (or grant reduction). (A case-file review showed higher levels of sanctions in
this site). This could potentially be a factor in explaining the MFIP reductions, although
these did not occur in the other site where participation is mandatory and sanctions were
incurred by participants (Crow Wing).
•
The ISP programs in Anoka and Ramsey increased the proportion of
participants who received SSI.
Few ISP participants receive SSI prior to enrolling in the ISP program. Across the eight
sites, only 15 individuals receive SSI benefits prior to ISP enrollment. By the end of the
two-year follow-up period, an additional 122 ISP participants began receiving SSI.
1
Although findings from the descriptive analysis show increases in the employment and earnings of St.
Louis ISP participants, the regression-adjusted differences are not statistically significant.
ES-11
Anoka and Ramsey stand out as having the largest fraction of ISP participants that began
receiving SSI after enrollment in ISP. In Ramsey, 30.1 percent of ISP participants began
receiving SSI benefits after ISP enrollment, while 18.6 percent of Anoka’s ISP
participants began receiving SSI after ISP enrollment. In the other six sites, the
percentage of ISP participants that begin receiving SSI after ISP enrollment is
significantly lower and ranges between 2.1 percent and 8.1 percent.
•
ISP participants in Anoka and Ramsey experienced unexpected statistically
significant changes in participants’ outcomes.
The analysis in Ramsey shows statistically significant changes in the unexpected
direction. The regression-adjusted differences show statistically significant decrease in
earnings and a statistically significant increase in the MFIP receipt rate. Similarly,
Anoka’s participants experience a statistically significant decline in their employment
rate. These decreases in employment and earnings may be explained by the increases in
SSI receipt in these sites. It is difficult for individuals to work once they receive SSI, both
because individuals must have significant mental or physical health problems to be
eligible for the program and because the SSI program includes many rules that
discourage work. In addition, Ramsey’s focus on rehabilitative services and improving
everyday functioning may contribute to the increased MFIP receipt. Individuals may stay
on MFIP longer than they would have in absence of the program in order to continue to
these services.
•
The Employability Measure scores show modest statistically significant gains
on many elements in four sites—Anoka, Chisago, St. Louis, and Washington.
The results in Hennepin and Crow Wing are mixed and show follow-up
Employability Measure scores that are both higher and lower than the initial
scores. Ramsey makes no statistically significant gains, but experiences a
statistically significant decline in one of the Employability Measure domains.
Results from the Employability Measure scores should be interpreted cautiously because
only a proportion of the ISP population completed a follow-up Employability Measure
instrument so the results are not necessarily representative of the entire population. The
analysis shows that Anoka has statistically significant gains at the six- and 12-month
follow-ups in every domain except for legal and safe living environment. In Chisago,
three domains—education, health, and financial—make statistically significant gains in
both follow-up periods, while four domains—child behavior, housing, social support, and
transportation—have a statistically significant gain in one of the follow-up periods. In St.
Louis, nine average scores (all except legal and health) make statistically significant
gains in at least one follow-up period. Washington ISP participants’ average scores make
statistically significant gains during the first follow-up period in nine Employability
Measure domains (all except legal and social support).
Discussion of Key Findings and Policy Implications
Many states and localities share an interest in finding strategies to more effectively assist
individuals who receive cash assistance for long periods. Since this study uses non-
ES-12
experimental regression analysis of small programs to examine the relationship between
the ISP programs and employment, earnings, and MFIP receipt, the results should be
interpreted cautiously. However, several implications can be drawn from the findings.
The positive employment and earnings results in Hennepin County indicate the
promise of this model which includes co-located staff with expertise in a number of
areas including employment, mental health, substance abuse, domestic violence, and
public health. The Hennepin ISP is notable because of the number of organizational
partners as well as staff within the organization who provide on-site assistance in specific
areas of expertise. In addition, case managers work closely with ISP participants and
maintain low caseloads. The program is also located at a community health clinic that has
many resources, including a food bank and medical, dental, and mental health clinic. The
Hennepin ISP contracts with several other organizations to provide most of these
specialized services to ISP participants and all staff are under the same management
structure. Staff meet formally and informally to discuss the specific needs of individual
cases. Finally, the Hennepin program includes a monthly support group that elicits high
levels of participation by individuals in the ISP program.
Two other sites (Anoka and Crow Wing) also use a team approach bringing together staff
with expertise in different areas to provide services to ISP participants. However, while
we cannot determine the specific reasons there are limited changes in economic outcomes
over time in these sites, there are potential factors to consider. In Anoka, the ISP
population has more mental and physical disabilities and the program has a strong focus
on helping participants become eligible for SSI, which makes achieving increases in on
employment and earnings more difficult. In Crow Wing, staff with expertise in child
protection and substance abuse primarily provide supervision and case consultation rather
than direct services. Crow Wing is also the only ISP site where the ISP services enhanced
the existing MFIP program, rather than being a stand-alone program.
Overall, while caution should be used given the limitations of the analysis of participant
outcomes, the positive results for ISP participants in Hennepin warrant further
consideration and evaluation of this approach. In particular, an evaluation using a random
assignment research design would provide more definitive information on the effects of
the program. Some aspects of the model may be difficult to replicate, given the unique
array of services available at the community health clinic where the program is housed.
This is particularly true in rural areas with more limited service environments.
Increased employment and earnings may not be an appropriate goal for some longterm welfare recipients. Anoka and Ramsey counties both serve a population with
significant mental and physical health issues, and the ISP program resulted in increases in
SSI receipt and declines in employment and earnings among ISP participants. Reflecting
the nature of their target population, assisting individuals in applying for SSI is a key
service in both programs and the two programs have the lowest proportion of individuals
receiving employment-related assistance among the ISPs. Given the significant barriers
faced by the ISP populations in these sites, this emphasis on obtaining long-term income
support may be appropriate for some individuals, particularly given the TANF time
limits. However, these results also illustrate the challenge of identifying a target group
ES-13
that can potentially benefit from services geared toward self-sufficiency rather than being
more appropriate for income support programs for the disabled. A key issue is to identify
appropriate screening and assessment tools to distinguish between those with temporary
disabilities who have the potential to work, sometimes with accommodations and
supports, and those with a significant level of permanent disability.
A service brokering approach with low caseloads for staff may not be sufficient to
address the persistent challenges of long-term cash assistance recipients. In the
service brokering sites, program staff have very low caseloads and work with participants
intensively to provide referrals to the services needed. However, while these services
could have resulted in improvements in non-economic outcomes, the ISP participants in
sites that used this approach did not experience increases in employment or earnings, and
in some sites MFIP receipt declined. While caution should be used given small sample
sizes on many of these sites, it may be that the brokered services, although coordinated
by ISP staff, do not bring the full range of services needed to address participants’ needs.
The service brokering approach can face limitations when other organizations have
capacity or budgetary constraints. In addition, in this project, some of the sites using this
approach are in rural areas and may face service shortages in particular areas.
The non-economic benefits of the ISP programs remain difficult measure but are
important to consider given the broad range of services provided. In addition to
improving economic outcomes, the ISP initiative has relatively broad goals that include
improving family-related outcomes and addressing the needs of children as well as adults
in the family. Towards this end, the ISP programs generally provide a broad range of
assistance in addition to employment-related services, particularly assistance with mental
and physical health issues and issues related to their children. Indeed, over time, there are
some increases in Employability Measure scores, an instrument designed to capture
improvements in personal and family-related outcomes, although these results should be
interpreted cautiously given data limitations. Nonetheless, given the hard-to-employ
population served by the ISPs, it is important to recognize that these programs may
achieve improved outcomes in areas other than employment gains and welfare
reductions. There is a need to continue to develop measures of alternative measures of
program accomplishments.
Across all the sites, employment and earnings levels were very low both before and
after enrollment in the program, indicating the significant challenge of designing
effective program services for this population. In spite of these interventions, even in
the most successful sites, the populations targeted by the ISPs continue to fare relatively
poorly over time in terms of their overall employment and earnings and MFIP receipt.
These findings are consistent with a range of other studies that show it is extremely
difficult to improve the economic success of those who face significant barriers to
employment. Overall, these results illustrate the persistent challenge of finding strategies
to effectively assist individuals who receive cash benefits for long periods.
ES-14
I.
Introduction
The passage of the Personal Responsibility and Work Opportunity Reconciliation Act
(PRWORA) in 1996 set the course for a work-oriented welfare system by establishing the
Temporary Assistance to Needy Families (TANF) program, requiring many welfare recipients to
enter the labor market and imposing a lifetime limit on cash assistance of 60 months. In the wake
of these reforms, policymakers and program operators have a renewed interest in what kinds of
services and supports are best able to help long-term welfare recipients find and keep jobs.
Despite advances in the development of programs that help recipients find jobs, a significant
portion of the welfare caseload remains on the rolls for long periods either not working or
working sporadically. Many of those who remain on welfare have multiple barriers to
employment that make it difficult to successfully move from welfare to work. Because of its
wide-ranging needs, this population is often involved in multiple but uncoordinated service
delivery systems.
In 2005, the Minnesota Department of Human Services (DHS) initiated a new effort that seeks to
address the needs of long-term cash assistance recipients in the Minnesota Family Investment
Program (MFIP), many of whom are in danger of reaching their time limit on cash assistance
benefits. To this end, DHS provided grants to eight sites to address the multiple needs of longterm MFIP recipients. Reflecting its focus on bringing together multiple service systems to
address the needs of this population, the project is known as the Minnesota Integrated Services
Project (ISP). ISP aims to improve both economic and family-related outcomes for this
population by increasing access to more comprehensive services that address multiple needs,
coordinating services provided by multiple service systems, and focusing on the needs of both
adults and children in the household.
This paper is the final report in an ongoing evaluation of the Minnesota ISP funded by the
McKnight Foundation and DHS, focusing on the changes in employment, earnings, and MFIP
receipt of ISP participants in each site over a two-year follow-up period. This chapter of the
report provides an overview of the ISP and sites and projects included in the initiative. Chapter II
provides a description of each of the ISP programs including key partners, target group,
demographic and barrier-related characteristics of ISP participants, services provided, and
overall implementation experiences. Chapter III discusses changes in employment, earnings,
MFIP receipt and family-related outcomes for ISP participants. Chapter IV summarizes key
findings for each ISP site. Chapter V provides a conclusion and recommendations for moving
forward.
Policy and Program Context
Like most TANF programs across the country, the Minnesota Family Investment Program
(MFIP) requires all cash assistance recipients to work or participate in employment-related
services or risk financial penalties (known as sanctions) and establishes a lifetime limit on the
receipt of cash benefits of 60 months. MFIP also provides a generous earned income disregard,
1
which allows recipients to keep more of their benefits when they go to work until their income
reaches the exit level. While MFIP has successfully moved some individuals off welfare and into
work, concerns have grown over how to address the needs of those who remain on cash
assistance for long periods.
Research and program experience have recognized the substantial number of welfare recipients
with barriers to sustained employment (Burt 2002). Forty percent of the caseload that receives
cash assistance through the TANF program nationally has been identified with significant
barriers, 20 percent of those with multiple barriers (Loprest 2001). In 2003, 13 percent of MFIPeligible adult cases had a severe mental health diagnosis, and 17 percent generated a child
protection assessment (Minnesota DHS 2004). A longitudinal study of long-term MFIP
recipients finds large proportions have been homeless, have health or mental health problems, or
have been treated at some point for chemical dependency (Minnesota DHS 2002). Many
problems of the hard to employ are concurrent (e.g., mental illness and substance abuse; mental
illness and very basic skill functioning; domestic violence, child abuse, and health and
behavioral problems among family members).
Little is known about what strategies work best for assisting hard-to-employ welfare recipients
with these challenges to improve their economic outcomes. Many evaluations of welfare-to-work
programs that operated in the 1980s and 1990s included subgroup analyses for cash assistance
recipients facing more serious barriers to employment. These programs typically required
participation in job search, vocational training, and basic education, sometimes in a fixed
sequence or sometimes using various assessments to determine services. The studies show that
the programs generally increase earnings about as much for the most disadvantaged recipients
(defined as longer-term welfare recipients with no high school diploma and recent work history)
as for less disadvantaged recipients (Michalopoulos 2005). However, because they start at such a
low level, the earnings of the most disadvantaged recipients remain very low in spite of the
gains. In addition, many of these past programs did not serve those with the most serious
physical or mental health problems because these types of recipients were typically exempted
from program participation requirements.
A few studies have tested special programs with more intensive services aimed at the hardest-toemploy recipients, although their results are sobering. A recent study of the PRIDE program in
New York City that targeted welfare recipients who have physical or mental health conditions
that limit their employability and used a mix of tailored unpaid work experience, job search, and
basic education found PRIDE generated relatively large increases in employment but again that
outcome levels remain low -- two-thirds of those in PRIDE did not work at all during a two-year
follow-up period (Bloom et al. 2007). In the Substance Abuse Research Demonstration, an
experimental evaluation of a case management intervention for women on TANF who were
substance abusers found the program increased participation in treatment and led to some
reductions in substance use, but had no effect on employment and earnings (Morgenstern and
Blanchard, 2006). Finally, an evaluation of a program in Minneapolis, Minnesota that focused on
reduced caseloads to facilitate more attention employment-related barriers and more
2
comprehensive assessments found no effect on employment and earnings in an experimental
evaluation (Le Blanc et al, 2007).
The Minnesota Integrated Services Project is designed to address the multi-faceted needs of
long-term MFIP recipients. In developing the project, state administrators recognized that a
significant portion of this population receive diagnosis or services through other public systems,
including mental health, chemical dependency, disability, child protection, domestic violence,
and services for children with special needs (Chazdon 2005). Moreover, many of these barriers
were not diagnosed or identified until individuals were close to meeting their time limits on cash
assistance and little time was available to provide needed assistance. This reflects that the current
service delivery systems providing support to those with significant challenges are generally
organized around single-issue expertise, with limited communication or coordination across
different systems (Loprest and Martinson, 2008). While there are numerous local public agencies
and publicly-funded private organizations that provide employment services and supports for
low-income individuals, communication among the agencies providing these different services
can be difficult because of varying goals, target populations, eligibility rules, and program
practices, resulting in fragmented services for the families who need them most (Zedlewski et al
2007).
To encourage a more coordinated response from the human services system, DHS provided
grants to eight sites across the state to address the multiple needs of long-term MFIP recipients.
The ISP is focused on improving the performance of the current MFIP for this hard-to-employ
population by providing more comprehensive and integrated services to address the particular
needs of each family member.
Defining Service Integration
To understand the ISPs in Minnesota, it is first useful to discuss the meaning of “service
integration” as well as the potential benefits and drawbacks of this approach based on past
studies and efforts in this area. The desire to simplify and streamline client processes through
service integration is often cited as a solution to the wide range of uncoordinated programs that
exist at the local level (Ragan 2003). Over the years, the terms “integration” and “coordination”
as well as “collaboration” and “linkages” have often been used interchangeably and with varying
connotations and meanings. It is generally recognized that there is no single definition of service
integration (Corbett and Noyes 2004). Service integration efforts are organized with different
goals, management, structure, and partners, although they share the common goal of creating a
system that improves outcomes for clients.
While the coordination of service delivery systems usually takes place at the local level, studies
have shown that a initiative to coordinate may either be locally developed (“bottom-up”
coordination) or may be encouraged or imposed by federal or state officials (“top-down”
coordination) (Martinson 1999). With top-down integration, federal and state officials may
develop requirements that local agencies coordinate the delivery of specific types of services, or
offer advice or incentives to promote collaboration. Coordination is sometimes required in
3
legislation; at other times, requirements are contained in administrative communications ranging
from the personal initiatives of key officials to joint policy statements or agency regulations.
Studies also recognize a distinction between administrative and operational service integration
strategies (Ragan 2003; see also U.S. General Accounting Office 1992). Administrative
strategies are “behind the scenes” system changes, such as reorganizing government agencies to
consolidate program administration and functions; collaborating in planning, management, and
oversight; integrating a wide range of service providers in local systems; and blending funding
streams. In contrast, operational strategies are those that directly affect client/worker processes,
including co-locating staff from multiple programs and organizations; developing common client
intake, assessment, and case management services; consolidating case plans and staff functions;
and integrating staff from multiple agencies into teams. Administrative service integration
strategies typically have more ambitious goals and are focused on reforming the delivery system.
Operational strategies have more modest goals and are focused on linking clients to existing
services and uniting various service providers, without altering the program budgeting or funding
process, service agency responsibility, or organizational structures. The most comprehensive
examples of service integration occur where both operational and administrative changes have
been implemented (Ragan 2003).
Studies point to the substantial benefits that can accrue to both clients and programs through
service coordination and integration (Martinson 1999). These efforts often enable clients to
access a wider range of services than would otherwise be available. Agencies may be able to
reduce duplicative services with coordination or they may be able to provide new or expanded
services. Clients may also experience reduced barriers to accessing services—primarily though a
simplified referral process that reduces the cost and time associated with accessing services.
From the agency perspective, the benefit is to reduce the duplication of services, refocus
resources on new or extended services, offer a wider range of services, and increase knowledge
and communication among program staff.
While there are clearly many benefits of coordinated services to both clients and programs, past
studies and experiences show that there are barriers that make coordination and integration
difficult (Sandfort 2004; Corbett and Noyes 2004; Hutson 2004). These include bureaucratic
barriers and “turf” protection, differing philosophies or missions, differences in performance
measures, legal or regulatory issues, incompatible management information systems, and
different eligibility rules. The combination of these factors can be daunting to some coordination
efforts and are most likely responsible for problematic past efforts.
Project Goals and Sites
Minnesota DHS identified four primary goals of the Integrated Service Projects to address issues
facing long-term welfare recipients: (1) to identify employment barriers earlier in the family’s
time on cash assistance; (2) to work with both adults and children in each family; (3) to change
fundamentally the way services are delivered so they are provided in a manner that is accessible,
4
integrated, and cost-effective; and (4) to identify policy and system issues that interfere with the
delivery of services to the adults and children in these families.
While this “top-down” service integration effort was initiated at the state level, DHS did not
provide a specific definition of “service integration.” While certain partners were mandated
(county human services agency, a managed health care plan, and a community-based health
clinic), sites were given significant discretion in determining how they structured and operated
their service integration models. This approach builds on the county–administered welfare
system in Minnesota, where counties are given significant latitude in designing a range of
programs.
The Minnesota ISP aims to improve both economic outcomes related to improved employment,
earnings, and welfare receipt, as well as other noneconomic outcomes related to family
functioning. These outcomes include improving family outcomes on a range of measures such as
safe living environment, personal skills, social support, child behavior, physical and mental
health, housing, transportation, and legal issues. To assist the sites in measuring progress on
these family-related outcomes, DHS provided an instrument for program staff at all sites to
assess and track participants’ status in these areas an ongoing basis, known as the Employability
Measure. DHS also provided screening tools designed to assess barriers in several areas
including mental health, chemical dependency, learning disabilities, and criminal history, known
as the MFIP Self-Screen and the Brief Screening Tool for Special Learning Needs (discussed
further below).
Eight sites representing diverse locations across the state were selected for the ISP: Anoka
County, Chisago County, Crow Wing County, Hennepin County, Ramsey County, the Red Lake
Indian Nation, St. Louis County, and Washington County (see Exhibit 1-1). Two of the sites
serve surrounding counties under their ISP grant: the St. Louis program also serves Carlton,
Itasca, and Koochiching counties and the Chisago program serves a five-county area that also
includes Isanti, Kanabec, MilleLacs, and Pine counties. The Hennepin site serves a neighborhood
in Minneapolis. Each site received funding to operate their program for three years, although
recently resources were provided to extend the ISPs an additional year.
The ISP sites represent a range of urban, rural, and suburban locations with substantial variation
in many socioeconomic characteristics (see Table 1-1). Hennepin and Ramsey counties are urban
counties with strong economies, but their MFIP caseload comprises a greater proportion of
minorities and immigrants. Both Anoka and Washington are suburban counties with higher
levels of education and income and lower levels of poverty than the other sites, although the
MFIP caseload in Anoka is similar to the urban counties in terms of its lower education levels
and diverse racial composition. The St. Louis program encompasses a wide geographic area that
includes one urban area (Duluth). The Chisago and Crow Wing programs operate in primarily
rural areas. These sites also have higher levels of unemployment and lower levels of education
5
Exhibit 1-1
Location of the Minnesota Integrated Services Projects
Map created using U.S. Census Bureau Tiger Map Service
6
Table 1-1
Economic and Demographic Profile of the Minnesota Counties in which the Integrated Services Project Sites are Located
Measure
Minnesota
Anoka
Red Lake1
Chisago
Crow Wing
Chisago
Isanti
Kanabec
Mille Lacs
Pine
For Total Population:
Race/Ethnicity
White
Black
Hispanic/Latino
American Indian
Asian
89%
4%
3%
1%
3%
94%
2%
2%
1%
2%
0%
0%
0%
100%
0%
97%
1%
1%
1%
1%
98%
0%
1%
1%
0%
97%
0%
1%
1%
0%
94%
0%
1%
5%
0%
94%
1%
2%
3%
0%
98%
0%
1%
1%
0%
Education Level
No diploma
High School graduate
Some college
College graduate
12%
29%
24%
35%
9%
32%
28%
30%
39%
33%
22%
7%
11%
37%
27%
24%
13%
38%
26%
22%
20%
42%
22%
16%
19%
40%
23%
18%
21%
41%
23%
15%
14%
34%
25%
28%
$47,111
$56,874
$57,754
$64,261
$22,813
$19,969
$52,012
$57,335
$50,127
$55,996
$38,520
$43,603
$36,977
$44,054
$37,379
$44,058
$37,589
$44,847
Families in female-headed households below poverty
level, 1999
19%
13%
52%
15%
19%
23%
22%
28%
26%
Unemployment Rate, November 20052
3.8%
3.7%
5.0%
4.1%
4.1%
5.5%
6.3%
5.6%
4.8%
Unemployment Rate, November 20072
4.0%
4.1%
5.3%
4.9%
4.9%
6.6%
6.3%
6.3%
5.2%
26,941
1,270
1,098
126
94
76
81
136
219
Race/Ethnicity
White
Black
Hispanic/Latino
American Indian
Asian
Mixed
38%
37%
5%
9%
9%
1%
62%
28%
2%
3%
3%
2%
15%
1%
0%
82%
0%
1%
90%
5%
1%
1%
1%
2%
95%
0%
0%
2%
1%
1%
93%
3%
0%
1%
1%
1%
86%
1%
0%
10%
1%
1%
91%
0%
2%
4%
1%
1%
90%
3%
3%
2%
0%
2%
Immigrant to U.S.
19%
15%
0%
1%
0%
0%
0%
1%
2%
Education Level
No diploma
High School graduate
Some college
College graduate
44%
48%
8%
1%
38%
50%
11%
1%
51%
45%
4%
0%
35%
57%
7%
1%
32%
59%
10%
0%
38%
58%
4%
0%
35%
57%
9%
0%
32%
61%
7%
0%
29%
59%
16%
0%
Suburban
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Median Income, 1999
Household income
Family income
For MFIP Caseload:
Adults on MFIP Caseload, October 2005
Setting
Continued on next page
7
Table 1-1 (continued)
Economic and Demographic Profile of the Minnesota Counties in which the Integrated Services Project Sites are Located
Measure
Minnesota
Hennepin
Ramsey
St. Louis
Washington
St. Louis
Carlton
Itasca
Koochiching
For Total Population:
Race/Ethnicity
White
Black
Hispanic/Latino
American Indian
Asian
89%
4%
3%
1%
3%
81%
9%
4%
1%
5%
77%
8%
5%
1%
9%
95%
1%
1%
2%
1%
92%
1%
1%
5%
0%
95%
0%
1%
3%
0%
96%
0%
1%
2%
0%
94%
2%
2%
0%
2%
Education Level
No diploma
High school graduate
Some college
College graduate
12%
29%
24%
35%
9%
21%
23%
46%
12%
25%
22%
41%
13%
32%
25%
30%
16%
37%
25%
22%
15%
33%
26%
26%
18%
38%
22%
23%
6%
26%
26%
42%
$47,111
$56,874
$51,711
$65,985
$45,722
$57,747
$36,306
$47,134
$40,021
$48,406
$36,234
$44,025
$36,262
$43,608
$66,305
$74,576
Families in female-headed households below poverty
level, 1999
19%
17%
22%
27%
17%
31%
33%
10%
Unemployment Rate, November 20052
3.8%
3.5%
3.8%
4.7%
4.8%
5.3%
6.3%
3.4%
Unemployment Rate, November 20072
4.0%
3.8%
4.0%
5.0%
4.8%
6.5%
6.9%
3.6%
26,941
7,802
6,955
1127
141
203
76
459
Race/Ethnicity
White
Black
Hispanic/Latino
American Indian
Asian
Mixed
38%
37%
5%
9%
9%
1%
18%
66%
2%
5%
6%
1%
21%
45%
5%
3%
24%
1%
72%
9%
1%
14%
1%
2%
79%
1%
1%
18%
0%
1%
70%
1%
1%
26%
0%
1%
89%
0%
1%
9%
0%
0%
62%
21%
8%
2%
5%
1%
Immigrant to U.S.
19%
25%
32%
1%
0%
0%
7%
8%
Education Level
No diploma
High school graduate
Some college
College graduate
44%
48%
8%
1%
46%
46%
7%
1%
52%
40%
7%
0%
32%
60%
8%
1%
32%
61%
6%
1%
30%
60%
10%
0%
29%
68%
3%
0%
33%
56%
11%
0%
Urban
Urban
Urban/Rural
Rural
Rural
Rural
Suburban
Median Income, 1999
Household income
Family income
For MFIP Caseload:
Adults on MFIP Caseload, October 2005
Setting
Sources: U.S. Census 2000, Summary File 3 - Sample Data; Bureau of Labor Statistics, Local Area Unemployment Statistics; MFIP caseload data from the Minnesota Department of Human
Services; Minnesota Department of Employment and Economic Development (DEED).
1
Statistics on the unemployment rate and for the MFIP caseload are for Beltrami County, the county where most residents on the Red Lake Reservation live. Statistics for Red Lake Reservation
alone are not available in these areas.
2
Unemployment rates not seasonally adjusted.
8
than many of the other sites. Finally, the Red Lake program operates on the Red Lake
Reservation, a very disadvantaged area in terms of its economy, education, and income levels. 1
The ISP Evaluation and Data Sources
The ISP evaluation, sponsored by DHS and funded by the McKnight Foundation and DHS, is a
multicomponent study employing a range of research strategies and data sources. The evaluation
includes an implementation study and a study of participants’ employment, welfare, and familyrelated outcomes based on administrative data and information collected through the
Employability Measure.
This report focuses on describes the longer-run economic outcomes for program participants.
The report examines the outcomes of individuals who enrolled in the program after its inception
in April 2005 through June 2006. This results in 987 participants across all the sites distributed
as follows: Anoka County, 306 participants; Chisago County, 82; Crow Wing County, 86;
Hennepin County, 93; Ramsey County, 123; Red Lake, 46; St. Louis County, 156; and
Washington County, 95. The following data sources are used in the study:
•
Baseline demographic data. Participant demographics were from MFIP administrative
data and from the ISP Baseline Data form (see appendix B), completed by ISP staff at the
time of enrollment in the program.
•
ISP Baseline Data Form, MFIP Self-Screen, Brief Screening Tool for Special
Learning Needs, and Employability Measure. Participants’ employment-related
barriers at the point of enrollment in the program are examined through four sources. The
ISP Baseline Data Form includes 25 items developed by DHS for the ISP evaluation and
is administered by program staff at enrollment. It includes primarily self-reported
responses to questions but could also include information which the staff person had
through prior knowledge, reviewing a case history, or observation. It covers a range of
barriers including education, mental and physical health, criminal history, housing,
domestic violence, and transportation.
ƒ
The MFIP Self-Screen, based on self-reported responses of participants to 16 items,
assesses participants’ barriers in the areas of mental health and chemical dependency.
Items are weighted 1, 2, or 3 with a total score of 3 being the threshold indicating that
a referral for mental health or chemical dependency is needed.
ƒ
The Brief Screening Tool for Special Learning Needs examines the prevalence of
learning disabilities or special needs in this area. It includes 13 items weighted with a
1, 2, 3, or 4, with a total score 12 being the threshold indicating that additional
assistance is needed.
1
Unemployment rates on Table 1-1 are presented for Beltrami County where most of the Red Lake Reservation is
located. While comparable unemployment rate data is not available for Red Lake Reservation alone, unemployment
rates are significantly higher (over 70 percent) on the reservation (Garrigan 2007).
9
ƒ
The Employability Measure is designed to assess MFIP participants in 11 areas
related to employment: child behavior, dependent care, education, financial, health
(including mental, physical, and chemical health), housing, legal, personal skills, safe
living environment, social support, and transportation. During face-to-face meetings,
program staff trained on the administration of the instrument determine the level of
the participant in each area on a scale from 1 to 4 or 5, depending on the area being
measured. The Employability Measure is designed to measure status and change over
time in each domain, and it is readministered about every six months after ISP
enrollment when possible. (See Appendix A for a copy of the Employability
Measure.)
•
Unemployment Insurance (UI), MFIP receipt, and Supplemental Security Income
(SSI) data. UI records provide quarterly employment and earnings, and MFIP records
provide monthly cash assistance information. We also have information on the date that
MFIP recipients began to receive SSI benefits.
•
Field research. Information on program operations was collected primarily during twoday site visits conducted in November 2006, when the programs had been operating for
12 to 18 months, depending on the site. During each visit, we held discussions with
representatives of the key partners of each project, including managers and line staff. We
also conducted phone interviews with program managers in each site in the fall of 2007
and spring of 2008. Finally, we reviewed a number of documents related to the ISP
project for each site including the ISP grant applications, quarterly reports submitted to
DHS, and other documents provided by the site.
•
Case-file review data. These data are used to examine the type and level of service
receipt among ISP participants. During visits to ISP sites in November 2006, Urban
Institute researchers, guided by ISP staff who worked on each case, recorded the services
and type of assistance received by a sample of participants based on documentation in
their case file. Due to resource constraints, twenty cases were selected at random from
each site. To ensure an adequate follow-up period that reflected the range of services
provided, the sample was selected to provide a minimum of six months after program
enrollment. The sample was randomly selected from the group of ISP participants who
enrolled from program inception (which varied from April to August 2005 across the
sites) through May 2006, and data was collected on services provided through October
2006. Overall, although sample sizes are small, this allows for a 6 to 18 month follow-up
period after enrollment to document the receipt of services.
•
Focus groups. To provide an understanding of participants’ experiences in the program,
focus groups with participants in four sites were conducted—Anoka, Hennepin, Ramsey,
and Washington. The focus groups were conducted in August 2007 by Urban Institute
researchers. Each ISP site was responsible for recruiting past and current participants to
attend the focus group using a list of randomly selected ISP participants who had been in
10
the program for at least six months provided by the Urban Institute. Each focus group
session lasted approximately 90 minutes, and participants received an incentive payment
for attending.
Earlier ISP evaluation reports describe the implementation of the ISP projects and provide
more in-depth descriptions of participant characteristics and program services (see Martinson
et al 2007; Martinson et al 2006) and a summary of focus groups conducted with ISP
participants regarding their experiences in the program (Parnes and Johnson 2007).
11
II.
The ISP Programs: Service Strategies and Participant
Characteristics
This chapter begins with an overview of the strategies used by the ISP sites used to
provide and integrate services for hard-to-employ MFIP recipients. It then describes the
key features and characteristics of the participants served in each of the programs.
Overview of ISP Program Models: What Type of Service Integration Was
Achieved?
A key goal of the Minnesota ISP is to develop partnerships with other service delivery
systems, including public agencies and community-based organizations, and “integrate”
services that address the needs of long-term MFIP recipients. This chapter provides an
overview of how the ISPs designed their programs and integrated services for long-term
MFIP recipients. While the ISP sites clearly developed unique and individualized
approaches to service integration, there are some patterns in the models adopted. Overall,
as outlined in Exhibit 2-1, we observed three different general approaches for integrating
services in the ISP sites:
•
Team-based approach. Three sites (Anoka, Crow Wing, and Hennepin) use a
team-based approach that involves bringing staff with expertise in different areas
to provide services to ISP participants, with all staff housed at the same physical
location. Participants may work with different staff or more than one staff person
depending on their needs and the issues they are facing. Even within this single
approach, there were different models. Anoka brought together a
multidisciplinary team with staff from different divisions within the county
human services division, to provide expertise in a range of areas. Participants are
assigned to case managers based on their needs, but also work with more than
one staff person if needed. The program also includes an emphasis on applying
for SSI, with a staff person working with participants exclusively on this issue.
Another example of this approach is to contract with other organizations that
bring expertise in needed areas. For example, Hennepin County contracted with
other organizations to provide on-site staff with expertise in substance abuse and
domestic violence. Hennepin was also able to use a psychologist and public nurse
already on staff within the organization to develop a team that provided services
in these areas. Finally, the program in Crow Wing enhances the current MFIP by
incorporating joint supervision of program staff from the child protection division
and the inclusion of a chemical dependency supervisor for case consultation, both
from within the Department of Human Services, and a public health nurse.
12
Exhibit 2-1
Minnesota Integrated Services Projects
Team-based approach
Anoka. This, project, housed in the Anoka County Human Services Division, features a multidisciplinary service team that provides
intensive case management and service coordination for participants. Staff specialize in specific areas, including juvenile and
criminal justice, employment and vocational rehabilitation, public health, child protection, mental health, chemical dependency, and
housing, and participants are assigned to a case manager based on the barriers they are facing. A staff disability advocate assists
participants with the SSI application process. Whenever possible, services for the family are provided in-house by the project team,
though team members also connect with other professionals involved with the family.
Crow Wing. Operated by Crow Wing County Social Services (CWCSS), this project builds on a segment of the county’s existing
MFIP program that targets hard-to-employ MFIP recipients, the Tier 3 program. MFIP recipients who have not found a job through
the county’s standard MFIP program are transferred to a MFIP outreach specialist at CWCSS who provides case management
services and referrals to appropriate community resources. Supervisors from Child Protection Services (CPS) and Chemical
Dependency divisions at CWCSS provide guidance and enhance coordination of services. An ISP specialist works with chemically
dependent mothers, and a nurse from the Crow Wing Public Health Agency is also available to provide services as needed and
participate in monthly staff meetings.
Hennepin. Sponsored by NorthPoint Health and Wellness Center, Inc., a community-based health and human services agency, the
core service of this program is one-on-one case management provided by Family Facilitators employed by NorthPoint as well as
several MFIP employment service providers. Family Facilitators connect participants and their families with services in the
community that address employment and other barriers. Staff from African American Family Services and Turning Point, two
community-based social service agencies, assist participants with chemical dependency and domestic violence issues, and an onsite
psychologist provides mental health assessments and counseling.
Service brokering approach
Chisago. This program operates in a five-county region and is managed by Communities Investing in Families, a nonprofit
organization with experience working with low-income families. Family advocates work one on one with participants to address
barriers, refer them to additional resources in the community, and coordinate with MFIP employment counselors and other service
providers who work with their clients.
Red Lake. This project is operated by the Tribal Council of the Red Lake Band of Chippewa Indians. Through multidisciplinary
case management, community workers link hard-to-employ MFIP recipients with appropriate services and programs on the
reservation, such as GED courses. The program also provides transportation assistance and instruction in traditional work activities
for clients, such as wreath-making, beading, and gardening.
St. Louis. This project is operated in four counties by a set of community action agencies and the Minnesota Chippewa Tribe.
Family employment advocates assess the needs of families and work with them one on one to help connect them with appropriate
community resources to address a range of issues, including transportation, housing, substance abuse, child care, child support,
probation, education, mental health, physical health, and domestic violence.
Washington. Operated by HIRED, a nonprofit organization that provides MFIP employment services, this project aims to reduce
the likelihood that residents will relocate and assist those who have relocated to reestablish services in Washington County. Its
larger goal is to facilitate case coordination across systems for families involved with multiple service providers. Integrated
Services coordinators complete an in-depth assessment, make individualized referrals to a wide range of services, and communicate
with other professionals involved with the family to better coordinate services. The program has established a close working
relationship with the county’s community mental health clinic to ensure quick access to psychological evaluations and mental
health services for clients.
Single service approach
Ramsey. This initiative integrates mental health rehabilitation expertise into the county MFIP employment services program, while
accessing new funding outside the regular MFIP allocation. The ISP provides financial support to several providers to meet
capacity and certification standards to provide services under Adult Rehabilitative Mental Health Services (ARMHS). ARMHS aim
to help individuals with serious mental illness improve functionality, and services are billed directly to Medical Assistance
(Minnesota’s Medicaid program). Each agency has flexibility to provide ARMHS services or partner with an agency that provides
ARMHS services.
13
•
Service brokering approach. Four sites (Chisago, St. Louis, Red Lake, and
Washington) use an approach that involves putting program staff in a “service
brokering” role. Here, staff are responsible for coordinating referrals to other
services in the community based on the individual needs of participants. It is also
used to some extent by sites using the team-based approach when specific
services beyond the expertise of program staff are needed. Under this approach,
primarily through the efforts of line staff, the ISPs coordinate a wide range of
services that address the multiple needs of individuals on their caseload including
physical or mental health, substance abuse, housing, special needs of children and
domestic violence. ISP staff may draw on the expertise of specific program
partners, but they are responsible for overall coordination of services.
•
Single service approach. Ramsey County is unique among the sites in that it
focuses on providing in-depth assistance in one service area—assistance with
mental health issues—and thus does not fit under either of the models discussed
above. This program systemically brings rehabilitation expertise in mental health
into the county MFIP but by design does not generally address the other service
needs of participants.
Overall, sites using a team-based approach are able to provide more an integrated set of
services than those using a service brokering approach. Sites using the team approach
generally provide participants easier access to specialized services that are often offered
in-house and have more opportunities to bring together staff with different expertise to
discuss specific cases. Under this approach, staff with expertise in a range of areas are
easily accessible by ISP participants, with no additional referrals or scheduling needed to
receive assistance. Because team members are co-located and under the same
management structure, staff can discuss how to best coordinate service needs in different
substantive areas for individual cases through frequent formal meetings, as well as
informal discussions. Sites relying more on a service brokering approach were generally
not as successful in bringing these different workers or systems together to provide more
coordinated services, although they sometimes had phone contact with other individual
workers involved with the family, typically on an as-needed basis. In addition, ISP staff
in these systems sometimes had to deal with capacity or budgetary constraints at
organizations where they made referrals which could affect the timeliness and nature of
services received by ISP participants.
The ISPs have clearly focused on developing operational service integration strategies,
with most integration occurring at the staff level rather than involving the coordination of
larger service delivery systems. As discussed in the interim ISP report (see Martinson et
al, 2007), there appear to be several reasons why more ambitious levels of service
integration achieved were not achieved by most of the sites. First, the basic parameters of
the ISP initiative may not have been sufficient to achieve system-wide change. DHS did
not provide specific guidance on the type of service integration to be established, in large
part reflecting the flexibility generally given to counties in the MFIP program. While
providing flexibility was an important element of this initiative, an unintended effect may
14
been that it did not provide the leverage needed to involve other service delivery systems,
many of which faced their own set of demands and constraints. Second, the projects were
designed to operate for a limited duration (3 years initially) and sites did not typically
undertake longer-term planning that more ambitious efforts may require. Third, because
the ISP programs are small, system-wide integration, which would potentially affect a
much greater number of families, did not generally appear warranted to some. Finally, in
two sites, the ISP was launched in several counties simultaneously. Both these sites found
it difficult to manage both implementation across several counties and within a single
service delivery system, let alone working with multiple service systems in multiple
counties.
Description of the ISP Programs
This chapter describes the key features of each of the ISP programs, including a project
summary, institutional partners, staffing, target population and program size, recruitment
and enrollment procedures, the type of services provided, and a discussion of the site’s
overall implementation experiences. For each site, it also provides information on the
nature of the population served at the time they enrolled in the program, including their
demographic characteristics and prevalence of employment-related barriers. In the four
sites where focus groups were conducted (Anoka, Hennepin, Ramsey, and Washington) a
summary of these are also provided.
This chapter draws several data sources including site visits conducted in 2005 and 2006,
case-file reviews of random sample of 18-20 cases in each site to examine service receipt,
data on participant characteristics collected at the time of program enrollment, focus
groups in four sites, and phone interviews with program managers in 2007 and 2008.
Table 2-1 presents selected demographic and employment-related barriers for each of the
ISP sites and Appendix B presents tables providing detailed participant characteristics for
each site. Table 2-2 presents a summary of the types of services participants received
through the ISP program based on a case-file review. Much of the information below is
summarized from the interim ISP evaluation report (see Martinson et al 2007).
Anoka County: Partners for Family Success
Project summary. The Anoka County Integrated Services Project, known as Partners for
Family Success (PFS), aims to better coordinate services for families with multiple
barriers through a centrally located service team staffed by experts from each of five
departments under the Human Services Division (corrections, community social services
and mental health, community health and environmental services, income maintenance,
and the job training center). Based on a previous service integration effort in Anoka
County Human Services Division, the core components of this program are intensive case
management and coordination of services for clients. The program goals include an
emphasis on refining service needs and reducing the number, or level, of outside service
providers involved with each family. The program also has a strong emphasis on and
staff person dedicated to assisting individuals in applying for SSI (known as the “SSItrack”). Other staff on the PFS team include a rehabilitation/job counselor, who provides
rehabilitation assessment and serves as the job counselor for most participants, and a
15
Table 2-1
Characteristics of ISP Participants at Enrollment, by Site
Anoka
Chisago
Average age
Average age of youngest child
34.6
7.3
31.6
6.0
Race and ethnicity
White, Non-Hispanic
Black, Non-Hispanic
Native American
Hispanic
Asian
67.3
23.1
4.6
1.3
3.6
%
%
%
%
%
92.7
1.2
3.7
2.4
0.0
%
%
%
%
%
91.8
3.5
3.5
1.2
0.0
%
%
%
%
%
11.8
86.0
2.2
0.0
0.0
%
%
%
%
%
25.0
55.8
3.3
4.2
11.7
%
%
%
%
%
2.2
0.0
97.8
0.0
0.0
%
%
%
%
%
72.8
3.3
21.2
0.7
2.0
%
%
%
%
%
77.7
13.8
2.1
5.3
1.1
%
%
%
%
%
Highest degree attained
Less than high school
High school diploma or GED only
Some college
College degree
38.2
47.4
13.1
1.3
%
%
%
%
37.8
52.4
4.9
4.9
%
%
%
%
24.4
62.8
11.6
1.2
%
%
%
%
33.3
59.1
5.4
2.2
%
%
%
%
44.7
48.0
7.3
0.0
%
%
%
%
50.0
47.8
2.2
0.0
%
%
%
%
33.5
53.5
11.0
1.9
%
%
%
%
28.4
56.8
14.7
0.0
%
%
%
%
Score above threshold on Special Learning Needs
Screen1
41.2 %
28.8 %
26.7 %
27.6 %
35.3 %
6.5 %
27.6 %
24.2 %
Mental condition that makes it hard to work
64.1 %
42.7 %
34.9 %
24.7 %
82.9 %
6.5 %
39.1 %
31.6 %
Ever diagnosed with depression
59.5 %
67.1 %
62.8 %
50.5 %
82.9 %
19.6 %
66.7 %
53.7 %
Physical condition that makes it hard to work
45.8 %
26.8 %
27.9 %
17.2 %
39.0 %
13.0 %
23.1 %
31.6 %
Ever in chemical dependency treatment
14.1 %
28.0 %
26.7 %
23.7 %
16.3 %
39.1 %
27.6 %
27.4 %
Score above threshold on MFIP Mental Health and
2
Substance Abuse Self-Screen
91.8 %
66.7 %
77.1 %
83.6 %
72.6 %
41.3 %
73.1 %
69.5 %
9.2 %
23.0 %
28.6 %
23.0 %
12.1 %
0.0 %
19.2 %
27.7 %
Ever convicted of a felony
11.8 %
12.2 %
4.7 %
28.0 %
18.7 %
6.5 %
18.6 %
23.2 %
Ever homeless
20.3 %
29.3 %
39.5 %
52.7 %
37.4 %
19.6 %
53.2 %
31.6 %
Confirmed domestic violence in past year
Crow Wing
30.2
4.7
Hennepin
Ramsey
Red Lake
St. Louis
31.5
5.8
34.1
5.9
32.5
5.7
29.3
4.2
Washington
31.5
5.3
Continued on next page
16
Table 2-1 (continued)
Characteristics of ISP Participants at Enrollment, by Site
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
Percent of ISP Participants Scoring 1 or 2 on
the following Employability Measure Scores
Child Behavior
34.5 %
35.0 %
25.3 %
25.6 %
49.4 %
13.9 %
36.4 %
38.9 %
Dependent Care
27.3 %
39.7 %
29.8 %
14.0 %
50.0 %
27.8 %
49.6 %
40.2 %
Education
39.4 %
32.5 %
24.7 %
27.9 %
41.5 %
50.0 %
25.0 %
32.6 %
Health (Physical, Mental, and Chemical)
85.1 %
60.8 %
55.4 %
32.6 %
87.8 %
19.4 %
61.1 %
61.1 %
Housing
33.3 %
31.3 %
34.1 %
31.4 %
23.2 %
18.4 %
32.6 %
49.5 %
Financial
42.6 %
57.5 %
62.4 %
39.5 %
53.7 %
29.7 %
72.9 %
87.4 %
Legal
20.8 %
17.9 %
11.8 %
18.6 %
24.4 %
21.1 %
21.3 %
38.7 %
Safe Living Environment
17.7 %
22.1 %
14.1 %
15.1 %
30.9 %
15.8 %
17.6 %
34.0 %
Personal Skills
59.9 %
35.0 %
42.4 %
30.2 %
69.5 %
31.6 %
43.1 %
51.6 %
Social Support
49.0 %
72.5 %
63.5 %
25.6 %
73.2 %
34.2 %
69.4 %
58.9 %
Transportation
39.5 %
53.8 %
58.8 %
24.4 %
48.8 %
54.1 %
65.3 %
59.6 %
Number of Observations3
306
82
86
93
123
46
156
95
Source: Authors' tabulations of ISP data collected from participants by program staff at enrollment and administrative data, provided by the Minnesota Department of Human
Services.
1
Persons who receive a score above the threshold of 12 on the "Special Learning Needs Screen" should be referred for further assistance.
2
A score above 3 on the MFIP Self-Screen indicates that an individual may have a mental health or chemical dependecy problem and should be referred for a professional
assessment.
3
Employability Measure scores are not available for all participants, thus the number of observations for the percentage of participants scoring 1 or 2 on Employability Measure
scores are as follows: Anoka 235, Chisago 60, Crow Wing 75, Hennepin 86, Ramsey 79, Red Lake 36, St. Louis 129, Washington 90.
17
Table 2-2
Types of Services and Assistance Received by ISP Participants Who Completed
Initial Assessment within a Six-Month Follow-Up Period, by Site 1
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
Completed ISP Assessment2
75.0 %
100.0 %
100.0 %
100.0 %
85.0 %
100.0 %
95.0 %
100.0 %
Additional Assessment Completed by
Outside Agency
35.0 %
45.0 %
60.0 %
11.1 %
70.0 %
5.0 %
20.0 %
57.9 %
Assistance with Mental Health Issue
Participated in Counseling or Therapy
35.0 %
35.0 %
65.0 %
40.0 %
95.0 %
60.0 %
55.6 %
38.9 %
75.0 %
75.0 %
25.0 %
15.0 %
60.0 %
60.0 %
42.1 %
42.1 %
Assistance with Physical Health Issue
20.0 %
20.0 %
35.0 %
16.7 %
50.0 %
0.0 %
30.0 %
31.6 %
Assistance with Substance Abuse Issue
Participated in Substance Abuse Treatment
20.0 %
10.0 %
30.0 %
5.0 %
40.0 %
10.0 %
11.1 %
5.6 %
5.0 %
5.0 %
0.0 %
0.0 %
35.0 %
20.0 %
21.1 %
21.1 %
Assistance in Applying for Supplemental
Security Income Program
90.0 %
30.0 %
25.0 %
16.7 %
40.0 %
10.0 %
20.0 %
31.6 %
40.0
15.0
15.0
0.0
15.0
15.0
5.0
5.0
10.0
5.0
5.0
5.0
%
%
%
%
%
%
%
%
%
%
%
%
70.0
35.0
35.0
10.0
5.0
5.0
10.0
5.0
20.0
15.0
10.0
10.0
%
%
%
%
%
%
%
%
%
%
%
%
100.0
65.0
55.0
30.0
30.0
40.0
45.0
5.0
45.0
0.0
25.0
0.0
%
%
%
%
%
%
%
%
%
%
%
%
55.6
5.6
0.0
0.0
11.1
5.6
5.6
0.0
0.0
11.1
5.6
11.1
%
%
%
%
%
%
%
%
%
%
%
%
30.0
10.0
10.0
5.0
5.0
5.0
0.0
0.0
0.0
5.0
5.0
0.0
%
%
%
%
%
%
%
%
%
%
%
%
15.0
5.0
0.0
0.0
0.0
0.0
0.0
0.0
5.0
0.0
0.0
5.0
%
%
%
%
%
%
%
%
%
%
%
%
70.0
45.0
40.0
20.0
0.0
0.0
0.0
5.0
25.0
0.0
20.0
5.0
47.4
21.1
31.6
10.5
0.0
15.8
0.0
10.5
0.0
0.0
5.3
0.0
%
%
%
%
%
%
%
%
%
%
%
%
30.0
20.0
0.0
10.0
5.0
%
%
%
%
%
45.0
45.0
25.0
20.0
0.0
%
%
%
%
%
80.0
65.0
40.0
35.0
20.0
%
%
%
%
%
44.4
33.3
0.0
22.2
16.7
%
%
%
%
%
35.0
20.0
10.0
10.0
10.0
%
%
%
%
%
65.0
40.0
5.0
35.0
0.0
%
%
%
%
%
85.0 %
89.5
70.0 %
89.5
10.0 %
10.5
45.0 %
5.3
10.0 %
10.5
Continued on next page
%
%
%
%
%
Assistance with Child-Related Issues
Assistance with Child Care
Assistance with Child Protection Issues
Assistance with Parenting Issues
Referred for Assessment
Assistance with Mental Health Issue
Assistance with Physical Health
Assistance in Applying for SSI for Child
Referral to Head Start or other Preschool
Referral to Tutoring or Mentor Program
Contacted Staff at Child's School
Assistance with Other Child-Related Issues
Participated in Employment-Related
Services
Individual Job Search
Structured Job Search Class
Education and Training
Supported Work
%
%
%
%
%
%
%
%
%
%
%
%
18
Table 2-2 (continued)
Types of Services and Assistance Received by ISP Participants Who Completed
Initial Assessment within a Six-Month Follow-Up Period, by Site 1
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
Assistance with Domestic Violence
10.0 %
15.0 %
25.0 %
5.6 %
5.0 %
0.0 %
15.0 %
15.8 %
Assistance with Criminal Justice Issue
15.0 %
25.0 %
30.0 %
11.1 %
0.0 %
10.0 %
15.0 %
47.4 %
Assistance with Housing Issue
35.0 %
50.0 %
85.0 %
33.3 %
15.0 %
10.0 %
55.0 %
68.4 %
Assistance with Transportation
25.0 %
55.0 %
65.0 %
22.2 %
25.0 %
65.0 %
80.0 %
68.4 %
Assistance with Household-Related Issues
10.0 %
20.0 %
45.0 %
22.2 %
25.0 %
0.0 %
35.0 %
57.9 %
Assistance with Other Public Programs
0.0 %
15.0 %
55.0 %
0.0 %
5.0 %
0.0 %
40.0 %
5.3 %
Participated in Cultural-Related Activities
0.0 %
0.0 %
0.0 %
0.0 %
0.0 %
20.0 %
0.0 %
0.0 %
15.0 %
20.0 %
45.0 %
83.3 %
10.0 %
0.0 %
55.0 %
0.0 %
0.0 %
5.0 %
35.0 %
5.6 %
0.0 %
0.0 %
5.0 %
15.8 %
5
6
12
5
4
3
8
7
Attended Support Group
Sanctioned Since Enrollment
Average Number of Services Received by
Participant After Assessment
Distribution of Number of Services
Received After Assessment
No Services Received
1-2 Services Received
3-7 Services Received
8-12 Services Received
13 or more Services Received
Number of Observations
10.0
50.0
5.0
30.0
5.0
%
%
%
%
%
20
0.0
15.0
50.0
30.0
5.0
20
%
%
%
%
%
0.0
0.0
5.0
50.0
45.0
20
%
%
%
%
%
11.1
5.6
77.8
5.6
0.0
18
%
%
%
%
%
15.0
10.0
60.0
15.0
0.0
20
%
%
%
%
%
10.0
35.0
55.0
0.0
0.0
20
%
%
%
%
%
0.0
10.0
25.0
60.0
5.0
20
%
%
%
%
%
0.0
5.3
52.6
31.6
10.5
%
%
%
%
%
19
Source: Review of ISP casefiles by Urban Institute staff.
1 Percentages do not sum to 100 percent because individuals can receive assistance in more than one area.
2 Completed ISP assessment is defined as completing four screening tools: ISP Baseline Data Collection Form, the MFIP Self-Screen, the Brick Screening Tool for Special
Needs, and the Employability Measure. At the time of the casefile review, some sites (Chisago, Crow Wing, Red Lake, and Washington) required ISP participants to
complete the forms prior to enrollment, while in others (Anoka, Hennepin, Ramsey, and St. Louis), enrollment could occur prior to completing the screening tools.
19
public health nurse, who works with participants to identify health concerns, develop a
plan of care, and connect them to preventive health care.
Sponsoring organizations and institutional partners. PFS is housed within the Anoka
County Human Services Division team and is staffed by representatives from various
departments within the division, including staff with expertise in juvenile and criminal
justice, developmental disabilities, public health, vocational rehabilitation, mental health,
chemical dependency, and child protection. In part because this project was developed
from an already-established program, the partnerships for this program have remained
relatively stable over the course of ISP. PFS developed formal partnerships with a
community-based health clinic, Central Center for Family Resources, and a managed
health care plan, Medica/United Behavioral Health. PFS coordinators have consistently
referred participants to Central Center for Family Resources for psychological
assessments and counseling throughout the program’s existence. Medica/United
Behavioral Health initially played a more limited role in the program, although this
partnership has evolved over time. With support from a grant from the Medica
Foundation, PFS added a public health nurse to the PFS team.
Staffing. There are currently 13 employees under the project, including staff that
specialize in child protection, criminal justice, public health, vocational rehabilitation,
mental health, chemical dependency, developmental disabilities, SSI, and MFIP
eligibility. Five employees are fully supported by supplemental county funding. Team
members maintain ongoing connections and support from their respective county
departments. The PFS team is housed at a single location, and all team members work in
close proximity to one another.
Target group and enrollment levels. PFS targets families receiving multiple services in
Anoka County who have multiple barriers to attaining sustained employment.
Participants do not need to be MFIP recipients, although non-MFIP cases are not
supported by ISP funds. Participating families must have children in the household, must
demonstrate resiliency (as determined by program staff), and must be willing to work
with PFS. For clients on MFIP, the program targets those who have been receiving MFIP
assistance for less than 52 months unless they are in a priority group or need help
applying for SSI. In addition to targeting a general hard-to-employ population, the Anoka
program has a strong focus on assisting individuals or their children in applying for the
SSI program. As of April 2008, the program’s cumulative enrollment was 587 families,
and 210 families were served in the first quarter of 2008. Of these families, 138 were on
the SSI track and worked exclusively with a staff person on this issue while the
remaining 72 families received a broader range PFS services.
20
Participant characteristics. On average, PFS participants are approximately 35 years
old. Over two-thirds of participants are white and nearly one-quarter are black. Nearly 40
percent have not attained a high school degree. Further, based on results from a reading
test, approximately one-third of participants read at an 8th grade level or below. Results
from the Brief Screening Tool for Special Learning Needs show that over 40 percent of
participants scored above the threshold indicating that additional assistance was needed.
Mental and physical health problems are a significant barrier for participants in Anoka
County. About two-thirds of participants (64 percent) reported having a mental condition
that makes it hard to work, and nearly 60 percent of participants have ever been
diagnosed with depression. Nearly half of PFS participants (46 percent) reported that they
had a physical condition that makes it hard to work. A significant proportion of
participants also report having a family member with an illness or disability making it
hard to work (38 percent). In addition, the MFIP Mental Health and Substance Abuse
Self-Screen shows that 92 percent of participants in Anoka scored at a level indicating
that an individual may have a mental health or chemical dependency problem and should
be referred for a professional assessment. The Employability Measure assesses ISP
participants in 11 areas related to employment. A score of 1 or 2 indicates an area that
prevents or seriously interferes with employment. Thus, it is useful to look at the percent
of participants with a score of 1 or 2 in each area at baseline (see table 2-1). Overall, 85
percent of Anoka’s ISP participants received a score of 1 or 2 on the health domain
(which includes mental, physical, and mental issues) of the Employability Measure. The
program’s focus on SSI advocacy partially explains the notable prevalence of mental and
physical health barriers among PFS participants.
Recruitment and enrollment. PFS referrals can come from any source in the county.
Initially, the majority of referrals to the program came from MFIP financial workers and
employment counselors. However, referrals from other county departments have
increased throughout the program’s duration as other departments have gained a better
understanding of PFS’ role and the ways in which the program can help families involved
in multiple systems. In particular, an increasing number of referrals have come from the
child protection department, with now approximately a quarter of referrals come from
this source. Referred clients are contacted by an ISP intake worker who conducts an indepth initial screening and determines whether the referral is appropriate. After referrals
are reviewed and accepted, clients are assigned to a case manager according to their
specific needs and the ISP assessment tools are completed.
Types of services provided. Based on the results of participants’ assessments, PFS staff
work with participants to develop individualized and comprehensive plans that identify a
set of services to address participants’ goals and barriers. As discussed, Anoka’s ISP
team includes a disability advocate who works exclusively with SSI-track participants.
Individuals on the SSI-track do not have complete the full ISP assessment, although
many do. Other PFS team members may be used for consultation or assigned to families
as a secondary worker, where appropriate. Staff have low caseloads, ranging between 15
and 20 cases. Key program services include case management, integration of services
within the PFS team, a parent support group, assistance in moving to SSI, ready access to
21
other professionals that can provide support, home visits, referrals to outside agencies and
programs, and a focus on children in the family. In Anoka, individuals on the SSI-track
can receive SSI-related services without completing the ISP assessment, which explains
the somewhat lower percentage of participants that completed the ISP assessment in this
site (75 percent).
The case-file review in Anoka shows notably, 90 percent of participants in the sample
received assistance with applying for SSI, the most commonly received service according
to this analysis. Approximately half of ISP participants in Anoka County receive
assistance solely focused on this activity, while the remainder receive SSI assistance in
addition to other services. Assistance with child-related issues is also common, with 40
percent of PFS participants receiving assistance in this area including assistance with
child care and child protection services, making referrals for additional assessments of
the child, and providing services to address the child’s mental health.
To address the high incidence of mental health barriers in this population, PFS case
workers provide assistance related to mental health. This typically includes assistance in
diagnosing mental health problems and referrals to professionals who provide counseling,
therapy, or drug treatment. Over one-third of participants received assistance in this area,
and the same proportion participated in counseling or therapy. One-fifth of participants
received assistance with substance abuse, and one-fifth received assistance with physical
health issues. Assistance in these areas generally consists of referrals to doctors,
specialists, and treatment programs. About one-third of participants received
employment-related assistance in this area. The prevalence of SSI-track participants in
the ISP program, who are likely to have difficulty working, likely resulted in lower levels
of assistance in this area than may be expected.
Participant experiences. Six women participated in the focus group in Anoka, all of
whom had been involved in PFS for a year or longer. Mental health problems were a
common theme among participants in Anoka. Five out of six participants expressed
feelings of isolation and depression, and three had contemplated suicide. Overall,
participants in the focus group were extremely positive about the program and expressed
the feeling that their participation in PFS had resulted in significant improvements in
their lives. Specifically, five participants felt that the emotional support derived from
their PFS worker and referrals to psychological services had improved their mental
health. Three participants felt that they were able to be a better parent to their children as
a result of feeling more emotionally and mentally stable since enrolling in PFS. The PFS
program impacted other aspects of participants’ lives as well. All participants in the
group felt that, to some extent, they were in a better financial position and had increased
their ability to be self-sufficient. Overall, there were little participants in the focus group
wanted to change about the program. Suggestions included developing a comprehensive
directory of community services and expanding the program size.
Implementation experiences. The Anoka ISP formally integrates a wide range of
services by staffing the program with partners from divisions within the county’s human
22
service department, including staff with expertise in juvenile and criminal justice,
developmental disabilities, public health, vocational rehabilitation, mental health,
chemical dependency, and child protection in one location with a single supervisor. In
part because this project was developed from an already-established program, the
partnerships for this program have remained relatively stable over time. One exception is
that Anoka added a public health nurse as a partner (with funding from the Medica
Foundation) to assist in providing preventive care.
While it was initially difficult to move beyond “silos” and create this integrated team,
program managers in Anoka attribute their ability to bring together a wide range of
services to support they received from upper levels of management within the county’s
human service department. Program staff note that system integration has been a priority
of the department from the beginning.
In terms of challenges, it took time for PFS staff to develop the “trust” of other
departments within the agency. At first, some programs were reluctant to make referrals
because they did not understand the goals or services of PFS, but this improved over time
as a better understanding of how PFS worked with families in multiple service systems
developed. One accomplishment was to improve the number of referrals from child
protection to PFS, based on the positive experiences of a number of participants during
the initial stages of the program.
Another challenge for PFS program was the relatively disadvantaged nature of the
population served and its dual service tracks. Because the program featured a major
component that involved moving individuals to SSI, it served many participants who
faced severe barriers and challenges. Some participants were not motivated to take the
steps to move toward employment because of their significant barriers. Program staff had
to work to develop appropriate referrals and service strategies for individuals on both
service tracks.
Chisago: Chisago County Integrated Services Project
Project summary. From 2005-2007, the Chisago program operated in a five-county
region that in addition to Chisago included Isanti, Kanabec, Mille Lacs, and Pine
counties. During this period, case coordinators in each county worked one on one with
participants to address their barriers to employment and refer them to additional
assessment and resources in the community. Case coordinators coordinated with other
service providers who work with their clients, including child protection, probation,
Women, Infants, and Children (WIC), public health, and mental health. Case coordinators
can refer participants to Five County Mental Health Crisis Services to receive
individualized treatment plans to stabilize their mental health issues. Supported
employment is provided to some participants through a partnership with RISE, Inc.
Recently, beginning in January 2008, Chisago County split off to become its own
program, while the other four remaining counties (Isanti, Kanabec, Mille Lacs, and Pine)
continued to operate as a multi-county site with Isanti County serving as the fiscal host
23
(the multi-county site is referred to as the Isanti program). The focus of this report is on
the program structure and services provided before this split occurred (the evaluation
focuses on participant outcomes through the end of 2007).
Sponsoring organizations and institutional partners. Communities Investing in
Families (CIF), a nonprofit that works with community groups in the area to help lowincome families, coordinates and oversees the Chisago ISP. Isanti County Health and
Human Services, Kanabec County Health and Human Services, Mille Lacs County
Health and Human Services, and Pine County Health and Human Services are also
important program partners. Pine Technical College also provides fiscal and
programmatic support. Coordinators can refer families to Five County Mental Health, the
project’s mental health partner. A substance abuse program (Rum River Recovery) and
child care resource and referral program are secondary partners and their services are
accessed on an as-needed basis. RISE, Inc., a supported employment provider and an
initial ISP partner, is now less involved with the project due to budget cuts resulting from
the program’s split with Chisago County. Further, the health plan (Medica) has only
played a minimal role in the program.
Staffing. Prior to 2008, the project was overseen by the CIF executive director. Day-today supervision was provided by a full-time coordinator for the region. There were four
part-time case coordinator positions. Three case coordinators are employed by Pine Tech
and one is employed by Kanabec County Family Services. One case coordinator was also
the part-time regional SSI advocate for all counties. In addition, Chisago County ISP has
a full-time supported employment case manager from RISE and a part-time social worker
to provide services to ISP clients in the region. The ISP team also includes a half-time
MFIP social worker who works with families with child protection issues. In addition, the
program added a social security advocate who assists ISP families with the SSI
application process (this position is not funded by the ISP grant).
Target group and enrollment levels. This project targets families receiving MFIP who
are among the hardest to serve and have multiple barriers to self-sufficiency, including
mental health, chemical dependency, poor work history, and housing issues. As of spring
2008, the five-county program cumulatively served 292 families. In the first quarter of
2008, the Chisago program served 32 families and the four-county Isanti program served
42 families.
Participant characteristics. Across the five counties, the average age of ISP participants
is 32 years old. About 84 percent of ISP participants are women, and the majority (93
percent) are white. About 4 percent are Native American, and a small percentage are
black or Hispanic. Nearly 38 percent do not have a high school diploma or a GED.
According to a reading test, about 6 percent read below a 4th grade level and 39 percent
scored at a 9th grade reading level or above. The rate of learning disabilities among
Chisago ISP participants is high. Over one-third of participants report that they have been
diagnosed with a learning disability at some point in their lifetime. On the Brief
Screening Tool for Special Learning Needs about 29 percent of participants scored above
24
the threshold that indicates they may have a learning disability and should be referred for
further assistance.
The level of mental health problems among participants is also markedly high. Over twothirds of participants have been diagnosed with depression in their lifetime, and about 43
percent report that they have a mental condition that makes it hard to work. On the MFIP
Mental Health and Substance Abuse Self-Screen, two-thirds of participants scored at a
level suggesting that an individual may have a mental health or chemical dependency
problem and should be referred for a professional assessment. Rates of substance abuse
are also high – nearly one-third of participants report abusing drugs or alcohol in the past
year, and 28 percent have been in chemical dependency treatment at some point in their
lifetime. Physical health problems pose another barrier to work for some participants in
Chisago. About 27 percent of participants have a physical health problem that makes it
hard to work, and 27 percent report that they have a family member with an illness or
disability that makes it difficult to work. Overall, about 61 percent of Chisago’s ISP
participants received a score of 1 or 2 on the health domain (which includes physical,
mental, and chemical issues) of the Employability Measure and over 70 received these
low scores on the social support domain. Many participants face more than one serious
barrier to employment: over two-thirds of participants have at least two barriers and 28
percent of participants face four or more barriers to employment.
Recruitment and engagement. Referral and recruitment strategies vary slightly from
county to county. Generally, case coordinators receive referrals from MFIP employment
counselors. In three of the four counties, the employment counselor initially introduces
the idea of working with ISP to the participant. Because the program targets the hard-toserve, many referred participants are at risk of sanction, and ISP is often recommended as
a way to come into compliance. Case coordinators aim to avoid “cold calling”
participants, as they want to emphasize the voluntary nature of the ISP. Once a
participant expresses interest in the program, a case coordinator will contact him or her
and set up a time to conduct a home visit. During the first few meetings, case
coordinators complete an initial assessment, including the ISP baseline forms
Types of services provided. Once enrolled, clients meet with a case coordinator
regularly. Case coordinators can refer clients to a variety of community resources,
including in-depth assessment (e.g., mental health, chemical dependency), supported
employment, job training, rehabilitation services, housing assistance, and others. Case
coordinators may also contact other professionals who work with their clients with the
goal of developing a more unified case plan for the participant.
The case-file review shows that child-related assistance was the most commonly received
service in Chisago, with 70 percent of participants in our sample receiving assistance in
this area. Most services in this area consist of referring families to special programs for
the child or contacting another program involved with the child. For example,
coordinators often refer participants to child care programs or assist with applying for
child care subsidies. With an MFIP social worker who specializes in child protection
issues on staff, assistance with child protection cases is also common.
25
Reflecting the high incidence of mental health problems among this population,
assistance with mental health issues is also common – 65 percent of participants received
assistance in this area. Coordinators often refer participants to various organizations that
provide assessments and counseling, including Five County Mental Health Crisis
Services, a partner organization. Notably, 40 percent of participants in our sample
participated in counseling or therapy. Assistance with substance abuse issues was also
relatively high, with 30 percent of participants receiving assistance in this area.
Housing and transportation are two other areas with which participants received
assistance; around half of the participants in our sample received assistance in each of
these areas. The area served by this program is predominantly rural and lacks
comprehensive public transportation, making it difficult for participants without a reliable
car to get to work. Further, program staff noted that housing costs in the area are high and
many participants struggle to find affordable housing. Forty-five percent of participants
received assistance with employment-related services, most commonly supervising
individual job search.
Implementation experiences. The Chisago program faced unusual challenges because
their initiative involved a regional focus with five counties. Ultimately, this approach did
not work well because of the differing service environments across the counties and the
program recently split into two separate programs as discussed above.
Overall, the Chisago primarily relied on a service brokering model where staff made
referrals on an as-needed basis. The role of a mental health partner, Five County Mental
Health and River Recovery Services, played a more active role as the program
progressed. However, the supported employment, substance abuse, and child care
resource and referral programs remained as secondary partners throughout the study
period. In general, during the study period, the Chisago program was not able to formally
engage partners that provided expertise in a broad range of areas.
Crow Wing County: Crow Wing County Integrated Services Project
Project summary. Crow Wing’s ISP builds on a segment of the county’s existing MFIP
program that targets hard-to-employ MFIP recipients, operated by Crow Wing County
Social Services (CWCSS) and known as the Tier 3 program. Through ISP, this project
was able to bring in greater coordination with child protection and chemical dependency
services than had existed in the past. For the ISP/Tier 3 program, MFIP recipients who
have been identified as having multiple barriers that affect their ability to obtain and
maintain employment are transferred to an MFIP outreach specialist at CWCSS who
provides case management services and referrals to appropriate community resources.
Using resources from the ISP grant, Tier 3 services are augmented by involving
supervisors from child protection services (CPS) and chemical dependency divisions at
CWCSS to provide ongoing guidance and enhance coordination with these services. In
addition, an ISP specialist whose position was modeled after the Healthy Moms/Healthy
Children chemical dependency program for mothers assists the MFIP outreach specialists
26
with case management and home visits for participants with chemical dependency issues.
A public health nurse from the Crow Wing Public Health Agency is also available to
provide services as needed and participate in monthly staff meetings. Because ISP is part
of Crow Wing’s MFIP program, participation is mandatory and individuals can be
sanctioned for noncompliance.
Sponsoring organizations and institutional partners. Crow Wing County’s ISP is
operated by Crow Wing County Social Services (CWCSS). Key partners include Crow
Wing County Child Protection Services (CWCCPS), Crow Wing County Chemical
Dependency Unit (a division of CWCSS), Crow Wing County Public Health, and South
Country Health Care Alliance.
Staffing. ISP/Tier 3 staff include a director; an income maintenance supervisor
responsible for project planning, budgeting, and program oversight; an MFIP supervisor
responsible for day-to-day operations; four MFIP outreach specialists (two are part-time);
and a Family Service Aid (all the above from CWCSS). A child protection supervisor
from Crow Wing County Protective Services provides supervision and coordination with
child protection services routinely. A supervisor from the Crow Wing County Adult
Mental Health Chemical Dependency Division plays an ongoing, consultative role on the
project. Finally, a manager from the Crow Wing County Department of Public Health is
available for information and guidance.
Target group and enrollment levels. All cases in the Tier 3 program are a part of ISP.
To be eligible for ISP/Tier 3, individuals must have multiple barriers to employment that
can include chemical dependency issues, mental health issues, physical health issues, low
IQ, and lack of education. In addition, mothers who are under 18 years old and eligible
for MFIP are automatically placed in Tier 3/ISP. Crow Wing does not have to enroll
additional families in the ISP program. As of April 2008, the Crow Wing Tier 3 program
had cumulatively served 155 families, with 120 families served in the first quarter of
2008.
Participant characteristics. Crow Wing ISP participants are, on average, 30 years old
and have about two children. The vast majority of participants (about 92 percent) are
white, with a small percentage of black and Native American participants. Almost onequarter lack a high school degree or GED. Results from a reading test suggest that many
Crow Wing ISP participants can read at a high school level or above. Nearly 84 percent
scored at a 9th grade level or above, while only 9 percent read at an 8th grade level or
below. Self-reported data and results from the Brief Screening Tool for Special Learning
Needs suggest that some participants have learning disabilities. About 20 percent of
Crow Wing ISP participants report that they have been diagnosed with a learning
disability in their lifetime. On the Special Learning Needs Screen, almost 27 percent of
participants scored at a level that suggests they should be referred for further assistance.
The rate of mental health problems among Crow Wing ISP participants is high. Nearly 63
percent of participants report that they have ever been diagnosed with depression, and 35
percent have a mental health condition that makes it hard to work. Further, 77 percent
27
scored above a level on the MFIP Mental Health and Substance Abuse Self Screen that
indicates they may have a mental health or chemical dependency problem and should be
referred for professional assessment. About 19 percent reported abusing drugs or alcohol
in the past year, and 27 percent have been in treatment for chemical dependency at some
point in their lifetime.
Self-reported data suggest that many Crow Wing ISP participants have unstable housing
histories. Nearly 40 percent of ISP participants have ever been homeless, and 33 percent
have been evicted. At the time of enrollment, the most common housing situation was
unsubsidized rental, with approximately 38 percent of participants living in this
arrangement. About 27 percent of participants lived in a subsidized rental, and 12 percent
owned their own home. Almost two-thirds of ISP participants in Crow Wing received a
score of 1 or 2 on the financial (62 percent) and social support (64 percent) domains of
the Employability Measure.
Recruitment and engagement. Crow Wing MFIP employment counselors and financial
workers identify cases that appear to meet the criteria for the Tier 3 program and make a
referral. The MFIP supervisor and child protection supervisor review these referrals and
make a final determination regarding enrollment in the ISP/Tier 3 program. In addition,
mothers who are under 18 years of age and on MFIP are considered a priority; these
referrals bypass the waitlist and are enrolled in ISP immediately. Those assigned to the
ISP/Tier 3 program are assigned to an MFIP outreach specialist who replaces their MFIP
employment counselor. The outreach specialist obtains available documentation on
participants (including background information from the child protection system), after
which a meeting is set up with the participant to conduct assessments.
Types of services provided. The ISP assessment consists of completion of all of the ISP
baseline forms, and an individual is not considered an ISP participant until all these forms
are completed. Based on assessment results and participant needs, Crow Wing ISP staff
develop an employment/social service plan that documents key steps for participants to
take. Referrals are made as needed for a range of services including mental health and
domestic violence, with special attention given to CPS and chemical dependency, given
added program expertise in these areas. The MFIP outreach specialist maintains weekly
contact with participants with a caseload of approximately 20 per worker and is in contact
with professionals from other programs and systems in the community that can include
probation, Head Start professionals, and schools. One of the part-time MFIP outreach
specialists works with mothers under the age of 18 who are automatically placed in ISP.
The ISP specialist assists the MFIP outreach specialists with home visits and case
management.
The case-file review shows very high levels of assistance in multiple areas, and the
highest levels among all the ISPs. Notably, all participants received assistance with childrelated issues. For example, MFIP outreach specialists help participants with child care
arrangements, assist with parenting issues, and refer participants’ children to doctors or
other services. Further, the program’s connection with child protection results in greater
28
communication between ISP and the child protection department, and participants
frequently receive assistance with child protection issues.
Nearly all participants (95 percent) also received assistance with mental health issues.
Services in this area include referrals to counselors and psychologists for assessments and
counseling. Forty percent of participants received assistance with substance abuse issues.
The Family Service Aid staff specializes in working with mothers who have past or
current chemical dependency issues, which may explain the high level of substance
abuse-related service receipt. Furthermore, a supervisor from the county Adult Mental
Health/Chemical Dependency agency acts as a consultant for the ISP program. Program
staff also address participants’ physical health issues; over one-third of participants
received physical health-related assistance. For example, participants can be referred to
the public health nurses that are part of the ISP team to help managing medications and
treatment plans.
Employment-related assistance was also common, with about 80 percent of participants
receiving this type of assistance. Specifically, MFIP specialists frequently assist
participants with individual job search, as well as refer them to structured job search
classes or work-related education and training. Assistance with housing and
transportation is also common, with 85 percent and 65 percent of participants receiving
these types of assistance, respectively. For example, MFIP specialists assist participants
with applying for Section 8 housing or with arranging for financial assistance toward car
repair. Reflecting the mandatory nature of the Crow Wing ISP program, about one-third
of participants had been sanctioned for noncompliance resulting in a reduction in their
MFIP grant.
Another key component of Crow Wing’s ISP is the Lifeworks program, a weekly group
session for ISP participants. Lifeworks includes two “levels” – Lifeworks 1 and
Lifeworks 2. Lifeworks 1, which lasts for eight weeks, focuses on basic skills, including
decision making, creating a routine, and managing daily tasks, and also addresses mental
health and other issues as they arise. After completing Lifeworks 1, participants can
move on to Lifeworks 2, which expands on the issues raised in Lifeworks 1 and serves as
a support group for ISP participants. Lifeworks 2 is ongoing, and participants can attend
group session for as long as they wish (or as long as their worker feels it is necessary). In
addition, Teen Works, a group for younger moms, began meeting in April 2008. Teen
Works covers many of the same issues as Lifeworks, with a focus on the specific issues
that young mothers face.
Implementation experiences. The Crow Wing ISP program augments the existing MFIP
program with a set of services designed to meet the needs of hard-to-employ MFIP
recipients and the program is mandatory for ISP participants. Unlike the other sites using
a team approach, the Crow Wing program primarily integrates services by incorporating
supervision rather than direct service provision. These supervisors play an important role
in providing case consultations to staff, particularly in the areas of child protection and
chemical dependency issues. However, service brokering, where staff make referrals to
29
existing service providers in the community are still the primary mechanism through
which participants receive services.
The relatively smaller size of this county, where many services were already colocated,
made integrating services at this level relatively more straightforward than in other
locations. The program managers note that support from the higher management levels
within the county was also critical for successfully implementing their service integration
model. In addition, because the program was an enhancement to the existing MFIP
program, rather than a new programmatic effort, fewer implementation issues were
encountered in this site.
Hennepin County: Gateway to Success
Project Summary. Housed at NorthPoint Health and Wellness Center, Inc., the core
service of Hennepin County’s ISP, called Northside Families Gateway to Success
(Gateway), is case management services focusing on family development provided by
family facilitators. Family facilitators seek to connect participants and their families with
services in the community that address employment and other barriers faced by
participants and their families. Family facilitators are from MFIP employment service
providers, with experience addressing employment issues for hard-to-employ
populations. An on-site psychologist from NorthPoint is available to provide assessments
and counseling on mental health issues to ISP participants, and Tubman Family Alliance
and Turning Point provide on-site staff assistance on domestic violence and substance
abuse issues, respectively, and there is a staff person who specializes in youth issues.
Although not officially part of the Gateway program, NorthPoint Health and Wellness
Center also has many resources for families colocated at the same facility, including a
food bank and a medical, dental, and mental health clinic. The Gateway program includes
an emphasis on promoting “family empowerment,” a topic which is addressed through
monthly support group meetings, which all participants are required to attend.
Sponsoring organizations and institutional partners. Gateway is sponsored by and
located within NorthPoint Health and Wellness Center, Inc., a community health and
human services center that also includes an on-site medical, dental, and mental health
clinic. Gateway’s organizational partners include three MFIP employment service
providers (Minneapolis Urban League, HIRED, and Pillsbury United Communities), two
community-based social service providers (Turning Point and Tubman Family Alliance),
and Metropolitan Health Plan. The Urban League provides employment and job
development services to Gateway participants through its employment resource center.
Staffing. The Gateway service team includes a mix of NorthPoint employees and staff
from partner organizations. Both HIRED and Pillsbury United Communities employ one
of Gateway’s five family facilitators; the remaining three are NorthPoint employees. In
addition, the team includes a part-time men’s advocate and a male and female youth
advocate. A staff member from Turning Point is onsite 20 hours per week to work with
Gateway families with chemical dependency issues, and a Tubman Family Alliance
employee serves as a domestic violence advocate. In addition, a NorthPoint psychologist
30
is onsite to provide assessments and counseling to Gateway families and a nurse
practitioner from the NorthPoint health clinic assists in case consultations as needed. The
staff also includes a part-time youth specialist.
Target group and enrollment levels. The program primarily focuses on MFIP families
who reside in North Minneapolis or are served by a participating North Minneapolis
MFIP employment service provider. (Future plans include expanding to a larger
geographic area.) In addition, eligible MFIP families must meet at least one of the
following criteria: (1) involvement in the adult or juvenile criminal justice system; (2)
involvement in child protective systems; (3) involvement in behaviorial and physical
health services to include mental health, chemical dependency, chronic or debiliating
medical issues and development disability concerns; (4) 18- or 19-year-old parent
involved in educational programs at the West Broadway Community School located in
North Minneapolis; or (5) involvement in shelter system or documented recent episodes
of homelessness. As of April 2008, the program’s cumulative enrollment was 187
families, and 88 families were actively participating in the program.
Participant Characteristics. On average, Gateway participants are approximately 32
years old. The vast majority of participants (86 percent) are black, and 12 percent are
white. A small proportion of participants are Native American. Gateway participants are
predominantly women (89 percent). About one-third of Gateway participants in
Hennepin did not have a high school diploma or GED at the time of enrollment.
According to the reading test, while 64 percent of participants can read at a 9th grade
level or above, one-quarter of participants read at an 8th grade level or below, and nearly
10 percent read below a 4th grade level. Learning disabilities appear to be fairly common
among Gateway participants, with 20 percent of participants reporting that they have
been diagnosed with a learning disability at some point in their life. Further, nearly 28
percent of participants scored at a level on the Brief Screening Tool for Special Learning
Needs that suggests they should receive additional assistance addressing their learning
disability.
Many Gateway participants and their families face physical and mental health issues that
may limit their ability to find and maintain employment. Nearly one-fifth of participants
reported that physical health condition limited their ability to work, and a similar
proportion had a family member with an illness or disability that made it hard for them to
work. Levels of mental health issues among participants are even higher. Half of
Gateway participants reported ever being diagnosed with depression, and one-quarter had
a mental health condition that made it hard to work. Strikingly, on the MFIP Mental
Health and Substance Abuse Self Screen, 84 percent of participants scored above the
threshold that indicates that an individual may have a mental health or chemical
dependency problem and should be referred for a professional assessment.
A number of Gateway participants have a criminal background, which can potentially
limit their employment opportunities. Notably, one-third of participants have spent time
in jail or prison, and 28 percent have been convicted of a felony. Gateway families’
current and past housing situations are indicative of their level of economic disadvantage.
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Over half of Gateway participants (53 percent) have ever been homeless, and over 25
percent have been evicted at some point in their life. However, few were living in
emergency housing at the time they enrolled in Gateway. Upon enrollment, 44 percent of
Gateway families were living in subsidized housing.
Recruitment and engagement. The program primarily relies on MFIP employment
counselors from the three key partner as well as the other North Minneapolis MFIP
employment service providers in Hennepin County to refer appropriate individuals from
their caseloads. MFIP employment counselors use the county’s data sharing system to
identify participants involved in more than one county system. To increase referrals,
county administrators of the MFIP program refer all MFIP recipients who meet the
program’s eligibility criteria. ISP program staff are then able to contact these individuals’
MFIP employment counselors and request that they refer them to Gateway. Gateway
management and staff have provided information sessions to all North Minneapolis MFIP
employment service providers in the county. NorthPoint also occasionally hosts informal
recruiting events with food and door prizes for eligible families, during which they are
introduced to the program, staff, and other participating families. While Gateway has
continued to receive a steady flow of referrals, a growing group of inactive participants
spurred the creation of a new staff position in 2007 to enhance Gateway’s recruitment
effort. This staff person regularly visits MFIP employment service providers in the area
to educate their staff and management about Gateway services and generally serves as a
liaison between these employment service providers and the ISP.
Once participants are referred to Gateway and their eligibility is verified, family
facilitators contact participants to gauge their interest in the program. If they choose to
enroll, an initial one-on-one meeting is scheduled, during which a family facilitator goes
over program components and begins the intake process. At this point, an individual is
considered a Gateway participant. Over the next month, the baseline assessment forms
are completed. In addition, participants may be referred to Gateway’s onsite psychologist
for a psychological assessment as the family facilitator sees fit.
Types of services provided. Soon after participants are enrolled in Gateway, they work
with their family facilitator to develop a comprehensive case plan that addresses the
needs and barriers identified (this could involve referrals to a wide range of services and
service providers). Family facilitators attempt to maintain regular contact with the
participants to monitor progress on achieving these goals. Many participants meet with
their family facilitator on a weekly basis and may be in contact by phone as frequently as
every day. Caseloads range from 20 to 25. Family facilitators connect participants and
their families with on-site program. A monthly support group is also an important
program activity.
Based on the case-file review, Gateway participants receive assistance in a number of
areas. They commonly receive services to address their mental health barriers, with over
55 percent of participants in our sample receiving assistance in this area. For example,
family facilitators often refer participants to NorthPoint’s onsite psychologist for
assessments and counseling (nearly 40 percent of participants participated in counseling
32
or therapy). Participants also benefit from having the NorthPoint health clinic onsite,
where family facilitators can refer them for medical consultations and treatment;
approximately 17 percent of participants received assistance with physical health issues.
Approximately 44 percent of participants received assistance with employment-related
issues, and one-third received help with an individual job search. Since 2007, Gateway
participants receive employment and job training services through the Urban League –
located across the street from NorthPoint – and have access to their Resource Room,
where they can search for jobs, create and update resumes, enroll in training and
educational programs, and develop soft skills.
Over half of Gateway participants received assistance with child-related issues, reflecting
Gateway’s focus on serving the entire family. As noted, Gateway’s team includes staff
members whose expertise is working with male and female youth. Family facilitators also
refer children of Gateway families for assessments, counseling, medical care, or other
services as necessary.
In addition to individualized referrals and services, a major component of the program is
a monthly empowerment group, which all participants are encouraged to attend (over 80
percent of participants in our sample attended the group). The empowerment group is
focused on job readiness, job search skills, understanding poverty, and developing
“personal power.” Sessions are based on curriculum developed by program consultants
and facilitated by program staff. To supplement the empowerment groups, Gateway
began sponsoring an informal support group in June 2007 for Gateway participants called
the Women’s Café. The group functions as a drop-in session for two hours each month,
and anyone who is interested may attend. There is no set agenda for the meetings – group
participants control the flow and content of the discussion. Gateway’s in-house
psychologist and domestic violence specialist are also available during this time should
any issues arise that require their attention.
Gateway has undertaken several new initiatives in 2007. The first, Model Families, is
aimed at Gateway’s upwardly mobile families. Gateway identified a cohort of 20 families
whom they feel, with a small amount of additional support, have the potential to move to
a higher level of self-sufficiency, and program staff envision them eventually becoming a
part of the Gateway team and lending their expertise and experiences to current Gateway
participants. Families Opening Doors is another new Gateway project that pairs Gateway
families with a local artist to work together on an ongoing art project.
33
Participant experiences. Overall, participants in the Hennepin program reported having
strong relationships with their facilitator and were overwhelmingly positive in their
reviews of Gateway. The focus group held with 12 Gateway participants indicated that
they drew upon the group sessions for support and encouragement and this was a critical
program activity to them. These participants stressed how positive it was for them to be
able to discuss events and issues in their lives with others in the group sessions. Focus
group participants also reported receiving a wide range of services through their ISP
worker including help paying bills, managing money, transportation, and help with
disability assistance. The program helped several participants through various stages of
the job search process, including job leads and interview skills. Access to mental and
physical health services was another area in which Gateway facilitators aided
participants. Two participants were seeing a doctor located in the NorthPoint complex for
counseling. Several other participants received assistance with their children, with. In
particular, three participants mentioned the Gateway mentoring program for youth.
Implementation experiences. The Gateway program is notable because of the number of
on-site organizational partners with specific areas of expertise that are part of the
program. The Hennepin program includes staff that with expertise in employment,
substance abuse, and domestic violence as well as an on-site psychologist, public nurse,
and youth specialist. While not technically part of the Gateway program, because the
program is colocated at a community health clinic, many other resources are available onsite. Compared to the other ISP sites, the Gateway program has made more changes to
their program services over time, most recently adding initiatives focused on more
upwardly mobile families. Program managers stress that a key to eliciting the ongoing
participation of staff from these organizations is that the Gateway provided resources to
cover at least a portion of the staff person’s time. This way other organizations see a
benefit to participating in the program.
Ramsey County: Integrated Services Project
Project summary. The Ramsey County initiative is designed to develop and integrate
rehabilitation expertise in mental health into the county MFIP program, while accessing
new funding outside the regular MFIP allocation. The ISP provides financial support to
several county MFIP employment service providers and Goodwill/Easter Seals to meet
capacity and certification standards to provide services under Adult Rehabilitative Mental
Health Services (ARMHS). Services provided by ARMHS-certified providers aim to help
individuals with mental illness or poor mental health improve functionality, with the goal
of obtaining employment. Once providers are determined capable of delivering this set of
mental health services, they become certified as an ARMHS service provider and are able
to bill Medical Assistance (Minnesota’s Medicaid program) directly for services. Once
certified to provide ARMHS services, providers are able to deliver the services to eligible
MFIP clients. Each agency has flexibility in how they decide to bring ARMHS services
into their MFIP programs.
Sponsoring organizations and institutional partners. Ramsey County Community
Human Services Department, while officially the lead agency, was heavily involved in
34
the planning phase (particularly the Mental Health division of this agency), but has been
less involved in the operational phase of the program. When the program began providing
services, Ramsey County Workforce Solutions, the county MFIP employer service
provider, took on more of an overall coordinating role. Ramsey County has several
providers involved in providing ARMHS services, although there have been changes over
time. As of April 2008, ISP ARMHS providers include Employment Action
Center/Health Choices; HIRED; Family Support Services, Inc.; South Metro Human
Services; Health Choices; Goodwill/Easter Seals; and Lifetrack Resources. Hmong
American Partnership was initially slated to become ARMHS certified, but chose not to
participate due to funding constraints, and Mental Health Resources chose to drop out of
the ISP after providing ARMHS services for two years, in large part because of
budgetary issues.
Target group and enrollment levels. Ramsey County’s ISP targets MFIP participants
with serious mental illness who are stabilized enough for rehabilitation. Before enrolling
in ISP, participants must receive a diagnostic assessment by a mental health professional.
After a diagnostic is completed indicating the clients’ medical necessity for receiving
mental health services, a functional assessment is performed with the clients, which
examines client functionality in 14 different life areas. To be eligible for ARMHS,
individuals must have at least moderate impairment in 3 or more of the 14 areas. As of
April 2008, 707 adults had enrolled in the program cumulatively, and 309 participants
were served in the first quarter of 2008.
Participant characteristics. The average age of Ramsey County ISP participants is 34
years old. Over half of ISP participants are black, while one-quarter are white. Twelve
percent of participants are Asian, reflecting Ramsey County’s significant Hmong
population. About 13 percent of ISP participants are not U.S. citizens. ISP participants
are predominantly women (89 percent).
Many ISP participants in Ramsey County have low levels of education. A strikingly high
proportion (45 percent) have no high school diploma or GED. Available data suggests
English literacy may be a problem for some participants in Ramsey County. Based on
results of a reading, 9 percent of participants cannot read, and one-third read at an 8th
grade level or below. This is likely related to the finding that 10 percent of participants
cannot communicate in English, which reflects the county’s relatively large population of
non-U.S. citizens. According to self reports and results from the Brief Screening Tool for
Special Learning Needs, a number of ISP participants have learning disabilities. About 31
percent of participants reported that they have been diagnosed with a learning disability
at some time in their life. Further, over one-third of participants (35 percent) scored
above the threshold that suggests they have a learning disability and should be referred
for further assistance.
As expected given Ramsey County’s focus on providing services to participants with
mental health issues, the level of mental health issues among participants is striking.
Eighty-three percent of participants have been diagnosed with depression in their
lifetime, and a similar percentage reported having a mental health problem that makes it
35
difficult to work. As measured using the MFIP Mental Health and Substance Abuse Self
Screen, nearly three-quarters of participants scored at a level indicating that an individual
may have a mental health or chemical dependency problem and should be referred for a
professional assessment. Nearly 40 percent of participants in Ramsey County reported
having a physical health problem that limits their ability to work. Overall, nearly 89
percent of Ramsey’s ISP participants received a score of 1 or 2 on the health domain
(which includes physical, mental, and chemical issues) of the Employability Measure and
over 70 percent received these low scores on the social support domain.
Some ISP participants in Ramsey County have a criminal record, which can serve as
another barrier to employment. Nearly one-fifth of ISP participants have been convicted
of a felony, and a similar proportion have spent time in jail or prison. Over one-third have
been homeless at some point in their lifetime, reflecting the level of economic
disadvantage among this population. In addition, approximately one-fifth reported that
they had been evicted at some point prior to enrolling in ISP. The most common housing
situation at the time of enrollment is subsidized housing, with 44 percent of participants
living in a subsidized rental. No ISP participants in Ramsey County owned their home at
the time of enrollment. Overall, nearly 80 percent participants experienced two or more
barriers, and nearly half had three or more barriers.
Recruitment and engagement. Participants are referred to the program by their MFIP
employment counselor. MFIP employment counselors make referrals for ARMHS based
on the MFIP assessment (particularly the MFIP self-screening tool) and their knowledge
of the client. Also, the ARMHS practitioners train MFIP staff on signs used to identify
potential participants. Before ARMHS services may begin, clients must receive a
diagnostic assessment by a mental health professional. After a diagnostic is completed
indicating the clients’ medical necessity for receiving mental health services, a functional
assessment is performed with the clients, which examines client functionality in 14
different life areas. To be eligible for ARMHS, individuals must have at least moderate
impairment in 3 or more of the 14 areas. ISP workers cannot begin billing ARMHS for
any services they provide until these assessments have been completed. The providers
used different methods with some enrolling clients when they were diagnosed with
mental illness, even if it was before they agreed to participate in the program and
completed the assessment. The case-file review shows that some participants (15 percent)
did not complete the assessment and continue in the ISP program. These represent
instances where staff were unable to engage participants after they had been diagnosed
with a mental illness and officially enrolled in the program.
Types of services provided. The Ramsey County ISP program has a strong emphasis on
improving basic functionality and managing mental health treatment and services are
often provided in the home. Once clients are deemed eligible, an ARMHS case worker
develops a treatment plan with the clients. Services under ARMHS may include training
on basic living skills; education on mental health symptoms, medications, and side
effects; parenting; problem-solving; communication; budgeting and money management;
household management; employment and school-related skills; and engaging individuals
in the community such as employers or family members to support the clients. The
36
frequency of meetings and services with clients varies, but are generally frequent and
intensive. An ARMHS provider generally assigns 12 to 15 clients to a worker.
Not surprisingly, the case-file review shows that assistance with mental health was the
most commonly received service among participants in the sample, with 75 percent of
participants receiving assistance in this area. Since Ramsey County explicitly focuses on
mental health and participants must have a documented serious mental illness to receive
services under ISP, it is not surprising that a large majority of participants in the sample
received mental health assistance. Further, Ramsey County has a high percentage of
individuals who participated in counseling or therapy (75 percent), also likely a factor of
the program’s primary emphasis on mental health. Physical health is another area
addressed by ARMHS workers, with half of the participants in our sample receiving
assistance in this area. The rehabilitative nature of the program in Ramsey appears to
result in some individuals receiving assistance with physical as well as mental health
issues. For example, ARMHS providers may refer participants to doctors and specialists
or help them monitor their medications.
Forty percent of participants received assistance applying for SSI, again reflecting the
mental and physical health barriers of this population. Goodwill/Easter Seals, the largest
ARMHS provider in the ISP program, has a disability on staff who is available to assist
participants with the SSI application process. About one-third of ISP participants
received employment-related assistance. Because the program focuses on assisting
individuals with mental health issues with rehabilitation so they can function in their
daily life, employment-related assistance may be less common than assistance in other
areas.
Participant experiences. The five focus group participants in the Ramsey County
reported that a key goal of the ISP was individualized goal-setting. They also reported
frequent contact with their counselors that varied by participant from daily to monthly,
but all participants said that they felt they could contact their counselor whenever
necessary. Employment and education were two areas in which participants said their
counselor helped them, including help applying for college and financial aid and
assistance with resumes and job leads. ISP counselors helped their clients keep track of
their commitments by making reminder calls about things like doctor’s appointments and
ISP counselors also provided advice on how to better communicate with to their children.
All participants received assistance with mental health and when asked if the program
had affected their mental health, each participant spoke up to say that it had affected them
in a positive direction. However, a significant issue for three of the participants was the
perceived “nosiness” of their counselor. And while all participants reported receiving
numerous services, their reactions to the program were mixed. All of the participants
agreed that turnover among ISP counselors was a problem. There was a consensus among
the group that it was difficult to become comfortable with several different counselors.
Implementation experiences. The Ramsey County project serves the most
disadvantaged population of all of the ISP sites, those facing significant mental health as
well as other barriers. Serving this population combined with the billing requirements for
37
ARMHS, created significant financial problems for some providers. In addition, the
intensive data collection required for ARMHS billing has been a challenge for the
providers.
The Ramsey County initiative was initially designed to develop self-sustaining funding
sources once the ISP funding ended. However, the Ramsey program experienced some
difficulties with this financing model. Many of the referrals received for the ISP were
individuals with severe mental illness who were often not at a point where they were
ready to engage in the rehabilitative services provided by ARMHS. The ISP often had to
provide a range of other services before an individual was ready to enter ARMHS, such
as assistance with pressing physical health issues, family issues, or housing needs. In
addition, staff had to complete a number of assessment instruments (see discussion
above) before individuals could be determined eligible for ARMHS. These additional
services were typically not billable to ARMHS, and this put some providers in a difficult
financial situation, causing several to drop out of the program.
Finally, given their high levels of multiple barriers, engaging ISP participants in program
services was particularly difficult in Ramsey County and was an ongoing challenge. The
level of effort required to enroll and elicit ongoing participation was more than
anticipated, and also was not billable to ARHMS. Some providers also did not receive
adequate numbers of appropriate referrals, requiring more training and communication
with MFIP employment counselors. Overall, however, while some providers could not
make this model financially viable, several (particularly Goodwill Easter Seals and
HIRED) have addressed these challenges and serve significant numbers of Ramsey
County ISP participants.
Red Lake: Mino Aanokii
Project summary. Operating on a Chippewa Tribe Indian reservation, Red Lake’s ISP,
called Mino Aanokii (Good Work), focuses on addressing the needs of families who face
multiple barriers to employment. Through multidisciplinary case management,
community workers link hard-to-employ MFIP recipients with appropriate services and
programs on the reservation.
Sponsoring organizations and institutional partners. The Tribal Council of the Red
Lake Band of Chippewa Indians operates the program. Red Lake had difficulties
engaging many of the proposed program partners, primarily owing to lack of stable
leadership. By the second year of the project, partners that were listed in the original
grant, such as Red Lake Family Children Services, Red Lake Chemical Health Programs,
and Red Lake Comprehensive Health/Mental Health Department, were still not
participating in the project. Though the MFIP (New Beginnings) and Beltrami County
Human Services are technically considered partners, they had very little contact with ISP
staff. New Beginnings primarily serves as a referral source and Red Lake Tribal Council
served as the fiscal agent.
38
Staffing. Staff include four community workers. The program is under the supervision of
the executive director of the tribe. All staff members are tribal employees.
Target group and enrollment levels. The project targets hard-to-employ MFIP
recipients with multiple barriers to employment including chemical dependency, mental
health issues, and learning disabilities. While the program focuses on those who have
received over 45 months of MFIP, all MFIP participants are eligible to participate in the
program. By end of 2007, program staff reported that the Red Lake project had
cumulatively served approximately 50 families, with 15 participants served in the first
quarter of 2008.
Participant characteristics. ISP participants are, on average, about 33 years old and
have about two children. Not surprisingly, Red Lake serves a primarily Native American
population – about 98 percent of participants are Native American and 2 percent are
white. The majority of ISP participants (91 percent) are women.
Education levels among ISP participants in Red Lake are low. Fifty percent of ISP
participants do not have a high school degree or GED. About 48 percent report having a
high school diploma or GED as their highest level of education, Based on a reading test,
nearly three-quarters of ISP participants can read at a 9th grade level or above. There is a
particularly low incidence of learning disabilities in the Red Lake site, with only 7
percent reaching the threshold on Brief Screening Tool for Special Learning Needs that
indicated a problem was likely. Further, only 13 percent reported that they had been
diagnosed with a learning disability at some point in their life. Native American
participants in Red Lake may be less likely to report such problems as learning
disabilities or they may have attended schools where fewer diagnostic tools were
available or administered for identifying learning disabilities.
A small proportion of participants – about 13 percent – reported that they have a physical
condition that makes it difficult to work. Similarly, only about 7 percent of participants
say they have a mental condition that makes it difficult to work. On the MFIP Mental
Health and Substance Abuse Self Screen, 41percent of participants scored above the
threshold for referral for a mental or chemical health assessment. This difference could
possibly be because of reluctance to report this type of information given the small
community in Red Lake. However, over one-third (35 percent) of participants disclosed
that they have abused drugs or alcohol over the past year, and 39 percent have been in
chemical dependency treatment at some point in their lifetime. About one-fifth of
participants in Red Lake have ever been homeless. Public housing is the most common
housing arrangement, with approximately 39 percent of participants.
Recruitment and engagement. Program staff receives referrals from the New
Beginnings program and then contacts these individuals to determine their interest in
participating in the program.
Types of services provided. The initial meeting with clients lasts two to three hours and
includes administration of several assessment tools (including the Employability Measure
39
and TABE test). Community workers also develop an employability development plan,
which includes participants’ goals. The goal is for five community workers to each carry
a caseload of 20 clients. Participants may be referred to services on the reservation, such
as GED courses. The program also provides transportation assistance for many clients
and instruction in traditional work activities for clients such as wreath-making, beading,
and gardening.
The case-file review shows that employment-related assistance was one of the most
commonly received services, with 65 percent of participants receiving assistance in this
area. ISP workers assist participants with individual job search, as well as refer
participants to education and training programs. Assistance with transportation was also
common – 65 percent of participants received services in this area. The ISP program
owns several vans to transport participants without a vehicle to and from appointments.
The provision of cultural-related activities was unique to Red Lake. This program
provided referrals to instruction in traditional Native American work activities for clients
such as wreath-making, beading, and gardening. One-fifth of participants took part in
cultural-related activities.
Implementation experiences. Of all the sites, the program in Red Lake clearly had the
most trouble getting the program off the ground and implementing their model as
intended. Primarily owing to changes in leadership and shifting priorities, key
partnerships were never established, and the level of services provided to participants is
relatively low. Partners that were listed in the original grant, such as Red Lake Family
Children Services, Red Lake Chemical Health Programs, and Red Lake Comprehensive
Health/Mental Health Department, were still not participating in the project. Though the
MFIP (New Beginnings) and Beltrami County Human Services were technically
considered partners, they had very little contact with ISP staff. This program also
operates in an area with particularly limited resources and weak economic conditions,
presenting another set of challenges.
St. Louis County: Project Hope
Project summary. The HOPE (Hope and Opportunity in the Pursuit of Employment)
Project operates in four counties in northeastern Minnesota: St. Louis, Carlton,
Koochiching, and Itasca counties. In this project, family employment advocates assess
the needs of families and work with them one on one to help connect them with
appropriate resources in their communities. Family employment advocates, who are
employed by the community action agencies and the Minnesota Chippewa Tribe, work
with participants on a range of issues including transportation, housing, substance abuse,
child care, child support, probation, education, mental health, physical health, and
domestic violence. The HOPE Project also provides funding to expand the Circles of
Support program, a program in which participants are matched with community members
who support their move out of poverty.
Sponsoring organizations and institutional partners. The HOPE Project is overseen
and coordinated by the Arrowhead Economic Opportunity Agency (AEOA), a nonprofit
40
community action agency in St. Louis County. Community action agencies and human
services departments in each of the four counties served also play a key role, including
the St. Louis County Public Health and Social Services and Community Action Duluth in
St. Louis County; Carlton County Public Health and Human Services and the Lakes and
Pines Community Action Council in Carlton County; Koochiching County Human
Services, Itasca County Human Services, and Kootasca Community Action Agency in
Itasca and Koochiching counties.
Staffing. The project director is employed by AEOA, and the community action agencies
and the Minnesota Chippewa Tribe employ seven family employment advocates. The
community action agencies also employ five full-time Circles of Support coordinators
who recruit volunteers for the program, organize weekly Circles of Support meetings, and
train community volunteers and participants. The family employment advocates in St.
Louis County’s Virginia location, Itasca County, and Carlton County are located in
workforce centers with MFIP employment counselors, but advocates in St. Louis
County’s Duluth location and Koochiching County are housed in a separate location. The
Minnesota Chippewa Tribe’s advocate works at the workforce center in Carlton County
and the Minnesota Chippewa Tribe office in Duluth.
Target group and enrollment levels. The HOPE Project targets participants who have
been on MFIP for 24 to 48 months, although it includes families who have been on MFIP
for fewer months if they meet other eligibility criteria. Other criteria include being a
member of a racial or ethnic community, having one or more disabilities, and lacking
substantial work history. Participants should also be motivated and willing to work. As of
April 2008, the program has cumulatively served 261 families. In the first quarter of
2008, Project Hope served 98 families.
Participant characteristics. The average age of HOPE participants is about 29 years
old. Over one-fifth of participants (21 percent) are Native American, and approximately
73 percent are white. In the St. Louis ISP program, about 54 percent of participants have
a high school degree or GED as their highest level of education. Based on a reading test,
two-thirds of participants can read at a 9th grade level or above. Nearly one-quarter of
participants have been diagnosed with a learning disability at some point in their lifetime.
On the Brief Screening Tool for Special Learning Needs, about 27 percent scored at a
level indicating they may have a learning disability and should be referred for further
assistance.
Nearly one-quarter of participants in St. Louis report having a physical condition that
makes it difficult to work and about 19 percent have a family member with an illness or
disability making it hard for the participant to work. Mental heath problems are even
more prevalent among HOPE participants. Approximately two-thirds of participants have
ever been diagnosed with depression, and 39 percent have a mental condition that makes
it difficult to work. On the MFIP Mental Health and Substance Abuse Self Screen, nearly
three-quarters of participants scored above the threshold indicating that they may have a
mental health or chemical dependency issue and should be referred to a professional for
41
assessment. About 19 percent of participants say they had abused drugs or alcohol in the
past year, and 28 percent had ever been in chemical dependency treatment.
Over half of HOPE participants have been homeless at some time in their lifetime.
Further, about one-third have ever been evicted. At enrollment, the most common
housing situation was subsidized rentals, with 50 percent of participants living in this
arrangement. Only 6 percent of participants owned their own home. Over two-thirds of
ISP participants in St. Louis received a score of 1 or 2 on the financial (73 percent) and
social support (70 percent) domains of the Employability Measure.
Recruitment and engagement. Family employment advocates receive most of the
referrals from MFIP employment counselors. Family employment advocates and Circles
of Support coordinators refer and recruit clients for Circles of Support. The program is
entirely voluntary, and the exact recruitment and enrollment procedures vary slightly by
county. In general, once a referral is received from the MFIP employment counselor, the
advocate contacts the client to schedule an initial meeting. Most family employment
advocates make the first contact with potential participants by calling, sending an
introduction letter and brochure, or stopping by their house. During the initial meeting,
the advocate presents the program and the services offered and gauges the participant’s
interest. If interested, the participant is enrolled in the program at that time and the
advocate begins completing the assessment instruments. While most individuals go on to
complete the assessment and participate in the ISP program, a small percentage (5
percent) did not complete the full ISP assessment based on the case-file review.
Types of services provided. Advocates work with participants to create and write out a
plan detailing the participants’ goals. Advocates work closely with participants on
achieving these goals. Some family employment advocates meet with the participants,
their MFIP employment counselor, and their financial worker so they can coordinate their
plans. Each advocate carries a caseload of about 20 participants. They have introduced
some specialization with counselor, particularly those with mental health expertise. If
they know they are facing certain barriers they are better able to refer them to staff with
certain expertise.
The HOPE Project is also expanding the Circles of Support program that already existed
in Duluth (in St. Louis County), Grand Rapids (in Itasca County), and International Falls
(in Koochiching County). The program expanded to Virginia and Hibbing (in St. Louis
County), but the Virginia program closed in November 2006. As part of Circles of
Support, participants are matched with three community “allies” who volunteer to attend
regular group meetings and support the participants’ efforts to find and maintain
employment. Allies assist in any way possible to help participants move out of poverty.
Circles of Support also offers weekly leadership meetings with participants and allies to
discuss issues relating to self-reliance or advocacy. Staff report that approximately onethird of HOPE families have been involved in Circles as opposed to the original goal of
one-half of HOPE families. Staff’s views regarding which families should participate in
Circles have evolved over time, and HOPE advocates now recognize that Circles may not
be appropriate for the hardest-to-serve families or those in crisis. In addition, a new
42
position has been created in Virginia that integrates the HOPE advocate and employment
counselor roles into one.
The case-file review shows that assistance on employment-related services were
particularly common, with 85 percent of participants receiving services in this area. Many
participants received assistance with individual job search, and advocates also refer
participants to employment-related education and training programs. Reflecting a focus
on the family, the rate of child-related assistance was also quite common, with 70 percent
of participants receiving services in this area. This type of assistance typically consists of
assistance finding child care or applying for child care subsidies, assistance with child
protection issues, referrals to Head Start or other preschool programs, or assistance with
parenting issues. Further, advocates occasionally contact a child’s school if a problem
arises.
To address the high level of mental health problems among this population, HOPE
advocates often provide assistance with mental health issues. Sixty percent of participants
received help in this area. This type of assistance generally consists of referrals for
counseling or support groups. Further, 60 percent of participants attended counseling or
therapy. High percentages of participants also received assistance with transportation and
housing (70 and 55 percent, respectively). This project serves a suburban and rural area,
where public transportation is scarce and affordable housing hard to find. Thus, advocates
often help participants find funding for car repairs, offer bus passes or gas cards, and
assist participants with rental applications or applications for Section 8 vouchers.
Implementation experiences. This program faced unique challenges given the broad,
relatively rural geographic area covered by the project. While managers understood the
importance of colocated services, this was difficult for this site given the distances
between many service providers and participants. Case managers were required by
necessity to rely on phone contact. Throughout the ISP project, the HOPE project also
had difficulty establishing a strong connection with the Minnesota Chippewa Tribe. In
the end, they could not define how the ISP project could be helpful to the tribe, and the
goals of this partnership were never well-defined.
The St. Louis program is notable for maintaining a strong focus on employment while
also addressing barriers. In addition, Circles of Support has been an important program
component. However, there were some difficulties operationalizing this model within the
ISP program. Staff found that if participants had too many barriers, it was difficult to
maintain the interest of the ally in working with the participant. They started
implementing stricter eligibility requirements to screen individuals for Circles of Support,
but this ran counter to its goals. Overall, program staff, while supportive of Circles of
Support and while it appeared helpful in a few cases, in the end ISP staff did not feel it
was appropriate for the ISP population.
43
Washington County: Integrated Services Project
Project summary. Washington County’s ISP aims to stabilize particularly families who
receive MFIP assistance, reduce the likelihood that residents will relocate, and assist
those who have relocated from another county to reestablish services in Washington
County. A larger goal of the project is to facilitate communication and cooperation
among counties to develop a process for transitioning services for families relocating
across counties. Integrated services coordinators complete in-depth assessments and
make individualized referrals to a wide range of services. In addition the ISP has
established a close working relationship with Human Services, Inc., to ensure access to
psychological evaluations and mental health services for clients. Child protection workers
and other professionals involved with the family work with ISP staff to coordinate
services.
Sponsoring organizations and institutional partners. The Washington County ISP is
sponsored by Washington County Community Services and operated by HIRED, a
nonprofit organization that provides MFIP employment services (not in Washington
County, but in several surrounding counties.) Washington County’s list of partners has
evolved over the lifetime of the project. Current partners include Human Services, Inc.
(HSI), a community mental health center that has been involved since the beginning of
the project. HSI has played a stable and integral role providing assessment and diagnoses
to ISP participants. ISP also maintains connections with several departments in the
county, including child protection, adult mental health, vocational rehabilitation,
community corrections, and public health. For the most part, these relationships are
largely on a worker-to-worker level: IS coordinators discuss shared clients with workers
in these departments on an as needed basis. ISP and vocational rehabilitation have,
however, set up regular monthly meetings to discuss case planning and share resources.
Blue Cross/Blue Shield of Minnesota was initially identified as a partner to fulfill the
state’s requirement that ISPs partner with a health care plan, but this partnership never
came to fruition. Similarly, Common Health Clinic – also an initial partner – has played a
minimal role.
Staffing. Program staff are employed by HIRED and housed at the Washington County
Workforce Center and offices nearby. Staff include an ISP manager, four integrated
services (IS) coordinators, and one part-time data entry specialist.
Target group and enrollment levels. The project initially targeted MFIP recipients that
had been receiving assistance for 12 to 48 months and were transitory (i.e., those who had
moved during the last year or were facing eviction), but found that this population was
smaller than they had anticipated. To increase participation, the program accepted
individuals who are not transitory but who have a number of other barriers to stability
that may cause them to eventually lose housing (including significant mental health
issues, chemical abuse, involvement in the criminal justice system, or children doing
poorly in school). Beginning in January 2008, Washington County ISP revamped their
program to refocus heavily on particularly mobile MFIP families who have recently
moved in or out of Washington County. In particular, the ISP aims to serve MFIP
44
participants moving from Washington County to Ramsey or Hennepin County (referred
to as “transfer outs”), as well as those families moving into Washington County from any
other locality who fail to schedule their initial MFIP interview (known as “transfer ins”).
The ISP also continues to serve a smaller group of participants who reside in Washington
County and are nearing their MFIP time limit. As of December 2007, 174 families had
enrolled in the program cumulatively. However, to prepare for the renewed focus on
transient MFIP recipients, the ISP transferred the majority of their caseload back to the
county MFIP program in fall 2007. ISP coordinators chose to retain 16 current clients
who they felt needed intensive support. New referrals began in January 2008, and as of
April 2008, the program was serving 34 families.
Participant characteristics. The average age of ISP participants is approximately 32
years old. Over three-quarters of participants are white, about 14 percent are black. A
small percentage of participants are Native American, Hispanic, or Asian. More than one
in four ISP participants have not received a high school diploma or GED in Washington
County. Based on results from a reading test, about 71 percent of participants read at a
9th grade level or above. Results from the Special Learning Needs Screen suggest that
some ISP participants may struggle with learning disabilities. Nearly one-fourth of
participants scored at a level indicating that they may have a learning disability and
should be referred for further assistance. About 18 percent reported that they had been
diagnosed with a learning disability at some point in their lifetime.
Physical health problems appear to serve as a barrier to work for a number of participants
in Washington County. Nearly one-third of participants reported that they had a physical
health condition that makes it difficult to work. Rates of mental illness and associated
problems were high in Washington County. On the MFIP Mental Health and Substance
Abuse Self Screen, about 70 percent of participants scored at a level indicating they may
have a mental health or chemical dependency problem and should be referred for
professional assessment. Over half of participants have been diagnosed with depression
in their lifetime, and nearly one-third reported that they have a mental condition that
makes in hard to work. Rates of reported substance abuse are also fairly high. About 27
percent of participants reported they have abused drugs or alcohol in the past year, and
the same percentage have been in chemical dependency treatment at some point. Overall,
61 percent of ISP participants in Washington County received a low score of 1 or 2 on
the health domain (which includes mental, physical, and mental issues) of the
Employability Measure.
A fairly high proportion of ISP participants have a criminal background, according to
self-reported data. Nearly one-quarter of participants have been convicted of a felony,
and about one-third have spent time in prison. Nearly one-third of participants have been
homeless at some point in their lifetime, although only 1 percent were living in
emergency housing at the time of enrollment. About one-quarter of participants live in
subsidized housing, and a similar percentage rent in the private market.
Recruitment and engagement. Referrals are made by MFIP employment counselors.
Employment counselors refer families clients who are moving out of Washington County
45
and into Ramsey or Hennepin County to the program (or vice versa) or who have
frequently relocated within Washington County. All MFIP recipients who have recently
moved into Washington County and fail to schedule their initial MFIP interview are
automatically referred to the ISP program. Once referred to the program, participation is
mandatory, and nonparticipation can result in a sanction.
When an individual is referred to the program, he or she is assigned an IS coordinator,
who makes contact with the client and sets up an initial meeting. ISP services begin with
the completion of the Employability Measure and HIRED’s Full Family Assessment,
which collects comprehensive information on the client’s history and integrates a number
of screening tools, including the MFIP Self-Screen. Clients are referred to Human
Services, Inc., for psychological assessment and mental health services, as needed. For
clients who are transferring out of Washington County, the ISP director sends an email to
HIRED’s data specialist in whichever county they are moving to informing them of the
transfer.
Types of services provided. After assessments are completed, IS coordinators work with
participants to develop an employment plan aimed at addressing barriers to employment,
the components of which are determined on an individual basis and evolve over time.
Staff conduct case conferences that include the multiple providers serving individual
families to determine how best to meet families’ needs and avoid duplication.
The case-file review shows employment-related services are the most commonly received
service in Washington County, with nearly 90 percent of participants receiving assistance
in this area. Washington County’s ISP program is infused with a strong employmentfocused philosophy—it is located within the Washington County WorkForce Center, and
the county has instituted employment-related performance measures for the ISP program,
including attempting to meet the state’s 50 percent participation rate. IS coordinators
most commonly assist participants with individual job search. Assistance with housing
and transportation was also quite common in Washington County, where 68 percent of
participants received assistance in both of these areas. Washington County is an affluent,
suburban county with limited affordable housing and public transportation. Thus, it is not
surprising that participants need assistance in these areas.
Nearly half of ISP participants received assistance with child-related issues. The most
common service in this area is assistance with child protection issues. The program has a
partnership with the child protection agency, and IS coordinators are in contact with child
protection workers, allowing them to discuss shared clients and coordinate case plans on
an individual basis. Other child-related services provided by ISP include providing
information on child care programs, assistance with parenting skills, and referrals to
counseling or other services for participants’ children.
A relatively high percentage of participants – 47 percent – received assistance with
criminal justice issues, reflecting the number of participants with a criminal background.
Interviews with ISP staff suggest that methamphetamine use has become endemic in
Washington County, and some ISP participants have drug-related offenses on their
46
records. In addition, IS coordinators have developed one-on-one relationships with
workers in community corrections, which has increased communication regarding shared
clients.
About 42 percent of participants received assistance with mental health, reflecting the
level of mental health problems among this population. Nearly one-third of participants
received assistance applying for SSI. Washington County has a contract with SSI
advocacy organizations to provide assistance with the SSI application process to ISP
participants. A similar proportion of participants also received assistance with physical
health issues. Services in this area include referrals to doctors, specialists, and public
health nurses.
Participant experiences. Based on experiences of the eight women on Washington in
the focus group, employment assistance was the primary function of the ISP program,
including referrals for workshops and assistance with resumes and job leads. Some
participants received assistance with transportation issues, including a car donation and
gas cards. Two participants said they received mental health services as a result of their
enrollment, and some of the assistance that participants received was related to their
children, including assistance with child care and a referral to counseling. Focus group
participants’ reactions to their relationship with their ISP worker were varied. While
some characterized the ISP program as providing emotional support, two thought their
worker was negative. After hearing about the different services that participants in the
group had received, three participants said they felt that their worker had not
communicated to them all of the available services. This appeared to be an area in which
variation in experience was due to differing ISP workers. The most common suggestion
was to make continuing education more accessible to participants. For some participants,
there were disagreements with their ISP workers over enrolling in higher education that
were closely related to a belief that ISP was too focused on employment without
considering job quality.
Implementation experiences. The Washington program is notable for its focus on
transient families. The ISP had difficulty reaching this target population, but has now
refocused its effort to identify and serve this group. This program maintains a strong
emphasis on employment, and is one of the few ISP sites where individuals can be
sanctioned for noncompliance with ISP activities.
Program managers in Washington focused on improved service coordination at the staff
level and involved few organizational partners. To achieve broader, more systemic
change, managers reported that would have had to implement the program differently
from the beginning, by involving a broader range of institutional partners and developing
a greater level of “top down” support to bring service delivery systems together. The
Washington ISP had intended for the child protection system to a play a central role in
the program, and while staff from ISP does coordinate with these workers, they become
less involved in the ISP project over time, primarily due to budget constraints.
47
III.
Change in ISP Participants’ Employment, Earnings, Government
Benefits Receipt, and Employability Measure Scores
The ISP programs are designed to improve participants’ economic levels including increased
employment and earnings and decreased welfare receipt levels as well as improve a range of
outcomes related to family stability such as living environment, personal skills, social support,
child behavior, physical and mental health, housing, transportation, and legal issues. This chapter
describes the change in ISP participants’ employment, earnings, and benefit receipt two years
before they enrolled in the ISP program and two years after enrollment. In addition, we examine
changes in participants’ Supplemental Security Income (SSI) receipt and Employability Measure
scores, which provide information on the extent to which participants face barriers in 11 areas
related to family stability.
We begin with a discussion of participants' employment and earnings, followed by their MFIP
receipt. Within these sections, we present findings from both descriptive and multivariate
regression analyses. The descriptive analyses show patterns and changes in the outcomes over
time. The multivariate regression analysis provides estimates of the relationship between ISP
participation and participants’ employment and MFIP outcomes, controlling for county
economic conditions and participant characteristics. The economic and participant characteristics
incorporated into the analysis are the counties’ annual unemployment rate and participants’
gender, age, race, and educational attainment measured at enrollment. 1 A key benefit of the
regression analysis over the descriptive analysis is that it takes account of the effects of the
economy, as measured by the unemployment rate, on outcomes. It is important to note that the
regressions cannot rule out that unmeasured factors aside from the program services may be
responsible for some of the estimated relationships. These possible factors could include
variables that have been shown to be related to MFIP outcomes, such as having a second parent
in the household, mobility, serious mental health and chemical dependency diagnoses, marital
status, number of children, whether the person is a U.S. citizen or speaks English, and county
poverty rate. Also, small sample sizes make it more difficult to detect statistically significant
relationships, particularly gains that are small in magnitude.
Employment and Earnings
The employment and earnings information presented here is based on unemployment insurance
(UI) data, which measure these levels quarterly. We rely on UI data because they provide the
most complete reporting of employment and earnings before and after enrollment. 2 We examine
1
More information on the methodology used for the regression-adjusted outcomes are presented in Appendix C.
The UI records consist of employer reports to the state UI agency. All employers subject to the state UI tax are
required to report employee earnings quarterly. Although these data will cover most civilian employees, earnings
reports are not required, for example, for self-employed individuals, most independent contractors, and military and
federal employees. In addition, UI records will miss earnings for individuals who work “off the books” or for cash
and for those who work out of state (since records are collected at the state level).
2
48
participants’ employment and earnings in the eight quarters (two years) prior to enrollment, the
quarter of enrollment, and up to eight quarters after enrollment. 3
Patterns of employment and earnings in the two years before and after participants enrolled in
each of the ISP programs are shown in Exhibit 3-1. In general, employment rates and average
earnings fell in the quarters leading up to enrollment and increased in the quarters following
enrollment. For example, in Anoka County about 40 percent of ISP participants were employed
two years before enrollment, but only about 15 percent were employed in the quarter of
enrollment. During the two subsequent years, the employment rate fluctuated between 20 percent
and 30 percent (Exhibit 3-1, top panel). Average earnings show a similar pattern. Among all ISP
participants in Anoka, average quarterly earnings were roughly $1,000 two years before
enrollment, fell to about $250 in the quarter of enrollment, and then increased in the two years
after enrollment. The pattern of quarterly earnings among employed ISP participants in Anoka is
similar, although the dollar values are higher, ranging between $1,000 and $3,500.
This pattern of falling and then rising employment and earnings is present in most sites. The sites
that deviate most from this pattern are Crow Wing, Ramsey, and Red Lake. Ramsey, for
example, generally had declining employment and earnings across the four-year period. In nearly
every site, there are some sharp increases and decreases in employment and earnings between
quarters. This could be due in part to the relatively small number of participants in the sites.
Anoka has 306 ISP participants, but the other seven sites have significantly fewer participants—
between 46 and 156 participants. Examining all ISP participants in the state together shows
substantially smoother patterns of employment and earnings across the four-year period (Exhibit
3-1, bottom panel).
The observed earnings dip prior to ISP enrollment is consistent with patterns for participants of
job training and other social programs and has been widely documented in the literature (e.g.,
Ashenfelter 1978; Heckman and Smith 1999; Mueser, Troske, and Gorislavsky 2007).
Individuals often enroll in these programs soon after encountering particularly difficult
circumstances or crises, such as the loss of a job. When programs disproportionately attract
individuals who are not employed due to a recent job loss, analyses of earnings data reveal a
pattern of reduced employment and earnings just prior to enrollment. For some, the reduced
earnings are likely a temporary disruption to an otherwise steady earnings path. For others, the
reduced earnings may be due to a decline in the individuals’ ability to obtain work at wages that
they could previously command.
The observed increase in earnings following enrollment has also been documented in this
literature (e.g., Ashenfelter 1978; Heckman and Smith 1999; Mueser et al. 2007). The increase
could be due to the program or due to individuals’ natural return to their higher and more
permanent earnings path (Ashenfelter 1978). So that our comparison of ISP participants’ preand post-enrollment earnings is based on their more permanent and steady level of earnings, we
define the pre- and post-enrollment periods to exclude the period immediately before and after
enrollment. Specifically, we follow the recent literature (Mueser et al. 2007) and define the preand post-enrollment periods as follows:
3
This analysis uses data through December 2007, so two years of follow-up is only available for persons who
enrolled in the ISP program prior to January 2006—70 percent of the sample (686 participants).
49
Exhibit 3-1
Average Quarterly Employment Rate and Earnings Amount
in the Two Years Before and After Enrollment
Anoka
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Chisago
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Note: All earnings are in 2007 dollars.
50
Exhibit 3-1 (continued)
Average Quarterly Employment Rate and Earnings Amount
in the Two Years Before and After Enrollment
Crow Wing
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Hennepin
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Note: All earnings are in 2007 dollars.
51
Exhibit 3-1 (continued)
Average Quarterly Employment Rate and Earnings Amount
in the Two Years Before and After Enrollment
Ramsey
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Red Lake
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Note: All earnings are in 2007 dollars.
52
Exhibit 3-1 (continued)
Average Quarterly Employment Rate and Earnings Amount
in the Two Years Before and After Enrollment
St. Louis
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Washington
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Note: All earnings are in 2007 dollars.
53
Exhibit 3-1 (continued)
Average Quarterly Employment Rate and Earnings Amount
in the Two Years Before and After Enrollment
All Sites
Employment Rate
Quarterly Earnings
All ISP Participants
Quarterly Earnings
Employed ISP Participants
Note: All earnings are in 2007 dollars.
54
•
•
Pre-enrollment period: employment and earnings in the fifth through eighth quarter
before enrollment.
Post-enrollment period: employment and earnings in the fifth through eighth quarter after
enrollment.
Table 3-1 (top two panels) shows the average quarterly employment rate and earnings levels in
the pre- and post-enrollment periods, for each site. 4 Consistent with different target groups served
by these programs, the pre-enrollment employment rates differ substantially across the sites
(between 23.6 percent and 48.8 percent; Table 3-1, row 1). Keeping with this finding, the
average quarterly earnings of participants also varies across the sites (from $545 to $1,173; Table
3-1, row 5). 5
We also examine changes in employment and earnings between the pre- and post-enrollment
periods. The rows labeled "absolute difference" simply present the difference in ISP participants’
employment rate and average earning between these two time periods. In general, the patterns
show increases in employment and earnings between the pre- and post-enrollment periods, but
many of the increases are not statistically significantly different from zero (Table 3-1, rows 3 and
6). We measure statistical significance at the 1, 5, and 10 percent level. Statistical significance at
the 1 percent level, for example, identifies differences (e.g., an increase in earnings) as
statistically significant if we are 99 percent confident that the true difference (e.g., the earnings
increase) is not zero. The regression-adjusted differences between the pre-and post-enrollment
periods, which control for the county unemployment rate and selected participant characteristics,
are generally smaller in magnitude and have lower levels of statistical significance than the
absolute differences (Table 3-1, rows 4 and 8). 6
The employment and earnings findings vary across the sites and are as follows: 7
•
Anoka: In the pre-enrollment period, 32.5 percent of Anoka’s ISP participants were
employed and the average quarterly earnings among all participants was $868 (or $3, 472
annually). Between the pre- and post-enrollment periods, participants in Anoka did not
experience statistically significant increases in their employment or earnings. In fact, the
absolute differences show that the percent of participants employed fell by 7.7 percentage
points. 8 The regression-adjusted numbers show a similar statistically significant decline
4
All earnings are in 2007 dollars.
The average of participants’ quarterly earnings appears low because these calculations include persons with zero
earnings.
6
These regression-adjusted differences are based on the regression models describe above and in Appendix C.
7
Part of the difference in statistical significance of the sites is owing to the difference in the number of participants.
It is less likely that a fixed difference will be statistically significant if the site has a small number of participants
versus a large number of participants.
8
In Anoka, also we examine employment and earnings patterns separately for individuals who were on the “SSI
track” and only received assistance in this area and for those on the “non-SSI track” (i.e., ISP track) who received a
broader range of services. We did not find a notable absolute difference in the employment rate outcome for the two
groups, although the absolute difference in quarterly earnings differs between the two groups. Those in Anoka on
the SSI track experienced a statistically significant drop in quarterly earnings (of $276), while the earnings of those
on the non-SSI track had no statistically significant change (Appendix B Table B-5 presents the results separately
for the two groups in Anoka).
5
55
Table 3-1
Changes in Employment, Earnings, and MFIP Receipt Over Time
Ramsey
Red Lake
31.7%
52.1%
20.4% ***
11.3% *
23.6%
15.1%
-8.5% **
-5.8%
42.4%
32.9%
-9.5%
-14.8%
41.5%
51.2%
9.7% **
4.9%
41.6%
43.5%
1.9%
2.6%
$1,158
$1,241
$83
-$14
$974
$1,881
$907 ***
$465 *
$545
$244
-$301 **
-$233 *
$986
$983
-$3
-$145
$911
$1,237
$327 **
$268
$1,100
$1,226
$126
$48
47.3%
30.6%
-16.7% ***
-19.0% ***
43.5%
38.9%
-4.6%
-3.1%
61.6%
56.1%
-5.5%
-2.5%
60.6%
74.0%
13.3% ***
8.7% *
79.2%
81.8%
2.6%
4.7%
56.9%
46.0%
-10.9% **
-4.3%
61.6%
42.2%
-19.4% ***
-16.4% ***
$308
$195
-$113 ***
-$138 ***
$278
$232
-$46
-$38
$427
$382
-$45
-$16
$495
$517
$22
-$9
$657
$699
$42
$56
86
93
123
46
Anoka
Chisago
Crow Wing
Employment Rate
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
32.5%
24.8%
-7.7% ***
-7.3% **
47.9%
49.1%
1.3%
2.7%
48.8%
49.0%
0.2%
0.2%
Quarterly Earnings
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
$868
$745
-$124
-$148
$1,173
$1,459
$286
$296
61.2%
56.2%
-4.9%
-0.9%
Hennepin
St. Louis
Washington
Employment and Earnings
MFIP Receipt
MFIP Receipt Rate
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
Monthly MFIP Benefit Amount
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
1
Number of Observations
$430
$355
-$75 ***
-$37
306
82
$406
$295
-$111 ***
-$76 *
156
$405
$268
-$138 ***
-$129 ***
95
Note: All earnings are in 2007 dollars. MFIP benefits include food and cash assistance. Cash assistance is in nominal dollars, food assistance is in 2007 dollars.
* = p<.1; ** = p<.05; *** = p<.01
1. This analysis uses data through December 2007, so ISP participants who enrolled in the program after December 2005 do not have data for two full years after
enrollment. Depending on the month enrolled, ISP participants have between 18 and 24 months of data after enrollment (i.e., between 6 and 12 months of data in the postenrollment period). Two years of follow-up is available for 70 percent of the sample (686 participants) and at least 21 months of follow-up is available for 87 percent of the
sample (859 participants). All 987 ISP participants have at least 18 months of follow-up data.
56
in the employment rate (7.3 percentage points) between the pre- and post-enrollment
periods. 9
•
Chisago: Nearly half (47.9 percent) of Chisago’s ISP participants were employed in the
pre-enrollment period. At the same time, average quarterly earnings among all ISP
participants was $1,173 (or $4,692 annually). Between the pre- and post-enrollment
periods both employment and earnings increased, but neither increase was statistically
significantly different from zero. The regression-adjusted numbers also show no
statistically significant increase.
•
Crow Wing: Crow Wing’s employment rate was 49 percent in both the pre- and postenrollment periods, so no increases occurred between these two time periods. ISP
participants’ average quarterly earnings did increase slightly, but the increase was not
statistically significantly different from zero. Average quarterly earnings were $1,158 in
the pre-enrollment period and $1,241 in the post-enrollment. The regression-adjusted
numbers also show no statistically significant difference.
•
Hennepin: Hennepin experienced statistically significant increases in both their
employment and earnings between the pre- and post-enrollment periods. Hennepin’s
employment rate increased by 20.4 percentage points (from 31.7 percent to 52.1 percent)
and average quarterly earnings increased by $907 (from $974 to $1,881). The results
from the regression analysis also show statistically significant increases in employment
and earnings for Hennepin participants, although the increases are more modest than the
absolute differences (11.3 percentage points and $465, respectively).
•
Ramsey: Consistent with the decline in employment and earnings shown in Exhibit 3-1,
ISP participants experienced statistically significant declines in both outcomes between
the pre- and post-enrollment periods. The employment rate was 23.6 percent in the preenrollment period and 15.1 percent in the post-enrollment period, a difference of 8.5
percentage points. ISP participants’ average quarterly earnings fell by $301, from $545 in
the pre-enrollment period to $244 in the post-enrollment period. The regression adjusted
numbers also show declines in employment and earnings, although only the decline in
earnings is statistically significantly different from zero.
•
Red Lake: In the pre-enrollment period, 42.4 percent of ISP participants were employed
and the average quarterly earnings among all participants was $986 (or $3,944 annually).
Between the pre- and post-enrollment periods, participants in Red Lake did not
experience statistically significant increases in their employment or earnings. The
regression adjusted numbers also show no statistically significant difference.
•
St. Louis: The descriptive results show that ISP participants in St. Louis experienced
statistically significant increases in both employment and earnings between the pre- and
post-enrollment periods. St. Louis’s increases were more modest than Hennepin’s, but
still substantial. The employment rate increased by 9.7 percentage points between the
pre- and post-enrollment periods (from 41.5 percent to 51.2 percent) and average
quarterly earnings increased by $327 (from $911 to $1,237). However, results from the
9
The regression-adjusted difference in the employment rate is similar in Anoka’s non-SSI and SSI tracks (-7.3 and 7.6 percentage points, respectively), but only the decline in the SSI track is statistically significantly different from
zero (at the 10 percent level, see Appendix B, Table B-5).
57
regression analysis show no statistically significant increase. While the regressionadjusted increases in employment and earnings are not statistically significant, the
magnitudes are not small (4.9 percent and $268, respectively).
•
Washington: ISP participants’ employment rate was 41.6 percent in the pre-enrollment
period and 43.5 percent in the post-enrollment period. This increase of roughly 2
percentage points, however, is not statistically significantly different from zero. Average
quarterly earnings also increased slightly, but again, the difference is not statistically
significant. Earnings in the pre-enrollment period were $1,100 and earnings in the postenrollment period were $1,226 (a difference of $126). The regression-adjusted numbers
also show no statistically significant difference.
Of the eight ISP programs studied, Hennepin’s program is the only that showed statistically
significant increases in participants’ employment and earnings based on both the absolute and
regression-adjusted differences. The descriptive results show that ISP participants in St. Louis
experienced statistically significant increases in employment and earnings, but the regressionadjusted differences are not statistically significant. The other six sites did not have a statistically
significant increase in either employment or earnings. However, two sites—Anoka and
Ramsey—experience unexpected statistically significant declines in their employment and/or
earnings outcomes.
MFIP Receipt and Benefit Levels
Patterns of MFIP receipt and MFIP benefit levels are inversely related to trends in employment
and earnings. That is, MFIP receipt tends to increase in the two years before ISP enrollment and
fall in the two years after enrollment (Exhibit 3-2). At the time of enrollment, the vast majority
of ISP participants were MFIP recipients (between 88 percent and 98 percent across the sites).
These high rates of receipt at enrollment are consistent with the fact that ISP was targeted at
MFIP recipients. Average monthly MFIP benefit amounts among all ISP participants follow the
same pattern as MFIP receipt rates. The average MFIP benefit amount among those who receive
a benefit is, however, relatively flat over this four year period (not shown). This is not surprising
since the MFIP cash portion of the benefits was unchanged over this period and the MFIP food
portion increased modestly with inflation.
As with our analysis of employment and earnings, we compare MFIP receipt and MFIP benefit
levels in the pre- and post-enrollment periods. Since we have monthly (versus quarterly)
information on MFIP benefits, we define the pre-enrollment period to include the 13th through
24th month before enrollment and the post-enrollment period to include the 13th through 24th
month after enrollment.
The bottom two panels of Table 3-1 show the MFIP receipt rate and average monthly MFIP
benefits in the pre- and post-enrollment periods for each site. As discussed above, the rows
labeled “absolute difference” present the simple difference in ISP participants’ employment rate
and average earning between the two time periods. The rows labeled “regression-adjusted
differences” present the differences based on results from the regression models, which control
for the county unemployment rate and selected participant characteristics. The pre-enrollment
MFIP receipt rates range between 44 percent and 79 percent across the sites and average monthly
58
Exhibit 3-2
Average Monthly MFIP Benefit Receipt and Benefit Amount
in the Two Years Before and After Enrollment
Anoka
Monthly MFIP Benefit Amount
All ISP Participants
MFIP Receipt Rate
Chisago
MFIP Receipt Rate
Monthly MFIP Benefit Amount
All ISP Participants
Note: MFIP benefits include food and cash assistance. Cash assistance is in nominal dollars, food assistance is in 2007 dollars.
59
Exhibit 3-2 (continued)
Average Monthly MFIP Benefit Receipt and Benefit Amount
in the Two Years Before and After Enrollment
Crow Wing
Monthly MFIP Benefit Amount
All ISP Participants
MFIP Receipt Rate
Hennepin
MFIP Receipt Rate
Monthly MFIP Benefit Amount
All ISP Participants
Note: MFIP benefits include food and cash assistance. Cash assistance is in nominal dollars, food assistance is in 2007 dollars.
60
Exhibit 3-2 (continued)
Average Monthly MFIP Benefit Receipt and Benefit Amount
in the Two Years Before and After Enrollment
Ramsey
Monthly MFIP Benefit Amount
All ISP Participants
MFIP Receipt Rate
Red Lake
MFIP Receipt Rate
Monthly MFIP Benefit Amount
All ISP Participants
Note: MFIP benefits include food and cash assistance. Cash assistance is in nominal dollars, food assistance is in 2007 dollars.
61
Exhibit 3-2 (continued)
Average Monthly MFIP Benefit Receipt and Benefit Amount
in the Two Years Before and After Enrollment
St. Louis
Monthly MFIP Benefit Amount
All ISP Participants
MFIP Receipt Rate
Washington
MFIP Receipt Rate
Monthly MFIP Benefit Amount
All ISP Participants
Note: MFIP benefits include food and cash assistance. Cash assistance is in nominal dollars, food assistance is in 2007 dollars.
62
Exhibit 3-2 (continued)
Average Monthly MFIP Benefit Receipt and Benefit Amount
in the Two Years Before and After Enrollment
All Sites
MFIP Receipt Rate
Monthly MFIP Benefit Amount
All ISP Participants
Note: MFIP benefits include food and cash assistance. Cash assistance is in nominal dollars, food assistance is in 2007 dollars.
63
benefits range between $278 and $657. 10 In general, the patterns show decreases in MFIP receipt
and benefits over time, but only a subset of these declines are statistically significant. 11,12
The patterns of MFIP receipt and benefit levels in each site are as follows:
•
Anoka: The MFIP receipt rate was 61.2 percent in the pre-enrollment period and 56.2
percent in the post-enrollment period. This decline, however, is not statistically
significantly different from zero. Anoka’s ISP participants did, however, experience a
significant absolute decline in the average monthly MFIP benefit amount received—from
$430 to $355. 13 Results from the regression analysis, however, show no statistically
significant decrease in the MFIP receipt rate or MFIP benefit amount between the preand post-enrollment periods. 14
•
Chisago: Between the pre- and post-enrollment periods, Chisago experienced statistically
significant decreases in both their MFIP receipt rate and average monthly benefit
amounts. The absolute and regression-adjusted differences are similar for both outcomes.
Results based on the regression model show that the MFIP receipt rate fell by 19.0
percentage points and average monthly benefits fell by $138 between the pre- and postenrollment periods. 15
•
Crow Wing: In the pre-enrollment period, 43.5 percent of ISP participants received MFIP
benefits and the average monthly benefit was $278. Between the pre- and post-enrollment
periods, Crow Wing ISP participants did not experience statistically significant decreases
in either the benefit receipt rate or monthly benefit amount.
•
Hennepin: Over three-fifths (61.6 percent) of Hennepin’s ISP participants received MFIP
in the pre-enrollment period. At the same time, average monthly MFIP benefit amounts
were $427. Between the pre- and post-enrollment periods both the MFIP receipt rate and
benefit amount declined, but neither decline was statistically significantly different from
zero. The regression adjusted numbers also show no statistically significant decline.
•
Ramsey: In the pre-enrollment period, 60.6 percent of Ramsey’s ISP participants received
MFIP benefits and the average monthly benefit was $495. Between the pre- and post-
10
The average of participants’ monthly MFIP benefits appears low because these calculations include persons with
zero benefits.
11
Consistent with our discussion of employment and earnings above, we measure statistical significance at the 1, 5,
and 10 percent level. For example, a decrease in MFIP receipt is identified as statistically significantly different
from zero at the 1 percent level if we are 99 percent confident that the decrease is not zero.
12
As mentioned above, part of the difference in statistical significance of the sites is owing to the difference in the
number of participants. A fixed difference is less likely to be statistically significant if the site has a small versus a
large number of participants.
13
In Anoka, we examine these patterns separately for individuals who were on the SSI track and for those on the
non-SSI track who received a broader range of services. Descriptive analysis based on absolute differences between
the pre- and post-enrollment periods did not find any notable difference in the MFIP receipt rate outcome for the two
groups, although there is a difference between the two groups’ change in the MFIP benefit amount. ISP participants
who are not on the SSI track experienced a statistically significant drop in MFIP benefits, while the benefits of those
on the SSI track had no statistically significant change (Appendix B Table B-5).
14
The regression-adjusted differences are not statistically significant for Anoka’s SSI and non-SSI tracks (Appendix
B Table B-5).
15
The regression-adjusted differences are slightly higher than the absolute differences, but these differences (e.g.,
16.7 percent and 19.0 percent) are not statistically significantly different from one another.
64
enrollment periods, participants in Ramsey did not experience statistically significant
decreases in the rate of MFIP receipt or in the dollar value of MFIP benefits. In fact, the
descriptive numbers show that the percent of participants receiving MFIP benefits
increased significantly by 13.3 percentage points (from 60.6 percent to 74 percent). The
regression-adjusted difference also shows a statistically significant increase in the percent
of ISP participants who received MFIP benefits (an increase of 8.7 percentage points).
•
Red Lake: In the pre-enrollment period, MFIP receipt rates and average monthly benefits
were quite high. Nearly 80 percent of ISP participants received MFIP benefits and the
average monthly MFIP benefit amount was $657. Between the pre- and post-enrollment
periods, participants in Red Lake did not experience statistically significant decreases in
either of these outcomes.
•
St. Louis: In the pre-enrollment period, 56.9 percent of St. Louis ISP participants received
MFIP benefits and the average monthly benefit was $406. The descriptive results show
that ISP participants in St. Louis experienced statistically significant decreases in both the
MFIP receipt rate (by 10.9 percentage points) and MFIP benefit amounts (by $111)
between the pre- and post-enrollment periods. Results from the regression analysis,
however, show a statistically significant decrease in the MFIP benefit amount only (by
$76).
•
Washington: Washington ISP participants experienced statistically significant decreases
in their MFIP receipt rate and average monthly MFIP benefits over time. Their MFIP
receipt rate decreased by a substantial 19.4 percentage points (from 61.6 percent to 42.2
percent) and average monthly benefits decreased by $138 (from $405 to $268). The
results from the regression analysis also show statistically significant decreases in the
MFIP receipt rate (16.4 percentage points) and benefit amounts ($129) between the preand post-enrollment periods.
Based on the descriptive analysis, four sites experienced statistically significant decreases in
their MFIP receipt rate and/or average monthly MFIP benefit amount between the pre- and postenrollment periods—Anoka, Chisago, St. Louis, and Washington. Results based on the
regression analysis, however, show fewer statistically significant declines. Chisago and
Washington continue to show statistically significant declines in both their MFIP receipt rate and
benefit amount, but only St. Louis’s MFIP benefit amount fell significantly and Anoka had no
statistically significant MFIP declines. Three of the sites—Crow Wing, Hennepin, and Red
Lake—had no statistically significant change in their MFIP receipt rate or MFIP benefit amount
between the pre- and post-enrollment periods. Finally, Ramsey experienced an unexpected
statistically significant increase in the MFIP receipt rate.
Supplemental Security Income Receipt
In this section, we examine ISP participants’ receipt of Supplemental Security Income, a federal
program providing income support to the disabled. Overall, in addition to earnings and MFIP,
the data revealed that a nontrivial number of ISP participants receive income from SSI (Table 32).
65
Table 3-2
ISP Participants' Supplemental Security Income
Timing of Enrollment in SSI
Began Receiving SSI before ISP Enrollment
Number
Percent
Began Receiving SSI after ISP Enrollment
Number
Percent
Number of Observations
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
5
1.6%
0
0.0%
0
0.0%
7
7.5%
3
2.4%
0
0.0%
0
0.0%
0
0.0%
57
18.6%
3
3.7%
7
8.1%
5
5.4%
37
30.1%
1
2.2%
10
6.4%
2
2.1%
306
82
86
93
123
46
156
95
66
An analysis of the SSI data shows that, in some sites, an important aspect of the ISP program
was increasing ISP participants’ receipt of SSI benefits. Data on SSI receipt among ISP
participants shows that few ISP participants received SSI prior to enrolling in the ISP program.
Across the eight sites, only 15 individuals received SSI benefits prior to ISP enrollment—seven
participants in Hennepin, five participants and Anoka, and three participants in Ramsey. By the
end of the two-year follow-up period, an additional 122 ISP participants began receiving SSI.
The extent to which ISP participants began receiving SSI benefits varies considerably across the
sites. Two sites—Anoka and Ramsey—stand out as having the largest fraction of ISP
participants begin receiving SSI after enrollment in ISP. This finding is consistent with the
relatively high incidence of mental and physical health barriers for people enrolled in ISP in
these two sites. In Ramsey, 30.1 percent of ISP participants began receiving SSI benefits after
ISP enrollment, while 18.6 percent of Anoka’s ISP participants began receiving SSI after ISP
enrollment. 16 In the other six sites, the percentage of ISP participants that began receiving SSI
after ISP enrollment was significantly lower and ranged between 2.1 percent and 8.1 percent.
Summary of Patterns of Employment, Earnings, MFIP, and SSI
This section briefly summarizes the patterns of employment, earnings, and MFIP receipt in the
pre- and post-enrollment periods, for each of the eight sites (Table 3-1). We look to identify
whether ISP sites experienced increases in participants’ employment and earnings and decreases
in their MFIP receipt and benefit levels. In addition, we summarize patterns of SSI receipt (Table
3-2).
•
Anoka: Results from the descriptive analysis show that ISP participants in Anoka
experienced statistically significant declines in employment and MFIP benefit amounts
between the pre- and post-enrollment periods. The regression-adjusted differences,
however, show a statistically significant decline in Anoka’s employment rate only. This
decline in employment is in the unexpected direction, and could be due to the fact that
Anoka’s ISP program targeted individuals who were likely to be eligible for SSI, and
assisted them in applying for this benefit. An evaluation of SSI data shows that nearly
one-fifth (18.6 percent) of Anoka’s ISP participants began receiving SSI benefits after
enrolling in the ISP program.
•
Chisago: ISP participants in Chisago did not experience statistically significant increases
in either employment or earnings, but their MFIP receipt rate and average monthly
benefit amount significantly decline between the pre-and post-enrollment periods. No ISP
participants were receiving SSI before ISP enrollment, and only 4 percent of participants
became SSI recipients after enrolling in the ISP program.
•
Crow Wing: ISP participants in Crow Wing did not experience statistically significant
changes in employment, earnings, MFIP receipt, or average monthly MFIP benefit
amount. Crow Wing also had a modest increase in SSI receipt after ISP enrollment: 8.1
percent of participants began receiving SSI after ISP enrollment.
16
Anoka’s SSI track had the biggest share of participants who began receiving SSI after ISP enrollment: 1.2 percent
of participants on this track were receiving SSI before enrollment, and 23.0 percent began receiving SSI after
enrollment. However, those not specifically targeted to be enrolled in SSI also had a nontrivial fraction of
participants begin receiving SSI after ISP enrollment. Of this non-SSI track group, 2.1 percent began receiving SSI
before enrollment, while 13.5 percent began receiving SSI after ISP enrollment (see Appendix B Table B-6).
67
•
Hennepin: ISP participants in Hennepin experienced statistically significant increases in
both employment and earnings between the pre- and post-enrollment periods. However,
neither their MFIP receipt rate nor average monthly MFIP benefit amount decreased
significantly. Hennepin is the only site where more people began receiving SSI before
enrollment than after. Still, 5.4 percent of their population began receiving SSI after ISP
enrollment (compared to 7.5 percent that began receiving SSI before enrollment).
•
Ramsey: ISP participants in Ramsey did not experience statistically significant increases
in employment or earnings, nor did they experience statistically significant decreases in
MFIP receipt or average monthly MFIP benefit. In fact, results from the regression
analysis show that two of these four outcomes had statistically significant changes in the
unexpected direction (earnings decreased and the MFIP receipt rate increased).
Consistent with the fact that Ramsey’s program targeted individuals with mental illness,
ISP participants in Ramsey were the most likely to become SSI recipients—30.1 percent
of recipients began receiving SSI after enrolling in the ISP program. The SSI and MFIP
receipt rates can both increase because families that receive MFIP can have a family
member who receives SSI.
•
Red Lake: ISP participants in Red Lake did not experience statistically significant
changes in employment, earnings, MFIP receipt, or average monthly MFIP benefit. Also,
only one person in Red Lake enrolled in SSI after becoming an ISP participant.
•
St. Louis: The descriptive results show that ISP participants in St. Louis experienced
statistically significant increases in employment and earnings, as well as statistically
significant declines in the MFIP receipt rate and MFIP benefit amounts. However, results
from the regression analysis show a statistically significant decrease in the MFIP benefit
amount only. No ISP participants were receiving SSI before ISP enrollment, while 6.4
percent of participants became SSI recipients after enrolling in the ISP program.
•
Washington: ISP participants in Washington did not experience statistically significant
increases in either employment or earnings, but their MFIP receipt rate and average
monthly benefit amounts fell. ISP participants in Washington were the least likely to
begin receiving SSI—only 2.1 percent began receiving SSI after ISP enrollment.
Findings from the descriptive analysis show that Hennepin and St. Louis are the only two site
that experienced increases in employment and earnings between the between the pre- and postenrollment periods. St. Louis also experience decreases in MFIP receipt and benefit amounts, but
the MFIP receipt and average monthly MFIP benefits of Hennepin participants did not
significantly change. Two sites—Chisago and Washington—experienced significant decreases in
their MFIP receipt rate and MFIP benefit amount, but no significant increases in employment or
earnings. Two additional sites did not experience statistically significant increases in
employment or earnings, or experience statistically significant decreases in MFIP receipt or
MFIP benefits—Crow Wing and Red Lake. Finally, Anoka and Ramsey had statistically
significant changes in the unexpected direction. These two programs served the populations with
the most significant mental and physical health issues. The overall timing of SSI enrollment by
ISP participants in these two sites reveals that an important function of the ISP programs is
moving participants onto SSI, and this could have affected their changes in employment and
MFIP receipt.
68
Results from the regression analysis are similar to those from the descriptive analysis, although
there are some differences. A strength of the regression analysis over the descriptive analysis is
that it controls for the effects of the county unemployment rate and selected participant
characteristics on outcomes. Based on the regression analysis, Hennepin’s program is the only
one associated with increases in participants’ employment and earnings. 17 Although Hennepin
ISP participants’ employment and earnings increased, their MFIP receipt and benefit levels did
not significantly decline. This may be due to the income disregard which allows participants to
keep some of their MFIP benefits when earnings are below a specified level. While we cannot
untangle the features of the Hennepin program that may be driving these relationships, this
program is unique in its co-location of staff with expertise in mental health, domestic violence,
substance abuse services, and public health.
Three sites—Chisago, St. Louis, and Washington—experienced statistically significant decreases
in their MFIP receipt rate and/or monthly MFIP benefit amounts, but no statistically significant
increases in employment or earnings. The reasons for these MFIP declines in the absence of
employment and earnings gains are not clear. An analysis of ISP participants who reached their
time limit during the study period shows that this was not a reason for the MFIP declines, with
few individuals reaching their time limit and little variation across the sites. St. Louis ISP
participants had increases in employment and earnings that are not statistically significant, but
the magnitudes are not small, and these higher earnings could potentially account for the decline
in MFIP benefits in this site. Washington is one of two sites where participation in ISP is
mandatory and noncompliance could result in a MFIP sanction (or grant reduction). This could
potentially be a factor in explaining the MFIP reductions, although these did not occur in the
other site where participation is mandatory (Crow Wing).
The Anoka and Ramsey programs are statistically significantly related to some of the participant
outcomes examined, but in the unexpected direction. The decrease in employment and earnings
in these two sites may be explained by the increases in SSI receipt. It is difficult for individuals
to work once they receive SSI, both because individuals must have significant mental or physical
health problems to be eligible for the program and because the SSI program includes many rules
that discourage work. 18 In addition, Ramsey’s focus on rehabilitative services and improving
everyday functioning may contribute to the increased MFIP receipt. Individuals may stay on
MFIP longer than they would have in absence of the program in order to continue to these
services.
The programs in two sites—Crow Wing and Red Lake—are not associated with increases in
employment or earnings nor are they associated with decreases in the MFIP receipt rate or
average monthly MFIP benefits.
As mentioned above, the regression analysis cannot rule out that unmeasured factors aside from
the program services may be responsible for some of the estimated relationships. These possible
factors could include variables that have been shown to be related to MFIP outcomes, such as
17
Although findings from the descriptive analysis show increases in the employment and earnings of St. Louis ISP
participants, the regression-adjusted differences are not statistically significant.
18
The fairly onerous SSI application process discourages recipients from attempting work and risking loss of all
benefits. Combining work with benefits is currently uncommon in part because the reduction in benefits as earnings
rise is relatively high (Loprest and Martinson 2008).
69
having a second parent in the household, mobility, serious mental health and chemical
dependency diagnoses, marital status, number of children, whether the person is a U.S. citizen or
speaks English, and county poverty rate. The estimated relationship between the ISP programs
and participants’ employment, earnings, and MFIP outcomes should be viewed as upper bounds.
Also, small sample sizes make it more difficult to detect statistically significant relationships,
particularly gains that are small in magnitude.
ISP Participant Scores on the Employability Measure
The Minnesota ISP program aims to improve both economic outcomes related to improved
employment, earnings, and welfare receipt, as well as other noneconomic outcomes related to
family functioning. To measure status and change on these personal and family-related areas,
DHS developed an Employability Measure instrument that the agency was piloting at several
employment services providers at the time the ISP began and decided to use it for this project.
The Employability Measure assesses ISP participants in 11 areas related to employment: child
behavior, dependent care, education, financial, health (including mental, physical, and chemical
health), housing, legal, personal skills, safe living environment, social support, and
transportation. During face-to-face meetings, ISP staff, who were trained on the administration
of the instrument, determine the level of the participant in each area on a scale from 1 to 5. 19 A
score of 1 or 2 indicates an area that prevents or seriously interferes with employment, while a
score of 4 or 5 indicates an area of strength (Appendix A provides a copy of the instrument.).
For the ISPs, the Employability Measure instrument was intended to be administered by program
staff at enrollment (i.e., initial) and every six months after ISP enrollment (e.g., six-month
follow-up, 12-month follow-up, etc.), so that changes over time in each domain could be
measured. Although the time period covered by our data allows all ISP participants to be
observed up to 18-months after enrollment, many ISP participants, especially welfare leavers,
did not receive follow-up Employability Measure scores and a few did not receive initial scores.
The percent of ISP participants who received both an initial and six-month follow-up
Employability Measure score in each of the eight sites is as follows: Anoka (59 percent),
Chisago (57 percent), Crow Wing (58 percent), Hennepin (41 percent), Ramsey (26 percent),
Red Lake (17 percent), St. Louis (65 percent), and Washington (83 percent). Fewer than onequarter of ISP participants in each site received both an initial and 18-month follow-up score, so
we do not report the 18-month change. Because this analysis of Employability Measure scores is
based on only a subset of ISP participants who completed the instrument more than once, the
results should be interpreted cautiously.
Our analysis examines the change in ISP participants’ Employability Measure scores between:
(1) the initial and six-month follow-up period and (2) the initial and 12-month follow-up period.
To ensure that the over time comparisons are valid, calculations of the change in Employability
Measure scores are restricted to the subset of individuals who have a score report in each of the
two time periods being compared. For example, the change in the Employability Measure scores
between the initial period and the six-month follow-up period is restricted to individuals who
have Employability Measure scores recorded at both intervals (i.e., in the initial period and the
19
Most scores are based on a scale from 1 to 5. Five areas—child behavior, health, legal, social support, and
transportation—are reported to be based on a scale from 1 to 4/5, with no difference between 4 and 5. Thus we treat
these areas as having scores based on a scale from 1 to 4.
70
six-month follow-up period) and the change between the initial and 12-month period includes
only those who have scores at both of these intervals.
Below we describe the Employability Measure findings for each site, except Red Lake where no
follow-up scores are available (Table 3-3). As with our earlier analyses, we measure statistical
significance at the 1, 5, and 10 percent level.
•
Anoka: At the initial interview, only three of Anoka’s average Employability Measures
are above three—dependent care, legal, and safe living environment. However,
statistically significant gains are made in the follow-up periods in nine of the 11
domains—child behavior, dependent care, education, health, housing, financial, personal
skills, social support, and transportation. The two remaining domains—legal and safe
living environment—did not experience statistically significant gains in either the sixmonth or 12-month follow-up periods.
•
Chisago: Two measures, safe living environment and legal, have average scores above
three at the initial interview in Chisago; the remaining Employability Measures have
average initial scores below three. Three measures—education, health, and financial—
make statistically significant gains in both follow-up periods, while four measures—child
behavior, housing, social support, and transportation—have a statistically significant gain
in one of the follow-up periods. The dependent care, legal, safe living environment, and
personal skills domains do not have statistically significant changes recorded in either of
the follow-up periods.
•
Crow Wing: Participants in Crow Wing’s ISP have average initial scores above three in
the areas of dependent care, legal, and safe living environment. After 12 months the only
statistically significant gain was in safe living environment. One measure, dependent
care, has a statistically significant drop during the 12-month follow-up, indicating that, on
average, participants lost stability in their dependent care situations over this time period.
However, this registered decline is based on less than 40 percent of Crow Wing’s ISP
participants.
•
Hennepin: ISP participants in Hennepin, at the initial interview, had scores of at least
three on five measures—child behavior, dependent care, legal, safe living environment,
and social support. At the six-month follow-up, child behavior, financial, personal skills,
and transportation had all make significant gains, and financial and personal skills make
another significant gain after 12 months. The safe living environment average score drops
by a statistically significant amount, but still remains above three.
•
Ramsey: At the initial interview, three Employability Measure average scores are above
three—dependent care, legal, and safe living environment; the remaining scores are
below three. No average score in Ramsey makes a statistically significant gain in either
of the two follow-up periods. The scores in education have a statistically significant
decrease in both the six- and 12-month follow-ups. However, both of these registered
declines are based on less than 30 percent of Ramsey’s ISP participants.
•
St. Louis: Average initial Employability Measure scores for participants in St. Louis
exceed a score of three in two areas—legal services and safe living environment. Nine
average scores (all except legal and health) make statistically significant gains in at least
one follow-up period. Five measures—dependent care, education, financial, social
71
Table 3-3
Changes in Employability Measure Scores in the 6 and 12 Month Follow-Up Periods
Anoka
Chisago
Crow Wing
Hennepin
Child Behavior
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.83
0.30 ***
0.47 ***
2.63
0.25 *
0.19
Dependent Care
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
3.22
0.39 ***
0.80 ***
2.92
0.22
0.16
3.51
-0.06
-0.45 *
Education
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.53
0.27 ***
0.34 ***
2.51
0.28 ***
0.37 ***
2.84
-0.04
0.13
2.79
0.11
0.47
Health (physical and mental)
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
1.68
0.34 ***
0.53 ***
2.19
0.38 ***
0.38 **
2.36
0.16
0.00
2.68
-0.08
-0.07
2.98
0.15
0.11
3.13
0.21 **
0.33
3.55
-0.05
0.07
Ramsey
Red Lake
St. Louis
Washington
2.77
-0.03
-0.25
-
2.76
0.12 *
0.10
2.77
0.29 ***
0.09
3.10
0.13
0.21
-
2.67
0.41 ***
0.42 **
2.89
0.41 ***
0.21
2.53
-0.34 **
-0.31 *
-
2.71
0.12 **
0.18 **
2.65
0.16 *
0.22 *
1.81
0.03
-0.25
-
2.34
2.27
0.06
0.30 ***
0.15
0.31 **
Continued on next page
72
Table 3-3 (continued)
Changes in Employability Measure Scores in the 6 and 12 Month Follow-Up Periods
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
2.82
-0.03
-0.33
2.91
-0.13
0.00
-
2.72
0.17 **
0.14
2.45
0.32 ***
0.14
2.47
-0.03
-0.13
-
2.03
0.36 ***
0.63 ***
1.69
0.48 ***
0.43 ***
3.34
0.11
0.13
3.28
0.25
0.19
-
3.24
0.00
0.03
3.68
-0.26 *
-0.47
3.47
-0.34
0.19
-
Housing
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.67
0.42 ***
0.57 ***
2.79
0.34 **
0.27
2.92
0.02
-0.03
Financial
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.44
0.38 ***
0.51 ***
2.15
0.62 ***
0.45 **
2.38
0.14
0.28
Legal
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
3.34
0.03
0.13
3.35
0.23
0.15
Safe Living Environment
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
3.89
0.05
0.25
3.83
0.34
0.30
3.53
0.00
-0.16
3.64
0.20
0.38 *
2.50
0.32 **
0.53 **
Red Lake
St. Louis
Washington
2.97
0.12
-0.08
3.61
3.18
0.20 **
0.36 **
0.25
0.04
Continued on next page
73
Table 3-3 (continued)
Changes in Employability Measure Scores in the 6 and 12 Month Follow-Up Periods
Anoka
Chisago
Crow Wing
Personal Skills
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.28
0.40 ***
0.66 ***
2.85
0.04
0.17
2.68
-0.08
-0.09
Social Support
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.56
0.24 ***
0.37 ***
2.32
0.21 *
0.17
2.38
-0.02
-0.06
Transportation
Month of Enrollment
Initial to 6 month difference
Initial to 12 month difference
2.71
0.16 **
0.29 *
2.04
0.32 **
0.33
2.36
0.02
0.16
Hennepin
2.89
0.24 *
0.80 ***
3.00
-0.08
0.13
2.87
0.13 *
0.27
Ramsey
Red Lake
St. Louis
Washington
2.09
0.03
-0.25
-
2.61
0.08
0.40 **
2.48
0.26 **
0.25
1.97
0.19
-0.06
-
2.26
0.33 ***
0.54 ***
2.51
0.18
0.22
2.19
0.09
-0.19
-
2.17
0.34 ***
0.42 ***
2.25
0.22 *
0.16
Number of Observations
Month of Enrollment
180
47
50
38
32
8
102
79
Initial to 6 month difference
180
47
50
38
32
8
102
79
Initial to 12 month difference
78
31
32
15
16
0
65
51
Note: * = p<.1; ** = p<.05; *** = p<.01. To ensure that the over time comparisons are valid, calculations of the change in Employability Measure scores are
restricted to the subset of individuals who have a score reported in each of the two time periods being compared. For example, the change in the Employability
Measure scores between the initial period and the 12-month follow-up period is restricted to individuals who have Employability Measure scores recorded in
both periods (i.e., in the initial period and the 12-month follow-up period). Child behavior, health, legal, social support, and transportation scores are on a 4 point
scale. All other categories are based on a 5 point scale. Calculations are not provided for sites with fewer than 15 participants observed at both enrollment and at
the follow-up period.
74
support, and transportation—have statistically significant increases in both follow-up
periods.
•
Washington: ISP participants in Washington, at the initial interview, had only one
Employability Measure score above three—safe living environment. At the six-month
follow-up, however, nine of the 11 scores make statistically significant gains (all except
legal and social support). Further, three domains—education, health, and financial—
make another significant gain after 12 months.
Overall, Anoka, Chisago, St. Louis, and Washington show modest statistically significant gains
on many Employability Measure scores. The results in Hennepin and Crow Wing are mixed and
show follow-up Employability Measure scores that are both higher and lower than the initial
scores. Ramsey makes no statistically significant gains, but experiences a statistically significant
decline in one of the Employability Measure domains. When interpreting these results it is
important to keep in mind that only a proportion of the of the overall ISP participant population
completed a follow-up Employability Measure. Thus, especially in sites where the Employability
Measure sample size is a very small proportion of the entire population, results are not
necessarily representative. In addition, it should be noted that while staff receive training on
administering the Employability Measure, there may be differences across staff and sites in
determining individual scores.
75
IV.
Summary of ISP Participant Outcomes for Each Site
This chapter summarizes the results of the analyses of outcomes for ISP participants for each
site, specifically participant outcomes for employment and earnings, MFIP and SSI receipt, and
Employability Measure scores.
Anoka. Housed in the county’s human services agency, the Anoka ISP program features a
multidisciplinary service team that provides case management and service coordination for
participants. Staff specialize in specific areas, including juvenile and criminal justice,
employment and vocational rehabilitation, public health, child protection, mental health,
chemical dependency, and housing. Participants are assigned to a case manager based on the
barriers they are facing. A staff disability advocate assists participants with the SSI application
process (called the “SSI track”). Whenever possible, services for the family are provided inhouse by the project team, though team members also connect with other professionals involved
with the family. Compared to most of the other ISPs, ISP participants in Anoka faced more
significant barriers to employment, particularly mental and physical health issues.
Results from the descriptive analysis show that ISP participants in Anoka experienced a
statistically significant decline in employment and MFIP benefit amounts between the pre- and
post-enrollment periods. The regression-adjusted differences, however, only show a statistically
significant decline in Anoka’s employment rate. This decrease in the employment rate is similar
for participants on both the SSI and non-SSI track, but only the decrease in the SSI track is
statistically significantly different from zero. Reflecting the relatively high incidence of mental
and physical health problems among ISP participants in this site, there is also a relatively large
increase in SSI receipt over the follow-up period. Because it is difficult to work once individuals
receive SSI, the decreases in employment may be explained by this increase in SSI receipt. On
the Employability Measure, statistically significant gains are made in both follow-up periods in
child behavior, dependent care, education, health, housing, financial, personal skills, social
support, and transportation. The two remaining domains—legal and safe living environment—
did not experience statistically significant gains in either the six-month or 12-month follow-up
periods.
Chisago. The Chisago program operates in a five-county region and is managed by a nonprofit
organization with experience working with low-income families. Family advocates work one-onone with participants to address barriers and refer them to additional resources in the community.
This site had some operational difficulties stemming from its regional focus involving five
counties (and recently split into two separate programs) and was not able to formally engage
organizational partners that could have provided expertise in a broad range of areas.
ISP participants in Chisago did not experience statistically significant increases in either
employment or earnings, but their MFIP receipt rate and average monthly benefit amount
significantly decline between the pre-and post-enrollment periods. In three domains of the
Employability Measure—education, health, and financial—ISP participants in Chisago make
statistically significant gains in both the six- and 12-month follow-up periods, while four
measures—child behavior, housing, social support, and transportation—have a statistically
significant gain in just one of the follow-up periods.
76
Crow Wing: Operated by Crow Wing County Social Services, this project builds on a segment
of the county’s existing MFIP program that targets hard-to-employ MFIP recipients. Crow Wing
is the only site where the ISP services enhance the existing MFIP program, rather than being a
stand-alone program, and where participation in ISP is mandatory. MFIP recipients who have not
found a job through the county’s standard MFIP program are transferred to a specialist who
provides case management services and referrals to appropriate community resources. Crow
Wing primarily integrates services using supervisors with specialized expertise in child
protection services and chemical dependency to provide guidance to staff. The Crow Wing ISP
provided very high level of assistance in many areas according the casefile review.
ISP participants in Crow Wing did not experience statistically significant changes in
employment, earnings, MFIP receipt, or average monthly MFIP benefit amount. On the
Employability Measure, after 12 months the only gain was in the safe living environment
domain. One measure, dependent care, has a statistically significant drop during the 12-month
follow-up. However, this registered decline is based on less than 40 percent of Crow Wing’s ISP
participants, so should be interpreted with caution.
Hennepin. Operated by a community-based health and human services agency, the core service
of this program is one-on-one case management provided by program staff that connects
participants and their families with services in the community. Staff from two community-based
social service agencies are located at the program office and assist participants with chemical
dependency and domestic violence issues. In addition, an onsite psychologist provides mental
health assessments and counseling. Monthly support groups are a primary program activity.
ISP participants in Hennepin experienced statistically significant increases in both employment
and earnings between the pre- and post-enrollment periods. However, neither their MFIP receipt
rate nor average monthly MFIP benefit amount decreased significantly. This may be due to the
income disregard which allows participants to keep some of their MFIP benefits when earnings
are below a specified level. This program is unique in its co-location of staff with expertise in
several areas including mental health, domestic violence, and substance abuse services. On the
Employability Measure, the child behavior, financial, personal skills, and transportation domains
all make significant gains after six months, and financial and personal skills make another
significant gain after 12 months. The safe living environment average score drops by a
statistically significant amount.
Ramsey: The ISP initiative in Ramsey County integrates mental health rehabilitation expertise
into the county MFIP employment services program. The ISP provides financial support to
several providers to meet capacity and certification standards necessary to provide services under
Adult Rehabilitative Mental Health Services (ARMHS). ARMHS aims to help individuals with
serious mental illness improve functionality, and services are billed directly to the state’s
Medicaid program. Reflecting the focus of the Ramsey ISP on addressing mental health issues,
there is a very high incidence of participants with barriers in this area.
ISP participants in Ramsey did not experience statistically significant increases in employment
or earnings, nor did they experience statistically significant decreases in MFIP receipt or average
monthly MFIP benefit. In fact, results from the regression analysis show that two of these four
outcomes had statistically significant changes in the unexpected direction (earnings decreased
77
and the MFIP receipt rate increased). Reflecting the high level of participants with mental health
problems, there is also a relatively large increase in SSI receipt among ISP participants over the
follow-up period, which may be an important factor in explaining the decrease in earnings. In
addition, Ramsey’s focus on rehabilitative services and improving everyday functioning for
those with serious mental health issues may contribute to the increased MFIP receipt, as
individuals may stay on MFIP longer than they would have without the program in order to
continue to receive these services. On the Employability Measure, there are no statistically
significant gains in any domain and the scores in education have a statistically significant
decrease in both follow-up periods. However, both of these registered declines are based on less
than 30 percent of Ramsey’s ISP participants.
Red Lake. This project is operated by the Tribal Council of the Red Lake Band of Chippewa
Indians. Staff link hard-to-employ MFIP recipients with appropriate services and programs on
the reservation, such as GED courses. The program can also provide transportation assistance
and instruction in traditional work activities, such as wreath-making, beading, and gardening.
This program operates on an Indian reservation with relatively poor economic conditions, and
experienced changes in leadership and priorities over the study period. Overall, the level of
services provided to ISP participants is relatively low. ISP participants in Red Lake did not
experience statistically significant changes in employment, earnings, MFIP receipt, or average
monthly MFIP benefit.
St. Louis. This project is operated in four counties by a group of community action agencies and
an Indian tribe. Program staff assess the needs of families and work with them one on one to help
connect them with appropriate community resources to address a range of issues. Circles of
Support, where participants are matched with “community allies” to provide support, is a key
program component. The St. Louis ISP faced some operational issues given the broad, relatively
rural area covered by the project, and the distance between many service providers and
participants made service coordination more challenging.
The descriptive results show that ISP participants in St. Louis experienced statistically
significant increases in employment and earnings, as well as statistically significant declines in
the MFIP receipt rate and MFIP benefit amounts. However, results from the regression analysis
show a statistically significant decrease in the MFIP benefit amount only. While the increase in
earnings in St. Louis is not statistically significant in the regression analysis, the magnitude is not
small and this could potentially account for the decline in MFIP benefits. On the Employability
Measure, nine scores (all except legal and health) make statistically significant gains in at least
one follow-up period. Five measures—dependent care, education, financial, social support, and
transportation—have statistically significant increases in both the six- and 12-month follow-up
periods.
Washington. Operated by a nonprofit organization that provides MFIP employment services,
this project aims to reduce the likelihood that residents will relocate and assists those who have
relocated to reestablish services. Program staff make individualized referrals to services in the
community and communicate with other professionals involved with the family to better
coordinate services. The county’s community mental health clinic provides access to
psychological evaluations and mental health services for clients. Participation in the ISP program
is mandatory, and individuals can be sanctioned for non-compliance. During the study period,
78
the Washington ISP site faced difficulties engaging a transient population, and focused on
serving individuals with barriers that could cause them to lose housing. This site was also unable
to involve a broad range of institutional partners beyond the mental health clinic.
Washington did not experience statistically significant increases in either employment or
earnings, but their MFIP receipt rate and average monthly benefit amounts fell. The reductions in
MFIP receipt without earnings gains may be attributable to the mandatory nature of the ISP
program in Washington, where non-compliance could result in a grant reduction. For the
Employability Measure, ISP participants’ average scores make statistically significant gains
during the six-month follow-up period in nine of the 11 domains (all except legal and social
support). Further, three domains—education, health, and financial—make another significant
gain after 12 months.
79
V.
Discussion of Key Findings and Policy Implications
Many states and localities share an interest in finding strategies to more effectively assist
individuals who receive cash assistance for long periods. This report examines ISP
participants’ employment, earnings, and MFIP receipt using descriptive and multivariate
regression analysis, but we cannot rule out that unmeasured factors aside from the
program services may be responsible for some of the estimated relationships. Also, small
sample sizes make it difficult to detect statistically significant relationships, particularly
gains that are small in magnitude. Thus, the results should be interpreted cautiously.
However, several implications can be drawn from the findings.
The positive employment and earnings results in Hennepin County indicate the
promise of this model which includes co-located staff with expertise in a number of
areas including employment, mental health, substance abuse, domestic violence, and
public health. The Hennepin ISP is notable because of the number of organizational
partners as well as staff within the organization who provide on-site assistance in specific
areas of expertise. In addition, case managers work closely with ISP participants and
maintain low caseloads. The program is also located at a community health clinic that has
many resources, including a food bank and medical, dental, and mental health clinic. The
Hennepin ISP contracts with several other organizations to provide most of these
specialized services to ISP participants and all staff are under the same management
structure. Staff meet formally and informally to discuss the specific needs of individual
cases. Finally, the Hennepin program includes a monthly support group that elicits high
levels of participation by individuals in the ISP program.
Two other sites (Anoka and Crow Wing) also use a team approach bringing together staff
with expertise in different areas to provide services to ISP participants. However, while
we cannot determine the specific reasons there are limited changes in economic outcomes
over time in these sites, there are potential factors to consider. In Anoka, the ISP
population has more mental and physical disabilities and the program has a strong focus
on helping participants become eligible for SSI, which makes achieving increases in
employment and earnings more difficult. In Crow Wing, staff with expertise in child
protection and substance abuse primarily provide supervision and case consultation rather
than direct services. Crow Wing is also the only ISP site where the ISP services enhanced
the existing MFIP program, rather than being a stand-alone program.
Overall, while caution should be used given the limitations of the analysis of participant
outcomes, the positive results for ISP participants in Hennepin warrant further
consideration and evaluation of this approach. In particular, an evaluation using a random
assignment research design would provide more definitive information on the effects of
the program. Some aspects of the model may be difficult to replicate, given the unique
array of services available at the community health clinic where the program is housed.
This is particularly true in rural areas with more limited service environments.
Increased employment and earnings may not be an appropriate goal for some longterm welfare recipients. Anoka and Ramsey counties both serve a population with
80
significant mental and physical health issues, and the ISP program resulted in increases in
SSI receipt and declines in employment and earnings among ISP participants. Reflecting
the nature of their target population, assisting individuals in applying for SSI is a key
service in both programs and the two programs have the lowest proportion of individuals
receiving employment-related assistance among the ISPs. Given the significant barriers
faced by the ISP populations in these sites, this emphasis on obtaining long-term income
support may be appropriate for some individuals, particularly given the TANF time
limits. However, these results also illustrate the challenge of identifying a target group
that can potentially benefit from services geared toward self-sufficiency rather than being
more appropriate for income support programs for the disabled. A key issue is to identify
appropriate screening and assessment tools to distinguish between those with temporary
disabilities who have the potential to work, sometimes with accommodations and
supports, and those with a significant level of permanent disability.
A service brokering approach with low caseloads for staff may not be sufficient to
address the persistent challenges of long-term cash assistance recipients. In the
service brokering sites, program staff have very low caseloads and work with participants
intensively to provide referrals to the services needed. However, while these services
could have resulted in improvements in non-economic outcomes, the ISP participants in
sites that used this approach did not experience increases in employment or earnings, and
in some sites MFIP receipt declined. While caution should be used given small sample
sizes on many of these sites, it may be that the brokered services, although coordinated
by ISP staff, do not bring the full range of services needed to address participants’ needs.
The service brokering approach can face limitations when other organizations have
capacity or budgetary constraints. In addition, in this project, some of the sites using this
approach are in rural areas and may face service shortages in particular areas.
The non-economic benefits of the ISP programs remain difficult measure but are
important to consider given the broad range of services provided. In addition to
improving economic outcomes, the ISP initiative has relatively broad goals that include
improving family-related outcomes and addressing the needs of children as well as adults
in the family. Towards this end, the ISP programs generally provide a broad range of
assistance in addition to employment-related services, particularly assistance with mental
and physical health issues and issues related to their children. Indeed, over time, there are
some increases in Employability Measure scores, an instrument designed to capture
improvements in personal and family-related outcomes, although these results should be
interpreted cautiously given data limitations. Nonetheless, given the hard-to-employ
population served by the ISPs, it is important to recognize that these programs may
achieve improved outcomes in areas other than employment gains and welfare
reductions. There is a need to continue to develop measures of alternative measures of
program accomplishments.
Across all the sites, employment and earnings levels were very low both before and
after enrollment in the program, indicating the significant challenge of designing
effective program services for this population. In spite of these interventions, even in
the most successful sites, the populations targeted by the ISPs continue to fare relatively
poorly over time in terms of their overall employment and earnings and MFIP receipt.
81
For example, average quarterly earnings were less than $2,000—or less than $8,000 per
year—in the post-enrollment period. These findings are consistent with a range of other
studies that show it is extremely difficult to improve the economic success of those who
face significant barriers to employment. Overall, these results illustrate the persistent
challenge of finding strategies to effectively assist individuals who receive cash benefits
for long periods.
82
REFERENCES
Ashenfelter, Orley. 1978. “Estimating the Effect of Training Programs on Earnings.” The
Review of Economics and Statistics 60(1): 47–57.
Bloom, Dan, Cynthia Miller and Gilda Azurdia. 2007. “The Employment Retention and
Advancement Project: Results from the Personal Roads to Individual Development and
Employment (PRIDE) Program in New York City.” New York: MDRC.
Burt, Martha. 2002. “The Hard-to-Serve: Definitions and Implications.” In Welfare Reform:
The Next Act, edited by Alan Weil and Kenneth Finegold. Washington, DC: The Urban
Institute Press.
Chazdon, Scott. 2005. “The Minnesota Integrated Service Project.” Presentation for the
WELPAN Research Conference, Madison, WI.
Corbett, Thomas, and Jennifer L. Noyes. 2004. “Service and Systems Integration: A
Collaborative Project.” Focus 23(2): 30–34.
Heckman, James J., and Jeffrey A. Smith. 1999. “The Pre-Programme Earnings Dip and the
Determinants of Participation in a Social Programme. Implications for Simple
Programme Evaluation Strategies.” The Economic Journal 109 (July): 313–48.
Hutson, Rutledge Q. 2004. “Providing Comprehensive, Integrated Social Services to
Vulnerable Children and Families: Are There Legal Barriers at the Federal Level to
Moving Forward?” Part of the Cross-Systems Innovation Project of the National
Governors Association, the Hudson Institute, and the Center for Law and Social Policy.
Washington, DC: Center for Law and Social Policy.
Garrigan, James. 2007. “Testimony of Red Lake Band of Chippewa Indians before the U.S.
Senate Committee on Indian Affairs.” July 12, 2007
LeBlanc, Allen, Cynthia Miller, and Karin Martinson. 2007. “The Employment Retention
and Advancement Project: Results from the Minnesota ERA Site.” New York, NY:
Manpower Demonstration Research Corporation.
Loprest, Pamela. 2001. “How Are Families Who Left Welfare Doing over Time? A
Comparison of Two Cohorts of Welfare Leavers.” Economic Policy Review. New York:
Federal Reserve Bank of New York.
Loprest, Pamela, and Karin Martinson. 2008. Supporting Work for People with Significant
Challenges. Washington, DC: The Urban Institute.
Martinson, Karin. 1999. “Literature Review on Service Coordination and Integration in the
Welfare and Workforce Development Systems.” Washington, DC: The Urban Institute.
83
Martinson, Karin, Caroline Ratcliffe, Elizabeth Harbison, and Joanna Parnes. 2007.
Minnesota Integrated Services Project: Participant Characteristics and Program
Implementation. Washington, DC: The Urban Institute.
Michalopoulos, Charles. 2005. “Does Making Work Still Pay?” New York: MDRC.
Minnesota Department of Human Services. 2002. “Minnesota Family Investment Program
Longitudinal
Study:
Approaching
the
60-Month
Time
Limit.”
http://edocs.dhs.state.mn.us/lfserver/Legacy/DHS-4450G-ENG.
Minnesota Department of Human Services, Program Assessment and Integrity Division.
2004. “Characteristics of December 2003 Minnesota Family Investment Program: Cases
and Eligible Adults.” http://edocs.dhs.state.mn.us/lfserver/Legacy/DHS-4219E-ENG.
Minnesota Department of Human Services, Program Assessment and Integrity Division.
2006.
“Employability
Measure
Pilot
Study
Final
Report.”
http://edocs.dhs.state.mn.us/lfserver/Legacy/DHS-4966-ENG.
Morgenstern, Jon and Kimberley A. Blanchard. 2006. “Welfare Reform and Substance
Abuse Treatment for Welfare Recipients.” Alcohol Research and Health 29 (1):63-67.
Mueser, Peter R., Kenneth R. Troske, and Alexey Gorislavsky. 2007. “Using States
Administrative Data to Measure Program Performance.” The Review of Economics and
Statistics 89(4): 761–83.
Parnes, Joanna and Heidi Johnson. 2008. Minnesota Integrated Services Project: Voices of
Program Participants. Washington, DC: The Urban Institute.
Ragan, Mark. 2003. “Building Better Human Service Systems: Integrating Services for
Income Support and Related Programs.” Prepared for the Annie E. Casey
Foundation/Casey Strategic Consulting Group. Albany, NY: Rockefeller Institute of
Government.
Sandfort, Jodi. 2004. “Why is Human Services Integration so Difficult to Achieve?” Focus
23(2): 35–39.
U.S. General Accounting Office. 1992. “Integrating Human Services: Linking At-Risk
Families with Services More Successful than System Reform Efforts.” Washington, DC:
U.S. General Accounting Office.
Zedlewski, Sheila, Pamela Holcomb, and Pamela Loprest. 2007. Hard-to-Employ Parents: A
Review of Their Characteristics and the Programs Designed to Address Their Needs.
Washington, DC: The Urban Institute.
84
APPENDIX A
Employability Measure
A-1
The Employability Measure
The Minnesota Department of Human Services (DHS) and the Minnesota Department of
Employment and Economic Development developed the Employability Measure to
assess a person’s status and progress in 11 life areas related to employment: child
behavior, dependent care, education, financial, health, housing, legal, personal skills, safe
living environment, social support, and transportation. The Employability Measure is
intended to provide a systematic way of measuring a person’s current status in these areas
and thus progress over time in overcoming barriers to obtaining and retaining a job.
Completing the EM gives job counselors information to use in writing employment plans
and gives employment services providers, counties, tribes, and the state data on barriers
and strengths of program participants. The measure is not an in-depth assessment, so
referrals to other assessments or services may follow.
DHS began piloting the Employability Measure in January 2005 to test the reliability,
validity, and utility of the measure. Shortly after this pilot began, the Integrated Services
Project (ISP) was implemented in eight sites. At the same time that pilot testing of the
measure was taking place, DHS required that each of the eight ISPs use the
Employability Measure as a way to study family-related status and outcomes.
In the Integrated Services Project, ISP workers who have been trained on the EM
complete the measure during their initial meetings with an ISP participant and every six
months thereafter, if possible, for persons still participating in ISP. During face-to-face
meetings, they interview participants about the eleven life areas to determine their current
level in each area. Six areas have five levels (1 to 5, low to high) and the other five
areas—child behavior, health, legal, social support, and transportation—have four levels
(1 to 4/5, with no differentiation between the 4 or 5 score).
In the ISP study, differences in those moving from level 1 to level 2 and from level 2 to
level 3 are most relevant. The higher levels represent strengths and should be noted, but
the programs were designed to address the needs of those at the lower levels. Therefore,
differences over time in the proportion of individuals at levels 1 or 2 are important
indicators of progress.
The ISP sites continue to use the Employability Measure, as well as several other sites.
The pilot testing of the measure at seven Employment Services providers ended in June
2006, and the final report can be found on the DHS web site (Minnesota DHS, 2006).
DHS is now revising the EM, with input from job counselors experienced in its use,
county, tribal, employment services provider, and state staff, and members of
racial/ethnic groups and language and culture groups.
For more information, contact Anne Lauer of DHS at 651-431-3941.
A-2
EMPLOYABILITY MEASURE
Minnesota Department of Human Services
Minnesota Department of Employment and Economic Development
April 26, 2005
Copyright © 2004 by Minnesota Department of Human Services
A-3
EMPLOYABILITY MEASURE
Child Behavior
Level
4/5
Effect of actions of children in the family on participant’s employability
Children exhibit positive behaviors.
•
•
3
Behavior problems do not prohibit or limit employment.
•
•
•
2
All children have strong attendance and excelling in school AND
All children exhibit pro-social behaviors (chores, service activities, youth group, sports, music, arts, or other extra-curricular activities)
All children attending school regularly AND
All children getting school work done and making progress AND
All children have generally positive behaviors
Considerable time spent dealing with children’s behavior affects job attendance or job search.
For example,
•
School truancy
•
Attending school but not making appropriate progress
•
Frequent misbehavior requiring parent to visit school or child care provider
•
Other risk behavior (sexual, anger, impulsiveness, destructive behavior, problematic social relationships)
1
Participant is unable to get or sustain a job due to time necessary to deal with children’s behavior
problems.
For example,
•
Involved with a gang
•
Frequently suspended, expelled, or truant from school
•
Addicted to drugs/alcohol
•
Involved in illegal activities
•
Asked to leave child care provider due to child’s behavior
How are your children doing in school? Attending regularly? Making progress? How are they doing overall?
Are any of your children having behavior problems at school or at home?
Do you ever miss work due to your children’s behavior?
Any problems with your children with things like gangs, drugs, illegal activities, or pregnancy?
Reason for level chosen:
A-4
4/26/05
EMPLOYABILITY MEASURE
Dependent Care
Level
5
For children under age 13 and/or vulnerable adults
Care arrangements are good and not subsidized.
For example,
•
Dependent care not needed (e.g., not responsible for any children under 13 or vulnerable adults)
•
Good quality provider
•
Care facility is safe
•
Back-up care arrangements are available (for example, employer-provided sick leave includes sick child/adult care)
4
Care arrangements are good and subsidized.
For example,
•
Good quality provider
•
Care facility is safe
•
Back-up care arrangements are available (for example, employer-provided sick leave includes sick child/adult care)
3
Care arrangements are generally stable with a few exceptions.
For working participants,
•
Care provider is stable and safe AND
•
Receiving child care or adult care subsidy if needed AND
•
Back-up child care arrangement usually available (occasional problems, for example when child sick)
For other participants,
•
If child care need arises (for example, for job search or a new job), care arrangements are available and accessible AND
•
Child care assistance eligibility has been approved if needed
2
Care arrangements are unstable or not reliable.
For example,
•
Only available care is by unreliable or unwilling family member or friend
•
No back-up care arrangement available
•
Care not available during work hours
•
No sick care or sick days available
•
Care too costly to be sustained
•
Care not culturally appropriate
•
If needed for job search or a new job, care arrangements would be difficult to set up
1
Care arrangements are completely absent or detrimental to the child or adult.
For example,
•
No care available or accessible (for example, not eligible for child care assistance, providers too far away)
•
Child or adult has special needs not accepted by providers
•
Care is unaffordable
•
Unwilling or refuses to use child care or dependent care
•
Care not safe or perceived as unsafe
What do you do for child care? (or adult care if needed in the home to care for a family member who is ill or
incapacitated)
Are you receiving a child care subsidy? (If not) have you applied for child care assistance?
For your children under 12, what do you do for child care for summer vacation, snow days, holidays, and sick children
or when provider can’t take children that day?
(If not employed or not previously doing job search) For job search or a new job, how would you handle child care?
Reason for level chosen:
A-5
4/26/05
EMPLOYABILITY MEASURE
Level
5
Education
Participant’s education, training and job readiness
Excellent education attained.
•
•
•
4
Able and eager to learn new things (life-long learner) AND
Advanced education credentials (usually college grad or beyond) AND
More options for employment and earnings
Participant uses education for advanced opportunities.
For example,
•
Certificate or diploma or successful college coursework (for example, associate degree, technical college, apprenticeship)
•
Previous successful experience in competitive employment
•
Any learning disability or physical or mental disability is managed
•
Able and willing to learn more to advance
•
Independent learner
•
May be attending college and working part-time
3
Participant is job ready with functional education.
•
•
•
2
Education adequate to get low-level competitive employment
May have low-level certificate (like Certified Nurse Assistant – CNA)
Usually, but not necessarily, high school diploma or GED
Basic education is in process.
For example,
•
Getting training, like GED, ESL, ABE, skills training, etc.
•
In supported employment
•
If learning disability diagnosed, working on it
•
Able to learn
•
Has done some recent training but still needs to work on skills to obtain employment – especially literacy skills or English
1
Education is inadequate for employment.
Examples of reasons that participant is not job ready:
•
Does not like to learn or lacked opportunity to learn
•
No GED or high school diploma
•
Does not enjoy learning
•
Dropped out of school
•
Illiterate or very poor reader
•
No education available in original country
•
Very limited education ability (for example, low IQ, severe mental or physical condition that interferes with learning)
Do you have a high school diploma or GED? (If yes) Which one?
Did you like school?
Do you like to read? Did you have any trouble with reading in school?
Have you ever been in special education classes?
(If appropriate) Have you had any schooling beyond high school? (If yes) What type?
(If appropriate) Do you have any certifications or professional licenses?
Reason for level chosen:
A-6
4/26/05
EMPLOYABILITY MEASURE
Level
5
Financial
Family income in relation to expenses
Family has income well above basic living expenses.
•
•
•
4
Family income is adequate.
•
•
•
3
Very little or no discretionary money AND
Very little cushion for emergencies
Income is sometimes adequate to meet basic living expenses (food, shelter, and other expenses
particular to this family like medical care, child care, etc.)
•
•
•
1
Income/employment is stable AND
Close to 100% of income is from earnings and/or child support AND
Limited discretionary income
Income is stable, but pays only for basic living expenses (food, shelter, and other expenses particular
to this family like medical care, child care, etc.)
•
•
2
Income/employment is stable AND
No income from public sources AND
Financial cushion for emergencies and discretionary spending
One or more major sources of income are erratic or unstable (for example, child support payments, earnings)
May have new job but poor employment history
May not be paying basic bills even though income appears sufficient
Income is inadequate to meet basic living expenses (food, shelter, and other expenses particular to
this family, like medical care, child care, etc.).
Note: Responses to first five questions will be needed for Employability Measure data entry on WF1 or TEAMS.
Are you currently working? (If yes) Where? (If you cannot tell, ask whether it is subsidized employment.)
How long have you worked there?
(If yes) How many hours per week do you work?
(If yes) How much do you earn per hour?
How many jobs have you had in the last 6 months?
Did you receive MFIP cash and Food Support for this month? (If yes) How much?
Do you have Medical Assistance?
Do you receive any other type of income like child support or SSI? (If yes) Type and amount? How often do you receive
this income?
Are you current on your rent and utilities? (If no) Why not?
Do you have concerns about having enough money to buy food?
Do you have any money saved?
Reason for level chosen:
A-7
4/26/05
EMPLOYABILITY MEASURE
Level
4/5
Health
Family physical, mental, and chemical health
Family has no serious physical, mental, or chemical health concerns.
•
•
•
3
Physical, mental, or chemical health concerns are stabilized.
•
•
•
•
•
2
Generally good health for self and all family members AND
If working, not at risk of losing employment AND
If working, employer provides personal/sick leave benefit
Following any treatment plans, including taking medication AND
Chronic conditions may be present among self or family members, but they are managed and do not present barriers to employment or job
search AND
If working, employer offers flexibility to deal with time off for medical reasons, either sick/personal leave or unpaid leave AND
If working, slight risk of losing employment because health concerns occasionally interfere with work attendance or performance AND
If not employed, health concerns do not prevent job search
Serious physical, mental, or chemical health concerns often interfere with work attendance or
performance or job search.
For example,
•
Work absences due to health concerns place client at risk of losing job
•
Access to health care provider limited (for example, appointment times, clinic location, or referrals to specialists)
•
Lack of access to culturally appropriate and acceptable care
•
Treatment routinely needed during work day or multiple medical appointments each month for self or family members
•
Poor work history because of health issues for self or family members
1
Extremely serious physical, mental, or chemical health concerns prevent employment.
For example,
•
Incapacitated or ill family member needing care
•
Documented medical condition preventing work
•
More than one family member with very serious health problems that are not managed
•
Medication or treatment does not control condition
•
Not compliant with treatment plan, leading to negative health consequences (includes not taking medication because cannot afford)
•
Cannot care for self and personal care is not available (for example, quadriplegia, recovering from surgery, terminal illness)
How is your general health? Do you have concerns about your health?
Do you or any family members have any medical conditions that affect your ability to work or look for work?
Do you need to take medications daily?
Who is your doctor?
Do you or anyone in your household use tobacco, smokes or drinks alcohol? How much and how often?
Is there any type of health care that you or a family member need but are not getting? (If yes) What is it? Why aren’t you
getting it?
(If you suspect health concerns that the participant has not mentioned (for example, depression or bipolar disorder) you
could ask a general question like the following) What is a typical day like for you?
Reason for level chosen:
A-8
4/26/05
EMPLOYABILITY MEASURE
Housing
Level
5
Condition of structure and stability of family’s living situation
Home ownership or market-rate rental housing meets family needs and requires no government
assistance.
•
•
4
Family has stable non-subsidized housing.
•
•
3
Paying all housing expenses with own money AND
Requires NO government assistance for housing, such as MFIP, emergency assistance or fuel or energy assistance
Non-subsidized and non-shared housing
May receive assistance toward housing needs, such as MFIP, emergency assistance or fuel or energy assistance
Living situation is stable.
For example,
•
Subsidized rental housing (for example, Section 8) or public housing
•
Supportive housing (housing with services provided to help with daily living)
•
Transitional housing
•
Living in home of family or friends in a stable living situation
•
May be shared housing or help from family or friends
2
Family is at risk of losing housing or is in temporary housing.
For example,
•
Has an Unlawful Detainer that is limiting their ability to get housing
•
In temporary or unstable housing including shelters or with family or friends
•
In danger of being evicted (for example, late on rent, bad behavior, foreclosure)
•
Frequent moves (three or more times in last year)
•
May be shared housing or help from family or friends
1
Housing is nonexistent, dangerous, or structurally substandard.
For example,
•
Lives in unsafe and/or substandard housing (for example, serious problems with things like insects, rodents, broken windows or appliances,
chronic plumbing problems, etc.)
•
Homeless with no shelter or other options available
Do you like where you live?
Do you have any concerns about having a place to live?
Do you rent or own? (If renting) Is it subsidized?
Do you share housing with anyone? (If yes) With whom? How much of the housing costs do you pay?
How long have you been there?
How many times have you moved in the last year?
Are you current with your rent and utilities?
Have you ever been evicted? (If yes) When and why?
Reason for level chosen:
A-9
4/26/05
EMPLOYABILITY MEASURE
Level
4/5
Legal
Family’s criminal or civil legal issues affecting participant’s employment
No significant legal issues affect employment.
•
•
3
Past issues have been resolved (for example, probation expired, license restored, divorce finalized, etc.) OR
Never had any legal issues
Work is possible, but legal issues interfere.
For example,
•
Probation restrictions
•
Issues requiring court appearances like open child protection case, divorce, child custody case, bankruptcy, etc.
2
Legal issues limit work opportunities.
For example,
•
Felony conviction limiting type or hours of work, including preferred or previous work
•
Professional license or driver license required for doing a particular job revoked due to child support nonpayment, DUI, professional
malfeasance, etc.
•
Job lost due to legal issues
1
Participant is legally forbidden to work
For example,
•
No work permit
•
Under threat of deportation
•
Incarcerated or scheduled to be incarcerated
In order to assist you better with finding work, I need to find if there are any legal issues that prevent or limit you from
working or from the type of work you can do:
i. Are you currently on probation or parole?
ii. Do you have community service obligations?
iii. Are you going to court for any reason?
iv. Any other convictions?
Have you lost a professional license or driver’s license needed for your job?
(If answer yes to any of above) How does this affect working or looking for work?
Reason for level chosen:
A-10
4/26/05
EMPLOYABILITY MEASURE
Level
5
Personal Skills
Participant’s self-management and job-seeking skills
Participant’s skills are sufficient to handle and make the best out of ordinary and extraordinary life
and work situations.
Skill areas include:
•
Able to manage work and home responsibilities well
•
Able to effectively manage crisis situations both at work and home
•
Conflict resolution, time management, problem solving are all part of daily functioning
•
Has retained a job more than 12 months OR proven ability to get, hold and manage job and home responsibilities
4
Participant’s skills are sufficient to adequately manage ordinary life and work situations.
Skill areas include:
•
Has back-up plans and/or is able to problem solve for unforeseen circumstances (for example, furnace out and able to make arrangements
and show up to work)
•
Has retained a job for more than 6 months or left a job to take a better job (better pay, benefits, hours, etc.)
•
Successful job-seeking skills
3
Participant is learning to manage daily routines, work routine, and problem solving.
For example,
•
Adequate or improving job seeking skills (interviewing, applications, etc.)
•
Has retained a job for 2- 6 months
•
Learning soft skills (although may have minor conflicts, time management issues, or reprimands at work, etc.)
2
Participant has limited skills to perform activities of daily living and work.
For example,
•
Learning job seeking skills (interviewing, applications, etc.)
•
Occasionally cannot solve problems, time management conflicts, or personal conflicts in personal life and work life
•
Unable to hold employment longer than 2 months due to lack of soft skills (for example, not calling in when sick, tardiness or absenteeism,
chaotic life and not able to balance work and personal life)
1
Participant’s ability to perform activities of daily living and work is very limited.
For example,
•
Unable to manage conflict, problem solving, communications with others, or time demands
•
Personal maintenance skills lacking (for example, poor hygiene, oral health, grooming, clothing)
•
Lacks budgeting skills (may have or may need vendor payments or a representative payee)
•
May be in or need sheltered workshops or supportive work environment
•
May not be able to get or hold onto a job more than a very short period of time due to lack of personal skills
(If not employed) When were you last employed? How long did you work there?
How many jobs have you had in the last three years? What is the longest any of these jobs lasted?
Have you ever had conflicts with co-workers or supervisors? (If yes) What about?
Have any other kinds of problems come up at work?
Have you ever been fired? (If yes) What happened?
(If not employed) What steps do you need to take to get a job?
Do you feel you need help with budgeting money?
Reason for level chosen:
A-11
4/26/05
EMPLOYABILITY MEASURE
Level
5
Safe Living Environment
Neighborhood and household safety
Family members are as safe as possible from violence both at home and in the neighborhood.
•
•
•
4
Family members are safe from violence and the impact of violence most of the time.
•
•
•
3
Family interactions are nonviolent (any formerly violent abuser continues to refrain from violence) AND
Participant characterizes the neighborhood as safe most of the time AND
Participant feels comfortable going out to work (safe to leave family, safe to travel through neighborhood)
Family members are working toward being free from violence at home and in the neighborhood.
•
•
•
•
•
2
All household members can avoid or leave unsafe situations AND
Participant characterizes the neighborhood as safe AND
Family interactions are nonviolent
Abuser is developing a support system and skills to interact nonviolently
Violent abuser (may or may not be household member) begins to refrain from violence
Participant feels safe enough to go out to work (for example, leaving children at home, traveling through neighborhood)
If required, vulnerable person has a safety plan that is being followed or is working on one
Neighborhood is usually safe place to live
Family members have safety problems at home or in their neighborhood.
For example,
•
Occasional shootings, break-ins, drug dealing in the neighborhood
•
Violent behavior of abuser (may or may not be household member) is unresolved, but interventions have been initiated
•
If needed, children/vulnerable adults placed in stable situation outside the home
•
Police may be called, but infrequently
•
Some involvement of helping agencies like domestic violence advocate or battered women’s shelter
•
Order for protection in place if needed
1
Either home or neighborhood is extremely dangerous and any interventions are ineffective.
For example,
•
Violent abuser threatens safety of household members
•
Police frequently called to respond to violence in the home or neighborhood
•
Victim of or impacted by frequent shootings, break-ins, drug dealing, etc. in the neighborhood
•
Limited involvement of helping agencies in violent household situation
•
Frequent battered women’s shelter visits followed by return to abusive situation
•
No safety plan or safety plan is ineffective
Do you feel safe in your neighborhood? (If no) Why not?
Do you feel safe at home? (free from violence in the home )
(If no) Do you currently have a safety plan? (If yes) Are you following it?
(If does not feel safe) Do you currently have an Order for Protection (OFP) against anyone? (If yes) Why and against
whom?
Have you received services from a domestic abuse center or women’s shelter? (If yes) What happened?
Reason for level chosen:
A-12
4/26/05
EMPLOYABILITY MEASURE
Level
4/5
Social Support
Positive, helpful personal influences on the participant
There are supportive interactions with reliable adults and/or community organizations.
•
•
•
3
Some positive support is usually available.
•
•
•
2
Network of friends or family or fellow members of one or more community organizations AND
Some are role models AND
They help participant overcome barriers
A number of generally reliable supports such as other adults and community organizations AND
Support sometimes, but may not always be there AND
Destructive behaviors of others have little effect on work, direct or indirect
Participant has limited positive support.
For example,
•
Few stable, mature adults are involved in the participant’s life
•
Very limited connection to community organizations
•
Destructive behavior of others influences the participant
1
Participant has no effective positive social support.
For example,
•
Other people sabotage efforts to work
•
No supportive adults and no connection to any community organizations (church, schools, etc.
•
Isolated
•
Destructive behaviors of others greatly affect or harm the participant
Do you have a support network of friends and family? Who are they?
How well do you get along with your family?
Do you have anyone you can confide in? (friend, mentor, counselor, elder, therapist)
Tell me about your friends, what kinds of things do you do with your friends?
Is there anyone who is not supportive of your working or who causes problems so you cannot go to work?
Do you regularly attend any groups or organizations? (church, support groups, coaching, sports, etc)
Reason for level chosen:
A-13
4/26/05
EMPLOYABILITY MEASURE
Transportation
Level
4/5
Getting to work
Transportation is dependable and reliable.
•
•
•
3
Transportation is not a barrier to employment AND
Could be good car or convenient public transit AND
Reliable alternative transportation
Transportation arrangements meet most needs.
For example,
•
Has valid driver’s license and up-to-date insurance and registration and vehicle is generally reliable
•
Public transportation meets most daily work needs but has limitations (route, hours, convenience, etc.)
•
No reliable alternative transportation
2
Transportation is unreliable.
•
•
•
•
•
1
Public transportation not always available when needed
Has access to a vehicle that is not reliable
Vehicle maintenance and repairs are unaffordable
Time spent commuting is excessive (child care drop-offs, scheduling, etc.)
Only expensive private transportation for hire (taxis, Dial-a-Ride, etc.) is available
Transportation is not adequate to meet work or job search needs.
•
•
•
Car transportation is not adequate: driving illegally (no license or no insurance) or no access to vehicle AND
Public transportation is not adequate: unavailable or unaffordable or participant refuses to use AND
Other transportation arrangements are not adequate: getting rides, walking, etc. are unavailable or inconsistently available
How do you get around? How well does this work?
Do you have good alternative transportation? (If yes) What is it?
How long does it take you to get to work (or job club or job search)?
Is public transportation available where you live? (If yes) Is it available when you need it?
Do you have a driver’s license? (If no) Why not? (suspended, revoked, never got one)
(If participant owns car) Do you have insurance coverage on your car right now?
Reason for level chosen:
A-14
4/26/05
APPENDIX B
Supplemental Tables
B-1
Table B-1
Demographic Characteristics of ISP Participants at Enrollment, by Site
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
Average Age
34.6
31.6
30.2
31.5
34.1
32.5
29.3
31.5
Age Group
18-19
20-24
25-29
30-34
35-39
40+
0.3
11.4
22.2
18.6
19.0
28.4
%
%
%
%
%
%
2.4
26.8
20.7
14.6
12.2
23.2
%
%
%
%
%
%
1.2
20.9
38.4
11.6
12.8
15.1
%
%
%
%
%
%
0.0
24.7
20.4
17.2
19.4
18.3
%
%
%
%
%
%
0.8
11.4
26.8
13.8
23.6
23.6
%
%
%
%
%
%
2.2
17.4
26.1
15.2
15.2
23.9
%
%
%
%
%
%
1.3
26.3
33.3
16.7
11.5
10.9
%
%
%
%
%
%
0.0
25.3
26.3
11.6
17.9
18.9
%
%
%
%
%
%
Race and Ethnicity
White, Non-Hispanic
Black, Non-Hispanic
Native American
Hispanic
Asian
67.3
23.1
4.6
1.3
3.6
%
%
%
%
%
92.7
1.2
3.7
2.4
0.0
%
%
%
%
%
91.8
3.5
3.5
1.2
0.0
%
%
%
%
%
11.8
86.0
2.2
0.0
0.0
%
%
%
%
%
25.0
55.8
3.3
4.2
11.7
%
%
%
%
%
2.2
0.0
97.8
0.0
0.0
%
%
%
%
%
72.8
3.3
21.2
0.7
2.0
%
%
%
%
%
77.7
13.8
2.1
5.3
1.1
%
%
%
%
%
U.S. Citizenship
94.1 %
100.0 %
98.8 %
98.9 %
87.0 %
100.0 %
96.8 %
95.8 %
Gender
Female
Male
87.3 %
12.7 %
84.1 %
15.9 %
96.5 %
3.5 %
89.2 %
10.8 %
89.4 %
10.6 %
91.3 %
8.7 %
95.5 %
4.5 %
92.6 %
7.4 %
Children
Average number of children
Average age of youngest child
Marital Status
Married
Never married
Divorced
Separated
Widowed
1.9
7.3
13.4
51.6
17.0
17.0
1.0
1.7
6.0
%
%
%
%
%
Cohabiting with non-married partner
4.9 %
Number of Observations
306
9.8
47.6
18.3
24.4
0.0
1.8
4.7
%
%
%
%
%
9.3
53.5
16.3
20.9
0.0
2.0
5.8
%
%
%
%
%
13.2 %
10.8 %
82
86
5.4
75.3
7.5
11.8
0.0
2.3
5.9
%
%
%
%
%
11.4
63.4
11.4
12.2
1.6
2.3
5.7
%
%
%
%
%
9.4 %
7.3 %
93
123
6.5
87.0
2.2
4.3
0.0
2.1
4.2
%
%
%
%
%
18.2 %
46
9.7
64.5
8.4
16.8
0.6
1.8
5.3
%
%
%
%
%
7.4
62.1
11.6
17.9
1.1
%
%
%
%
%
5.8 %
4.3 %
156
95
Source: Authors' tabulations of ISP data collected from participants by program staff at enrollment and administrative data, provided by the Minnesota Department of Human Services.
B-2
Table B-2
Educational Attainment and Prevalence of Learning Disabilities for ISP Participants at Enrollment, by Site
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
Highest degree attained
Less than high school
High school diploma or GED only
Some college
College degree
38.2
47.4
13.1
1.3
%
%
%
%
37.8
52.4
4.9
4.9
%
%
%
%
24.4
62.8
11.6
1.2
%
%
%
%
33.3
59.1
5.4
2.2
%
%
%
%
44.7
48.0
7.3
0.0
%
%
%
%
50.0
47.8
2.2
0.0
%
%
%
%
33.5
53.5
11.0
1.9
%
%
%
%
28.4
56.8
14.7
0.0
%
%
%
%
Reading level
Cannot read
3rd grade level or below
4th to 8th grade level
9th grade level or above
Don't know/hasn't been assessed
1.6
5.9
25.2
35.1
32.1
%
%
%
%
%
0.0
6.1
26.8
39.0
28.0
%
%
%
%
%
0.0
2.3
7.0
83.7
7.0
%
%
%
%
%
0.0
9.9
14.3
63.7
12.1
%
%
%
%
%
8.6
15.5
18.1
45.7
12.1
%
%
%
%
%
0.0
2.2
13.0
73.9
10.9
%
%
%
%
%
0.6
0.0
9.0
66.7
23.7
%
%
%
%
%
1.1
2.1
17.0
71.3
8.5
%
%
%
%
%
Proficiency in English
Cannot communicate in English
Communicate in English with difficulty, needs interpreter
Communicate in English without interpreter,
some misunderstandings
Fluent in English
1.0 %
2.6 %
5.2 %
0.0 %
0.0 %
0.0 %
1.2 %
0.0 %
2.3 %
0.0 %
0.0 %
2.2 %
10.2 %
5.1 %
2.5 %
0.0 %
0.0 %
2.2 %
1.3 %
0.0 %
3.2 %
1.1 %
0.0 %
6.4 %
91.2 %
100.0 %
96.5 %
97.8 %
82.2 %
97.8 %
95.5 %
92.6 %
Learning Disabilities
Ever diagnosed as having a learning disability
29.4 %
34.1 %
19.8 %
20.4 %
30.9 %
13.0 %
22.4 %
17.9 %
Special Learning Needs Screen1
Score above threshold of 12 (%)
Average score
41.2 %
9.8
28.8 %
7.6
26.7 %
8.2
27.6 %
7.8
35.3 %
8.8
6.5 %
3.3
27.6 %
7.3
24.2 %
6.8
Number of Observations
306
82
86
93
123
46
156
95
Source: Authors' tabulations of ISP data collected from participants by program staff at enrollment, provided by the Minnesota Department of Human Services.
Persons who receive a score above the threshold of 12 on the "Special Learning Needs Screen" should be referred for further assistance.
1
B-3
Table B-3
Prevalence of Physical and Mental Health, Substance Abuse, and Domestic Violence Barriers for ISP Participants at Enrollment, by Site
Anoka
Physical Health
Physical condition that makes it hard to work
Family member with illness or disability making it hard to
work
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
45.8 %
38.2 %
26.8 %
26.8 %
27.9 %
18.6 %
17.2 %
22.6 %
39.0 %
21.1 %
13.0 %
6.5 %
23.1 %
18.6 %
31.6 %
14.7 %
Mental Health
Ever diagnosed with depression
Mental condition that makes it hard to work
59.5 %
64.1 %
67.1 %
42.7 %
62.8 %
34.9 %
50.5 %
24.7 %
82.9 %
82.9 %
19.6 %
6.5 %
66.7 %
39.1 %
53.7 %
31.6 %
Substance Abuse
Abused drugs or alcohol during the last year
Ever in chemical dependency treatment
Lived with someone abusing drugs or alcohol in past year
11.8 %
14.1 %
13.7 %
32.9 %
28.0 %
45.1 %
18.6 %
26.7 %
31.4 %
16.1 %
23.7 %
16.1 %
17.1 %
16.3 %
19.5 %
34.8 %
39.1 %
23.9 %
18.6 %
27.6 %
26.9 %
27.4 %
27.4 %
22.1 %
MFIP Mental Health and Substance Abuse Self-Screen 1
Score above threshold of 3 (%)
Average score
91.8 %
8.4
66.7 %
6.6
77.1 %
11.2
83.6 %
8.1
72.6 %
7.4
41.3 %
3.5
73.1 %
8.3
69.5 %
6.5
Domestic Violence
No evidence of family violence in past year
Suspected family violence in past year
Confirmed domestic violence in past year
87.3 %
3.6 %
9.2 %
62.2 %
14.9 %
23.0 %
67.9 %
3.6 %
28.6 %
74.7 %
2.3 %
23.0 %
75.9 %
12.1 %
12.1 %
100.0 %
0.0 %
0.0 %
76.3 %
4.5 %
19.2 %
71.3 %
1.1 %
27.7 %
82
86
93
Number of Observations
306
123
46
156
95
Source: Authors' tabulations of ISP data collected from participants by program staff at enrollment, provided by the Minnesota Department of Human Services.
A score above 3 on the MFIP Self-Screen indicates that an individual may have a mental health or chemical dependecy problem and should be referred for a professional assessment.
1
B-4
Table B-4
Criminal History, Housing Situations, Modes of Transportation, and Prevalence of Multiple Barriers for ISP Participants at Enrollment, by Site
Anoka
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
Criminal Background
Ever convicted of a felony
Ever in jail or prison
11.8 %
16.3 %
12.2 %
28.0 %
4.7 %
34.9 %
28.0 %
33.3 %
18.7 %
19.5 %
6.5 %
43.5 %
18.6 %
36.5 %
23.2 %
32.6 %
Housing History
Ever homeless
Ever evicted
Average number of moves in past 12 months
20.3 %
21.2 %
0.9
29.3 %
42.7 %
1.1
39.5 %
32.6 %
1.6
52.7 %
25.8 %
1.3
37.4 %
21.1 %
0.8
19.6 %
8.7 %
0.8
53.2 %
34.0 %
1.4
31.6 %
25.3 %
0.8
Current Living Situation
Living in emergency housing
Living with friends
Living in public housing
Living in subsidized rental
Living in unsubsidized rental
Living in own home
Other current living situation
2.6
16.0
2.0
38.6
23.5
8.8
8.5
3.7
11.0
1.2
17.1
47.6
8.5
11.0
1.2
9.4
4.7
27.1
37.6
11.8
8.2
2.2
7.7
1.1
39.6
33.0
1.1
15.4
0.9
11.1
10.3
43.6
28.2
0.0
6.0
0.0
6.5
39.1
15.2
17.4
8.7
13.0
0.0
6.4
9.0
50.0
17.3
6.4
10.9
1.1
12.6
9.5
24.2
26.3
3.2
23.2
Valid Driver's License
57.2 %
69.5 %
68.6 %
33.3 %
29.3 %
43.5 %
64.1 %
65.3 %
Primary Mode of Transportation
Own car
Access to someone else's car
Rides with others
Public transportation
Walk
Other
None
43.1
12.1
15.7
21.2
0.7
1.3
5.9
%
%
%
%
%
%
%
58.0
7.4
29.6
1.2
2.5
0.0
1.2
%
%
%
%
%
%
%
66.3
8.1
7.0
4.7
11.6
2.3
0.0
%
%
%
%
%
%
%
19.1
10.1
5.6
62.9
0.0
1.1
1.1
%
%
%
%
%
%
%
19.8
6.9
18.1
50.0
3.4
1.7
0.0
%
%
%
%
%
%
%
47.8
10.9
15.2
15.2
4.3
2.2
4.3
%
%
%
%
%
%
%
44.2
9.6
17.3
17.3
7.7
0.6
3.2
%
%
%
%
%
%
%
54.7
10.5
15.8
8.4
6.3
2.1
2.1
%
%
%
%
%
%
%
Number of Barriers to Employment1
Zero
One
Two
Three
Four or More
5.2
18.3
30.7
25.5
20.3
%
%
%
%
%
13.5
18.9
24.3
14.9
28.4
%
%
%
%
%
10.7
33.3
29.8
13.1
13.1
%
%
%
%
%
17.2
33.3
27.6
13.8
8.0
%
%
%
%
%
0.9
19.8
30.2
27.6
21.6
%
%
%
%
%
32.6
45.7
19.6
2.2
0.0
%
%
%
%
%
24.4
23.7
25.0
13.5
13.5
%
%
%
%
%
13.8
23.4
26.6
16.0
20.2
%
%
%
%
%
Number of Observations
306
%
%
%
%
%
%
%
82
%
%
%
%
%
%
%
86
%
%
%
%
%
%
%
93
%
%
%
%
%
%
%
123
%
%
%
%
%
%
%
46
%
%
%
%
%
%
%
156
%
%
%
%
%
%
%
%
%
%
%
%
%
%
95
Source: Authors' tabulations of ISP data collected from participants by program staff at enrollment, provided by the Minnesota Department of Human Services.
1
Seven barriers identified at the time of enrollment are included in this analysis: mental condition that makes it difficult to work, abused alcohol/drugs in the last year, confirmed family violence in
the last year, physical condition makes it hard to work, family member with illness making it hard to work, ever diagnosed with a learning disability or reading below the 9th grade level, and score of
fair or poor on housing Employability Measure.
B-5
Table B-5
Changes in Employment, Earnings, and MFIP Receipt Over Time in Anoka
Anoka
All
Anoka
Non-SSI track
Anoka
SSI Track
Employment and Earnings
Employment Rate
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
32.5%
24.8%
-7.7% ***
-7.3% **
Quarterly Earnings
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
36.2%
29.1%
-7.1% *
-7.3%
29.4%
21.2%
-8.2% **
-7.6% *
$868
$745
-$124
-$148
$904
$957
$53
-$81
$838
$562
-$276 *
-$226
61.2%
56.2%
-4.9%
-0.9%
56.7%
50.3%
-6.4%
4.1%
MFIP Receipt
MFIP Receipt Rate
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
Monthly MFIP Benefit Amount
Pre-enrollment period
Post-enrollment period
Absolute difference
Regression-adjusted difference
$430
$355
-$75 ***
-$37
$398
$294
-$104 ***
-$18
65.0%
61.3%
-3.7%
-4.0%
$457
$407
-$50
-$42
Number of Observations
306
141
165
Note: All earnings are in 2007 dollars. MFIP benefits include food and cash assistance. Cash
assistance is in nominal dollars, food assistance is in 2007 dollars.
* = p<.1; ** = p<.05; *** = p<.01
B-6
Table B-6
ISP Participants' Supplemental Security Income in Anoka
Supplemental Secury Income (SSI)
Timing of Enrollment in SSI
Began Receiving SSI before ISP Enrollment
Number
Percent
Began Receiving SSI after ISP Enrollment
Number
Percent
Number of Observations
Anoka
All
Anoka
Non-SSI Track
Anoka
SSI Track
5
1.6%
3
2.1%
2
1.2%
57
18.6%
19
13.5%
38
23.0%
306
141
165
B-7
APPENDIX C
Regression Approach
C-1
Appendix C: Regression Approach
This section provides details of the regression analysis. This approach allows us to estimate the
relationship between ISP participation and participants’ employment and MFIP outcomes,
controlling for both county economic conditions and participant characteristics. As described in
the text, employment and earnings are measured quarterly, while MFIP receipt and benefit levels
are measured monthly. The analysis uses data on ISP participants over a roughly four year
period—from two years prior to ISP enrollment through up to two years after ISP enrollment. 1
For each outcome, the regression equations are estimated using data on ISP participants in all
periods. Analyses are carried out separately for each of the eight sites.
Employment and MFIP receipt are measured as binary outcomes (employed, yes or no; receive
MFIP, yes or no). In the employment equation, for example, the dependent variable equals 1 if
the participant is employed and equals 0 if the participant is not employed. For employment and
MFIP receipt we estimate logit models, which take account of the binary nature of these
outcomes. 2 The earnings and MFIP benefit amounts are dollar amounts, set equal to zero for
those with no earnings or no benefits. We model these outcomes using ordinary least squares
(OLS) models. 3 The basic model can be written as
Yit = α + TimePeriod t β t + X it δ + ε it .
[1]
If the outcome being examined is earnings, for example, then the dependent variable Yit
represents the earnings of participant i in time period (i.e., quarter) t. The explanatory variables
in the model are Xit and TimePeriodt. The variable Xit is a vector of county and participant
characteristics, consisting of the county annual unemployment rate and participants’ gender, age
(under 25 [omitted], 25-29, 30-34, 35-39, 40 and older), race (white non-Hispanic, non-white
[omitted]), and educational attainment measured at enrollment (less than high school, high
school only, more than high school [omitted]). Only the county unemployment rate and
participant’s age vary over time. The employment and earnings equations use quarterly data, so
the variable TimePeriodt represents a vector of 16 indicator variables for the eight quarters prior
to enrollment and the eight quarters after enrollment. 4 Similarly, the MFIP outcomes use
monthly data so the variable TimePeriodt represents a vector of 48 indicator variables for the 24
1
ISP participants who enrolled after December 2005 do not have data for two full years after enrollment. Depending
on the month enrolled they have between 18 and 24 months of data after enrollment.
2
We also estimated the employment and MFIP receipt outcomes using OLS models (i.e., estimated a linear
probability model). Comparing the results from the OLS model to those from the logit model, shows that the
regression-adjusted differences are very similar across the two models.
3
In preliminary analyses, we also estimated the earnings and MFIP benefit amount outcomes using log and Poisson
models. The log model does not directly handle zeros (because the log of zero is undefined) and the findings were
sensitive to how the zeros were treated. The Poisson model is predominantly used for describing counts and thus
handles zero values quite well. Like the log regression, the Poisson model gives less weight to the cases with large
values and thus helps have the estimates describe the middle of the distribution. For our analysis of earnings and
MFIP benefit amount, our final estimates are based on the OLS model because our goal is to describe the variation
in average earnings and benefits—appropriately weighting the share with zeros. The OLS model essentially uses a
difference in means, so our measure of pre-post change in earnings appropriately combines the change in the
employment rate and the change in the earnings for those who are employed. Also, the results of the OLS and
Poisson model are qualitatively similar.
4
The omitted time period is the quarter of enrollment.
C-2
months before and after enrollment. 5 Finally, ε it represents the error term which is assumed to
be uncorrelated with the TimePeriod and X variables. Estimating Equation 1 provides estimates
of the coefficients (α, β, and δ) and a description of the relationship between the explanatory
variables and the outcome being examined (e.g., earnings). These regression coefficients (and
odds ratios from the logit models) are presented in Appendix Tables C-1 through C-6.
Once the regression equation is estimated, the regression coefficients ( α̂ , β̂ , and δ̂ ) are used to
calculate the regression-adjusted difference between participants’ outcomes in the pre-enrollment
and post-enrollment period. As described in Section III, the pre-enrollment period is defined as
the fifth through eighth quarter (or 13th through 24th month) before enrollment and the post
enrollment period is defined as the fifth through eighth quarter (or 13th through 24th month) after
enrollment.
To demonstrate our approach more clearly, we use ISP participants’ employment as the outcome
of interest. The estimated relationship is obtained in three main steps: (1) calculate the
probability of employment in each of the pre-enrollment quarters and then take the average
across the quarters (this is the estimated pre-enrollment probability of employment), (2) calculate
the probability of the employment in each of the post-enrollment quarters and then take the
average across the quarters (this is the estimated post-enrollment probability of employment),
and (3) subtract the “estimated pre-enrollment probability of employment” from the “estimated
post-enrollment probability of employment”—this is the regression-adjusted difference.
For the outcomes estimated using logit equations (employment and MFIP receipt), obtaining the
regression-adjusted difference requires a number of specific calculations based on specifications
and assumptions underlying the logit equation. Below we break these steps down using the
employment equation as an example. Also, in the steps below, we referred to the pre-enrollment
quarters as quarters -5 through -8 (i.e., the fifth through eighth quarters before enrollment) and
post-enrollment quarters as quarters 5 through 8 (i.e., the fifth through eighth quarters after
enrollment).
ˆ for each ISP participant in each of the pre- and post-enrollment periods,
1. Calculate Y
it
where
ˆ = α
ˆ + TimePeriod t βˆ t + X it δˆ , for t = - 5, - 6, - 7, - 8, 5, 6, 7, and 8.
Y
it
2. Calculate the probability of employment based on the logit specification for each
individual in each of the pre- and post-enrollment periods, where
Pr(Employm ent) it = Pit =
e
ˆ
Y
it
1+ e
5
ˆ
Y
it
, for t = - 5, - 6, - 7, - 8, 5, 6, 7, and 8.
The omitted time period is the month of enrollment.
C-3
3. Calculate the probability of employment in each of the pre- and post-enrollment periods.
That is, for each time period t, calculate the average probability of employment over ISP
participants.
ˆ
Y
1 N
1 N e it
Pr(Employment)t = ∑Pit = ∑
, for t = - 5, - 6, - 7, - 8, 5, 6, 7, and 8; N = number of persons.
ˆ
N i=1
N i=1
Y
it
1+e
4. Calculate the average probability of employment in the pre-enrollment period and the
post-enrollment period.
Pr( Employment)pre−enroll =
1 −8
∑ Pr( Employment)t (t = - 5, - 6, - 7, and - 8)
4 t =−5
Pr( Employment )post −enroll =
1 8
∑ Pr( Employment )t (t = 5, 6, 7, and 8) .
4 t =5
5. Calculate the regression-adjusted difference for employment as
Pr( Employment)post−enroll − Pr( Employment)pre−enroll .
Calculating the regression-adjusted difference for the earnings and MFIP benefit amount
outcomes, which are based on ordinary least squares (OLS) regression equations, is substantially
more straightforward. For these outcomes, the regression-adjusted difference can be calculated
directly from the estimated coefficients on the TimePeriod variable (i.e., the βt coefficients in
Equation 1). Using the earnings outcome as an example, let the coefficients on this TimePeriod
variable in the pre-enrollment period be represented by β-5, β-6, β-7, and β-8, and in the postenrollment period by β5, β6, β7, and β8 or fewer quarters for people enrolled after December 2005.
In this case, the regression-adjusted difference for earnings can be calculated as
Average of (β5, β6, β7, β8) – Average of (β-5, β-6, β-7, β-8).
This calculation is more straightforward because of the linearity built into the ordinary least
squares model.
It is important to note that this is a non-experimental regression analysis and does not have the
rigor of experimental design involving random assignment. These regressions cannot rule out
that unmeasured factors aside from the program services may be responsible for some of the
estimated relationships. Also, small sample sizes make it more difficult to detect statistically
significant relationships, particularly of gains that are small in magnitude.
C-4
Table C-1
Logit Regression of ISP Participants' Employment-Regression Coefficients
Anoka
Time Period
Number of Quarters Prior to Enrollment
Quarter 8
1.475***
[0.303]
Quarter 7
1.251***
[0.298]
Quarter 6
1.193***
[0.282]
Quarter 5
1.052***
[0.253]
Quarter 4
1.046***
[0.248]
Quarter 3
0.890***
[0.233]
Quarter 2
0.806***
[0.202]
Quarter 1
0.343**
[0.172]
Number of Quarters Following Enrollment
Quarter 1
0.434***
[0.140]
Quarter 2
0.560***
[0.186]
Quarter 3
0.715***
[0.185]
Quarter 4
0.778***
[0.193]
Quarter 5
0.977***
[0.197]
Quarter 6
0.982***
[0.220]
Quarter 7
0.892***
[0.274]
Quarter 8
0.686**
[0.286]
Economic Characteristics
County Unemployment Rate
-0.213
[0.248]
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
1.022**
[0.403]
0.990***
[0.379]
0.671*
[0.346]
0.672**
[0.329]
0.753**
[0.300]
0.48
[0.331]
0.233
[0.304]
0.106
[0.242]
1.152***
[0.386]
0.923***
[0.356]
0.620**
[0.298]
-0.0981
[0.294]
0.179
[0.283]
0.545**
[0.251]
0.262
[0.265]
0.201
[0.242]
0.591
[0.472]
0.176
[0.438]
0.437
[0.399]
0.262
[0.395]
0.496
[0.341]
-0.0779
[0.246]
-0.498*
[0.302]
-0.401*
[0.218]
0.179
[0.490]
-0.22
[0.456]
-0.213
[0.465]
-0.259
[0.427]
-0.236
[0.351]
0.185
[0.282]
0.275
[0.281]
0.192
[0.264]
0.357
[0.431]
0.396
[0.465]
0.535
[0.447]
0.513
[0.403]
0.715*
[0.416]
0.697*
[0.367]
0.811**
[0.368]
0.377
[0.258]
0.209
[0.364]
0.522
[0.344]
0.341
[0.311]
0.415
[0.301]
0.464*
[0.262]
0.204
[0.216]
0.177
[0.209]
-0.0222
[0.185]
0.09
[0.264]
-0.0974
[0.256]
0.207
[0.260]
0.176
[0.255]
-0.0567
[0.259]
-0.456*
[0.269]
-0.329
[0.259]
-0.534**
[0.210]
0.365
[0.226]
0.660***
[0.238]
0.610**
[0.255]
0.808***
[0.267]
0.917***
[0.292]
0.674*
[0.404]
0.995**
[0.438]
1.220***
[0.457]
0.245
[0.214]
-0.0316
[0.271]
0.197
[0.277]
0.103
[0.295]
0.38
[0.295]
0.334
[0.288]
0.735*
[0.426]
1.192***
[0.409]
-0.0895
[0.180]
0.124
[0.225]
0.261
[0.218]
0.516**
[0.250]
0.648**
[0.296]
0.636**
[0.304]
0.950***
[0.297]
1.205***
[0.343]
-0.139
[0.248]
0.205
[0.270]
0.0318
[0.286]
-0.452
[0.330]
-0.492
[0.395]
-0.66
[0.414]
-0.52
[0.405]
-0.554
[0.468]
0.247
[0.387]
0.381
[0.359]
0.494
[0.390]
-0.152
[0.525]
-0.785
[0.634]
-0.398
[0.609]
0.000165
[0.666]
-0.00563
[0.806]
0.0883
[0.150]
0.351*
[0.199]
0.247
[0.217]
0.395*
[0.221]
0.566**
[0.239]
0.579**
[0.272]
0.599**
[0.281]
0.531*
[0.292]
0.273
[0.227]
0.675**
[0.273]
0.44
[0.271]
0.0879
[0.284]
0.318
[0.280]
0.413
[0.268]
0.129
[0.284]
-0.0565
[0.309]
-0.461
[0.313]
-0.925**
[0.406]
-1.220**
[0.489]
0.654
[0.438]
0.789*
[0.405]
-0.0987
-0.208*
[0.281]
[0.120]
Continued on next page
C-5
Table C-1 (continued)
Logit Regression of ISP Participants' Employment-Regression Coefficients
Anoka
Individual Characteristics
Age 25-29
Age 30-34
Age 35-39
Age 40+
Female
White, non-hispanic
Less Than High School Education
High School Education
Constant
Number of Observations
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
0.0967
[0.227]
-0.0728
[0.260]
-0.118
[0.273]
-0.809***
[0.259]
0.0244
[0.225]
-0.000714
[0.165]
-0.247
[0.262]
0.0359
[0.249]
-0.746
[1.017]
-0.0708
[0.329]
-0.0659
[0.291]
0.199
[0.418]
0.0474
[0.292]
0.263
[0.348]
-0.525
[0.410]
0.301
[0.542]
0.384
[0.531]
1.535
[1.698]
0.142
[0.307]
0.181
[0.433]
0.0498
[0.393]
-0.833*
[0.492]
1.230**
[0.590]
0.35
[0.411]
0.571
[0.513]
0.603
[0.471]
2.396
[2.277]
0.154
[0.323]
0.497
[0.363]
1.048***
[0.382]
0.24
[0.406]
0.591
[0.447]
0.333
[0.455]
0.702
[0.604]
0.778
[0.576]
2.466
[1.956]
0.26
[0.358]
0.243
[0.394]
-0.558
[0.451]
-0.239
[0.455]
0.599
[0.586]
-0.793**
[0.333]
-1.138***
[0.347]
-0.452
[0.318]
-3.699**
[1.789]
0.0963
[0.501]
0.0967
[0.527]
0.562
[0.626]
-0.195
[0.623]
1.525***
[0.563]
0.748***
[0.275]
-0.156
[0.458]
0.0584
[0.486]
-6.712***
[2.170]
0.528**
[0.209]
0.830***
[0.270]
0.585**
[0.296]
0.834***
[0.295]
0.61
[0.453]
0.356*
[0.197]
-0.417
[0.293]
-0.428*
[0.258]
-0.937
[1.514]
0.204
[0.305]
0.1
[0.431]
-0.404
[0.403]
-0.0731
[0.456]
-0.0982
[0.308]
-0.498
[0.342]
-0.417
[0.430]
-0.218
[0.368]
1.154
[0.834]
5114
1365
1444
1515
2006
741
2609
1556
Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
C-6
Table C-2
Logit Regression of ISP Participants' Employment-Odds Ratios
Anoka
Time Period
Number of Quarters Prior to Enrollment
Quarter 8
4.373***
[1.327]
Quarter 7
3.492***
[1.040]
Quarter 6
3.298***
[0.931]
Quarter 5
2.864***
[0.723]
Quarter 4
2.847***
[0.707]
Quarter 3
2.435***
[0.568]
Quarter 2
2.239***
[0.453]
Quarter 1
1.409**
[0.243]
Number of Quarters Following Enrollment
Quarter 1
1.543***
[0.217]
Quarter 2
1.751***
[0.326]
Quarter 3
2.044***
[0.377]
Quarter 4
2.178***
[0.420]
Quarter 5
2.657***
[0.524]
Quarter 6
2.670***
[0.588]
Quarter 7
2.440***
[0.669]
Quarter 8
1.985**
[0.567]
Economic Characteristics
County Unemployment Rate
0.808
[0.200]
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
2.779**
[1.119]
2.691***
[1.021]
1.956*
[0.678]
1.957**
[0.643]
2.123**
[0.638]
1.616
[0.534]
1.262
[0.384]
1.112
[0.269]
3.164***
[1.221]
2.517***
[0.896]
1.858**
[0.553]
0.907
[0.267]
1.196
[0.338]
1.724**
[0.432]
1.299
[0.344]
1.223
[0.295]
1.805
[0.852]
1.192
[0.522]
1.549
[0.618]
1.3
[0.513]
1.643
[0.560]
0.925
[0.227]
0.608*
[0.183]
0.670*
[0.146]
1.196
[0.587]
0.802
[0.366]
0.808
[0.376]
0.772
[0.329]
0.789
[0.277]
1.203
[0.339]
1.317
[0.369]
1.212
[0.319]
1.428
[0.616]
1.486
[0.691]
1.708
[0.763]
1.67
[0.673]
2.045*
[0.851]
2.007*
[0.736]
2.250**
[0.828]
1.457
[0.377]
1.233
[0.449]
1.686
[0.580]
1.406
[0.437]
1.514
[0.456]
1.590*
[0.417]
1.227
[0.265]
1.194
[0.250]
0.978
[0.181]
1.094
[0.289]
0.907
[0.232]
1.23
[0.320]
1.193
[0.305]
0.945
[0.245]
0.634*
[0.171]
0.719
[0.186]
0.586**
[0.123]
1.441
[0.326]
1.934***
[0.460]
1.840**
[0.470]
2.244***
[0.599]
2.503***
[0.730]
1.962*
[0.793]
2.704**
[1.185]
3.387***
[1.549]
1.277
[0.273]
0.969
[0.262]
1.218
[0.337]
1.109
[0.327]
1.462
[0.431]
1.396
[0.403]
2.085*
[0.888]
3.294***
[1.347]
0.914
[0.165]
1.132
[0.255]
1.298
[0.283]
1.676**
[0.419]
1.911**
[0.565]
1.888**
[0.575]
2.585***
[0.767]
3.335***
[1.144]
0.87
[0.216]
1.227
[0.332]
1.032
[0.295]
0.637
[0.210]
0.611
[0.242]
0.517
[0.214]
0.594
[0.241]
0.574
[0.269]
1.281
[0.496]
1.463
[0.525]
1.638
[0.639]
0.859
[0.451]
0.456
[0.289]
0.672
[0.409]
1
[0.666]
0.994
[0.802]
1.092
[0.164]
1.421*
[0.283]
1.28
[0.278]
1.484*
[0.327]
1.761**
[0.421]
1.784**
[0.485]
1.821**
[0.511]
1.701*
[0.496]
1.314
[0.298]
1.963**
[0.535]
1.553
[0.421]
1.092
[0.311]
1.375
[0.385]
1.511
[0.404]
1.138
[0.323]
0.945
[0.292]
0.631
[0.198]
0.396**
[0.161]
0.295**
[0.144]
1.924
[0.843]
2.202*
[0.892]
0.906
0.813*
[0.254]
[0.0976]
Continued on next page
C-7
Table C-2 (continued)
Logit Regression of ISP Participants' Employment-Odds Ratios
Anoka
Individual Characteristics
Age 25-29
Age 30-34
Age 35-39
Age 40+
Female
White, non-hispanic
Less Than High School Education
High School Education
Constant
Number of Observations
Chisago
Crow Wing
Hennepin
Ramsey
1.102
[0.250]
0.93
[0.241]
0.889
[0.242]
0.445***
[0.115]
1.025
[0.231]
0.999
[0.165]
0.781
[0.205]
1.037
[0.258]
0.474
[0.483]
0.932
[0.307]
0.936
[0.272]
1.22
[0.510]
1.049
[0.307]
1.301
[0.453]
0.592
[0.243]
1.352
[0.732]
1.468
[0.780]
4.639
[7.878]
1.152
[0.354]
1.199
[0.519]
1.051
[0.413]
0.435*
[0.214]
3.421**
[2.019]
1.418
[0.583]
1.77
[0.909]
1.828
[0.862]
10.97
[24.99]
1.167
[0.377]
1.644
[0.596]
2.851***
[1.088]
1.271
[0.517]
1.806
[0.808]
1.395
[0.635]
2.019
[1.219]
2.177
[1.253]
11.78
[23.04]
1.296
[0.465]
1.275
[0.503]
0.572
[0.258]
0.787
[0.358]
1.821
[1.066]
0.452**
[0.150]
0.320***
[0.111]
0.636
[0.202]
0.0247**
[0.0443]
5114
1365
1444
1515
2006
Red Lake
St. Louis
Washington
1.101
[0.552]
1.102
[0.581]
1.754
[1.098]
0.823
[0.513]
4.597***
[2.590]
2.112***
[0.581]
0.856
[0.392]
1.06
[0.515]
0.00122***
[0.00264]
1.696**
[0.355]
2.294***
[0.620]
1.796**
[0.532]
2.303***
[0.680]
1.84
[0.834]
1.427*
[0.281]
0.659
[0.193]
0.652*
[0.168]
0.392
[0.593]
1.226
[0.374]
1.106
[0.476]
0.668
[0.269]
0.93
[0.424]
0.906
[0.279]
0.608
[0.208]
0.659
[0.283]
0.804
[0.296]
3.17
[2.644]
741
2609
1556
Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
C-8
Table C-3
OLS Regression of ISP Participants' Earnings
Anoka
Time Period
Number of Quarters Prior to Enrollment
Quarter 8
922.5***
[221.6]
Quarter 7
826.7***
[217.5]
Quarter 6
737.9***
[193.3]
Quarter 5
546.5***
[151.4]
Quarter 4
543.9***
[138.3]
Quarter 3
453.5***
[131.0]
Quarter 2
288.5***
[83.82]
Quarter 1
7.393
[33.71]
Number of Quarters Following Enrollment
Quarter 1
73.16**
[33.95]
Quarter 2
152.4***
[54.84]
Quarter 3
272.7***
[62.82]
Quarter 4
379.5***
[82.14]
Quarter 5
567.6***
[113.2]
Quarter 6
617.1***
[133.7]
Quarter 7
717.1***
[202.5]
Quarter 8
541.6***
[191.5]
Economic Characteristics
County Unemployment Rate
-121.1
[199.9]
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
1334***
[314.0]
1355***
[330.6]
969.7***
[301.3]
767.9***
[241.5]
683.5***
[195.1]
546.2***
[195.9]
253.1
[166.3]
203.4
[154.2]
1467***
[299.2]
1300***
[285.0]
1073***
[275.1]
491.6**
[211.6]
587.2***
[214.2]
508.3**
[199.9]
366.5**
[177.7]
114
[156.6]
1254***
[425.3]
967.0**
[381.1]
986.1***
[368.2]
709.5**
[300.7]
797.9***
[262.1]
99.78
[157.6]
-118
[139.8]
-211.7**
[106.2]
276.3
[204.8]
223.9
[178.5]
196.3
[169.7]
134.8
[128.1]
86.51
[95.32]
154.2*
[89.27]
149.5*
[76.50]
57.11
[56.51]
353.7
[292.3]
320.2
[275.2]
266.3
[256.9]
230.3
[225.7]
339.8
[206.6]
443.5*
[237.8]
379.0**
[176.2]
237.9*
[129.4]
441.2
[274.9]
416.0*
[244.7]
343.8
[220.8]
364.5*
[209.6]
373.2**
[180.7]
243.5*
[137.4]
53.37
[113.3]
20.24
[82.83]
724.6***
[231.2]
556.5**
[220.7]
859.2***
[239.4]
342.6*
[174.5]
212.8
[147.7]
11.27
[152.4]
-3.32
[142.3]
-136.9
[99.57]
395.6***
[117.9]
726.6***
[169.6]
932.1***
[227.2]
1106***
[221.2]
1341***
[254.0]
1159***
[329.4]
1483***
[391.5]
1630***
[404.0]
201.2
[152.3]
270.4
[185.2]
416.7**
[204.8]
554.8**
[224.6]
921.8***
[253.2]
860.2***
[255.1]
1135***
[346.0]
1358***
[372.1]
40.83
[122.9]
363.5**
[171.3]
597.7***
[201.5]
914.2***
[212.5]
1116***
[274.4]
1230***
[314.2]
1496***
[361.5]
1935***
[416.8]
88.43
[54.25]
154.6**
[66.19]
76.3
[68.24]
-14.64
[77.47]
12.81
[122.9]
-50.94
[122.1]
-24.68
[145.0]
-37.94
[175.7]
84.17
[162.1]
251.4
[196.3]
570.1*
[298.8]
193.5
[378.4]
-100.5
[408.8]
385.1
[487.8]
404.4
[594.8]
-113.6
[725.9]
283.6***
[101.1]
367.0***
[109.6]
480.4***
[112.6]
665.5***
[145.9]
665.9***
[145.6]
660.0***
[170.9]
646.4***
[192.4]
657.4***
[211.9]
239.9*
[134.6]
686.1***
[189.3]
655.6***
[188.8]
520.8***
[172.8]
639.7***
[198.8]
758.4***
[202.2]
848.9***
[279.6]
426
[264.2]
-558.4*
[295.9]
-1056***
[337.6]
-1253***
[433.5]
299.7
[222.5]
400.9
[453.7]
121.3
-27.67
[212.2]
[94.45]
Continued on next page
C-9
Table C-3 (continued)
OLS Regression of ISP Participants' Earnings
Anoka
Individual Characteristics
Age 25-29
Age 30-34
Age 35-39
Age 40+
Female
White, non-hispanic
Less Than High School Education
High School Education
Constant
Observations
R-squared
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
104.3
[160.2]
170.6
[174.2]
276.3
[200.1]
-155.3
[149.9]
-94.33
[178.7]
111.7
[107.6]
-326.2*
[196.0]
-230.4
[197.8]
846.8
[806.4]
209.1
[249.5]
291.6
[221.8]
825.0**
[383.3]
378.5
[233.3]
595.4***
[188.8]
-886.3*
[524.2]
-17.48
[403.8]
281
[420.3]
3054*
[1603]
374.4
[245.8]
599.6
[378.9]
326.7
[307.4]
-184.5
[336.9]
445.2
[348.8]
-90.35
[431.3]
-69.28
[397.5]
11.49
[362.7]
5344***
[1864]
176.2
[211.3]
708.6*
[368.5]
1580***
[496.2]
497.9
[368.3]
573.5
[470.1]
135.2
[470.2]
148.8
[631.4]
221.3
[559.4]
4148**
[1799]
-20.35
[141.2]
186.3
[194.1]
-131.8
[162.0]
-69.68
[155.5]
48.01
[171.9]
-239.3***
[88.29]
-417.4**
[195.1]
-163.9
[199.9]
-731.1
[832.3]
248.1
[433.4]
156.9
[372.1]
1138
[792.2]
-99.94
[355.7]
1028***
[363.2]
221
[297.7]
1026
[701.9]
941.7
[655.3]
-3749
[2701]
351.2**
[159.8]
568.2**
[221.9]
354.6*
[210.4]
466.2**
[218.9]
438.5
[277.8]
181.3
[157.3]
-621.3**
[302.1]
-590.7**
[281.8]
-433.3
[1098]
291.6
[186.6]
366.5
[375.7]
101.9
[265.7]
314.7
[286.9]
-565.7
[419.8]
-128.4
[232.6]
-311.9
[343.0]
-175.4
[344.5]
1241
[749.7]
5114
0.038
1365
0.111
1444
0.067
1515
0.124
2006
0.061
741
0.076
2609
0.078
1556
0.054
Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
C-10
Table C-4
Logit Regression of ISP Participants' MFIP Receipt-Regression Coefficients
Anoka
Time Period
Number of Months Prior to Enrollment
Month 24
Month 23
Month 22
Month 21
Month 20
Month 19
Month 18
Month 17
Month 16
Month 15
Month 14
Month 13
Month 12
Month 11
Month 10
Month 9
Month 8
Month 7
-3.794***
[0.391]
-3.763***
[0.390]
-3.715***
[0.386]
-3.558***
[0.385]
-3.466***
[0.380]
-3.482***
[0.373]
-3.444***
[0.372]
-3.441***
[0.368]
-3.371***
[0.367]
-3.111***
[0.357]
-3.005***
[0.355]
-2.974***
[0.354]
-2.968***
[0.352]
-2.945***
[0.351]
-2.783***
[0.350]
-2.653***
[0.347]
-2.553***
[0.342]
-2.379***
[0.338]
Chisago
-4.122***
[0.565]
-3.888***
[0.551]
-3.880***
[0.552]
-3.645***
[0.578]
-3.423***
[0.561]
-3.108***
[0.559]
-2.971***
[0.537]
-3.007***
[0.539]
-2.966***
[0.534]
-2.884***
[0.524]
-2.973***
[0.528]
-2.809***
[0.523]
-2.702***
[0.521]
-2.745***
[0.519]
-2.728***
[0.498]
-2.707***
[0.539]
-2.394***
[0.519]
-2.257***
[0.516]
Crow Wing
-4.016***
[0.637]
-3.994***
[0.632]
-3.991***
[0.623]
-4.082***
[0.638]
-3.859***
[0.622]
-3.745***
[0.625]
-3.726***
[0.587]
-3.566***
[0.576]
-3.354***
[0.581]
-3.045***
[0.528]
-2.877***
[0.526]
-2.589***
[0.526]
-2.785***
[0.505]
-2.673***
[0.502]
-2.813***
[0.494]
-2.607***
[0.490]
-2.492***
[0.478]
-2.632***
[0.477]
Hennepin
-1.929***
[0.601]
-1.889***
[0.589]
-1.829***
[0.564]
-1.811***
[0.519]
-1.860***
[0.513]
-1.952***
[0.511]
-1.719***
[0.520]
-1.758***
[0.499]
-1.610***
[0.491]
-1.451***
[0.477]
-1.535***
[0.458]
-1.627***
[0.432]
-1.666***
[0.418]
-1.662***
[0.412]
-1.440***
[0.385]
-1.458***
[0.339]
-1.255***
[0.363]
-0.849**
[0.368]
Ramsey
-3.446***
[0.858]
-3.499***
[0.856]
-3.484***
[0.830]
-3.444***
[0.821]
-3.312***
[0.816]
-3.290***
[0.812]
-3.246***
[0.811]
-3.196***
[0.805]
-3.046***
[0.794]
-2.958***
[0.788]
-3.047***
[0.805]
-2.947***
[0.795]
-2.782***
[0.786]
-2.780***
[0.783]
-2.914***
[0.775]
-2.912***
[0.772]
-2.833***
[0.771]
-2.824***
[0.771]
Red Lake
-1.438*
[0.736]
-1.155
[0.758]
-1.146
[0.759]
-1.281*
[0.750]
-1.270*
[0.748]
-1.127
[0.755]
-1.104
[0.758]
-1.109
[0.761]
-1.112
[0.761]
-1.294*
[0.741]
-1.295*
[0.747]
-1.301*
[0.740]
-1.361*
[0.747]
-1.509**
[0.677]
-1.272**
[0.619]
-0.773
[0.700]
-0.97
[0.602]
-0.931
[0.701]
St. Louis
Washington
-3.048***
-2.958***
[0.436]
[0.558]
-2.952***
-2.825***
[0.428]
[0.577]
-2.856***
-2.674***
[0.414]
[0.573]
-2.782***
-2.627***
[0.412]
[0.574]
-2.611***
-2.673***
[0.391]
[0.574]
-2.423***
-2.719***
[0.388]
[0.573]
-2.424***
-2.626***
[0.379]
[0.574]
-2.573***
-2.671***
[0.371]
[0.573]
-2.666***
-2.805***
[0.370]
[0.574]
-2.658***
-2.803***
[0.364]
[0.574]
-2.595***
-2.807***
[0.370]
[0.552]
-2.513***
-2.710***
[0.359]
[0.571]
-2.417***
-2.840***
[0.353]
[0.574]
-2.384***
-2.658***
[0.341]
[0.593]
-2.302***
-2.515***
[0.330]
[0.594]
-2.125***
-2.465***
[0.335]
[0.594]
-2.007***
-2.202***
[0.321]
[0.596]
-1.754***
-1.850***
[0.316]
[0.604]
Continued on next page
C-11
Table C-4 (continued)
Logit Regression of ISP Participants' MFIP Receipt-Regression Coefficients
Anoka
Month 6
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
-2.063***
[0.334]
-2.095***
[0.326]
-1.760***
[0.328]
-1.249***
[0.330]
-0.895***
[0.322]
-0.667**
[0.291]
-2.062***
[0.518]
-1.880***
[0.515]
-1.742***
[0.540]
-1.239**
[0.547]
-0.969*
[0.514]
-0.875*
[0.515]
-2.519***
[0.479]
-2.254***
[0.473]
-2.138***
[0.470]
-1.866***
[0.454]
-1.616***
[0.414]
-0.979***
[0.365]
-0.578
[0.357]
-0.235
[0.346]
-0.038
[0.336]
0.207
[0.328]
0.211
[0.283]
-0.111
[0.229]
-2.721***
[0.773]
-2.378***
[0.778]
-2.099***
[0.785]
-1.861***
[0.664]
-1.549**
[0.640]
-0.408
[0.418]
-0.278
[0.725]
-0.54
[0.716]
-0.755
[0.707]
-0.952
[0.604]
0.0277
[0.531]
0.448
[0.442]
-1.579***
[0.315]
-1.519***
[0.306]
-1.149***
[0.301]
-1.144***
[0.289]
-0.575**
[0.286]
-0.515**
[0.242]
Number of MonthsFollowing Enrollment
Month 1
-0.46
[0.405]
Month 2
-0.943**
[0.387]
Month 3
-1.245***
[0.381]
Month 4
-1.774***
[0.372]
Month 5
-1.771***
[0.373]
Month 6
-2.055***
[0.370]
Month 7
-2.306***
[0.362]
Month 8
-2.460***
[0.356]
Month 9
-2.395***
[0.355]
Month 10
-2.442***
[0.349]
Month 11
-2.613***
[0.344]
-0.199
[0.442]
-0.368
[0.521]
-0.878*
[0.516]
-1.690***
[0.516]
-1.936***
[0.518]
-2.320***
[0.522]
-2.837***
[0.511]
-2.978***
[0.516]
-3.070***
[0.523]
-3.199***
[0.502]
-3.332***
[0.505]
0.539
[0.548]
0.237
[0.718]
-0.648
[0.565]
-0.984*
[0.511]
-1.623***
[0.503]
-1.689***
[0.506]
-2.112***
[0.507]
-2.346***
[0.509]
-2.553***
[0.491]
-2.656***
[0.492]
-2.655***
[0.493]
-0.000542
[0.150]
-0.0975
[0.311]
-0.508
[0.339]
-0.645*
[0.363]
-0.957***
[0.371]
-0.772**
[0.347]
-0.643*
[0.340]
-0.778**
[0.359]
-0.966**
[0.394]
-1.033***
[0.393]
-1.037***
[0.394]
-0.0148
[0.723]
-0.437
[0.726]
-0.984
[0.729]
-0.982
[0.729]
-1.18
[0.732]
-1.348*
[0.732]
-1.918***
[0.733]
-2.224***
[0.734]
-2.414***
[0.734]
-2.439***
[0.736]
-2.385***
[0.695]
1.157
[0.850]
-0.00592
[0.511]
0.426
[0.752]
0.42
[0.748]
-0.0388
[0.737]
-0.589
[0.712]
-0.759
[0.691]
-0.33
[0.709]
-0.531
[0.704]
-0.744
[0.682]
-1.038
[0.666]
-0.167
0.729
[0.207]
[0.897]
-0.509**
-0.858
[0.243]
[0.646]
-0.732**
-1.275**
[0.292]
[0.622]
-0.876***
-1.598***
[0.296]
[0.609]
-1.171***
-1.999***
[0.315]
[0.606]
-1.170***
-2.325***
[0.314]
[0.601]
-1.490***
-2.460***
[0.318]
[0.604]
-1.591***
-2.604***
[0.310]
[0.605]
-1.777***
-2.735***
[0.313]
[0.605]
-2.034***
-2.822***
[0.318]
[0.605]
-2.187***
-2.763***
[0.305]
[0.599]
Continued on next page
Month 5
Month 4
Month 3
Month 2
Month 1
-1.322**
[0.624]
-1.122*
[0.631]
-0.88
[0.586]
-0.43
[0.614]
-0.232
[0.626]
0.00196
[0.645]
C-12
Table C-4 (continued)
Logit Regression of ISP Participants' MFIP Receipt-Regression Coefficients
Anoka
Month 12
Month 13
Month 14
Month 15
Month 16
Month 17
Month 18
Month 19
Month 20
Month 21
Month 22
Month 23
Month 24
Economic Characteristics
County Unemployment Rate
Individual Characteristics
Age 25-29
Age 30-34
Age 35-39
Age 40+
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
-2.749***
[0.350]
-2.954***
[0.351]
-3.086***
[0.347]
-3.166***
[0.348]
-3.294***
[0.350]
-3.329***
[0.346]
-3.429***
[0.348]
-3.530***
[0.352]
-3.547***
[0.355]
-3.752***
[0.371]
-3.818***
[0.373]
-3.847***
[0.374]
-3.827***
[0.374]
-3.418***
[0.484]
-3.661***
[0.489]
-3.538***
[0.509]
-3.696***
[0.507]
-3.819***
[0.520]
-4.118***
[0.543]
-4.369***
[0.538]
-4.472***
[0.578]
-4.508***
[0.580]
-4.547***
[0.591]
-4.712***
[0.575]
-4.495***
[0.604]
-4.682***
[0.608]
-2.759***
[0.497]
-2.997***
[0.505]
-3.061***
[0.488]
-3.183***
[0.511]
-3.472***
[0.524]
-3.497***
[0.531]
-3.632***
[0.541]
-3.685***
[0.560]
-3.857***
[0.560]
-4.267***
[0.670]
-4.192***
[0.685]
-4.513***
[0.699]
-4.289***
[0.680]
-1.308***
[0.382]
-1.366***
[0.384]
-1.535***
[0.372]
-1.513***
[0.404]
-1.695***
[0.408]
-1.780***
[0.400]
-1.916***
[0.405]
-1.947***
[0.419]
-2.143***
[0.441]
-2.054***
[0.434]
-1.872***
[0.445]
-2.147***
[0.462]
-2.253***
[0.464]
-2.572***
[0.708]
-2.542***
[0.710]
-2.563***
[0.719]
-2.552***
[0.726]
-2.686***
[0.730]
-2.707***
[0.738]
-2.917***
[0.743]
-2.817***
[0.736]
-2.921***
[0.738]
-2.949***
[0.737]
-3.035***
[0.739]
-2.925***
[0.738]
-2.987***
[0.783]
-0.987
[0.663]
-0.931
[0.696]
-0.739
[0.709]
-0.545
[0.722]
-0.88
[0.732]
-0.862
[0.754]
-1.014
[0.753]
-1.167
[0.774]
-1.350*
[0.784]
-1.271
[0.789]
-1.348*
[0.799]
-0.727
[0.871]
0.549
[0.992]
-2.186***
[0.305]
-2.262***
[0.308]
-2.513***
[0.322]
-2.538***
[0.317]
-2.582***
[0.327]
-2.635***
[0.326]
-2.746***
[0.337]
-3.024***
[0.348]
-3.170***
[0.354]
-3.063***
[0.358]
-3.018***
[0.354]
-3.306***
[0.364]
-3.391***
[0.382]
-2.767***
[0.597]
-2.978***
[0.579]
-3.149***
[0.579]
-3.315***
[0.581]
-3.306***
[0.583]
-3.347***
[0.584]
-3.342***
[0.583]
-3.325***
[0.565]
-3.463***
[0.561]
-3.563***
[0.574]
-3.610***
[0.580]
-3.859***
[0.562]
-3.778***
[0.565]
0.484**
[0.240]
0.849***
[0.303]
0.975**
[0.488]
0.195
[0.532]
-0.738
[0.496]
-0.423
[0.460]
0.510*
[0.304]
0.0379
[0.156]
-0.203
[0.244]
-0.245
[0.266]
-0.271
[0.282]
-0.095
[0.269]
0.311
[0.256]
0.628*
[0.376]
0.28
[0.437]
0.306
[0.297]
0.029
[0.301]
-0.0814
[0.440]
0.185
[0.360]
0.577
[0.409]
-0.149
[0.340]
0.205
[0.374]
-0.54
[0.432]
-0.365
[0.397]
0.323
[0.364]
0.278
[0.404]
0.806*
[0.415]
0.282
[0.399]
-0.587
[0.625]
-0.194
[0.826]
-0.837
[0.692]
0.292
[0.608]
-0.256
-0.498*
[0.241]
[0.300]
-0.212
0.0331
[0.280]
[0.459]
-0.257
-0.333
[0.309]
[0.377]
-0.278
-0.453
[0.340]
[0.374]
Continued on next page
C-13
Table C-4 (continued)
Logit Regression of ISP Participants' MFIP Receipt-Regression Coefficients
Anoka
Female
White, non-hispanic
Less Than High School Education
High School Education
Constant
Observations
Chisago
Crow Wing
Hennepin
Ramsey
0.486**
[0.235]
-0.536***
[0.162]
0.237
[0.241]
0.348
[0.223]
1.518
[1.090]
0.302
[0.376]
0.118
[0.320]
0.622
[0.482]
0.459
[0.431]
-2.502
[1.609]
1.338*
[0.802]
0.215
[0.423]
0.493
[0.345]
0.441
[0.289]
-4.036
[2.530]
0.335
[0.499]
0.43
[0.447]
0.775
[0.576]
0.564
[0.511]
0.591
[1.989]
-0.232
[0.454]
0.683**
[0.343]
0.879*
[0.497]
0.522
[0.476]
6.104***
[2.183]
14777
3952
4167
4386
5838
Red Lake
St. Louis
Washington
-0.118
[0.677]
-1.550***
[0.561]
-0.937*
[0.499]
6.564***
[2.512]
0.316
[0.449]
-0.0547
[0.210]
-0.00514
[0.336]
0.354
[0.284]
-0.344
[1.659]
-0.0233
[0.451]
-0.462
[0.291]
-0.212
[0.459]
-0.0939
[0.388]
3.755***
[1.057]
2116
7566
4516
Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
C-14
Table C-5
Logit Regression of ISP Participants' MFIP Receipt-Odds Ratios
Anoka
Time Period
Number of Months Prior to Enrollment
Month 24
Month 23
Month 22
Month 21
Month 20
Month 19
Month 18
Month 17
Month 16
Month 15
Month 14
Month 13
Month 12
Month 11
Month 10
Month 9
Month 8
Month 7
0.0225***
[0.00880]
0.0232***
[0.00904]
0.0244***
[0.00940]
0.0285***
[0.0110]
0.0313***
[0.0119]
0.0307***
[0.0115]
0.0319***
[0.0119]
0.0320***
[0.0118]
0.0344***
[0.0126]
0.0446***
[0.0159]
0.0495***
[0.0176]
0.0511***
[0.0181]
0.0514***
[0.0181]
0.0526***
[0.0185]
0.0619***
[0.0217]
0.0705***
[0.0244]
0.0778***
[0.0266]
0.0926***
[0.0313]
Chisago
0.0162***
[0.00916]
0.0205***
[0.0113]
0.0207***
[0.0114]
0.0261***
[0.0151]
0.0326***
[0.0183]
0.0447***
[0.0250]
0.0513***
[0.0275]
0.0494***
[0.0266]
0.0515***
[0.0275]
0.0559***
[0.0293]
0.0512***
[0.0270]
0.0603***
[0.0315]
0.0670***
[0.0349]
0.0643***
[0.0333]
0.0654***
[0.0325]
0.0667***
[0.0359]
0.0913***
[0.0474]
0.105***
[0.0540]
Crow Wing
0.0180***
[0.0115]
0.0184***
[0.0116]
0.0185***
[0.0115]
0.0169***
[0.0108]
0.0211***
[0.0131]
0.0236***
[0.0148]
0.0241***
[0.0141]
0.0283***
[0.0163]
0.0350***
[0.0203]
0.0476***
[0.0251]
0.0563***
[0.0296]
0.0751***
[0.0395]
0.0618***
[0.0312]
0.0691***
[0.0346]
0.0600***
[0.0296]
0.0737***
[0.0361]
0.0827***
[0.0396]
0.0719***
[0.0343]
Hennepin
0.145***
[0.0873]
0.151***
[0.0891]
0.161***
[0.0905]
0.163***
[0.0849]
0.156***
[0.0798]
0.142***
[0.0725]
0.179***
[0.0932]
0.172***
[0.0861]
0.200***
[0.0982]
0.234***
[0.112]
0.215***
[0.0987]
0.196***
[0.0849]
0.189***
[0.0790]
0.190***
[0.0783]
0.237***
[0.0913]
0.233***
[0.0788]
0.285***
[0.104]
0.428**
[0.157]
Ramsey
0.0319***
[0.0274]
0.0302***
[0.0259]
0.0307***
[0.0255]
0.0320***
[0.0262]
0.0364***
[0.0297]
0.0373***
[0.0303]
0.0389***
[0.0316]
0.0409***
[0.0330]
0.0476***
[0.0378]
0.0519***
[0.0409]
0.0475***
[0.0382]
0.0525***
[0.0417]
0.0619***
[0.0486]
0.0620***
[0.0486]
0.0543***
[0.0421]
0.0544***
[0.0420]
0.0588***
[0.0454]
0.0594***
[0.0458]
Red Lake
0.237*
[0.175]
0.315
[0.239]
0.318
[0.241]
0.278*
[0.208]
0.281*
[0.210]
0.324
[0.245]
0.332
[0.251]
0.33
[0.251]
0.329
[0.250]
0.274*
[0.203]
0.274*
[0.204]
0.272*
[0.201]
0.256*
[0.192]
0.221**
[0.150]
0.280**
[0.174]
0.461
[0.323]
0.379
[0.228]
0.394
[0.277]
St. Louis
Washington
0.0474***
0.0519***
[0.0207]
[0.0289]
0.0523***
0.0593***
[0.0224]
[0.0342]
0.0575***
0.0690***
[0.0238]
[0.0395]
0.0619***
0.0723***
[0.0255]
[0.0415]
0.0735***
0.0690***
[0.0287]
[0.0396]
0.0886***
0.0660***
[0.0344]
[0.0378]
0.0886***
0.0724***
[0.0336]
[0.0415]
0.0763***
0.0692***
[0.0283]
[0.0396]
0.0695***
0.0605***
[0.0257]
[0.0347]
0.0701***
0.0606***
[0.0255]
[0.0348]
0.0747***
0.0604***
[0.0276]
[0.0333]
0.0811***
0.0665***
[0.0291]
[0.0380]
0.0891***
0.0584***
[0.0315]
[0.0335]
0.0922***
0.0701***
[0.0314]
[0.0415]
0.100***
0.0809***
[0.0330]
[0.0480]
0.119***
0.0850***
[0.0399]
[0.0505]
0.134***
0.111***
[0.0431]
[0.0659]
0.173***
0.157***
[0.0548]
[0.0950]
Continued on next page
C-15
Table C-5 (continued)
Logit Regression of ISP Participants' MFIP Receipt-Odds Ratios
Anoka
Month 6
Month 5
Month 4
Month 3
Month 2
Month 1
0.127***
[0.0425]
0.123***
[0.0402]
0.172***
[0.0565]
0.287***
[0.0946]
0.409***
[0.131]
0.513**
[0.150]
Number of MonthsFollowing Enrollment
Month 1
0.631
[0.256]
Month 2
0.390**
[0.151]
Month 3
0.288***
[0.110]
Month 4
0.170***
[0.0631]
Month 5
0.170***
[0.0634]
Month 6
0.128***
[0.0474]
Month 7
0.0997***
[0.0361]
Month 8
0.0854***
[0.0304]
Month 9
0.0912***
[0.0323]
Month 10
0.0869***
[0.0303]
Month 11
0.0733***
[0.0252]
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
0.266**
[0.166]
0.326*
[0.205]
0.415
[0.243]
0.651
[0.400]
0.793
[0.496]
1.002
[0.646]
0.127***
[0.0660]
0.153***
[0.0786]
0.175***
[0.0946]
0.290**
[0.158]
0.379*
[0.195]
0.417*
[0.214]
0.0806***
[0.0386]
0.105***
[0.0496]
0.118***
[0.0554]
0.155***
[0.0702]
0.199***
[0.0821]
0.376***
[0.137]
0.561
[0.200]
0.791
[0.273]
0.963
[0.324]
1.23
[0.403]
1.235
[0.350]
0.895
[0.205]
0.0658***
[0.0509]
0.0928***
[0.0721]
0.123***
[0.0961]
0.155***
[0.103]
0.212**
[0.136]
0.665
[0.278]
0.758
[0.549]
0.583
[0.418]
0.47
[0.332]
0.386
[0.233]
1.028
[0.546]
1.566
[0.692]
0.206***
[0.0649]
0.219***
[0.0670]
0.317***
[0.0953]
0.318***
[0.0920]
0.563**
[0.161]
0.597**
[0.144]
0.819
[0.362]
0.692
[0.361]
0.416*
[0.214]
0.184***
[0.0953]
0.144***
[0.0747]
0.0983***
[0.0513]
0.0586***
[0.0299]
0.0509***
[0.0263]
0.0464***
[0.0243]
0.0408***
[0.0205]
0.0357***
[0.0180]
1.715
[0.939]
1.268
[0.911]
0.523
[0.296]
0.374*
[0.191]
0.197***
[0.0993]
0.185***
[0.0935]
0.121***
[0.0614]
0.0957***
[0.0488]
0.0778***
[0.0382]
0.0702***
[0.0346]
0.0703***
[0.0346]
0.999
[0.150]
0.907
[0.282]
0.602
[0.204]
0.525*
[0.191]
0.384***
[0.143]
0.462**
[0.160]
0.526*
[0.179]
0.459**
[0.165]
0.381**
[0.150]
0.356***
[0.140]
0.355***
[0.140]
0.985
[0.712]
0.646
[0.469]
0.374
[0.273]
0.375
[0.273]
0.307
[0.225]
0.260*
[0.190]
0.147***
[0.108]
0.108***
[0.0794]
0.0895***
[0.0657]
0.0872***
[0.0642]
0.0921***
[0.0640]
3.18
[2.702]
0.994
[0.508]
1.531
[1.151]
1.522
[1.139]
0.962
[0.709]
0.555
[0.395]
0.468
[0.324]
0.719
[0.510]
0.588
[0.414]
0.475
[0.324]
0.354
[0.236]
0.846
2.073
[0.175]
[1.859]
0.601**
0.424
[0.146]
[0.274]
0.481**
0.280**
[0.140]
[0.174]
0.416***
0.202***
[0.123]
[0.123]
0.310***
0.135***
[0.0976]
[0.0820]
0.310***
0.0978***
[0.0976]
[0.0588]
0.225***
0.0854***
[0.0717]
[0.0516]
0.204***
0.0740***
[0.0631]
[0.0447]
0.169***
0.0649***
[0.0530]
[0.0393]
0.131***
0.0595***
[0.0416]
[0.0360]
0.112***
0.0631***
[0.0342]
[0.0378]
Continued on next page
C-16
Table C-5 (continued)
Logit Regression of ISP Participants' MFIP Receipt-Odds Ratios
Anoka
Month 12
Month 13
Month 14
Month 15
Month 16
Month 17
Month 18
Month 19
Month 20
Month 21
Month 22
Month 23
Month 24
Economic Characteristics
County Unemployment Rate
Individual Characteristics
Age 25-29
Age 30-34
Age 35-39
Age 40+
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
0.0640***
[0.0224]
0.0521***
[0.0183]
0.0457***
[0.0159]
0.0422***
[0.0147]
0.0371***
[0.0130]
0.0358***
[0.0124]
0.0324***
[0.0113]
0.0293***
[0.0103]
0.0288***
[0.0102]
0.0235***
[0.00870]
0.0220***
[0.00820]
0.0213***
[0.00797]
0.0218***
[0.00814]
0.0328***
[0.0159]
0.0257***
[0.0126]
0.0291***
[0.0148]
0.0248***
[0.0126]
0.0219***
[0.0114]
0.0163***
[0.00883]
0.0127***
[0.00681]
0.0114***
[0.00661]
0.0110***
[0.00640]
0.0106***
[0.00626]
0.00898***
[0.00517]
0.0112***
[0.00674]
0.00926***
[0.00563]
0.0633***
[0.0315]
0.0500***
[0.0252]
0.0468***
[0.0229]
0.0415***
[0.0212]
0.0310***
[0.0163]
0.0303***
[0.0161]
0.0265***
[0.0143]
0.0251***
[0.0141]
0.0211***
[0.0118]
0.0140***
[0.00939]
0.0151***
[0.0104]
0.0110***
[0.00767]
0.0137***
[0.00932]
0.270***
[0.103]
0.255***
[0.0979]
0.215***
[0.0801]
0.220***
[0.0889]
0.184***
[0.0749]
0.169***
[0.0674]
0.147***
[0.0596]
0.143***
[0.0598]
0.117***
[0.0517]
0.128***
[0.0557]
0.154***
[0.0684]
0.117***
[0.0539]
0.105***
[0.0487]
0.0764***
[0.0541]
0.0787***
[0.0559]
0.0771***
[0.0554]
0.0779***
[0.0566]
0.0682***
[0.0498]
0.0667***
[0.0493]
0.0541***
[0.0402]
0.0598***
[0.0440]
0.0539***
[0.0398]
0.0524***
[0.0386]
0.0481***
[0.0355]
0.0537***
[0.0396]
0.0505***
[0.0395]
0.373
[0.247]
0.394
[0.274]
0.477
[0.339]
0.58
[0.419]
0.415
[0.304]
0.422
[0.318]
0.363
[0.273]
0.311
[0.241]
0.259*
[0.203]
0.281
[0.221]
0.260*
[0.208]
0.483
[0.421]
1.732
[1.719]
0.112***
[0.0343]
0.104***
[0.0321]
0.0810***
[0.0261]
0.0790***
[0.0250]
0.0757***
[0.0247]
0.0717***
[0.0234]
0.0642***
[0.0216]
0.0486***
[0.0169]
0.0420***
[0.0149]
0.0467***
[0.0167]
0.0489***
[0.0173]
0.0366***
[0.0133]
0.0337***
[0.0129]
0.0629***
[0.0375]
0.0509***
[0.0295]
0.0429***
[0.0249]
0.0363***
[0.0211]
0.0367***
[0.0214]
0.0352***
[0.0206]
0.0353***
[0.0206]
0.0360***
[0.0203]
0.0313***
[0.0176]
0.0284***
[0.0163]
0.0271***
[0.0157]
0.0211***
[0.0119]
0.0229***
[0.0129]
1.623**
[0.390]
2.337***
[0.708]
2.651**
[1.294]
1.215
[0.646]
0.478
[0.237]
0.655
[0.301]
1.665*
[0.506]
1.039
[0.162]
0.816
[0.200]
0.783
[0.208]
0.763
[0.215]
0.909
[0.245]
1.365
[0.350]
1.873*
[0.705]
1.324
[0.578]
1.358
[0.404]
1.029
[0.310]
0.922
[0.406]
1.204
[0.433]
1.782
[0.729]
0.862
[0.293]
1.228
[0.459]
0.583
[0.252]
0.694
[0.276]
1.382
[0.503]
1.32
[0.534]
2.239*
[0.928]
1.326
[0.529]
0.556
[0.348]
0.824
[0.680]
0.433
[0.300]
1.339
[0.814]
0.774
0.608*
[0.187]
[0.182]
0.809
1.034
[0.227]
[0.475]
0.773
0.717
[0.239]
[0.270]
0.758
0.636
[0.257]
[0.238]
Continued on next page
C-17
Table C-5 (continued)
Logit Regression of ISP Participants' MFIP Receipt-Odds Ratios
Anoka
Female
White, non-hispanic
Less Than High School Education
High School Education
Constant
Observations
Chisago
Crow Wing
Hennepin
Ramsey
1.626**
[0.382]
0.585***
[0.0948]
1.267
[0.306]
1.416
[0.316]
4.561
[4.971]
1.352
[0.509]
1.125
[0.360]
1.863
[0.898]
1.583
[0.681]
0.0819
[0.132]
3.810*
[3.057]
1.24
[0.525]
1.637
[0.565]
1.554
[0.449]
0.0177
[0.0447]
1.398
[0.698]
1.538
[0.688]
2.171
[1.250]
1.757
[0.899]
1.805
[3.590]
0.793
[0.360]
1.979**
[0.679]
2.410*
[1.197]
1.685
[0.802]
447.5***
[976.9]
14777
3952
4167
4386
5838
Red Lake
St. Louis
Washington
0.889
[0.602]
0.212***
[0.119]
0.392*
[0.196]
709.3***
[1782]
1.372
[0.616]
0.947
[0.199]
0.995
[0.335]
1.424
[0.405]
0.709
[1.176]
0.977
[0.440]
0.63
[0.184]
0.809
[0.371]
0.91
[0.353]
42.74***
[45.20]
2116
7566
4516
Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
C-18
Table C-6
OLS Regression of ISP Participants' MFIP Benefit Amount
Anoka
Time Period
Number of Months Prior to Enrollment
Month 24
Month 23
Month 22
Month 21
Month 20
Month 19
Month 18
Month 17
Month 16
Month 15
Month 14
Month 13
Month 12
Month 11
Month 10
Month 9
Month 8
-407.1***
[45.00]
-403.5***
[44.18]
-406.5***
[42.76]
-391.3***
[43.14]
-361.2***
[42.50]
-359.2***
[42.26]
-341.1***
[41.17]
-350.0***
[39.71]
-339.4***
[39.21]
-314.1***
[36.11]
-277.3***
[35.16]
-275.7***
[33.77]
-277.1***
[32.80]
-263.3***
[32.13]
-252.8***
[30.88]
-233.5***
[29.82]
-198.6***
[26.80]
Chisago
-513.7***
[59.29]
-508.5***
[60.47]
-497.1***
[58.76]
-452.6***
[60.65]
-419.7***
[60.02]
-374.8***
[56.69]
-343.3***
[56.51]
-328.4***
[56.46]
-303.6***
[55.26]
-302.5***
[53.04]
-313.6***
[52.23]
-318.8***
[51.08]
-290.8***
[51.12]
-287.1***
[52.74]
-266.5***
[52.17]
-288.5***
[50.04]
-256.7***
[51.85]
Crow Wing
-458.6***
[69.13]
-458.6***
[66.48]
-470.2***
[66.01]
-471.7***
[67.78]
-446.1***
[67.89]
-425.5***
[67.26]
-400.5***
[67.49]
-358.1***
[69.00]
-339.5***
[65.79]
-315.6***
[55.59]
-296.6***
[54.91]
-249.0***
[56.99]
-221.1***
[56.44]
-243.0***
[53.86]
-271.8***
[50.59]
-250.3***
[48.62]
-266.7***
[44.41]
Hennepin
-301.9***
[87.56]
-260.0***
[84.94]
-273.3***
[81.25]
-270.7***
[73.26]
-272.8***
[72.05]
-289.4***
[72.91]
-260.4***
[73.24]
-274.9***
[69.51]
-242.5***
[67.11]
-227.6***
[62.68]
-236.8***
[61.59]
-247.2***
[58.86]
-247.7***
[55.55]
-223.9***
[55.90]
-207.4***
[49.05]
-201.6***
[44.91]
-181.7***
[47.23]
Ramsey
-260.8***
[72.73]
-258.5***
[73.14]
-273.7***
[69.42]
-273.3***
[68.21]
-262.4***
[67.53]
-238.4***
[66.78]
-229.1***
[67.00]
-221.3***
[63.95]
-206.2***
[60.88]
-193.1***
[60.20]
-183.3***
[57.92]
-178.6***
[51.49]
-170.0***
[47.41]
-171.3***
[44.95]
-173.3***
[43.44]
-158.2***
[41.25]
-150.5***
[38.61]
Red Lake
-197.7***
[70.12]
-175.3**
[67.93]
-207.5***
[67.05]
-163.1**
[73.02]
-151.0**
[73.92]
-129.0*
[72.90]
-116.5
[72.76]
-114.9
[72.99]
-145.0**
[69.46]
-187.4**
[73.56]
-163.4**
[64.00]
-162.3***
[59.00]
-172.0***
[62.61]
-196.3***
[58.78]
-162.9***
[56.57]
-108.1**
[52.43]
-100.5**
[47.95]
St. Louis
Washington
-414.4***
-264.8***
[67.20]
[46.43]
-378.9***
-268.9***
[65.06]
[46.18]
-369.6***
-234.0***
[62.53]
[45.92]
-379.1***
-239.3***
[59.49]
[42.99]
-348.5***
-233.1***
[57.98]
[44.34]
-320.6***
-216.3***
[55.80]
[45.24]
-309.8***
-205.9***
[54.30]
[45.97]
-326.0***
-230.0***
[51.35]
[44.56]
-335.1***
-245.8***
[49.98]
[43.68]
-339.1***
-243.6***
[47.91]
[42.41]
-324.1***
-225.8***
[49.21]
[45.02]
-295.6***
-224.8***
[47.51]
[43.79]
-290.2***
-225.7***
[44.89]
[44.32]
-282.4***
-211.1***
[44.92]
[45.42]
-269.6***
-191.9***
[41.96]
[44.77]
-248.1***
-169.9***
[39.20]
[45.53]
-237.0***
-126.1***
[36.65]
[44.44]
Continued on next page
C-19
Table C-6 (continued)
OLS Regression of ISP Participants' MFIP Benefit Amount
Anoka
Month 7
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
-179.7***
[25.15]
-155.0***
[23.49]
-131.9***
[23.29]
-117.8***
[21.93]
-69.54***
[18.61]
-42.94***
[15.29]
-27.86**
[12.96]
-236.9***
[47.57]
-187.5***
[49.15]
-128.7**
[51.12]
-134.9**
[54.33]
-70.3
[48.96]
-66.89
[42.15]
-96.12***
[35.69]
-249.3***
[43.31]
-217.2***
[45.85]
-174.1***
[42.34]
-174.1***
[41.03]
-124.7***
[37.70]
-93.62***
[32.07]
-30.97
[24.00]
-120.5***
[43.99]
-54.19
[38.35]
-14.03
[35.32]
3.959
[30.99]
32.98
[28.60]
9.733
[25.06]
6.915
[21.16]
-146.3***
[36.74]
-130.4***
[35.45]
-126.3***
[33.19]
-99.96***
[30.01]
-77.59***
[26.64]
-58.99***
[22.39]
-16.48
[15.13]
-112.1**
[44.50]
-90.08**
[43.43]
-109.9**
[52.50]
-103.7**
[50.88]
-75.8
[48.25]
-39.04
[40.35]
26.37
[28.98]
-201.8***
[34.68]
-155.4***
[33.03]
-154.4***
[32.58]
-128.0***
[30.94]
-110.3***
[30.55]
-74.66***
[26.37]
-31.26
[19.65]
Number of MonthsFollowing Enrollment
Month 1
-22.07*
[12.48]
Month 2
-32.31**
[14.92]
Month 3
-54.73***
[17.19]
Month 4
-93.71***
[18.11]
Month 5
-105.1***
[18.90]
Month 6
-141.5***
[20.15]
Month 7
-176.0***
[20.95]
Month 8
-211.2***
[21.12]
Month 9
-206.6***
[20.90]
Month 10
-224.3***
[21.26]
35.07
[24.98]
29.52
[30.09]
-56.2
[38.17]
-175.6***
[42.45]
-219.9***
[45.51]
-276.0***
[47.43]
-320.7***
[49.55]
-351.4***
[48.64]
-366.8***
[52.42]
-417.0***
[43.55]
78.84***
[25.63]
53.89
[33.87]
10.17
[39.54]
-48.34
[38.39]
-104.3**
[44.50]
-126.3***
[43.03]
-165.9***
[46.04]
-206.4***
[45.34]
-241.6***
[46.46]
-277.1***
[43.72]
26.27
[17.26]
-27.37
[29.51]
-57.35
[39.87]
-88.59**
[42.95]
-86.52*
[46.44]
-107.3**
[46.55]
-80.42*
[43.81]
-100.7**
[45.67]
-114.8**
[45.63]
-128.4***
[48.15]
-10.18
[13.98]
-8.18
[16.40]
-39.27**
[18.97]
-52.66**
[20.15]
-59.15***
[22.44]
-63.34***
[23.23]
-112.4***
[27.93]
-120.1***
[28.63]
-129.1***
[27.74]
-139.8***
[30.71]
-3.098
[33.92]
-32.69
[35.67]
19.2
[38.69]
7.365
[40.87]
-20.56
[47.92]
-50.01
[48.14]
-66.87
[50.26]
-51.28
[48.75]
-76.91
[59.76]
-102.0*
[54.32]
16.43
-14.53
[14.41]
[27.26]
-28.45
-36.35
[20.24]
[29.63]
-63.53**
-115.3***
[25.37]
[35.88]
-103.1***
-138.1***
[28.66]
[37.80]
-140.7***
-161.4***
[31.25]
[43.03]
-158.1***
-231.8***
[32.82]
[43.43]
-171.0***
-252.7***
[33.76]
[44.82]
-201.4***
-259.1***
[32.49]
[46.28]
-234.8***
-268.6***
[33.88]
[45.34]
-282.7***
-282.1***
[34.94]
[43.57]
Continued on next page
Month 6
Month 5
Month 4
Month 3
Month 2
Month 1
-98.86**
[42.25]
-64.15
[39.23]
-56.68
[35.51]
-56.61*
[31.08]
-17.61
[27.99]
11.96
[25.31]
12.89
[21.36]
C-20
Table C-6 (continued)
OLS Regression of ISP Participants' MFIP Benefit Amount
Anoka
Month 11
Month 12
Month 13
Month 14
Month 15
Month 16
Month 17
Month 18
Month 19
Month 20
Month 21
Month 22
Month 23
Month 24
Economic Characteristics
County Unemployment Rate
Chisago
Crow Wing
Hennepin
Ramsey
Red Lake
St. Louis
Washington
-249.0***
[21.44]
-271.7***
[22.46]
-301.1***
[23.61]
-318.8***
[23.85]
-334.6***
[24.54]
-360.0***
[25.57]
-373.7***
[25.44]
-369.0***
[26.23]
-393.1***
[28.68]
-404.3***
[28.81]
-443.6***
[34.65]
-450.0***
[35.93]
-463.0***
[36.61]
-464.3***
[36.65]
-418.4***
[45.34]
-428.5***
[44.83]
-456.0***
[43.68]
-439.3***
[44.49]
-465.0***
[45.59]
-472.3***
[47.36]
-511.5***
[53.07]
-542.1***
[56.88]
-573.8***
[58.44]
-588.4***
[60.94]
-569.2***
[63.20]
-583.7***
[63.61]
-568.0***
[67.41]
-564.7***
[68.87]
-270.5***
[45.56]
-283.0***
[45.94]
-335.2***
[46.11]
-329.7***
[44.76]
-342.8***
[46.73]
-400.0***
[44.62]
-387.6***
[46.25]
-396.4***
[48.76]
-428.7***
[47.67]
-420.0***
[54.46]
-524.5***
[74.23]
-520.2***
[75.43]
-541.8***
[76.40]
-524.1***
[77.08]
-142.0***
[50.95]
-169.6***
[51.49]
-179.5***
[51.63]
-222.8***
[48.93]
-233.2***
[54.15]
-228.5***
[57.98]
-236.6***
[58.13]
-283.3***
[59.20]
-320.7***
[62.85]
-340.6***
[65.46]
-321.1***
[65.80]
-299.8***
[64.09]
-329.6***
[67.03]
-355.6***
[65.85]
-144.6***
[31.75]
-175.4***
[38.52]
-182.5***
[39.45]
-179.1***
[43.52]
-196.3***
[48.09]
-211.9***
[48.08]
-200.0***
[52.10]
-220.9***
[52.31]
-225.3***
[52.97]
-274.6***
[55.65]
-288.3***
[55.71]
-306.6***
[58.83]
-282.4***
[61.33]
-312.8***
[67.29]
-109.1**
[52.56]
-105.7*
[52.84]
-93.42
[58.73]
-60.5
[61.05]
-37.61
[66.01]
-56.08
[73.21]
-73.02
[80.12]
-94.13
[84.12]
-141
[90.87]
-155.1
[92.75]
-136.5
[98.85]
-198.2*
[106.5]
-117.9
[110.6]
-99.55
[123.6]
-289.3***
[32.92]
-304.1***
[33.72]
-309.6***
[34.20]
-339.5***
[35.24]
-369.4***
[35.03]
-388.0***
[38.40]
-394.8***
[39.19]
-416.9***
[40.48]
-463.8***
[42.85]
-473.6***
[45.36]
-460.5***
[44.52]
-453.6***
[45.25]
-492.1***
[48.75]
-487.8***
[51.89]
-243.8***
[44.10]
-267.5***
[42.55]
-303.6***
[43.14]
-316.4***
[43.31]
-342.7***
[42.95]
-354.5***
[41.90]
-350.0***
[41.92]
-361.8***
[40.35]
-360.7***
[40.49]
-381.6***
[38.52]
-396.8***
[40.33]
-400.9***
[44.27]
-407.9***
[49.10]
-403.2***
[50.03]
111.9***
[41.90]
129.8***
[45.57]
167.1**
[79.11]
36.56
[96.52]
-117.1
[84.88]
-51.95
[71.49]
95.19*
20.09
[54.76]
[19.53]
Continued on next page
C-21
Table C-6 (continued)
OLS Regression of ISP Participants' MFIP Benefit Amount
Anoka
Individual Characteristics
Age 25-29
Age 30-34
Age 35-39
Age 40+
Female
White, non-hispanic
Less Than High School Education
High School Education
Constant
Observations
R-squared
Chisago
Crow Wing
Hennepin
Ramsey
22.28
[39.93]
-0.273
[44.24]
10.78
[48.06]
-12
[43.63]
15.63
[42.41]
-70.88**
[28.74]
41.12
[48.76]
16.37
[44.69]
274.8
[181.1]
45.35
[44.79]
135.9*
[71.17]
46.78
[75.78]
9.769
[46.62]
36.15
[58.79]
-1.626
[69.93]
84.85
[89.32]
50.52
[84.78]
-129.2
[225.0]
17.53
[41.58]
-28.17
[63.98]
94.1
[69.03]
104.3
[67.41]
93.8
[140.9]
-20.62
[58.95]
21.39
[64.43]
23.37
[55.52]
-365.7
[389.9]
-7.488
[60.73]
146.1*
[78.27]
-62.71
[74.81]
-94.6
[71.20]
110.1
[88.98]
8.318
[73.85]
-181.4
[208.9]
-195.2
[196.9]
605.2
[434.8]
126.4*
[67.18]
102.6
[83.44]
173.9**
[83.09]
25.65
[72.39]
5.006
[88.71]
-11.9
[62.83]
161.9
[99.72]
152.4
[99.70]
1022***
[362.4]
14777
0.091
3952
0.159
4167
0.138
4386
0.108
5838
0.097
Red Lake
St. Louis
Washington
-35.69
[124.5]
107.9
[153.1]
-44
[130.2]
47.89
[111.7]
-103.6
[149.3]
219.5**
[84.28]
91.28
[109.9]
51.73
[84.46]
1116**
[426.2]
7.015
[41.91]
-13.82
[46.88]
11.87
[59.25]
11.66
[91.00]
4.451
[86.54]
-69.56
[45.18]
77.94
[63.76]
104.3*
[55.99]
155.1
[315.1]
-48.58
[52.19]
38.59
[88.54]
-34.37
[64.58]
-19.35
[71.51]
-19.2
[63.23]
-58.26
[49.53]
-66.9
[102.9]
-87.22
[83.17]
701.5***
[175.7]
2165
0.059
7566
0.093
4516
0.111
Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
C-22