La Follette
Policy Report
Volume 25, Number 1, Fall 2015
Director’s Perspective
Poverty Symposium
Showcases Longtime
Milwaukee Partnership
In our long tradition of partnering on
research and scholarship in the city of
Milwaukee, the La Follette School held
a half-day conference on “Urban Men
in Poverty: Problems and Solutions” in
April in partnership with the Marquette
University Law School.
La Follette School economist Geoffrey
Wallace organized the conference, and
he kicked it off with extensive insights
into facts and figures on problems and
solutions related to urban poverty, including reasons for focusing on men and on
urban centers in studying these issues.
Geoffrey described “a mass retreat from
employment for less skilled men” in
recent times, a trend that is particularly
strong among African American men. You
can read more about Geoffrey’s observations in this issue of the La Follette Policy
Report.
Professor Charles Franklin of Marquette University Law School presented
data on the high percentage of young
men who are arrested and incarcerated
by their mid-30s and how that harms their
Director’s Perspective
continued on page 15
Inside this Issue
The Measurement of Poverty:
An Evolving Story..............................................8
Racial Variation in the Effect of Incarceration
on Neighborhood Attainment..........................11
The Effect of Child Support on the Work
and Earnings of Custodial Mothers: New
Evidence...........................................................16
Adult Male Poverty
is Cause for Concern
By Geoffrey Wallace
P
oor working-aged men tend to be ignored in discussions of poverty among policymakers, academics, and the popular press. Part of the reason for this lapse is that
their poverty rates are lower than those of other groups; these men are also dismissed
as being “undeserving” poor.
Indeed, the official poverty rates for nonelderly and elderly men are below
those of children, nonelderly adult women, and elderly adult women. However,
the government’s supplemental poverty measure that incorporates noncash government benefits and refundable tax credits finds poverty rates for adult men that
are in line with those of other demographic groups. The government directs noncash refundable tax credits and noncash transfers to families with children, which
explains higher male poverty relative to other groups in the supplemental poverty
measure.
Figure 1 shows the 2013 official and supplemental poverty rates for children,
adults 19 to 64, and older adults. The supplemental measure indicates a greater
share of men live in poverty: 14.8 percent of men ages 19-64 and 11.9 percent
of men 65 and older, compared to official rates of 11.7 percent and 6.8 percent.
Reducing male poverty should be more of policy concern for two reasons. First,
working-age male poverty rates vary substantially by demographic group, with
some groups experiencing a high incidence of poverty; moreover, poverty for this
group has far-reaching generational repercussions that affect children.
Male poverty affects child poverty. In 2013, about one-third of men whom the
supplemental poverty measure deemed poor lived in households with at least one
child. More than 4 percent of poor nonelderly men who do not live with children
are responsible for paying support for noncustodial children. Reducing the incidence of male poverty by increasing income, reducing net tax liability, or reducing
out-of-pocket medical expenses could lead to substantial reductions in child poverty. For example, eliminating out-of-pocket medical expenses for poor nonelderly
adult men or increasing their income by 10 percent would reduce the supplemental
measure’s child poverty rate by more than a percentage point.
Incidence and Composition of Male Poverty in 2013
Table 1 shows the supplemental poverty measure rates in 2013. The numbers in the
table were obtained by matching demographic information from the 2014 Current
Population Survey Annual Social and Economic Supplement (CPS) to the 2013
Supplemental Poverty Measure Research File. As Table 1 shows, the incidence of
poverty varies dramatically across subgroups. Among men defined as poor by the
Figure 1. Poverty Rates by Age, Group, and Sex, 2013
22%
Poverty Rates According to Official Measure
20.2%
20%
18%
Poverty Rates According to Supplemental
Measure
16.8%
16.7%
Percentage of Poor People
16%
15.2%
14.8%
15.8%
14%
11.9%
11.7%
12%
11.6%
10%
8%
6.8%
6%
4%
2%
0
Children Ages
0 to 18 years
Men Ages
19 to 64 Years
Women Ages 19 to 64 Years
Men 65 Years
and Older
Women 65 Years
and Older
Source: Authors’ calculations
supplemental poverty measure, those ages 19 to 24 have a high
incidence of poverty at 22.4 percent poor—a rate higher than
the child poverty rate of 16.7 percent. Poverty rates for men
older than 25 are relatively low. This situation is especially true
for men older than 65.
For men ages 19 to 64, race and location are important
determinants of poverty status as measured by the supplemental measure and shown in Table 1. Male poverty rates among
blacks and Hispanics are 22.3 and 24.6 percent respectively,
compared to 10.5 percent for non-Hispanic whites and 16.1
percent for other racial-ethnic groups. The male poverty rate
in the western United States is 3 percentage points higher
than poverty rates in the South, the next highest poverty rate
region. The high rate of western male poverty can be attributed to the relatively high cost of shelter in the West. Controlling for household size and composition and for metropolitan status, the poverty thresholds (the minimum money
a household needs to not be living in poverty) applicable to
Geoffrey Wallace is an associate professor of public affairs and
economics at the La Follette School. His research is in labor
economics, the economics of marriage and the family, and policy
issues relating to poverty. This article is based on a presentation
he gave in April at the La Follette School conference on “Urban
Men in Poverty: Problems and Solutions” held in partnership with
the Marquette University Law School.
2 / La Follette Policy Report
poor men in the West are more than $1,000 higher than the
poverty thresholds applicable to men in the Northeast, the
region with the next most expensive shelter costs. The poverty
rate in metro areas, particularly in principal cities, is higher
than the poverty rate in non-metro areas. In the principal cities identified in the survey, the nonelderly adult male poverty
rate is 19.3 percent compared to 15.2 percent for metro areas
as a whole (which includes principal cities) and 12.5 percent
for rural areas.
Rates of poverty for nonelderly men in families (including
cohabitating arrangements) are fairly low. Moreover, nonelderly men living with children have a poverty rate that is lower
than the average rate across the entire male nonelderly adult
population. Men in married-couple families face the lowest
risk of poverty (9.4 percent) followed by men who reside in
resource-sharing units headed by a cohabitating couple (14.9
percent). Both of these groups experienced less than the average incidence of poverty in 2013. Men living in other types of
family and non-family arrangements have a high incidence of
poverty: Poverty rates for men in male-headed families, femaleheaded families, and non-families are 21.6 percent, 25.4 percent, and 25.7 percent, respectively.
Education level and worker status have a large influence on
the incidence of poverty. Nearly one-third of nonelderly adult
men without a high school education are poor. Men with just
a high school education and no college have a poverty rate of
17.4, while men with a college degree or more have a poverty
www.lafollette.wisc.edu
Fall 2015
rate of 6.3 percent. Nonelderly adult men who reported to the
CPS that they did not work at jobs or for businesses in 2013
have a poverty rate of 36.6 percent. Men who reported working
part time or for less than a full year experienced a poverty rate
of 22.5 percent. Nonelderly adult men who work full time all
year round have a very low incidence of poverty.
The last column of Table 1 shows the percentage of the
nonelderly adult poor men within each subgroup. A subgroup
can comprise high share of poor men because they make up
a large share of the population, because they have a high rate
of poverty, or both. Groups falling into the first category
include white males (45.1 percent of nonelderly poor adult
men), males living in metropolitan areas, males living in families with or without children, men in married-couple families,
men with some college but no degree, and men who worked
full time year round. Men who make up a large share of the
poor because of high poverty rates include men ages 19 to 24
and men not living in families. Groups of men who represent
a large share of the poor because of high poverty rates and
because they make up a large share of the population include
black and Hispanic men, men residing in the West, men with
a high school education or less, and men who worked less than
full time and year round.
The connections among poverty, education, and worker
status are striking. More than 60 percent of nonelderly poor
adult men have a high school diploma or less education, and
more than 75 percent of poor men work less than full time
and year round. Almost 43 percent of nonelderly adult poor
men have a high school diploma or less education and work
less than full time year round, and nearly 60 percent of these
poor less educated and un- and underemployed men are black
or Hispanic.
Why Aren’t More Poor Men Working?
Responses in the CPS give some insight into the reasons men
reported for not working in a calendar year, including being
ill or disabled, taking care of home or family, going to school,
not being able to find work, or other reasons. This information provides evidence of significant barriers to employment
for the approximately 50 percent of poor men who did not
work in 2013. More than 35 percent of nonelderly poor adult
men who did not work in 2013 listed illness and disability as
the primary reason. Another 10 percent said they were taking
care of home or family, which might suggest a relative’s health
problems. Nearly 18 percent of poor men who did not work
in 2013 reported difficulty finding a job as the primary reason.
There is considerable heterogeneity in reported reasons for
not working across age groups. Within the group of poor men
ages 19 to 24 who did not work in 2013, only 8.3 percent
reported not working because of illness or disability, and 60
percent reported not working because they were enrolled in
school. Among nonelderly poor men 45 and older who did not
work in 2013, more than 51.5 percent reported disability or
illness as the primary reason, with another 21 percent reporting
that they were taking care of home or family. For this group of
older men, the inability to find a job does not appear to be an
Fall 2015
Table 1: Male Poverty per the Supplemental
Poverty Measure, 2013
Percentage
Percentage
of Poor Men
within Subgroup
within
Subgroups
that is Poor
Subgroup
By Age
19-24 years
25-34 years
35-44 years
45-54 years
55-64 years
65+ years
22.4
15.2
13.0
12.4
13.3
11.9
18.2
19.9
15.7
16.0
15.7
14.5
By Race/Ethnicity for Ages 19-64
White (non-Hispanic)
10.5
Black (non-Hispanic)
22.3
Hispanic 24.6
Other 16.1
45.1
17.9
28.9
8.2
By Region for Ages 19-64
Northeast
Midwest
South West
13.215.4
12.216.9
14.9
33.5
17.934.2
By Metro for Ages 19-64
Metro Non-metro
Not identifiable 15.2
87.7
12.511.8
10.8
0.6
By Family for Ages 19-64
Non-family
Family without children
Family with children
25.730.3
12.5
37.5
12.4
32.2
By Head of Family Type for Ages 19-64
Married couple
9.4
Cohabitating couple
14.9
Male-headed family
21.6
Female-headed family
25.4
Male nonfamily
25.7
37.9
9.0
11.4
11.3
30.3
By Education Level for Ages 19-64
Less than high school
32.2
24.9
High school, no college
17.4
36.4
Some college
13.4
26.4
College+
6.312.3
By Employment Status for Ages 19-64
Did not work
36.6
Did not work full time
or year around
22.5
Worked full time year round
5.6
46.5
30.5
23.1
Source: 2014 Current Population Survey Annual Social and Economic
Supplement to the 2013 Supplemental Poverty Measure Research File
www.lafollette.wisc.edu
La Follette Policy Report / 3
important factor in explaining the
lack of employment in 2013, with
less than 15 percent indicating that
they did not work because of difficulty to finding work. Black and
Hispanic men of all ages are more
likely to report not working due to
difficulty finding work.
Poor Men
and Sources of Income
Table 2: Income Sources and Deductible Expenses of Poor Men
Ages 19 to 64 not in Families or Cohabitating Relationships
Average Amount
Across All
Nonelderly Men
Net Individual Resources
Total Cash Income
Total earnings
Own earnings
Other earnings
Unemployment Insurance, welfare,
Supplemental Security Income
Social Security Other cash transfers
Total Noncash Transfers
Total Tax Liability Federal tax liability before credits
Federal tax credits Payroll taxes
State taxes
Total Deductible Expenses
Child support
Work expenses
Out-of-pocket medical expenses
$4,795
7,244
5,046
5,046
0
Percentage
with Income
from Source
Average
78.3
79.4
52.9
52.9
0
$6,919
9,184
9,540
9,540
0
Nearly 50 percent of poor men did
not work in 2013. Of those who
699
12.2
5,742
did work, most worked less than
690
7.9
8,683
full time year round. This finding
809
27.0
2,997
suggests that poor men have mod611
25.2
2,421
est earnings, which prompts several
-552
46.2
-1,201
questions: What other sources of
-146
22.5
-648
income do these men have? What
77
26.5
290
is their tax liability? To what extent
-421
51.2
-822
do they incur expenses such child
-63
17.8
-375
support payments, work expenses,
-2,508
86.2
2,909
and out-of-pocket medical expens-348
5.8
-6,007
es? The following analysis addresses
-653
52.3
-1,248
these questions by breaking non-1,508
75.3
-2,002
elderly poor men into rough three
equal groups based on their livingPoverty Threshold 11,787
family arrangements and examining income sources and expenses of
Poverty Gap
6,992
each group. As tables 2-4 show, the
groups are nonelderly adult men Notes: Numbers may not add due to rounding or to state tax credits. Also, an individual may have negative net
who are not in families (or cohabi- resources.
Source: 2014 Current Population Survey Annual Social and Economic Supplement to the 2013 Supplemental
tating), nonelderly adult men in Poverty Measure Research File
families without children, and
nonelderly adult men in families with children. The tables are
Table 2 provides information on income sources available
organized in a way that is consistent with the calculation of the
to poor men who are not in a family or cohabitating relationsupplemental poverty measure.
ship. On average for a man not in a family or cohabitating
The supplemental poverty measure defines net individual
relationship, a poverty gap of $6,992 exists between net indior family resources as the sum of earnings, cash transfers, nonvidual resources of $4,795 and the supplemental poverty meacash transfers, net tax liability, and deductible expenses. These
sure poverty threshold of $11,787.
income sources appear in the top panel of each table. DeductAlthough 79.4 percent of poor working-age non-family
ible expenses include work expenses, child support payments,
men have some net cash income, average levels are quite low,
and out-of-pocket medical expenses and are shown in the botaveraging $7,244. The biggest source of cash income is earntom panel of each table. Net individual or family resources are
ings, which 52.9 percent of men report receiving, an average of
compared to the supplemental poverty thresholds to determine
$5,046. Rates of receipt of Unemployment Insurance, welfare,
an individual’s or sharing unit’s poverty status. The poverty gap
and Supplemental Security Income are low, but these income
is the amount of additional resources the individual or famsources are important for the men who rely on them. Traditionily would need in order not to be poor—to reach the poverty
ally, few people in the 19-to-64 age range collect Social Security,
threshold. In each of these three tables, the first column shows
but the 7.9 percent of men who do receive Social Security averthe average amount associated with the income or expense catage $8,683 in payments. About a quarter of poor working-age
egory across all nonelderly men, the second column shows a
men receive noncash transfers and other cash transfers, which
positive (income) or negative (expense) percentage of workcan represent anything from educational assistance to financial
ing age men with income from each source, and the last colassistance from friends and relatives.
umn shows the average of the positive (resources) or negative
Poor working-age men by and large do not receive any help
(expenses) values.
from the tax system, and they have other expenses that decrease
4 / La Follette Policy Report
www.lafollette.wisc.edu
Fall 2015
welfare, and Supplemental Security
Income are low. Evidence that some
Table 3: Income Sources and Deductible Expenses
of these men live with older relatives
of Poor Men Ages 19 to 64 in Families without Children
comes in the form of high rates of
Social Security receipt relative to
Average Amount
Percentage
nonfamily men. A little over a third
Across All with Income
of the men live in resource-sharing
Nonelderly Men
from Source
Average
units that receive cash transfers
Net Family Resources
$12,525
89.9
$14,749
other than Unemployment Insur Total Cash Income
20,049
93.0
21,577
ance, welfare, Supplemental Secu Total earnings
14,533
70.7
20,545
rity Income, and Social Security,
Own earnings
5,987
48.8
12,257
and a little over a quarter live in
Other earnings
8,546
51.2
16,689
resource-sharing units that receive
Unemployment Insurance, welfare,
noncash transfers. Similar to poor
Supplemental Security Income
1,182
16.4
7,207
nonfamily men, poor family men
Social Security 2,748
22.5
12,223
without children pay taxes and do
Other cash transfers
1,585
34.6
4,575
not receive much in the way of cred Total Noncash Transfers
927
28.7
3,228
its from the tax systems. Their fami Total Tax Liability -1,697
65.6
-2,642
lies also have substantial work and
Federal tax liability before credits
-494
40.4
-1,222
out-of-pocket medical expenses.
Federal tax credits 201
35.7
564
The income sources of the 32
Payroll taxes
-1,214
70.4
-1,725
percent of nonelderly poor men
State taxes
-191
27.1
-724
who live with children are shown
Total Deductible Expenses
-6,754
96.0
-7,037
in Table 4. These men have an aver Child support
-188
3.0
-6,346
age poverty threshold of $30,173,
Work expenses
-1,527
70.5
-2,165
net family resources of $19,991,
Out-of-pocket medical expenses
-5,039
89.1
-5,657
an average poverty gap of $10,182,
and income sources that are quite
Poverty Threshold 22,799
different from men living in famiPoverty Gap
10,274
lies without children. How do their
income sources differ? First of all
Notes: Numbers may not add due to rounding or to state tax credits. Also, an individual or a family may have
their own earnings are substantially
negative net resources.
higher than the other two groups of
Source: 2014 Current Population Survey Annual Social and Economic Supplement to the 2013 Supplemental
men. Second, they are far more likePoverty Measure Research File
ly to receive noncash transfers (78
percent do) and when they do, the amounts of these transfers
their net resources. About half of the men face some tax liability
received are higher. Lastly, their families receive a substantial
with an average amount of $552. Because their income levels
amount of assistance from the tax system. Although the famiare typically very low, most of this liability comes from paylies of 28 percent of these men do have some net tax liability,
roll taxes. The largest source of deductible expenses are outon average they are receiving net benefits from the tax system
of-pocket medical expenses that average $1,508 per year and
in the amount of $1,062 (largely through the earned income
are incurred by three-quarters of nonelderly poor men. For the
tax credit). On average, their families have about $2,100 in tax
roughly half of men who do work, deductible work expenses
liability from federal, state, and payroll taxes, but this liability is
average $1,248 per year.
more than completely offset by tax credits that average $3,145.
The income sources of the 37 percent of nonelderly poor
Similar to families without children, families with children
men who live with family members (including cohabitating
have substantial deductible expenses: $7,600 on average. Outpartners) but do not live with any children are shown in Table
of-pocket medical expenditures are similar to families without
3. The average poverty gap for these nonelderly poor men
children, but due to child-care cost, work expenses for families
is $10,274, with an average threshold of $22,799. Levels of
with children are higher.
employment and earnings for men in families are similar to
those of nonfamily men; about half the poor nonelderly family
men not living with children work with average earnings for
Conclusions
workers at $12,257 per year. About half of these poor men live
Male poverty is a problem that historically has not received
with family members who earn income. On average, working
much attention in academic and policy circles or in the popurelatives provide $8,546 in earnings, a level that is more than
lar press. Part of this lack of attention could be attributed to
$2,500 greater than the average level of earnings of the males
the fact that males have traditionally been thought of as a low
themselves. Rates of receipt of Unemployment Insurance,
Fall 2015
www.lafollette.wisc.edu
La Follette Policy Report / 5
poverty group. While this status is
undoubtedly still true for elderly
Table 4: Income Sources and Deductible Expenses
males, nonelderly adult males have
of Poor Men Ages 19 to 64 in Families with Children
supplemental poverty measure
poverty rates that are very similar
Average Amount Percentage
to those of children and women.
Across All with Income
Moreover, nearly one-third of non
Nonelderly Men
from Source
Average
elderly poor men living in resourceNet Family Resources
$19,991
95.8
$21,382
sharing units with children and
Total Cash Income
23,623
95.3
24,818
more than 4 percent of nonelderly
Total earnings
19,997
83.9
23,824
poor men who do not live with
Own
earnings
11,517
66.5
17,318
children are responsible for paying
Other earnings
8,480
50.6
16,769
child support for a noncustodial
Unemployment
Insurance,
welfare,
child: Male poverty and child pov Supplemental Security Income
1,238
18.1
6,831
erty are related.
Social Security 1,261
10.3
12,280
Nonelderly men differ in their
Other cash transfers
1,128
28.0
4,031
risk of being poor. Men ages 19 to
Total
Noncash
Transfers
2,733
78.4
3,488
64 are at an elevated risk for pov Total Tax Liability 1,062
28.1
-3,317
erty, including young men, black
Federal
tax
liability
before
credits
-327
25.7
-1,275
men, Hispanic men, and urban
Federal
tax
credits
3,145
71.8
4,380
men, men residing in the West, and
Payroll
taxes
-1,661
83.4
-1,992
men who are not in families or are
State taxes
-94
22.7
-803
in female-headed families. There
Total
Deductible
Expenses
-7,728
97.7
-7,601
are strong links between education
Child support
-155
2.4
-6,515
level and the risk of poverty, with
Work
expenses
-2,168
83.5
-2,597
those with a high school degree or
Out-of-pocket
medical
expenses
-5,105
91.0
-5,610
less education facing a much higher
risk of poverty. Employment and
Poverty Threshold 30,173
poverty also are related: 5.6 percent
of nonelderly men who reported
Poverty Gap
10,182
working full time year round in
2013 were poor compared to more Notes: Numbers may not add due to rounding or to state tax credits. Also, an individual or a family may have
than 36 percent of men who report- negative net resources.
Source: 2014 Current Population Survey Annual Social and Economic Supplement to the 2013 Supplemental
ed not working.
Poverty Measure Research File
The extent to which nonelderly
poor men receive assistance from the tax and transfer system is
Hispanic men and that being an ex-offender may represent a
highly dependent on whether they reside in households with
substantial barrier to employment, as a 2003 study by Devah
children. Men living in family units without children are highly
Pager suggests. Moreover, although not reflected in the data
dependent on earnings as a source of resources, while men livsource used to calculate poverty and employment rates in this
ing in families with children have higher earnings and are far
analysis, child support payments and arrears may discourage
more likely to receive noncash transfers (food stamps, housing
formal sector work by acting as a large lump-sum tax on earnassistance, energy assistance, etc.) and net benefits from the tax
ings of poor men, as a 2012 study by Daniel Miller and Ronald
system.
Mincy suggests.
Looking at the future of male poverty, the news is good
A source of potential good news is found in the deductand bad. The bad news first: Male poverty is closely tied to
ible expenditures of poor men and their families and the ways
education and employment, neither of which is very tractable
in which policy is likely to reduce these expenses in the near
in the near term. Forty-seven percent of nonelderly men who
future. As seen in the analysis of income sources and expenses,
were poor in 2013 reported no formal sector employment for
the resource-sharing units to which poor men belong face subthe calendar year. While information on the duration of nonstantial out-of-pocket medical expenditures. For nonelderly
employment is not available, it seems likely that four years into
poor men who are not in families, average out-of-pocket medithe recovery from the Great Recession many of these men have
cal expenditures account for more than 20 percent of the povnot worked in recent years and will not work in future years.
erty gap. For nonelderly poor men in families (with and withIllness and disability are important reasons for non-employout children) average out-of-pocket medical expenses account
ment as is difficulty finding a job, particularly for black and
for 50 percent of the poverty gap. Indeed, high out-of-pocket
Hispanic men. It seems likely that the ex-offender population
medical expenditures may be an important determinant of
is heavily represented in this group of poor detached black and
male poverty status. Comparing poor and non-poor men with
6 / La Follette Policy Report
www.lafollette.wisc.edu
Fall 2015
resources less than two times the supplemental measure’s poverty threshold, out-of-pocket medical expenses were $1,000
higher for the families of poor men than for the families of
their non-poor counterparts.
Not only are out-of-pocket medical expenditures seemingly important in determining male poverty, male poverty is
closely associated with not working—and the most frequent
reason poor men report not working is illness or disability. A
major feature of the Affordable Care Act was the expansion of
Medicaid to cover all adults up to 135 percent of the poverty
line. At present 30 states and Washington, D.C., have opted
to take the Medicaid expansion, one state (Utah) is undecided,
and 19 states (mostly in the South and West) have rejected the
expansion. Short- and long-term Medicaid expansion has the
potential to dramatically improve the prospects of poor men
and their families. To help ease male poverty in the short-term,
out-of-pocket medical expenses should be substantially reduced
for poor men and their families in states that opted to expand
Medicaid. In the long-run, Medicaid expansion may lead to
increased access to health care, better health outcomes, reduced
rates of disability and illness, and higher employment rates. u
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policyreport@lafollette.wisc.edu
Policy Report
Robert Haveman
Faculty Editor
Karen Faster
Senior Editor
Faculty
Susan Webb Yackee Director, Professor of Public Affairs
and Political Science
Hilary Shager Associate Director
Rebecca M. Blank University Chancellor
Menzie Chinn Professor, Public Affairs and Economics
Maria Cancian
Professor, Public Affairs and Social Work
J. Michael Collins Associate Professor of Public Affairs
and Human Ecology
Mark Copelovitch Associate Professor, Public Affairs
and Political Science
Jim Doyle
Adjunct Associate Professor of Public Affairs
Dennis Dresang
Professor Emeritus, Public Affairs
and Political Science
Jason Fletcher
Associate Professor of Public Affairs
Bob Hanle
Adjunct Associate Professor of Public Affairs
Robert Haveman
Professor Emeritus, Public Affairs and
Economics
Pamela Herd
Professor, Public Affairs and Sociology
Karen Holden
Professor Emeritus, Public Affairs
and Consumer Science
Leslie Ann Howard Adjunct Associate Professor of Public Affairs
Valerie Kozel
Adjunct Associate Professor of Public Affairs
Robert J. Lavigna Adjunct Associate Professor of Public Affairs
Christopher
Lecturer, Public Affairs and Economics
McKelvey
Robert Meyer
Research Professor, Public Affairs
Donald Moynihan Professor, Public Affairs
Dave Nelson
Associate Lecturer of Public Affairs
Gregory Nemet
Associate Professor, Public Affairs
and Environmental Studies
Andrew Reschovsky Professor Emeritus, Public Affairs and
Applied Economics
Timothy Smeeding Professor, Public Affairs
Emilia Tjernström Assistant Professor of Public Affairs
Geoffrey Wallace Associate Professor, Public Affairs and
Economics
David Weimer
Professor, Public Affairs and Political Science
John Witte
Professor Emeritus, Public Affairs and
Political Science
Barbara Wolfe
Professor, Public Affairs, Economics,
and Population Health Sciences
© 2015 Board of Regents of the University of Wisconsin System. The La
Follette Policy Report is a semiannual publication of the Robert M. La
Follette School of Public Affairs, a teaching and research department of the
College of Letters and Science at the University of Wisconsin–Madison. The
school takes no stand on policy issues; opinions expressed in these pages
reflect the views of individual researchers and authors. The University of
Wisconsin-Madison is an equal opportunity and affirmative-action educator
and employer. We promote excellence through diversity in all programs.
Fall 2015
www.lafollette.wisc.edu
La Follette Policy Report / 7
The Measurement of Poverty: An Evolving Story
By Rebecca Blank
W
hen the War on Poverty was launched in 1964, there
were no government statistics on the size and composition of the poor population; indeed, there was no accepted
definition of poverty or what it meant to be called “poor.” A
statistical measure of poverty was needed to indicate how many
people were poor, show how the prevalence of poverty was concentrated among different groups, and enable tracking of the
poor population over time. Such a measure could also provide
a crude indicator of the effectiveness of antipoverty policies.
The Creation of a Poverty Measure
in the Johnson Administration
When the Johnson administration asked the Social Security Administration to propose a poverty definition, Mollie
Orshansky was put in charge. She calculated a poverty threshold that presumes the resource-sharing unit to be the family
(defined as two or more related individuals residing in the same
dwelling); the threshold for families of two or more in 1963
was based on the following definition:
poverty threshold = 3 × subsistence food budget
The subsistence food budget was the Economy Food Plan
defined by the U.S. Department of Agriculture in 1961: the
funds needed for “Temporary or emergency use when funds
are low.” The multiplier of 3 was based on the average family
of two or more spending one-third of their after-tax income on
food, as indicated in the 1955 Household Food Consumption
Survey; the multiplication of the food budget by 3 indicated
the income necessary to support that level of food consumption. While this approach did yield a poverty threshold stated
in dollars, it rests on an arbitrary basis. With this threshold
La Follette School Professor Rebecca
Blank is chancellor of the University
of Wisconsin–Madison. Prior to becoming chancellor, she spent four years in
top positions of the U.S. Department of
Commerce, including service as deputy
secretary and as acting secretary for
more than a year. Before her government
service, she served as dean and professor of public policy and economics in the
Gerald R. Ford School of Public Policy at the University
of Michigan from 1999 to 2008.
8 / La Follette Policy Report
as a starting point, an equivalence scale based on relative food
expenditures among different family types was developed to
produce poverty thresholds for families of different sizes and
configurations.
Over time, these poverty thresholds have been updated
annually by changes in the consumer price index, which reflects
the price of a basket of goods a typical urban consumer purchases. Aside from these annual threshold updates, there have
been very few changes to the 1965 measure, which is often
referred to as the “official” poverty line.
To calculate the scope of poverty, “family resources” must
be defined to compare them to the poverty threshold to determine if a family is or is not poor. Orshansky used a family’s
pretax cash income; hence, a family whose pretax cash income
fell below the poverty threshold for a family of their size and
configuration would be considered poor. The overall poverty
rate was calculated as the total number of people living in families whose income was below the poverty threshold, divided by
the total population. Separate rates were calculated for different
subpopulations (such as by race and ethnicity, age, gender, or
family composition).
This poverty calculation has been used since the mid-1960s
and is the nation’s official poverty measure. It is an “absolute”
measure so that if low-income families experience real income
growth, an increasing number of them will move above this
threshold and the poverty rate would automatically fall. In
1963, the poverty line was about 49 percent of median income,
but by the early 2000s it had fallen to less than 30 percent of
median income.
The original presentation of these official poverty rates
occurred in the 1964 Economic Report of the President. The
results of Orshansky’s measurement efforts are shown in the
solid line of Figure 1. While 22.4 percent of the population
was poor in 1959, this number fell rapidly to a low of 11.1
percent in 1973. It has never been this low in any year since.
After 1973, the poverty rate stagnated, rising in recessions and
falling in good times. The last year for which data are available
is 2013, when the official poverty rate was 14.5 percent, much
higher than in the late 1960s and early 1970s. In short, official
poverty rates have shown no noticeable long-term trend in the
United States—and, in particular, have not declined—for 40
years despite the poverty line as a percentage of median income
falling nearly 20 percentage points.
www.lafollette.wisc.edu
Fall 2015
Figure 1. Poverty Rates, 1969-2012
20%
Supplemental Poverty
Measure
15%
Official Poverty Measure
10%
5%
0%
1969
197419791984198919941999200420092014
Source: Supplemental Poverty Measure data from Fox, L., Garfinkel, I., Kaushal, N., Waldfogel, J., & Wimer, C. (2015).Waging war on poverty:
Poverty trends using a historical supplemental poverty measure. Journal of Policy Analysis and Management, 34; official poverty measure data from the
Current Population Survey Annual Social and Economic Supplement. Figure adapted from Haveman, R., Blank, R., Moffitt, R., Smeeding, T. and Wallace, G.
(2015), The war on poverty: Measurement, trends, and policy. Journal of Policy Analysis and Management, 34.
Growing Criticisms
of the Official Poverty Measure
Some Progress
Within a decade of its creation, the official poverty measure
began to be criticized and suggestions for alternative measurement approaches began to be heard. All of its components were
questioned—the definition of the resource-sharing unit, the use
of pretax cash income, and the thresholds (which were thought
to be too low). With improved data, analysts doubted that the
threshold should be benchmarked to food alone. Moreover,
the official poverty thresholds were criticized for not reflecting
substantial differences in the costs of living across locations or
increases in standards of living.
The absolute nature of the U.S. poverty threshold, based
on data from the 1950s and adjusted for inflation over time,
means that there is no conceptual justification to the current
poverty line; it is simply an arbitrary dollar amount. Using a
threshold calculated in 1964 (based on 1950s data) to estimate
poverty in 2014 is to use a 50-year-old categorization. Because
this measure is based on cash income, it is not affected by the
many in-kind antipoverty programs initiated in the United
States during the past five decades. Because tax measures do
not affect the definition of pretax income, substantial increases
in after-tax income among low-income families due to the several expansions of the earned income tax credit over the years
had no impact on the measure of poverty. In essence, the very
definition created in the Johnson administration to help understand poverty has led to serious misunderstandings because of
its growing inadequacy over time.
Fall 2015
Over the years, a variety of formal efforts recognized some
of these issues in attempts to update and refine a new and
improved poverty measure. In the early 1990s, the poverty measure was formally reviewed by a panel created by the
National Academy of Sciences. The panel recommended alterations to the definition of the poverty threshold and adjustment
of these thresholds for cost-of-living differences across regions
and rural/urban areas. Moreover, the panel recommended a
resource definition that measured after-tax income (since taxes
are mandatory payments) plus imputed in-kind benefits from
major near-cash programs (primarily Food Stamps and housing assistance). The value of health insurance was not imputed
but the panel recommended that out-of-pocket expenditures
on health care be subtracted from after-tax income, since these
resources are not available to be spent on food, shelter, and
clothing. Work-related expenses, including child care, were also
proposed to be subtracted from resources; these were treated as
necessary expenditures in order to earn a living.
The National Academy of Sciences recommendations led
to a substantial body of follow-up research and a few cities or
regions have implemented a version of the recommendations.
Starting in 2011, the U.S. Census Bureau began to regularly
report a supplemental poverty measure, which is loosely based
on the academy recommendations; other alternative poverty
measures using more expansive resource definitions, taking
account of taxes and in-kind benefits, were also published.
But neither the supplemental poverty measure nor alternative
www.lafollette.wisc.edu
La Follette Policy Report / 9
poverty rates published by the Census Bureau generated sufficient support in Congress to revise the official poverty measure.
Figure 1 shows the supplemental and official poverty measures. In addition to pretax cash income, which is the basis
for the official measure, the supplemental measure takes into
account in-kind benefit programs and benefits conveyed
through the tax system in the resource measure. The supplemental poverty measure also deducts work-related expenses and
out-of-pocket health-care expenses from income. Because the
supplemental measure poverty thresholds are based on expenditures on food, housing, and clothing (rather than just food)
and are adjusted over time as the composition of expenditures
changes, the supplemental poverty measure is a quasi-relative
poverty measure. The measure accounts for differences in housing costs among areas, and it uses an improved equivalence
scale to determine thresholds for different types of families.
The supplemental measure indicates that poverty has declined
over time, rather than being essentially flat as the official measure implies.
Alternative Approaches
to Poverty Measurement
The supplemental poverty measure is an effort to update poverty measurement, but its approach is conceptually similar to
Orshansky’s with a poverty threshold based on expenditures on
necessities and a resource measure based on family resources.
Other approaches have been proposed to measure poverty in
fundamentally different ways. For example, many have suggested that poverty be defined as the share of the population below
some point in the income distribution. Researchers proposed
in 1967 and in 1990 a poverty threshold be set at 50 percent of
median income. In contrast to the absolute poverty lines used
in the official measure, a relative measure of poverty would
remain constant even if all incomes are growing proportionally
across the distribution.
An alternative approach is to define a poverty threshold
by estimating the cost of a comprehensive basket of necessary
expenditures. Rather than using data on expenditures to determine a poverty threshold, such an approach would require an
objective determination of what is “necessary” and what is a
“reasonable cost” for those things deemed necessary. Efforts
have been made in the United States to create such baskets. The
European Union has recently funded several major research
projects to create low-income expenditure baskets and compare
the resulting poverty measure to other approaches.
A further alternative focuses on using expenditure data rather
than income data in calculating the resource side of the poverty measure. Of course, this method requires reliable measures
of family expenditures (as opposed to income), which are not
as frequently collected in many countries (although the United
States has an annual expenditure survey). The U.S. 2014 Economic Report of the President suggests the supplemental poverty
measure shows similar trends to an expenditure-based measure.
A final alternative is to measure material hardship directly,
rather than to assume that it is created by low income levels. Material hardship measures are only imperfectly correlated with income and clearly provide additional information
about economic need. The European Union has adopted this
approach, which supplements a poverty measure with multiple
other measures of deprivation. The European Union requires
each member state to report on 14 indicators of social exclusion, including a relative poverty measure, labor market, child
well-being, and health outcomes. u
Top Policy and Management Scholars
The La Follette School
welcomes to its faculty
u
Emilia Tjernström, whose dissertation at
u
Rourke O’Brien, who is completing a
University of California, Davis, employed field
Robert Wood Johnson post-doctoral
experiments to examine the impact of food
fellowship at Harvard University.
policy innovations in developing countries.
Our faculty include three fellows
of the National Academy of Public Administration
u
Robert J. Lavigna
u
Donald Moynihan
10 / La Follette Policy Reportwww.lafollette.wisc.edu
u
David Weimer
Fall 2015
Racial Variation in the Effect
of Incarceration on Neighborhood Attainment
By Michael Massoglia
T
he U.S. prison population has quadrupled since the mid1970s, leaving the United States with the highest incarceration rate in the world. This dramatic expansion reflects
one of the largest policy experiments of the 20th century, and
researchers and policymakers are just beginning to understand
the impact this experiment has had on U.S. society. Imprisonment rates are higher for African Americans and Hispanics
than for whites; indeed, the incarceration rate for blacks is more
than six times larger than the rate for whites. Incarceration has
become an increasingly common part of the life course, especially for black males with low levels of education. It is reasonable to assume that rising imprisonment has contributed to
existing racial inequalities in U.S. society.
In fact, studies indicate that imprisonment has disproportionately disadvantaged minority ex-inmates, their families,
and their communities. Disproportionate incarceration plays
a role in racial variation in earnings as well as certain aspects of
health. Additionally, felon disenfranchisement, or the restriction of voting rights among ex-offenders, disproportionately
affects African Americans, which has major implications for
state and federal elections. Finally, because of large racial discrepancies in incarceration rates, black children are actually
more likely to have an incarcerated mother than white children
are to have an incarcerated father.
Recent research suggests that minority ex-inmates also
may be disadvantaged in the residential environment. Racial
and ethnic minority ex-inmates live in poorer and more disadvantaged neighborhoods after prison as compared to white
ex-inmates. These studies are limited, however, by their inability to account for the quality of the neighborhood in which
prisoners lived prior to incarceration. In fact, the quality of
the neighborhood of origin for the typical minority prisoner
Michael Massoglia is a professor of sociology at the University of Wisconsin–Madison. He spoke at the La Follette School’s
spring symposium “Urban Men in Poverty:
Problems and Solutions” in Milwaukee.
This article is adapted from an American
Sociological Review piece he wrote with
Glenn Firebaugh, a professor of sociology
and demography at Pennsylvania State
University, and Cody Warner, an assistant professor of sociology at
Montana State University.
Fall 2015
is worse than the neighborhood of origin for the typical white
prisoner. In 1980, for example, the average minority person
in U.S. urban areas lived in a lower quality neighborhood (as
measured by the poverty rate) than nine out of 10 whites.
Given the magnitude of the neighborhood racial divide,
whites tend to have more to lose than minorities from a spell
of confinement. Incarceration is much more unusual in white
communities than in black communities. Because neighborhoods where incarceration is unusual are less likely to welcome
their straying members, whites might be less inclined than
African Americans to return to their neighborhoods of origin.
Whether this neighborhood response will result in a white
released prisoner moving to a poorer neighborhood is an open
question.
Although blacks reside in the poorest neighborhoods after
prison, we do not know whether this pattern reflects an incarceration effect or existing racial residential inequalities. Hence,
in this study we attempt to measure the effect of incarceration
on residential quality controlling for the quality of the preimprisonment neighborhood. Does this effect of incarceration
vary by race? To answer this question, we use a unique nationally representative longitudinal dataset that allows us to track
individuals as they transition between prisons and communities across roughly 30 years.
We first describe the types of neighborhoods where exinmates reside. We then discuss residential attainment and
on the consequences of incarceration, developing testable
hypotheses on incarceration’s impact on neighborhood quality. We discuss our data and methods. The key here is our use
of fixed-effects models that enable us to estimate the impact
of incarceration while accounting for prisoners’ neighborhoods
of origin. We find that incarceration has a negative effect on
neighborhood attainment only for whites and not for minority ex-inmates or even ex-inmates as a whole. After examining
this effect further, we conclude by noting the importance of a
deeper and more nuanced understanding of the lasting effects
of incarceration on America’s growing felon class.
The Neighborhood Quality of Ex-Inmates
As a group, individuals who have been incarcerated live in less
desirable neighborhoods than do individuals without histories of incarceration. The best evidence of this comes from the
Returning Home Project, in which researchers tracked released
offenders across several metropolitan areas. More than one-half
www.lafollette.wisc.edu
La Follette Policy Report / 11
of the released inmates followed in Chicago settled in seven of
ex-inmates might be explicitly targeted and excluded from
the 77 community areas (aggregates of tracts that reflect neighsome neighborhoods or communities.
borhoods). High rates of poverty and disadvantage typify these
For the nearly 80 percent of prisoners who are released with
seven areas.
supervisory parole, the close monitoring of living arrangements
Evidence that post-prison neighborhood environment
may create barriers to finding adequate and stable housing.
affects recidivism and that one’s residence shapes one’s life sugCorrectional agencies often require pre-approval of housing
gests the importance of ascertaining ex-inmates’ residential
choices, and in many respects housing discrimination against
destinations. Indeed, given the
former inmates is now legalAs a group, individuals who have
large racial disparities in conly sanctioned. For example,
finement, growth in the prison
individuals convicted of drug
been incarcerated live in less desirable
population may have important
crimes can be banned from
implications for racial inequali- neighborhoods than do individuals without public housing, which, ironities across a number of dimencally, is specifically intended
histories of incarceration. Evidence that
sions tied to neighborhood
to provide assistance to those
post-prison neighborhood environment
context (e.g., health and labor
most in need of housing. Moremarket outcomes) as an outover, as an economically maraffects recidivism and that one’s residence
growth of its (presumed) effect
ginalized subgroup, ex-inmates
shapes one’s life suggests the importance
on neighborhood attainment
may also encounter commeritself.
cial rental agencies that simply
of ascertaining ex-inmates’ residential
The observed association
refuse to rent to them. Faced
destinations.
between incarceration and
with such discrimination,
neighborhood attainment does
many ex-inmates may have few
not necessarily reflect a causal relationship. Because ex-inmates
options outside the most disadvantaged neighborhoods.
are more likely to be male, young, poor, unemployed, a racial
Given these considerations, our first hypothesis is: Conor ethnic minority, and have a low level of education than other
trolling for neighborhood of origin and other determinants of
citizens, we statistically control for these factors in measuring
residential location, ex-inmates will tend to reside in more disthe causal effect of incarceration on neighborhood quality.
advantaged neighborhoods following release from prison than
However, controlling for these individual characteristics is
the neighborhoods in which they lived prior to incarceration.
insufficient to ensure that we have estimated the causal effect
Because of the considerable racial disparities in both patof incarceration on neighborhood disadvantage. It is also necterns of neighborhood quality and rates of incarceration, this
essary to control for the quality of the neighborhood in which
general expectation is likely to be a complicated one. In parreleased prisoners lived prior to prison.
ticular, we attempt to determine if the neighborhood quality
We therefore account for both individual traits and neighconsequences of imprisonment will be greater for individual
borhood of origin prior to prison in estimating the effect of
whites or for individual racial/ethnic minorities. While prior
incarceration on neighborhood quality. Our data are a combistudies find that black parolees live in neighborhoods with
nation of individual data from the 1979 National Longitudinal
more concentrated disadvantage and more residential instaSurvey of Youth and contextual (tract-level) data from the U.S.
bility than white parolees, they reach this conclusion without
Census.
information on neighborhood of origin. Given extensive racial
residential inequality, incarceration may do little to actually
change the neighborhood trajectories of minority ex-inmates,
Our Research Hypotheses
while white inmates have more to lose given their advantaged
Because the quality of one’s neighborhood is an established
starting points. Hence, our second hypothesis is that the incarmarker of social standing, Americans are willing to pay more
ceration effect on neighborhood attainment will differ for Afrito live in more desirable neighborhoods. Although incarceracan Americans, Hispanics, and whites.
tion is rarely considered in studies of neighborhood quality, we
expect that incarceration does affect the quality of neighborData
hoods. For example, as a form of coercive mobility, incarceration, at least temporarily, forcibly removes individuals from
Our analysis uses the 1979 National Longitudinal Survey of
their communities. Upon release, ex-inmates might experience
Youth, a data collection that began with 12,686 individuals
constrained residential options stemming directly or indirectly
between 14 and 22 years old. Respondents were interviewed
from their incarceration. Inmates suffer from fractured social
yearly from 1979 to 1994 and biennially since 1994; data colties and an increased likelihood of divorce, meaning residences
lection is ongoing. With permission of the Bureau of Labor
prior to prison may be neither available nor welcoming upon
Statistics we gained access to restricted data that identify
release. Moreover, because incarceration limits employment
respondents’ geographic locations (state, county, and census
opportunities and depresses wages, ex-inmates are often unable
tract) at each wave of data collection. As a result, we were able
to afford to live in more desirable neighborhoods. Finally,
to construct individual residential histories that cover almost
12 / La Follette Policy Reportwww.lafollette.wisc.edu
Fall 2015
three decades. Treating census tracts as proxies for neighborhoods, we merged the extensive individual-level 1979 National
Longitudinal Survey of Youth data with characteristics of
respondents’ neighborhoods (e.g., rates of neighborhood poverty and unemployment). This combination of individual and
neighborhood data covering almost 30 years provides us with a
unique and high quality best dataset for examining individual
and neighborhood characteristics before and after prison.
racial variation in residential attainment—white respondents
reside in the best neighborhoods, black respondents residing in
the most disadvantaged neighborhoods, and Hispanic respondents in-between. Our data further reflect racial variation in
many of our control measures. Whites have higher levels of
educational attainment, homeownership, and employment
than do African Americans, and are less likely to live in public
housing and to have incomes below the poverty line.
When we observe the simple difference between neighborhood disadvantage and ex-inmate status and race/ethnicity,
Measurement
two findings stand out. First, we find striking racial disparities
in neighborhood attainment, with blacks and Hispanics who
Incarceration and Ex-Inmate Status
have never served time in prison living, on average, in more
In the 1979 National Longitudinal Survey of Youth, incarceradisadvantaged neighborhoods than whites who have been in
tion status is measured yearly by a residential location indicator,
prison. Second, all of the ex-inmate racial groups live in more
which allows us to observe prison sentences across time with
disadvantaged neighborhood environments than do individucertainty.
als like them who have not been incarcerated. This pattern
A total of 628 respondents who were in prison but subselends credence to our first hypothesis—there is a detrimental
quently released were interviewed during the three decades of
effect of incarceration on neighborhood quality—but it is not
observation in the survey. Additional data restrictions leave us
a definitive test.
with 558 ex-inmates (288 blacks, 166 whites, and 104 HispanIn fact, the question of whether the incarceration experiics) whom we follow for an average of 5.5 survey waves followence—rather than individual characteristics or pre-prison
ing release from prison.
neighborhood conditions—drives this relationship between
incarceration and neighborhood disadvantage remains open.
Neighborhood Disadvantage
To more reliably assess the effects of being incarcerated (being
an ex-inmate) on neighborhood disadvantage, we focus on
We measure neighborhood disadvantage using census tract
estimated statistical results that reveal the effect of being an
measures of the poverty rate, the joblessness rate (percentage
ex-inmate while controlling for a host of variables and neighof working-age people unemployed or out of the labor force),
borhood of origin factors that could confound this relationthe percentage of families that are female-headed, and the
ship. These statistical findpercentage of households that
ings provide little support for
receive public assistance. Using
We emphasize that there is substantial
Hypothesis 1. They strongly
these four measures, we formed
and meaningful racial variation in
suggest that, having controlled
an index of neighborhood qualfor neighborhood of origin
ity; higher values of this index
incarceration’s effects across different
and other determinants of resiindicate poorer quality neighlife domains. In some cases incarceration
dential location, there is not a
borhoods.
negative effect of incarceration
In our statistical estimates,
apparently contributes to racial and ethnic
on neighborhood quality—exwe also account for wide variinequalities. In other cases, such as the
inmates do not reside in more
ety of individual characteristics
disadvantaged neighborhoods
that can change over time, such
results presented here, the incarceration
following prison, compared
as the number of moves across
effect is more pronounced for whites.
with the types of neighborneighborhoods that a responhoods they resided in before
dent makes, educational attainprison.
ment, poverty status, type of residence, family and employment
To assess Hypothesis 2, we estimated the same statistical
status, and age. In our statistical analysis, we examine how
relationships for each of the racial groups. This analysis indineighborhood disadvantage changes over time for individuals
cates significant racial variation in the effect of incarceration
holding constant the basic characteristics of the neighborhood
on neighborhood quality. Our statistical results show that, after
where a person previously resided.
accounting for neighborhood of origin, it is whites, not African
Americans or Hispanics, whose neighborhood environments
Results
are most affected by a prison spell. This finding suggests that
We have 2,629 valid observations of ex-inmates, and their racial
for blacks and Hispanics (but not for whites), the association
characteristics represent racial patterns in the incarcerated popbetween incarceration and neighborhood disadvantage found
ulation—approximately one-half are African American, 30 perin the overall data is, in fact, attributable to the individual
cent are white, and 20 percent are Hispanic. The characteristics
traits or pre-prison neighborhood histories of the ex-inmates
of the neighborhoods in which they live also reflect well-known
themselves. It also suggests that the statistically insignificant
Fall 2015
www.lafollette.wisc.edu
La Follette Policy Report / 13
effect of incarceration on neighborhood disadvantage for all exinmates collectively masks the significant effect of incarceration
for whites. In separate estimates, we also find that for white
ex-inmates the adverse neighborhood effect of incarceration
appears to increase over time.
In summary, this additional analysis provides a more complete, and complex, picture of the incarceration–neighborhood
disadvantage relationship than provided by previous research.
After accounting for numerous characteristics and pre-prison
neighborhood context, we find no effect of incarceration on
neighborhood disadvantage for African Americans or Hispanics. In contrast, incarceration has a significant negative effect on
neighborhood attainment for whites, and this penalty appears
to intensify across time.
To ensure that our results are robust, we performed additional statistical analyses. These ‘sensitivity studies’ address factors that could yield biased statistical results. For example, our
results could be biased if the set of factors that we measure and
statistically control for is incomplete.
Across nine statistical tests, two facts never change. First,
for whites the effect of incarceration is always adverse, and the
coefficient is always statistically significant. Second, the effects
of incarceration on African Americans and Hispanics are never
statistically significant. We see no evidence to reject our main
findings.
As we have emphasized, our results suggest that whites have
much more to lose with regard to neighborhood quality from
being incarcerated than to blacks or Hispanics. This explanation is plausible because disparities in pre-prison neighborhood
environments for whites, Hispanics, and African Americans
are massive: while blacks and Hispanics live in neighborhoods
that are greatly disadvantaged prior to incarceration relative to
the mean, whites live in neighborhoods that are well above the
mean level of quality.
Our finding that whites have more to lose from a spell of
incarceration than do African Americans raises an important
question: Why is the incarceration penalty not more severe
for whites than for African Americans in other domains where
whites are also more advantaged, such as wages? The answer,
we suspect, is that blacks and whites differ much more with
regard to neighborhood environment than they do with regard
to wages or employment. Indeed, in our study we show that in
2008 the difference in the average hourly wage for blacks and
whites in the 1979 National Longitudinal Survey of Youth data
was substantially less than the racial difference in neighborhood
disadvantage.
Discussion and Conclusions
Given the dramatic swelling of the ex-inmate population in the
United States, understanding the lasting effects of incarceration
on ex-inmates, their families, and their communities is critical. While most research on the consequences of incarceration
focuses on individual and family outcomes, much less is known
about incarceration’s effect on residential outcomes such as
neighborhood quality.
By using nationally representative longitudinal data to
examine change in neighborhood attainment across time, we
discovered that white ex-inmates live in significantly more disadvantaged neighborhoods after a prison spell than they did
before. We found no effect for neighborhood characteristics of
ex-inmates as a group, or for African American or Hispanic exinmates. Additionally, and again for whites only, incarceration’s
adverse effect on neighborhood attainment intensifies during
the years following release from prison.
What remains to be determined is whether the pre- and
post-prison disparity for whites could reflect the number
of arrests or criminal convictions as opposed to the effect of
incarceration. While our data are relatively limited in terms
of measures of arrests and criminal convictions, the weight of
the evidence suggests that the pre- and post-prison difference
we observed for whites reflects primarily the effect of a prison
spell, not the effect of criminal offending or a criminal record.
Incarceration automatically removes individuals from their
neighborhoods; a criminal record does not. In other words, the
causal chain appears to be: conviction g prison sentence g
uprooted from current neighborhood g move to a new, more
disadvantaged neighborhood upon release from prison.
What if conviction does not lead to a prison spell? The chain
of events would be different. Because conviction itself does not
necessarily, or even likely, uproot an individual from his neighborhood, individuals who do choose to move are likely to do so
voluntarily. However, a definitive answer to this question awaits
further research.
Our findings have a number of policy implications. To say
that incarceration tends to harm whites more than African
Americans with respect to neighborhood attainment is not
to say that incarceration effects always tend to be greater for
whites or are always inconsequential for African Americans.
Rather, we emphasize that there is substantial and meaningful racial variation in incarceration’s effects across different life
domains. In some cases incarceration apparently contributes to
racial and ethnic inequalities. In other cases, such as the results
presented here, the incarceration effect is more pronounced for
whites. There is evidence that this is also the case for mortality
and recidivism. Policymakers should be attentive to these differences in fashioning policies to temper the societal costs of
mass incarceration.
We noted earlier that the steep rise in the prison population
is largely policy-driven, rather than being tied to any dramatic
increase in criminal activity. As such, reductions in the use of
incarceration must also be driven by policy. Clearly a balance
needs to be struck between public safety and the costs of incarceration. In a time when federal and state budgets are being
strained, many observers have started to question the current
balance, noting that increased public funds directed to the correctional system come at the expense of funds for education,
health, or any number of other public goods and services. Even
if the prison boom has peaked, the consequences of that boom
will be felt for decades to come, as large numbers of prisoners are reintegrated into U.S. society. Results presented in this
article provide a strong reminder of the need for effective policies concerning that reintegration process. u
14 / La Follette Policy Reportwww.lafollette.wisc.edu
Fall 2015
Director’s Perspective continued from page 1
prospects for employment and stable lives. Franklin, who is
director of the Marquette University Law School Poll, also
presented results from a public opinion poll of Wisconsin voters that captured opinions on several government policies,
particularly those related to services for low-income people.
Next Professor David Pate of the University of Wisconsin–
Milwaukee Department of Social Work reported on his work
over more than two decades examining the lives of men living
in poverty.
Professor Mike Massoglia of the University of Wisconsin–Madison Department of Sociology then described the
devastating impact of incarceration and correction policy. He
has found, for instance, that there are serious impacts on an
individual level (jobs, wages, health); on a family level (family
functioning and spousal relationships); on a community level
(neighborhood impacts, civic participation and representation); and on a state level (creating and perpetuating historic
patterns for the already disadvantaged). This issue of the
Policy Report includes an article by him on racial variation
and the effect of incarceration on neighborhoods.
Finally, Harry Holzer, a Georgetown University professor of
public policy, summarized the day by challenging the crowd
of more than 200 to “find the gems” in the research and
“figure out how to replicate them and take them to scale.”
For example, Holzer pointed to programs focused on prevention (Big Brother/Big Sister and career academies) and
re-connection (Job Corps, Jobs Plus) of disconnected youth
before they become offenders. For offenders, he recommended transitional jobs and programs like the Strong Fathers’
Initiative in New York.
Marquette University President Mike Lovell agreed with
Holzer, saying “the only way we’re going to face and overcome the problems of urban men in poverty is by working
together.”
I could not agree more. And I hope you find this type of
work by the La Follette School as important as I do. A podcast
of the entire program is available at http://go.wisc.edu/4dvi04.
I am especially pleased that this symposium builds on the
decades of contributions the La Follette School has made in
Milwaukee, including 25 years of capstone projects with the
city of Milwaukee, a national conference on the future of big
cities, evaluation of welfare/work programs such as Milwaukee’s New Hope and Wisconsin Works (W2), research on the
Milwaukee Parental School Choice program, and dozens of
other partnerships on topics ranging from gangs and youth
violence to value-added education research.
In the years to come, it is our pledge that efforts by the
La Follette faculty, students, staff, alumni and friends will
continue to help make a difference.
On Wisconsin!
The La Follette School
of Public Affairs
Congratulates
University of Wisconsin–Madison
Chancellor
Rebecca Blank
for winning the
2015 Daniel Patrick
Moynihan Prize
from the American Academy
of Political and Social Science
Fall 2015
www.lafollette.wisc.edu
La Follette Policy Report / 15
The Effect of Child Support on the Work
and Earnings of Custodial Mothers: New Evidence
By Laura Cuesta and Maria Cancian
T
he poverty rate in 2012 for families in which a child lives
with only the biological mother was more than four times
(40.9 percent) that of two-parent families (8.9 percent), and
almost twice the poverty rate observed among custodial-father
families (22.6 percent), according to the U.S. Census Bureau.
Because cash assistance for low-income families has declined
dramatically in the last decade, and receipt of child support (i.e.,
money payments from parents who do not live with their children) is often small and irregular, the earnings of these mothers
are a crucial source of income for these families. Given that child
support payments also contribute to the income of these families, observers suppose the decision of whether to work likely
depends on whether mothers receive child support. Indeed, they
may think the receipt of child support is a substitute for earnings by the mothers, who then reduce their work and earnings.
Observers tend to suppose that the more child support a custodial mother receives, the less she is likely to work for pay.
Our new research published in the July 2105 Children and
Youth Services Review suggests otherwise, and policymakers
who focus on increasing child support payments by non-custodial parents may want to take note.
Most previous research suggests that child support has a
small, negative effect on the work and earnings of custodial
mothers. However, this research is limited in that it fails to
adequately control for difficult-to-observe characteristics of
mothers that may also affect their willingness to work—things
such as the mothers’ motivation, self-advocacy, and perceptions
about receiving welfare benefits. As we note in our Children and
Youth Services Review article, the failure to adequately account
for these unobserved factors that may also influence mother’s
work and earnings suggests that the estimates in the literature may not be “causal.” Another weakness in this research is
that never-married mothers are excluded from these analyses.
Because these women face rather different incentives to work—
largely due to policies focused on them—the overall estimates
may be affected by their exclusion.
Our study in Children and Youth Services Review takes
advantage of a statewide randomized experiment to provide
more convincing estimates of the effect of child support on
the employment of low-income, custodial mothers. For custodial mothers who participate in the Temporary Assistance
for Needy Families (TANF) program, we examine the effect
of child support on their work. Does the receipt of child support payments reduce the likelihood of working, or the hours
worked, for these mothers?
Laura Cuesta is assistant professor of social
work at Rutgers University. She is interested
in the role of public policies in the wellbeing
of disadvantaged children and families. Her
current research looks at the effects of child
support policy; international approaches to
child support; and the interaction of child support policies with welfare programs. Her dissertation examined the role of child support on Laura Cuesta the wellbeing of custodial-mother families in the
United States and Colombia. She received her doctorate in social
welfare from the University of Wisconsin–Madison. Maria Cancian
is professor of public affairs and social work at the University of
Wisconsin–Madison. Her research considers
the relationship between public policies and
changes in marriage, fertility, and employment,
with a focus on the implications of child support policy for the well-being of divorced and
never-married families, the employment and
income of women who have received welfare,
and the impact of married women’s growing employment and earnings on marriage
Maria Cancian
patterns and the inter- and intra-household
distribution of income. She received her doctorate in economics
from the University of Michigan. This article is a summary of a
paper recently published in Children and Youth Services Review.
Policy Context
With the enactment of welfare reform in the mid-1990s, the
United States moved from cash income support for (mainly)
poor single mothers to the promotion of self-sufficiency. TANF
created this focus on mothers’ employment by conditioning cash
assistance on parents’ work efforts. In Wisconsin, the TANF program (called W-2) attempts to recreate some features of the job
market as part of a strategy to promote work. For example, to
promote employment among participants, W-2 cash assistance
does not vary with family size or the mothers’ hours of work.
As discussed in our Children and Youth Services Review article,
the Wisconsin TANF program has other characteristics, including the approach to child support for families receiving cash
assistance. Unlike the program in other states, in Wisconsin,
16 / La Follette Policy Reportwww.lafollette.wisc.edu
Fall 2015
most custodial mothers in the original W-2 program were
allowed to receive all child support paid on behalf of their children. Moreover, Wisconsin ignored child support income when
determining TANF eligibility. This policy was consistent with
the W-2 emphasis on replicating the job market. Just as workers’ wages are not affected if they receive child support, neither
would child support reduce W-2 cash payments. In addition to
this policy differing from that in other states, it contrasted with
prior Aid to Families with Dependent Children (AFDC) policy.
AFDC allowed for a maximum $50 child support to be passed
through; only $50 was also disregarded in determining AFDC
cash benefits. Most other states maintained this $50 passthrough and disregard, or eliminated it completely. Because
Wisconsin’s approach was more generous than the norm, the
federal government required the state to apply for a waiver. As
a condition for granting the waiver, the federal government
required an experimental evaluation of the policy.
Wisconsin’s approach, and the required evaluation, provided the opportunity to evaluate the effects of child support on a
host of individual and family outcomes. In the fall of 1997, the
experiment was inaugurated; both recipients of AFDC transitioning to TANF and new applicants to TANF were randomly
assigned to one of two pass-through eligibility statuses. Participants in the experimental group received the full amount
of child support paid by the noncustodial parent, while participants in the control group received a partial pass-through
of the first $50 per month, or 41 percent of the amount paid,
whichever was larger.
Our Children and Youth Services Review article considers
mothers’ labor supply during the initial experiment beginning
in 1997. Later, in July 2002, all custodial parents began to
receive the full pass-through of child support. In 2003, with
the end of the federal waiver that required the experiment, Wisconsin stopped allowing custodial parents on TANF to receive
all child support paid on behalf of their children.
Prior Research
Given the importance of child support income and mothers’
earnings for the economic well-being of low-income, custodialmother families, it is surprising that the literature that examines policies that simultaneously encourages child support collections and mothers’ employment is so small. The few earlier
studies have considered the effect of child support income on
the work effort of divorced and separated mothers who were
both recipients of AFDC benefits and nonrecipients. Moreover,
as noted in our Children and Youth Services Review article, this
research has come to somewhat different conclusions. While
research by John Graham and Andrea Beller published in the
late 1980s suggests that child support is negatively associated
with the number of hours worked, the negative effect was only
one-third to one-half the effect of other sources of income other
than earnings. This smaller effect is likely due to the fact that
child support is a risky income source, leading mothers to work
and earn to offset this risk. Research by Wei-Yin Hu published
in 1999 shows a small, negative effect of child support on the
labor supply of mothers who are not on AFDC (and, hence,
Fall 2015
likely to be better off than a welfare sample) and a positive effect
among mothers receiving cash welfare. Hu found that child support increases the number of hours mothers on AFDC worked.
Methods
In our Children and Youth Services Review study, we use survey and administrative data from the Wisconsin Child Support
Demonstration Evaluation, which uses an experimental design
to evaluate the effects of child support on several outcomes.
The evaluation study collected three waves of information
about the custodial mother, one child (referred to as the “focal”
child), and that child’s noncustodial father. Because custodial
mothers may make decisions about work and earnings only
after they observe a regular pattern of child support payments
or an increased amount of child support income, the effects of
the experiment may take some time to be observed. Hence, we
base our study on employment and earnings two years after the
1997 starting date (i.e., between the last month of 1999 and
the first half of 2000). The final sample includes 2,085 mothers
who were interviewed in 1997 and two years later. Our data
provided comprehensive information on employment history,
child support, demographics, and socioeconomic characteristics of the mother and her family.
When randomized experiments are perfectly implemented,
the simple comparison of mean values between experimental
and control groups provides the unbiased causal effect. However, no randomized experiment is free from some randomly
occurring differences in initial characteristics, and this experiment is no different. To accommodate this problem, we rely on
statistically adjusted means rather than simple means.
We proceed from more simple and direct estimates of the
effect of child support receipt on mothers’ employment and
hours worked to estimates that attempt to adjust for the fact
that child support receipt and employment are interdependent.
The simple and direct estimates provide a context for the more
advanced analyses that exploit the experimental design in the
Children and Youth Services Review study.
All models include other variables than simply the receipt of
child support payments. These variables include the mother’s
age, race, education, her AFDC and employment history, child
support receipt history, number of children, and residency. In
addition, the focal child’s age, the noncustodial father’s average
annual earnings, the number of legal fathers associated with the
mother are included.
Results
The straightforward estimates directly compare mothers who
receive child support and those who do not. They suggest that
the mothers who receive child support are more likely to work
(62.5 percent) than those who do not (53.2 percent), a difference of 9.3 percentage points. These estimates also suggest that,
on average, custodial mothers receiving child support work
three more hours per week than nonrecipients.
More complex estimates that include control variables show
that mothers who received any child support in 1999 are 6.1
percentage points more likely to work than those who did
www.lafollette.wisc.edu
La Follette Policy Report / 17
not—the positive “effect” of child support on a mother’s participation in the labor market remains even after controlling for
observed characteristics. The estimates also suggest that hours
worked are positively related to the receipt of child support. On
average, mothers who received child support are estimated to
work between 1.7 to 2.4 hours more per week than those who
did not receive child support.
Findings from the straightforward estimates suggest that
child support has a positive and statistically significant relationship with custodial mothers’ labor supply. The findings in our
Children and Youth Services Review study are consistent with
those reported in Hu’s 1999 nonexperimental study. However,
these estimates may be biased since they are unable to capture
differences in unobserved characteristics that may influence
child support receipt and participation in the labor market.
In our experimental estimates, we take advantage of the random assignment of mothers to experimental (full pass-through
of child support) and control groups to estimate the effect of
child support on custodial mothers’ labor supply. While those
in the experimental group have a higher probability of working,
this estimate is not statistically significant. This positive estimate
strongly suggests that child support receipt does not reduce the
likelihood of working for pay among mothers participating in
W-2. The estimated effect of being in the experimental group
on hours worked is also positive but again is not statistically significant; this result also suggests that the receipt of child support
does not reduce the number of hours worked for these mothers.
As we report in Children and Youth Services Review, one explanation of this result is that mothers do not take into account child
support payments in making their decisions regarding employment. The risky nature of child support payments suggests that
this may not be an irrational decision. To assess this possibility we
estimated the effect of child support payment receipt on mother’s
employment for those mothers who are likely to have regular and
reliable payments (e.g., at least $1,000 of child support was paid
in an earlier year). The results did not change substantially, suggesting that this explanation may not hold.
Discussion and Conclusions
The overarching question of this study was whether child support receipt reduces the work effort of custodial mothers participating in TANF. Results from our direct estimates are consistent with previous analyses for custodial mothers receiving
cash welfare; when unobserved differences between mothers
who do and do not receive child support are not taken into
account, additional support is positively associated with hours
worked as in Hu’s 1999 study. However, once the variation in
child support received associated with random assignment to
alternative pass-through and disregard policies is used, there is
no discernible effect of child support receipt on the likelihood
of working for pay or hours worked. The difference between
our results in Children and Youth Services Review and those of
prior analyses may be due to the confounding of child support
effects with other unobserved characteristics that affect mothers work. Alternatively, findings from prior research may reflect
the behavioral response of custodial mothers who are relatively
better off than a welfare sample. The vast majority of the sample
in our study comprises mothers who have never been married,
while other studies focused on divorced and separated mothers and explicitly excluded never-married mothers. Moreover,
we examine this issue after work-focused welfare reform, which
may have changed work incentives among low-income mothers.
Our findings should be interpreted cautiously, as we note
in Children and Youth Services Review. First, our results cannot
be generalized to all custodial-mother families as child support
payments are particularly irregular and small among disadvantaged families like those participating in TANF. As a result, the
custodial mother may be more likely to make decisions about
labor supply without considering potential monetary contributions from the noncustodial father; small and irregular amounts
of child support may actually underscore the importance of
working for pay among these women.
Second, there is one characteristic of the Wisconsin experiment that may make it a weaker test. Although custodial
mothers in the experimental group received on average higher
amounts than those in the control group, the difference was
small. Hence, while we were able to address selection into child
support receipt by taking advantage of the random assignment
into experimental and control groups, the actual treatment
(child support income) was relatively modest. It is noteworthy,
however, that this limited treatment is arguably a strength for
a policy-relevant test: low-income women are likely to receive
low and irregular payments.
Third, by the time the experiment was conducted, Wisconsin had built a relatively strong institutional framework supporting work-focused welfare interventions; this environment
may have created unique conditions that strengthen incentives
to participate in the labor market and helped to avoid the negative effects of child support income on mothers’ employment.
It is important to note, though, that most states have adapted
to changes introduced by welfare reform; the Wisconsin experience may be relevant for other states.
Finally, there is one consideration that we have not been able
to effectively deal with. It may be the case that the payment of
child support by the noncustodial father may be affected by the
work and earnings of the mother. If they are, one could expect,
for instance, that the more the mother works and earns, the
less regular would be the father’s payment. This possible effect
would tend to bias our results downward; the true estimate of
the effect of child support on mothers’ work would be more
positive than we report and potentially significant.
In spite of these caveats, our results have implications for
social welfare policies in the United States. Some states have
implemented programs aimed at improving the employability
of noncustodial fathers, in hope that these interventions will
ultimately result in more steady and increased child support
payments. As we note in Children and Youth Services Review,
our findings suggest that this effect would not likely be offset
by changes in mothers’ employment, even in the context of
a full child support pass-through. Policies encouraging child
support collections and mothers’ employment are not necessarily incompatible; efforts to increase the labor supply of both
parents are likely to be worthy. u
18 / La Follette Policy Reportwww.lafollette.wisc.edu
Fall 2015
Award-winning faculty. Cutting edge research.
The La Follette School congratulates Donald Moynihan
for his election as president of the Public Management Research Association.
He is the winner of the Association for Public Policy Analysis and Management’s 2014 Kershaw
award, which honors a scholar younger than 40 who has made a distinguished contribution
to public policy analysis and management. Public Administration Review included two of his
articles among the 75 most influential articles published in its 75-year history.
Quality Research
in the Best Journals
Funded
Research
Top rankings in public affairs
publications: University ranked 2nd for
articles faculty published in the four
“top” public affairs journals for 2009 to
2013 and 4th for public administration
research.
Publications in top field and
David Weimer
disciplinary journals, including recent
publications in Health Affairs and
the Journal of Health Politics (David Weimer), American
Political Science Review (Susan Webb Yackee), American
Journal of Political Science (Donald Moynihan), Journal
of Health Economics, American Sociological Review, and
Proceedings of the National Academy of Science (Jason
Fletcher), Public Administrative Review and Journal of
Public Administration Research and Theory (Pamela Herd),
JAMA Pediatrics (Barbara Wolfe).
Fall 2015
The La Follette School’s interdisciplinary
researchers develop innovative
projects with support from federal and
private funding sources: nearly $35
million in National Institutes of Health
funding (Pamela Herd);
$1.2 million in funding from the U.S.
Susan Yackee
Treasury (J. Michael Collins); $500,000
from the Burroughs Wellcome Fund
(Susan Webb Yackee); $460,000 from the National Institute
of Child Health and Human Development, $350,000 from the
William T. Grant Foundation, $92,000 from Russell Sage,
(Jason Fletcher); $397,000 from the Smith Richardson
Foundation, and $100,000 from the Annie E. Casey
Foundation (Barbara Wolfe).
www.lafollette.wisc.edu
La Follette Policy Report / 19
La Follette Policy Report
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