CONSUMER CREDIT DEPENDENCY:
AN EXPERIMENTAL APPROACH TO MEASURING POVERTY AND THE
IMPACTS OF WELFARE POLICY
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Stephanie N. Monjaraz
FALL
2013
© 2013
Stephanie N. Monjaraz
ALL RIGHTS RESERVED
ii
CONSUMER CREDIT DEPENDENCY:
AN EXPERIMENTAL APPROACH TO MEASURING POVERTY AND THE
IMPACTS OF WELFARE POLICY
A Thesis
by
Stephanie N. Monjaraz
Approved by:
__________________________________, Committee Chair
Terri Sexton, Ph.D.
__________________________________, Second Reader
Suzanne O’Keefe, Ph.D.
____________________________
Date
iii
Student: Stephanie N. Monjaraz
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator ___________________
Kristin Kiesel, Ph.D.
Date
Department of Economics
iv
Abstract
of
CONSUMER CREDIT DEPENDENCY:
AN EXPERIMENTAL APPROACH TO MEASURING POVERTY AND THE
IMPACTS OF WELFARE POLICY
by
Stephanie N. Monjaraz
Consumer debt has risen at an average annual rate of 4.1 percent over the last 20 years
and while that is not in itself shocking, when compared to the average annual growth of
the family household income which has only grown by an annual average rate of 0.6
percent over the same time period the width of the gap between consumer debt and
income growth is. Given this large outweigh, this study argues that the official poverty
threshold should be revised and account for this modern debt conundrum. This study uses
longitudinal data from the Panel Study of Income Dynamics and estimates the effect
specific welfare policies have on alternative poverty measures. We contribute to the
literature by statistically evaluating a new experimental poverty measure, debt poor,
which accounts for the negative impact interest payments, on non-mortgage debt, have on
a household’s disposable income. The 17 welfare policies analyzed in this study directly
impact poverty and are grouped into three categories: eligibility requirements, financial
incentives to work, and time limits. State-level, household-level and women specific
entity and time fixed effects were specified for over 7,000 households residing in 48
v
different states during 2001-2009. The results indicate that financial incentives to work
and time limits have significant and robust effects on alternative poverty measures. States
with stricter time limit policies reduce poverty by 0.06-0.9 percentage points. While
states with time limit exemptions for ill/incapacitated individuals increases poverty 0.07
percentage points. Suggesting that the existing welfare programs actually encourage
individuals to remain on welfare Overall, the results are mixed. Nonetheless, the concept
of welfare policies impacting debt poor households should not be ignored. Future studies
should reevaluate an experimental poverty measure that considers interest payments on
consumer debt.
_______________________, Committee Chair
Terri Sexton, Ph.D.
_______________________
Date
vi
ACKNOWLEDGEMENTS
There are a few people who I would like to thank and recognize for all of their efforts in
helping me succeed. First I would like to thank Dr. Sexton and Dr. O’Keefe for all their
support and dedication during the progression of this thesis. I am very privileged to have
had these professors commit their summer to my research and grateful for all their
feedback. Throughout my coursework at Sacramento State, these professors taught me
the fundamentals of microeconomic theory, the empirical process and applications of
econometric techniques. Not only have these two professors shaped my economic
framework but so have my previous professors. Thanks to them, I have been able to
successfully apply my education in the workforce and my personal life. Secondly, I
would like to thank my friends and family for their immeasurable love and support.
Being 300-500 miles away, their routine phone calls and various packages have helped
me focus on reaching my goals. I would also like to personally thank my friend Jessi for
extending her time and impeccable English grammar onto this thesis. Lastly I would like
to thank my boyfriend Jake for believing in me, and all of his unimaginable support and
commitment. My professors provided me with the fundamentals and Jake pushed me
towards publishing my research, without them, I would still be writing…From the bottom
of my heart, thank you.
vii
TABLE OF CONTENTS
Page
Acknowledgements ..................................................................................................... vii
List of Tables ................................................................................................................ x
List of Figures ............................................................................................................. xi
Chapter
1. INTRODUCTION ..................................................................................................1
2. LITERATURE .........................................................................................................8
2.1 Poverty Literature: Specific Welfare Policies............................................10
2.2 Experimental Poverty Measures ................................................................13
3. METHODOLOGY ................................................................................................18
3.1 Methodology for Calculating an Experimental Poverty Measure .............19
3.2 Dependent Variables ..................................................................................21
3.3 Independent Variables ...............................................................................23
4. DATA ....................................................................................................................26
4.1 PSID Individual Level Data .......................................................................26
4.2 Urban Institute Welfare Policies ................................................................27
4.3 Explanatory Variables: Welfare Policy Variables .....................................27
4.3.1 Eligibility Requirements ...................................................................28
4.3.2 Financial Incentives to Work ............................................................30
4.3.3 Time Limits.......................................................................................35
4.4 Explanatory Variables: Macroeconomic Controls .....................................36
viii
4.5 Household Demographic Variables ...........................................................40
5.
EMPIRICAL MODEL..........................................................................................41
5.1 Fixed Effects Model ...................................................................................42
5.2 Probability Models: Logit and Probit ........................................................43
5.3 Fixed Effects Logit Model .........................................................................44
5.4 Ensuring Robust Estimates ........................................................................45
6.
RESULTS .............................................................................................................47
6.1 Poverty Rates: Fixed Effects Models .........................................................47
6.2 Experimental Measures: Two-way Fixed Effects Logit Model .................55
7.
CONCLUSION.....................................................................................................64
Appendix A. State-Level Entity Effects ....................................................................69
Appendix B. Additional Tables .................................................................................70
References ....................................................................................................................73
ix
LIST OF TABLES
Tables
Page
1.
2001-2009 Average State Poverty Rates and Measures ................................... 23
2.
Datasets and Sources Summary ........................................................................ 26
3.
Brief Description of Time Limit Policies ......................................................... 35
4.
Inflation Adjusted Explanatory Variables, 2009 Dollars .................................. 36
5.
Two-way State-level Random Effects Models ................................................. 54
6.
Household-Level Fixed Effects Logit Model ................................................... 60
7.
Household-Level Fixed Effects Odds Ratios .................................................... 61
x
LIST OF FIGURES
Figures
Page
1.
Total Consumer Credit in U.S., A Two Decade Outlook, Seasonally Adjusted 2
2.
Commercial Bank Interest Rate on Credit Card Plans........................................ 8
3.
Percentage Change in the U.S. Official Poverty Thresholds by Family Size ... 20
4.
U.S. Total Number of TANF Recipient Caseloads per Year, in Thousands .... 24
5.
State Average Number of Returns Receiving the Earned Income Tax Credit . 34
6.
Average Annual Unemployment Rate .............................................................. 37
7.
2001-2009 Gross State Product Per Capita by State ......................................... 37
8.
Fair Market Rents Growth Rate, 2001-2009..................................................... 38
9.
2001-2009 Gini Coefficient per State ............................................................... 39
10.
Logit and Probit Predicted Residuals ................................................................ 43
xi
1
Chapter 1
INTRODUCTION
Orshansky (1969) once said, “Poverty, like beauty, lies in the eyes of the
beholder”. Indeed, this seemingly harsh and judgmental statement is bounded with truth.
Poverty is an unfortunate burden that many American families face. Yet, the working
definition of what defines poverty is archaic and really does not accurately reflect the
struggles of low-income households who do not meet these guidelines. In fact, the
definition of poverty dates back to the 1960s and in the recent literature has been
challenged (HHS, 2012). According to the 1995 National Academy of Sciences’ (NAS)
poverty recommendations, the official poverty threshold is ineffective at capturing true
poverty in this new century and must be redefined. Every year the poverty threshold is
adjusted for inflation using the Consumer Price Index (CPI). However, societal changes
cannot solely be captured by changes in baskets of consumer goods. Changes that directly
affect disposable income should be considered when measuring poverty.
According to the Federal Reserve Bank G 19 report, in 2011 the average U.S
household credit card debt was $15,580; and approximately 46.7 percent of households
had credit card debt (FRB, 2012). In 1960 Americans’ personal debt amounted to roughly
55 percent of national income and by 2007 it elevated to 133 percent (Brooks, 2009).
Given this rise in consumer debt, the U.S. poverty threshold should reflect the prevalence
of debt in American society. Consequently, this study argues that the official poverty
threshold should be revised and applies an experimental approach to defining and
2
measuring poverty. Figure 1 demonstrates the time path revolving and non-revolving
consumer debt has taken over the past twenty-three years.
Figure 1: Total Consumer Credit in U.S., A Two Decade Outlook, Seasonally Adjusted
2,500,000
Revolving
Non-Revolving
Dollars
2,000,000
1,500,000
1,000,000
500,000
1990-01
1990-12
1991-11
1992-10
1993-09
1994-08
1995-07
1996-06
1997-05
1998-04
1999-03
2000-02
2001-01
2001-12
2002-11
2003-10
2004-09
2005-08
2006-07
2007-06
2008-05
2009-04
2010-03
2011-02
2012-01
2012-12
0
Year
Source: FRB G19, 2012
The poverty threshold is estimated by the U.S. Census Bureau, and is still
calculated using Orshansky’s1963 method. In essence, Orshansky derived food budgets
for families under economic stress. Using food consumption and expenditure data from
the U.S. Department of Agriculture, minimum food intake/costs were then estimated, and
framed the poverty threshold (Short, 2012). Today this method is still used to estimate
poverty, the CPI is used to account for inflation and different thresholds are now
calculated that vary by family size and age. In order to determine who is in poverty, the
U.S. Census Bureau compares families’ total (before tax) income to specific thresholds, if
a household’s total income is less than the family specific thresholds then they are
considered poor.
3
There are numerous limitations with how the poverty threshold is defined; most of
these limitations are problematic as they lack the consideration of expenses that directly
impact consumers’ total disposable income. These limitations include: taxes, capital
gains, social security, and noncash benefits (i.e. public housing, and transfer payments).
However, a major limitation is that the thresholds do not vary geographically. Differences
across counties’ and states’ cost of living are not considered. For instance, in 2012 a
single member household in San Francisco and Sacramento CA, with the same total
income, would be considered poor under the same threshold (income less than $11,170).
Clearly, the cost of living in San Francisco is significantly higher than in Sacramento. As
a result, geographic differences such as cost-of-living standards should be considered
when estimating a poverty threshold. This study uses the Department of Housing and
Urban Development’s Fair Market Rents (FMR) as a proxy for estimating how cost of
living differences across states, impact various poverty measures.
This thesis contributes to the literature by developing an experimental approach to
poverty and examining how specific welfare policies affect alternative poverty measures.
The experimental approach involves analyzing a poverty rate that considers interest
payments on non-mortgage debt. Additionally, in order to relate the findings in this study
to the literature, the official U.S Census poverty rate was also examined. Seventeen
comprehensive and specific welfare policies are evaluated and are based on McKernan
and Ratcliffe’s (2006) conceptual framework. All seventeen welfare policies were
grouped into three categories: eligibility requirements, financial incentives to work, and
time limits. Most of these policy variables are indicator variables. However a few are
4
measured in real dollars, percentages and as categorical variables. Moreover, examining
how specific welfare policies affect alternative poverty outcomes is extremely important
as the variation in welfare policies over time, and within states, can capture the uncertain
relationship between policy and poverty.
Annual household data from the Panel Study of Income Dynamic (PSID) was
used for deriving the experimental poverty measures. The data extends from 2001-2009
and uses various economic variables to control for the 2008 housing crisis. As welfare
policies impact households and states differently, household-level entity effects and statelevel entity effects were used to outline two main model specifications. These model
specifications estimate five different poverty outcomes which are grouped as rates or
binary variables. In particular, the first specification uses a fixed effects model, with
state-level entity and time effects, to examine how specific welfare policies affect five
poverty rates. While the second uses a fixed effect logit model, with household-level
entity and time effects, to examine how specific welfare policies affect five binary
poverty outcomes. State and household entity effects are specified, in order to estimate
how specific welfare policies affect alternative entity-groups and poverty outcomes. The
application of using an experimental poverty measure that considers the cost of serving
non-mortgage debt as an outcome, when analyzing specific welfare policies, is a new
contribution to the literature.
The experimental poverty measures derived in this study adjust the head of
household’s total (before-tax) annual income by the cost of servicing non-mortgage debt,
denoted as debt poor. If the debt-adjusted income levels are below the corresponding
5
family size poverty threshold then the household is considered debt poor. Additionally,
the sample was restricted to specifically capture how single female head of households
with dependents were affected by this debt poor measure. This study uses four outcome
variables that are specific to female households and various demographics (i.e. education
and race). The experimental poverty measure examined in this study is conservative
because it is based on survey data and participants are more likely to underestimate their
total non-mortgage debt.
Alternative poverty specifications are thoroughly analyzed in this study in order
to statistically estimate how specific welfare policies affect various poverty outcomes.
Unlike the bulk of the literature, this study examines individual welfare policies. Prior
studies have examined the overall effect welfare reform (i.e. total caseloads) have on
poverty, employment, and income. However, the results indicate no significant effect and
in many cases, have contradicting results.
When evaluating panel data, fixed effects are preferable to random effects as they
control for all omitted time-invariant differences across states. One popular approach for
testing the validity of the fixed effects is the Hausman test. According to the Hausman
test all of the derived poverty rates with state-level entity effects were collinear, except
the U.S. Census poverty rates. This indicates that the sample does not have enough within
variation across state entities. Moreover, when state-level entity and time effects are
specified, the random effects strongly explain the variation in these experimental poverty
rates. Contrary, when evaluating individual-level entity effects in the logit models, the
6
Hausman test suggests that the fixed effects are valid and explain 35 percent of the
variation within entities.
The results suggest that financial incentives to work and time limit welfare
policies are large determinants of poverty and are statistically significant in explaining
the variation in poverty rates. For instance, when considering how state-level entity and
time effects impact the official poverty rates, the results suggest that states with
intermittent time limit policies decrease poverty by 0.06-0.9 percentage points, (p<0.01).
This suggests that states with stricter time limit requirements decrease poverty, as
limiting welfare benefits makes a recipient more inclined to enter the labor force. These
results are intuitive and are consistent with McKernan and Ratcliffe’s (2006) study.
After examining the state-level and household-level fixed effects estimated
models, the overall results did not appear robust or consistent. However, when examining
each model specification group individually, robustness was apparent, indicating that
when controlling for state and household-level fixed effects, welfare policies have
varying and inconclusive impacts on poverty. Prior studies that have examined overall
welfare reform (i.e. TANF caseloads) effects on poverty have also found contradicting
results. Thus, to a limited extent, these results shed some light on the fact that welfare
might have a direct impact on poverty when state-level and individual-level entity effects
are specified. By examining specific welfare policies, this study illustrates how different
policies have varying effects on poverty. Moreover if researchers simply estimate overall
welfare caseloads, the actual impact may be dismissed.
7
The remaining sections are as follows; chapter two discusses the literature on the
modern debt conundrum, welfare reform studies, and the literature on experimental
poverty measures. Chapter three follows with the experimental poverty methodology,
providing an extensive discussion of how the experimental poverty measure was derived.
Chapter four describes the data and focuses primarily on defining the welfare policies and
control variables. Chapter five follows by defining two empirical models analyzed in this
study and includes an overview on the steps taken in order to ensure efficient estimators.
Lastly, chapter six present the results, and chapter seven ends discussing the conclusions,
caveats, and future extensions.
8
Chapter 2
LITERATURE
Borrowing and credit have become an integral part of society, according to
the Federal Reserve Board, “the percentage of families holding credit cards issued by
banks has risen from about 16 percent in 1970 to about 71 percent in 2004” (FBR, 2006).
Overall, the previous research concerning consumer debt is extensive and primarily
focused on why credit has become a modern phenomenon. Figure 2 illustrates the trend
of commercial bank interest rate on consumer credit cards. Prior research suggests that
the rise in consumer credit dependency is a result of telecommunications, data processing
(FRB, 2006; Clemmitt, 2008; and Ruben, 2009), deregulation of interest rates (Christen
and Morgan, 2005; FRB, 2006), and income inequality (Rajan, 2010; Azzimonti, et al
(2012); Pressman and Scott, 2009).
Figure 2 : Commercial Bank Interest Rate on Credit Card Plans
Percent
15%
13%
1995-02
1995-11
1996-08
1997-05
1998-02
1998-11
1999-08
2000-05
2001-02
2001-11
2002-08
2003-05
2004-02
2004-11
2005-08
2006-05
2007-02
2007-11
2008-08
2009-05
2010-02
2010-11
2011-08
2012-05
2013-02
11%
Year
Source: FRB G.19 Report, 2013
9
Pressman and Scott (2009), motivated by the current rise in consumer debt,
develop a new strategy for measuring poverty and income inequality in the United States.
Unlike previous studies, Pressman and Scott consider the effect interest payments have
on consumer welfare. Using 1983-2007 data from the Federal Reserve Board’s Survey of
Consumer Finances, Pressman and Scott use household total revolving debt to estimate
how servicing debt impacts the household’s total annual income. In particular, by
subtracting household’s annual interest payments on revolving/non-revolving debt from
total income, a debt poor measure was derived. A household is then considered ‘debt
poor’ if they lie below the official poverty threshold. Additionally, using this same
interest payment adjusted income measure, Pressman and Scott estimate income
inequality by deriving a new Gini Coefficient1.
Their results indicate that during 1983-2007, roughly 4 million US citizens were
‘debt poor’ but not officially considered poor since they lied above the poverty threshold.
Furthermore, Pressman and Scott argue that the current poverty and income inequality
measures are faulty and must be redefined. However, because Pressman and Scott did not
apply statistical methods to their debt poor measure, policy implications are difficult to
motivate. This study attempts to add support and statistical validity to Pressman and
Scott’s claim. In particular, this study applies Pressman and Scott’s debt poor
methodology and specifies a fixed effects logit model to identify the factors that
influence the probability of being debt poor. Moreover, this study derives a debt poor
1
The Gini Coefficient is the ratio of income received by the top 10 percent of household earners relative to
the bottom and the ratio of the standard deviation of log income.
10
experimental poverty measure that is supported with economic significance and is
compared to the official poverty rate to estimate the marginal effects of debt.
Overall, the literature surrounding this modern debt conundrum is extensive
(Schooley and Worden (2009); Christen and Morgan (2005); Lee et al. (2007); FRB,
(2006)). However, the direct impact that debt has on poverty rates in America is minimal
at best. Consequently, this thesis argues that interest payments from total non-mortgage
debt should be considered when measuring U.S poverty, as interest payments directly
impact a household’s disposable income. Additionally, studies that consider poverty as an
outcome are rare. Instead, the literature focuses on welfare caseloads as an outcome.
This study contributes to the literature by considering the direct impact that consumer
debt has on disposable income and specifies alternative poverty outcomes. Future policy
implications can be assessed as this study estimates the effect of 17 specific welfare
policies on a debt-adjusted poverty measure and the official poverty rate.
2.1 – Poverty Literature: Specific Welfare Policies
The literature examining poverty as an outcome is limited and in many cases
insignificant. According to McKernan and Ratcliffe (2006), the bulk of poverty research
tends to focus on the impact of welfare reform on earnings, total caseloads (i.e. aid and
filings), employment, but not poverty (Ziliak, Gundersen, & David, 2000; Zedlewski,
2001; & Moffitt, 1999). Most of these studies provide mixed and insignificant results as
they tend to focus on comprehensive state and federal welfare reform measures, and not
specific policies. Examining specific welfare policies is optimal as some policies may
11
have inverse effects on poverty and analyzing a comprehensive measure may not disclose
the individual policy effects.
McKernan and Ratcliffe (2006), examine the effect of 19 specific welfare policies
on deep poverty and poverty for never married mothers and children of never married
mothers. These 19 selected policies are measured on a monthly basis from 1986-2000,
and are grounded in a conceptual framework that is based on how policies can influence
poverty. Welfare reform and social/economic demographic data are primarily aggregated
from the Survey of Income Program Participation (SIPP) and the 1996-2010 Urban
Institute’s Welfare Rules database.
McKernan and Ratcliffe (2006) use a weighted least squares model and apply
state-specific and time fixed effects when analyzing welfare policies. Including time and
entity fixed effects allows the variation over time and across states to capture the
relationship between policy and poverty. Their results coincide with their hypotheses and
are robust. Their results suggest that more generous financial incentives to work and
lenient eligibility requirements for welfare recipients reduce deep poverty. Their results
are intuitive as more lenient eligibility requirements should lower the poverty rate
because families can supplement earnings with welfare payments. This thesis considers
the same welfare policies as the McKernan and Ratcliffe (2006) study. However, unlike
McKernan and Ratcliffe, this study contributes to the literature by investigating how
specific welfare policies affect both the traditional U.S poverty rate and a debt poor
experimental poverty rate.
12
Similar to the bulk of the literature, Blank (1997) examines various determinates
that led to the growth in public assistance caseloads during 1977-1997, and primarily
focuses on the participation in Aid to Families with Dependent Children (AFDC)
program. Blank analyzes the effect that eligibility requirements had on total caseload
benefits. According to Blank, caseloads are measured as the product of the number of
eligible participants and the take-up rate2. Two approaches are used for measuring takeup rates and are defined as administrative and Current Population Survey (CPS) take-up
rates. The administrative take-up rate is measured by dividing actual caseload data by
eligibility estimates. The CPS take-up rate is measured by taking the CPS-determined
AFDC usage data and dividing it by eligibility.
Blank derived four main logged dependent variables: AFDC for two parent
families (AFDC-Basic), AFDC for unemployed parents (AFDC-Up), AFDC-Total, and
AFDC for child only cases as a share of the female population (AFDC-Child Only).
Utilizing an OLS weighted regression with weights contingent on state population. Her
study includes various control variables (i.e. state specific minimum wage, and
unemployment rate) that help better predict the underlying trend in rising caseloads. State
specific explanatory variables such as the percentage of immigrant population, average
household demographics, unemployment, and private/public health insurance are also
evaluated. The results indicate that during 1984-1995, increases in caseload benefits were
due to changes in eligibility requirements. However, overall, Blank’s study fails to reveal
2
According to the Bureau of Labor Statistics, take-up rates measure employer-provided Medicare plans.
Take-up rates are a subset of the participation rate, which is the percentage of workers who are covered by
the plan. Unlike the participation rate, take-up rates narrowly identify only those workers who have access
to the plan (Kronson, 2009).
13
statistically significant results, indicating that perhaps her specification was inadequate in
explaining the drastic changes in public assistance caseloads.
The main limitations of Blank’s study rest on the data and methodology.
According to Blank (1996) the data used to examine the changes in caseloads over time
consist of multiple sources that were pooled together. For instance, one aggregated
measure of AFDC caseloads consists of annual state data from the Department of Health,
Education and Welfare (1969-1980), the Department of Health, and Human Services
(1982-1993), and the Urban Institute 1981-1996 dataset. These multiple data sources
impose bias into Blank’s study due to systematic differences in methodologies and
measurement error. Panel techniques, such as fixed effects, were integrated into Blank’s
study yet the data were pooled from various cross-sectional and time series sources.
Unlike Blank’s study, this thesis mitigates measurement bias by using one strongly
balanced panel dataset from the Panel Study of Income Dynamics (PSID) when
measuring key outcome variables.
2.2 – Experimental Poverty Measures
Historically, experimental poverty measures attempt to capture the
underestimated poor population that the official U.S poverty rate fails to evaluate. Many
of these experimental measures include examining after tax income, impacts of the
Earned Income Tax Credit (Grogger, 2004b), child support expenditures (Wong and
Wong, 2004), and various transfer payments. All such measures try to incorporate the
impact they have on household total annual income. However, interest payments on nonmortgage debt also reduce a household’s disposable income. This study contributes to
14
the literature by considering a new experimental poverty measure, debt poor, which has
not been statistically evaluated.
Iceland et al. (2001) examine the effects of welfare reform on poverty using an
alternative experimental measure that is based on the 1995 National Academy of
Sciences (NAS) recommendations. These recommendations include adjusting the U.S.
official poverty rate by noncash government benefits, taxes, out-of pocket medical
expenditures, job-related expenses, and the Earned Income Tax Credit (EITC). Since
poverty measurements are based on family income and size thresholds, not on work
status, Iceland et al. contributes to the literature by constructing an experimental poverty
measure that estimates a ‘family-based’ work status threshold. Accordingly, a full-time
working family is defined as working at least 1,750 hours per year, whereas a part-time
working family works 50-1,750 hours annually. Moreover, Iceland et al. focus on
working families with children and examine the effect welfare policies have on both the
official and experimental poverty rates. Similarly, this thesis considers the NAS poverty
recommendations, when developing the debt poor framework, particularly examining
how the EITC impacts debt poor households.
Iceland et al. (2001) use the 1998 Current Population Survey to derive a new
poverty rate and to analyze the official poverty rate and welfare policies. Their findings
suggest that the official poverty rate underestimates poverty. In particular, in 1997 the
U.S poverty rate was 13.3 percent, while their alternative measure was 16.1 percent.
Iceland et al. argue that child care costs greatly outweigh food stamps and other noncash
15
benefits. Indeed, the U.S poverty threshold does a poor job of accounting for the many
struggling families.
Short and Garner (2002) estimate an experimental poverty measure that considers
how medical expenditures for different households (i.e. grouped by size, race, and age)
impact income. Short & Garner examine two experimental measures that consider
medical out-of-pocket expenditures (MOOP) and compare them to the official poverty
rate3. Moreover these two measures consider medical out-of-pocket expenses subtracted
from income (MSI), and medical out-of-pocket expenses added to the threshold (MIT).
Their results indicate that in 2000 the poverty rate in the U.S. would have been 0.5-0.9
percentage points higher if out-of-pocket medical expenses were considered4.
When considering medical out-of-pocket costs for different demographic groups,
Short and Gardner find that, relative to the official poverty rate, the experimental poverty
rate for the elderly is higher, whereas children and African Americans experience lower
poverty rates. Some of these results are intuitive, however most are unclear, hence further
research needs to explore alternative poverty measures. Similarly, this thesis attempts to
measure how health insurance (private versus public) explains the variation in the number
of debt poor households and considers various household characteristics when examining
the prevalence of debt and poverty. However, due to data restrictions and a lack of
variation, the effect private/public insurance has on poverty could not be examined. In
3
Medical out-of-pocket expenditures include health insurance premiums, medical services, drugs, and
medical supplies. According to Short and Garner, the method for deriving MOOP is complex and involves
using the 1987 National Medical Expenditure Survey.
4
In 2000 the U.S. poverty rate was 11.3 percent, MSI and MIT experimental poverty measures were 12.2
and 12.7.
16
particular, less than one percent of the population provided data on health insurance.
Therefore, health insurance could not be evaluated.
According to the U.S Census, in 2004 the national poverty rate for single female
head of households with dependent children (30.5%) was nearly three times larger than
the overall aggregate (12.7%). As a consequence, in some specifications, I will restrict
the sample to single female heads of households with at least two children and derive
four additional debt poor measures that examine how education and race impact poverty.
The restricted single female household sample will be used to capture the marginal effect
that debt has on single women with more than one child relative to the entire sample.
Furthermore, given the rise in consumer debt, it is imperative to analyze an experimental
measure of poverty that considers the impact that debt has on household disposable
income. In order to forecast and gauge the potential future spending in welfare programs,
a poverty rate that considers interest payments from non-mortgage debt must be
considered.
This study considers the debt conundrum and evaluates how state and household
fixed effects contribute to the variation in an experimental poverty measure. By
examining the empirical effect specific welfare policies have on debt poor households
this thesis contributes a new poverty approach to the literature. Using two different model
specifications ten poverty outcomes are considered: five poverty rates and five binary
poverty measures. The poverty rates outcomes use a fixed effects model with state-level
entity effects while the binary poverty measures specify a fixed effects logit model with
individual-level entity effects. Because poverty is highest amongst women, the binary
17
poverty outcomes were restricted to a women-specific sample and used for evaluating an
unrestricted sample.
18
Chapter 3
METHODOLOGY
Poverty in the United States is calculated by comparing a household’s total
(before tax) income to a specific poverty threshold. According to the Department of
Health and Human Services the Census calculates various poverty thresholds that vary by
family size and age. The methodology for deriving these thresholds coincides with
Orshansky’s 1963 poverty definition and is adjusted annually by the CPI (HHS, 2013).
This study uses two official poverty rates from the U.S. Census, calculates five binary
experimental poverty measures and three experimental poverty rates. These measures are
used for estimating the effect specific welfare policies have on alternative poverty
outcomes. This study primarily focuses on how robust the attained results are with
McKernan and Ratcliffe’s (2006) study and understanding how these results change
when the prevalence of interest payments on non-mortgage debt is considered.
Unlike the official poverty rate, prior to comparing a household’s total annual
income to the specific poverty threshold, annual interest payments from total nonmortgage debt were subtracted from the head of household’s total annual income. If this
annual adjusted income variable is below the specific poverty threshold then the
household is considered debt poor. The following sections describe how the debt poor
dependent variable was derived. Because an experimental poverty measure that considers
interest payments from debt has never been evaluated, the proceeding sections are
extensive.
19
3.1– Methodology for Calculating an Experimental Poverty Measure
According to Pressman and Scott (2009) the annual interest payment on credit
card debt can be calculated by multiplying the individual’s annual debt by its associated
interest rate. Following this approach, average interest rates from commercial banks were
obtained from the Federal Reserve Board’s (FRB) G19 consumer report. Moreover, by
using family level data from the PSID and the FRB interest rates, the total head of
households’ annual interest payments from total non-mortgage, ‘other’, debt was
estimated. In order to estimate a poverty measure, the household’s total annual interest
payments on other debt was subtracted from their total annual income, see Equation 1.
Orshansky’s 1963 poverty thresholds vary from one-member family sizes to nine
and are subdivided by age. Evaluating only households younger than 65 with dependents
under 18, this thesis was able to estimate the number of households that were below the
specific poverty threshold. Figure 3 illustrates how the family size poverty thresholds
have changed during 2001-2009. If the ist household in state ‘s’ at time ‘t’ has an interest
payment adjusted income less than the specific poverty threshold then the household is
considered debt poor, see Equation 2.
Equation 1:
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑖𝑛𝑐𝑜𝑚𝑒𝑖𝑠𝑡 = [(𝑡𝑜𝑡𝑎𝑙 𝑎𝑛𝑛𝑢𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒𝑖𝑠𝑡 ) − (𝑜𝑡ℎ𝑒𝑟 𝑑𝑒𝑏𝑡𝑖𝑠𝑡 ∗ 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑟𝑎𝑡𝑒𝑢.𝑠. )]
Equation 2:
𝐷𝑒𝑏𝑡 𝑝𝑜𝑜𝑟𝑖𝑠𝑡 = 1 𝑖𝑓 𝑎𝑑𝑗. 𝑖𝑛𝑐𝑜𝑚𝑒𝑖𝑠𝑡 𝑖𝑠 < 𝑝𝑜𝑣𝑒𝑟𝑡𝑦 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑𝑛 ; 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
20
Figure 3: Percentage Change in the U.S. Official Poverty Thresholds by Family Size
4.0%
3.5%
2001-02
Percent Change
3.0%
2002-03
2.5%
2003-04
2.0%
2003-04
1.5%
2005-06
1.0%
2006-07
0.5%
2007-08
2008-09
0.0%
-0.5%
1
2
3
4
5
6
7
8
9
Family Size
Source: U.S. Census, ACS 2013
In order to properly estimate valid experimental poverty measures, a few
household characteristics need to be considered. These characteristics include family size,
educational attainment, unemployment and marital status. Each household characteristic
was grouped and analyzed one at a time.
In particular, ‘family size’ is a summation variable that aggregates the number of
kids and marital status of the individual household. The number of kids ranges from zero
to nine and marital status is described with either a one for not married, or a two for
married households. Each poverty threshold varies by year and family size.
Consequently, identification variables were generated to match each household’s family
size to its corresponding threshold value and year. Moreover, ‘family size’ is used for
identifying the households with annual adjusted income that falls below the
corresponding poverty threshold.
21
3.2– Dependent Variables
The dependent variables in this study consist of rates and binary variables. Five
different poverty rates per state are considered, two of which were not derived in this
study but collected from the U.S. Census American Community Survey (ACS). The
motivation behind including the Census poverty rate is to facilitate an assessment of
results attained in McKernan and Ratcliffe’s (2006) study. The remaining three poverty
rates were derived using the PSID dataset. These poverty rates include, a PSID state
poverty rate that calculates poverty using the same approach as the U.S. Census, an
experimental poverty rate that applies the debt poor methodology discussed in section
3.1, and a poverty rate that calculates the marginal effect of serving debt (i.e. the
difference between the experimental poverty rate and PSID poverty rate). These PSID
poverty rates were estimated by aggregating the total number of households per state and
counting the number of households whose income levels (adjusted or unadjusted for
interest payments) fell below the specific threshold.
Lastly, the remaining dependent variables are binary, and only consider debt poor
outcomes. In particular, five binary experimental poverty measures are examined
including an overall measure of debt poor households and four women-specific debt poor
measures. All four women-specific measures examine a restricted sample of single
female households with at least two children. The purpose for these women-specific
measures is geared towards capturing the individuals within the sample who are most
likely to be truly poor. In particular, according to the literature, single women with
children are more likely to be poor, (Fitzgerald and Ribar (2004); Schoeni and Blank
22
(2000); Weber, Edwards, and Duncan’s (2003)). According to the U.S Census, in 2004,
the U.S. poverty rate was 12.7 percent. However, when considering only single female
heads of households with dependents the poverty rate more than doubled to 30.5 percent
(U.S. Census Bureau 2004a, 2004b). As a result, the sample for four binary dependent
variables was restricted to single-female households with at least two dependents.
All four women-specific dependent variables consider household demographic
characteristics, with the exception of the first. Using the same methodology as 3.1, if the
interest payment adjusted income levels for single women with at least two children is
below the family size threshold, then they are considered debt poor, denoted single1. The
second and third poverty measures both consider the base case (single1), education and
race. In particular, ‘single2’ considers only non-white females and ‘single3’ evaluates
females with education levels less than or equal to 14 years. If a household has less than
14 years of education, ‘single3’ indicates that the individual may have some college
experience but no post high school degree. Moreover,‘single3’ will help describe whether
a person with only a high school degree, or equivalent, may be more prone to poverty as
they would have a harder time competing in the labor force.
The last female poverty measure evaluates race and education levels
simultaneously, denoted single4. Moreover, ‘single4’ is a combination of the prior
measures. Table 1 summarizes the descriptive statistics for all of the experimental
poverty measures evaluated in this study. According to Table 1, during 2001-2010, on
average, approximately two percent of households were not considered poor when in fact
interest payments from non-mortgage debt put them below the poverty line. The mean
23
values for the poverty measures that consider single women are all very similar and range
between 2.6 and 3.3 percent.
Table 1: 2001-2009 Average State Poverty Rates and Measures
Variables
Obs.
Mean
Std. dev
Min
Max
Census Official Poverty Rate
432
0.105
0.031
0.046
0.189
Census Female Poverty Rate
432
0.158
0.036
0.076
0.276
State Poverty Rate
432
0.095
0.067
0.000
0.231
Experimental Poverty Rate
432
0.102
0.072
0.000
0.255
Debt Poor Marginal Effect
432
0.007
0.028
0.000
0.140
Experimental Poverty Measure: All Thresholds Considered
Debt Poor (Unrestricted)
76,914
0.130
0.336
0
1
25,164
Debt Poor (Restricted)
0.262
0.440
0
1
Experimental Poverty Measures: Single Women with ≥ Two Dependents*
Single1 (Debt Poor Restricted) 25,164
0.262
0.440
0
1
Single2 (non-white)
20,640
0.298
0.457
0
1
Single3 (edu ≤ 14)
22,374
0.289
0.453
0
1
Single4 (non-white & edu≤ 14) 16,281
0.325
0.468
0
1
* Each experimental measure observes single women with at least two kids however; additional
characteristics are included and are specified in parentheses.
3.3–Independent Variables
Macroeconomic measures and specific welfare policies are important explanatory
variables used for analyzing the variation in poverty outcomes over time and within
states. Identifying the key determinants of poverty is crucial, especially when poverty is
the outcome as economic shocks impact poverty differently. This study uses various
macroeconomic variables for examining how income inequality, cost-of-living, state
income per capita, unemployment, and a tax credit affect alternative poverty outcomes
during 2001-2009. Additionally, examining the effect seventeen specific welfare policies
have on poverty is the focus of this study. These policy variables were selected in the
24
analysis because they all impact poverty directly. Furthermore, these key determinants of
poverty are characterized along two dimensions, economic controls and welfare policy
variables. Details pertaining to these two dimensions are discussed further in chapter 4.
However, their motivation and overall hypothesized relationships with poverty are briefly
discussed below.
According to the U.S Department of Health and Human Services, during the
1970s, total welfare caseloads began to quickly rise. From 1970 to 1995, welfare
caseloads grew at an annual rate of 13 percent. Such drastic growth led to reform,
replacing Aid for Families with Dependent Children (AFDC) with Temporary Assistance
for Needy Families (TANF). Many public policy critics argued that the rising trend
illustrated a failure of alleviating poverty. Instead, the AFDC program appeared to be
encouraging matriarchy and discouraging work. In any case, TANF generated a welfare
reform policy and, in most cases, is believed to be the leading cause of the decrease in
total recipient caseloads (see Figure 4).
Figure 4: U.S. Total Number of TANF Recipient Caseloads per Year, in Thousands
Total Number of Caseloads
7,000
6,000
5,000
4,000
3,000
2,000
1,000
Source: ACF, HHS
Year
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
0
25
This study investigates how welfare policies impact debt poor states and
households. Since TANF allows states to implement stricter/looser eligibility
requirements for welfare recipients, the policies examined are geared towards reflecting
the differences in policies across states. Seventeen welfare policies are examined and
were selected using McKernan and Ratcliffe (2006) typologies from the Department of
Health and Human Services. Theses typologies were applied in this study because they
are known to directly affect poverty.
Additionally, economic control variables are important determinants of poverty as
fluctuations in income inequality, cost-of-living, Gross State Product (GSP) per capita,
and unemployment rate directly impact poverty levels. Improvements in the conditions of
the economy are hypothesized to reduce poverty through positive effects on wages and
employment. Chapter four discusses these variables more thoroughly.
26
Chapter 4
DATA
In order to estimate a model that demonstrates how differences in state specific
welfare policies explain the variation in the probability that a household is debt poor, it is
imperative to control for unobservable differences across states and over time. As such,
this study uses a variety of datasets to derive five experimental poverty measures
including longitudinal data from the Panel Study of Income Dynamics (PSID) and the
Urban Institute (see Table 2).
Table 2: Datasets and Sources Summary
Datasets & Sources
Main Family Data, PSID
Welfare Rules Database (WRD), Urban Institute
Regional Data GSP per capita, BEA
Local Area Unemployment Statistics (LAUS), BLS
Minimum Wage, DOL
EITC Data, Brookings Institute
Interest Rate Data, Federal Reserve Board (FRB) G19
Unit of Measure
Individual family panel
State specific panel
State level time-series
State level time-series
State level time-series
State level time-series
National average time-series
*Time period ranges from 2001-2009 and includes both panel and time series datasets
4.1 – PSID Individual Level Data
The Panel Study of Income Dynamics (PSID) is a longitudinal survey that has
been collecting data since 1968. It samples over 18,000 individuals living in 5,000
families nationwide and collects demographic, health and economic status data for each
family member (PSID, 2013). Initially households were surveyed on an annual basis
however, as of 2001 households have been surveyed on a biennial basis.
In this study, 8,546 heads of households from 48 different states are examined
over a nine year period (2001-2009). Puerto Rico and the Virgin Islands are not included
in this sample due to a lack of consistent data. Alaska, Hawaii and Vermont were
27
dropped from the sample because the number of surveyed participants was very low.
Additionally, this dataset has demographic and economic characteristics for each head of
household (i.e. race, gender, education, income, unemployment status, etc).
4.2 – Urban Institute Welfare Policies
The Urban Institute began gathering data about state welfare programs in the mid1990s. Due to welfare reform, states’ flexibility in designing and managing welfare
programs largely increased and left many policy makers perplexed (WRD, 2013). As a
result, the Urban Institute’s Welfare Rules Database is a panel series that collects data on
states’ cash assistance programs. In this study, 48 states are observed over nine periods
(2001-2009).
4.3– Explanatory Variables: Welfare Policy Variables
McKernan and Ratcliffe (2006) examine 19 specific policy variables that were
selected using a specific typology strategy that narrowed down the key state program
rules that directly affect poverty. These typologies were developed by McKernan and
Ratcliffe’s conceptual framework, assistance from the Department of Health and Human
Services, and a technical working group.5 Moreover, these 19 specific policies only
include welfare related policies that are hypothesized to directly affect poverty in the
short and medium run.
This study investigates how specific welfare policies affect the variation in the
percentage of debt poor households during years preceding, and shortly after, the 2008
economic recession (2001-2009). Since the TANF legislation allows states to implement
5
See Fender, McKernan, and Bernstein (2002) for a detailed description on how these typologies were
derived.
28
different eligibility requirements for welfare recipients (i.e. stricter or looser policies), the
policies examined in this study are geared towards capturing the differences across state
policies.
This study analyzes the same 19 specific policies that McKernan and Ratcliffe
(2006) examine however, a few policy variables were analyzed differently, or omitted,
due to the complexity of their derivations and data availability.6 Following McKernan
and Ratcliffe’s study, each specific welfare policy is grouped into three broad categories:
eligibility requirements, financial incentives to work, and time limits. Each category
includes policies that help capture the variation in welfare policies across different states.
In particular, state-level minimum wage directly impacts financial incentives due to labor
supply and demand shocks. Table 3 summaries the welfare variables analyzed in this
study and their hypothesized effects on poverty are described in Table 4.
4.3.1 – Eligibility Requirements
There are three main policy variables that directly impact a state’s eligibility
requirement for welfare recipients; family cap, earned income disregards for eligibility
tests, and vehicle asset exemption. According to McKernan and Ratcliffe (2006), more
lenient eligibility requirements should lower the poverty rate because families can
supplement earnings with welfare payments.
6
Each state has welfare policies that vary in max benefits and work requirements for recipients. The earned
income disregards while working during month 12 policy variable was omitted from this analysis because
McKernan and Ratcliffe only provide their assumptions and not their derivations. There assumptions
suggest that multiple welfare variables were used to derive this variable however the details are omitted.
Additionally, due to the time consuming process for collecting data, the percentage of Earned Income Tax
Credit (refundable) was not analyzed.
29
(1) Family Cap: States with family cap policies deny eligibility to newborn children
of welfare recipients. This is an indicator variable, if a specific state has
implemented a family cap policy then ‘wf_famcap’ equals 1, 0 otherwise.
Moreover, if a state has a family cap policy, then poverty in that state will be
higher because of the monetary burden that a child imposes.
(2) Earned Income Disregards for Eligibility Tests: The earned income disregard
(EID) is the total percent of an applicant’s earnings that is not included when
calculating welfare benefits. According to the Welfare Rules Database, each state
specifies the amount of earned income that a recipient is able to disregard for a
certain amount of months. Overall there is a large variation in how much a state
disregards (McKernan and Ratcliffe, 2006).
The data for this policy variable has three parts, dollar values, percentages,
and string variables. Moreover, in order to evaluate the EID in this study, three
policy variables were generated. The first consists of the maximum earned income
that is disregarded from a monthly check during the first three months of aid. The
second EID variable calculates the maximum percent of monthly earned income
that is disregarded from the remaining months, i.e. month 12. Finally, because
some states do not impose explicit net income tests for eligibility purposes, the
third EID policy variable is a binary regressor, “if the state either imposes no net
income test at application or does impose a net income test, but the calculation of
the test and disregards allowed for the test are no different from those used to
30
calculate the benefit, the no explicit net income test variable receives a 1; if not, it
receives a 0” (McKernan and Ratcliffe, 2006).
Moreover, analyzing these three earned income disregard (EID) policy
variables is crucial for this study because they directly impact poverty. In
particular, the higher the percent and or dollar value of the earned income that is
disregarded, the lower the poverty rate because the income disregarded increases
a recipient’s total income. Similarly, if a state does not enforce explicit net
income tests for eligibility purposes then, all else equal, the poverty rate is
expected to decrease as a household has more earned income at their disposal.
(3) Vehicle Asset Exemption for Applicants: Vehicle exemption policies quantify the
asset value (i.e. equity or fair market price) of an applicant’s vehicle and are used
for calculating the exempt portion of a vehicle’s value. Intuitively, a high state
vehicles asset exemption will allow people with better cars to qualify for welfare,
making it possible that this exemption leads to more recipients, and lowers the
state’s poverty rate. A higher asset exemption value indicates that a vehicle is in
good condition and worth more. Moreover, states with high vehicle exemptions
are expected to have lower poverty rates because the likelihood that an applicant
is able to maintain a job is dependent on a reliable car.
4.3.2 – Financial Incentives to Work
Welfare policies that are expected to increase earned or unearned income are
considered a financial incentive and/or disincentive to work. Most of these policies
generate ambiguous effects on poverty due to substitution and income effects. There are
31
six policies that impact income and directly affect poverty, the following describes each
policy in detail.
(4) FLSA Federal-level Minimum Wage: The Fair Labor Standards Act (FLSA)
outlines minimum wage and overtime standards for non-exempt private and
public employees in the U.S. Since some states do not mandate businesses to
comply with a minimum wage requirement, the FLSA federal measure is
analyzed to determine the minimum wage baseline for these states.
(5) State-level Minimum Wage: Historical state-level minimum wages are reported in
this study. A zero value indicates that the state has no minimum wage policy.
These states include AL, LA, MS, SC, and TN. According to economic theory,
the effects of a minimum wage on labor market outcomes are ambiguous at best.
Changes in the minimum wage depend upon labor supply/demand conditions and
the relative magnitude of income and substitution effects.
On one hand, an increase in the minimum wage causes a decrease in the
number of hours worked due to the income effect, while the substitution effect
increases the number of hours worked. Thus, overall the net effect on hours
worked and income is ambiguous. When considering labor demand and supply
conditions, the market effects on poverty are also ambiguous. For instance, an
increase in the minimum wage would increase quantity of labor supplied and
decrease quantity of labor demanded. Given the increase in minimum wage, if
demand for labor is elastic, fewer workers would be employed with higher wages.
32
If the unemployment rate increases significantly then the poverty rate increases,
however the converse can occur and poverty could decline.
(6) Most Severe Sanctions for Non-Compliance Amount: This variable summarizes
state sanction policies initiated when a recipient fails to comply with work
requirements, denoted, wf_sevamount. Depending on if the recipient is rational,
severe sanctions for non-compliance may cause total income to increase or
decrease. In particular, if the severe sanctions cause welfare recipients to comply,
then earned income would increase and poverty would decline. However, if
recipients fail to comply then unearned income would decline and cause the
poverty rate to increase. Due to inverse fluctuations between earned and unearned
income, the impact of ‘wf_sevamount’ on poverty is ambiguous.
The Urban Institute reports a state’s maximum amount of benefits lost due
to non-compliance with job requirements in dollar values. Each state’s monetary
loss in benefits varies by amount and duration. This variable represents the
maximum dollar value of lost benefits in a given year. Using the Consumer Price
Index, this policy variable was converted to real 2009 dollars.
(7) Duration of Most Severe Sanctions for Non-Compliance: This variable indicates
the duration of non-compliance penalties and ranges from 0 to 4. If a state does
not specify actual sanctions, only provides warnings, then a value equal to zero is
indicated. If the non-compliance policy is permanent then a value equal to five is
indicated, see below.
0 – warning, no actual sanctions
1 – 1 month
33
2 – 2-5 months, reapply
3 – 6-11 months
4 – 12-36 months
(8) Treatment of Child Support Income: The standard AFDC policy disregards $50
for child support costs and is considered pass-through/transfer income. Since
welfare reform, TANF now allows states to deviate from the AFDC $50 passthrough allotment. Three categories are specified in this policy variable, 1, 2 and
3. These categorical variables were specified because they follow McKernan and
Ratcliffe’s (2006) methodology. In any case, if the pass-through income is less
than $50 then a one is indicated, if the pass-through is equal to $50 a two is
indicated and, if the pass-through is greater than $50 a three is indicated.
According to McKernan and Ratcliffe, the higher pass-through of child support
expenditures, the higher total income and thus the lower the poverty rate.
(9) State Average Number of Returns Receiving the Earned Income Tax Credit
(EITC): The Brookings Institute reports the total number of returns receiving the
EITC for various geographical locations, extending from 1997 to 2010. According
to the Brookings Institute, all data are derived from the Internal Revenue Service's
Stakeholder Partnerships, Education, and Communication (IRS-SPEC) Return
Information Databases. The EITC is a refundable federal income credit that is
applicable to working individuals and families with low to moderate earned
income levels (IRS, 2013). The EITC allows recipients to keep more of their
earned income at the end of each year, thereby reducing poverty. By definition,
34
the official poverty rate is a before tax measure thus including a tax measure
allows us to estimate the effects tax credits have on poverty.
By definition, the official poverty rate is a before tax measure thus
including the EITC was. According to the Center on Budget and Policy Priorities
(CBPP), since the early 1990s the EITC has significantly contributed to the
increase of women in the workforce, helped encourage education, and reduced
poverty (CBPP, 2013). However, the EITC is expected to have an ambiguous
effect on poverty given the inverse effects caused by the substitution and income
effects. In particular, an increase in EITC can cause the poverty rate to decrease
if the substitution effect leads to more hours worked. However, the poverty rate
may increase if recipients reduce their total hours worked and replace earned
income with their tax credit.
Figure 5: State Average Number of Returns Receiving the Earned Income Tax Credit
2,500,000
California
2,000,000
Florida
New York
1,500,000
Alabama
Arizona
1,000,000
Nevada
500,000
D.C.
Delaware
Year
2009
2008
2007
2006
2005
2004
2003
2002
-
2001
Total Average Number of Returns
3,000,000
35
4.3.3 – Time Limits
Time limit policies restrict welfare recipients from receiving benefits for a
prolonged period of time. This study considers six different time limit variables because
states differ in the length of time during which recipients can receive benefits.
According to McKernan and Ratcliffe, all six time limit policy variables are expected to
have ambiguous effects on poverty because they have ambiguous effects on income. In
particular, long/short duration may increase/decrease unearned income because of
welfare benefits however, poverty may increase or decrease depending on earned income
and the net effects on income, see Table 2 for details on these time limit variables.
Table 3: Brief Description of Time Limit Policies
Policy Name
No Time Limits
Intermittent Time
Limits
Time Limits for Ill
Members
Time Limits for
Cooperation
Duration of Time
Limits
Time Limit Exemption
for Dependents
Time Limits (6)
Variable
Metric Definition
tl_notl
(0/1)
Equals 1 if State does not have time
limits
tl_interm
(0/1)
Equals 1 if State has intermittent life time
limits
tl_ill
(0/1)
Equals 1 if State has any type of time
limit exemption for either ill/
incapacitated recipients or caring for
ill/incapacitated individuals
tl_coop
(0/1)
Equals 1 if the state extends time limits
for recipients who are unemployed and
cooperating with the welfare
requirements.
tl_months
(m)
Indicates a state’s maximum number of
months
tl_child
(m)
Indicates if the state has time limit
exemptions for recipients with
dependents under ‘x’ months of age. The
months of the dependent are reported,
(i.e. if a child is 4 months old then the
variable equals 4 for that state.
36
4.4 – Explanatory Variables: Macroeconomic Controls
Considering changes in the economy is important because if omitted, the
estimates would be biased. Household and state-level fixed effects control only for timeinvariant characteristics. However, because this study analyzes a time period with an
economic crisis, time variant regressors that differ across states such as: the state-level
unemployment rate, gross state product (GSP) per capita, Gini coefficient, and changes in
the cost of living (i.e. the growth rate of Fair Market Rents) must be specified. In
particular, the 2008 recession impacted these macroeconomic variables differently thus,
including these control variables allows us to estimate the specific effect they had on
state-level and household-level poverty outcomes. Lastly, given that a few welfare
benefits and economic control variables have monetary values associated with them, the
variables were adjusted for inflation using the Consumer Price Index (CPI), see Table 3
for a list of these real valued variables.
Table 4: Inflation Adjusted Explanatory Variables, 2009 Dollars
Gross State Product (GSP) per capita
Fair Market Rate (FMR)
State and Federal Minimum Wage
Total Income and Non-mortgage Debt
Vehicle Asset Exemptions
Severe Sanctions for Non-Compliance
(1) State-Level Annual Average Unemployment Rate: The Bureau of Labor Statistics
Local Area Unemployment Statistics (LAUS) collects monthly data for many
geographic areas. This study uses LAUS unemployment rates to derive an annual
average unemployment rate per state. Figure 5 illustrates the nine year trend of the
annual average unemployment rate for a sample of states.
37
Figure 6: Average Annual Unemployment Rate
14
Alaska
12
Alabama
Arizona
8
Arkansas
2011
2010
2009
2008
2007
Florida
2006
0
2005
Delaware
2004
2
2003
Colorado
2002
California
4
2001
6
2000
Percentage
10
Georgia
Year
(2) Gross State Product (GSP) Per Capita: GSP is the state level equivalence of
Gross Domestic Product. It measures a state’s market value of all final goods and
services produced within the state in a given year. Gross state product per capita
is expected to reduce poverty as income and poverty are directly related. Figure 6
provides a good picture of how GSP per capita varied in various states during
2001-2009.
Figure 7: 2001-2009 Gross State Product Per Capita by State
35,000,000
California
30,000,000
NewYork
25,000,000
Florida
20,000,000
Arizona
15,000,000
Alabama
Utah
10,000,000
Delaware
5,000,000
D.C.
Soource: BEA, 2013
Year
2009
2008
2007
2006
2005
2004
2003
2002
-
2001
Millions of Dollars (inflation adj.)
40,000,000
38
(3) –Fair Market Rents: The fair market rents (FMR) for two bedroom housing units
is available on the U.S Department of Housing & Urban Development (HUD)
website. The FMR indicates the average cost of housing for a two-bedroom unit
in a given state and, is primarily used as a payment standard for the Housing
Choice Voucher Program (HUD, 2010). According to HUD, eligible voucher
recipients receive a housing subsidy; the total subsidy is determined by the FMR
and income (see Equation 3). States with higher FMRs are expected to have lower
poverty levels.
Equation 3:
𝐻𝑜𝑢𝑠𝑖𝑛𝑔 𝑆𝑢𝑏𝑠𝑖𝑑𝑦 = 0.30(𝑚𝑜𝑛𝑡ℎ𝑙𝑦 𝑖𝑛𝑐𝑜𝑚𝑒) − 𝐹𝑀𝑅
Housing is a major factor in many cost-of-living estimates (BLS, Austin
Chamber, CSER). In order to analyze a proxy for changes in the cost-of-living
over time, this study calculates the FMR growth rate. Figure 7 summarizes the
growth rate of state-level FMR during 2001-2009.
7.3
7.2
7.1
7
6.9
6.8
6.7
6.6
6.5
6.4
6.3
6.2
6.1
6
5.9
D.C.
California
Delaware
New York
Arizona
Florida
Year
2009
2008
2007
2006
2005
2004
2003
2002
Alabama
2001
Fair Market Rents Growth Rate
Figure 8: Fair Market Rents Growth Rate, 2001-2009
39
(4) –Gini Coefficient: This study uses the Current Population Survey (CPS) annual
Gini coefficient to measure income inequality for each state. The Gini coefficient is
an index measure of income inequality that ranges between zero (perfect equality)
and one (perfect inequality).Moreover, the closer the index value is to one, the
higher the income inequality in that state. Figure 8 illustrates the nine year average
Gini index per state and is ranked by states with high to low income inequality.
Figure 9: 2001-2009 Gini Coefficient per State
0.6
Alabama
Arizona
California
Delaware
D.C.
Florida
NewYork
Utah
Gini Coefficient
0.55
0.5
0.45
0.4
0.35
Source: U.S. Census, ACS
2009
2008
2007
2006
2005
2004
2003
2002
2001
0.3
Year
4.5 – Household Demographic Variables
Household demographic variables were also evaluated when the fixed effects
were not time-invariant (i.e. random effects model). These variables provide detailed
information about the household such as marital status, education attainment, and race.
By including these variables, omitted variable bias is reduced because household
characteristics are important determinants of poverty. In particular, an additional child is
hypothesized to increase poverty due to reduced annual earned income, especially for
single parent households, and due to the increase in the poverty threshold. Additionally,
40
according to the U.S. Census, poverty rates differ tremendously for households according
to their race, gender, marital status, and/or educational attainment.
41
Chapter 5
EMPIRICAL MODEL
This study utilizes two different empirical models, fixed effects and fixed effects
logit models, for analyzing state-level and household entity poverty rates. Both models
use the same panel however the former aggregates the household-level panel to the state
level. Each state has unique weights that correspond to the total number of households
residing in each state during the sample period, 2001-2009. The state-level fixed effects
model specifies the five poverty measures as an outcome and estimates the effects that
specific welfare policies have on the official and experimental poverty rates. On the other
hand, household-level entity effects are used when specifying a two-way fixed effects
logit model. This model estimates the impact that 17 specific welfare policies have on
the probability that the ist head of household, in state ‘s’ at time ‘t’, is debt poor. Fixed
effects are valuable when analyzing panel data as they help mitigate omitted variable bias
by having individuals serve as their own controls (Allison, 2009).
Time effects are preferable for this study as they capture any effects that vary over
time but are constant across the entire panel. For instance, welfare recipients residing in
the same state would be exposed to identical welfare policies that vary over time. After
testing the validity of the time effects in both models, the state-level fixed effects model
indicated that the time entities were not fixed. Thus, only time fixed effects are specified
in the household-level fixed effects logit model. In any case, estimating entity and/or time
fixed effects will help ensure consistent and efficient estimators.
42
5.1 – Fixed Effects Model
The fixed effects model has two main assumptions, (1) assumes that unobservable
characteristics within an entity (i.e. head of household or State) may impact the
dependent variable and (2) each entity is unique and has its own error and constant term.
Moreover, if both assumptions hold then the estimates are unbiased because the
regressors are uncorrelated with the error term. However, if these assumptions do not
hold, and the cross-sections are not independent, then the error and constant terms are
correlated and random effects must be applied (Torres-Reyna, 2011).
Equation 4
𝑌𝑠𝑡 = 𝑎𝑖 + 𝛽′1 𝑊𝑠𝑡 + 𝛽′2 𝑋𝑠𝑡 + 𝜇𝑠 + 𝜂𝑡 + 𝑣𝑖𝑡
Equation 4 describes the state-level fixed effects model utilized for analyzing the
poverty rate outcomes where ‘Yst ’ represents one of the four poverty rates (i.e. U.S.
Census rate, PSID state rate, experimental rate, and the marginal effect rate) in state ‘s’ at
time ‘t’ (year) and ‘𝑎𝑖 ’ is a unique constant term that allows group-specific estimates to
be derived for each state. Additionally, ‘W’ and ‘X’ are the independent variables
denoting two different matrices, where ‘W’ corresponds to all of the specific welfare
policies and ‘X’ represents the economic control variables, such as, real GSP per capita,
FMR, annual average unemployment rate, and the Gini coefficient. Lastly, ‘𝜇𝑠 ’
symbolizes the state fixed effects, ‘𝜂𝑡 ’ is the time fixed effects and ‘𝑣𝑖𝑡 ’ is the i.i.d
stochastic error term.
43
5.2 – Probability Models: Logit and Probit
Logistic functions are non-linear models that have useful econometric advantages.
They allow researchers to estimate the probability of an event by analyzing a binary
dependent variable and restricting the observed values [0, 1], such that:
𝑌𝑖 =∈ {0,1}
Unlike linear models, such as OLS, logistic models are able to produce probabilities by
transforming the binary dependent variable to a continuous variable.
In this study, a fixed effects logit model was used to estimate the probability of
debt poor. Probit models are similar to logit models in that they are both non-linear
functions that estimate probabilities and only differ by their distribution. According to
(Agresti, 2013), when estimated, both models produce similar probabilities. However,
conditional logit models are preferable when the degrees of freedom in the sample are
relatively larger than the number of observations. Figure 8 illustrates the predicted
residuals for the logit and probit models.
Figure 10: Logit and Probit Predicted Residuals
In order to verify that the logistic cumulative distribution is preferable, both
models were estimated. After comparing the Akaike Information Criterion (AIC) and
44
predicted probabilities for both models, the logit model proved to be a stronger model
based on a minimized AIC.
5.3 – Fixed Effects Logit Model
Fixed effects logit models divide the data into groups, the group estimates are
then determined by the unobserved differences across the groups (Gould, 1999). This
study uses a fixed effects logit model for modeling the impact that household specific
welfare policies have on the probability of the ‘ith’ household being debt poor. A fixed
effects logit model is preferable when considering welfare policies as important
determinants of debt poor households because the individuals serve as their own controls
and the fixed effects control for time invariant factors (i.e. public sentiment towards
welfare). Moreover, applying fixed effects to a logit model is useful for estimating the
predicted probabilities of an event.
Equation 5 summarizes the second empirical model. Where 𝑌𝑖𝑡 is the probability
that the 𝑖 𝑠𝑡 head of household at time ‘𝑡’ is debt poor and 𝑎𝑖 is a unique constant that
allows group-specific estimates to be derived for each individual household. Equation 4
suggests that each household has varying intercepts but constant slopes. Similar to
Equation 4, ‘W’, ‘X’, ‘𝜂𝑡 ’ and ‘𝑣𝑖𝑡 ’ represent the same vectors, time fixed effects and
stochastic error term. However, ‘𝑌𝑖𝑡 ’ describes the probability that the ‘ist’ household in
‘s’ state at time ‘t’ is debt poor and ‘𝜑𝑖 ’are the household level fixed effects.
Equation 5
𝑌𝑖𝑠𝑡 = 𝑎𝑖 + 𝛽′1 𝑊𝑖𝑠𝑡 + 𝛽′2 𝑋𝑖𝑠𝑡 + 𝜑𝑖 + 𝜂𝑡 + 𝑣𝑖𝑡
Random effects can also be estimated for both empirical models however, fixed
effects are preferred because random effects do not control for omitted variable bias.
45
Another advantage involves the correlation between the individual effects and the
regressors. Unlike random effects, the fixed effects estimator allows these measures to be
correlated and is consistent. This study uses the Hausman test to verify if the second
assumptions of the fixed effects hold for both empirical models. The following equation
summarizes the null and alternative hypotheses for the Hausman test.
Hausman Test: 𝐻𝑜 : 𝑎1 = 𝑎2 = 𝑎3 = ⋯ = 𝑎𝑘
𝐻1 : 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑛𝑢𝑙𝑙 ′ 𝑠 𝑖𝑠 𝑛𝑜𝑡 𝑡𝑟𝑢𝑒
According to the Hausman test, if one of the entities is not unique and does not have its
own error and constant term then the fixed effects are not valid. After applying the
Hausman test to all five models, the fixed effects proved to be valid.
5.4 – Ensuring Robust Estimates
Panel series are often viewed as an efficient analytical method for analyzing
econometric data as their unique modeling techniques effectively mitigate omitted
variable bias (Asteriou and Hall, 2009). However, panel data estimations suffer from the
prevalence of serial correlation, heteroskedasticity and cross-sectional dependence. As a
result, proper statistical methods were applied to ensure efficient and consistent
estimations.
Serial correlation and heteroskedasticity violate two conditions of the classical
econometric model, suggesting that regressors are not independently and identically
distributed (i.i.d). Serial correlation is very common in panel series because past entity
observations are highly likely to be correlated with present observations. Additionally, if
there are sub-groups in the sample with differing variances, heteroskedasticity becomes
46
evident. According to econometric theory, in order to evaluate an efficient and consistent
estimator, a data series must be identically distributed across all regressors. When this
condition holds, the error terms are homoskedastic and do not vary by sub-groups (Stock
and Waston, 2011).
Consequently, in order to ensure the estimated standard errors are robust to these
issues, heteroskedastic auto-correlated consistent (HAC) standard errors were specified.
HAC standard errors are critical when conducting statistical analysis with longitudinal
data because they are robust to heteroskedastic errors and serial correlation. Without
HAC standard errors, the estimated standard errors would be inflated and the coefficients
would become insignificant. Moreover HAC standard errors disentangle the serial
correlation within each household’s characteristics and ensure that the errors are nonvariant.
Cross-sectional dependence (CD) is another common issue in panels, especially if
a common shock affects all individuals (Hsiao, Pesaran, and Pick, 2007). Again, classical
econometric models assume that cross-sections are independent and if violated,
estimators are inconsistent. Given the 2008 financial crisis, the cross-sections may not be
independent across the panel. As a result, this study used Pesaran (2004) CD test to
evaluate independence across cross-sections. Unfortunately, given the large number of
household observations, none of the CD tests could be estimated on the entire sample7.
7
See Pesaran (2004) and Friedman (1997) for more details on CD test
47
Chapter 6
RESULTS
After ensuring that the two-way fixed effects (i.e. entity and time) were valid, and
controlling for HAC standard errors, fixed effects models and fixed effects logit
specifications were estimated. Five two-way fixed effects models were estimated to
evaluate the effects that specific welfare policies had on different poverty rates: U.S.
Census official rate, experimental rate, a marginal poverty rate that estimates the percent
increase interest payments on non-mortgage debt have on poverty, and the U.S. Census
women-specific poverty rate8. Additionally, five two-way fixed effects logit models were
used to estimate the probability of debt poor households for a restricted/unrestricted
women sample.
6.1 – Poverty Rates: Fixed Effects Models
First, I estimate the state-level fixed effects and analyze the impact specific
welfare policies have on three different poverty rates. Fixed and random effects were
specified for the purpose of validation. The purpose of this study is two folds and consists
of comparing my results with McKernan and Ratcliffe’s (2006) study and estimating
experimental measures. As a result, Table 5 summarizes the random effects results of five
different models, please see appendix A for the fixed effect results. As a quick reference,
Table 5, column 2, contains the results from McKernan and Ratcliffe’s study9.
8
After estimating the PSID derived poverty rate that mimics the same methodology used by the U.S.
Census revealed near zero coefficients and was omitted from the analysis.
9
Note, the results in Table 5 Column 2 were not re-estimated, they were taken from McKernan and
Ratcliffe’s (2006) study.
48
All five models underwent a validation procedure, in particular a Hausman test
was conducted to ensure that the entity fixed effects were efficient, AIC scores were
estimated to see which model had that strongest specification and the within group Rsquares were compared. These procedures indicated that only the U.S. Census official
and women specific poverty rates had valid state-level fixed effects. However, all of the
derived experimental poverty measures (i.e. debt poor experimental poverty rate, and the
marginal effect poverty rate) did not. This indicates that the state-level effects are
collinear and a random effects specification is more efficient. These results are not
surprising; many studies specify random effects models when estimating welfare policy
effects. In particular, McKernan and Ratcliffe’s study uses a weighted least squares
model when estimating the effect specific welfare policies have on poverty. Given
McKernan and Ratcliffe’s specification, this study overlooked the Hausman test and
considered the effect specific welfare policies have on various poverty rates using
random effects. Since the fixed effects were efficient estimators for the U.S. Census
poverty rates, Appendix A provides the summarized results for the state-level fixed
effects model.
This section primarily focuses on discussing Table 5 by comparing columns one
and two. These results reveal the robustness of the random effects models and allows the
results of this thesis to be extended towards the literature. Since McKernan and Ratcliffe
estimated a women-specific poverty rate, column one summarizes the results for the U.S.
Census American Consumer Survey (ACS) official poverty rate for female heads of
households. Again, column two summarizes the results from McKernan and Ratcliffe’s
49
(2006) study. Due to data restrictions, these two poverty rates differ by data source,
survey duration, and population size. McKernan and Ratcliffe acquired a monthly female
head of household poverty rate from the U.S. Census Survey of Income Program
Participants (SIPP). Their study observed households on a monthly basis from 19882002, which allowed them to have a sample size 95 percent larger than this thesis.
However, in order to see how robust McKernan and Ratcliffe’s results were, this thesis
compares their findings with alternative poverty measures (i.e. columns 1 and 3-5).
Table 5 summarizes the state random effects results for five different poverty
outcomes. The U.S. Census official poverty rates are summarized in columns 1 and 3.
Column 2 summarizes McKernan and Ratcliffe (2006) results, column 4 describes the
results for the derived debt poor experimental poverty rate, and column 5 summarizes the
marginal effect interest payments on credit card debt have on poverty. In general, when
analyzing the poverty rate outcomes, the majority of the specific welfare policies were
statistically insignificant. However, relative to McKernan and Ratcliffe’s results, columns
one and three findings are fairly similar. Nearly all of the welfare policies had the same
hypothesized signs as McKernan and Ratcliffe. For instance, if the unemployment rate
increases by one percent in a given year, the poverty rate for female head of households
with dependents increases by 0.009-0.5 percentage points. While the overall poverty rate
(i.e. U.S. Census official poverty rate) increases by 0.3 percentage points (Table 5,
Column 3). Both of these estimated coefficients in columns 1 and 3 are statistically
significant at the 95 percent confidence level.
50
Contrary, when interpreting the percentage of earned income disregarded during
month 12, even though the results are statistically significant, the estimated coefficients
are not as expected. For instance, a one percent increase in a state’s earned income
disregards during month 12 would increase poverty for female headed households by 0.60.8 percentage points (columns 1 and 3). All five models had inconsistent estimated
coefficient. Intuitively, a higher percentage of income that is disregarded should decrease
poverty because households have more earned income at their disposal (assuming the
substitution effect dominates). However, given these results, the income effect dominates
for both the U.S. Census poverty rates (Table 5 Column 1 and 3) causing poverty to
decrease as the percentage of disregards increases.
There were some limitations present when comparing the results of this thesis
with McKernan and Ratcliffe’s study. In particular, McKernan and Ratcliffe’s data varied
by month, not annually. They specified a few models varying by month, 12-months and
24-month durations10. They concluded that most of these welfare policies analyzed
became significant the larger the time frame. In particular, a 24-month estimated model
was more likely to have more statistically significant results than a 12-month model. This
indicates that, the lack of monthly variation could be contributing to the lack of
significant welfare policy estimates.
When examining the specific welfare policy variables individually, the majority
of them were statistically insignificant. However, because most welfare policies often
have related components, statistical significance for individual policies is not anticipated
10
Table 5 presents McKernan and Ratcliffe’s results for their t=12 months model.
51
(McKernan and Ratcliffe, 2006). Thus, I examined the joint significance of the welfare
policy variables using a Wald test. Overall, financial incentive to work and time limit
variables revealed consistent joint significant results (one-percent significance level). In
contrast, eligibility requirements (i.e. family cap, earned income disregards and vehicle
exemptions) failed to reveal joint significance in most cases.
According to McKernan and Ratcliffe (2006), their results were very robust to
different time lags and specifications. Family cap in particular is statistically significant
relative to the other models estimated in Table 5. In McKernan and Ratcliffe’s study,
family cap was very robust, with a positive coefficient and statistical significance.
Contrary, in this study, family cap is positive however, not statistically significant.
Intuitively, states with a family cap policy would have a positive effect on poverty
because a family cap policy does not raise family benefits when a new child is born.
Moreover, a welfare dependent household would be financially worse off. Similarly, the
remaining eligibility requirement welfare policies have the same expected signs as the
literature (Table 5, column 2) yet are statistically insignificant. Given these results and
after conducting a Wald test, it became apparent that there is not enough between and
within variation in these eligibility requirement welfare policies to impact poverty.
When examining the financial incentives to work related policies, six out of the
seven policy variables had the same expected signs but only two were statistically
significant. In particular, treatment of child support income is a categorical variable and it
is highly significant (P<0.01). Focusing on columns 1-2 only, the magnitude of treatment
of child support income suggests that going from a state where the pass-through of child
52
support income is below the AFDC limit (i.e. less than $50) to a state where the pass
through is above $50 decrease poverty for women heads of households by 0.63-3.2
percentage points, whereas, the overall official poverty rate (Table 5, Column 3)
increases by 0.95 percentage points. These initial results make sense for women-specific
poverty rates as more pass through income is alleviating women out of poverty.
However, the impact the pass-through policies have on poverty has a positive effect when
gender is not specified, indicating that the importance of pass-through child support
income policies may be washed away and ignored if gender is not considered from a
policy analyst perspective.
The state-specific minimum wage was not statistically significant in explaining
the women-specific poverty rates, columns 1 and 3 however, these results are consistent
with Gundersen and Ziliak (2000) and McKernana and Ratcliffe (2006), who find that a
higher state minimum wage increases poverty. In any case, both outcomes, a
higher/lower poverty rate are possible because of the labor market and consumer
interactions. For instance, the minimum wage depends on the price elasticity of demand
for labor. If demand is elastic then an increase in the wage will decrease the quantity
demanded of labor by a larger percentage, causing employment and income to fall.
However, if demand is inelastic then a decrease in employment will be more than offset
by the increased wage and poverty would decline as income increases. In these results it
is hard to identify which effect dominates because none of the coefficients are consistent
or significant.
53
Lastly, roughly half of the time limit policy variables were consistent with
McKernan and Ratcliffe’s results. Only the intermittent time limits and time limits
exemptions for ill or disadvantaged recipients were significant at the one-percent level
(Table 5, Column 3). According to McKernan and Ratcliffe, they are the only researchers
that have analyzed time limit policies and found statistically significant results. The
hypothesized effects for all of the time limit explanatory variables were ambiguous.
However, both columns two and three indicate that states with intermittent time limit
policies decrease poverty by 0.93-1.4 percentage points, with one percent significance.
This suggests that states with stricter time limit requirements decrease poverty. These
results make sense as, stricter policies that restrict the amount and/or time of welfare
benefits would motivate recipients to enter the labor force.
Overall these results indicate that specific policies have varying impacts on
poverty. Prior studies have mostly looked at overall welfare reform (i.e. TANF caseloads)
and have found contradicting results. Thus, these results shed some light on the fact that
welfare might have a direct impact on poverty. By examining specific welfare policies,
this study illustrates how different policies have varying effects on poverty. Moreover if
researchers simply estimate overall welfare caseloads, the actual impact may be
dismissed. Although the majority of the welfare policies examined did not reveal
significance when examined individually, a Wald test indicated that financial incentives
to work and time limit policies were consistently highly significant (p<0.0000). When
examining McKernan and Ratcliffe’s (2006) results, the largest limitation was contingent
on their 14 year sample and monthly varying dataset. Contrary, this study analyzed
54
annual data for nine years and appeared to have contradicting results due to insufficient
time-series data and the lack of time lags.
Table 5: Two-way State-level Random Effects Models
Random Effects
Control Variables
RGSP per capita (in $1,000s)
Unemployment rate
Eligibility Requirements
Family cap (0/1)
Vehicle exemption (in $1,000s)
EID (in $100s)
No explicit EID test
Financial Incentives to Work
Max monthly benefits (in $100s)
EID during month 12 (percent)11
State-level minimum wage
Most severe sanction amount ($)
Most severe sanction duration (0-5)
Treatment of child support (0-2 scale)
State average EITC (in $100s)
Time Limits (Binary Variables)
Duration of lifetime time limits (12m)
No time limits
Intermittent time limit
Time limit exemption for illness
Time limit exemption for child (m)
Time limit extension if cooperating
Time
Years
11
Women
Poverty
Rate
(1)
-0.0551
(0.041)
0.479**
(0.221)
McKernan
&
Ratcliffe
(2)
-0.001
(0.000)
0.5000
(0.388)
Official
Poverty
Rate
(3)
-0.0569**
(0.0263)
0.340***
(0.0644)
Experimental
Poverty
Rate
(4)
0.0117
(0.010)
0.00863
(0.0188)
Marginal
Effect
Rate
(5)
0.016
(0.012)
0.00724
(0.0188)
0.431
(0.625)
-0.0654
(0.067)
0.118
(0.082)
0.701
(0.784)
1.300**
(0.422)
-0.0310
(0.044)
0.1830
(0.372)
0.8510
(0.540)
0.227
(0.443)
0.0135
(0.023)
0.148*
(0.077)
0.105
(0.559)
-0.0927
(0.118)
-0.0012
(0.008)
0.120
(0.129)
-0.833
(1.548)
-0.0971
(0.125)
-0.0005
(0.008)
0.626
(0.581)
-1.455*
(0.764)
-0.127
(0.132)
-6.338*
(3.694)
0.216
(0.196)
-0.238
(1.609)
0.109
(0.104)
-3.176***
(0.439)
0.186
(0.301)
-0.4200
(0.409)
0.1800
(0.222)
0.6220
(0.366)
-0.004*
(0.002)
0.3270
(0.1740)
-0.628*
(0.239)
0.1270*
(0.0680)
0.0288
(0.061)
-7.833**
(3.268)
-0.0485
(0.0445)
-2.872
(2.052)
0.0301
(0.0399)
0.945***
(0.0911)
-0.0105
(0.194)
-0.0923
(0.089)
8.263
(7.100)
-0.00575
(0.0183)
1.575
(2.669)
0.0181
(0.0132)
0.0442
(0.0410)
0.446*
(0.252)
-0.0886
(0.0857)
1.692
(3.641)
-0.00552
(0.0190)
2.606
(1.653)
0.0177
(0.0134)
0.0488
(0.0391)
0.493*
(0.29)
0.0541
(0.034)
2.617
(2.139)
-0.690
(0.908)
0.176
(0.120)
0.0144
(0.025)
0.406
(0.941)
Annual
2001-09
0.006
(0.0200)
-2.199
(1.411)
-1.431*
(0.589)
1.297*
(0.512)
0.02
(0.023)
-0.066
(0.571)
Monthly
1988-02
-0.00109
(0.0085)
-0.821
(0.685)
-0.930**
(0.453)
-0.0746**
(0.0291)
0.00101
(0.0043)
0.307
(0.429)
Annual
2001-09
0.00243
(0.0023)
-0.0710
(0.123)
-0.0221
(0.0860)
0.0673***
(0.0115)
8.16e-05
(0.0012)
-0.0370
(0.125)
Annual
2001-09
0.00267
(0.0022)
0.0151
(0.123)
-0.0254
(0.0937)
0.0665***
(0.0123)
0.00057
(0.0001)
-0.0202
(0.111)
Annual
2001-09
McKernan & Ratcliffe (2006) were able to derive a non-percent value for this policy variable; however
their methodology was not fully disclosed.
55
Overall
With-in
0.296
0.449
0.579
0.327
0.418
0.719
0.145
0.146
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
N=384 (McKernan & Ratcliffe (2006) sampled 51 states, N=7,533), 48 states, models include time effects.
Time-specific effects are included.
6.2 – Experimental Measures: Two-way Fixed Effects Logit Model
This next section focuses on the probability of a household being debt poor. Five
experimental poverty logit models with household entity fixed and random effects were
estimated. Table 6 (columns 1-4) provide the results for the women-restricted poverty
outcomes (i.e. single, non-white, head of household women with an education level less
than a college degree and at least two dependents). Table 6, column 5 summarizes the
results for the unrestricted household fixed effect logit model (i.e. not women-specific).
This model, denoted as experimental poverty measure in Table 5 is the experimental debt
poor measure for the entire sample. When estimating the same debt poor measure in the
unrestricted sample (Column 4, Table 5) the estimated coefficients were very robust
relative to the restricted model (Column 5, Table 6). Overall the unrestricted and
restricted fixed effect models are similar. However, ‘Single1’ appears to be a stronger
specification contrary to the remaining four specifications and, real gross state product
(RGSP), unemployment, Gini Coefficient and all of the welfare policy variables
estimated coefficients are consistent with theory.
The estimated coefficients of the logit model require transformation because they
are not intuitive. In particular, the coefficients represent changes in the logit for each unit
change in the predictor. Accordingly, the odds ratios (OR) were estimated, Table 7
summarizes the odds ratios for each explanatory variable described in Table 6. An odds
56
ratio is the odds of an outcome in one group relative to the odds of an outcome in another
group and it is derived by taking the expediential value of the estimated logit coefficients.
Unlike probabilities, the odds ratio can be greater than one because it is a ratio (i.e. varies
between zero and infinity). According to Table 7, overall, the majority of the odds ratios
were at or below one, indicating that the odds of poverty with exposure to most outcomes
are slightly lower or relatively the same. Moreover, only 3 out of the 16 explanatory
variables had an odds ratio greater than one.
When considering Table 6, only columns 1 (single female heads of households)
and 3 (single female heads of households with at least some) had robust results that were
consistent with theory. When considering race and the unrestricted debt poor sample,
columns 2 and 5, the results were not intuitive. In particular, the unemployment rate had a
negative relationship with poverty suggesting that, a one percent increase in the
unemployment rate causes poverty to decrease. Even though the results in columns 2 and
5 were not statistically significant, the estimated coefficient poses endogeneity issues.
Lastly, column 4 also had results that paralleled columns 2 and 5 however; it had a lot
more statistically significant coefficients. Column 4 combines all of the single female
head of household characteristics, i.e. single non-white female heads of households with
some college and at least two children, single 4. Given these mixed and contradicting
results, this section focuses on column 3 because it is the most robust model.
Similarly to the state-level entity effect poverty rate models, an F-test was
estimated. All of the control variables, financial incentives to work and time limit policy
variables had a strong joint significant (p<0.000). However, the eligibility requirement
57
policy variables were not. Unlike the random effect model, estimated in section 6.1 and
summarized in Table 5, the fixed effect model drops collinear variables. As a result, half
of the eligibility requirement variables were dropped from all five logit fixed effects
models. Roughly half of the estimated coefficients in Table 6 column 3 were statistically
significant with 95 percent confidence intervals. The remaining half had the same
expected signs as McKernan and Ratcliffe’s (2006) study, with the exception of time
limits for children under ‘x’ amount of months.
Referencing the statistically significant odds ratios on Table 7, the Gini
coefficient was the strongest control variable observed in these results. Given the
magnitude of the Gini coefficient odds ratio, (exponential value of 43.57= 8.36E18), a
different approach was taken in order to better interpret the impact that the Gini
coefficient had on the probability of single female heads of households with some college
and at least two children being debt poor, single 3. Moreover, in order to better interpret
the effect that the Gini coefficient has on debt poor single female households with at least
two dependents predictors, the Gini coefficient was standardized. Standardizing the Gini
coefficient (mean= 0, standard deviation =1) provides a means for comparing the effect
of variables measured in different metrics (Long and Freese, 1997). In this case, the Gini
coefficient is a fraction between zero and one. Generally speaking, the Gini coefficient
varies per year and by state. Moreover, the results in Table 7 indicate that a one standard
58
deviation increase in the Gini Coefficient, on average, is three times a standard deviation
in the odds ratio of being debt poor12.
According to Szklo and Nieto (2007) the higher the confidence intervals, the more
likely the odds ratios are estimated poorly as confounding variables are highly likely to
be affiliated. Given these results and the results from section 6.1, there is definitely an
endogeneity problem in this analysis. Whether it is a confounding variable or strong seam
bias, measurement error, or other external sampling issues, future studies need to
consider the various confounding variables associated with empirical analyses that
investigate poverty as an outcome. In any case, this section will briefly discuss the results
of Single1 only, because it is the only specification with results consistent with theory.
There were six statistically significant welfare policies and economic control
variables where exposure did not affect the outcome. These include RGSP per capita,
vehicle exemption assets, maximum monthly benefits, average annual EITC filings,
duration of time limits, time limit exemptions for children under ‘x’ months, and time
limit extensions for co-operating recipients. According to the correlation matrix all of the
explanatory variables had moderate to low correlations. Time limit policies and economic
control variables, in particular, had the largest correlation coefficients (i.e. 0. 46).
Additionally, there were no obvious implications of multicollinearity as the largest
correlation coefficient was 0.699 (i.e. annual average unemployment rate and the
minimum wage). Overall the Gini coefficient and family cap policies had exposures
associated with higher odds of being in poverty (i.e. debt poor).
12
To put it another way, a one-percentage point increase in the Gini coefficient, on average, leads to a 27
standard deviation increase in debt poor single-female households with at least two dependents.
59
When considering most severe duration of time limits, going from a state with no
actual sanctions, just warnings, to a state where the sanctions are permanent (i.e. over 36
months) nearly doubles odds of a single female head of household with at least two
dependents being debt poor. Additionally, a state where the pass-through of child support
income is less than $50, to a state where the pass through is above $50 is 20 percent more
likely to be debt poor. However, this odds ratio is not statistically significant and has an
estimated sign that is against intuition. More money should reduce the probability of a
household from being debt poor.
Lastly, the time limit variables had results that were fairly consistent overall with
the literature. Columns two through five in Table 6 have similar coefficients with the
same estimate signs across all variables. Additionally, these results have the same
estimated signs as the state-level random effect model (i.e. Table 5, column one). States
with time limits exemptions for ill and/or disadvantaged households barely increases the
odds of being debt poor (OR=1.01). Whereas women households within states with time
limit exemption for children under a certain amount of months have lower odds of being
debt poor, with marginal impacts. Moreover, the odds of exposure to debt poor decreases
as a child become older. For instance, a female headed household with a child that is one
month relative to a six month child decreases the odds of being debt poor by nearly 20
percentage points (i.e. 0.94 odds ratio to 0.75). Intuitively this makes sense as a child
ages, because a single female head of household can return to the workforce and increase
their earned income.
60
Table 6: Household-Level Fixed Effects Logit Model
Household Fixed Effects
Control Variables
Gini Coefficient (σ)
Fair Market Rents, (g)
RGSP per capita ($1k)
Unemployment rate (%)
Eligibility Requirements
Family cap (0/1)
Vehicle exemption ($1k)
Financial Incentives to Work
Max monthly benefits
Minimum wage
Most severe sanction duration (0-5)
Treatment of child support (0-2)
State Average EITC
(1)
Single 1
(2)
Single 2
(3)
Single 3
(4)
Single 4
(5)
Debt Poor
52.27***
(19.69)
-0.00191
(0.00178)
-1.98e-04***
(6.00e-05)
0.00815
(0.0366)
59.91***
(22.23)
0.000299
(0.00209)
-1.77e-04**
(7.18e-05)
-0.00902
(0.0411)
43.57**
(20.20)
-0.00167
(0.00180)
-1.92e-04***
(6.10e-05)
0.00507
(0.0373)
47.62**
(20.73)
-0.00773***
(0.00213)
-3.37e-04***
(7.34e-05)
-0.0854*
(0.0486)
61.98***
-16.55
-0.0029**
-0.00139
9.69e-1**
(4.07e-05)
-0.0134
(0.0279)
0.420**
(0.168)
-4.84e-05**
(2.25e-05)
0.126
(0.190)
-1.99e-05
(2.56e-05)
0.336*
(0.173)
-5.40e-05**
(2.32e-05)
-0.203
(0.203)
-3.47e-05
(2.80e-05)
0.230*
-0.125
-2.52E-05
-1.76E-05
-0.00131***
(0.00046)
0.0223
(0.0430)
0.0161
(0.0140)
0.194
(0.166)
-0.00174
(0.00195)
-0.00116**
(0.00056)
-0.0385
(0.0597)
0.0471***
(0.0157)
0.177
(0.193)
0.000783
(0.00223)
-0.00129***
(0.00047)
0.0107
(0.0432)
0.0195
(0.0142)
0.192
(0.167)
-0.00334*
(0.00199)
-0.00291***
(0.00064)
-0.186***
(0.0613)
0.0633***
(0.0162)
0.414**
(0.174)
0.00143
(0.00241)
-0.000433
-0.000341
-0.0419
-0.0351
-0.0165
-0.0109
0.344**
-0.149
0.00124
-0.00149
Time Limits (Binary Variables)
Duration of lifetime time limits (m)
0.00690**
0.00640*
0.00761**
0.00269
(0.00345)
(0.00382)
(0.00366)
(0.00425)
Intermittent time limit
-0.0550
-0.198
-0.169
-0.212
(0.232)
(0.260)
(0.241)
(0.281)
Time limit exemption for illness
0.0376
-0.0933
0.0895
-0.727***
(0.173)
(0.227)
(0.176)
(0.240)
Time limit exemption for child (m)
-0.0292***
-0.0180
-0.0577***
-2.44e-05
(0.0107)
(0.0110)
(0.0142)
(0.0213)
Time limit extension coop
-0.450*
-0.434
-0.463**
-1.059***
(0.231)
(0.270)
(0.236)
(0.315)
Observations
9,808
7,334
9,344
7,096
Number of id
1,226
1,008
1,168
887
Pseudo R-Squared
0.0382
0.0155
0.0372
0.0389
Chi-Squared
303.9
93.22
283.9
229.3
Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
0.00322
-0.00264
0.0569
-0.183
0.340***
-0.116
-0.0180***
-0.00551
-0.335*
-0.178
17,648
2,206
0.0365
498.4
61
Table 7: Household-Level Fixed Effects Odds Ratios
Household Fixed Effects Odds Ratios
Control Variables
Gini Coefficient (σ)
Fair Market Rents, (g)
RGSP per capita ($1k)
Unemployment rate (%)
Eligibility Requirements
Family cap (0/1)
Vehicle exemption ($1k)
Financial Incentives to Work
Max monthly benefits
Minimum wage
Most severe sanction duration (0-5)
Treatment of child support (0-2)
State average EITC
Time Limits (Binary Variables)
Duration of lifetime time limits (m)
Intermittent time limit
Time limit exemption for illness
Time limit exemption for child (m)
Time limit extension coop
(1)
Single 1
(2)
Single 2
(3)
Single 3
(4)
Single 4
(5)
Debt Poor
3.760
0.998
1.000
1.008
4.220
1.000
1.000
0.990
3.080
0.998
1.000
1.005
3.360
0.992
1.000
1.089
4.740
0.370
1.000
0.987
1.522
1.000
1.13
1.000
1.399
1.000
0.816
1.000
1.259
1.000
0.999
1.023
1.016
1.214
0.998
1.000
0.96
1.05
1.19
1.000
0.999
1.011
1.02
1.212
0.997
0.997
0.83
1.065
1.513
1.001
1.000
0.959
0.984
1.411
1.001
1.007
0.946
1.038
0.971
0.638
1.01
0.82
0.91
0.98
0.65
1.008
0.845
1.094
0.944
0.629
1.003
0.809
0.483
1.000
0.347
1.003
1.059
1.405
0.982
0.715
Using household level fixed effects to estimate the probability that a household is
debt poor did not reveal statistically sound results. Although most of the estimated effects
that the welfare policy variables had on the predictor were consistent with the literature,
the impacts were marginal and not robust. When considering a female restricted
predictor, the results were intuitive and consistent with theory however, debt poor as a
predictor, revealed strong endogeneity issues.
After consideration of the random effects and fixed effects for both state and
household-level entities, the fixed effects appear to be stronger as the estimated
coefficients have signs consistent with theory. Overall the derived poverty rates had
smaller within and between variations than the U.S. Census poverty rates however, they
produced estimates that were consistent with theory (i.e. RGSP). Although, many of the
welfare policy estimated coefficients changed signs when the U.S. Census poverty rate
62
was the dependent variable, both of the state-level entity effects and household-level
experimental models had nearly identical coefficients. The biggest limitations in these
results are contingent on the average annual unemployment rate for the household-level
entity fixed effects logit models. All of the unemployment rates were negatively related
to poverty. These results are outlandish and strongly suggest that endogeneity issues
impact the results.
Omitted variable bias, confounding variables, seam bias and measurement error
are highly likely culprits of the wrong estimated signs. Because this study used survey
data, seam bias is hard to control for. Moreover, considering ways to overcome sampling
issues like measuring seam bias and researching possible confounding factors affiliated
with poverty are needed in order to estimate a robust experimental model. Even though
these results are very limited, future studies need to consider the effect specific welfare
policies have on alternative poverty measures. Additionally, estimating specific welfare
policies revealed to have mixed effects on poverty. These effects, in most cases, differed
between the poverty outcome specifications suggesting that prior literature that strictly
examined the overall effect of welfare reform was inconsistent.
This study went beyond the scope of the literature and examined the effect
specific welfare policies had on alternative poverty measures using state-level,
household-level, and women-specific random effects and fixed effects entities. In many
cases, type two error was present as estimated coefficients were sought out to be highly
statistically significant but with the wrong sign. In particular, in Table 7, the annual
average unemployment rate was statistically significant at the one-percent level however;
63
the results indicated that a one-percentage point increase in the state’s average annual
unemployment rate consistently lowers the odds of households (gender/non-gender
specific) being debt poor. The odds ratios were between 0.88 -1.00. Moreover, these
contradicting results suggest that model misspecification and confounding factors are
distorting the actual effects.
64
Chapter 7
CONCLUSION
According to Pressman and Scott (2009), over the past 20 years total consumer
debt has risen at an annual rate of 4.1 percent, which is much higher than the annual
growth rate of median household income, 0.6 percent. Given this large rise in consumer
debt the official poverty threshold must be revised. Merely adjusting the poverty
threshold by changes in inflation does not justify the true number of people struggling
with debt, and hovering right above the threshold to make ends meet. Policy analyst
should be forward looking and consider the growing trend of consumer debt as it may
impose a larger welfare dependent society. This study calculates an experimental poverty
measure, debt poor, which accounts for interest payments on non-mortgage debt and
estimates the effect 17 specific welfare policies have on the probability of debt poor
households. Although the results of this thesis are not robust or statistically sound,
welfare analyst should strongly consider the acceleration of consumer debt and its
impacts on society.
This analysis uses the Panel Study of Income Dynamics family-level data and the
Federal Reserve Board G. 19 interest rates to estimate state specific experimental poverty
measures that vary by household demographics. Data for specific welfare policies and
economic predictors were acquired from the Urban Institute’s Welfare Rules Database,
the Department of Labor, Bureau of Labor Statistics, and the Bureau of Economic
Analysis. The bulk of the literature examines the overall impact that welfare reform has
on total caseloads. However, this study contributes to the literature by considering the
65
effect 17 specific welfare policies have on a non-traditional poverty outcome that
considers interest payments on total non-mortgage debt.
State-level entity and household-level fixed and random effects models were
estimated to control for unobserved differences across groups. When examining the statelevel entity and household-level entity effects for the experimental poverty measures, the
Hausman test had mixed results. Consequently, a robust fixed effects logit model with
clustered standard errors was estimated. On one hand, the fixed effects for the individualentity specifications were efficient. Contrary, the fixed effects for the state-level entity
specification were inefficient. Such finding were also found in the literature, as a result,
the state-level entity effect model used random effects to explain the variation specific
welfare policies had on five different poverty rates. On the other hand, the logit model
used fixed effects to estimate the probability of being debt poor for female specific and
non-gender specific households.
The results suggest that most of the individual welfare policy variables were
statistically insignificant for both state-level and household-level entity effects. However,
after examining the Wald test, financial incentives to work and time limit welfare policies
had large joint statistical significance. The overall examined welfare policies were
consistent with McKernan and Ratcliffe’s (2006) study. Only a couple time limit policy
variables had contracting results. Given that estimating the effect time limit policies have
on poverty; these results indicate that future studies need to examine time limits more
thoroughly.
66
In order to interpret past monetary values, in current terms, all monetary variables
were adjusted for inflation using the CPI. However, most of the policy variables were
binary, and lack sufficient variation across time. Moreover, given the lack of variation,
the fixed effects model might be inappropriate for estimating the effect welfare policies
have on debt poor and could potentially lead to insignificant results.
For robustness checks, a few fixed effects models were estimated with only
caseloads (TANF) and economic control variables, omitting the specific welfare policy
variables. Overall the change in 𝑅 2 was negligible and the coefficients were robust across
specifications. However, even though the results were robust, the experimental poverty
measures that considered individual-level entity effects revealed the most specification
errors. The occurrence of endogeneity was obvious as the coefficient on annual
unemployment rate was consistently negative and in some cases statistically significant.
Moreover, estimated coefficients with the wrong hypothesized signs suggest endogeneity
issues, such as measurement errors and omitted variable bias. Perhaps including valid
instrumental variables would help alleviate the observable issues with the results.
According to economic theory, good instruments are natural occurring shocks, since this
sample captures the 2008 housing crisis, introducing shock variables that accounts for the
crisis might serve valid instruments (i.e. changes in predatory lending, house
foreclosures, and mortgage debt).
Prior literature examining welfare reform and the prevalence of poverty is very
limited and in many cases the consensus is mixed. According to McKernan and Ratcliffe
(2006), the effect that specific welfare policies have on the poverty rate is not extensive.
67
The bulk of the literature focuses on analyzing caseloads (e.g., Council of Economic
Advisers 1999; Danielson and Klerman 2004; Grogger 2004a-b, Moffitt 1999; &
Zedlewski 2001) and the effect welfare reform has on employment, earnings, income and
other outcomes. After the 1995 National Academy of Sciences published their report on
measuring poverty, the literature on estimating experimental poverty measures
significantly increased, yet poverty as an outcome is not commonly seen in the literature.
Moreover, this study contributes to the literature by calculating a new experimental
poverty measure that adjusts a household’s total annual income by the interest payments
made on non-mortgage debt.
There were many limitations to this analysis hence, from a policy perspective; the
results should be taken lightly. In short, it may appear that this study argues that
households with a tremendous amount of consumer debt should be considered poor and
thus receive government aid. However, consumer debt is sometimes acquired by
individuals who purchase big ticket items and are not struggling to make ends meet.
Consumer debt is used for establishing credit in most cases; hence people rack up debt in
hopes to increase their credit scores. Furthermore, because I was not able to decipher
between the households that were debt dependent with those who were purchasing big
ticket items and increasing their credit accessibility, this debt poor measure is not ideal.
In any case, the purpose of this study serves to motivate the discussion of the
accumulation of consumer debt and the inclining associated interest rates. When
considering welfare policy, analyst should be wary of the modern debt conundrum that
this thesis analyzes.
68
‘Buying on credit’ has grown tremendously this decade. Given this change in
society and the negative impact it poses on consumer’s disposable income, the cost of
serving non-mortgage debt should be considered when the U.S. Census estimates poverty
in America. Even though this study was unable to statistically demonstrate the effect
specific welfare policies have on debt poor households, this experimental approach to
poverty should not be ignored. Given the growth in consumer debt, the poverty definition
needs to consider all of the factors that reduce an individual’s disposable income.
Additionally, understanding how the continuous growth in consumer debt will impact
future welfare policies is critical for preventing the growth of welfare dependent states.
From a policy perspective managing consumer debt is vastly important as consumers
maybe more likely to file for bankruptcy and apply for welfare benefits. Future studies
should modify and expand this experimental approach to poverty and considering interest
payments on non-mortgage debt has large future implications.
69
Appendix A:
State-Level Entity Effects
State-level Fixed Effects Model
Control Variables
RGSP per capita (in thousands)
Unemployment rate
Average years of education
Gini coefficient
Fair market rents (%)
Eligibility Requirements
Family cap (0/1)
Vehicle exemption (in $1,000s)
EID (in $100s)
No explicit EID test
Financial Incentives to Work
Max monthly benefits (in $100s)
EID during month 12
State-level minimum wage
Most severe sanction amount ($)
Most severe sanction duration (0-5 scale)
Treatment of child support income (0-2)
State Average EITC (in $100s)
Time Limits (Binary Variables)
Duration of lifetime time limits (months)
No time limits
Intermittent time limit
Time limit exemption for illness
Time limit exemption for child (months)
Time limit extension if cooperating
Overall
With-in
Women
Official
Poverty
Rate
Official
Poverty
Rate
Experimental
Poverty Rate
Marginal
Effect Poverty
Rate
-0.0524
(0.04)
0.483**
(0.221)
-0.290
(0.302)
1.345
(2.848)
-0.0112*
(0.00590)
-0.0652
(0.043)
0.329***
(0.0689)
0.252
(0.502)
-0.00107
(0.00302)
0.020*
(0.011)
0.0125
(0.0188)
-0.414
(0.429)
0.0008*
(0.0005)
0.0143
(0.013)
0.00607
(0.0188)
-0.104
(0.212)
-0.434
(0.427)
0.00078
(0.0005)
0.483
(0.627)
-0.066
(0.068)
0.0108
(0.008)
0.673
(0.800)
0.0581
(0.426)
0.020
(0.025)
-
-0.110
(0.121)
-0.048
(0.076)
-
-0.0977
(0.125)
-0.0070
(0.007)
0.0087
(0.007)
-1.523*
(0.790)
-0.00140
(0.0013)
-5.908*
(3.525)
0.198
(0.194)
-0.324
(1.545)
0.102
(0.101)
-3.18***
(0.425)
0.0025
(0.0025)
0.00023
(0.0007)
-0.0216
(0.0526)
0.0620*
(0.0318)
0.935***
(0.103)
-0.0029
(0.0037)
-0.0009
(0.0009)
-0.00702
(0.0202)
0.0193
(0.0130)
0.0539
(0.0412)
0.0047*
(0.0028)
-0.0009
(0.0009)
1.266
(3.821)
-0.00434
(0.0181)
3.003*
(1.758)
0.0172
(0.0133)
0.0480
(0.0387)
0.0049*
(0.0029)
0.0540
(0.0336)
2.609
(2.116)
-0.662
(0.919)
0.176
(0.121)
0.0110
(0.0250)
0.386
(0.935)
0.299
0.415
-0.00266
(0.0087)
-1.065**
(0.485)
-0.907**
(0.439)
-0.0820**
(0.0309)
0.00025
(0.0048)
0.238
(0.453)
0.0529
0.721
0.00250
(0.0023)
-0.0120
(0.123)
-0.0241
(0.0892)
0.0674***
(0.0119)
0.00051
(0.0012)
-0.0345
(0.123)
0.0332
0.146
0.00265
(0.0025)
0.0114
(0.124)
-0.0249
(0.0939)
0.0661***
(0.0124)
0.0006
(0.001)
-0.0223
(0.110)
0.284
0.145
70
Appendix B:
Additional Tables
Eligibility Requirements (4)
Variable
Metric Definition
wf_famcap
(0/1)
Equals 1 if State has family cap provision.
wf_veh
($)
Indicates the portion of a recipient’s
vehicle fair market value that is excluded
from welfare benefit requirements.
Earned Income
wf_eid
($)
Indicates the three month maximum
Disregards (EID)
amount of total monthly earned income
disregarded
EID Remainder
wf_eidr
(%)
Indicates the nine month maximum
percent of earned income disregarded
from earned income (i.e. after
disregarding a portion of earned income,
wf_eid, a percentage of total monthly
earnings is disregarded for the remaining
9 months).
No Explicit EID Tests
wf_eidtest
Equals 1 if the state does not have any
explicit EID test for eligibility purposes.
Financial Incentives to Work (7)
Policy Name
Variable
Metric Definition
Most Severe Sanctions
wf_sevamou
($)
Indicates the max monetary loss of
for Non-Compliance
nt
benefits due to non-compliance.
Most Serve Sanctions for wf_sevmont
(#)
Indicates the duration of severe sanctions
Non-Compliance,
hs
for non-compliance. Values are
Duration
categorical and range between 0-36
months. If value equals,
0 – Indicates a warning, state has no
sanctions
1 – Indicates 1 month sanctions
2 – Indicates 2-5 month sanctions,
reapply
3 – Indicates 6-11 month sanctions
4 – Indicates 12-36 month sanctions
Treatment of Child
wf_childsinc (0-2) 1: if wf_childsinc < $50
Support Income
2: if wf_childsinc = $50
3: if wf_childsinc > $50
Maximum Monthly
wf_maxben
($)
Indicates the states maximum monthly
Benefits
benefits for welfare recipients.
Average Earned Income
Aveitc
($)
Indicates the state average EITC benefits
Tax Credit (EITC)
State-Level Minimum
min_wage
($)
State specific minimum wage, all states
Wage
without a minimum wage are set equal
zero
FLSA Minimum Wage
fedFLSA
($)
Federal level minimum wage
Policy Name
Family Cap
Vehicle Asset
Exemption
71
Policy Name
No Time Limits
Intermittent Time
Limits
Time Limits for Ill
Members
Time Limits for
Cooperation
Duration of Time
Limits
Time Limit Exemption
for Dependents
Fair Market Rents
(FMR)
Gini Coefficient
State Average Annual
Unemployment Rate
Time Limits (6)
Variable
Metric Definition
tl_notl
(0/1)
Equals 1 if State does not have time limits
tl_interm
(0/1)
Equals 1 if State has intermittent life time
limits
tl_ill
(0/1)
Equals 1 if State has any type of time limit
exemption for either ill/ incapacitated
recipients or caring for ill/incapacitated
individuals
tl_coop
(0/1)
Equals 1 if the state extends time limits
for recipients who are unemployed and
cooperating with the welfare
requirements.
tl_months
(m)
Indicates a state’s maximum number of
months
tl_child
(m)
Indicates if the state has time limit
exemptions for recipients with dependents
under ‘x’ months of age. The months of
the dependent are reported, (i.e. if a child
is 4 months old then the variable equals 4
for that state.
Controls (3)
fmr
($)
Indicates the growth rate in a state’s FMR
for a two-bedroom house, and is used as a
proxy for cost-of-living over time.
gini
(#)
Indicates the income inequality level in
each state. It is an index ranging from [01], perfect inequality denoted, 1.
aaunemp_r
(#)
Annual average state unemployment rate.
*Metric indicates binary variables (0/1), dollar values ($), percents (%), &numerical values (#) i.e. index,
range, duration
**Unit of measure: ith head of household
Variables
Real Total Income
Real Total Debt
Interest Payments
Adjusted Income
Race: Non-white
Gender: Female
Average years of Edu
Average Age
Obs.
76,914
76,914
76,914
76,914
76,914
76,914
76,914
76,914
Mean
74,158.6
9,246.6
1,292.3
72,866.4
0.4
0.3
12.4
44.0
Std. dev
85,804.6
27,989.9
3,942.8
85,449.7
0.5
0.5
3.1
9.0
Min
-115,731.6
0
0
-115,731.6
0
0
0
21
Max
2,392,281
2,200,000
314,820
2,392,281
1
1
17
91
72
Variables
Gini Coefficient
FMRs
ln(FMR)
Average EITC
ln(EITC)
Annual Average Unemployment
Rate
ln(RGSP)
Variables
Obs.
Mean
76,914 0.45
76,914 605.74
76,914 6.37
76,914 315.65
76,914 5.52
Std. dev
0.04
168.91
0.25
228.66
0.69
Min
0.01
370.26
5.91
39.60
3.68
Max
0.54
1324.00
7.19
1819.16
7.51
76,914
5.28
1.63
2.59
13.42
76,914
11.93
0.94
10.03
14.48
Metric*
Obs.
Mean
Std. dev
Min
Max
Eligibility Requirements (4)
Family Cap
(0/1)
76,914
0.43
0.50
0
1
Vehicle Assets Exemptions
($)
76,914
3,631
4,760
0
18,100
EID
($)
76,914
70.87
81
0
330
EID Net Income Tests
(0/1)
76,914
0.439
0.496
0
1
Financial Incentives to Work (8)
State Minimum Wage
($)
76,914
5.13
1.74
0
8.55
EID During Month 12
(%)
76,914
0.13
0.16
0
0.56
Average Earned Income Tax
($)
76,914
289
210
33
1,505
Credit
Severe Sanctions Non($)
76,914
0.86
0.26
0
1
Compliance
Duration of Severe Sanctions
(m)
76,914
1.91
0.962
0
4
Permanent Sanctions
(0/1)
76,914
0.13
0.31
0
1
Treatment of Child Support
(#)
76,914
0.03
0.24
0
2
Income
Maximum Monthly Benefits
($)
76,914
697
280
205
1,430
Time Limits (6)
No Time Limits
(0/1)
76,914
0.069
0.253
0
1
Duration of Time Limits
(m)
76,914
50
20
0
60
Intermit Time Limits
(0/1)
76,914
0.174
0.379
0
1
Time Limits for Co-operating
(0/1)
76,914
0.244
0.430
0
1
Time Limits for Ill Members
(0/1)
76,914
0.550
0.498
0
1
Time Limits for Dependents <'x'
(m)
76,914
4
12
0
60
Months
*Metric indicates binary variables (0/1), real 2009 dollars ($), range of numbers (#), number
of months (m), & percents (%)
73
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