Work Incentives and the Food Stamp Program (Hoynes and Schanzenbach, NBER Working Paper 16198)
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NBER Working Paper No. 16198, Work Incentives and the Food Stamp Program, by Hilary Williamson Hoynes and Diane Whitmore Schanzenbach, dated July 2010. The abstract states that the authors use the cross-county introduction of the program in the 1960s and 1970s to estimate its impact on the extensive and intensive margins of labor supply, earnings, and family cash income. It states that they find modest reductions in employment and hours worked when food stamps are introduced, and that the results are larger for single-parent families. The introduction states that food stamps is the largest cash or near cash means tested transfer program, with spending in 2009 of 50 billion dollars.
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NBER WORKING PAPER SERIES
WORK INCENTIVES AND THE FOOD STAMP PROGRAM
Hilary Williamson Hoynes
Diane Whitmore Schanzenbach
Working Paper 16198
http://www.nber.org/papers/w16198
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
July 2010
We are grateful to Bob Schoeni and Donna Nordquist for help with the PSID. Alan Barreca and Rachel
Henry Currans-Sheehan provided excellent research assistance. Schanzenbach thanks the Joint Center
for Poverty Research USDA Food Assistance and Nutrition Research Innovation and Development
Grants in Economics Program and the Population Research Center at the University of Chicago for
generous financial support. The views expressed herein are those of the authors and do not necessarily
reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peer-
reviewed or been subject to the review by the NBER Board of Directors that accompanies official
NBER publications.
© 2010 by Hilary Williamson Hoynes and Diane Whitmore Schanzenbach. All rights reserved. Short
sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided
that full credit, including © notice, is given to the source.
Work Incentives and the Food Stamp Program
Hilary Williamson Hoynes and Diane Whitmore Schanzenbach
NBER Working Paper No. 16198
July 2010
JEL No. H31,J22
ABSTRACT
Labor supply theory makes strong predictions about how the introduction of a social welfare program
impacts work effort. Although there is a large literature on the work incentive effects of AFDC and
the EITC, relatively little is known about the work incentive effects of the Food Stamp Program and
none of the existing literature is based on quasi-experimental methods. We use the cross-county introduction
of the program in the 1960s and 1970s to estimate the impact of the program on the extensive and
intensive margins of labor supply, earnings, and family cash income. Consistent with theory, we find
modest reductions in employment and hours worked when food stamps are introduced. The results
are larger for single-parent families.
Hilary Williamson Hoynes
Department of Economics
University of California, Davis
One Shields Ave.
Davis, CA 95616-8578
and NBER
hwhoynes@ucdavis.edu
Diane Whitmore Schanzenbach
School of Education and Social Policy
Northwestern University
Annenberg Hall, Room 205
2120 Campus Drive
Evanston, IL 60208
and NBER
dws@northwestern.edu
I. Introduction
A central question in public finance, one that has generated decades of research, is how tax
and transfer programs affect labor supply. The early literature typically estimated cross sectional
models thereby ignoring the endogeneity of net wages, net income, and selection into transfer
programs. Concerns about biases from these cross sectional approaches led to methodological
innovations, in particular structural modeling in the presence of kinked budget constraints.1
Structural approaches had their own limitations, including concerns about sensitivity to choice of
utility function, stochastic assumptions, and so on. The “new public finance” approach followed,
with a reliance on using policy-induced variation and quasi-experimental methods to analyze impacts
on labor supply. Quasi-experimental approaches have been used to analyze the impact of wide range
of policies on labor supply such as federal taxes, the Earned Income Tax Credit, Medicaid, social
security, and welfare reform.
Our paper contributes to the literature on taxes, transfers and labor supply by using a quasi-
experimental approach to estimate the impact of the food stamp program (FSP) on labor supply. The
FSP is a federal means tested program, providing benefits to buy food for families who are income
and asset eligible. Importantly, while the primary goal of the food stamp program is to increase food
consumption, Hoynes and Schanzenbach (2009) show that most households are inframarginal and
thus food stamp benefits can be treated as an income transfer.
Food stamp benefits are the fundamental safety net in the U.S., being the only public
assistance program that is available to all family types (most programs are targeted on female headed
households, children, or the elderly). In fact, Food stamps is the largest cash U.S. cash or near cash
means tested transfer program with spending in 2009 of 50 billion dollars compared to 30 billion for
1
For example, innovating papers that use structural models applied to kinked budget constraints include Burtless
and Hausman (1978) analyzing the negative income tax, Hausman (1980) on the federal income tax, and Moffitt
(1983) on AFDC.
1
TANF and 40 billion for the federal EITC.2 The importance of the FSP program is particularly
apparent in the current great recession, where more than 1 in 9 persons is receiving food stamps.
Today, food stamp benefits (recently renamed Supplemental Nutrition Assistance Program or
SNAP benefits) are paid via electronic debit card that can be swiped at the checkout line like a debit
card, and can be used to purchase most grocery store food goods. Recipients are allotted a benefit
amount B equal to the difference between the federally defined maximum benefit level for a given
family size (i.e. G, the guarantee amount) and the amount that the family is deemed to be able to
afford to pay for food on its own according to the benefits formula (essentially 30 percent of cash
income, less some deductions). During the time period studied here, the program was slightly
different. Through 1978, there was a “purchase requirement” which allowed recipients to buy G
dollars worth of food stamp coupons for a price P set by a federal schedule and capped to be no
higher than 30 percent of income Y.3 The difference between G and P is called the “bonus
coupon amount” and is analogous to today’s benefit level. During this period, benefits were paid
out via coupons that were slightly smaller than dollar bills that could be used to purchase almost all
food goods at grocery stores.4
A central challenge for the empirical food stamp literature is that the program is federal and
exhibits no variation across states, which is an approach commonly used in the quasi-experimental
literature.5 Further, the universal nature of the program means there are no ineligible groups to serve
as controls, which is another common approach in the quasi-experimental literature. Instead, the
2
Food stamp program statistics are available at http://www.fns.usda.gov/pd/SNAPsummary.htm; AFDC/TANF
statistics are available at http://www.acf.hhs.gov/programs/ofa/data-reports/index.htm, and EITC program data is
available at http://www.taxpolicycenter.org/TaxFacts/index.cfm.
3
In practice, the price averaged about 25 percent of income for a family of 4. See Appendix Table 4 for the 1969
price schedule.
4
Benefits cannot be used for hot food intended for immediate consumption, vitamins, alcohol, pet food or paper
products. During the time period studied in this paper, they also could not be used to purchase food that was
“obviously imported”.
5
There was some cross-state variation in eligibility standards in the earliest years until federal standards were
adopted in the January 1971 amendments to the Food Stamp Act.
2
typical food stamp study in some way compares recipients to nonrecipients leading to a possible bias
if there is selection into program participation (Currie 2003). The small existing literature on the
labor supply effects of the food stamp program uses structural estimation with little attention to
exogenous variation in the program.6
In this paper, we take a very different approach to estimating the labor supply effects of the
FSP using the introduction of the program as it was phased in across U.S. counties over a relatively
gradual period. We utilize the natural experiment afforded by the nationwide roll-out of the modern
Food Stamp Program during the 1960s and early 1970s. Our identification strategy uses the sharp
timing of the county-by-county rollout of the FSP, which was initially constrained by congressional
funding authorizations (and ultimately became available in all counties by 1975). While the existing
literature limits attention to hours worked, we examine the impacts of the program on labor force
participation, annual hours, earnings, and total family cash income. Further, reflecting the universal
eligibility in the FSP, we examine impacts on all families, including married couples and female
headed households. Our “program introduction” research design has the appeal of relying on non-
marginal changes in incentives faced by consumers.7
Safety net programs, such as AFDC, TANF and food stamps, are designed to insure a basic
level of consumption in low-income families. Consequently, benefits in traditional income support
programs feature a guarantee—a benefit level if the family has no income. As earnings or income
increases, benefits are reduced leading to an implicit tax rate on earnings (called the benefit reduction
rate or BRR). As described above, benefits in the FSP take this form; for example in 2010 a family of
three has a food stamp guarantee of $526 per month and the benefit is phased-out using a benefit
6
These studies include Fraker and Moffitt (1988), Hagstrom (1996), and Keane and Moffitt (1998) and are
discussed below.
7
This “program introduction” research design has been taken in recent analyses of other social programs such as
Head Start (Ludwig and Miller 2007), Medicare (Finkelstein and McKnight 2008), Title I (Cascio et al. 2006), and
in our own prior work on the food stamp program (Almond, Hoynes and Schanzenbach 2010, Hoynes and
Schanzenbach 2009).
3
reduction rate of 30 percent.8 Notably, the benefit reduction rate in the food stamp program is lower
than the rate under the old AFDC program or most states’ TANF programs.9
As is well known, a family’s labor supply response to the income transfer program may
partially offset the income and consumption enhancing goals of the program. The guarantee produces
an income effect and the benefit reduction rate reduces the net wage leading to an income and
substation effect. Standard static labor supply theory predicts that the program will reduce labor
supply on both the extensive (employment) and intensive (hours conditional on work) margins. As a
result, it may cost more than $1 in income support payments to increase a low-income family’s
available cash and near-cash resources by $1.
We use data from the Panel Study of Income Dynamics (PSID) from 1968-1978 to examine
the impact of the FSP on labor supply, earnings, and income. We employ a difference-in-difference
model where the treatment is at the county level, with controls for county and year fixed effects and
state linear time trends. In this model, identification requires that there are no contemporaneous
county level trends that are correlated with food stamp introduction and family economic outcomes.
We also estimate a triple difference model that uses variation across subgroups with varying
propensities of being affected by food stamps. Our results are robust to adding controls for possible
confounders and we also present event study models that further support the validity of the research
design.
Overall, our results indicate that people behave as the theory predicts. In the pooled (full)
sample we find modest reductions in employment and hours worked after food stamp benefits are
introduced. The impacts are larger but also less precisely estimated when limited to female headed
households. We find no significant impacts of the FSP on earnings or family income.
8
There are additional deductions for dependent care, child support, medical costs, high housing costs, and 20
percent of earned income.
9
Up until 1967, the benefit reduction rate in the AFDC program was 100%. It was reduced to 67% in 1967, then
increased again to 100% in 1981. After federal welfare reform, and the conversion to TANF, there is substantial
variation across states in the programs’ BRR.
4
The remainder of the paper is as follows. Section II summarizes the prior literatures and
Section III provides a history and summary of the Food Stamp Program. Section IV describes the
expected effects of the FSP on labor supply and Section V describes the data. Section VI presents the
results and Section VII discusses the results in the context of the existing literature. Section VIII
concludes.
II. Prior Literature
While there is a sizable literature examining the impacts of the food stamp program on family
consumption, nutrition, and family well-being, there is little research examining its impacts on labor
supply. The prior literature, which is based mostly on structural estimation, finds modest impacts of
the FSP on labor supply.
The prior studies of the effect of FSP on labor supply include Fraker and Moffitt (1988),
Hagstrom (1996), and Keane and Moffitt (1998). Fraker and Moffitt (1988) use structural models and
kinked budget constraints to estimate the impact of food stamps on labor supply for a sample of
female heads of household. They specify a utility function and model the choice of hours of work
(zero, part time, full time) and participation in the AFDC and food stamp programs. Fraker and
Moffitt find that the FSP reduces hours of work by participants by 1 hour per week among food
stamp recipients, or since mean weekly hours worked for Food Stamp participants is about 9.5, a
9 percent reduction. Keane and Moffitt (1998) extend this paper, also looking at female headed
households, by simultaneously modeling AFDC, food stamps, Medicaid and housing benefits. They
find larger elasticities than Fraker and Moffitt, but do not report simulations for - effect of the overall
FSP on labor supply. Hagstrom (1996) estimates the impact of the FSP on married couple’s labor
supply using a multinomial logit model. He reports small impacts of changes in the food stamp
guarantee and BRR on labor supply but does not report simulations for the total effect of the FSP on
labor supply. Notably, all of these studies use cross sectional models where food stamp benefits do
5
not vary across families.10 Moffitt (2002) summarizes the literature by concluding “the Food Stamp
program has little effect on work disincentives.” In this paper, we find labor supply effects that are
larger than the prior literature.
The vast literature on labor supply effects of a variety of income transfer programs is well
summarized elsewhere (Moffitt 1992; Hoynes 1997; Moffitt 2002). The empirical literature on the
AFDC program may also be useful here because there is substantial overlap in participation between
the two programs. The general findings from the literature on the AFDC program (which provided
cash income support) are that AFDC reduces labor supply among program participants by 10 to
50 percent. In particular, the influential paper by Moffitt (1983) finds that annual hours worked by
AFDC recipients are 546 lower per year because of the program. This translates into a reduction of
208 hours per year among female headed households as a whole. The earlier work by Hausman
(1981) finds even larger effects, a reduction of 1024 hours per year for AFDC recipients. These
estimates suggest that income support programs can have large work disincentive effects. Compared
to the food stamp program, AFDC benefits and the benefit reduction rate are higher, thus we would
expect the work disincentive effects to be lower in the food stamp program.
III. Introduction of the Food Stamp Program
President Kennedy’s first executive order was to introduce the modern Food Stamp Program
by establishing eight county-level programs. The number of pilot programs grew to 43 by 1963. The
pilot programs were seen as a great success, and were credited for improving diets of low-income
families while also strengthening markets for farm commodities (Johnson, 1964). Lyndon Johnson
expanded the program and made it permanent when he signed the Food Stamp Act on August 31,
1964. The Act gave local areas the authority to introduce a federally funded FSP in their area.
10
The papers use data from a single year, so the food stamp schedule varies only by family size (which is a source
of variation not used in these papers). The effective benefit will vary by state since AFDC income is included in a
family’s countable income (and AFDC payments vary by state).
6
Counties introduced the program at a steady rate over the next decade, until Amendments to the Food
Stamp Act in 1973 required all remaining counties to adopt the program by 1975.
Figure 1 summarizes the overall pattern of FSP introduction. The figure plots the percent of
counties offering FSP, where the counties are weighted by their 1970 population. Note this is not the
food stamp caseload, but represents the percent of the national population that lived in an area
offering a FSP. The figure shows that there was a long ramp up period between 1964 and 1975,
leading to the eventual universal coverage of the FSP. For example in 1967 (our first year of income
data from our main data source – the PSID) a bit less than half of the population lived in counties
with FSP; by 1972 coverage rose to over 80 percent of the population. According to Berry (1984),
funding limits were a major factor in the timing that counties moved off of the waiting list and were
able to start up their program: “The program was quite in demand, as congressmen wanted to reap
the good will and publicity that accompanied the opening of a new project. At this time there was
always a long waiting list of counties that wanted to join the program. Only funding controlled the
growth of the program as it expanded” (pp. 36-37).
In this paper we rely on variation across counties in the timing of the original introduction of
the FSP to isolate the impact of the program on labor supply behavior. Consequently the causal
identification of the impact of FSP relies on the exogeneity of the county implementation of the
program. An important starting point is to recognize that counties applied for funds to start a local
food stamp program and were funded subject to current allocations by Congress. Notably, the county
application was voluntary.
In the early 1960s, some counties provided food aid to the poor via the Commodity
Distribution Program (CDP). The Federal government would purchase surplus commodities to
support farmers and distribute a portion of them directly to low-income families. The CDP was
criticized not only for being inefficient relative to normal market channels for distributing food, but
also because the timing of the goods distribution was irregular and only a narrow variety of
7
commodities were available.11 At the time the Food Stamp Act was signed, only about one-third of
families living in poverty were participating in the CDP program. Part of this was likely due to the
fact that many counties did not offer the program.12 In general, the CDP was preferred by agricultural
interests and the FSP was preferred by advocates for the poor.
Because of the 10 year ramp up to countrywide availability of the FSP, we can exploit that
variation as a natural experiment of exposure to the program. For this research design to be a valid
approach to studying the labor supply impact of the FSP, though, counties’ FSP start dates much be
exogenous to other underlying county-level trends in labor supply. To explore the exogeneity of FSP
adoption, we take several approaches. Below we show event-study analyses that document the
absence of pre-trends in our outcomes variables and show a sharp change in measures of labor supply
after the FSP is introduced. In addition, here we explore what county-level characteristics predict
when counties adopt the program.
Given the politics of the FSP relative to the CDP (Kotz 1969; Berry 1984), we expect that
northern, urban counties with large poor populations would likely adopt the FSP relatively earlier,
while Southern counties and those with strong agricultural interests in general are likely to adopt the
program later. To measure this, we take county-level characteristics measured in 1960 – that is,
before even the introduction of the pilot programs – and use them to predict the timing of a county’s
eventual adoption of FSP. The dependent variable in this analysis is the month and year of the
county’s food stamp start date expressed as an index equal to 1 in January 1961. The independent
variables include the percent of the 1960 population that lives in an urban area, is black, is less than
age 5, is age 65 or older, has income less than $3000 (in 1959$), the percent of the county’s land
used for farming, and log of the county’s total population (constructed from the 1960 Census of
11
The most frequently available commodities were flour, cornmeal, rice, dried milk, peanut butter and rolled wheat
(Citizens’ Board of Inquiry, 1968).
12
We have not been able to construct a consistent time series for county participation in the CDP, so we are unable
to use this information in the empirical analysis.
8
Population and Census of Agriculture). All regressions are weighted by the 1960 county
population.13
The results are presented in Appendix Table 1 and include specifications with and without
state fixed effects, and with and without the early pilot counties (which were clearly non-random).
Positive coefficients indicate that the county characteristic predicts a later FSP start date. We find
that counties that have a larger overall population and a higher share that is poor, black, very young,
or elderly implement FSP earlier. Consistent with the political history of the program, counties with
more land used for farming implement the program later. We also find that the relationship between
start date and county characteristics is weaker (in absolute value) in Southern counties. While these
results show statistically significant impacts of county characteristics in predicting the timing of the
introduction of FSP, the quantitative importance of these characteristics is small and most of the
variation remains unexplained. To control for possible differences in trends across counties that may
be spuriously correlated with the county treatment effect, we include interactions of the 1960 pre-
treatment county characteristics with time trends in all of our models (as in Acemoglu et al. 2004 and
Hoynes and Schanzenbach 2009). Note that the main effects of the 1960 county characteristics are
absorbed by the county fixed effects. The results are little impacted by the inclusion of these trends.
These findings are consistent with Berry’s characterization that the exact timing of FSP introduction
across counties was driven largely by fiscal constraints and not the lack of desire by counties to
introduce the program.
The FSP was introduced around the same time as other programs that were part of the federal
“war on poverty.” For example, this period included the introduction of Medicaid, Medicare, Head
Start, and the Supplemental Nutrition Program for Women, Infants and Children (WIC), and saw
expansions of AFDC, Social Security, and disability insurance programs. If these other programs
13
We drop observations from Alaska throughout the paper due to inconsistencies in mapping FSP service areas to
standard county FIPS codes. Here we also drop the (few) counties where the percent of land used for farming is
greater than 100 percent, and very small counties with population less than 1,000 because of missing data.
9
mainly varied at the state level, then our controls for state linear time trends or state-year fixed
effects should absorb their impacts. Nonetheless, to control for possible coincident expansions of
other programs we also include annual measures of county per-capita transfer payments for cash
income support, medical care, and retirement and disability programs (see data section below).
IV. Labor Supply Predictions of Food Stamp Introduction
Food stamp benefits have the structure of a traditional income support program, with a
guaranteed income benefit which is reduced with family income at the legislated benefit reduction
rate. We illustrate the labor-leisure tradeoff with and without food stamps in Figure 2. Like other
means-tested programs, the FSP alters the household’s labor-leisure tradeoff increasing after tax and
transfer income at earnings up to the breakeven point. In particular, the food stamp benefit is largest
at zero hours of work, and benefits are reduced as income and earnings are increased leading to an
implicit tax rate on earned income. The benefit reduction rate in the food stamp program is
30 percent, which is lower than other means tested transfer programs (e.g. 67 or 100 percent in
AFDC, the precursor to TANF).14
In Figure 2a, the x axis measures the amount of leisure consumed, and the y axis measures
total income including the cash value of in-kind transfers through the food stamp program.15 Prior to
the introduction of the FSP, the budget constraint is a straight line with a slope equal to the
individual’s wage W. The simple static labor supply model states that an individual maximizes her
utility subject to this budget constraint, and assuming a positive labor supply choice, chooses some
combination of consumption of goods and leisure at points illustrated for consumers with different
14
Today there is a deduction for 20 percent of earnings, and as a result the effective benefit reduction rate is lower
than 0.30. During the early years of the FSP, there was a “purchase requirement” which allowed recipients to buy G
dollars worth of food stamp coupons for a price P set by a federal schedule. The price increased by $3 for every
additional $10 in income (see Appendix Table 4 for the 1969 price schedule).
15
For simplicity we model food stamp benefits as cash in the amount of the face value of the coupons, and assume
there are no other welfare programs in place. We ignore the purchase requirement and model the program based on
the bonus coupon amount. We also ignore that F is flat across small ranges of incomes. Adding any of these to the
model complicates the graphs but does not change the prediction or the intuition.
10
preferences by A and A^, consuming leisure in amount and ^, respectively. If her offer wage is
below her reservation wage (the slope of the indifference curve at zero hours of work) then it will be
optimal to remain out of the labor force, as illustrated by point A (at maximum leisure choice , or
hours=0).
As illustrated in Figure 2b, the introduction of the FSP alters the budget constraint by adding
non-labor income G, and rotating the slope of the budget constraint to W(1-t) where t is the tax rate
on benefits as income increases (0.3 during this time period). For the individual supplying zero
hours of work and consuming only leisure, consumption opportunities increase by the FSP
“guarantee” amount G.
As is well known in the analysis of traditional income support programs, this combination of
a guaranteed income and benefit reduction rate leads unambiguously to predictions of reductions in
the intensive and extensive margins of labor supply. In this case, both the income effect of the benefit
as well as the income and substitution effect from the benefit reduction rate leads, unambiguously, to
a predicted decline in employment (extensive margin), hours worked (intensive margin), and (if
wages are fixed) earnings. In addition, family cash income (which as measured does not include food
stamp benefits) would also be predicted to fall. (Of course, family total after transfer income
including food stamps is likely to increase.)
Referring back to Figure 2, our representative individual who was, prior to the introduction of
the food stamp program, in the labor force and consuming at point A, is predicted to increase their
leisure (reduce their hours worked) choosing a consumption bundle A’ with leisure amount ' where
' . Alternatively, it is possible that the combination of the negative income and substitution
effects can push them out of labor market to point A^’.
As discussed above, the FSP is run by the U.S. Department of Agriculture, and throughout
most of its history the benefit and eligibility criteria have been uniform across states. Thus, at a point
11
in time, the guarantee and benefit reduction rate are constant across all eligible families of a given
family size.16 The prior literature examines the period after the FSP was expanded to a national
program. Without variation across states that is often utilized in analysis of government programs,
the literature relies on structural estimation where the emphasis is on identifying parameters of the
utility function. Despite no variation in the FSP program parameters G and t, by estimating the
parameters of the utility function in the presence of the budget constraint, the studies simulate the
effect of marginal changes to the FSP (change in G or t) or out of sample predictions of the total
effect of the FSP. Instead, we use the rollout of the FSP in a quasi-experimental difference-in-
difference design whereby we compare counties that have implemented the FSP to other counties
who have not yet implanted. We measure whether family labor supply responds to the introduction of
the program as predicted by economic theory. Notably, this is a non-marginal calculation; instead we
identify the labor supply with and without the program in place.
V. Data
In order to utilize the county-level variation in FSP rollout, we require a dataset that covers as
much of the rollout period as possible (1963-1975) and provides information on county of residence.
The Panel Study of Income Dynamics (PSID) is a panel data set that began in 1968 with a sample of
about 5,000 households. Subsequently all members and descendants of the original survey families
were re-interviewed annually. The original 1968 sample consists of two subsamples: a nationally
representative subsample of 3,000 households (Survey Research Center subsample) and a subsample
of 1,900 households selected from an existing sample of low income and minority populations
(Survey of Economic Opportunity subsample). To adjust for this nonrandom composition, the PSID
includes weights designed to eliminate biases attributable to attrition, and to the oversampling of low
income groups. All analyses use the weights provided by the PSID.
16
The food stamp guarantee, G, varies by size of family.
12
The central focus of the PSID is labor market and demographic variables, containing
substantial detail on income, employment, and family composition. From this, we measure the annual
hours worked and annual earnings over the previous year for both the household head and spouse (if
applicable), whether the family reports receiving any food stamp benefits, and total annual family
cash income (not including the value of food stamps). We use data from interview years 1968 to
1978.17 We stop the sample in 1978 because of a significant change in the structure of food stamp
payments that begins in 1979.18
We limit the sample to include families with a family head that is less than 65. We exclude
elderly families because they have lower food stamp takeup rates (see Appendix Table 2) and are less
attached to the labor market. Our nonelderly headed sample consists of 48,168 family-year
observations. In some specifications, we limit the sample to nonelderly heads with a high school
education or less (37,474 observations), female headed households with children
(7,280 observations), or nonwhite, female headed households with children (5,464 observations) to
represent samples with a higher FSP participation rates. Note, unlike other means tested transfer
programs, the FSP is available to married and unmarried families alike as long as they are income
eligible. Reflecting their lower family incomes, however, female headed households have
significantly higher eligibility and participation rates compared to their married counterparts.
Appendix Table 3 presents some basic descriptive statistics for the main analysis samples.
All dollar amounts are in 2005 dollars. Over 90 percent of heads report working at some point in the
previous year in the overall sample, with 1,947 average annual hours. Female headed households
report much lower rates of employment (71 percent overall, and 62 percent for nonwhite female
heads) and hours worked (1068 and 864, respectively).
17
We have to drop the food stamp variable for the 1969 interview year because the survey does not separate FSP
benefits from other sources of food assistance (e.g. commodity distribution program, other in-kind benefits) in that
year. This reduces our sample sizes when FSP participation is the dependent variable.
18
Most notably, the purchase requirement was eliminated. As a result, participants were simply given stamps worth
the bonus coupon amount B instead of having to purchase the entire G amount of food stamps at a discounted price.
13
The public-use release of the PSID includes only state level identifiers for each family-year.
Through special arrangement, we have obtained county level identifiers for each family in each year.
We merge the PSID data using these county identifiers with three additional data sets of county
variables. First, the key treatment or policy variable is the month and year that each county
implemented a food stamp program, which comes from USDA annual reports on county food stamp
caseloads (USDA, various years). We code the FSP policy variable equal to one if the respondent’s
county of residence has the program by January of that year.19 Second, the 1960 City and County
Data Book—which compiles data from the 1960 Census of Population and Census of Agriculture—is
used to measure economic, demographic, and agricultural variables for the counties pre-treatment
(before FSP is rolled out) period. In particular, we use: the percent of the 1960 population that lives
in an urban area, is black, is less than 5, is 65 or over, has income less than $3,000 (1959$), the
percent of land in the county that is farmland, and log of the county population. Finally, we use
Bureau of Economic Analysis, Regional Economic Information System (REIS) data to construct
annual, county real per capita income and government transfers to individuals, including cash public
assistance benefits (Aid to Families with Dependent Children AFDC, Supplemental Security Income
SSI, and General Assistance), medical spending (Medicare and Military health care), and cash
retirement and disability payments for each county-year.
VI. Results
a. Difference-in-Difference Approach
We begin by estimating a difference-in-difference model using the 1968-78 PSID. This
compares labor supply measures across counties and over time relative to when the FSP was
introduced. Specifically, we estimate the following model:
19
This is measured for the same year as is reported on in the annual labor supply and income data. That is, since the
1968 survey asks about labor supply and income during calendar year 1967, the corresponding FSP variable for that
year reflects availability of the program in their county in 1967.
14
(1) yict FSPct X it CB60c * t REISct c t st ict
where yict is the outcome variable (such as head’s employment status or annual hours worked) for
family i living in county c in year t. FSPct is an indicator variable equal to one if there is a Food
Stamp Program in county c at time t. Xit are family demographic characteristics (urban residence,
education and race of head, female headed household indicator, and state unemployment rate), CB60c
are 1960 county characteristics (interacted with linear time), and REISct are county-level per capita
income transfer program data, ηc and λt are county- and year-fixed effects respectively, and μst are
either state-specific linear time trends or state by year fixed effects.
As described in Section IV above, we include pre-treatment county characteristics (CB60)
interacted with linear time trends to control for the observable determinants of county food stamp
adoption. The variables in CB60 include the percent of land in farming and the percent of population
black, urban, age less than five, age greater than 65 and with income less than $3,000. We also
include per-capita county income transfers (REISct) for (1) retirement and disability programs, (2)
medical care (Medicare, Medicare, and military health care), and (3) cash public assistance (AFDC,
SSI, and general assistance) to control for coincident introduction or expansion of other programs
that are not swept out by state linear time trends or state*year fixed effects. All estimates are
weighted using the PSID family weight, and standard errors are clustered at the county level.
Results are presented in Table 1. Each cell provides estimates from a separate regression and
only the coefficient on the FSP availability variable is reported (along with the mean of the
dependent variable). Columns (1)-(2) cover all non-elderly families. Panel A presents the “first
stage” impact of program introduction on an indicator variable for whether the family reports any
receipt of food stamps in that year. We find that about 3 percent of the overall sample reports
receiving food stamps when the program is in place. The dependent variables in panels B through D
are whether the head reported any employment during the year, the head’s annual hours, and the
15
head’s annual earnings, and the dependent variable in panel E is the log of total family cash income.
Two specifications are reported for each subsample—the odd columns use state linear time trends
and the even columns use state-by-year fixed effects.20 State-by-year fixed effects may do a better job
accounting for concurrent expansion in other welfare programs. The results are largely unchanged
when state-by-year effects are included.
The results in column (1) and (2) indicate no effect of FSP introduction on head’s work
behavior in the overall nonelderly population. Note that hours and earnings are unconditional
measures – that is, they include zero values for nonworkers. Any measured effect, therefore, can
come about from a change in the extensive or intensive margins of work. We similarly find in
Panel E no impact of FSP on log of family income.
Our identification strategy is based on the introduction of a county-level FSP program, but
only the low-income residents of treated counties who are eligible to enroll (and, further, who
actually do enroll in the program) should be directly impacted by the program. As shown in
Appendix Table 2, program participation varies widely with education, race and family type.21 To
account for this, we first limit the overall sample to sub-groups that are likely to be impacted by the
program (and similarly, in Table 3 we show placebo regressions limited to sub-groups that are
unlikely to be impacted by the program).
Columns (3)-(4) limit the sample to nonelderly families with a head who has 12 or fewer
years of education. The first stage results in Panel A show that 4.3 to 4.8 percent of families report
receiving food stamp benefits after the program is introduced in their county. There is no evidence
that head’s work effort declines in response to the program. In fact when state-by-year fixed effects
are included, head’s work force participation the prior year actually increases by a statistically
20
The sample sizes change slightly in this specification because we drop observations if the state-year cell has fewer
than five observations.
21
The table calculates FSP participation rates for families by the head’s race, marital status, and educational
attainment, and presence of children using the PSID pooled over the years 1976-78, which is the period in our data
after the FSP was implemented in all counties.
16
significant 2 percentage points. The coefficients on head’s annual earnings and family income are
negative, but never statistically significant.
Columns (5)-(6) limit the sample to female-headed families. This sample reports participating
in the program at much higher rates: between 19 and 22 percent report receiving benefits after the
program is introduced in their county. Employment and earnings responses for this sample are
completely in line, although not always statistically significantly, with the theoretical predictions
outlined in Figure 2. Among all female heads, the point estimate on annual employment status shows
a statistically insignificant 4 percentage point reduction with FSP introduction. Further, although
employment rates do not change, there is evidence that female heads reoptimize along the intensive
margin as predicted by economic theory by reducing their annual hours of work. In the specification
controlling for linear time trends, annual hours are reduced by an average of 183 hours (on a mean of
1068). The results are slightly larger when state-by-year fixed effects are included in the model. Head
annual earnings and log of family income both appear to decline as well, but the effects are not
precisely estimated.
Finally, we further limit the sample to nonwhite female-headed families in columns (7)-(8).
The estimated first stage of food stamp takeup rate is largest for these families with an estimated 25
to 38 percent reporting receipt of food stamps after the program is introduced. These families appear
to adjust on the extensive margin, with estimates varying from insignificant 4.8 to a statistically
significant 9.7 percentage point reduction in labor force participation. Annual hours worked and
earnings (which, as noted above, include zeros for non-workers) decline commensurately and are
statistically significantly different from zero. The impact on overall family cash income is imprecise
and not consistently signed across the specifications.
The estimates in Table 1 represent average treatment effects across the sub-sample, which
includes FSP participants and nonparticipants. We can use the average participation rates in
Appendix Table 2 to convert the estimates to the implied treatment on the treated (TOT) effect by
17
dividing the coefficient by the sample’s FSP participation rate.22 This suggests a TOT effect on
annual hours of 500-650 hours for female headed households and 289-517 for nonwhite female
headed households.
Existing evidence suggests that the labor supply of a family’s secondary worker is more
responsive to income transfer programs, so we might expect the spouse’s labor supply to decline by
more than the head’s in a married-couple family (Eissa and Hoynes 2006; Hoynes 1996). To test this,
in Table 2 we present results limited to married couple families and show the impact of FSP
introduction on labor supply separately for heads and spouses.23 As in Table 1, the odd columns
control for state-by-linear time trends while the even columns include state-by-year fixed effects.
Columns (1) and (2) present results for all nonelderly married families. Only 2 percent of families in
this sample report receiving benefits after the FSP is introduced. There is no evidence in this sample
that heads or spouses alter their labor supply efforts in response to the introduction of the program.
Head’s annual employment status, hours and earnings are never statistically significantly impacted
by FSP introduction, and the point estimate is usually positive. Spouse’s employment behavior in this
overall sample is never statistically significantly impacted although the point estimates are consistent
with a work disincentive effect of food stamps.
In columns (3) and (4) we target the sample to married couples where the head has a high
school diploma or less. Here the takeup rate of the program is only slightly higher – 3 percent of the
sample reports receiving benefits – and the impacts on head and spouse labor supply are also
reasonably precisely estimated zeroes. The results so far suggest that it is primarily single female
household heads that alter their labor supply in response to the FSP.
22
We inflate by the participation rates in Appendix Table 2 instead of the (smaller) coefficient from Panel A for
consistency with the triple-difference results reported further below.
23
In most, but not all families, the head is male and the spouse is female. For this reason we will continue to use the
terms head/spouse rather than husband/wife.
18
As a check to insure that the labor market findings are not spurious, in Table 3 we present
placebo regressions looking for signs of an “effect” of the FSP on groups that are unlikely to have
received treatment. Columns (1) and (2) report results for all high-income (defined as annual income
greater than $50,000 in inflation-adjusted 2005 dollars) families, the next pair of columns limits the
sample to high-income married couples with children, and the final pair limits the sample even
further to white married high-income families with children. As shown in Panel A, these families are
very unlikely to report receiving any food stamp benefits. The coefficients on head’s employment,
hours worked and earnings are small and not statistically different from zero. The sign on annual
earnings is generally wrong-signed – indicating an increase in earnings after FSP is introduced in
their county – with sizeable standard errors. Effects on spouse reporting any employment here are
small and generally wrong-signed, while spousal hours worked and earnings bounce around and are
imprecise. These findings indicate that there is no “treatment effect” on families that do not receive
treatment and lend further credibility to our research design.
b. Triple-difference approach
Unlike virtually all other U.S. public assistance programs, there is no categorical eligibility
for the food stamp program. That is, eligibility depends on income and asset tests but it is not
targeted to particular demographic groups, such as single parents with children. This argues for using
broad samples, such as all nonelderly families or low educated nonelderly families as we did above.
However, it is clear from the analysis in Tables 1 and 2 that we may not have the power to detect
effects in a broad sample.
At the same time, the participation rates in Appendix Table 2 show that while food stamp
participation is highest among single parent families with children, the participation is widespread
and varying across many demographic groups. For example, among families where the head has less
than 12 years of education, 14 percent of married couples with children and 14 percent of single
nonelderly persons with no children participate in food stamps (compared to 46 percent of single
19
parent families with children). The rates are uniformly higher among black families, with 56 percent
of single nonelderly parent families with children (where the head has less than 12 years of
education) participating in food stamps.
In the end, in choosing our preferred sample for this analysis, we face a tradeoff between
sample size (using the larger sample of nonelderly families but with overall lower participation rates)
and targeting (using the smaller sample female heads of household with higher participation rates).
Here, we refine these earlier results by using the nonelderly sample, but adopting a triple-difference
specification that accounts for different probabilities of being affected by food stamps. Using the
participation rates in Appendix Table 2, we consider 24 groups g that are defined by race (white,
nonwhite), marital status (married, not married), presence of children (yes, no), and educational
attainment (<12, 12, >12). Following Bleakley (2007) and Hoynes and Schanzenbach (2009), we use
these participation rates (Pg) to scale the food stamp treatment variable, and estimate the following
model where the unit of observation is the family-year:
(2) yict FSPct FSPct Pg CB60c * t REISct g g * t c t st ict
In addition to the variables include in equation (1), we also include fixed effects for group and group
linear time trends. We include the main effect for FSP as well as the interaction with Pg . The
coefficient on the main effect can be interpreted as the impact on a group with zero risk of being on
food stamps, and we therefore expect the coefficient on the main effect to be zero.
The results are presented in Table 4. In column (1) we include state linear time trends, in
column (2) we include state by year fixed effects, and in column (3) we augment equation (2) with
interaction terms between Pg and all control variables. This is a triple-difference specification: across
counties, over time, and across groups. Because we inflate by participation rate, the coefficient of
interest, δ, can be interpreted as the impact of the treatment on the treated.
20
The results in Table 4 are consistent with the theoretical predictions that the introduction of
an income support program induces households to consume more leisure. Further, the results are
remarkably consistent across specifications. Participating heads reduce employment by 24 to 27
percentage points when the FSP is introduced in the county. Head’s annual hours also appear to
decline with p-values hovering just over 10 percent. The point estimates on earnings and log family
income suggest a commensurate decline, but the estimates never approach statistical significance. In
each case, as expected, the coefficients on the main effect for FSP are uniformly small and not
different from zero. This is a placebo-test of sorts and the small coefficients give us additional
confidence about the validity of our design.
Table 5 repeats the specifications described above but limits the sample to married couples
with a spouse present. Head’s labor supply is not statistically significantly affected by the
introduction of FSP, though all point estimates are negative. The same pattern holds for spouse’s
labor supply. Employment, hours and earnings all have a negative point estimates but are not
statistically significantly different from zero. We do find that, relative to their baseline values, these
responses are larger for spouses than heads as predicted from the literature.
c. Event study analysis
Our results so far have been quite robust to different ways of controlling for confounding
background characteristics. This suggests that our results are identified off of discrete changes in
outcomes at the time of FSP introduction. If instead identification were being driven by underlying
trends or variation in other characteristics, we would expect more sensitivity of our coefficients to
changes in the manner we account for these possible confounders.
To directly examine the timing of the shifts in labor supply relative to the introduction of
FSP, we return to the female headed household sample and conduct an event study analysis. We use
the female headed household sample because they have the highest FSP participation rates and
largest responses to FSP introduction in the results presented above. The event study approach
21
requires a discrete treatment, so using the full sample with adjustment for group specific participation
rates does not lend itself to this approach.
The advantage of an event study analysis is it traces out the trend in labor supply year-by-
year for the periods leading up to and following FSP adoption. This allows for rigorous testing of a
“pre-trend,” the presence of which would raise concerns about our identification strategy. It also
allows for an analysis of the dynamics of the policy effect—for example whether they grow with
time since adoption. We are able to estimate an event study analysis here because we have a discrete
policy variable that is implemented at different times across the counties in our sample.
Specifically, we estimate the following equation:
3
(3) yict j 1( ct j ) CB60c * t REISct c t st ict
j 3
where τct denotes the year relative to the introduction of the FSP, defined so that τct=0 if for county c
the FSP began at any point in calendar year t. For τct<0, work effort decisions were made prior to the
introduction of the FSP. All coefficients are measured relative to the omitted coefficient (τct=-1). We
run the model with and without the inclusion of our controls for potential FSP policy endogeneity
(that is, the 1960 county characteristics interacted with linear time trends and REIS county controls)
and include state linear time trends (μst). In order to eliminate potential compositional effects, we
restrict the sample to a balanced panel of counties having observations for all 7 event periods (3 years
prior to FSP implementation and 3 years afterwards). Because our data begin in 1968, this means we
exclude counties where FSP started in 1970 or earlier.
We produce event study graphs for each of the labor supply outcomes for the female headed
households. Figure 3A plots the event-year coefficients from the estimation of equation (3) on
whether the head was employed last year. The figure shows an absence of pre-trends, and shows a
sharp turn downward after FSP is introduced. The estimates with and without controls for policy
endogeneity are very similar. Figure 3B repeats the exercise with annual hours worked by the head.
22
Again there is no pre-trend in hours worked prior to the introduction of FSP. Immediately after the
program is introduced, there is a discrete drop in hours worked, and the coefficients continue to be
negative and relatively flat in the subsequent years.
These results show strong evidence that our county adoption of FSP is identifying the causal
impact of the food stamp program. These figures show no trend in the outcome variables leading up
to the program introduction and sharp changes in the outcome following county program
introduction. In our prior work analyzing the impact of the FSP introduction on infant health
(Almond, Hoynes and Schanzenbach 2010) we used county FSP caseload data to show that the food
stamp programs seemed to ramp up quite quickly in the 1-2 years following program adoption. This
is consistent with the outcome results in Figures 3A and 3B.
The remaining panels in Figure 3 shows impacts of food stamp introduction on head’s annual
earnings and the log of family income. Figure 3C, which reports estimates on head’s earnings, may
show a small downward trend prior to the introduction of FSP, but there is still a sharp drop in
earnings immediately following the introduction of the program. Finally, Figure 3D, which reports
estimates for (log) family income, the impact of FSP here is – consistent with the small and
imprecise coefficients in the earlier tables – not sharp and does not imply an important impact of FSP
on total income.
d. Specification check using Decennial Census
As a final robustness check, we estimate the impact of FSP program introduction on labor
supply using public use decennial Census data from the county-level aggregate files—known as the
STF files. Here, we are limited to the variables and samples that have been pre-tabulated by the
Census. We are able to estimate models for female labor force participation rates, male labor force
participation rates, labor force participation rates for women with a child under age six, and the
23
fraction of families with income below 10,000 (in real 1979 dollars).24 We pool county outcomes
from the 1960, 1970 and 1980 Censuses and estimate models similar to those presented above, with
decade fixed effects replacing the year fixed effects. The regressions are weighted using county
population and the standard errors are clustered on county. The results are presented in Table 6—
with panel A reporting estimates for all persons and panel B reporting estimates for nonwhites (for
variables that are available).25
It is important to note that these treatment groups are broader than the targeted samples used
in the PSID and there is no way to weight the treatment by group participation rate because the data
are county wide averages. With that said, the results show a relatively statistically precise, small
negative estimated work disincentive effect. For example, the estimate for males (females) shows
that implementing a food stamp program leads to a statistically significant 0.003 (0.002) decrease in
the labor force participation rate compared to the mean value of 0.76 (0.40). Using an average
participation rate of 7.4, these imply TOT impacts on women of 2.7 percentage points (in italics in
the table) or about 7 percent. For men, the TOT impacts are 4.1 percentage points or 5 percent. In
addition, the probability that overall family cash income (not including food stamps) is less than
$10,000 per year (in 1979 dollars) increases by a statistically significant 1 to 3 percentage points.
VII. Discussion
Overall, the evidence from the PSID and decennial Census is consistent with our theoretical
predictions. We generally find that the introduction of the food stamp program leads to lower rates of
employment and hours. The evidence is less clear for earnings and family income as our results never
24
The labor force participation rates are defined for persons age 16 and over.
25
The smallest geographic area identified in the census public use micro data is the county group, these are
contiguous groups of counties with a combined population of 100,000 or more We estimated models using the 1970
and 1980 public use micro data IPUMS data where we aggregated the FSP treatment across all counties in the
country group. Further, we had to combine county groups to accommodate the changing county group boundaries
between the 1970 and 1980 Census. In the end, this aggregation was substantial and the results had very low power.
The results are available on request.
24
approach statistical significance for these outcomes. The validity of the research design and estimates
is confirmed by several additional results. First, the impacts are larger in subsamples with higher
participation rates (e.g. female heads of household) and essentially zero for placebo groups such as
highly educated married couples. Second, the event study results show that the policy introduction is
unrelated to trends in the outcome variables, and labor supply sharply declines with the introduction
of the program. Beyond the sign and statistical significance, however, what do our magnitudes tell
us about the work disincentive effects of the food stamp program? How do our results compare to the
literature?
Our preferred estimates use the pooled sample and the triple difference specification
(Tables 4 and 5), as this takes advantage of the universal nature of the FSP program. However, the
existing food stamp literature does not provide any estimates for comparison. As a check and to
gauge the magnitude of the expected labor supply effects of the food stamp program, we simulated
the impact of the program on annual hours worked in our PSID sample using estimated labor supply
elasticities from the Negative Income Tax experiments (Robins and West 1983). The simulations for
the full nonelderly sample predicted a 20-24 hour reduction in annual hours, which scaled up by the
group FS participation rate implies about a 300 hour reduction in hours for participating households.
This is quite similar to our (insignificant) estimated reduction in Table 4. Overall, we take this as a
useful exercise which corroborates our estimates of modest work incentive effects in the food stamp
program in the full sample.26
In the female headed household subsamples where we find our largest and most robust
estimates, there is more scope to compare our results to the literature. We focus on the results for
26
More specifically, we assign the compensated wage and income effects based on family type (female headed
households are assigned the effects for “single females” and all male headed singles/families are assigned the effects
for “husbands”). We assign maximum food stamp benefits and the food stamp breakeven income level by family
size using the 1975 food stamp parameters in Table 2.2 from MacDonald (1977). The change in net wages is -0.3
reflecting the food stamp benefit reduction rate. We assume no nonlinear response to the implementation of food
stamps and therefore simulate the change in hours for those families with head’s earnings below the food stamps
breakeven point. Full results of the simulation are available on request.
25
annual hours, since that is the measure used in the literature. The Table 4 results show an increase in
hours of 183-238 for all female heads, implying a TOT impact of 500-600 hours per year (or more
than a 50 percent reduction). This is considerably larger than the Fraker and Moffitt (1988) results,
whose structural model results indicate that food stamp participants reduce their work effort by less
than 100 hours per year. It is also instructive to compare our results to the related literature
examining the impacts of the AFDC program on labor supply. As summarize above in Section II,
AFDC is estimated to reduce labor supply (annual hours) among program participants by 10 to
50 percent (546 hour reduction reported in Moffitt 1983). Our estimates for female headed
households are on the high end of this range. At the same time we would have expected the FSP to
yield smaller impacts than AFDC given the programs lower benefit reduction rate and lower
guarantee.
VIII. Conclusion
In this paper we present evidence on the work incentive effects of food stamps, the largest
cash or near-cash transfer program in the safety net. This paper provides an important contribution to
the literature on work incentives of social welfare programs, and is the first paper on employment
effects of the food stamp program that uses a quasi-experimental research design. The impacts of
food stamps on work behavior have been difficult for researchers to isolate because there is little
cross-state or over-time program variation to exploit. Here we use county variation in the adoption of
the program between 1963 and 1975 to identify the impact of food stamps. Using the PSID, we find
modest reductions overall in employment rates and hours worked, and larger reductions among
female headed households. Our results suggest a larger work disincentive effect of FSP for female
heads than had previously been concluded in the literature.
Even though there have been changes in the population of the United States, in the labor
force attachment of women, and some of the parameters of FSP have changed, these results are still
26
relevant for today’s policy debates. To date, there is little credible evidence on the impact of FSP on
work behavior. Understanding the incentives in FSP is important since such a large fraction of
Americans rely on FSP, and it is one of the few remaining safety net programs that does not have a
substantial work requirement component
27
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29
Figure 1: Cumulative Percent of Counties with Food Stamp Program, 1960-1975
100
1973 Amend:
1961: Pilot 1964 FSA: Manditory FSP by
Programs Counties Can 1975
Counties Participating in FSP (weighted %)
Initiated Start FSP
80
60
40
PSID annual
20 data coverage
starts
0
1960 1962 1964 1966 1968 1970 1972 1974
Year
Source: Author’s tabulations of county FSP start dates. Counties are weighted by their 1960 populations.
30
Figure 2a: Income-Leisure Diagram Before Food Stamps
Income
A
A^ A~
leisure
l l
Figure 2b: Income-Leisure Diagram After Food Stamps
Income
A’
A~’
G
ℓ’ leisure
31
Figure 3a: Event Study Estimates of Impact of FSP on Head Any Work Last Year
Female headed households with children only
Head work (any last year)
0.05
0.00
-3 -2 -1 0 1 2 3
-0.05
Include county controls for
-0.10 FSP endogeneity
Exclude county controls for
policy endogeneity
-0.15 Unbalanced sample
-0.20
event time in years
Figure 3b: Event Study Estimates of Impact of FSP on Head Annual Hours
Female headed households with children only
Head annual hours
200
100
0
-3 -2 -1 0 1 2 3
-100
Include county controls for
FSP endogeneity
-200
Exclude county controls for
policy endogeneity
-300
Unbalanced sample
-400
event time in years
32
Figure 3c: Event Study Estimates of Impact of FSP on Head Earnings
Female headed households with children only
Head earnings
3000
2000
1000
0
-3 -2 -1 0 1 2 3
-1000
Include county controls for
-2000 FSP endogeneity
Exclude county controls for
-3000
policy endogeneity
-4000 Unbalanced sample
-5000
event time in years
Figure 3d: Event Study Estimates of Impact of FSP on Head Earnings
Female headed households with children only
ln( Family Income)
0.10
0.05
0.00
-3 -2 -1 0 1 2 3
-0.05 Include county controls for
FSP endogeneity
Exclude county controls for
-0.10 policy endogeneity
Unbalanced sample
-0.15
event time in years
Notes: Each figure plots coefficients from an event-study analysis. Coefficients are defined as years relative to the year the Food Stamp Program
is implemented in the county. The sample is limited to female-headed households with children. The balanced sample lines include information
on a county only if there are at least 3 years pre- and post-implementation data. The specification includes controls for demographics, unrestricted
fixed effects for county and year, county per capita transfers, state-specific linear time trends, and 1960 county characteristics interacted with
linear time.
33
Table 1: Impacts of Food Stamp Introduction on Labor Supply and Family Income, by Group
Nonwhite
All nonelderly households Nonelderly, Head Educ<=12 Female Heads Female Heads
(1) (2) (3) (4) (5) (6) (7) (8)
A. Any Food Stamps =1
County FSP Implemented 0.028 0.028 0.043 0.048 0.223 0.193 0.377 0.247
(0.007)*** (0.009)*** (0.009)*** (0.010)*** (0.047)*** (0.045)*** (0.075)*** (0.058)***
Number of Observations 43,673 43,673 34,060 34,060 5,681 6,256 4,264 4,688
B. Head Any Work =1
County FSP Implemented 0.000 0.010 0.006 0.019 -0.043 -0.040 -0.048 -0.097
(0.005) (0.008) (0.007) (0.010)* (0.037) (0.050) (0.041) (0.055)*
Dependent variable mean 0.926 0.926 0.904 0.904 0.707 0.071 0.605 0.615
C. Head Annual hours
County FSP Implemented 8 34 16 36 -183 -238 -158 -282
(20) (25) (24) (31) (77)** (97)** (74)** (88)***
Dependent variable mean 1947 1947 1879 1879 1068 1068 864 864
D. Head Annual Earnings
County FSP Implemented 270 -445 -32 -219 -533 -1065 -1193 -3169
(729) (960) (643) (966) (1112) (1329) (986) (1317)**
Dependent variable mean 41742 41742.00 34600 34600 14194 14194 10022 10022.00
E. Log(Family Income)
County FSP Implemented 0.004 -0.003 -0.001 -0.008 -0.046 -0.096 -0.037 0.014
(0.015) (0.020) (0.017) (0.023) (0.051) (0.060) (0.064) (0.086)
Number of Observations 48,168 48,148 37,474 37,447 7,280 6,890 5,464 5,175
1960 Cty Vars * Linear Time X X X X X X X X
Year and County Fixed Effects X X X X X X X X
Per Capita Cty Transfers X X X X X X X X
State x Linear Time X X X X X X X X
State x Year FE X X X X
Notes: Each parameter is from a separate regression of the outcome variable on a dummy variable equal to 1 if the county-year observation had a food stamp program in place by January of that year.
The sample includes non-elderly PSID families using interview years 1968-1978. Observations from Alaska are dropped because of missing data on food stamp program start dates. All outcome
variables correspond to annual measures taken as of the interview (in spring of the interview year). Demographic controls include dummies for education, number of children, number of adults, race,
urban location and state unemployment rate. 1960 county variables include log of population, percent of land in farming, percent of population black, urban, age<5, age>65 and with income less than
$3,000, each interacted with a linear time trend. Per capita county transfer income comes from the BEA REIS and includes measures for public assistance (AFDC, General Assistance), medical care
(Medicare, Medicaid, military), and retirement and disability benefits. Estimates are weighted using the PSID weight and clustered on county. Standard errors are in parenthesis and ***, **, and *
indicate that the estimates are statistically significant at the 1%, 5% and 10% levels, respectively.
34
Table 2: Impacts of Food Stamp Introduction on Labor Supply and Family Income, Married-Couple
Households Only
All nonelderly married-couple Nonelderly, Head Educ<=12 married-
households with spouse present couple households with spouse present
(1) (2) (3) (4)
A. Any Food Stamps =1
County FSP Implemented 0.016 0.018 0.026 0.029
(0.006)*** (0.008)** (0.008)*** (0.009)***
Number of Observations 28,543 28,543 21653 21,653
B. Head Any Work =1
County FSP Implemented 0.000 0.006 0.006 0.018
(0.004) (0.006) (0.007) (0.009)**
Dependent variable mean 0.964 0.964 0.954 0.954
C. Head Annual hours
County FSP Implemented 9 36 13 42
(21) (27) (26) (33)
Dependent variable mean 2162 2162 2115 2115
D. Head Annual Earnings
County FSP Implemented 741 -258 290 -65
(845) (1133) (730) (1085)
Dependent variable mean 49598 49598 41547 41547
E. Spouse Any Work =1
County FSP Implemented -0.001 -0.003 -0.002 -0.002
(0.013) (0.016) (0.017) (0.021)
Dependent variable mean 0.556 0.56 0.544 0.544
F. Spouse Annual hours =1
County FSP Implemented -149 1 -339 8
(317) (28) (342) (34)
Dependent variable mean 710 710 704 704
G. Spouse Annual Earnings
County FSP Implemented 2 -240 11 -392
(22) (408) (27) (480)
Dependent variable mean 9840 9840 8732 8732
H. Log(Family Income)
County FSP Implemented 0.005 -0.003 -0.005 -0.013
(0.012) (0.017) (0.015) (0.020)
Number of Observations 31,443 31,438 23,797 23,781
1960 Cty Vars * Linear Time X X X X
Year and County Fixed Effects X X X X
Per Capita Cty Transfers X X X X
State x Linear Time X X X X
State x Year FE X X
Notes: Each parameter is from a separate regression of the outcome variable on a dummy variable equal to 1 if the county-year observation had a
food stamp program in place by January of that year. The sample includes married, non-elderly PSID families using interview years 1968-1978.
Observations from Alaska are dropped because of missing data on food stamp program start dates. All outcome variables correspond to annual
measures taken as of the interview (in spring of the interview year). Demographic controls include dummies for education, number of children,
number of adults, race, urban location and state unemployment rate. 1960 county variables include log of population, percent of land in farming,
percent of population black, urban, age<5, age>65 and with income less than $3,000, each interacted with a linear time trend. Per capita county
transfer income comes from the BEA REIS and includes measures for public assistance (AFDC, General Assistance), medical care (Medicare,
Medicaid, military), and retirement and disability benefits. Estimates are weighted using the PSID weight and clustered on county. Standard
errors are in parenthesis and ***, **, and * indicate that the estimates are statistically significant at the 1%, 5% and 10% levels, respectively.
35
Table 3: Impacts of Food Stamp Introduction on Labor Supply and Family Income, Placebo Samples
All high income (>50K) White High Income Married
households High Income Married w/Children w/Children
(1) (2) (3) (4) (5) (6)
A. Any Food Stamps =1
County FSP Implemented 0.005 0.004 0.003 0.012 0.002 0.008
(0.003) (0.005) (0.004) (0.007) (0.003) (0.018)
Number of Observations 16,796 18,010 9,813 10,491 1,947 1,528
B. Head Any Work =1
County FSP Implemented -0.007 -0.015 0.000 0.000 -0.004 -0.012
(0.006) (0.007) (0.003) (0.005) (0.008) (0.020)
C. Head Annual hours
County FSP Implemented -10 -35 16 7 -26 -73
(22) (32) (26) (36) (49) (82)
D. Head Annual Earnings
County FSP Implemented 775 -1063 1059 52 6917 421
(1022) (1376) (1322) (2012) (4,049)* (6128)
E. Spouse Any work last year
County FSP Implemented 0.003 -0.011 0.014 0.009
(0.023) (0.029) (0.047) (0.083)
F. Spouse Annual hours
County FSP Implemented -14 20 -93 -40
(35) (45) (60) (110)
G. Spouse Annual Earnings
County FSP Implemented -283 -106 -1631 -1042
(538) (728) (1057) (1910)
H. Log(Family Income)
County FSP Implemented 0.012 0.006 0.003 0.003 0.022 -0.033
(0.010) (0.014) (0.013) (0.017) (0.031) (0.053)
Number of Observations 20,003 19,844 11,742 11,561 2,349 1,669
1960 Cty Vars * Linear Time X X X X X X
Year and County Fixed Effects X X X X X X
Per Capita Cty Transfers X X X X X X
State x Linear Time X X X
State x Year FE X X X
Notes: Each parameter is from a separate regression of the outcome variable on a dummy variable equal to 1 if the county-year observation had a
food stamp program in place by January of that year. The sample includes non-elderly PSID families using interview years 1968-1978.
Observations from Alaska are dropped because of missing data on food stamp program start dates. All outcome variables correspond to annual
measures taken as of the interview (in spring of the interview year). Demographic controls include dummies for education, number of children,
number of adults, race, urban location and state unemployment rate. 1960 county variables include log of population, percent of land in farming,
percent of population black, urban, age<5, age>65 and with income less than $3,000, each interacted with a linear time trend. Per capita county
transfer income comes from the BEA REIS and includes measures for public assistance (AFDC, General Assistance), medical care (Medicare,
Medicaid, military), and retirement and disability benefits. Estimates are weighted using the PSID weight and clustered on county. Standard
errors are in parenthesis and ***, **, and * indicate that the estimates are statistically significant at the 1%, 5% and 10% levels, respectively.
36
Table 4: Impact of Food Stamp Introduction on Labor Supply and Family Income
Triple Difference Estimates for Nonelderly Sample
(1) (2) (3)
Head Any work last year
County FSP Implemented x Pg -0.266 -0.273 -0.240
(0.081)*** (0.083)*** (0.067)***
County FSP Implemented 0.019 0.029 0.017
(0.007)*** (0.009)*** (0.006)***
Head Annual hours
County FSP Implemented x Pg -281 -302 -268
(183) (188) (168)
County FSP Implemented 25 52 24
(24) (27)* (23)
Head Annual Earnings
County FSP Implemented x Pg -6885 -6929 -4673
(6142) (6168) (5648)
County FSP Implemented 830 -22 694
(906) (1099) (889)
Log(Family Income)
County FSP Implemented x Pg -0.062 -0.137 -0.058
(0.111) (0.124) (0.112)
County FSP Implemented 0.013 0.005 0.013
(0.016) (0.021) (0.016)
Number of Observations 48,168 48,148 48,168
1960 Cty Vars * Linear Time X X X
Per Capita Cty Transfers X X X
Group Fixed Effects, Group * linear time X X X
Year Fixed Effects (main and x Pg) X X X
County Fixed Effects X X X
State x Linear Time X X
State x year fe X
Pg x Other Covariates (except area fixed
effects) X
Notes: Each parameter is from a separate regression of the outcome variable on the food stamp implementation dummy multiplied by a group
food stamp participation rate. The food stamp implementation dummy equals one if the county-year observation had a food stamp program in
place by January of that year. The group food stamp participation rate is calculated for each education-race-marital status-presence of children
cell using the 1976-78 PSID. The sample includes all years 1968-78 and excludes observations from Alaska. For details on this sample selection,
see text. All outcome variables correspond to annual measures taken as of the interview (in spring of the interview year). Demographic controls
include dummies for education, number of children, number of adults, race, urban location and state unemployment rate. 1960 county variables
include log of population, percent of land in farming, percent of population black, urban, age<5, age>65 and with income less than $3,000, each
interacted with a linear time trend. Per capita county transfer income comes from the BEA REIS and includes measures for public assistance
(AFDC, General Assistance), medical care (Medicare, Medicaid, military), and retirement and disability benefits. Estimates are weighted using
the PSID weight and clustered on county. Standard errors are in parenthesis and ***, **, and * indicate that the estimates are statistically
significant at the 1%, 5% and 10% levels, respectively.
37
Table 5: Impact of Food Stamp Introduction on Labor Supply and Family Income
Triple Difference Estimates for Nonelderly, Married-Couple Sample
(1) (2) (3)
Head Any work last year
County FSP Implemented x Pg -0.107 -0.111 -0.106
(0.086) (0.087) (0.073)
County FSP Implemented 0.005 0.012 0.005
(0.005) (0.007)* (0.005)
Head Annual hours
County FSP Implemented x Pg -102 -120 -153
(405) (416) (393)
County FSP Implemented 13 41 15
(32) (36) (31)
Spouse Any work last year
County FSP Implemented x Pg -0.163 -0.117 -0.071
(0.284) (0.292) (0.259)
County FSP Implemented 0.009 0.004 0.004
(0.02) (0.024) (0.02)
Spouse Annual hours
County FSP Implemented x Pg -204 -154 -39
(569) (584) (500)
County FSP Implemented 15 13 6
(38) -41 (34)
Log(Family Income)
County FSP Implemented x Pg -0.241 -0.230 -0.199
(0.315) (0.326) (0.302)
County FSP Implemented 0.019 0.011 0.017
(0.021) (0.025) (0.020)
Number of Observations 31,443 31,438 31,443
1960 Cty Vars * Linear Time X X X
Per Capita Cty Transfers X X X
Group Fixed Effects, Group * linear time X X X
Year Fixed Effects (main and x Pg) X X X
County Fixed Effects X X X
State x Linear Time X X
State x year fe X
Pg x Other Covariates X
Notes: Each parameter is from a separate regression of the outcome variable on the food stamp implementation dummy multiplied by a group
food stamp participation rate. The food stamp implementation dummy equals one if the county-year observation had a food stamp program in
place by January of that year. The group food stamp participation rate is calculated for each education-race-presence of children cell using the
1976-78 PSID. The sample includes all years 1968-78 and excludes observations from Alaska. The sample includes only married couples. For
details on this sample selection, see text. All outcome variables correspond to annual measures taken as of the interview (in spring of the
interview year). Demographic controls include dummies for education, number of children, number of adults, race, urban location and state
unemployment rate. 1960 county variables include log of population, percent of land in farming, percent of population black, urban, age<5,
age>65 and with income less than $3,000, each interacted with a linear time trend. Per capita county transfer income comes from the BEA REIS
and includes measures for public assistance (AFDC, General Assistance), medical care (Medicare, Medicaid, military), and retirement and
disability benefits. Estimates are weighted using the PSID weight and clustered on county. Standard errors are in parenthesis and ***, **, and *
indicate that the estimates are statistically significant at the 1%, 5% and 10% levels, respectively.
38
Table 6: Impact of Food Stamp Introduction on Labor Supply and Family Income
1960, 1970, 1980 Census STF Analysis
Labor Force Participation Rate
Females 16 and Males 16 and Females with Family Income <
over over children<6 $10,000 (1979$)
(1) (2) (3) (4)
A. All Races
County FSP Implemented -0.002 -0.003 0.004 0.009
(0.001)* (0.001)** (0.008) (0.002)***
Treatment on Treated -0.027 -0.041 0.054 0.122
Dependent variable mean 0.396 0.762 0.337 0.238
Number of Observations 7,898 7,898 7,898 7,898
B. Nonwhites
County FSP Implemented 0.002 -0.004 n/a 0.027
(0.004) (0.003) (0.006)***
Dependent variable mean 0.457 0.703 0.455
Number of Observations 7,443 7,321 7,093
1960 Cty Vars * decade fixed effects X X X X
Per capita county transfer payments X X X X
Decade fixed effects X X X X
County fixed effects X X X X
Notes: Each parameter is from a separate regression of the outcome variable on a dummy variable equal to 1 if the county-year observation had a food stamp program in place in that year. Data are from
1960-1980 Census county-level STF files. Counties in Alaska are dropped because of missing data on food stamp program start dates. Demographic controls include dummies for education, number of
children, number of adults, race, urban location and state unemployment rate. 1960 county variables include log of population, percent of land in farming, percent of population black, urban, age<5,
age>65 and with income less than $3,000, each interacted with a linear time trend. Per capita county transfer income comes from the BEA REIS and includes measures for public assistance (AFDC,
General Assistance), medical care (Medicare, Medicaid, military), and retirement and disability benefits. Estimates are weighted using the PSID weight and clustered on county. Standard errors are in
parenthesis and ***, **, and * indicate that the estimates are statistically significant at the 1%, 5% and 10% levels, respectively.
39
Appendix Table 1: Determinants of County Level Food Stamp Program Start Date
Analysis Using the 1960 City and County Data Book
All Counties Limiting to post-pilot counties
(1) (2) (3) (4)
Percent of land in farming -0.025 0.124 0.114 0.136
(0.830) (0.028)*** (0.027)*** (0.033)***
Percent of pop with income<$3000 0.005 -0.544 -0.347 0.085
(0.050) (0.092)*** (0.088)*** (0.147)
Percent of pop urban 0.214 -0.068 -0.040 -0.001
(4.36)** (0.041) (0.039) (0.053)
Percent of pop black -0.326 -0.208 -0.212 -0.474
(4.36)** (0.070)*** (0.067)*** (0.145)***
Percent of pop age < 5 -3.566 -2.329 -2.954 -3.557
(4.92)** (0.625)*** (0.593)*** (0.786)***
Percent of pop age > 65 -1.030 -0.982 -1.133 -3.048
(2.49)* (0.390)** (0.371)*** (0.524)***
log population -11.229 -9.139 -7.819 -7.335
(13.44)** (0.752)*** (0.718)*** (0.932)***
South * % of land in farming -0.125
(0.058)**
South * % pop with income<$3000 -0.603
(0.188)***
South * % pop urban -0.110
(0.080)
South * % pop black 0.373
(0.165)**
South * % pop age < 5 0.787
(1.222)
South * % pop age > 65 3.467
(0.754)***
South * log population 0.645
(1.548)
State Fixed Effects X X X
Number of Observations 2,957 2,957 2,939 2,939
R squared 0.14 0.56 0.55 0.56
Notes: The data are at the county level and the dependent variable is equal to the calendar month (normed to 1 in January 1961) that the county
began offering the Food Stamp Program. The control variables come from the City and County Data Book for 1960. Alaska counties are dropped
due to missing data on the food stamp program. Very small counties (with population less than 1,000) are dropped because of missing data on
some control variables. A small number of counties are dropped because the variable percent of land in farming exceeds 100 percent. Estimates
are weighted using the 1960 county population.
40
Appendix Table 2: Food Stamp Participation Rates by Demographic Group
Education Group
High School
All Less than HS Grad More than HS
A. All Races
All family types 0.08 0.14 0.06 0.02
Single with children 0.32 0.46 0.23 0.15
Married with children 0.07 0.14 0.06 0.01
Single, no children 0.07 0.14 0.05 0.03
Married, no children 0.02 0.04 0.01 0.01
Single, no children elderly 0.07 0.10 0.03 0.01
Married, no children elderly 0.03 0.05 0.00 0.00
B. White
All family types 0.05 0.10 0.04 0.02
Single with children 0.22 0.38 0.14 0.07
Married with children 0.05 0.12 0.05 0.01
Single, no children 0.05 0.11 0.04 0.03
Married, no children 0.01 0.03 0.00 0.01
Single, no children elderly 0.05 0.07 0.02 0.01
Married, no children elderly 0.02 0.04 0.00 0.00
C. Nonwhite
All family types 0.22 0.28 0.18 0.09
Single with children 0.51 0.56 0.44 0.43
Married with children 0.16 0.22 0.14 0.03
Single, no children 0.13 0.20 0.09 0.04
Married, no children 0.06 0.10 0.02 0.02
Single, no children elderly 0.24 0.25 0.11 0.00
Married, no children elderly 0.10 0.13 0.00 0.00
Notes: Weighted means of food stamp participation rates using families in the 1976-1978 Panel Study of Income Dynamics. These years were
chosen because by 1976 all counties had implemented food stamp programs yet it was before the elimination of the purchase requirement in
1979.
41
Appendix Table 3: Descriptive Statistics for PSID Samples
All Nonelderly Singles and Families Nonelderly, Head <=12 Female Headed Households Nonwhite Female Headed Households
N Mean Min Max N Mean Min Max N Mean Min Max N Mean Min Max
FSP participation 43693 0.068 0 1 34084 0.087 0 1 6608 0.289 0 1 4951 0.439 0 1
Head any work last year 48168 0.926 0 1 37474 0.904 0 1 7280 0.707 0 1 5464 0.615 0 1
Head Annual hours 48168 1947 0 5824 37474 1879 0 5824 7280 1068 0 4628 5464 864 0 4628
Head Annual earnings 48168 41742 0 503346 37474 34600 0 439860 7280 14194 0 117854 5464 10022 0 75516
Spouse any work last year 31443 0.556 0 1 23797 0.544 0 1
Spouse annual hours 31443 9840 0 116806 23797 8732 0 103129
Spouse annual earnings 31443 710 0 7980 23797 704 0 7980
Log(Family Income) 48168 10.763 6.617 13.129 37474 10.645 6.645 12.994 7280 10.193 6.714 12.640 5464 9.939 6.714 11.781
County FSP prorgam implemented 48168 0.760 0 1 37474 0.761 0 1 7280 0.804 0 1 5464 0.821 0 1
Urban county 48168 0.622 0 1 37474 0.595 0 1 7280 0.669 0 1 5464 0.724 0 1
Education<12 years 48168 0.335 0 1 37474 0.487 0 1 7280 0.481 0 1 5464 0.608 0 1
Education=12years 48168 0.353 0 1 37474 0.513 0 1 7280 0.376 0 1 5464 0.304 0 1
Education>12 years 48168 0.312 0 1 37474 0 0 0 7280 0.142 0 1 5464 0.088 0 1
White 48168 0.857 0 1 37474 0.823 0 1 7280 0.634 0 1 5464 0.000 0 0
State unemployment rate 48168 5.82 2 12.5 37474 5.74 2 12.5 7280 6.00 2 12.5 5464 5.99 2 12.5
County % black, 1960 48168 9.68 0 81.3 37474 9.90 0 81.3 7280 12.39 0 62.1 5464 19.72 0.1 62.1
County % urban, 1960 48168 70.15 0 100 37474 67.06 0 100 7280 74.98 0 100 5464 81.99 0 100
County % farmland, 1960 48168 44.75 0 239.8 37474 45.54 0 239.8 7280 41.36 0 126.6 5464 36.37 0 116.2
County % ?$3,000, 1960 48168 20.94 5.5 74.4 37474 22.09 5.5 74.4 7280 20.52 5.5 68 5464 21.52 5.5 68
County % <5 years, 1960 48168 11.25 5.6 18.2 37474 11.19 5.6 18.2 7280 11.21 6.7 18.2 5464 11.26 7.4 15.1
County %>65 years, 1960 48168 9.28 1 24.9 37474 9.39 1 24.9 7280 9.25 2.8 24.9 5464 8.96 2.8 24.9
log(1960 county population) 48168 12.29 7.72 15.61 37474 12.15 7.72 15.61 7280 12.64 7.72 15.61 5464 13.19 8.74 15.61
County per cap ret. and dis. 48168 983.03 0 23532 37474 992.42 0 23532 7280 1025.32 0 23532 5464 985.65 0 4539.11
County per cap medical payments 48168 219.00 0 6646.72 37474 216.82 0 6646.72 7280 236.25 0 6646.72 5464 246.56 0 1282.09
County per cap cash PA payments 48168 224.99 0 14071 37474 224.54 0 14071 7280 284.00 0 14071 5464 332.98 0 2714.17
Note: PSID interview years 1968-78. Observations from Alaska are dropped because of missing data on food stamp program start dates. All outcome variables correspond to annual measures taken as of
the interview (in spring of the interview year).
42
Appendix Table 4: 1969 Food Stamp Price Schedule
(Price charged for total monthly coupon allocation, prior to the 1978 elimination of the purchase
requirement)
Family Family Family
size =2 size =3 size=4
(1) (2) (3)
Total monthly
coupon 56 84 106
allocation
Monthly income
Monthly purchase price
range
0-19 1 1.5 2
20-29 1 1.5 2
30-39 4 4 4
40-49 7 7 7
50-59 10 10 10
60-69 12 13 13
70-79 15 16 16
80-89 18 19 19
90-99 21 21 22
100-109 23 24 25
110-119 26 27 28
120-129 29 30 31
130-139 31 33 34
140-149 34 36 37
150-169 36 40 42
170-189 46 48
190-209 52 54
210-229 58 60
230-249 64 66
250-269 66 72
270-289 72
290-309 76
310-329 80
330-359 80
360 and higher 82
43
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