Do SNAP participants expand non-food spending when they receive more SNAP Benefits?—Evidence from the 2009 SNAP benefits increase

Food Policy 65 (2016) 9–20

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Do SNAP participants expand non-food spending when they receive more SNAP Benefits?—Evidence from the 2009 SNAP benefits increase Jiyoon Kim Department of Economics, Doermer School of Business, Indiana University – Purdue University, Fort Wayne, IN 46805-1499, USA

a r t i c l e

i n f o

Article history: Received 19 October 2015 Received in revised form 8 October 2016 Accepted 18 October 2016 Available online 27 October 2016 JEL codes: I38 D12 Keywords: SNAP ARRA Expenditure response Food stamp Consumer expenditure survey

a b s t r a c t This study examines the expenditure response to the largest increase in Supplemental Nutrition Assistance Program benefits, instituted in April 2009. Investigating the effects in both food and nonfood spending categories, I find that the rise in SNAP benefits increased not only food at home expenditures, but also housing, transportation, and education expenditures of SNAP households relative to those of non-SNAP households. Specifically, the SNAP benefit increase leads to the reduced out-of-pocket spending on food for infra-marginal SNAP recipients, and the freed up resources allowed households with bounded budgets to fund other essential needs, such as paying mortgage, rent, utility fee, transportation expenses as well as tuition. Examining non-food expenditures provides a more complete picture of the impact of the SNAP benefit increase by shedding light on the spillover effect of the policy change. The result also derives policy implication on ongoing debate about SNAP allotment generosity. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction The Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp Program, is the federal government’s largest nutrition assistance program, with a maximum monthly benefit of $194 per person (CBPP, 2016). The program began in the 1960s and since then the participation rate has increased dramatically. The SNAP allotment has steadily gone up, but given that many households still lack access to sufficient food and that food insecurity is rising, debate continues about the value of generous SNAP benefits (USDA Economic Research Report, 2015a). In direct response to the Great Recession, SNAP maximum monthly benefits increased on April 1, 2009, by 13.6% as part of the American Recovery and Reinvestment Act (ARRA). This was the largest one-time increase in SNAP benefits (Fig. 1). My study quantifies the effects on SNAP household consumption choices of this unprecedentedly large boost in SNAP benefits. By employing a simple difference-in-differences strategy, I compare consumption of SNAP households with that of lowincome non-participants before and after the increase in SNAP benefits. I use the Consumer Expenditure Survey (CEX) data from E-mail address: [email protected] http://dx.doi.org/10.1016/j.foodpol.2016.10.002 0306-9192/Ó 2016 Elsevier Ltd. All rights reserved.

2007 to 2011, which covers two years before and two years after the 2009 SNAP benefit increase. I find that after ARRA total household expenditures rose more for SNAP households than for low-income non-participants by $407 per quarter. When I investigate each expenditure category separately, the increase is pronounced in the subcategories of food (strongly driven by food at home), housing (mainly by shelter costs), and education. Yet I find null results for tobacco and food away from home expenditures—a result relevant to policy. The evidence suggests that the SNAP benefit increase in 2009 helped participating households to expand food expenditures and allocate extra resources to meet other pending household needs. This paper adds value to existing studies in two respects. First, almost all studies of SNAP’s consumption effects estimate the marginal propensity to consume (MPC) food out of food stamps and compare this to the MPC food out of cash, using data collected decades ago. But in recent years the size and characteristics of the population served by SNAP have changed dramatically. Thus, studies using earlier data tell us little about the behavior of contemporary SNAP participants and have limited relevance to current discussions of policy. In this paper, I do not repeat the question of whether food spending under SNAP is greater than that induced by cash transfers. Most importantly, I focus on the current program

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J. Kim / Food Policy 65 (2016) 9–20

135 125 115 105 95 85 75 65 55 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

Fig. 1. Average Monthly SNAP Benefits per Person ($2009). Source: Author’s calculation using program data from ‘‘SNAP National Level Summary: Participation and Costs, 1969–2014” from Food and Nutrition Service, U.S. Department of Agriculture. .

and examine the effectiveness of the SNAP benefit increase in expanding the food and non-food choices of needy families. Second, my study examines the effects of the benefit increase on non-food consumption. As previous studies have shown, the MPC on food with food stamps is greater than the MPC on food with equivalent cash. However, little attention has been paid to the fact that the MPC food with food stamps is less than one. Indeed, SNAP increases both food and non-food spending. My contribution is to break down the impact into sub-spending categories. The goal of SNAP is to alleviate hunger and malnutrition by increasing food purchasing power (Food Stamp Act of 1977). Thus, any effects on overall consumption through impacts on non-food expenditures can be considered as spillover effects. I find that the increase in SNAP benefits provides considerable support to spend on housing (paying mortgage or rent) and education (paying tuition). This outcome is consistent with the intent of the policy. Many economists believe that consumption is a better measure of well-being than income, particularly for those at the bottom of the income distribution, because it reflects permanent income, the insurance value of government programs, and private and government transfers (Meyer and Sullivan, 2006). Hence, studying consumption at the level of sub-spending categories reveals the welfare effects of the benefit increase during a time of economic hardship.

2. Background and institutional detail In response to the Great Recession, Congress passed the American Recovery and Reinvestment Act (ARRA) in February 2009. It is commonly referred to as ‘‘the Stimulus Package” or ‘‘the Recovery Act.” At roughly $800 billion, it was the largest fiscal stimulus programs in American history (Wilson, 2012). A wide range of tax credits and direct transfers was provided to low-income groups. For example, unemployment benefits were extended to a maximum of up to 99 weeks and increased by $25 a week, and unemployment benefits were excluded from taxation for the first $2400. Additionally, a one-time direct cash payment of $250 was given to Social Security recipients, people on Supplemental Security Income, and veterans receiving disability and pensions. The Earned Income Tax Credit (EITC) was expanded for families with at least three children. Billions of dollars in welfare payments were distributed through Temporary Assistance for Needy Family

(TANF), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), free school lunch programs, job training and the employment service. In particular, the maximum SNAP monthly allotments were increased 13.6% above the previous level. This benefit increase was the largest since the initiation of the program. Fig. 1 depicts a trend of average monthly SNAP benefits per person. A peak occurred in 2009 as a result of ARRA. Table 1 reports the maximum benefit amount for a family of 4 per fiscal year and the percent change in benefit level. Table 2 presents the amount increased by family size. The increase in SNAP benefits from the 2009 ARRA was $18 to $24 per person per month—large enough to allow low-income households to spend on other categories in addition to food. 3. Theoretical framework I present a neoclassical model of consumer choice and predict the effect of a SNAP benefit increase on household food and non-food spending. Fig. 2, based on the standard Southworth (1945) model, wherein consumers allocate a fixed budget between food and all other goods, provides the overarching conceptual framework. Panel A shows that the budget line shifts out horizontally in
keeping with the amount of the increase B00F , from CD to EF. The slope stays the same because the transfers do not affect the relative price between food and non-food. Panel B depicts shifts in the consumption choices of two types of SNAP recipients, infra-marginal (A) and extra-marginal (B). 3.1. Infra-marginal recipient A household that spends on food an amount greater than their SNAP allotment is infra-marginal, and the vast majority of SNAP recipients fall into this category (Hoynes et al., 2014). Such households treat the in-kind transfer as cash (Whitmore, 2002). After the SNAP benefit rises, infra-marginal recipients choose the optimal consumption bundle denoted by A2 rather than the previous optimal bundle A1 . This simple static consumer choice theory predicts (1) that household food expenditure will rise with the new budget set but (2) that the increase in food spending will be less than the added dollars provided by the SNAP benefit. Expenditures on nonfood goods will rise as a result of reduced out-of-pocket spending on food.

J. Kim / Food Policy 65 (2016) 9–20 Table 1 Maximum monthly SNAP benefits for a family of 4. Source: Food and Nutrition Service, U.S. Department of Agriculture. Year

Maximum benefit for a family of 4

Percent change in benefit level

Oct 1994–Sept 1995 Oct 1995–Sept 1996 Oct 1996–Sept 1997 Oct 1997–Sept 1998 Oct 1998–Sept 1999 Oct 1999–Sept 2000 Oct 2000–Sept 2001 Oct 2001–Sept 2002 Oct 2002–Sept 2003 Oct 2003–Sept 2004 Oct 2004–Sept 2005 Oct 2005–Sept 2006 Oct 2006–Sept 2007 Oct 2007–Sept 2008 Oct 2008–March 2009 April 2009–Sept 2009 Oct 2009–Oct 2013 Nov 2013–Sept 2014

$386 $397 $400 $408 $419 $426 $434 $452 $465 $471 $499 $506 $518 $542 $588 $668 $668 $632

2.8% 0.75% 2% 2.7% 1.7% 1.9% 4.1% 2.9% 1.3% 5.9% 1.4% 2.4% 4.6% 8.5% 13.6% 0% 5.4%

Table 2 Monthly SNAP benefit increase from the 2009 ARRA. Source: Food and Nutrition Service, U.S. Department of Agriculture.

*

Household size

Increase

Household size

Increase

1 2 3 4

$24 $44 $63 $80

5 6 7 8

$95 $114 $126 $144

For each extra person, a household receives $18.

3.2. Extra-marginal recipient The in-kind nature of SNAP should only matter for households whose benefit exceeds their desired food budget. For the extramarginal type, the SNAP allotment is large enough to result in zero dollars spent of out-of-pocket on food. Hence, food expenditure is equal to the SNAP benefits. When the benefit is increased, extramarginal recipients move from B1 to B2 , thereby increasing their food expenditures by the exact amount of a SNAP benefit increase. Spending on non-food goods remains unchanged. From the empirically well-known fact that most SNAP recipients are infra-marginal, my model derives two testable hypotheses: (1) Under ARRA, a majority of SNAP recipients will increase both food and non-food expenditures. (2) The resulting increase in food expenditures will be less than the incremented amount of the SNAP benefit. Fig. 3 presents total food spending decomposed into cash spending and SNAP spending as a function of household net income for the infra-marginal group only. When family size is fixed, the maximum SNAP benefit appears as a horizontal line. A family with zero income is eligible for the maximum SNAP allotment, and this becomes their food expenditure. As income grows, the SNAP benefit decreases proportionally; the current benefit reduction rate is 0.3.1 I hypothesize that total food spending increases with income.2 In Panel A, a household participating 1 To find the household’s allotment (i.e., SNAP = Maximum allotment  0.3  [net income]) the net monthly income of the household is multiplied by 0.3 and then subtracted from the maximum allotment for the household size. This is because SNAP households are expected to spend about 30% of their resources on food (http://www. fns.usda.gov/snap/eligibility). 2 As predicted in Engel’s law, the share of income spent on food declines with income level. In high-income households the share declines but the absolute dollar amount spent on food is greater than the total food expenditures of low-income households.

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in SNAP spends the total AC on food; AB comes from the SNAP benefits and BC comes out of pocket. Reflecting the ARRA increase in benefits, in Panel B the maximum SNAP benefit line shifts up 13.6%. Accordingly the SNAP benefit line also shifts up, although the slope is unchanged. Total food spending rises for SNAP households (households whose income is less than 130% of the poverty line3), but in non-SNAP households it does not change. After ARRA, the same hypothetical household’s total spending on food now reaches AC 0 , increased by CC 0 . This rise is less than the increase in SNAP benefits, BB0 . In other words, infra-marginal recipients treat the increased amount of the SNAP like an increase in cash income, and this leads to a rise in both food and non-food spending. It is equally important to examine to which non-food categories the difference between BB0 and CC 0 has been allocated by the infra-marginal households. The predictions of the model guide an empirical test of my research question: After ARRA, by how much did food expenditures go up in response to the rise in SNAP benefits? Did the increase in SNAP benefits lead to an increase in non-food expenditures, and if so, in which categories? In this study, I provide evidence on how food expenditures respond to the change in SNAP allotments, not to the introduction of the SNAP program. A number of papers about SNAP’s effect on consumption have addressed shifts in food expenditure, but few have studied shifts in non-food expenditures, which constitutes another great part of low-income households’ life. 4. Literature review Classic consumer theory predicts that households that spend more on food than their SNAP allotment (i.e., the infra-marginal group) will treat the in-kind transfer as cash. When the SNAP benefit is increased, these households spend more on food and on non-food items. This prediction has been empirically confirmed. Most previous studies, however, have asked whether SNAP leads to larger increases in food purchases than a similar sized cash transfer does, and they pay little attention to the impact on non-food items. Previous papers (summarized in Fraker (1990) and Levedahl (1995)) typically estimate the marginal propensity to consume (MPC) food out of food stamps and the MPC food out of cash to measure the primary goal of food stamps as the increased food consumption. According to Fraker (1990), the MPC food out of cash is 0.03–0.17, whereas the MPC food out of food stamps is about 0.17–0.47. Unfortunately, many MPC estimates have been confounded by failures to disentangle the unobserved characteristics of SNAP participants. Another strand of the literature addresses this selection issue by examining the major cash-out randomized trials of the early 1990s, when Food Stamp benefits were paid to a random subset of recipients in cash rather than food stamps. But scholars have reached inconsistent conclusions regarding how converting food stamps into cash affects the food expenditure decisions of recipients (Moffitt, 1989; Ohls et al., 1992; Fraker et al., 1995; Levedahl, 1995; Breunig and Dasgupta, 2002, 2005). This paper makes two principal contributions to the literature. First, most previous studies examine data obtained 20 years ago or more. The current SNAP has evolved considerably since its inception. Important changes include the elimination of the pur3 In the model, I assume (1) that every household whose income is less than 130% of the poverty line participates in the SNAP and (2) that those whose income is more than 130% of the poverty line do not. Empirically, SNAP eligibility is not strictly enforced by income threshold: those with high income may be eligible and those with low income may not when other factors such as asset test, broad-based categorical eligibility, and stigma of usage are considered.

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J. Kim / Food Policy 65 (2016) 9–20

Panel A: Impact of SNAP Benefit Increase on Budget Constraint Other goods

Panel B: Consumption Decisions in Response to SNAP Benefit Incre Other goods

C

E

A 1

B’’

B*

*

2

Budget constraint with SNAP benefit increase

F

* A*

with SNAP

1

B

D

F

Food

BF1

BF2

AF1

AF2

Food

Fig. 2. SNAP benefit increase in the 2009 ARRA and a consumer’s choice. Note: Figs. 2 and 3 are directly adopted from Hoynes et al. (2014). Hoynes et al. (2014) analyze the introduction of the SNAP program, while I analyze the increase in SNAP benefits.

Panel A: Food Spending for SNAP-participating Households

Panel B: Changes in Food Spending after the SNAP Benefit Incre

Fig. 3. SNAP benefit increase in the 2009 ARRA and allocation of food expenditures.

chase requirement,4 the switch to electronic benefit transfer (EBT), and a decline in black markets for food stamps. The characteristics of SNAP participating households also have changed over time. Participation in the program has sky-rocketed, rising from 2% of the U.S. population in 1970 to nearly 14% in 2011 (USDA SNAP Annual Summary, 2015b). Accordingly, there is a compelling need to study the current program. This paper analyses how the increase in SNAP allotments affects SNAP household consumption choices—an issue relevant to contemporary policy making. Second, I do not compare the MPC food out of SNAP to that of cash transfers, a topic that has been already studied by many researchers. The fact that has been overlooked is that the MPC food out of SNAP is less than 1. In other words, participants increase nonfood purchases as well as food purchases with each dollar of SNAP benefits. Still under-examined in the literature is the question of how SNAP participants allocate their resources to nonfood

4 Families were required to make a cash payment up front to receive their food stamp benefits. For example, if a family was able to afford to spend $50 on food, but the cost of the thrifty food plan was $70, the family could purchase $70 in food stamps for the cash price of $50. Under the current SNAP program, they would receive $20 without outlaying any cash.

spending categories. My contribution is to break down this effect into subcategories. Two previous studies have examined the impacts of the 2009 SNAP benefit increase. Using the Current Population Survey Food Security Supplement (CPS-FSS), Nord and Prell (2011) compare food spending in December 2009 and December 2008. To provide an estimate of changes in food expenditures attributable to ARRA, they use a difference-in-differences approach to net out the effect of year-to-year changes in food prices and any other factors that affected the SNAP-eligible and non-eligible groups similarly. They find that the increase in food expenditures is 2.2% greater in SNAP-eligible households than in non-eligible households. Using the SNAP receipt information in CPS, they compare SNAP participant and non-participant groups: median food expenditures increased 9.1% among SNAP-participant households compared to 3.4% among SNAP-eligible non-participating households. This finding may overstate the impact given that selection into SNAP is positively correlated with tastes for food consumption (Moffitt, 1983; Currie, 2004). Most recently, Beatty and Tuttle (2015, hereafter BT) used CEX data to examine several SNAP benefit increases from 2007 to 2010 (including the largest increase in 2009 ARRA). These changes, they conclude, caused households to increase food-at-home

J. Kim / Food Policy 65 (2016) 9–20

expenditures and the share allocated to food-at-home. To create a comparison group, they employ a difference-in-differences strategy, using the Coarsened Exact Matching approach (CEM)5 and conclude that an estimate of the MPC out of the increase in SNAP benefits to be 0.48. Both of these studies examine only food expenditures. But evidence strongly suggests that overall consumption more accurately measures well-being of the disadvantaged (Meyer and Sullivan, 2006, 2008, 2012). Consequently, I examine the effects of the SNAP benefit increase on both total household expenditures and individual spending categories. Changes in expenditures on spending categories identify possible allocation across categories within households, and are thought to reflect the spillover effects of the policy change. Several points distinguish my study from BT. First, BT do not focus solely on the 2009 ARRA SNAP benefit increase. Instead, their analysis examines smaller benefit changes, such as the 4.6% benefit increase in 2007 and the 8.5% increase in 2008. To isolate the impact of the SNAP benefit increase during the years that followed the Great Recession, my study focuses exclusively on a 13.6% benefit increase instituted in 2009 ARRA. Second, in the CEX, SNAP participation is reported only for the first and last interviews, but it is imputed for the rest of the interviews (i.e., data is replaced with the value from either the first or the last interview).6 BT examine only households that completed two or three interviews in a row7 to estimate within-household specifications. Hence, their analysis sample is prone to the SNAP imputation problem, and it is confined to families without attrition. It is worth noting that in the CEX economically advantaged households complete higher number of interviews with less attrition. My paper uses only the first quarterly interview, which reports actual income and program participation. I also examine only one interview per consumer unit no matter how many quarterly interviews they have completed. This strategy allows me to bypass the confounding relationship between number of interviews and households’ social economic status (SES). Third, as noted above, BT use household fixed effects to estimate the impact of the benefit increase. So their identification is due to change in spending within households whose first interview was before the increase and last interview was after. However, a potential limitation is that the CEX follows addresses across survey quarters and not specific households. Although they drop any households whose demographic variables were inconsistent over the survey period, this could still confound the result. Moreover, benefit changes typically occur each year in October, with an exception of April 2009. Therefore, interviews before the change occur during summer months and interviews after the change occur during fall and winter months, conflating program effects with seasonality. Lastly, looking at changes in consumption over one or two quarters might not reveal spending that occurs over longer time periods. Using cross-household variation, I compare a group of households whose first interview occurred before April 2009 to another group of households whose first interview occurred after April 2009.

5 A comparison group is generated with a distribution of explanatory variables similar to the treatment group, which improves balance between the two groups, and thereby the groups differ only by SNAP participation. 6 Income and program participation data in the middle quarters are imputed, unless there is anyone turning 14 years old or any members who previously did not work but are now working. 7 They limit their analysis to households that had at least three interviews in a row in their placebo test.

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5. Data and the empirical identification strategy 5.1. Data The Consumer Expenditure Survey (CEX), administered by the Bureau of Labor Statistics, is the only survey in the U.S. that collects a complete record of consumption expenditure data on hundreds of categories of goods and services. In addition to the buying habits of the nation’s households, the CEX reports household characteristics, income, and program participation. The CEX consists of a quarterly interview survey and a diary survey. In the interview survey, each consumer unit8 is interviewed every three months, providing a short panel of up to five consecutive quarters. In the diary survey, respondents keep track of all of their purchases for two consecutive weeks. This paper examines data from the interview survey, which is designed to obtain data on the types of expenditures respondents can recall for a period of three months or longer. Relatively large and regular expenditures, such as rent, utilities, health care, major durable goods, as well as food, are captured in the interview survey. The analysis sample is drawn from the years 2007 to 2011, which includes two years before and two years after the April 2009 benefit increase. My pre-ARRA sample period starts with interviews conducted in April of 2007 and runs through interviews carried out in February of 2009 (which covers expenditures from January 2007 to January 2009). My post-ARRA baseline sample period starts with interviews conducted in August 2009 and runs through interviews carried out in June 2011 (thus covering expenditures from May 2009 to May 2011). To avoid the distinctive consumption patterns of households headed by the young and the old, I restrict my sample to households whose heads are at least 20 years old and less than 65 years old. I drop from the sample any households with implausibly low expenditures and implausibly high expenditures (2.4% of the analysis sample). My analysis focuses on households with income below 185% of the poverty line,9 such that non-SNAP households earning up to 185% of the poverty line provides as similar income and expenditure levels as possible with those of SNAP households. Table 4 confirms that the expenditure level in every spending category balances well across the two groups. Although each consumer unit could be interviewed for up to five consecutive quarters, for every consumer unit I use the first interview only. In other words, every household contributes once to the analysis sample no matter how many interviews they have completed. This is again because of income and program participation information in the CEX: expenditure information pertaining to the quarter or to individual months is collected in each quarterly interview, but information about income and program participation for the previous 12 months is collected in only two of the five interviews—during the first interview and the last interview. Final sample size is 8700 households—4006 households before the 2009 ARRA and 4694 households after ARRA. It is worth noting that SNAP receipt can be measured with substantial error in the CEX. Hoynes et al. (2014) point out that the number of households reporting SNAP receipt in the CEX is approximately 60% of those recorded in SNAP administrative records. I use only the first quarterly interviews of the CEX that report nonimputed data on program participation, thus underreporting is

8 The consumer unit is either the members of a household, a person living alone or with others, or two or more persons living together who make joint expenditure decisions. I use the terms ‘‘consumer unit” and ‘‘household” interchangeably to describe the unit of analysis. 9 The reason is that the broad based categorical eligibility policy adopted by many states has effectively raised the gross income eligibility threshold by as much as 185 or 200% of poverty around 2009 (Laird and Trippe, 2014).

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J. Kim / Food Policy 65 (2016) 9–20

rather smaller compared to the sequence of following quarterly interviews. Table 3 shows the proportion of households in the CEX analysis sample that reported SNAP receipt in comparison to the administrative participation rates released by USDA. The participation rates are not significantly off between the two data sets, but are only one to two percentage points lower in the CEX. However, this could mean a big difference in percentage terms. Beatty and Tuttle (2015) also carefully document the underreporting issue in the CEX, arguing that the control group almost certainly contains SNAP participants. This contaminated control group will spend a greater amount on food and other categories than would an uncontaminated control group, making the difference between the two groups smaller. Consequently, to the extent that there exists underreporting in the CEX and the control group erroneously contains some SNAP recipients, the estimates from this paper may underestimate the true impact of the SNAP benefit increase. Table 4 summarizes the mean expenditure for each spending category in the two groups (SNAP and non-SNAP) before and after the 2009 ARRA. It is notable that food expenditures fell in the nonSNAP group, but rose in the SNAP group after 2009, resulting in a simple difference-in-differences of $138. This trend in food expenditure is strongly driven by food at home. The story remains the same for the budget share allocated to food and food at home. A similar trend is evident in housing category, primarily induced by shelter costs. The simplest difference-in-differences estimators, which rely on sample mean of expenditures, are positive for every category except tobacco. 5.2. Empirical approach Going beyond simply comparing the sample mean of the two groups before and after the ARRA, I examine the effects of the SNAP benefit increase by using the difference-in-differences (DID) identification strategy:

C i:t ¼ b0 þ b1  SNAP i þ b2  Afteri;t þ b3  SNAP i;t  After i;t þ b4  X i:t þ i:t

ð1Þ

where C i;t denotes dollar amounts of consumption for household, i, in quarter, t; SNAP is an indicator for SNAP participating households. It should be noted, however, that the timing may be rather imperfect because changes in many other policies could have been simultaneously happening around the time of ARRA in 2009. Hence, I carefully design before and after groups such that the model compares before and after April 1, 2009, the day on which SNAP benefit increased. Households that completed their first interview before February 2009 (the month of ARRA implementation) are included in before group and After takes a value of zero. Those that completed their first interview after August 2009 are included in the after group, and After takes on a value of one. The after group reports its consumption from May 2009, immediately after the ARRA SNAP increase. Several characteristics of the SNAP households potentially could affect consumption, regardless of the policy. Thus, I control for a vector of demographic characteristics, X i;t , such as age, agesquared, race, education, marital status, employment status of the head, family size, the number of children less than 18 years old, the proportion of children younger than 5 years old, the proportion of children over 5 years old, the proportion of elderly over 65 years old, whether the household resides in a metropolitan statistical area (MSA), and the region of residence. Lastly, to control for seasonality and the yearly changes that affect all households, I include the month (January to December) and year (2007 to 2010) fixed effects. This is a standard difference-in-differences model with b3 measuring the difference between the change in the SNAP group’s consumption pre- to post-ARRA and the change

Table 3 Supplemental nutrition assistance program participation rate. Year

CEX (%)

USDA Administrative Data (%)

2007 2008 2009 2010 2011

6.58 7.90 9.69 11.96 12.47

8.74 9.28 10.92 13.03 14.34

Note: SNAP participation rates in the CEX are calculated by the author using all consumer units from the first quarter interviews of the CEX 2007–2011. Administrative SNAP participation rates are calculated using program data from ‘‘SNAP National Level Summary: Participation and Costs, 1969–2014” from Food and Nutrition Service, U.S. Department of Agriculture. http://www.fns.usda.gov/sites/ default/files/pd/SNAPsummary.pdf.

in the non-SNAP group’s consumption pre- to post-ARRA. Changes in consumption common to both groups are netted out. In the main analysis, the term SNAP is 1 if households report any SNAP receipt for the past 12 months and 0 if households report no SNAP receipt and earning up to 185% of the poverty line. However, direct comparison of SNAP recipients and non-recipients can be confounded by self-selection and underreporting of SNAP receipts. Analysts need to be especially cautious about selection into SNAP at the time of ARRA enactment because the increase in SNAP benefits can shift self-selection probabilities: some households that were not motivated to participate under the pre-ARRA benefit level now might be enticed to participate by the larger benefit. These households are likely to be at the margin of eligibility, better off than the average pre-ARRA participants and worse off than the average pre-ARRA non-participants (Nord and Prell, 2011). Their inclusion into SNAP participant group could improve average participant household consumption status, even though the SNAP benefit increase might not improve their economic well-being. Accordingly, for a robustness check, I use the income cutoff to define the SNAP eligible treatment group. In this analysis, SNAP is 1 if household’s income falls below 130% of the poverty line and is 0 if household’s income is above 130% but below 185% of the poverty line. The point of this robustness check is to bound the effect of the underreporting and misclassification of SNAP participation. 6. Findings 6.1. SNAP participants vs Non-participants Table 5 presents the main results. In Panel A, the treatment group includes households that received SNAP benefits during the previous 12 months. The regression covers two years before ARRA (January 2007 to January 2009) and two years after ARRA (May 2009 to May 2011). Each column specifies a different expenditure category. The coefficient on the SNAP variable indicates the difference in expenditures between the two groups before ARRA. As expected, SNAP group has far lower expenditures all along the categories, showing that they are worse off at baseline. The coefficient on the After variable estimates the pre- to post-change for the non-SNAP group. The estimate of primary interest is the coefficient on the interaction term. This is the difference in the pre- to post-ARRA consumption change between the two groups. Using the detailed information provided by the CEX, I decompose the aggregate household spending into major expenditure categories.10 Table 5 shows the overall change in consumption (column 1) and the contribution to the overall change from various components of consumption (column 2 to 11). Note that Food at home (column 3) and Food away (column 4) are subcategories of Food (column 2); Shelter (column 6) and Utility (column 7) are subcategories

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J. Kim / Food Policy 65 (2016) 9–20 Table 4 Quarterly expenditures ($2009) by SNAP participation before and after the SNAP benefit increase in April 2009. Spending categories

(1) (2) Before ARRA (Jan 2007–Jan 2009)

(3) (4) After ARRA (May 2009–May 2011)

(5) Difference in difference

SNAP

No-SNAP

SNAP

No-SNAP

Total expenditure ($) Food ($) (Share) Food at home ($) (Share) Food away ($) (Share)

5221.89 1262.50 24.2% 1043.17 20.0% 219.75 4.2%

6098.37 1381.52 22.7% 1004.89 16.5% 376.88 6.2%

5228.78 1320.40 25.3% 1135.78 21.7% 185.05 3.5%

5704.40 1301.25 22.8% 963.87 16.9% 337.64 5.9%

400.86 138.17 0.9% 133.63 1.3% 4.54 0.4%

Housing ($) (Share) Shelter ($) (Share) Utility ($) (Share)

2183.86 41.8% 1313.63 25.2% 600.13 11.5%

2616.56 42.9% 1677.61 27.5% 619.27 10.2%

2318.71 44.3% 1400.84 26.8% 619.88 11.9%

2589.69 45.4% 1655.12 29.0% 617.31 10.8%

161.72 0.0% 109.70 0.1% 21.71 0.3%

Transportation ($) (Share) Entertainment ($) (Share) Education ($) (Share)

1042.03 20.0% 236.42 4.5% 42.08 0.8%

1312.41 21.5% 314.36 5.2% 250.98 4.1%

865.46 16.6% 255.84 4.9% 67.80 1.3%

1093.93 19.2% 289.17 5.1% 219.75 3.9%

41.91 1.1% 44.61 0.5% 56.95 0.8%

Tobacco ($) (Share)

143.14 2.7%

81.31 1.3%

136.68 2.6%

85.35 1.5%

10.50 0.3%

Note: Sample includes all consumer units with income less than 185% of the poverty line from CEX 2007–2011. SNAP is a group that reported any SNAP receipt during the past 12 months; Non-SNAP is a group that reported no SNAP receipt. All expenditures are expressed in 2009 dollars.

Table 5 [Main results] Effects of the 2009 SNAP benefit increase on households’ expenditures difference-in-differences estimation: before (2007–2009) and after (2009–2011). Quarterly expenditure ($2009) (1) Total expenditure

(2) Food

(3) Food at home

(4) Food away

(5) Housing

(6) Shelter

(7) Utility

(8) Transportation

(A) Sample: SNAP participants (treatment) compared to non-participants with income 6 185% of the poverty line (control) SNAP 1489.60*** 231.90*** 114.65*** 117.13*** 552.45*** 394.83*** 102.78*** 195.82* [164.139] [34.241] [27.420] [16.715] [62.184] [44.651] [17.193] [104.791]

(9) Entertainment

(10) Tobacco

(11) Education

71.70*** [19.901]

60.29*** [10.966]

98.57*** [25.122]

After ARRA

91.75 [355.386]

0.17 [64.856]

30.79 [45.049]

30.91 [40.985]

18.79 [135.138]

3.94 [107.058]

45.82 [32.639]

64.80 [207.295]

22.75 [40.811]

9.48 [17.439]

75.57 [81.505]

SNAP*After ARRA

407.70** [194.837]

125.63*** [40.510]

123.76*** [33.096]

1.89 [19.850]

141.66* [79.391]

100.67* [59.778]

25.10 [21.201]

20.91 [116.357]

38.49* [22.896]

11.38 [14.008]

65.74* [36.637]

Observations

8700

8700

8700

8700

8700

8700

8700

8700

8700

8700

8700

R-squared

0.233

0.243

0.318

0.056

0.205

0.155

0.330

0.045

0.044

0.052

0.054

(6) Shelter

(7) Utility

Quarterly expenditure ($2009) (1) Total expenditure

(2) Food

(3) Food at home

(4) Food away

(5) Housing

(8) Transportation

(B) Sample: households with income 6 130% of the poverty line (treatment) compared to those with 130% < income 6 185% (control) LowInc 1364.83*** 160.19*** 101.91*** 58.21*** 433.46*** 265.20*** 109.66*** 402.06*** [165.513] [34.738] [27.154] [19.258] [63.390] [48.142] [14.599] [109.156]

(9) Entertainment

(10) Tobacco

(11) Education

85.16*** [27.395]

5.62 [7.981]

94.77*** [33.632]

After ARRA

174.75 [365.429]

20.24 [66.115]

63.40 [49.149]

43.09 [38.382]

11.98 [138.553]

60.56 [104.908]

40.99 [35.314]

98.56 [223.872]

20.22 [44.818]

15.37 [19.024]

140.84 [92.516]

LowInc*After ARRA

190.26 [209.656]

100.40** [42.518]

99.95*** [32.768]

0.48 [24.163]

29.78 [85.586]

91.86 [65.533]

2.10 [20.063]

236.32* [133.612]

32.76 [31.392]

15.66 [12.284]

60.99 [43.586]

Observations

8592

8592

8592

8592

8592

8592

8592

8592

8592

8592

8592

R-squared

0.235

0.242

0.319

0.050

0.210

0.154

0.336

0.046

0.045

0.045

0.054

Note: These are coefficients from estimating Eq. (1). Regressions include demographic variables, month and year dummies. Sampling weight is used. Column (3) and (4) are subcategories of Column (2). Column (6) and (7) are subcategories of Column (5). *** p < 0.01. ** p < 0.05. * p < 0.1.

16

J. Kim / Food Policy 65 (2016) 9–20

of Housing (column 5). All other columns are independent categories. The coefficient on the food expenditures interaction term is statistically significant at $125.63, mainly driven by food at home expenditures. This makes sense because the SNAP benefits can only be used for designated food items, and it is used mostly for food at home.11 The magnitude of the interaction term is $123.76 for food at home. This change in quarterly expenditure is equivalent to an increase in monthly expenditure of $41. The mean household size in the SNAP group is 3.2 (Appendix B Table B1). Therefore, $41 of monthly household expenditure corresponds to about $12.8 per person. As shown in Table 2, the average increase in monthly SNAP benefits per person ranges from $18 to $24, depending on family size. Thus, the increase in food expenditures is less than the full SNAP benefit increase; this, too, is consistent with the predictions of the theoretical framework. This finding confirms that SNAP recipients are infra-marginal participants who treat an increase in in-kind transfers as an increase in disposable income. A pure income effect predicts an increase in both food and non-food spending. SNAP benefits can be used only to purchase food. By reducing out-of-pocket spending on food, the freed up resources allow households with bounded budgets to fund other needs. This is precisely what we see in Table 5 Panel A. After the 2009 ARRA, expenditures on housing (mainly driven by shelter cost, which includes rent or mortgage payment) as well as entertainment (which includes fees, admissions, and leisure equipment) and education (which includes tuition and school-related expenses) rose more for SNAP households than non-SNAP households. It provides suggestive evidence that SNAP participants were able to afford other essential parts of life in response to the boost in benefits. SNAP receipts in the CEX are fairly underreported, and so the results produced from the analysis of SNAP participants should be interpreted with caution. If the non-SNAP group erroneously includes SNAP households that refused to report their participation, the estimates would be underestimated and regarded as a lower bound of the effect of the SNAP benefit increase. 6.2. Households with income 6 130% vs households with 130% < income 6 185% In Table 5 Panel B, I implement a robustness check by making use of SNAP’s income eligibility criteria to define a treatment group. SNAP is the one element of the safety net that is truly universal – available to any households with low incomes.12 Although some states have expanded the eligibility cutoff, any U.S. household is eligible for SNAP if its gross monthly income13 is less than 130% of the poverty line. I use this income cutoff to identify the SNAP treatment group; a comparison group has income14 greater than the eligibility cutoff but lower than 185% of the poverty line. The point of this robustness check is to see how big a bias the results might have from misclassification or underreporting of SNAP participation. It is worth noting that the income reported in the CEX does not have the same accounting period as the one used to define SNAP 11 The CEX ‘‘food at home” concept is the closest match to the items that can be purchased with SNAP benefits. This measure collects spending on food at groceries, convenience stores, farmers markets and home delivery services, minus the cost of paper products, cleaning supplies, pet food, and alcohol (Hoynes et al., 2014). 12 To be eligible for SNAP benefits households have to meet both a monthly gross income test and a monthly net income test unless all members are receiving Supplemental Security Income (SSI) or Temporary Assistance for Needy Families (TANF). Other households with one or more elderly members only have to meet the net income test. 13 Gross income is a household’s total income before any deductions have been made. 14 The income used for SNAP eligibility is a cash, pre-tax measure and does not include in-kind benefits or tax credits. Consequently, I use the before-tax income minus SNAP benefits to determine the treatment and control groups.

eligibility: CEX reports the amount of income during the past 12 months, whereas gross monthly income determines SNAP eligibility. Hence, my analysis sample contains more long-term poor households. However, it naturally excludes such households whose income was consistently high over the course of year, but a sudden negative shock introduced a plunge in income, pulling it below the SNAP eligibility line. Interaction terms are statistically significant and positive for food, strongly driven by food at home, as well as transportation categories. The SNAP eligible group’s total expenditures rose after ARRA by $190 per quarter than the control group with slightly higher income, but not significant. The magnitude of the interaction term of food expenditure—$100.4—is smaller than what is observed for SNAP participants in Table 5 Panel A. This suggests that the impact of policy change could be diluted by the inclusion of non-SNAP low-income households not affected by the ARRA SNAP increase. Consequently, Table 5 Panel B can be interpreted as the global effect of ARRA on low-income households overall. The increase in transportation expenditures for low-income households aligns with the findings of Meyer and Sullivan (2006). Using the CEX, they describe underlying trends in income and consumption for single mother headed families between 1993 and 2003, when dramatic changes in welfare and tax policies took place, including expansions in the EITC and welfare waivers as well as passage of the welfare reform. Analyzing various components of consumption, they find that spending on housing and transportation accounts for much of the increase in consumption in the bottom quintiles of income distribution. They report that the rise in housing consumption is mainly driven by increases in out-of-pocket rent; the rise in transportation is associated with increased work by single mothers. 6.3. Sensitivity snalysis 6.3.1. The placebo period To alleviate concerns that what is picked up might be the differential trends across groups, I run the same specification over the placebo period when there was no change in SNAP benefits—the postARRA period (2010–2011). As shown in Table 1, there was a sequence of small SNAP benefit increases in pre-ARRA period but no changes in post-ARRA period, so I restrict the placebo test only to the post-ARRA period15 and show both SNAP participants (Panel A) and SNAP eligible households (Panel B). In Table 6, I find no effect and none of the coefficients on the interaction term is statistically significant, regardless of the choice of treatment group. The absence of significant results in the placebo periods reaffirms the finding that SNAP households clearly responded to the largest benefit increase in 2009. 6.3.2. Treatment group compositional change pre- and post-ARRA The main issue with the current model specification is a possible compositional shift between treatment and control group over time. For example, suppose that I define the treatment group as households with income under the 130% of the poverty line and the comparison group as households with income under the 185% line, but above the 130% line. There are two identifying assumptions: (1) the difference between the two groups is fixed over time, and (2) there is no shift across groups. However, a household whose income was above 130% of the poverty line before ARRA could have suffered extreme economic hardship and fallen below 130% after ARRA. In this case, incorporating the pre-ARRA control group into the postARRA treatment group could improve estimates of the consumption of the latter group caused by life cycle consumption trends. 15 Another reason is that the time period of pre-ARRA years was unusually volatile due to a severe recession

17

J. Kim / Food Policy 65 (2016) 9–20 Table 6 [Placebo test] years after ARRA: households’ expenditures before (2010) and after (2011). Quarterly expenditure ($2009) (1) Total expenditure

(2) Food

(3) Food At home

(4) Food away

(5) Housing

(6) Shelter

(7) Utility

(8) Transportation

(A) Sample: SNAP participants (treatment) compared to non-participants with income 6 185% of the poverty line (control) 84.33** 22.73 106.93*** 423.45*** 264.39*** 92.13*** 287.63*** SNAP 1171.22*** [180.614] [37.691] [31.277] [19.112] [77.850] [61.207] [20.603] [99.291]

(9) Entertainment

(10) Tobacco

(11) Education

55.29** [22.182]

56.77*** [14.841]

55.91 [36.131]

After ARRA

111.33 [157.154]

30.99 [30.014]

15.96 [20.190]

15.05 [20.473]

78.45 [63.907]

87.62* [49.804]

1.18 [15.607]

46.96 [86.077]

7.36 [19.391]

10.35 [8.322]

23.73 [43.248]

SNAP*After ARRA

42.66 [243.507]

66.79 [50.667]

42.65 [42.325]

24.13 [25.385]

38.29 [104.935]

70.42 [81.311]

10.65 [27.337]

118.12 [131.481]

51.84* [28.754]

15.32 [19.518]

38.46 [50.789]

Observations

4633

4633

4633

4633

4633

4633

4633

4633

4633

4633

4633

R-squared

0.236

0.254

0.332

0.052

0.210

0.154

0.340

0.047

0.055

0.051

0.057

(4) Food away

(5) Housing

(6) Shelter

(7) Utility

(8) Transportation

(9) Entertainment

(10) Tobacco

(11) Education

24.09* [14.593]

1.20 [48.826]

Quarterly expenditure ($2009) (1) Total expenditure

(2) Food

(3) Food at home

(B) Sample: households with income 6 130% of the poverty line (treatment) compared to those with 130% < income 6 185% (control) LowInc 1257.79*** 103.71*** 28.46 75.12*** 404.14*** 174.16*** 102.51*** 159.35 58.67** [189.125] [36.119] [27.300] [21.559] [83.699] [62.028] [19.771] [101.505] [25.885] After ARRA

180.35 [217.678]

32.81 [39.754]

19.80 [30.148]

12.96 [24.105]

155.81 [101.624]

121.72 [74.700]

23.21 [24.137]

12.95 [124.184]

1.57 [27.124]

4.67 [16.548]

48.72 [43.501]

LowInc*After ARRA

49.08 [265.165]

65.69 [49.031]

33.37 [37.362]

32.28 [29.190]

117.31 [115.945]

74.08 [87.464]

24.60 [28.058]

29.89 [158.305]

11.01 [31.873]

1.37 [19.050]

53.25 [58.238]

Observations

4633

4633

4633

4633

4633

4633

4633

4633

4633

4633

4633

R-squared

0.240

0.252

0.332

0.045

0.212

0.150

0.345

0.046

0.056

0.045

0.057

Note: These are coefficients from estimating Eq. (1). Regressions include demographic variables, month and year dummies. Sampling weight is used. Column (3) and (4) are subcategories of Column (2). Column (6) and (7) are subcategories of Column (5). *** p < 0.01. ** p < 0.05. * p < 0.1.

Similarly, SNAP participants before ARRA could differ from SNAP participants after ARRA in a number of ways. Ideally, the only difference within a treatment group that we want to see across time is the increase in SNAP benefits. All other characteristics should be balanced to identify the effect of the 2009 ARRA. To ensure that the composition of the treatment group is fixed during the period of study, I report summary statistics of the households included in treatment group. Appendix B Table B1 presents summary statistics for the households that reported SNAP receipts in the CEX. A slight upward trend over time is evident in the head’s education as well as in marital status, but otherwise there are no notable differences in other characteristics before and after ARRA. After 2009 these households received considerably more unemployment benefits, SNAP benefits, and Medicaid. As noted in Appendix B Table B2, households with income less than 130% of the poverty line look surprisingly balanced before and after, with the exception of age of head, number of earners, and race of head. Again, low-income households received significantly more unemployment benefits, SNAP benefits, and Medicaid after the 2009 ARRA, which reflects the ARRA provisions that were targeted at low-income households. My analysis yields convincing evidence that there was no compositional shift within the treatment group during the study period. Otherwise, the estimator could seriously be confounded by reflecting changing attributes of the group over time, failing to isolate the effect of the SNAP benefit increase. 6.3.3. Treatment group defined by education, employment, and marital status of head Given that the SNAP receipt in the CEX is underreported and therefore incomplete compared to administrative data, I use the

demographic characteristics of household head to identify the treatment and control group. Disadvantageous social economic status such as low education, single or unemployed head predicts SNAP participation (USDA, 2009). Across different definitions of a treatment group, the results are strikingly similar, which confirms that the ARRA SNAP increase had a positive impact on a wide range of disadvantaged groups. Panel A of Table 7 reports the results when the treatment group consists of households with a loweducated (i.e. less than high school degree) single head. It shows measurable impact on total expenditures, strongly driven by increases in food and transportation expenditures. Panel B of Table 7, which uses the employment status of the household head to define the treatment group, reveals sizable increases in total expenditures that are induced mostly by changes in food at home and housing expenditures (both shelter cost and utility fee). The finding of consistent results across different definitions of the treatment group confirms that ARRA protected disadvantaged families from negative consumption shocks. Moreover, it helps expanding the limited budgets of disadvantaged families, thereby ensuring that they could maintain a basic level of both food and non-food consumption. 7. Conclusion Given that consumption is generally favored over income as a measure of the well-being of the poor (Meyer and Sullivan, 2008), the overall welfare effects of the policy change can be assessed by examining consumption responses in various spending categories induced by increases in in-kind transfers. I accomplish this by focusing on a plausibly exogenous and the largest one-time increase in SNAP benefits that started on April 1,

18

J. Kim / Food Policy 65 (2016) 9–20

Table 7 [Sensitivity analysis] Effects of the 2009 SNAP benefit increase on households’ expenditures difference-in-differences estimation: before (2007–2009) and after (2009–2011). Quarterly expenditure ($2009) (1) Total expenditure

(2) Food

(3) Food at home

(4) Food away

(5) Housing

(6) Shelter

(7) Utility

(8) Transportation

(A) Sample: households with low educated single head (treatment) compared to the rest with income 6 185% (control) 30.35 21.95 8.37 168.71* 17.06 44.43 15.50 LowEdSingle 352.66* [213.120] [50.100] [39.489] [27.519] [101.487] [80.457] [27.244] [136.422]

(9) Entertainment

(10) Tobacco

(11) Education

117.70*** [32.327]

49.38*** [17.846]

33.66 [30.279]

After ARRA

9.25 [313.338]

17.51 [64.566]

8.38 [45.193]

25.84 [40.301]

12.33 [133.800]

25.69 [105.717]

39.47 [32.373]

20.40 [203.999]

36.34 [40.586]

8.61 [17.130]

90.90 [82.024]

LowEdSingle * After ARRA

507.75*** [183.837]

148.39*** [47.443]

98.58** [39.065]

49.79** [23.283]

93.93 [89.359]

75.82 [69.637]

10.07 [26.849]

291.63*** [107.998]

21.88 [27.566]

24.37 [16.228]

31.93 [32.996]

Observations

8700

8700

8700

8700

8700

8700

8700

8700

8700

8700

8700

R-squared

0.211

0.238

0.317

0.048

0.196

0.146

0.326

0.044

0.044

0.045

0.053

(5) Housing

(6) Shelter

(7) Utility

(8) Transportation

(9) Entertainment

(10) Tobacco

(11) Education

348.53*** [84.581]

43.81** [21.193]

8.84 [7.910]

12.67 [35.527]

Quarterly expenditure ($2009) (1) Total expenditure

(2) Food

(3) Food at home

(4) Food away

(B) Sample: households with unemployed head (treatment) compared to the rest with income 6 185% (control) Unemp 836.34*** 109.51*** 35.38* 74.02*** 214.56*** 177.79*** 35.96** [131.191] [29.298] [21.179] [18.182] [61.821] [46.720] [14.119] After ARRA

69.31 [319.895]

21.49 [66.504]

17.05 [46.195]

38.50 [42.175]

39.11 [135.586]

21.78 [107.504]

51.20 [32.531]

37.36 [207.040]

21.28 [42.087]

0.59 [17.850]

94.85 [88.457]

Unemp*After ARRA

434.58** [170.355]

46.77 [37.006]

63.38** [27.726]

16.61 [21.861]

188.38** [81.496]

166.18*** [62.386]

38.60** [19.034]

67.62 [109.666]

37.22 [24.584]

14.46 [11.479]

0.57 [47.654]

Observations

8700

8700

8700

8700

8700

8700

8700

8700

8700

8700

8700

R-squared

0.211

0.237

0.317

0.047

0.196

0.147

0.325

0.043

0.043

0.044

0.053

Note: These are coefficients from estimating Eq. (1). Regressions include demographic variables, month and year dummies. Sampling weight is used. Column (3) and (4) are subcategories of Column (2). Column (6) and (7) are subcategories of Column (5). *** p < 0.01. ** p < 0.05. * p < 0.1.

2009. Comparing SNAP households with non-participants, I find that the benefit increase had sizable positive effects on food, housing, and education expenditures. As predicted by the theoretical framework, the increased amount of food expenditures was smaller than the increased amount of the SNAP benefit. This occurred because SNAP households redirected their freed up resources to non-food items such as housing, transportation, education, and entertainment. In other words, the rise in SNAP benefits resulted in the increase in food expenditures, and also allowed recipients to meet other essential household needs. This is an important finding with the concerns magnified for households with low income and low assets at the time of economic downturn. As Johnson et al. (2006) find that households with low income or low liquid wealth quickly consume most of their tax rebates and save little of them for future periods, it indicates that consumption by the poor immediately mirrors the transfers received. In light of this, consumption would be a superior measure of well-being when households are not saving some of their transfers for future use. My study provides compelling evidence that during the economic crisis, the SNAP benefit boost not only shifted up food spending but also improved expenditures in other essential spending categories of low-income households. This reflects the positive spillover effects of the SNAP benefit increase. In November 2013, ARRA expired and SNAP benefits were reduced for the first time in its history. As demonstrated here, the increase in SNAP benefits enabled the vast majority of inframarginal households to increase both food and non-food purchases. As a follow-up to this study, it would be useful to examine

how households behaved once the one-time SNAP boost came to an end and monthly benefits were cut substantially.

Appendix A. Items included in each expenditure category in the CEX A.1. Food Spending on food includes food consumed at home (mainly through grocery shopping), and food eaten away from home. Alcoholic beverages are reported in a separate category. a. Food at home b. Food away from home.

A.2. Housing a. Shelter: For home owners, shelter cost includes mortgage interest, property taxes, maintenance, repairs, and insurance. For home renters, this includes rent for the rented dwelling b. Utility: Fees for natural gas, electricity, fuels, telephone service, and water c. House operation: Domestic services, babysitting, and child day care d. House equipment: textiles, furniture, floor coverings, and home appliances.

19

J. Kim / Food Policy 65 (2016) 9–20 Table B1 Households who participated in SNAP Past 12 months.

Age of head

Pre-ARRA (N = 1424)

Post-ARRA (N = 890)

Difference

T-statistics

38.60

38.90

0.03

0.58 ⁄⁄

Head, married Own home Family size Number of earners Number of children less than 18 years old

0.25 0.18 3.24 0.83 1.59

0.28 0.18 3.20 0.82 1.50

0.04 0.01 0.04 0.01 0.09

2.04 0.35 0.48 0.22 1.47

Head, less than HS Head, HS grad

0.37 0.52

0.32 0.54

0.05⁄⁄⁄ 0.02

2.64 0.90

Head, college grad Head, white Head, black Head, other race Head, female Urban Number of hours worked by head per week Number of weeks worked by head Social security income Supplemental security income

0.10 0.64 0.30 0.05 0.75 0.92 34.67 18.03 1464.28 1322.12

0.14 0.66 0.28 0.06 0.72 0.94 34.85 17.49 1690.13 1185.11

0.03⁄⁄ 0.02 0.02 0.01 0.02 0.02 0.18 0.53 225.84 137.01

2.54 0.81 1.26 0.82 1.29 1.59 0.25 0.57 1.33 1.00

Unemployment compensation Public assistance income Income from child support payment

3767.91 3192.49 3250.70

6690.48 2823.05 3628.55

2922.57⁄⁄⁄ 369.44 377.85

4.75 1.13 0.79

Income from SNAP

1829.46

2053.36

223.90⁄⁄⁄

2.66

Medicaid receipt Medicare receipt

0.84 0.21

0.88 0.20

0.04⁄⁄ 0.01

2.49 0.30

Pre-ARRA (N = 2673)

Post-ARRA (N = 3268)

Difference

T-Statistics

Age of head Head, married Own home Family size

37.41 0.29 0.25 2.59

38.10 0.27 0.24 2.60

0.69⁄⁄ 0.01 0.01 0.01

1.96 1.08 1.16 0.25

Number of earners Number of children less than 18 years old Head, less than HS Head, HS grad Head, college grad Head, white Head, black

0.86 1.02 0.26 0.56 0.17 0.73 0.21

0.83 1.00 0.25 0.57 0.18 0.72 0.21

0.04⁄ 0.02 0.01 0.01 0.01 0.01 0.00

1.88 0.65 1.31 0.44 0.93 0.83 0.14

Head, other race Head, female Urban Number of hours worked by head per week

0.06 0.61 0.94 34.61

0.07 0.61 0.94 34.03

0.01⁄ 0.01 0.00 0.58

1.72 0.56 0.44 1.31

Number of weeks worked by head Social security income Supplemental security income

22.61 1343.39 677.43

20.64 1357.19 633.24

1.96⁄⁄⁄ 13.79 44.19

3.33 0.14 0.71

Unemployment compensation Public assistance income Income from child support payment

3770.75 2948.96 3381.55

6575.52 2910.77 3732.46

2804.77⁄⁄⁄ 38.19 350.91

6.14 0.12 0.83

Income from SNAP Medicaid receipt Medicare receipt

539.49 0.75 0.29

777.05 0.78 0.26

237.56⁄⁄⁄ 0.04⁄⁄ 0.03

6.09 2.19 1.87

Table B2 Households with income 6 130% of the poverty line.

A.3. Transportation a. b. c. d. e. f. g.

Vehicle purchases Vehicle finance charges Gasoline and motor oil Maintenance and repairs Vehicle insurance Vehicle rental, and leases Public transportation.

A.4. Education a. School books, supplies, and equipment b. Tuition for school c. Other school-related expenses. A.5. Entertainment a. Fees and admissions b. Televisions, radios, and sound equipment

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J. Kim / Food Policy 65 (2016) 9–20

c. Pets, toys, and playground equipment, and others. A.6. Other expenditures a. Personal care products and services: products for hair, oral hygiene products, cosmetics and bath products, electric personal care appliances, etc. b. Health: health insurance, medical services, prescription drugs, medical supplies c. Tobacco products d. Miscellaneous: safety deposit box rental, checking account fees, bank service charges e. Cash contributions. Appendix B. Summary statistics: treatment group pre- and postARRA See Tables B1 and B2. References Beatty, Timothy K.M., Tuttle, Charlotte, 2015. Expenditure response to increases in in-kind transfers: evidence from the supplemental nutrition assistance program. Am. J. Agric. Econ. 97 (2), 390–404. Breunig, Robert V., Dasgupta, Indraneel, 2002. A theoretical and empirical evaluation of the functional forms used to estimate the food expenditure equation of food stamp recipients: comment. Am. J. Agric. Econ. 84 (4), 1156– 1160. Breunig, Robert V., Dasgupta, Indraneel, 2005. Do intra-household effects generate the food stamp cash-out puzzle? Am. J. Agric. Econ. 87 (3), 552–568. Center on Budget and Policy Priorities, 2016. A quick guide to SNAP eligibility and benefits, pp. 1–5. Currie, Janet, 2004. The Take Up of Social Benefits. NBER Working Paper No. 10488. Fraker, Thomas M., 1990. The Effects of Food Stamps on Food Expenditure: A Review of the Literature. Food and Nutrition Service, U.S. Department of Agriculture, Alexandria, VA. Fraker, Thomas M., Martini, Alberto P., Ohls, James C., 1995. The effect of food stamp cashout on food expenditures: an assessment of the findings from four demonstrations. J. Hum. Resour. 30 (4), 633–649. Hoynes, Hilary W., McGranahan, Leslie, Schanzenbach, Diane, 2014. SNAP and Food Consumption. University of Kentucky Center for Poverty Research Discussion Paper Series, DP2014-03.

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