Abstract

This report uses 1985-2000 data from the 1979 cohort of the National Longitudinal Survey of Youth to examine the

effects of the Food Stamp Program on obesity. The effects are found to differ by gender, level of benefits, and duration of participation. Results suggest that, for females, current program participation increases Body Mass Index (by

0.5 index point on average) as well as the probability of being obese (between 2 and 5 percentage points). Current

program participation was not found to have significant effects for males. Long-term participation is found to

increase obesity for females and males.

The Effects of Food Stamps on Obesity

Contractor and Cooperator Report No. 34

September 2007

By Charles Baum, Middle Tennessee State University

This study was conducted by Middle Tennessee State University under a

cooperative research contract with USDA’s Economic Research Service (ERS)

Food and Nutrition Assistance Research Program (FANRP): contract number

43-3AEM-4-80097 (ERS project representative: Dean Jolliffe). The views

expressed are those of the authors and not necessarily those of ERS or

USDA.
Charles L. Baum The Effects of Food Stamps on Obesity 

Contents 

Executive Summary..................................................................................................................... iii

Introduction....................................................................................................................................1 

The Theoretical Model ..................................................................................................................7 

Data ...............................................................................................................................................11 

 Descriptive Statistics................................................................................................................16 

Estimation Methodology and Identification ..............................................................................22 

Ordinary Least Squares (OLS) Models....................................................................................23 

Instrumental Variable Models..................................................................................................24 

Fixed Effects Models ...............................................................................................................29 

Dynamic Models: Change in BMI and Transition Equations..................................................30 

Dynamic Models: Hazard Models ...........................................................................................31 

Models with Lagged Food Stamp Benefits..............................................................................33 

Results...........................................................................................................................................34 

OLS Results .............................................................................................................................34 

Instrumental Variables Results ................................................................................................35 

Fixed Effects Results ...............................................................................................................38 

Dynamic Results: Change in BMI and Transition Equations..................................................39 

Dynamic Models: Hazard Rate Results ...................................................................................40 

Lagged Food Stamp Benefits Results......................................................................................42 

Conclusions...................................................................................................................................46 

References.....................................................................................................................................52 

Tables............................................................................................................................................60 

Figures...........................................................................................................................................82 

Appendix Tables...........................................................................................................................90 

ii
Charles L. Baum The Effects of Food Stamps on Obesity 

Executive Summary (3-pages) 

An increasing number of Americans are obese. In fact, the latest estimates indicate 

that about 30 % of adult Americans are currently obese, which is roughly a 100 % increase from 

25 years ago. As a result of the dramatic increase in obesity, adult Americans are now more 

likely to be obese than to smoke cigarettes. 

Public health officials in the United States have become increasingly alarmed about 

the growing prevalence of obesity in part because recent research indicates that societal costs of 

obesity, which include medical expenses, exceed those of cigarette smoking and alcoholism. 

Further, the medical literature finds that obesity increases morbidity and mortality by increasing 

the prevalence of diabetes, cardiovascular disease, stroke, cancer, hypertension, dyslipidemia, 

gout, sleep apnea, and osteoarthritis. Some have asserted that obesity will soon overtake tobacco 

as the leading preventable cause of death. In response, Americans are estimated to spend over 30 

billion dollars on weight loss programs annually (Philipson and Posner, 1999). 

Many non-economic societal changes do not initially appear to explain why the 

prevalence of obesity is increasing. An increasing portion of Americans are exercising and 

dieting, and Americans currently possess more knowledge of the consequences of obesity than 

ever before. Yet, Americans are more likely to be obese now than ever. Perhaps obesity is an 

economic phenomenon where individuals select their optimal weight, which may not be 

medically optimal (the weight that minimizes mortality), by comparing the marginal benefit with 

the marginal cost of losing or gaining weight. If so, then economic factors may serve to explain 

at least a portion of the increasing prevalence of obesity. 

I examine the effect of an economic factor -- the Food Stamp Program (FSP) -- on 

obesity. Prior to the Food Stamp Act of 1964 (and other food assistance programs passed during 

the twentieth century), poverty was assumed to be associated with a decrease in food 

consumption. However, various twentieth century government entitlement programs changed 

this by constructing a safety net that helps prevent those in poverty from starvation. The Food 

Stamp Act does this by guaranteeing an allotment of food for those below the poverty level. 

Perhaps as a result, the literature suggests Food Stamps increase food consumption by more than 

an equivalent amount of cash would, even for households that receive less in Food Stamp 

benefits than they spend on food. Further, some recent evidence suggests Food Stamp recipients 

consume significantly more sugar and meat than eligible non-recipients. 

In this project, I estimate the relationship between Food Stamp benefits and BMI and 

the probability of being obese with National Longitudinal Survey of Youth (NLSY79) data. I 

first use ordinary least squares (OLS) regression models to estimate the effects of Food Stamp 

receipt on BMI and logits to estimate the effects of Food Stamp receipt on the probability of 

being obese. Next, in an attempt to control for potential unobserved heterogeneity bias, I reestimate the models using predicted Food Stamp benefits derived from a two-step estimator that 

first predicts Food Stamp benefits using Instrumental Variables (IV). I also estimate individualspecific fixed effects models to control for potentially biasing effects of individual-specific 

unobserved heterogeneity. In addition, I estimate dynamic models that explain the change in 

BMI, the conditional probability of becoming obese (conditional on not being obese), the 

conditional probability of no longer being obese (conditional on being obese initially), and the 

hazard rate for becoming obese at a particular time conditional on not yet being obese. Finally, I 

re-estimate these models using alternative Food Stamp variable specifications including one that 

iii
Charles L. Baum The Effects of Food Stamps on Obesity 

identifies the effects of current and past program receipt to explore timing effects such as 

whether Food Stamps have contemporaneous or lagged effects. In another, I examine patterns of 

receipt over the preceding two years to identify the effects of receiving benefits in one spell 

either short-term (for less than 9 months), medium-term (between 9 and 23 months), or longterm (24 months) and the effects of received benefits during multiple spells. Each of these 

model specifications are estimated using two samples: a low-income sub-sample (defined as 

those with no more than a high school education) and a sub-sample of respondents approximated 

to be eligible for Food Stamp benefits. Models are estimated with these samples separately for 

males and females. 

Descriptive statistics from the 1979 NLSY cohort show that FSP participation and 

obesity are correlated for females. For example, 27.8 % of low-income females on Food Stamps 

are obese compared to 19.0 % of low-income female non-recipients. Similarly, among females 

approximated to be eligible for benefits, those receiving Food Stamps are more likely to be 

obese: 28.3 % versus 17.7 %. This association is much weaker for males. OLS models 

controlling for a spate of observable characteristics essentially enable us to compare individuals 

who are alike in all observable ways (the same gender, race/ethnicity, education, income, etc.) 

except Food Stamp receipt. In these models, female Food Stamp recipients are significantly 

more likely to be obese (and to have higher BMI) than female non-recipients. For example, 

providing a low-income female non-recipient with Food Stamp benefits is predicted to increase 

the probability of being obese between roughly 2 and 5 percentage points (and to increase BMI 

by a majority of an index point). In corresponding models that examine males, Foods Stamps 

have statistically insignificant effects on BMI and obesity. 

Of course, OLS estimates could be biased by unobserved characteristics correlated 

with both FSP participation and obesity (and BMI). However, alternative specifications used to 

control for these sources of bias fail to reject the null hypothesis that OLS results are not biased. 

Where instruments are deemed valid, the evidence suggests the OLS and IV estimates are not 

statistically different from one another. Fixed effects models also support conclusions with OLS 

results, with many instances of significant positive effects of Food Stamp benefits on BMI and 

the probability of being obese, specifically for females. 

The dynamic models also show positive effects of Food Stamp benefits for females. 

Such benefits significantly increase the typical change in BMI between surveys and significantly 

increase the hazard of becoming obese (conditional on not yet being obese). Further, lagged 

Food Stamp benefits (from previous years) often significantly increase BMI and obesity. Again, 

these conclusions tend to be true primarily for females, with insignificant effects for males. 

These models also show that long-term Food Stamp receipt increases BMI and obesity, even in 

some instances for males, but receiving assistance from the FSP for a more limited period does 

not have this side-effect. 

In some cases, dynamic effects are larger than those found in models identifying 

contemporaneous effects. For example, receiving Food Stamps continually during the two years 

preceding the interview date increases obesity by at least 10 percentage points. Over a 15-year 

period, the hazard models suggest Food Stamp benefits significantly decrease the probability of 

not yet being obese between 10 and 20 percentage points. Similarly, for eligible females, 

positive effects of Food Stamps become successively larger with the inclusion of additional 

lagged Food Stamp covariate terms. 

iv
Charles L. Baum The Effects of Food Stamps on Obesity 

These results can be used to approximate the amount to which Food Stamp benefits 

have contributed to the increase in obesity. During the 1976-1980 period covered by NHANES 

II, the prevalence of obesity was 15.0 %, with roughly 20.9 million obese adult American adults 

aged 20 through 74. In the 2003-2004 NHANES surveys, the prevalence of obesity was 32.2 %, 

with roughly 62.1 million obese American adults. Thus, as the prevalence of obesity doubled 

between these periods, the number of obese American adults increased by 41.2 million. 

However, between 1978 and 2003, the number of Food Stamp recipients increased from just 16.0 

million to 21.3 million, which is an increase of 5.3 million (though more than that moved 

through the program). Using reputedly conservative OLS estimates, assume Food Stamps 

increase the probability of being obese by 4 percentage points. This suggests that 212,000 

additional Americans became obese due to Food Stamps (4 % of 5.3 million = 212,000) between 

these periods. This would account for half of one % (0.5 %) of the 41.2 million increase in obese 

American adults (212,000/41.2 million = 0.005). Had this approximated contribution to obesity 

by the FSP not been made, the prevalence of obesity would be 32.1 % rather than 32.2 % (after 

subtracting 212,000 from 62.1 million obese American adults). Thus, while the effects of Food 

Stamps on obesity appear statistically significant at the individual-recipient level, the FSP has 

probably had virtually an immeasurably small impact on the growing prevalence of obesity. 

There are reasons to believe that the estimated contribution of the FSP to the 

increasing prevalence of obesity is an underestimate: dynamic models indicate larger positive 

effects once cumulative effects of Food Stamps (or effects of Food Stamp receipt from past 

periods) are included in the analysis. However, this is likely to have a small impact on the 

prevalence of obesity because only a small minority of recipients receive benefits long-term. 

Further, though the increase in Food Stamp receipt has been 5.3 million, more than that have 

moved through the program during which time they were evidently more susceptible to 

becoming obese. However, this is likely to have a small impact on the prevalence of obesity 

because results show that receiving benefits short-term (and medium-term) does not have a 

significant effect on obesity. 

Regardless, since the FSP appears inadvertently to increase obesity, policymakers 

should seek ways to provide food assistance without exacerbating obesity. One approach might 

be to redouble efforts to educate newly-certified Food Stamp recipients about healthy and 

nutritious eating habits and weight management (or weight reduction). Such action might 

change the FSP from being a contributor to obesity to a benefactor reducing this prevalence. 

v
The Effects of Food Stamps on Obesity 

I. Introduction 

An increasing number of Americans are obese, where obese is defined as having a 

body mass index (BMI) of 30 or more and where BMI equals weight in kilograms divided by 

height in meters squared (CDC, 2006a). In fact, the latest estimates indicate that about 30 % of 

adult Americans are currently obese, which is roughly a 100 % increase from 25 years ago 

(Kuczmarski et al., 1994; VanItallie, 1996; Flegal et al., 1998; Flegal et al., 2002; Ogden et al., 

2006). These increases are found for both males and females, as well as for various races. As a 

result of the dramatic increase in the prevalence of obesity, adult Americans are now more likely 

to be obese than to smoke cigarettes: only 22.5 % of adult Americans currently smoke 

(Campaign for Tobacco-Free Kids, 2004). 

Public health officials in the United States have become increasingly alarmed about 

the growing prevalence of obesity in part because recent research indicates that societal costs of 

obesity exceed those of cigarette smoking and alcoholism (Sturm, 2002). One such cost is the 

medical expense that obesity imposes. Sturm (2002) and Finkelstein, Fiebelkorn, and Wang 

(2003) estimate that obesity increases inpatient and outpatient spending by about 36 %. In 

accord, Wolf and Colditz (1998) estimate that the year-1995 economic costs of obesity were 

almost 100 billion, about half of which were direct medical costs. The medical literature finds 

that obesity increases morbidity and mortality (Pi-Sunyer, 1994; Stevens et al., 1998; Calle et al., 

1999) by increasing the prevalence of diabetes, cardiovascular disease, stroke, cancer, 

hypertension, dyslipidemia, gout, sleep apnea, and osteoarthritis (McGinnis and Foege, 1993; PiSunyer, 1994; Wolf and Colditz, 1998; Must et al., 1999; Chow et al., 2000; Rauscher, 2000; 

Castro-Rodriguez et al., 2001; Field, 2001; Michaud et al., 2001; Kenchaiah et al., 2002). Some 
Charles L. Baum The Effects of Food Stamps on Obesity 

have asserted that obesity will soon overtake tobacco as the leading preventable cause of death 

(Mokdad et al., 2004). Currently, the medical literature estimates that obesity contributes to 

between 111,909 and 365,000 premature adult deaths in the U.S. each year compared to 435,000 

premature deaths due to tobacco (Allison et al., 1999; Mokdad et al., 2004; Flegal et al., 2005; 

Mokdad 2005). 

Since federal, state, and local governments pay roughly half the cost of health care in 

the United States, obesity will have a substantial impact on public programs such as Social 

Security and Medicare/Medicaid. Finkelstein et al. (2003) estimate that obese Medicare 

(Medicaid) recipients cost $1,486 ($864) more per year than such recipients of normal weight. 

Obese individuals are also shown to experience social penalties in their interpersonal activities 

and in the “marriage market” and to earn lower wages (Register and Williams, 1990; Gortmaker 

et al., 1993; Hamermesh and Biddle, 1994; Averett and Korenman, 1996; Pagan and Davila, 

1997; Baum and Ford, 2004; Cawley, 2004). In response to the various costs of obesity, 

Americans are estimated to spend over 30 billion dollars on weight loss programs annually 

(National Institute of Diabetes and Digestive Kidney Diseases, 1996; Cawley, 1999; Philipson 

and Posner, 1999). 

Many non-economic societal changes do not initially appear to explain why the 

prevalence of obesity is increasing. Consider the following: (i) an increasing portion of 

Americans are exercising and dieting, (ii) Americans currently possess more knowledge of the 

consequences of obesity than ever before via government and medical awareness campaigns, (iii)

biological factors, though capable of explaining weight differences within a cohort, should not 

have changed enough to explain differences in obesity over the last half-century, and (iv) weight 

is no longer a status symbol indicating prosperity (Philipson and Posner, 1999; Philipson, 2001). 

2
Charles L. Baum The Effects of Food Stamps on Obesity 

Yet, Americans are more likely to be obese now than ever. Perhaps obesity is an economic 

phenomenon where individuals select their optimal weight, which may not be medically optimal 

(the weight that minimizes mortality), by comparing the marginal benefit with the marginal cost 

of losing or gaining weight. If so, then economic factors may serve to explain at least a portion 

of the increasing prevalence of obesity. 

Fortunately, researchers have recently begun to examine the economic causes of 

obesity. For example, Philipson (2001), Philipson and Posner (1999), and Lakdawalla and 

Philipson (2002) suggest that increased obesity is the result of jobs becoming more sedentary; 

Anderson, Butcher, and Levine (2003a, 2003b) find evidence that maternal employment 

increases childhood obesity because working mothers have less time to prepare healthy meals; 

Cutler, Glaeser, and Shapiro (2003) assert that technological advances in food preparation 

making food readily available have caused hyperbolic consumers (defined as those who lack 

self-control) to overeat; Chou, Grossman, and Safer (2004) find that BMI and obesity have 

significantly increased due to increases in the number of restaurants and decreases in food prices 

(prices in fast-food and full-service restaurants and the price of home food); and DeCicca (2004) 

and Gruber and Frakes (2005) examine the effects of cigarette taxes on BMI and obesity (finding 

conflicting results). 

I seek to examine the effect of another economic factor -- the Food Stamp Program 

(FSP) -- on obesity. Prior to the Food Stamp Act of 1964 (and other food assistance programs 

passed during the twentieth century), poverty was assumed to be associated with a decrease in 

food consumption. However, various twentieth century government entitlement programs 

changed this by constructing a safety net that helps prevent those in poverty from starvation. 

The Food Stamp Act does this by guaranteeing an allotment of food for those below the poverty 

3
Charles L. Baum The Effects of Food Stamps on Obesity 

level. The program does this by providing participants with electronic benefits cards with credit 

redeemable for food (USDA, 2003a). In 2005, FSP participants averaged $92.70 in monthly 

benefits at a cost of $31.0 billion to the government (USDA, 2006a). Ironically, it is in the 

period since the Food Stamp Act’s passage that the prevalence of obesity has increased so 

dramatically. Between 1971 and 1974, the FSP served between 9.3 and 12.8 million participants 

annually (USDA, 2006a), and the prevalence of obesity in the United States was 14.5 % (Flegal 

et al., 2002). These statistics have doubled. In 2005, the FSP served an estimated 25.7 million 

participants (USDA, 2006a), and the prevalence of obesity is currently over 30 % (Flegal et al., 

2002). 

The FSP potentially increases obesity by increasing food consumption. The FSP 

potentially increases food consumption by making the monetary cost of food zero for eligible 

individuals up to their Food Stamp allotment (though since FSP participation rates are well 

below 100 %, non-monetary costs such as stigma and the opportunity cost of applying and recertifying for the benefits likely remain significant). A survey of the literature suggests a dollar 

of Food Stamps increases food consumption between $0.17 and $0.47, which is more than an 

equivalent amount of cash would (Fraker, 1990). It is not surprising that this would be true for 

constrained households (those who receive an amount of Food Stamp benefits that is greater than 

the amount they would otherwise spend on food), but this also appears to be true for the other 85 

to 95 % of Food Stamp households that are unconstrained (households that receive an amount of 

Food Stamp benefits that is less than the amount they would otherwise spend on food) (Fraker, 

1990). Further, recent evidence by Wilde, McNamara, and Ranney (1999) suggests Food Stamp 

recipients consume significantly more sugar and fat than eligible non-recipients. 

4
Charles L. Baum The Effects of Food Stamps on Obesity 

Economic theory suggests that the quantity of a good demanded increases as the price 

decreases. If true, then the FSP may at least partially explain why income is not positively 

associated with obesity. Otherwise, the utility maximizing framework in economics suggests 

that the demand for “normal goods” is increasing in income. Assuming food is a normal good, 

food consumption should be increasing in income as well. Consequently, we might assume that 

obesity is more prevalent among those with greater income. The opposite is often true. 

Socioeconomic status (SES) in developed countries has been found to be negatively associated 

with weight and obesity for females, though this relationship is weaker for men (Sobal and 

Stunkard, 1989; Stunkard and Sorensen, 1993; Jeffery, French, Forster, and Spry, 1991; Lantz et 

al., 1998; Wardle, Walter, and Jarvis, 2002), with recent evidence even suggesting those in 

poverty have not experienced the largest increases in obesity (Chang and Lauderdale, 2005). 

Only a couple of studies have examined the effects of FSP participation on obesity.1 

In the nutrition literature, seminal work by Gibson first examined this link. Separately 

examining men and women using a standard multivariate regression framework, Gibson (2003) 

finds that FSP participation among low-income women (but not men) is significantly associated 

with increased obesity.2

 Similarly, Chen, Yen, and Eastwood, using 1994-1996 Continuing 

Survey of Food Intakes by Individuals (CSFII) data, and Meyerhoefer and Pylyphuck (2006), 

using 2000-2003 Medical Expenditure Panel Survey (MEPS) data, find that contemporaneously-

 1

 However, economic studies have found that food stamp receipt increases food expenditures 

(Fraker, Devaney, and Cavin, 1986; Devaney and Fraker, 1989; Fraker, 1990; Fraker, Martini, 

and Ohls, 1995) and nutrient intake (Devaney and Moffitt, 1991; Rose, Habicht, and Devaney, 

1997; Basiotis, Kramer-LeBlanc, and Kennedy, 1998; Wilde, McNamara, and Ranney, 1999). 

2

 Gibson also examines the effects of the FSP on overweight in children (Gibson, 2004, 2006). 

5
Charles L. Baum The Effects of Food Stamps on Obesity 

measured Food Stamp receipt has statistically significant positive effects on low-income women 

but not on low-income men. All three studies make some attempt to control for systematic 

unobserved differences between Food Stamp recipients and non-recipients: Gibson uses 

individual-specific fixed effects models, Chen et al. use Heckman’s (1978) simultaneousequations framework to model the decision to participate in the FSP and obesity jointly, and 

Meyerhoefer and Pylypchuk essentially instrument Food Stamp receipt using state FSP outreach 

expenditures and state recertification length. However, none explore the dynamic relationship 

between Food Stamps and obesity where current weight is linked to past weight and past Food 

Stamp receipt. This seems like an important omission because contemporaneously-measured 

Food Stamp receipt would not be expected to have an instantaneous and substantial effect on 

weight. Instead, since current weight is not independent from past weight, the Food Stampweight relationship is likely much more complex. 

In this project, I estimate the relationship between Food Stamp benefits and BMI and 

the probability of being obese with National Longitudinal Survey of Youth (NLSY79) data. I 

first use ordinary least squares (OLS) regression models to estimate the effects of Food Stamp 

receipt on BMI and logits to estimate the effects of Food Stamp receipt on the probability of 

being obese. Next, in an attempt to control for potential unobserved heterogeneity bias, I reestimate the models using predicted Food Stamp benefits derived from a two-step Instrumental 

Variables (IV) estimator. I also estimate individual-specific fixed effects models to control for 

potentially biasing effects of individual-specific unobserved heterogeneity. In addition, I 

estimate dynamic models that explain the change in BMI, the conditional probability of 

becoming obese (conditional on not being obese), the conditional probability of no longer being 

obese (conditional on being obese initially), and the hazard rate for becoming obese at a 

6
Charles L. Baum The Effects of Food Stamps on Obesity 

particular time conditional on not yet being obese. Finally, I re-estimate these models using 

alternative Food Stamp variable specifications. One of these identifies the effects of current and 

past program receipt to explore timing effects such as whether Food Stamps have 

contemporaneous or lagged effects. Another estimates the effects of short-term, medium-term, 

and long-term Food Stamp receipt, as well as the effects of receiving benefits in multiple spells. 

Each of these model specifications are estimated using two samples: a low-income sub-sample 

(defined as those with no more than a high school education) and a sub-sample of respondents 

approximated to be eligible for Food Stamp benefits. Models are estimated with these samples 

separately for males and females. 

OLS results suggest Food Stamp receipt significantly increases female BMI by less 

than one index point and significantly increases the female probability of being obese by a 

couple of percentage points (two to five). In corresponding models that examine males, Foods 

Stamps have statistically insignificant effects on BMI and obesity. The IV and fixed effects 

models provide virtually no evidence that OLS estimates are biased, and the dynamic models 

indicate, if anything, that the cumulative effects of Food Stamp receipt are somewhat larger than 

those indicated by OLS, specifically for females. Evidence also suggests that long-term Food 

Stamp receipt increases BMI and obesity, even for males, but receiving assistance from the FSP 

for a more limited period does not have this consequence. Regardless, even though Food Stamps 

have significant positive effects on BMI and obesity, these effects are small, and such benefits, 

consequently, are approximated to have played a very minor role in the increasing prevalence of 

obesity. 

II. The Theoretical Model

7
Charles L. Baum The Effects of Food Stamps on Obesity 

Expanding on Philipson and Posner (1999) and Lakdawalla and Philipson’s (2002) 

intuition for why consumers might rationally be at weights that are not medically optimal, I 

develop the following theoretical model to explain how Food Stamp Benefits potentially affect 

weight. I assume that utility is a function of current weight (W), where deviations from ideal 

weight (W0) decrease utility, 

tW )( = |W0 – W(t)|, (1) 

with UW <0, where subscripts indicate partial derivatives. Ideal weight is defined as the weight 

an individual would choose if such weight could be attained without cost. W0 may or may not be 

one’s optimal weight in a medical sense. I assume that utility is also a function of food (F) and 

other consumption (C), 

U(t) = U( tW )( , F(t), C(t)), (2) 

where U is an instantaneous utility function with UF>0, UC>0, UFF<0, and UCC<0. Thus, the 

utility function is assumed to be twice continuously differentiable and concave. I also assume 

that the marginal effect of ideal weight deviations is larger for those further from their ideal 

weight (U WW ≤ 0). In this model, current decisions affect future weight because weight is 

formed dynamically according to the following accumulation process, 

W(t+1) = W(t) - δW(t) + F(t), (3) 

where W(t+1) is future weight and δ is the rate of depreciation of the stock of weight over time. 

Perhaps δ represents the caloric expenditure required to live. Thus, 

∂W(t)/∂t = F(t) - δW(t). (4) 

8
Charles L. Baum The Effects of Food Stamps on Obesity 

For simplicity, I assume that the utility function is separable over time in C, F, and W , that the 

rate of time preference is constant and given by σ, and that a lifetime is infinite. This generates a 

continuous-time lifetime utility function given by 

U = e ∫

∞

0

-σt

U[ tW )( , F(t), C(t)]dt. (5) 

Consumers maximize this utility function subject to a continuous-time lifetime budget 

constraint, 

∫

∞

0

e

-rt[C(t)+P(t)F(t)]dt ≤ I0, (6)

where P(t) is the price of food at time t, r is the interest rate, and I0 is the discounted value of 

lifetime income. In this setup, the price of other consumption is assumed to be the numeraire 

(normalized to be 1), and the capital market is assumed to be perfect. Maximizing the utility 

function (5) subject to the lifetime budget constraint (6) and the weight accumulation process (3 

and 4) yields the following first-order conditions: 

UC = λe

-(r-σ)t (7) 

and 

UF = λP(t)e-(r-σ)t

 - e ∫

∞

0

-δ(t-τ) UW WW (τ) dτ , (8) 

with WW >0 for those overweight and WW <0 for those underweight and where λ is the Lagrange 

multiplier, which is interpreted as the marginal utility of income. These first order conditions can 

be interpreted as follows. The marginal discounted lifetime utility of other consumption equals 

the marginal discounted lifetime cost (equation 7). Equation 8 shows that the total price of food 

9
Charles L. Baum The Effects of Food Stamps on Obesity 

consists of two parts. The first is the monetary price, P, which is discounted over time. The 

second is the discounted marginal utility of weight change. Since deviations from ideal weight 

decrease utility (and since food increases weight), the total cost of food is greater than the 

monetary cost for those overweight; however, the total cost of food is less than the monetary cost 

for those underweight. Combining the two first order conditions, 

P(t) UC = UF + e ∫

∞

0

-δ(t-τ) UW WW (τ) dτ , (9) 

shows that the marginal discounted lifetime utility of food plus the effect of food on the marginal 

discounted lifetime value of weight change must equal the marginal discounted lifetime utility of 

other consumption. 

Solving for the endogenous variables (C, F, and W ), one can see that weight (W) is a 

function of prices (such as the price of food, P), lifetime income (I0), the weight depreciation 

rate, and the rate of time preference. Increasing P unambiguously decreases food consumption 

for someone overweight, which decreases weight. 

The model can be extended to show the effect of Food Stamp benefits. Many treat 

Food Stamp benefits as an increase in income (for example, see Fraker and Moffitt, 1988). 

However, assuming food from the FSP cannot be resold, then FSP receipt should not be treated 

as income when the utility maximizing quantity of food is less than the amount of food received 

through the FSP. To incorporate FSP receipt into the model, maximize 

L = e ∫

∞

0

-σt

U[ tW )( , F(t), C(t)]dt - λ e ∫

∞

0

-rt[C(t)+P(t)F1(t)]dt ≤ I0, (10) 

where F(t) = F1(t) + FSP(t), (11) 

10
Charles L. Baum The Effects of Food Stamps on Obesity 

where ∂W(t)/∂t = F1(t) + FSP(t) - δW(t), (12) 

and where F1 is purchased food, FSP is food received through the FSP, and total food 

consumption is F. This setup assumes that a unit of food from either source (purchased and/or 

received from the FSP) yields the same utility. Referring to equation (9), increasing FSP 

prompts a decrease in purchased food consumption (and an increase in other consumption) by 

decreasing the marginal utility of food. In addition, for those over their ideal weight, increasing 

Food Stamp benefits increases the marginal utility of weight change, which reinforces the 

decrease in purchased food consumption. For those under their ideal weight, increasing FSP 

decreases the marginal utility of weight change, which augments the incentive to decrease 

purchased food consumption. Consequently, both those over and under their ideal weight will 

reduce purchased food consumption, though total food consumption (and weight) increases for 

both types because purchased food consumption falls by less than FSP food rises. 

III. Data

I use 1979-cohort NLSY79 data to estimate the effects of Food Stamp benefits on 

obesity. In 1979, the NLSY79 began annually interviewing a cohort of 12,686 respondents 

(6,283 of whom were female) who were between the ages of 14 and 21. The cohort initially 

included oversamples of blacks, Hispanics, low-income whites, and military personnel. 

However, the military sample was dropped in 1984 and the low-income whites were dropped in 

1990, and I do not include either in my analysis for that reason. Because I include the black and 

Hispanic oversamples, I use sampling weights throughout the analysis. After the 1994 survey, 

the NLSY79 began interviewing biennially, and these respondents have since been reinterviewed on that basis. In each survey, the NLSY79 collects information on each 

respondent’s individual characteristics. In addition, questions about weight were asked in the 

11
Charles L. Baum The Effects of Food Stamps on Obesity 

1981, 1982, 1985, 1986, 1987, 1989, 1990, 1992, 1993, 1994, 1996, 1998, 2000, and 2002 

surveys, and questions about height were asked in the 1981, 1982, and 1985 surveys (Center for 

Human Resource Research, 2004). I assume that height does not change after the 1985 survey 

because NLSY79 respondents are at least 20 years of age at that time, and I exclude pregnant 

females and new mothers (women who have given birth within a year) because their reported 

weight may not be representative of their non-pregnancy weight. 

The NLSY79 measures of weight and height are self-reported. Unfortunately, selfreported weight (and, to a lesser extent, self-reported height) potentially is measured with error. 

Fortunately, the National Health and Nutrition Examination Survey (NHANES) – NHANESIII 

(1988-1994), in particular – contains both actual and reported weight. Cawley (2000) uses this 

data to determine the extent of measurement error in weight (and height) and finds that those 

overweight underestimate their weight and those underweight overestimate their weight. Though 

the NLSY79 only collects self-reported weight, Cawley, using NHANESIII data, is able to 

predict actual weight for NLSY79 respondents from their self-reported weight. He does this by 

regressing actual weight on self-reported weight (and its squared value) using NLSY79-aged 

NHANESIII respondents. Then, he uses gender- and race-specific NHANESIII results to adjust 

self-reported weight in NLSY79 data. I use his procedure to adjust my NLSY79 data, and my 

results (as well as results from alternative correction specifications) are available upon request. 

The NLSY79 is not a simple random sample (where every observation has an equal 

probability of selection). Instead, it is a multi-stage, stratified sample with respondents who are 

geographically clustered (Center for Human Resource Research, 2004). As a result, respondents 

within clusters will tend to be similar, causing conventional standard errors (that assume a 

random sample design) to be too small. However, survey design commands in STATA allow 

12
Charles L. Baum The Effects of Food Stamps on Obesity 

researchers to adjust standard errors for stratification and clustering (STATA, 2001), and I adjust 

my standard errors using this procedure. Fortunately, the severity of understated standard errors 

due to sampling design will be abated in later surveys as respondents move, mixing more 

uniformly throughout the United States. 

The NLSY79 surveys that collect information about weight identify how much each 

respondent weighs in pounds at the time of the survey. To measure obesity, I calculate each 

individual’s body mass index (BMI). Obesity is defined as a BMI of 30 or more (CDC, 2006b). 

The Centers for Disease Control and Prevention (CDC) considers adults to be underweight if 

their BMI is less than 18.5 and to be overweight if their BMI is 25 to 30. The CDC’s method for 

identifying obesity in those under age 21 is through age- and gender-specific BMI growth charts 

(rather than simply using greater than or equal to 30 as the cutoff). For consistency across 

respondents, I use a cutoff of 30 for all respondents. 

To maintain a consistent sample across all survey years, I eliminate respondents who 

do not provide valid weight (and height) information in all surveys that collect weight 

information. Otherwise, changes in sample average weight over time might reflect attrition 

(where, perhaps, low-income individuals who weigh more might be more likely to drop out, 

downward-biasing the sample average weight in later survey years, for example). However, I 

make one exception: I do not entirely eliminate female respondents who have missing weight 

information due to giving birth; instead, these respondents’ weight observation is ignored from 

survey years when pregnant (or recently after giving birth) as described above, but their prior and 

future non-pregnant weight observations (from other survey years) are included in the analysis. 

The NLSY79 also collects extensive information on each respondent’s welfare 

experiences. However, information on welfare program participation is not collected for 

13
Charles L. Baum The Effects of Food Stamps on Obesity 

NLSY79 respondents under the age of 18 who are not married, not in college, and without 

children. I only include weight observations from the 1985 and successive NLSY79 surveys 

(when respondents are at least 20 years of age) so Food Stamp usage will not increase simply 

because youths cross the 18-year threshold. The NLSY79 identifies whether each respondent 

receives Food Stamp benefits in each month covered by the survey. The NLSY79 also identifies 

the amount of Food Stamp benefits received in each month. Thus, it is possible to determine not 

only whether respondents received Food Stamp benefits but also the amount received. I create 

two variables to measure Food Stamp benefits. The first Food Stamp benefits variable is discrete 

and equals one if the respondent receives such benefits (and zero otherwise). The other variable 

is continuous and equals the amount of Food Stamp benefits received (over the twelve months 

preceding the survey date). 

It is also possible with NLSY79 data to approximate whether respondents are eligible 

for Food Stamp benefits. Determining potential Food Stamp benefits is a complex process that 

hinges on the government’s eligibility criteria. To be eligible for Food Stamp benefits, a 

household must: 

• Have gross monthly income less than a household size-specific amount, 

though the gross income test is disregarded if the household contains an 

elderly (aged 60 and over) or disabled member. 

• Have net monthly income (gross income minus 20 % of earned monthly 

income, a standard deduction, child support payments, a dependent care 

deduction, an excess shelter cost deduction, and medical expenses for elderly 

and disabled household members) less than a household size-specific amount. 

• Have assets whose value is less than a specified amount. This amount is not 

specific to household size, but the amount is higher if the household contains 

an elderly or a disabled member. Further, the full value of the family’s 

vehicles is not counted – instead, only each vehicle’s value above a yearspecific threshold amount is counted as an asset. 

14
Charles L. Baum The Effects of Food Stamps on Obesity 

Households that receive Aid to Families with Dependent Children (AFDC -- now Temporary 

Aid to Needy Families [TANF]) or Supplemental Security Income (SSI) are automatically 

eligible for benefits. However, if such households have sufficient income, then their Food Stamp 

allotment may be zero. 

The NLSY79 collects the information needed to identify gross income, net income, 

household size, the ages of household members, the value of assets, and vehicle values in the 

1985 through 2000 surveys. In particular, in these survey years, the NLSY79 collects 

information on: wages, salary, farms, businesses, and/or military service (each identified 

separately) for the respondent and the respondent’s spouse (and for the respondent’s partner in 

1994 and successive surveys); the earned income (one component) of other household members; 

unemployment compensation, AFDC benefits, SSI benefits, veterans’ benefits, disability 

payments, income from “other types” of welfare assistance, income from trusts and/or estates, 

childcare support, and alimony for the respondent and the respondent’s spouse (and for the 

respondent’s partner in 1994 and successive surveys); welfare income (one component) for all 

other household members; and the value of savings and checking accounts, money markets, 

credit union savings, U.S. savings bonds, IRAs and/or Keoghs, certificates of deposit, personal 

loans, common stock, preferred stock, stock options, corporate or government bonds, mutual 

funds, estates or trusts, and any item worth more than $500 such as furniture or jewelry. The 

NLSY79 also collects information on child support payments made by the respondent and the 

respondent’s spouse or partner in 1994 and successive surveys. 

Unfortunately, approximating Food Stamp eligibility using the NLSY79 has some 

limitations. First, as mentioned above, the NLSY79 does not collect “partner” income or child 

support payments prior to 1994. This is a shortcoming because measures of partner income are 

15
Charles L. Baum The Effects of Food Stamps on Obesity 

used to determine Food Stamp eligibility when the partner is considered part of the household. 

Child support payments are important because they are deducted from gross income to determine 

net income. Second, the NLSY79 does not collect information on shelter costs (such as utility 

payments), so each individual’s excess shelter cost deduction is not identified. A third limitation 

is that NLSY79 respondents report annual measures of earned income, unearned income, and 

childcare payments. This is problematic because Food Stamp eligibility is determined monthly. 

A fourth limitation is that the NLSY79 does not identify whether households contain disabled 

members. This is important because for households with a disabled member the gross income 

limit is disregarded, the excess shelter cost deduction is not capped, medical expenses above a 

threshold amount per month ($35 in 2004) are deductible, and the asset limit is higher ($3,000 

instead of $2,000 in 2004) (Dean and Rosenbaum, 2002). A final limitation is that, though the 

NLSY79 asks respondents for the total value of all vehicles, it does not identify the number of 

vehicles. This is important because only each vehicle’s value above a threshold amount ($4,650 

in 2004) is counted as an asset. Further, the NLSY79 does not collect any asset information 

prior to the 1985 survey or in the 2002 survey, which explains why observations from the 2002 

survey wave, which does collect weight information, are not included in the analysis. 

Descriptive Statistics

I first select respondents with no more than a high school education to use in the 

analysis because they should be more likely to be eligible for Food Stamps than a sample of all 

NLSY79 respondents (without education restrictions). After making the necessary exclusions, 

my sample contains 11,748 male and 10,558 female person-year observations across the 11 used 

surveys that collect weight information. In this sample when weighted, about 47 % are female, 

13.6 % are African-American (referred to as black from henceforth for brevity), and 5.9 % are 

16
Charles L. Baum The Effects of Food Stamps on Obesity 

Hispanic.3 I also perform the analysis using a sub-sample of those approximated to be eligible 

for Food Stamps based on the program’s gross and net income tests and asset test. The 

corresponding sub-sample of those approximated to be eligible for Food Stamp benefits contains 

3,681 male and 4,799 female person-year observations. 

Table 1 gives the sample means for the key dependent variables (BMI and the 

probability of being obese) and the key explanatory variables (Food Stamp receipt and the 

amount of Food Stamps received) for low-income males and females, as well as these mean 

statistics for corresponding low-income Food Stamp recipients and non-recipients. As shown in 

this table, 5 % of low-income males and 14.7 % of low-income females receive Food Stamp 

benefits. Among those who receive Food Stamps, household benefits average $2,090 for males 

and $2,738 for females per year. This is reasonably similar to that found in USDA 

administrative data: Food Stamp households receive an average of $212.90 in benefits per month 

(USDA, 2006a) and receive benefits for an average of 11 months per year (USDA, 2006b). 

Table 1 also shows that average BMI and the probability of being obesity are higher for lowincome female Food Stamp recipients than low-income female non-recipients. Specifically, 27.8 

% of low-income female recipients are obese compared to 19.0 % of low-income female nonrecipients. Similarly, low-income female recipients have higher BMIs: 26.8 versus 25.3. 

However, the same is not true for low-income males, where, for example, low-income male 

recipient BMI is slightly lower than corresponding low-income male BMI for non-recipients. 

 3

 Even when weighted, descriptive statistics from the NLSY79 are not necessarily comparable to 

those from other data sets (such as NHANES) because the NLSY79 is a cohort of respondents no 

more than seven years apart in age (aged 35 to 42 in 2000, for example). 

17
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 2 presents these descriptive statistics for the sub-sample of observations 

approximated to be eligible for Food Stamp benefits. BMI and obesity are not universally higher 

in the eligible sub-sample than in the low-income sub-sample. However, both eligible male and 

eligible female Food Stamp recipients have higher BMIs and are more likely to be obese than 

corresponding eligible non-recipients. As we would expect, those in the eligible sub-samples are 

more likely to receive Food Stamp benefits. 

I next illustrate how BMI changes over time for these cohorts by plotting BMI for 

each year in which the NLSY79 collects weight information. Presented in figure 1 for lowincome males and in figure 2 for low-income females, BMI increases over time from, for 

example, 24.7 (23.3) in 1985 to 28.8 (28.3) in 2000 for low-income males (females). BMI is 

higher for low-income males than low-income females, ranging from about 1.5 index points 

higher in 1985 to 0.5 index points higher in 2000. This is surprising because NHANES data 

shows the opposite. This appears to be a characteristic unique to the NLSY79 cohort. 

Regardless, others have found male BMI to be larger than female BMI in NLSY79 data (Cawley, 

2004). 

BMI growth paths for the sub-sample approximated to be eligible are presented in 

figures 3 and 4. Compared to low-income females, BMI initially is lower in the low-income 

female sub-sample, but eligible female BMI becomes larger by 1989 and remains so through the 

year-2002 survey. Conversely, low-income male BMI is higher than eligible male BMI 

throughout. Further, while BMI is somewhat higher for low-income males than low-income 

females (with this gap narrowing over time), the opposite is true in some years for the eligible 

sub-samples. 

18
Charles L. Baum The Effects of Food Stamps on Obesity 

Presented in these figures (figures 1 through 4) is also BMI for corresponding Food 

Stamp recipients and non-recipients. In the low-income sub-samples, BMI tends to be higher for 

Food Stamp recipients than non-recipients, specifically for females. This differential also tends 

to grow over time. For example, in the low-income female sub-sample, recipient BMI is initially 

0.7 index points higher than that for low-income female non-recipients, but this differential 

grows to 3.3 index points by year-2000. The recipient-non-recipient BMI difference is perhaps a 

bit starker and more consistent in the eligible sub-samples than in the low-income sub-samples. 

For example, eligible male recipient BMI is higher than eligible male non-recipient BMI in most 

years. 

I next present corresponding figures for the growth in the prevalence of obesity. 

Shown in figures 5 and 6 for low-income males and low-income females, respectively, the 

prevalence of obesity tends to increase over time. For example, for low-income males (females), 

this increase is from 11.57 (9.92) % in 1985 to 31.61 (33.95) % in 2000. Presented in figures 7 

and 8, eligible males are initially more likely to be obese than eligible females, but this reverses 

by the early 1990s. Further, by year-2000, the prevalence of obesity is higher in the eligible 

female sub-sample than in any of the other sub-samples examined. 

Obesity figures 5 through 8 also present the prevalence of obesity for corresponding 

Food Stamp recipients and non-recipients. As was the case with BMI, obesity tends to be more 

prevalent for Food Stamp recipients than their non-recipient counterparts. For example, in the 

low-income female sub-sample, recipients are roughly five percentage points more likely to be 

obese, and this gap grows to almost 20 percentage points by year-2000. Further, the recipientnon-recipient obesity differential is larger for females than males, both in the low-income subsample and in the sub-sample of those eligible for benefits. 

19
Charles L. Baum The Effects of Food Stamps on Obesity 

Somewhat differently, Ver Ploeg, Mancino, and Lin (2006) find that in NHANES 

data the BMI gap between Food Stamp recipients and eligible non-participants increased 

between 1976-1980 and 1988-1994 but then decreased thereafter, with the BMI of eligible nonparticipants becoming larger by 1999-2002. Ver Ploeg et al. also find that the probability of 

being overweight increased less for Food Stamp participants than for eligible non-participants 

between 1972 and 2002 in NHANES data. Differences in trends between my NLSY79 sample 

and Ver Ploeg et al.’s NHANES sample may be due to following a particular cohort over time 

that, by definition, ages versus examining a cross-section of respondents with no aging. That is, 

obesity does not level off in my sample because the NLSY79 cohort continues to age. 

Regardless, I find that obesity increases faster after 1996 for Food Stamp recipients than for nonrecipients. Some of this could be due to the decrease in the FSP caseload during this period. For 

example, the number of Food Stamp recipients in NLSY79 data between 1994 and 2000 declines 

by 50 %, with non-obese recipients being the ones more likely to leave the FSP. 

The descriptive statistics and correlations presented thus far in tables and figures do 

not necessarily represent the causal effects of the FSP on obesity. To identify causal effects, I 

use multivariate regression analysis to hold constant potentially confounding factors. First, I 

control for individual characteristics with a standard set of demographic variables. The 

demographic covariates include controls for gender, race/ethnicity, age, education, marital status, 

household composition (number of children, household size), household income, urban 

residence, state of residence, and survey year of response. Descriptive statistics for these (and 

other) variables are presented in table 1 for the low-income sub-samples and in table 2 for the 

sub-samples approximated to be eligible for Food Stamp benefits. 

20
Charles L. Baum The Effects of Food Stamps on Obesity 

I also control for local (county or SMSA) economic conditions (descriptive statistics 

presented in tables 3 and 4) because economic conditions may affect participation in public 

assistance programs. To do this, I include variables identifying the local unemployment rate, 

potential earnings (proxied by local per capita income), the % of the local labor force that is 

female, the % of the local population with a high school education and a college education, the 

% of the local population employed, and the % of the local labor force in manufacturing and 

wholesale/retail trade. 

I also control for state political orientation (descriptive statistics presented in tables 3 

and 4) because liberal states may have more generous FSP eligibility criteria and may also be 

less likely to stigmatize those who receive welfare (Figlio, Gundersen, and Ziliak, 2000; Ziliak, 

Gundersen, and Figlio, 2003). I control for state political orientation with variables indicating 

how often members of the state’s congressional delegation (representatives and senators 

separately) cast liberal votes as measured by Americans for Democratic Action (ADA) as well as 

the political affiliation of the governor and state legislature (representatives and senators 

separately) (Americans for Democratic Action, 2005). 

I also control for AFDC/TANF Program characteristics. The 1996 welfare reform act 

(the Personal Responsibility and Work Opportunity Reconciliation Act -- PRWORA) began 

allowing states to develop their own TANF eligibility standards, benefits, and time limits. Since 

that time, states have tailored unique TANF programs. I include a TANF dummy variable equal 

to one if PRWORA welfare reform is in force and (six) pre-PRWORA welfare waiver dummy 

variables equal to one if a pre-welfare reform state waiver is in force either terminating or 

reducing benefits due to time limits, changing work exemption policies, changing sanctions for 

violations, increasing earned income disregards, changing family cap rules, or implementing 

21
Charles L. Baum The Effects of Food Stamps on Obesity 

work requirements (see Crouse, 1999). In addition, I include controls for other state TANF 

program characteristics. States differed in their monthly maximum benefit levels (for example, 

state-specific household size-specific maximum AFDC/TANF benefits for a family of four) prior 

to PRWORA; after PRWORA, states began differing in their time limits in which recipients may 

receive TANF benefits (months of allowable lifetime receipt), whether household benefits are 

capped for births occurring during participation spells (family caps), child age (in months) for 

which caregivers are exempt from work requirements, their most severe sanctions for program 

violations (whether the most severe sanction is full or permanent instead of partial and 

temporary), their income/asset limits, and their earned income disregards (flat dollar amounts 

and percentages of earnings disregarded from benefits calculation for the first month with 

earnings). If PRWORA’s TANF work requirements increase employment and, consequently, 

earnings, then they may affect Food Stamp benefits by making recipients ineligible for Food 

Stamps due to the program’s income test. Furthermore, state TANF programs may affect Food 

Stamp benefits because those eligible for TANF benefits are automatically eligible for Food 

Stamps. Information required to create these variables is obtained from a report on state 

AFDC/TANF policies by Crouse (1999) and from the Urban Institute’s online Welfare Rules 

Database (The Urban Institute, 2005). 

IV. Estimation Methodology and Identification 

My goal is to identify the causal effects of Food Stamp benefits on obesity. In this 

context, ‘causal effect’ is defined as it would be, for example, in the evaluation of a social 

program: a causal effect is the average effect on a nonrandom treated subpopulation, where 

covariates attempt to control for the nonrandom assignment of the treatment (Angrist and 

22
Charles L. Baum The Effects of Food Stamps on Obesity 

Krueger, 1999). This differs from the causal effect of a medical treatment that is randomly 

assigned to a sub-sample. 

Ordinary Least Squares (OLS) Models

To identify causal effects, I estimate the probability of being obese using multivariate 

regression analysis. However, modeling whether BMI is above or below the threshold that 

defines obesity is not necessarily a better approach than a continuous model of BMI (Jolliffe, 

2004). This is because the obesity threshold is likely to be somewhat arbitrary. For example, 

moving from a BMI of 29 to 30 is probably not substantially worse than moving from a BMI of 

28 to 29. Further, the dichotomous measure of obesity does not change when those obese gain 

weight (and starkly changes from 0 to 1 when those barely below the obesity threshold gain 

small amounts of weight). Therefore, I estimate an additional set of regressions modeling 

obesity with a continuous BMI measure in OLS regression framework. 

The key variables in these models are a measure of weight (W) such as the 

probability of being obese or BMI and Food Stamp benefits (FSB). Formally, I estimate 

Wit = β0 + β1Xit + β2FSBit + εit (13) 

for observation i at time t, where X is a vector of covariates and ε is the error term. When the 

outcome is measured as a discrete variable such as the probability of being obese, the models 

take the logit specification. In this model and in all others that use multiple observations from 

the same respondent, I adjust my standard errors to account for respondent-specific correlation 

because respondents potentially provide multiple observations (from multiple survey years). 

Otherwise, standard errors will be understated and significance levels will be overstated. 

23
Charles L. Baum The Effects of Food Stamps on Obesity 

Even within the context of multivariate regression analysis, estimates are susceptible 

to various sources of bias. One potential source of bias is due to unobserved heterogeneity, 

where obese respondents systematically differ from their non-obese counterparts in ways that are 

difficult for researchers to measure. In particular, unobserved characteristics, which may be 

time-variant, may be correlated with obesity and FSP participation. For example, suppose that 

those who are “food insecure” are more likely to apply for and receive Food Stamps. If these 

same food insecure individuals are more likely to be obese because they are prone to eating 

disorders such as binge-eating when food is available (the arguments of Gibson, 2003, and 

Townsend et al., 2001), then obesity and Food Stamp benefits will both be correlated with food 

insecurity. If food insecurity is unobserved to the researcher, then the Food Stamp benefit 

variable will pick of the effects of Food Stamps as well as the effects of food insecurity on 

obesity. If Food Stamp benefits increase obesity and food insecurity increases obesity and Food 

Stamp benefits, then the positive Food Stamp benefit coefficient would be biased upward. In 

general, if Food Stamp benefits are correlated with any unobserved characteristic that is also 

correlated with obesity, then OLS regression will not identify the causal effects of Food Stamp 

benefits on obesity, producing unobserved heterogeneity bias. 

Another type of bias is reverse causality and/or simultaneity bias. Reverse causality 

bias would exist if obesity determines (or influences) FSP participation. Simultaneity bias would 

exist if obesity and FSP participation affect each other (or are determined concurrently). It 

would seem easy to argue that consumers decide (at least monthly) how much to eat and whether 

to apply for and, if eligible, use Food Stamps. 

Instrumental Variable Models

24
Charles L. Baum The Effects of Food Stamps on Obesity 

I attempt to control for potential unobserved heterogeneity bias using three 

approaches. In my first approach, I use a two-step procedure that first predicts Food Stamp 

benefits with exogenous characteristics and then uses the predicted estimate of Food Stamp 

benefits to estimate the effects of Food Stamp benefits on obesity in a second-stage model. More 

formally, I estimate 

FSBit = α0 + α1Xit + α2Zit + εit (14) 

and 

Wit = β0 + β1Xit + β2 it

^

FSB + εit (15) 

for observation i at time t, where X and ε are as defined above, is the predicted value of 

Food Stamp benefits, and Z is a vector of instruments that predict Food Stamp benefits but have 

no effect on weight. (This procedure is often referred to as instrumental variable estimation. 

The goal is to generate unbiased estimates by finding instruments that are as highly correlated as 

possible with the endogenous regressor [Food Stamp benefits] but that are uncorrelated with the 

error, ε.) 

^

FSB

 As instruments, I use some of the household characteristics required to determine 

Food Stamp eligibility. Specifically, these characteristics include the value of vehicles and the 

presence of an elderly member. Eligibility criteria will serve as exogenous instruments 

identifying Food Stamp benefits if (i) Food Stamp eligibility criteria significantly explain Food 

Stamp receipt and (ii) Food Stamp eligibility criteria do not significantly affect obesity 

independently of Food Stamp benefits. Certainly the value of vehicles and the presence of an 

elderly member affect Food Stamp benefits because they directly determine eligibility (for 

example, the presence of a senior raises gross income and asset limits). However, these 

25
Charles L. Baum The Effects of Food Stamps on Obesity 

instruments will be invalid if they affect obesity (for example, if non-car owners burn more 

calories). Ultimately, F-tests will be used to determine whether household Food Stamp 

eligibility characteristics are indeed valid instruments for Food Stamp benefits. Comparisons 

(using Hausman tests) with OLS models containing the actual measure of the Food Stamp 

benefit variable will indicate whether OLS estimates are indeed biased. 

My second approach is similar to my first approach except that I use another type of 

instrument. In this approach, I exploit state variation in Food Stamp eligibility laws (Kabbani 

and Wilde, 2003, use similar instruments). This is possible because the 1996 welfare reform act, 

PRWORA, began allowing states to develop their own Food Stamp eligibility standards (within a 

set of federal guidelines). In 2002, The Farm Security and Rural Investment Act (the Farm Bill) 

further expanded the options and waivers available to states. Since that time, states have utilized 

various options and waivers. My second approach predicts Food Stamp benefits with 

instruments that reflect variation in state Food Stamp eligibility laws. Therefore Z becomes a 

vector of state Food Stamp eligibility criteria. A sampling of these options and waivers are listed 

below roughly in chronological order: 

• PRWORA gives states the option of extending Food Stamp benefits to able-bodied 

adults without dependents (ABAWDs) (defined as adults aged 18 through 50 not 

providing care to a dependent) whose benefits would otherwise have expired if they 

live in an economically depressed area with high unemployment (10 % or higher) or 

insufficient job opportunities (GAO, 1999; Rosenbaum, 2002). 

• PRWORA gives states the option of making work and training requirement penalties 

more severe (Gabor and Botsko, 2001; USDA, 2003b, p. 18). 

• PRWORA gives states the option of disqualifying Food Stamp recipients for failing 

to meet obligations specified by other means-tested assistance and benefit programs 

such as TANF (Gabor and Botsko, 1998, 2001; USDA, 2003b, p. 19). 

• States have the option of requiring recipients to report changes in income either 

periodically (periodic reporting) or as circumstances change (incident reporting) 

(USDA, 2003b, p. 1). 

26
Charles L. Baum The Effects of Food Stamps on Obesity 

• States that use incident reporting have the option of first adjusting food stamp 

benefits when the certification period expires (USDA, 2003b, p. 2). 

• States that use incident reporting may be granted waivers reducing the types of 

changes that must be reported (USDA, 2003b, p. 3). 

• The 2001 Agriculture Appropriations Act gives states the option of replacing the 

FSP’s vehicle rule with a more liberal vehicle rule from a TANF assistance or noncash benefit program (Super and Dean, 2001; Center on Budget and Policy Priorities, 

2003; and USDA, 2003b, p. 6). 

• Effective November 2000, states have the option of making households that receive 

means-tested cash assistance or means-tested non-cash benefits funded by a TANF or 

TANF Maintenance of Effort (MOE) assistance program automatically eligible for 

Food Stamp benefits (Super and Dean, 2001; USDA, 2003b, p. 8). 

• States have the option of using a standard utility allowance (rather than a household’s 

actual utility costs) in calculating a household’s excess shelter cost deduction (Posey, 

2002; USDA, 2003b, p. 10; Dean and Rosenbaum, 2002). 

• The Farm Bill gives states the option of disregarding changes in deductions used to 

calculate net income during the certification period (Dean and Rosenbaum, 2002; 

Rosenbaum, 2002; Posey, 2002; USDA, 2003b, p. 2). 

• The Farm Bill allows states to continue providing Food Stamp benefits to households 

formerly receiving TANF benefits for up to five months after cessation of TANF 

assistance (Hayes, 2002; Dean and Rosenbaum, 2002; GAO, 2002; Posey, 2002; 

USDA, 2003b, p. 4). 

• The Farm Bill gives states the option of reducing the sources of income and types of 

assets used to determine eligibility (Posey, 2002; Rosenbaum, 2002; USDA, 2003b, 

p. 5). However, some limitations apply. 

• The Farm Bill gives states the option of excluding child support payments from 

income (USDA, 2003b, p. 12). 

Because Food Stamp eligibility criteria vary between states and over time within 

states, I identify the effects of Food Stamp benefits using variation in Food Stamp eligibility 

policies. Certainly state Food Stamp eligibility criteria should affect Food Stamp benefits. 

Further, it seems reasonable to assume that state Food Stamp eligibility criteria are unrelated to 

obesity when controlling for Food Stamp benefits. For example, state legislatures probably do 

not alter Food Stamp eligibility criteria based on a state’s prevalence of obesity. Again, F-tests 

are used to determine whether state Food Stamp eligibility criteria are indeed valid instruments. 

27
Charles L. Baum The Effects of Food Stamps on Obesity 

However, comparing the effects of various Food Stamp eligibility options and 

waivers across states will produce misleading results if such differences are due to state-specific 

effects that are not the result of the options and waivers. To control for such effects, I include 

state-specific dummy variables in the regression models. Thus, the models identify the effects of 

state Food Stamp eligibility criteria on Food Stamp benefits by examining the effects of state 

Food Stamp eligibility changes on changes in Food Stamp benefits. Similarly, estimates will 

also be biased if the usage of the options and waivers is correlated with but not due to yearspecific effects. To control for year-specific effects, I include a dummy variable for each year 

covered by the model (one for each survey year). The year dummy variables pick up national 

trends while variation in state Food Stamp eligibility criteria across states within a particular year 

identify the effects of state Food Stamp eligibility criteria. Thus, the effects of state eligibility 

criteria are identified from variation over time and across states. 

To estimate the effects of state Food Stamp eligibility criteria on Food Stamp 

benefits, I use data on each state’s use of FSP options and waivers as well as when these options 

and waivers went in force and when they were changed, if applicable. I gather this information 

from Gabor and Botsko (1998), Super and Dean (2001), and Knaus (2003), and the Urban 

Institute (see Finegold, Margrabe, and Ratcliffe, 2006). The NLSY79 identifies each 

respondent’s state of residence, enabling me to link measures of state Food Stamp eligibility 

criteria with each respondent. 

The proposed IV procedure has some shortcomings. First, instruments that are 

weakly correlated with the endogenous covariate in the first stage model often generate large 

second stage standard errors. Even more troubling, the IV literature shows that IV estimates may 

still be biased when the identification variables are weakly associated with the endogenous 

28
Charles L. Baum The Effects of Food Stamps on Obesity 

covariate (and when there is a relationship, even if only a weak one, between the instruments and 

the second stage error). With weak instruments, Nelson and Startz (1990a,b), Bound, Jaeger, and 

Baker (1995), and Staiger and Stock (1997) argue that IV estimates may be biased in the 

direction of OLS. Again, for IV to produce unbiased estimates, the instruments must 

significantly explain the endogenous covariate. However, Bound et al. (1995) show that in large 

samples, instruments may appear to have statistically significant effects when, in fact, their 

relationship to the endogenous covariate is weak. That is, in large samples, testing the null 

hypothesis that the instruments have no effect may produce statistically significant p-values with 

relatively small F-statistics. Thus, Bound et al. (1995) conclude that finding appropriate 

instruments is more difficult than previously thought. To help determine instrument quality, the 

IV literature recommends reporting the F-statistic for the instruments (Bound et al., 1995; Staiger 

and Stock, 1997). 

Fixed Effects Models

In my third approach to control for potential unobserved heterogeneity bias, I use 

NLSY79 data to estimate individual-specific fixed effects models that compare multiple 

observations from the same respondent. I assume that the effects of Food Stamp benefits on 

BMI and obesity can be estimated from the following model: 

Wist = β0 + β1Xit + β2FSBit + νi + εist (16) 

where νi is an individual-specific factor representing respondent i’s unobserved characteristics, 

and ε is the error term for respondent i at time t. In this context, if correlation between FSB and 

νi exists and if the heterogeneity component (νi) is unobserved to the researcher, then estimates 

will be biased. That is, because νi is unobserved, it cannot explicitly be included, subjecting the 

29
Charles L. Baum The Effects of Food Stamps on Obesity 

estimates to potential unobserved heterogeneity bias. However, if this individual-specific 

unobserved component is the same across observations from the same respondent (time invariant 

for each respondent), then it can be identified and controlled for with respondent-specific dummy 

variables. In practice, the individual-specific fixed effects model essentially compares 

observations over time from the same respondent. Just as continuous-outcome fixed effects 

models are estimated with respondent-specific dummy variables, discrete-outcome fixed effects 

models take the logit functional form and include respondent dummy variables (fixed effects 

logit models can be estimated in STATA, 2001). Unfortunately, the fixed effects model has 

some limitations. First, if the respondent’s unobserved component is not constant over time, 

then the estimates may still be biased. Another limitation of the fixed effects model is that it 

does nothing to control for reverse causality (or simultaneity), where obesity determines FSP 

participation. I also estimate a corresponding fixed effects linear probability model for the 

probability of being obese. The advantage of using the logit model fixed effects specification is 

that the outcome variable is constrained to be between zero and one. Conversely, predicted 

outcomes using the linear probability model specification may be negative or larger than 100 %. 

The advantage of using the linear probability model specification is that respondents with no 

variation in the outcome variable are included. Conversely, the logit model specification does 

not include respondents who are either never obese or always obese across NLSY79 survey 

years. 

Dynamic Models: Change in BMI and Transition Equations

The third part of my analysis examines dynamic effects of Food Stamp benefits on 

BMI and obesity. Because current weight is not independent from past weight, 

30
Charles L. Baum The Effects of Food Stamps on Obesity 

contemporaneously-measured covariates probably affect weight change. Thus, I first estimate 

another model specification using change in BMI as the dependent variable, 

∆Wit = β0 + β1Xit + β2FSBit + εit, (17) 

where ∆ indicates change. A variant on this specification is to estimate conditional transition 

equations. Such equations estimate the probability of becoming obese from a non-obese subsample and the probability of no longer being obese from an obese sub-sample. Formally, 

Prob (Obeset = 1 | Obeset-1 ≠ 1) = Φ( { Wit – Xitβit} / σ ), (18) 

where Φ is the normal cumulative distribution function, σ is the standard deviation of the error 

(where the error is assumed to be normally distributed), and Xi is a vector of covariates redefined 

to include FSBit. For consistency, I measure these changes (the change in BMI and the 

conditional changes in obesity) over a two-year time interval. Specifically, I examine changes 

between the 1985 and 1987 surveys, the 1987 and 1989 surveys, 1990 and 1992, 1992 and 1994, 

1994 and 1996, 1996 and 1998, 1998 and 2000, and 2000 and 2002 (with weight observations 

from 1986, 1989, and 1993 disregarded because they cannot be matched with corresponding 

two-year-later weight measurements). 

Dynamic Models: Hazard Models

Since less than 10 % of NLSY79 youths were obese in 1985 but almost 30 % were by 

2000, obesity could be estimated using a survivor function (“surviving” is defined in this context 

as not yet being obese). This specification uses hazard rates to estimate the effects of Food 

Stamp benefits on the probability of becoming obese between NLSY79 surveys (ten betweensurvey transitions from the 1985 through 2000 surveys that collect weight information). I follow 

the approach taken by Prentice and Gloeckler (1978) and Meyer (1990, 1995) and use a hazard 

31
Charles L. Baum The Effects of Food Stamps on Obesity 

specification that does not impose parametric restrictions on the underlying baseline hazard 

function. If the baseline hazard function were estimated by assuming a parametric form (such as 

a Weibull form) and that parametric form were incorrect, then estimates would be inconsistent. 

This hazard model also gives consistent estimates when there is censored data. Here, the data is 

censored after the year-2000 NLSY79 survey. 

Let λi(t) be the hazard rate for observation i in year t, where the hazard rate is defined 

as the probability of becoming obese in year t given that the respondent is not yet obese. 

Formally, if Ti is the year in which a respondent becomes obese, then the hazard rate is defined 

as 

)( [ ]| lim 0 t

h

tTtThtprob

i

i i

h = λ ≥≥>+

⎯⎯→ + . (19) 

Empirically, the hazard rate is 

λi(t) = λ0(t)exp(Xi′β) (20) 

where λ0(t) is the baseline hazard rate for year t and Xi is a vector of covariates (including 

FSBit). Following Meyer (1990, 1995), given that a respondent is not yet obese by year t, the 

probability that the respondent does not become obese in year t+1 is 

∫ ∫

+ +

−=≥+≥ −=

1 1

0 ],)()'exp(exp[])(exp[]|1[

t

t

t

t

i i i i tTtTprob λ duu X λβ duu (21) 

which gives 

tTtTprob t)))('exp(exp(]|1[ i i i ≥ + =≥ − X β + γ (22) 

with 

32
Charles L. Baum The Effects of Food Stamps on Obesity 

∫

+

=

1

0 })(ln{)(

t

t

γ t λ duu (23) 

because Xi is constant between t and t+1. Thus, the likelihood function is 

∏ ∏ =

−

=

−−= + +−

N

i

k

t

ii i

i

i l k t

1

1

0

βγ γ β βγ ))}')(exp(exp())]')(exp(exp(1{[),( δ X X (24) 

where N is the number of respondents, ki is the minimum of Ti and ten and δi equals one if t 

equals Ti and zero otherwise. The first term equals one unless a respondent becomes obese 

between years t and t+1. The second term is the probability that a respondent is not yet obese by 

year t. 

Models with Lagged Food Stamp Benefits 

As specified, FSBit identifies the effects of contemporaneous FSP receipt (at time t); 

however, past Food Stamp benefits may affect past weight which affects current weight. In the 

final portion of models, the Food Stamp variable identifies the effects of program receipt from 

past periods. In the first of these models, FSBit is specified as a vector such that 

FSBit = (FSBit, FSBit-1, FSBit-2, ... FSBit-n) (25) 

where n is the number of past periods examined (with results presented for n equal to one, three, 

and seven). This shows whether Food Stamp receipt affects weight contemporaneously or with a 

lag. In another set of models, I estimate the effects of patterns of past Food Stamp receipt (using 

the classification system proposed by Murphy and Harrell, 1992). Specifically, I estimate the 

effects of receiving Food Stamp benefits over the preceding two years in one spell either shortterm (for less than 9 months), medium-term (between 9 and 23 months), or long-term (24 

months). These models also estimate the effects of receiving benefits during multiple spells. 

33
Charles L. Baum The Effects of Food Stamps on Obesity 

V. Results 

OLS Results

First, I estimate the effects of Food Stamp benefits on BMI and the probability of 

being obese in OLS (and logit) regressions separately using sub-samples of low-income males 

and females. Table 5 reports the effects of the Food Stamp benefit covariates. Model 1 (for both 

sub-samples) examines the effect of receiving Food Stamps (a dichotomous variable) on BMI, 

model 2 examines the effect of the amount of Food Stamps received (a continuous variable) on 

BMI, and model 3 includes both the dichotomous and the continuous Food Stamp benefits 

variables (from models 1 and 2). Correspondingly, models 4, 5, and 6 re-estimate models 1, 2, 

and 3 using the probability of being obese as the outcome variable instead of BMI. For each 

model, I also report the predicted value of the outcome variable for not receiving and for 

receiving Food Stamp benefits (or for receiving $2,400 in Food Stamp benefits during the year, 

which is roughly the annual average for recipients). Appendix tables A1 through A4 present the 

effects of the other covariates for the low-income sub-samples (appendix tables A1 and A2) and 

the eligible sub-samples (appendix tables A3 and A4). 

Initial results for low-income females suggest that Food Stamps significantly increase 

BMI and obesity. This is true for females for both the FSP participation variable in models 1 and 

4 and, at the 10 % level, for the amount of Food Stamp benefits received in models 2 and 5. 

However, the amount of Food Stamp benefits received never has a statistically significant effect 

when both are included in the same model. The coefficient point estimates suggest switching 

from not receiving Food Stamp benefits to receiving them (or from not receiving Food Stamp 

benefits to receiving $2,400 in Food Stamp benefits per year) increases BMI by an average of 

about 0.5 index points across models 1, 2, and 3. The same change increases the probability of 

34
Charles L. Baum The Effects of Food Stamps on Obesity 

being obese between roughly 2 and 5 percentage points. However, Food Stamp benefits do not 

significantly affect males in any of the models. 

Though the models in table 5 use low-income NLSY79 respondents, corresponding 

results may mask the effects of Food Stamp benefits because a portion of included observations 

are ineligible for benefits. Therefore, I next re-estimate the models (in table 5) including only 

observations approximated to be eligible for Food Stamp benefits based on state- and yearspecific eligibility criteria and household characteristics. Results using eligible respondents, 

which are presented in table 6, suggest that Food Stamp benefits significantly increase BMI and 

the probability of being obese for females but not for males. The magnitudes of the Food Stamp 

variable coefficients are comparable, if not somewhat larger, to those in table 5 for the lowincome sub-samples. For example, receiving Food Stamps is projected to increase low-income 

female obesity by 5.1 % but to increase obesity among eligible respondents by 6.3 %. Further, 

the Food Stamp covariates typically have larger t-statistics in table 6. For example, Food Stamp 

receipt significantly affects obesity at the 5 % level among low-income females but at the 1 % 

level among eligible respondents. 

Instrumental Variables Results

The OLS results thus far establish correlation between Food Stamp benefits and BMI 

and the prevalence of obesity holding multiple observable factors constant. However, these 

models do not necessarily establish whether Food Stamps cause recipients to gain weight. This 

is because Food Stamp benefits and BMI/obesity could be correlated with other unobserved 

characteristics. In an effort to identify the causal effects of Food Stamp benefits, I next reestimate the models in tables 5 and 6 using a measure of predicted Food Stamp benefits rather 

than actual Food Stamp benefits (however, I do not present corresponding results from the model 

35
Charles L. Baum The Effects of Food Stamps on Obesity 

specification that includes both Food Stamp receipt and the amount of Food Stamps received 

because in this model only the dichotomous measure tends to be statistically significant -- see 

tables 5 and 6). 

I use two sets of instruments: first, I use household characteristics that determine 

program eligibility, and, second, I use state eligibility criteria. Sample first-stage IV Food Stamp 

receipt model results using the first set of instruments for the low-income sub-samples are 

presented in appendix table A5 and sample model results using the second set of instruments for 

the low-income samples are presented in appendix table A6. Corresponding first-stage results 

for the eligible sub-samples are in appendix tables A7 and A8. These first-stage results show 

that household vehicle values significantly decrease Food Stamp receipt, but, surprisingly, the 

presence of a senior significantly decreases Food Stamp receipt (see appendix tables A5 and A7). 

First-stage results in appendix tables A6 and A8 show that USDA-approved state outreach plans 

significantly increase Food Stamp receipt. 

Second-stage BMI and obesity results for the low-income sub-samples are presented 

in table 7 and results for the eligible sub-samples are presented in table 8. All of these tables 

(presenting second-stage results) report F-statistics (and indicators of statistical significance) for 

(i) the significance of the instruments in the first-stage models, (ii) the validity of the exclusion 

restrictions (excluding the instruments from the second-stage models), and (iii) Hausman tests of 

the null hypothesis that the OLS results and IV results are not statistically different. 

Reported in table 7 for the low-income sub-samples of males and females, household 

characteristics (household vehicle values and the presence of a senior) significantly explain Food 

Stamp benefits in first-stage models at the 1% level. Considering the low-income sub-sample of 

males, second-stage exclusion restrictions appear valid, and results show that Food Stamp 

36
Charles L. Baum The Effects of Food Stamps on Obesity 

benefits continue to have statistically insignificant effects on BMI and obesity (as with OLS 

results), with Hausman tests being unable to reject the null hypothesis of no difference between 

OLS and IV estimates. Unfortunately, second-stage Food Stamp benefit results for the lowincome sub-sample of females are not valid because F-tests suggest the instruments should not 

be excluded from second-stage BMI and obesity models. Perhaps females without vehicles face 

a higher time cost of food consumption due to lack of transportation to grocery stores or a higher 

monetary cost of food consumption when cheaper supermarkets are not within walking distance. 

Furthermore, those without vehicles may travel more by foot, consequently burning more 

calories. 

Results in table 8 considering the eligible sub-samples are similar to those in table 7 

for the low-income sub-samples. For example, the instruments continue to explain Food Stamp 

benefits significantly at the 1 % level. Hausman tests continue to fail to reject the null 

hypothesis that the OLS and IV estimates are not statistically different for the sub-sample of 

eligible males. Further, the instruments are again invalid for the sub-sample of eligible females 

because F-tests indicate they should be included in the second-stage BMI and obesity models. 

I next re-estimate the models presented in tables 7 and 8 using state eligibility laws as 

instruments. Corresponding low-income sub-sample results are presented in table 9 and results 

for the sub-samples of eligible respondents are presented in table 10. State eligibility laws tend 

to have statistically significant effects on Food Stamp benefits in first-stage models for lowincome males, but for low-income females they only have significant effects on the amount of 

Food Stamps received at the 10 % level. Further, F-tests suggest the instruments are properly 

excluded from second-stage BMI and obesity models for both low-income males and females. 

Where the instruments are valid, Hausman tests do not reject the null hypothesis that the OLS 

37
Charles L. Baum The Effects of Food Stamps on Obesity 

and second-stage IV Food Stamp estimates are not statistically different. Continuing, presented 

in table 10, corresponding results for the sub-sample of respondents approximated to be eligible 

for benefits show that the instruments do not significantly affect Food Stamp benefits for males. 

In models 1 and 3, the instruments affect eligible females at the 10 % level, but Hausman tests 

fail to reject the null hypothesis that the OLS and IV estimates are statistically different. 

In sum, where valid, the IV models provide no evidence that positive OLS effects of 

Food Stamp benefits are inconsistently large: in none of the models where the instruments are 

deemed valid do Hausman tests indicate OLS bias away from zero. However, OLS and IV 

estimates may appear to be the same because second-stage IV results are estimated imprecisely. 

Fixed Effects Results

IV estimates are only as strong as their instruments. When instruments are weak, IV 

estimates often are as well with large standard errors. Another approach to control for potential 

unobserved heterogeneity bias is to estimate individual-specific fixed effects models. 

Controlling for time-invariant unobserved heterogeneity, my fixed effects specifications 

essentially compare changes in Food Stamp receipt over time with changes in BMI and the 

probability of being obese over time. Table 11 presents individual-specific fixed effects model 

results for the low-income sub-sample and table 12 presents corresponding results for those 

approximated to be eligible. For each sample, I use two fixed effects specifications to model 

obesity: a fixed effects logit model and a fixed effects linear probability model. The advantage 

of using the logit model specification is that the outcome (the probability of being obese) is 

constrained to be between zero and 100 % (predicted outcomes using the linear probability 

model specification may be negative or larger than 100 %). The advantage of using the linear 

probability model specification is that respondents with no variation in the outcome variable 

38
Charles L. Baum The Effects of Food Stamps on Obesity 

(obesity) across survey years are included (the logit model specification does not include 

respondents who are either never obese or always obese across NLSY79 survey years). 

Fixed effects models using the low-income sub-sample provide some evidence that 

Food Stamp benefits increase BMI and obesity. Presented in table 11, the amount of Food 

Stamps received significantly increases BMI for low-income males at the 10 % level. For lowincome females, receiving Food Stamps significantly increases BMI and the probability of being 

obese (in both the logit and linear probability fixed effects specifications). Much in agreement, 

results in table 12 using the eligible sub-samples show that receiving Food Stamps significantly 

increases BMI at the 5% level for males and females. Thus, there is at least some evidence that 

when controlling for time-invariant individual-specific characteristics, Food Stamps increase 

BMI and obesity. In none of the models is Food Stamp receipt found to decrease these 

measures. 

Dynamic Results: Change in BMI and Transition Equations

Weight is not independently determined each period; instead, as defined in the 

theoretical model, contemporaneous weight is weight stock net of contemporaneous weight 

change. To begin to account for this, I next model the effects of Food Stamp benefits on the 

change in BMI and on transition equations for the conditional probability of becoming obese 

(conditional on not being obese) and the conditional probability of no longer being obese 

(conditional on being obese) between surveys. Table 13 presents Food Stamp covariate results 

from these models for the low-income sub-samples and table 14 presents corresponding results 

for the eligible sub-samples. 

Shown in table 13, results indicate that Food Stamp benefits significantly increase the 

change in BMI between surveys for the low-income female sub-sample in model 1 (and at the 10 

39
Charles L. Baum The Effects of Food Stamps on Obesity 

% level for males in model 2). For example, in model 1, with no Food Stamps, low-income 

female BMI is predicted to change by 0.57 index points between surveys; however, with such 

benefits, BMI is predicted to increase by almost 0.8 index points, which is an increase of over 

0.2 index points. To put this effect in perspective, consider the following. Weighted means 

indicate the NLSY79 low-income female sub-sample prevalence of obesity in 1998 was 0.298 

(with average BMI of 27.4). By year-2000, the sample prevalence of obesity increased to 0.330, 

with BMI increasing by 0.6 index points (from 1998) to 28.0. Had BMI instead increased by 0.8 

index points, as model 1 in table 13 suggests with Food Stamp receipt, the year-2000 lowincome female prevalence of obesity would have been 0.334, which is .4 percentage points 

higher. However, Food Stamp benefits do not significantly affect the conditional probability of 

becoming obese and the conditional probability of no longer being obese for the low-income 

sub-sample of females. Also of note, for males at the 10 % level, Food Stamps decrease the 

probability of transitioning from obese to no longer obese, decreasing this transition rate from 

13.9 % to 8.2 % (model 5). 

Results are different when using the eligible sub-sample. In table 14, Food Stamp 

benefits have statistically insignificant effects on eligible females, but, interestingly, for eligible 

males, significantly decrease the probability of transitioning from obese to not obese. Food 

Stamps continue to increase the change in BMI at the 10 % level for eligible males (model 2). 

Dynamic Models: Hazard Rate Results

I next estimate a hazard model for becoming obese (conditional on not yet being 

obese) to show the cumulative effect of Food Stamp benefits over time. I explore two hazard 

specifications for both the dichotomous Food Stamp covariate and the continuous Food Stamp 

covariate. The first includes the Food Stamp covariate but does not interact it with duration 

40
Charles L. Baum The Effects of Food Stamps on Obesity 

variables, resulting in a proportional effect of Food Stamps over time (model 1 for Food Stamp 

receipt and model 2 for the amount of Food Stamps). The second hazard specification interacts 

the Food Stamp covariate with duration dummy variables, allowing such benefits to have 

different effects in each period (model 3 for Food Stamp receipt and model 4 for the amount of 

Food Stamps received). I do not present corresponding Food Stamp covariate results in tables 

(though coefficient results are available upon request) because in non-linear hazard 

specifications it is difficult to interpret the magnitudes of the effects. Instead, I first discuss 

whether Food Stamps have statistically significant effects, and then I illustrate the magnitudes of 

these effects in figures, described below. 

Referring to the F-statistic for tests of the joint significance of the set of Food Stamp 

benefit covariates, Food Stamp benefits have statistically insignificant effects for low-income 

males. Results are mixed for low-income females. The dichotomous Food Stamp covariate 

specification has significant, positive effects on females in models 1 and 3, but the amount of 

Food Stamps received has insignificant effects in models 2 and 4. Much the same is true for the 

eligible sub-samples, where receiving Food Stamps has significant, positive effects for eligible 

females (in models 1 and 3) but insignificant effects for eligible males (except for model 4, 

where effects are significant and positive). 

To illustrate the magnitudes of the Food Stamp effects on the obesity hazard, I next 

plot the predicted survivor rates for the annual probability of not yet being obese without Food 

Stamp receipt and with Food Stamp receipt using model 3 estimates. Then, I do the same using 

model 4 estimates, predicting the annual survivor rates with no Food Stamp benefits and with 

$2,400 in annual Food Stamp benefits in each year. I do not present corresponding figures for 

hazard models 1 and 2 because they are more restrictive, constraining Food Stamp benefits to 

41
Charles L. Baum The Effects of Food Stamps on Obesity 

have a proportional effect in each period. The two sets of simulations are presented in figures 9 

and 10 for low-income males and in figures 11 and 12 for low-income females. Corresponding 

eligible sub-sample simulations are presented in figures 13 through 16. 

Figure 11 for low-income females suggests that receiving Food Stamp benefits 

decreases the survivor rate by over 12 percentage points by year-2000. The effect of receiving 

$2,400 annually in Food Stamp benefits is even larger, decreasing the probability of not yet 

being obese by year-2000 by about 20 percentage points in figure 12. The magnitudes of these 

effects are somewhat smaller for corresponding males in figures 9 and 10, though low-income 

male point estimates have been noted to be statistically insignificant. These effects are about the 

same size for the eligible female sub-sample, where, for example, receiving Food Stamp benefits 

decreases the probability of not yet being obese roughly 20 percentage points by year-2000. 

Lagged Food Stamp Benefits Results

Food Stamp benefits received today would not instantaneously be expected to 

substantially change weight; instead, because today’s weight is much the same as yesterday’s 

weight, Food Stamp receipt would be expected to affect weight somewhat slowly, perhaps being 

measurable only over a significant amount of time. Thus, I next examine the lagged effects of 

Food Stamp benefits on BMI and obesity (the effects of Food Stamp benefits from prior periods). 

Examining the effects of Food Stamp benefits from prior periods on current weight may also 

address concerns about reverse causality: sequentially, weight today does not affect Food Stamp 

receipt from prior periods. 

To examine the effects of prior periods’ Food Stamp receipt, I estimate three lagged 

specifications. In the first, I include contemporaneous Food Stamp benefits and one-year lagged 

Food Stamp benefits. In the second, I include contemporaneous Food Stamp benefits and one-, 

42
Charles L. Baum The Effects of Food Stamps on Obesity 

two-, and three-year lagged Food Stamp benefits. The third specification adds lagged Food 

Stamp benefits from the previous fourth, fifth, sixth, and seventh years.4

 I do this separately for 

both the dichotomous Food Stamp receipt covariate specification and the continuous amount of 

Food Stamps received covariate specification for both BMI and the probability of being obese. 

This results in twelve models for each sample. Results for the low-income sub-sample are 

presented in table 15 for males and in table 16 for females and for the eligible sub-sample in 

tables 17 for males and in table 18 for females. All of the tables contain the predicted value of 

BMI and the predicted probability of being obese for no Food Stamp benefits in the considered 

periods and for receiving Food Stamp benefits (or receiving $2,400 in benefits) in each period 

considered. 

For low-income males, Food Stamp benefits tend to have statistically insignificant 

effects however specified (see table 15). However, when examining low-income females (table 

16), results show that dichotomously-measured one-year lagged Food Stamp receipt (models 1 

and 4) significantly affects BMI and obesity, but contemporaneous Food Stamp receipt does not. 

This remains true when additional lags are included, suggesting one-year lagged Food Stamp 

receipt affects weight, not contemporaneous receipt. However, the results are less conclusive 

when Food Stamp benefits are measured continuously. Significant effects for other lags 

occasionally appear but not systematically. 

 4

 I do not include additional lags (from earlier years) because they are unavailable for a portion 

of the observations. For example, while Food Stamp benefits from 1979 are collected seven 

years prior to first weight observation used in the analysis from 1985, benefits in 1978 are not, 

resulting in missing values for eight-year lags for weight observations from 1985. 

43
Charles L. Baum The Effects of Food Stamps on Obesity 

Much the same is true for the eligible sub-samples. For example, Food Stamps 

generally have statistically insignificant effects on males, and one-year lagged Food Stamp 

receipt significantly affects female BMI and obesity at the 5 % level, with contemporaneous 

Food Stamp benefits having insignificant effects. However, when measured continuously as the 

amount of benefits received, neither contemporaneous nor one-year lagged effects are 

significant. Other lagged Food Stamp covariates sporadically have significant effects, but it is 

difficult to discern a consistent pattern. 

In sum, results are mixed but provide some evidence that one-year lagged Food 

Stamp benefits significantly affect BMI and obesity for females when measured dichotomously. 

Simulated effects of receiving Food Stamp benefits with lags are somewhat larger. For example, 

switching from no Food Stamp receipt to receiving Food Stamp benefits for low-income females 

with one-year lags (model 1, table 16) increases BMI from 26.1 to 26.9, which is about a 0.8 

point increase. This in the initial OLS specification without lags results in a 0.7-point increase 

(from 26.0 to 26.7 in model 1, table 5). This change for eligible females is 1.2 BMI index points 

(model 1, table 18) with one-year lags and 0.9 BMI index points without (model 1, table 6). 

Further, there is some evidence that such simulated effects continue to grow larger as additional 

lags are added. For eligible females, adding two- and three-year lags increases the simulated 

effect of receiving Food Stamps to a 1.5-point increase in BMI (model 2, table 18), and adding 

four additional lags (model 3, table 18) results in a 2-point increase in BMI. 

In a related set of models, I estimate the effects of patterns of FSP participation on 

BMI and obesity. In particular, I divide those who have received Food Stamps at some point 

over the 24 months preceding the interview date into four mutually exclusive and exhaustive 

groups (the reference category is not receiving Food Stamps over the last two years). Those who 

44
Charles L. Baum The Effects of Food Stamps on Obesity 

received benefits during only one spell were classified as receiving benefits either short-term (for 

less than 9 months), medium-term (between 9 and 23 months), or long-term (24 months). Those 

who received benefits during multiple spells were assigned to a separate category. This 

classification was based on reports suggesting recipients could be accurately classified in one of 

these four ways (Murphy and Harrell, 1992). Of the Food Stamp recipients in my sample, about 

31 % receive benefits short-term, 19 % medium-term, and 34 % for a long-term, with 16 % 

experiencing multiple spells. 

Presented in table 19 for the low-income sub-sample, results indicate long-term Food 

Stamp receipt significantly increases low-income male obesity and low-income female BMI and 

obesity. For example, long-term low-income female recipients are predicted to be almost 10 

percentage points more likely to be obese than their non-recipient counterparts. Further, they are 

predicted to be over 5 percentage points more likely to be obese than their short-term and 

medium-term recipient counterparts, and short-term and medium-term Food Stamp receipt do not 

have statistically significant effects on BMI and obesity. 

Long-term Food Stamp receipt significantly increases BMI and obesity in table 20 for 

both eligible males and eligible females. For example, long-term Food Stamp receipt again 

increases female obesity by about 10 percentage points; furthermore, long-term receipt increases 

eligible male obesity by about 15 percentage points. There is also some evidence that multiple 

spells of Food Stamp receipt increase BMI and obesity for eligible females. However, shortterm and medium-term Food Stamp receipt again do not appear to increase BMI or obesity. This 

suggests that extensive use of Food Stamps increases BMI and obesity, but receiving assistance 

from the FSP for more limited periods does not have this consequence. 

45
Charles L. Baum The Effects of Food Stamps on Obesity 

Results are largely the same in alternative specifications that examine, for example, 

the preceding 48 months (instead of the preceding 24 months) and that separately categorize 

multiple spell recipients by duration (short-term, multiple spells; medium-term, multiple spells; 

and long-term, multiple spells). However, by definition, none are in the long-term, multiple spell 

category because all long-term recipients experience (by construction) one long spell, suggesting 

Murphy and Harrell’s (1992) categories are sufficiently exhaustive. 

VI. Conclusions 

Medical researchers generally agree that obesity results from sustained caloric intake 

sufficiently in excess of caloric expenditures from physical activity. Because food and activity 

choices are subject to time and budget constraints, economic factors that affect these constraints 

potentially influence obesity as well. This study examines one such factor, the FSP, which is 

hypothesized potentially to increase obesity by effectively supplying food to eligible recipients at 

no (or reduced) cost, consequently increasing caloric intake, particularly for those in low-income 

households. Indeed, research typically suggests food expenditures increase between $0.17 and 

$0.47 per dollar of Food Stamp benefits (Fraker, 1990), and that this increase is larger than that 

generated by an equivalent amount of cash, even for households that receive less in Food Stamp 

benefits than they spend on food. Some recent evidence even suggests Food Stamp recipients 

consume significantly more sugar and meat than eligible non-recipients (Wilde et al., 1999). 

Descriptive statistics from the 1979 NLSY cohort show that FSP participation and 

obesity are often correlated for females. For example, 27.8 % of low-income females on Food 

Stamps are obese compared to 19.0 % of low-income female non-recipients. Similarly, among 

females approximated to be eligible for benefits, those receiving Food Stamps are more likely to 

be obese: 28.3 % versus 17.7 %. This association is much weaker for males. OLS models 

46
Charles L. Baum The Effects of Food Stamps on Obesity 

controlling for a spate of observable characteristics essentially enable us to compare individuals 

who are alike in all observable ways (the same gender, race/ethnicity, education, income, etc.) 

except Food Stamp receipt. In these models, female Food Stamp recipients are significantly 

more likely to be obese (and to have higher BMI) than female non-recipients. For example, 

providing a low-income female non-recipient with Food Stamp benefits is predicted to increase 

the probability of being obese between roughly 2 and 5 percentage points (and to increase BMI 

by a majority of an index point). However, the effects of Food Stamps on low-income males are 

typically statistically insignificant. These results are largely the same for the sample of 

respondents approximated to be eligible for benefits. 

Of course, OLS estimates could be biased by unobserved characteristics correlated 

with both FSP participation and obesity (and BMI). However, alternative specifications used to 

control for these sources of bias fail to reject the null hypothesis that OLS results are not biased. 

Where instruments are deemed valid, the evidence suggests the OLS and IV estimates are not 

statistically different from one another. Fixed effects models also support conclusions with OLS 

results, with many instances of significant positive effects of Food Stamp benefits on BMI and 

the probability of being obese, specifically for females. 

The dynamic models also show positive effects of Food Stamp benefits for females. 

Such benefits significantly increase the typical change in BMI between surveys and significantly 

increase the hazard of becoming obese (conditional on not yet being obese). Further, lagged 

Food Stamp benefits (from previous years) often significantly increase BMI and obesity. Again, 

these conclusions tend to be true primarily for females, with insignificant effects on males. 

These models also show that long-term Food Stamp receipt increases BMI and obesity, even in 

47
Charles L. Baum The Effects of Food Stamps on Obesity 

some instances for males, but receiving assistance from the FSP for a more limited period does 

not have this side-effect. 

In some cases, dynamic effects are larger than those found in models identifying 

contemporaneous effects. For example, receiving Food Stamps continually during the two years 

preceding the interview date increases obesity by at least 10 percentage points. Over a 15-year 

period, the hazard models suggest Food Stamp benefits significantly decrease the probability of 

not yet being obese between 10 and 20 percentage points. Similarly, for eligible females, 

positive effects of Food Stamps become successively larger with the inclusion of additional 

lagged Food Stamp covariate terms. 

The size of my contemporaneously-measured effect of Food Stamp receipt on obesity 

(for example, a 4 percentage-point increase would change a 20 % prevalence of obesity by 20 %) 

is larger than that found by Gibson (2003) and Meyerhoefer and Pylypchuk (2006), who predict 

that Food Stamp receipt increases the prevalence of obesity by 9.1 % and 6.7 %, respectively, but 

smaller than that found by Chen et al. (2005), who predict a 40 % increase. However, my 

dynamic models also reveal that Food Stamp receipt potentially has larger effects. For example, 

receiving Food Stamps long-term increases the probability that females are obese by 10 

percentage points, which is a 50 % change in obesity. These dynamic models also show that (i)

short-term (and medium-term) Food Stamp receipt does not significantly increase obesity and (ii) 

long-term Food Stamp receipt significantly increases obesity for males. Gibson, Chen et al., and 

Meyerhoefer and Pylypchuk’s contemporaneously-measured effects were statistically 

insignificant for males. 

These results can be used to approximate the amount to which Food Stamp benefits 

have contributed to the increase in obesity. During the 1976-1980 period covered by NHANES 

48
Charles L. Baum The Effects of Food Stamps on Obesity 

II, the prevalence of obesity was 15.0 %, with roughly 20.9 million obese American adults aged 

20 through 74 (Flegal et al., 1998, 2002). In the 2003-2004 NHANES surveys examined by 

Ogden et al. (2006), the prevalence of obesity was 32.2 %, with roughly 62.1 million obese 

American adults. Thus, as the prevalence of obesity doubled between these periods, the number 

of obese American adults increased by 41.2 million. However, between 1978 and 2003, the 

number of Food Stamp recipients increased from just 16.0 million to 21.3 million, which is an 

increase of 5.3 million (USDA, 2006a). Using reputedly conservative OLS estimates, assume 

Food Stamps increase the probability of being obese by 4 percentage points. This suggests that 

212,000 additional Americans became obese due to Food Stamps (4 % of 5.3 million = 212,000) 

between these periods. This would account for half of one % (0.5 %) of the 41.2 million 

increase in obese American adults (212,000/41.2 million = 0.005). Had this approximated 

contribution to obesity by the FSP not been made, the prevalence of obesity would be 32.1 % 

rather than 32.2 % (after subtracting 212,000 from 62.1 million obese American adults). Thus, 

while the effects of Food Stamps on obesity appear statistically significant at the individualrecipient level, the FSP has probably had virtually an immeasurably small impact on the growing 

prevalence of obesity. 

Of course, there are reasons to believe that the estimated contribution of the FSP to 

the increasing prevalence of obesity is an underestimate: dynamic models indicate larger positive 

effects once cumulative effects of Food Stamps (or effects of Food Stamp receipt from past 

periods) are included in the analysis. However, this is likely to have a small impact on the 

prevalence of obesity because only a minority of recipients receive benefits long-term (for at 

least two years). Further, though the increase in Food Stamp receipt has been 5.3 million, more 

than that have moved through the program during which time they were evidently more 

49
Charles L. Baum The Effects of Food Stamps on Obesity 

susceptible to becoming obese. For example, Rank and Hirschl (2005) project that about half of 

all Americans will receive Food Stamps at some point during their lifetimes. However, this is 

likely to have a small impact on the prevalence of obesity because results show that receiving 

benefits short-term (and medium-term) does not have an effect on obesity. 

Regardless, even in the absurd case where we assume Food Stamps cause all 

recipients to become obese, the FSP would play only a minor role in the increasing prevalence of 

obesity. For example, assume that all the additional 5.3 million Food Stamp recipients (between 

1976-1980 and 2003-2004) became obese (and were not obese to begin with). This would 

indicate only 12.8 % of the increase in the number of obese American adults is due to Food 

Stamps (5.3 million/41.2 million = 0.128). Were this assumed contribution to obesity by the FSP 

not made, then the prevalence of obesity would be 29.5 % rather than 32.2 % (after subtracting 

5.3 million obese American adults due to the FSP from 62.1 million obese American adults). 

Even in this admittedly-outrageous example, the contribution of the FSP to obesity would be 

minor. 

Regardless, since the FSP appears inadvertently to increase obesity, policymakers 

should seek ways to provide food assistance without exacerbating obesity. One approach might 

be to redouble efforts to educate newly-certified Food Stamp recipients about healthy and 

nutritious eating habits and weight management (or weight reduction). Such action might 

change the FSP from being a contributor to obesity to a benefactor reducing this prevalence. 

Many consider the increasing incidence of obesity to be reaching epic proportions. 

Since medically recommended diets and exercise appear to be doing little to abate the everincreasing prevalence of obesity, identifying economic factors that potentially contribute to this 

epidemic has become more important. Ultimately, successfully identifying this and/or other such 

50
Charles L. Baum The Effects of Food Stamps on Obesity 

factors enables public officials to better direct policy designed to control the incidence of 

obesity, particularly among those with low incomes where obesity is more prevalent. 

51
Charles L. Baum The Effects of Food Stamps on Obesity 

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VanItallie, Theodore B. (1996). “Prevalence of Obesity.” Obesity, 25 (4): 887. 

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Charles L. Baum The Effects of Food Stamps on Obe

59

Whitfield, R. A. (1982). “A Nutritional Analysis of the Food Stamp Program.” American 

Journal of Public Health, 72 (8): 793-799. 

Wardle, Jane, Jo Walter, and Martin J. Jarvis. (2002). “Sex Differences in the Association of 

Socioeconomic Status with Obesity.” American Journal of Public Health, 92 (8): 1299-1304. 

Ver Ploeg, Michele, Lisa Mancino, and Biing-Hwan Lin. (2006). “Food Stamps and Obesity: 

Ironic Twist or Complex Puzzle?” United States Department of Agriculture, Economic Research 

Service, Amber Waves, February. 

Ziliak, James P., Craig Gundersen, and David N. Figlio. (2003). “Food Stamp Caseloads over 

the Business Cycle.” Southern Economic Journal, 69 (4): 903-919. 

Wolf, Anne M., and Graham A. Colditz. (1998). “Current Estimates of the Economic Cost of 

Obesity in the United States.” Obesity Research, 6 (2): 97-106. 

Wilde, Parke E., Paul E. McNamara, and Christine K. Ranney. (1999). “The Effect of Income 

and Food Programs on Dietary Quality: a Seemingly Unrelated Regression Analysis With Error 

Components.” American Journal of Agricultural Economics, 81 (4): 959-971. 

sity 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 1: Descriptive Statistics: The Low-Income Sub-Sample 

Low-Income Males Low-

Dependant Variables Full Sample Recipients Non-recipients Full Sample Body Mass Index 26.620 (0.160) 26.596 26.621 25.597 (0.175) Prevalence of Obesity 0.201 (0.012) 0.213 0.201 0.203 (0.011) Food Stamp Variables

Received Food Stamps 0.050 (0.004) - - 0.147 (0.011) Amount of Food Stampsa 2.090 (0.176) - - 2.738 (0.085) 

Demographic Characteristics

Black (=1 if black) 0.129 (0.017) 0.220 0.124 0.143 (0.019) Hispanic (=1 if Hispanic) 0.062 (0.010) 0.100 0.060 0.056 (0.009) Age 30.536 (0.094) 29.746 30.578 30.724 (0.094) Education 11.602 (0.038) 10.850 11.641 11.617 (0.033) Marital Status (=1 if married) 0.577 (0.014) 0.552 0.579 0.568 (0.015) Children 1.012 (0.032) 1.444 0.989 1.461 (0.037) Family Size 3.296 (0.035) 3.801 3.270 3.499 (0.038) Household Income ($1,000s)b 51.099 (1.226) 26.095 52.418 45.888 (1.069) 

Urban (=1 if urban residence) 0.705 (0.030) 0.660 0.707 0.694 (0.032) 

Sample means with standard errors in parentheses. There are 11,748 low-income males and 10,558 low-income 

excluded. b Annual household income in year-2005 dollars. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 2: Descriptive Statistics: The Eligible Sub-Sample 

Eligible Males E

Dependant Variables Full Sample Recipients Non-recipients Full Sample Body Mass Index 25.688 (0.179) 26.432 25.546 25.771 (0.233) Prevalence of Obesity 0.155 (0.012) 0.218 0.144 0.218 (0.012) Food Stamp Variables

Received Food Stamps 0.159 (0.012) - - 0.388 (0.017) Amount of Food Stampsa 2.223 (0.202) - - 2.904 (0.085) 

Demographic Characteristics

Black 0.247 (0.030) 0.248 0.247 0.291 (0.031) Hispanic 0.072 (0.012) 0.082 0.070 0.071 (0.010) Age 28.482 (0.169) 29.997 28.194 29.302 (0.120) Education 12.154 (0.091) 11.260 12.324 12.204 (0.079) Marital Status 0.267 (0.016) 0.492 0.224 0.244 (0.014) Children 0.567 (0.038) 1.283 0.431 1.431 (0.043) Family Size 3.466 (0.051) 3.706 3.420 3.699 (0.050) Household Income ($1,000s)b 37.084 (1.295) 24.479 39.474 33.321 (0.995) 

Urban 0.775 (0.026) 0.708 0.787 0.752 (0.026) 

Sample means with standard errors in parentheses. There are 3,681 eligible males and 4,799 eligible females. a z

Annual household income in year-2005 dollars.
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 3: Descriptive Statistics for the Low-Income Sub-Sample 

Low-Income Males Low-Income Females

Economic Conditions Variables

Local Unemployment Rate 0.067 (0.002) 0.068 (0.002) 

Local Per Capita Income 22.668 (0.312) 22.630 (0.324) 

Portion of Local Labor Force Female 0.409 (0.003) 0.411 (0.003) 

Local Population High-School Educated 0.670 (0.009) 0.667 (0.009) 

Local Population College-Educated 0.152 (0.004) 0.153 (0.004) 

Local Population Employed 0.433 (0.004) 0.433 (0.004) 

Local Labor Force in Manufacturing 0.212 (0.009) 0.210 (0.009) 

Local Labor Force in Wholesale/Retail Trade 0.185 (0.002) 0.185 (0.002) 

Political Variables

Representative’s ADA Ranking 0.414 (0.010) 0.418 (0.011) 

Senator’s ADA Ranking 0.519 (0.019) 0.511 (0.020) 

Democrat Governor 0.478 (0.021) 0.470 (0.020) 

Portion of State House Democrat 0.576 (0.009) 0.589 (0.009) 

Portion of State Senate Democrat 0.565 (0.012) 0.584 (0.013) 

Welfare Characteristics Variables

Post-TANF (=1 if TANF in force) 0.178 (0.002) 0.181 (0.003) 

Pre-Welfare Reform Waiver #1 0.029 (0.004) 0.029 (0.004) 

Pre-Welfare Reform Waiver #2 0.048 (0.005) 0.042 (0.005) 

Pre-Welfare Reform Waiver #3 0.062 (0.005) 0.057 (0.005) 

Pre-Welfare Reform Waiver #4 0.073 (0.009) 0.067 (0.008) 

Pre-Welfare Reform Waiver #5 0.046 (0.006) 0.046 (0.007) 

Pre-Welfare Reform Waiver #6 0.013 (0.002) 0.016 (0.003) 

State Maximum Benefits 749.245 (23.436) 742.919 (25.414) 

No Time Limita 0.075 (0.029) 0.050 (0.020) 

Time Limita,b 55.981 (0.792) 55.454 (1.053) 

Family Capsa 0.500 (0.044) 0.510 (0.044) 

Child Agea 10.068 (0.700) 10.629 (0.848) 

Severe Sanctionsa 0.707 (0.038) 0.677 (0.038) 

Earned Income Disregardsa 104.224 (8.952) 101.086 (10.252) 

Earned Income Disregardsa 51.056 (2.482) 52.516 (2.386) 

Asset Limita,c 2337.411 (95.034) 2356.376 (99.054) 

No Asset Limita 0.076 (0.028) 0.078 (0.030) 

TANF Vehicle Exemptiona,d 6607.612 (285.549) 6384.336 (281.977) 

Vehicles Included in TANF Asset Testa 0.552 (0.042) 0.549 (0.042) 

Household Eligibility Characteristics

62
Charles L. Baum The Effects of Food Stamps on Obesity 

Vehicle Value 7.027 (0.268) 6.195 (0.258) 

Senior Present 0.087 (0.007) 0.056 (0.006) 

State Eligibility Characteristics

FSP Vehicle Asset Limita 0.069 (0.033) 0.068 (0.027) 

EBTa 0.484 (0.034) 0.510 (0.035) 

Non-Parental Adults Caregiversa,e 0.669 (0.038) 0.645 (0.041) 

Simplified Periodic Reportinga,f 0.734 (0.041) 0.761 (0.041) 

Categorical Eligibilitya 0.783 (0.032) 0.781 (0.031) 

Severe Sanctionsa,g 0.494 (0.043) 0.450 (0.043) 

Outreach Plana 0.489 (0.045) 0.447 (0.045) 

Sample means with standard errors in parentheses. There are 11,748 low-income males and 10,558 

low-income females. a

 Only post-welfare reform observations are used for the descriptive statistic. b

Only observations in states with time limits are used in the descriptive statistics. c

 Only observations in 

states with asset limits are used in the descriptive statistics. d

 Only observations in states without 

vehicle exclusions are used in the descriptive statistics. e The excluded category is only parents are 

allowed to be considered the caregiver. f The excluded category is incident reporting. g

 Sanctions for 

E&T offenses are either extended beyond that normally required by the Food Stamp Program, 

permanent (instead of temporary), or applied to the entire household (instead of only the offending 

member).

63
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 4: Descriptive Statistics for the Eligible Sub-Sample 

Eligible Males Eligible Females

Economic Conditions Variables

Local Unemployment Rate 0.072 (0.002) 0.071 (0.002) 

Local Per Capita Income 22.739 (0.306) 22.544 (0.290) 

Portion of Local Labor Force Female 0.410 (0.003) 0.412 (0.003) 

Local Population High-School Educated 0.666 (0.007) 0.662 (0.007) 

Local Population College-Educated 0.158 (0.004) 0.156 (0.003) 

Local Population Employed 0.428 (0.004) 0.425 (0.004) 

Local Labor Force in Manufacturing 0.201 (0.007) 0.197 (0.007) 

Local Labor Force in Wholesale/Retail Trade 0.184 (0.002) 0.184 (0.002) 

Political Variables

Representative’s ADA Ranking 0.404 (0.010) 0.411 (0.011) 

Senator’s ADA Ranking 0.527 (0.020) 0.519 (0.020) 

Democrat Governor 0.533 (0.020) 0.502 (0.021) 

Portion of State House Democrat 0.589 (0.009) 0.602 (0.009) 

Portion of State Senate Democrat 0.576 (0.012) 0.593 (0.012) 

Welfare Characteristics Variables

Post-TANF (=1 if TANF in force) 0.106 (0.007) 0.123 (0.006) 

Pre-Welfare Reform Waiver #1 0.014 (0.003) 0.019 (0.003) 

Pre-Welfare Reform Waiver #2 0.030 (0.005) 0.030 (0.004) 

Pre-Welfare Reform Waiver #3 0.035 (0.005) 0.040 (0.004) 

Pre-Welfare Reform Waiver #4 0.061 (0.009) 0.066 (0.009) 

Pre-Welfare Reform Waiver #5 0.027 (0.005) 0.028 (0.004) 

Pre-Welfare Reform Waiver #6 0.010 (0.003) 0.011 (0.002) 

State Maximum Benefits 750.899 (21.019) 740.061 (25.093) 

No Time Limita 0.070 (0.029) 0.068 (0.028) 

Time Limita,b 56.933 (0.669) 55.771 (0.769) 

Family Capsa 0.464 (0.052) 0.471 (0.045) 

Child Agea 11.384 (1.317) 10.834 (0.887) 

Severe Sanctionsa 0.627 (0.051) 0.631 (0.043) 

Earned Income Disregardsa 106.345 (9.193) 127.742 (17.088) 

Earned Income Disregardsa 49.891 (2.587) 49.770 (2.165) 

Asset Limita,c 2225.738 (82.006) 2357.200 (97.718) 

No Asset Limita 0.032 (0.017) 0.076 (0.024) 

TANF Vehicle Exemptiona,d 6057.164 (250.667) 6144.415 (242.010) 

Vehicles Included in TANF Asset Testa 0.582 (0.048) 0.554 (0.045) 

Household Eligibility Characteristics

64
Charles L. Baum The Effects of Food Stamps on Obesity 

Vehicle Value 2.852 (0.189) 2.445 (0.153) 

Senior Present 0.223 (0.016) 0.116 (0.009) 

State Eligibility Characteristics

FSP Vehicle Asset Limita 0.000 (0.000) 0.073 (0.041) 

EBTa 0.526 (0.048) 0.482 (0.038) 

Non-Parental Adults Caregiversa,e 0.614 (0.050) 0.624 (0.048) 

Simplified Periodic Reportinga,f 0.784 (0.043) 0.785 (0.038) 

Categorical Eligibilitya 0.732 (0.046) 0.764 (0.038) 

Severe Sanctionsa,g 0.524 (0.052) 0.525 (0.045) 

Outreach Plana 0.495 (0.053) 0.472 (0.044) 

Sample means with standard errors in parentheses. There are 3,681 eligible males and 4,799 eligible 

females. a

 Only post-welfare reform observations are used for the descriptive statistic. b Only 

observations in states with time limits are used in the descriptive statistics. c

 Only observations in 

states with asset limits are used in the descriptive statistics. d

 Only observations in states without 

vehicle exclusions are used in the descriptive statistics. e The excluded category is only parents are 

allowed to be considered the caregiver. f The excluded category is incident reporting. g

 Sanctions for 

E&T offenses are either extended beyond that normally required by the Food Stamp Program, 

permanent (instead of temporary), or applied to the entire household (instead of only the offending 

member).

65
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 5: The Effects of Food Stamp Benefits on BMI and Obesity: Low-Income OLS Results

 Body Mass Index Probability of Being Obese

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Low-Income Males 

Received Food Stamps 0.073 - 0.010 0.098 - 0.070 

(0.420) - (0.406) (0.201) - (0.196) 

Amount of Food Stamps - 0.014 0.013 - 0.024 0.014 

- (0.082) (0.062) - (0.041) (0.036) 

Predicted Values 

With No Food Stamps 26.737 26.737 26.737 0.209 0.209 0.209 

With Food Stamps (=2,400) 26.774 26.772 26.779 0.224 0.218 0.225 

Low-Income Females 

Received Food Stamps 0.617* - 0.394 0.304** - 0.264* 

(0.345) - (0.359) (0.126) - (0.137) 

Amount of Food Stamps - 0.153* 0.090 - 0.056* 0.015 

- (0.080) (0.075) - (0.032) (0.026) 

Predicted Values 

With No Food Stamps 26.098 26.135 26.089 0.221 0.226 0.221 

With Food Stamps (=2,400) 26.716 26.502 26.700 0.272 0.249 0.272 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 lowincome males and 10,558 low-income females. The models include individual characteristics 

variables, economic variables, political variables, state AFDC/TANF program characteristic 

variables, state dummy variables, and year dummy variables. 

66
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 6: The Effects of Food Stamp Benefits on BMI and Obesity: Eligible OLS Results

Body Mass Index Probability of Being Obese

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Eligible Males 

Received Food Stamps 0.196 - 0.292 0.289 - 0.307 

(0.395) - (0.411) (0.200) - (0.213) 

Amount of Food Stamps - -0.013 -0.046 - 0.031 -0.009 

- (0.061) (0.057) - (0.033) (0.046) 

Predicted Values 

With No Food Stamps 25.804 25.842 25.805 0.154 0.159 0.154 

With Food Stamps (=2,400) 26.000 25.809 25.986 0.190 0.168 0.189 

Eligible Females 

Received Food Stamps 0.906** - 0.878** 0.381*** - 0.389***

(0.370) - (0.407) (0.129) - (0.139) 

Amount of Food Stamps - 0.121* 0.011 - 0.040 -0.003 

- (0.071) (0.071) - (0.030) (0.024) 

Predicted Values 

With No Food Stamps 26.078 26.327 26.075 0.220 0.241 0.220 

With Food Stamps (=2,400) 26.984 26.618 26.981 0.283 0.257 0.284 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible 

males and 4,799 eligible females. The models include individual characteristics variables, economic 

variables, political variables, state AFDC/TANF program characteristic variables, state dummy 

variables, and year dummy variables. 

67
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 7: The Effects of Predicted Food Stamp Benefits on BMI and Obesity: Low-Income OLS 

Results

Body Mass Index Probability of Being Obese

Low-Income Males Model 1 Model 2 Model 3 Model 4

Received Food Stamps 0.810 - 0.862 - 

(1.498) - (0.582) - 

Amount of Food Stamps - -1.916 - -0.162 

- (1.605) - (0.889) 

Predicted Values 

With No Food Stamps 26.716 27.052 0.204 0.219 

With Food Stamps (=2,400) 27.526 22.452 0.361 0.163 

F-Statistic for Instruments 52.16*** 21.49*** 52.16*** 21.49*** 

F-Statistic for Restrictions 1.360 1.360 0.320 0.320 

F-Statistic for Hausman Test 0.130 1.570 1.610 0.040 

Low-Income Females 

Received Food Stamps 1.149 - 0.209 - 

(0.995) - (0.354) - 

Amount of Food Stamps - 3.224** - 2.025** 

- (1.371) - (0.827) 

Predicted Values 

With No Food Stamps 26.002 24.164 0.225 0.136 

With Food Stamps (=2,400) 27.151 31.586 0.260 0.818 

F-Statistic for Instruments 75.55*** 13.52*** 75.55*** 13.52*** 

F-Statistic for Restrictions 4.08** 4.08** 8.91** 8.91** 

F-Statistic for Hausman Test 0.060 5.05** 0.260 5.68** 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 lowincome males and 10,558 low-income females. The models include individual characteristics 

variables, economic variables, political variables, state AFDC/TANF program characteristic variables, 

state dummy variables, and year dummy variables. The instruments are household vehicle values and 

the present of a senior adult. 

68
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 8: The Effects of Predicted Food Stamp Benefits on BMI and Obesity: Eligible OLS 

Results 

Body Mass Index Probability of Being Obese

Eligible Males Model 1 Model 2 Model 3 Model 4

Received Food Stamps 0.364 - 0.994 - 

(1.513) - (0.624) - 

Amount of Food Stamps - -0.720 - 0.513 

- (0.938) - (0.582) 

Predicted Values 

With No Food Stamps 25.819 26.150 0.142 0.144 

With Food Stamps (=2,400) 26.183 24.422 0.287 0.335 

F-Statistic for Instruments 29.78*** 14.33*** 29.78*** 14.33*** 

F-Statistic for Restrictions 0.82 0.82 1.08 1.08 

F-Statistic for Hausman Test 0.03 0.62 1.66 0.72 

Eligible Females 

Received Food Stamps 1.600 - 0.425 - 

(1.252) - (0.453) - 

Amount of Food Stamps - 2.444*** - 1.289*** 

- (0.899) - (0.482) 

Predicted Values 

With No Food Stamps 25.837 23.063 0.223 0.096 

With Food Stamps (=2,400) 27.438 28.928 0.295 0.502 

F-Statistic for Instruments 53.73*** 9.73*** 53.73*** 9.73*** 

F-Statistic for Restrictions 3.39** 3.39** 6.11** 6.11** 

F-Statistic for Hausman Test 0.13 6.77** 0.01 6.77** 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible 

males and 4,799 eligible females. The models include individual characteristics variables, economic 

variables, political variables, state AFDC/TANF program characteristic variables, state dummy 

variables, and year dummy variables. The instruments are household vehicle values and the present 

of a senior adult. 

69
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 9: The Effects of Predicted Food Stamp Benefits on BMI and Obesity: Low-Income OLS 

Results 

Body Mass Index Probability of Being Obese

Low-Income Males Model 1 Model 2 Model 3 Model 4

Received Food Stamps 0.969 - 0.737 - 

(1.670) - (0.640) - 

Amount of Food Stamps - -0.235 - -0.309 

- (1.582) - (0.744) 

Predicted Values 

With No Food Stamps 26.708 26.814 0.206 0.223 

With Food Stamps (=2,400) 27.677 26.248 0.337 0.125 

F-Statistic for Instruments 16.97*** 1.83* 16.97*** 1.83* 

F-Statistic for Restrictions 0.60 0.60 6.36 6.36 

F-Statistic for Hausman Test 0.19 0.04 0.86 0.20 

Low-Income Females 

Received Food Stamps 0.196 - -0.511 - 

(1.221) - (0.449) - 

Amount of Food Stamps - -0.575 - 0.313 

- (1.166) - (0.463) 

Predicted Values 

With No Food Stamps 26.208 26.624 0.254 0.200 

With Food Stamps (=2,400) 26.404 25.243 0.177 0.331 

F-Statistic for Instruments 5.68 1.94* 5.68 1.94* 

F-Statistic for Restrictions 0.48 0.48 4.25 4.25 

F-Statistic for Hausman Test 0.46 0.39 4.42** 0.31 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 lowincome males and 10,558 low-income females. The models include individual characteristics 

variables, economic variables, political variables, state AFDC/TANF program characteristic 

variables, state dummy variables, and year dummy variables. The instruments are state eligibility 

characteristics. 

70
Charles L. Baum The Effects of Food Stamps on Obesity 

71

Table 10: The Effects of Predicted Food Stamp Benefits on BMI and Obesity: Eligible OLS 

Results 

Eligible Sample Body Mass Index Probability of Being Obese

Eligible Males Model 1 Model 2 Model 3 Model 4

Received Food Stamps -0.412 - 0.313 - 

(1.901) - (0.885) - 

Amount of Food Stamps - -0.583 - 0.188 

- (1.070) - (0.798) 

Predicted Values 

With No Food Stamps 25.956 26.101 0.158 0.159 

With Food Stamps (=2,400) 25.543 24.701 0.199 0.221 

F-Statistic for Instruments 8.09 1.44 8.09 1.44 

F-Statistic for Restrictions 0.22 0.22 3.68 3.68 

F-Statistic for Hausman Test 0.07 0.08 0.01 0.05 

Eligible Females 

Received Food Stamps -0.344 - -0.470 - 

(1.936) - (0.643) - 

Amount of Food Stamps - -0.181 - 0.208 

- (1.185) - (0.352) 

Predicted Values 

With No Food Stamps 26.752 26.862 0.301 0.206 

With Food Stamps (=2,400) 26.408 26.427 0.222 0.288 

F-Statistic for Instruments 11.86* 1.71 11.86* 1.71 

F-Statistic for Restrictions 1.51 1.51 4.72 4.72 

F-Statistic for Hausman Test 0.63 0.06 2.03 0.24 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible 

males and 4,799 eligible females. The models include individual characteristics variables, economic 

variables, political variables, state AFDC/TANF program characteristic variables, state dummy 

variables, and year dummy variables. The instruments are state eligibility characteristics. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 11: The Effects of Food Stamp Benefits on BMI and Obesity: Fixed Effects Models with the Low-Inc

Body Mass Index Probability of Being Obese Pro

Model Specification OLS OLS Logit Logit 

Model 1 Model 2 Model 3 Model 4 M

Low-Income Males 

Received Food Stamps 0.037 - -0.207 - -

(0.091) - (0.255) - (

Amount of Food Stamps - 0.081* - -0.049 

- (0.044) - (0.151) 

Low-Income Females 

Received Food Stamps 0.221** - 0.427** - 0.

(0.092) - (0.200) - (

Amount of Food Stamps - 0.041 - 0.076 

- (0.038) - (0.060) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 low-income males 

dropped from the logit model due to all positive or all negative outcomes values) and 10,558 low-income female

dropped from the logit model due to all positive or all negative outcomes values). The models include individua

economic variables, political variables, state AFDC/TANF program characteristic variables, state dummy variab

variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 12: The Effects of Food Stamp Benefits on BMI and Obesity: Fixed Effects Models with the Eligible 

Body Mass Index Probability of Being Obese Pro

Model Specification OLS OLS Logit Logit 

Model 1 Model 2 Model 3 Model 4 M

Eligible Males 

Received Food Stamps 0.271** - -0.082 - 

(0.137) - (0.457) - (

Amount of Food Stamps - 0.113* - 0.079 

- (0.058) - (0.197) 

Eligible Females 

Received Food Stamps 0.335** - 0.368 - 0

(0.134) - (0.261) - (

Amount of Food Stamps - 0.008 - 0.030 

- (0.048) - (0.071) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible males (1,94

from the logit model due to all positive or all negative outcomes values) and 4,799 eligible females (2,609 of whthe logit model due to all positive or all negative outcomes values). The models include individual characteristicvariables, political variables, state AFDC/TANF program characteristic variables, state dummy variables, and ye
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 13: The Effects of Food Stamp Benefits on the Change in BMI, Becoming Obese, and No 

Longer Being Obese: Change Models with the Low-Income Sub-Sample

 BMI Change Becoming Obese No Longer Obese

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Low-Income Males 

Received Food Stamps 0.173 - 0.049 - -0.640* - 

(0.115) - (0.266) - (0.389) - 

Amount of Food Stamps - 0.053* - -0.036 - -0.188 

- (0.030) - (0.091) - (0.186) 

Predicted Values 

With No Food Stamps 0.482 0.486 0.071 0.071 0.139 0.137 

With Food Stamps (=2,400) 0.655 0.616 0.074 0.066 0.082 0.095 

Low-Income Females 

Received Food Stamps 0.225** - 0.072 - 0.159 - 

(0.109) - (0.184) - (0.257) - 

Amount of Food Stamps - 0.042 - 0.006 - 0.007 

- (0.030) - (0.050) - (0.067) 

Predicted Values 

With No Food Stamps 0.567 0.589 0.079 0.080 0.121 0.125 

With Food Stamps (=2,400) 0.792 0.691 0.084 0.081 0.138 0.127 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. To maintain a consistent 

time interval for measured changes, included observations are those with valid weight observations from 

the 1985 and 1987 surveys, the 1987 and 1989 surveys, 1990 and 1992, 1992 and 1994, 1994 and 1996, 

1996 and 1998, 1998 and 2000, and 2000 and 2002. There are 8,489 low-income males (1,953 of whom 

are obese and 6,536 of whom are not) and 6,944 low-income females (1,785 of whom are obese and 

5,159 of whom are not). The models include individual characteristics variables, economic variables, 

political variables, state AFDC/TANF program characteristic variables, state dummy variables, and year 

dummy variables. 

74
Charles L. Baum The Effects of Food Stamps on Obesity 

75

Table 14: The Effects of Food Stamp Benefits on the Change in BMI, Becoming Obese, and No 

Longer Being Obese: Change Models with the Eligible Sub-Sample

 BMI Change Becoming Obese No Longer Obese

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Eligible Males 

Received Food Stamps 0.085 - 0.194 - -2.49*** - 

(0.164) - (0.344) - (0.708) - 

Amount of Food Stamps - 0.071* - -0.155 - -0.766* 

- (0.036) - (0.130) - (0.442) 

Predicted Values 

With No Food Stamps 0.578 0.568 0.068 0.073 0.214 0.192 

With Food Stamps (=2,400) 0.663 0.740 0.080 0.053 0.052 0.068 

Eligible Females 

Received Food Stamps 0.021 - -0.049 - 0.209 - 

(0.140) - (0.211) - (0.377) - 

Amount of Food Stamps - 0.013 - -0.044 - 0.001 

- (0.037) - (0.062) - (0.080) 

Predicted Values 

With No Food Stamps 0.813 0.805 0.094 0.097 0.110 0.121 

With Food Stamps (=2,400) 0.835 0.838 0.090 0.089 0.127 0.121 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. To maintain a consistent 

time interval for measured changes, included observations are those with valid weight observations from 

the 1985 and 1987 surveys, the 1987 and 1989 surveys, 1990 and 1992, 1992 and 1994, 1994 and 1996, 

1996 and 1998, 1998 and 2000, and 2000 and 2002. There are 2,446 eligible males (425 of whom are 

obese and 2,021 of whom are not) and 2,981 eligible females (826 of whom are obese and 2,155 of 

whom are not). The models include individual characteristics variables, economic variables, political 

variables, state AFDC/TANF program characteristic variables, state dummy variables, and year dummy 

variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 15: The Effects of Lagged Food Stamp Benefits on BMI and Obesity: OLS Results, Low-Income Male Sub-

Body Mass Index Probability o

Low-Income Males Model 1 Model 2 Model 3 Model 4 MoReceived Food Stamps -0.180 (0.320) -0.246 (0.295) -0.252 (0.287) 0.030 (0.165) -0.020 1 Year Lag 0.391 (0.366) 0.210 (0.304) 0.204 (0.296) 0.119 (0.167) -0.012 2 Year Lag - - 0.113 (0.279) 0.099 (0.261) - - 0.096 3 Year Lag - - 0.416 (0.299) 0.466* (0.266) - - 0.258* 4 Year Lag - - - - -0.210 (0.251) - - - 5 Year Lag - - - - -0.055 (0.243) - - - 6 Year Lag - - - - 0.086 (0.242) - - - 7 Year Lag - - - - 0.352 (0.282) - - - Predicted Values With No Food Stamps 26.729 26.712 26.704 0.209 0.2With Food Stamps 26.940 27.203 27.394 0.232 0.2Amount of Food Stamps 0.016 (0.078) 0.007 (0.067) 0.013 (0.062) 0.024 (0.039) 0.019 1 Year Lag -0.007 (0.040) -0.011 (0.033) -0.013 (0.032) 0.005 (0.020) 0.002 2 Year Lag - - 0.027 (0.050) 0.027 (0.049) - - 0.011 3 Year Lag - - 0.059 (0.128) 0.058 (0.107) - - 0.036 4 Year Lag - - - - -.228*** (0.083) - - - 5 Year Lag - - - - -0.030 (0.024) - - - 6 Year Lag - - - - 0.350* (0.184) - - - 7 Year Lag - - - - 0.096 (0.090) - - - Predicted Values With No Food Stamps 26.738 26.733 26.725 0.210 0.2Food Stamps =2,400 26.760 26.930 27.381 0.220 0.2

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 observations in the male l

models include individual characteristics variables, economic variables, political variables, state AFDC/TANF program dummy variables, and year dummy variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 16: The Effects of Lagged Food Stamp Benefits on BMI and Obesity: OLS Results, Low-Income Female Su

Body Mass Index Probability o

Low-Income Females Model 1 Model 2 Model 3 Model 4 MoReceived Food Stamps 0.161 (0.299) 0.075 (0.280) 0.065 (0.274) 0.084 (0.124) 0.059 1 Year Lag 0.659** (0.265) 0.418* (0.254) 0.418* (0.253) 0.314** (0.126) 0.286** 2 Year Lag - - 0.276 (0.231) 0.266 (0.227) - - -0.079 3 Year Lag - - 0.249 (0.224) 0.240 (0.192) - - 0.197* 4 Year Lag - - - - -0.036 (0.200) - - - 5 Year Lag - - - - -0.066 (0.205) - - - 6 Year Lag - - - - 0.265 (0.209) - - - 7 Year Lag - - - - -0.091 (0.258) - - - Predicted Values With No Food Stamps 26.056 26.009 26.000 0.218 0.2With Food Stamps 26.877 27.027 27.061 0.286 0.2Amount of Food Stamps 0.111 (0.069) 0.073 (0.059) 0.069 (0.058) 0.034 (0.028) 0.026 1 Year Lag 0.135 (0.100) 0.075 (0.086) 0.072 (0.083) 0.069 (0.048) 0.058 2 Year Lag - - 0.095 (0.086) 0.082 (0.083) - - -0.011 3 Year Lag - - 0.165** (0.077) 0.148** (0.065) - - 0.068** 4 Year Lag - - - - 0.057 (0.062) - - - 5 Year Lag - - - - -0.035 (0.043) - - - 6 Year Lag - - - - 0.012 (0.060) - - - 7 Year Lag - - - - 0.049 (0.056) - - - Predicted Values With No Food Stamps 26.113 26.065 26.052 0.225 0.2Food Stamps =2,400 26.705 27.046 27.142 0.266 0.2

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 10,558 observations in the female 

models include individual characteristics variables, economic variables, political variables, state AFDC/TANF program dummy variables, and year dummy variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

 Table 17: The Effects of Lagged Food Stamp Benefits on BMI and Obesity: OLS Results, Eligible Male Sub-Sam

Body Mass Index Probability 

Eligible Males Model 1 Model 2 Model 3 Model 4 MoReceived Food Stamps 0.191 (0.313) 0.141 (0.298) 0.170 (0.304) 0.274 (0.176) 0.210 1 Year Lag 0.009 (0.397) -0.095 (0.374) -0.108 (0.374) 0.024 (0.183) -0.211 2 Year Lag - - -0.161 (0.372) -0.120 (0.364) - - 0.309 3 Year Lag - - 0.653 (0.428) 0.830** (0.416) - - 0.247 4 Year Lag - - - - -0.542 (0.360) - - - 5 Year Lag - - - - 0.419 (0.364) - - - 6 Year Lag - - - - -0.048 (0.369) - - - 7 Year Lag - - - - -0.383 (0.402) - - - Predicted Values With No Food Stamps 25.804 25.766 25.791 0.154 0With Food Stamps 26.004 26.305 26.011 0.191 0Amount of Food Stamps -0.010 (0.060) -0.004 (0.053) 0.002 (0.051) 0.032 (0.032) 0.032 1 Year Lag -0.027 (0.033) -0.025 (0.030) -0.024 (0.029) 0.001 (0.017) 0.000 2 Year Lag - - 0.006 (0.036) 0.004 (0.036) - - 0.008 3 Year Lag - - -0.087 (0.153) -0.016 (0.130) - - -0.013 4 Year Lag - - - - -0.166 (0.145) - - - 5 Year Lag - - - - -0.036** (0.015) - - - 6 Year Lag - - - - 0.032 (0.193) - - - 7 Year Lag - - - - 0.046 (0.103) - - - Predicted Values With No Food Stamps 25.847 25.857 25.863 0.159 0Food Stamps =2,400 25.757 25.592 25.484 0.169 0

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 observations in the male eli

include individual characteristics variables, economic variables, political variables, state AFDC/TANF program characte

variables, and year dummy variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 18: The Effects of Lagged Food Stamp Benefits on BMI and Obesity: OLS Results, Eligible Female Sub-Sa

Body Mass Index Probability 

Eligible Females Model 1 Model 2 Model 3 Model 4 MoReceived Food Stamps 0.261 (0.332) 0.186 (0.318) 0.144 (0.316) 0.172 (0.124) 0.158 1 Year Lag 1.012*** (0.334) 0.809** (0.346) 0.807** (0.346) 0.328** (0.138) 0.333** 2 Year Lag - - -0.017 (0.337) -0.093 (0.340) - - -0.167 3 Year Lag - - 0.611** (0.304) 0.313 (0.290) - - 0.240** 4 Year Lag - - - - 0.035 (0.320) - - - 5 Year Lag - - - - 0.313 (0.309) - - - 6 Year Lag - - - - 0.498 (0.318) - - - 7 Year Lag - - - - 0.007 (0.389) - - - Predicted Values With No Food Stamps 25.900 25.774 25.634 0.211 0With Food Stamps 27.173 27.363 27.658 0.294 0Amount of Food Stamps 0.098 (0.065) 0.059 (0.057) 0.054 (0.057) 0.030 (0.027) 0.022 1 Year Lag 0.096 (0.103) 0.025 (0.091) 0.010 (0.088) 0.045 (0.043) 0.037 2 Year Lag - - 0.104 (0.079) 0.065 (0.078) - - -0.022 3 Year Lag - - 0.264*** (0.082) 0.227*** (0.071) - - 0.091**4 Year Lag - - - - 0.059 (0.066) - - - 5 Year Lag - - - - 0.045 (0.048) - - - 6 Year Lag - - - - 0.067 (0.080) - - - 7 Year Lag - - - - 0.061 (0.067) - - - Predicted Values With No Food Stamps 26.283 26.116 26.041 0.238 0Food Stamps =2,400 26.749 27.201 27.451 0.267 0

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 4,799 observations in the female 

include individual characteristics variables, economic variables, political variables, state AFDC/TANF program characte

variables, and year dummy variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 19: The Effects of Lagged Food Stamp Benefits on BMI and Obesity: Low-Income OLS Results 

Low-Income Males Low-In

Body Mass Index Probability of Being Obese Body Mass Index 

Model 1 Model 2 Model 1 

Short-Term -0.403 -0.144 0.317 

(0.342) (0.098) (0.314) 

Medium-Term 0.222 0.097 0.064 

(0.548) (0.260) (0.417) 

Long-Term 1.413 0.675** 1.502*** 

(1.022) (0.291) (0.513) 

Multiple Spells 0.489 0.218 0.493 

(0.670) (0.315) (0.483) 

Predicted Values With No Food Stamps 26.726 0.208 26.012 Food Stamps, Short-Term 26.323 0.187 26.328 Food Stamps, Medium-Term 26.930 0.222 26.090 Food Stamps, Long-Term 28.125 0.325 27.532 Food Stamps, Multiple Spells 27.222 0.244 26.698 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,713 low-income males 

females. The models include individual characteristics variables, economic variables, political variables, state A

characteristic variables, state dummy variables, and year dummy variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Table 20: The Effects of Lagged Food Stamp Benefits on BMI and Obesity: Eligible OLS Results 

Eligible Males Elig

Body Mass Index Probability of Being Obese Body Mass Index 

Model 1 Model 2 Model 1 

Short-Term -0.495 -0.160 0.455 

(0.368) (0.246) (0.566) 

Medium-Term 0.149 0.254 0.714 

(0.551) (0.273) (0.456) 

Long-Term 1.599* 0.985*** 1.796*** 

(0.894) (0.284) (0.528) 

Multiple Spells -0.535 -0.029 0.928* 

(0.604) (0.332) (0.549) 

Predicted Values With No Food Stamps 25.823 0.153 25.821 Food Stamps, Short-Term 25.329 0.135 26.276 Food Stamps, Medium-Term 25.927 0.182 26.572 Food Stamps, Long-Term 27.385 0.298 27.730 Food Stamps, Multiple Spells 25.320 0.157 27.346 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,651 eligible males and 4

models include individual characteristics variables, economic variables, political variables, state AFDC/TANF prvariables, state dummy variables, and year dummy variables. 
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 1 

Body Mass Index: Low-Income Male Sub-Sample

15

17

19

21

23

25

27

29

31

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Body Mass Index

Full Sample Food Stamp Recipients No Food Stamp Receipt

Figure 2 

Body Mass Index: Low-Income Female Sub-Sample

15

17

19

21

23

25

27

29

31

33

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Body Mass Index

Full Sample Food Stamp Recipients No Food Stamp Receipt

82
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 3 

Body Mass Index: Eligible Male Sub-Sample

15

17

19

21

23

25

27

29

31

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Body Mass Index

Full Sample Food Stamp Recipients No Food Stamp Receipt

Figure 4 

Body Mass Index: Eligible Female Sub-Sample

15

17

19

21

23

25

27

29

31

33

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Body Mass Index

Full Sample Food Stamp Recipients No Food Stamp Receipt

83
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 5 

Prevalence of Obesity: Low-Income Male Sub-Sample

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Prevalence of Obesity

Full Sample Food Stamp Recipients No Food Stamp Receipt

Figure 6 

Prevalence of Obesity: Low-Income Female Sub-Sample

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Prevalence of Obesity

Full Sample Food Stamp Recipients No Food Stamp Receipt

84
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 7 

Prevalence of Obesity: Eligible Male Sub-Sample

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Prevalence of Obesity

Full Sample Food Stamp Recipients No Food Stamp Receipt

Figure 8 

Prevalence of Obesity: Eligible Female Sub-Sample

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Prevalence of Obesity

Full Sample Food Stamp Recipients No Food Stamp Receipt

85
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 9 

Survival Rates for Not Yet Being Obese: Low-Income Male Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u rv iv a l R a te

No Food Stamp Receipt Received Food Stamp Benefits

Figure 10 

Survival Rates for Not Yet Being Obese: Low-Income Male Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u r v i v a l R a t e

Food Stamp Benefits = $0 Food Stamp Benefits = $2,400

86
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 11 

Survival Rates for Not Yet Being Obese: Low-Income Female Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u rv iv a l R a te

No Food Stamp Receipt Received Food Stamp Benefits

Figure 12 

Survival Rates for Not Yet Being Obese: Low-Income Female Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u rv iv a l R a t e

Food Stamp Benefits = $0 Food Stamp Benefits = $2,400

87
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 13 

Survival Rates for Not Yet Being Obese: Eligible Male Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u rv iv a l R a te

No Food Stamp Receipt Received Food Stamp Benefits

Figure 14 

Survival Rates for Not Yet Being Obese: Eligible Male Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u r v i val R a t e

Food Stamp Benefits = $0 Food Stamp Benefits = $2,400

88
Charles L. Baum The Effects of Food Stamps on Obesity 

Figure 15 

Survival Rates for Not Yet Being Obese: Eligible Female Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u rv iv a l R a te

No Food Stamp Receipt Received Food Stamp Benefits

Figure 16 

Survival Rates for Not Yet Being Obese: Eligible Female Sub-Sample

0.00

0.20

0.40

0.60

0.80

1.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

S u r v i v a l R a t e

Food Stamp Benefits = $0 Food Stamp Benefits = $2,400

89
Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A1: The Effects of Other Covariates: OLS Results, Low-Income Male Sub-Sample 

 Body Mass Index Probability of Being Obese

Household Income 0.001 (0.002) -0.002* (0.001) 

Black -0.505 (0.329) -0.135 (0.178) 

Hispanic 1.628*** (0.478) 0.559*** (0.202) 

Age 0.096 (0.061) 0.038 (0.030) 

Education Level 0.144 (0.119) 0.060 (0.061) 

Marital Status 0.721*** (0.216) 0.315** (0.124) 

Children 0.146 (0.121) 0.004 (0.057) 

Household Size 0.015 (0.060) 0.030 (0.034) 

Urban -0.465* (0.282) -0.228* (0.135) 

Local Unemployment Rate 0.350 (6.047) -0.118 (2.723) 

Local Per Capita Income -0.039 (0.043) -0.040* (0.022) 

Portion of Local Labor Force Female -0.601 (4.510) 2.225 (2.394) 

Local Population High-School Educated 2.841 (2.503) 2.191* (1.315) 

Local Population College-Educated 3.025 (4.758) 0.538 (2.469) 

Local Population Employed -11.136** (4.544) -2.341 (2.256) 

Local Labor Force in Manufacturing 0.746 (2.449) -0.113 (1.283) 

Local Labor Force in Wholesale/Retail Trade 1.463 (6.276) -3.588 (3.293) 

Representative’s ADA Ranking -0.398 (0.436) 0.095 (0.310) 

Senator’s ADA Ranking -0.589*** (0.213) -0.237 (0.152) 

Democrat Governor 0.002 (0.104) 0.038 (0.072) 

Portion of State House Democrat -1.941** (0.852) -1.164** (0.538) 

Portion of State Senate Democrat -0.665 (0.789) -0.362 (0.537) 

Post-TANF -0.939 (1.478) -1.108 (1.399) 

Pre-Welfare Reform Waiver #1 -0.019 (0.283) -0.174 (0.177) 

Pre-Welfare Reform Waiver #2 -0.315 (0.330) -0.276 (0.192) 

Pre-Welfare Reform Waiver #3 0.032 (0.352) -0.004 (0.214) 

Pre-Welfare Reform Waiver #4 -0.059 (0.251) -0.052 (0.121) 

Pre-Welfare Reform Waiver #5 -0.155 (0.383) 0.103 (0.161) 

Pre-Welfare Reform Waiver #6 -0.068 (0.372) -0.017 (0.207) 

State Maximum Benefits -0.001 (0.001) -0.001 (0.001) 

Time Limit 0.486 (1.385) 0.829 (0.679) 

No Time Limit 0.006 (0.020) 0.012 (0.010) 

Family Caps -0.260 (0.317) -0.195 (0.182) 

Child Age -0.033** (0.013) -0.020*** (0.007) 

Severe Sanctions -0.988*** (0.378) -0.350* (0.197) 

Earned Income Disregards 0.001 (0.002) 0.001 (0.001) 

Earned Income Disregards 0.004 (0.006) 0.001 (0.003) 

Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit 0.897 (1.709) 1.262 (1.007) 

90
Charles L. Baum The Effects of Food Stamps on Obesity 

Vehicles Included in TANF Asset Test 0.001 (0.001) 0.002** (0.001) 

TANF Vehicle Exemption -0.154 (0.394) -0.196 (0.227) 

Intercept 27.069*** (4.261) -15.569*** (6.241) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 low-income males. 

91
Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A2: The Effects of Other Covariates: OLS Results, Low-Income Female Sub-Sample 

 Body Mass Index Probability of Being Obese

Household Income -0.008** (0.003) -0.003* (0.002) 

Black 2.211*** (0.451) 0.554*** (0.165) 

Hispanic 1.261** (0.526) 0.293 (0.224) 

Age 0.250*** (0.072) 0.096*** (0.031) 

Education Level -0.291** (0.151) -0.104* (0.057) 

Marital Status 0.159 (0.283) 0.035 (0.130) 

Children -0.250 (0.156) -0.073 (0.058) 

Household Size 0.138 (0.100) 0.056 (0.038) 

Urban -0.445 (0.353) -0.173 (0.149) 

Local Unemployment Rate 10.125* (5.737) 6.543** (2.664) 

Local Per Capita Income 0.058 (0.061) 0.020 (0.025) 

Portion of Local Labor Force Female 5.882 (4.977) 3.445 (2.212) 

Local Population High-School Educated 2.102 (3.232) 1.253 (1.249) 

Local Population College-Educated -6.003 (5.907) -3.020 (2.697) 

Local Population Employed 1.414 (4.958) 1.630 (2.028) 

Local Labor Force in Manufacturing -1.204 (2.778) -0.721 (1.227) 

Local Labor Force in Wholesale/Retail Trade -10.167 (8.083) -4.146 (3.754) 

Representative’s ADA Ranking 0.326 (0.562) 0.200 (0.382) 

Senator’s ADA Ranking -0.112 (0.283) 0.239 (0.157) 

Democrat Governor -0.063 (0.136) 0.041 (0.073) 

Portion of State House Democrat -2.169** (1.057) -1.122** (0.468) 

Portion of State Senate Democrat -0.743 (1.065) 0.130 (0.557) 

Post-TANF -4.585* (2.735) -16.617*** (5.377)) 

Pre-Welfare Reform Waiver #1 -0.030 (0.404) 0.030 (0.191) 

Pre-Welfare Reform Waiver #2 0.053 (0.436) -0.098 (0.213) 

Pre-Welfare Reform Waiver #3 0.133 (0.453) 0.121 (0.218) 

Pre-Welfare Reform Waiver #4 0.058 (0.324) -0.283* (0.158) 

Pre-Welfare Reform Waiver #5 -0.046 (0.352) 0.133 (0.169) 

Pre-Welfare Reform Waiver #6 -0.379 (0.488) -0.241 (0.230) 

State Maximum Benefits 0.002 (0.001) 0.001 (0.001) 

Time Limit 0.716 (1.479) 0.613 (0.756) 

No Time Limit 0.010 (0.020) 0.006 (0.010) 

Family Caps 0.624* (0.376) 0.006 (0.202) 

Child Age 0.030** (0.015) 0.001 (0.007) 

Severe Sanctions -0.224 (0.422) -0.040 (0.223) 

Earned Income Disregards 0.002 (0.001) 0.001 (0.001) 

Earned Income Disregards -0.009 (0.007) -0.007** (0.003) 

Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit -0.071 (2.097) 0.872 (1.197) 

92
Charles L. Baum The Effects of Food Stamps on Obesity 

Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -0.217 (0.481) -0.133 (0.244) 

Intercept 19.449*** (5.563) -6.761 (6..633) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 10,558 low-income 

females. 

93
Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A3: The Effects of Other Covariates: OLS Results, Eligible Male Sub-Sample 

 Body Mass Index Probability of Being Obese

Household Income 0.007** (0.003) 0.001 (0.002) 

Black -0.072 (0.327) 0.087 (0.202) 

Hispanic 2.043*** (0.592) 0.678** (0.269) 

Age 0.224*** (0.069) 0.145*** (0.038) 

Education Level 0.039 (0.065) 0.002 (0.038) 

Marital Status 0.398 (0.332) 0.163 (0.189) 

Children 0.155 (0.153) 0.014 (0.070) 

Household Size 0.023 (0.064) 0.018 (0.039) 

Urban -0.262 (0.427) -0.300 (0.253) 

Local Unemployment Rate -1.291 (6.044) 2.397 (3.652) 

Local Per Capita Income -0.032 (0.049) -0.041 (0.033) 

Portion of Local Labor Force Female -8.066 (5.306) 0.344 (3.335) 

Local Population High-School Educated 1.862 (3.594) 3.067* (1.898) 

Local Population College-Educated -3.254 (5.512) -5.365* (3.266) 

Local Population Employed -0.835 (5.278) 3.827 (2.991) 

Local Labor Force in Manufacturing -0.417 (2.828) -1.509 (1.636) 

Local Labor Force in Wholesale/Retail Trade 3.276 (8.199) -2.821 (5.090) 

Representative’s ADA Ranking -2.484*** (0.948) -0.207 (0.657) 

Senator’s ADA Ranking -0.639 (0.536) -0.403 (0.381) 

Democrat Governor -0.064 (0.245) 0.047 (0.156) 

Portion of State House Democrat -0.109 (1.755) -1.544 (1.171) 

Portion of State Senate Democrat -0.874 (1.860) -0.301 (1.249) 

Post-TANF -5.078 (3.348) -19.281*** (3.144) 

Pre-Welfare Reform Waiver #1 2.125* (1.088) 0.972 (0.596) 

Pre-Welfare Reform Waiver #2 -0.374 (0.940) -0.555 (0.719) 

Pre-Welfare Reform Waiver #3 -0.715 (1.092) -0.105 (0.861) 

Pre-Welfare Reform Waiver #4 0.501 (0.632) -0.039 (0.343) 

Pre-Welfare Reform Waiver #5 0.078 (0.856) 0.104 (0.494) 

Pre-Welfare Reform Waiver #6 -0.038 (1.128) 0.126 (0.607) 

State Maximum Benefits -0.004* (0.002) -0.003* (0.002) 

Time Limit -1.632 (3.512) -1.876 (1.506) 

No Time Limit -0.015 (0.054) -0.016 (0.021) 

Family Caps 1.363* (0.736) 0.378 (0.362) 

Child Age 0.055* (0.032) 0.009 (0.015) 

Severe Sanctions 0.044 (0.865) 0.151 (0.457) 

Earned Income Disregards 0.002 (0.004) 0.001 (0.002) 

Earned Income Disregards 0.003 (0.014) -0.004 (0.007) 

Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit -5.826 (5.637) -2.004 (2.590) 

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Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -1.201 (0.931) -0.523 (0.511) 

Intercept 28.140*** (6.683) -2.567 (3.558) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible males. 

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Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A4: The Effects of Other Covariates: OLS Results, Eligible Female Sub-Sample 

 Body Mass Index Probability of Being Obese

Household Income -0.003 (0.003) 0.001 (0.001) 

Black 2.015*** (0.544) 0.551*** (0.187) 

Hispanic 1.022* (0.628) 0.018 (0.266) 

Age 0.190** (0.082) 0.100*** (0.029) 

Education Level -0.254** (0.106) -0.117*** (0.042) 

Marital Status 0.093 (0.363) 0.133 (0.157) 

Children -0.239 (0.175) -0.114* (0.060) 

Household Size 0.139 (0.096) 0.074** (0.036) 

Urban -0.062 (0.490) 0.263 (0.207) 

Local Unemployment Rate -0.524 (7.292) 1.506 (3.212) 

Local Per Capita Income 0.003 (0.076) 0.003 (0.033) 

Portion of Local Labor Force Female -2.380 (6.235) -2.573 (2.442) 

Local Population High-School Educated -4.695 (4.035) -1.404 (1.517) 

Local Population College-Educated -2.020 (6.757) -2.524 (2.813) 

Local Population Employed 9.977 (6.542) 5.042** (2.378) 

Local Labor Force in Manufacturing -1.320 (4.101) -1.087 (1.490) 

Local Labor Force in Wholesale/Retail Trade -0.465 (10.130) -1.295 (3.670) 

Representative’s ADA Ranking 1.178 (0.995) 0.485 (0.476) 

Senator’s ADA Ranking -0.793 (0.591) -0.093 (0.256) 

Democrat Governor -0.289 (0.271) 0.059 (0.120) 

Portion of State House Democrat -2.883 (2.022) -1.480* (0.837) 

Portion of State Senate Democrat -2.434 (2.105) -0.569 (0.929) 

Post-TANF -6.433** (3.031) -12.867*** (2.729) 

Pre-Welfare Reform Waiver #1 2.925** (1.283) 1.182*** (0.387) 

Pre-Welfare Reform Waiver #2 -0.968 (1.205) -0.092 (0.380) 

Pre-Welfare Reform Waiver #3 -0.489 (1.235) -0.875** (0.430) 

Pre-Welfare Reform Waiver #4 0.672 (0.615) 0.208 (0.251) 

Pre-Welfare Reform Waiver #5 -0.571 (0.957) 0.173 (0.312) 

Pre-Welfare Reform Waiver #6 -0.278 (1.014) -0.838* (0.446) 

State Maximum Benefits 0.007*** (0.003) 0.002** (0.001) 

Time Limit 1.637 (3.213) 0.707 (1.323) 

No Time Limit 0.029 (0.045) 0.019 (0.021) 

Family Caps 1.532 (0.989) 0.052 (0.364) 

Child Age 0.014 (0.036) -0.001 (0.013) 

Severe Sanctions -2.350** (0.993) -0.332 (0.369) 

Earned Income Disregards 0.005** (0.002) 0.001 (0.001) 

Earned Income Disregards 0.012 (0.015) 0.001 (0.006) 

Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit 2.657 (5.021) 0.280 (1.918) 

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Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -2.945** (1.254) -1.022** (0.480) 

Intercept 22.096*** (6.201) 7.041*** (2.066) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 4,799 eligible females. 

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Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A5: Food Stamp Receipt: OLS Results, Low-Income Sub-Sample Sample

Low-Income Males Low-Income Females

Household Income -0.033*** (0.006) -0.016*** (0.002) 

Black 0.062 (0.202) 1.077*** (0.130) 

Hispanic -0.134 (0.204) 0.207 (0.178) 

Age -0.028 (0.030) -0.054** (0.024) 

Education Level -0.163*** (0.044) -0.225*** (0.039) 

Marital Status 0.666*** (0.160) -1.173*** (0.115) 

Children 0.200*** (0.054) 0.605*** (0.051) 

Household Size 0.080*** (0.031) -0.011 (0.032) 

Urban 0.072 (0.184) -0.105 (0.139) 

Vehicle Value -0.149*** (0.0210 -0.172*** (0.020) 

Senior Present -0.189 (0.158) -0.279** (0.135) 

Local Unemployment Rate 4.502* (2.643) 0.072 (2.187) 

Local Per Capita Income -0.014 (0.023) 0.009 (0.019) 

Portion of Local Labor Force Female -1.018 (2.142) -0.624 (1.852) 

Local Population High-School Educated -1.658 (1.381) -2.864** (1.120) 

Local Population College-Educated 4.451* (2.561) 4.248** (2.158) 

Local Population Employed -5.058** (2.359) -6.561*** (1.761) 

Local Labor Force in Manufacturing 2.258* (1.199) -0.318 (0.997) 

Local Labor Force in Wholesale/Retail Trade 5.812 (3.642) 3.238 (3.059) 

Representative’s ADA Ranking -0.395 (0.424) 0.061 (0.355) 

Senator’s ADA Ranking -0.358 (0.243) -0.557*** (0.183) 

Democrat Governor -0.015 (0.109) 0.088 (0.087) 

Portion of State House Democrat -0.267 (1.200) -1.076* (0.654) 

Portion of State Senate Democrat 0.225 (0.918) -0.814 (0.724) 

Post-TANF -12.735*** (2.438) -20.342*** (2.521) 

Pre-Welfare Reform Waiver #1 -0.321 (0.362) -0.091 (0.284) 

Pre-Welfare Reform Waiver #2 0.665* (0.389) -0.119 (0.264) 

Pre-Welfare Reform Waiver #3 -0.445 (0.412) 0.144 (0.281) 

Pre-Welfare Reform Waiver #4 -0.231 (0.230) 0.033 (0.173) 

Pre-Welfare Reform Waiver #5 0.022 (0.373) -0.105 (0.232) 

Pre-Welfare Reform Waiver #6 -0.132 (0.399) 0.548* (0.310) 

State Maximum Benefits 0.001 (0.001) -0.001 (0.001) 

Time Limit -0.505 (1.453) -0.560 (1.336) 

No Time Limit -0.029 (0.018) 0.011 (0.020) 

Family Caps 0.337 (0.487) 0.483* (0.277) 

Child Age 0.006 (0.012) -0.011 (0.011) 

Severe Sanctions -0.877** (0.416) -0.105 (0.302) 

Earned Income Disregards -0.002 (0.002) 0.002* (0.001) 

Earned Income Disregards 0.004 (0.008) 0.004 (0.005) 

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Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit -0.363 (2.367) 0.001 (1.392) 

Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -0.157 (0.478) -0.077 (0.351) 

Intercept 24.332*** (0.253) 8.147*** (2.055) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 low-income males 

and 10,558 low-income females. 

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Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A6: Food Stamp Receipt: OLS Results, Low-Income Sub-Sample

Low-Income Males Low-Income Females

Household Income -0.040*** (0.006) -0.019*** (0.003) 

Black 0.237 (0.208) 1.235*** (0.134) 

Hispanic -0.086 (0.210) 0.215 (0.181) 

Age -0.042 (0.031) -0.073*** (0.025) 

Education Level -0.194*** (0.046) -0.254*** (0.040) 

Marital Status 0.540*** (0.166) -1.461*** (0.119) 

Children 0.216*** (0.055) 0.652*** (0.052) 

Household Size 0.079** (0.031) -0.021 (0.031) 

Urban 0.142 (0.183) -0.054 (0.140) 

FSP Vehicle Asset Limit 0.089 (0.081) -0.022 (0.136) 

EBT 0.424 (0.314) 0.186 (0.224) 

Non-Parental Adult Caregivers -0.611 (0.538) -0.073 (0.302) 

Simplified Periodic Reporting 0.182 (0.426) -0.307 (0.313) 

Categorical Eligibility -0.611 (0.481) -0.404 (0.317) 

Severe Sanctions -0.188 (0.410) -0.170 (0.286) 

Outreach Plan 1.457*** (0.445) 0.434 (0.311) 

Local Unemployment Rate 3.293 (2.653) 0.087 (2.213) 

Local Per Capita Income -0.015 (0.024) 0.013 (0.019) 

Portion of Local Labor Force Female -0.815 (2.131) -0.291 (1.884) 

Local Population High-School Educated -1.532 (1.383) -3.222*** (1.152) 

Local Population College-Educated 3.870 (2.552) 4.467** (2.243) 

Local Population Employed -5.140** (2.377) -6.777*** (1.750) 

Local Labor Force in Manufacturing 2.197* (1.219) -0.174 (1.007) 

Local Labor Force in Wholesale/Retail Trade 4.982 (3.688) 2.752 (3.102) 

Representative’s ADA Ranking -0.209 (0.437) 0.066 (0.353) 

Senator’s ADA Ranking -0.345 (0.239) -0.546*** (0.180) 

Democrat Governor -0.040 (0.108) 0.097 (0.086) 

Portion of State House Democrat 0.149 (1.228) -0.981 (0.664) 

Portion of State Senate Democrat -0.064 (0.900) -0.733 (0.710) 

Post-TANF -12.217*** (2.548) -18.804*** (2.988) 

Pre-Welfare Reform Waiver #1 -0.378 (0.374) -0.192 (0.285) 

Pre-Welfare Reform Waiver #2 0.520 (0.368) -0.053 (0.251) 

Pre-Welfare Reform Waiver #3 -0.479 (0.380) 0.083 (0.273) 

Pre-Welfare Reform Waiver #4 -0.114 (0.234) 0.080 (0.179) 

Pre-Welfare Reform Waiver #5 0.094 (0.332) -0.100 (0.228) 

Pre-Welfare Reform Waiver #6 -0.033 (0.369) 0.378 (0.300) 

State Maximum Benefits 0.002 (0.001) 0.001 (0.001) 

Time Limit 1.292 (1.896) -0.557 (1.604) 

No Time Limit -0.004 (0.024) 0.010 (0.024) 

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Charles L. Baum The Effects of Food Stamps on Obesity 

Family Caps 0.703 (0.512) 0.288 (0.304) 

Child Age -0.004 (0.015) -0.012 (0.013) 

Severe Sanctions -1.421*** (0.523) 0.008 (0.363) 

Earned Income Disregards -0.005 (0.003) 0.002** (0.001) 

Earned Income Disregards 0.008 (0.008) 0.006 (0.006) 

Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit 0.254 (2.347) -1.149 (1.518) 

Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -0.260 (0.458) 0.075 (0.345) 

Intercept 10.023*** (2.014) 1.424*** (0.183) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 11,748 low-income males 

and 10,558 low-income females. 

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Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A7: Food Stamp Receipt: OLS Results, Eligible Sub-Sample Sample 

Eligible Males Eligible Females

Household Income -0.009*** (0.003) -0.005*** (0.001) 

Black -0.083 (0.214) 0.789*** (0.129) 

Hispanic -0.163 (0.240) 0.025 (0.185) 

Age 0.038 (0.031) -0.002 (0.024) 

Education Level -0.102*** (0.035) -0.209*** (0.029) 

Marital Status 1.355*** (0.156) -0.497*** (0.125) 

Children 0.138** (0.057) 0.695*** (0.055) 

Household Size -0.031 (0.032) -0.175*** (0.036) 

Urban 0.307 (0.208) -0.094 (0.157) 

Vehicle Value -0.149*** (0.028) -0.145*** (0.020) 

Senior Present -0.399*** (0.165) -0.227* (0.141) 

Local Unemployment Rate 6.982** (2.916) 4.438* (2.483) 

Local Per Capita Income -0.001 (0.024) 0.008 (0.018) 

Portion of Local Labor Force Female -5.373** (2.562) 0.466 (2.017) 

Local Population High-School Educated -3.833** (1.615) -2.216* (1.179) 

Local Population College-Educated 3.275 (2.957) 2.428 (2.137) 

Local Population Employed -1.118 (2.821) -4.717** (1.916) 

Local Labor Force in Manufacturing 2.186* (1.308) -0.025 (1.030) 

Local Labor Force in Wholesale/Retail Trade 14.002*** (4.327) 1.807 (3.170) 

Representative’s ADA Ranking 0.360 (0.586) -0.096 (0.421) 

Senator’s ADA Ranking -0.338 (0.318) -0.305 (0.235) 

Democrat Governor -0.092 (0.131) 0.212** (0.105) 

Portion of State House Democrat 0.545 (1.470) -0.353 (0.887) 

Portion of State Senate Democrat -0.210 (1.307) -0.183 (0.873) 

Post-TANF -16.595*** (1.431) -19.107*** (2.631) 

Pre-Welfare Reform Waiver #1 -0.174 (0.597) -0.412 (0.457) 

Pre-Welfare Reform Waiver #2 0.714 (0.566) -0.693* (0.399) 

Pre-Welfare Reform Waiver #3 -1.287** (0.568) 0.983* (0.548) 

Pre-Welfare Reform Waiver #4 -0.198 (0.306) 0.053 (0.249) 

Pre-Welfare Reform Waiver #5 0.352 (0.498) -0.149 (0.359) 

Pre-Welfare Reform Waiver #6 0.501 (0.574) 0.999** (0.472) 

State Maximum Benefits 0.001 (0.001) -0.001 (0.001) 

Time Limit -1.056 (1.834) -2.487* (1.363) 

No Time Limit -0.014 (0.025) -0.029 (0.021) 

Family Caps 0.435 (0.514) 0.750** (0.365) 

Child Age 0.017 (0.013) -0.003 (0.014) 

Severe Sanctions -1.119** (0.524) -0.330 (0.424) 

Earned Income Disregards 0.001 (0.002) 0.002 (0.001) 

Earned Income Disregards 0.011 (0.009) 0.004 (0.007) 

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Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit 2.633 (3.079) -0.626 (2.033) 

Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -0.695 (0.664) -0.188 (0.518) 

Intercept 24.332*** (0.253) 6.673*** (2.041) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible males and 

4,799 eligible females. 

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Charles L. Baum The Effects of Food Stamps on Obesity 

Appendix Table A8: Food Stamp Receipt: OLS Results, Eligible Sub-Sample Sample 

Eligible Males Eligible Females

Household Income -0.012*** (0.003) -0.008*** (0.002) 

Black 0.046 (0.221) 0.904*** (0.129) 

Hispanic -0.124 (0.242) 0.091 (0.184) 

Age 0.024 (0.031) -0.017 (0.024) 

Education Level -0.120*** (0.037) -0.231*** (0.030) 

Marital Status 1.262*** (0.151) -0.703*** (0.121) 

Children 0.174*** (0.057) 0.727*** (0.054) 

Household Size -0.045 (0.032) -0.188*** (0.034) 

Urban 0.355 (0.203) -0.028 (0.158) 

FSP Vehicle Asset Limit 0.089 (0.081) 0.209* (0.112) 

EBT 0.209 (0.426) 0.220 (0.311) 

Non-Parental Adult Caregivers -0.277 (0.507) -0.495 (0.376) 

Simplified Periodic Reporting 0.337 (0.530) -0.302 (0.401) 

Categorical Eligibility -0.397 (0.570) -0.261 (0.4190 

Severe Sanctions 0.370 (0.506) -0.537 (0.385) 

Outreach Plan 1.378*** (0.613) 0.890** (0.407) 

Local Unemployment Rate 6.445** (2.915) 3.696 (2.464) 

Local Per Capita Income -0.007 (0.026) 0.005 (0.019) 

Portion of Local Labor Force Female -4.523* (2.481) 0.153 (2.004) 

Local Population High-School Educated -3.521** (1.577) -2.536** (1.200) 

Local Population College-Educated 2.456 (2.879) 3.016 (2.173) 

Local Population Employed -0.385 (2.706) -4.809** (1.934) 

Local Labor Force in Manufacturing 1.795 (1.287) 0.090 (1.032) 

Local Labor Force in Wholesale/Retail Trade 12.085*** (4.290) 1.616 (3.184) 

Representative’s ADA Ranking 0.376 (0.578) -0.140 (0.415) 

Senator’s ADA Ranking -0.290 (0.311) -0.189 (0.229) 

Democrat Governor -0.130 (0.129) 0.189* (0.106) 

Portion of State House Democrat 0.236 (1.423) -0.267 (0.853) 

Portion of State Senate Democrat -0.206 (1.294) -0.054 (0.852) 

Post-TANF -18.642*** (1.851) -18.434*** (2.501) 

Pre-Welfare Reform Waiver #1 -0.260 (0.576) -0.463 (0.444) 

Pre-Welfare Reform Waiver #2 0.582 (0.549) -0.785** (0.385) 

Pre-Welfare Reform Waiver #3 -1.169*** (0.562) 1.020* (0.522) 

Pre-Welfare Reform Waiver #4 -0.208 (0.312) 0.100 (0.246) 

Pre-Welfare Reform Waiver #5 0.391 (0.468) -0.131 (0.355) 

Pre-Welfare Reform Waiver #6 0.559 (0.559) 0.901** (0.455) 

State Maximum Benefits 0.002 (0.001) -0.001 (0.001) 

Time Limit -0.366 (2.403) -2.321 (1.700) 

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No Time Limit 0.004 (0.034) -0.016 (0.023) 

Family Caps 0.931 (0.624) 0.396 (0.453) 

Child Age 0.001 (0.018) -0.002 (0.016) 

Severe Sanctions -1.610** (0.660) -0.082 (0.535) 

Earned Income Disregards 0.001 (0.002) 0.003* (0.001) 

Earned Income Disregards 0.018* (0.010) 0.009 (0.008) 

Asset Limit 0.001 (0.001) 0.001 (0.001) 

No Asset Limit 1.929 (3.350) -2.394 (2.572) 

Vehicles Included in TANF Asset Test 0.001 (0.001) 0.001 (0.001) 

TANF Vehicle Exemption -0.957 (0.659) 0.155 (0.576) 

Intercept 10.023*** (2.014) 1.424*** (0.183) 

Standard errors are in parentheses. *p < 0.10, **p < 0.05, and ***p < 0.01. There are 3,681 eligible males and 

4,799 eligible females. 

105