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The effects of SNAP and WIC programs on nutrient intakes of children
Food Policy 35 (2010) 576–583
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The effects of SNAP and WIC programs on nutrient intakes of children Steven T. Yen * Department of Agricultural Economics, The University of Tennessee, 302 Morgan Hall, 2621 Morgan Circle, Knoxville, TN 37996-4518, United States
a r t i c l e
i n f o
Article history: Received 6 November 2008 Received in revised form 12 May 2010 Accepted 19 May 2010
Keywords: SNAP WIC Dual treatment effect model
a b s t r a c t Nutritional effects of participation by young children in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) in the United States are investigated. A system of nutrient equations with dual endogenous programs is estimated by the maximum-likelihood procedure. WIC is found to increase the intakes of three of the four important nutrients for WIC children, including iron, potassium, and fiber. SNAP only has a small and negative effect on fiber intake. The additional benefit of SNAP participation is non-existent given participation in WIC. Ó 2010 Elsevier Ltd. All rights reserved.
Introduction The Supplemental Nutrition Assistance Program (SNAP, formerly Food Stamp Program) and the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) are among the largest and most widely available Federal food assistance programs in the United States (US), second only to the National School Lunch Program (NSLP). Administered by the Food and Nutrition Service (FNS) of the US Department of Agriculture (USDA), the SNAP is the nation’s key nutrition safety net which provides inkind benefit for low-income households to buy food. Program cost for the SNAP was $53.638 billion for fiscal year 2009, putting food on the table for 15.23 million households and 33.72 million individuals each month (USDA-FNS, 2010). WIC provides non-monetary benefits in the forms of nutritious foods, nutrition education, and health referrals to participating low-income individuals. Participants include pregnant, postpartum, and breastfeeding women, and infants and children up to age 5 who are at nutritional risk. Also administered by the USDA-FNS, WIC is a Federal grant program (not an entitlement program as SNAP is) that provides funds to WIC state agencies to pay for WIC foods, nutrition education, and administrative costs. To be income eligible, an applicant’s income must fall at or below 185% of the US Poverty Income Guidelines (e.g., $40,793 for a family of four in 2010). Participation by the person or a family member(s) in certain other benefit programs, such as the SNAP, Medicaid, or Temporary Assistance for Needy Families (TANF), automatically qualifies the person for the WIC. During 2009, the WIC program cost was $6.477 billion, with a total par-
* Tel.: +1 865 974 7474; fax: +1 865 974 4829. E-mail address: [email protected] 0306-9192/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2010.05.010
ticipation of 9.12 million persons (USDA-FNS, 2010). In most WIC state agencies, a participant receives checks or vouchers each month to purchase specific foods designed to supplement their diets. The foods provided are high in one or more of the following nutrients: protein, calcium, iron, and vitamins A and C. These nutrients were identified in the original WIC statute (1972) but are no longer named in the enabling WIC legislation (in effect since 1978).1 Empirical literature Fox et al. (2004) review the literature on the effects of USDA’s food and nutrition assistance programs (including WIC and SNAP) on a variety of outcome variables, ranging from household food expenditures, nutrient availability, food insecurity, and individual dietary intake to health outcomes such as overall health, birth outcomes and obesity. Currie (2000) reviews earlier studies on US food and nutrition programs, and Moffitt (2000) contains the literature on other welfare programs. Butler et al. (1985), using data from the Food Stamp Cash Out Project, find the SNAP effects on nutrient intakes negligible, and that controlling for endogeneity of participation with a selectionbias technique does not affect the results. Devaney and Moffitt (1991) use data from the 1979–1980 Survey of Food Consumption in Low-Income Households and found that the dietary effects of
1 Recent findings however suggest that young children do not face nutritional risks of inadequacy of these nutrients (Devaney et al., 2005; IOM, 2006a). Devaney et al. (2005) also find that the degree of inadequate usual intakes of micronutrients is low among 4–8 year old SNAP participants, and for WIC eligible participants/nonparticipants age 1–3. The major nutritional concerns are excess intake of fat and overweight.
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S.T. Yen / Food Policy 35 (2010) 576–583
the SNAP benefit on nutrient availability are considerably larger than those of cash income, and that, like Butler et al. (1985), endogenizing SNAP participation does not cause notable differences. Using nutrient adequacy ratios for a sample of preschool children, Rose et al. (1998) find positive effects of both WIC and SNAP on nutrient intakes. Treating these benefits as exogenous, regression results suggest that the SNAP benefit increases iron and zinc intakes by 12.3% and 9.2%, respectively, of the preschooler’s RDAs; WIC benefit has greater effects on iron and zinc intakes than cash income, but neither program affects the intakes of fat, saturated fat or cholesterol. Oliveira and Gundersen (2000), using a sample of income-eligible children, find significant effects of WIC on the intakes of iron, vitamin C, vitamin A, vitamin B6, and folate; contrary to findings by Rose et al. (1998), however, WIC does not affect zinc intake. Further, by limiting analysis to children living in a household with an infant or woman on WIC to control for self-selection bias, they find that participation in WIC has a positive effect on iron, folate and vitamin B6, but does not affect the other nutrients reported (vitamins A and C). Butler and Raymond (1996), renewing attention to endogenous program participation, present the only exception to the existing literature by considering a system of nutrient equations with a single endogenous SNAP variable. Controlling for participation in the SNAP with a switching regression model, they find that food stamp income has small effects on nutrient intakes among the elderly. The above nutrient intake literature suggests that estimates of program effects differ depending on methodology, particularly in the treatment of SNAP and WIC participation. Comparison to results from previous studies is therefore difficult due to differences in methodology and data (previous studies differ among themselves as well). While there have been attempts to correct for selectivity bias caused by SNAP and/or WIC participation, there is a greater tendency among previous studies to treat these variables as exogenous. Yet, findings by Butler and Raymond (1996) and Oliveira and Gundersen (2000) that controlling for program participation can produce notably different (opposite) program effects are particularly interesting. There has not been an attempt to simultaneously endogenize both SNAP and WIC participation and to examine the effects of program interactions. Further, except Butler and Raymond (1996), Oliveira and Gundersen (2000), and Rose et al. (1998), most previous studies have examined single outcomes. More recent studies have investigated the role of SNAP in household food insecurity (Yen et al., 2008) obesity and health care spending (Chen et al., 2005; Meyerhoefer and Pylypchuk, 2008), and the role of WIC in nutritional risk (Bitler et al., 2005), pregnancy, and birth outcomes (Bitler and Currie, 2005). In many studies with multiple outcomes such as nutrients or food expenditures, the outcome equations are often analyzed on an equation-by-equation basis (e.g., Butler et al., 1985; Devaney and Fraker, 1989) or with exogenous program participation (e.g., Rose et al., 1998). These single-equation or exogenous-program approaches are unappealing as consumers typically make food choices from a bundle of commodities and each food item typically contains multiple nutrients. Further, participation in SNAP and WIC is likely the results of individual decisions, made simultaneously with the food/nutrient intake decisions. On statistical grounds, such endogeneity is caused by correlations among unobservables which may affect both program participation and nutrient intake. When these cross-equation correlations are ignored in statistical estimation, statistical biases and inefficiency can result from a failure to accommodate simultaneity and non-random selection into programs. In addition, while participation in multiple programs is the rule, rather than exception, most studies address the effects of single programs, one at a time. Studies that address participation in multiple programs are rare (exceptions including Keane and Moffitt (1998)), and studies with multiple
outcomes and multiple programs are, to our knowledge, nonexistent. This paper addresses participation and effectiveness of SNAP and WIC in a multi-equation framework for nutrient intakes with endogenous SNAP and WIC participation. Empirical analysis is conducted for young children in the US. The nutrients selected for the current analysis are iron, potassium, vitamin E, and fiber. These nutrients were identified by a recent Institute of Medicine (IOM) panel as to need improvement for WIC children (Devaney et al., 2005; IOM, 2006a, pp. 46–86).
Empirical specification and econometric model The nutrient intake equations are motivated by a theoretical framework in which consumer preference is defined over utilitygenerating attributes (nutrients) which are produced with market goods (food items). Maximization of utility subject to the nutrientproducing technology and fixed budget yields nutrient demand equations (Lancaster, 1971). The econometric model consists of two equations for binary program participation outcomes (d1i, d2i) and m 2 equations for continuous nutrient intake (y3i, . . . , ymi) for individual i. Participation in each program is governed by a probit mechanism such that
dki ¼ 1 if z0i ak þ uki > 0 ¼ 0 if z0i ak þ uki 6 0;
ð1Þ
k ¼ 1; 2
where zi is a vector of exogenous variables and, for program k, ak is a conformable parameter vector and uki is a random error. Both program variables appear in each nutrient intake equation as potentially endogenous regressors (treatments):
log yki ¼ x0i bk þ ck1 d1i þ ck2 d2i þ uki ;
k ¼ 3; :::; m
ð2Þ
where xi is a vector of exogenous variables and, for nutrient k, bk is a parameter vector, ck1 and ck2 are scalar parameters, and uki are random error terms which capture the effects of unobserved factors on the program participation and nutrient intake decisions. Assume the error terms ui = [u1i, . . . , umi]0 in (1) and (2) are distributed as m-dimensioned normal with zero means and covariance matrix R [qh‘rhr‘], where qh‘ are correlations and rh are standard deviations such that r1 = r2 = 1 (because parameters a1 and a2 are identified only up to a scale due to the binary outcomes of d1i and d2i). The error correlation q12 accommodates common unobserved factors governing decisions in both SNAP and WIC, correlations qk1 and qk2 (for k P 3) accommodate endogeneity of SNAP and WIC in the nutrient equations, and the other correlations (qh‘ for h, ‘ P 3, h – ‘) reflect associations among unobserved factors in the nutrient equations. Such correlations and endogeneity are testable hypotheses. To construct the likelihood function, partition the error vector ð1Þ0 ð2Þ0 ð1Þ ð2Þ to ui ¼ ½ui ; ui 0 such that ui ¼ ½u1 ; u2 0 and ui ¼ ½u3 ; . . . ; um 0 with corresponding partitioning of R at the second row and column
R
R11 R12 R21 R22
ð3Þ
such that R11 is 2 2, R12 ¼ R021 is 2 (m 2), and R22 is (m 2) (m 2). Denote uki ¼ log yki ðx0i bk þ ck1 d1i þ ck2 d2i Þ, k = 3, . . . , m. Then, the sample likelihood contribution for a participant in both programs (d1i = 1, d2i = 1) is m Y
Ld1i ¼1;d2i ¼1 ¼
!
y1 ki
k¼3 Z 1 z0i a1
f ðu3i ; . . . ; umi Þ
Z
1
z0i a2
gðu1i ; u2i ju3i ; . . . ; umi Þdu1i du2i
ð4Þ
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S.T. Yen / Food Policy 35 (2010) 576–583
Qm
where the product term k¼3 y1 ki is the Jacobian of transformation from (log y3i, . . . , log ymi) to (y3i, . . . , ymi) and, using properties of the multivariate normal distribution (Kotz et al., 2000), f(u3i, . . . , umi) is the marginal probability density function of ð2Þ ui Nð0; R22 Þ and g(u1i, u2i|u3i, . . . , umi) is the conditional density ð1Þ ð2Þ of ui jui Nðl1j2 ; X1j2 Þ, with conditional mean and variance ð2Þ l1j2 ¼ R12 R1 ¼ ½n1i ; n2i 0 22 ui
"
ð5Þ #
x21 x12 : X1j2 ¼ R11 R12 R R ¼ x21 x22 1 22
0 21
ð6Þ
The likelihood contributions for other sample regimes are similar to (4), with changes in the integration limits such that a finite lower (upper) limit z0i ak would correspond to participation (non-participation) in program k. Define dichotomous indicators j1i = 2d1i 1, j2i = 2d2i 1, and let g1i ¼ j1i ðz0i a1 þ n1i Þ=x1 , g2i ¼ j2i ðz0i a2 þ n2i Þ=x2 , and si = j1ij2ix12/(x1x2) using (5) and (6). Then, the likelihood function for an independent sample of n observations is
L¼
n m Y Y i¼1
! y1 ki
f ðu3i ; . . . ; umi ÞU2 ðg1i ; g2i ; si Þ
ð7Þ
k¼3
where U2(g1i, g2i; si) is the bivariate standard normal cumulative distribution function with standard normal variates (g1i, g2i) and correlation si. Maximum-likelihood (ML) estimation is carried out by maximizing the likelihood function (7). The model is a generalization of the double- and multiple-selection models (Catsiapis and Robinson, 1982; Tunali, 1986) and multiple-treatment effect model (Keane and Moffitt, 1998) in that there are multiple outcome equations. Evaluation of programs is based on the conditional mean of each dependent variable yki. The conditional means are
Eðyki jd1i ; d2i Þ ¼ expðx0i bk þ ck1 d1i þ ck2 d2i Þexpðr2 =2Þ
U2 ðj1i z0i a1 þ j1i rqk1 ; j2i z0i a2 þ j2i rqk2 ; j1i j2i q21 Þ U2 ðj1i z0i a1 ; j2i z0i a2 ; j1i j2i q21 Þ ð8Þ
for k = 3, . . . , m. Detailed derivations are available in Yen and Rosinski (2009). Using (8), the average treatment effect (ATE), in the spirit of Heckman and Vytlacil (2005) with one treatment, of participation in both programs can be calculated as
ATEWIC;SNAP ¼
n 1X ½b Eðyki jd1i ¼ 1; d2i ¼ 1Þ b Eðyki jd1i ¼ 0; d2i ¼ 0Þ n i¼1
ð9Þ b are evaluated based on (8) at the where the conditional means EðÞ ML estimates of parameters. Likewise, the ATEs of WIC conditional on non-participation and participation in SNAP are, respectively,
ATEWICjSNAP¼0 ¼
n 1X ½b Eðyki jd1i ¼ 1; d2i ¼ 0Þ b Eðyki jd1i ¼ 0; d2i ¼ 0Þ n i¼1
ð10Þ ATEWICjSNAP¼1 ¼
n 1X ½b Eðyki jd1i ¼ 1; d2i ¼ 1Þ b Eðyki jd1i ¼ 0; d2i ¼ 1Þ n i¼1
ð11Þ The ATEs of SNAP conditional on WIC non-participation and participation are similar. To further explore the effects of explanatory variables on nutrient intakes, marginal effects are calculated by differentiating (differencing, in the case of a discrete variable) the conditional means in (8). These treatment and marginal effects are not trivial and depend on the whole set of parameters.
Data sources and sample Data are drawn from the 1994–1996 Continuing Survey of Food Intakes by Individuals (CSFII) and its 1998 Supplemental Children’s Survey (USDA-ARS, 2000). The CSFII 1994–1996 includes intake data for 4253 children up to age 9. The CSFII 1998, conducted in response to the Food Quality Protection Act of 1996 and designed to be combined with CSFII 1994–1996, adds intake data for 5559 children up to age 9. For each year, dietary data for individuals were collected through in-person interviews using 24-h recalls. These dietary data were collected for two nonconsecutive days for some of the children and for a single day for other children (see selection of sample below). A food instruction booklet was used to probe for a complete description of every food item and the amount eaten. The nutritive values of each food eaten, including the nutrients considered in this study, were then calculated using the weight of the food and data from the Survey Nutrient Database (USDAARS, 2006). Further details on the sample design, data collection and coding, and nutrient calculation are described in the survey documentation (USDA-ARS, 2000). To address joint participation decisions, the sample is limited to children eligible for both WIC and SNAP. We consider households at or below 130% of the Federal poverty guidelines who were income eligible for the SNAP. Since WIC income requirement is more generous, at or below 185% of the Federal poverty guidelines, households who were income eligible for the SNAP would also be income eligible for WIC. In addition, children who participated in Medicaid or TANF, who were authorized to participate in the SNAP, who resided in a household receiving income from the Aid to Families with Dependent Children (AFDC) program, or who were individually determined to be at nutritional risk by a health professional are also eligible. Because the CSFII data do not allow determination of nutritional risk, (family) income eligibility is used as a proxy for WIC eligibility. A similar procedure is followed by Oliveira and Gundersen (2000) and Rose et al. (1998) in determining WIC eligibility.2 Because the CSFII does not contain information on the nutrient contribution of breast milk and exclusion of breast-fed children could cause sample selectivity bias, children under 2 years of ages are excluded.3 Because WIC ends at the child’s fifth birthday, children age P 5 are excluded. In addition, the CSFII includes nutrient intake information for individuals with first-day intake only, second-day intake only, and 2-day intakes. To address some of the data issues, the sample is limited to young children with complete 2-day intake data, for which 2-day average intake data are used in the analysis. The final sample includes 1446 children. The first endogenous variable, WIC participation, is coded from response (provided by a caregiver or parent) to the following question: ‘Are you receiving benefits under the Women, Infants and Children (WIC) Program at the present time?’ The other endogenous program variable, SNAP participation, is coded from response to the question: ‘Did any member of your household receive food stamps in any of the last 12 months?’ An implicit assumption in modeling such joint program participation is that receipt of food stamp benefits by (any member(s) in) the household during the
2 The WIC population evaluated can be very different from the full, targeted WIC population of children in households with income less than 185% poverty, and the results estimated on the full WIC population may be different. Bitler et al. (2003), for instance, find that up to 23% of the WIC population have income above 185% income. This would suggest that a significant share of the WIC population is excluded by limiting the population to those with income less than 130% of the federal poverty income level. 3 Breastfeeding status was reported only by children age 63. The sample may include a few children who were still breast feeding, but most if not all of the nutrients should be coming from food and beverages by that age. Nutritional guidelines are also better established for older children.
S.T. Yen / Food Policy 35 (2010) 576–583
past 12 months, regardless of whether the household currently receives the benefits, can affect nutrient intake by the child. We include four nutrients identified by the IOM (2006a) as critical (needing improvement): iron, potassium, vitamin E, and fiber. Some of these nutrients were also investigated by Oliveira and Gundersen (2000) and Rose et al. (1998), Devaney et al. (2005), and in a report by the IOM (2006a). In IOM (2006a) and Devaney et al. (2005), intakes of nutrients are calculated as the percentages of the most recent Dietary Reference Intakes (DRI) developed by the IOM (2000, 2003), because two age groups in the DRIs were combined. This is the case as well in the current study as children in the 2–3 and 4 age groups are combined so the same procedure is followed. Intakes of iron and vitamin E are calculated using the recommended daily allowance (RDA) values (IOM, 2006b, p. 328, p. 234). Intakes of potassium and fiber, for which information is not available to calculate Estimated Average Requirements (EARs) used in calculation of the RDAs, are calculated as the percentages of Adequate Intakes (AIs) (IOM, 2006b, p. 370, p. 110). The explanatory variables include household characteristics such as per capita annual income, family size, and home ownership; household head’s characteristics such as education (
579
tions but not the potassium equation. None of the corresponding correlations are significant between the nutrients and SNAP equation. Significance of the error correlations between WIC and nutrient equations suggests endogeneity of sample selection and program participation, implying that failure to accommodate such endogeneity can cause biased parameter estimates in the nutrient intake equations. Correlation between the error terms of the two programs is positive and significant at the 1% level, which justifies joint estimation of the two program participation equations.5 Significance of error correlations among the nutrient equations also justifies joint estimation of the nutrient equations to improve statistical efficiency. Among the determinants of program participation, household income, race (being black or being white), being employed, and age (=4) of children are all negative factors of WIC participation at the 10% level of significance or lower. For no obvious reason, children 3 years of age are more likely to participation in the WIC than younger children (age 2). The negative effect of age (=4) on WIC participation is in agreement with findings from other studies that participation in WIC falls as children get older (e.g., USDA-ARS, 2000). Household income, home ownership, residing in the South and in a suburban area, being Hispanic, and having an employed parent are all negatively associated with SNAP participation, whereas being black or being white, residing in the Northeast, and having only one adult in the household contribute to SNAP participation. Because directions and magnitudes of the program effects depend on coefficients of the program variables as well as the selectivity terms (see (8)), further comparisons of the effects of programs and explanatory variables are made by calculating ATEs and marginal effects. Program effects
Estimation results One practical empirical issue is the choice of regressors to explain program participation and nutrient intakes. Unlike a linear system or in instrumental variable estimation for which exclusion conditions are needed for parameter identification (e.g., Currie and Cole, 1993; Butler and Raymond, 1996), the nonlinear identification criteria are met due to the functional form and distributional assumptions for ML estimation of the current system. However, to avoid overburdening functional form and distributional assumptions for parameter identification in the absence of exclusion restrictions, categorical variables for child’s age and household head’s education dummies are used in the WIC and SNAP participation equations; whereas child’s age and household head’s education, both in years, are included only in the nutrient intake equations. Importantly, preliminary estimation without these exclusion restrictions did not produce discernable differences in parameter estimates and, more importantly, program effects from the reported estimates with exclusions, which lends evidence to robustness of the current results.4 Parameter estimates ML estimates are presented in Table 1. The error term of the WIC participation equation is significantly (at the 5% level of significance) correlated with those of the iron, vitamin E, and fiber equa4
Delineating the separate effects of variables on program participation and nutrient intake is always difficult. Some of the regressors may serve as proxies for unobservables. For example, education may reflect the effect of stigma, an important determinant of program participation (Moffitt, 1983) which is not available in the CSFII; it may also reflect the knowledge of nutrition. The ability to afford education may also be reflected in income.
The ATEs of programs on nutrient intakes are presented in Table 2. The effects of WIC are positive on three of the four nutrients, conditional on non-participation or participation in SNAP. Participation in WIC (only) increases the intakes of iron (by 16.3%DRI), potassium (5.5%DRI), and fiber (3.4%DRI), but not vitamin E. The effects of WIC are similar conditional on participation in SNAP: iron (19.8%DRI), potassium (5.5%DRI), and fiber (4.1%DRI). Taking participation in both programs into consideration, the combined effects are increases in the intakes of iron (by 18.6%DRI) and potassium (4.7%DRI), whereas the conflicting effects of WIC and SNAP cancel out for iron. Participation in SNAP alone decreases fiber intake slightly (by 2.71%DRI) which is marginally significant (pvalue = 0.11). In sum, the effects of SNAP are nearly non-existent, and participation in SNAP brings no additional benefit given participation in WIC. Our insignificant and small effects of SNAP are similar to findings for the elderly by Butler and Raymond (1996), who control for SNAP participation and argue that the positive program effects on nutrient intakes in earlier findings could be the result of selfselection into the SNAP by individuals who are more interested in maintaining good nutrition. Our insignificant effects of SNAP suggest that, contrary to the finding by Butler and Raymond (1996), selection is not an issue for this sample of children. Marginal effects of explanatory variables on nutrient intakes Table 3 presents the marginal effects of explanatory variable on nutrient intakes, conditional on non-participation in both 5 Imposing independence between error terms of SNAP and WIC produced only slightly different parameter estimates, effects of programs, and marginal effects of explanatory variables on nutrition intakes.
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S.T. Yen / Food Policy 35 (2010) 576–583
Table 1 ML estimation of nutrient equations with endogenous WIC and SNAP participation.a Variable
Constant Income, per capita ($1000) Household size Home owner Healthy Meals/snacks Male Northeast Midwest South City Suburban Black White Hispanic One adult Employed Age 3 Age 4
Program participation
log(%DRI)
WIC
SNAP
Iron
Potassium
Vitamin E
Fiber
0.079 (0.274) 0.079*** (0.029) 0.001 (0.023) 0.108 (0.086) 0.196 (0.179) 0.138 (0.085) 0.076 (0.070) 0.169 (0.119) 0.154 (0.115) 0.020 ((0.100) 0.168* (0.101) 0.011 (0.098) 0.296** (0.120) 0.177* (0.099) 0.054 (0.115) 0.023 (0.126) 0.146* (0.083) 0.311*** (0.085) 0.175* (0.094) 0.084 (0.075) 0.007 (0.094) 0.052 (0.150)
0.963*** (0.313) 0.336*** (0.031) 0.023 (0.024) 0.354*** (0.092) 0.045 (0.203) 0.098 (0.092) 0.111 (0.078) 0.295** (0.145) 0.049 (0.123) 0.208* (0.110) 0.032 (0.109) 0.198* (0.104) 0.549*** (0.141) 0.231** (0.111) 0.271** (0.128) 0.786*** (0.143) 0.302*** (0.087) 0.105 (0.105) 0.045 (0.104) 0.010 (0.094) 0.167 (0.107) 0.885*** (0.228)
4.451*** (0.197) 0.017 (0.021) 0.011 (0.008) 0.041 (0.039) 0.146** (0.062) 0.008 (0.032) 0.069** (0.028) 0.041 (0.049) 0.023 (0.044) 0.032 (0.041) 0.042 (0.040) 0.069* (0.040) 0.046 (0.054) 0.010 (0.041) 0.024 (0.050) 0.022 (0.057) 0.119*** (0.037)
3.710*** (0.171) 0.009 (0.018) 0.002 (0.006) 0.012 (0.032) 0.031 (0.054) 0.084*** (0.026) 0.058*** (0.022) 0.025 (0.038) 0.063* (0.033) 0.048 (0.033) 0.048 (0.031) 0.077** (0.031) 0.106** (0.045) 0.054* (0.033) 0.085** (0.041) 0.038 (0.050) 0.032 (0.027)
3.795*** (0.233) 0.005 (0.026) 0.009 (0.009) 0.015 (0.046) 0.159** (0.076) 0.032 (0.037) 0.084*** (0.030) 0.022 (0.052) 0.032 (0.048) 0.037 (0.045) 0.022 (0.044) 0.015 (0.045) 0.034 (0.062) 0.006 (0.045) 0.053 (0.053) 0.032 (0.071) 0.076* (0.042)
3.313*** (0.203) 0.013 (0.023) 0.011 (0.009) 0.014 (0.042) 0.099 (0.062) 0.053 (0.035) 0.084*** (0.030) 0.021 (0.053) 0.004 (0.046) 0.077* (0.043) 0.031 (0.042) 0.069 (0.043) 0.007 (0.060) 0.025 (0.044) 0.236*** (0.054) 0.043 (0.064) 0.097** (0.038)
0.075*** (0.019) 0.013** (0.005) 0.517*** (0.092) 0.051 (0.159) 0.478*** (0.023)
0.045*** (0.014) 0.004 (0.004) 0.037 (0.094) 0.120 (0.141) 0.368*** (0.012)
0.016 (0.020) 0.011* (0.006) 0.277** (0.109) 0.030 (0.203) 0.509*** (0.014)
0.048** (0.020) 0.016*** (0.005) 0.400*** (0.107) 0.045 (0.178) 0.505*** (0.019)
0.414*** (0.055) 0.545*** (0.037) 0.694*** (0.027)
0.539*** (0.036) 0.609*** (0.038)
0.608*** (0.025)
Age Head’s education WIC SNAP Standard dev. (ri) Error correlation (qij) SNAP Iron Potassium Vitamin E Fiber Log likelihood a * ** ***
0.317*** (0.048) 0.523*** (0.097) 0.140 (0.153) 0.281** (0.121) 0.394*** (0.116) 28557.665
Asymptotic standard errors in parentheses. p < 0.10. p < 0.05. p < 0.01.
0.161 (0.197) 0.203 (0.223) 0.009 (0.238) 0.081 (0.210)
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S.T. Yen / Food Policy 35 (2010) 576–583 Table 2 Effects of WIC and SNAP participation on nutrient intakes (%DRI).a Nutrient
Program participation status WIC given SNAP = no
WIC given SNAP = yes
WIC & SNAP
SNAP given WIC = no
SNAP given WIC = yes
Iron
16.33*** (4.58) 5.48*** (1.33) 2.00 (2.86) 3.43** (1.69)
19.78*** (4.21) 5.54*** (1.19) 3.31 (2.75) 4.14*** (1.47)
18.56*** (5.89) 5.35*** (1.62) 5.36 (3.66) 1.43 (2.10)
1.22 (4.73) 0.20 (1.35) 2.05 (3.06) 2.71 (1.71)
2.23 (5.49) 0.14 (1.63) 3.36 (3.49) 2.00 (2.00)
Potassium Vitamin E Fiber a ** ***
Asymptotic standard errors in parentheses. p < 0.05. p < 0.01.
Table 3 Marginal effects of continuous explanatory variables on nutrient intakes (% DRI).a WIC non-participants, SNAP non-participants Iron Continuous explanatory variables Income, per capita 0.07 (1.78) Household size 1.68 (1.40) Age 11.23*** (2.88) Head’s education 1.93** (0.75) Binary explanatory variables Home owner 5.33 (4.66) Healthy 17.13** (8.22) Meals/snacks 4.06 (4.57) Male 12.15*** (3.73) Northeast 10.59 (6.84) Midwest 6.12 (6.28) South 5.14 (5.42) City 9.26* (5.55) Suburban 9.30* (5.36) Black 3.64 (6.99) White 1.05 (5.25) Hispanic 3.70 (6.54) One head 7.20 (7.76) Employed 14.41*** (4.93) Age 3 6.35*** (2.26) Age 4 2.64 (1.76)
Asymptotic standard errors in parentheses. p < 0.10. p < 0.05. p < 0.01.
Potassium
Vitamin E
WIC participants, SNAP participants Fiber
Iron
Potassium
Vitamin E
Fiber
0.41 (0.65) 0.03 (0.73) 2.37*** (0.75) 0.22 (0.23)
0.47 (1.18) 0.75 (1.48) 1.32 (1.68) 0.92* (0.50)
0.14 (0.71) 0.54 (0.64) 2.46** (1.00) 0.84*** (0.31)
0.01 (2.14) 1.84 (1.61) 12.72*** (3.19) 2.19** (0.97)
0.27 (0.59) 0.05 (0.53) 2.59*** (0.83) 0.24 (0.26)
0.33 (1.23) 0.79 (1.29) 1.40 (1.79) 0.98* (0.59)
0.13 (0.83) 0.55 (0.54) 2.54** (1.04) 0.86*** (0.31)
1.33 (1.34) 1.91 (2.64) 4.12*** (1.33) 2.71** (1.10) 0.43 (1.91) 3.37* (1.78) 2.05 (1.58) 2.23 (1.52) 4.55*** (1.54) 6.58*** (1.92) 3.28** (1.64) 5.24** (2.15) 0.05 (2.18) 2.59** (1.28) 0.58 (0.60) 0.06 (0.43) 0.06 (0.28) 0.39 (0.57) 1.89 (2.39)
0.04 (3.15) 11.46* (5.86) 3.46 (3.05) 7.44*** (2.46) 1.26 (4.04) 3.83 (4.11) 2.68 (3.58) 2.90 (3.63) 1.47 (3.54) 5.66 (4.47) 2.07 (3.45) 5.39 (4.29) 3.80 (4.69) 6.07* (3.16) 1.95* (1.16) 1.09 (0.80) 0.55 (0.54) 0.31 (1.27) 0.90 (5.17)
1.18 (1.74) 3.88 (2.76) 3.44** (1.73) 4.69*** (1.42) 0.15 (2.59) 0.97 (2.34) 3.79* (2.02) 2.37 (2.01) 3.39* (1.99) 1.67 (2.45) 0.37 (1.90) 13.28*** (2.94) 2.62 (2.93) 4.21** (1.74) 1.69** (0.72) 0.77 (0.54) 0.41 (0.39) 0.02 (0.74) 0.47 (2.64)
4.65 (5.24) 18.35* (9.55) 5.47 (5.25) 14.15*** (4.20) 12.66* (7.63) 8.30 (7.10) 5.20 (6.10) 11.77* (6.29) 10.85* (6.05) 0.54 (7.64) 2.56 (5.91) 5.09 (7.39) 5.83 (7.95) 15.67*** (5.59) 8.88*** (3.23) 4.94* (2.93) 2.37 (2.20) 0.13 (3.00) 3.59 (8.75)
1.26 (1.47) 2.11 (2.88) 4.50*** (1.45) 3.00** (1.19) 0.68 (2.05) 3.58* (1.93) 2.34 (1.73) 2.38 (1.65) 4.85*** (1.67) 6.72*** (2.06) 3.47* (1.78) 5.57** (2.35) 0.88 (2.25) 2.72** (1.38) 0.65 (0.66) 0.19 (0.51) 0.11 (0.29) 0.32 (0.49) 2.18 (2.77)
0.29 (3.33) 11.87* (6.26) 3.97 (3.26) 8.06*** (2.55) 0.89 (4.28) 4.41 (4.37) 2.76 (3.76) 3.45 (3.84) 1.56 (3.74) 6.64 (4.55) 2.52 (3.67) 5.87 (4.55) 3.72 (4.65) 6.13* (3.34) 2.54* (1.48) 1.69 (1.17) 0.77 (0.75) 0.36 (1.26) 1.33 (5.97)
1.52 (1.80) 3.75 (2.85) 3.77** (1.79) 4.95*** (1.44) 0.07 (2.64) 1.32 (2.41) 3.78* (2.07) 2.75 (2.08) 3.55* (2.06) 2.46 (2.48) 0.07 (1.97) 13.93*** (3.04) 2.38 (2.72) 4.17** (1.79) 2.13** (0.94) 1.26 (0.81) 0.59 (0.55) 0.07 (0.82) 0.29 (3.07)
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programs and also on participation in both programs.6 The marginal effects of most variables on nutrient intakes are fairly consistent and the magnitudes very close, between the two program participation scenarios. Being healthy has the most notable and positive effects on the intake of two of the nutrients: iron (17.1%DRI) and vitamin E (11.5%DRI). Age is also a key factor. Conditional on non-participant in both programs, a 1 year increase in age decreases iron intake by 11.23%DRI and fiber intake by 2.46%DRI, but increases potassium intake by 2.37%DRI. The effects of age on these nutrients are similar conditional on participation in both programs. Income and household size do not affect the intake of any nutrient. Being healthy increases iron and vitamin E. Not surprisingly, availability of meals/snacks at day-care facilities increases potassium and fiber. Boys have higher intakes of all nutrients than girls. There is also evidence of regional differences, with children residing in the Northeast having higher intake of iron conditional on WIC and SNAP participation, those in the Midwest having higher intake of potassium, and those in the South having lower intakes of fiber than children in the West, conditional on both participation and non-participation in WIC and SNAP. Urbanization is also a factor. Compared with children in rural areas, children residing in the city have higher intakes of iron, and those in a suburban area having higher iron, potassium, and fiber. Race and ethnicity also make a difference, with blacks and whites both having lower intakes of potassium, and the Hispanics having higher intakes of potassium and fiber than children of other races/ethnicity. Education of the household head does not affect any of the nutrients. Finally, on age, children 3 years of age have higher intakes of iron, vitamin E, and fiber, while children 4 years of age have lower iron, than younger children (age 2).
Concluding remarks WIC and SNAP are two important food and nutrition assistance programs administered by the USDA to improve the nutritional well being of the low-income individuals, and there is continued interest in investigating the roles of these programs in achieving their goals. This study investigates the determining factors of participation in SNAP and WIC, and the effects of these programs on nutrient intakes of young children. Since participation in programs and intakes of nutrients are likely to be joint decisions and consumers typically make food and nutrition choices from a bundle of commodities (and most foods typically contain multiple nutrients), there are behavioral reasons to model these decisions in a system. On statistical grounds, accommodating endogeneity of program participation also ameliorates statistical (simultaneousequation) bias and estimating the nutrient equations in a system improves statistical efficiency. This study presents the first attempt to estimate a system of nutrient equations with dual endogenous programs. Although the CSFII data are somewhat dated compared to the more recent National Health and Nutrition Examination Survey (NHANES) data, they remain one of the richer data sources which contain both program participation and nutrient intake information (the NHANES contains limited socio-demographic characteristics which are important for estimation of the statistical model considered in this study). Our empirical evidence supports the system approach to estimation of the nutrient equations. WIC participation is found to increase the intakes of iron, potassium, and fiber, whereas the effect of SNAP is nearly non-existent, conditional or unconditional on WIC participation. Further studies might therefore investigate the need for WIC participants to be also on SNAP and vice versa—a query which might call for the use of experimental data. 6 Marginal effects conditional on other participation scenarios are similar and are available upon request.
This study focuses on the effects of WIC and SNAP on the levels of nutrient intakes. The nature of nutritional well being has changed and become more complex over time. An increasingly important question to address may be the extent to which WIC and SNAP should focus on improving the quality of children’s diet rather than just the quantities (levels) of nutrients. Other food and nutritional issues have also emerged among young children and the general population alike. Obesity, for instance, has become a serious health concern for children and adolescents (US CDC, 2010). This changing health and nutrition landscape should prompt the Federal governments to reexamine existing food assistance programs. Further studies might therefore investigate the effects of WIC, SNAP and other food assistance programs on outcomes besides nutrient intakes, such as diet quality, overweight, and obesity. The modeling approach developed in this study emphasizes the often-ignored sample selectivity in multiple program participation in a multi-outcome framework and should be of use in the investigation of other programs and diet and health outcomes, such as pregnancy and birth outcomes (Bitler and Currie, 2005) and food insecurity (Bitler et al., 2005; Yen et al., 2008). Finally, the econometric model developed in this study relies on the multivariate normality assumption. Further studies might consider a semiparametric or nonparametric approach to addressing multiple program effects without the normality assumption. Acknowledgements Research for this paper was supported by US Department of Agriculture’s Cooperative Agreement No. 58-4000-7-0029, and by USDA’s Research Innovation and Development Grants in Economics (RIDGE) Program through a Grant from the Nutrition Department, University of California, Davis. The views in this paper are those of the author and do not necessarily reflect the views or policies of the USDA. A very early version of this paper was presented at the North American Summer Meeting of the Econometric Society, June 2002. The research method, specifically evaluation of program effects, draws crucially on result of my collaborative effort with Jan Rosinski. Margaret Andrews, David Eastwood, Joanne Guthrie, Lucia Kaiser, Panagiotis Kasteridis, Biing-Hwan Lin, David Ribar, Douglass Shaw, David Smallwood, Parke Wilde, and three anonymous reviewers provided helpful comments and suggestions. The usual disclaimer applies.
Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foodpol.2010.05.010. References Bitler, M.P., Currie, J., 2005. Does WIC work? The effects of WIC on pregnancy and birth outcomes. Journal of Policy Analysis and Management 24 (1), 73–91. Bitler, M.P., Currie, J., Scholz, J.K., 2003. WIC eligibility and participation. Journal of Human Resources 38 (Suppl.), 1139–1179. Bitler, M., Gundersen, C., Marquis, G.S., 2005. Are WIC nonrecipients at less nutritional risk than recipients? An application of the food security measure. Review of Agricultural Economics 27 (3), 433–438. Butler, J.S., Raymond, J.E., 1996. The effect of the Food Stamp Program on nutrient intake. Economic Inquiry 34 (4), 781–798. Butler, J.S., Ohls, J.C., Posner, B., 1985. The effect of the Food Stamp Program on the nutrient intake of the eligible elderly. Journal of Human Resources 20 (3), 405– 420. Catsiapis, G., Robinson, C., 1982. Sample selection bias with multiple selection rules: an application to student aid grants. Journal of Econometrics 18 (3), 351–368. Chen, Z., Yen, S.T., Eastwood, D.B., 2005. Effects of food stamp participation on body weight and obesity. American Journal of Agricultural Economics (Proceedings) 87 (5), 1167–1173.
S.T. Yen / Food Policy 35 (2010) 576–583 Currie, J., 2000. US food and nutrition programs. In: Moffitt, R.A. (Ed.), Means-Tested Transfer Programs in the United States. The University of Chicago Press, Chicago, IL, pp. 199–289 (Chapter 4). Currie, J., Cole, N., 1993. Welfare and child health: the link between AFDC participation and birth weight. American Economic Review 83 (4), 971–985. Devaney, B., Fraker, T., 1989. The effect of food stamps on food expenditures: an assessment of findings from the Nationwide Food Consumption Survey. American Journal of Agricultural Economics 71 (1), 99–104. Devaney, B., Moffitt, R., 1991. Dietary effects of the Food Stamp Program. American Journal of Agricultural Economics 73 (1), 202–211. Devaney, B., Kim, M., Carriquiry, A., Camaño-Garcia, G., 2005. Assessing the Nutrient Intakes of Vulnerable Subgroups. Contractor and Cooperator Report No. 11. US Department of Agriculture, Washington, DC.
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Meyerhoefer, C., Pylypchuk, Y., 2008. Does participation in the Food Stamp Program affect the prevalence of obesity and health care spending? American Journal of Agricultural Economics 90 (2), 287–305. Moffitt, R., 1983. An economic model of welfare stigma. American Economic Review 73 (5), 1023–1035. Moffitt, R.A. (Ed.), 2000. Means-Tested Transfer Programs in the United States. The University of Chicago Press, Chicago, IL. Oliveira, V., Gundersen, C., 2000. WIC and the Nutrient Intake of Children. Food Assistance and Nutrition Research Report No.5. US Department of Agriculture, Washington, DC. Rose, D., Habicht, J.P., Devaney, B., 1998. Household participation in the Food Stamp and WIC programs increases the nutrition intakes of preschool children. Journal of Nutrition 128 (3), 548–555. Tunali, I., 1986. A general structure for models of double-selection and an application to a joint migration-earnings process with remigration. Research in Labor Economics 8 (B), 235–284. US Centers for Disease Control and Prevention (US CDC), 2010. Childhood Overweight and Obesity. (accessed 12.05.10). USDA-ARS (US Department of Agriculture, Agricultural Research Service), 2000. Continuing Survey of Food Intakes by Individuals 1994–96, 1998. CD-ROM, Washington, DC. USDA-ARS, 2006. USDA National Nutrient Database for Standard Reference, Release 19. (accessed 03.07). USDA-FNS (US Department of Agriculture, Food and Nutrition Service), 2010. Program Data: Supplemental Nutrition Assistance Program (Last modified: 05/ 03/2010).
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Food Policy journal homepage: www.elsevier.com/locate/foodpol
The effects of SNAP and WIC programs on nutrient intakes of children Steven T. Yen * Department of Agricultural Economics, The University of Tennessee, 302 Morgan Hall, 2621 Morgan Circle, Knoxville, TN 37996-4518, United States
a r t i c l e
i n f o
Article history: Received 6 November 2008 Received in revised form 12 May 2010 Accepted 19 May 2010
Keywords: SNAP WIC Dual treatment effect model
a b s t r a c t Nutritional effects of participation by young children in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) in the United States are investigated. A system of nutrient equations with dual endogenous programs is estimated by the maximum-likelihood procedure. WIC is found to increase the intakes of three of the four important nutrients for WIC children, including iron, potassium, and fiber. SNAP only has a small and negative effect on fiber intake. The additional benefit of SNAP participation is non-existent given participation in WIC. Ó 2010 Elsevier Ltd. All rights reserved.
Introduction The Supplemental Nutrition Assistance Program (SNAP, formerly Food Stamp Program) and the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) are among the largest and most widely available Federal food assistance programs in the United States (US), second only to the National School Lunch Program (NSLP). Administered by the Food and Nutrition Service (FNS) of the US Department of Agriculture (USDA), the SNAP is the nation’s key nutrition safety net which provides inkind benefit for low-income households to buy food. Program cost for the SNAP was $53.638 billion for fiscal year 2009, putting food on the table for 15.23 million households and 33.72 million individuals each month (USDA-FNS, 2010). WIC provides non-monetary benefits in the forms of nutritious foods, nutrition education, and health referrals to participating low-income individuals. Participants include pregnant, postpartum, and breastfeeding women, and infants and children up to age 5 who are at nutritional risk. Also administered by the USDA-FNS, WIC is a Federal grant program (not an entitlement program as SNAP is) that provides funds to WIC state agencies to pay for WIC foods, nutrition education, and administrative costs. To be income eligible, an applicant’s income must fall at or below 185% of the US Poverty Income Guidelines (e.g., $40,793 for a family of four in 2010). Participation by the person or a family member(s) in certain other benefit programs, such as the SNAP, Medicaid, or Temporary Assistance for Needy Families (TANF), automatically qualifies the person for the WIC. During 2009, the WIC program cost was $6.477 billion, with a total par-
* Tel.: +1 865 974 7474; fax: +1 865 974 4829. E-mail address: [email protected] 0306-9192/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2010.05.010
ticipation of 9.12 million persons (USDA-FNS, 2010). In most WIC state agencies, a participant receives checks or vouchers each month to purchase specific foods designed to supplement their diets. The foods provided are high in one or more of the following nutrients: protein, calcium, iron, and vitamins A and C. These nutrients were identified in the original WIC statute (1972) but are no longer named in the enabling WIC legislation (in effect since 1978).1 Empirical literature Fox et al. (2004) review the literature on the effects of USDA’s food and nutrition assistance programs (including WIC and SNAP) on a variety of outcome variables, ranging from household food expenditures, nutrient availability, food insecurity, and individual dietary intake to health outcomes such as overall health, birth outcomes and obesity. Currie (2000) reviews earlier studies on US food and nutrition programs, and Moffitt (2000) contains the literature on other welfare programs. Butler et al. (1985), using data from the Food Stamp Cash Out Project, find the SNAP effects on nutrient intakes negligible, and that controlling for endogeneity of participation with a selectionbias technique does not affect the results. Devaney and Moffitt (1991) use data from the 1979–1980 Survey of Food Consumption in Low-Income Households and found that the dietary effects of
1 Recent findings however suggest that young children do not face nutritional risks of inadequacy of these nutrients (Devaney et al., 2005; IOM, 2006a). Devaney et al. (2005) also find that the degree of inadequate usual intakes of micronutrients is low among 4–8 year old SNAP participants, and for WIC eligible participants/nonparticipants age 1–3. The major nutritional concerns are excess intake of fat and overweight.
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the SNAP benefit on nutrient availability are considerably larger than those of cash income, and that, like Butler et al. (1985), endogenizing SNAP participation does not cause notable differences. Using nutrient adequacy ratios for a sample of preschool children, Rose et al. (1998) find positive effects of both WIC and SNAP on nutrient intakes. Treating these benefits as exogenous, regression results suggest that the SNAP benefit increases iron and zinc intakes by 12.3% and 9.2%, respectively, of the preschooler’s RDAs; WIC benefit has greater effects on iron and zinc intakes than cash income, but neither program affects the intakes of fat, saturated fat or cholesterol. Oliveira and Gundersen (2000), using a sample of income-eligible children, find significant effects of WIC on the intakes of iron, vitamin C, vitamin A, vitamin B6, and folate; contrary to findings by Rose et al. (1998), however, WIC does not affect zinc intake. Further, by limiting analysis to children living in a household with an infant or woman on WIC to control for self-selection bias, they find that participation in WIC has a positive effect on iron, folate and vitamin B6, but does not affect the other nutrients reported (vitamins A and C). Butler and Raymond (1996), renewing attention to endogenous program participation, present the only exception to the existing literature by considering a system of nutrient equations with a single endogenous SNAP variable. Controlling for participation in the SNAP with a switching regression model, they find that food stamp income has small effects on nutrient intakes among the elderly. The above nutrient intake literature suggests that estimates of program effects differ depending on methodology, particularly in the treatment of SNAP and WIC participation. Comparison to results from previous studies is therefore difficult due to differences in methodology and data (previous studies differ among themselves as well). While there have been attempts to correct for selectivity bias caused by SNAP and/or WIC participation, there is a greater tendency among previous studies to treat these variables as exogenous. Yet, findings by Butler and Raymond (1996) and Oliveira and Gundersen (2000) that controlling for program participation can produce notably different (opposite) program effects are particularly interesting. There has not been an attempt to simultaneously endogenize both SNAP and WIC participation and to examine the effects of program interactions. Further, except Butler and Raymond (1996), Oliveira and Gundersen (2000), and Rose et al. (1998), most previous studies have examined single outcomes. More recent studies have investigated the role of SNAP in household food insecurity (Yen et al., 2008) obesity and health care spending (Chen et al., 2005; Meyerhoefer and Pylypchuk, 2008), and the role of WIC in nutritional risk (Bitler et al., 2005), pregnancy, and birth outcomes (Bitler and Currie, 2005). In many studies with multiple outcomes such as nutrients or food expenditures, the outcome equations are often analyzed on an equation-by-equation basis (e.g., Butler et al., 1985; Devaney and Fraker, 1989) or with exogenous program participation (e.g., Rose et al., 1998). These single-equation or exogenous-program approaches are unappealing as consumers typically make food choices from a bundle of commodities and each food item typically contains multiple nutrients. Further, participation in SNAP and WIC is likely the results of individual decisions, made simultaneously with the food/nutrient intake decisions. On statistical grounds, such endogeneity is caused by correlations among unobservables which may affect both program participation and nutrient intake. When these cross-equation correlations are ignored in statistical estimation, statistical biases and inefficiency can result from a failure to accommodate simultaneity and non-random selection into programs. In addition, while participation in multiple programs is the rule, rather than exception, most studies address the effects of single programs, one at a time. Studies that address participation in multiple programs are rare (exceptions including Keane and Moffitt (1998)), and studies with multiple
outcomes and multiple programs are, to our knowledge, nonexistent. This paper addresses participation and effectiveness of SNAP and WIC in a multi-equation framework for nutrient intakes with endogenous SNAP and WIC participation. Empirical analysis is conducted for young children in the US. The nutrients selected for the current analysis are iron, potassium, vitamin E, and fiber. These nutrients were identified by a recent Institute of Medicine (IOM) panel as to need improvement for WIC children (Devaney et al., 2005; IOM, 2006a, pp. 46–86).
Empirical specification and econometric model The nutrient intake equations are motivated by a theoretical framework in which consumer preference is defined over utilitygenerating attributes (nutrients) which are produced with market goods (food items). Maximization of utility subject to the nutrientproducing technology and fixed budget yields nutrient demand equations (Lancaster, 1971). The econometric model consists of two equations for binary program participation outcomes (d1i, d2i) and m 2 equations for continuous nutrient intake (y3i, . . . , ymi) for individual i. Participation in each program is governed by a probit mechanism such that
dki ¼ 1 if z0i ak þ uki > 0 ¼ 0 if z0i ak þ uki 6 0;
ð1Þ
k ¼ 1; 2
where zi is a vector of exogenous variables and, for program k, ak is a conformable parameter vector and uki is a random error. Both program variables appear in each nutrient intake equation as potentially endogenous regressors (treatments):
log yki ¼ x0i bk þ ck1 d1i þ ck2 d2i þ uki ;
k ¼ 3; :::; m
ð2Þ
where xi is a vector of exogenous variables and, for nutrient k, bk is a parameter vector, ck1 and ck2 are scalar parameters, and uki are random error terms which capture the effects of unobserved factors on the program participation and nutrient intake decisions. Assume the error terms ui = [u1i, . . . , umi]0 in (1) and (2) are distributed as m-dimensioned normal with zero means and covariance matrix R [qh‘rhr‘], where qh‘ are correlations and rh are standard deviations such that r1 = r2 = 1 (because parameters a1 and a2 are identified only up to a scale due to the binary outcomes of d1i and d2i). The error correlation q12 accommodates common unobserved factors governing decisions in both SNAP and WIC, correlations qk1 and qk2 (for k P 3) accommodate endogeneity of SNAP and WIC in the nutrient equations, and the other correlations (qh‘ for h, ‘ P 3, h – ‘) reflect associations among unobserved factors in the nutrient equations. Such correlations and endogeneity are testable hypotheses. To construct the likelihood function, partition the error vector ð1Þ0 ð2Þ0 ð1Þ ð2Þ to ui ¼ ½ui ; ui 0 such that ui ¼ ½u1 ; u2 0 and ui ¼ ½u3 ; . . . ; um 0 with corresponding partitioning of R at the second row and column
R
R11 R12 R21 R22
ð3Þ
such that R11 is 2 2, R12 ¼ R021 is 2 (m 2), and R22 is (m 2) (m 2). Denote uki ¼ log yki ðx0i bk þ ck1 d1i þ ck2 d2i Þ, k = 3, . . . , m. Then, the sample likelihood contribution for a participant in both programs (d1i = 1, d2i = 1) is m Y
Ld1i ¼1;d2i ¼1 ¼
!
y1 ki
k¼3 Z 1 z0i a1
f ðu3i ; . . . ; umi Þ
Z
1
z0i a2
gðu1i ; u2i ju3i ; . . . ; umi Þdu1i du2i
ð4Þ
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S.T. Yen / Food Policy 35 (2010) 576–583
Qm
where the product term k¼3 y1 ki is the Jacobian of transformation from (log y3i, . . . , log ymi) to (y3i, . . . , ymi) and, using properties of the multivariate normal distribution (Kotz et al., 2000), f(u3i, . . . , umi) is the marginal probability density function of ð2Þ ui Nð0; R22 Þ and g(u1i, u2i|u3i, . . . , umi) is the conditional density ð1Þ ð2Þ of ui jui Nðl1j2 ; X1j2 Þ, with conditional mean and variance ð2Þ l1j2 ¼ R12 R1 ¼ ½n1i ; n2i 0 22 ui
"
ð5Þ #
x21 x12 : X1j2 ¼ R11 R12 R R ¼ x21 x22 1 22
0 21
ð6Þ
The likelihood contributions for other sample regimes are similar to (4), with changes in the integration limits such that a finite lower (upper) limit z0i ak would correspond to participation (non-participation) in program k. Define dichotomous indicators j1i = 2d1i 1, j2i = 2d2i 1, and let g1i ¼ j1i ðz0i a1 þ n1i Þ=x1 , g2i ¼ j2i ðz0i a2 þ n2i Þ=x2 , and si = j1ij2ix12/(x1x2) using (5) and (6). Then, the likelihood function for an independent sample of n observations is
L¼
n m Y Y i¼1
! y1 ki
f ðu3i ; . . . ; umi ÞU2 ðg1i ; g2i ; si Þ
ð7Þ
k¼3
where U2(g1i, g2i; si) is the bivariate standard normal cumulative distribution function with standard normal variates (g1i, g2i) and correlation si. Maximum-likelihood (ML) estimation is carried out by maximizing the likelihood function (7). The model is a generalization of the double- and multiple-selection models (Catsiapis and Robinson, 1982; Tunali, 1986) and multiple-treatment effect model (Keane and Moffitt, 1998) in that there are multiple outcome equations. Evaluation of programs is based on the conditional mean of each dependent variable yki. The conditional means are
Eðyki jd1i ; d2i Þ ¼ expðx0i bk þ ck1 d1i þ ck2 d2i Þexpðr2 =2Þ
U2 ðj1i z0i a1 þ j1i rqk1 ; j2i z0i a2 þ j2i rqk2 ; j1i j2i q21 Þ U2 ðj1i z0i a1 ; j2i z0i a2 ; j1i j2i q21 Þ ð8Þ
for k = 3, . . . , m. Detailed derivations are available in Yen and Rosinski (2009). Using (8), the average treatment effect (ATE), in the spirit of Heckman and Vytlacil (2005) with one treatment, of participation in both programs can be calculated as
ATEWIC;SNAP ¼
n 1X ½b Eðyki jd1i ¼ 1; d2i ¼ 1Þ b Eðyki jd1i ¼ 0; d2i ¼ 0Þ n i¼1
ð9Þ b are evaluated based on (8) at the where the conditional means EðÞ ML estimates of parameters. Likewise, the ATEs of WIC conditional on non-participation and participation in SNAP are, respectively,
ATEWICjSNAP¼0 ¼
n 1X ½b Eðyki jd1i ¼ 1; d2i ¼ 0Þ b Eðyki jd1i ¼ 0; d2i ¼ 0Þ n i¼1
ð10Þ ATEWICjSNAP¼1 ¼
n 1X ½b Eðyki jd1i ¼ 1; d2i ¼ 1Þ b Eðyki jd1i ¼ 0; d2i ¼ 1Þ n i¼1
ð11Þ The ATEs of SNAP conditional on WIC non-participation and participation are similar. To further explore the effects of explanatory variables on nutrient intakes, marginal effects are calculated by differentiating (differencing, in the case of a discrete variable) the conditional means in (8). These treatment and marginal effects are not trivial and depend on the whole set of parameters.
Data sources and sample Data are drawn from the 1994–1996 Continuing Survey of Food Intakes by Individuals (CSFII) and its 1998 Supplemental Children’s Survey (USDA-ARS, 2000). The CSFII 1994–1996 includes intake data for 4253 children up to age 9. The CSFII 1998, conducted in response to the Food Quality Protection Act of 1996 and designed to be combined with CSFII 1994–1996, adds intake data for 5559 children up to age 9. For each year, dietary data for individuals were collected through in-person interviews using 24-h recalls. These dietary data were collected for two nonconsecutive days for some of the children and for a single day for other children (see selection of sample below). A food instruction booklet was used to probe for a complete description of every food item and the amount eaten. The nutritive values of each food eaten, including the nutrients considered in this study, were then calculated using the weight of the food and data from the Survey Nutrient Database (USDAARS, 2006). Further details on the sample design, data collection and coding, and nutrient calculation are described in the survey documentation (USDA-ARS, 2000). To address joint participation decisions, the sample is limited to children eligible for both WIC and SNAP. We consider households at or below 130% of the Federal poverty guidelines who were income eligible for the SNAP. Since WIC income requirement is more generous, at or below 185% of the Federal poverty guidelines, households who were income eligible for the SNAP would also be income eligible for WIC. In addition, children who participated in Medicaid or TANF, who were authorized to participate in the SNAP, who resided in a household receiving income from the Aid to Families with Dependent Children (AFDC) program, or who were individually determined to be at nutritional risk by a health professional are also eligible. Because the CSFII data do not allow determination of nutritional risk, (family) income eligibility is used as a proxy for WIC eligibility. A similar procedure is followed by Oliveira and Gundersen (2000) and Rose et al. (1998) in determining WIC eligibility.2 Because the CSFII does not contain information on the nutrient contribution of breast milk and exclusion of breast-fed children could cause sample selectivity bias, children under 2 years of ages are excluded.3 Because WIC ends at the child’s fifth birthday, children age P 5 are excluded. In addition, the CSFII includes nutrient intake information for individuals with first-day intake only, second-day intake only, and 2-day intakes. To address some of the data issues, the sample is limited to young children with complete 2-day intake data, for which 2-day average intake data are used in the analysis. The final sample includes 1446 children. The first endogenous variable, WIC participation, is coded from response (provided by a caregiver or parent) to the following question: ‘Are you receiving benefits under the Women, Infants and Children (WIC) Program at the present time?’ The other endogenous program variable, SNAP participation, is coded from response to the question: ‘Did any member of your household receive food stamps in any of the last 12 months?’ An implicit assumption in modeling such joint program participation is that receipt of food stamp benefits by (any member(s) in) the household during the
2 The WIC population evaluated can be very different from the full, targeted WIC population of children in households with income less than 185% poverty, and the results estimated on the full WIC population may be different. Bitler et al. (2003), for instance, find that up to 23% of the WIC population have income above 185% income. This would suggest that a significant share of the WIC population is excluded by limiting the population to those with income less than 130% of the federal poverty income level. 3 Breastfeeding status was reported only by children age 63. The sample may include a few children who were still breast feeding, but most if not all of the nutrients should be coming from food and beverages by that age. Nutritional guidelines are also better established for older children.
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past 12 months, regardless of whether the household currently receives the benefits, can affect nutrient intake by the child. We include four nutrients identified by the IOM (2006a) as critical (needing improvement): iron, potassium, vitamin E, and fiber. Some of these nutrients were also investigated by Oliveira and Gundersen (2000) and Rose et al. (1998), Devaney et al. (2005), and in a report by the IOM (2006a). In IOM (2006a) and Devaney et al. (2005), intakes of nutrients are calculated as the percentages of the most recent Dietary Reference Intakes (DRI) developed by the IOM (2000, 2003), because two age groups in the DRIs were combined. This is the case as well in the current study as children in the 2–3 and 4 age groups are combined so the same procedure is followed. Intakes of iron and vitamin E are calculated using the recommended daily allowance (RDA) values (IOM, 2006b, p. 328, p. 234). Intakes of potassium and fiber, for which information is not available to calculate Estimated Average Requirements (EARs) used in calculation of the RDAs, are calculated as the percentages of Adequate Intakes (AIs) (IOM, 2006b, p. 370, p. 110). The explanatory variables include household characteristics such as per capita annual income, family size, and home ownership; household head’s characteristics such as education (
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tions but not the potassium equation. None of the corresponding correlations are significant between the nutrients and SNAP equation. Significance of the error correlations between WIC and nutrient equations suggests endogeneity of sample selection and program participation, implying that failure to accommodate such endogeneity can cause biased parameter estimates in the nutrient intake equations. Correlation between the error terms of the two programs is positive and significant at the 1% level, which justifies joint estimation of the two program participation equations.5 Significance of error correlations among the nutrient equations also justifies joint estimation of the nutrient equations to improve statistical efficiency. Among the determinants of program participation, household income, race (being black or being white), being employed, and age (=4) of children are all negative factors of WIC participation at the 10% level of significance or lower. For no obvious reason, children 3 years of age are more likely to participation in the WIC than younger children (age 2). The negative effect of age (=4) on WIC participation is in agreement with findings from other studies that participation in WIC falls as children get older (e.g., USDA-ARS, 2000). Household income, home ownership, residing in the South and in a suburban area, being Hispanic, and having an employed parent are all negatively associated with SNAP participation, whereas being black or being white, residing in the Northeast, and having only one adult in the household contribute to SNAP participation. Because directions and magnitudes of the program effects depend on coefficients of the program variables as well as the selectivity terms (see (8)), further comparisons of the effects of programs and explanatory variables are made by calculating ATEs and marginal effects. Program effects
Estimation results One practical empirical issue is the choice of regressors to explain program participation and nutrient intakes. Unlike a linear system or in instrumental variable estimation for which exclusion conditions are needed for parameter identification (e.g., Currie and Cole, 1993; Butler and Raymond, 1996), the nonlinear identification criteria are met due to the functional form and distributional assumptions for ML estimation of the current system. However, to avoid overburdening functional form and distributional assumptions for parameter identification in the absence of exclusion restrictions, categorical variables for child’s age and household head’s education dummies are used in the WIC and SNAP participation equations; whereas child’s age and household head’s education, both in years, are included only in the nutrient intake equations. Importantly, preliminary estimation without these exclusion restrictions did not produce discernable differences in parameter estimates and, more importantly, program effects from the reported estimates with exclusions, which lends evidence to robustness of the current results.4 Parameter estimates ML estimates are presented in Table 1. The error term of the WIC participation equation is significantly (at the 5% level of significance) correlated with those of the iron, vitamin E, and fiber equa4
Delineating the separate effects of variables on program participation and nutrient intake is always difficult. Some of the regressors may serve as proxies for unobservables. For example, education may reflect the effect of stigma, an important determinant of program participation (Moffitt, 1983) which is not available in the CSFII; it may also reflect the knowledge of nutrition. The ability to afford education may also be reflected in income.
The ATEs of programs on nutrient intakes are presented in Table 2. The effects of WIC are positive on three of the four nutrients, conditional on non-participation or participation in SNAP. Participation in WIC (only) increases the intakes of iron (by 16.3%DRI), potassium (5.5%DRI), and fiber (3.4%DRI), but not vitamin E. The effects of WIC are similar conditional on participation in SNAP: iron (19.8%DRI), potassium (5.5%DRI), and fiber (4.1%DRI). Taking participation in both programs into consideration, the combined effects are increases in the intakes of iron (by 18.6%DRI) and potassium (4.7%DRI), whereas the conflicting effects of WIC and SNAP cancel out for iron. Participation in SNAP alone decreases fiber intake slightly (by 2.71%DRI) which is marginally significant (pvalue = 0.11). In sum, the effects of SNAP are nearly non-existent, and participation in SNAP brings no additional benefit given participation in WIC. Our insignificant and small effects of SNAP are similar to findings for the elderly by Butler and Raymond (1996), who control for SNAP participation and argue that the positive program effects on nutrient intakes in earlier findings could be the result of selfselection into the SNAP by individuals who are more interested in maintaining good nutrition. Our insignificant effects of SNAP suggest that, contrary to the finding by Butler and Raymond (1996), selection is not an issue for this sample of children. Marginal effects of explanatory variables on nutrient intakes Table 3 presents the marginal effects of explanatory variable on nutrient intakes, conditional on non-participation in both 5 Imposing independence between error terms of SNAP and WIC produced only slightly different parameter estimates, effects of programs, and marginal effects of explanatory variables on nutrition intakes.
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Table 1 ML estimation of nutrient equations with endogenous WIC and SNAP participation.a Variable
Constant Income, per capita ($1000) Household size Home owner Healthy Meals/snacks Male Northeast Midwest South City Suburban Black White Hispanic One adult Employed Age 3 Age 4
Program participation
log(%DRI)
WIC
SNAP
Iron
Potassium
Vitamin E
Fiber
0.079 (0.274) 0.079*** (0.029) 0.001 (0.023) 0.108 (0.086) 0.196 (0.179) 0.138 (0.085) 0.076 (0.070) 0.169 (0.119) 0.154 (0.115) 0.020 ((0.100) 0.168* (0.101) 0.011 (0.098) 0.296** (0.120) 0.177* (0.099) 0.054 (0.115) 0.023 (0.126) 0.146* (0.083) 0.311*** (0.085) 0.175* (0.094) 0.084 (0.075) 0.007 (0.094) 0.052 (0.150)
0.963*** (0.313) 0.336*** (0.031) 0.023 (0.024) 0.354*** (0.092) 0.045 (0.203) 0.098 (0.092) 0.111 (0.078) 0.295** (0.145) 0.049 (0.123) 0.208* (0.110) 0.032 (0.109) 0.198* (0.104) 0.549*** (0.141) 0.231** (0.111) 0.271** (0.128) 0.786*** (0.143) 0.302*** (0.087) 0.105 (0.105) 0.045 (0.104) 0.010 (0.094) 0.167 (0.107) 0.885*** (0.228)
4.451*** (0.197) 0.017 (0.021) 0.011 (0.008) 0.041 (0.039) 0.146** (0.062) 0.008 (0.032) 0.069** (0.028) 0.041 (0.049) 0.023 (0.044) 0.032 (0.041) 0.042 (0.040) 0.069* (0.040) 0.046 (0.054) 0.010 (0.041) 0.024 (0.050) 0.022 (0.057) 0.119*** (0.037)
3.710*** (0.171) 0.009 (0.018) 0.002 (0.006) 0.012 (0.032) 0.031 (0.054) 0.084*** (0.026) 0.058*** (0.022) 0.025 (0.038) 0.063* (0.033) 0.048 (0.033) 0.048 (0.031) 0.077** (0.031) 0.106** (0.045) 0.054* (0.033) 0.085** (0.041) 0.038 (0.050) 0.032 (0.027)
3.795*** (0.233) 0.005 (0.026) 0.009 (0.009) 0.015 (0.046) 0.159** (0.076) 0.032 (0.037) 0.084*** (0.030) 0.022 (0.052) 0.032 (0.048) 0.037 (0.045) 0.022 (0.044) 0.015 (0.045) 0.034 (0.062) 0.006 (0.045) 0.053 (0.053) 0.032 (0.071) 0.076* (0.042)
3.313*** (0.203) 0.013 (0.023) 0.011 (0.009) 0.014 (0.042) 0.099 (0.062) 0.053 (0.035) 0.084*** (0.030) 0.021 (0.053) 0.004 (0.046) 0.077* (0.043) 0.031 (0.042) 0.069 (0.043) 0.007 (0.060) 0.025 (0.044) 0.236*** (0.054) 0.043 (0.064) 0.097** (0.038)
0.075*** (0.019) 0.013** (0.005) 0.517*** (0.092) 0.051 (0.159) 0.478*** (0.023)
0.045*** (0.014) 0.004 (0.004) 0.037 (0.094) 0.120 (0.141) 0.368*** (0.012)
0.016 (0.020) 0.011* (0.006) 0.277** (0.109) 0.030 (0.203) 0.509*** (0.014)
0.048** (0.020) 0.016*** (0.005) 0.400*** (0.107) 0.045 (0.178) 0.505*** (0.019)
0.414*** (0.055) 0.545*** (0.037) 0.694*** (0.027)
0.539*** (0.036) 0.609*** (0.038)
0.608*** (0.025)
Age Head’s education WIC SNAP Standard dev. (ri) Error correlation (qij) SNAP Iron Potassium Vitamin E Fiber Log likelihood a * ** ***
0.317*** (0.048) 0.523*** (0.097) 0.140 (0.153) 0.281** (0.121) 0.394*** (0.116) 28557.665
Asymptotic standard errors in parentheses. p < 0.10. p < 0.05. p < 0.01.
0.161 (0.197) 0.203 (0.223) 0.009 (0.238) 0.081 (0.210)
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S.T. Yen / Food Policy 35 (2010) 576–583 Table 2 Effects of WIC and SNAP participation on nutrient intakes (%DRI).a Nutrient
Program participation status WIC given SNAP = no
WIC given SNAP = yes
WIC & SNAP
SNAP given WIC = no
SNAP given WIC = yes
Iron
16.33*** (4.58) 5.48*** (1.33) 2.00 (2.86) 3.43** (1.69)
19.78*** (4.21) 5.54*** (1.19) 3.31 (2.75) 4.14*** (1.47)
18.56*** (5.89) 5.35*** (1.62) 5.36 (3.66) 1.43 (2.10)
1.22 (4.73) 0.20 (1.35) 2.05 (3.06) 2.71 (1.71)
2.23 (5.49) 0.14 (1.63) 3.36 (3.49) 2.00 (2.00)
Potassium Vitamin E Fiber a ** ***
Asymptotic standard errors in parentheses. p < 0.05. p < 0.01.
Table 3 Marginal effects of continuous explanatory variables on nutrient intakes (% DRI).a WIC non-participants, SNAP non-participants Iron Continuous explanatory variables Income, per capita 0.07 (1.78) Household size 1.68 (1.40) Age 11.23*** (2.88) Head’s education 1.93** (0.75) Binary explanatory variables Home owner 5.33 (4.66) Healthy 17.13** (8.22) Meals/snacks 4.06 (4.57) Male 12.15*** (3.73) Northeast 10.59 (6.84) Midwest 6.12 (6.28) South 5.14 (5.42) City 9.26* (5.55) Suburban 9.30* (5.36) Black 3.64 (6.99) White 1.05 (5.25) Hispanic 3.70 (6.54) One head 7.20 (7.76) Employed 14.41*** (4.93) Age 3 6.35*** (2.26) Age 4 2.64 (1.76)
Asymptotic standard errors in parentheses. p < 0.10. p < 0.05. p < 0.01.
Potassium
Vitamin E
WIC participants, SNAP participants Fiber
Iron
Potassium
Vitamin E
Fiber
0.41 (0.65) 0.03 (0.73) 2.37*** (0.75) 0.22 (0.23)
0.47 (1.18) 0.75 (1.48) 1.32 (1.68) 0.92* (0.50)
0.14 (0.71) 0.54 (0.64) 2.46** (1.00) 0.84*** (0.31)
0.01 (2.14) 1.84 (1.61) 12.72*** (3.19) 2.19** (0.97)
0.27 (0.59) 0.05 (0.53) 2.59*** (0.83) 0.24 (0.26)
0.33 (1.23) 0.79 (1.29) 1.40 (1.79) 0.98* (0.59)
0.13 (0.83) 0.55 (0.54) 2.54** (1.04) 0.86*** (0.31)
1.33 (1.34) 1.91 (2.64) 4.12*** (1.33) 2.71** (1.10) 0.43 (1.91) 3.37* (1.78) 2.05 (1.58) 2.23 (1.52) 4.55*** (1.54) 6.58*** (1.92) 3.28** (1.64) 5.24** (2.15) 0.05 (2.18) 2.59** (1.28) 0.58 (0.60) 0.06 (0.43) 0.06 (0.28) 0.39 (0.57) 1.89 (2.39)
0.04 (3.15) 11.46* (5.86) 3.46 (3.05) 7.44*** (2.46) 1.26 (4.04) 3.83 (4.11) 2.68 (3.58) 2.90 (3.63) 1.47 (3.54) 5.66 (4.47) 2.07 (3.45) 5.39 (4.29) 3.80 (4.69) 6.07* (3.16) 1.95* (1.16) 1.09 (0.80) 0.55 (0.54) 0.31 (1.27) 0.90 (5.17)
1.18 (1.74) 3.88 (2.76) 3.44** (1.73) 4.69*** (1.42) 0.15 (2.59) 0.97 (2.34) 3.79* (2.02) 2.37 (2.01) 3.39* (1.99) 1.67 (2.45) 0.37 (1.90) 13.28*** (2.94) 2.62 (2.93) 4.21** (1.74) 1.69** (0.72) 0.77 (0.54) 0.41 (0.39) 0.02 (0.74) 0.47 (2.64)
4.65 (5.24) 18.35* (9.55) 5.47 (5.25) 14.15*** (4.20) 12.66* (7.63) 8.30 (7.10) 5.20 (6.10) 11.77* (6.29) 10.85* (6.05) 0.54 (7.64) 2.56 (5.91) 5.09 (7.39) 5.83 (7.95) 15.67*** (5.59) 8.88*** (3.23) 4.94* (2.93) 2.37 (2.20) 0.13 (3.00) 3.59 (8.75)
1.26 (1.47) 2.11 (2.88) 4.50*** (1.45) 3.00** (1.19) 0.68 (2.05) 3.58* (1.93) 2.34 (1.73) 2.38 (1.65) 4.85*** (1.67) 6.72*** (2.06) 3.47* (1.78) 5.57** (2.35) 0.88 (2.25) 2.72** (1.38) 0.65 (0.66) 0.19 (0.51) 0.11 (0.29) 0.32 (0.49) 2.18 (2.77)
0.29 (3.33) 11.87* (6.26) 3.97 (3.26) 8.06*** (2.55) 0.89 (4.28) 4.41 (4.37) 2.76 (3.76) 3.45 (3.84) 1.56 (3.74) 6.64 (4.55) 2.52 (3.67) 5.87 (4.55) 3.72 (4.65) 6.13* (3.34) 2.54* (1.48) 1.69 (1.17) 0.77 (0.75) 0.36 (1.26) 1.33 (5.97)
1.52 (1.80) 3.75 (2.85) 3.77** (1.79) 4.95*** (1.44) 0.07 (2.64) 1.32 (2.41) 3.78* (2.07) 2.75 (2.08) 3.55* (2.06) 2.46 (2.48) 0.07 (1.97) 13.93*** (3.04) 2.38 (2.72) 4.17** (1.79) 2.13** (0.94) 1.26 (0.81) 0.59 (0.55) 0.07 (0.82) 0.29 (3.07)
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programs and also on participation in both programs.6 The marginal effects of most variables on nutrient intakes are fairly consistent and the magnitudes very close, between the two program participation scenarios. Being healthy has the most notable and positive effects on the intake of two of the nutrients: iron (17.1%DRI) and vitamin E (11.5%DRI). Age is also a key factor. Conditional on non-participant in both programs, a 1 year increase in age decreases iron intake by 11.23%DRI and fiber intake by 2.46%DRI, but increases potassium intake by 2.37%DRI. The effects of age on these nutrients are similar conditional on participation in both programs. Income and household size do not affect the intake of any nutrient. Being healthy increases iron and vitamin E. Not surprisingly, availability of meals/snacks at day-care facilities increases potassium and fiber. Boys have higher intakes of all nutrients than girls. There is also evidence of regional differences, with children residing in the Northeast having higher intake of iron conditional on WIC and SNAP participation, those in the Midwest having higher intake of potassium, and those in the South having lower intakes of fiber than children in the West, conditional on both participation and non-participation in WIC and SNAP. Urbanization is also a factor. Compared with children in rural areas, children residing in the city have higher intakes of iron, and those in a suburban area having higher iron, potassium, and fiber. Race and ethnicity also make a difference, with blacks and whites both having lower intakes of potassium, and the Hispanics having higher intakes of potassium and fiber than children of other races/ethnicity. Education of the household head does not affect any of the nutrients. Finally, on age, children 3 years of age have higher intakes of iron, vitamin E, and fiber, while children 4 years of age have lower iron, than younger children (age 2).
Concluding remarks WIC and SNAP are two important food and nutrition assistance programs administered by the USDA to improve the nutritional well being of the low-income individuals, and there is continued interest in investigating the roles of these programs in achieving their goals. This study investigates the determining factors of participation in SNAP and WIC, and the effects of these programs on nutrient intakes of young children. Since participation in programs and intakes of nutrients are likely to be joint decisions and consumers typically make food and nutrition choices from a bundle of commodities (and most foods typically contain multiple nutrients), there are behavioral reasons to model these decisions in a system. On statistical grounds, accommodating endogeneity of program participation also ameliorates statistical (simultaneousequation) bias and estimating the nutrient equations in a system improves statistical efficiency. This study presents the first attempt to estimate a system of nutrient equations with dual endogenous programs. Although the CSFII data are somewhat dated compared to the more recent National Health and Nutrition Examination Survey (NHANES) data, they remain one of the richer data sources which contain both program participation and nutrient intake information (the NHANES contains limited socio-demographic characteristics which are important for estimation of the statistical model considered in this study). Our empirical evidence supports the system approach to estimation of the nutrient equations. WIC participation is found to increase the intakes of iron, potassium, and fiber, whereas the effect of SNAP is nearly non-existent, conditional or unconditional on WIC participation. Further studies might therefore investigate the need for WIC participants to be also on SNAP and vice versa—a query which might call for the use of experimental data. 6 Marginal effects conditional on other participation scenarios are similar and are available upon request.
This study focuses on the effects of WIC and SNAP on the levels of nutrient intakes. The nature of nutritional well being has changed and become more complex over time. An increasingly important question to address may be the extent to which WIC and SNAP should focus on improving the quality of children’s diet rather than just the quantities (levels) of nutrients. Other food and nutritional issues have also emerged among young children and the general population alike. Obesity, for instance, has become a serious health concern for children and adolescents (US CDC, 2010). This changing health and nutrition landscape should prompt the Federal governments to reexamine existing food assistance programs. Further studies might therefore investigate the effects of WIC, SNAP and other food assistance programs on outcomes besides nutrient intakes, such as diet quality, overweight, and obesity. The modeling approach developed in this study emphasizes the often-ignored sample selectivity in multiple program participation in a multi-outcome framework and should be of use in the investigation of other programs and diet and health outcomes, such as pregnancy and birth outcomes (Bitler and Currie, 2005) and food insecurity (Bitler et al., 2005; Yen et al., 2008). Finally, the econometric model developed in this study relies on the multivariate normality assumption. Further studies might consider a semiparametric or nonparametric approach to addressing multiple program effects without the normality assumption. Acknowledgements Research for this paper was supported by US Department of Agriculture’s Cooperative Agreement No. 58-4000-7-0029, and by USDA’s Research Innovation and Development Grants in Economics (RIDGE) Program through a Grant from the Nutrition Department, University of California, Davis. The views in this paper are those of the author and do not necessarily reflect the views or policies of the USDA. A very early version of this paper was presented at the North American Summer Meeting of the Econometric Society, June 2002. The research method, specifically evaluation of program effects, draws crucially on result of my collaborative effort with Jan Rosinski. Margaret Andrews, David Eastwood, Joanne Guthrie, Lucia Kaiser, Panagiotis Kasteridis, Biing-Hwan Lin, David Ribar, Douglass Shaw, David Smallwood, Parke Wilde, and three anonymous reviewers provided helpful comments and suggestions. The usual disclaimer applies.
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