The effect of food stamp cashout on undernutrition

Economics Letters 67 (2000) 75–85 www.elsevier.com / locate / econbase

The effect of food stamp cashout on undernutrition John A. Bishop a , John P. Formby b , Lester A. Zeager c , * a

b

Department of Economics, East Carolina University, Greenville, NC 27858 -4353, USA Department of Economics, Finance, and Legal Studies, University of Alabama, Tuscaloosa, AL 35487 -0224, USA c Department of Economics, East Carolina University, Greenville, NC 27858 -4353, USA Received 25 January 1999; accepted 9 September 1999

Abstract We investigate the effect of food stamp cashout on undernutrition using household data from cashout experiments in Alabama and San Diego, California. We apply dominance methods to test for differences in nutrient distributions at or below the recommended dietary allowances.  2000 Elsevier Science S.A. All rights reserved. Keywords: Food stamp program; (Under)Nutrition; Cashout; Stochastic dominance JEL classification: I38 (Provision and effects of welfare programs)

1. Introduction The architects of the US Food Stamp Program (FSP) aimed to help low-income households eat a nutritious diet by supplementing their food purchasing power. Since its inception, the FSP has been providing benefits in the form of food coupons rather than cash, reflecting the popular notion that coupons are more likely than cash to raise food consumption and improve nutrition. The effect on recipient behavior of in-kind versus cash benefits has been a longstanding interest of economists (Southworth, 1945), but direct comparisons of consumer responses to cash and coupon benefits were not possible until recently. To investigate the issue, the US Department of Agriculture (USDA) – the agency responsible for oversight of the FSP – sponsored cashout demonstration projects in 1989. Two of these projects were designed as classic experiments. In cooperation with state and local social service agencies, USDA randomly assigned large numbers of households to treatment (cash) and control (food coupon) groups *Corresponding author. Tel.: 11-252-328-6408; fax: 11-252-328-6743. E-mail address: [email protected] (L.A. Zeager) 0165-1765 / 00 / $ – see front matter  2000 Elsevier Science S.A. All rights reserved. PII: S0165-1765( 99 )00244-X

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

76

in Alabama and San Diego, California, which provides an ideal opportunity for comparing the effects of cash and coupons on actual behavior. In both experiments, substantial proportions of households have nutrient deficits, especially for folacin, vitamin E, and calcium. Other households are well above requirement thresholds and are not undernourished. To compare relative undernutrition in the treatment and control groups, we apply dominance methods as proposed by Kakwani (1989). Comparing the nutrient distributions rather than summary statistics (e.g. the distribution means) permits us to focus on undernourished households.1 Kakwani (1989) has shown that cumulative distributions of nutrients can be used to evaluate relative undernutrition in the same way that cumulative distributions of incomes have been used to evaluate relative poverty (Foster and Shorrocks, 1988). Kakwani’s approach has been applied in India (Kakwani, 1989), Indonesia (Ravallion, 1990, 1992), Puerto Rico (Bishop et al., 1996), and the United States (Bishop et al., 1992). Statistical inference procedures have also been developed for identifying significant differences between two cumulative distributions. We apply Kakwani’s approach and the statistical inference procedures to data from the cashout experiments. A dominance approach is well suited to the cashout data for two reasons. First, the classic experimental design of the cashout demonstrations ensures that any differences between the nutrient distributions of cash and coupon recipients are attributable to the treatment variable. Second, the specific form of the distribution of nutrient requirements in the population does not need to be known; we need only assume the populations have the same requirement distributions — a reasonable assumption for comparisons of the treatment and control groups in the cashout experiments. The remainder of the paper is organized as follows. The next section provides a brief description of the methods used in the paper. After describing the data from the experiments, the empirical results are presented and discussed. A final section offers concluding comments.

2. The dominance approach to comparing relative undernutrition The starting point for most studies of undernutrition is a set of human dietary requirements for different age and sex groups. By far the most widely recognized of these is the Recommended Dietary Allowances (RDA) developed by the Food and Nutrition Board of the National Research Council of the National Academy of Sciences. Yet, as the nutritionists readily acknowledge, the adequate nutrition level for the human body cannot be established with exact precision; any requirement threshold, such as the RDA, involves an element of arbitrariness.2 Moreover, in evaluating nutrient distributions it is important to remember that ‘there is no agreed-upon benefit to levels above 100%’ of the RDA (Emmons, 1986, p. 1690). These concerns can be addressed by evaluating relative undernutrition based on cumulative 1

In their reports to the USDA, Ohls et al. (1992) and Fraker et al. (1992) compare nutrient availability at the means of the treatment and control groups of the two experiments. They also recognize the value of comparing cumulative nutrient distributions, but consider only a few nutrients and apply no statistical inference procedures to the distributional comparisons. 2 Devaney and Fraker (1986, p. 729) treat the RDA accordingly: ‘The RDA are not dietary ‘requirements’ in the usual sense. Rather, the RDA are intakes of nutrients judged to be adequate for nearly all persons in the United States and are set such that they exceed the requirements (except for food energy) for most individuals.’ Butler and Raymond (1996, p. 783) cite several sources in the nutrition literature that propose using two-thirds the RDA as the criterion for adequate nutrition.

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

77

distribution functions (cdfs) of nutrients for two populations, truncated in the neighborhood of the RDA, assuming that the populations have the same distribution of nutrient requirements. Let F1 (x) and F2 (x) be the cdfs of nutrients for two populations. Widely known procedures developed by nutrition scientists can be used to adjust nutrients for the composition and size of the household, the ages of its members, and the number of meals eaten away from home. Kakwani (1989, p. 539) then establishes the following proposition: Proposition 1. if F1 (x)> F2 (x) for all values of x, then the undernutrition in population 1 will always be greater than or equal to that in population 2. Proposition 1 is known as first-order dominance (FOD) of F1 by F2 . An analogous theorem on the construction of poverty orderings based on income distributions for two populations is derived by Foster and Shorrocks (1988), who truncate the distributions at the poverty line. Following Foster and Shorrocks, the distributions of nutrients could be truncated in the neighborhood of the requirement thresholds (RDA). Silberberg (1985) and Butler and Raymond (1996) also propose limiting attention to the persons who are at risk of undernutrition. The logic of this approach is made very clear by Butler and Raymond (1996, p. 781), ‘ . . . above some level, extra nutrients are superfluous. If an individual has attained a nutritionally adequate diet, then whether or not food stamp income allows him or her to consume more nutrients is irrelevant.’ In the construction of dominance tests, it is convenient to compare the inverses of the cdfs in Proposition 1, x 5 F 21 1 ( p), where p represents a quantile in the nutrient distribution (e.g. the bottom 20 percent) and x is an order statistic, which is the highest nutrient level in the quantile. We approximate the inverse cdf, or quantile function, as the step function formed by the vector of conditional mean nutrient levels in the sample quintiles. We then conduct pairwise tests of corresponding conditional means for each sample, which permits us to distinguish among three possible outcomes — dominance, statistical equivalence, and a significant crossing. To make multiple comparisons we follow the approach of Bishop et al. (1989), who developed a pairwise, joint-testing procedure for a predetermined number of points, k, in the distributions.3 The points can be the conditional means of the quintiles, deciles, or other quantiles in the two samples. That is, we test the null hypothesis: H0 : m1i 5 m2i

i 5 1,2, . . . ,k

(1)

where m1i and m2i are the conditional means in populations 1 and 2. An appropriate test statistic for H0 is: T i 5 ( mˆ 1i 2 mˆ 2i ) / [Qˆ 1i /N1 1 Qˆ 2i /N2 ]

1/2

i 5 1,2, . . . ,k

(2)

where Q 1i and Q 2i denote asymptotic variances and N1 and N2 are the number of observations in samples 1 and 2. Bishop et al. (1989), show that for large samples, each of the T i is asymptotically standard normal and provide a procedure for estimating Q 1i and Q 2i . We treat the individual tests at k points of the quantile function as sub-hypotheses of a joint test. To maintain the size of the joint test (i.e. to control the type I error), we use critical values for the Student 3

This procedure is closely related to earlier work by Beach and Davidson (1983) and Beach and Richmond (1985).

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

78

Maximum Modulus (SMM) distribution (Miller, 1981). An approximately a -level test of the equality of two vectors of conditional quantile means rejects each of the k sub-hypotheses if uT i u . ma (k,`), where ma (k,`) is the upper-a critical value of the SMM distribution with ` degrees of freedom.4 The SMM critical values are obtained from Stoline and Ury (1979). In general, joint tests of the conditional means generate only partial orderings of distributions. If no pair of conditional means is significantly different, then the two quantile functions are statistically equivalent. If a positive (negative) and significant difference exists at one or more quantile means and no negative (positive) significant differences exist, then we have FOD. Finally, if tests yield both positive and negative significant differences, the two quantile functions intersect and no FOD ranking is possible. However, truncated dominance may still hold below reasonable requirement thresholds. The next section describes the nutrient data provided by the cashout experiments.

3. Data The data for this study were obtained from parallel surveys, conducted in 1990, of households that participated in food stamp cashout demonstrations in Alabama and San Diego, California. The Alabama sample included residents of rural (52 percent) and urban (48 percent) counties; the California sample was drawn entirely from San Diego county. Therefore, the two experiments represent not only different regions, but contrasting social settings as well. Fraker et al. (1995b, p. 388), point out that the implementation of the two experiments also differed. The San Diego experiment was scheduled to run 5 years or longer and was given much publicity. In contrast, the Alabama experiment ran only 8 months and received little publicity. We represent nutrient status by nutrient availability, which exceeds nutrient intake because some food is wasted or fed to pets. For comparability across households that differ in size and composition, we express nutrients in adult male equivalent (AME) units. In Tables 1 and 2 we compare nutrient availability to the RDA in the Alabama and San Diego experiments. In both experiments, nutrient availability at the sample means surpasses the RDA for food energy and each nutrient in the treatment and control groups. But 16 percent of the Alabama sample and about 25 percent of the San Diego sample fall short of the RDA for food energy. Moreover, the incidence of undernutrition is more than 10 percent in each sample for phosphorus, iron, vitamin B 6 , magnesium, vitamin E, calcium, and folacin. In contrast, relatively few households in either sample experience a shortfall in protein, vitamin C, thiamin, niacin, and vitamin B 12 . In a 1983 study of low-income families in Cleveland, Emmons (1986) finds that diets contain inadequate amounts of vitamin B 6 , vitamin E, iron, calcium, and magnesium (her study provides no information on folacin). Using the 1977–1978 Nationwide Food Consumption Survey, Silberberg (1985) concludes that consumption of folacin and magnesium may be inadequate. Thus, our findings reinforce the results of previous studies using other data sources. Before moving to our main results, we point out that the number of observations generated by the 4

Bishop and Formby (1999), provide evidence that the test is conservative and has good power against dominance in sample sizes of 50 or more per quantile. Given the conservative nature of the test, we use a one-tailed test at the 10-percent level of significance.

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

79

Table 1 Nutrient availability relative to the RDA: Alabama a Nutrient

RDA

Sample mean: coupon recipients

Proportion of coupon recipients below RDA

Sample mean: cash recipients

Proportion of cash recipients below RDA

Energy (kcal) Protein (g) Vitamin E (IU) Vitamin C (mg) Thiamin (mg) Niacin (mg) Vitamin B 6 (mg) Vitamin B 12 (mg) Folacin (mg) Calcium (mg) Phosphorus (mg) Magnesium (mg) Iron (mg)

2700 56 15 45 1.4 18 2 3 400 800 800 350 10

4670 161 20.3 153 3.2 42 3.1 10.7 444 940 1675 466 18.4

0.16 0.01 0.41 0.09 0.08 0.06 0.24 0.08 0.54 0.49 0.11 0.34 0.20

4722 162 20.5 151 3.2 42 3.2 10.3 446 971 1703 473 18.5

0.16 0.01 0.41 0.08 0.09 0.08 0.26 0.09 0.51 0.47 0.11 0.35 0.18

a

The Alabama sample contains 2184 observations, including 1025 coupon recipients and 1159 cash recipients.

FSP cashout experiments is sufficient to perform statistical comparisons of distributions of nutrients by quintile. Even the smaller San Diego sample contains more than 90 observations per quintile in the treatment and control groups. Information about the randomness of the treatment and control groups is given by Fraker et al. (1995a, p. 639).5 Table 2 Nutrient availability relative to the RDA: San Diego a Nutrient

RDA

Sample mean: coupon recipients

Proportion of recipients below RDA

Sample mean: cash recipients

Proportion of cash recipients below RDA

Energy (kcal) Protein (g) Vitamin E (IU) Vitamin C (mg) Thiamin (mg) Niacin (mg) Vitamin B 6 (mg) Vitamin B 12 (mg) Folacin (mg) Calcium (mg) Phosphorus (mg) Magnesium (mg) Iron (mg)

2700 56 15 45 1.4 18 2 3 400 800 800 350 10

4001 163 16.9 164 2.9 37.6 3.2 9.7 468 972 1478 519 16.1

0.24 0.04 0.53 0.06 0.08 0.09 0.22 0.05 0.47 0.45 0.12 0.28 0.24

3797 153 16.8 161 2.8 36.1 3.1 9.0 451 931 1399 495 15.8

0.25 0.03 0.55 0.06 0.11 0.07 0.23 0.05 0.53 0.45 0.12 0.28 0.24

a

5

The San Diego sample contains 935 observations, including 463 coupon recipients and 472 cash recipients.

For additional information on the cashout data and the contexts in which they were collected (see, Fraker et al., 1992, 1995a,b; Ohls et al., 1992).

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

80

4. Empirical results The food energy and nutrient distributions (cdfs) for the experimental (cash) and control (coupon) groups in the cashout experiments contain all of the information necessary to apply the first-order dominance criterion in Proposition 1. As noted above, it is both equivalent and more convenient to conduct the inference tests and present the results in terms of the inverse cdf, F 21 1 , which is widely

Fig. 1. Distributions of food energy availability and key micronutrients in Alabama.

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

81

referred to as the quantile function. Fig. 1 shows quantile functions estimated at quintiles for food energy and selected micronutrients in the Alabama sample. Fig. 2 provides similar information for San Diego. Some of the quantile functions appear to cross and others appear not to differ. By applying inference tests we can answer the following questions. Do the quantile functions differ? If yes, where and how do they differ?

Fig. 2. Distributions of food energy availability and key micronutrients in San Diego.

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

82

Table 3 reports inference tests for first-order stochastic dominance comparisons of distributions of food energy, vitamin B 6 , and vitamin E availability for the cash and coupon recipients in Alabama. By inspection of the food energy quintile means for coupon and cash recipients (panel a), it appears that the quantile functions intersect. That is, coupon recipients have higher mean food energy availability in the bottom two quintiles than cash recipients, while the opposite is true in the remaining quintiles. But the test statistic reveals that no difference in quintile means is statistically significant. Therefore, we reject the null hypothesis and conclude that the distributions of food energy for coupon and cash recipients are statistically equivalent. In Table 3 we also find apparent crossings in quantile functions for vitamins B 6 and E (panels b and c). But the inference tests reveal that, in both cases, only the difference in the bottom quintile is statistically significant. Thus, for vitamins B 6 and E, coupon recipients FOD cash recipients, which means undernutrition is greater for cash recipients than coupon recipients. For all other micronutrients not shown in Table 3, we find no significant differences between cash and coupon recipients, as in the food energy comparison. The corresponding results for San Diego are shown in Table 4. Once again, as in Alabama, there is an apparent crossing in quantile functions for food energy availability in the treatment (cash) and control (coupon) groups, but the differences are not significant at any of the quintiles (panel a). Hence, coupon and cash recipients are statistically equivalent with respect to the availability of food energy.

Table 3 Dominance comparisons of nutrient distributions in Alabama: cash vs. coupon recipients Sample quintile

Test statistic a

Quintile mean: Cash recipients

Coupon recipients

(a) Food energy availability (kcal per adult male equivalent per day, RDA52700) 1 2137.40 2238.87 21.30 2 3306.04 3334.10 20.35 3 4253.59 4206.49 0.50 4 5470.21 5348.04 0.91 5 8433.48 8224.15 0.72 (b) Vitamin B 6 availability (mg per adult male equivalent per day, RDA52) 1 1.30 1.41 22.11* 2 2.15 2.18 20.52 3 2.87 2.80 0.93 4 3.74 3.66 0.86 5 5.73 5.67 0.31 (c) Vitamin E availability (IU per adult male equivalent per day, RDA515) 1 5.50 6.19 22.11* 2 11.54 12.13 21.15 3 17.48 17.33 0.27 4 24.93 23.81 1.38 5 43.21 41.88 0.73 a *

The test statistic for the differences in quintile means is the Student Maximum Modulus (SMM) — a joint test statistic. Significantly different from zero at the 10-percent level using a one-tailed test.

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

83

Table 4 Dominance comparisons of nutrient distributions in San Diego: cash vs. coupon recipients Sample quintile

Test statistic a

Quintile mean Cash recipients

Coupon recipients

(a) Food energy availability (kcal per adult male equivalent per day, RDA52,700) 1 2036.90 2016.95 0.23 2 2850.66 2919.38 20.76 3 3462.75 3601.93 21.31 4 4336.07 4533.07 21.25 5 6301.61 6937.74 21.88 (b) Phosphorous availability (mg per adult male equivalent per day, RDA5800) 1 734.65 732.55 2 1024.48 1040.86 3 1289.05 1330.55 4 1619.98 1707.32 5 2326.72 2581.16

0.07 20.47 20.86 21.58 22.19*

(c) Vitamin B 6 availability (mg per adult male equivalent per day, RDA52) 1 1.50 1.53 20.42 2 2.21 2.20 0.13 3 2.81 2.86 reformat20.49 4 3.44 3.68 22.00 5 5.37 5.91 21.45 a *

The test statistic for the differences in quintile means is the Student Maximum Modulus (SMM) — a joint test statistic. Significantly different from zero at the 10-percent level using a one-tailed test.

Table 4 also shows the importance of taking into account the RDA in nutrition comparisons. Consider phosphorous availability (panel b) for coupon and cash recipients. Coupon recipients have greater phosphorous availability than cash recipients in every quintile except the first. However, only one of these differences is statistically significant and it is in the fifth quintile — far above the recommended amount for phosphorus (RDA5800). In this case, it is reasonable to use truncated dominance, which leads to the conclusion that coupon and cash recipients are equivalently undernourished with respect to phosphorus. Comparisons for all other micronutrients in the San Diego experiment reveal no significant differences between coupon and cash recipients. For vitamin B 6 (Table 4, panel c), the difference in means in the fourth quintile is nearly significant, but the means are far above the recommended allowance (RDA52) at this quintile. Therefore, we find no evidence that cash recipients are more undernourished than coupon recipients in the San Diego experiment. Overall, our findings reveal that food stamp cashout leads to few changes in undernutrition. Among these changes, reductions in the availability of vitamins E and B 6 in Alabama may be cause for concern, because the proportions of the sample below the RDA are relatively high for these nutrients (more than 40 percent for vitamin E) and the significant change in availability occurs in the lowest quintile for both samples. The finding that cashout alters few dimensions of undernutrition in Alabama and has no significant effects in San Diego is consistent with a standard analysis of in-kind versus

84

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

cash transfers in economic theory. The theory predicts that conversion from coupon to cash benefits will effect behavior only if food assistance exceeds the desired level of food consumption in the absence of assistance — a condition met in only a small proportion of food stamp households.

5. Conclusions We make comparisons of nutrient distributions for cash and coupon recipients who participated in two food stamp cashout experiments in Alabama and San Diego, California. A substantial proportion of households participating in the experiments fall short of recommended levels for food energy and a variety of nutrients. To focus attention on households who are at or below the nutrient requirement threshold (RDA), we make truncated dominance comparisons of the nutrient distributions of cash and coupon recipients in the experiments. We find that cashout leads to greater undernutrition for cash recipients than coupon recipients in only two of 26 cases (vitamins E and B 6 in Alabama). The modest effects of the cashout on behavior are consistent with the standard analysis of in-kind versus cash transfers in economic theory.

Acknowledgements The authors wish to thank Steven Carlson (Food and Nutrition Service, USDA) and Thomas Fraker (Mathematica Policy Research) for making the cashout data available and James Ohls for valuable comments. The authors gratefully acknowledge financial support from the University of North Carolina Institute of Nutrition. Lester Zeager wishes to thank the College of Arts and Sciences, East Carolina University, for release time for this project.

References Beach, C.M., Davidson, R., 1983. Distribution-free statistical inference with Lorenz curves and income shares. Rev. Econ. Stud. 50, 723–735. Beach, C.M., Richmond, J., 1985. Joint confidence intervals for income shares and Lorenz curves. Int. Econ. Rev. 26, 439–450. Bishop, J.A. Formby, J.P., 1999. Tests of significance for Lorenz partial orders. In: Silber, J. (Ed.), Income Inequality Measurement: From Theory to Practice. Springer, Berlin (in press). Bishop, J.A., Formby, J.P., Thistle, P.D., 1989. Statistical inference, income distributions, and social welfare. In: Slottje, D.J. (Ed.), Research on Economic Inequality, Vol. 1, JAI Press, Greenwich, CT, pp. 49–82. Bishop, J.A., Formby, J.P., Zeager, L.A., 1992. Nutrition and nonparticipation in the US Food Stamp program. Appl. Econ. 24, 945–949. Bishop, J.A., Formby, J.P., Zeager, L.A., 1996. Relative undernutrition in Puerto Rico under alternative food assistance programs. Appl. Econ. 28, 1009–1017. Butler, J.S., Raymond, J.E., 1996. The effect of the Food Stamp program on nutrient intake. Econ. Inquiry 34, 781–798. Devaney, B., Fraker, T., 1986. Cashing out food stamps: Impacts on food expenditures and diet quality. J. Policy Anal. Manag. 5, 725–741. Emmons, L., 1986. Food procurement and the nutritional adequacy of diets in low-income families. J. Am. Dietet. Assoc. 86, 1684–1693.

J. A. Bishop et al. / Economics Letters 67 (2000) 75 – 85

85

Foster, J.E., Shorrocks, A.F., 1988. Poverty orderings. Econometrica 56, 173–177. Fraker, T.M., Martini, A.P., Ohls, J.C., Ponza, M., Quinn, E.A. (Eds.), 1992. The Evaluation of the Alabama Food Stamp Cash-Out Demonstration, Vols. I and II, Mathematica Policy Research, Inc, Washington. Fraker, T.M., Martini, A.P., Ohls, J.C., 1995a. The effect of food stamp cashout on food expenditures. J. Hum. Resour. 30, 633–649. Fraker, T.M., Martini, A.P., Ohls, J.C., Ponza, M., 1995b. The effect of cashing-out food stamps on household food use and the cost of issuing benefits. J. Policy Anal. Manag. 14, 372–392. Kakwani, N., 1989. On measuring undernutrition. Oxford Econ. Papers 41, 528–552. Miller, R.G., 1981. Simultaneous Statistical Inference, 2nd Ed, Wiley, New York. Ohls, J.C., Fraker, T.M., Martini, A.P., Ponza, M. (Eds.), 1992. The Effects of Cash-Out On Food Use By Food Stamp Participants in San Diego, Mathematica Policy Research, Inc, Princeton. Ravallion, M., 1990. Income effects on undernutrition. Econ. Dev. Cult. Change 38, 489–515. Ravallion, M., 1992. Does undernutrition respond to incomes and prices?. Dominance tests for Indonesia. World Bank Econ. Rev. 6, 109–124. Silberberg, E., 1985. Nutrition and the demand for tastes. J. Polit. Econ. 93, 881–900. Southworth, H.M., 1945. The economics of public measures to subsidize food consumption. J. Farm Econ. 27, 38–66. Stoline, M.R., Ury, H.K., 1979. Table of the studentized maximum modulus distribution and an application to multiple comparisons among means. Technometrics 21, 87–93.