| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
RESEARCH AND PRACTICE |
Lori Kowaleski-Jones is with the Department of Family and Consumer Studies, University of Utah, Salt Lake City. Greg J. Duncan is with the Institute for Policy Research, Northwestern University, Evanston, Ill.
Correspondence: Requests for reprints should be sent to Lori Kowaleski-Jones, PhD, Department of Family and Consumer Studies, University of Utah, 225 S 1400 East, Room 228, Alfred Emery Bldg, Salt Lake City, UT 84112–0080 (e-mail: lori.kowaleski-jones{at}fcs.utah.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. This study sought to estimate the impact on birthweight of maternal participation in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).
Methods. WIC estimates were based on sibling models incorporating data on children born between 1990 and 1996 to women taking part in the National Longitudinal Survey of Youth.
Results. Fixed-effects estimates indicated that prenatal WIC participation was associated with a 0.075 unit difference (95% confidence interval [CI] = –0.007, 0.157) in siblings' logged birthweight. At the 88-oz (2464-g) low-birthweight cutoff, this difference translated into an estimated impact of 6.6 oz (184.8 g).
Conclusion. Earlier WIC impact estimates may have been biased by unmeasured characteristics affecting both program participation and birth outcomes. Our approach controlled for such biases and revealed a significant positive association between WIC participation and birthweight.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
In the present study, we addressed these concerns by estimating WIC effects with a national sample of children and using sibling fixed-effects models to account for unmeasured heterogeneity among the mothers of sample children. Specifically, we studied a sample of children born between 1990 and 1996 to mothers participating in the National Longitudinal Survey of Youth (NLSY), and we used merged NLSY mother–child data files to estimate the effects of WIC participation on birthweight. We eliminated the biasing effects of persistent characteristics of mothers—both measurable and unmeasurable—and thus the estimates from this study constitute a methodological advance over previous studies. A disadvantage of the sample is that all of the children were born to somewhat older mothers. In addition, all of the data were reported by the mothers, and standard errors for the fixed-effects models that we estimated were considerably larger than standard errors for more conventional regression models.
|
THE WIC PROGRAM |
|---|
|
TOPABSTRACTINTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
Much of the research on the effects of WIC participation on children has focused on the potential benefits of increased use of prenatal care, increased Medicaid savings, and better infant outcomes.6,9–12 In particular, studies indicate that the WIC program is beneficial in the promotion of nutrition supplementation during pregnancy, which has been linked to more positive birth outcomes.12–15 The WIC program has also drawn some recent criticism. Besharov and Germanis16 argued that the benefits of the WIC program may have been overstated because many of the earlier studies suffered from issues of selection bias. In the present study, we responded to this criticism by using methodological techniques that accounted for bias arising from persistent unmeasured family characteristics that could affect both program participation and child outcomes.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
 | (1) |
i is an error term that includes unobserved variables and random error.
In constructing our ordinary least squares (OLS) model estimates for equation 1
, we used Huber–White robust standard errors that accounted for the lack of independence of observations based on siblings born to the same mother.17 These OLS estimations would yield biased estimates of ß1 if unobservable determinants of Y were also correlated with P.
The sibling fixed-effects estimator removes bias from time-invariant maternal and family components of by subtracting the average for all siblings in a given family from each child's value:
 | (2) |
Fixed-effects models do not eliminate bias from time-varying covariates that affect both WIC participation and birth outcomes. If mothers were systematically more likely to use WIC during a second pregnancy because they had learned of the program during a difficult first pregnancy, then the results from sibling models would probably be biased by the fact that mothers were undergoing a learning process that would disproportionately favor younger siblings. However, it is likely that a considerable amount of variability in WIC participation across births is exogenous to the family and may instead be produced by state variations in program implementation.18
Sample
Our data were drawn from the 1996 and earlier survey waves of the NLSY, a nationally representative sample of men and women. When initially interviewed in 1979, the women in our sample were aged 14 to 21 years. The study oversampled Black, Hispanic, and economically disadvantaged White youths.19 Beginning in 1990, the NLSY obtained maternal reports on whether WIC benefits had been received in the preceding calendar year. Our sample consisted of the 1984 children born to NLSY mothers between 1990 and 1996 for whom prenatal WIC participation status had been recorded. It is important to note that the women in our sample were between the ages of 25 and 38 years when they gave birth; thus, the sample consisted of relatively older mothers.
For our sibling-based analyses, we identified 969 children born between 1990 and 1996 who had 1 or more siblings also born between 1990 and 1996. Most of the sibling groups in this sample included just 2 children. In the majority of these groups (349 of 453), mothers did not participate in the WIC program during their pregnancies. Thirty-three of the sibling groups represented situations in which mothers' WIC participation included all of the children who were a part of this sample.
The 71 discordant-sibling groups in which siblings differed in terms of their mother's participation in the WIC program before their birth were the key source of variance in estimations of our sibling fixed-effects regression models. The number of discordant-sibling groups was consistent with previous studies on the WIC and Head Start programs20,21 involving similar modeling techniques22 and, as shown subsequently, provided acceptably precise estimates of ß1. The majority of the discordant-sibling groups (49, or 74%) followed the pattern of no participation in the WIC program in the first observed pregnancy and participation before a subsequent birth. However, a sizable percentage (26%) of the discordant-sibling groups followed the opposite pattern of WIC program usage.
Measurement
Prenatal WIC participation was measured with a dichotomous variable based on maternal reports of receipt of WIC benefits in the calendar year preceding the birth. Recent research indicates that a potentially important aspect of WIC participation is timing—early vs late—in terms of the mother's pregnancy.1 The NLSY provides only annual data on receipt of WIC benefits, an analytic cost that had to be weighed against the advantages of the size, national scope, and sibling representation of our sample. Table 1
presents weighted descriptive information about the children in the total, sibling, and discordant-sibling samples. Approximately 12% of the children in both the total sample and the sibling sample had mothers who received WIC benefits during their pregnancy. Based as they are on WIC use by at least 1 sibling, mean rates of WIC use in the discordant-sibling sample were larger.
|
.
|
Time-varying characteristics included in both OLS and fixed-effects models were mothers' reports of smoking and reports of drinking during the given pregnancy. We also accounted for maternal prenatal food stamp participation, family income (not including food stamps) in the calendar year before the birth, maternal residence in an urban area in the year before the birth, maternal education as of the year before the birth, child sex, and ethnic identity. Past research has shown significant differences in birthweight according to minority status.23
Finally, we controlled for birth order of the child, because first-born children may be less likely to receive WIC benefits. In our multivariate analyses, we initially controlled only for child demographic characteristics. Subsequently, we included a full set of prenatal variables and maternal characteristics. We did not include time-invariant prenatal and maternal characteristics in the sibling fixedeffects regression analysis, because these characteristics did not differ between siblings.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
presents coefficients and standard errors from OLS and fixed-effects models of the natural log of birthweight. Experimentation with raw birthweight and the square root of birthweight as alternative forms of our dependent variable produced coefficients with similar significance levels and impact estimates. The natural log transformation yielded the most consistently significant estimates of the relationship between our independent variables and birthweight. Missing data on independent variables other than WIC participation produced substantial sample size variability across our models.
Because missing dummy variables create problems in fixed-effects regression models, we adopted the strategy of substituting previous-wave values whenever possible (e.g., for family income); when this strategy was not feasible, we used mean substitution. Experimentation indicated that our results were robust across various missing data treatments. Specifically, we estimated parallel sets of coefficients in which we (1) deleted all cases involving missing data in the final set of explanatory variables, (2) used mean substitution of the missing data, and (3) set missing values to zero and included dummy variables for missing data. Table 2 notes coefficients that differed from zero at the .01, .05, and .10 levels of statistical significance; our inclusion of the .10 level was based on the relatively small sample sizes driving our fixedeffects estimates.
Estimates based on simple OLS models (Table 2) implied that participation in WIC was associated with a statistically insignificant 0.025 increase in logged birthweight. Controls for a more extensive set of maternal and child characteristics more than doubled the OLS-based estimated impact, to a statistically significant 0.055. This increase resulting from controls on observable characteristics suggested a negative selection process in which the mothers most likely to deliver low-birthweight infants were more likely to participate in WIC.
The more complete OLS model also suggested that prenatal maternal smoking and drinking were associated with significant decrements in birthweight. In addition, higher maternal cognitive skills were significantly linked to higher birthweights. We found a significant negative association between prenatal receipt of food stamps and birthweight; this result appeared to be tied to the fact that mean birthweights were considerably lower among women who received food stamps but not WIC benefits than among those who demonstrated the opposite pattern (111.83 vs 125.35 oz [3131.24 vs 3509.8 g]). Consistent with other research,28 we observed both race and sex differences in birthweight (Table 2). Finally, firstborn children were more likely to be lower in birthweight than higher-birth-order children.
The sibling fixed-effects models presented in Table 2 also suggested a positive impact of prenatal WIC participation on birthweight. Note that the sample sizes for the fixed-effects models were based on all children but that the key WIC impact estimates were driven by the within-family WIC and birthweight differences for the 71 sibling pairs discordant in terms of maternal WIC usage.
In the case of the model that controlled only for child sex and birth order ( Table 2), the estimated impact was a statistically insignificant 0.034. After the full set of prenatal maternal variables had been taken into account, prenatal WIC participation was associated with a 0.075-unit difference in siblings' logged birthweight. Note the large standard error associated with this coefficient, however. The 0.075 point estimate implied a WIC impact of 6.6 oz (184.8 g) at the 88-oz (2464-g) low-birthweight cutoff, an effect considerably larger than those revealed in previous studies. The size of the standard error argues against attaching considerable weight to this particular point estimate, but such an estimate supports the conclusion that the impact is indeed positive.
We also estimated models in which we constrained the samples in analysis 1 of Table 2 to be identical with the samples in analysis 2. The WIC coefficient did not change in the OLS model (0.023, SE = 0.017), and it increased slightly in the fixed-effects model (0.052, SE = 0.037). Given that the change in the WIC coefficient was slight, it appears that the change in coefficients in the models presented in Table 2 was more a function of the addition of covariates than of a difference in sample composition.
The major source of bias in our fixedeffects estimates derived from maternal conditions that influenced both pregnancyspecific WIC participation and child outcomes. To address these concerns, we considered a variety of maternal health measures in the year before the birth of the child, including the average pregnancy weight of the mother and whether she reduced calories or sodium during pregnancy, sought prenatal care, or had health conditions limiting work. These supplementary results indicated that none of the primary relationships of interest were altered substantially by inclusion of these additional variables.
We also estimated models that controlled for the effects of gestational length, testing the hypothesis that WIC may be helpful in increasing gestational length and in turn increasing birthweight. Babies born preterm had significantly lower birthweights than babies born full term, but inclusion of this control did not alter the main relationship between prenatal WIC participation and birthweight. We also assessed the interactive effects of WIC participation and birth order so as to capture the potential effects of "referral bias" (i.e., mothers may be more likely to seek WIC benefits in their second pregnancy than in their first). We did not find evidence of a significant interaction.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
There are several limitations to our approach. First, our sample consisted of children born to relatively older mothers. As a result, our findings may not generalize to children born to mothers in other age groups. Second, our sibling sample was fairly small, although it was sufficient in size to detect a 7% impact on birthweight with 90% power. Third, our sibling-based analyses may not generalize to children with no siblings. These limitations suggest the need for further research on the connection between WIC participation and birthweight. Given our finding that the impact of WIC appeared to increase with our fixed-effects controls for time-invariant family characteristics, we suggest that further research attend to the omitted-variable–bias problem.
Our results suggest a pattern in which more complete models accounting for background and prenatal factors produced the largest estimates of effects of prenatal WIC participation on birthweight. This pattern of findings implies a negative selection into WIC in which women with the greatest needs are most likely to participate. The bias-reducing methodology used here should be considered in future program evaluations.
|
Acknowledgments |
|---|
Helpful comments were provided by Andrea Beller, Douglas Besharov, Peter Germanis, Christine Ross, Sonia Salari, Ken Smith, Nick Wolfinger, and Cathleen Zick.
|
Footnotes |
|---|
Accepted for publication October 10, 2001.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONTHE WIC PROGRAMMETHODSRESULTSDISCUSSIONReferences |
|---|
2. Blank RM. It Takes a Nation: A New Agenda for Fighting Poverty. Princeton, NJ: Princeton University Press; 1997.
3.
Endozien J, Switzer B, Ryan R. Medical evaluation of the Special Supplemental Feeding Program for Women, Infants, and Children. Am J Clin Nutr. 1979;32:677–692.
4. Kennedy E, Gershoff S, Reed R, Austin J. Evaluation of the effect of WIC supplemental feeding on infant birth weight. J Am Diet Assoc. 1982;80:220–227.[Medline]
5.
Kotelchuck M, Schwartz J, Anderka M, Finison K. WIC participation and pregnancy outcomes: Massachusetts Statewide Evaluation Project. Am J Public Health .1984;74:1086–1092.
6. Rush D. Evaluation of the Special Supplemental Food Program for Women, Infants, and Children (WIC). Research Triangle Park, NC: Research Triangle Institute; 1987.
7. Devaney B, Bilheimer L, Schore J. The Savings in Medicaid Costs for Newborns and Their Mothers Resulting From Prenatal Participation in the WIC Program. Vol. 2. Washington, DC: US Dept of Agriculture; 1991.
8. Devaney B, Schirm A. Infant Mortality Among Medicaid Newborns in Five States: The Effects of Prenatal WIC Participation. Washington, DC: US Dept of Agriculture; 1993.
9. Devaney B, Bilheimer L, Schore J. Medicaid costs and birth outcomes: the effects of prenatal WIC participation and the use of prenatal care. J Policy Analysis Manage. 1992;11:573–592.[Medline]
10.
Schramm W. WIC participation and its relationship to newborn medical costs in Missouri: a cost/benefit analysis. Am J Public Health. 1985;85:729–731.
11. Avruch S, Cackley AP. Savings achieved by giving WIC benefits to women prenatally. Public Health Rep. 1995;110:27–34.[Medline]
12.
Metcoff J, Costiloe P, Crosby WM, et al. Effect of food supplementation (WIC) during pregnancy on birth weight. Am J Clin Nutr. 1985;41:933–947.
13. Kolasa KM, Weismiller DG. Nutrition during pregnancy. Am Fam Physician. 1997;56:205–212, 216–218.[Medline]
14.
Rush D, Sloan NL, Leighton J, et al. The National WIC Evaluation: evaluation of the Special Supplemental Food Program for Women, Infants, and Children, V: longitudinal study of pregnant women. Am J Clin Nutr. 1988;48:439–483.
15.
Caan B, Horgen D, Morgen S, et al. Benefits associated with WIC supplemental feeding during the inter-pregnancy interval. Am J Clin Nutr. 1987;45:29–41.
16. Besharov DJ, Germanis P. Is WIC as good as they say? Public Interest. 1999;134:1–7.
17. Stata Statistical Software: Release 6.0. College Station, Tex: Stata Corp; 1999.
18. Food Assistance: A Variety of Practices May Lower the Costs of WIC. Washington, DC: US General Accounting Office; 1997. Publication GAO/RED-97-225.
19. Baker PC, Keck CK, Mott FL, Quinlan SV. NLSY Child Handbook—Revised Edition. Columbus, Ohio: Center for Human Resource Research, Ohio State University; 1993.
20. Hicks L, Langham R, Takenaka J. Cognitive and health measures following early nutritional supplementation: a sibling study. Am J Public Health. 1982;72:1111–1118.
21. Currie J, Thomas D. Does Head Start make a difference? Am Econ Rev. 1995;85:341–364.
22. McCormick MC, Brooks-Gunn J, Workman-Daniels K, Turner J, Peckham GJ. The health and developmental status of very-low birth weight children at school age. JAMA .1992;267:2204–2208.[Abstract]
23. Currie J, Cole N. Welfare and child health: the link between AFDC participation and birth weight. Am Econ Rev. 1993;83:971–985.
24.
McCormick MC, Brooks-Gunn J. Concurrent child health status and maternal recall of events in infancy. Pediatrics. 1999;104:1176–1181.
25.
Axelsson G, Rylander R. Validation of questionnaire-reported miscarriage, malformation and birthweight. Int J Epidemiol. 1984;13:94–98.
26. Case R, Rieckhoff M, Beebe SA, Pinto-Martin J. Obstetric and perinatal events: the accuracy of maternal report. Clin Pediatr. 1992;31:200–204.
27. Lederman SA, Paxton A. Maternal reporting of pre-pregnancy weight and birth outcome: consistency and completeness compared with the clinical record. Maternal Child Health J. 1998;2:123–126.
28. Cramer J. Racial and ethnic differences in birth weight: the role of income and financial assistance. Demography .1995;32:231–247.[Medline]
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
