the effect of health and poverty on early childhood cognitive development

The Effect of Health and Poverty on Early ChildhoodCognitive Development

David M. Welsch & David M. Zimmer

Published online: 8 December 2009# International Atlantic Economic Society 2009

Abstract Although evidence of a link between socioeconomic status and child healthhas been researched extensively, much less attention has been devoted to studying thelink between child health and cognitive development. This paper seeks to determinewhether early childhood illnesses and poverty significantly impede cognitive develop-ment. The empirical model attempts to control for observed and unobservedheterogeneity through the use of panel data models. Results indicate that a child’scognitive development is not directly related to health problems acquired after birth orsocioeconomic standing. Rather, cognitive development is primarily influenced byunobserved child- and family-specific factors that happen to be correlated with healthand socioeconomic status. On the other hand, birth weight appears to affect cognitiveperformance later in childhood, even after taking unobserved heterogeneity into account.

Keywords National longitudinal survey of youth . Peabody tests . Panel data .

Internal instruments

JEL I18 . I32 . C23

Introduction

The literature on U.S. children consistently finds that income, wealth, and parentaleducation are positively associated with cognitive development (Guo and Harris

Atl Econ J (2010) 38:37–49DOI 10.1007/s11293-009-9198-2

The authors wish to thank editor John M. Virgo and anonymous referees for helpful comments. All errorsare ours.

D. M. WelschDepartment of Economics, University of Wisconsin – Whitewater, Whitewater, WI, USAe-mail: [email protected]

D. M. Zimmer (*)Department of Economics, Western Kentucky University, Grise Hall, Room 426,1906 College Heights Blvd., Bowling Green, KY 42101, USAe-mail: [email protected]

2000; Waldfogel et al. 2002; Aughinbaugh and Gittleman 2003; Ruhm 2004; Tayloret al. 2004; Paxson and Schady 2007; Todd and Wolpin 2007). However, much lessattention has been devoted to studying the link between child health and cognitivedevelopment. Healthcare workers have long been aware that particular healthconditions, such as anemia and lead poisoning, are detrimental to cognitivedevelopment, but the literature lacks studies on the effects of more commonlyacquired illnesses, especially those that do not directly affect brain function.1

Performance on math and reading tests as early as age seven has been shown topredict educational attainment and wages as adults (Case & Paxson, NBERWorkingPaper #12466; Connolly et al. 1992). Currie and Thomas (NBER Working Paper#10435) and Feinstein (2003) find that the predictive power of early life test scoresis particularly large for children from low-income families. Therefore, if either healthor poverty is causally related to cognitive development, then policies that aim toincrease access to health care or reduce poverty, such as public health insuranceexpansions or income transfers, would be expected to improve cognitive outcomes.On the other hand, if the link between health and cognitive outcomes is primarilydue to unmeasured child-specific traits, then these policy reforms would have limitedimpact on cognitive performance and might be inefficient uses of public resources.

The empirical complication is that child and family characteristics that areunobserved (to the researcher) might simultaneously affect health, poverty, andcognitive development (Blau 1999; Guo and Harris 2000). In a recent study closelyrelated to the one presented in this paper, Kaestner and Grossman (NBER WorkingPaper #13764) examine the effect of weight on child cognitive achievement. Toaddress unobserved confounding factors, they estimate longitudinal models thatinclude child-specific fixed effects. The empirical approach in this paper follows asimilar procedure. We find that while health appears to be positively associated withcognitive development, these relationships disappear when unobserved heterogene-ity is taken into account.

Data

Data are drawn from the 1979 National Longitudinal Survey of Youth (NLSY),which originally consisted of 12,686 individuals who were between ages 14 and 21in 1979. This cohort has been interviewed annually or biennially since 1979.Although the primary focus of the survey is on labor market behavior, in 1986 theNational Institute of Child Health and Human Development sponsored a biennialsupplemental survey of children born to women of the 1979 NLSY cohort. Forpurposes of the present study, a panel of children from the years 1986, 1988, 1990,1992, 1994, 1996, 1998, and 2000 was extracted, although not all children haveinformation available in all eight periods, resulting in an unbalanced panel. Thepanel is terminated in the year 2000 because the health measures used in this paper

1 A separate strand of literature examines the link between child health and school performance in lessdeveloped countries. These studies typically examine health problems that are more applicable to low-income countries, such as malnutrition and water-transmitted infections (Wisniewski, unpublishedmanuscript, University of Minnesota; Glewwe and Jacoby 1995; Glewwe et al. 2001; Alderman et al.2001; Block 2007).

38 D.M. Welsch, D.M. Zimmer

are not available beyond 2000. The final estimation sample consists of 54,119 child/year observations.

Cognitive development is measured by four tests administered to NLSYrespondents. The four tests are measured in terms of percentile performance,ranging from 1 to 99.2 Three of the tests, the Peabody Individual Achievement Tests(PIAT), are administered to all children five years of age and older. Of these threetests, the Mathematics Assessment Test (PIATM) measures mathematical ability ascommonly taught in American schools, the Reading Recognition Test (PIATR)measures word recognition and pronunciation, and the Reading ComprehensionAssessment Test (PIATC) measures a child’s ability to comprehend sentences. Thefourth test, the Peabody Picture Vocabulary Test-Revised (PPVT), is administered toall children 3 years of age and older and measures vocabulary and overall verbalability. These four tests allow us to investigate the effects of health and poverty ondifferent areas of cognitive achievement. These tests have been used in other studiesof cognitive development of U.S. children (Guo and Harris 2000; Paxson andSchady 2007; Todd and Wolpin 2007; Kaestner & Grossman, NBER Working Paper#13764).3

The NLSY also records detailed information on child health. We define broadmeasures of health similar to those used by Guo and Harris (2000). Two of thehealth variables may be present at birth, or they might be acquired during childhood,although as discussed below, the data appear to indicate that these variables measurehealth problems acquired after birth. These two variables are dichotomous indicatorsof whether the child has a condition that requires medical attention, and whether thechild has a condition that limits school activities. A third health variable, birthweight in pounds, captures health problems present at birth. Because birth weight ishighly correlated with health problems later in life, this variable is commonly used instudies of child health.

Table 1 shows average performance on the four cognitive tests by health andpoverty status. The largest and perhaps most unsurprising finding is that childrenabove the poverty line score better than children below the poverty line; for sometests the difference is greater than ten percentile points. The tables also reveal thathealthy children have higher scores than unhealthy children. A child born above themedian birth weight scores two to six percentile points higher than children bornbelow the median birth weight, and the magnitude of this effect does not appear tovary with respect to poverty status. Children without medical conditions or school-limiting conditions also have higher scores, but the absolute differences are larger forchildren above the poverty line. Interestingly, having a condition that requiresmedical attention does not appear to substantially affect scores for children below thepoverty line.

One must interpret these numbers with caution. Sample means of explanatoryvariables in Table 2 reveal that socioeconomic characteristics of children and their

2 There are several different measures of the Peabody test scores. We use the percentile scores becausetheir empirical distributions most closely resemble those of the normal distribution.3 As noted by Paxson and Schady (2007), children in the pre-1992 waves had unusually young mothers,and the Peabody tests for these years have some censoring problems at the lower and upper ends of thedistribution of scores. However, after excluding those observations, our results were similar to thosereported below, although we lost some precision due to smaller sample sizes.

The Effect of Health and Poverty on Early Childhood Cognitive Development 39

Table 1 Mean test scores by health and poverty status

HasConditionRequiringMedicalAttention

Does NotHave ConditionRequiringMedicalAttention

HasConditionLimitingSchoolActivities

Does NotHave ConditionLimiting SchoolActivities

BirthWeightBelowMedian

BirthWeightAboveMedian

BelowPovertyLine

PIATM 36.5 38.6 27.1 39.0 36.6 40.8

PIATR 42.3 46.2 30.1 46.7 44.9 47.4

PIATC 41.2 41.7 28.0 42.4 40.5 43.4

PPVT 23.0 20.8 15.2 21.3 19.8 22.9

AbovePovertyLine

PIATM 48.6 51.8 33.8 52.1 49.1 53.6

PIATR 55.5 59.2 37.6 59.6 57.1 60.5

PIATC 52.2 55.3 36.1 55.6 52.9 56.8

PPVT 39.0 38.1 27.2 38.5 35.4 40.7

Table 2 Sample means by health status

Variable HasConditionRequiringMedicalAttention(N=3,282)

Does NotHaveConditionRequiringMedicalAttention(N=50,837)

HasConditionLimitingSchoolActivities(N=1,110)

Does Not HaveConditionLimitingSchoolActivities(N=53,009)

BirthWeightBelowMedian(N=26,430)

BirthWeightAboveMedian(N=26,577)

Age of child inmonths

96.5 112.1 115.4 111.1 116.0 106.4

Child is female 0.40 0.49 0.36 0.49 0.54 0.43

Child is black 0.27 0.32 0.34 0.32 0.40 0.24

Child isHispanic

0.15 0.18 0.18 0.17 0.17 0.18

Mom is married 0.60 0.61 0.52 0.61 0.55 0.68

Family size 4.25 4.29 4.47 4.28 4.24 4.32

Mom isemployed

0.58 0.63 0.54 0.62 0.61 0.63

Mom’seducation

12.5 12.2 12.0 12.2 12.1 12.4

Mom’s age 32.4 32.8 32.8 32.7 32.6 32.9

Child coveredby privateinsurance

0.62 0.46 0.49 0.47 0.42 0.52

Child coveredby Medicaid

0.28 0.16 0.39 0.16 0.19 0.14

Family inpoverty

0.24 0.23 0.31 0.23 0.27 0.19


families vary with respect to child health. For example, unhealthy children are morelikely to have mothers who are not married and not employed. Unhealthy childrenare also more likely to be enrolled in Medicaid and live below the poverty line.Taken together, these numbers suggest that healthy and unhealthy children differ inother ways besides health. In addition to the numbers reported in Table 2, it ispossible that family characteristics that are unobserved (to the researcher) lead topoor cognitive development and poor health.

Empirical Strategies

Two of the three health measures, having a condition requiring medical attention andhaving a condition that limits school activities, and our measure of poverty varyacross time. We estimate the effects of these variables using panel data models thatinclude child-specific fixed effects. The third health measure, birth weight, does notvary across time, and therefore we employ an alternative instrumental variablesapproach based on calculated internal instruments.

Fixed Effect Models

The regression model takes the typical form found in the education productionfunction literature:

TSit ¼ ci þ dt þ X0itb þ gILLit þ lPOVit þ "it ð1Þ

where TSit is the test score of interest, Xit is a vector of child and familysocioeconomic characteristics with estimable coefficients β, ILLit is a dichotomousvariable indicating the presence of one of the time-varying health conditions, POVitis a dichotomous variable indicating whether the family lives below the federalpoverty line, and εit is a random error term. The vector Xit includes measures of thechild’s age (in months), gender, race, family size, mother’s age at birth, mother’seducation, and separate indicators for whether the mother is married and employed.The vector also includes an indicator of whether the child is covered by privateinsurance and whether the child is covered by Medicaid/SCHIP. Controlling forinsurance status is important, as children with better access to care might be morelikely to be diagnosed with health problems. Finally, the models include yeardummies dt. The main focus of this paper are the terms γ and λ which measure theextent, if any, to which health problems and poverty affect cognitive outcomes. As itis presently written, Eq. 1 measures the effect of health conditions and poverty oncontemporaneous cognitive performance. As health problems and poverty statusmight have delayed or accumulated effects, Eq. 1 is also re-estimated by replacingILLit and POVit with an indicator of illness and being in poverty in a precedingperiod. Standard errors adjust for clustering at the child level.

The term ci is a child-specific fixed effect that captures time-invariantheterogeneity. If ci=0 for all i, then Eq. 1 can be estimated by ordinary leastsquares (OLS) by treating the panel as a pooled cross section. However, under OLSany unobserved child-specific heterogeneity is absorbed into the error term, and


consequently estimates of γ and λ might over-represent or under-represent the extent towhich health conditions and poverty affect exam scores. This is a problem in thepresent application, as child- and family-specific traits, such as lifestyle and diet, arelikely to affect both health and cognitive performance and may be correlated withpoverty.4 The benefit of the fixed effects approach, as explained by Todd and Wolpin(2003), is that inputs into cognitive outcomes are treated as endogenous with respect tounobserved endowments. Kaestner and Grossman (NBER Working Paper #13764)explain that the model is in quasi reduced form because determinants of cognitiveachievement are proxied by explanatory variables, but health is treated as endogenous.5

In fixed effect models, coefficients of time-invariant explanatory variables are notidentified. Fortunately, the two time-varying health measures appear to exhibitsufficient time variation. Among children who report ever having a conditionrequiring medical attention, only 37 children have the condition in every period forwhich they appear in the survey. Similarly, among children with conditions limitingschool activities, only 3 have the condition in all survey periods. The “within”coefficients of variation of having a condition that requires medical attention andhaving a condition that limits school activities are 3.12 and 5.81, respectively. Notonly do the health variables exhibit more within-child variation than other time-varying variables, but they also exhibit more within-child than between-childvariation. These numbers suggest that, in contrast to birth weight, these healthconditions develop or are acquired after birth.

On the other hand, birth weight is measured at the beginning of life and thus doesnot vary over time. For birth weight, we rely on an estimation procedure recentlyproposed by Lewbel (unpublished manuscript, Boston College), discussed in thefollowing subsection.

Internal Instruments Approach

Consider the following equation

TSi ¼ X0i b þ gBWi þ "i ð2Þ

where BWi is the birth weight of child i and everything else is as defined above.6 Thedata are treated as a pooled cross section. As birth weight is likely correlated with

5 Fixed effects models can have large inconsistency if key variables, such as health status, exhibitmeasurement error. Cameron and Trivedi (2005, p. 905) show that this inconsistency diminishes as timebetween panel observations become larger. Therefore, although we would prefer annual observations werethey available, one advantage of the biennial survey used in this paper is that inconsistency due tomeasurement error is reduced.

4 An alternative estimation approach is to focus on a sample of children with siblings, and include siblingfixed effects. However, the sibling specification has two potential weaknesses. First, it ignoresapproximately 21% of children in the sample without siblings. Second, when sibling endowments differ,as is likely true more often than not, sibling effects models not only are more biased than child effectsmodels, but they are also more biased than OLS models (Ruhm 2004). For these reasons, we do not pursuethis approach.

6 We instrument for birth weight and not poverty, despite potential concerns about the endogeneity ofpoverty. We do this for two reasons. First, our findings for poverty confirm previous results found in theliterature, while our health result is new. Second, preliminary analysis suggested that the degree ofendogenity of birth weight was larger in magnitude than that of poverty.


the error term, uncovering the causal effect of birth weight typically requires aninstrumental variable approach such as two-stage least squares (2SLS). The methodrequires an external instrument that is correlated with birth weight but uncorrelatedwith cognitive scores. Unfortunately, there are no plausible instruments thatsignificantly affect birth weight without also influencing cognitive achievement.

In lieu of valid external instruments, we draw upon recent advances ineconometrics to construct “internal” instruments. The idea behind the approach isto impose restrictions on higher moments in order to achieve identification. Severalrecent examples of variants of this approach are Dagenais and Dagenais (1997);Lewbel (1997); Cragg (1997); Sentana and Fiorentini (2001), and Rigobon (2002,2003). The concept was recently generalized by Lewbel (unpublished manuscript,Boston College), who provides Monte Carlo evidence that restrictions on highermoments may yield nontrivial identification. The approach was recently employedby Block (2007) in an empirical study similar to the one presented in this paper. Hisfocus was on the effect of maternal nutrition knowledge on child micronutrientstatus.

Consider the following triangular system:

TSi ¼ X0i b1 þ gBWi þ "i1 ð3Þ

BWi ¼ X0i b2 þ "i2: ð4Þ

Lewbel suggests the following two-step estimator. In step one, Eq. 4 is estimatedby OLS. Letting b"i2 be the estimated residuals from this regression, the second stepinvolves 2SLS estimation of Eq. 3 using Xi and Xi � X

� �

b"i2 as instruments, where Xis the sample mean of Xi. Standard errors are calculated using a block bootstrapprocedure (Efron 1979), with blocking at the child level. Thus, consistent estimatesof γ may be obtained even when external instruments are not available; the onlyrequirement is that εi2 exhibits heteroskedasticity (i.e. Var "i2 Xij½ � varies acrosschildren). A standard Breusch and Pagan (1979) test is calculated to confirm thepresence of first stage heteroskedasticity; the test statistic, shown at the bottom ofTable 5, allows rejection of homoskedasticity.7

The Lewbel approach allows one to use all Xi to construct the instrumentsXi � X� �

b"i2, or one may use only a subset of Xi. Because not all Xi are stronglyassociated with birth weight, and in order to avoid problems associated with weakinstruments, we focus on a subset of Xi that appears to be strongly associated withbirth weight; the variables are child’s age, mother’s age at birth, gender, race, andfamily size.8

For instruments to be appropriate they must satisfy two conditions. First, theinstruments must be correlated with birth weight, which is testable via first-stage F-tests. Second, instruments must be uncorrelated with the error terms (or moreinformally test scores), conditional on other explanatory variables. To check this

7 The test statistic is asymptotically distributed as chi-squared with degrees of freedom equal to thenumber of independent variables.8 A previous version of the paper constructed instruments based on all independent variables. Results werequalitatively similar to those presented here, although coefficient estimates were slightly smaller inmagnitude.


condition, Hansen (1982) chi-square statistics of overidentifying restrictions arecalculated. The null hypothesis of the Hansen test is that the internal instruments arenot correlated with the error term in the test score equations for all four test scoremodels. P-values of the F and Hansen tests appear in the following table

PIATM PIATR PIATC PPVT

F-test <0.01 <0.01 <0.01 <0.01

Hansen test 0.69 0.17 0.49 0.35

The F-test statistic is rejected for all tests, indicating that the instruments pass thefirst requirement. The p-values for the Hansen tests do not support rejection of thenull hypothesis at conventional levels of statistical significance, lending support tothe internal instruments.

Results

We first present and discuss results for the time varying measures of health. This isfollowed by discussion of estimates of time-invariant birth weight. For brevity, wedo not discuss estimates of control variables. These estimates, which were consistentwith expectations as well as previous studies, are available from the authors uponrequest.

OLS Estimates

Equation (1) is estimated by both OLS and fixed effects. The OLS estimates ignorethe longitudinal nature of the data by setting ci=0 for all i. Consequently, theseestimates are likely biased by the presence of unobserved child- and family-specificheterogeneity. We present and discuss OLS estimates before turning attention tofixed effect models; we also present results from a constant-only model. Twodichotomous measures of health are examined separately as independent variables:whether the child has a condition requiring medical attention and whether the childhas a condition limiting school activities. We examine each of the four test measuresin separate regressions.

The second sets of results in Tables 3 and 4 present OLS estimates of Eq. 1.Children below the poverty line perform worse than children not born into poverty.The poverty variable likely captures some unmeasured components of familysocioeconomic status.

It is noteworthy that, in comparison to the constant-only model, the magnitudes ofthe effects of the health variables do not substantially diminish, and in some casesbecome larger. This indicates that observable child- and family-specific socioeco-nomic characteristics do not substantially affect the relationship between health andcognitive performance. On the other hand, the magnitude of the effects of poverty dodiminish substantially once socioeconomic variables are included, indicating thatobserved differences in scores above and below the poverty line may be largelydriven by child- and family-specific traits, and not necessarily by poverty per se.


Children with a condition requiring medical attention score approximately fivepercentile points lower on the math test, four points lower on the reading recognitiontest, two points lower on the reading comprehension test, and one point lower on thepicture vocabulary test. Having a condition that limits school activities appears to beassociated with quantitatively larger reductions in scores: approximately 15percentile points lower on the math test, 18 points on the reading recognition test,15 points on the reading comprehension test, and nine points on the picturevocabulary test.

Table 3 OLS and fixed effects estimates of health condition requiring medical attention


Coeff. St. Err. Coeff. St. Err. Coeff. St. Err. Coeff. St. Err.

OLS (constant only)

Health Condition −2.94a 0.81 −3.80a 0.87 −2.49a 0.86 −1.25 0.95

Family in poverty −13.1a 0.51 −13.0a 0.57 −13.3a 0.57 −17.2a 0.56

OLS (including explanatory variables)

Health Condition −4.54a 0.73 −4.03a 0.80 −2.38a 0.79 −1.38b 0.83

Family in poverty −1.12a 0.51 −1.02b 0.58 −1.46a 0.56 −1.56a 0.56

FIXED EFFECTS

Health Condition −0.37 0.58 0.10 0.55 1.20b 0.69 0.33 0.88

Family in poverty 0.06 0.44 1.08a 0.42 −0.05 0.49 0.18 0.54

a significant at 0.05 levelb significant at 0.10 level

Standard errors adjusted for clustering at child level.

Table 4 OLS and fixed effects estimates of health condition that limits school activities



OLS (constant only)

School condition −16.24a 1.19 −20.33a 1.27 −17.97a 1.29 −9.71a 1.35



School condition −15.00a 1.34 −17.56a 1.18 −14.97a 1.15 −8.61a 1.82


FIXED EFFECTS

School condition −0.59 0.82 −1.07 0.77 −0.68 0.94 −0.78 1.10

Family in poverty 0.06 0.44 1.09a 0.42 −0.04 0.49 0.18 0.54

a significant at 0.05 level

Standard errors adjusted for clustering at child level.


These numbers provide evidence that having a health condition and living inpoverty are significantly associated with cognitive development. However, theseresults must be interpreted with caution, as cognitive performance is likely to beinfluenced by other child- and family-specific factors separate from health andpoverty status, and these others factors are likely to be correlated with a child havinga medical condition or being in poverty.

Longitudinal Estimates

The bottom sets of results in Tables 3 and 4 show fixed effect estimates. Neitherhaving a condition requiring medical attention nor having a condition limiting schoolactivities appears to significantly encumber cognitive performance. In fact, lack ofstatistical significance notwithstanding, three of the four coefficients of the healthvariables in Table 4 are positive, one of which is marginally significant. For all fourtests scores, coefficients of both health measures are far smaller in magnitudecompared to corresponding estimates from the OLS models. The second row of eachtable shows the effects of poverty on test scores. Poverty is not a significantdeterminant of math, reading comprehension, or picture vocabulary scores, butcuriously, poverty is positively related to reading recognition performance. Equallyinteresting, coefficients of most of the other explanatory variables, aside from thepreviously-mentioned measures of child age and maternal education, lose statisticalsignificance and magnitude when fixed effects are included in the model. Thesefindings are similar to Kaestner and Grossman’s, (NBER Working Paper #13764)who find that weight is not related to cognitive scores after controlling for child-specific fixed effects.

Models that include lagged health and poverty indicators (results available uponrequest) produce results that are qualitatively, and in most places quantitatively, similarto results from our baseline models. Specifically, ordinary least squares regressionssuggest that health problems are negatively associated with cognitive scores 2 years inthe future. The effects of school-limiting health problems are larger in magnitude thanthe effects associated with conditions that require medical attention. Also similar to thebaseline models, almost all coefficients of interest are not statistically significant whenthe longitudinal nature of the data is taken into account.

Effects of Birth Weight

The estimates of the effects of birth weight on cognitive development are presentedin Table 5. In the OLS estimates birth weight, measured in pounds, is positivelyassociated with cognitive scores, although the results are quantitatively small. Achild born one pound heavier scores approximately one percentile point higher on allfour tests (approximately two percentile points higher when no control variables areincluded). Stated differently, low birth weight appears to be associated with slightlylower cognitive performance later in childhood.

To control for possible unobserved heterogeneity that might affect a baby’sweight at birth and later cognitive performance, Eqs. 3 and 4 are estimated by theinternal instruments approach outlined above. The model is identified so long as thevariance of error term in Eq. 4 exhibits heteroskedasticity. The large value of


the Breusch-Pagan test statistics, presented at the bottom of Table 5, indicates thepresence of heteroskedasticity. In contrast to results for health conditions requiringmedical attention and health problems that limit school activities, when unobservedheterogeneity is taken into account, the coefficients of birth weight increase inmagnitude and remain significant. A child born one pound heavier scoresapproximately three to four percentile points higher on cognitive tests. These resultsare similar to Matte et al. (2001) and Reichman (2005), who find that birth weight isrelated to intelligence quotient scores later in childhood.

Discussion

Several potential weaknesses of the empirical strategies should be noted. First, fixedeffects models can only control for the influence of unmeasured factors that are fixedover time. Second, although the internal instruments approach used to estimate theeffects of birth weight is capable of accommodating time-varying unobservedfactors, the implementation requires strong assumptions on the variance of the first-stage error term, although this assumption seems to be satisfied. Keeping thesepotential limitations in mind, the results of the models provide useful insights intothe links between early childhood health problems, poverty, and cognitiveachievement, an area of research that has received little attention regarding U.S.children.

The two health measures that vary over time and are likely acquired after birth donot appear to directly affect cognitive development once unobserved heterogeneity is

Table 5 OLS and two-stage least squares estimates of birth weight



OLS (constant only)

Birth weight (in pounds) 2.07a 0.23 1.65a 0.20 1.82a 0.22 2.16a 0.23




Family in poverty −1.03b 0.55 −0.94b 0.51 −1.41a 0.56 −1.54a 0.58

TWO-STAGE LEAST SQUARES


Family in poverty −0.91b 0.52 −0.81 0.60 −1.32a 0.53 −1.48a 0.55

Breusch-Pagan test of first-stage heteroskedasticity

11.92a

a significant at 0.05 levelb significant at 0.10 level

OLS standard errors adjusted for clustering at child level.

2SLS standard errors calculated by block bootstrap.


taken into account. Rather, cognitive development is primarily influenced byunobserved child- and family-specific factors that happen to be correlated withhealth and socioeconomic status. These unobserved factors might involve lifestyle,nutrition, educational environment, general parenting ability of mothers and/orfathers, and unreported diseases (or early stages of diseases). Note that theseunobserved factors do not include information already included in the model, such aspoverty, health insurance status, family size, and maternal marital, employment, andeducational status. On the other hand, birth weight appears to affect cognitiveperformance later in childhood, even after taking unobserved heterogeneity intoaccount. This result is intuitive, as birth weight is potentially beyond families’ andespecially children’s control.

Fixed effects estimates presented above suggest that temporary changes in afamily’s poverty status do not affect cognitive performance, but this does not implythat standard of living is not important. Indeed, Blau (1999) finds that permanentincome has a larger impact than temporary income. The more important implicationsof this paper relate to policies that aim to treat or eliminate childhood healthproblems. Although worthy endeavors in their own right, results in this papersuggest that public resources would be more optimally redirected to identifyunmeasured factors that simultaneously affect child health and cognitive perfor-mance. Finally, the results of this paper confirm findings from recent research thatlow birth weight is an important determinant of cognitive development. Therefore,increased resources should be devoted to identifying and treating prenatal factorsthat medical professionals suspect are correlated with birth weight, such as maternalhealth, environmental factors, and genetic anomalies.

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the effect of health and poverty on early childhood cognitive development

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