Chapter 15 - Human capital development before age five*

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Abstract

This chapter seeks to set out what economists have learned about the effects of early childhood influences on later life outcomes, and about ameliorating the effects of negative influences. We begin with a brief overview of the theory which illustrates that evidence of a causal relationship between a shock in early childhood and a future outcome says little about whether the relationship in question is biological or immutable. We then survey recent work which shows that events before five years old can have large long term impacts on adult outcomes. Child and family characteristics measured at school entry do as much to explain future outcomes as factors that labor economists have more traditionally focused on, such as years of education. Yet while children can be permanently damaged at this age, an important message is that the damage can often be remediated. We provide a brief overview of evidence regarding the effectiveness of different types of policies to provide remediation. We conclude with a list of some of the many outstanding questions for future research.

Introduction

The last decade has seen a blossoming of research on the long term effects of early childhood conditions across a range of disciplines. In economics, the focus is on how human capital accumulation responds to the early childhood environment. In 2000, there were no articles on this topic in the Journal of Political Economy, Quarterly Journal of Economics, or the American Economic Review (excluding the Papers and Proceedings), but there have been five or six per year in these journals since 2005. This work has been spurred by a growing realization that early life conditions can have persistent and profound impacts on later life. Table 1 summarizes several longitudinal studies which suggest that characteristics that are measured as of age 7 can explain a great deal of the variation in educational attainment, earnings as of the early 30s, and the probability of employment. For example, McLeod and Kaiser (2004) use data from the National Longitudinal Surveys and find that children’s test scores and background variables measured as of ages 6 to 8 predict about 12% of the variation in the probability of high school completion and about 11% of the variation in the probability of college completion. Currie and Thomas (1999b) use data from the 1958 British Birth Cohort study and find that 4% to 5% of the variation in employment at age 33 can be predicted, and as much as 20% of the variation in wages. Cunha and Heckman (2008) and Cunha et al. (2010) estimate structural models in which initial endowments and investments feed through to later outcomes; they arrive at estimates that are of a similar order of magnitude for education and wages. To put these results in context, labor economists generally feel that they are doing well if they can explain 30% of the variation in wages in a human capital earnings function.

This chapter seeks to set out what economists have learned about the importance of early childhood influences on later life outcomes, and about ameliorating the effects of negative influences. We begin with a brief overview of the theory which illustrates that evidence of a causal relationship between a shock in early childhood and a future outcome says little about whether the relationship in question is biological or immutable. Parental and social responses are likely to be extremely important in either magnifying or mitigating the effects of a shock. Given that this is the case, it can sometimes be difficult to interpret the wealth of empirical evidence that is accumulating in terms of an underlying structural framework.

The theoretical framework is laid out in Section 2 and followed by a brief discussion of methods in Section 3. We do not attempt to cover issues such as identification and instrumental variables methods, which are covered in some depth elsewhere (cf Angrist and Pischke (2009)). Instead, we focus on several issues that come up frequently in the early influences literature, including estimation using small samples and the potentially high return to better data.

Section 4 discusses the evidence for long-term effects of early life influences in greater detail, while Section 5 focuses on the evidence regarding remediation programs. The discussion of early life influences is divided into two subsections corresponding to in utero influences and after birth influences. The discussion of remediation programs starts from the most general sort of program, income transfers, and goes on to discuss interventions that are increasingly targeted at specific domains. In surveying the evidence we have attempted to focus on recent papers, and especially those that propose a plausible strategy for identifying causal effects. We have focused on papers that emphasize early childhood, but in instances in which only evidence regarding effects on older children is available, we have sometimes strayed from this rule. A summary of most of the papers discussed in these sections is presented in Tables 4 through 13. A list of acronyms used in the tables appears in Appendix A. We conclude with a summary and a discussion of outstanding questions for future research in Section 6.

Section snippets

Conceptual framework

Grossman (1972) models health as a stock variable that varies over time in response to investments and depreciation. Because some positive portion of the previous period’s health stock vanishes in each period (e.g., age in years), the effect of the health stock and health investments further removed in time from the current period tends to fade out. As individuals age, the early childhood health stock and the prior health investments that it embodies become progressively less important.

In

Methods

As discussed above, we confine our discussion to methodological issues that seem particularly germane to the early influences literature. One of these is the question of when sibling fixed effects (or maternal fixed effects) estimation is appropriate. Fixed effects can be a powerful way to eliminate confounding from shared family background characteristics, even when these are not fully observed. This approach is particularly effective when the direction of unobserved sibling-specific

Empirical literature: evidence of long term consequences

What is of importance is the year of birth of the generation or group of individuals under consideration. Each generation after the age of 5 years seems to carry along with it the same relative mortality throughout adult life, and even into extreme old age.

Kermack et al. (1934) in The Lancet (emphasis added).

In this section, we summarize recent empirical research findings that experiences before five have persistent effects, shaping human capital in particular. A hallmark of this work is the

Empirical literature: policy responses

The evidence discussed above indicates that prenatal and early childhood often have a critical influence on later life outcomes. However, by itself this evidence says little about the effectiveness of remediation. Hence, this section discusses evidence about whether remediation in the zero to five period can be effective in shaping future outcomes. In so doing, we take a step away from explicit consideration of an early-childhood shock ug as in Section 2. Instead, we focus on the specific

Discussion and conclusions

There has been an explosion of research into the early determinants of human capital development over the past 10 years. The work surveyed in this chapter conclusively shows that events before five years old can have large long term impacts on adult outcomes. It is striking that child and family characteristics measured at school entry do as much to explain future outcomes as factors that labor economists have more traditionally focused on, such as years of education. Yet evidence for long term

Appendix A

The following acronyms are used in this chapter:

AFDC = Aid to Families with Dependent Children

BCS = British Birth Cohort Study of 1970.

BPI = Behavioral Problems Index

BW = birth weight

CESD = Center for Epidemiological Depression scale

CCT = Conditional Cash Transfer

COHS = County Organized Health System

CPS = Current Population Survey

DDST = Denver Developmental Screening Test

ECLS-B = Early Childhood Longitudinal Study—Birth Cohort

ECLS-K = Early Childhood Longitudinal Study—Kindergarten Class of

Appendix B

Human capital of a child is produced with a CES technology:h=Aγ(I¯1+μg)ϕ+(1γ)I2ϕ1/ϕ,where μg is an exogenous shock to (predetermined) period 1 investments. Parents value their consumption and the human capital of their child:Up=U(C,h)=Bθ(C)φ+(1θ)hφ1/φ,and have the budget constraint:I¯1+I2+C=y¯.

Absent discounting, the marginal utility from consuming equals the marginal utility from investing:UC*=δUδhδhδhδhδI2*.θCφ1=(1θ)hφ1A[]1ϕ1(1γ)I2*ϕ1θ(y¯I¯1I2*)φ1=(1θ)Aφ1[]φ1φA[]1ϕ1(1γ)I2*

Appendix C

Sibling a has human capital ha, which is affected by a period 1 investment shock of μg:ha=Aγ(I¯1a+μg)ϕ+(1γ)I2aϕ1/ϕ.

Sibling b does not experience a shock to first period investments:hb=BγI¯1bϕ+(1γ)I2bϕ1/ϕ.

Assume further that first period investments do not distinguish between the two siblings (absent the shock experienced by sibling a):I¯1a=I¯1b=I¯1.

Parents have Cobb-Douglas utility that cares only about the human capital of their two children:Up=U(ha,hb)=(1α)logha+αloghb.

The parents exhaust

Appendix D

In general, we need to observe the baseline investments I¯1 and I¯2 to estimate parameters of the production function ϕ and γ. However, nearly all datasets with measures of human capital h and an observable investment shock μg lack measures of human capital investments I¯1 and I¯2. We can still make progress in estimating parameters of the production function despite not observing I¯1 and I¯2, so long as we expect baseline investment levels to be similar: I¯1I¯2. For μg = μg,17

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    *

    We thank Maya Rossin and David Munroe for excellent research assistance, participants in the Berkeley Handbook of Labor Economics Conference in November 2009 for helpful comments, and Christine Pal and Hongyan Zhao for proofreading the equations.

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