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Birth Cohort and the Black-White Achievement Gap: The Role of Health Soon After Birth. Kenneth Y. Chay, Brown University and NBER Jonathan Guryan, University of Chicago GSB and NBER Bhashkar Mazumder, Chicago Fed March, 2009. Overview.

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birth cohort and the black white achievement gap the role of health soon after birth

Birth Cohort and the Black-White Achievement Gap: The Role of Health Soon After Birth

Kenneth Y. Chay, Brown University and NBER

Jonathan Guryan, University of Chicago GSB and NBER

Bhashkar Mazumder, Chicago Fed

March, 2009

  • Large gap in measured skill between blacks and whites in US
    • Jencks & Phillips (1998); Dickens & Flynn (2006); Fryer & Levitt (2004,2006);
  • Evidence that the gap converged during 1980s
    • Jencks & Phillips (1998); Dickens & Flynn (2006); Neal (2006)
  • …but stopped in 1990s
    • Neal (2006)
  • We argue that much of the convergence in 1980s is due to cohort effects rather than year (of test) effects
    • Expands the set of possible explanations for the cause of convergence
    • Suggests that there is scope for interventions earlier in life
  • Cohort-based test score gains line up very well with improvements in infant health“infant health hypothesis”
    • Large reductions in infant mortality rates of blacks relative to whites in South in 1960s
    • About 70% of decline due to decline in Post Neonatal Mortality (PNMR = deaths b/w 28 days and 1 year/ 1000 live births)
    • Much smaller relative improvements in infant health for Blacks outside South
    • Improvement in black AFQT scores may have been a result of better black infant health of those born between 1963 and early 1970’s
    • Timing lines up in comparisons across regions as well as across states within the South

PNMR Decline

Begins in 1964


Begins with

1963 Cohort

AFQT Detail

  • Improvements in infant health may be due to hospital integration
    • Almond, Chay and Greenstone (2008) –hospital integration, Medicare
    • Present new data on change in hospital admissions by age
    • Test score gains much more highly correlated with PNMR than other measures of early life health (NMR flat, low birth weight gets worse!)
  • We briefly consider some competing explanations: family background, income, school desegregation and other civil rights era policies
    • Look at cohort timing (e.g. did they affect earlier cohorts?)
    • Within and across region variation (e.g. food stamp rollout begins in North)
    • Competing stories still suggest an important role for infant health
  • Results imply sizable long-term benefits to early life investments in health/human capital
  • Leave mechanisms to future research…one possibility
    • Large fraction of PNMR is complications from diarrhea
    • Medical studies link diahrrea in first two years of life to cognitive function
      • Lorentz et al (Pediatr Infect Dis J, 2006) link early childhood diahrrea to cognitive function 6 to 9 years later in study in Brazil shantytown(they also have multiple other published studies using a variety of cognitive outcomes)
test score data
Test Score Data

NAEP - Long term trends micro data (NAEP-LTT)

“Nation’s Report Card”

Random sample of 9, 13, and 17 year olds in school

Same testing frame – designed to be comparable over time

Math and Reading

1971 to 2004

About 525,000 students over 14 years

AFQT from US Military applicants

Universe of applicants from 1976 to 1991

Sample restrictions: men aged 17-18 or 17-20 at time of application.

AFQT score combines math and reading from ASVAB

Must correct for selection on those who choose to apply

Large sample: 2,916,935 (1,977,118) white men;

1,154,348 (725,480) black men

Summary stats


Figure 2A: Black and White NAEP Scores by Year, US

Notes: Figure plots black and white average scaled NAEP Math and Reading score, along with their difference, by year

for the entire United States, regression adjusted for race-specific subject and age effects.


Notes: Figure plots racial differences in average scaled NAEP Math and Reading scores, normalized by the standard deviation

of test scores by survey year, age, and subject. Subject-specific regressions adjust for race-specific age effects.

selection into who takes afqt
Selection into who takes AFQT

Approach to sample selection: 

Rich set of fixed effects and differencing (including within region)(one needs a complicated alternative story to explain results)

Use estimated selection probabilities, (inverted) as weights (IPW)Hirano, Imbens, and Ridder (Econometrica); Wooldridge (JOE)

Near ideal application of IPW because we know the universe of test takers

We divide the number of AFQT takers in (state-race-cohort-age-year) cells by population size of cell.

Denominators come from: i) births (Vital Statistics); and ii) cell population around test year (Censuses). Nearly identical results. (Detail)

This “removes” selection bias across cells.

Varies along full interaction of cohort-age-time – sweeps out added bias over and above fixed effects. (we cannot interact age by race by region by time )

In practice not much effect once we got to 17-18 yr old sample (chart)

selection charts
Selection Charts

Figure 3A: Shows prob of selection for 17 and 18 year olds separately in each region

Applications are countercyclical, blacks more likely to apply

Patterns similar across regions within age groups, ie. Differencing across regions handles a lot of selection

Clear time pattern in selection but NOT by cohort (e.g. renorming)

Figure 3B: Combines 17 and 18 year olds

South has slightly higher probability of enlistment

Minimal fluctuations in regional difference

Figure 3C: Shows Low Education (<= 2 years of Ed)

Not much variation –flat during the 1980s, Common to both regions

Figure 3D: Cross State Differences (Al/MS vs TN/VA, NY/IL)

a potential explanation the infant health hypothesis
A potential explanation: The Infant Health Hypothesis

Does the timing of the convergence in black-white PNMR by region & state match the convergence in the black-white AFQT scores?

Timing need not be in the exact same “years”

PNMR recorded by date of death, not birth

PNMR a proxy for infant health (e.g. 0-2 or 0-3 yr olds)Ex: Improvements in health of 0-24 month olds will show up as lower PNMR in the year following the year of birth. i.e. AFQT will lead PNMR by one year.

PNMR could lag actual improvements in latent infant health (Almond, Chay and Greenstone, 2008)

Inherent selection problem on survival, but arguably biases our results down

estimation used for figure 4
Estimation Used for Figure 4

For each region or state we estimate:





Subscripts: i(individual), c (birth year), a (age), t (calendar yr)

Estimate and plot separately by region/state (s)

Baseline group is 17 year olds in 1984, with 3-4 yrs of HS

How we separate cohort, age and year


Fig 4A:

Black-white gaps in South, Border, Rustbelt: PNMR

Fig 4B:

Black-white gaps in South, Border, Rustbelt: AFQT


Fig 4C:

Between region B-W gaps and white levels: PNMR

Fig 4D:

Between region B-W gaps and white levels: AFQT

estimation used for tables
Estimation Used for Tables



Age by year, Education by year interactions


Diff-in-diffs-in-diffs estimate:

  • Ex: S = 2 (South); S = 1 (Rustbelt)
  • Alabama, Mississippi comparisons (sharp change):
  • Contrast 1961-63 and 1966-68 birth cohorts

Table 4: South v. Rustbelt AFQT Cohort Diff-in-diffs, 60-62 to 70-72

with various additional fixed effects


Table 5: Comparison of AL-MS and other state groups, 61-63 to 69-71

with various additional fixed effects


…Back to the NAEP

  • AFQT results imply about 0.3 of standard deviation effect over 10 successive cohorts
  • Similar specification on 17 year olds in NAEP suggests 0.4 standard deviation gain
  • Recall, NAEP is representative so we’re not worried about selection



Table 1: Change between birth cohorts in black-white NAEP score gap of 17-year olds, South and North

how well does pnmr explain the cross cohort changes in afqt
How well does PNMR explain the cross-cohort changes in AFQT?
  • We know take the estimated cross-cohort change in AFQT from 61-63 to 67-69 in each of 22 states as dependent variable.
  • Run this on cross-cohort change in PNMR (62-64 to 68 to70)
  • Also include specifications with:
    • NMR
    • Mother’s education (percent hs dropouts from natality data)
    • Migration (percent moved out)

Table 6: Association of racial convergence from the early to late 1960s birth cohorts

in AFQT scores and infant mortality


Figure 6D: Pre-post changes in black-white AFQT and PNMR

Mean, 75th percentile and 25th percentile

why did black infant health improve
Why did black infant health improve?

Results point to a particular source that improved health in early life, the integration of southern hospitals

Almond, Chay and Greenstone (2008) show hospital integration led to reductions in black PNMR in Mississippi in particular

No corresponding effect on NMR

If right, raises question whether results are evidence of …

… stronger effects of health interventions at early ages

… or, only improvements in healthcare access at early ages

Use NHIS data on admissions to try tease this out

other potential causes of afqt infant health convergence
Other potential causes of AFQT & infant health convergence
  • War on Poverty led to many social programs implemented at similar time
  • However, alternative stories should have the following features:
    • Effects in the South but not North
    • Should affect successive cohorts
    • Should affect the same cohorts experiencing test score gains
    • Should match cross state differences in AFQT gains
  • School Desegregation
    • Slow rollout, only some urban districts by 1968
    • Deseg often either all-grades-at-once, or high-schools first
    • Deseg in ’68 should have affected those born before ’63
    • Empirically, we find that year effects dominate cohort effects
other potential causes of afqt infant health convergence1
Other potential causes of AFQT & infant health convergence
  • Civil Rights Act
    • Parental permanent income gains should have affected earlier cohorts
    • Empirically does not explain cross-state gains (within South) in AFQT
    • Perceived increased returns to investing in HK would have to be sudden
  • AFDC: Caseload growth in AL-MS below national average in this period
  • Food stamps: (Hoynes & Schanzenbach)
    • AL-MS-NC rolled out Food stamps later than IL-OH-MI, mostly after ’67
    • Target of early rollout in South was predom. white rural counties
  • Medicaid: AL-MS last to adopt Medicaid Jan. 1, 1970
other potential causes of afqt infant health convergence2
Other potential causes of AFQT & infant health convergence
  • Head Start (from Ludwig & Miller data):
    • AL-MS had less penetration in ’68 than IL-MI-OH, and no more growth ’68-’72
    • Many early Head Start programs segregated in South
  • Family Background
    • Secular gains in decades prior to 1960s
    • Not explain cross-state gains in test scores
  • Many alternate explanations still imply an important LR effect of early life investments in health, HK on HK accumulation

Figure XX: Competing hypotheses

A. Between area differences in black-white differences in mother’s education


C. Between area differences in log income of black men

Notes: Based on Social Security tax records merged to the March 1978 Current Population Survey. Results come from a series of annual cross-sections that use the Tobit model to correct for censoring due to top-coding at the taxable maximum. Sample is restricted to 19-51 year-old black men.

  • In both NAEP and Military Applicant AFQT
    • Increases in black test scores beginning with the 1963 birth cohort
      • Over 10 birth cohorts black-white test score gap closedby about 40 % of SD in NAEP, and by about 30 % of SD in AFQT
    • Cohort-based convergence only seen in South, and only among blacks
  • Possible explanation
    • Infant Health Hypothesis
    • Lines up with timing of convergence in infant health measure (PNMR)
      • South v. Rustbelt
      • AL/MS v. TN/VA v. SC/NC
  • Cohort convergence in AFQT appears closely related to PNMR but not NMR or LBW
  • Strongly suggestive evidence that hospital integration may have played an important role.
  • Implies early life investments in health and human capital have important long-term effects
  • Mechanism unclear
    • PNMR used as proxy for infant health
    • Diarrhea/pneumonia are leading causes of PNMR
      • ECD linked to cognitive skills
  • Plan to assess costs/benefits of greater hospital access