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

Kenneth Y. Chay, Brown University and NBERJonathan Guryan, University of Chicago GSB and NBER

Bhashkar Mazumder, Chicago Fed

March, 2009

1

Overview

• 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

2

Overview• 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

3

4

0

5

10

15

20

25

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75

PNM

R pe

r 100

0 bi

rths

Year

Trends in PNMR

Blacks, South Blacks, Rustbelt Whites, South Whites, Rustbelt

NMR vs PNMR

6

PNMR DeclineBegins in 1964

AFQT RiseBegins with1963 Cohort

AFQT Detail

Overview• 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

7

Overview

• 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)

8

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 menSummary stats

10

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

12Notes: Figure plots black and white average scaled NAEP Math and Reading score, along with their difference, by yearfor the entire United States, regression adjusted for race-specific subject and age effects.

13

Figure 2A: Black-white gap in standardized NAEP scores by calendar year of exam, United States

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Year of exam

Bla

ck-w

hite

ga

p in

sta

nd

ard

ize

d N

AE

P s

core

Reading Math 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.

14

16

Figure 2C. Black-white differences in NAEP scores by year of birth, US

0

1.5

3

4.5

6

7.5

9

10.5

12

13.5

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

Year of birth

Wh

ite N

AE

P s

core

(d

ivid

ed

by

s.d

. o

f sc

ore

s)

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

Bla

ck-w

hite

ga

p in

sta

nd

ard

ize

d N

AE

P s

core

White Black-white

17

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1953-54 1957-58 1961 1962-64 1965 1966-67 1968-69 1970-71 1972-73 1974-75 1976-77 1978-79

Year of birth

Bla

ck-w

hite

ga

p in

sta

nd

ard

ize

d N

AE

P s

core

South North

Figure 2D. Black-white cohort differences in NAEP scores, South vs North

Selection into who takes AFQT

• Approach to sample selection: 1. Rich set of fixed effects and differencing (including within region)

(one needs a complicated alternative story to explain results) 2. 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)19

p̂

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)22

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

27

28

0

10

20

30

40

50

60

70

0

2

4

6

8

10

12

14

16

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75

PNM

R, N

MR

per

1000

bir

ths

Year

B-W DIfference in PNMR, NMR, LBW in South

B-W,PNMR South B-W NMR South B-W, LBW South

Estimation Used for Figure 4

29

TicatW c

W tW a

W X icatW W icat

W

Bicat

BBicat

Ba

Bt

Bc

Bicat XT

(1a)

(1b)

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

cB c

W S

For each region or state we estimate:

(whites)

(blacks)

32

Fig 4A: Black-white gaps in South, Border, Rustbelt: PNMR

Fig 4B:Black-white gaps in South, Border, Rustbelt: AFQT

0

2

4

6

8

10

12

14

16

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75

PNM

R pe

r 100

0 bi

rths

Year

South Border Rustbelt

-26

-24

-22

-20

-18

-16

-14

-12

-10

-8

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75Pe

rcen

tile

Year

South Border Rustbelt

33

Fig 4C: Between region B-W gaps and white levels: PNMR

Fig 4D:Between region B-W gaps and white levels: AFQT

-4

-2

0

2

4

6

8

10

12

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75

PNM

R pe

r 100

0 bi

rths

Year

South-Rustbelt, B-W Border-Rustbelt, B-WSouth-Rustbelt, Whites

-6

-4

-2

0

2

4

6

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75Perc

entil

e

Year

South-Rustbelt, B-W Border-Rustbelt, B-WSouth-Rustbelt, Whites

Estimation Used for Tables

36

Baseline

(2a)

(2b)

Diff-in-diffs-in-diffs estimate:

S,ricat

S,rS,ricat

S,ra

S,rt

S,rc

S),r(post

S),r(pre

S,ricat

X

ccT

72197016219601

S,ricat

,rt

S,ricat

S,t

S,ricat

S,rS,ricat

,rat

S,at

S,ra

S,rt

S,rc

S),r(post

S),r(pre

S,ricat

XX

X

ccT

72197016219601

post(B ),2 post

(W ),2 pre(B ),2 pre

(W ),2 post(B ),1 post

(W ),1 pre(B ),1 pre

(W ),1 •Ex: S = 2 (South); S = 1 (Rustbelt)

•Alabama, Mississippi comparisons (sharp change): Contrast 1961-63 and 1966-68 birth cohorts

Age by year, Education by year interactions

37

Black-white difference in AFQT scoresEducation fixed effects Race-education fixed effects

Average in Change by Average in Change by1960-1962 1970-1972 1960-1962 1970-1972

(1a) (1b) (2a) (2b)

A. South -25.76*** 12.69*** -23.46*** 9.08***

(0.82) (0.79) (0.73) (0.62) {PNMR, birth year} {14.05} {-8.27}

B. Rustbelt -21.01*** 5.10*** -18.99*** 2.01**

(0.88) (0.86) (0.75) (0.66) {PNMR, birth year} {5.95} {-1.49}

C. South – Rustbelt -4.75*** 7.60*** -4.47*** 7.06***

(1.17) (1.13) (1.01) (0.88) {PNMR, birth year} {8.10} {-6.78}

Table 3: South v. Rustbelt AFQT Cohort Diff-in-diffs, 60-62 to 70-72

38

South-Rustbelt difference in black-white AFQT gap

(1) (2) (3) (4) (5) (6)

1960 to 1962 average -4.75*** -4.59*** -4.47*** -3.91*** -3.46*** ---

(1.17) (1.04) (1.01) (1.18) (1.05)

1960-1962 to 1970-1972 7.60*** 7.04*** 7.06*** 6.36*** 5.62*** 7.13***

Change (1.13) (0.81) (0.88) (1.18) (0.92) (1.22)

Region-race-cohort Y Y Y Y Y YRegion-race-time Y Y Y Y Y YRegion-race-age Y Y Y Y Y Y

Region-education Y Y Y Y YRace-education Y Y Y YRegion-race-education Y Y

Age-time Y Y YRegion-age-time Y YRace-age-time Y Y

Education-time Y Y YRegion-education-time Y YRace-education-time Y YRegion-race-educ-time Y

Table 4: South v. Rustbelt AFQT Cohort Diff-in-diffs, 60-62 to 70-72with various additional fixed effects

39

Comparison of black-white AFQT gaps in Alabama-Mississippi andIllinois-New York Tennessee-Virginia

(1a) (1b) (1c) (2a) (2b) (2c)

1961-1963 to 1969-1971 6.55 6.85 5.59 3.54 3.22 3.13 Change in AFQT gap [9.94] [5.80] [5.13] [10.33] [2.85] [3.16]

Change in black-white infant health gap PNMR (per 1,000) -5.25 -2.02 NMR (per 1,000) 1.80 -0.69 LBW (per 100) 1.13 0.29

State-race-cohort Y Y Y Y Y YState-race-time Y Y Y Y Y YState-race-age Y Y Y Y Y Y

Education fixed effects Y Y Y Y Y YState-education Y Y Y YRace-education Y Y Y YState-race-education Y Y Y Y

Age-time Y YRace-age-time Y YEducation-time Y YRace-education-time Y Y

Sample size 591,646 591,646 591,646 304,469 304,469 304,469

Table 5: Comparison of AL-MS and other state groups, 61-63 to 69-71with various additional fixed effects

40

…Back to the NAEP

40

• 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

41

Black-white difference in NAEP scores (in standard deviations)scores by birth cohort

(1971, 1980, 1990 surveys)Math scores by birth cohort

(1978, 1990 surveys)Early 50s and 60s cohorts Early 60s and 70s cohorts Early 60s and 70s cohorts

Average in Change by Average in Change by Average in Change by1953-1954 1962-1963 1962-1963 1972-1973 1961 1972-1973

(1a) (1b) (2a) (2b) (3a) (3b)A. South Black-white NAEP gap -1.300*** -0.222*** -1.522*** 0.828*** -1.281*** 0.698***

(0.031) (0.052) (0.042) (0.084) (0.030) (0.076)

Sample Size 9,966 5,020 7,164

B. North Black-white NAEP gap -1.201*** -0.086* -1.287*** 0.460*** -1.154*** 0.293***

(0.035) (0.048) (0.033) (0.073) (0.030) (0.072)

Sample Size 20,762 11,122 16,573

C. South – North Black-white NAEP gap -0.099** -0.136* -0.235*** 0.368*** -0.127*** 0.405***

(0.047) (0.071) (0.053) (0.111) (0.042) (0.104)

Sample Size 30,728 16,142 23,737

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?

42

• 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)

43

-4

-2

0

2

4

6

8

10

12

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2

Black-white PNMR gap (1968-70 minus 1962-64)

Bla

ck-w

hite

AF

QT

ga

p (

19

67

-69

min

us

19

61

-63

)

South Border States Rustbelt

y = 1.37 – 0.720∙x, R-squared=0.520 [2.57] [3.91]

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

44

Difference in AFQT gap between 1961-1963 and 1967-1969 birth cohorts (1) (2) (3) (4) (5) (6) Between cohort diff in racial gap in PNMR (per 1,000) -0.720*** -0.690*** -0.690*** -0.690*** -0.655***

[3.91] [3.62] [3.79] [3.93] [3.50] NMR (per 1,000) 0.358 0.200 0.187 [1.37] [0.95] [0.69] Migrate out of state 0.200** 0.174 (percent) [2.28] [1.28] Mother HS dropout -0.257* -0.095 (percent) [1.88] [0.40] Constant 1.37** 4.27*** [2.57] [7.32] R-squared 0.520 0.085 0.546 0.567 0.571 0.604 Number of states 22 22 22 22 22 22

Table 6: Association of racial convergence from the early to late 1960s birth cohortsin AFQT scores and infant mortality

45

-4

-2

0

2

4

6

8

10

12

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Black-white NMR gap (1968-70 minus 1962-64)

Bla

ck-w

hite

AF

QT

ga

p (

19

67

-69

min

us

19

61

-63

)

South Border States Rustbelt

y = 4.27 + 0.358∙x, R-squared=0.085 [7.32] [1.37]

Figure 6B: Pre-post changes in black-white AFQT and NMR

46

Figure 6D: Pre-post changes in black-white AFQT and PNMRMean, 75th percentile and 25th percentile

-6

-3

0

3

6

9

12

15

18

21

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2

Black-white PNMR gap (1970-72 minus 1962-64)

Bla

ck-w

hite

AF

QT

ga

p (

196

9-7

1 m

inu

s 1

961

-63

)

Mean 75th pct-tile 25th pct-tile

47

-36

-32

-28

-24

-20

-16

-12

-8

-4

1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973

Year of birth

Bla

ck-w

hite

AF

QT

ga

p

25th %tile Median 75th %tile

Appendix Fig. A2: B-W conditional quantile gap in AFQT in South

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

48

49

-8

-6

-4

-2

0

2

4

6

0 1 2 3 4 5 6-8 9-11 12-14 15-18

Age

Bla

ck-w

hite

ga

p in

ho

spita

l ad

mis

sio

n (

pe

r 1

00

bo

ys)

South, July 1962 to June 1964 South, July 1965 to June 1967 North, average

Figure 7A: Black-white hospital admission rate differences by age (boys)

50

Figure 7B: Convergence in B-W hospital admission gap after July 1962 to June 1964

-4

-2

0

2

4

6

8

10

0 1 2 3 4 5 6-8 9-11 12-14 15-18

Age

Ra

cia

l co

nve

rge

nce

in h

osp

ital a

dm

issi

on

(p

er

10

0 b

oys

)

South, by July 1965 to June 1967 South, by Jan. 1971 to Dec. 1972 North, by Jan. 1971 to Dec. 1972

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

51

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

52

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

53

54

Figure XX: Competing hypothesesA. Between area differences in black-white differences in mother’s education

-2.100

-1.800

-1.500

-1.200

-0.900

-0.600

-0.300

0.000

0.300

0.600

1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979

Child's year of birth

Bet

wee

n a

rea

, bla

ck-w

hite

diff

in m

othe

r's e

duc

atio

n

South-Rbelt (1960) South-Rbelt (1970) South-Rbelt (1980)

ALMS-MIOH (1960) ALMS-MIOH (1970) ALMS-MIOH (1980)

55

B. Between area differences in racial gap in probability of migration in and out

-50

-40

-30

-20

-10

0

10

20

30

40

1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979

Child's year of birth

Bet

we

en

are

a, b

lack

-wh

ite d

iff in

mig

ratio

n p

erc

en

t

ALMS-TNVA, In ALMS-MIOH, In ALMS-TNVA, Out ALMS-MIOH, Out

56

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.

Summary• 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

57

Summary

– 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

58