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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.

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Presentation on theme: "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."— Presentation transcript:

1 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,

2 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

3 Overview Cohort-based test score gains line up very well with improvements in infant healthinfant 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 1970s – Timing lines up in comparisons across regions as well as across states within the South 3

4 4 NMR vs PNMR

5 5

6 6 PNMR Decline Begins in 1964 AFQT Rise Begins with 1963 Cohort AFQT Detail

7 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!)low birth weight 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

8 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

9 9

10 Test Score Data NAEP - Long term trends micro data (NAEP-LTT) Nations Report Card Random sample of 9, 13, and 17 year olds in school17 year olds 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 or 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 10

11 11

12 Figure 2A: Black and White NAEP Scores by Year, US 12 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.

13 13 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 14

15 Figure 2C: Black and white NAEP scores by year of birth, US 15 Notes: Figure plots black and white average scaled NAEP Math and Reading score, along with their difference, by year of birth for the entire US, regression adjusted for race-specific subject and age effects. Cohort trend Break ?

16 16 Figure 2C. Black-white differences in NAEP scores by year of birth, US

17 17 Figure 2D. Black-white cohort differences in NAEP scores, South vs North

18 Fraction of cohort with >=12 years completed education, by race and region (2000 census) 18 Return

19 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)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 yr old sample (chart)chart 19

20 Inverse Probability Weighting We estimate denominators in 3 ways: IPW_ST - Natality files number that survive to age 1 By cohort*race*state IPW_CENSUS_ST - Census number living in state at entry time By cohort*race*state Use decennial census, and where lived 5-years ago to get estimates Use closest estimate (e.g for where cohort lived in 1979). IPW_CENS_EDCAT - Census number living in state at entry time, by education By cohort*race*state*education Education is years of completed education at entry – 11 Use nearest census to estimate fraction of cohort*race*state in each completed education category 20 Return

21 21 White and Black-White differences in AFQT scores, uncorrected and corrected for sample selection, South-Rustbelt Difference Return

22 Selection Charts Figure 3A: Shows prob of selection for 17 and 18 year olds separately in each region Figure 3A – 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 Figure 3B – South has slightly higher probability of enlistment – Minimal fluctuations in regional difference Figure 3C: Shows Low Education (<= 2 years of Ed) Figure 3C: – Not much variation –flat during the 1980s, Common to both regions Figure 3D: Cross State Differences (Al/MS vs TN/VA, NY/IL) Figure 3D: 22

23 23 Return

24 24 Return

25 25 Figure 3C: Fraction of Low Education (<= 2 years of hs) taking AFQT by test year Return

26 26 Figure 3D: Diff in selection AL-MS v. TN-VA & IL-NY Return

27 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 28

29 Estimation Used for Figure 4 29 (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 For each region or state we estimate: (whites) (blacks)

30 How We Identify Cohort Effects Well known problem that cohort, age and time cannot be separately identified without some assumptions Advantages to our AFQT data – Cohorts are observed taking test in multiple years – We have exact date of birth and date of entry, therefore, using annual measures, we observe individuals from the same cohort at two different ages in a given year (smooths out cohort and year, Appendix Table)Appendix Table Include dummy for age 18, year dummies (drop 1 year –not 2), full set of cohort dummies We do not control for race-age-year interactions, so cohort picks up differences in age profile across years Results are not sensitive to how we normalize time – Extensive set of robustness checks (e.g. Deaton and Paxson (1995) approach) 30

31 31 Table A1: Age of entry by year of birth and year of AFQT exam

32 32 Fig 4A: Black-white gaps in South, Border, Rustbelt: PNMR Fig 4B: Black-white gaps in South, Border, Rustbelt: AFQT

33 33 Fig 4C: Between region B-W gaps and white levels: PNMR Fig 4D: Between region B-W gaps and white levels: AFQT

34 34 Figure 6: Cross-state differences A. PNMR within South B. AFQT within South

35 35 Figure 6: Cross-state differences: AL-MS and other state groups C. PNMR C. AFQT

36 Estimation Used for Tables 36 Baseline (2a) (2b) Diff-in-diffs-in-diffs estimate: Ex: S = 2 (South); S = 1 (Rustbelt) Alabama, Mississippi comparisons (sharp change): Contrast and birth cohorts Age by year, Education by year interactions

37 37 Black-white difference in AFQT scores Education fixed effectsRace-education fixed effects Average inChange byAverage inChange by (1a)(1b)(2a)(2b) A. South *** *** *** 9.08 *** (0.82)(0.79)(0.73)(0.62) {PNMR, birth year}{14.05}{-8.27} B. Rustbelt *** 5.10 *** *** 2.01 ** (0.88)(0.86)(0.75)(0.66) {PNMR, birth year}{5.95}{-1.49} C. South – Rustbelt *** 7.60 *** *** 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, to 70-72

38 38 South-Rustbelt difference in black-white AFQT gap (1)(2)(3)(4)(5)(6) 1960 to 1962 average *** *** *** *** *** --- (1.17)(1.04)(1.01)(1.18)(1.05) to *** 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-cohortYYYYYY Region-race-timeYYYYYY Region-race-ageYYYYYY Region-educationYYYYY Race-educationYYYY Region-race-educationYY Age-timeYYY Region-age-timeYY Race-age-timeYY Education-timeYYY Region-education-timeYY Race-education-timeYY Region-race-educ-timeY Table 4: South v. Rustbelt AFQT Cohort Diff-in-diffs, to with various additional fixed effects

39 39 Comparison of black-white AFQT gaps in Alabama-Mississippi and Illinois-New YorkTennessee-Virginia (1a)(1b)(1c)(2a)(2b)(2c) to 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) NMR (per 1,000) LBW (per 100) State-race-cohortYYYYYY State-race-timeYYYYYY State-race-ageYYYYYY Education fixed effectsYYYYYY State-educationYYYY Race-educationYYYY State-race-educationYYYY Age-timeYY Race-age-timeYY Education-timeYY Race-education-timeYY Sample size591, ,469 Table 5: Comparison of AL-MS and other state groups, to with various additional fixed effects

40 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 were not worried about selection

41 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 cohortsEarly 60s and 70s cohorts Average inChange byAverage inChange byAverage inChange by (1a)(1b)(2a)(2b)(3a)(3b) A. South Black-white NAEP gap *** *** *** *** *** *** (0.031)(0.052)(0.042)(0.084)(0.030)(0.076) Sample Size9,9665,0207,164 B. North Black-white NAEP gap *** * *** *** *** *** (0.035)(0.048)(0.033)(0.073)(0.030)(0.072) Sample Size20,76211,12216,573 C. South – North Black-white NAEP gap ** * *** *** *** *** (0.047)(0.071)(0.053)(0.111)(0.042)(0.104) Sample Size30,72816,14223,737 Table 1: Change between birth cohorts in black-white NAEP score gap of 17- year olds, South and North

42 How well does PNMR explain the cross-cohort changes in AFQT? 42 We know take the estimated cross-cohort change in AFQT from to 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 Mothers education (percent hs dropouts from natality data) Migration (percent moved out)

43 43 Figure 6A: Pre-post changes in black-white AFQT and PNMR

44 44 Table 6: Association of racial convergence from the early to late 1960s birth cohorts in AFQT scores and infant mortality

45 45 Figure 6B: Pre-post changes in black-white AFQT and NMR

46 46 Figure 6D: Pre-post changes in black-white AFQT and PNMR Mean, 75 th percentile and 25 th percentile

47 47 Appendix Fig. A2: B-W conditional quantile gap in AFQT in South

48 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 49 Figure 7A: Black-white hospital admission rate differences by age (boys)

50 50 Figure 7B: Convergence in B-W hospital admission gap after July 1962 to June 1964

51 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

52 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,

53 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 – 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 54 Figure XX: Competing hypotheses A. Between area differences in black-white differences in mothers education

55 55 B. Between area differences in racial gap in probability of migration in and out

56 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 year-old black men.

57 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 closed by 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

58 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

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