Environmental Inequality and the Black-White Test

Score Gap Anna Aizer

Janet CurriePeter SimonPatrick Vivier

Racial Disparities in Test Scores

Large literature examining causes of the gap• Reviewed by Jencks and Phillips, eds (2008 and 2011) and Magnuson

and Waldfogel, eds (2008)• Consider the following factors:

• Family income• School segregation• Family structure• Parenting practices• Quality of educational inputs• School segregation (Reber, 2010)• Neighborhood segregation (Card and Rothstein, 2007)

• Even considering all the above factors, substantial gaps remain

New sources to consider?

• Jencks and Phillips (2008) recommend looking at other, less traditional sources, to explain the persistence of the gap• Chay, Guryan and Mazumder (2009) alternative explanation: relative

improvement in infant health of African Americans for cohorts born in the American south between 1963 and 1971.

New Source: Environmental Inequality• Large literature documenting that African American and low income

families’ greater exposure to environmental toxins (Brown, 1995; Brulle and Pellow, 2006; Ringquist, 2005; Mohai and Saha, 2006)• Cause of the disparities:• Intentional siting of hazards in low income/minority neighborhoods• Housing prices reflect environmental quality (Chay and Greenstone, 2005;

Currie et al, 2014)• Disparities in regulatory response to an existing hazard

Environmental Justice (EJ)

• Inequity in the regulatory response to pollution by race/class led to new federal regulation in 1994:• “Preventing discrimination in the development, implementation and

enforcement of environmental laws, regulations and policies” (EPA)

• Research examining the extent to which the regulatory response has become less discriminatory is mixed• Sites in areas with a high share of black families or poor families still less likely

to receive superfund designation (O’Neal, 2007)• Conditional on superfund designation, no longer appears to be any disparity

in the duration of clean up (Burda and Harding, 2014)

The Questions:

• Can environmental inequality explain any of the racial gap in test scores?• African-Americans disproportionately exposed to pollutants• Conditional on exposure, may have fewer resources to counter negative

effects (eg, nutrition)

• Can environmental regulation reduce disparities in test scores?

Focus on a Specific Pollutant: Lead• Long epidemiological literature linking lead exposure with diminished

cognition• Lead has no biological value and is toxic. Mimics calcium in the body,

interfering with renal, endocrine, cardiovascular and nervous systems.• In the brain, lead damages the prefrontal cerebral cortex, hippocampus and

cerebellum (Finkelstein et al, 1998)• Nutrition affects rates of absorption

• Children particularly sensitive (Lidsky and Schneider, 2003) • Greater exposure (inhalation and ingestion)• Conditional on exposure, lead more likely to be absorbed by children• Conditional on absorption, lead more likely to affect a developing nervous

system

Historical Sources of Lead

• Gasoline: in the 1970s, a gallon of gasoline contained 4.0 grams of lead• Household Paint: contained high lead content (10-40% for some

colors) prior to world war II• Post war, manufacturers drastically reduced the amount of lead contained in

paint.

Regulatory Response

• 1970, US Surgeon General issued his first formal statement on lead poisoning, naming it a national health problem. • Since then, 2 major regulatory changes:• Eliminating lead from gas: 1985 0.5 grams/gallon, 1986 0.1 grams/gallon,

1996 zero• Eliminating lead from household paint: completely de-leaded in 1978

• Lead in the air has declined from 2.5 ug/m3 in 1980 to less than 0.5 by 2012 (EPA)• Nationally, preschool age children with lead levels in excess of 10 has

declined from 8.6% in 1998 to 1.3% in 2004.

Is Lead Still a Problem?• Lead remains a hazard due to residual lead found in the air, dust and soil. • 4.5 million households in the US are still exposed to high levels of lead

and half a million preschool aged children have elevated blood lead levels (CDC, 2011)• Disadvantaged children disproportionately affected (1999-2002 NHANES)

• Lead levels 50% higher for African American Children• Lead levels 30% higher for low income children

• Elimination of elevated blood levels a goal in Healthy People 2020, the ten year national objectives for improving the health of all Americans• More recent focus on the danger of low lead levels.

• CDC “lead levels of concern” which have declined over time from 60, to 10 to 5 and now they no longer use the term – any level above zero is a concern.

Greater Exposure of African-Americans to Lead • Nationally, African Americans more likely to live in old (pre 1978)

housing• 73% of African Americans vs. 63% of whites• Racial disparities greater than income disparities

• Racial differences greater when we limit to within region/area

RI Share in Housing BuiltPre 1978 Pre 1945

Black 0.83 0.52White 0.74 0.37Hispanic 0.83 0.53<=100% FPL 0.81 0.43>=200% FPL 0.76 0.4

African American Families Concentrated in Historic, Urban Centers

Providence Rest of RI

Share housing built pre 1978 0.81 0.68Share housing built pre 1945 0.49 0.27

Share black population living in: 0.86 0.14Share white population living in: 0.51 0.49

Share population <100% FPL living in: 0.77 0.23Share population >100% FPL lining in: 0.55 0.45

Share population <100% FPL & black living in: 0.89 0.11Share population <100% FPL & white living in: 0.6 0.4Calculations from US census IPUMS data for 1990, 2000 and 2010

Sample includes all children <=10 years old

Structure of This Paper:

• Document disparities in lead levels by race and income• Examine a policy aimed at reducing lead levels among RI children• Disproportionate declines in the lead levels of African American & the poor

• Link declining disparities in lead levels with declining disparities in test scores• Establish a causal relationship between lead levels and future cognitive

achievement• Identification issues

Data

• Data on child blood lead levels (BLLs) in first 72 months of life (RIDOH) linked with third through eight grade test scores (RIDE)• On average, 4.7 (median=4) BLLs per child• Date of test, location of child, method, level• NECAP test scores for reading and math in grades 3-8, free-lunch status, IEP• Linked with vital statistics data: maternal race, ethnicity, education, marital

status, birth weight, gender, month prenatal care initiated, birth order• Covers birth cohorts 1997-2004 in the state.

• In RI, 80% of all children screened at least once by 36 months• In RI, 15-20% in private school as of 2010• Final sample of 57,000 linked children

Part I: Documenting Disparities in Lead Levels & Trends

Appendix Table 1: Within Tract Correlations Between Lead Levels and Child and Family Characteristics

Avg lead level Married at Birth -0.244

[0.0214]Maternal age at birth -0.0280

[0.00174]Mother African-American 0.328

[0.0325]Maternal education in years -0.0526

[0.00371]Mother has at least one risk factor 0.172

[0.0223]Always free/reduced lunch 0.359

[0.0267]male 0.169

[0.0160]Birth order 0.207

[0.00870]Birth weight/100 -0.0147

[0.00180]Gestation in Weeks 0.0389

[0.00510]Month prenatal care initiated 0.0865

[0.00730]

Observations 53,994R-squared 0.206

Part II: Why the disproportionate decline in lead levels for some groups?• Policy by the DOH and the HRC to reduce lead levels in RI children• DOH: Required all landlords of homes in which a child had an elevated lead level to obtain a

“Lead Free” certificate.• State AG to file suit against non-compliant landlords

• HRC: Required all landlords to obtain “Lead Safe” certificates to rent their homes• Provided training and information to landlords about lead abatement• Low-interest loans for abatement: section 8, family day care• No resources for enforcement, only the threat of litigation• Target neighborhoods with historically high lead burden – contracted with community based

organizations in the 4 “core cities”, the oldest cities in the state with greatest share of disadvantaged children• 35% of children in the core cities poor vs. 9.4% rest of the state• 36% of children in the core cities African American vs. 2% rest of state• 73% of African American children and 46% of the poor live in the core cities

• In 1997, 331 certificates issued; by 2010, 41,000 had been issued.

Did Certificates Work?

Leadi= α1certificates/old homesnt + α2 Xic + α3Xi

m +

α4 Xntn + γn + γt + µi

• Xic : BW, birth order, gestation, gender, year of birth, free lunch status

• Xim: Maternal characteristics: race, ethnicity, years of schooling, marital

status at birth, month prenatal care initiated, maternal age at birth• Xn

tn : share poor, median income, share African American, share Hispanic, share housing build pre-war, share housing built post 1978, ln(population)• Year of birth and tract FE

Table 1: First Stage - Predicting a Child's Average Lead Level(1) (2) (3) (4)

DOH certificates at birth/old housing units -0.0878 -0.184

[0.0152] [0.0285]

(DOH certificates at birth/old housing units) Squared 0.000502

[0.000125]

HRC certificates at birth/old housing units -0.113

[0.0147]

Loans at birth/old housing units -0.00707

[0.00280]

Observations 57,303 57,303 57,303 57,303

R-squared 0.204 0.204 0.205 0.204

F Statistic (excluded instruments) 33.18 17.19 59.00 12.25

Table 3: Certificates and Lead Levels: Heterogeneity by Child Characteristic (1) (2) (3) (4)

DOH certificates at birth/old housing units -0.0785 -0.0372 -0.129 -0.0836

[0.0156] [0.0252] [0.0394] [0.0174](DOH Certificates at birth/old housing)*Black -0.0637

[0.0232](DOH Certificates at birth/old housing)*Free Lunch -0.0630

[0.0250](DOH Certificates at birth/old housing)*Maternal Education 0.00347

[0.00308](DOH Certificates at birth/old housing)*Male -0.00817

[0.0162]

Observations 57,303 57,303 57,303 57,303

R-squared 0.205 0.204 0.204 0.204

Can lead certificates explain disproportionate decline in lead among black children?• More certificates in neighborhoods with a larger share black• Within neighborhoods, black children’s lead levels disproportionately

affected by certificate availability• Of the 2.3 point decline in average lead levels among African

Americans for 1998-2004 birth cohorts, 21% is explained by the rise in certificates

Part III: Does Lead Exposure Reduce Cognitive Achievement?• Review the existing work• Discuss identification issues & strategy• Present IV estimates based on certificates as an IV• Examine impact of lead exposure on • Reading and math scores• Distributional impacts• Test scores over time (3rd through 8th grade)• Heterogeneous effects by child characteristic

Existing Research on Lead and Child Outcomes• Long epidemiological literature linking lead and cognitive and non-

cognitive outcomes• Known for centuries that high levels of lead are associated with worse

cognitive outcomes• More recent focus on low levels of lead• Individual-level analysis:

• linked preschool lead levels with test scores and behavioral outcomes including ADHD and hyperactivity (Chardramouli et al, 2009; Canfield et al, 2003; Nigg et al, 2010, and Wasserman, 1997).

• McLaine et al (2013) links preschool lead levels with kindergarten reading readiness among Providence children.

Two Challenges to Estimating

• Endogneity of lead exposure: omitted variable bias/confounding• Bellinger (2008) “It has long been recognized that due consideration must be

given to the possibility that any observed association between lead exposure and neurodevelopment is, in part, an artifact of residual confounding…Indeed, adjusting for SES and related covariates can result in reductions of 50% of more in the magnitude of lead’s regression coefficient.”

Measurement Error in Blood Lead Levels (BLLs)

1. Individual blood lead levels (BLLs) measured with considerable error• “The ratio of imprecision to measurement value, particularly at BLLs

<10ug/dL, is relatively high.” (CDC, 2007) • The CDC allows measurement error up to 4 ug/dl or 10%, whichever is higher.

2. Infrequent testing & short half life of lead in blood• We have on average 4 measures for each child for the first 72 months of life• Lead half life in blood is 36 days

• Remains in tissue much longer

Cohort Level Analysis – identification more straight forward, but ecological fallacy• Lead and Cognition• Ferrie, Rolf and Troesken (2012) – variation from lead piping, examining

impact on intelligence tests of WWII enlistees. • Effects only for low SES children.

• Lead and Crime• Masters et al(1998); Reyes (2007); Nevin (2000 and 2007); Mielke and Zahran

(2012); Nilsson (2014) and Reyes (2014)• Dramatic declines in lead levels (from the de-leading of gasoline) linked with

the dramatic declines in crime rates

Lead, SES and Cognitive Outcomes

IV Strategy

• Use certificates in one’s tract at birth to identify impact of lead on future cognitive achievement• Exclusion restriction: neighborhoods that see more growth in the

number of certificates not otherwise changing in ways that would independently affect child cognitive outcomes

Table 4: Lead and Reading Test Scores (Avg)

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

OLS Tract FE Tract FE Tract FE IV Mom FE Mom FE IV

Avg lead level -1.029 -0.274 -1.931 -0.105 -3.197

[0.0188] [0.0190] [0.837] [0.0413] [1.609]

Randomly Drawn Lead Level -0.0792

[0.0142]

Observations 60,582 57,310 57,310 57,303 57,310 57,303

R-squared 0.048 0.138 0.136 0.062

Table 5: Lead Levels and Third Grade Reading Proficiency (1) (2) (3) (4) (5) (6)

Substantially below Proficient <30 Proficient >=40 with Distinction >56

Panel A: Reading Tract FE IV Tract FE IV Tract FE IV

Avg lead level 0.00541 0.0790 -0.00858 -0.176 -0.00281 0.0205

[0.000537] [0.0261] [0.000795] [0.0450] [0.000729] [0.0308]

Observations 53,978 53,972 53,978 53,972 53,978 53,972

R-squared 0.033 0.067 0.058

Average of dependent variable 21% 71% 10%

Table 5: Lead Levels and Third Grade Math Proficiency (1) (2) (3) (4) (5) (6)

Substantially below Proficient <30 Proficient >=40 with Distinction >56

Panel B: Math Proficiency Tract FE IV Tract FE IV Tract FE IV

Avg lead level 0.00484 0.0644 -0.00479 -0.0316 -0.00176 0.00381

[0.000619] [0.0279] [0.000844] [0.0355] [0.000591] [0.0246]

Observations 54,035 54,029 54,035 54,029 54,035 54,029

R-squared 0.032 0.066 0.034

Average of dependent variable 12% 60% 14%

Table 6: Lead Levels and Reading Proficiency Over Time

(1) (2) (3) (4)

Third Grade Eighth Grade

FE FE IV FE FE IV

Avg lead level -0.00792 -0.0710 -0.00782 -0.120

[0.00135] [0.0626] [0.00111] [0.0648]

Observations 16,511 16,511 17,850 17,850

R-squared 0.069 0.070

2 sets of robustness checks

• Alternative measures of lead• Alternative sets of Instruments

Table 7: Different Measures of Lead and Reading Test Scores (Avg) (1) (2) (3) (4) (5) (6) (7) (8)

FE IV FE IV FE IV FE IV Average level - censor at 25 -0.278 -1.925

[0.0192] [0.833]Lead Area Under the Curve (AUC) -0.252 -1.038

[0.0147] [0.432]Max lead level -0.140 -1.285

[0.0107] [0.573]Geometric Mean of Lead -0.309 -3.631

[0.0207] [1.990]

Observations 57,310 57,303 57,309 57,302 57,310 57,303 56,278 56,271

R-squared 0.138 0.140 0.138 0.140

Table 8: Second Stage Estimates of Lead and Average Reading Scores from Alternative IVs (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Avg lead level -1.825 -1.736 -1.931 -1.812 -1.939 -1.717 -1.277 -1.770 -1.892 -1.807

[0.775] [0.752] [0.837] [0.816] [0.679] [0.622] [0.553] [0.594] [0.666] [0.611]Observations 57,303 57,303 57,303 57,303 57,303 57,303 57,303 57,303 57,303 57,303First Stage Instruments:

DOH Certificates at birth/1000*Original (1997) Lead Level X X

DOH Certificates at age 5/1000*Original (1997) Lead Level X

DOH certificates at birth/old housing units X X X

DOH certificates at age 5/old housing units X

HRC certificates at birth/1000*Original (1997) Lead Level X

HRC certificates at birth/Old housing units X X X

HRC certificates at age 5/Old housing units X

Total certificates at birth/1000*Original (1997) Lead Level X

Total certificates at birth/old housing units X

Additional robustness exercises:

• Including more controls:• race*year FE• free lunch* year FE• Lead level in 1997 (in tract)*year

• Propensity score estimates• Limit to tracts with predicted high numbers of certificates (by 2004)

Summary (thus far)• Racial disparities in lead levels and test scores• Lead has a negative and causal impact of cognitive achievement that

does not fade over time and is not heterogeneous• Policy in 1997 disproportionately lowered the lead levels of African

American children • The declines did not simply reflect declines more generally by income• Important spatial component • African American children concentrated in urban areas with higher initial lead

levels that received more “treatment” over this time period

• Did the narrowing of the racial gap in lead levels result in a reduction in the racial gap in test scores?

Trends in Lead Over Time

Two exercises:

• Calculate what share of the reduction in the black-white test score gap was due to the reduction in lead levels based on the causal estimate of the impact of lead on educational outcomes• Note: assume the impact of lead on test scores not vary by race

• Examine directly how the gap in lead levels relates to the gap in test scores

Trends in disparities in test scores

• Racial gap in test scores fell from 9.7 for those born in 1998 to 6.3 for those born in 2004 (from 70% to 45% of a standard deviation). • For lead, the gap fell from 2.2 to 0.9 over this same period• Based on the range of causal estimates, the decline in the gap in lead

levels explains 37-76% of the decline in the test score gap.• Income gap in test scores and lead both fell,but by smaller amounts:• Income test score gap fell from 9.3 to 8.4 (67% to 60% of a std deviation)• Income lead level gap fell from 1.83 to 0.99

Table 9: Disparities in Lead and Disparities in Test Scores - Tract and County Level

(1) (2) (3) (4)

White-Black Reading Scores

Tract Level Disparities County Level Disparities

OLS IV OLS IV

White-Black Lead Scores -0.746 -2.101 -1.422 -1.321

[0.127] [1.487] [0.822] [2.179]

Observations 1,736 1,695 40 40

R-squared 0.146 0.077 0.853 0.853

Each observation is a tract (county) year. Also included are county (tract) and year of birth fixed effects.

All regressions weighted by tract (county) population

Note that the White-Black lead score difference is negative, generally, since white lead levels are lower than black lead levels.

Conclusions• RI implemented a policy that targeted and disproportionately reduced

the lead levels of African American and low income children• The resulting declines in racial disparities in lead exposure can explain

a substantial share of the recent decline in racial test score disparities.• Eliminating the black-white test score gap single most effective way to

reduce racial economic inequality (Jencks and Phillips, 2011).• If we continue to target environmental regulation at children at

greatest risk, then declines in disparities in future economic outcomes may follow.