The Impact of the Flint Water Crisis on Fertility

[Date]

The Impact of the Flint Water Crisis on Fertility

Daniel Grossman, West Virginia University David Slusky, University of Kansas

June 18, 20182018 IRP Summer Research Workshop

The Effect of an Increase in Lead in the Water System on Fertility, Pregnancy, and

Birth Outcomes: The Case of Flint, Michigan

Daniel Grossman, West Virginia University and

David J.G. Slusky, University of Kansas

July 10, 2017 “We were drinking contaminated water in a city that is literally in the middle of the Great Lakes, in the middle of the largest source of fresh water in the world.” – Dr. Mona Hanna-Attisha

2

College of Business and Economics

Flint water leached corrosion

Source: City of Flint website

3

Research questions

• What is the impact of a high level of lead in the water supply on– The fertility rate?– The sex ratio for live births?– Birth weight?

• Are the results robust?

4

Preview of results: fertility rates drop

• Investigating the fertility and health effects at birth of the Flint Water change

• Fertility rates decreased 12%• Sex ratios skew female births (as in

Sanders and Stoecker 2015)• Weaker evidence of lower birth weight• No evidence of behavioral changes

5

Lead has many terrible consequences

• Lead in the water raises blood lead levels• Lead crosses the placenta, affecting fetus• Lead is an abortifacient• Lead is associated with criminality and

behavior and educational problems• There is no “safe” level of lead

6

Sources: Troesken 2006; Hall 1905; Edwards et al. 2009; Hanna-Attischa et al. 2016; Edwards 2014; Taylor et al. 2014; Feigenbaum and Muller 2016; Clay et al. 2014; Reyes 2007, 2015; Aizer et al. 2016; Aizer and Currie 2017; Billings and Schnepel 2018, Guzze 2016; Clay et al. 2018

Lagged gasoline lead correlated w/ crime

7

Source: Reyes 2007

Individual evidence for cognitive channel

8

Source: Aizer, Currie, Simon, and Vivier 2015

150+ years of data that ↑lead means ↓kids

• Men exposed have lower fecundity • Pregnant women get prenatal

abnormalities, reduced gestation, miscarriage, & reduced birth weight

• Clay, Portnykh, and Severnini (2018) use the Interstate Highway System & the Clean Air Act and find reductions in the birth rate and worsening birth outcomes

9

Sources: Men: Paul 1860; Hamilton and Hardy 1983; Assennato et al. 1987; Coste et al. 1991; Winder 1993; Alexander et al. 1996; Lin et al. 1996; Bonde and Kolstad 1997; Apostoli, Porru, & Bisanti 1999; Apostoli et al. 2000; Hernberg2000; Sallmén, Lindbohm, and Nurimnen 2000; Sallmén 2001; Shaiau, Wang, & Chen 2004; Wirth & Mijal 2010; Vigehet al. 2011; Wu et al. 2012; Eibensteiner 2013. Women: Borja-Aburto et al. 1999; Hertz-Picciotto 2000; Joffe et al. 2003; Bellinger 2005; Hu et al. 2006; Cleveland et al. 2008; Vigeh et al. 2010; Zhu et al. 2010; Taylor, Golding, & Emond 2014

10

2014

Pre 2014 Flint receives water from Detroit Water and Sewerage Department (DWSD)

Governor appoints Emergency Manager

Water rates (prices) hit extreme levels

2011

March 2014: Flint and Genesee County plan own pipeline to Lake Huron

April 2014: Flint changes water source to Flint River, Genesee County stays with DWSD

2012 2013

Timeline of events in Flint

Flint passes ordinance that all connections with any water main be made with lead pipe

1897

11

Mar 2015 Aug 2015Aug-Dec 2014

Problems begin to emerge. Fecal coliform, GM water switch, TTHM

Emergency manager stresses water is safe, refuses to return to DWSD

Citizens complain about color, taste of water

Dr. Edwards independently tests Flint water lead levels, 19 times more corrosive than DWSD.

Dr. Hanna-Attisha finds increased blood level in Flint children

Sep 2015

Timeline of events in Flint (cont.)

2016: Governor apologies & asks for help

12

Source: Getty Images, March 17, 2016

Identification strategy: Flint water change

• Flint changed its water source from Lake Huron to the Flint River in April 2014

• Flint River water had numerous problems with contradictory chemical solutions

• New water leached lead out of pipes• So far, 15 individuals have been indicted• Yet, government budgets are potentially

about to be cut, including for testing water• Flint is not the only place with lead issues

13

Sources: Edwards et al. 2017, EPA, Reuters 2017, Drum 2016, Doleac 2017

Flint kids w/ high lead rose from ’13→‘15

14

Source: Hanna-Attisha et al. 2016

Specifically in the first few months

15

Source: Zahran et al. 2017

2 other papers looking at more outcomes

• Using Zillow home sales data– Home prices have declined significantly– No effect on the number of houses sold

• Using county-level Nielsen scanner data– Bottle water sales increased immediately– Water filter sales rose after October 2015

• Using Michigan education data– 6-9 pp fewer scoring proficient in math– 12-14 pp fewer scoring proficient in reading

16

Source: Christensen, Keiser, and Lade 2017; Sauve-Syed 2017

Reviewing 3 health economics models

• Grossman Model of Health Capital (Grossman 1972)

• Selection and Scarring (Bozzoli, Deaton, Quintana-Domeque 2009)

• Fetal Origins Hypothesis (Barker 1992, 1995; Currie 1999; Almond and Currie 2010)

• Not mutually exclusive

17

Demand for health (Grossman 1972)

• Health is durable stock; depreciates with age; also directly in utility function

• Determines amount of time available for market activities

• Death when health < minimum stock• Can be increased (decreased) through

investment (disinvestment)• Contaminants in water can be considered

a disinvestment in health

18

19

Mean increases

SelectionMean decreases

ScarringPDF PDF

Scarring & selection (Bozzoli et al. 2009)

Could disentangle with generational data

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Source: Gørgens, Meng, Vaithianathan 2012

• 1959–1961 Great Chinese Famine• Taller children are more likely to survive• But appeared to be no change in height• Could be because scarring + selection = 0• Disentangled using survivor’s children’s

height to proxy for unscarred height• Scarring results is that children < 5 who

survived grew up to be 1-2 cm shorter

Fetal origins hypothesis (Barker 1992, 1995)

• In utero environment can program a person for later life health

• Increased risk of hypertension, cardiovascular disease

• Latent health effect

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• Selective attrition (culling)– The least healthy fetuses don’t survive– Lower fertility rates, but increased health

• Direct effect on infant health (scarring)– Shifts the health distribution to the left– Births that did occur measurably less healthy

• Indirect or latent effect on adult health

22

These models lead to several channels

Data: Michigan Vital Statistics 2008-15

• Birth data– Date of birth– Geocoded to the census tract level– Weeks of gestation– Gender of the child– Birth weight

• Define conception date = date of birth – weeks of gestation

23

We compare Flint to other Michigan cities

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Note: Size of point is proportional to population. Green are cities with outlier fertility rates

25

Outcomes• General fertility rate• Male-female ratio

=1 if conception date >= Nov 2013 & born in Flint(affected >= half of pregnancy)

Clustered at city level

City fixed effects

Month#yearfixed effects

Empirical structure: City panel over time

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑐𝑐𝑐𝑐 = 𝑎𝑎 + 𝛽𝛽1𝑊𝑊𝑎𝑎𝑂𝑂𝑂𝑂𝑊𝑊𝑐𝑐𝑐𝑐 + 𝛼𝛼𝑐𝑐 + 𝛿𝛿𝑐𝑐 + 𝜀𝜀𝑐𝑐𝑐𝑐

For• c city• t month and

year

Look at annual general fertility rate (GFR)

26

𝐺𝐺𝐺𝐺𝐺𝐺15−49𝑐𝑐𝑐𝑐=

𝐵𝐵𝐵𝐵𝑊𝑊𝑂𝑂𝐵𝐵𝐵 𝑂𝑂𝑂𝑂 𝑤𝑤𝑂𝑂𝑂𝑂𝑂𝑂𝑤𝑤 𝑎𝑎𝑎𝑎𝑂𝑂 15 − 49𝑐𝑐𝑐𝑐𝑃𝑃𝑂𝑂𝑃𝑃𝑂𝑂𝑃𝑃𝑎𝑎𝑂𝑂𝐵𝐵𝑂𝑂𝑤𝑤 𝑂𝑂𝑜𝑜 𝑤𝑤𝑂𝑂𝑂𝑂𝑂𝑂𝑤𝑤 𝑎𝑎𝑎𝑎𝑂𝑂 15 − 49𝑐𝑐𝑐𝑐∗ 12 ∗ 1000

Monthly GFR is extremely noisy

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28

Result easier to see in when smoothed

5560

6570

75G

FR (1

3 m

onth

mov

ing

aver

age)

1/07 1/09 1/11 1/13 1/15Conception Month and Year

15 Most Populous Cities in MI (excluding Flint) Flint

PrePre

Post

Post

0

10

20

30

40

50

60

70

80

Rest of Michigan Flint

GFR

Fertility rates decline in Flint

29

Pre PrePost

Post

49.649.8

5050.250.450.650.8

5151.251.4


Share of babies who

are male

Sex ratio shifts toward girls in Flint

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In regression: GFR ↓12%; Sex ratio ↓1.8%

31

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

ΔGFR

-0.016

-0.014

-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

ΔSex Ratio

95% confidence interval 95% confidence interval

Our results are plausible in the literature

• Our question has not been studied in the economics literature

• But it has in the environmental science literature

• Edwards (2014) studied lead in water in Washington, D.C. in the 2000s

• Found that it led to a 12% decrease in fertility rate (same as us)

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How many fewer kids?

33

• Women 15-49 in Flint 26,000• Coefficient -7.5• Months of treatment 17• Adjust for monthly vs. annual 1 / 12• Adjust for GFR per 1000 women 1 / 1000• 26,000 * -7.5 * 17 / 12 / 1000

≈ 276 fewer children

34

Primary robustness checks

• Flexible start date• Subsample analyses• American Time Use Survey (county level)• Google Trends (4-county level)• Dropping each control city• Randomization Inference• Synthetic Control• Imperfect Synthetic Control

Effect is not sensitive to start month

35

-12

-8

-4

0

4

8

12

Nov-06 Nov-07 Nov-08 Nov-09 Nov-10 Nov-11 Nov-12 Nov-13 Nov-14 Nov-15

Effect on GFR

Month "Treatment" Begins

Treatment for primaryspecification (Nov '13)

Flint mothers are relatively uneducated

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No High School

Degree, 25%

Only High School

Degree, 35%

Some College (No Degree),

30%

Post-secondary

Degree, 10%

0% 20% 40% 60% 80% 100%

Fertility results are on more educated (?)

37

-40-30-20-10

01020304050ΔGFR

95% confidence interval

Sex ratio also on more educated (?)

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-8%-6%-4%-2%0%2%4%6%

ΔSex Ratio95% confidence interval

(1) (2) (3) (4) (5)All No High

School Degree

Only High School Degree

Some College

(No Degree)

Post-secondary

DegreePanel A. GFR

-8.188*** 26.81** 9.124* -20.27*** -9.876***(1.112) (9.224) (4.836) (3.170) (2.079)

R-squared 0.247 0.091 0.101 0.149 0.204Mean 76.92 109.2 85.75 68.01 49.53

Panel B. Sex Ratio-0.0169*** 0.00944 0.00816 -0.0162* -0.0481***(0.00403) (0.0176) (0.00755) (0.00882) (0.00705)

R-squared 0.008 0.055 0.077 0.054 0.060Mean 0.493 0.505 0.501 0.523 0.487

Results are on some college (no degree)

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Why are the results from the educated?

• Avoidance by more educated is possible, but unlikely to explain disparity in sex ratio

• College graduate group in Flint is very small (< 100 births post water change)

• The “some college” are really different– Not necessarily a convex combination of

college graduates and high school graduates– Many dropped out due to a shock, making

them more disadvantaged than those with no college (Pollak and Lundberg 2014)

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Using populous counties instead of cities

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Results hold at greater aggregation

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

Main Result Counties County Groups

ΔGFR


Time spend having sex (ATUS) increases!

43

0

0.01

0.02

0.03

County-level CBSA-level

ΔShare reporting

time spent having sex


Flint Water (blue) & Lead (red)

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Source: Google Trends; http://edition.cnn.com/2016/01/20/health/flint-water-crisis-timeline/index.html

0

10

20

30

40

50

60

70

80

90

100

Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17

Rel

ativ

e Se

arch

Vol

ume

First Hanna-Attisha report comes out

Situation becomes a national story; Governor apologizes and asks for federal and state aid

Nothing in April 2014

Abortion (g), condom (b), & contraception (r)

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Source: Google Trends

0

10

20

30

40

50

60

70

80

90

100


Rel

ativ

e Se

arch

Vol

ume

Baby

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0

10

20

30

40

50

60

70

80

90

100


Rel

ativ

e Se

arch

Vol

ume

Moving (blue) & escape (red)

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0

10

20

30

40

50

60

70

80

90

100


Rel

ativ

e Se

arch

Vol

ume

Help

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0

10

20

30

40

50

60

70

80

90

100


Rel

ativ

e Se

arch

Vol

ume

Results are robust to omitting cities

49

Ann Arbor

Dearborn

Detroit

Farmington Hills

Grand Rapids

Kalamazoo

Lansing

Livonia

Rochester Hills

Southfield

Sterling Heights

Troy

Warren

Westland

Wyoming

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

ΔGFR

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Check using randomization inference

• Inference problem with only having one treated unit (Flint)

• Follow Cunningham & Shah (2017) which study RI decriminalizing indoor prostitution

• They assign treatment to each of the other 49 states in the US and look at distribution

• We’ll do this by assigning treatment to each of the 15 other cities in Michigan

• Variant of Fisher’s (1935) permutation test

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Flint’s is the largest in magnitude

02

Freq

uenc

y

-10 -5 0 5Average Treatment Effect

Flint Comparison cities

Check using synthetic control method

• Want the mix of control cities that best matches Flint on pre-trends and levels

• Minimizes standard deviation between Flint and the weighted control covariates

• Covariates include– Fertility rate– Demographics: maternal educational

outcomes, race/ethnicity, sex of the child

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Source: Abadie and Gardeazabal 2003; Abadie et al. 2010

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1st used to study terrorism in Basque C.

Source: Abadie and Gardeazabal 2003

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Check with a Catalonia as a placebo

Source: Abadie and Gardeazabal 2003

Synthetic control weights for Flint

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Synthetic Flint WeightAnn Arbor 0Dearborn 0Detroit 0.599Farmington Hills 0Flint 0Grand Rapids 0Kalamazoo 0Lansing 0Livonia 0Rochester Hills 0Southfield 0Sterling Heights 0Troy 0Warren 0.401Westland 0Wyoming 0

Synthetic Flint better matches for GFR

56

The difference is bigger than placebos

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Clearer if Wyoming is excluded

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Average treatment effect is the largest

Imperfect synthetic control method

• Powell (2017) has a brand new method• Solves two problems

– Improves inconsistent pre-period match due to temporary spikes by matching pre-period outcomes predicted from flexible time trends

– Allows for treated group being an outlier by using treated group’s presence in other group’s synthetic controls

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Example of second fix for outliers

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Treated Unit Control Unit A Control Unit B

Outcome Variable

• Can’t use positive weights on A and B to match the treated unit

• But can weight the treated unit and B to match A

• Can then invert this match to get a weight for the treated unit

• Conceptually: allowing negative weights

Much better pre-period match

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4050

6070

80G

FR

0 20 40 60 80 100Months

Flint Synthetic Control

Resulting inference is consistent

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Flint

02

Freq

uenc

y

-10 -5 0 5 10Average Treatment Effect

Flint Comparison cities

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𝐵𝐵𝐵𝐵𝑊𝑊𝑂𝑂𝐵𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑐𝑐𝑐𝑐 = 𝑎𝑎 + 𝛽𝛽1𝑊𝑊𝑎𝑎𝑂𝑂𝑂𝑂𝑊𝑊𝑖𝑖𝑐𝑐𝑐𝑐 + 𝛽𝛽2𝑋𝑋𝑖𝑖𝑐𝑐𝑐𝑐 + 𝛾𝛾𝑖𝑖𝑐𝑐𝑐𝑐 + 𝛿𝛿𝑐𝑐 + 𝜀𝜀𝑖𝑖𝑐𝑐𝑐𝑐

Analogous framework for birth outcomes

Birth outcomes• Birth weight• Gestational age• Gestational growth

=1 if conception date >= Nov 2013 & born in Flint(affected >= half of pregnancy)

Clustered at city level

Census tract fixed effect

Month and year fixed effects

For• i individual• c city• t month and

year

Controls for • Child: sex, race, ethnicity• Mother: marital status,

educational attainment

Pre

Pre

Post

Post

2900

2950

3000

3050

3100

3150

3200

3250


Birth weight (g)

Birth weight decline is ambiguous

65

Analogously ambiguous in a regression

66

-70-60-50-40-30-20-10

0102030

Difference inDifferences

+CensusTract FE

+Time FE +Child SexControl

+MomControls

ΔBirth weight (g)


Other outcomes also mostly ambiguous

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-0.01

0.00

0.01

0.02

0.03

0.04

Share low birth weight

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

Gestational Age (weeks)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Gestational Growth (g/week)


0.0

002

.000

4.0

006

.000

8D

ensi

ty

0 2000 4000 6000 8000grams

Flint Pre-Water Flint Post-WaterNon-Flint Pre-Water Non-Flint Post-Water

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Birth weight shows a slight leftward shift

Netting out selection from scarring

• Assume GFR ↓ 12.0% came from left tail of BW distribution (μ = 3082 g, σ = 632 g)

• 𝐸𝐸 𝑋𝑋 𝑋𝑋 > 𝜇𝜇 + 𝜎𝜎Φ−1 𝑃𝑃 = 𝜇𝜇 +𝜎𝜎𝜎𝜎 Φ−1 𝑝𝑝

1−𝑝𝑝

• = 3242 g• = 3217 g after diff in diff trend• Actual post μ = 3042 g • ↓ 5.4% on 3217 g (-175 g)

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Source: Bozzoli, Deaton, and Quintana-Domenque (2009)

Results are robust to many checks, e.g.:

• Ln(births)• Fixed effect Poisson• Stratifying by mother’s race or age • Omitting cities with outlier GFR• Only children conceived <= Sep 2014• Flint vs. Genesee County• Falsification: Rest of Genesee vs. others

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-12-10-8-6-4-202

Main Result Omitting AnnArbor &

Wyoming

Onlyconceptionsbefore Sept

2014

Flint vs.Genesee

Genesse w/oFlint vs. Rest

ΔGFR(pre-period mean=62)

Results are robust to many checks

71


Water switch → scarring and selection

• 12% decrease in fertility rate• ≈ 276 fewer children born in Flint• 1.9% shift in sex ratio toward more female• 5.4% decrease in birth weight of survivors• Not explained by a behavioral responses• Cutting government funding could cause

this tragedy to repeat itself in other cities

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This project is just the beginning

• We will purchase linked Medicaid and birth data for the births affected the water crisis

• We will investigate its impact on– Medicaid take up rates – Previously forgone preventive care– Wasteful medical spending

• Also allows us to follow mothers as they move out of Flint and calculate avoidance

73

Questions?

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[Date]

Thank you!

The Impact of the Flint Water Crisis on Fertility

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