Shin-Yi ChouLehigh University & NBER

Jin-Tan Liu (劉錦添 )National Taiwan University & NBER

Michael GrossmanCity University of New York Graduate Center & NBER

Theodore JoyceBaruch College & NBER

Parental Education and Child Health: Evidence from a Natural Experiment in Taiwan

臺灣過去 50年，兩大公共政策 1968 年 九年國教 1995 年 全民健保

Duflo (2001), “Schooling and Labor Market Consequences of School Construction in Indonesia:Evidence from an Unusual Policy Experiment,” American Economic Review, 91, 795-813.

Currie and Moretti (2002),”Mother’s Education and the

Intergenerational Transmission of Human Capital: Evidence from College Openings,” Quarterly Journal of Economics, 118, 1495-1532.

Clark and Hsieh (2000), “Schooling and Labor Market Impact of the 1968 Nine-Year Education Program in Taiwan,” Working Paper, Department of Economics, Princeton University

Education and Health:

Michael Grossman (1972), “On the Concept of Health Capital and The Demand for Health,” Journal of Political Economy, 80, 223-255.

Michael Grossman (2000), “The Human Capital Model,” in Culyer and Newhouse eds. Handbook of Health Economics, Vol. 1. Elesevier Science B.V.

Michael Grossman and Robert Kaestner (1997), “Effects of Education on Health,” in Behrman and Stacery eds. The Social Benefits of Education, University of Michigan Press.

Education and Health

Michael Grossman, “Education and Nonmarket Outcomes,” in Hanushek and Welch eds. Handbook of the Economics of Education, Elsevier Science.

(NBER working papers No. 11582).

Conceptual Foundations: 1. Productive efficiency 2. Allocative efficiency Causality? Y = X B + U “The third variable” may cause schooling and health to vary

in the same direction. “The time preference hypothesis”

Empirical Methods:

1. Include past health measures in regressions

2. Siblings or twins samples: control for unmeasured third variables differences in outcomes due to differences in schooling between siblings or twins.

3. Instrumental variables method (IV): variables are correlated with schooling but not correlated with omitted third variables, such as ability, inherited genetic traits, and time preference.

Instrumental Variable (IV)

Y = X B + U OLS is biased when U is correlated with X Use an IV Z for X βIV = (Z’X)-1 (Z’Y)

βIV is consistent when Z satisfies two conditions:

1) Z is uncorrelated with U 2) Z is correlated with X

Instrumental Variable (IV)

Random encouragement designs: 1. To test the effect of flu vaccine on flu: The IV (the letter) is randomly assigned, but not the treatment (flu vaccine). 2. Distance to hospital with operating facilities as an IV for surgery in heart attacks. 3. Distance to school as an IV for schooling 4. Policy Reforms

How to find IV?

1.Lleras-Muney (2005), Compulsory Education Laws from 1915 to 1939 US Censuses of Population for 1960, 1970, 1980 The effect of education on mortality

2. Arendt (2005), Compulsory School Reform in Denmark in 1958 and 1975 The impact of schooling on self-rated health

3. Spasojevic (2003), 1950 Swedish Comprehensive School Reform.

Difference-in-Difference (DD)Estimators

“Natural Experiments” Actual policy changes to identify the effects of policies on outcomes

DD: to compare outcomes before and after a policy change for a group affected by the change (Treatment Group, T) to a group not affected by the change (Control Group, C).

DD = [E(Y1 ︳ T) – E(Y0 ︳ T)] –

[E(Y1 ︳ C) – E(Y0 ︳ C)]

Yi, t = α+ β2 Time + β3 Treatment

+ β4 (Time* Treatment) + ui,t

DD:

Meyer, Bruce D. (1995), “Natural and Quasi-experiments in Economics,” Journal of Business and Economic Statistics, 13(2), 151-161.

Angrist, Joshua D. and Alan B. Krueger (1999), “Empirical Strategies in Labor Economics,” Handbook of Labor Economics.

MIT, Harvard: Jonathon, Gruber Duflo, Esther

前言 探討主題 :

Does the parents’ attainment of education affect the health of their children?

研究困難 : Unobserved characteristics that affect both the parents’ education levels and the health of their children.

本文實證方法 :Compulsory schooling laws in Taiwan affects the levels of Parental education, but is uncorrelated with children’s health

本文大綱

1968 年臺灣九年國民義務教育 Data and sample Effect of 1968-Reform on Education Effect of Parental Education on child health

outcomes

?

研究背景 :1968 年延長九年國民義務教育 Extended from 6 years to 9 years 140 new high schools were opened

Number of junior high schools per thousand primary school graduates increased from 0.8 (1967-1968 academic year) to 1.3 (1968-1969 academic year)

The percentage of primary school graduates who entered junior high school increased from 56% (1967-1968 academic year) to 77% (1968-1969 academic year)

Intensity of school construction varies across regions.

Junior High Schools per Thousand Primary School Graduates Source: Ministry of Education, Educational Statistics of Republic of China.

( 每 1000 位國小畢業生中國中的數目 )

Number of First Year JH Students to Number of Primary School Graduates Source: Ministry of Education, Educational Statistics of Republic of China.

( 每位國小畢業生進入國中的比例 )

Program Intensity in 1968

City/countyIntensity of the 1968 program City/county

Intensity of the 1968 program

Taipei city 0.23 Chaiyi county 0.07Kaohsiung city 0.02 Tainan county 0.23Taipei county 0.13 Kaohsiung county 0.05Ilan county 0.06 Pingtung county 0.24Taoyuan county 0.14 Taitung county 0.57Hsinchu County 0.05 Hualien county 0.39Miaoli county 0.19 Penghu county 0.76Taichung County 0.22 Keelung city 0.17Changhua county 0.02 Taichung city 0.13Nantou county 0.17 Tainan city 0.09Yunlin county 0.12

* Program intensity is defined as number of new junior high schools in 1968 per thousand children ages 12-14 in 1967.

(1968 年新設國中數目佔 1967 年每 1000 位 12 歲至 14 歲學童的比率)

Econometric Method:

控制組 /對照組 : children over/under the age of 11 in 1968

Validation of our strategy: The higher the program intensity is, the larger

the effect of education reform Program intensity was independent with initial

schooling levels

Enrollment Rate in 1966 vs. Program Intensity Source: Ministry of Education, Educational Statistics of Republic of China.

ProgramEn

roll

men

t R

ate

Percentage of Workers in Agriculture in 1967 vs. Program Intensity Source: The data on percentage of workers in agriculture are from the Taiwan Agricultural Yearbook.

Program

Data and Sample

Birth and death certificates, 1978-1999, total 22 years

Sample Size: 5,576,868~6,099,832. Child Health outcomes include the probabilities

of:low/very low birth weight(less than 2500 grams/1500 grams), pre-maturity, mortality,etc.

Sample: women or men satisfying these: Between 1- and 20-years old in 1968 Between 22- and 45-years old when they or their

wives gave birth in 1978-1999

Basic approach:

Sijt is the number of years of formal schooling completed by mother (or father) i born in city/county j with her/his child born in year t.

Indices: i: mother (or father), j: city/county, t: year C: cohort dummies, P: program intensity, T: treatment group dummy, R: region of birth(city/county), Y: year dummies, 1978 is an omitted year

Effect of 1968 Education Reform on Education

k l

ijttljkkk

tkkk

jkkijk

ikkijt YRYRTPCS99

794

99

793

21

221 )(

19 19

1979 l =1979

Effects of Education Reform on Parents’ Educational Attainment (Basic Approach)

(1) (2) (3) (4) (5)15-20 15-20 12-14 12-20 15-200-5 6-11 9-11 0-11 0-11

Treatment dummy*Program intensity 1.029 0.45 0.098 0.332 0.461 (2.07) (1.76) (0.54) (1.71) (1.69)

(1) (2) (3) (4) (5)15-20 15-20 12-14 12-20 15-200-5 6-11 9-11 0-11 0-11

Treatment dummy*Program intensity 1.094 0.771 0.098 0.58 0.782 (2.43) (2.94) (0.54) (3.12) (2.84)

Control group (fathers ages in 1968)Treatment group (fathers ages in 1968)

Panel A: First Stage Results on Mother's Education

Control group (mothers ages in 1968)Treatment group (mothers ages in 1968)

Panel B: First Stage Results on Father's Education

β (

β (

)

)

1968 年九年國教對個人教育成就的影響 :

Education reform not only has a positive impact on the educational attainment of the treatment groups, but also has a larger impact for the younger women.

The 12-14 year-olds may not be a pure control group.

Education reform has a bigger impact on father’s educational attainment than on mother’s.

Education reform had a larger impact on the education of younger fathers.

1968 年九年國教對個人教育成就的影響 ( 續 ):

Full specification: replace P x T by P x C(Program Intensity)

Coefficient series {k}:

decreases sharply when k=13 (13 years old in year 1968)

fluctuates near 0 when k=14 ~ 19 all positive for k=0 ~ 11, and decreases from 0 to

11

21

2

99

794

99

793

21

22

19

0

19

01 )(

k lijttljkk

ktkk

kjkk

kikjk

kikkijt YRYRCPCS

191999

l=1979k=1979

Coefficients of the Interactions between the Age in 1968 and Program Intensity in the Years of Schooling Regression

Mothers Fathers

1968 年九年國教對教育成就的影響

Restricted Estimation: assume k=0 when k 15. We delete the cohort aged 12-14 from our sample. The F-ratios are 15.73 and 16.96 for mother’s and father’s

samples, respectively, when the enrollment rate and the percentage of agricultural share are employed as regressors.

for every junior high school constructed per 1000 children between the ages of 12 and 14, Mothers 0-5, 6-11: receive 1.0 and 0.72

additional years Fathers 0-5, 6-11: receive 0.84 and 0.77

additional years

Effect of Parental Education on Child Health Outcome

Basic approach in the first stage Under OLS estimation, higher parental

educational attainments significantly reduce the risk of all adverse health outcomes (prematurity, etc.)

Under the 2SLS estimation, mother’s years of schooling shows significant impacts only on low and very low birthweight and prematurity.

Under the OLS estimation, father’s years of schooling has similar effect as mother’s.

Under 2SLS, father’s years of schooling has smaller effect as mother’s.

Panel A: Mother's Years of Schooling on Child Health Outcomes

OLS 2SLSNeonatal Mortality -0.0001 -0.0006

( -9.18) ( -0.39)Infant Mortality -0.0003 -0.003

(-26.58) ( -1.26)Postneonatal Mortality -0.0002 -0.0024

(-26.91) ( -1.32)Low Birthweight -0.0018 -0.0166

(-52.37) ( -2.36)Very Low Birthweight -0.001 -0.0113

(-57.11) ( -3.13)Premature -0.0009 -0.0165

(-26.64) ( -2.45)Panel B: Father's Years of Schooling on Child Health Outcomes

OLS 2SLSNeonatal Mortality 0 -0.0008

( -6.29) ( -1.02)Infant Mortality -0.0003 -0.0028

(-24.79) ( -2.25)Postneonatal Mortality -0.0002 -0.002

(-27.06) ( -2.08)Low Birthweight -0.0018 -0.0128

(-52.21) ( -3.54)Very Low Birthweight -0.0008 -0.0088

(-46.10) ( -4.90)Premature -0.0007 -0.0091

(-21.64) ( -2.63)

P*T (0-11 vs 15-20)

P*T (0-11 vs 15-20)

Effects of Parental Schooling on Child Health Outcomes: OLS and TSLS (Basic Approach in the First Stage)

Effect of Parental Education on Child Health Outcome

Restricted estimation in the first stage Similar to previous results except that father’s

years of schooling reduces infant and postneonatal mortality.

Effects of Parental Schooling on Child Health Outcomes:OLS and TSLS (Restricted Estimation in the First Stage)

Mother's Education on Child Health OutcomesOLS 2SLS

Neonatal Mortality -0.0001 -0.0002( -9.22) ( -0.23)

Infant Mortality -0.0003 -0.0012(-26.66) ( -0.82)

Postneonatal Mortality -0.0002 -0.0009(-26.98) ( -0.87)

Low Birthweight -0.0018 -0.0139(-52.63) ( -3.37)

Very Low Birthweight -0.001 -0.0072(-57.12) ( -3.47)

Premature -0.0009 -0.0137(-27.01) ( -3.46)

Father's Education on Child Health OutcomesOLS 2SLS

Neonatal Mortality 0 -0.0003( -6.30) ( -0.30)

Infant Mortality -0.0003 -0.0028(-24.75) ( -2.20)

Postneonatal Mortality -0.0002 -0.0026(-26.99) ( -2.61)

Low Birthweight -0.0018 -0.0126(-52.39) ( -3.36)

Very Low Birthweight -0.0008 -0.0076(-46.15) ( -4.09)

Premature -0.0007 -0.0074(-21.86) ( -2.07)

Further questions:

1. Control group, treatment group Age: 2. Clustering 3. Group data regression Weights? 4. Mother’s education vs. Father’s education

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