estimating the social returns to education · driven by “affective” thinking (a focus on...
TRANSCRIPT
Estimating the Social Returns to Education
Suncica Vujic
Keynote LectureEconometric Modelling in Economics and Finance
Annual Conference of the Institute of Economic SciencesBelgrade
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 1 / 46
Research QuestionDoes education generate returns beyond individual’s own lifetime earnings?
Standard approach = Education as a (financial) investment(Borjas (2019), Labor Economics)
Private rate of return to schooling = increase in a worker’s earningsresulting from an additional year of education
Social rate of return to schooling = increase in national incomeresulting from an additional year of education
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 2 / 46
Research QuestionDoes education generate returns beyond individual’s own lifetime earnings?
More recent literature = Nonpecuniary benefits of education(Oreopoulos and Salvanes (2011))
Outside the labour market, additional years of education have effect on:Crime reduction(Lochner and Moretti (2004), Machin, Marie, and Vujic (2011), Machin, Marie, and Vujic (2012))
Enhanced political engagement and attitudes in democracy(Dee (1998), Milligan, Moretti, and Oreopoulos (2004))
Improvements in health(Lleras-Muney (2005), Chen and Lange (2008), Lundborg (2013))
Better lifestyle behaviours(Park and Kang (2008), Etilé and Jones (2011))
Better marriage market prospects(Goldin (1992))
Etc.Therefore, education generates social returns, which exceed privatereturns to education
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 3 / 46
How to Estimate the Social Returns to Education?
Two issues when drawing causal inference:
1 More education may be correlated with a wide array of otherfactors, like persistence, ability, family background, etc.
2 More education generates more income, and higher income willaffect people’s lives as well.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 4 / 46
How to Estimate the Social Returns to Education?
(Borjas (2019), Labor Economics)
Estimate Mincer’s HCEF by OLS:(Mincer (1974))
ln(wagei) = β0+β1educi+J∑
j=2
βjXji+εi
⇒ β1 rate of return to education
But if E(εi |educi) 6= 0 due to
Measurement errorReverse causalityOmitted variables
⇒ β1 biased and inconsistent
⇒ Earnings differentials acrossworkers do not estimate thereturns to education!
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 5 / 46
How to Estimate Social Returns to Education?
Instrumental variables (IV) estimator (selection on unobservables)(Angrist and Krueger (1991), Harmon and Walker (1995), Oreopoulos (2006))
Models based on identical twins(Ashenfelter and Krueger (1994), Miller, Mulvey, and Martin (1995), Isacsson (2004))
Difference-in-Differences (DiD)(Duflo (2001))
Regression Discontinuity Design (RDD)(Buscha and Dickson (2015))
Propensity score matching (selection on observables)(Blundell, Dearden, and Sianesi (2005))
Heckman selection model(Carneiro, Heckman, and Vytlacil (2011))
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 6 / 46
Overview of the Talk
“If anyone giving a keynote presents only one paper, they are not doing their job.”(Daniel Hamermesh, BDLE 2019)
Education and crime (Machin, Marie, and Vujic (2011))Education and fertility (James and Vujic (2016, 2019))Education and health (Webbink, Vujic, Koning, and Martin (2012))
Mechanism? Education affects preferences (patience and riskaversion), reducing time inconsistency problem.“Teen fertility, criminal activity, and smoking are risky behaviours often considereddriven by “affective” thinking (a focus on immediate feelings) rather than “cognitive”thinking (a focus on long-term benefits and costs).”(Oreopoulos and Salvanes (2011))
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 7 / 46
1. Education and Crime: MMV (2011)
The Crime Reducing Effect of EducationMachin, Marie & Vujic (EJ, 2011)
Consider a simple least squares regression of a measure ofoffending for a particular age cohort a in year t (Oat ) with aneducation variable (Eat ) as an explanatory variable andXjat(j = 1,2, . . . , J) being a set of other control variables(proportion British born, proportion employed, proportionnon-white and proportion living in London):
Oat = α0 + α1Eat +J∑
j=0
δjXjat + εat (1)
= α0 + α1Eat +J∑
j=0
δjXjat + (unobservablesat + εat)
E(Eat |εat) = E(Eat |unobservablesat) 6= 0 (2)
Unobservables – think of patience!Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 8 / 46
1. Education and Crime: MMV (2011)
The Crime Reducing Effect of EducationMachin, Marie & Vujic (EJ, 2011)
Use instrumental variables (IV) approach (“statistical trick”).Use raising of the minimum school leaving age (RoSLA) from 15to 16 in 1973 to account for the endogeneity of education.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 9 / 46
1. Education and Crime: MMV (2011)
The Crime Reducing Effect of EducationMachin, Marie & Vujic (EJ, 2011)
Logic of the instrumental variable in this paper:EXOGENOUS: Legislation which raised minimum school leavingage (RoSLA) from 15 to 16 in 1973 cannot be correlated with theunobserved determinants of crime rate changes.RELEVANT: Or...the only reason raising of RoSLA is related tocrime is because this law affected 15-year-old pupils to stay atschool one extra year.
Measures of education:Age left schoolNo qualifications (more appropriate in the LATE context)
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 10 / 46
1. Education and Crime: MMV (2011)
IV ApproachMachin, Marie & Vujic (EJ, 2011)
First-stage (reduced form) crime & education regressions:
Oat = β0 + β1SLAat +J∑
j=0
φjXjat + νat (3)
Eat = δ0 + δ1SLAat +J∑
j=0
ϕjXjat + υat (4)
The crime structural form yielding causal estimates:
Oat = θ0 + θ1Eat +J∑
j=0
σjXjat + εat , (5)
where the IV estimate of the coefficient on the education variable in (5)is the ratio of the reduced form coefficients in (3) and (4), θ1 = β1/δ1.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 11 / 46
1. Education and Crime: MMV (2011)
Discontinuities around RoSLA IncreaseNo Educational Qualifications/Age Left School
No Educational Qualifications
.15
.2.2
5.3
.35
Pro
port
ion w
ith N
o Q
ualif
ications
1950 1955 1960 1965
Year of Birth
Age Left School
15.8
16
16.2
16.4
Avera
ge A
ge L
eft S
chool
1950 1955 1960 1965
Year of Birth
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 12 / 46
1. Education and Crime: MMV (2011)
Discontinuities around RoSLA IncreaseConviction Rate
−.3
−.2
−.1
0.1
Male
Convic
tion R
ate
1950 1955 1960 1965
Year of Birth
Table
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 13 / 46
1. Education and Crime: MMV (2011)
The Crime Reducing Effect of EducationMachin, Marie & Vujic (EJ, 2011)
“The paper uses a statistical trick to find a causal link between low education and crime.”
Number of Google Scholar citations ∼ 400
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 14 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: MotivationJames & Vujic (2016)
Trend in teen fertility rates(WB data)
Trend in age-specific fertility rates(ONS-UK data)
Policy makers have focused on the drivers of both teen fertility and delays inchildbearing.What has been examined less is the role of education.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 15 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: MotivationJames & Vujic (2016)
Education-fertility relationship is also plagued by the endogeneityof the individual choice to participate in both activities, hence it isempirically difficult to prove.Use exogenous education shocks in an IV/RD-DD setting.We exploit two different variations in schooling:
1 Easter Leaving Rule (ELR)2 Education Expansion (EE) (EER, 2019)
Allows the examination of: Qualifications (inc. degree), years ofschooling, post-compulsory education.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 16 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: ELRJames & Vujic (2016)
Starting from Education Act of 1962, up until 1997, ELR:
Determined when in the school year people could leave school.Those born between the 1st September and the 31st Januarycould leave at Easter.Those born between the 1st February and the 31st August had tostay until the end of the summer.
1 Within-cohort discontinuity – a nominal difference of up to twomonths of required schooling.
2 “High stakes” exams taken in the summer term (May-June) – GCEO-levels, CSE, GCSE.
The rule only had bite after the RoSLA in 1973.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 17 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: Empirical StrategyJames & Vujic (2016)
RDD: Exploits the discontinuity point at 31st January/1st February.Define ELR:
ELRi =
{1, if Mobi = 2, . . . ,80, otherwise
RDD reduced form regression:
Fi = γ0 + γ1ELRi + f (Mobi − c) + λXi + νi
where Fi = timing of fertility (age at first pregnancy; probability ofbecoming a teen mother; probability of delaying pregnancy).The discontinuity in this case is not sharp – specify first stage:
Qi = β0 + β1ELRi + f (Mobi − c) + δXi + εi
where Qi = whether the woman has any academic qualifications.But, seasonality of month of birth...(Buckles and Hungerman (2013))
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 18 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: Empirical StrategyJames & Vujic (2016)
Given this possible threat to our identification strategy⇒ RD-DD.(Danzer and Lavy (2018))
Additional control group – compares the discontinuity for thepost-RoSLA cohorts with the pre-RoSLA cohorts.
Fuzzy RD-DD: IV (LATE) with ELR × RoSLA as an instrument.Quality of instruments? ELR: Family Background
Xi : age, age squared, non-white, living in London, birth cohortdummies, year of survey dummies.
First Stage
Qi = β0+β1ELRi +β2RoSLAi +β3ELRi×RoSLAi +Xiδ+f (Mobi−c)+εi
Reduced Form
Fi = γ0+γ1ELRi +γ2RoSLAi +γ3ELRi×RoSLAi +Xiλ+ f (Mobi−c)+νi
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 19 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: Birth After Age 26 & 30James & Vujic (2016)
Figure: The mean of the timing of fertility by month of birth
ELR reduced forms ELR IV/OLS
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 20 / 46
2. Education and Fertility: JV (2019)
Education and Fertility Timing: ConclusionsJames & Vujic (2016)
No impact on teen births but effects further up the age distribution.Points to a signalling effect of improved labour marketopportunities as a result of holding qualifications.Why effects only at or after 26?
ELR led to more quals −→ more education −→ improved LMparticipation, employment and earnings.(Anderberg and Zhu (2014), Dickson and Smith (2011), Del Bono and Galindo-Rueda (2007))
Formal education is a more important driver of employmentand LF participation decisions for women than it is for men.(Del Bono and Galindo-Rueda (2007))
But, more education −→ reduction in marriage rates; less childrenoverall; more likely to remain childless.(Fort, Schneeweis, and Winter-Ebmer (2014))
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 21 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Koning & Martin (HE, 2012)
A large literature has established the importance of both cognitiveand non-cognitive ability in social and economic life.(Gregg and Machin (2000), Borghans, Duckworth, Heckman, and Ter Weel (2008), Cunha, Heckman, and Schennach(2010), Cunha and Heckman (2007), Heckman (2006), Heckman, Stixrud, and Urzua (2006), Heckman (2008))
Basic intelligence, acquired skills, social skills, self-control, andpersistence matter for success in life.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 22 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Worldwide 10-20% of children and adolescents experience mentaldisorders (WHO).Most common mental health problems in children:
DepressionAnxietyBehaviour disordersAttention deficit hyperactivity disorder (ADHD)
We try to empirically establish the long-term effects of childhoodconduct disorder problems on human capital using data fromAustralian Twin Register (ATR).Childhood mental health problems have high human and financialcosts for families and society at large.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 23 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Conduct disorder (American Psychiatric Association – APA):“Disruptive behaviour disorder.”“A repetitive and persistent pattern of behaviour in which the basicrights of others or major age-appropriate societal norms or rulesare violated.”Categories: aggression to people and animals; destruction ofproperty; deceitfulness or theft; serious violation of rules
Human capital is broadly defined:Positive human capital: grade repetition, marks in school, andeducational attainment.‘Negative’ human capital: being arrested, spent time in jail,physically attacking others, etc.(Currie and Stabile (2007))
Use within twin-pair estimation approach.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 24 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
An individual i has human capital HCi determined by:
HCi = βCDi + αAi + ui
CDi = conduct disorderAi = unobserved ‘ability’ (genetic traits + family background)
Let conduct disorder be determined by:
CDi = δAi + ξi
Ai = unobserved ‘ability’ (genetic traits + family background)ξi = conduct disorder-specific random term
Due to the unobserved genetic traits and family backgroundcharacteristics affecting both conduct disorder and human capitalmeasures, we get a standard result that OLS is upward biased:
plim(βOLS) = β + ασA,CD
σ2CD
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 25 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Let HC1j and HC2j denote human capital outcome of the first and secondtwin in the jth twin pair.
We then get:HC1j = βCD1j + µj + ε1j
HC2j = βCD2j + µj + ε2j
The unobserved component is again made up of two parts. The firstpart, µj , denotes unobserved genetic and family backgroundcharacteristics that vary between twin pairs but not within pairs. Thesecond part, ε1j and ε2j , denote unobserved factors specific to each twin.
Taking first differences and applying OLS gives:
HC1j − HC2j = βWTP(CD1j − CD2j) + ε1j − ε2j
βWTP = within-twin-pair estimator of conduct disorder
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 26 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Adding explanatory variables, we estimate the following equation usingOLS and twin FE approaches:
HCij = βCDij + γXij + µj + εij
Xij = mother’s and father’s education, age and age squared, gender,weight at birth.
Some of these covariates drop out in the twin FE specification.
FE estimate of β might still be biased due to the measurement error inconduct disorder.
Measurement error is exacerbated by differencing and even more sowhen differencing between identical twins.(Griliches (1979), Bound and Solon (1999) – “double trouble”)
We try to control for this by: (a) applying three different measures ofconduct disorder; (b) otherwise, use IV approach where one measure ofCD is instrumented with a second measure of CD (‘AK instrument’).(Ashenfelter and Krueger (1994))
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 27 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Associations between CD & HC Measures: Lowess
OLS/Twin FEs
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 28 / 46
3. Education and Health: WVKM (2012)
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Early conduct disorder problems (pre-18) have detrimentallong-term effects on human capital accumulation, as well as onviolent and criminal behaviour.
Conduct disorder is more deleterious if these behaviours occurearlier in life.
Effective treatments early in life might yield high returns.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 29 / 46
3. Education and Health: WVKM (2012)
Summary of the Talk
Part of economics of education(Mincer (1958, 1974), Becker (1968, 1975), Arrow (1973), Spence (1973), Hanushek (1981), Card (1999), Heckman(2000), Holmlund, Lindahl, and Plug (2011),...)
Establishing causal rather than correlational relationshipsImportant for policy evaluation
‘Evidence based’ approach as a guiding principle in educationpolicy and practice.Showing what works.Developing (cost-) effective education interventions.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 30 / 46
3. Education and Health: WVKM (2012)
Scope for Policy Interventions/Future Research
Important to quantify ALL returns to education in order to guide appropriatepolicy intervention.
Unlike production returns to education, private/public decisions on education arerarely guided having in mind non-production (social) benefits of education:
“I’ll go to the university because I will get better job/pay.” (,)“I’ll go to the university because I will be healthier; live longer; smoke less;vote democratic; have happier marriage, etc.” (/)
Years of schooling and degree attainment provide limited information on what itis about education that produces both pecuniary and nonpecuniary returns.
⇒ Expand the range of measures to include the more qualitativedimensions of education.
Separating nonpecuniary from pecuniary returns to education?
Ideally, use two separate sources of exogenous variation – one that affectsschooling and another that affects income.Alternatively, condition on observable income variation not related toschooling.
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 31 / 46
3. Education and Health: WVKM (2012)
Thank you for your attention!
Suncica Vujic
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 32 / 46
Appendix
MMV (2011): Baseline Estimates (Property Crimes)Men, Aged 18-40, Born 1953-1961, Discontinuity Sample
Table 4a: The Causal Effect of Education on Crime, by Broad Types of Crime:
Log(OID Property Convictions Per 1000 Population)A. Population Weighted No Qualifications Age Left School
(1) (2) (3) (4) (5)Crime Education Crime Education Crime
Reduced Reduced Structural Reduced StructuralForm Form Form Form Form
SLA Increase −0.096 −0.113 0.375(0.039) (0.019) (0.055)
No Qualification 0.851(0.370)
Age Left School −0.257(0.108)
F -test F (1,117) F (1,117) F (1,117)=6.02 =36.34 =46.13
[p=0.016] [p=0.000] [p=0.000]B. Inverse Distance Weighted No Qualifications Age Left SchoolSLA Increase −0.135 −0.135 0.445
(0.037) (0.021) (0.058)No Qualification 0.999
(0.306)Age Left School −0.303
(0.089)F -test F (1,117) F (1,117) F (1,117)
=13.58 =42.14 =58.89[p=0.000] [p=0.000] [p=0.000]
Back
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 33 / 46
Appendix
Family Background Characteristics (ELR)
Table: Family Background Characteristics (Easter Leaving Rule)
(1) (2) (3) (4) (5) (6) (7) (8)
Degree Post School Quals Some Quals No quals or schooling
Father Mother Father Mother Father Mother Father Mother
RD -0.00927 -0.0316 0.0122 -0.0123 0.00480 0.0192 0.00660 -0.0247(0.0432) (0.0338) (0.0705) (0.0600) (0.0766) (0.0766) (0.0739) (0.0755)
Observations 2,339 2,340 2,339 2,340 2,339 2,340 2,339 2,340R2 0.021 0.008 0.004 0.005 0.020 0.021 0.015 0.019
RD-DD -0.0116 -0.00387 -0.0139 0.00744 -0.0256 -0.00113 -0.0175 -0.0225(0.0142) (0.0105) (0.0253) (0.0197) (0.0280) (0.0266) (0.0284) (0.0279)
Observations 4,852 4,860 4,852 4,860 4,852 4,860 4,852 4,860R2 0.023 0.012 0.014 0.026 0.043 0.078 0.036 0.074
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Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 34 / 46
Appendix
Education and Fertility Timing: First StageJames & Vujic (2016)
Table: Easter Leaving Rule and Qualifications
(1) (2) (3) (4) (5) (6) (7) (8)
ELR 0.0448*** 0.00657(0.00742) (0.00650)
RoSLA*ELR 0.0300*** 0.0360*** 0.0368*** 0.0386*** 0.0366*** 0.0301***(0.00462) (0.00510) (0.00510) (0.00568) (0.00659) (0.00804)
Constant 51.13*** 28.71*** 40.23*** 37.41*** 66.00*** 70.60*** 86.71*** 120.4***(8.212) (7.141) (5.400) (5.840) (6.715) (7.488) (9.047) (12.42)
Window All All All Sep-Jun Sep-Jun Oct-May Nov-Apr Dec-MarRoSLA period Post Pre Both Both Both Both Both Both
Quadratic spline No No No No Yes Yes Yes YesObservations 66,574 105,568 172,142 142,268 142,268 113,999 84,586 56,939
R2 0.015 0.014 0.068 0.066 0.067 0.067 0.068 0.068F -test 36.40 1.021 42.17 50.01 52.13 46.08 30.75 14.03
p-value 0.00 0.31 0.00 0.00 0.00 0.00 0.00 0.00
Back
Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 35 / 46
Appendix
Education and Fertility Timing: Reduced FormsJames & Vujic (2016)
Table: Effect of the Easter Leaving Rule on Age at First Birth: Teen Births
(1) (2) (3) (4) (5) (6) (7)Age at First Birth < 16 < 17 < 18 < 19 < 20 < 21
RD-DD −0.0444 −0.000215 0.00151 0.00276 −0.000961 −0.00326 −0.00264(0.0536) (0.000636) (0.00108) (0.00187) (0.00271) (0.00348) (0.00409)
RD-DD −0.0299 −0.000226 0.00142 0.00255 −0.00131 −0.00371 −0.00316Quadratic Spline (0.0536) (0.000635) (0.00108) (0.00186) (0.00271) (0.00347) (0.00409)
Observations 90,815 137,979 137,979 137,979 137,979 137,979 137,979
Table: Effect of the Easter Leaving Rule on Age at First Birth: Delayed Fertility
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)≥ 21 ≥ 22 ≥ 23 ≥ 24 ≥ 25 ≥ 26 ≥ 27 ≥ 28 ≥ 29 ≥ 30
RD-DD 0.00264 0.000691 0.000601 0.00664 0.00777 0.0114** 0.0148*** 0.0146*** 0.0171*** 0.0181***(0.00409) (0.00455) (0.00488) (0.00508) (0.00518) (0.00519) (0.00515) (0.00508) (0.00500) (0.00493)
RD-DD 0.00316 0.00129 0.00123 0.00730 0.00845 0.0121** 0.0155*** 0.0153*** 0.0177*** 0.0187***Quadratic Spline (0.00409) (0.00455) (0.00488) (0.00508) (0.00518) (0.00519) (0.00515) (0.00508) (0.00500) (0.00493)
Observations 137,979 137,979 137,979 137,979 137,979 137,979 137,979 137,979 137,979 137,979
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Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 36 / 46
Appendix
Education and Fertility Timing: OLS & IVJames & Vujic (2016)
Table: OLS and IV Estimates of Education on Fertility Timing: Evidence from the Easter Leaving Rule
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Age 1st Birth Teen Birth (<21) Aged 24 and over Aged 27 and over Aged 30 and overOLS IV OLS IV OLS IV OLS IV OLS IV
Quals 1.216*** −1.066 −0.071*** −0.012 0.137*** 0.116 0.112*** 0.309** 0.081*** 0.405***(0.029) (1.171) (0.002) (0.095) (0.003) (0.141) (0.003) (0.147) (0.003) (0.146)
Observations 75,500 75,500 113,999 113,999 113,999 113,999 113,999 113,999 113,999 113,999First Stage F Stat 46.5 46.1 46.1 46.1 46.1Hausman test (p-value) 0.041 0.54 0.88 0.18 0.020
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Suncica Vujic (University of Antwerp) Estimating the Social Returns to Education Belgrade, October 29, 2019 37 / 46
Appendix
What is the Effect of Early Conduct Disorder on HC?Webbink, Vujic, Konning & Martin (2012)
Effect of Early Conduct Disorder on ± Human Capital
APA Definition Grade Marks high High school Attacking Arrested since Jailrepetition school graduation others 18 years old
OLS 0.096 −0.267 −0.136 0.179 0.124 0.048(0.019)*** (0.026)*** (0.019)*** (0.022)*** (0.017)*** (0.011)***
N 5224 5210 5226 2140 2138 2136FE All 0.057 −0.180 −0.054 0.146 0.076 0.020
(0.018)*** (0.031)*** (0.020)*** (0.029)*** (0.019)*** (0.010)**N 5224 5210 5226 2140 2138 2136FE MZ 0.091 0.025 −0.036 0.162 0.067 0.022
(0.026)*** (0.045) (0.032) (0.044)*** (0.028)** (0.013)*N 2220 2220 2218 876 876 874
Back
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Appendix
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Appendix
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Appendix
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Appendix
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Appendix
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Appendix
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Appendix
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