Estimating the Social Returns to Education · driven by “affective” thinking (a focus on immediate feelings) rather than “cognitive” thinking (a focus on long-term beneﬁts

Estimating the Social Returns to Education

Suncica Vujic

[email protected]

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


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


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.


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!


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))


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))


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



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.




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)



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.



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



Discontinuities around RoSLA IncreaseConviction Rate

−.3

−.2

−.1

0.1

Male

Convic

tion R

ate

1950 1955 1960 1965

Year of Birth

Table




“The paper uses a statistical trick to find a causal link between low education and crime.”

Number of Google Scholar citations ∼ 400


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.



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.



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.



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))



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



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



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))


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.



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.




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.




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




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




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))




Associations between CD & HC Measures: Lowess

OLS/Twin FEs




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.



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.



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.



Thank you for your attention!

Suncica Vujic

[email protected]


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


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

Back


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


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

Back


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

Back


Appendix


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


Appendix

References I

Anderberg, D. and Y. Zhu (2014).What a difference a term makes: the effect of educational attainment on marital outcomes on the UK.Journal of Population Economics 27, 387–419.

Angrist, J. D. and A. B. Krueger (1991).Does compulsory school attendance affect schooling and earnings?The Quarterly Journal of Economics 106(4), 979–1014.

Arrow, K. J. (1973).Higher education as a filter.Journal of Public Economics 2, 193–216.

Ashenfelter, O. and A. Krueger (1994).Estimates of the economic return to schooling from a new sample of twins.The American Economic Review 84(5), 1157–1173.

Becker, G. S. (1968).Crime and punishment: An economic approach.Journal of Political Economy 76, 169–217.

Becker, G. S. (1975).Human capital.New York: Columbia University Press.

Blundell, R., L. Dearden, and B. Sianesi (2005).Evaluating the effect of education on earnings: models, methods and results from the national child development survey.Journal of the Royal Statistical Society: Series A (Statistics in Society) 168(3), 473–512.


Appendix

References II

Borghans, L., A. L. Duckworth, J. J. Heckman, and B. Ter Weel (2008).The economics and psychology of personality traits.Journal of Human Resources 43(4), 972–1059.

Borjas, G. J. (2019).Labor Economics (8th ed.).Irwin: McGraw-Hill.

Bound, J. and G. Solon (1999).Double trouble: on the value of twins-based estimation of the return to schooling.Economics of Education Review 18, 169–182.

Buckles, K. S. and D. M. Hungerman (2013).Season of Birth and Later Outcomes: Old Questions, New Answers.Review of Economics and Statistics 95, 711–724.

Buscha, F. and M. Dickson (2015).The Wage Returns to Education over the Life-Cycle: Heterogeneity and the Role of Experience.Technical report, IZA Discussion Papers 9596.

Card, D. (1999).The causal effect of education on earnings.In O. Ashenfelter and D. Card (Eds.), The Handbook of Labour Economics, Volume 3A, Chapter 30, pp. 1801–1863.

North-Holland: Elsevier.

Carneiro, P., J. J. Heckman, and E. J. Vytlacil (2011).Estimating marginal returns to education.The American Economic Review 101(6), 2754–2781.


Appendix

References III

Chen, K. and F. Lange (2008).Education, Information, and Improved Health: Evidence from Breast Cancer Screening.Technical report, IZA Discussion Papers 3548.

Cunha, F. and J. J. Heckman (2007).The technology of skill formation.The American Economic Review: Papers and Proceedings 97 (2), 31–47.

Cunha, F., J. J. Heckman, and S. M. Schennach (2010).Estimating the technology of cognitive and noncognitive skill formation.Econometrica 78(3), 883–931.

Currie, J. and M. Stabile (2007, July).Mental health in childhood and human capital.Working Paper 13217, National Bureau of Economic Research.

Danzer, N. and V. Lavy (2018).Paid parental leave and children’s schooling outcomes.The Economic Journal 128(608), 81–117.

Dee, T. S. (1998).Competition and the quality of public schools.Economics of Education Review 17 (4), 419–427.

Del Bono, E. and F. Galindo-Rueda (2007).The long term impacts of compulsory schooling: Evidence from a natural experiment in school leaving dates.Discussion Paper CEE DP 74, Centre for the Economics of Education, London School of Economics, London.


Appendix

References IV

Dickson, M. and S. Smith (2011).What determines the return to education: An extra year or a hurdle cleared?Economics of Education Review 30(6), 1167–1176.

Duflo, E. (2001).Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment.The American Economic Review 91, 795–813.

Eisenhauer, P., J. J. Heckman, and S. Mosso (2015).Estimation of dynamic discrete choice models by maximum likelihood and the simulated method of moments.International Economic Review 56(2), 331–357.

Etilé, F. and A. M. Jones (2011).Schooling and smoking among the baby boomers – An evaluation of the impact of educational expansion in France.Journal of Health Economics 30(4), 811–831.

Fort, M., N. Schneeweis, and R. Winter-Ebmer (2014).More schooling, more children? Compulsory schooling and fertility in Europe.CESifo Working Paper 5068, Centre for Economic Studies & Ifo Institute, Munich.

Goldin, C. (1992, June).The meaning of college in the lives of american women: The past one-hundred years.Working Paper 4099, National Bureau of Economic Research.

Gregg, P. and S. J. Machin (2000).Child development and success or failure in the youth labour market.In D. G. Blanchflower and R. B. Freeman (Eds.), NBER Comparative Labour Market Series, pp. 247–288. Chicago: The

University of Chicago Press.


Appendix

References V

Griliches, Z. (1979).Sibling models and data in economics: Beginnings of a survey.Journal of Political Economy 87 (5), 37–64.

Hanushek, E. A. (1981).Education policy research - An industry perspective.Economics of Education Review 1(2), 193–223.

Harmon, C. and I. Walker (1995).Estimates of the economic return to schooling for the United Kingdom.The American Economic Review 85(5), 1278–1286.

Heckman, J. J. (2000).Policies to foster human capital.Research in Economics 54, 3–56.

Heckman, J. J. (2006).Skill formation and the economics of investing in disadvantaged children.Science 312(5782), 1900–1902.

Heckman, J. J. (2008).Schools, Skills, and Synapses.Economic Inquiry 46(3), 289–324.

Heckman, J. J., J. Stixrud, and S. Urzua (2006).The effects of cognitive and noncognitive abilities on labour market outcomes and social behaviour.Journal of Labor Economics 24(3), 411–481.


Appendix

References VI

Holmlund, H., M. Lindahl, and E. Plug (2011).The causal effect of parents’ schooling on children’s schooling: A comparison of estimation method.Journal of Economic Literature 49(3), 615–651.

Isacsson, G. (2004).Estimating the economic returns to educational levels using data on twins.Journal of Applied Econometrics 99, 99–119.

James, J. and S. Vujic (2016).From High School to the High Chair: Education and Fertility Timing.Technical Report 48/16, Bath Economics Research Paper.

James, J. and S. Vujic (2019).From High School to the High Chair: Education and Fertility Timing.Economics of Education Review 69, 1–24.

Lleras-Muney, A. (2005).The relationship between education and adult mortality in the United States.Review of Economic Studies 72(1), 189–221.

Lochner, L. and E. Moretti (2004).The effect of education on crime: Evidence from prison inmates, arrests, and self-reports.The American Economic Review 94, 155–189.

Lundborg, P. (2013).The health returns to schooling - What can we learn from twins?Journal of Population Economics 26, 673–701.


Appendix

References VII

Machin, S. J., O. Marie, and S. Vujic (2011).The Crime Reducing Effect of Education.The Economic Journal 121, 463–484.

Machin, S. J., O. Marie, and S. Vujic (2012).Youth Crime and Education Expansion.German Economic Review (Special Issue on Economics of Crime) 13(4), 366–384.

Miller, P., C. Mulvey, and N. Martin (1995).What do twins studies reveal about the economic returns to education? A comparison of Australian and U.S. findings.The American Economic Review 85(3), 586–599.

Milligan, K., E. Moretti, and P. Oreopoulos (2004).Does education improve citizenship? Evidence from the United States and the United Kingdom.Journal of Public Economics 88(9–10), 1667–1695.

Mincer, J. (1958).Investment in human capital and the personal income distribution.The Journal of Political Economy 66, 281–302.

Mincer, J. (1974).Schooling, Experience and Earnings.New York: Columbia University Press.

Oreopoulos, P. (2006).Estimating average and local average treatment effects of education when compulsory schooling laws really matter.The American Economic Review 96(1), 152–175.


Appendix

References VIII

Oreopoulos, P. and K. G. Salvanes (2011).Priceless. The nonpecuniary benefits of schooling.Journal of Economic Perspectives 25(1), 159–184.

Park, C. and C. Kang (2008).Does education induce healthy lifestyle?Journal of Health Economics 27 (6), 1516–1531.

Spence, M. (1973).Job market signaling.The Quarterly Journal of Economics 87 (3), 355–374.

Webbink, D., S. Vujic, P. Koning, and N. Martin (2012).The Effect of Childhood Conduct Disorder on Human Capital: Evidence from a Cohort of Young Australian Twins.Health Economics 21(8), 928–945.


Documents

Estimating the Social Returns to Education · driven by “affective” thinking (a focus on immediate feelings) rather than “cognitive” thinking (a focus on long-term beneﬁts