hc-lecture 2 returns to education

8/13/2019 HC-Lecture 2 Returns to Education

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Economics of Human Capital

Lecture 2

Technology vs educationIan Walker

Lancaster University Management [email protected]



Overview of lecture

• Inequality – Changes in D and S – Other factors

• Intergenerational transmission• School quality• Signalling

• Education and growth• Next week – Externalities and peer effects – Post school investments



Some facts• Returns to education in the U.S. fell during the

1970s when there was a very sharp increase in thesupply of educated workers.

• Returns to education then began a sharp rise in80s.

• Rise slowed in the 1990s.• This conclusion is robust to many sensible ways of

measuring education returns. – In a standard Mincer wage equation, return to year of

education rose from about 7.5% in 1980 to 10% in 1990. – Large increase between college and HS grads.

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Some facts

• Also an increase in the estimated return to“experience” (for younger cohorts). – Note: 'returns to experience' typically estimated

x-section data, so this pattern probably has nomeaningful predictive power for lifecycleearnings.

• Many other economies experienced a rise inearnings differentials in the 1980s – but only in the UK was the rise as pronounced as

in the U.S.

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Some facts

• Very large increases: US and UK – And Germany – recent work by Dustmann, et al using Germany

social security records• Modest increases: Australia, Canada, Japan,Spain, and Sweden

• No noticeable changes: France and Italy• Modest falls: Netherlands

5



Some facts

• After the 1990s, rise in overall inequality inthe US gave way to a 'polarization„ – wage inequality rising in the upper-half of the

wage distribution – but attenuating or even declining in the lower

half.

• Some share of the rise in overall inequality isdue to a rise in 'residual' inequality – That is, the inequality remaining after partialling

out the estimated contribution of observables(such as education and experience). 6



Changes in the supply of skills

• Important for understanding wage structurechanges. – remarkable growth in the supply of skills among

all advanced economies.• But large cross-country differences in timing• US had particularly rapid rise in 70s (partly

due to the Vietnam war) and slowdown in80s.• UK had gradual rise in mid 60‟s to 70, flat,

then sharp rise in early 90‟s to 00. 7



Theory of skill premia

• Demand and supply• Competitive market• Two types of workers (L, H), imperfect subs

• Wages equal marginal products so

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• So skill premium is

• Taking logs

• So

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• Expansion in education (rise in H/L) reducesreturns to education

• But large expansion in H/L has occurred inmost countries since 1945

• Yet ω has risen in most countries• Something must be happening to A H/AL to

counteract the rise in H/L …. • Labour saving technical progress

– AH rising faster than A L 10



Katz and Murphy

• KM estimate

where γ 2 is 1/ σ

• That is, they assume that A H/AL can becaptured by a time trend

• KM uses CPS 63-87 (25 obs) and find

• So σ=1/0.71=1.4

• See also Katz and Goldin‟s “race” book 11



Other evidence

• Blau and Kahn JPE 1996 found that labourmarket institutions (unions and wagecentralization) are much better predictors of

cross-country wage inequality than aresupply and demand indices• They find no evidence that D and S explain

any of the cross-country differences in therelative earnings of the less-skilled.

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Other evidence

• Leuven, Oosterbeek and van Ophem, EJ2004 use IALS data rather tha education – Weak link between IALS scores and schooling

• Estimate relative wages of skill j acrosscountries as a fn of relative D and Sdifferences

• They find β<0 and support D and S story

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Intergenerational transmission

• Becker‟s Woytinsky lecture book 1967 • Becker / Tomes – JPE 1976, JoLE 1986• Parents altruistic• Max log C 1 + log C 2

• Perfect credit market – So invest in child education up to the point where

education returns = 1+r

• But – Contractual issue – Imperfect credit 14



Evidence

• Many studies regress schooling against logparental income

• Positive coefficient

– Children‟s education may be a consumptiongood for the parents, Or credit constraints

• Some studies include parental education

– Income coeff reduces• But parental income may affect quality of

education rather than quantity

• Parental income measurement error 15



Evidence

• So common (Becker/Tomes) to runregressions like – Schooling=controls + α · log parental income

• This captures a school quality effect – Coeff ≈ 0.3 – Small effect: if your twice as rich as me then your

kids will be 10% (α2) richer than mine – But ME might bias α towards 0 – Children usually observed at early age in

lifecycle compared to parents

– And its probably not linear 16



Parents • Long history of research on intergenerational

mobility in social science – Earnings elasticity of father and son‟s earnings:

• 0.40 - 0.50 in US (Solon, 1999), 0.60 in UK (Dearden et al1997)

– Elasticity of educational mobility:• 0.25 to 0.40 in UK (Dearden et al.,1997)

• Mechanism?

– Is it causal? – Is it parental education or income or both thatmatters?

• UK policy context – Raising min SLA to 18, abolish child poverty



Wider context

• Private returns to education widely studied – High returns suggest possible underinvestment – So encouraging more human capital formation might be

welfare improving

• Growing literature on social returns: – Health and education.• Deaton and Paxson (1999), Lleras-Muney (2002).• Currie and Moretti (2002) – Mother‟s education and child birth

weight in developing world and US.

• Children‟s human capital – An externality of sorts

• Survey of correlation studies by Haverman and Wolfe ( JEL 1995)• Suggest strong associations



Intergenerational Transfer:Nurture, Nature, or what?

• Better educated are better at parenting – Higher home productivity as well as in the paid

marketplace

• Better educated make better investments – Including investing in the human capital of their children

• Better educated are better peers – Cultural transmission

• Better educated have better genes – Unobserved characteristics of the parents may be

genetically transmitted to the children.



Literature ReviewChildren of identical twins

• Eliminates (half of) the nature effects? – As genetically alike as siblings – but cousins – so (slightly) different nurture

• Behrman and Rosenzweig ( AER 2002, 2005) and Antonovics and Golberger ( AER 2005) – Differences between the children of US MZ twins

• Small effect of father‟s education, no effect of mother‟s – But terrible data – Bingley et al (2009): Parental Schooling and Child

Development: Learning from Twins Parents . SFI WP – Much better data

• Conventional effects of DZ mother‟s education • no effect of MZ mothers



Literature Review Adoptees• Eliminates the nurture effect?

– But selective adoption? Differential treatment?• Mostly small samples

– Sacerdote (2002), Dearden et al (1997)• Small effect of adoptive father‟s educ on adopted sons • About the same as on natural sons

• Two bigger datasets control for selection – Sacerdote (2007)

• Korean adoptees randomly assigned to US parents• Some impact of adopted mother‟s education

– But very small when father‟s education included

– Bjorklund et al (QJE 2006)• Swedish data registers

– Use pre-adoption info to control for selection – Finds post- adoption mother‟s education matters (a little)



Literature ReviewInstrumental Variables

• Identifies causal (nurture) effect? – Most studies focus on RoSLA as an IV – Only one study estimates effects on completed schooling

• Children and parents matched from registers

• Black, Devereux and Salvanes ( AER , 2005) – Cross sectional variation in SLA in Norway

• Uses completed schooling – OLS supports evidence of impact, IV does not

• but (weak) evidence of mother/son influences• Effect of 0.12 years for low education sample



Literature ReviewInstrumental Variables

• Early outcomes – Children and parents in same household

• Oreopoulos, Page and Stevens, (JoLE 2006) – Cross sectional variation in Min SLA in USA:

• Outcome is grade repetition: – OLS and IV

• Significant effects for sum of parent‟s educations – Insignificant when entered separately

• Other studies – Grade repetition in HE

• Carneiro et al (2007)• Maurin and McNally (2008)

– Suggestive of an effect that parental HE has an effect – But weak IVs



Holmlund, et a l , IZA WP 2008• One large Swedish dataset• Three identification strategies

– Twins, Adoptees and IV (RoSLA)• Twins

– zygosity of twins unknown• Identifies bounds• Small effects of mum‟s educ (0-0.06), larger for dad‟s (0.1-0.12)

• Adoptees – Some evidence of non-random assignment

• Large effects of mum‟s education (0.11), none for dad‟s

• IV – RoSLA coincided with other reforms

• And its a local effect

• 0.06 years effect of mum‟s education, none of dad‟s



What else matters?

• Intergenerational correlation studies – show that education and income matters (to somedegree) for children‟s living standards

• But what else matters (as well)?

• Poor environment – neighbourhood, housing, teen mum, marriage, schools,peers......

• Adds almost nothing to explanatory power

• Poor parental involvement – Low propensity to plan ahead, high time preference• Adds about 1/3 rd to the explanatory power



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School quality

• Historical OLS focus - but endogenous CS – Krueger and Hanuschek meta‟s in EJ

• Experiments

• RD / IV “Maimonides‟ rule” or demographics – Usually “fuzzy” so IV not RD, estimates a LATE – Uses variation at one grade only – Uses school average CS – Sorting by income into good schools near cap

• Urquiola and Verhoogen ( AER 2009)

– Window size – precision vs bias trade off

• NEW - Siblin differences



Seminal studies of causal effect

• STAR data - Krueger ( QJE 99) – large significant and lasting effects of lower CS• K, K+1, K+2, K+3 (0.20 σ, 0.28 σ, 0.22 σ, 0.19 σ)

– No effects of classroom assistant - sorry

• Maimonides‟ rule- Angrist and Lavy ( QJE 1999) – Finds large effects of CS cuts in

• K+4, K+5 (0.17 σ, 0.26 σ)

• Cohort size - Hoxby ( QJE 2000) – Connecticut local cohort size variation as IV – Discontinuity from mandated CS caps

• FE - Rivkin et al (Econometrica 2005)

– Texan administrative database 28



CS variation | C (D ± ε) samplesMaimonides’ ( Angrist/Lavy) method

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CS

D

Cohort

size

D 2

D

3

D

ε

D/2



Cohort variation | classes samplesIV (Hoxby) method

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CS

D

Cohort

sizeC=1 C=2 C=3 C=4



Recent studies of causal effect

• Browning and Heinesen, "Class size, teacher hoursand educational attainment", Scan JE (2008)

• Leuven, Oosterbeek, and Rønning, “Quasi -experimental Estimates of the Effect of Class Sizeon Achievement in Norway”, Scan JE (2008).

• Lindahl, “Home versus school learning: A newapproach to estimating the effect of class size on

achievement”, Scan JE , (2005)• Fredriksson, Öckert, and Oosterbeek, “Long -term

effects of class size”, mimeo (2012)

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Issues

• Focus has been on education outcomes noteconomic effects• Long term effects of CS

– Frederiksson et al , mimeo 2012 – Chetty et al, QJE 2012

• Structural modellers object to exclusion ofother inputs into educational production – Inputs probably negatively correlated with CS – Some evidence that there is “home production”,

Lindahl ( Scan JE 2008)32



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Krueger CBA

• Krueger STAR estimates – Causal effects of CS on early test scores

• Effects of early test scores on later test

scores• Effects of later test scores on earnings• So multiplies one causal effect by two

correlations• Computes PV is earnings effect and cost of

extra teachers

– Finds Benefits ≈ Double costs



Signals

• Wages reflect not (just) the productivityenhancing effect of education (HC theory)but an effect on earnings of the underlying

ability that education signals (Spence,QJE 1970). – Both signalling an HC theories imply that there is

a positive correlation between earnings andeducation

• “This fact makes it virtually impossible tocome up with a valid test of the screening

hypothesis …..” Lazear AER 1977 34



• Firms maximise profits. Individuals maximiseincomes (W) net of education costs (C).

• Firms believe that people with S=S* are H-types and pay them W H, and believe thatS=0 people are L-types and pay them W L

• H-types choose S=S* and L-types S=0.• Expectations fulfilled• Separating equil

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Human Capital and GrowthMacro evidence• Aggregating a Mincer HCEF to the economy

level we get

• Differencing removes technologicaldifferences that are part of the error termterms to give

• so the S coeff shows how returns havechanged over time, while the S coefficientgives the (social) rate of return in j at time t .36

ln jt jt jt jt w r S e

ln j j jt jt j jt w r S r S e



Evidence

• O‟Neill (JPE 1995) – (social) return rising

• The idea that growth rates should converge

is in a feature of many macro-studies – those below their steady-state growth rateshould catch up with those above.

• – where W* is the steady state level of GDP pc

• Then the macro growth equation wouldbecome

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*1 j jt j jW W W u

1 , 1 ...... j jt j t jW W rS e



Tests

• Many unconvincing attempts. One idea – signalling says that relative education matters – HC says absolute education matters

• Chevalier et al Economic Journal 2004

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Result

• What does the signalling model predict in thiscase?

• People with GED scores of 40 − 44 willearn more if they receive the GEDcertificate than not.

• But Human Capital model implies that since

ability is comparable among these groups,wages will be also.

• Found 20% effect after 5 years41



Next

• Investment in post-schooling skills• Externalities and peer effects

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hc-lecture 2 returns to education

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