DI

SC

US

SI

ON

P

AP

ER

S

ER

IE

S

Forschungsinstitut zur Zukunft der ArbeitInstitute for the Study of Labor

College Expansion and the Marginal Returns to Education: Evidence from Russia

IZA DP No. 8735

December 2014

Olga BelskayaKlara Sabirianova PeterChristian Posso

College Expansion and the Marginal

Returns to Education: Evidence from Russia

Olga Belskaya UNC‐Chapel Hill

Klara Sabirianova Peter

UNC‐Chapel Hill, IZA and CEPR

Christian Posso

UNC‐Chapel Hill

Discussion Paper No. 8735 December 2014

IZA

P.O. Box 7240 53072 Bonn

Germany

Phone: +49-228-3894-0 Fax: +49-228-3894-180

E-mail: iza@iza.org

Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

IZA Discussion Paper No. 8735 December 2014

ABSTRACT

College Expansion and the Marginal Returns to Education: Evidence from Russia*

This paper evaluates whether the expansion of higher education is economically worthwhile based on a recent surge in the number of campuses and college graduates in Russia. Our empirical strategy relies on the marginal treatment effect method in both normal and semi‐parametric versions, and estimating policy‐influenced treatment parameters for the marginal students who are directly affected by college expansion. We use high‐quality panel data with multiple wage observations, many birth cohorts, disaggregated location information, and past economic conditions. We find that college expansion attracts individuals with lower returns to college, but the returns for marginal students vary considerably depending on the scale of expansion and the type of location where new campuses are opened. Marginal individuals in smaller cities and locations without college campuses receive the largest benefits from new campuses. The results provide important implications for the design of policies targeting the expansion of higher education. JEL Classification: J31, I21, P36 Keywords: education, returns to college, marginal treatment effect,

policy‐relevant treatment effect, local instrumental variable, selection bias, college expansion, Russia

Corresponding author: Klara Sabirianova Peter Department of Economics Carolina Population Center University of North Carolina-Chapel Hill Chapel Hill, NC 27599 USA E-mail: kpeter@unc.edu

* We are thankful to Brian McManus, Saraswata Chaudhuri, Donna Gilleskie, and Tiago Pires for useful comments. We also acknowledge helpful comments received from participants of the UNC Applied Microeconomics Workshop and Southern Economic Association meetings in Atlanta, GA.

1  

1.Introduction

Overthelast20years,manycountrieshaveexpandedhighereducationandincreasedthe

number of college graduates. Between 1990 and 2010, the number of students in tertiary

educationper100,000peoplehastripledinBrazil, IndiaandRussia,andincreasedtwelve‐foldin

China; thenumberof collegeshas also increased several‐fold in the largest developing countries

(Carnoy et al. 2013). Answering pertinent policy question – whether such rapid and massive

collegeexpansion iseconomicallyworthwhile–requiresproperanalytical tools. Toevaluate the

effectivenessofcollegeexpansionpolicies,analystsneedtoestimatethereturnstocollegeforthe

marginalindividualwhopreviouslywouldnothavehadanopportunitytostudyincollegeandwho

isaffectedbycollegeexpansion.

Weevaluatethemarginalreturnstocollegeinresponsetocollegeopeningsusingthecase

ofRussia.1 Between1992and2003, thenumberof students inRussianhighereducationalmost

tripled, the number of universities increased two‐fold, and the number ofmunicipalitieswith at

leastonecampusmorethandoubled.2ThelegalframeworkfortheRussiancollegeexpansionwas

provided by the 1992 Law on Education,which allowed the opening of private universities and

legalized tuition‐based programs and branches in public universities (Federal Law 1992).3 The

earlyexpansionperiodwasaccompaniedbyarapidincreaseintheaveragecollegewagepremium,

ascanbeseeninFigure1.Followingthelow‐returnSovietperiodwithitscompressedcentralized

wage structure, the OLS‐estimated averagewage premium per year of college education surged

from 2.8 percent in 1990 to about 8.7 percent in 1998 during Russia’s transition to a market

economy,butthenthepremiumstabilized.

                                                            1WeprovideabriefsummaryoftheSovietandRussiansystemsofhighereducationinthewebappendix.2SourcesareRegionsofRussia(1995,2004)andauthors’calculations.3 Russian institutions of higher education are referred to by a variety of names, including “Universities,”“Institutes,” “Academies,” and “Higher Schools”. For compatibility of terminology with internationalliteratureonthistopic,thispaperusestheterms“Universities”and“Colleges”,eventhoughthelattertermisnottechnicallyaccuratefromtheRussianlanguagepointofview.Thus,suchtermsas“College,”“University,”and“Institutionofhighereducation”areusedinterchangeablythroughoutthispaper.

2  

Ourempiricalstrategyreliesonthemarginaltreatmenteffect(MTE)methodinitsnormal

and semi‐parametric versions (Heckman and Vytlacil 2001, 2005, 2007) and a construction of

policy treatment parameters (Heckman and Vytlacil 2001; Carneiro et al. 2010). Unlike the

previousMTEstudiesthatarebasedoneithercross‐sectionaldataorone‐cohortpanelssuchasthe

NationalLongitudinalSurveyofYouth(NLSY),westudymultiplecohorts,includingonesthatmade

their college decision in the pre‐expansion period (1985‐1992) as well as cohorts that entered

collegeduring theexpansionperiod(1993‐2003). Thus,wecanevaluate theoutcomesofactual,

real‐world college expansion rather than a hypothetical increase in the probability of college

attendance.

The identificationofMTErelieson thevariation invariablesdirectlyaffecting thecollege

decision. These variables capture the institutional environment and economic conditions that

prevail during individuals’ late teenage years, the time when individuals make their college

decisions.4Ourkeyinstrument,thenumberofcampusesinthemunicipalityofresidenceatage17,

measurestheextentofcollegeexpansioninRussiaovertimeandacrosslocations.Thisinstrument

is an improvement over a commonly used binary instrumental variable (IV) ‐ the presence of a

collegeintheU.S.countyofresidenceduringteenageyears(Card1995;CameronandTaber2004),

asitallowsforcomputingpolicy‐relevanttreatmenteffectsseparatelyfortheestablishmentofthe

firstcampusinthelocalitythatneverhadacollegeaswellasforthemarginalchangeinthenumber

ofcampuses.Sincetheinstrumentisnumeric,itspolynomialfunctionalformisutilizedtocapture

the strong non‐linear effects of college availability on college attainment and on the marginal

returns. Wealsoprovideseveralargumentsandstatisticalteststooffsetpotentialconcernswith

thevalidityofthisinstrument.

ConsistentwiththeMTEliterature,wefindthatthereturns tocollegeareheterogeneous,

and that individualswith thehighestpropensity togo tocollegeenjoy the largestmarginalgains

                                                            4SeeCard(2001)forareviewofthevariables(mainlycostshifters)thataffecttheschoolingdecision.

3  

from college education. We also find strong evidence of the positive sorting of individuals into

college basedonunobservedgains. Themagnitudeof the sorting gain inRussia – a5.7percent

wageincreaseperyearofcollege–fallsbetweenthecorrespondingestimateof2percentforChina

(HeckmanandLi2004)and7.6percentfortheU.S.(Carneiroetal.2011b).5

Inadditiontoconventionaltreatmentparameters,weestimatethereturnstocollegeforthe

individualswhochangecollegeparticipation inresponsetoamarginal increase inthenumberof

campuses.Theestimatedmarginalpolicy‐relevanttreatmenteffectparameterof9.6percentwage

increase per year of college indicates large gains for the marginal individuals affected by the

establishmentofnewcampuses.Ourpolicysimulationsshowthattheopeningofacollegecampus

in constrained municipalities – smaller non‐capital cities or municipalities that did not have

institutionsofhighereducationpriortocollegeexpansion–attractsstudentswithhigherreturns

compared to the effect of the same policy in unconstrained municipalities with more college

choices.Inanotherpolicysimulation,weshowthatifthenumberofcampusespermunicipalitydid

not increase or remained at the 1992 level, then a considerable portion of populationwith high

potentialgainsfromcollegeeducationwouldnothavebeenabletorealizethesegains.Wealsofind

larger discounted net benefits for students from constrained municipalities by applying the

estimatedreturnsinthetraditionalcost‐benefitanalysis.

Our paper contributes to several strands of the literature. First, we contribute to the

literatureon themarginal treatmenteffectof collegeeducation.6 Mostof this literatureuses the

NationalLongitudinalSurveyofYouth(NLSY)andconsiderschangesintuitionasapolicyaffecting

the collegedecisions (Carneiroetal. 2010, 2011b;Carneiro andLee2009;Heckmanetal. 2014;

                                                            5 Our results support a “comparative advantage model” of the labor market rather than a “single abilitymodel”,which isconsistentwiththepast literatureonself‐selectionthatgoesbacktoRoy(1951). A largenumber of earlier studies established empirical evidence of the non‐random sorting of individuals intodifferent levelsofeducationandcalled for theselectivitycorrection in thereturns toschooling (e.g.,WillisandRosen1979,Garen1984,BjörklundandMoffitt1987). 6Outsidethefieldofeducation,theMTEmethodisonlybeginningtobeappliedinasystematicway;e.g.,seeBasu et al. (2007) and Evans and Basu (2011) for theMTE applications in health related topics. Moffitt(2014)usestheMTEmethodtoestimatetheeffectofatransferprogramonlaborsupply.

4  

HeckmanandVytlacil2001,2005).MTEestimatesoutsidetheU.S.arerareandoftenlimitedtoa

singlecrosssection(Carneiroetal.(2011a)forIndonesia;HeckmanandLi(2004)forChina;Kyui

(2013)forRussia;Zamarro(2010)forSpain).7CollegeexpansionintheMTEliteratureiseithernot

modelled at all or simulated as an additive exogenous change in the probability of college

attendance.Thekeycontributionofourstudyistheestimationoftheexpansion‐relatedmarginal

treatmenteffects. Tothebestofourknowledge, this isthefirststudythatutilizesdataonactual

college openings to derive policy‐relevant treatment parameters and evaluate counterfactual

scenariosofcollegeexpansion.Wealsoshowthatthereturnstocollegeformarginalstudentsvary

considerably depending on the scale of college expansion and the location of new campuses.

Additionally,weextendtheMTEliteraturebyusinghigherqualitypaneldatawithmultiplewage

observations, several birth cohorts, disaggregated location information, and past economic

conditions. As shown by Heckman et al. (2006), cohort‐based models fitted on repeated cross

sectionsprovidemore reliable estimatesof the returns toeducation than theestimatesobtained

fromasinglecrosssection.

Second, our study is directly related to the literature that evaluates the effect of college

expansion on the returns to college. A number of earlier papers (Katz andMurphy 1992, Topel

1997,amongothers)linkedtheincreasedsupplyofcollegegraduateswithaloweraveragecollege

wage premium. Carneiro et al. (2011b) and Moffitt (2008) show that an increase in college

participation leads to a decline in the marginal returns to college. We also find that college

expansion draws individuals with lower marginal returns to college. However, the general

equilibriumeffectswereeitheroffsettingornotstrongenoughtoshiftconsiderablytheaggregate

                                                            7 Carneiro et al. (2011a) computes the MTE of post‐secondary education in Indonesia in 2000 using thedistance from the current village center to the nearest secondary school as an identifying restriction.HeckmanandLi (2004) estimate theMTEof collegedegree fora cross‐sectional sampleof youngChineseworkersin2000,usinglimitedexclusionrestrictionssuchasparentalincomeandparentaleducation.Kyui(2013) estimates standard college‐related treatment parameters using 2006 RLMS and provides the firstapplicationof theMTEmethodtoRussia. Zamarro(2010)developsanewmethod forestimatingMTEformultipleschoolinglevelsandappliesittotheevaluationoftheeducationreforminSpain.

5  

equilibriumskillpricesandtheirdistribution.Inthispaper,wedonotdisentanglevaryinggeneral

equilibriumeffectsofcollegeexpansionandleavethisquestionforfutureresearch.8

Third, thispaper contributes to the literatureevaluating the effectofnewuniversitieson

collegeenrollment. Wefindthattheopeningof the firstcampusinRussianmunicipalitieswhere

there were previously no colleges increases the probability of receiving a college degree by 11

percentage points. Studies fromother countries also report positive, but lower effects of a new

university on college enrollment, e.g., 8 percentage points in Italy (Oppedisano 2011) and 6.4

percentagepointsinCanada(Frenette2009).

Fourth,ouruseofthenumberofcampusesinthemunicipalityofresidenceatage17asa

supply‐side instrument for college education builds upon previous applications of supply‐side

shifters in identifying the returns to schooling; some earlier examples include construction of

elementary schools in Indonesia (Duflo 2001), a dummy for living near college (Card 1995), the

distance to the nearest college (Kane and Rouse 1995) and supply disruptions caused by awar

(IchinoandWinter‐Ebmer2004).

Finally,ourpapercontributes toa large literatureon thereturns toeducationduring the

transitiontoamarketeconomy(Andrénetal.2005,Brainerd1998,Fangetal.2012,Fleisheretal.

2005, Gorodnichenko and Sabirianova Peter 2005,Münich et al. 2005, Yang 2005, amongmany

others). We improve this literature by estimating the distribution of returns, applying a more

rigorous identification strategy, and deriving policy‐relevant parameters for individuals at the

marginofchoice. Yet,wealsoconfirmapreviouslydocumentedpatternof increasingreturns to

schoolingduringtheearlytransitionperiod,followedbytheirlevellingoutinthelaterperiod.

Ourpaperisstructuredasfollows.Inthenextsection,wepresenttheempiricalframework

used in this study. Section 3 discusses the data and identifying variables. Section 4 presents

                                                            8Intheaccompanyingpaper,weshowthattheexpansionofhighereducationinRussiaworsenedthequalityof college entrants, reduced resources per student, and increased the market returns to college quality(BelskayaandSabirianovaPeter2014).

6  

estimates of the marginal treatment effect and discusses the identification validity. Section 5

performspolicysimulationsandSection6concludes.

2.EconometricFramework

Modelset‐up

To estimate the heterogeneous returns to college, we follow a semi‐structural method

developed by Carneiro etal. (2011b). The decision rule of an individual i is characterized by a

latentvariablemodelofcollegeenrollment:

∗ 0 ,where ∗ (1)

isabinaryvariableindicatingcollegeenrollment;itequalsoneforcollegegraduatesand

zeroforhighschoolgraduates.9Weapproximate byalinearformgivenby ,where isa

vectorofobservablecharacteristicsthataffectthecollegedecision.Vector includesthreetypeof

variables: (i) variables that affect both the schooling decision and wages, ; (ii) variables that

measuretheextentofcollegeexpansionatthetimewhenindividualsmaketheircollegedecisions,

;and(iii)otherinstrumentalvariables/exclusionrestrictionsthatalsoshiftthelatentvariable ∗,

:

(2)

Weassume that is anunobserved to theeconometricianerror term that is statistically

independentof .Itcapturesthemarginalcostofobtainingacollegeeducationforindividuali.

Let P denote the probability of selecting into treatment (college) given . It is

convenient to rewrite the selection equation (2) using the following innocuous transformation.

Define ,where isacumulativedistributionfunction,and isdistributeduniformlyin

                                                            9Followingthetraditionoftheliterature,thecollegedrop‐outdecisionisnotmodelledinthisstudy.Russiahasarelativelyhighcompletionrateof79percentofcollegeentrantscompletingatertiary“typeA”programcomparedtotheOECDaverageof69percentandtheU.S.collegecompletionrateof56percent(OECD2008).

7  

theunitinterval[0,1].Differentvaluesof denotedifferentpercentilesof .Thus,theselection

equationbecomes

(3)

Let the outcome of interest be ln , where is an hourly wage. The potential

outcomesaredefinedaccordingtothelevelofeducationachievedsuchthat

, , for 0, 1 (4)

where , is a vector of observable characteristics that affect hourly wages, and

includeexogenouswagedeterminantsthatareexcludedfromthecollegechoiceequation(e.g.,an

individual’sageattimetandunanticipatedtransitoryshockstolocallabormarkets). Weassume

that islinear, .

Theobservedoutcome canbewritteninaswitchingregressionform:

, 1 ,

, 1 ,

, , ,

(5)

The gross returns to college education are given by∆ , ,

, , andthemodelassumesthatagentsknowtheirreturns.

Themeanoutcome , conditional on ( , ), is a sumofmeanoutcomes for

eachlevelofeducationandweightedbytheprobabilityofbeingateachlevelofeducation:

E | , E | 1, , E | 0, , 1

E , , | , 6

The vector ( , ) is observed by both the agent and econometrician, while ( , , ) is

knownbytheagentbutunobservedbytheeconometrician.Tounderstandthepossibleeffectsof

unobserved endowments in thewage equation (’s) on the returns,we consider three cases: (i)

unobservedendowmentsarehomogeneous,i.e. , , ̅ forallindividuals,suchthatthelast

term in equation (6) cancels out; (ii) unobserved endowments are heterogeneous but mean

8  

independentofcollegedecisions, i.e.,E , , | , E , , ;againthelast

terminequation(6)cancelsout; and(iii)unobservedendowmentsareheterogeneous, , , ,

andcorrelatedwiththeunobservedcharacteristicsfromthecollegedecisionequation( ),inwhich

casethelastterminequation(6)cannotbeignored.

Inthefirsttwocases,thestandardIVapproachmightbesufficienttoidentifytheaverage

returns to college education. In the third case, which is more realistic, individuals, who are

observationally identical from an econometrician’s point of view, may make different college

decisions;thatis,unobservedcharacteristics influencingthecollegedecisionarecorrelatedwith

unobserved endowments ′s in the wage equation. As a result, the returns to college, for

observationally identical individuals, will depend upon a conditional mean component

E | and an individual‐specific unobserved component

E , , | , .

In the third case, the standard IV approach may not be appropriate for estimating the

parameters of interest. It is widely recognized that an IV estimator identifies the local average

treatmenteffect(LATE),whichistheexpectedgainfromthetreatment(e.g.,collegegraduation)of

thoseindividualswhoswitchfromnotreatmenttotreatmentwhenaninstrumentchanges.Inthe

caseofseveralinstruments,eachoftheIVsidentifiesadifferentmarginofthereturntocollegethat

varies across individuals. In general, there isno simple interpretationof the IVestimator in the

presenceofseveralinstrumentsthatinduceindividualstogotocollege.10

MarginalTreatmentEffectfromtheNormalSelectionModel

The marginal treatment effect (MTE) approach allows researchers to obtain the entire

distribution of the individual‐specific returns (i.e., , , | , (Heckman and

Vytlacil2005,2007).Usingequation(6),the , isdefinedasfollows

                                                            10HeckmanandVytlacil(2005,2007)presentalimitednumberofcaseswhereitispossibletodeterminetheexactaveragetreatmentparameteridentifiedbyeachoftheinstruments.

9  

,E | ,

E , , | , 7

Weassumethat includesatleastoneexclusionrestriction,and and arestatistically

independentof , , , , .Wefirstestimate , fromtheparametricselectionmodelthat

assumes a multivariate normal distribution of errors, , , , , ~N 0, . Under the above

assumptions,E , , | , Cov , , Cov , , Φ ,whereΦ ∙ is

theinverseofthestandardnormalcumulativedistributionfunction,and , isgivenby

, Cov , , Cov , , Φ (8)

HeckmanandVytlacil (2005,2007)showhow,basedon theMTEdistribution, to recover

several standard treatment parameters such as the average treatment effect in the population

, the average treatment effect on the treated , the average treatment effect on the

untreated , the ordinary least squared estimator , and the IV estimator . In

short, each treatment parameter can be obtained as a weighted average of , , where

weightsaregiveninAppendixTableA3.

PolicyParameters

In addition to the standard treatment effect parameters,we also calculate the returns to

collegeforthoseindividualswhowenttocollegeduetothecollegeexpansionpolicy.Theobjective

hereistocalculatethegainsofthemarginalgraduateswhowouldhavehadlimitedaccesstohigher

educationifthecollegeexpansiondidnotoccur,buttheychosetogotocollegeinresponsetothe

openingofmorecollegesintheplaceoftheirresidence. Inotherwords,weareinterestedinthe

returns of the marginal graduates whose college decision was influenced by an increase in the

instrument inequation(2).

To illustrate our parameter of interest, we perform the following experiment using the

estimatedparametersofselectionequation(2).Supposethatanindividual getsadrawgivenby ̃

10  

such that ̃ , which implies that 0. Further, suppose that the total

numberofcollegecampusesavailabletoanindividual inhisregionofresidenceattheageof17

increasessuchthatthenewnumberofcampusesisgivenby ∗ .Ourparameterofinterest

is the average of , for those individuals who switch their decision because of the

expansion,suchthat ̃ ∗ ,whichimpliesthat 1.

Heckman and Vytlacil (2001) develop a policy relevant treatment effect ( )

parameterthatmeasurestheaveragereturnstocollegeeducationforindividualsinducedtochange

their schooling decisions in response to a specific policy (for instance, ∗ ). Essentially,

istheaverage , forswitchersandisdefinedas:

, , | | , , , 0, , , 1 (9)

To identify , , the support condition needs to be met, that is, the range of

∗, , must be contained in the range of , , . PRTE is generally applied to a fixed

discretepolicychange.Examplesofspecificpoliciesevaluatedwiththismethodinthepastinclude

atuitionchangeintheUS(HeckmanandVytlacil2001;Carneiroetal.2010,2011b;Eisenhaueret

al. 2015) and a 10 percent reduction in the distance to upper secondary schools in Indonesia

(Carneiro et al. 2011a). The PRTE approach has not been applied yet with respect to college

expansion.

Alternatively,Carneiroetal.(2010)proposedthemarginalpolicyrelevanttreatmenteffect

(MPRTE ),whichcorrespondstoamarginalchangeinpolicy.Thatis,

lim → , . (10)

TheMPRTEmethodessentiallyestimatesthemeanbenefitsofcollegeforthemarginalindividuals

whoareindifferentbetweenparticipatingandnotparticipatingincollege.Theestimatorrequires

theavailabilityofacontinuousinstrument,assuminganinfinitesimalchangein.

11  

MarginalPolicyEffectsUsingLocalIV

AnalternativeapproachtoestimatingMTEisbasedonthesemi‐parametricmethodoflocal

instrumental variables (Heckman and Vytlacil 2005). Under the assumption that and are

statisticallyindependentof , , , , ,equation(6)canbere‐writtenasfollows:

E | , (11)

where E , , | , for ∈ .

TheMTEestimationprocessfollowsCarneiroandLee(2009)andconsistsofthreesteps:

1) In the first step, we obtain the propensity score, ̂ , from a probit regression of on

, , .

2) In the second step, we estimate , using a partially linear regression estimator of

Robinson (1988) and then compute ̂ . The estimates

of , areprovidedinthewebappendix.

3) In the third step, we estimate a locally quadratic regression ofR on ̂ and calculate the

derivativesoftheconditionalmeanestimate ′ ̂ .

Aftercompletingthesesteps,theMTEestimatesareobtainedaccordingtoequation(7):

,| ,

′ ̂ (12)

Comparedto thenormalselectionmodel, the local IVmethod forestimatingMTE ismore

flexibleasitdoesnotassumethenormalstructureoftheerrorterm.Butitisalsomorerestrictive

in the sense that the MTE can be estimated with this method only over the common support

of ,whichrarelytakestheentirefullunitinterval[0,1]. Sinceitispracticallyimpossibleto

estimate theMTEover the fullunit interval, thestandardtreatmentparameterssuchasATE,TT,

TUT,aswellasPRTEcannotbeidentifiedusingthesemi‐parametricapproach. Nonetheless, it is

possibletoidentifytheMPRTEparametersbecauseonlythemarginalsupportof isrequired

inthiscase(Carneiroetal.2010).Inourempiricalwork,weapplythelocalIVmethodtorecover

12  

the marginal returns to college for the individuals responding to a marginal change in the

probabilityofcollegeparticipationandinthenumberofcampuses.

Thefollowingsectiondiscusseshoweachofthemodelvariables , , ismeasuredin

thedata.

3.DataandIdentificationVariables

The primary data source for this study is the 1995‐2011Russia LongitudinalMonitoring

Survey–HigherSchool ofEconomics (RLMS‐HSE),which is anationally representative stratified

sampleofhouseholdsof theRussianFederation.11 We limit thesample tohighschoolgraduates,

age25andolder,assumingthatthemajorityofpeoplecompletetheirformaleducationbyage25.

Wealso restrict our sample topeoplebornbetween1968and1986. Thus, considering that the

collegedecision inRussia is typicallymadeatage17,oursample includes individualswhomade

theircollegedecisioninthepre‐expansionperiod(1985‐1992)aswellasthosewhodiditduring

the expansion period (1993‐2003). Compared to the previous studies that are based on either

cross‐sectional data or one‐cohort panels such as NLSY, our multi‐cohort panel not only has

multiplewageobservationsovertheindividuallifecycle,butalsopermitstheanalysisofschooling

decisionsforseveralcohorts,thusmakingthestudyofcollegeexpansionfeasible.

Thewage variable is the log of deflated hourlywage rate at primary job. Asmentioned

above, we restrict the period of college decision making to years 1985‐2003. Our preferred

specification employs individual wage data from the post college decision period, 2004‐2011.

However,intherobustnesschecks,weconsiderotherintervalsofwagedata.

                                                            11RLMS‐HSEisorganizedbytheNationalResearchUniversityHigherSchoolofEconomics,Moscowtogetherwith theCarolina Population Center at theUniversity ofNorthCarolina at ChapelHill and the Institute ofSociology at the Russian Academy of Sciences. The panel started in 1994. The RLMS‐HSE surveyedindividualsin32outof83regionsandallsevenfederaldistrictsoftheRussianFederation(accordingtotheofficial classification of regions as of January 1, 2010). We drop the year 1994, because the educationquestionnaireinthatyearisincompatiblewiththeoneinsubsequentsurveyrounds.

13  

To estimate the college decision equation (2), we define the treatment group as college

graduatesandabove(fouryearsormoreoftertiaryeducationwithadiploma)whereasthecontrol

groupassecondaryschoolgraduates. Thelattercategoryincludesgraduatesofbothgeneraland

professionalsecondaryschools,butexcludescollegedrop‐outs.12Insomeoftherobustnesschecks,

thedefinitionofthetreatmentgroupincludescollegedrop‐outswiththreeormoreyearsofcollege

education.Figure2Ashowsthattheshareofcollegegraduatesincreasedfrom23percentin1995

to39percentin2011inthe25‐44agegroupoftheRLMSsample.

We linkedRLMSrespondentswith the local characteristicsat age17,whichareavailable

either at the level of municipality (such as the number of campuses by category) or at a more

aggregate regional level (such as the size of cohort, earnings, and unemployment rate). The

locationofrespondentsatage17isnotdirectlyreportedbytheRLMSrespondents,but itcanbe

inferredinthemajorityofcasesfromthesurveyquestionsrelatedtomigrationhistory,completion

ofhighschool,andcollegelocation.Forexample,“locationat17”isthesameastheplaceofcurrent

residenceinthreeinstances:(i) for individualswhopermanentlymovedtotheirpresent location

before they turned 18 years old (including those who never moved); (ii) for individuals who

completed high school at present location; and (iii) for individualswho are born in the place of

currentresidenceandwhotemporarilymovedtoanotherlocationafterage17.Collegelocationis

taken as “location at 17” for those respondents who reported to reside in the same community

beforegoingtocollege. Thus, locationatage17isknownwithhighconfidencefor83percentof

oursample.13Thelocationoftheremaining17percentofoursampleatage17(e.g.,thosebornina

differentmunicipalityandmovedtoacurrentresidenceaftercompletingcollege)isunknown.We

                                                            12Professional secondaryschools includevocational schools (“PTU”), technical schools (“technikums”)andthe specialized schools that train associate professionals in various fields (such as medicine, education,business,etc.). Theyoffer3‐4‐yearprogramsafter9yearsofsecondaryschoolsor2‐3‐yearprogramsafter11yearsofcompletegeneralsecondaryeducation.13OursampleexcludesrespondentswhowerebornandstudiedoutsideRussia.

14  

imputetheirlocationatage17basedoncollegelocationandthetypeofbirthplacesuchasvillage,

township,andcity(seeAppendixTableA1fordetailsofimputation).14

Table 1 categorizes the variables used in the wage and schooling equations. Individual

characteristics such as gender (=1 if female), nationality (=1 if ethnicallyRussian), andmother’s

education(=1ifmotherhasacollegedegree)enterboththewageandschoolingequations.Since

currenturban residence is likely tobeanticipatedat the timeof collegedecision, it theoretically

explains both the wage and schooling outcomes. Year dummies, age, and age squared are

controlled for in the wage equation, while four birth cohort dummies with 5‐year intervals are

included in the college equation. Dummies for theMoscow city and seven federal districts are

determinedatage17inthecollegeequationandatthecurrenttimeinwageequations.

Anothervariablethatisexpectedtoaffectboththecollegedecisionandwageistheregional

cohortsize,whichisthelogofregionalpopulationofage17atthetimewhenanindividualwas17.

Individualsfromlargercohortsarelikelytofacemoreintensecompetitionforalimitednumberof

universityslotsintheirregionandthushavealowerprobabilityofgettingintocollege.Thewage

effectoflargercohortsisambiguousduetoadownwardpressurefromthelaborsupplysideandan

upward pressure from a biggermarket size and potentially higher demand for labor. The data

source for this variable is the Russian Census and other official demographic statistics (see

AppendixTableA1fordetails). Historically,thecohortsizevariedsignificantlynotonlybetween

regionsbutalsowithinregionsovertimeduetothelargedemographicchangesassociatedwiththe

long‐lasting effects ofWWII and low birth rates in the 1990s in Russia (see Figure 2B). In our                                                            14Inthe1990s,morethanhalfoftheinternalmigrationflowswereregisteredwithinagivenregion,e.g.,fromruralareastocitiesofthesameregion(DemographyYearbookofRussia2002).Furthermore,inourRLMSsample,62percentofcollegestudentswiththeknownplaceofbirthstudiedinthesamemunicipalityastheirbirthplace,22percentstudiedinadifferentmunicipalityofthesameregion,andonly16percentstudiedinadifferentregion.Giventhesefacts,wecanrandomlyassignthemissinglocationatage17basedonthetypeof birthplace (village, township, and city), college location for students, and current residence for non‐students. For example, if a studentwasborn in a township, then an arbitrary townshipwouldbe chosenwithin the region of his college as a place of his college decision. For non‐students,we choose a randommunicipalityofthesametypeastheirbirthplacewithintheregionoftheircurrentresidence.Notethatsuchimputationsaffectonlyonevariableofinterest,whichisthenumberofcampusespermunicipality,sinceallotherlocalvariablesareconstructedattheregionallevel.

15  

sampleperiodbetween1985and2003,however, the fluctuationsovertimewererelativelymild,

thoughsignificantregionalvariationremained.

NumberofCampusesperMunicipality

Thekeyidentificationvariablethroughwhichweperformpolicysimulationsisthenumber

ofcampusesavailableinamunicipalityatage17.WediscussitsidentificationvalidityinSection4.

This variable is constructed based on the opening/closing dates of campuses from the Russian

university database (Belskaya and Sabirianova Peter 2014). The number of campuses in each

municipalitymeasuresthelocalavailabilityofcollegeeducationatthetimewhenindividualsmake

theircollegeenrollmentdecisions,anditservesasaproxyfor inequation(2).Itisalsobroken

downbycategoriesofpublic‐privateandmaincampus‐branch.

Thedescriptivestatisticsforthisvariableconfirmarapidexpansionofcollegeavailabilityin

Russiafollowingthe1992LawonEducation.Between1985and2003,thenumberofcampusesin

theRussianuniversitydatabasesurgedfrom812to2245,andthenumberofmunicipalitieswithat

least one campus increased from 198 to 442. Excluding Moscow city, the average number of

campusespermunicipality in theRLMSsamplemore thandoubledover the sampleperiod,with

mean=6.9andstd.dev.=13.9(seeFigure2C).Theupsurgeofnewcampuseswasalsoparticularly

strikingintheMoscowregionwheretheirnumberquadrupled.

Despiteseeminglylargechangesinthenumberofcampusesovertime,theshareofwithin‐

municipalityvariance isonlyabout13percentof thetotalvariance. Thevariancedecomposition

reported in Table 2 indicates that most variation in the total number of campuses is between

municipalities(87percent).However,thecompositionofvariancediffersconsiderablybythetype

ofcampus. For instance, the fact that thenumberofpublicuniversitiesbarelychangedsince the

Soviettimesisreflectedintheneartozerowithin‐municipalityvariance(lessthan1percentofthe

total variance); practically all the variation in this variable comes from the differences between

municipalities.Atthesametime,thewithin‐municipalityvariationovertimeissubstantialforthe

16  

numberofbranches(bothpublicandprivate)andforthenumberofprivateuniversities(32to55

percent each). These features of the main variable will be utilized in the validity tests of our

instrument.

LocalLaborMarketConditions

Besides the number of campuses per municipality at age 17, we also use labor market

conditions in the location of residence at the time of college decisions as additional exclusion

restrictions.LocalearningsandunemploymentratearefrequentlyusedasinstrumentsintheMTE

literature (e.g., Cameron and Heckman 1998, Cameron and Taber 2004, Carneiro et al. 2011b,

Zamarro2010,Eisenhaueretal.2015,amongothers).15 Inourcase, regionalearningsat17and

regionalunemploymentrateat17capturetheopportunitycostofgoingtocollege.

Tocontrol for thepotentialcorrelationbetween localwage/unemploymentatage17and

current individualwage,we include (i) the contemporaneous (transitory) component of regional

variablesinthewageequationsand(ii)thepermanent(predicted)componentofregionalvariables

inboththeschoolingandwageequations(similartoCameronandHeckman1998;Carneiroetal.

2011b);seeTable1.

The transitory shocks to regional labor markets are proxied by the deviation of

contemporaneous regional earnings and unemployment rate from the trend‐adjusted regional

average. Specifically, regional transitoryearningsaredefinedas ̂ ina regressionof the logof

regionalmonthlyearningsinregionrandyeartonthesetofregionaldummies andaquadratic

timetrend,asinln . Theregionaltransitoryunemploymentrateisdefined

similarly.

Thepermanent regionalvariablesarepredicted fromtheregressionsof regionalmonthly

earningsandunemploymentrateonregiondummiesandaquadratic time trendover theperiod

                                                            15Local tuitionasanothercommonlyused IV isnotapplicable toourcasebecausecollege tuitionwasnotcharged during the Soviet periodand only 28 percent of all public students paid a fee for their educationduringoursampleperiodofcollegeexpansion,1993‐2003(EducationinRussia,2008).

17  

1995‐2011.Essentially,thesearetrend‐adjustedaveragecharacteristicsofthelocallabormarket.

Asanalternativemeasureofpermanentearnings,wecomputeaverageregionalmonthlyearnings

overthefirst10yearsfollowingthecollegedecision.Toaccountforthepossibilitythatindividuals

makingacollegedecisionundertheSovietcentralizedwagestructuremaynothaveanticipatedthe

futuremarketearningsduetoalargestructuralbreakintheeconomy,wealsocomputepermanent

regional earnings separately for the Soviet period (1980‐1991) and the market period (1992‐

2011).

In essence, with the above structure of regional variables, we assume that individuals

making their college decision are not only influenced by the observed contemporaneous

characteristicsoftheir locationatage17,butalsohaveanimperfect foresightatage17of future

locallabormarketconditions.

In addition to the regional monthly earnings and unemployment rate at age 17, the

opportunity cost of going to college is captured by the country‐level skill wage ratio at age 17,

whichistheratioofaveragewagesofmanualworkerstoaveragewagesofnon‐manualworkersin

industry(seeAppendixTableA1fordatasources).ThroughouttheSovietperiod,manualworkers

were rewardedwithhighpay,whilewagesofnon‐manualworkerswereartificially compressed.

Figure2Dshowsthatthewageratioplummetedfrom0.94to0.55duringoursampleperiod(1985‐

2003),reflectingahistorictrendoftherisingwagepremiumforskilledworkers.

SummaryStatisticsfortheEstimationSample

Thebaselineestimationsample(age25,age17[1985,2003],wage[2004,2011],and

non‐missingvariables)amountsto17,911person‐yearobservations.Conditionalontheexogenous

sample constraintsbasedonage, surveyyear, andbirth cohortsand for agivendefinitionof the

college variable, wage is missing for about 20 percent of respondents either due to non‐

employment or non‐reporting. In robustness checks,we apply the inverse propensity score re‐

weightingtodealwiththeselectionintowages(i.e.,employmentorreportingofwages).Withthe

18  

exception of mother’s education, the missing values in other covariates are trivial (about 0.6

percent).16 Table A1 in the appendix details how each variable is constructed. The descriptive

statistics inTable3 is reportedseparately forcollegegraduatesandsecondaryschoolgraduates.

Asexpected,collegegraduatesaremorelikelytobe female,resideinurbanareas,haveamother

withcollegedegree, live inmunicipalitieswithmorecampusesatage17,andearnahigherwage

rate.Interestingly,whencollegegraduateswere17,theyresidedinareaswithlowerearningsand

higher unemployment rates, but their regions of residence at the time of the survey had better

permanentlabormarketcharacteristics.

4.MarginalTreatmentEffect

BaselineEstimates

In thissection,wereport theMTEestimatesof themarginalbenefitsofcollegeeducation

anddiscuss thevalidityof exclusion restrictions. Webeginbyestimating thecomponentsof the

switching regression model given in Equations (2)‐(4). The random variables , , , , are

assumed tohave amultivariatenormaldistribution, and themodel is estimatedusingmaximum

likelihood. Table 4 presents the estimates of thewage equation for secondary school graduates

(column1), thewageequationforcollegegraduates(column2), thecollegeequation(column3),

andthemarginaleffectsofvariablesinthecollegeequation(column4).Theprobabilityofhavinga

college degree,P , is significantly higher among females, ethnically non‐Russians, urban

residents,andthosewhosemotherwenttocollege.Asexpected,individualsfromsmallerregional

cohortsatage17tendtohaveahigherrateofcollegeattainment,astheyfacelesscompetitionfora

givennumberof collegeopenings in their region. Morepeople go to college in the regionswith

higher permanent earnings, but college decisions do not appear to be responsive to regional

                                                            16Becausemother’seducationisunknownforthe12percentofourestimationsample,weincludeabinaryindicator formissing values of this variable to prevent sample loss. We drop observations withmissingvaluesinothercovariates.

19  

unemployment rates and to regional earnings at age 17. Predictably, better salaries of manual

workers relative to non‐manual workers ‐ observed at age 17 ‐ deter people from pursuing a

collegedegree.

Theinstrumentsarejointlystrongpredictorsofcollegedecision(p‐value=0.000).Thekey

instrumentmeasuring the college availability at the timewhen an individualwas 17 is the total

number of college campuses per municipality. This variable serves as a cost shifter of college

decisions.Inordertocapturepotentialnon‐lineareffectsofcollegeavailabilityandtoaccountfor

the fact that some of the larger cities had a wide choice of colleges even before the college

expansion (e.g., Moscow had 100 colleges in 1990), we add a quadratic term on the number of

campuses. Because the distribution of the number of campuses is clumping at zero, we also

introduceabinaryvariabletoindicatemunicipalitieswithnocampuses(mean=0.386).Thelatter

variableisanalogoustoacommonlyusedbinaryIV,suchasthepresenceofacollegeinthecounty

of residence during teenage years (Card 1995; Cameron and Taber 2004). Results in Table 4

suggest that the opening of the first campus in a municipality without a college increases the

probabilityofreceivingacollegedegreeby0.114,i.e.,by11percentagepoints,onaverage,ceteris

paribus.Theestablishmentofadditionalcampusesalsoimprovesthecollegeattainment,thoughat

adiminishing ratepernewcampus. Thus, theeffectof collegeavailabilityoncollegeattainment

appearstobemuchstrongerfor“constrained”municipalitieswithfewercampuses.

Theestimatesofthewageequationsforthetwogroupsarestandard.17Theresultssuggest

that each grouphas a comparative advantage in the labormarket, that is, the individualswith a

higherpropensitytogotocollege(lowv) tendtodowell inthelabormarketoncetheygraduate

                                                            17Theresultsworthmentioningare:averylargegenderwagegapinthecontrolgroup(0.45logpointsor56percent difference favoring males) compared to 27 percent in the treatment group; mother’s educationaffectswagesofcollegegraduates,butnotwagesofsecondaryschoolgraduates;theindividualwagerateinbothgroups ispositively influencedbyhigher transitoryandpermanent localearnings;only in thecontrolgroup, the wage rate responds positively to local unemployment (both transitory and permanent); thecohortswithlargerregionalpopulationat17receiveahigherwagepremiumtoday.

20  

( 0.150), but the same individuals would be worse off if they don’t go to college (

0.222).

FollowingCarneiroetal.(2011b)andHeckmanetal.(2010),weperformatestforselection

ongains(i.e.,whetherreturnstocollegearecorrelatedwithS). Asimpletestinvolvesestimating

equation(6)wherethelasttermisapproximatedwithapolynomialin ̂ (obtainedfromtheprobit

of college equation) and testing whether the coefficients on higher order polynomial terms are

jointlystatisticallysignificant.Theresultsofthistestsupportthehypothesisthatindividualsinour

sampleselectoncollegegains(seeAppendixTableA2).Thetestrejectsthatthereturnstocollege

arenotcorrelatedwithSorthat , isconstantin .

Another test for selection on gains relies on the estimated parameters from the normal

switching regression model (Table 4). The null hypothesis that the slope of the MTE is zero,

i.e., : 0,isrejectedatthe1percentlevel.Weestimatethat 0.372witha

standarderrorof0.065. This findingsupports theconclusionof “selectionongains” inTableA2

thatdoesnotimposethejointnormalityassumption.

UsingtheestimatedparametersinTable4,wecomputetheMTEaccordingtoEquation(8).

Figure3plots theestimatedMTE foragridofvaluesvbetweenzeroandone,18with90‐percent

confidencebands,evaluatedatmeanvaluesofX.Weobtainannualizedestimatesofthereturnsto

collegebydividingtheMTEby4.5,whichistheaveragedifferenceinyearsofschoolingbetween

thetreatmentandcontrolgroupsinoursample.ThenegativeslopeofMTEimpliesthatindividuals

withlowvaluesofv(thosewhoaremorelikelytogotocollege)havethelargestmarginalreturns

tooneyearofcollegeeducation.Conversely,individualswithhighvaluesofvhavelowMTE.The

heterogeneity in theMTE across the distribution of v is substantial: the returns vary from ‐18.2

percent for the highest vpersonwhowould lose from attending college to 32.9 percent for the

lowestvperson,withtheaveragereturnof7.3percentperoneyearofcollegeeducation.

                                                            18 In practice,we restrict the grid of values of to be between0.001 and 0.999,with 999 equally spacedpoints.

21  

Given the vast differences in labor market institutions and data characteristics between

RussiaandtheU.S.,ourMTEestimatesforRussiaturnedouttobesurprisinglyclosetothefindings

ofCarneiroetal.(2011b).Inasampleof28to34‐yearoldwhitemalesfromNLSY,theyfindthat

thecollegereturnsintheU.S.varyfrom‐15.6percentto28.8percentperyearofcollegewiththe

meanof6.7percentandtheMTEslope 0.239.Atthesametime,ourMTEestimates

forRussiaareloweratthemeanandhaveamuchlargervariancethanthecorrespondingestimates

obtained by Heckman and Li (2004) in a cross‐sectional sample of Chineseworkers (theirMTE

rangesfrom5to15percentperyearofcollege,mean=10.8).

AlternativeInstruments

InTable5,ourmaininstrument–numberofcampusespermunicipality–isbrokendown

by categories ofmain campus‐branch (column1), public‐private19 (column2),Moscow vs. other

cities(column3).Overall,theseresultsarenotdifferentfromthebaselinespecification.Wefinda

negativeMTEslopeofsimilarmagnitude,averagereturnsof7to8percentperyearofcollege,and

aclearconcaverelationshipbetweencollegeavailabilityandcollegeattainment forall indicators.

Butthecollegeprobabilityfunctionisestimatedtobeconsiderablymoreconcavewithrespectto

the number of branches. This result could be partly explained by branches rather than main

campuses being opened in less‐populated areas where the first few local branches may have a

substantial impact on local college attainment (hence, large positive linear term), andwhere the

establishmentoffurtherbranchesmayalsoquicklyovercrowdalocalmarketforhighereducation

services(hence,largenegativequadraticterm).Lowertuitionfeesandlaxeradmissioncriteriain

branches could also contribute to a high response of the probability of going to college to the

openingofthefirstfewbranchesinlocality.

                                                            19Weuseadummyindicatorforprivatecampusesinsteadofthenumberofcampusesduetoconsiderableclusteringofobservationsatvaluesofzeroandone:only10percentofallmunicipalitiesintheRLMSsamplehadmorethanoneprivatecampus.

22  

Anothernoteworthyfindingisthattheestablishmentofaprivatecampusinamunicipality

withexistingpublic campusesdoesnothavea statistically significanteffecton theprobabilityof

college attainment.20 This result could be due to the fact that private education institutions in

Russiaarecharginghigher tuition,areoftensmall insizeandtendtoopen in largercitieswhere

individualshaveotheroptionsofpursuingacollegedegree.InthethirdcolumnofTable5,wetry

toisolatetheeffectofcollege‐crowdedMoscowbytheinteractionofthenumberofcampusesper

municipalityat17withMoscowresidenceat17inalinearspecification.Theestimatessuggestthat

thecollegeattainmentinMoscowisnotaffectedbyafurtherincreaseinthenumberofcampuses,

buttheopeningofanadditionalcampusinothermunicipalitieswithatleastonecampusincreases

theprobabilityof receivingacollegedegreeby0.4percentagepoints. Allparametersof interest

remainakintothebaselinespecification.

Starting with the paper of Card (1995), the literature raised two major issues with the

validityofabinaryindicatorforcollegepresenceasaninstrument, includingnon‐randomcollege

construction and the Tiebout‐type geographic sorting of individuals in response to college

presence. These are legitimate concerns with regard to the number of campuses as well. The

validityoftheinstrumentisgoingtobecompromisedif:

′ inwageequationaffect incollegeequation;thatis,anticipatedwageshocksin2004‐2011

influencewherenewcampusesareopenedin1985‐2003;

and ′ are jointly determined; for example, individualswith, let say, positivewage draws

choosetoresideinthelocationofnewcampuses.

Toisolatethepotentialcorrelationbetweenfutureexpectedearningsandcampusopenings,

bothwageandschoolingequationscontrolforthepredictedregionalearningsandunemployment

rate in the place of residence at 17. Adding district fixed effects and a dummy for the type of

                                                            20 Interestingly, in a paper on the effect of mother’s education on birth outcomes in the U.S., Currie andMoretti (2003) also found that the opening of public colleges has a larger effect onmother’s educationalattainmentthantheopeningofprivatecolleges.

23  

location serves the same purpose. These controls partly address the above concerns, including

non‐randomcollegeopeningsinresponsetohigherregionalincomeandthesortingofindividuals

with higher wage draws into the locations with better labor market conditions, which also

happenedtobeplaceswithmorecampuses.

ThevariancedecompositioninTable2givesususefulhintstocheckwhetherourbaseline

results are driven by the time‐series or cross‐sectional variation in the number of campuses. A

largewithin‐municipalityvariationinthenumberofcampusesovertimeprovidesmoreroomfor

the endogenousdecisions to open campuses in response to unobserved (by the econometrician)

futurewageshocks.Atthesametime,thecross‐sectional/geographicvariationthatiscreatedina

different economic system long before the college expansion started does not allow for such

strategic behavior. Table 2 shows that most of the variation in the number of campuses is

geographic rather than over time (87percent vs. 13percent). Furthermore,we re‐estimate our

baselinespecificationwiththenumberofpubliccollegesonly. Thisnumberremainedpractically

thesamesincetheSoviettimes;thewithin‐municipalityvarianceislessthan1percentofthetotal

variance (Table 2). We can reasonably assume that the college construction decision under the

centrally‐planned industrial structure is not correlated with wage innovations in the market

economy. The alternative specificationwith the number of public colleges does not change the

valueandtheslopeofthemarginaltreatmenteffect(column4ofTable5).

We also ran several placebo tests by adding future college openings in the college

equation.21Thegoalhereistocheckwhethercollegedecisionsareinfluencedbythefuturecollege

expansion. One of such tests is shown in column 5 of Table 5 and suggests that newly opened

campuses in the same municipality between the ages of 25 and 30 do not have any significant

impactonthelikelihoodofgoingtocollegeatage17.

                                                            21Forexample,wecontrolledforthenumberofcampusesestablishedinthesamemunicipalitybetweentheages of 30 and 35; thenwe tried adding new campuses opened between 2007 and 2011. None of thesevariableshaveanysignificantimpactonthelikelihoodofgoingtocollegeatage17.

24  

Therefore, provided that the anticipated local labor market conditions are sufficiently

controlled for, the number of campuses at age 17 can be used as a sensible instrument for the

marginaltreatmenteffectestimationandsubsequentpolicysimulations.

AlternativeSpecifications

Beforecalculatingthetreatmentparameters,wecheckwhetherourresultsaresensitiveto

changes in specifications. Specifically, we focus on the sensitivity of the mean component of

MTE, 1 0 , and the covariance componentofMTE, ( ). Theestimatesof additional

specifications arepresented inTable6. In all of these specifications, the instruments are jointly

strongpredictorsofcollegedecisions,andthenullhypothesisthattheMTEslopeiszero, :

0, is rejected at the one percent level. In column 1, the model is estimated using an

alternative definition of the treatment group that includes college dropouts with three ormore

years of higher education. The rationale for changing the dependent variable is that college

dropoutswithsomeyearsofschoolingmightalsohavebeen“treated”bystudyingincollege.

Incolumn2,weuseanalternativedefinitionofregionalpermanentearningscalculatedas

averageregionalearningsoverthefirsttenyearssincecollegedecisionatage17.Specificationin

column 3 applies the same definition as in column 2 but replaces permanent earningswith the

Sovietperiodaverageregionalearnings(1980‐1991)forindividualswhoturned17beforemarket

reforms. Our motivation here is that teenagers raised under central planning may not have

foreknown future earnings in the market economy. As we introduce Soviet earnings, the

association between individual wages and regional permanent earnings becomes weaker but

remainsstatisticallysignificant.22Overall,modificationsinthefirstthreecolumnsdonotcauseany

significantdeviationoftheestimatedMTEfromabaselinespecification.

To check for potential bias due to non‐randommissingwages, specification in column 4

applies the inversepropensityweighting(IPW)toourbaselinespecification,wheretheweight is

                                                            22Resultsareshowninwebappendix.

25  

the inverse of the predicted probability of having non‐missing wages. The propensity score is

constructed using the model covariates and , which are available for all respondents,

includingthosewithmissingwages.23TheMTEestimatesarenotaffectedbytheIPWcorrection.

Wealsoestimatethemodelwithoutassumingthejointnormalitybetweentheerrorsofthe

wage and college equations. Figure 4 shows the MTE using the semiparametric procedure we

describedinsection2.Overall,theshapeoftheMTEandpolicyparameterswediscussinthenext

sectionareconsistentwiththefullyparametricnormalmodel.

Next,we testwhether our results change ifwe usewagedata for different survey years.

Figure5plotstheaveragetreatmenteffectandtheMTEslopealongwiththe95‐percentconfidence

interval for different periods of the wage data; the point estimates from the 1995‐2011 sample

period is also shown in column5 of Table 6. Although the average returns to college appear to

decline over time, there is no statistically significant (at the 5 percent level) difference in the

averagereturnstocollegebetweendifferentsurveyperiods. TheMTEslopeisalsoconstantover

different periods. It seems that the general equilibrium effects of college expansion are either

cancelingeachotheroutortheyarenotsufficientlystrongtoshifttheequilibriumskillpricesand

theirdistribution.Byattractingmarginalstudentswithlowerreturns,collegeexpansionmayalter

theaggregatecompositionofcollegegraduatesandthusputadownwardpressureontheaverage

returnstocollege. However,thecompositioneffectinourestimatesisintertwinedwiththeprice

effect that partly could be demand‐driven. That is, skill‐biased demand shocks along with the

positiveproductivityspilloversfromtheincreasedstockofhumancapitalmaycompensateforthe

supply‐side effects keeping the equilibrium college wage premium constant over time.

Disentangling these varying general equilibrium effects of college expansion remains an area for

                                                            23 See Table 1 for the list of covariates. The MTE estimates are not sensitive if the IPW correction isperformedseparatelyformissingwagesamongtheemployedduetonon‐reporting(15percentofallmissingwages)andformissingwagesduetonon‐employment.TheprobitmodelsandMTEestimateswithIPWfordifferentsourcesofmissingdataareshowninwebappendix.

26  

futureempiricalanalysis.Thepolicytreatmentparametersdiscussedinthenextsectionshouldbe

interpretedfromapartialequilibriumperspective.

5.PolicyTreatmentParameters

ConventionalTreatmentParameters

Recallthegrossreturnstocollegeeducationaregivenby∆ , ,

, , , withE ∆ | E | . Following Heckman and Li (2004), the probability

limitoftheOLSestimatorcanbewrittenas:

∆ E | , 1 E | , 0

E , , 1 E , , 0

E ∆ | E , 1 E , 0

E ∆ | E , , | 1 E , 1 E , 0 (ATE)(Sortinggain)(SelectionBias)

E ∆ | , 1 E , 1 E , 0 , (11)(TT)(SelectionBias)

whereATE=E ∆ | is the average treatment effect of college education for a randomly chosen

individual; TT=E ∆ | , 1 is the treatment effect on the treated; the sorting gain,E ,

, | 1 TT ATE,isthemeangainoftheunobservablesforpeoplewhoselectcollege,and

the selection bias E , 1 E , 0 ] = OLS ‐ TT is the mean difference in

unobservablesbetweensecondaryschoolgraduatesandcollegegraduatesifthelatterwouldnotgo

tocollege.

InTable7,wereporttheabovetreatmentparameters,whichareconstructedbyintegrating

MTEwiththeappropriateweightsdevelopedbyHeckmanandVytlacil(2005)(AppendixTableA3

and Figure A1). Standard errors are bootstrapped, and all parameters are annualized. For a

randomlychosenindividual,theaveragetreatmenteffectofoneyearofcollegeeducationisabout

7.3percent.Thetreatmenteffectonthetreatedisa13percentwageincreaseforcollegegraduates

27  

comparedwithwhattheywouldreceivewithoutcollegedegree. Atthesametime,thetreatment

effectontheuntreated(TUT)isonly1.9percentwageincreaseforsecondaryschoolgraduatesif

theywould go to college. TheOLS estimate is 7.7 percent. We estimate the sorting gain of 5.7

percent and the selection bias of – 5.3 percent. Positive sorting gain,E , , | 1 0,

impliesthatindividualssortintocollegeonthebasisofunobservedgains.Negativeselectionbias

means that if college graduates did not complete college education, they would be worse off in

terms of the unobserved wage component in comparison with secondary school

graduates,E , 1 E , 0 . In other words, both college and secondary school

graduateshaveacomparativeadvantageinthelabormarket,whichisconsistentwiththeanalysis

ofWillis and Rosen’s (1979) seminal paper. An IV estimate of the returns to college (with the

propensityscore ̂ usedasIV)is16.1percent,anditispredictablyupwardbiasedcomparedtoATE

duetoheterogeneity( , , )andpositivesortinggain.AsevidentfromAppendixFigureA1,the

IVestimandweighsahighervaluedsegmentof , moreheavily.

PolicyEffects

Inadditiontoconventionaltreatmentparameters,wealsoestimatethetreatmenteffectfor

individualsatthemarginofindifferencebetweengoingandnotgoingtocollege( ). As

shown by Carneiro et al. (2010), the marginal treatment effect at the indifference point is

equivalent to the effect of the marginal policy change, which is college expansion in our case.

Following Carneiro etal. (2010),we assess themarginal returns to college for three alternative

policyregimes.Thefirstpolicyexogenouslyincreasestheprobabilityofgraduatingfromcollegeby

aninfinitesimalamount,sothat . Analternativepolicychangestheprobabilityof

collegebyatinyproportion(1+), 1 ∙ .Thethirdpolicymayinvolveasmallchange

in one of the continuous components ofZ. In ourZ vector,wehave a directmeasure of college

expansion, , , which is the number of campuses in the municipality of residencem in year t

28  

(correspondingtoage17),suchthat , .24Usingequation(10)andweightsprovided

in Appendix Table A3 and Figure A1, we calculatemarginal policy‐relevant treatment effects of

threepolicyregimesfromthenormalselectionmodelandreporttheminTable8,PanelA.Similar

toCarneiroetal.(2010),wefindthattheMPRTEforamarginaladditivechangeinPisestimatedto

behigher(9.9percent)thantheMPRTEforamarginalproportionalchangeinP(7.4percent),but

theestimatesforRussiaaregreaterinmagnitudeinbothcases.Thethirdpolicyregime,whichis

more explicit and not yet reported in the literature, has returns of 9.6 percent per one year of

college for amarginal personwho is indifferent between going or not going to college andwho

wouldchangecollegeparticipationinresponsetoamarginalincreaseinthenumberofcampuses.

Alternatively,we reportMPRTEparameters from theMTEdistribution estimatedusing a

semi‐parametricmethodoflocalIV.TheestimationprocessisdescribedinSection2,andtheMTE

estimates,evaluatedatmeanvaluesofX,areplottedwith90‐percentconfidencebandsinFigure4.

We find that the semi‐parametricmethodproducesMTEwith the same shape as the parametric

one,butwithsomewhatlargerstandarderrors.Similartothenormalmodel,theMTEisdeclining

in , and we reject the null hypothesis that the returns to college are not correlated with S or

that , is constant in based on the test results reported in Appendix Table A2. The

common support of the P(Z) estimated from our sample ranges from aminimum of 0.070 to a

maximumof0.938.25PanelBofTable8presentstheMPRTEparametersfromthesemi‐parametric

model for the threealternativepolicy regimesdescribedabove.26 Inparticular, theMPRTE fora

marginalchangeinthenumberofcampusesisestimatedtobe10.7percentwageincreaseforone

yearofcollege.Theseestimatesareonlyslightlyhigher(byaboutonepercentagepoint)thanthe

                                                            24Inoursample,thenumberofcampusespermunicipalityvariesfrom0to299andistreatedasacontinuousvariable.Aninfinitesimalchangein , canbeinterpretedasanewclassroomoranadditionalstudentslotinalocality.25CommonsupportisdefinedastheintersectionofthesupportofP(Z)givenS=1andthesupportofP(Z)givenS=0.76observations,or0.4percentofoursample,falloutsidethecommonsupport.26AMPRTEparameterisaweightedaverageoftheMTEvaluesestimatedsemi‐parametricallyonthebasisofequation(12).TheMPRTEweightsarereportedinAppendixTableA3andalsoplottedinFigureA1.

29  

MPRTEestimates fromthenormalmodel,soweadheretoourearlierconclusionswithregardto

themarginalreturnstocollege. Regardlessofthemethod,wefindthatamarginalstudentatthe

indifferencepointenjoysrelativelyhighreturnstocollegeinRussia,eventhoughhisreturnsare,as

expected,lowerthanthoseearnedbycurrentcollegegraduates(TT).

Next, we perform a few policy experiments for a discrete change in the number of

campuses. The experiments are evaluated using the policy‐relevant treatment effect (PRTE)

estimatorproposedbyHeckmanandVytlacil(2001)andgiveninequation(9).27Inourcase,PRTE

captures theaverageMTE for the individualswhochangedcollegeparticipation in response toa

fixed increase in thenumberof campuses from the1992pre‐expansion level, ,

∆ , .Inthefirstsimulation,weaddonecampusineachmunicipalitytothe1992levelin1993‐

2003andfindthePRTEreturnstobe9.8percentwageincreaseperyearofcollege.Inthesecond

setofsimulations,weaddonecampuspermunicipality indifferent locations. Results inTable8,

Panel C show that the returns to college vary depending on the place where campuses are

established. The returns are estimated to be larger for constrained municipalities, including

smallernon‐capitalcities,ruraldistricts,andmunicipalitiesthatdidnothaveinstitutionsofhigher

educationin1992(9.9percentwageincreaseinallthreecases).Atthesametime,theopeningofa

campus in localities with previously existing campuses or in the largest regional cities‐capitals

attractsindividualswithlowerreturnstocollege(7.7and7.5percent,respectively).

Inthefinalsetofsimulations,weaskwhatwouldthereturnstocollegebefortheaffected

individuals(thosewhoshiftedtotreatment)ifthenumberofcampusesincreasedbyonlyahalfof

the actual increase, ,∗

, , , ), or by 50 percent more of the actual increase,

,∗

, , , ).ComparedtoearliersimulationsinPanelCwhereweadduniformly

                                                            27ThePRTErequiresthattheempiricalsupportofthedistributionof tobewithintheunitinterval,andthattheempiricalsupportof hastobecontainedinthesupportof .Theseconditionsaresatisfiedinoursetting.Giventhemultivariatenormalstructureoftheerrors,thesupportof istheunitintervalbyconstruction. Additionally, the empirical support of is given by (0.044, 0.949), while the empiricalsupportof is(0.045,0.949)for=1.

30  

onecampusinselectedmunicipalities,herewesimulatechangeswhicharelargeinmagnitudeand

varyinginsizedependingonthescaleofactualcollegeexpansionineachmunicipality.Onaverage,

fouradditionalcampusespermunicipalitywereestablishedbetween1992and2003.Predictably,

moresizeablecollegeexpansionsattract individualswithlowerreturns,thoughthescaleeffect is

notlinear.PRTEisestimatedtobe9.8percentforaone‐campusestablishmentpolicy,8percentfor

halfexpansion,6percentforfullexpansion,and6.2percentfor1.5ofactualexpansion.

Our PRTE estimates of the returns to college imply that the present value of additional

earningsstream foramarginalstudentovera33‐yearworking life (age22‐55) isequal toabout

$41,657 gains in non‐capital cities vs. $36,261 in capital cities (see details of calculations in

AppendixTableA4).Thedifferenceinnetbenefitsbetweenthetwotypesofcitiesisevengreater,

since students in capital cities, onaverage,paymore tuition and foregohigher earningswhile in

schoolcomparedtostudentsinsmallercities.Aroughcost‐benefitcalculationinTableA4suggests

that the net present benefits for amarginal student amount to $15,357 in non‐capital cities and

$7,334incapitalcitiesaftersubtractingthepresentvalueofforegoneearnings,averagetuitionand

othercollege‐relatedexpenses.Consideringthatrelocationtoalargecityimposesadditionalcosts

(e.g., transportation, greater living expenses, etc.), new students in constrained municipalities

clearlybenefitedfromcollegeopeningsintheplaceoftheirresidence.Thestockofhumancapital

intheremotelabormarketsisalsolikelytoincrease,ascollegegraduatestendtostayinthearea

wheretheyreceivetheireducation(Groen2004;Winters2011).

5.Conclusions

This paper estimates marginal returns to college education in Russia. Despite the vast

differences in labormarket institutionsanddatacharacteristicsbetweenRussiaand theU.S.,our

resultsareconsistentwiththepreviousU.S.literatureonMTEinthatwealsofind(i)alargedegree

ofheterogeneityinreturnstocollege,varyingfrom‐18to33percentincreaseinlifetimewagesfor

31  

a year of college; (ii) a negatively‐sloped MTE showing greater marginal benefits from college

among individuals with the highest propensity to go to college; (iii) the positive sorting of

individuals intocollegebasedonobservedandunobservedmarketgainsassociatedwithcollege;

(iv)lowerreturnsforamarginalstudentthanforanaveragestudent(10vs.13percentperyearof

college);(v)a largerIVestimateofthereturnstocollegecomparedtoanOLSestimate;16and8

percent, respectively. Implicitly these results support the MTE approach and the need for the

precisecharacterizationofdifferenttreatmentparameters.

Themain focusof thisstudy isontheevaluationof the large‐scalecollegeexpansionthat

occurredinRussiaandresultedinmassopeningsofnewcolleges,bothpublicandprivate,andtheir

branches inmany localitieswhere collegeeducationwasnotpreviouslyoffered. Specifically,we

areinterestedinthereturnsofthemarginalindividualwhoswitchedintotreatmentasaresultof

college expansion. Unlike previousMTE studieswhere college expansion is characterized by an

exogenous hypothetical shift in the probability of attending college by some random amount or

proportion,weevaluateactual, real‐worldcollegeexpansionusing thenumberof campuses (and

theirtypes)atahighlydisaggregatedlevelofmunicipality.

We establish that individualswith lower returns enter colleges asmore campuses open.

However, for the marginal individuals who are affected by the establishment of an additional

campusatthetimeofmakingacollegedecision,theoverallgainsfromattendingcollegearelarge

andpositive;weestimatea10percentwagegainfortheseindividuals.Furthermore,wefindthat

the opening of a new campus in constrained municipalities – smaller non‐capital cities or

municipalitiesthatdidnothaveinstitutionsofhighereducationbeforecollegeexpansion–attracts

students with higher returns compared to the effect of the same policy in unconstrained

municipalities with at least one college existing before the expansion. Our policy estimate also

indicates that if the number of campuses permunicipality did not increase, then a considerable

32  

shareofpopulationwithhighpotentiallabormarketgainsfromcollegewouldnothavebeenable

torealizethesegainsinthemarket.

Other resultshighlight the importantdistinctionbetweenpublic andprivate colleges and

between main campuses and branches and show that public campuses and local branches are

estimated tobemoreeffective in influencing local collegeparticipation. Wealso find thecollege

probabilityfunctiontobeconcavewithrespecttothenumberofcampuses,withaverylargekink

point for the firstcampusopened ina locality. Theeffectofeachadditionalcampuson the local

college attainment diminishes and eventually vanishes in college‐rich locations such asMoscow

city.

Overall, our findings show the direction for future policies targeted at expanding college

educationindevelopingcountriesandidentifyinglocationsforthefuturecollegeconstructionthat

wouldattractindividualswiththehighestpotentialgainsfromadditionaleducation.

References

Andrén, Daniela, John Earle, and Dana Sapatoru, 2005. The Wage Effects of Schooling underSocialismandinTransition:EvidencefromRomania,1950‐2000,JournalofComparativeEconomics33(2):300‐323.

Basu, Anirban, James J. Heckman, Salvador Navarro‐Lozano, and Sergio Urzua, 2007. Use ofInstrumental Variables in the Presence of Heterogeneity and Self‐selection: An Application toTreatmentsofBreastCancerPatients,HealthEconomics16(11):1133‐1157.

Belskaya,Olga, andKlara SabirianovaPeter, 2014.HowDoes theExpansionofHigherEducationChange theReturns toCollegeQuality? Insights fromSixtyYearsofRussianHistory,UNCChapelHillWorkingPaper.

Björklund,Anders,andRobertMoffitt,1987.TheEstimationofWageGainsandWelfareGains inSelf‐SelectionModels,ReviewofEconomicsandStatistics69(1):42‐49.

Brainerd,Elizabeth,1998.WinnersandLosersinRussia’sEconomicTransition,AmericanEconomicReview88(5):1094‐1116.

Cameron,Stephen,andJamesJ.Heckman,1998.LifeCycleSchoolingandDynamicSelectionBias:ModelsandEvidenceforFiveCohortsofAmericanMales,JournalofPoliticalEconomy106(2):262‐333.

33  

Cameron,Stephen,andChristopherTaber,2004.EstimationofEducationalBorrowingConstraintsUsingReturnstoSchooling,JournalofPoliticalEconomy112(1):132‐182.

Card, David, 1995. Using Geographic Variation in College Proximity to Estimate the Return toSchooling,inAspectsofLabourMarketBehaviour:EssaysinHonourofJohnVanderkamp,ed.byLouisN.Christofides,E.KennethGrant,andRobertSwidinsky.Toronto:UniversityofTorontoPress,201‐222.

Card,David,2001.Estimating theReturn toSchooling:ProgressonSomePersistentEconometricProblems,Econometrica69(5):1127‐1160.

Carneiro,Pedro,andSokbaeLee,2009.EstimatingDistributionsofPotentialOutcomesUsingLocalInstrumentalVariableswithanApplicationtoChangesinCollegeEnrollmentandWageInequality,JournalofEconometrics149(2):191‐208.

Carneiro,Pedro, JamesHeckman,andEdwardVytlacil, 2010.EvaluatingMarginalPolicyChangesandtheAverageEffectofTreatmentforIndividualsattheMargin,Econometrica78(1):377‐394.

Carneiro, Pedro, James Heckman, and Edward Vytlacil, 2011b. Estimating Marginal Returns toEducation,AmericanEconomicReview101(4):2754‐2781.

Carneiro,Pedro,MichaelLokshin,CristobalRidao‐Cano,andNithinUmapathi,2011a.AverageandMarginalReturnstoUpperSecondarySchoolinginIndonesia,PolicyResearchWorkingPaper5878.

Carnoy, Martin, Prashant Loyalka, Maria Dobryakova, Rafiq Dossani, Isak Froumin, KatherineKuhns, Jandhyala B. G. Tilak, and Rong Wang, 2013. University Expansion in a Changing GlobalEconomy:TriumphoftheBRICs?Stanford,California:StanfordUniversityPress.

Currie,Janet,andEnricoMoretti,2003.Mother'sEducationandtheIntergenerationalTransmissionofHumanCapital:Evidence fromCollegeOpenings,Quarterly JournalofEconomics118(4):1495‐1532.

DemographyYearbookofRussia,2002.Moscow:Goskomstat.

Duflo,Esther,2001.SchoolingandLaborMarketConsequencesofSchoolConstructioninIndonesia:EvidencefromanUnusualPolicyExperiment,AmericanEconomicReview91(4):795‐813.

EducationinRussia,2008.Moscow:Goskomstat

Eisenhauer,Philipp,JamesJ.Heckman,andEdwardVytlacil,2015.TheGeneralizedRoyModelandtheCost‐BenefitAnalysisofSocialPrograms,JournalofPoliticalEconomy,132(2),forthcoming.

Evans, Heather, and Anirban Basu, 2011. Exploring Comparative Effect Heterogeneity withInstrumentalVariables:PrehospitalIntubationandMortality,Health,EconometricsandDataGroupWorkingPapers11/26.

Fang,Hai,KarenEggleston,JohnRizzo,ScottRozelle,andRichardZeckhauser,2012.TheReturnstoEducation in China: Evidence from the 1986 Compulsory Education Law, Working Paper, No.18189,NationalBureauofEconomicResearch,Cambridge,MA.

FederalLaw,1992.Obobrazovanii[OnEducation],July10,1992.

34  

Availableathttp://www.consultant.ru/popular/edu/.

Fleisher,Belton,KlaraSabirianovaPeter,andXiaojunWang,2005.ReturnstoSkillsandtheSpeedofReforms:EvidencefromCentralandEasternEurope,China,andRussia, JournalofComparativeEconomics33(2):351‐370.

Frenette, Marc, 2009. Do Universities Benefit Local Youth? Evidence from the Creation of NewUniversities,EconomicsofEducationReview28(3):318‐328.

Garen,John,1984.TheReturnstoSchooling:ASelectivityBiasApproachwithaContinuousChoiceVariable,Econometrica52(5):1199‐1218.

Gorodnichenko, Yuriy, and Klara Sabirianova Peter, 2005. Returns to Schooling in Russia andUkraine: A Semiparametric Approach to Cross‐Country Comparative Analysis, Journal ofComparativeEconomics33(2):324‐350.

Groen, Jeffrey A., 2004. The Effect of College Location on Migration of College‐Educated Labor,JournalofEconometrics121(1‐2):125‐142.

Heckman, James J., JohnEricHumphries,GregoryVeramendi, andSergioUrzua,2014.Education,HealthandWages,WorkingPaperNo.19971,NationalBureauofEconomicResearch,Cambridge,MA.

Heckman,JamesandEdwardVytlacil,2001.Policy‐RelevantTreatmentEffects,AmericanEconomicReview91(2):107‐111.

Heckman, James and Edward Vytlacil, 2005. Structural Equations, Treatment Effects, andEconometricPolicyEvaluation,Econometrica73(3):669–738.

Heckman,JamesandEdwardVytlacil,2007.EconometricEvaluationofSocialPrograms,HandbookofEconometrics,Volume6B:4877‐5143.

Heckman,JamesandXuesongLi,2004.SelectionBias,ComparativeAdvantageandHeterogeneousReturnstoEducation:EvidencefromChinain2000,PacificEconomicReview9(3):155‐171.

Heckman, James, Daniel Schmierer, and Sergio Urzua, 2010. Testing the Correlated RandomCoefficientModel,JournalofEconometrics158(2):177‐203.

Heckman, James, Lance Lochner, andPetraTodd, 2006. Earnings Functions, Rates ofReturn andTreatment Effects: The Mincer Equation and Beyond, Handbook of the Economics of Education,Volume1,Chapter7:307‐458.

Ichino,AndreaandRudolfWinter‐Ebmer,2004.TheLong‐RunEducationalCostofWorldWar II,JournalofPoliticalEconomy22(1):57‐87.Kane,ThomasandCeciliaRouse,1995.Labor ‐MarketReturnstoTwo‐andFour‐YearCollege,AmericanEconomicReview85(3):600‐614.

Kane, Thomas J. and Cecilia Elena Rouse, 1995. Labor‐Market Returns to Two‐ and Four‐YearCollege,AmericanEconomicReview85(3):600‐614.

Katz, Lawrence and Kevin Murphy, 1992. Changes in Relative Wages, 1963‐1987: Supply andDemandFactors,QuarterlyJournalofEconomics107(1):35‐78.

35  

Kyui, Natalia, 2013. Expansion of Higher Education, Employment andWages: Evidence from theRussianTransition,BankofCanadaWorkingPaper2013‐45.

Moffitt, Robert, 2008. Estimating Marginal Treatment Effects in Heterogeneous Populations,Annalesd’EconomieetdeStatistique91/92:239‐261.

Moffitt, Robert (2014). Estimating Marginal Treatment Effects of Transfer Programs on LaborSupply,WorkingPaper.

Münich, Daniel, Jan Svejnar, and Katherine Terrell, 2005. Returns to Human Capital Under theCommunismWageGridandduringtheTransitiontoaMarketEconomy,ReviewofEconomicsandStatistics83(1):100‐123.

OECD,2008.EducationataGlance:OECDIndicators.OECDPublishing.

Oppedisano,Veruska,2011.The(Adverse)EffectsofExpandingHigherEducation:Evidence fromItaly,EconomicsofEducationReview30(5),997‐1008.

RegionsofRussia.Moscow:Goskomstat,variousyears.

Robinson,Peter,1988.Root‐N‐ConsistentSemiparametricRegression,Econometrica56(4):931–54.

Roy,Andrew,1951.SomeThoughtsontheDistributionofEarnings,OxfordEconomicPapers3(2):135‐146.

Topel, Robert, 1997. ‘Factor Proportions and Relative Wages: The Supply‐Side Determinants ofWageInequality,JournalofEconomicPerspectives11(2):55–74.

Willis,RobertandSherwinRosen,1979.EducationandSelf‐Selection,JournalofPoliticalEconomy87(5):S7‐S36.

Winters, John V., 2011. Why Are Smart Cities Growing?Who Moves andWho Stays? Journal ofRegionalScience51(2):253‐270.

Yang, Dennis Tao, 2005. Determinants of Schooling Returns during Transition: Evidence fromChineseCities.JournalofComparativeEconomics,33(2):244‐264.

Zamarro, Gema, 2010. Accounting for Heterogeneous Returns in Sequential Schooling Decisions,JournalofEconometrics156(2):260‐276.

36  

Table1:VariablesinWageandCollegeEquations

Variable TimeframeLevelof

aggregationCommon

Wageeq

CollegeeqEandI

Female Fixed individual x Urbanresidence Fixed individual x EthnicallyRussian Fixed individual x Mother’seducation Fixed individual x Age,agesquared Currentyear individual xBirthcohorts Fixed individual xFederaldistricts Currentyear individual xFederaldistrictsat17 Age17 individual xSurveyyeardummies Currentyear individual xRegionalcohortsizeat17 Age17 region x Permanentregionalearnings Fixed region x Permanentunemploymentrate Fixed region x Transitoryregionalearnings Currentyear region xTransitoryunemploymentrate Currentyear region xRegionalearningsat17 Age17 region xUnemploymentrateat17 Age17 region xSkillwageratioat17 Age 17 country xNumberofcampusesat17 Age17 municipality x

Notes: The vector includes wage determinants; E is the number of campuses in the municipality ofresidenceatage17;theIvectorincludesotherexclusionrestrictionsinthecollegedecisionequation;andthecommonvector belongstobothequations. 

Table2:VarianceDecompositionfortheNumberofCampuses

Variables MeanTotal

variance

Within‐municipality

%

Between‐municipality

%Totalnumberofcampuses 1.43 77.3 86.8 13.3Numberofcolleges‐public 0.77 22.3 99.4 0.6Numberofcolleges‐private 0.27 17.5 59.6 40.4Numberofbranches‐public 0.33 1.0 68.3 31.8Numberofbranches‐private 0.07 0.2 44.9 55.1

Notes: Thenumber of campuses, colleges, and branches is calculated for everymunicipality in theRLMSestimation sample. The panel ofmunicipalities used in the variance decomposition is balancedwith 733municipalitiesand19timeperiodsfrom1985to2003.Acampusreferstoallbuildingsofthesamecollegeinonemunicipality.Branchisacampuslocatedoutsidethemunicipalityofthemaincampus.Totalnumberofcampusesisthesumofthenumberofmaincampuses(whichisequivalenttothenumberofcolleges)andthenumberofbranches.

37  

Table3:SampleStatistics

S=0(N=10,962)

S=1(N=6,949)

Meancomparison

t‐test(p‐value)Hourlywagerate(log) 3.831 4.286 0.000 (0.751) (0.721) Age 32.855 31.919 0.000 (4.808) (4.708) Regionaltransitoryearnings(log) 0.020 0.013 0.001 (0.139) (0.146) Regionaltransitoryunemploymentrate,% 0.111 0.239 0.000 (1.687) (1.641) Female 0.492 0.606 0.000 (0.500) (0.489) Urbanresidence(binary) 0.754 0.873 0.000 (0.431) (0.333) EthnicallyRussian(binary) 0.907 0.911 0.319

(0.291) (0.284) Mother’seducation(binary) 0.067 0.302 0.000 (0.249) (0.459) Mother’seducationmissing(binary) 0.117 0.115 0.648 (0.322) (0.319) Regionalcohortsizeat17(log) 10.456 10.551 0.000 (0.610) (0.674) Regionalpermanentearnings(log) 8.519 8.626 0.000

(0.387) (0.396) Regionalpermanentunemploymentrate,% 6.841 6.201 0.000

(2.615) (2.886) Nofcampusespermunicipality 11.879 26.432 0.000 (36.070) (57.131) Municipalitywithnocampuses(binary) 0.465 0.260 0.000 (0.499) (0.439) Skillwageratioat17 0.760 0.725 0.000

(0.128) (0.126) Regionalearningsat17(log) 7.875 7.844 0.000

(0.463) (0.460) Regionalunemploymentrateat17,% 4.817 5.808 0.000

(5.209) (5.164)Notes:Descriptive statistics are provided for the baseline estimation sample (age 25, age 17 [1985,2003],wage [2004,2011],andnon‐missingvariables). Thet‐testcomparesmeansofvariablesbetweencollegegraduates(S=1)andgraduatesofsecondaryschools(S=0).ThedefinitionofallvariablesisdescribedinAppendixTableA1.Standarddeviationsareinparentheses.

38  

Table4:MaximumLikelihoodEstimatesoftheNormalSwitchingRegressionModel 

VariablesWageequations CollegeequationS=0 S=1 Coefficients ME

Age 0.013 0.055*** … … (0.015) (0.019) Agesquared ‐0.001 ‐0.060** … … (0.023) (0.028) Regionaltransitoryearnings(log) 0.733*** 0.780*** … … (0.095) (0.119) Regionaltransitoryunemploymentrate,% 0.010** ‐0.001 … … (0.005) (0.006) Female ‐0.465*** ‐0.239*** 0.374*** 0.142*** (0.013) (0.017) (0.020) (0.008)Urbanresidence(binary) 0.212*** 0.308*** 0.183*** 0.069*** (0.015) (0.024) (0.031) (0.011)EthnicallyRussian(binary) ‐0.040** ‐0.015 ‐0.065* ‐0.027* (0.019) (0.024) (0.038) (0.015)Mother’seducation(binary) ‐0.041 0.167*** 1.053*** 0.403*** (0.034) (0.036) (0.029) (0.010)Mother’seducationmissing(binary) ‐0.021 0.110*** 0.156*** 0.059*** (0.017) (0.023) (0.031) (0.012)Regionalcohortsizeat17(log) 0.191*** 0.064*** ‐0.181*** ‐0.066*** (0.017) (0.018) (0.026) (0.010)Regionalpermanentearnings(log) 0.618*** 0.796*** 0.281*** 0.112*** (0.040) (0.051) (0.051) (0.019)Regionalpermanentunemploymentrate,% 0.020*** 0.003 ‐0.000 0.002 (0.005) (0.006) (0.009) (0.003)

Instruments

Nofcampusespermunicipality … … 0.011*** 0.005*** (0.001) (0.000)Nofcampusespermunicipalitysquared/100 … … ‐0.003*** ‐0.001*** (0.000) (0.000)Municipalitywithnocampuses(binary) … … ‐0.312*** ‐0.110*** (0.026) (0.010)Skillwageratioat17 … … ‐1.313*** ‐0.527*** (0.368) (0.145)Regionalearningsat17(log) … … 0.040 0.016 (0.043) (0.017)Regionalunemploymentrateat17,% … … ‐0.007 ‐0.003 (0.006) (0.002)

‐0.372***(0.065) 0.330***(0.057);annualized=7.3

2–testforjointsignificanceofinstruments=360.9*** 2–testforindependenceofequations=40.6*** Notes: ***Significantat1%.**Significantat5%.*Significantat10%. N=17,911(age25,age17 [1985,2003],wage[2004,2011],andnon‐missingvariables).Thistableshowsthemaximumlikelihoodestimatesofwage and college equations for the normal switching regression model. Robust standard errors are inparentheses.Thestandarderrorof( iscomputedusingtheDeltamethod.Thecollegeequationalsoincludes dummies for four cohorts,Moscow residence at age 17, and seven federal districts at age 17;wageequationsincludedummiesforsurveyyears,currentMoscowresidence,andsevenfederaldistrictsatthetimeofthesurvey.

39  

Table5:CollegeEquation:AlternativeSetofInstruments

Variables (1) (2) (3) (4) (5)Nofcolleges(maincampuses) 0.007*** … … … … (0.002) Nofcollegesquared/100 ‐0.002*** … … … … (0.000) Nofbranches 0.065*** … … … … (0.013) Nofbranchessquared/100 ‐0.287*** … … … … (0.100) Municipalitywithnocampuses(dummy) ‐0.258*** ‐0.250*** ‐0.324*** ‐0.276*** ‐0.312*** (0.030) (0.029) (0.026) (0.027) (0.026)Numberofpubliccampuses … 0.024*** … … … (0.002)Nofpubliccampusessquared/100 … ‐0.018*** … … … (0.003) Municipalitywithaprivatecampus(dummy) … ‐0.008 … … … (0.031)Nofcampuses … … 0.009*** … 0.011***

(0.001) (0.001)xMoscowresidencyat17 … … ‐0.009*** … …

(0.001) Nofpubliccolleges … … … 0.025*** …

(0.002)Nofpubliccollegessquared/100 … … … ‐0.020*** …

(0.003)Nofcampusessquared/100 … … … … ‐0.003***

(0.000)ChangeinNofcampusesbetweenages25‐30 … … … … ‐0.001

(0.001) ‐0.353*** ‐0.352*** ‐0.387*** ‐0.373*** ‐0.370***

(0.066) (0.066) (0.063) (0.064) (0.066) 0.374*** 0.369*** 0.318*** 0.347*** 0.338***

(0.061) (0.060) (0.054) (0.058) (0.059)2–testforjointsignificanceofinstruments 388.4 412.2 352.3 394.4 362.1p‐value [0.000] [0.000] [0.000] [0.000] [0.000]2–testforindependenceofequations 49.6 49.2 44.0 50.3 41.6p‐value [0.000] [0.000] [0.000] [0.000] [0.000]Log‐likelihood ‐24,466 ‐24,451 ‐24,480 ‐24,460 ‐24,476Notes: N=17,911.***Significantat1%.**Significantat5%.*Significantat10%.N=17,911.TableshowsthemaximumlikelihoodestimatesofthecoefficientsoftheNofcampusespermunicipalityinthecollegeequationusing alternative definitions. The number of campuses, colleges, and branches is computed at the level ofmunicipality.Allspecificationsusethesamesetofvariablesandthesamesampleconstraintsasinthebaselinespecification reported in Table 4 (age 25, age 17 [1985, 2003], wage [2004, 2011], and non‐missingvariables).Thestandarderrorof( iscomputedusingtheDeltamethod.Robuststandarderrorsareinparentheses.

40  

Table6:SwitchingRegressionModelParametersfromAlternativeSpecifications

Includesdropouts

10‐yearearningsaverage

Sovietearnings IPWWage1995‐

2011

(1) (2) (3) (4) (5) ‐0.363 ‐0.353 ‐0.365 ‐0.369 ‐0.355

(0.064) (0.070) (0.064) (0.053) (0.061) 0.345 0.367 0.355 0.397 0.434

(0.054) (0.077) (0.064) (0.047) (0.057)2–testforjointsignificance 370.6 164.0 170.1 517.3 237.8ofinstruments[p‐value] [0.000] [0.000] [0.000] [0.000] [0.000]

2–testforindependenceof 39.4 41.2 40.6 32.1 81.5equations[p‐value] [0.003] [0.003] [0.001] 0.000 [0.000]

Log‐likelihood ‐26,100 ‐24,680 ‐24,750 30,589 ‐31,592Numberofobservations 18,823 17,911 17,911 17,911 22,584Notes:Tablereportsstatisticsforalternativespecificationsofthenormalswitchingregressionmodel.Unlessnotedotherwise, all specificationsuse thesamesetofvariablesand thesamesampleconstraintsas in thebaselinespecificationreportedinTable4(age25,age17[1985,2003],wage[2004,2011],andnon‐missingvariables).Incolumn1,thetreatmentgroup,S=1,includescollegedropoutswiththreeormoreyearsofcollege.Incolumn2,weuseanalternativedefinitionofregionalpermanentearningscalculatedasaverageregional earnings over the first ten years since college decision at age 17. The specification in column 3appliesthesamedefinitionasincolumn2butreplacespermanentearningswiththeSovietperiodaverageregionalearnings(1980‐1991)forindividualswhoturned17beforemarketreforms.Incolumn4,weapplythe inverse propensityweight from the probitmodel of non‐missingwages on covariates and . Incolumn 5,wage [1995, 2011]. The standard error of ( is computed using the Deltamethod.Robuststandarderrorsareinparentheses,andp‐valuesareinbrackets.

41  

Table7:TreatmentParametersTreatmentParameter Estimatedreturn

AverageTreatmentEffect(ATE) 0.073 (0.026)TreatmentontheTreated(TT) 0.130 (0.041)TreatmentontheUntreated(TUT) 0.019 (0.020)InstrumentalVariables(IV) 0.161 (0.051)OrdinaryLeastSquares(OLS) 0.077 (0.024)

Sortinggain(TT‐ATE) 0.057Selectionbias(OLS‐TT) ‐0.053Notes: The standard treatment parameters are obtained by integrating the , using weightingfunctionsofHeckmanandVytlacil(2005):

, , ,where , , , , .The weighting functions , are defined in Appendix Table A3. Linear IV estimates use P(Z) as aninstrument. Bootstrappedstandarderrorsareinparentheses(250replications). Thereportedreturnsareannualizedbydividing theestimatesby4.5,which is thedifference inaverageyearsof schoolingbetweentreatmentandcontrolgroups.Tablereportstheresultsunderthebaselinespecification(Table4).

42  

Table8:PolicyParameters

PolicyExperiment PolicyParameter

PanelA:MPRTEfromthenormalselectionmodel(forinfinitesimal) 0.099

(0.011)1 ∙ 0.074

(0.013), 0.096

  (0.011)

PanelB:MPRTEfromthesemi‐parametricmodel(forinfinitesimal) 0.110

(0.012)1 ∙ 0.086

(0.014), 0.107

  (0.012)

PanelC:PRTEfrompolicysimulationsAddingacampusineachmunicipality  0.098

,∗

, 1 (0.011)Openingafirstcampusin1993‐2003 0.099

,∗ 1if , 0, ∈ 1993,2003 (0.011)

Openinganadditionalcampusin1993‐2003 0.077,

∗, 1if , 0, ∈ 1993,2003 (0.012)

Addingacampusincapitalcitiesin1993‐2003 0.075,

∗, 1, ∈ 1993,2003 (0.012)

Addingacampusinnon‐capitalcitiesin1993‐2003 0.099,

∗, 1, ∈ 1993,2003 (0.010)

Addingacampusinruraldistrictsin1993‐2003 0.099,

∗, 1, ∈ 1993,2003 (0.011)

50%ofactualexpansion 0.080 ,

∗, , , ) (0.012)

Actualexpansion( , , ) 0.060 ,

∗, (0.013)

150%ofactualexpansion 0.062 ,

∗, , , ) (0.013)

Notes:Tablereportsmarginalpolicy‐relevanttreatmenteffect(MPRTE)forthenormalselectionandsemi‐parametricmodelsaswellaspolicy‐relevanttreatmenteffect(PRTE)frompolicyexperiments.Thereportedreturnsareperyearofcollege. , isthenumberofcampusesinthemunicipalityofresidencematage17inyeart; ,

∗ isthesimulatednumberofcampuses.TheMTEestimatesarebasedonequation(8)inPanelsAandC;equation(12)inPanelB.TheMPRTEareobtainedbyintegratingthe , usingtheweightingfunctions reported inAppendixTableA3. The reportedPRTE estimates are computedusingQuasi‐MonteCarlosimulationwithHaltonsequences;theseestimatesareessentiallyidenticaltothePRTEestimatesbasedonthePRTEweightingfunction.Bootstrappedstandarderrorsareinparentheses(250replications).

43  

Figure1:AverageReturnstoCollegeEducationinRussia

 

Notes:ReturnstocollegeeducationarecalculatedfromtheOLSregressionofthelogofmonthlyearningsatprimary job on college degree, gender, age, age squared, urban place of birth, a dummy for Russiannationality,andsevenfederaldistricts.Thecomparisongroupincludesgraduatesofgeneralandprofessionalsecondaryschools,butexcludescollegedrop‐outs.Earningsfor1985and1990arereportedretrospectively.Estimationisperformedforeachyearseparatelyusingthesampleof25‐to55‐yearolds.Reportedaretheestimatedcoefficientsoncollegedegreeandtheoveralltrendfittedusingnon‐parametricsmoothing(lowess;bandwidth=0.4). Returns are in percent andper yearof college (dividedby4.5). Also shown is the90%confidenceintervalcomputedusingrobuststandarderrors.Theverticallinedemarks1991asthebreakupoftheUSSR.

USSR period

0

1

2

3

4

5

6

7

8

9

10

1985 1990 1995 2000 2005 2010

44  

Figure2:TrendsinKeyVariables

 

Notes:PanelAshowsthepercentshareofcollegegraduatesinRLMSamong25‐44‐yearoldindividuals.Thecomparison group includes graduates of general and professional secondary schools, but excludes collegedrop‐outs.PanelBdepictstheaveragesizeof17‐year‐oldpopulationinthousandspeopleacrossallRussianregions. Vertical linesdefine the sampleperiod– from1985 to2003. Also shown is the90%confidenceinterval. Panel C shows the average number of campuses permunicipality at age 17 in the RLMS sample,excludingMoscowcity. PanelDdisplaystheratioofwagesofmanualworkerstothewagesofnon‐manualworkersintheindustrialsectorduringthesampleperiod1985‐2003.   

2025

3035

40

1995 1998 2001 2004 2007 2010

Age 25-44A. Share of college graduates, %

Sample period

1020

3040

1950 1960 1970 1980 1990 2000 2010

per region, 000sB. Average population age 17

03

69

1215

1985 1988 1991 1994 1997 2000 2003

per municipalityC. Average N of campuses

.5.6

.7.8

.91

1985 1988 1991 1994 1997 2000 2003

D. Manual/non-manual wage ratio

45  

Figure3:MarginalTreatmentEffectEstimatedfromaNormalSelectionModel

Notes:The figureplots theMTEestimates fora gridof valuesv betweenzeroandone,with a90‐percentconfidenceinterval,evaluatedatmeanvaluesofX.Weestimateaparametricnormalselectionmodelgiveninequations (1)‐(4) by maximum likelihood. The figure is computed using equation (8) and the estimatedparametersinTable4.Thefixedcovariancecomponentof , isgivenby 0.372withastandarderrorof0.065. Thereportedreturnsareperyearofcollege. ThestandarderrorsarecomputedusingtheDeltamethod.   

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

MT

E(x

,v)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0v

CI 90% MTE(x, v)

46  

Figure4:MarginalTreatmentEffectEstimatedfromaSemi‐ParametricModelwithLocalIV 

 

Notes:The figureplots theMTEestimates fora gridof valuesv betweenzeroandone,with a90‐percentconfidence interval, evaluated at mean values of X. Estimation steps are described in Section 2. Theestimation is performed using a Robinson's (1988) double residual estimator to obtain the non‐linearrelationbetween the log ofwages and ,where ̂ is thepredictedprobability of graduating from college.Then, we use a Kernel quadratic local polynomial regression to evaluate the derivatives of equation(11), ′ ̂ ,withabandwidthof0.32. The reported returnsareperyearof college. 90percent standarderrorbandsarecalculatedusingthebootstrap(250replications).

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

MT

E(x

,v)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0v

CI 90% MTE_semi(x, v)

47  

Figure5:AverageTreatmentEffectEstimatesforDifferentSamplePeriods

 Notes: ;  1 0 .Figurereportstheaveragereturnstocollege(ATE)andthe MTE slope along with the 95‐percent confidence interval under the baseline specification byadding/subtractingoneadditionalsurveyyeartopreservethepanelfeaturesofwageseries.Thelastyearofeach survey period is always 2011 to keep the college decision period (1985‐2003) constant forcomparabilitypurposes.Yearindicatesthebeginningofthesurveyperiod.Theredlinecorrespondstothebaselineestimatesforthe2004‐2011surveyperiod(Table4).

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1995 1996 1998 2000 2001 2002 2003 2004 2005 2006Year

CI 95% ATE

A. ATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1995 1996 1998 2000 2001 2002 2003 2004 2005 2006Year

CI 95% MTE slope

B. MTE slope

48  

AppendixTableA1:DescriptionofVariables

Generalnotes:1. Thesourceforallindividual‐levelvariablesisRLMS2. Estimationsamplecovers1995‐1996,1998,2000‐2011timeperiods;variablesareavailableforallyears,unlessnoted

otherwise3. Thesampleisrestrictedtoindividualsbornbetween1968and1986andolderthan25atthetimeofthesurvey.4. Russianregionsincludetwofederalcities(MoscowcityandSt.Petersburg)and81territories,whichareaggregatedinto

sevenfederaldistricts.5. Duetomultiplechangesintheadministrative‐territorialstructureofRussia,allpastregionaldataarecollectedbasedon

theclassificationofregionsaccordingtothe2008amendmenttotheConstitutionoftheRussianFederation.6. Atthetimeofthesurvey,respondentsresidedin32regions,buttheygraduatedfromuniversitieslocatedin73regions7. MunicipalityinRLMSisdefinedaccordingtothe5‐digitmunicipalitycodetakenfromtheNationalClassificationof

AdministrativeandTerritorialDivision(OKATOinRussian).OKATOcodeconsistsoftwodigitsforaregionandthreedigitsforeithercityordistrict(county‐equivalent)withinaregion.

8. Municipalityatage17isthesameascurrentmunicipalityforindividualswhomovedtocurrentmunicipalitybefore17,finishedsecondaryschoolincurrentlocation,orcurrentlyresideintheplaceoftheirbirth.Municipalityatage17isthesameascollegelocationifindividualsresidedinthesameplacebeforecollege.Municipalityatage17isimputedbyrandomlyselectingamunicipalityforagiventypeofbirthplace(city,township,andvillage)withintheregionofcurrentresidenceforindividualswhodidnotattendcollegeandmovedtoanewlocationafterage17.Municipalityatage17isimputedbyrandomlyselectingamunicipalityforagiventypeofbirthplace(city,township,andvillage)withintheregionofcollegeforindividualswhoattendedcollegeandmovedtoanewlocationafterage17.

DependentVariables

Hourlywagerate(log) =Laborearningspermonthatprimaryjob/Hoursofworkpermonthatprimaryjob

Laborearningspermontharedefinedas:

monthlyaverage(overthelast12months)after‐taxlaborearningsforanemployee[1998‐2011];

totalaccumulatedwagedebtdividedbythenumberofmonthsofoverduewagesforemployeeswithwagearrears[1995‐1996];

monetary portion of lastmonth earnings for employees with nowagearrears[1995‐1996];

monetary portion of lastmonth earnings for self‐employed; self‐employed include individuals reportingplace ofworkother thanan organization as well as those involved in regular individualeconomicactivities[allyears].

Hoursofworkpermontharedefinedasfollows:

Usualhoursofworkinatypicalweektimesfour[1998‐2011]; Actualhoursofworklastmonth[1995‐1996]; Unusually highhours are top codedat 480hourspermonth (16

hoursperdaytimes30days).

Collegedegree(binary) Twoalternativedefinitions:

(i) = 1 if an individual has a college degree or higher, = 0 if anindividual graduated from a general and/or professionalsecondaryschoolwithcredentials;

(ii) =1 if an individual has three ormore years of college educationwithorwithoutcollegedegree,=0ifanindividualgraduatedfromageneraland/orprofessionalsecondaryschool.

49  

CommonvectorXS

Female =1iffemale

Urbanresidence(binary) =1ifresidesinurbanlocationatthetimeofthesurvey

EthnicallyRussian(binary) =1ifethnicityisRussian

Mother’seducation(binary) =1ifmotherhasacollegedegree

Note:Availablein2006and2011surveys;extrapolatedtootheryearsbasedonindividualpanelidforcasesofconsistentreportingofmother’seducation.

Mother’seducationismissing =1ifmother’seducationismissing

Regionalcohortsizeat17(log) Logof17‐yearoldpopulationintheregionofresidenceattheageof17,1985‐2003.

Sources:Census1979,1989,2002;DemoscopeWeekly(www.demoscope.ru/);GoskomstatCentralStatisticalDatabase.

Regionalpermanentearnings(log) Threealternativedefinitions:

(i) Predictedearnings=predictedvaluefromtheregressionofthelogofregionalearningson83regionaldummiesandaquadratictrend,1994‐2011;

(ii) 10‐yearaverageearnings=thelogoftheaverageofregionalearningsoverthefirsttenyearssincecollegedecisionatage17;

(iii) 10‐yearaveragewithastructuralbreak

=10‐yearaverageearningsasin(ii)for17‐yearoldsinthemarketeconomyafter1991;

=thelogoftheaverageofregionalearningsoverthe1980‐1991periodfor17‐yearoldsintheSovietperiodbefore1992.

Note:Regionalearningsaretheaveragemonthlyearningsinagivenyeardeflatedin2000prices.

Sources:GoskomstatCentralStatisticalDatabase;LaborandEmployment(variousyears).

Regionalpermanentunemploymentrate,%

Predictedvaluefromtheregressionofregionalunemploymentrateon83regionaldummiesandaquadratictrend,1994‐2011.

Note:AlternativedefinitionswerenotusedbecauseofthelackofvariationintheunemploymentrateduringtheSovietperiod,whenunemploymentwasconsideredtobenon‐existent.

Sources:GoskomstatCentralStatisticalDatabase.

WageequationonlyXW

Age,agesquared Yearofsurveyminusyearofbirth;themodeofbirthyeariscomputedincasesofinconsistenciesacrossrounds.

Regionaltransitoryearnings(log) Residualfromtheregressionofthelogofregionalearningson83regionaldummiesandaquadratictrend,1994‐2011.

Sources:GoskomstatCentralStatisticalDatabase;LaborandEmployment(variousyears).

50  

Regionaltransitoryunemploymentrate,%

Residualfromtheregressionofregionalunemploymentrateon83regionaldummiesandaquadratictrend,1994‐2011.

Sources:GoskomstatCentralStatisticalDatabase

Federaldistricts(dummies) SetofdummiesforresidinginMoscowandsevenfederaldistrictsatthetimeoftheinterview.

Collegeequation,EandI

Nofcampusespermunicipality Totalnumberofcollegecampusesinthemunicipalityofresidenceatage17,1985‐2003.

Acampusreferstoallbuildingsofthesamecollegeinonemunicipality.Eachcollegehasamaincampus;somecollegesmayalsohavebrancheswithcampuseslocatedinothermunicipalities.

Subcategoriesofcampusesincludemaincampuses;branches;publiccampuses;privatecampuses.

Sources:RussianUniversityDatabase(BelskayaandSabirianovaPeter,2014).

Skillwageratioat17 Ratioofaveragewagesofmanualworkerstoaveragewagesofnon‐manualworkersintheindustrialsector(manufacturing+mining+electricity+selectedindustrialservices)atage17,1985‐2003.

Sources:Russianyearbooks(annualissuesfrom1985to2003)

Regionalearningsat17(log) Logofregionalearningsintheregionofresidenceatage17,1985‐2003.

Note:Regionalearningsaretheaveragemonthlyearningsinagivenyeardeflatedin2000prices.

Sources:GoskomstatCentralStatisticalDatabase;LaborandEmployment(variousyears).

Regionalunemploymentat17,% Unemploymentrateintheregionofresidenceatage17,1985‐2003.

Note:Availablefor1992‐2003;assumedzeroduringtheSovietperiod,whenunemploymentwasconsideredtobenon‐existent.

Sources:GoskomstatCentralStatisticalDatabase.

Birthcohorts(dummies) Setofdummiesforindividualsbornin19851988,19891993,19941998,and19992003.

Federaldistrictsat17(dummies) SetofdummiesforlivinginMoscowandsevenfederaldistrictsatage17.

51  

TableA2:TestsforSelectiononGains

PanelA.Testoflinearityof | , usingpolynomialsinP

DegreeofPolynomial 2 3 4 5

p‐valueofjointtestofnonlinearterms 0.000 0.000 0.000 0.000

PanelB.TestofequalityofLATEs(H0: 0)

RangesofvforLATE DifferenceinLATEs

p‐value Jointp‐valuej j+1

(0.00,0.04) (0.08,0.12) 0.035 0.000 0.0000(0.08,0.12) (0.16,0.20) 0.029 0.000 (0.16,0.20) (0.24,0.28) 0.025 0.000 (0.24,0.28) (0.32,0.36) 0.021 0.000 (0.32,0.36) (0.40,0.44) 0.019 0.000 (0.40,0.44) (0.48,0.52) 0.017 0.000 (0.48,0.52) (0.56,0.60) 0.016 0.000 (0.56,0.60) (0.64,0.68) 0.016 0.000 (0.64,0.68) (0.72,0.76) 0.017 0.000 (0.72,0.76) (0.80,0.84) 0.018 0.020 (0.80,0.84) (0.88,0.92) 0.019 0.044 (0.88,0.92) (0.96,1.00) 0.019 0.060

Notes toPanelA: Standard errors are bootstrapped (500 replications) to account for the fact that P isestimated. The computation of the test includes two steps. First, we estimate a probit model of collegeequationandpredictthepropensityscore . Second,weestimatealinearregressionof on , ∙ and ,where isapolynomialofdegree j=2,3,4,5. Eachcolumnpresentsthep‐valueofthetestassociated with the null hypothesis that neither nonlinear term has an explanatory power for eachpolynomial.NotestoPanelB:Wecomputethesemi‐parametricMTEasdescribedinSection2andpresentedinFigure4using250bootstrapreplications.LATEisthelocalaveragetreatmenteffect,anditisdefinedastheaverageofMTEineachoftheequally‐spacedintervalsofv,withthetotalof13non‐overlappingintervalsjseparatedbyadistanceof0.04. Thenullhypothesis is that theLATEsof twocontiguous intervalsareequal. Thep‐valueforthisnullhypothesisisshownincolumn4. Thep‐valueistheproportionofbootstrapb forwhich

inintervalj,where and for 1, … 250.Thelastcolumnreportsthep‐valueassociatedwiththenullhypothesisthatthedifferencesacross all adjacent LATEs are different from zero. The joint p‐value is the proportion of bootstrap b forwhich , where ∑ for all j intervals and ∑

for 1, … 250andalljandj+1intervals.

52  

TableA3:WeightsforTreatmentEffects

__________________________________________________________________________________________________________________

, 1

, |1

P|

, |1

1 P |

,∗, ,

∆, ∆ P∗ P

,| | |

E | | |

, | |1|

for astheinstrument

| , , | , ,,

, 0

, |1|

, |1

1 |

__________________________________________________________________________________________________________________

Source:HeckmanandVytlacil(2001,2005),Carneiroetal.(2010)

Notes:ATE=averagetreatmenteffect;TT=treatmenteffectonthetreated;TUT=treatmenteffectontheuntreated;PRTE=policyrelevanttreatmenteffect;MPRTE=marginalpolicyrelevanttreatmenteffect;IV=instrumentalvariables;OLS=ordinaryleastsquare.

53  

TableA4:Cost‐BenefitAnalysis

Regionalcapitals

Non‐capitalcities

COSTSPERCOLLEGESTUDENTA.Directcostsofcollegeattendance

Tuitionexpendituresforfee‐payingstudentsperyear $1,376 $1,059Shareofstudentspayingtuition 0.576 0.680Additionaldirectcosts(books,supplies,tutoring,etc.)peryear $72 $72Totaldirectcostsofcollegeattendanceperyear $865 $792

B.ForegonelabormarketearningsAverageannualnetearningsofhighschoolgraduates,age18‐30 $5,763 $4,706Probabilityofbeingemployedofhighschoolgraduates,age18‐30 0.831 0.854

Totalcostsofcollegeattendanceperyear $5,656 $4,809Presentvalueofcollegecosts $30,927 $26,300BENEFITSPERCOLLEGESTUDENTC.Improvedlabormarketincome

Rateofreturnstocollege(%) 7.5 9.9Probabilityofbeingemployedofcollegegraduates,age22‐55 0.920 0.929Additionalannualnetearningsofcollegegraduate,age22‐55 $1,789 $1,948

Presentvalueofadditionalearningsstream,32‐yearworklife $38,261 $41,657

Netpresentbenefitsofcollegeeducation $7,334 $15,357

Notes: Regional capitals are most populated cities in the region and have the highest concentration ofcolleges.Allmonetaryvaluesaredeflatedin2010pricesusingnationalCPIandthenconvertedtoUSdollars($1 = 30.6 rubles in December 2010). Tuition expenditures and the share of students paying tuition arecalculated from the RLMS questions available in 2006‐2008 surveys. Additional direct costs are fromGoskomstat(SocialConditionsandWelfareoftheRussianPopulation,2011)andassumedthesameforbothtypes of cities. Real earnings are net of tax, averaged over the 2004‐2011 period, and adjusted for theprobabilityofbeingemployed. Theratesofreturnsaretakenfromtable8(PRTEfromaddingacampusincapital and non‐capital cities). Additional annual net earnings of college graduate, age 22‐55 = (averageannual net earnings of high school graduates, age18‐30) x (percent rate of returns to college/100) x (4.5years of college) x (probability of being employed of college graduates, age 22‐55). The present value iscalculatedatage22over4.5pastyearsofcollegeand33futureyearsofworkinglifeat3percentrealinterestper annum. Calculations of net benefits do not account for earnings during school years, stipends andfinancial support, additional living expenses if a studentdecides tomove toa regional capital, and collegebenefitsbesidesearnings.

54  

FigureA1:MTEWeights

 

Notes: PanelAplotstheMTEweightsusedincomputingATE,TT,TUT,andIVwithestimatesprovidedinTable7;seeAppendixTableA3forformulas.PanelBplotstheweightsonMTEforthreedifferentversionsofMPRTE: 1) ; 2) 1 ∙ ; and 3) , . The MPRTE estimates arereportedinTable8. Thescaleofthey‐axis inPanelBisthescaleoftheMTE,notthescaleoftheweights,whicharescaledtofitthepicture.

0.0

0.5

1.0

1.5

2.0

2.5

We

igh

ts

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0v

ATE TT TUT IV

A. Standard treatment parameters

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

MT

E a

nd W

eig

hts

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0v

MTE MPRTE1 MPRTE2 MPRTE3

B. MPRTE weights