The Earnings of Soviet Workers: Evidence from the Soviet Interview Project
Author(s): Paul R. Gregory and Janet E. Kohlhase
Source: The Review of Economics and Statistics, Vol. 70, No. 1 (Feb., 1988), pp. 23-35
Published by: The MIT Press
Stable URL: http://www.jstor.org/stable/1928147
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THE EARNINGS OF SOVIET WORKERS: EVIDENCE
FROM THE SOVIET INTERVIEW PROJECT
Paul R. Gregoryand JanetE. Kohlhase*
Abstract-Micro data gatheredby theSovietInterviewProject
provide one of the firstopportunities
forWesternresearchers
to investigatethe determinants
of Soviet earnings.The data
show that Soviet labor marketsoperate in many respects
like U.S. labor markets,yet institutionaldifferences
remain.
The most strikinginstitutional
impactis that Soviet workers
are rewardedand penalized forpoliticalbehaviorexternalto
the firm.As in theUnited States,educationand experienceare
rewarded; men earn more than women. However,the Soviet
patternof returnsto educationis different,
returnsto experience are lower and occupationalsegregationof womenis less
important.
forthe Soviet InterCenter conductedinterviews
view Project (SIP) with2,793 formerSoviet citizens who immigratedto the United States betweenJanuary1, 1979 and April30, 1982.
This paper exploitstheuniqueSIP data to study
An augmentedhuman
Soviet wage differentials.
capital model assumes an active Soviet labor
marketin which workerscan manipulatemoney
rewardsby acquiringhumancapital,selectingjob
and by transmitting
loyaltysignals.
characteristics,
L. Introduction
Microdata on privilegesand politicalactivitiesare
is notpossibleto studySovietwagedifferen-combined with conventionaleconomic data to
estimate a reduced formearningsequation. We
tials using officialSovietdata. Sovietstatistical
confirmthat the Soviet labor marketworks in
authoritiesdo not publish data for correlating
many respectslike the U.S. labor market.Educaearningswith explanatoryfactorslike education,
tion and experienceare rewarded;men earn more
age, and experience.MeaningfulWesternresearch
thanwomen.Althoughtheeffects
are directionally
on wage differentials
has requiredmicrodata.AlThe
the same, they are quantitativelydifferent.
though the Soviets have conducted household
of returnsto educationis different,
returns
pattern
surveys,theirresultsare selectively
reportedin the
to experienceare lower,occupationalsegregation
formof sample averagesor simplecross tabulaof women is less important.The most striking
tions (Tsypin, 1978). Moreover,Soviet statistical
is that Soviet workersare
institutionaldifference
authoritieswould find it difficult
to obtain unrewardedand penalizedforpoliticalactionsexterbiased responsesto questionson politics,undernal to the enterprise.
groundeconomicactivity,
and privileges.
The immigrationof large numbersof former
Soviet citizensto Israel, West Germanyand the
II. ModellingtheSovietLabor Market
United States in the 1970s providesa rareopportunityto study large samples of formerSoviet A. Background
citizens. The firstmicrodata studies of Soviet
The Soviet labor marketbears a strongerrehousehold economic behaviorused a sample of
to capitalist marketsthan do other
semblance
1,000 immigrantsresiding in Israel who left
factor
markets.Despite a numberof restricSoviet
the Soviet Union in the early 1970s (Ofer and
Soviet
adultpopulationlargelyhas freethe
tions,
Vinokur,1979, 1981,1983,1984; Pickersgill,
1976).
of
of
choice
dom
occupation and location. The
More recently,the National Opinion Research
Sovietwage system,describedby Chapman(1979),
Received for publicationMay 28, 1986. Revision accepted Bergson (1964), and Kirsch (1972), combines
forpublicationMay 8, 1987.
flexicentralizedwage settingwithenterprise-level
*Universityof Houston.
setindustry
base pay rates
Data for this studywere producedby the Soviet Interview bility.State committees
Project.This projectwas supportedby GrantNo. 701 fromthe and skill differentials
for blue-collaremployees
National Council for Soviet and East European Research to
and detailedcompensationschedulesforhigh-level
JamesR. Millar,
the Universityof IllinoisUrbana-Champaign,
compensatefor
in this positions.Adjustmentcoefficients
PrincipalInvestigator.The analysisand interpretations
thesponsors.We
studyare thoseof theauthors,notnecessarily
uftdesirablejobs and locations. Bonuses, prewould like to express our gratitudeto Elizabeth Clayton, miums,piece rates,and reclassification
opportuniHerbertLevine, Kent Osband,
Steven Craig, James Griffin,
GertrudeSchroeder-Greenslade,
and anonymousrefereesfor ties give the managersome freedomto respondto
theirhelpfulcomments.
local labor marketconditions.
IT
Copyright(? 1988
[ 23 j
24
THE REVIEW OF ECONOMICS AND STATISTICS
The primarytaskof theSovietrewardsystemis imize regimestabilityand use thewage systemas
to induce people to takejobs amongoccupations, a controlinstrument.'Soviet workerscan be reindustriesand regionsto meet planned staffing gardedas accumulatinga stockof politicalcapital,
requirements.As a firstapproximation,one can whichcan be eitherpositiveor negative.Workers
wages to can add to theirstockby engagingin regime-supsay that the Sovietsuse market-clearing
allocate labor. As such, the Soviet incentivesys- port activitiesor make withdrawalsthroughacts
tem (like its capitalist counterpart)must offer of regimedisloyalty.Politicalauthoritiesdesireto
and productivity know the individual's stock of political capital.
compensatingwage differentials
The existenceof an activemarketis Insofar as Soviet authoritiescannot gatherinfordifferentials.
supportedby findingsof similarpatternsof wage mation on actual thoughtprocesses,theywould
differentials,
employmentand turnoverin Soviet have to screen using observable signals and
and capitalistlabor markets(Bergson,1964,chap. credentialsmuch like Westernmanagers(Spence,
6; Pryor,1985, chap. 8; Gregory,1973; Oferand 1973). Positive signals consist of organizational
Vinokur, 1981; Granick, 1987). Despite similar memberships,attendancerecords,and volunteer
differ- activitiesin supportof the regime.Anti-governpatterns,thereare importantinstitutional
ences betweenWesternand Soviet labor markets ment activities(strikes,attendingunsanctioned
(Schroeder, 1979; Feshbach, 1983; and Nash, meetings,protests)send out signalsof regimedis1966). The state bears virtuallyall the explicit loyalty.
In the Soviet system,privileges(such as access
(and many implicit) costs of highereducation.
Graduates of highereducation are assigned ad- to closed shops or clinicsor use of an officialcar)
to firstjobs. Administrative
posi- are allocated both by managersand by political
ministratively
tions are filledfroma partyor statenominations authorities.Rational managers(like theirWestlist. Soviet labor unions bear no resemblanceto ern counterpartsdescribed by Hashimoto and
unions.An internalpassportsystem Raisian, 1985) would preferentially
Western-style
allocate priviallocationof housinglimit leges to employeeswho acquire firm-specific
huand the administrative
the mobilityof workers.
man capital. Sovietmanagerialallocationof priviIf theSovietlabor marketbehavesgenerallylike leges, therefore,
shouldnot be expectedto depart
its Westerncounterpart,then standardWestern fromWesternpatterns.Rational Soviet political
of authorities,however,mightallocate privilegesinmodels (modifiedfor institutional
differences)
to the
human capital, compensatingwage differentials,dependentlyof theindividual'scontribution
and incentive-wagecontracting(Stiglitz, 1975; firm.Katsenelinboigen(1980) has hypothesized
Spence, 1973; Becker,1962; Mincer,1974) can be that Soviet officialsallocate privilegespreferapplied to Soviet wages. The standardmodel de- entiallyto high-levelpersonnelto increasepolitipicts the equilibriumwage as a functionof gener- cal control of the responsiblepositions in the
human capital (including economy.The higherthe position,the largerthe
alized and firm-specific
such as loyaltyto the percentageshare of privilegesin total compensaqualitative characteristics
and demographicattri- tion. Hence, privilegesmightbe allocated among
firm),job characteristics,
butes. Two additional factorsmust be added to Soviet workers in a manner not explained by
We
and job characteristics.
rewards workercharacteristics
adapt the model to Sovietcircumstances:
alloca- find empirical support for Katsenelinboigen's
forpoliticalloyaltyand the administrative
propositions.2
tion of privilegedin-kindbenefits.
The effectsof theconventionalfactorson wages
human 1 Politicalauthoritiesmayrewardregimeloyalistsby offering
are well known.General and firm-specific
worker
and
hence
raise
wages. easier access to higherpaying occupationsand encouraging
productivity
capital
raise compensa- higherpay withina given occupation.Appointmentsto reUndesirable job characteristics
positions are controlledby political authorities
affectjob pref- sponsible
tion. Demographiccharacteristics
throughthe nomenklatura.A record of political reliability
erences.The hypothesisconcerningpoliticalloyal- increases the chances of being admittedto highereducation.
the earnings of Soviet Personnelcommitteesmay selectworkerswithbetterpolitical
ty is straightforward:
Politicallydisloyalworkersmay be denied overtime
workersare positivelyaffected
by politicalloyalty records.
workor bonuses or maybe placed on higherpiece-ratenorms.
and are negativelyaffected
by politicaldisloyalty. 2 We ran separatelogitregressionson car, clinic,and closed
desireto max- shop privilegesand foundthat the receiptof theseprivileges
Rational Sovietpoliticalauthorities
THE EARNINGS OF SOVIET WORKERS
B. Data fromtheSovietInterview
Project
25
urban population,is not a representative
sample
of the referentpopulation.The greatestrisk of
bias would be a pureJewishdistortionthatcauses
SIP behavioralcoefficients
to differ
fromthereferent population. The SIP relationshipbetweenalcohol consumptionand income,forexample,could
not be generalized.We have no a prioriway of
identifying
such biases thatwould affectearnings.
Soviet Jews findit difficult
to enterthe highestlevel positions,and theymayhave to workharder
for advancement.The potentialbiases have been
addressed in considerable depth by Ofer and
Vinokur(1984), Andersonand Silver(1987b), and
Swafford(1987). It is our opinionthatsuchbiases
of thisstudy.
do not dictatethefindings
The interviewswere conductedbetweenApril
and December of 1983, and respondentswere
asked to speak about theirlives in the Soviet
Union priorto thebreakcaused by theemigration
decision.3For mostrespondents,
thiswas 1978 or
1979-the end of theirlast "normal" period of
life in the Soviet Union. Analysisof associated
case interviews
(Andersonand Silver,1987a) shows
thatdespitethe retrospective
natureof the survey
respondents'recall of objectiveeconomicdata is
quite accurate.
Over 90% of the SIP respondentswereJewish,
and theycame frommediumand large cities in
the European partsof the SovietUnion. The SIP
sample was gatheredunder favorableconditions
of theEarningsModel
for elicitinginformationon the referentUSSR C. Specification
population,the SovietEuropeanurbanpopulation
A reducedformearningsequationrelatesearnresidingin mediumto large cities.First,the SIP ings to conventionalhumancapitalfactorsas well
sample was stratifiedfrom over 33,000 cases as to measuresofjob characteristics,
privilegeand
according to geographic,educational, and na- political loyalty.The sample is restrictedto retionalitycharacteristics
of thereferent
population. spondentswho reportearningsfrom1978 or 1979.
Second, there is strikingsimilarity(see table 1)
LOG EARNINGS = ao + b1EXP + b2EXP2
betweenthe SIP samplemeansand referent
popu+?b3iEDi + b4MOVE + 2c1jREGj
lation means of standardeconomic and demo+ EC3mOCCm + d1AGE
+?c2kINDk
graphicvariablesnot used in stratifying
the sam+d2MARRIED + d3ADULTS + d4KIDS
ple. Althoughsimilarin otherrespects,the SIP
+d5MALE + e1CAR + e2CLINIC
sample is more highlyeducated and more con+ e3SHOP + e4SQMETERS + f1GOOD
centratedin serviceoccupationsthan the referent
+ f2GOOD: LEADERS + f3BAD
population. Third, most respondentswere not
politically active and expressedboth favorable
+ f4BAD: LEADERS
and unfavorableattitudestowardsthe Soviet sys+f5GOODL X BADL + gHOURS + u
tem (Millar and Clayton, 1987). A significant
(1)
number participatedregularlyin regime-support A
semi-logequationis estimated,the preferred
activities,and some evenoccupiedleadershipposi- functionalform for earningsequations (Rosen,
tions. A very small proportionwere leaders of
1977). The dependentvariableLOG EARNINGS
activities(table 2). Fourth,the is the natural log of nominalmonthlyearnings.
anti-government
proportionof SIP respondents
withprivileges(5% The explanatoryvariablesare groupedaccording
to 7%) appears to be reasonablegiventhe higher
to generalized human capital measures (the b
level of educationof theSIP sample.
coefficients),
job characteristics
(the c coefficients),
A self-selectedsample of immigrants,repredemographiccharacteristics
(the d coefficients),
of the Soviet
sentingprimarilyan ethnicminority
firm-specific
human capital (the e coefficients),
and
Since the
political
loyalty
(the f coefficients).
depends upon earningsand education(the latterin two of the
earnings,a variable
threecases). Privilegestend to be allocated accordingto the dependentvariableis monthly
of the positionas proxiedby earningsand edu- for hoursworked(HOURS) mustbe includedto
responsibility
cation.
Projectsee Millar adjust for part-timework. The u denotes the
3 For an overviewof the SovietInterview
and de(1987). For a discussionof the SIP samplingframeand proce- stochasticerrorterm.Variabledefinitions
dures, see Anderson,Silverand Lewis (1986), Andersonand scriptivestatisticsare givenin the appendix.
(1987). The data are availSilver(1987a, 1987b) and Swafford
Generalizedhumancapitalis measuredby years
Consortiumfor Political and
able from the Interuniversity
of
workexperience(EXP and EXP2), nine cateSocial Research,Universityof Michigan.
26
THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 1.-COMPARISONS OF THE USSR POPULATIONAND SOVIET INTERVIEWPROJECTSAMPLE:
EMPLOYMENT,HOUSING AND FAMILY SIZE
Variable
1. EMPLOYMENT
a. EmploymentStatusa(inpercent)
Employed
Going to school
leave
Retired,disabled,maternity
Keepinghouse
Other
b. Branchof Employment
(in percent)
Industry
forestry
Agriculture,
communication
Transportation,
Construction
Trade,cateringsupplies
Communalhousingand services
Health
Education
Artsand culture
Science
apparatus
Creditand Government
USSR Urban
Population
1978
38.3
0.1
12.3
11.8
10.5
4.6
5.6
8.0
1.7
3.8
3.3
100.00
12.9
24.0
3.2
SIP
1978-79
69.5
2.5
16.5
9.1
2.4
100.00
71.0
3.0
16.0
10.0
100.00
c. AverageMonthlyEarnings(in rubles)
160
All workers(excludingagriculture)
177
Industry
190
Transportation
139
Communication
191
Construction
124
Trade and catering
123
Communalhousing& services
116
Health
132
Education
108
Artsand culture
170
Science
147
Creditand government
apparatus
40.6
d. Hours workedperweek,industry
e. Time spentshoppingperworkday(in minutes)41.0
2. HOUSING
a. Space per capita (squaremeters)
b. Privateownership(percent)
3. AVERAGE FAMILY SIZE (persons)
Moscow
1978
27.0
0.0
9.6
7.1
9.2
4.7
5.2
5.4
1.5
19.2
5.3
100.00
30.5
0.2
6.3
8.9
14.1
5.5
7.0
10.3
3.8
9.9
3.5
100.00
166
174
188
158
183
126
121
129
149
144
170
172
159
167
187
129
191
135
140
132
141
162
185
165
40.0
60.0
13.5
37.0
3.2
a
USSR Employment status is for1979.
Sources: Items la, ld, le, 2a, 2b, 3 from Narodnoe KhoziaistvoSSR 1979, p. 375, p. 397; VestnikStatistiki,No. 6,
1981, p. 79.
Item lb: There is no urban population breakdown after the 1970 census. Therefore,we adjust the 1978 branch
employmentdistribution(Narodnoe KhoziaistvoSSR 1978, p. 366) by the urban to total USSR employmentratios in the
1970 census (}toqi vsesoiuznoiperepis' naseleniia 1970 goda, Vol. V, pp. 192-197). The Moscow distributionis from
Moskva v tsifrakh1979, p. 87.
Item lc: Narodnoe KhoziaistvoSSR 1978, p. 373. Moskva v tszfrakh1979, p. 93.
gories of completededucation(ED), and by job
are measured
moves (MOVE). Jobcharacteristics
by 16 regions(REG), 20 industries(IND), and 44
Demographic
occupational categories (oCC).4
4 It is importantto note thatthe occupationalbreakdownis
quite detailed (34 groups for high-levelworkersand 10 for
the44 occupationgroupsare
blue-collarworkers).For brevity,
paper(Gregory
not reportedherebut aredescribedin a working
and Kohlhase, 1986).
are givenby (AGE), maritalstatus
characteristics
(MARRIED), presence of other adult family
members(ADULTS) or children(KIDS) in the
familyand gender(MALE).
Political loyaltyis measuredby the memberships and activitiesreportedby SIP respondents
(such as membershipsin trade unions, workers
committees, komsomol), how frequentlythey
attended,and whethertheyplayedleadershiproles.
THE EARNINGS OF SOVIET WORKERS
27
TABLE 2.-POLITICAL ACTIVITY OF THE SIP SAMPLE
SupportActivities
in at
Participatedregularly
activity
least one regime-support
trade
such as partycommittees,
union,workerscommitteeor was a
komsomol(communistyouthleague)
activist;excludingleaders
Was a leaderin above activities
DisloyaltyActivities
Participatedin at least one
activitysuchas
antigovemment
art shows,studygroups,
unofficial
of
protests,strikes,distribution
illegalpublications;excluding
leaders
Was a leader in above activities
Variable
Name
High-Level
(percent)
Blue-Collar
(percent)
GOOD
29.7
25.4
GOOD: LEADER
29.7
17.9
BAD
29.0
15.9
BAD: LEADER
2.7
1.7
Soviet citizensroutinelybelong to certainorgani- disloyal by engagingin regimesupportactivities.
onlythose The coefficientson the education dummies are
zations(like thetradeunion); therefore,
who reported regular attendalnceor leadership expectedto be larger(positive)forhigherlevelsof
roles were placed in the four political groups education. The rewardsto experience(EXP and
describedbelow (table 2). Respondentswho self- EXP2) shouldincreaseat a decreasingrateso that
identifiedas being leaders,organizers,or officers a positivesignis expectedon EXP and a negative
were classified as GOOD: LEADERS. Regular sign on EXP2. The male gender(MALE) coeffiparticipants(but non-leaders)were classifiedas cient is likelyto be positivebased upon previous
GOOD. Respondentswho engagedin acts ofpolit- studiesof sex differentials.
in the same way
The signson the fourprivilegevariables(CAR,
ical disloyaltyweredifferentiated
An
term
interaction
as BAD: LEADERS or BAD.
CLINIC, SHOP, SQMETERS) are indeterfor
While privilegesserveas a proxyfordewas
included
to
test
minate.
x
BADL)
(GOODL
theyare also a
tradeoffsbetweenpoliticalloyaltyand disloyalty sirable employee characteristics,
fringebenefitthat could be tradedoffforlower
at the leadershiplevel.
We do not have direct data on firm-specificmoney wages (Dye and Antle, 1984; Leibowitz,
on 1983; Ehrenberg,1980). In the Sovietcase where
human capital,but SIP did gatherinformation
privilegestypicallycannot be purchasedlegally,
privileges-access to a closed shop (SHOP),
closed health clinic (CLINIC), use of an official themarginalvalue of privilegemaybe higherthan
in the West. The net effectdependsupon whether
car (CAR) and housing space (SQMETERS).
Such privilegesare included because they may the positiveeffectof desirableworkercharacterishuman tics dominatesthenegativeeffectof theprivilegeproxy (as argued above) for firm-specific
capital and forthe perceivedpoliticalimportance wage tradeoff.
Three furtherpointsmustbe made concerning
of the respondent.
are expectedon thepolitical the model. First,we includeregionaldummyvariPositivecoefficients
in local
loyaltyvariableswithgreaterrewardsforpolitical ables (REG) to controlfor differences
activism(GOOD: LEADER) thanforregularpar- amenities,climate,and livingcosts.5 Second, an
ticipation(GOOD). We expectnegativecoefficients
forpoliticaldisloyalty,withstronger
penaltiesfor 5 We experimentedwithdirectmeasuresof amenitiesforthe
than participators 18 cities from which a majorityof the respondentscame.
leaders (BAD: LEADER)
Measures such as per capita medical facilities,libraries,and
term(GOODL
(BAD). The signon theinteraction
factorsbut failedto yield
theaterswereenteredas explanatory
x BADL) is an empiricalissue. A negativesign significantcoefficients.
withmeasuresof
We also experimented
size coefficients.
shows that individualscan compensateforbeing citysize but failedto findsignificant
28
THE REVIEW OF ECONOMICS AND STATISTICS
instrumentalvariablesprocedureis used to esti- thisproposition,butnotwithoutsurprises.
Activist
mate hours of work(HOURS) as hoursdepends regimeloyalty(beingin the GOOD: LEADER catupon earningsper unitof time.6Third,theprivi- egory) is rewardedby higherearnings.With oclege coefficientsare subject to a simultaneous cupation held constant,a high-levelemployeein
equation bias which the compensationliterature the GOOD: LEADER categoryearns 6.1% more
recognizesbut does not solve (Ehrenberg,1980, than an employee with no regularpolitical inpp. 480-483; Brown,1980). As a componentof volvement.The returnrisesto 8.8% whenoccupathe fullwage, fringesbelongon theleft-handside; tion is not held constant.Thus, 2.7 percentage
as proxies for workercharacteristics,
theybelong points of the 8.8% monetaryrewardfor activist
on the right-handside. The data do not allow us loyalty comes in the form of placement in a
to solve this potentialbias, but the othercoeffi- higher-paying
occupation,whiletheremaining6.1
cientsare unaffected
by whethertheprivilegevari- percentagepoints representhigherpay withina
ables are included.
givenoccupation.The monetary
rewardforregime
loyaltyappears to come primarilyin the formof
III. RegressionResults
higherpay withina givenoccupationratherthan
as an admission ticketto higher-paying
occupaRegressionresultsfor 1,349 high-level(whitetions. Blue-collarworkersearn a similarreward
collar, technical,scientific,and managerial)emfor activist loyalty(about 9%), but the loyalty
ployees and for591 blue-collarworkersare given
rewardis exclusivelyin the formof a higherpay
in table 3. Separate models are estimatedfor
within a given occupation. Less-active regime
blue-collar and high-levelmanpowerbecause of
loyalty(being simplyin the GOOD category)does
the institutionaldifferencesin Soviet compennot yield higherearnings.To earn a rewardfor
sation practices for each group (described in
politicalloyalty,one mustbe an activist.
Chapman, 1979). Significancetestsforcoefficient
Regime disloyalty appears to be severely
heterogeneityshow that the coefficient
sets are
punished in the case of blue-collarworkers.A
indeed statistically
different.
blue-collarBAD: LEADER mayearnfrom34% to
fromregressionswith
Analysis of coefficients
36% less than a fellow workerwith otherwise
and without occupation provides information
identical characteristics.8 If blue-collar workers
about the channels throughwhich earningsare
sufferan earningsloss of thismagnitude,activist
affected.Accordingly,
table 3 reportstwocolumns
disloyaltyis the most importantdeterminantof
for each sample: the firstcolumn
of coefficients
blue-collar earnings. The activist disloyaltyof
includesdetailedoccupational(OCC) dummiesto
high-levelemployees is not punished by lower
hold therespondent'soccupationconstantand the
is negaearnings(the BAD:LEADER coefficient
second column omitsthe OCC dummies.Coeffitive but not significant).The SIP sample does
cients on the dummyvariables are interpreted
provide ample evidencethat activistanti-governrelativeto theomittedcontrolcategoriesidentified
mentactionsby high-level
employeesare punished
in the table notes.7
(Bahry and Silver,1987). Our resultsuggestsonly
that Soviet authoritiesuse punishmentotherthan
A. Loyaltyand Disloyalty
monetarysanctions.As in the case of nonactivist
The fivepolitical behaviorcoefficients
test the loyalty,nonactivistdisloyaltydoes not appear to
propositionthat Soviet authoritiesrewardloyalty carry any monetarysanctions.The BAD coeffiThe insignifiand penalize disloyalty.The regressionssupport cients are uniformlyinsignificant.
cant GOODL X BADL interactioncoefficients
6 The instruments
cannotcompensatefor
are the exogenousvariablesin the wage suggestthatSovietworkers
equation plus dummiesforsecond and privatejobs, spouse's disloyaltyby acts of regimesupport.
earningsand spouse's earningssquared, numberof children
A possible explanationfor the difference
beand the interactionof childrenand spouse's earnings.
tween
coefficients
is
that
blue-collar
the
disloyalty
7 The coefficient
on continuousvariables shows the percentagechangein earningsbroughtabout by a one unitchange "bad" activitiestend to be more factory-protest
in the explanatoryvariable.For dummyvariables,the coeffiin earnings
cient shows the approximatepercentagedifference
8 Note that the negativecoefficients
on activistregimedisbetween the dummy group and the omittedcontrol group
at the 10% level.
loyaltyare significant
(Halvorsen and Palmquist,1980).
THE EARNINGS OF SOVIET WORKERS
29
TABLE3.-DETERMINANTS
OF SOVIETEARNINGS,
INSTRUMENTAL
VARIABLES
ESTIMATESa
High Level Earnings
OccupationIncluded
Variable
Human Capital
EDO
ED1
ED2
ED3
ED4
ED6
ED 7
ED8
EXP
EXP2
Blue Collar Earnings
OccupationOmitted
0.0084
-0.0297
-0.0190
-0.1750
0.0064
0.0809
0.1279b
0.0258
-0.0004b
(0.04)
(-0.37)
(-0.15)
(-0.91)
(0.17)
(1.12)
(3.17)
(5.39)
-0.0956
-0.0985
-0.1116
-0.2524
0.0113
0.0884
(0.16)
0.0145
0.2238b
0.0312b
(-0.51)
(-1.19)
(-0.92)
(-1.24)
(0.28)
(1.27)
(5.58)
(6.61)
- 0.0005b
(-4.47)
PoliticalLoyalty
GOOD
0.0035
GOOD: LEADER 0.0607b
BAD
0.0087
BAD:LEADER
-0.0547
GOODL X BADL 0.0617
(0.16)
(2.21)
(0.39)
(-0.73)
(0.56)
0.0073
0.0878b
0.0137
-0.0781
0.0527
(0.31)
(3.07)
(0.60)
(-0.98)
(0.45)
Privilege
CAR
CLIN
SHOP
SQMETERS
0.0788b
0.0445
-0.009
0.0002
(2.31)
(0.84)
(-0.02)
(0.68)
0.0796b
0.0320
0.0443
0.0002
-0.0005
0.0126
0.0022
(-0.18)
(0.48)
(0.21)
0.0009
0.0172
0.0013
Demographic
A GE
MARRIED
ADULTS
KIDS
MALE
0.0056
-0.0268b
0.1809b
JobCharacteristics
HOURS
-0.0025
IND1
-0.0415
IND2
-0.1147
IND4
-0.0126
INDS
-0.0717
IND6
IND7
IND8
IND9
IND1O
IND11
0.1810
0.0889
0.0502
-0.0149
-0.0939
-0.0271
IND13
-0.1414
IND12
(-1.96)
(4.49)
-0.0195
0.2236b
(-0.17)
(-0.45)
(-1.13)
(-0.17)
0.0019
-0.0854
-0.0535
-0.0122
(-1.37)
-0.0500
(0.40)
-0.0468
0.0845c
-0.0037
-0.3587a
0.1707
(-0.99)
(1.63)
(-0.06)
(-1.73)
(0.53)
(2.04)
(0.68)
(0.89)
(0.98)
-0.0997
0.0087
0.0910
-0.0002
(-0.92)
(0.05)
(0.78)
(-0.57)
-0.1256
0.0385
0.1597
- 0.0002
(-1.13)
(0.22)
(1.40)
(-0.60)
(0.29)
(0.61)
(0.12)
(-1.36)
-0.0016
0.0582
-0.0015
0.0057
(-0.29)
(1.10)
(-0.09)
(0.22)
0.0001
0.0618
0.0050
0.0166
(0.02)
(1.16)
(0.30)
(0.62)
-0.0030
-0.3993
0.1053
0.1387
(-0.21)
(-1.29)
(0.46)
(0.91)
-0.0009
-0.3526
0.1458
0.0732
(-0.06)
(-1.18)
(0.64)
(0.48)
(-1.53)
(-0.84)
(0.70)
(0.11)
(-1.51)
(1.22)
(5.95)
(0.18)
(-0.86)
(-0.51)
(-0.16)
(-0.93)
-0.1244 a
(3.51)
(-1.65)
0.2870b
-0.1167
0.2034
0.1666b
0.1830
0.0035
-0.0298
0.0103
(1.62)
(2.29)
(1.02)
(0.05)
(-0.22)
(0.24)
-0.1729
-0.1010
0.2417
-0.0458
-0.2244
0.1257
(-1.12)
(-0.86)
(0.97)
(-0.46)
(-1.18)
(1.50)
-0.2352
-0.1018
0.1805
0.0095
-0.2484
0.1036
(-1.32)
-0.1209
(-1.06)
-0.2229
(-0.67)
-0.2500
-0.0847
(-1.04)
IND16
IND17
-0.1496b
-0.2005b
(-2.15)
(-3.44)
-0.1888b
-0.1951b
(-4.05)
(-2.04)
IND19
-0.0836b
-0.0004
(-0.01)
-0.0304
0.1867b
(1.50)
(1.32)
(0.29)
(-0.23)
(-0.76)
(-0.66)
(-1.04)
(-2.65)
(-2.05)
(-0.57)
-0.1413b
-0.0786
-0.0696
-0.0284
(-1.90)
(-0.88)
(1.73)
(0.22)
(-1.63)
(0.40)
(-1.76)
IND20
0.0218b
-0.0003a
-0.0404
0.0885a
0.0143
-0.3394c
0.1312
-0.1294a
-0.2089b
(0.14)
(-1.96)
(-1.54)
(-0.59)
(-0.60)
(0.82)
(-0.21)
(0.83)
(2.25)
(-0.85)
(-1.45)
IND18
(-1.21)
0.0377
-0.1959b
-0.0905
-0.0548
-0.0867
0.0415
-0.0210
0.0646
-0.0650
-0.1066
-0.0698
-0.0002
(-0.15)
(-2.29)
(-1.60)
(-0.75)
(-0.83)
(1.10)
(-0.09)
(0.84)
(1.74)
(-0.58)
(-1.96)
IND15
-0.0401
-0.2251a
-0.0937
-0.0705
-0.1194
0.0564
-0.0094
0.0637
0.0169a
-0.0457
-0.1079b
IND14
OccupationOmitted
coefficient (t-statistic) coefficient (t-statistic) coefficient (t-statistic) coefficient (t-statistic)
(-3.14)
MOVE
OccupationIncluded
(-2.80)
(-1.14)
(-0.69)
(-0.52)
-0.2586b
-0.5312b
-0.3851b
-0.3466b
-0.1732
-0.2000
-0.1660
(-3.26)
(-2.37)
(-2.62)
(-1.72)
-0.3577b
(-1.54)
(-5.32)
(-0.79)
-0.5082b
-0.1821b
-0.4471b
-0.3939b
(-3.32)
(-2.03)
-0.2230
(-1.43)
(-0.90)
-0.2047
(-1.16)
-0.2353a
(-1.30)
(6.10)
(-2.45)
(-2.60)
(-1.19)
(-1.86)
THE REVIEW OF ECONOMICS AND STATISTICS
30
TABLE 3. -(CONTINUED)
Blue Collar Earnings
High Level Earnings
OccupationIncluded
Variable
Region
REG2
REG3
REG4
REGS
REG6
REG7
REG8
REG9
REG10
REG11
REG12
REG13
REG14
REG15
REG16
Constant
OccupationIncluded
OccupationOmitted
coefficient (t-statistic) coefficient (t-statistic) coefficient (t-statistic) coefficient (t-statistic)
-0.0862b
0.0438
0.0775
-0.0943b
-0.2052b
-0.1340b
-0.2014b
-0.0580
0.3063a
-0.1112b
-0.0105
-0.1532b
0.1434
0.0008
-0.1544
4.76737b
0.51
0.47
R2
Mean Square Error 0.0983
1349
Sample Size
,K_
OccupationOmitted
(-3.33)
(0.65)
(0.65)
(-2.63)
(-3.83)
(-2.78)
(-4.15)
(-1.20)
(1.86)
(-2.28)
(-0.21)
(-2.70)
(0.60)
(0.02)
(-0.80)
(8.87)
-0.1176b
0.0185
-0.0160
-0.1085b
- 0.2475b
-0.1581b
-0.2381b
-0.0671
0.2639
-0.1264b
0.0067
-0.1487b
0.1439
-0.0246
-0.2525
4.3928b
0.41
0.39
0.1143
1349
(-4.29)
(0.30)
(-0.13)
(-3.03)
(-4.37)
(- 3.09)
(-4.67)
(-1.38)
(1.52)
(-2.45)
(0.13)
(-2.51)
(0.67)
(-0.53)
(-1.26)
(11.46)
0.0109
-0.0406
0.3513
0.0444
0.0358
-0.0846
0.0684
0.0205
-0.2724
-0.0015
0.0335
0.1166
0.3380
0.0465
-0.1133
4.7314b
0.36
0.27
0.1710
591
(0.14)
(-0.36)
(1.37)
(0.62)
(0.33)
(-0.88)
(0.73)
(0.20)
(-1.07)
(-0.02)
(0.40)
(0.98)
(1.02)
(0.54)
(-0.65)
(8.34)
0.0248
-0.0561
0.3134
0.0773
0.0777
-0.0700
0.0566
0.0469
-0.3220
0.0365
0.0368
0.1238
0.1785
0.0412
-0.1247
4.5914b
0.33
0.25
0.1760
591
(0.32)
(-0.48)
(1.21)
(1.07)
(0.73)
(-0.72)
(0.60)
(0.45)
(-1.26)
(0.40)
(0.44)
(1.03)
(0.53)
(0.46)
(-0.70)
(8.03)
Note: A two-stage estimationprocedure is used where an instrumentforhours worked is constructedin the firststage. The dependent variable in the second
stage is the natural log of monthlyearnings; omittedcategoriesare REG1, IND3, ED 5, no privilege,no political behavior, serviceworkers(blue collar) or entry
level engineers (high level). For brevitycoefficientson occupation are not reported; complete tables are available upon request fromthe authors.
a
Significantat 10% level, 2-tailed test.
b
Significantat 5% level, 2-tailed test.
c Significantat 10% level, 1-tailed test.
raises
education
higher
onlycompleted
which manpower
"bad" activities,
orientedthanwhile-collar
4-6
having
sample,
In
the
blue-collar
earnings.
(Bahry,
in nature
tend to be moreintellectual
a
by
lowers
less
earnings
or
of
schooling
years
reIf
the
regime
1987).
Silver,
and
Bahry
1987;
7-8
while
only
to
22%,
having
20%
substantial
to
threats
serious
more
as
protests
factory
gards
by 9%. Beyondthispoint,
it wouldimposeheaviersanc- yearslowersearnings
regimestability,
vocationaltraining,
additionalgeneraleducation,
tions.
educationdoes not
specialized
or evensecondary
earnings.
impact
significantly
B. Returnsto Education
on theearnHighereducationhas twoeffects
vocationaleducationand ings of high-level
Becauseof extensive
Sovietworkers.First,higher
in theSovietUnion,it educationenablespersonsto enterhigherpaying
schooling
correspondence
attain- occupations.Second,oncein a givenoccupation,
makeslittlesenseto measureeducational
variable(suchas yearsof personswithhighereducationearn morethan
mentas a continuous
is better othersin thesameoccupation
attainment
Rather,education
schooling).
withlesseducation.
of school- The 22.4% total educationeffecton high-level
categories
brokendownintodifferent
areusedas thecontrol earningsis composedof 9.6%(22.4-12.8)due to
ing.Highschoolgraduates
show that the education'simpacton occupationalchoiceand
group and alternateregressions
theout- 12.8%dueto theeffect
choiceof controlgroupdoes not affect
within
a given
ofeducation
come.
occupation.
education
higher
tocompleted
The 22.4%return
Table 3 revealsthathavingeitherverylow or
for
graduation) high-level
affectsSoviet (relativeto high-school
veryhigh educationalattainment
foundby Ofer and
to
that
ofeducation workersis close
increments
whilesuccessive
earnings,
fortheearly1970s.
142-143)
pp.
(1984,
Vinokur
earnings
rangesdo notaffect
in theintermediate
their
leastpartially,
at
confirm,
also
results
These
For high-level
relativeto highschoolgraduates.
THE EARNINGS OF SOVIET WORKERS
31
findingthat returnsto education increase with D. Female Earnings
higherlevels of schooling.However,we findno
Holding occupation constant,women working
statisticallysignificantearnings differencesbe- in high-leveland
blue-collarjobs earnedbetween
tweeneightyearsand completeduniversity
educa- 18% and 19% less than
men workingin the same
tion, while Ofer and Vinokurfinda monotonic industryand having equivalent
education,trainprogression.The Sovietpatternof risingreturnsis ing, and personal
characteristics.
For blue-collar
contraryto the Westernpatternof fallingratesof workers,the female earningsgap
rises to 29%
returnfor successivelyhigherlevels of schooling when occupationis not
held constant.This means
(Mincer,1974, p. 48; Becker,1975,p. 206).
that occupational segregationexplains about 10
The Soviet patternof highincrementalreturns
percentagepoints of the blue-collarfemaleearnto the most-highly
educatedmembersof societyis ings gap. For high-levelworkers,the femaleearnnot entirelyexpected.Because thestatebearsmost ings gap rises from18%
to 22% whenoccupation
of the costs of highereducation,the demand-side is not held constant.Only
4 percentagepointsof
effectshould drivedown returnsto the university the female
earningsgap is accountedforby oceducated. However,thereare a limitednumberof
cupational segregationin the case of high-level
entry positions in sought-after
universitiesand workers.
institutesand preferential
university
admissionsis
Our figure for the unexplained differential
one way the elite passes on its statusto the next
(about one-fifth)is close to Ofer and Vinokur
generation(Mathews, 1978; Voslesensky,1984).
(1981, p. 144), who findan unexplaineddifferenWhile speculative,our results suggest that the
tial of 21% to 25%. For high-levelSovietworkers,
supplyeffectdominatesthedemandeffect.
occupational segregationplays only a minorrole
in accounting for lower female earnings. For
C. Experience
blue-collar Soviet workers,the effectsof lower
Table 3 reveals that both high-leveland blue- earningswithina givenoccupationis twicethatof
and nonlinearly occupational segregation.Althoughthereis concollar earningsare significantly
related to years of experiencein the workforce. troversyabout the relativeimportanceof occupaThe initialyearof workexperienceraisesearnings tional segregationin explainingAmericanearning
by sex (summarizedin the accompabelow thatfoundby differentials
by 2% to 3%, a figureslightly
nying
note),9
we concludethatoccupationalsegreFor
emVinokur
Ofer and
(1981).
high-level
gation
a less importantrole in the Soviet
plays
of
at
33
ployees,earningspeak
years experience.
Blue-collarearningspeak somewhatlater,some- labor market.
wherebetween33 and 40 years,dependingupon
whetheroccupationis held constant.Thus white- E. Privilege
collar and blue-collarearningspeak at the same
There is no significantrelationshipbetween
age because of thelaterstartin thelabor forceof earnings and privilegefor blue-collar workers.
high-levelworkers.
Privilegesdo not have a significant
effecton highThe Sovietreturnto experience(2% to 3% in the level earnings(althoughmost of the coefficients
initialyear)appears to be lowerthanin theUnited are positive) except forthe use of an officialcar
States,whereestimatesare typicallyin the 5% to (CAR), whichcarries8% higherearnings.Positive
have also been foundfor
8% range (Mincer, 1974; Duncan and Hoffman, fringebenefitcoefficients
1979; Lang and Ruud, 1986). Althoughby no
means conclusive,our resultssuggestthatearnings 9
Oaxaca (1973) findsthatequalizingoccupationaldistribuare less sensitiveto experiencethanin theAmeri- tionswould reducethefemaleearningsgap by about 9%, about
can labor market.One possibleexplanationis that one-halfof the unexplainedgap. Malkiel and Malkiel (1973)
Americanfirms,in theirimplicitcontracting,
must findthat occupationalsegregationexplainsmost of the earnings differential
in the large corporationstudied. Although
rewardexperiencemoreto retainfirm-specific
hu- there are ongoing controversiesabout the relativeeffectsof
man capital. Soviet managersmay be aided in occupational segregationand lowerpay withinthe same job,
retainingexperiencedworkersby the lesser geo- we interprettheliteratureas arguingthatoccupationalsegregation is more importantin explainingthe Americanfemale
graphicmobilityof workersand by housingcon- earningsgap. For a surveyof thisliterature,
see Ehrenbergand
straints.
Smith(1982), pp. 396-400.
32
THE REVIEW OF ECONOMICS AND STATISTICS
the United States (Leibowitz,1983) so thisis not create artificial labor shortages in Moscow.
an unusual result.The privilegeresultssupportthe Moscow residentsmaybe better"connected"than
interpretation
that privilegesare used to reward residentsof otherregions.Marginalsocial producdesiredworkercharacteristics
(includingthe level tivitymay be higherat the centerin a centrallyof the position) because a positive coefficient planned economy.
means that such rewardsdominatethe substitution of privilegeforlowerwages. This conclusion
IV. Conclusions
must remainspeculativebecause of the potential
We use micro data gatheredfromthe Soviet
simultaneousequationbiases discussedabove.
InterviewProject to investigatethe determinants
of Sovietearnings.The uniqueSIP data set,unlike
F. Regionaland Industry
Effects
officialSoviet statistics,allows an in-depthstudy
Space limitationspreventa full discussionof
of the Soviet incentivestructure.Our analysis
regionaland brancheffects.Table 3 revealsthat
thatSovietlabormarketsoperatein many
confirms
the branchesin whichearningsare low are those
respects like U.S. labor markets.Yet immense
(trade,healthand physicalculture,and education)
betweenthe two nations
institutionaldifferences
that are typically identifiedas low priority
returnsto certainworkercharcause differential
branches.This findingsuggeststhatSovietauthoracteristics.The most strikinginstitutional
impact
itiesare indeed successfulin enforcing
theirbranch
is the Soviet practiceof rewardingand penalizing
prioritieson thewage system.
politicalbehaviorthatis externalto thefirm.
For the regionalcoefficients,
Moscow, whichis
The Soviet wage systemrewardsactivistregime
assumed to be the most desiredlocation in the
support.Workerswho are leaders of regime-supSoviet Union, is used as thecontrolregion.Nonport activitiesearn from7% to 9% more,ceteris
Moscow regions are expected to have positive
paribus. High-levelemployeesare rewardedfor
compensatingreal wage differentials
relativeto
activistregimeloyaltyprincipallyby higherpay
Moscow. The resultsdo not supportthe expectawithina given occupation,but theyare also retion. For blue-collarworkers,the regionalcoeffiwarded by admission to higher-paying
occupacients are uniformlyinsignificant.
For high-level
in
tions.Blue-collarworkersare rewardedentirely
workers,there are a numberof negativecoeffithe formof higherpay withina givenoccupation.
cients for non-Moscow regions. Because REG
Activistregimedisloyaltyof high-levelmanpower
in additionto amenities,
proxiesforseveraleffects
is surprisingly
notpunishedbylowermoneywages.
the coefficients
representthe combinedimpactof For blue-collar
workers,however,activist dismanyfactors.Thereare fourpossibleexplanations loyalty appears to resultin
significant
losses of
forfindingrelativelyhigherwages in Moscow for
earnings. Only political activismmatters.Nonhigh-levelworkers. Higher wages may be exleadershiprecordsof loyaltyor disloyaltydo not
plained by cost-of-living
differences
because we
affectearnings.
are using nominal earnings.10
Entrypermitsmay
As in the United States,Soviet labor markets
rewardeducationand experience.Yet thedifferent
10
settingforattaininghighereducation
Soviet pricingauthoritiesset zonal retailprices for food institutional
by in the Soviet Union translatesinto a differential
differentiated
products;nonfoodproductsare not officially
geographiczones. Food pricesin statestoresshouldbe higher patternof rewards.The returnto educationis low
by 8% in Moscow, Leningradand the Baltics than in the
for Soviet workersexceptat the upper and lower
Ukraine accordingto officialregulations(Kokorev,1978, pp.
184-190). It is also likelythatcollectivefarmfood pricesare bounds, contraryto the U.S. patternof declining
higherin Moscow than elsewhere,althoughthereis no firm rates of returnto higherlevels of education.For
data to support this point. We are gratefulto Gertrude
Schroeder-Greensladefor bringinginformationon regional high-levelmanpower,onlycompletedhighereduprice variationsto our attention.
cation yields a positiverate of return.For blueforMoscow, collar workers,only those with limitedgeneral
differential
We conclude that the cost-of-living
if it exists,is likely to be small. We estimatea 10% upper
bound on the highercost of livingin Moscow relativeto other educationearn less. Returnsto experienceappear
providedby lower thanin the United States.
information
WesternUSSR regions.Furthermore,
SIP respondentson cost of living(perceivedpovertystandard
Even the Sovietsystemencountersa substantial
inflationrate in the immediatecommunity)did
and five-year
highercost-of- gap between the earningsof otherwiseidentical
not indicate that Moscow had a significantly
living.
male and femaleworkers.However,theSovietgap
THE EARNINGS OF SOVIET WORKERS
is associated withdifferent
forcesthanthe gap in
the United States.The Sovietfemaleearningsgap
is about 20%, holdingotherfactorsincludingoccupation constant.Withoutoccupationheld constant,Soviet women earn from22% to 29% less.
Lower pay withina givenoccupationaccountsfor
a much higher proportion(two-thirdsto fourfifths)of the femaleearningsdifferential
than in
the United States.
A remainingmystery
concernsthe Sovietpractice of settingregional wage adjustmentcoefficients.Whilewe findsupportthatcentralindustry
prioritiesare capitalizedinto earnings,we findno
conclusive evidence of regional prioritiesbeing
capitalized. This will be the topic of futureresearch.
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APPENDIX
TABLE Al.-CHARACTERISTICS OF HIGH LEVEL AND BLUE COLLAR SAMPLES
High Level
Variable
Description
Mean
Standard
Deviation
Blue Collar
Mean
Standard
Deviation
Human Capital
EDO
ED1
ED2
ED3
ED4
EDS
ED6
ED7
ED8
EXP
EXP2
MOVE
0.000
0.003
0.018
0.000
0.054
0.132
0.005
0.047
0.154
0.071
0.213
0.361
0.009
0.094
0.051
0.220
0.002
0.047
0.017
0.129
0.071
0.266
0.048
0.583
19.275
0.257
0.442
0.214
0.493
9.634
0.367
0.223
0.044
0.091
22.184
0.482
0.417
0.205
0.288
10.695
464.276
0.085
412.661
0.278
606.34
0.091
514.391
0.288
0.297
0.297
0.290
0.027
0.013
0.457
0.457
0.454
0.163
0.112
0.254
0.179
0.159
0.017
0.008
0.436
0.384
0.366
0.129
0.092
car (= 1)
Use of official
Legal access to closedclinic(= 1)
Legal access to closed shop (= 1)
Squaremetersof livingspace
0.094
0.054
0.059
39.683
0.292
0.226
0.236
37.417
0.039
0.014
0.034
45.614
0.194
0.116
0.181
63.291
Age at end of last normalperiod
Married(= 1)
Householdsize minusKIDS
Childrenunder18 years
Male (= 1)
37.675
0.844
2.540
0.887
0.431
9.239
0.363
0.938
0.737
0.495
39.479
0.838
2.663
0.980
0.613
10.638
0.369
1.138
0.922
0.488
Less than4 yearsgeneralschool
4-6 yearsgeneralschool
7-8 yearsgeneralschool; 1-2 years
tradeschool
More than8 yearsgeneralschool; some
secondaryspecialtyschool
3 or moreyearstradeschool; 2 year
degreeprogram
Attestat(highschool)
Completesecondaryspecialtyschool
Some highereducation
Completedhigheror more
Publicsectorexperiencedefinedas
age - numberof yearsnotworking
at a publicsectorjob
Experiencesquared
Moved in last 5 years(= 1)
PoliticalLoyalty
GOOD
GOOD: LEADER
BAD
BAD: LEADER
GOODL X BADL
in good activities(= 1)
Participates
Leaderin good activitiesor Komactiv(= 1)
Participatesin bad activities(= 1)
Leader in bad activities(= 1)
Good leaderstimesbad leaders
Privilege
CAR
CLIN
SHOP
SQMETERS
Demographic
AGE
MARRIED
ADULTS
KIDS
MALE
35
THE EARNINGS OF SOVIET WORKERS
TABLE Al. -CONTINUED
Blue Collar
High Level
Variable
Description
Mean
Standard
Deviation
Mean
Standard
Deviation
165.779
90.996
162.037
88.451
5.012
38.918
38.918
0.010
0.009
0.096
0.018
0.047
0.007
0.022
0.003
0.027
0.005
0.095
0.057
0.007
0.021
0.024
0.114
0.160
0.052
0.179
0.044
0.432
4.949
8.973
0.098
0.094
0.294
0.132
0.213
0.081
0.148
0.054
0.163
0.072
0.293
0.232
0.086
0.145
0.155
0.318
0.367
0.222
0.383
0.205
4.967
42.137
42.137
0.005
0.007
0.169
0.017
0.142
0.015
0.036
0.007
0.080
0.014
0.076
0.151
0.003
0.008
0.174
0.022
0.017
0.017
0.015
0.025
0.484
3.811
8.199
0.071
0.082
0.375
0.129
0.349
0.123
0.185
0.082
0.271
0.116
0.265
0.358
0.058
0.092
0.380
0.147
0.129
0.129
0.123
0.157
0.262
0.235
0.033
0.440
0.424
0.180
0.108
0.127
0.039
0.311
0.333
0.194
0.006
0.107
0.035
0.041
0.041
0.048
0.003
0.043
0.039
0.077
0.310
0.183
0.198
0.198
0.214
0.054
0.203
0.194
0.008
0.161
0.041
0.058
0.066
0.044
0.005
0.069
0.102
0.092
0.368
0.198
0.233
0.248
0.205
0.071
0.254
0.304
0.043
0.003
0.059
0.002
0.203
0.054
0.235
0.047
0.030
0.003
0.112
0.025
0.172
0.058
0.315
0.157
JobCharacteristics
EARNINGS
LOG EARNINGS
HOURS
HRSWKLNP
IND1
IND2
IND3
IND4
IND5
IND6
IND7
IND8
IND9
IND1O
INDII
IND12
IND13
IND14
IND15
IND16
IND17
IND18
IND19
IND20
Nominalgrossmonthlyearnings
(in rubles)
Naturallog of grossmonthlyearnings
Predictedhoursworked
Hoursworkedperweek
chemical
Manufacturing:
Man: energy,metallurgy
Man: machinebuilding
Man: wood,buildingmaterial
Man: light-nofood
Man: light-food
Man: other
forestry
Agriculture,
Transportation
Communications
Construction
Trade,social catering
Material,technicalsupply
Otherproductiveservices
Municipaleconomy,housing
culture
Health-physical
Education
Culture& arts
Science
Credit,state,party
Regions
REG1
REG2
REG3
REG4
REG5
REG6
REG7
REG8
REG9
REG1O
REGII
REG12
REG13
REG14
REG15
REG16
Sample size
Moscow
Leningrad
EuropeanRSFSR, Excluding
Moscow-Leningrad
Non-EuropeanRSFSR
Kiev
Odessa
WestUkraine
East Ukraine,excludingKiev-Odessa
Balticcapitals
Baltic,excludingcapitals
Kishinev& Minsk
Byelorussia& Moldavia,excluding
capitals
Transcaucasiancapitals
Transcaucasian,excludingcapitals
CentralAsian capitals
CentralAsia, excludingcapitals
1349
591