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DOES INDONESIA HAVE A 'LOW PAY' CIVIL SERVICE?
Deon Filmer & David L. Lindauer
To cite this article: Deon Filmer & David L. Lindauer (2001) DOES INDONESIA HAVE A 'LOW PAY' CIVIL SERVICE?, Bulletin of Indonesian Economic Studies, 37:2, 189-205, DOI: 10.1080/00074910152390883
To link to this article: http://dx.doi.org/10.1080/00074910152390883
Published online: 17 Jun 2010.
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DOES
INDONESIA
HAVE
A
‘LOW
PAY’
CIVIL
SERVICE?
DeonFilmer
TheWorldBank,WashingtonDC
DavidL.Lindauer*
WellesleyCollege,WellesleyMA
Governmentofficialsandpolicy analystsmaintainthatIndonesia’s civilservants arepoorlypaid,andhavebeenfordecades,aconclusionthatissupportedby anec-dotalevidenceandcasualempiricism. Inthispaper,therelationshipbetween gov-ernmentandprivatecompensation levelsissystematicallyanalysedusingevidence fromtwolargehouseholddatasets,the1998Sakernasandthe1999Susenas.The resultssuggestthatgovernmentworkerswithahighschooleducationorless, repre-sentingthree-quarters ofthe civilservice,earna paypremiumovertheirprivate sectorcounterparts. Civilservantswithmorethanahighschooleducationearnless thantheywouldintheprivatesectorbut,onaverage,thepremiumisfarsmaller thanis commonlyalleged,andisinkeepingwithpublic/private differentials in othercountries.Theresultsproverobusttovaryingeconometric specifications and castdoubtonthepropositionthatlowpayisanexplanationforgovernment corrup-tion.
INTRODUCTION
Among academic writers and policy makersalike,Indonesiahasbeen char-acterisedashavinga‘lowpay’civil serv-ice.Thisisalongmaintainedandwidely sharedview.Smith(1975:722–3), refer-ring to the situation in 1970, suggests that ‘Indonesian public officials are among the most poorly paid in the world’, with official salaries covering onlyhalfof‘essentialminimalmonthly needs’.Smithgoesontocitelowsalary levels asakeydeterminant of govern-ment corruption. Gray (1979:85), also referringto the1970s,‘wonder[s]how Indonesian civil servants survive … if [thecivilservant]confineshimselftothe [official]nominalsalaryplusautomatic cash supplements’. Gray documents
sourcesofillegalincomeforpublic offi-cials,butismorecircumspectthanSmith about the causal connection between lowsalariesandcorruption.
government/private pay gap at the highestranks.Itfindsthat‘[w]herecivil serviceclerksmakeabouthalf[the sal-ary]oftheirprivatesectorcounterparts, director-generalsmakeone-tenthto one-fifteenth’(WorldBank2000:14).
Indonesian gove rnment officials sharetheseviews.A1970commission, theCommitteeofFour (citedinSmith 1975),attributedwidespreadpublic cor-ruptionto low salaries.Articles inThe StraitsTimes(30Marchand4April2000) suggestthatsimilarviewsareheldby contemporarypolicymakersandwere the basis for the huge increasein al-lowances, amounting to as much as 1,000%,giventosomestructuralstaff inApril2000.1
TheclaimthatIndonesiancivil serv-antsarelow-paidraisesmanyquestions. Salariesmaybelow,butrelativetowhat orwhom?Aregovernmentsalarieslow relativetointernational levelsorto do-mesticalternatives? Withacivilservice, includingthearmedservicesandpolice, ofover 4.6 million, arealllow-paid or onlythoseathigherranks?Beyondthese mattersoffact,theconsequencesoflow paywarrantfurtherscrutiny.Islowpay aprimarydeterminant ofcorruption?
EVIDENCEONPAYLEVELS
Beliefintheinadequacyofgovernment compensation maybewidespread, but theevidence tosubstantiate it has not been sufficient. Anecdotal evidence abounds.Civilservants,especiallyinthe managerialandprofessional ranks, of-tenclaim toknowpeoplewithsimilar qualifications who earn multiples of theirsalariesintheprivatesector. Aca-demicstudiesandpolicyanalysesattest to more rigorous comparisons. Smith (1975)conductedasurveyofalmost600 governmentofficialsandaskedthemto estimate their monthly expenditure needs.Onaverage,suchneedsfellwell below officialsalaries. Clark and
Oey-Gardiner(1991)employasimilar meth-odologyintheiranalysisoffaculty com-pensation at Indonesia’s public universities. They compare official salaries witharespondent’s identifica-tion of‘income needed’,and conclude thatgovernmentpayisbelow prevail-ing marketwages. But such compari-sonsarenotarobustwayofdetermining the adequac y of government pay. Expenditurebehaviourisnotexogenous toearnings.Ifexpenditures exceed offi-cialincome,thismayreflect opportuni-ties, both legal and illegal, that civil servants have to secure income from other sources, rather than any inad-equacyofgovernmentpay.
ThestudiesbyWirutomo(1991)and theWorldBank(2000)employa differ-ent method fromthat used by Smith (1975) and Clark and Oey-Gardiner (1991), comparing government pay at different salary ranks with compensa-tion offeredby asample ofprivate es-tablishments. Wirutomo describes his comparisongroupas‘bigprivatefirms’ visited bytheauthor.The WorldBank studyemploysapaysurveyundertaken by Watson Wyatt, an international human resource consulting firm. The WatsonWyattdatawerecompiledfrom asurveyof79companiesinJakarta,of which 77 weremultinationals, mostly NorthAmericanorEuropean,and80% were inbanking, information technol-ogy,insuranceorpharmaceuticals. Such narrowsamplesoffirmsshouldnotbe considered as representative either of domesticfirmsorofthelabourmarket alternatives facing most Indonesian civil servants.
large domestic concerns pay higher wagesthan dodomesticorsmaller en-terprises, other things being equal.2
Why such firms pay a premium for workers is a subject of some debate (Jenkins1990).Multinationals, large do-mestic firms, and many state-owned enterprises tend to have considerable ability to paytheir employees.This is because of the economic rents these firms often enjoy owing to protected productmarketsoreconomiesofscale. Suchfirmsmayusetheserentsto com-pensateemployeesinexcessofmarket wagesinthehopeofattractingand re-taining thebestworkers (anefficiency wageexplanation), inordertominimise labourunrestintheiroperations, orin responsetodirectgovernmentpressure. Thesuperiorcompensation receivedby Indonesian employees of foreign and largedomesticfirmsmayevenindicate thatsuchfirmspay‘toomuch’relative tothereservationwagesoftheir employ-ees.Butitishardertoarguethat previ-ous studies provide reliable evidence thatcivilservantsreceive‘toolittle’.
An alternative approach to evalu-atingthe relative positionof govern-mentpaywithintheIndonesianwage structure is to analyse data from In-donesia’slabourforce(Sakernas)and householdexpenditure(Susenas) sur-veys,bothundertakenannuallybythe g o v e r n m e n t’s sta ti sti c a l ag e n c y, BadanPusatStatistik(BPS).Theseare largehouseholdsurveys,which iden-tifywhetheran individual’s primary employmentisin governmentor the privatesector,andwhichprovide in-formation on monthly earnings and expenditure, oneducation and expe-rience,andonotherhumancapital at-tributes. Surprisingly, these surveys do not appear tohave been used be-fore to evaluate the relationship be-tweengovernmentcompensationand prevailingmarketwages.
GOVERNMENTANDPRIVATE
SECTORPAYCOMPARISONS
USINGSAKERNAS
Wageearnersrepresentaboutone-third ofthenation’slabourforceof90million. Theremainingtwo-thirdsareprimarily self-employed orfamilyworkersin ag-ricultureortheinformalsector.Among wage earners, roughly 4.6 million are civil servants or work for the armed servicesorthepolice.3
Three conclusions emerge fromthis simple comparison of mean earnings. First,theoverwhelmingmajorityof gov-ernment workers do not receive ‘low pay’comparedwiththeirprivatesector counterparts. Closetothree-quarters of allcivilservantshaveahighschool de-gree or less,and this groupearns pay that is eithercomparable to or higher than thatprevailing among similarly educatedworkersintheprivatesector. Second,civilservantswith post-second-aryeducationin1998didearnlessthan prevailing marketwages, but thepay ratiobetweentheprivateandpublic sec-torforthishighereducationcohortwas oftheorderof1.2:1to1.5:1.Thisiswell belowtheratiosreportedinearlier stud-ies, which were based on much nar-rowersamplesofprivatesectorjobsand hence of marketopportunities. Third, thepatternofgovernmentpay exceed-ingprivatecompensation forless edu-cated wo rkers and private pay exceeding government compensation
formoreeducatedworkers—the prob-lem of government salary compres-sion—isapatterncommontoothercivil services (Nunberg 1994). Indonesia’s situationdoesnotappearunique.
Sakernasis arich dataset:itallows comparisonofgovernmentandprivate pay based on worker attributes other thaneducation,suchasage,genderand location,whicharecommonlyfoundto besignificant determinants ofearnings. Tables2,3and4presentregression esti-matesthatincludethesevariables.The results confirm the basic findings re-ported in the simple comparisons of meansintable1.
Intable2,followingstandardhuman capital theory, the semi-logarithmic earningsequation(1)isestimated.The dependentvariableisthenatural loga-rithm ofmonthly earnings (E)of indi-viduali,andtheindependentvariables include age (A) and age-squared, to account for theexpected curvature in age–earningsprofiles.Fivediscrete
cat-TABLE1 MonthlyEarningsbyEducationLevel,1998 (Rp‘000/month,and%ofwageearnersincategory)
EducationLevel PrivateSector Government Ratio:Privateto
GovernmentPay
Primaryorlower 192 290 0.7:1
(42.2) (0.7)
Juniorhighschool 239 379 0.6:1
(13.7) (1.2)
Seniorhighschool 337 392 0.9:1
(23.5) (8.2)
Sometertiary 530 458 1.2:1
(3.2) (2.0)
Universityorhigher 771 520 1.5:1
(3.3) (2.1)
Alllevels 274 414 0.7:1
(85.8) (14.2)
egoriesofeducationareincluded(S2to S6). Theomitted category is ‘less than completedprimaryeducation’. Specifi-cations also inclu de the follo wing dummyvariables: government(G:1 = governmentanddefenceservices;0=all other),gender(M:1=male;0=female), andurban(U:1=urban;0=rural).
ln( Ei)=ac+aaAi+aa2Ai2+ås=2 6, bs s iS ,
+dgGi +dmMi +duUi +ei (1)
Age and age-squared have the ex-pected signs,and high degrees of sig-nificance. Education variables exhibit increasingandsignificant earnings dif-ferentialsassociatedwithhigherlevels ofschooling.Menearnasignificant pre-mium over women,as do urban over ruralwageworkers.Intheestimationon the entire wage sector, government
TABLE2 Determinants ofMonthlyEarningsofWageEmployees,1998
Variables AllWageEmployees UrbanEmployeesOnly
Coefficient (t-Statistic) Coefficient (t-Statistic)
Constant 10.41 (192.1) 10.42 (143.5)
Age 0.04 (15.9) 0.05 (13.8)
Age-squared –0.0004 (–11.5) –0.0004 (–9.0)
Education
Primary 0.32 (17.4) 0.34 (12.4)
Juniorhighschool 0.53 (26.0) 0.55 (18.1)
Seniorhighschool 0.82 (39.9) 0.86 (28.7)
Sometertiary 1.16 (41.5) 1.21 (31.5)
University 1.26 (33.9) 1.33 (29.3)
Dummyvariables
Government 0.10 (5.7) 0.002 (0.1)
Male 0.40 (33.6) 0.31 (22.6)
Urban 0.15 (10.9) -
-No.ofobservations 27,759 16,366
R2 0.39 0.39
F 652.4(10,1,027) 383.3(9,598)
Source:Asfortable1.
workers,onaverage,earnanestimated pay premium over the private sector, otherthingsbeingequal,ofabout10%.4
Ifthesampleisrestrictedtourban em-ployees,themagnitudeandsignificance ofthecoefficientsonage,educationand genderremainroughlythesame,butthe governmentpremiumis indistinguish-ablefromzero.Inotherwords,among urbanemployees,governmentand non-governmentworkers,onaverage,have thesame reportedearningsfrom their primaryjobifhumancapital character-isticsareheldconstant.
counterparts. Centralgovernment em-ployees, on the other hand, are cov-eredbyaunifiedsalarystructurethat does not differentiateon the basis of ruralorurbanlocation:rural govern-mentemployees,whoaccountfor one-third of all government employees, e ar n t h e s am e a m o u n t a s u r b an government employees. Thus if par-ity in pay between the government andprivatesectorsholdsintheurban sample, the same would not be ex-p ec ted i n th e ful l s a m pl e, w h ic h would tend to show a government premium.
Tables3and4extendtheanalysisby looking ‘behind’theaverage returnto government employment. The
regres-sion equations used to generate the results in tables 3 and 4 examine differencesbyeducationintheearnings structureofgovernmentandtheprivate sector. Added to the basic earnings functio n of table 2 are interactive dumm y var ia bles sho wing the interaction betw een government em ployment and education levels. Extendingequation(1)yields:
ln( ) Ei =ac+aaAi+aa2Ai2+
å
s=2 6, bs s iS,+dgGi+dmMi+du iU
+
å
s=2 6, bsIS Gs i, i+ei (2)The coefficients on the interaction terms( bsI)indicatewhetherthereisan
TABLE3 EarningsStructurebyEducation:GovernmentversusPrivateEmployees (AllWageEmployees), 1998
Variables Coefficient (t-Statistic) Coefficienton (t-Statistic)
Interaction Terma
Constant 10.43 (193.1)
Age 0.04 (15.6)
Age-squared –0.0004 (–11.2)
Education
Primary 0.32 (17.2) 0.02 (0.09)
Juniorhighschool 0.52 (25.3) 0.14 (0.78)
Seniorhighschool 0.80 (38.7) 0.05 (0.30)
Sometertiary 1.22 (37.6) –0.16 (–0.91)
University 1.36 (29.5) –0.28 (–1.59)
Dummyvariables
Government 0.11 (0.62)
Male 0.40 (33.6)
Urban 0.15 (10.9)
No.ofobservations 27,759
R2 0.39
F 451.2(15,1,027)
aCoefficientontheproductofeacheducationdummyvariableandthegovernmentdummy
variable.
most,around25%,withrelativelyweak statisticalsignificance. Theeconometric evidence,asinthesimple comparison ofaveragepayintable1,doesnot indi-catethatthegovernmentis,over all,a low wage employer, nor is there evi-denceof the huge private pay advan-tagesforeducatedworkersreportedby previousstudies.
These results are maintained after subjectingtheanalysisofpay differen-tials to more stringent econometric specifications. Two econometric prob-lems confrontthe earnings regression reportedinthispaper.First,workersfor whom we observe earnings are not a randomsampleofthepopulationbuta potentially self-selected one.Ifthis
po-TABLE4 EarningsStructurebyEducation:GovernmentversusPrivateEmployees (UrbanEmployeesOnly),1998
Variables Coefficient (t-Statistic) Coefficienton (t-Statistic)
Interaction Terma
Constant 10.44 (144.3)
Age 0.05 (13.4)
Age-squared –0.0004 (–11.4)
Education
Primary 0.34 (12.3) 0.10 (0.46)
Juniorhighschool 0.53 (17.3) 0.25 (1.15)
Seniorhighschool 0.84 (28.0) 0.06 (0.29)
Sometertiary 1.26 (29.6) –0.16 (–0.71)
University 1.43 (26.8) –0.26 (–1.14)
Dummyvariables
Government –0.003 (–0.21)
Male 0.31 (22.7)
Urban -
-No.ofobservations 16,366
R2 0.39
F 256.2(14,598)
aSeetable3,notea.
Source:Asfortable1.
additionalpremiumawardedto work-ers by education based on theirsector ofemployment. Theimpactonearnings ofgovernmentemploymentisnowthe sum ofthe coefficient on the ‘govern-ment’dummyvariable and the coeffi-cientontherelevantinteractive dummy variableoneducation( bs+bsI).
tential self-selection is correlated with thevariablesofinterest,thenthe uncor-rected estimates would be biased, as they would capture both a ‘participa-tion’ effectand adirecteffecton earn-ings.5 Second, unobserved household
and community characteristics corre-lated with theincluded characteristics (including the ‘government’ dummy variable)arenotspecifiedinthemodel. Notcorrecting for these would poten-tially biasthe estimates, for example, ascribingtogovernmentapay differen-tial actually awarded to unspecified worker attributes. Employing the ap-proachusedbyBehrmanandDeolalikar (1995)intheiranalysisofgender differ-entialsinthereturnstoschoolingin In-do nesia, we estimate alternative specificationsinanattempttocorrectfor these potential estimation problems. Theseapproachesyieldcoefficients that are littledifferentfrom thoseobtained usingthebasicformulations intables2 to4.(Theappendixprovidesdetailsof thealternativeeconometricapproaches.)
GOVERNMENTANDPRIVATE
SECTORPAYCOMPARISONS
USINGSUSENAS
Becausetheresultsongovernment ver-susprivatepayruncounterto conven-tionalwisdom,itisimportanttoidentify otherdatathatmightofferan independ-enttestoftherelationship. Inaddition toitslabourforcesurvey,BPSalso car-riesoutanannualhousehold expendi-ture survey (Susenas). This contains ques tions simila r to tho se in the Sakernas,and permits alternative esti-matesofhowgovernmentpayrelatesto prevailingmarketwages.
The 1999 Susenas was available for thepurposes ofthis study,and covers over 160,000 households. About one-third of these have household heads who report positive wage income. A comparisonofmeanearningsfromthis
sample(resultsnotshown)byeducation levelrevealsfindingssimilartothoseof table 1. Because of inflation, nominal earningsarehigherin1999thanin1998, but theratio ofgovernmentto private paybyeducationlevelissimilar.
Table 5 reports regression estimates for the subsample of wage earnin g household headsin the 1999 Susenas. Onlyhouseholdheadsareemployedin thispartoftheanalysisbecausethereis only one value of expenditure per household. Therefore, the right-hand side variables inthe modelneedto be aggregatedinsomewaysothatthereis onlyoneobservationperhousehold.We choose to record the characteristics (wageearningstatus,genderand edu-cation)oftheheadofthehousehold.This choicemaintainssimplicityinthemodel (forexample,therearenofractional edu-cation levels), andallows simple com-parisonstobemadebetweentheSusenas earnings and expenditure models de-scribedbelow,aswellaswiththealready reportedSakernasearningsmodel.
coeffi-cienton the‘government’dummy vari-ableintheSusenasthanintheSakernas isexpected.6
The secondregressionin table 5 of-fers anindirecttestofrelative govern-mentcompensation levels. Ituses the expenditureinformation intheSusenas, andthedependentvariableisnolonger earningsbutthelogarithm ofmonthly householdexpenditures perhousehold member. Ifgovernment pay is signifi-cantly lower than market wages, ex-penditurelevelsinhouseholdsheaded by government workers might be ex-pectedto beloweraswell. This is not thecase.Afterallowanceismadeforthe age,educationandgenderofthe house-holdhead,andforurban/rural location
andsizeofhousehold,theeffectof sec-torofemploymentonhousehold expen-dituresisnotsignificantlydifferentfrom zero. Households headed by govern-mentworkers,onaverage,donothave lower expenditures than their private sector counterparts.As in the results using theSakernas,ifinteraction terms are added by education level, average household expenditures are a little higher for those with a junior high school or lowereducation level and a governmentjob,atparityforhighschool graduates, and lower for those with more education and a government ratherthanaprivatesectorjob(table6).7
Theseresultsalone,basedonrelative household expenditures by sector of
TABLE5 Determinants ofEarningsandExpenditures ofHouseholdHeads WhoWereWageEmployees,1999
Independent DependentVariable
Variables
Ln(MonthlyEarnings) Ln(Household Expenditure
perPerson)
Coefficient (t-Statistic) Coefficient (t-Statistic)
Constant 10.51 (141.6) 12.48 (224.6)
Age 0.05 (14.4) –0.05 (–19.5)
Age-squared –0.0005 (–12.4) 0.0006 (19.5)
Education
Primary 0.26 (15.7) 0.11 (12.8)
Juniorhighschool 0.49 (27.1) 0.28 (25.6)
Seniorhighschool 0.73 (40.6) 0.49 (39.7)
Sometertiary 1.01 (42.8) 0.67 (35.9)
University 1.15 (25.7) 0.88 (33.9)
Dummyvariables
Government 0.04 (2.3) –0.01 (–1.0)
Male 0.47 (30.4) –0.12 (–10.5)
Urban 0.22 (18.5) 0.26 (21.9)
No.ofobservations 54,513 54,513
R2 0.32 0.34
F 617.8(10,1,689) 545.8(10,1,689)
employmentofthehouseholdhead,are animperfecttestoftherelationship be-tweenearningsinthegovernmentand theprivatesector.Households headed bycivilservantsmightrespondtolower wages by findingother sources of in-come,bothlegalandillegal,orby send-ing more family members into the labour force. Buttheabsence oflower expenditures am ong households headedbycivilservantsisatleast con-sistentwiththehypothesisthat govern-ment workers are not systematically underpaidrelative tomarket opportu-nities.Andwhenthesefindingsare com-bin ed w ith the Susenas results on relative earnings parity between the governmentandprivatesectors,a mu-tuallyconsistentpictureemerges.
The Susenasresultsrefertoanother year,aredrawnfromadifferentsample, permit use of expenditure as well as earningsdata,andconfirmthefindings from theSakernasdataon1998. There isnoevidencethatgovernmentisa‘low pay’employerfortheaverage govern-mentemployee.Evenforthemore edu-cated,whodoearnlessingovernment thantheywouldintheprivateeconomy, thedifferentialsarenotlargeandnotof the order of magnitude reported in previousstudies.
RECONCILINGTHEEVIDENCE
The estimates of government/private pay differentials obtained from BPS surveys are so differentfrom the find-ingsofearlierstudies,andfromofficial
TABLE6 Expenditures byEducationLevelforHouseholdsHeadedbyGovernmentversus PrivateSectorEmployees,1999
Variables Coefficient (t-Statistic) Coefficienton (t-Statistic)
Interaction Terma
Constant 12.49 (226.4)
Age –0.06 (–19.8)
Age-squared 0.0007 (19.7)
Education
Primary 0.11 (12.0) 0.07 (1.0)
Juniorhighschool 0.27 (23.3) 0.02 (0.3)
Seniorhighschool 0.49 (38.2) –0.08 (–1.2)
Sometertiary 0.74 (31.9) –0.25 (–3.6)
University 0.96 (29.6) –0.28 (–3.8)
Dummyvariables
Government 0.09 (1.3)
Male –0.12 (–10.7)
Urban 0.26 (21.8)
No.ofobservations 54,513
R2 0.34
aSeetable3,notea.
viewsoncivilservantpay,thatitis im-portanttotryto reconcile these differ-ences. Oneexplanation, that BPSdata areoflow qualityandcannotberelied upon,isnotpersuasive. Thebasicage– education–earningsprofilethatemerges from the regression analy sis is too similartoresultsfrom othercountries, bothinthedirectionandmagnitudeof specificcoefficientsandinthedegreeof explanatory power,toallowthe conclu-sionthatthesurveyisseriouslyflawed. Otherresearchers familiar with these data reach a s im ilar co nc lu sion (Behrman and Deolalikar 1995). A related explanation is that 1998 and 1999 were in the midst of the finan-cialcrisisandareatypicalyears. Con-cerning gov ernment/priv ate pay differentials, thismay be true.But the directionofbiasduringtheseyearsisto generate a smaller government pay premium (ora largergovernment pay deficit),becauseadjustmentsinnominal payduringthesecrisisyearshappened moreslowlyingovernmentthaninthe privatesector.
Anotherpossibility is thatBPSdata systematically under-represent earn-ings.Itiseasytoseewhyreported earn-ingsinthesurveymaybetoolow.When asked,‘Whatisthenetmonthlyincome you received from your primaryjob?’, individuals mayreportonlytheirbasic salaryandnotallowancesorfringe ben-efits.Alternatively, theymay be reluc-tant to reveal their true earnings to a government enumerator for fear that such information maybeused against them, for example, by tax authorities. Butforeitheromissiontoaccountforthe estimatedpatternofgovernmentversus privatepaywithin theSakernasor Su-senasdatarequiresthatindividualswho workintheprivatesectoraremore,not less,likelythangovernmentworkersto fo rg et to inc lu d e allo w a nc es and fringe benefits,or toconsciously
un-der-report actual earnings. If under-reportingisequallydistributedacross all workers, reported earnings will systematicallybetoolow, butthe es-timated differentialbetween govern-mentandprivateworkerswillremain unaffected.
Ifthereisabiasinreporting,itiscivil servants who moreoftenmay system-atically report lowerthan actual earn-ings.Privateemployersmayhaveless, notmore,complicatedsystemsof allow-ancesandfringebenefits,becausethey arenotasconstrained bylawand regu-lations in revising their salary scales. Governmentworkersareknownto re-ceive legal side-payments associated with theirpositions. Paymentssuchas honoraria,perdiempaymentsinexcess of actualtravel expenses, and project bonusesarelegallysanctionedformsof compensation ingovernment, areoften transacted in cash, are said to be less prevalentintheprivatesector,andmay notbeincludedby civilservantsin re-sponse to questions on earnings. The directionofpotentialunder-reporting of earningsdoesnotsuggestareasonwhy the estimated government premium overprivatecompensation eitheristoo largeorisinthewrongdirection.
illegalactionsofprivateworkers.Ifcivil servantsweretoincluderatherthan ex-cludetheir‘extra’earningsinresponse toquestionsabout‘averagenetmonthly income’inprimaryjobs,thentheir self-reportingofearningswould systemati-cally o verestimate o ffic ia l wages. Independent evidence on government payscalesrejects this interpretation of thedata.
In August 1998, the date of the Sakernassurveyusedinthispaper, gov-ernment salaries were based on 1997 salary scales. These scales cover four salary ranks,eachfurtherdividedinto fourorfivesub-ranks.Withineach sub-rank, salariesare determinedby years of service. Government employment datafromtheStatePersonnel Adminis-tration Board indicate the number of civilservantsbysub-rank.Selectingthe mid-pointsalarytorepresentthemean basicwageforeachofthe17sub-ranks resultsinanestimateofaverage govern-mentearnings in 1997 ofRp 310,000/ month.Becauseofthefinancialcrisis,a 15% across-the-board in crease in governmentsalaryscaleswasappliedin April1998,raisingtheaverageestimated basic salary in go ver nm ent to Rp 356,500/month. Statutory allow-ances,includingfamily,spouseandrice allowances, amounttoafurther15%of the basicsalary.Adding these supple-ments to the basic wage predicts an August 1998 estimate of Rp 410,000/ month.8 This isremarkably similarto
the 1998Sakernasestimateof official w ages for government w orkers o f Rp 414,000/month. Earnings reported by Sakernas appear torefer to official wagesonly.
Ifnotthroughdataaccuracy,howelse canthedifferingresults ofthevarious studies on government pay be recon-ciled? Earlier research ongovernment pay focused on specific occupational categories, often in the managerial
ranks,andcomparedpaylevelstoa nar-rowsetofwell-payingdomesticand for-eign enterprises. BPS data permit a different comparison, between broad educationcategoriesandrelativetothe entire labour market. Unfortunately, theseBPSdatadonotsupport compari-sonsat the very topof the occupation hierarchy, andareillsuited tojudging thereservation wagesofsenior manag-ers and professionals. More detailed humanresourcesurveysarerequiredfor thispurpose.
If the different survey designs are notperfectsubstitutes,andifthe em-pirical resultsfromthe varioustypes of surveys are accurate, then what maybemistakenistheinterpretation ofthedata.Theremaybea‘fallacyof association’,wheresignificantpay dif-ferentials between top government officials and senior corporate execu-tivesinthehighest-payingenterprises have been considered as pointing to likely pay gaps for allcivil servants. For lowerranks,which comprisethe majority of civil servants, this gap doesnotappeartoexistrelativetothe entire domesticlabourmarket.
face,andrequires moredetailed scru-tinythanisaffordedbythisanalysis.
PAY,CORRUPTIONAND
GOVERNMENTPERFORMANCE
Ithas long been alleged that the low pay of Indonesia’scivil service is re-sponsible for widespread corruption ingovernment.ResultsfromBPS sur-veys in 1998 and 1999 cast doubt on these conclusions. Most government employeesappearedtoearnamounts c o m p ar a bl e t o th e ir o p p o r t un i ty cost— that is , to the earnings they might have received in the private wage sector. These results m ay be even stronger as of the end of 2000. PresidentialdecreesinApril1999,and again in April and May 2000, raised nominal government salaries well in excess of price inflation. With more l i m i te d re c o v e r y i n th e m ar k e t economy, government pay may now exceed private pay forallbut a frac-tionofthenation’s4.6million govern-mentemployees.
Iftheassumptionoflowpayis inac-curate,so mustbe any simple linkage
betweenpayandcorruption. Howcan weexplain theprevalenceofpetty cor-ruption by lowerranking government workers,iftheyearnapremiumoverthe privatesector?Andcanthealleged‘big corruption’ amonghigher-ranking offi-cialsrealistically betiedtothepaythey receiverelativetotheirnon-government counterparts? Rather than identifying corruptbehaviourasaresponseto‘low pay’, itis more helpfulto view itas a response to opportunity. Soliciting bribes, arranging kickbacks or practis-ing extortion all represent calculated risks,wherecostsandbenefitsofcorrupt behaviour are weighed.If therisks of gettingcaughtarelowandpunishment minimal, corruption is apt toflourish. Increases in official pay raise the ex-pectedcostoflosingone’sjob.But un-lessactionsaretakentopunishcorrupt behaviour, payincreasesalonewilldo littletochangethecost/benefit calcula-tion, and corruption need not abate. Changesincompensation levelscanbe partofapackagetoreformcivilservant behaviour,butotherelementsare essen-tialtoreducingcorruptpractices.
NOTES
* TheauthorswouldliketothankBarbara Nunberg,who oversawthestudy that generatedthiswork,andJere Behrman, MartinRamaandtwoanonymousreferees for theircomments. DeonFilmerisan EconomistatTheWorldBank.DavidL. LindauerisStanfordCalderwood Profes-sorofEconomicsatWellesleyCollege.The findings,interpretations,andconclusions expressedinthispaperareentirelythose oftheauthors.Theydonotnecessarily rep-resenttheviewsofTheWorldBank,its Ex-ecutiveDirectors,or thecountriesthey represent.
1 Inadditiontobeingclassifiedbyrank, In-donesiancivilservantsmaybeclassified asfunctionalorstructuralstaff.Functional positions arefilledprimarilyby profes-sionals.Structuralstaff,whoinaddition totheircivilservicerankaredesignated
byechelon,occupy the top managerial positions,andconstituteabout10%ofall civilservants.
2 Graham(2000)reviewscross-country evi-denceofthesuperiorpayofferedby mul-tinationals ascomparedwithprevailing domesticwages.
3 Estimates ofthesizeofthelabourforce andofthenumberofwageearnersrefer to1999,andarebasedontheSakernasas reportedinBPS(1999),LabourForce Situa-tioninIndonesia,Jakarta,table15.9.
Gov-ernment employmentis drawn from
independent estimatesprovided bythe StatePersonnelAdministrationBoard (BKN,BadanKepegawaianNegara)and theMinistryofFinance.
variableonthedependentvariable. In-stead,inordertocalculatetherelative
ef-fect, , the coefficient, , must be
transformedaccordingto = e -1.When
the coefficient ona dummy variableis closetozero,thecoefficientisaclose ap-proximation to the relativeeffect. See HalvorsenandPalmquist(1980)fora com-pletederivation.
5 Thecanonicalexampleistherelationship betweeneducationandwages.According to economictheory, only individuals whosewageexceedsthethreshold ‘reser-vation wage’willparticipate inwage work.Onewould therefore expectthat individuals withmore education and higherwageswouldbeover-represented inasampleofwageworkers.Theeffectof thisselectionwouldbetounderestimate therelationship between educationand wagesforthepopulationasawhole,since low-education/low-wageindividualsare rareintheselectedsample.
6 Carryingoutthesameregressiononthe totalsampleofwageearners,andnotjust onhouseholdheads,yieldsresultsthatare evenmoresimilartothoseintheSakernas.
Forexample,thecoefficientonthe ‘gov-ernment’ dummy variableequals0.08 (withat-statisticof5.7)whentheentire sampleofindividualsaged16to60isused. 7 Intable6thesignoftheagevariables,as wellasthatofthemaledummyvariable, haschanged.Thisisbecausethevariables now refertothe ageand genderof the household head.These areintrinsically linkedtohouseholdsizeandcomposition, whichareincorporatedintothe depend-entvariable(i.e.householdexpenditures
percapita)butnotcontrolledforinthe re-gression.Whentheregressionincludesthe number ofhousehold membersand its square,theeffectsofageandofbeingmale becomesignificantlypositive,andallthe other coefficients arequalitatively un-changed.
8 Estimatesofofficialcompensationshould alsoincludethemeanvalueoffunctional andstructuralallowances(note1). How-ever,thereisnosimplewaytomapsuch allowancesontothesalaryscales.Ifthey wereincluded,theestimatedmeanlevel ofofficialcompensationwouldbehigher thanRp410,000/month.
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Behrman,Jere,andAnilDeolalikar (1995), ‘AreThereDifferentialReturnsof School-ingbyGender?TheCaseofIndonesian LabourMarkets,’ OxfordBulletin of Eco-nomicsandStatistics57(1):97–117. Clark,DavidH.,andMaylingOey-Gardiner
(1991),‘HowIndonesianLecturersHave AdjustedtoCivilServiceCompensation’,
Bulletin ofIndonesian Economic Studies
27(3):129–41.
Graham,Edward(2000),FightingtheWrong Enemy:AntiglobalActivists and Multina-tionalEnterprises, Institute for Interna-tionalEconomics,WashingtonDC. Gray,Clive(1979),‘CivilService
Compensa-tion inIndonesia’,Bulletin ofIndonesian EconomicStudies15(1):85–113.
Halvorsen,R.,andR.Palmquist(1980),‘The Interpretationof Dummy Variablesin Semi-logarithmic Equations’, American EconomicReview70:474–5.
Jenkins,Rhys(1990),‘ComparingForeign Sub-sidiariesandLocalFirmsinLDCs:
Theo-reticalIssues andEmpirical Evidence’,
JournalofDevelopment Studies26(2):205– 28.
Nunberg,Barbara(1994),‘Experiencewith CivilServicePay andEmployment Re-form:AnOverview’,inD.Lindauerand B.Nunberg(eds),Rehabilitating Govern-ment:PayandEmploymentReforminAfrica, TheWorldBank,WashingtonDC. Smith, Theodore(1975), ‘Stimulating
Per-formanceintheIndonesianBureaucracy: GapsintheAdministrator ’sToolKit’, Eco-nomic Development andCulturalChange
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Wirutomo,P.,etal.(1991),‘Labourinthe In-donesianPublicService’,inWoutervan Gin neke n (ed.), G overnment and Its Employees,AveburyandILO,Aldershot: 113–34.
APPENDIX
Intheiranalysisofgenderdifferentials inthereturnstoschoolingin Indone-sia, Behrman and Deolalikar (1995) outline tw o potential ec onometric problemsin earningsregression esti-mates. First, workers for whom we observe earnings are not a random sampleofthepopulationbuta poten-tially self-selected one. While the Sakernasdatadonotprovideentirely convincingvariablestoallowfor sta-tistically c orrecting the estimates, householddemographiccomposition variablescan beused in a first stage
modelto controlforthepotential se-lectivity of receiving wages. Specifi-cally, the pro bability of repo rting earnings and the determin ation of those (log) earnings are jointly esti-mated. Variables for the number of householdmembersunderage10,the numberagedbetween10and59,and the number aged 60 andover are in-cluded in the participation equation, butnotintheearningsdetermination equation.Theassumptionunderlying this restriction is thatthe age profile of the household determines the
op-APPENDIXTABLE1A SelectionCorrectedEstimates:Determinants ofMonthly EarningsofWageEmployees,1998
AllWageEmployees UrbanEmployeesOnly
Variables Coefficient (t-Statistic) Coefficient (t-Statistic)
Constant 10.4 72.5 12.4 105.9
Age 0.043 10.5 –0.006 1.26
Agesquared –0.0004 7.34 0.0003 4.81
Education
Primary 0.32 17.3 0.29 9.14
Juniorhighschool 0.53 26.0 0.54 16.2
Seniorhighschool 0.81 30.1 0.64 17.4
Sometertiary 1.15 23.3 0.72 13.8
University 1.25 24.9 0.90 16.05
Dummyvariables
Government 0.10 5.72 –0.009 0.44
Male 0.40 17.3 0.030 1.54
Urban 0.15 8.69 -
-SelectionmodelRhoa –0.015p-value=0.84 –0.829p-value<0.001
Chi-squaretestforjoint significance ofidentifying
instruments (df=3) 28.43p-value<0.001 4.91p-value=0.178
No.ofobservations 122,242(27,759wageworkers) 54,490(16,366wageworkers)
aSampleselectionusingHeckmanselectionmodel.Identifying instruments arethe
num-bersofhouseholdmembersaged0to9,10to59,and60andover.
APPENDIXTABLE1B Fixed-Effects Estimates:Determinants ofMonthly EarningsofWageEmployees,1998
AllWageEmployees UrbanEmployeesOnly
Variables Coefficient (t-Statistic) Coefficient (t-Statistic)
Constant 10.4 81.0 10.4 68.8
Age 0.046 7.70 0.049 6.04
Agesquared –0.0004 5.15 –0.0004 3.57
Education
Primary 0.29 7.98 0.34 6.28
Juniorhighschool 0.43 9.68 0.48 7.57
Seniorhighschool 0.71 13.4 0.84 12.4
Sometertiary 1.05 14.3 1.16 12.9
University 1.16 13.8 1.31 14.0
Dummyvariables
Government 0.07 1.74 –0.004 0.08
Male 0.36 19.2 0.29 12.9
Urban 12.4 1.06 -
-No.ofobservations 15,123 9,406
R2 0.762 0.748
Source:Sakernas,1998.
portunity cost ofparticipation in the wagelabourmarket,butdoesnot di-rectly determineearnings.
The second potential problem is of unobserved heterogeneity. There are potentially unobservedhouseholdand communitycharacteristics thatare cor-relatedwiththeincludedcharacteristics (including the ‘government’ dummy variable), but are not specified in the model.Notcorrecting forthesewould potentially biastheestimates.Inorder toallowforthispossibility,ahousehold fixed-effects modelofthe(log)earnings equation is estimated. An additional benefitofthisprocedureisthatif selec-tivityisbasedonhouseholdattributes, asisassumedinmostempirical appli-cations,thenthisfixed-effects approach shouldcontrolforselectivityinthewage labour market aswell asfor themore
genericpotentialunobserved heteroge-neityproblems.
becomesnegativeforthosewithatleast sometertiary education (results avail-ablefromauthors).
The fixed-effects estimates yield similarlyconsistentresults.Sincethis estimation method relies on within householdvariationtoidentifyan ef-fect,the significance of the resultsis lower.Nonetheless,thepointestimate ontheaveragepublicsectorpremium
