does indonesia have a ‘low pay’ civil...

Bulletin of Indonesian Economic Studies, Vol. 37, No. 2, 2001: 189–205

ISSN 0007-4918 print/ISSN 1472-7234 online/01/020189-17 © 2001 Indonesia Project ANU

DOES INDONESIA HAVE A ‘LOW PAY’ CIVIL SERVICE?

Deon Filmer

The World Bank, Washington DC

David L. Lindauer*

Wellesley College, Wellesley MA

Government officials and policy analysts maintain that Indonesia’s civil servantsare poorly paid, and have been for decades, a conclusion that is supported by anec-dotal evidence and casual empiricism. In this paper, the relationship between gov-ernment and private compensation levels is systematically analysed using evidencefrom two large household data sets, the 1998 Sakernas and the 1999 Susenas. Theresults suggest that government workers with a high school education or less, repre-senting three-quarters of the civil service, earn a pay premium over their privatesector counterparts. Civil servants with more than a high school education earn lessthan they would in the private sector but, on average, the premium is far smallerthan is commonly alleged, and is in keeping with public/private differentials inother countries. The results prove robust to varying econometric specifications andcast doubt on the proposition that low pay is an explanation for government corrup-tion.

INTRODUCTIONAmong academic writers and policymakers alike, Indonesia has been char-acterised as having a ‘low pay’ civil serv-ice. This is a long maintained and widelyshared view. Smith (1975: 722–3), refer-ring to the situation in 1970, suggeststhat ‘Indonesian public officials areamong the most poorly paid in theworld’, with official salaries coveringonly half of ‘essential minimal monthlyneeds’. Smith goes on to cite low salarylevels as a key determinant of govern-ment corruption. Gray (1979: 85), alsoreferring to the 1970s, ‘wonder[s] howIndonesian civil servants survive … if[the civil servant] confines himself to the[official] nominal salary plus automaticcash supplements’. Gray documents

sources of illegal income for public offi-cials, but is more circumspect than Smithabout the causal connection betweenlow salaries and corruption.

The remuneration of Indonesian civilservants in 1984 is considered byWirutomo et al. (1991), who find paritybetween private and government com-pensation for relatively unskilled work-ers (rank I), but a growing differentialin favour of the private sector at higherskill levels. At rank II the private to gov-ernment pay ratio is 2.7:1, and at thehighest government rank (IV), the ratiorises to 5.2:1. A recent report by theWorld Bank for the Consultative Groupon Indonesia paints a similar picturefor the late 1990s, with a growing

Deon Filmer and David L. Lindauer190

government/private pay gap at thehighest ranks. It finds that ‘[w]here civilservice clerks make about half [the sal-ary] of their private sector counterparts,director-generals make one-tenth to one-fifteenth’ (World Bank 2000: 14).

Indonesian government officialsshare these views. A 1970 commission,the Committee of Four (cited in Smith1975), attributed widespread public cor-ruption to low salaries. Articles in TheStraits Times (30 March and 4 April 2000)suggest that similar views are held bycontemporary policy makers and werethe basis for the huge increase in al-lowances, amounting to as much as1,000%, given to some structural staffin April 2000.1

The claim that Indonesian civil serv-ants are low-paid raises many questions.Salaries may be low, but relative to whator whom? Are government salaries lowrelative to international levels or to do-mestic alternatives? With a civil service,including the armed services and police,of over 4.6 million, are all low-paid oronly those at higher ranks? Beyond thesematters of fact, the consequences of lowpay warrant further scrutiny. Is low paya primary determinant of corruption?

EVIDENCE ON PAY LEVELSBelief in the inadequacy of governmentcompensation may be widespread, butthe evidence to substantiate it has notbeen sufficient. Anecdotal evidenceabounds. Civil servants, especially in themanagerial and professional ranks, of-ten claim to know people with similarqualifications who earn multiples oftheir salaries in the private sector. Aca-demic studies and policy analyses attestto more rigorous comparisons. Smith(1975) conducted a survey of almost 600government officials and asked them toestimate their monthly expenditureneeds. On average, such needs fell wellbelow official salaries. Clark and Oey-

Gardiner (1991) employ a similar meth-odology in their analysis of faculty com-pensation at Indonesia’s publicuniversities. They compare officialsalaries with a respondent’s identifica-tion of ‘income needed’, and concludethat government pay is below prevail-ing market wages. But such compari-sons are not a robust way of determiningthe adequacy of government pay.Expenditure behaviour is not exogenousto earnings. If expenditures exceed offi-cial income, this may reflect opportuni-ties, both legal and illegal, that civilservants have to secure income fromother sources, rather than any inad-equacy of government pay.

The studies by Wirutomo (1991) andthe World Bank (2000) employ a differ-ent method from that used by Smith(1975) and Clark and Oey-Gardiner(1991), comparing government pay atdifferent salary ranks with compensa-tion offered by a sample of private es-tablishments. Wirutomo describes hiscomparison group as ‘big private firms’visited by the author. The World Bankstudy employs a pay survey undertakenby Watson Wyatt, an internationalhuman resource consulting firm. TheWatson Wyatt data were compiled froma survey of 79 companies in Jakarta, ofwhich 77 were multinationals, mostlyNorth American or European, and 80%were in banking, information technol-ogy, insurance or pharmaceuticals. Suchnarrow samples of firms should not beconsidered as representative either ofdomestic firms or of the labour marketalternatives facing most Indonesiancivil servants.

Given their bases for comparison, itis not surprising that earlier studies con-clude that Indonesia’s civil servants arepaid less than their counterparts in theprivate sector. It is a well known result,after adjusting for worker education andexperience, that multinationals and

Does Indonesia Have a ‘Low Pay’ Civil Service? 191

large domestic concerns pay higherwages than do domestic or smaller en-terprises, other things being equal.2

Why such firms pay a premium forworkers is a subject of some debate(Jenkins 1990). Multinationals, large do-mestic firms, and many state-ownedenterprises tend to have considerableability to pay their employees. This isbecause of the economic rents thesefirms often enjoy owing to protectedproduct markets or economies of scale.Such firms may use these rents to com-pensate employees in excess of marketwages in the hope of attracting and re-taining the best workers (an efficiencywage explanation), in order to minimiselabour unrest in their operations, or inresponse to direct government pressure.The superior compensation received byIndonesian employees of foreign andlarge domestic firms may even indicatethat such firms pay ‘too much’ relativeto the reservation wages of their employ-ees. But it is harder to argue that previ-ous studies provide reliable evidencethat civil servants receive ‘too little’.

An alternative approach to evalu-ating the relative position of govern-ment pay within the Indonesian wagestructure is to analyse data from In-donesia’s labour force (Sakernas) andhousehold expenditure (Susenas) sur-veys, both undertaken annually by thegovernment’s statistical agency,Badan Pusat Statistik (BPS). These arelarge household surveys, which iden-tify whether an individual’s primaryemployment is in government or theprivate sector, and which provide in-formation on monthly earnings andexpenditure, on education and expe-rience, and on other human capital at-tributes. Surprisingly, these surveysdo not appear to have been used be-fore to evaluate the relationship be-tween government compensation andprevailing market wages.

GOVERNMENT AND PRIVATESECTOR PAY COMPARISONSUSING SAKERNASWage earners represent about one-thirdof the nation’s labour force of 90 million.The remaining two-thirds are primarilyself-employed or family workers in ag-riculture or the informal sector. Amongwage earners, roughly 4.6 million arecivil servants or work for the armedservices or the police.3

Earnings and other data fromSakernas are drawn from a representa-tive national sample of 50,000 house-holds. In the 1998 Sakernas survey therewere almost 28,000 observations on in-dividual wage earners, of whom 16.7%had the primary sector of economicactivity identified as ‘government ordefence service’. Earnings information,including compensation both in cashand in kind, is obtained from theresponse to the question: ‘What is theaverage net monthly income that you re-ceive from your primary activity/job?’.

Table 1 presents a comparison of gov-ernment and private pay by level of edu-cation. On average, governmentearnings at Rp 414,000/month exceed thenational non-government average ofRp 274,000/month. This is not surpris-ing, since government is moreeducation-intensive than private wagesector work. (In the Sakernas sample,49% of workers engaged in the privatewage sector have primary education orlower, compared with only 5% forworkers employed by government.)When data are disaggregated by edu-cation level, a government pay pre-mium remains at lower educationlevels; close to pay parity is achievedfor graduates of senior high school;and a private sector premium emergesfor those with some tertiary education(‘Diploma I/II’ or ‘Akademi/DiplomaIII’) or a university degree (‘Universi-tas/Diploma IV’).


Three conclusions emerge from thissimple comparison of mean earnings.First, the overwhelming majority of gov-ernment workers do not receive ‘lowpay’ compared with their private sectorcounterparts. Close to three-quarters ofall civil servants have a high school de-gree or less, and this group earns paythat is either comparable to or higherthan that prevailing among similarlyeducated workers in the private sector.Second, civil servants with post-second-ary education in 1998 did earn less thanprevailing market wages, but the payratio between the private and public sec-tor for this higher education cohort wasof the order of 1.2:1 to 1.5:1. This is wellbelow the ratios reported in earlier stud-ies, which were based on much nar-rower samples of private sector jobs andhence of market opportunities. Third,the pattern of government pay exceed-ing private compensation for less edu-cated workers and private payexceeding government compensation

for more educated workers—the prob-lem of government salary compres-sion—is a pattern common to other civilservices (Nunberg 1994). Indonesia’ssituation does not appear unique.

Sakernas is a rich data set: it allowscomparison of government and privatepay based on worker attributes otherthan education, such as age, gender andlocation, which are commonly found tobe significant determinants of earnings.Tables 2, 3 and 4 present regression esti-mates that include these variables. Theresults confirm the basic findings re-ported in the simple comparisons ofmeans in table 1.

In table 2, following standard humancapital theory, the semi-logarithmicearnings equation (1) is estimated. Thedependent variable is the natural loga-rithm of monthly earnings (E) of indi-vidual i, and the independent variablesinclude age (A) and age-squared, toaccount for the expected curvature inage–earnings profiles. Five discrete cat-

TABLE 1 Monthly Earnings by Education Level, 1998(Rp ‘000/month, and % of wage earners in category)

Education Level Private Sector Government Ratio: Private toGovernment Pay

Primary or lower 192 290 0.7:1(42.2) (0.7)

Junior high school 239 379 0.6:1(13.7) (1.2)

Senior high school 337 392 0.9:1(23.5) (8.2)

Some tertiary 530 458 1.2:1(3.2) (2.0)

University or higher 771 520 1.5:1(3.3) (2.1)

All levels 274 414 0.7:1(85.8) (14.2)

Source: Sakernas, 1998.


egories of education are included (S2 to

S6). The omitted category is ‘less than

completed primary education’. Specifi-cations also include the followingdummy variables: government (G: 1 =government and defence services; 0 = allother), gender (M: 1 = male; 0 = female),and urban (U: 1 = urban; 0 = rural).

ln( ) ,,

E A A Si c a i a i s s is= + + + =Âa a a b2

22 6

+ + + +d d d eg i m i u i iG M U (1)

Age and age-squared have the ex-pected signs, and high degrees of sig-nificance. Education variables exhibitincreasing and significant earnings dif-ferentials associated with higher levelsof schooling. Men earn a significant pre-mium over women, as do urban overrural wage workers. In the estimation onthe entire wage sector, government

TABLE 2 Determinants of Monthly Earnings of Wage Employees, 1998

Variables All Wage Employees Urban Employees Only

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)

EducationPrimary 0.32 (17.4) 0.34 (12.4)Junior high school 0.53 (26.0) 0.55 (18.1)Senior high school 0.82 (39.9) 0.86 (28.7)Some tertiary 1.16 (41.5) 1.21 (31.5)University 1.26 (33.9) 1.33 (29.3)

Dummy variablesGovernment 0.10 (5.7) 0.002 (0.1)Male 0.40 (33.6) 0.31 (22.6)Urban 0.15 (10.9) - -

No. of observations 27,759 16,366R2 0.39 0.39F 652.4 (10, 1,027) 383.3 (9, 598)

Source: As for table 1.

workers, on average, earn an estimatedpay premium over the private sector,other things being equal, of about 10%.4

If the sample is restricted to urban em-ployees, the magnitude and significanceof the coefficients on age, education andgender remain roughly the same, but thegovernment premium is indistinguish-able from zero. In other words, amongurban employees, government and non-government workers, on average, havethe same reported earnings from theirprimary job if human capital character-istics are held constant.

Why is there a difference in the ‘gov-ernment’ coefficient between the entirewage sector and the urban only sample?The entire wage sector sample includesrural private sector wage workers—primarily plantation labour—who tendto earn lower wages than their urban


counterparts. Central government em-ployees, on the other hand, are cov-ered by a unified salary structure thatdoes not differentiate on the basis ofrural or urban location: rural govern-ment employees, who account for one-third of all government employees,earn the same amount as urbangovernment employees. Thus if par-ity in pay between the governmentand private sectors holds in the urbansample, the same would not be ex-pected in the full sample, whichwould tend to show a governmentpremium.

Tables 3 and 4 extend the analysis bylooking ‘behind’ the average return togovernment employment. The regres-

sion equations used to generate theresults in tables 3 and 4 examinedifferences by education in the earningsstructure of government and the privatesector. Added to the basic earningsfunction of table 2 are interactivedummy variables showing theinteraction between governmentemployment and education levels.Extending equation (1) yields:

ln( ) ,,

E A A Si c a i a i s s is= + + + =Âa a a b2

22 6

+ + +d d dg i m i u iG M U

+ +=Â b es

Is s i i iS G

2 6, , (2)

The coefficients on the interactionterms (

bsI ) indicate whether there is an

TABLE 3 Earnings Structure by Education: Government versus Private Employees(All Wage Employees), 1998

Variables Coefficient (t-Statistic) Coefficient on (t-Statistic)Interaction

Terma

Constant 10.43 (193.1)Age 0.04 (15.6)Age-squared –0.0004 (–11.2)

EducationPrimary 0.32 (17.2) 0.02 (0.09)Junior high school 0.52 (25.3) 0.14 (0.78)Senior high school 0.80 (38.7) 0.05 (0.30)Some tertiary 1.22 (37.6) –0.16 (–0.91)University 1.36 (29.5) –0.28 (–1.59)

Dummy variablesGovernment 0.11 (0.62)Male 0.40 (33.6)Urban 0.15 (10.9)

No. of observations 27,759

R2 0.39F 451.2 (15, 1,027)

aCoefficient on the product of each education dummy variable and the government dummyvariable.



most, around 25%, with relatively weakstatistical significance. The econometricevidence, as in the simple comparisonof average pay in table 1, does not indi-cate that the government is, over all, alow wage employer, nor is there evi-dence of the huge private pay advan-tages for educated workers reported byprevious studies.

These results are maintained aftersubjecting the analysis of pay differen-tials to more stringent econometricspecifications. Two econometric prob-lems confront the earnings regressionreported in this paper. First, workers forwhom we observe earnings are not arandom sample of the population but apotentially self-selected one. If this po-

TABLE 4 Earnings Structure by Education: Government versus Private Employees(Urban Employees Only), 1998


Terma

Constant 10.44 (144.3)Age 0.05 (13.4)Age-squared –0.0004 (–11.4)

EducationPrimary 0.34 (12.3) 0.10 (0.46)Junior high school 0.53 (17.3) 0.25 (1.15)Senior high school 0.84 (28.0) 0.06 (0.29)Some tertiary 1.26 (29.6) –0.16 (–0.71)University 1.43 (26.8) –0.26 (–1.14)

Dummy variablesGovernment –0.003 (–0.21)Male 0.31 (22.7)Urban - -


R2 0.39F 256.2 (14, 598)

aSee table 3, note a.


additional premium awarded to work-ers by education based on their sectorof employment. The impact on earningsof government employment is now thesum of the coefficient on the ‘govern-ment’ dummy variable and the coeffi-cient on the relevant interactive dummyvariable on education (

b bs sI+ ).

Both the entire wage sector sample(table 3) and the urban only sample(table 4) suggest that those governmentworkers with a high school education orless earn a premium over their privatesector counterparts. On the other hand,government workers with some tertiaryeducation or a university degree earnless than they would in the private sec-tor. The respective premiums are, at


tential self-selection is correlated withthe variables of interest, then the uncor-rected estimates would be biased, asthey would capture both a ‘participa-tion’ effect and a direct effect on earn-ings.5 Second, unobserved householdand community characteristics corre-lated with the included characteristics(including the ‘government’ dummyvariable) are not specified in the model.Not correcting for these would poten-tially bias the estimates, for example,ascribing to government a pay differen-tial actually awarded to unspecifiedworker attributes. Employing the ap-proach used by Behrman and Deolalikar(1995) in their analysis of gender differ-entials in the returns to schooling in In-donesia, we estimate alternativespecifications in an attempt to correct forthese potential estimation problems.These approaches yield coefficients thatare little different from those obtainedusing the basic formulations in tables 2to 4. (The appendix provides details ofthe alternative econometric approaches.)

GOVERNMENT AND PRIVATESECTOR PAY COMPARISONSUSING SUSENASBecause the results on government ver-sus private pay run counter to conven-tional wisdom, it is important to identifyother data that might offer an independ-ent test of the relationship. In additionto its labour force survey, BPS also car-ries out an annual household expendi-ture survey (Susenas). This containsquestions similar to those in theSakernas, and permits alternative esti-mates of how government pay relates toprevailing market wages.

The 1999 Susenas was available forthe purposes of this study, and coversover 160,000 households. About one-third of these have household headswho report positive wage income. Acomparison of mean earnings from this

sample (results not shown) by educationlevel reveals findings similar to those oftable 1. Because of inflation, nominalearnings are higher in 1999 than in 1998,but the ratio of government to privatepay by education level is similar.

Table 5 reports regression estimatesfor the subsample of wage earninghousehold heads in the 1999 Susenas.Only household heads are employed inthis part of the analysis because there isonly one value of expenditure perhousehold. Therefore, the right-handside variables in the model need to beaggregated in some way so that there isonly one observation per household. Wechoose to record the characteristics(wage earning status, gender and edu-cation) of the head of the household. Thischoice maintains simplicity in the model(for example, there are no fractional edu-cation levels), and allows simple com-parisons to be made between the Susenasearnings and expenditure models de-scribed below, as well as with the alreadyreported Sakernas earnings model.

The first regression presents an earn-ings equation run on all wage earninghousehold heads. The results are essen-tially the same as those from theSakernas (table 2): earnings increasingbut at a decreasing rate with age; earn-ings increasing with education; andearnings higher for men and in urbanareas. The ‘government’ dummy vari-able is positive and remains significant(with a t-statistic of 2.3). But the relativeeffect of government employment onmonthly earnings, about 4%, is less thanhalf that found in the Sakernas data. Thismay be because the Susenas results, un-like those from the Sakernas, are forhousehold heads only, and most house-hold heads are male. Since womenworking for government earn a greaterpremium (or smaller deficit) over theirprivate wage alternative than do men,other things being equal, a smaller coeffi-


cient on the ‘government’ dummy vari-able in the Susenas than in the Sakernasis expected.6

The second regression in table 5 of-fers an indirect test of relative govern-ment compensation levels. It uses theexpenditure information in the Susenas,and the dependent variable is no longerearnings but the logarithm of monthlyhousehold expenditures per householdmember. If government pay is signifi-cantly lower than market wages, ex-penditure levels in households headedby government workers might be ex-pected to be lower as well. This is notthe case. After allowance is made for theage, education and gender of the house-hold head, and for urban/rural location

and size of household, the effect of sec-tor of employment on household expen-ditures is not significantly different fromzero. Households headed by govern-ment workers, on average, do not havelower expenditures than their privatesector counterparts. As in the resultsusing the Sakernas, if interaction termsare added by education level, averagehousehold expenditures are a littlehigher for those with a junior highschool or lower education level and agovernment job, at parity for high schoolgraduates, and lower for those withmore education and a governmentrather than a private sector job (table 6).7

These results alone, based on relativehousehold expenditures by sector of

TABLE 5 Determinants of Earnings and Expenditures of Household HeadsWho Were Wage Employees, 1999

Independent Dependent VariableVariables

Ln(Monthly Earnings) Ln(Household Expenditureper Person)

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)

EducationPrimary 0.26 (15.7) 0.11 (12.8)Junior high school 0.49 (27.1) 0.28 (25.6)Senior high school 0.73 (40.6) 0.49 (39.7)Some tertiary 1.01 (42.8) 0.67 (35.9)University 1.15 (25.7) 0.88 (33.9)

Dummy variablesGovernment 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. of observations 54,513 54,513R2 0.32 0.34F 617.8 (10, 1,689) 545.8 (10, 1,689)

Source: Susenas, 1999.


employment of the household head, arean imperfect test of the relationship be-tween earnings in the government andthe private sector. Households headedby civil servants might respond to lowerwages by finding other sources of in-come, both legal and illegal, or by send-ing more family members into thelabour force. But the absence of lowerexpenditures among householdsheaded by civil servants is at least con-sistent with the hypothesis that govern-ment workers are not systematicallyunderpaid relative to market opportu-nities. And when these findings are com-bined with the Susenas results onrelative earnings parity between thegovernment and private sectors, a mu-tually consistent picture emerges.

The Susenas results refer to anotheryear, are drawn from a different sample,permit use of expenditure as well asearnings data, and confirm the findingsfrom the Sakernas data on 1998. Thereis no evidence that government is a ‘lowpay’ employer for the average govern-ment employee. Even for the more edu-cated, who do earn less in governmentthan they would in the private economy,the differentials are not large and not ofthe order of magnitude reported inprevious studies.

RECONCILING THE EVIDENCEThe estimates of government/privatepay differentials obtained from BPSsurveys are so different from the find-ings of earlier studies, and from official

TABLE 6 Expenditures by Education Level for Households Headed by Government versusPrivate Sector Employees, 1999


Terma

Constant 12.49 (226.4)Age –0.06 (–19.8)Age-squared 0.0007 (19.7)

EducationPrimary 0.11 (12.0) 0.07 (1.0)Junior high school 0.27 (23.3) 0.02 (0.3)Senior high school 0.49 (38.2) –0.08 (–1.2)Some tertiary 0.74 (31.9) –0.25 (–3.6)University 0.96 (29.6) –0.28 (–3.8)

Dummy variablesGovernment 0.09 (1.3)Male –0.12 (–10.7)Urban 0.26 (21.8)


R2 0.34

aSee table 3, note a.



views on civil servant pay, that it is im-portant to try to reconcile these differ-ences. One explanation, that BPS dataare of low quality and cannot be reliedupon, is not persuasive. The basic age–education–earnings profile that emergesfrom the regression analysis is toosimilar to results from other countries,both in the direction and magnitude ofspecific coefficients and in the degree ofexplanatory power, to allow the conclu-sion that the survey is seriously flawed.Other researchers familiar with thesedata reach a similar conclusion(Behrman and Deolalikar 1995). Arelated explanation is that 1998 and1999 were in the midst of the finan-cial crisis and are atypical years. Con-cerning government/private paydifferentials, this may be true. But thedirection of bias during these years is togenerate a smaller government paypremium (or a larger government paydeficit), because adjustments in nominalpay during these crisis years happenedmore slowly in government than in theprivate sector.

Another possibility is that BPS datasystematically under-represent earn-ings. It is easy to see why reported earn-ings in the survey may be too low. Whenasked, ‘What is the net monthly incomeyou received from your primary job?’,individuals may report only their basicsalary and not allowances or fringe ben-efits. Alternatively, they may be reluc-tant to reveal their true earnings to agovernment enumerator for fear thatsuch information may be used againstthem, for example, by tax authorities.But for either omission to account for theestimated pattern of government versusprivate pay within the Sakernas or Su-senas data requires that individuals whowork in the private sector are more, notless, likely than government workers toforget to include allowances andfringe benefits, or to consciously un-

der-report actual earnings. If under-reporting is equally distributed acrossall workers, reported earnings willsystematically be too low, but the es-timated differential between govern-ment and private workers will remainunaffected.

If there is a bias in reporting, it is civilservants who more often may system-atically report lower than actual earn-ings. Private employers may have less,not more, complicated systems of allow-ances and fringe benefits, because theyare not as constrained by law and regu-lations in revising their salary scales.Government workers are known to re-ceive legal side-payments associatedwith their positions. Payments such ashonoraria, per diem payments in excessof actual travel expenses, and projectbonuses are legally sanctioned forms ofcompensation in government, are oftentransacted in cash, are said to be lessprevalent in the private sector, and maynot be included by civil servants in re-sponse to questions on earnings. Thedirection of potential under-reporting ofearnings does not suggest a reason whythe estimated government premiumover private compensation either is toolarge or is in the wrong direction.

The prevalence of non-legally sanc-tioned payments raises a third possibleexplanation for the failure of theSakernas and Susenas data to supportthe conventional view of significant payadvantages in the private sector. Theexistence of such payments, includingillegal surcharges levied on government-provided goods and services (for exam-ple, side-payments required to get alicence or permit approved), kickbackson government purchases, and graft in-volved in tax evasion is commonly ac-knowledged in Indonesia. Thesepayments may represent a significantsource of earnings for a larger numberof civil servants than do equivalent


illegal actions of private workers. If civilservants were to include rather than ex-clude their ‘extra’ earnings in responseto questions about ‘average net monthlyincome’ in primary jobs, then their self-reporting of earnings would systemati-cally overestimate official wages.Independent evidence on governmentpay scales rejects this interpretation ofthe data.

In August 1998, the date of theSakernas survey used in this paper, gov-ernment salaries were based on 1997salary scales. These scales cover foursalary ranks, each further divided intofour or five sub-ranks. Within each sub-rank, salaries are determined by yearsof service. Government employmentdata from the State Personnel Adminis-tration Board indicate the number ofcivil servants by sub-rank. Selecting themid-point salary to represent the meanbasic wage for each of the 17 sub-ranksresults in an estimate of average govern-ment earnings in 1997 of Rp 310,000/month. Because of the financial crisis, a15% across-the-board increase ingovernment salary scales was applied inApril 1998, raising the average estimatedbasic salary in government toRp 356,500/month. Statutory allow-ances, including family, spouse and riceallowances, amount to a further 15% ofthe basic salary. Adding these supple-ments to the basic wage predicts anAugust 1998 estimate of Rp 410,000/month.8 This is remarkably similar tothe 1998 Sakernas estimate of officialwages for government workers ofRp 414,000/month. Earnings reportedby Sakernas appear to refer to officialwages only.

If not through data accuracy, how elsecan the differing results of the variousstudies on government pay be recon-ciled? Earlier research on governmentpay focused on specific occupationalcategories, often in the managerial

ranks, and compared pay levels to a nar-row set of well-paying domestic and for-eign enterprises. BPS data permit adifferent comparison, between broadeducation categories and relative to theentire labour market. Unfortunately,these BPS data do not support compari-sons at the very top of the occupationhierarchy, and are ill suited to judgingthe reservation wages of senior manag-ers and professionals. More detailedhuman resource surveys are required forthis purpose.

If the different survey designs arenot perfect substitutes, and if the em-pirical results from the various typesof surveys are accurate, then whatmay be mistaken is the interpretationof the data. There may be a ‘fallacy ofassociation’, where significant pay dif-ferentials between top governmentofficials and senior corporate execu-tives in the highest-paying enterpriseshave been considered as pointing tolikely pay gaps for all civil servants.For lower ranks, which comprise themajority of civil servants, this gapdoes not appear to exist relative to theentire domestic labour market.

Even for senior ranks, the observedgap may not be the appropriate targetfor setting salaries. For many civilservants, basic salary and standard al-lowances do not capture the total com-pensation received. Furthermore,senior government officials and pro-fessionals worldwide tend to earn lessthan their private sector counterparts.Government employment possesses ‘acompensating differential’, wheregreater employment security, the ex-ercise of power, sometimes a less de-manding pace and the opportunity toserve one’s country can compensatefor lower earnings. Determining ad-equate compensation for the most sen-ior administrative and professionalcadre is a challenge all governments


face, and requires more detailed scru-tiny than is afforded by this analysis.

PAY, CORRUPTION ANDGOVERNMENT PERFORMANCEIt has long been alleged that the lowpay of Indonesia’s civil service is re-sponsible for widespread corruptionin government. Results from BPS sur-veys in 1998 and 1999 cast doubt onthese conclusions. Most governmentemployees appeared to earn amountscomparable to their opportunitycost—that is, to the earnings theymight have received in the privatewage sector. These results may beeven stronger as of the end of 2000.Presidential decrees in April 1999, andagain in April and May 2000, raisednominal government salaries well inexcess of price inflation. With morelimited recovery in the marketeconomy, government pay may nowexceed private pay for all but a frac-tion of the nation’s 4.6 million govern-ment employees.

If the assumption of low pay is inac-curate, so must be any simple linkage

between pay and corruption. How canwe explain the prevalence of petty cor-ruption by lower ranking governmentworkers, if they earn a premium over theprivate sector? And can the alleged ‘bigcorruption’ among higher-ranking offi-cials realistically be tied to the pay theyreceive relative to their non-governmentcounterparts? Rather than identifyingcorrupt behaviour as a response to ‘lowpay’, it is more helpful to view it as aresponse to opportunity. Solicitingbribes, arranging kickbacks or practis-ing extortion all represent calculatedrisks, where costs and benefits of corruptbehaviour are weighed. If the risks ofgetting caught are low and punishmentminimal, corruption is apt to flourish.Increases in official pay raise the ex-pected cost of losing one’s job. But un-less actions are taken to punish corruptbehaviour, pay increases alone will dolittle to change the cost/benefit calcula-tion, and corruption need not abate.Changes in compensation levels can bepart of a package to reform civil servantbehaviour, but other elements are essen-tial to reducing corrupt practices.

NOTES* The authors would like to thank Barbara

Nunberg, who oversaw the study thatgenerated this work, and Jere Behrman,Martin Rama and two anonymous refereesfor their comments. Deon Filmer is anEconomist at The World Bank. David L.Lindauer is Stanford Calderwood Profes-sor of Economics at Wellesley College. Thefindings, interpretations, and conclusionsexpressed in this paper are entirely thoseof the authors. They do not necessarily rep-resent the views of The World Bank, its Ex-ecutive Directors, or the countries theyrepresent.

1 In addition to being classified by rank, In-donesian civil servants may be classifiedas functional or structural staff. Functionalpositions are filled primarily by profes-sionals. Structural staff, who in additionto their civil service rank are designated

by echelon, occupy the top managerialpositions, and constitute about 10% of allcivil servants.

2 Graham (2000) reviews cross-country evi-dence of the superior pay offered by mul-tinationals as compared with prevailingdomestic wages.

3 Estimates of the size of the labour forceand of the number of wage earners referto 1999, and are based on the Sakernas asreported in BPS (1999), Labour Force Situa-tion in Indonesia, Jakarta, table 15.9. Gov-ernment employment is drawn fromindependent estimates provided by theState Personnel Administration Board(BKN, Badan Kepegawaian Negara) andthe Ministry of Finance.

4 In a semi-logarithmic equation the coeffi-cient on a dummy variable cannot be in-terpreted as the relative effect of the


variable on the dependent variable. In-stead, in order to calculate the relative ef-fect, g, the coefficient, a, must betransformed according to g = ea - 1. Whenthe coefficient on a dummy variable isclose to zero, the coefficient is a close ap-proximation to the relative effect. SeeHalvorsen and Palmquist (1980) for a com-plete derivation.

5 The canonical example is the relationshipbetween education and wages. Accordingto economic theory, only individualswhose wage exceeds the threshold ‘reser-vation wage’ will participate in wagework. One would therefore expect thatindividuals with more education andhigher wages would be over-representedin a sample of wage workers. The effect ofthis selection would be to underestimatethe relationship between education andwages for the population as a whole, sincelow-education/low-wage individuals arerare in the selected sample.

6 Carrying out the same regression on thetotal sample of wage earners, and not juston household heads, yields results that areeven more similar to those in the Sakernas.

For example, the coefficient on the ‘gov-ernment’ dummy variable equals 0.08(with a t-statistic of 5.7) when the entiresample of individuals aged 16 to 60 is used.

7 In table 6 the sign of the age variables, aswell as that of the male dummy variable,has changed. This is because the variablesnow refer to the age and gender of thehousehold head. These are intrinsicallylinked to household size and composition,which are incorporated into the depend-ent variable (i.e. household expendituresper capita) but not controlled for in the re-gression. When the regression includes thenumber of household members and itssquare, the effects of age and of being malebecome significantly positive, and all theother coefficients are qualitatively un-changed.

8 Estimates of official compensation shouldalso include the mean value of functionaland structural allowances (note 1). How-ever, there is no simple way to map suchallowances onto the salary scales. If theywere included, the estimated mean levelof official compensation would be higherthan Rp 410,000/month.

REFERENCESBehrman, Jere, and Anil Deolalikar (1995),

‘Are There Differential Returns of School-ing by Gender? The Case of IndonesianLabour Markets,’ Oxford Bulletin of Eco-nomics and Statistics 57 (1): 97–117.

Clark, David H., and Mayling Oey-Gardiner(1991), ‘How Indonesian Lecturers HaveAdjusted to Civil Service Compensation’,Bulletin of Indonesian Economic Studies27 (3): 129–41.

Graham, Edward (2000), Fighting the WrongEnemy: Antiglobal Activists and Multina-tional Enterprises, Institute for Interna-tional Economics, Washington DC.

Gray, Clive (1979), ‘Civil Service Compensa-tion in Indonesia’, Bulletin of IndonesianEconomic Studies 15 (1): 85–113.

Halvorsen, R., and R. Palmquist (1980), ‘TheInterpretation of Dummy Variables inSemi-logarithmic Equations’, AmericanEconomic Review 70: 474–5.

Jenkins, Rhys (1990), ‘Comparing Foreign Sub-sidiaries and Local Firms in LDCs: Theo-

retical Issues and Empirical Evidence’,Journal of Development Studies 26 (2): 205–28.

Nunberg, Barbara (1994), ‘Experience withCivil Service Pay and Employment Re-form: An Overview’, in D. Lindauer andB. Nunberg (eds), Rehabilitating Govern-ment: Pay and Employment Reform in Africa,The World Bank, Washington DC.

Smith, Theodore (1975), ‘Stimulating Per-formance in the Indonesian Bureaucracy:Gaps in the Administrator’s Tool Kit’, Eco-nomic Development and Cultural ChangeXXIII (4): 719–38.

Wirutomo, P., et al. (1991), ‘Labour in the In-donesian Public Service’, in Wouter vanGinneken (ed.), Government and ItsEmployees, Avebury and ILO, Aldershot:113–34.

World Bank (2000), ‘Indonesia: Seizing theOpportunity: Economic Brief for theConsultative Group’, World Bank, Ja-karta.


APPENDIXIn their analysis of gender differentialsin the returns to schooling in Indone-sia, Behrman and Deolalikar (1995)outline two potential econometricproblems in earnings regression esti-mates. First, workers for whom weobserve earnings are not a randomsample of the population but a poten-tially self-selected one. While theSakernas data do not provide entirelyconvincing variables to allow for sta-tistically correcting the estimates,household demographic compositionvariables can be used in a first stage

model to control for the potential se-lectivity of receiving wages. Specifi-cally, the probability of reportingearnings and the determination ofthose (log) earnings are jointly esti-mated. Variables for the number ofhousehold members under age 10, thenumber aged between 10 and 59, andthe number aged 60 and over are in-cluded in the participation equation,but not in the earnings determinationequation. The assumption underlyingthis restriction is that the age profileof the household determines the op-

APPENDIX TABLE 1A Selection Corrected Estimates: Determinants of MonthlyEarnings of Wage Employees, 1998

All Wage Employees Urban Employees Only

Variables Coefficient (t-Statistic) Coefficient (t-Statistic)

Constant 10.4 72.5 12.4 105.9Age 0.043 10.5 –0.006 1.26Age squared –0.0004 7.34 0.0003 4.81

EducationPrimary 0.32 17.3 0.29 9.14Junior high school 0.53 26.0 0.54 16.2Senior high school 0.81 30.1 0.64 17.4Some tertiary 1.15 23.3 0.72 13.8University 1.25 24.9 0.90 16.05

Dummy variablesGovernment 0.10 5.72 –0.009 0.44Male 0.40 17.3 0.030 1.54Urban 0.15 8.69 - -

Selection model Rhoa –0.015 p-value = 0.84 –0.829 p-value <0.001Chi-square test for jointsignificance of identifyinginstruments (df = 3) 28.43 p-value <0.001 4.91 p-value = 0.178No. of observations 122,242 (27,759 wage workers) 54,490 (16,366 wage workers)

aSample selection using Heckman selection model. Identifying instruments are the num-bers of household members aged 0 to 9, 10 to 59, and 60 and over.



APPENDIX TABLE 1B Fixed-Effects Estimates: Determinants of MonthlyEarnings of Wage Employees, 1998

All Wage Employees Urban Employees Only

Variables Coefficient (t-Statistic) Coefficient (t-Statistic)

Constant 10.4 81.0 10.4 68.8Age 0.046 7.70 0.049 6.04Age squared –0.0004 5.15 –0.0004 3.57

EducationPrimary 0.29 7.98 0.34 6.28Junior high school 0.43 9.68 0.48 7.57Senior high school 0.71 13.4 0.84 12.4Some tertiary 1.05 14.3 1.16 12.9University 1.16 13.8 1.31 14.0

Dummy variablesGovernment 0.07 1.74 –0.004 0.08Male 0.36 19.2 0.29 12.9Urban 12.4 1.06 - -

No. of observations 15,123 9,406R2 0.762 0.748


portunity cost of participation in thewage labour market, but does not di-rectly determine earnings.

The second potential problem is ofunobserved heterogeneity. There arepotentially unobserved household andcommunity characteristics that are cor-related with the included characteristics(including the ‘government’ dummyvariable), but are not specified in themodel. Not correcting for these wouldpotentially bias the estimates. In orderto allow for this possibility, a householdfixed-effects model of the (log) earningsequation is estimated. An additionalbenefit of this procedure is that if selec-tivity is based on household attributes,as is assumed in most empirical appli-cations, then this fixed-effects approachshould control for selectivity in the wagelabour market as well as for the more

generic potential unobserved heteroge-neity problems.

Both of these approaches yield esti-mates that are little different from thosein tables 2 to 4. In the all-Indonesia esti-mates the identifying instruments arejointly significantly different from zeroin the participation equation, but thepoint estimate of an approximate 10%average pay premium in the public sec-tor remains (appendix table 1A). In theurban-only sample, the identifyingvariables perform less well and arejointly insignificantly different fromzero. The resulting estimate of the aver-age government premium remains in-significantly different from zero. Theselection-corrected interactive modelsalso yield the point estimates that showthat the public sector premium dimin-ishes with the level of education, and


becomes negative for those with at leastsome tertiary education (results avail-able from authors).

The fixed-effects estimates yieldsimilarly consistent results. Since thisestimation method relies on withinhousehold variation to identify an ef-fect, the significance of the results islower. Nonetheless, the point estimateon the average public sector premium

is about 7% in the all-Indonesia sam-ple (and is significantly different fromzero at the 10% level), and remainsinsignificant in the urban-only sample(appendix table 1B). The interactionmodels again suggest that the publicsector premium becomes negativeonly for those with at least some terti-ary education (results available fromauthors).

does indonesia have a ‘low pay’ civil...

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