Public Choice 97: 107-120,1998. 107© 1998 Kiuwer Academic Publishers. Printed in the Netherlands.

Corruption and government size: A disaggregated analysis

RAJEEV K. GOEL and MICHAEL A. NELSON*Department of Economics, Illinois State University, Normal, IL 61790-4200. U.S.A.

Accepted 23 April 1996

Abstract. Using annual state-level data over 1983-1987, this paper examines the effect ofgovernment size on comiption hy public officials by including hoth demand and supply sideincentives for engaging in corrupt practices. Our objectives are twofold. First, we assess therelationship between the incidence of corruption and overall measures of the size of the fed-eral government and the state-local sector in each state. Second, we explore what kinds ofgovernment activities are more likely to be successful in deterring abuse of public office. Ourresults are generally suppt^rtive of Becker's "crime and punishment" model. Regarding theprimary focus of the paper, our results show that government size, in particular spending bystate govemments, does indeed have a strong positive influence on corruption.

1. Introduction

Media scrutiny and public interest in corruption by government officials hasheightened in recent years. Much of this attention has focused on the activ-ities of federal officials, especially in matters involving the military and thedefense industry. Campaign contributions and the power/infiuence of polit-ical action committees has been another area receiving special attention.Indeed, all this has led to calls for revising the federal bribery statute, espe-cially with regard to political contributions (Welch, 1989). Further, increasedcorruption by public officials, real or perceived, is often used as an argumentfor additional privatization of government activities.

While the literature on rent-seeking dates back at least to Niskanen {1971),much of this work has not distinguished between legal and illegal forms ofpublic sector rent-seeking activities. Numerous aspects of corrupt behaviorof public officials have been studied including the relationship between rent-seeking and corruption (Appelbaum and Katz, 1987; Nitzan, 1994a; Rose-Ackerman, 1978; Tollison, 1982), whether bribery promotes more efficient

" Earlier versions of this paper were presented at the 1995 Williamsburg Atlantic EconomicConference, the 1995 meetings of Southcm Economic Association and the University of Agri-culture in Nitra, Slovakia. We wish to thank Richard Arnould, S. Fredric Koch and Dan Richfor u.seful comments. Duane Abbott, Russell Anderson and Jih Young Chang provided valu-able research assistance. Insightful comments of a referee are also appreciated. All remainingerrors are our own.

108

bureaucratic supply (Lui, 1985), the link between market structure and cor-ruption (Rose-Ackerman, 1975), and the incentives for taking bribes (Allard,1988;Basuetal., 1992; Besley and McLaren. 1993;Cadot. 1987; Lui, 1986).

The above literature is primarily theoretical in its orientation. These studiestypically focus on individual incentives for engaging in corrupt practices anddo not explicitly consider the role of government size in encouraging thisbehavior. Focusing on the illegal aspects of rent-seeking (i.e., corruption), inthis paper we argue that the incentives for engaging in corrupt practices maybe demand or supply driven. Demand-driven incentives result from individualincentives to engage in corrupt practices. Supply-driven incentives stem fromthe large size of potential rents such as the size and magnitude of governmentspending.

The empirical research on corrupt activities associated with rent seekinghas been limited - mainly due to lack of appropriate data.' Notable excep-tions are Goel and Rich (1989) and Goel (1991 ).̂ Goel and Rich (1989) applya Becker (1968) model of criminal behavior to an aggregate data set on cor-rupt activity by level of government in the U.S. for 1970-83. Goel (1991)uses a similar data set to study the relationship between government size andbribery. These papers marshal! evidence consistent with a principal propo-sition of the Becker model that higher probabilities of detection or stifferpenalties lead to lower levels of corrupt activity by public officials.

In the present paper we build upon the earlier empirical work by applyinga Becker model to a data set consisting of annual state-level observations ofconvictions for public office abuse over the 1983-1987 period.^ Examplesof such convictions include public officials accepting bribes to expedite theirduties, to grant preferential treatment in awarding government contracts andlaw enforcement officials accepting bribes from convicts to exercise leniencyin punishment.

The incidence of corrupt activity by public officials varies greatly amongthe individual states. Similar variation is observed in the size of the publicsector engaged in law enforcement activities in each state. Aggregate datasets, consisting of annual observations for the entire state (or local) sector,mask these variations, and hence important insights into the determinants ofcorrupt activities by public officials may be lost. Technically, our data setpermits more precise measures of key explanatory variables and providesgreater within-sample variation for most of these variables.

Our objectives are twofold. First, we assess the relationship between theincidence of corruption and overall measures of the size of the federal gov-ernment and state-local sector. From a public policy perspective the focuson the state-local level is especially timely given the recent push in Congress

109

towards greater decentralization of government responsibilities. Second, weexplore what kinds of government activities or policies are more likely to besuccessful in deterring abuse of public office.

2. Convictions for abuse of public office

In an interesting paper Anechiarico and Jacobs (1994) trace the history ofefforts to control corrupt behavior by public officials. They maintain tbat thishistory shows several distinct periods where different approaches or strate-gies were taken to promote honest behavior by public officials and that effortsto curb corrupt activity have generally "'ratcheted up" over time. The mostrecent period, they contend, dates back to Watergate and the New York Cityfiscal crisis in the early 1970s. These events led to the current "mainstream"view of public administration, a view that emphasizes the increased use oflaw enforcement as a way to deal with corruption. This perspective also pre-scribes the use of broader definitions of corruption, including the targeting oflow-paid public administrators (p. 472).

The increased emphasis on law enforcement over the past two decadesto combat misconduct in public office is reflected in the trend in the num-ber of convictions of public officials, especially at the federal level. Thesetrends over the 1975-1991 period are displayed in Figure 1 for each lev-el of government.'̂ Convictions of federal officials, for instance, increasedthroughout the period, with a sharp spike up in 1983.'̂ In contrast, state con-victions exhibited an upward trend until the early 1980s and then stabilized.Convictions at the local level have risen more or less steadily throughout theperiod.

Conviction of public officials also varies considerably across Federal judi-cial districts when the data are aggregated to the state level. Table 1 dis-plays tbe number of convictions per thousand full-time employees (all levelsof government), by state, averaged over the years 1983-87.^ The data areordered according to conviction rates, with the state with the highest rate atthe top.

Examining these data it is clear that the abuse of public office by electedand appointed officials, at least convictions for such abuse, vary dramaticallyamong the states. For the period considered the range between the high andlow conviction rate state exceeds 10:1, a calculation that excludes Vermontwhere there were no public officials convicted of abuse during these years.A model to explain this interstate variation in conviction rates is presented intbe next section.

no

-lederai- stale- iocai

Figure i. Convictions for abuse of public office (by level of government)

3. Model aud data

Our empirical setup is inspired by the work of Becker (1968). A Beck-er "crime and punishment" model typically focuses on an individual whocompares the expected utilities of legal and illegal behavior, where the latterinvolves some probability of detection and punishment. Fundamental propo-sitions of this model are that tbe incidence of illegal behavior is directlyrelated to the potential gains from illegal activity and indirectly related tothe probability of conviction and the severity of punishment^

The estimated model takes the following form

CONV/EMP = /[DEFENSE. OFED. TEXP. JEMP. POLICE, USA,

FBI, ODOJ, SENT, RELSAL, UR] (1)

where CONV is the number of public officials (federal-state-local) convict-ed for abuse of public office in a state in a given time period and EMP istotal public sector employment in that state. Complete details, descriptivestatistics, and data sources for these variables, and lor each right-hand sidevariable in the model can be found in Table 2.

In the case of the public official, we assume that the potential gains fromcorrupt behavior are a function of variables describing the size and scope of

HITable I. Conviction rates of public employees, by state: 1983-1987 (convictions per thousandemployees)

Slate

TNAKSDMSALSCOKPAGANYWVILRIMTNVLACTARNCOHMAMIIDNMCA

Convictions

.132

.128

.121

.084

.079

.077

.076

.069Ml.064.060.060.057.056.055.050.049.049.047.046.044.043.043.041.040

State

MOFLKSCODEKYNEWIINMDTXVAHIlANJLHTMNMENHORAZWYNDWAVT

Convictions

.040

.040

.039

.038

.037

.037

.037

.035

.035

.033

.030

.028

.027

.026

.023

.023

.022

.021

.021

.020

.018

.016

.013

.012

.000

Source: Authors calculations. See Table 2 for details.

the public sector. The supply side infiuences (denoted by government size),demand-side factors (such as relative salaries of public officials) and thedeterrence and punishment variables, are described in further detail below.

Government size. An important issue of interest with respect to govern-ment size is to study whether there is a systematic relationship between thesize of government and the incidence of corruption.*^ It is, of course, reason-able to expect more political knavery with larger governments. A larger gov-ernment might also imply greater bureaucratic delay inducing rent-seekersto offer bigger bribes. However, some have argued that this relationship isnonlinear and the corrupt activity increases disproportionately with govern-ment size. For example, Scully (1991: 99) argues that, "The increase in the

112Tahie 2. A description of the variables used in tbe analysis

Variable name Variable description Descriptivestatisticsmean (st. dev)

Datasource

CONV/EMP Number of public officials convicted of abuse ofpublic office per thousand of full-time equivalentgovernment employees in a state

DEFENSE Real per capita gross stale product originating inthe federal military government industry^

OFED Real per capita gross state product originating infederal civilian government

TEXP Real per capita total expenditure by state andlocal governments

UR Annual average state unemployment rateRELSAL Average wage/salary of state and local govern-

ment employees, expressed as a fraction of stateper capita personal income

SENT Average incarceration sentence for convictedoffenders, all oftenders (in months).**

POLICE Real per capita police expenditures by state andlocal governments

JEMP Full-time equivalent state and local justice sys-tem (judicial and legal) employment per thou-sand population in 1985

USA Employment in Office of State's Attorneys perthousand population in the state. 1982

FBI Employment in the Federal Bureau of Investiga-tion per thousand population in the state. 1982

ODOJ Employment in other U.S. Depanment of Justiceactivities per thousand population in the state,1982

0.0471 (0.04)

$252 (223)

$427(224)

$3,200 (823)

7.5% (2.2%)

166.26% (17.25)

49.71 (8.88)

$101 (32)

.383 (.29)

0.016(0.004)

0.044(0.019)

0.166(0.74)

11>2]

[3.4]

[3.4]

[4]

[51[2,4]

[6]

[4]

[6]

[7]

17!

17|

Data sources:[1] U.S. Departtiieiit of Ju.sticc, Report to the Congress on the Activities and Operations ofthe Public Integrity Section for 1987, July 1988.[2] U.S. Departmetit of Commerce. Government Employment, (various years).[3] Data disk. "Gross State Product." Bureau of Economic Analysis, 1994.[4] U.S. Department of Commerce. Governmental Finances, (various years).[5] U.S. Department of Commerce, Statistical Abstract of the United States, (variousyears).[6] U.S. Department of Justice, Sourcebook of Criminal Justice Statistics, (various years).17] U.S. Department of Justice, Employment Fact Book, 1982.Notes; *AII nominal data converted to 1987 real dollars. ''Based on mean of stale data forthe years 1984, 1985, and 1988.

size and scope of government expenditure represents an enonnous rise in theopportunities for rent-seeking through budgetary reallocations".

113

We employ three measures of the size of potential rents to be captured(legally or illegally) by public officials.̂ Two of these variables pertain tothe size and scope of the federal government in a state, including the size ofthe defense industry (DEFENSE) (Goel, 1991) and the level of federal civil-ian government activities (OFED) in a state. Opportunities for rent-seekingat tbe state-local level are measured by TEXP. the total spending by state-local governments in each state. "̂ ' We make the distinction between defenseand non-defense federal activity in a state because convictions of individualsin the military are not included in the dependent variable (CONV) as thesegovernment employees operate under a separate judicial system."

Deterrence. Five variables are included in the model to account for theprobability that criminal behavior will be detected. Two of these variablespertain to state law enforcement activity, including JEMP, the number ofstate employees in tbe justice and legal services department, and, POLICE,the level of real police expenditures in the state. To supplement tbese mea-sures of deterrence, we add three variables describing federal law enforce-ment efforts in the state: USA, FBI, and ODOJ. USA represents employmentin State's Attorneys Offices in each state, FBI is Federal Bureau of Investi-gation employment in the state, and ODOJ measures other U.S. Departmentof Justice employment in the state. '̂ Each of tbe deterrence variables is nor-malized by state population.

The expected signs on the coefficients of tbe deterrence variables are ambigu-ous because data limitations forced us to measure criminal behavior as con-victions and not as the actual incidence of corruption (including undetectedabuse). A positive coefficient on a deterrence variable is expected if the actuallevel of criminal behavior remains largely unaffected and a greater proportionof these individuals are caught; or if tbe law enforcement officials themselvesare corrupt. On the other band, the sign will be negative if the activity leadsto a reduction in actual corrupt activity.

Severity of punishment. To measure the consequences of detection of mis-conduct in public office we include SENT in the model. This variable is theaverage jail sentence for convicted offenders for all criminals in the state.More severe sentences handed out by the state's judicial system should leadto lower rates of criminal behavior, including abuse of public office.

Other considerations. As to the other variables in equation (1), we wouldexpect lower relative government wages (RELSAL) to make bribe-takingmore attractive for government employees. Finally, following Goel and Rich(1989) the state unemployment rate (UR) is added to capture the macroeco-nomic influences on corruption. It is possible that pubHc officials are offeredmore bribes in periods of high unemployment because a greater number ofindividuals are seeking favors in periods of economic downturns. One could

114further argue that bribe givers have higher discount rates in periods of eco-nomic downturns (as impatient individuals are chasing fewer opportunities),which increases their willingness to offer bribes. On the other hand, whilewe do not have relevant data, one could argue that the size of bribes mightdiminish in recessionary times.

4. Results

We use LIMDEP computer software to conduct the empirical analysis. Twodata sets are used in the estimation, one panel and the other cross-sectional.The panel data set consists of annual observations for the 50 states over the1983-1987 period. Missing values for some variables in the model for select-ed years yielded a data set with 244 observations in total.

For many states the data reveal that the number of convictions of publicoffice abuse are highly volatile from one year to the next. Part of this volatili-ty may be attributed to the "lumpy" nature of some investigations into publicoffice abuse; for example, a large number of convictions in a state for someyear could represent the culmination of prosecution efforts towards a scandalinvolving many public office holders. To address this possibility, we also esti-mated the model with a cross-sectional data set where the dependent variableis the average state conviction rates over the 1983-87 period.'̂ ^ Presumably,any "lumpiness" in convictions for public office abuse is less significant whenviewed from the perspective of a five-year average.

Since we are analyzing proportions (grouped) data, a LOGIT model is esti-mated for both data sets. To account for the heteroskedasticity that is inherentin the estimation procedure (for details, see Greene, 1993: 653-654), eachobservation is weighted by the number of government employees (EMP) inthe state.'^

The maximum likelihood estimates for the full model using the panel dataset are reported as Model 1 in Table 3. Overall, the model performs wellwhen assessed by the log-likelihood statistic. As to the findings for our indi-vidual explanatory variables of corruption, there is little evidence that a largerdefense industry in a state (DEFENSE) is associated with a lower incidenceof conviction for abuse of public office. '̂ Larger non-military federal activityin a state's economy (OFED) has a statistically significant negative effect onsuch incidence. In contrast, larger state-local governments (TEXP) are posi-tively and significantly linked with higher rates of detected corruption. Giventhat the level of law enforcement effort is held constant elsewhere in theestimating equation, the positive coefficient for TEXP also suggests that thenumber of cases of actual abuse of public office increase disproportionatelywith the size of state-local government.'^

115Table 3. Determinants of state convictions of public officials* (Dep. Var. = eonvictions per10,000 govt. employees)

Explanatory variable

Constant

Oefense spending(DEFENSE)

Other federalgovernment (OFED)

State-localgovernment size(TEXP)

Slate unemploymentrale (UR)

Relative salary(RELSAL)

Average sentence(SENT)

Police expenditures(POLICE)

Judicial employment(JEMP)

State's ^tomeys(USA)

FBI employment(FBI)

Other Justice Dept.employment (ODOJ)

Log-likelihoodNo. observalions

Annual conviction rates: 1983-87

Model I""

-2.995***(13.51)-O.O(X)1(1.11)-0.0003**(2.38)0.0004***

(8.76)

0.047***(4.85)-0.008***(6.02)0.007**

(2.86)-0.012***

(10.26)-0.187**(2.51)30.734***(5.07)15.058***

(10.98)-1,829***(5.99)

-14902.49244

Model 2"

-2.678***(14.02)-0.00009(0.89)-0.0002**(2.25)0.0003***

(8.48)

0.044***(4.54)-0.007***(5.68)

-

-0.011***(9.84)-0.162**(2.20)34.065***(5.77)14.506***

(10.78)-1.885***(6.23)

-14906.56244

Average conviction rate: 1983-87

Model 11''

-3.401***(6.63)-O.(X)02(0.78)-0.0001(0.46)0.0003**

(2.97)

0.074**(2.57)-0.006*(1.74)0.007

(1.27)-0.011***(4.01)0.000004

(0.22)25,000*(L92)9.924**

(2.57)-1.630**(2.40)

-3019.2950

Mode! 22>'

-3.120***(6.77)-0,0002(0.66)-0.0001(0.34)0.0002**

(2.68)

0.071**(2.47)-0.005(1.52)

-

-O.OIO***(3.81)0,00001

(0.36)29.038**(2.32)9.013**

(2.38)-1.730**(2.58)

-3020.0950

Notes:" Absolute value of t-statistic in parenthesis.*" - statistically significant at the ten percent level; '* - five percent level; "*' - one percentlevel.

Since relatively low-level federal officials are included in the sample,, thefindings for the OFED variable support the perspective that corruption is sys-tematically more prevalent at higher levels of bureaucracy. Further, the con-trasting results for the government size variables (by the level of government)provide empirical support for Huntington's {1968: 68) conjecture that thereshould be greater corruption by officials of subnational governments than atthe federal level.'^

116The results for the detection probability variables are mixed. Examining

the findings for the police expenditures (POLICE) and state judicial employ-ment (JEMP) it appears that higher levels of both variables deter the inci-dence of actual corruption (including undetected). These findings are sup-portive of the Becker (1968) model proposition that greater resources devotedto law enforcement leads to reduced criminal behavior due to higher proba-bility of detection.'^

Of the federal enforcement employment at the state level, other JusticeDepartment employees (ODOJ) seem to have the greatest corruption-deterringeffect. The positive and statistically significant coefficient on the State's Attor-neys (USA) and FBI variables suggest that these offices have a greater effecton detection rates of public office abuse than on the actual level of corruptactivity.'^

The result for the penalty variable, average sentence (SENT), is not sup-portive with the prediction of the Becker (1968) model. The positive andstatistically-significant sign for this variable indicates that higher penaltyrates are associated with a greater incidence of (detected) corruption by pub-lic officials. One possible interpretation of this finding is that it simply repre-sents spurious correlation with the dependent variable. It should be recalledthat our penalty measure is the average incarceration for various offenses,and not necessarily offenses involving abuse of public office.

Turning briefiy to the other variables in the model, a higher state unem-ployment rate (UR) leads to higher levels of corruption. This result confirmsa similar finding by Goei and Rich (1989) and is consistent with the argu-ment that individual discount rates increase in periods of economic down-turns. Finally, higher govemment salaries (RELSAL) are found to discouragecorruption, as expected, reinforcing the belief that relatively low-paid gov-emment employees would engage in comapt practices to supplement theirwages.

In light of the unexpected results for the sentencing variable, we re-estimatedthe model without SENT. The panel data set estimates are reported as Model2 in Table 3. Examining these findings it is evident that ail the conclusionsfrom Model 1 still hold.

Finally, as to the cross-sectional set of regressions. Models 11 and 22, inTable 3, the main findings of our panel data are confirmed, although theeffects of non-defense federal spending (OFED) and judicial employment(JEMP) are no longer statistically significant. The influence of relative salariesof govemment employees (RELSAL), although of the expected sign, is alsostatistically weaker in this instance.

117

5. Conclusions

This paper uses a state-level data set to examine the effect of governmentsize on corrupt practices of public officials. Becker's (1968) "crime and pun-ishment" model provides the theoretical underpinnings of our work. On thepotential benefit side of corruption we include both demand and supply sideinfiuences. The costs of engaging in such activity include the probability ofdetection and punishment. Two sets of regression, one dealing with the paneldata and the other dealing with a cross-section of states, are used.

Regarding the primary focus of the paper, our results show that governmentsize, in particular spending by state governments, does indeed have a strongpositive infiuence on corruption. The effect of non-military federal spendingin a given state is negative in the panel regression. The contrasting resultsfor government size at the federal and state level might be due to the factthat while our data includes top state bureaucrats (with more discretionarycontrol leading to greater opportunities for bribery), only medium- and lowlevel federal officials are included. Whereas data limitations do not permit usto be more specific, this suggests that there are significant nonlinearities in therelation between government size and corruption. One important implicationof this finding is that if the objective of the federal government is to controlcorruption, then awarding greater control over spending to states might nolbe a good idea.

Several other interesting results emerge from our analysis. Given the posi-tive relationship between corruption and the size of state-local governmentsand a negative relationship between government salaries and corruption, itturns out that policy makers looking to reduce corruption by cutting govern-ment spending should be reducing expenditures other than those on employ-ee salaries. Further, it appears from our analysis that corruption increases inperiods of economic downturns. Finally, whereas police spending seems tomildly check corrupt practices, FBI and State's Attorneys employment haveno such effect. These latter federal enforcement activities appear to have lesseffect on the actual incidence of corruption and more effect on the level ofdetection.

Our results should be interpreted in the context of the measurement prob-lems and the availability of appropriate data. For example, individual attribut-es of rent seekers and corrupt officials, although difficult to obtain, mightexplain incentives for offering and accepting bribes. As better data becomeavailable further light can be shed on this important topic.

118

Notes

1. One major problem is the lack of data on the number of people who indulged in corruptpractices, but were not detected. Moreover, the figures on detection do not lell us if thosecharged with corruption are first time offenders or repeat offenders {see Feinstein. 1991).

2. In a related paper. Beck et al. (1992) marshall empirical support for McChesney's (1987)argument that politicians engage in their own rent-seeking activity (legal or illegal) bythreatening the elimination of private-sector transfers. However, their empirical work doesnot make any distinction between legal or illegal bribes to public officials.

3. The span of our data is constrained by the availability of data. Years prior to 1983 werenot included because there appears to be a major change in the reporting of convictionsfor abuse of public office at that point in time (see U.S. Department of Justice (1988) fordetails).

4. Over the past two decades the Depiirtinent of Justice has annually surveyed U.S. Attor-ney's offices in each Federal judicial district on the number of elected or appointed indi-viduals convicted for abuse of public office. For data collection U.S. Attorneys" officeswere given the following instructions, "For purposes of this questionnaire, a public cor-ruption case includes any case involving abuse of office by a public employee. We arenot excluding low-level employees or minor crimes, but rather focussing on the job-relatedness of the offense and whether the offense involves abuse of public trust placedin the employee". (U.S. Department of Justice. 1988: 44). Examples of cases included inthe data are corruption by police, corrections system employees, school officials, electedofficials like mayors and election commission employees, etc.

5. According to the Department of Justice (1988, Table 5.35) the jump in Federal convictionsm 1983 is attributable to two factors: greater focus on Federal corruption nationwide, andmore consistent reporting of abuses by lower-level employees.

6. The most recent year of published data on convictions at the state level is 1987.7. The relative effectiveness of detection probability and punishment severity in deterring

criminal behavior is a matter of some debate. Davis (1988: 383) finds that, "..irrespectiveof the criminal's attitude toward risk, an increase in the probability of detection is morelikely to deter crime than a comparable increase in penalties",

8. Nitzan (1994b) models legal rent-seeking where the sole objective of a politician is tomaximize transfer spending since the politician gets a share from the total spending.

9. The government si7.e measures enter our model as exogenous variables. An anonymousreferee points out that corrupt government officials might seek to increase government sizein anticipation of generating more corrupt opportunities. Hence, a simultaneous equationsframework may be preferable in order to investigate the possibility of a two-way causalitybetween corruption and government size. A fully-specified simultaneous equation model,including equations explaining defense spending, other federal spending and total state-local spending, is a substantial undertaking that is well beyond the scope of this paper.Moreover, it is also worth noting that models of government bureaucracy (e.g., Niskanen,1971) conclude that government officials seek to expand the size and scope of govern-ment in legal ways as well. It is not clear that corrupt officials would be more successfulat increasing govemment size relative to bureaucrats who seek to do this by legal means.From this perspective, at least, corruption, per se, may not have a direct effect on govem-ment size.

10. One should bear in mind that institutional details such as procurement and contractingmechanisms (although dif^cult to quantify) might contribute as much to corruption as thesize of potential rents.

11. In preliminary analysis we disaggregated non-defense spending into spending on welfareand "in-house" production (publicly-produced goods and services). None of the estimatedcoefficients were significant suggesting the possibility that the data were not reflectingthe institutional differences in the types of spending. Further, introduction of state level

119

dummy variables in the regression substantially diminished the statistical significance ofthe estimated coefficients.

12. Included in ODOJ are Justice Department employment in the Federal Prison System;Immigration and Naturalization; Drug Enforcement; U.S. Marshals Service; and. Offices,Boards, Divisions.

13. Average 1983-87 values were also used for the right-hand-side variables with the follow-ing exception. :̂ JEMP (1985 value) and USA. FBI, and ODOJ (all 1982 values).

14. Maximum likelihood estimates based on proportions data breaks down if any of the pro-portions are zero. There are 12 such cases in the panel data sel. and following Greene(1993: 655), the value of (CONV/EMP) was set to 0.001 in such cases.

15. Recall that our defense variable includes all activities surrounding the defense industry ina state, only some of which is performed by military personnel.

16. Detection rates of abuse are unlikely to rise to the extent that the level of law enforcementeffort is held constant elsewhere in the estimating equation.

17. Federal officials located outside of Washington DC have less discretionary control overspending (that might lead to greater bribery) relative to their state-level counterparts. Thislevel of institutional detail is not being captured by our analysis.

18. Caution needs to be exercised when interpreting the results for the police expendituresvariable hecause our measure includes all activities surrounding the preservation of lawand order, including traffic safety.

19. Another reason for the positive sign on USA and FBI might be that states with greatercorrupt activity attract increased federal deterrence resources.

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