fiscal consequences of public corruption: empirical evidence from state bond ratings

Fiscal Consequences of Public Corruption: Empirical Evidence from State Bond RatingsAuthor(s): Craig A. Depken II and Courtney L. LafountainSource: Public Choice, Vol. 126, No. 1/2 (Jan., 2006), pp. 75-85Published by: SpringerStable URL: http://www.jstor.org/stable/30026576 .

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Public Choice (2006) 126: 75-85 DOI: 10.1007/s 11127-006-4315-0 © Springer 2006

Fiscal consequences of public corruption: Empirical evidence from state bond ratings

CRAIG A. DEPKEN, II & COURTNEY L. LAFOUNTAIN The University of Texas at Arlington, Campus Box 19479, Arlington, TX 76019, U.S.A. (*Author for correspondence: E-mail: [email protected])

Accepted 6 October 2004

Abstract. Empirical analyses of public corruption focus predominantly on international differences; regional differences in public corruption within a single country receive little attention. We empirically investigate the effect of public corruption in the United States on state bond ratings, which previous research shows are inversely related to net interest costs on public debt. After controlling for various economic influences on bond ratings, we find that more corrupt states have lower bond ratings, which implies that taxpayers in more corrupt states face a negative pecuniary externality by paying a premium for debt.

JEL-classification: D73, H74, H71.

1. Introduction

Previous research suggests that public corruption can harm an economy in a variety of ways. Bureaucrats may over-regulate to increase the opportunities to collect bribes, thereby reducing the incentive to invest and diminishing overall economic performance (Myrdal, 1968; Mauro, 1995). The current regime is less likely to stay in power if the economy is not performing well, which reduces all bureaucrats' time horizons and increases their desire to collect current rents at the expense of projects with longer time horizons (Mauro, 1995). Bureaucrats may create artificial barriers to entry to generate monopoly rents from which bribes are extracted, at a cost to consumers of less product variety (Bliss & Di Tella, 1997). Competition to become a bureaucrat with the power to collect economic rents from corrupt activity can cause individuals to over-invest in political capital relative to human or physical capital (Ehrlich & Lui, 1999). Bureaucrats may skew public goods provision towards those that offer better opportunities to collect rents, such as highway construction or public housing projects, and away from those that do not, such as education or health care (Shleifer & Vishny, 1993). Finally, the cost of public projects may be higher when corrupt agents are in charge of procurement (Burguet & Che, 2004).

In this paper, we propose an additional channel through which public corruption may cause harm to those not directly engaged in corrupt behavior. Governments with more corrupt bureaucrats must offer a higher interest rate



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when they issue debt, increasing the burden on a state's taxpayers to repay the debt.

Why would debt be more costly for corrupt governments? Consider a government that finances a public goods project, such as new public housing, with debt. The cost of the project may be inflated if public corruption takes the form of kickbacks to the bureaucrat who awards the building contract, so a corrupt government may provide less public housing for the same cost. In addition, the new public housing may provide the highest return for corrupt bureaucrats but need not be the project that would generate the highest return for the state. In general, if public corruption slows economic growth or otherwise causes an economy to perform badly, then the more corrupt the government, the less likely (on the margin) that government will be able to pay off its debt in the future. Thus, the more corrupt the government, the riskier is the debt it issues and the higher the premium the government must pay to borrow. As government debt is ultimately serviced through taxation, higher net interest costs impose an extra burden on a government's constituents, i.e., a negative pecuniary externality is created by public corruption.

We explore this hypothesis by investigating how public corruption in the various U.S. states affects state bond ratings. Rubinfeld (1973) and Liu & Thakor (1984) show that bond ratings are inversely related to the net interest cost for state and municipal bonds, respectively. It follows that if public corruption has a negative impact on a state's bond rating then it will increase the interest a state pays on its debt, thereby increasing the burden taxpayers bear to repay that debt.

The majority of empirical studies of public corruption focus on the impact of corruption across countries, including studies on economic growth (Mauro, 1997; Mo, 2001), foreign direct investment (Wei, 2000), foreign aid (Alesina & Weder, 2002), and productivity (Lambsdorff, 2003). Our approach differs by focusing on the impact of different levels of corruption within a single country. This avoids many potential problems that international comparisons may encounter. First, our measure of corruption, defined below, is based on U.S. federal law and is therefore consistent across the various states. In contrast, using international data makes it difficult to consistently and accurately measure corruption across different countries and cultures (Mauro, 1995). Specifically, what is considered corruption in one country may not be considered unwar- ranted in other countries. Therefore, ad hoc definitions of corruption, without regard to local customs or regulations, may mischaracterize certain activities. In addition, it is difficult in international comparisons to control for national heterogeneities that may cause omitted variables bias in econometric results. Finally, we are able to explore regional effects of corruption that would be difficult, at best, to identify with international data.

Using data for U.S. states from 1995 through 2000, we find that a state's bond rating is inversely related to the level of public corruption in that state,



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after controlling for other economic influences. The effect is economically, as well as statistically, significant. Thus, the evidence we present is consistent with the hypothesis that the more corrupt a government, the riskier are the bonds it issues. Using Rubinfeld's (1973) estimates, we calculate the implied increase in the interest rate it pays on those bonds. Public projects financed with state debt cost more in states with more corruption than in states with less corruption, i.e. public corruption creates a negative pecuniary externality for a government's constituents.

2. Corruption and Bond Ratings: Empirical Analysis

To analyze the effect of public corruption on state bond ratings, we estimate the following model:

BONDRATEst = a + [CORRUPTIONst + yXst

+ t=1996

SEtYEARt + est,

where BONDRATEst is the average bond rating for state s in year t, CORRUPTION,t is the number of federal public corruption convictions per 100,000 residents in state s in year t, Xst is a vector of control variables, YEARt is a dummy variable for year t, (a, f, y, 8) are parameters to be estimated, and est is a stochastic error term. Table 1 lists the variables included in the analysis and summarizes their definitions, data sources, and descriptive statistics of the sample.

Three financial analysis services rate state government bonds: Moody's, Standard and Poor's, and Fitch. Each company uses a different scale with which to rate bonds: Moody's has 35 possible bond ratings, ranging from a highest rating of Aaa to a lowest rating of C, Standard and Poor's has 25, ranging from AAA to C, and Fitch has 19, ranging from AAA to C; each service has "plus" and "minus" rankings within most categories. We convert each service's ordinal bond rating into a cardinal score. The numerical rating for state s in year t by service j is Rstj E { 1,..., Nj}, where 1 corresponds to the lowest bond rating and Nj the highest bond rating by service j. We then normalize each cardinal ranking by the total possible rankings available by that service to obtain Rstj = Rstj/Nj. Finally, we average the available normalized ratings for a state (not every state offers general public debt, e.g., Nebraska, nor is every state rated by all three services in every year) to obtain a bond-rating index that falls between zero and one, with one indicating all three services gave a state their highest bond rating. While the natural lower limit of the index is zero, no state's score falls below 0.70 in our sample.

There is no single, well-accepted measure of public corruption for the United States. In this study, we use federal convictions of federal, state, and



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Table 1. Variable definitions and descriptive statistics

Variable Definition Mean Std. Dev. Min. Max.

BONDRATE Composite average of relative 0.891 0.067 0.720 1.000

Moody's, Standard &

Poor's, and Fitch bond

ratings e (0, 1]a CORRUPTION Federal public corruption 0.319 0.300 0.000 2.520

convictions per 100,000

capitab STATETAX State average tax burden as 9.893 1.209 6.000 13.000

percent of personal income e [0, 100]"

DEBTREV State debt as a percentage of 47.882 29.230 2.520 176.789

government revenue

E [0, 100]a PERCAPDEBT Real state per-capita debt in 2.229 1.505 0.528 6.619

thousands (2000 dollars)a PERCAPINC Real state per-capita income 30.606 5.876 19.947 47.548

in thousands (2000 dollars)d

UNEMP Annual state unemploymente 4.683 1.166 2.200 8.100

POP State population (millions)a 5.913 6.225 0.479 33.871

CONVICTS State's absolute number of 20.166 26.805 0.000 144.000 federal convictions for

public corruptionb YR95 Year 1995 (Yes = O)f 0.162 0.369 0.000 1.000

YR96 Year 1996 (Yes = 1)f 0.162 0.369 0.000 1.000

YR97 Year 1997 (Yes = 1)f 0.158 0.369 0.000 1.000

YR98 Year 1998 (Yes = 1)' 0.173 0.379 0.000 1.000

YR99 Year 1999 (Yes = 1)' 0.169 0.376 0.000 1.000

YROO Year 2000 (Yes = 1)' 0.173 0.379 0.000 1.000

aStatistical Abstract of the United States (various issues). bPublic Integrity Section, Department of Justice. CThe Tax Foundation. dBureau of Economic Analysis. eBureau of Labor Statistics. fSelf-generated, averages do not sum to one because of rounding.

local public officials for public corruption as reported by the Public Integrity Section of the Department of Justice, where public corruption is defined as "crimes involving abuses of the public trust by government officials" (Public Integrity Section, Criminal Division, U.S. Department of Justice, 2002, p. 1). Examples of the types of activities considered public corruption include accepting bribes, awarding government contracts to vendors without competitive



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bidding, accepting kickbacks from private entities engaged in or pursuing busi- ness with the government, overstating travel expenses or hours worked, selling of information on the criminal histories and law enforcement information to private companies, mail fraud, using government credit cards for personal pur- chases, sexual misconduct, falsifying official documents, theft of government computer equipment for an international computer piracy group, extortion, robbery, and soliciting bribes by police officers, possession with intent to distribute narcotics, and smuggling illegal aliens (Public Integrity Section, Criminal Division, U.S. Department of Justice, 2001; Public Integrity Sec- tion, Criminal Division, U.S. Department of Justice, 2002). Fredricksson, List, & Millimet (2003) and Goel & Nelson (1998) also use this data to measure public corruption.

The federal judiciary is divided into districts which comprise all or part of a particular state. For example, Texas has four districts, the Eastern, Western, Northern, and Southern, Mississippi has two federal districts, the Northern and Southern, and Connecticut has one district. To construct a state-level measure of public corruption, we aggregate federal convictions for public corruption from the federal district(s) to the state level for each year from 1995 to 2000. We then normalize total state public corruption convictions with state population for each year, so our measure of public corruption is the number of federal convictions per 100,000 residents. It is important to note that our data are federal corruption convictions of public officials at any level of government, but do not include any state or local corruption convictions of public officials. Thus, our measure most likely understates the actual number of public corruption convictions.

While this is one of many possible measures of public corruption, it has the advantage of being consistent across time and jurisdictions. Since the convictions occur at the federal level, the laws under which convictions occur are uniform across different states. Enforcement is not expected to be perfect (Becker, 1968). However we assume that more convictions per 100,000 residents reflects a more corrupt public sector in a given state. In our sample, Alaska in 2000 had the highest measure of public corruption with 2.5 convictions per 100,000 capita. Mississippi in 1999 is a close second with 2.13 convictions per 100,000 capita. We note that the data do not specify the root violation for each conviction. Therefore, we treat all convictions as homoge- neous for our purposes, even though public corruption includes a wide variety of activities, as described above.

Unfortunately, all three rating services are equally vague about the variables considered when determining bond ratings. However, Rubinfeld (1973) and Liu & Thakor (1984) show that the overall government debt to revenue ratio, vitality of the state economy, and diversification in employment and the tax base have an impact on state and municipal bond ratings. Thus, we include the state's per-capita tax burden (STATETAX), total state debt to government



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revenue ratio (DEBTREV), real per-capita state debt load (PCAPDEBT), real per-capita income (PCAPINCOME), and the state unemployment rate (UNEMP) as control variables. Once we include these control variables, the corruption variable will capture the impact on state bond ratings of corrupt bureaucrats providing a suboptimal mix of public goods, providing less public good for the cost than uncorrupt bureaucrats, or any other economic effects not explicitly captured by the controls.

We anticipate that a state's bond-rating index improves with a lower per- capita tax burden, lower total debt to total government revenue ratio, higher per-capita income, lower per-capita debt load, and lower unemployment rate. To control for yearly changes that may be common to the credit worthiness of all the states, we include yearly dummy variables for 1996 through 2000 (YR96-YR00); the reference year is 1995. The time trend of bond ratings is ambiguous, although the recession of 2000, if anticipated, might have reduced the credit-worthiness of all the states in the latter part of the 1990s.

Since the dependent variable is naturally bounded from above by one, we employ a Tobit estimator. Table 2 reports estimation results reflecting marginal impacts of the independent variables, evaluated at the sample means, and the impact of a one standard deviation increase in each variable above the mean on a state's cardinal bond rating for each reporting service. The estimated parameters are, in general, consistent with Rubinfeld (1973) and Liu & Thakor (1984). Moreover, the results are both economically and statistically significant, especially in the case of public corruption.

The coefficients on the control variables are consistent with our expec- tations. The state tax burden (which does not include federal taxes) has a detrimental impact on bond ratings. This might reflect the market's wariness of a state being able to raise tax revenues if tax burdens are already high, thereby making it more difficult to service public debt, ceteris paribus. If a state's tax burden is one standard deviation above the mean tax burden, then the state's bond rating will fall by approximately one third of a Moody's rating, 0.25 of an S&P rating, and 0.19 of a Fitch rating.

An increase in a state's debt to government revenue ratio is also detrimental to average bond ratings, although the level of per-capita state debt does not have a statistically significant impact on bond ratings. These results are consistent with ratings reflecting concern about the overall ability for a state to service public debt. An increase in a state's debt-to-revenue ratio equal to one standard deviation above the sample mean will cause the state's bond rating to decrease by 0.89 of a Moody's rating, 0.63 of a rating for S&P, and 0.48 of a rating for Fitch.

Higher per-capita income has a beneficial impact on a state's bond rating. A one standard deviation above the mean increase in a state's real per capita income correlates with a reduction of 0.43, 0.31, 0.23 of a rating for Moody's, S&P, and Fitch, respectively.



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Table 2. Tobit estimation results - Dependent variable: Average relative state bond rating

Marginal Gain/Loss in State bond ratings by 1 S.D. Change

Variable impact Moody's S&P Fitch

CORRUPTION -0.055a (4.11) -0.58 -0.41 -0.31 STATETAX -0.008a (2.49) -0.35 -0.25 -0.19 DEBTREV -0.001a (2.92) -0.89 -0.63 -0.48 PERCAPDEBT -0.004 (0.65) -0.22 -0.16 -0.12 PERCAPINC 0.002b (2.18) 0.43 0.31 0.23 UNEMP -0.032a (7.76) -1.34 -0.96 -0.73 YR96 -0.018 (1.27) -0.65 -0.46 -0.35 YR97 -0.023b (1.70) -0.83 -0.59 -0.44 YR98 -0.040a (2.81) -1.41 -1.00 -0.76 YR99 -0.044a (2.97) -1.57 -1.12 -0.85 YROO -0.045a (2.88) -1.59 -1.14 -0.86 INTERCEPT 1.166a (21.18) Observations 253 Censored Obs. 42

Log Likelihood 250.29

Ho : Zero Slopes X 128.49c

Dependent variable falls on the unit interval, reference year is 1995, marginal effects calculated at sample means. Absolute value of t-statistics reported in parentheses. aSignificant at the 0.05 level. bSignificant at the 0.10 level. cStatistically significant at the 0.01 level, including year dummy variables. Estimated change in bond rating calculated as the product of the marginal impact, the sample standard deviation (see Table 1), and the total possible number of ratings for each service, e.g., -0.055 x 0.300 x 35 = -0.58 Moody's ratings lost for a one standard deviation increase in public corruption.

The unemployment rate has a negative impact on a state's bond ratings. In fact, Table 2 demonstrates that the unemployment rate has the largest impact on average bond ratings. Since unemployment provides at least a partial diagnosis of a state's economic vitality and activity, it is not surprising that this economic indicator would be important. If a state's unemployment rate increases by one standard deviation above the mean, its bond rating will fall by one and a third Moody's ratings, approximately a full S&P rating, and approximately three fourths of a Fitch rating.

The year dummy variables indicate that, relative to 1995, average bond ratings declined after 1997. Perhaps this is symptomatic of the general increase in state spending that occurred during the late 1990s or a pre- scient forecast of the 2000 recession and the reduced state tax revenues that followed.



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Of particular interest is the coefficient on public corruption. We find that the number of public corruption convictions per 100,000 capita is inversely related with average state bond ratings and the impact is economically significant. A one standard deviation above the mean increase in a state's public corruption convictions per 100,000 capita causes the state's bond rating to fall by approximately 0.58 of a Moody's rating, 0.41 of a Standard and Poor's rating, and 0.31 of a Fitch rating. Thus, public corruption has a statistically but also an economically significant impact on state bond ratings, reflecting the market's wariness that more corrupt states will not fully service their debt. Moreover, the negative relationship between public corruption and states' bond ratings is robust to other specifications, including ordered probit analysis (not reported here but available from the authors upon request).'

The vague manner by which state bond ratings are determined naturally suggests the possibility of omitted variables bias in the regression analysis presented in Table 2. However, this very ambiguity makes it difficult to determine what additional explanatory variables would be appropriate to include; state-specific heterogeneities likely influence the bond-rating process but in ways that are difficult to predict. As is well known, omitted variables that are positively (negatively) correlated with state tax burdens, unemployment, per-capita debt, and government debt-to-revenue ratios and positively (negatively) correlated with state bond ratings would lead to upward (downward) bias in the parameter estimates presented in Table 2. It is difficult to predict the magnitude of possible parameter bias, but the relative stability of parameter estimates after including yearly interest payments on existing debt, personal and corporate income taxes, average property tax rates (all dropped from the final specification due to insignificance) suggest that parameter bias may not be a serious concern.2

What is the economic impact of a one standard deviation increase in the public corruption measure above the national mean? Rubinfeld (1973) shows that in the late 1960's a reduction in a municipality's bond rating from Moody's AAA to Moody's AA status (at the time, Moody's did not differentiate within investment grades) correlated with an increase in interest rates of approximately 20.6 basis points. Using current Moody's ratings, this would indicate approximately 5.15 basis points for every Moody's rating (say from AAA to AAA 1). From Table 2, a one standard deviation increase in public corruption above the national mean would cause a .60 reduction in a state's Moody's rating, or an increase in the cost of debt service of $18 per million dollars of debt (assuming an original interest rate of 5%). In 2000 the average state debt load was approximately $11 billion. Therefore, public corruption one standard deviation above the national average would increase annual interest payments by roughly $200,000 on a steady-state debt load at the national average.

Herein lies one of the fiscal consequences of public corruption: there is a negative pecuniary externality of public corruption beyond the rent seeking



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activities of corrupt officials and bureaucrats. These results are an indication of the indirect costs of public corruption and most likely underestimate the total costs of public corruption. Furthermore, our results show that regional differences in public corruption within a single country can impact economic variables of interest, something aggregated cross-country comparisons may be unable to detect.

3. Conclusion

This paper extends the empirical analysis of public corruption by turning attention to regional impacts of public corruption within a single country. We measure public corruption in the various U.S. states as the number of federal, state, and local public officials convicted in Federal district courts per 100,000 state residents. This measure of public corruption is consistent across time and the various states as federal law is common to the states.

To determine if public corruption creates a negative pecuniary externality, we investigate the impact of public corruption on state bond ratings. Bond ratings have been shown to be important determinants (and reflec- tions) of the cost of public capital, typically measured as the net interest costs on public debt. After controlling for other variables thought to influence bond ratings, we show that states with more corruption have lower bond ratings, and are therefore likely to incur higher net interest costs on public debt. Thus, corruption imposes extra costs on a states' residents because they pay more to service the debt incurred for public projects than they would have otherwise. In other words, public corruption creates a negative pecuniary externality, which provides a rationale for public corruption legislation.

Our empirical results are not intended to specifically address the relationship between a state's bond rating and net interest costs, which has been the focus of other research. Rather, including information on public corruption tests whether bond ratings, in as much as they reflect credit worthiness, are negatively related to public corruption. The empirical results we present suggest that this inverse relationship holds. The specific channels through which corruption reduces a state's credit-worthiness are an open question. Future research could focus on the impact of public corruption on the cost of public projects, on the transfer of federal money to the state, and a myriad of other variables of economic interest.

Acknowledgement

The authors thank Bill Crowder, Darren Grant, Roger Meiners, Mike Ward, and an anonymous referee for helpful comments. The usual caveat applies.



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Notes

1. At the suggestion of an anonymous referee, we estimated a specification including the interaction between corruption and public debt. The results presented in Table 2 are not qualitatively effected and the estimated parameter on the interaction term is positive and statistically significant. While this seems counterintuitive, the actual marginal impact of the interaction term is a highly non-linear function and therefore direct interpretation is difficult (Ai & Norton, 2003). However, the marginal effects of corruption and per-capita debt on the latent variable estimated in the Tobit model are still negative.

2. An additional concern is with measurement error in public corruption. The public corruption index used here likely understates the actual amount of public corruption because enforcement is not perfect and the Department of Justice data reflect only Federal convictions. If this measurement error is negatively correlated with improved bond ratings, the parameter estimates in Table 2 are likely biased upwards. However, the lack of data describing state and local corruption levels (Jacobs, 1999) makes it difficult to determine the extent of the measurement error and possible bias.

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