Public Sector Corruption in the US Local Debt ...

❙ 예산정책연구 제7권 제1호 2018. 05. pp.25-48

Public Sector Corruption in the U.S. Local Debt Finance: Do Local Governments Pay a Corruption Premium? *

**

Liu, Cheol and Tima T. Moldogaziev

Abstract This paper demonstrates that capital markets take surrounding environments in the states into consideration when determining prices on local debt, above and beyond the underlying features of municipal bonds. We empirically show that corruption premium exists in the US municipal bond market. Local governments in more corrupt states pay higher costs for long-term borrowing in the market. Moreover, fiscal institutions that are traditionally thought to bring fiscal discipline to local governments become ineffective in the states with relatively higher levels of public sector corruption. 󰋪 Keywords: public corruption; intergovernmental relations; local debt market; borrowing cost

투고일: 2018. 02. 26. 수정일: 2018. 04. 13. 게재확정일: 2018. 04. 25. * KDI School of Public Policy and Management, South Korea ([email protected]) ** Department of Public Administration and Policy, The University of Georgia, USA, ([email protected])

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예산정책연구 제7권 제1호

Introduction “The state is like a parent to a city. Sometimes you want to call Child and Family Services on them,” says James Spiotto, a Chicago attorney who specializes in local bond bankruptcy1). He underlines state roles in local financial distress in the U.S. and the influence of surrounding environments in the states on local bond evaluation. Recently, NASBO (2012) announced that investors pay attention to whether or not states have intervened in local fiscal distress, while credit rating agencies reflected the state bailout probabilities on local government credit ratings. However, the effect of surrounding political and managerial climates in the states on local debt management outcomes and the local bond markets has not been studied systematically. This work seeks to contribute to academic research on the relationship between public sector corruption and penalties that are placed on local governments from relatively more corrupt environments. Municipal governments are “instrumentalities” of the state. Local governments, as settled in legal precedent thanks to Dillon’s Rule, may have limited administrative and fiscal independence vis-à-vis their state governments. The strength of municipal fiscal health or the likelihood of fiscal distress may depend not only on “controllable” factors that localities can master but also on state law, formal and informal fiscal institutions, or state fiscal management practices or malpractices. A wise investor, therefore, should consider all the factors comprehensively when evaluating and buying bonds in the municipal debt market. One of such fundamental factors of risk is the quality of political or managerial environments in the state in which the locality is nested. In line with Spiotto’s assessment, this article demonstrates that capital markets take surrounding environments in the states into consideration when determining prices on local debt, above and beyond the underlying features of municipal bonds. We empirically show that corruption premium exists in the US local bond market. Local governments in more corrupt states pay higher costs for long-term borrowing in the market. Moreover, fiscal institutions that are traditionally thought to bring fiscal discipline to local governments become ineffective in the states with relatively higher levels of public sector corruption. This work offers empirical evidence for how public sector corruption distorts the costs of local government borrowing in the capital market, which is directly relevant to the design of policies that tackle corruption. 1) Bloomberg Government State and Municipal Finance Conference. June 7, 2012, Chicago, Il. (PEW 2013).

Public Sector Corruption in the U.S. Local Debt Finance: Do Local Governments Pay a Corruption Premium?

Literature Review Academic and practical knowledge about the determinants of municipal bond pricing is well developed. Following Poterba and Reuben (2001), we can summarize the literature on the factors affecting municipal bond price as follows:

R i,t = F (C i,t ; I i,t; L i,t; P i,t)

(1)

Here, R i,t means municipal I ’s bond price or yield in a given year t , C i,t implies the characteristics of the issue, I i,t captures the fiscal variables of the issuer, L i,t contains institutional and legal factors, and P i,t includes political variables potentially influencing bond pricing. The fundamental logic of the function is that the factors reducing the probability of future payment of current interest obligations will inflate bond yields, and vice versa. Therefore, all factors that affect the fiscal outcomes for local bond issuers are potential determinants of local borrowing costs, or yields. Characteristics of bond issues, C i,t, include credit ratings, underwriter reputation (often measured in terms of market share), underwriting sale method, bond insurance or other credit enhancements, bond type, issue size, call option, and time to maturity (Benson and Marks 2005; Butler et al. 2009; Capeci 1991, 1994; Guzman and Moldogaziev 2012; Joehnk and Kidwell 1979; Johnson and Kriz 2005; Peng and Brucato 2004; Robbins 2002; Simonsen et al. 2001). Fiscal and economic variables of the issuers, I i,t, capture the amount of borrowing and debt outstanding, personal income, revenue, budget surplus/deficit, unemployment, highest marginal tax rate, budgeting practices, and bankruptcy petition regime (Andersen 2014; Benson et al. 1984; Butler 2008; Capeci 1994; Denison et al. 2007; Marks and Raman 1985; Poterba and Rueben 2001; Robbins and Simonsen 2012; Wagner and Garrett 2012). In terms of surrounding fiscal environments, scholars describe the influence of institutional and legal variables (fiscal institutions, L i,t) on borrowing costs such as stringent accounting requirements, revenue/expenditure limitation laws, balanced budget rules, and borrowing restrictions (Bayoumi et al. 1995; Benson et al. 1984; Johnson and Kriz 2005; Poterba and Reuben 1999, 2001; Wagner and Garrett 2012). Finally, compared with other components in the function of bond borrowing costs, the impact of political or managerial variables, P i,t, has been under- investigated (Alt and Lowry 1994; Andersen 2014; Butler et al. 2009; Poterba 1994; Wagner and Garrett

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2012). We evaluate the impact of public sector corruption on local government borrowing costs as part of this set of risk factor. By focusing on the influence of surrounding environments in the states on local governments, this paper extends the existing literature on the determinants of the local bond yields as follows.

R i,t = F (C i,t; I i,t; L i,t; P i,t; S E j,i,t)

(2)

Here, S E j,i,t means a number of surrounding environmental factors of state j in which municipal i nested in a given year t . The factors include state level fundamentals that play a role in local fiscal outcomes, and specifically extend the known measures of political and managerial practices by adding the measure of relative levels of public sector corruption in the U.S. states to the mix. To reiterate, the fiscal outcomes of localities are by no means just a local concern. Local governments are “creatures of the states.” The states and municipalities are closely connected by a number of factors such as money transfers, intergovernmental aid, co-provision of public programs and public services, support of non-profit organizations and contracts with the private sector firms (Bowling and Wright, 1998; Coggburn and Schneider, 2003; Ho, 2007; Honalde, 2003; McCabe and Feiock, 2005; Rodridguez, 2007).

Conceptual Framework State level institutions constrain fiscal behavior and policy choices of local governments. Although home-rule and Dillon’s rule provisions are applied varyingly across the U.S. states, state constitutions and fiscal rules affect local revenue raising and spending, as well as the debt raising capacity, directly and indirectly (Honadle, 2003; McCabe and Feiock, 2005; Rodriguez, 2007). Overall, environments offering conditions that are conducive to stronger local government fiscal health are viewed positively in assessing default and bankruptcy risks at the municipal level. A number of state level political variables can affect the quality of local government management―executive, legislative, and judiciary decisions by the states influence the operation of their local governments. In addition, local residents elect state officials and state legislatures. The elected state officials and legislators must respond to local needs to maintain their future levels of electability. The nature of the relationship between state


governors and legislatures and municipalities’ top officials is also noteworthy in this perspective (NASBO 2012). State legislatures make many important decisions affecting local governments including economic development, infrastructure, environmental problems, regional development, and local public service delivery (Ho 2007). Governors’ decisions and agenda for taxing and spending programs affect the budget appropriations in legislatures, which in turn directly and indirectly influence local government finances (Berman 2006: 43). There is sufficient empirical evidence that state political environments affect local government policy choice sets (Hsieh 2008; Ni and Bretschneider 2007). Economic, demographic, and fiscal conditions in the states also affect significantly the fiscal outcomes for local governments. Economic and budgetary concerns at the state level can have substantial effect on how they respond to local fiscal distress. For example, a state’s property tax exemption for certain classes of properties or organizations reduces local revenues (Berman 2006: 52). Insufficient state financial resources and intergovernmental aids may jeopardize local governments as well (NASBO 2012; Honadle 2003). By following the corruption literature, we define public corruption as a “misuse of public office for private gain” (Liu and Mikesell 2014). This paper evaluates ‘illegal’ behaviors of public officials, actions that are reported in the U.S. Department of Justice’s database of pending and closed corruption cases involving public officials in each state. We expect that relative (and prevailing) levels of public sector corruption in the U.S. states are going to be directly relevant to local governments, specifically to their debt management functions. Existing literature on municipal bond borrowing costs demonstrates that public corruption can result in direct and indirect impact on capital markets and municipal bond prices. A substantial volume of empirical research provides evidence that corruption lowers the quality of public investment and infrastructure projects. Moreover, they often require costly maintenance costs thereafter. Corruption also hampers economic growth, productivity, procurement, and resource allocation (Beekman, Bulte, and Nillesen 2014; Brunetti, Kisunko, and Weder 1998; Celentani and Ganuza 2000; Ertimi and Saeh 2013; Hall and Jones 1999; Hessami 2010; Liu and Mikesell 2014; Mauro 1995, 1998; Tanzi and Davoodi 1997). It is plausible to expect that markets view the bonds issued by relatively more corrupt governments as investments of questionable credit quality. Research certainly confirms that the more corrupt a state public sector is, the riskier are the bonds issued by these governments and the higher are the yields that they must pay to borrow in the municipal

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debt market (Butler et al. 2009; Depken and LaFountain 2006). Depken and LaFountain (2006) find a significantly negative association between the level of public corruption of American states and their credit ratings. With the evidence, they argue that taxpayers residing in more corrupt states should pay a “corruption penalty” on their government debt. Butler et al. (2009) argue that a more corrupt American state government is likely to pay higher interest rates for their bonds to underwriting firms. These studies find two types of corruption penalty in the US state debt markets: lower credit ratings on corrupt state governments and higher interest rates to their bonds. It is well-documented that the US state bond markets exert disciplining actions on ill-disciplined fiscal sovereigns (e.g., Bayoumi et al. 1995). However, these studies did not clarify the mechanism how the bond markets and the holders of public bonds “discipline” the corrupt and “ill-behaving” US public bond issuers. Moldogaziev et al. (2017) develop the discussions and suggest a possible discipline mechanism. They argue that US state bond investors are likely to allow narrower retail markups when they purchase bonds issued by state governments with a higher level of public corruption. Noting that the existing studies mainly dealt with the corruption effects in the US “state” bond markets, Moldogaziev et al. (2017) call for research about corruption effects in the “local” debt markets because the costs of corruption are expected to be much more larger in the local markets. In order to contribute to the literature, we examine in this paper the corruption effects on the US “local” public debt by giving evidence with US local general obligation bonds issued by 4,450 cities, 1,282 counties, and 1,637 special purpose districts. Following the literature, we expect an adverse effect of public corruption on local bond prices, all else equal, because the capital market will exert pressure by demanding higher interest rates. There are a few reasons why we focus on local bonds, though we suspect that the detrimental effects of public corruption on bond prices will be reflected in yields of state as well as sub-state general obligation securities. In particular, local governments are less equipped with finance staff and knowledge, despite the fact that many bond deals in the municipal market are rather complex. It may be hard for local officials to understand how the markets work and local government managers may need to become heavily “dependent” on debt intermediaries such as financial advisors and investment banks, in making decisions on bond issuance and sales (Miller 1993). In addition, we speculate that the connections and ties between corrupt officials and debt intermediaries would be closer and stronger in sub-state settings. The highly decentralized and segmented local market may provide opportunities for, or create suspicion in the minds of the local investors of, corrupt deals between debt intermediaries and local officials.


Hypothesis How does corruption exercise adverse effects on local bond prices or yields? Firstly, we suspect that the markets will discipline bond issuers by assigning higher default risks due to their corruption, and rating their credit lower. Substantial empirical research provides evidence that corruption lowers the quality of public investment decisions and infrastructure projects. The projects of inferior quality will not perform up to the anticipated standards, but generate lower returns than planned. Moreover, they often require costly maintenance costs later. Corruption hampers economic growth, or otherwise makes an economy perform badly (Tanzi and Davoodi 1997; Depken and LaFountain 2006). Thus, investors and underwriters may perceive that these governments will be unable to pay off their debts in the future in a timely fashion. They may assume that bonds issued by corrupt governments are of questionable creditworthiness and demand greater compensation (yields). Consequently, the more corrupt a government, the riskier are the bonds issued by the government and the higher yields the government must pay to borrow (Butler et al. 2009: Depken and LaFountain 2006). Secondly, a number of practices and scandals in the municipal bond markets disclosed that corruption affects the prices of government bond prices directly. Corrupt officials that are prone to “misuse their public office for private gain” are likely to take advantage of their authority and attempt to levy bribes in the debt market. Corruption may take place in the municipal debt markets in the form of kickbacks and/or “entertainment” which are given to officials who have discretionary power on project contracts or bond deals (Depken and LaFountain 2006). The “pay-to-play” practices such as investment banks’ campaign contributions to politicians are in return for lucrative underwriting contracts (Butler et al. 2009). Although the payment is of extra personal benefit to the corrupt officials, it is not a free lunch to the taxpayers of the locality. The bribe-giving underwriters are likely to reflect the “implicit payments” on the final price of the locality’s bonds, which implies that the bond yields of the corrupt localities would be higher than less-corrupt governments, ceteris paribus. Thus, our hypothesis is: Hypothesis: Bonds issued in the relatively more corrupt states will result in greater levels of bond yields, because (a) the bond markets will assign lower credit-worthiness to municipalities from the states with greater levels of corruption and demand proper compensation and/or (b) financial intermediaries incorporate their “implicit payments” to corrupt individuals in the final prices of the bonds. Note, we add the variable of credit ratings into our regression model of the local bond yields because the models of bond yields in the extant literature conventionally include

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the variable and we assume that corruption can affect bond yields through multiple routes (“implicit payments”) which are not fully captured by credit ratings.

Data and Methods

Data: Serial Bonds Issued by U.S. Local Governments (2005–2010) We collected fixed rate general obligation (GO) bonds issued by U.S. local governments (cities, counties, and special districts; state debt is excluded) between January 31, 2005 and December 31, 2010. We chose this period because it accommodates a substantial period before and after the Great Recession of 2008-2010. In aggregate, 7,369 municipalities issued 17,481 general obligation bonds in this period, including 4,450 cities, 1,282 counties, and 1,637 special purpose districts. Issuance frequency varies both across the states, and within the states, across municipalities. Municipalities tend to sell their bonds by way of serial bonds rather than a single lump-sum term bond. Although they are components of a single bond deal, serial bonds have their own characteristics in terms of serial level maturities, call status, options, credit enhancement methods, par value, and so on. This makes the use of the series-level analysis the more appropriate unit for this work. The 17,481 GO bond issue deals yielded 252,616 serial bonds. We reduced our sample to 252,319 observations by omitting serial bond issues with missing observations for initial offering yields, which is the dependent variable of the study representing borrowing costs at the series level.

Econometric Model Our econometric model describing the influence of public sector corruption in the states on local bond borrowing costs is as follows: Yields of Local Serial Bonds = f (serial bond characteristics; bond market conditions; state fiscal institutions; state economic and demographic variables; state political variables; public corruption; fixed effects) The dependent variable is the initial offering yield for each serial bond in the sample. The initial offering yield implies the “yield at which municipal securities dealers offer securities to investors or to other municipal securities dealers” (MSRB 2013). Yields,


therefore, are expected to reflect the risk levels of local government bonds (Butler 2009; Moody’s Investors Service 2007).

Explanatory Variables We expect that the states can influence local finances through three broad avenues: fiscal institutions, state policies and programs, and intergovernmental transfers, all of which are included in regression models. Furthermore, our models include state political variables measuring political competitiveness (party rivalry), governors’ party affiliation, political party controlling legislature, and elections of governors and legislators, based on our review on literature. The extent of political competitiveness implies the degree of checks and balances not only among political parties, but also between the legislature and the executive branch. The models include two dummy variables implying a Democratic governor and a Democrat-controlled legislature. Political business cycle theory predicts the fiscal sustainability of a government will deteriorate during election years because incumbents will implement expansionary fiscal policies to get re-elected. For this reason, we include measures for electoral cycles in the states as well. Moreover, our regression models include a set of state economic and demographic variables, such as population, real per capita gross state product, and the ratio of total debt outstanding over gross state product. The main test variable of interest is the measure of public sector corruption. We proxy for it by using the count of U.S. public employees convicted of violating federal corruption-related laws. The historical conviction data are accessible from the Reports to Congress on the Activities and Operations of the Public Integrity Section (PIS), which is published by the U.S. Department of Justice (DOJ). We aggregated the conviction counts of public sector employees of each state-year in the sample. The conviction data include “accepting bribes, awarding government contracts to vendors without competitive bidding, accepting kickbacks from private entities engaged in or pursuing business with the government, overstating travel expenses or hours worked, selling information on criminal histories and law enforcement information to private companies, mail fraud, using government credit cards for personal purchases, 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 US public officials’ corruption-related behaviors comprehensively”(DOJ 2002).

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Compared with other corruption indexes, the PIS conviction measure has a number of advantages. First, the definition of public corruption, or “crimes involving abuses of the public trust by government officials”, is consistent with the corruption literature. Second, the conviction measure does not simply reflect the extent of judicial resources, prosecutors’ capacity, law enforcement, and slackness. Fixed-effect panel regressions did not find any significant associations between conviction numbers and these variables (results are omitted). A corruption ranking for the U.S. states based on the DOJ counts measure corresponds well to the general perception of public corruption in the U.S. Third, the measure is consistent across time and jurisdictions since DOJ applies the same federal corruption laws to all states and localities. Fourth, given that a complete and perfect measure of corruption is unavailable, the conviction numbers reported by DOJ are the most reliable, concrete, and objective indicators of corruption in the U.S. (see discussions in Depken and LaFountain 2006; Glaeser and Saks 2006; Liu and Mikesell 2014; Meier and Holbrook 1992). Finally, the public corruption variable is normalized per 10,000 public employees to bring the measure to a common denominator for comparability across the states.

Other Control Variables We control for four sets of covariates of initial offering yields of local bonds. The first set is related to bond issue characteristics. The second set covers the variables assessing market conditions. The third set accommodates multiple variables describing political situations. The last set captures economy and demography. [Table I] provides the descriptive statistics of all variables and data sources. Firstly, the issue characteristic variables include overall credit ratings, separate dummies of credit ratings from AAA to junk bonds, dummy of non-rated bonds, dummy of split rating, unlimited GO pledge, issue size, year to maturity, availability of call options, federal tax obligation, dummy of special district, and the qualification of underwriting banks. The set also contains some variables related to bond management practices such as bond insurance, negotiated sales, and consultation from financial advisors. Note that factors reducing the probability of future interest payment will inflate bond yields. We expect that an issue with a lower credit quality, split credit rating and longer maturity and call option will be associated with a higher yield.


[Table I] Descriptive Statistic (Observation: 252,616) Std. Variable Mean Min Max Dev. 3.507 1.037 0.08 9.62 Serial Bond Yield 0.460 0.292 0 2.44 Convictions per 10K public employee 0.303 0.194 0 2.21 Convictions per 100K population 6.176 0.298 5.12 6.94 Ln(caseloads per judge) 7.706 3.340 1 11 Credit Ratings (AAA=11, …, NR=1) 0.161 0.367 0 1 AAA (yes = 1; no = 0) 0.197 0.398 0 1 AA+ (yes = 1; no = 0) 0.216 0.411 0 1 AA (yes = 1; no = 0) 0.140 0.347 0 1 AA– (yes = 1; no = 0) 0.071 0.256 0 1 A+ (yes = 1; no = 0) 0.024 0.152 0 1 A (yes = 1; no = 0) 0.010 0.099 0 1 A– (yes = 1; no = 0) 0.011 0.103 0 1 BBB (yes = 1; no = 0) 0.001 0.028 0 1 Low Credit (yes = 1; no = 0) 0.170 0.376 0 1 Non-rating (yes = 1; no = 0) 0.683 0.465 0 1 Split Rating (yes = 1; no = 0) 0.738 0.439 0 1 GO Unlimited (yes = 1; no = 0) 15.619 1.471 9.62 20.94 Ln(Issue Size) 8.870 5.688 0.003 40.03 Maturity, in years 0.414 0.493 0 1 Call Option (yes = 1; no =0) 0.071 0.257 0 1 Federally Taxable (yes = 1; no =0) 0.148 0.355 0 1 Special District (yes = 1; no = 0) 0.587 0.492 0 1 Bank Qualified (yes = 1; no = 0) 0.416 0.493 0 1 Insured (yes = 1; no = 0) 0.418 0.493 0 1 Negotiated Bids (yes = 1; no = 0) 0.706 0.455 0 1 Financial Advisor (yes = 1; no = 0) 4.472 0.307 3.820 6.010 Market Yield, Bond Buyer 20 Index Market Volatility, st.dev. of the 10.645 7.662 2.000 50.318 8-week Moving Average in Basis Points, Bond Buyer 20 Index Year Dummies (2005–2010, yes = 1; no = 0) 39.356 11.704 12.05 63.104 Political Competitiveness† Legislative Control 0.540 0.360 0 1 (Unified Democratic = 1; else = 0)‡ Governor's Affiliation 0.554 0.497 0 1 (Democrat = 1; else = 0) ‡ Year of Gubernatorial Election 0.186 0.389 0 1 (yes = 1; no = 0) ‡ Year of Senate Election 0.426 0.494 0 1 (yes = 1; no = 0) ‡ Year of House Election 0.499 0.500 0 1 (yes = 1; no = 0) ‡

Source Bloomberg U.S. DOJ U.S. DOJ U.S. DOJ Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg Bond Buyer Bond Buyer

Carl Klarner, Department of Political Science, Indiana State University (link below)

† Ranney alternative measure of competition; †, ‡ from http://www.indstate.edu/polisci/ klarnerpolitics.htm

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A bond obliged to pay taxes to the federal government and/or issued by special districts is less attractive to investors and also associated with a higher yield. A lower yield will be set to an issue guaranteed by unlimited GO pledge and/or by qualified banks, because it is believed to be more reliable and attractive to investors. Markets are likely to ask higher yields for special districts because of their riskier business-type activities and narrow purposes with “high risk”. A larger issue size is inversely associated with bond yields because a larger trade volume can ease investor concerns about liquidity premium (Butler et al. 2009; Fairchild and Koch 1998; Guzman and Moldogaziev 2012; Peng and Brucato 2004). We pay special attention to the following features related to bond management practices: purchase of insurance, receipt of financial advisory service, and the method of sale. We expect that underwriters will demand lower yields for bonds receiving financial intermediary services from bond insurers and financial advisors. An issuer often enhances bond credit by purchasing insurance and reduces the default risk of a bond. It may also give a positive signal regarding its bonds to the markets by receiving financial advisory services, which are believed to mitigate for agency problems (Moldogaziev and Luby 2012; Vijayakumar and Daniels 2006). Negotiated sales are believed to pay greater yields due to lack of competition (Robbins and Simonsen 2007). Secondly, we assess market conditions through the prevailing market yields of municipal bonds and the volatility of market yields, which follows the primary market bond pricing literature (Guzman and Modogaziev 2012; Peng and Brucato 2004). The Bond Buyer 20 interest rate index for GO bonds proxies for market yield rates. The standard deviation of an eight week moving average of the Bond Buyer 20 index measures market yield volatility. Year dummies account for year-specific effects. Thirdly, our models include political variables measuring political competitiveness (party rivalry), governors’ party affiliation, political party controlling legislature, and elections of governors and legislators. Political science studies describe how various political and institutional variables exercise substantial effects on government fiscal outcomes. However, studies on how the political variables influence local bond yields are lacking. Applying the same logic used for the other sets of determinants above, we study the impact of political variables on local bond yields by examining how they are associated with the levels of bond yields for local bonds. The extent of political competitiveness or political party rivalry in legislature implies the degree of checks and balances not only among political parties, but also between the legislature and the executive branch. We expect that there will be an inverse association between the extent of political competitiveness and bond yields, because a higher level of accountability through strong checks and balances will reduce the default risk of local bonds (Eichler


2014). Andersen et al. (2014) argue that investor sentiments can be affected by the preference of politicians in charge of fiscal policy. This explains why we add variables capturing the political ideologies of governors and legislatures. The models include two dummy variables implying a Democratic governor and a Democrat-controlled legislature. Political business cycle theory predicts the fiscal sustainability of a government will deteriorate during election years because incumbents will implement expansionary fiscal policies to get re-elected. But it is also argued that politicians may do the opposite so as not to be blamed for extravagant government spending under economic downturns (Block and Vaaler 2004). We control for this controversial factor by including dummies of gubernatorial election, Senate election, and House election. Fourthly, our regression models include a set of economic and demographic variables, i.e. population, gross economic products, and the total amount of debt outstanding. We explain the details of the variables and implications in the model robustness check section later.

Empirical Findings

Baseline Regressions Our main empirical findings appear in [Table II], which shows the results from multivariate OLS regressions, with year fixed effects and robust standard errors. For robustness purposes, errors were also clustered at the state level. Based on these serial yield regressions, we conclude that our core test variables of interest are significant covariates of local government borrowing costs. Levels of public sector corruption are associated with higher serial level initial offering yields. In each model, the coefficient for the corruption measure is statistically significant at 0.001 percent level. These coefficients are 5 ~ 6 basis points in models I and II, which is the public sector corruption premium that local governments must face in relatively more corrupt states. Is the size of corruption premium substantial? For comparison, notice that the linear specification of the credit rating measure has coefficient ranging between 4~9 basis points.2) Taken as a comparison benchmark, the corruption premium would be equivalent to a change between 0.3 to 1.3 credit rating notches, with an average change of 0.8 units along the credit rating scale. 2) Alternatively, we used the counts of convictions per 100,000 of population as a proxy for levels of public corruption. With this proxy, we followed the same strategies as for reported models I-IV. Notably, the variations do not substantially change the results from the core regressions.

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[Table II] The Impact of Public Corruption on Municipal Bond Yields Baseline Model I (2010) and Robustness Checks (Models II~IV, 2005–2010)

Corruption (employee, t) Corruption (employee, t-1) Corruption (employee, 3 avg.) Ln(caseloads per judge) Credit Ratings GO Unlimited Ln(Issue Size) Maturity, in years Call Option Federally Taxable Special District Bank Qualified Insured Negotiated Bids Financial Advisor Market Yield Market Volatility Fiscal Law Intervention Ln(State Aid/GSP) Ln(population) Ln(GSPper capita) Ln(debt/GSP) Political Competitiveness Legislative Control Governor's Affiliation Gubernatorial Election Senate Election House Election Constant Observations F –value R-squared

Model I Coeff. S.E.

Model II Coeff. S.E.

0.06***

0.05***

0.012

Model III Coeff. S. E.

0.004 0.04***

0.02

0.013

0.13***

***

***

Model IV Coeff. S. E.

0.006

0.13*** ***

0.004

0.006

0.01*

0.005

0.14***

0.006

***

-0.09 0.001 -0.04 -0.04*** 0.007 -0.05*** -0.01*** 0.003 -0.03*** 0.19*** 0.001 0.11*** 0.06*** 0.010 0.08*** 1.15*** 0.010 1.31*** 0.19*** 0.009 0.09*** -0.03*** 0.008 -0.09*** 0.17*** 0.007 -0.04*** 0.14*** 0.006 0.07*** 0.03*** 0.007 -0.01** 0.75*** 0.009 0.56*** -0.01*** 0.0002 -0.005*** 0.04** 0.011 0.08*** 0.011 0.03* 0.016 0.06*** *** 0.04 0.007 0.02*** * -0.08 0.036 -0.06*** *** 0.06 0.010 0.01***

0.0004 0.003 0.001 0.0004 0.003 0.007 0.004 0.003 0.002 0.003 0.003 0.005 0.0002

-0.04 -0.05*** -0.03*** 0.11*** 0.08*** 1.31*** 0.09*** -0.09*** -0.04** 0.07*** -0.01*** 0.56*** -0.005***

0.0005 -0.04 0.0005 0.003 -0.05*** 0.003 0.001 -0.02*** 0.001 0.0004 0.11*** 0.0004 0.003 0.08*** 0.003 0.007 1.31*** 0.007 0.004 0.09*** 0.004 0.003 -0.09*** 0.003 0.002 -0.04*** 0.002 0.003 0.07*** 0.003 0.003 -0.01*** 0.003 0.0001 0.56*** 0.005 0.0002 -0.005*** 0.0002

0.005 0.002 0.015 0.003

0.07*** 0.02*** -0.06*** 0.02***

0.005 0.002 0.015 0.003

0.07*** 0.02*** -0.06*** 0.02***

0.005 0.002 0.015 0.003

0.003*** 0.0004

0.0001

0.0002***

0.0001

0.0001

0.0001

***

-0.11

***

0.011

0.0003* ***

-0.03

***

0.005

***

-0.04

***

0.005

***

-0.04

***

-0.03

0.007

0.01

0.003

0.02

0.003

0.02

0.02***

0.015

-0.02***

0.004

-0.03***

0.004

-0.03***

0.004 0.006 0.088 232,244 16,148.77*** 0.76

***

***

-2.81***

0.176 40,063 9,203.63*** 0.88

-0.03 0.05*** -0.07*

0.005 0.006 0.037 232,244 16,151.42*** 0.76

***

-0.03 0.06*** -0.91***

0.005 0.003 0.004

-0.03 0.06*** -0.96***

0.005 0.006 0.089 232,244 16,138.85*** 0.76

Credit ratings measured using different functional forms result in consistent and robust conclusions. Transformations of issue size and maturity do not change results. Year dummies (2005-2010) included, but omitted for brevity; Model I uses only 2010 data. Senate and House election variables are dropped automatically due to collinearity in Model I. Without reporting the results, we ran the same regressions with an alternate corruption proxy, or the count of convictions per 100,000 of population. The regression results are consistent and robust, not reported; *p < .05, **p < .01, ***p < .001


At the same time, coefficients for fiscal institutions and their association with local government borrowing costs are significant and positive in models I and II. Findings suggest that markets demand higher yields on bonds issued by localities from states with stricter fiscal limitations. Empirical results further show that state demographic and economic variables are significantly associated with local government serial bond yields. The variables capture government size (natural log of population), economic growth (real per capita gross state product), and debt burden (the ratio of total debt outstanding to gross product). Finally, empirical results also show that state political variables are significantly associated with local government serial bond yields. Political party rivalry, ideology of legislatures and governors, and elections of governors and legislators significantly affect local borrowing costs. Empirical results for all other control covariates in our regressions correspond with ex ante expectations. Unlimited GO pledge, higher credit quality, larger trade volume, qualified bank, purchase of insurance and financial advisors, and bond market volatility are inversely associated with local government serial bond yields. Conversely, we find that years to maturity, call options, federally taxable bonds, special district debt, negotiated sales, and higher bond market yields are likely to result in increased serial bond yields.

Robustness Checks To evaluate the robustness of empirical results in the core regressions, we apply a number of strategies as summarized in [Table II] (Models III and IV). Overall, the significant associations between main covariates and local government borrowing costs remain even after a number of model and functional form re-specifications. First, we displace the number of convictions per 10,000 public employees with the numbers of convictions per 100,000 of population. Differently from the baseline regression models, in addition, we displace conviction measures at time t with those at time (t-1) and the average of the previous three years’ conviction measures (or, those at times t-2, t-1, and t). Notably, the variations do not change our conclusion from the baseline regressions, that markets demand higher yields for bonds issued by localities from more corrupt environments. A positive association between corruption and bond yields tends to remain in the same ballpark (4 ~ 7 basis points) in these alternative specifications of our major variables. Second, we also apply an alternative proxy for public corruption, or caseloads per federal judge, by assuming that a larger caseload implies more corruption in the region.

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The coefficients of the natural log of per judge caseload variables are in the range of 12 ~ 13 basis points. We interpret that a one percent change in per judge caseloads is associated with a 12 ~ 13 basis points increase in local bond yields. The coefficients are significantly positive in all the regressions, which implies that a positive association between corruption and bond yields does not change with an alternative proxy for corruption. Third, we run the same regressions with multiple dummies of all credit ratings. The regression results remain the same across the variations. Not only does a significantly positive relationship between public corruption and local bond yields remain, but also the regression results on all the other variables including issue characteristics, market conditions, and political situations still correspond with those of the baseline regressions. Fourth, for further robustness purposes, we re-estimate our baseline regressions with additional covariates which control for the economic and fiscal health of governments. The variables capture government size (natural log of population), economic growth (real per capita gross product), and fiscal health (the ratio of total debt outstanding to gross product). We expect a positive association between population and bond yields, because a larger population implies a heavier fiscal burden to a government. Underwriters will demand higher yields for governments with larger debt outstanding because they assume that the fiscal health of those governments is worse. The bond markets will set lower yields on bonds issued by a good economy. After running a number of regressions with all covariates, we report representative results in [Table II]. Again, the additional economic variables do not change the results of the baseline regressions.

Policy Implications and Conclusion Models V and VI in [Table III] display a set of additional regression results after interacting the corruption variable with some bond management practice variables, i.e. insurance, financial advisor, and negotiated sales. The table displays some interesting results and suggests several policy implications. The general format of regression models V and VI in [Table III] corresponds with that in models I and II in [Table II] except adding three interaction variables: Corruption*Insurance, Corruption*Financial Advisor, and Corruption*Negotiated Sales. The first observation is that corruption penalty completely disappears when the


localities in corrupt environments purchase bond insurance and/or hire financial advisors. [Table II] shows that a direct association between corruption and bond yields is significantly positive, and direct associations between yields and both insurance and financial advisor are significantly negative. Note, however, the coefficients of the two interaction terms in [Table III], Corruption*Insurance and Corruption*Financial Advisor, are significantly negative in all the conventional significance levels. This implies that markets are likely to set lower yields (implying no corruption premium) on bonds issued by localities in more corrupt states but supported by bond insurance and/or financial advisors, while they demand a corruption premium on bonds issued by localities from more corrupt states without insurance and/or financial advisors. This implies that corrupt localities did or can mitigate penalties on their corruption imposed by the markets, i.e. corruption premium, by purchasing insurance and financial advisory services from a third party. However, historical episodes illustrate that the existence of insurance and financial advisors may not necessarily guarantee safe interest payments from municipal governments, including corrupt ones. Markets should take into consideration some possible willful tactics undertaken by municipalities to “enhance away” from the effects of public corruption. The second observation is that corruption premium tends to increase with bonds sold by negotiation. Given that both [Table II] and [Table III] display a significantly positive association between yields and negotiated sales, the coefficients of the interaction terms between corruption and negotiated sales, Corruption*Negotiated Sales, are also positive in models V and VI at [Table II]. This implies that corruption premium will increase with a negotiated sale, compared with a competitive sale. The finding corresponds with the general understanding on negotiated sales. Most negotiated sales may be arranged through personal connections and compromising deals between government officials and market shareholders, which provides much more opportunity for corruption compared with competitive / arm’s length processes.

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[Table III] The Impact of Public Corruption on Local Bond Yields Policy Implications 　　 Corruption (employee) Ln(caseloads per judge) Corrupt*Insured Corrupt*Negotiated Corrupt*Fin.Advisor Credit Ratings GO Unlimited Ln(Issue Size) Maturity, in years Call Option Federally Taxable Special District Bank Qualified Insured Negotiated Bids Financial Advisor Market Yield Market Volatility Political Competitiveness Legislative Control Governor's Affiliation Gubernatorial Election Senate Election House Election Constant Observations F –value R-squared

Model V Coeff.

Model VI S. E.

***

0.10

***

0.13

***

-0.04

**

0.03

**

-0.03

***

-0.04

***

-0.06

***

-0.02

***

0.11

***

0.10

***

1.30

***

0.12

***

-0.09

**

-0.01

Coeff.

0.01 0.004 0.007 0.008 0.009 0.0004 0.003 0.001 0.0004 0.003 0.006 0.003 0.003 0.004

S. E. ***

0.01

***

0.004

***

0.007

*

0.009

***

0.009

***

0.0005

***

0.003

***

0.001

***

0

***

0.003

***

0.007

***

0.004

***

0.003

**

0.004

***

0.005

0.10 0.13 -0.04

0.02 -0.04

-0.04 -0.06 -0.02

0.11 0.08 1.31 0.10 -0.09

-0.01

***

0.005

0.01

0.005

0.01

0.006

0.005

***

0.005

***

0.0002

***

0.0001

***

0.004

0.07

***

0.55

***

-0.005

0.0002

　

　

0.07 0.56

-0.005 -0.001 -0.02

　

　

　

　

0.01**

0.003

　

　

-0.02***

0.004

***

0.005

***

0.005

-0.06

0.04

　

　

　

　

-0.06

0.038

-0.03 0.05

232,244

232,244

***

13,544.38***

0.76

0.76

13,544.23

Standard errors are robust. Year dummies (2005-2010) included, but omitted for brevity. *p < .05, **p < .01, ***p < .001


[Table IV] reports the rankings of the states based on average local government serial bond yields (column I, from lowest to highest), convictions per 10,000 public employees (column II, from least corrupt to most corrupt), and convictions per 100,000 of population (column III, from least corrupt to most corrupt), on average in the period 2005–2010. The 10 least corrupt states are Idaho (the least), South Carolina, Vermont, Kansas, New Hampshire, Minnesota, Wyoming, Oregon, and Utah. The 10 most corrupt states are Louisiana (the most), Kentucky, Alaska, Virginia, Alabama, Montana, Maryland, Arizona, New Jersey, and Missouri. A simple estimation shows significant waste of taxpayers’ money when public corruption penalty in the 10 most corrupt states is considered. The local governments in these 10 most corrupt states paid 3.54 percent interest on their bonds in the period 2005– 2010, on average. The localities in the 10 least corrupt states paid 3.40 percent interest on their bonds. The difference is 14 basis points. Our sample descriptive statistics show that average annual bond issuance by localities in the 10 most corrupt states was $33.1 million, while localities in the remaining 40 states issued $22.2 million annually, on average. This implies that the localities in the 10 most corrupt states would spend $1.2 million (=$33.1M*0.0354) annually just to cover interest on their debt service, on average. This paper demonstrates that capital markets take surrounding environments in the states into consideration when determining prices on local debt, above and beyond the underlying features of municipal bonds. We empirically show that corruption premium exists in the US municipal bond market. Local governments in more corrupt states pay higher costs for long-term borrowing in the market. Moreover, fiscal institutions that are traditionally thought to bring fiscal discipline to local governments become ineffective in the states with relatively higher levels of public sector corruption.

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[Table IV] Rankings of States: Bond Yields and Public Corruption, on Average, 2005–2010 Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Yield Wyoming Connecticut New Mexico Massachusetts Montana North Carolina New Jersey Oregon Maine Kentucky Pennsylvania Missouri Utah Minnesota Georgia New Hampshire Kansas Wisconsin Tennessee North Dakota Indiana Rhode Island Virginia Mississippi Washington Ohio Maryland South Carolina Colorado Oklahoma Iowa New York Vermont Alabama Delaware Texas Idaho Illinois California Michigan Alaska Louisiana Florida Nebraska Nevada Arizona Hawaii Arkansas

2.920 3.228 3.257 3.264 3.295 3.313 3.338 3.355 3.366 3.367 3.377 3.379 3.380 3.384 3.387 3.389 3.407 3.414 3.426 3.434 3.450 3.460 3.464 3.465 3.469 3.471 3.510 3.512 3.512 3.529 3.531 3.536 3.540 3.576 3.580 3.624 3.633 3.691 3.737 3.750 3.782 3.784 3.789 3.791 3.855 3.861 4.022 4.258

Corruption (Employee) Idaho South Carolina Vermont Kansas New Hampshire Minnesota Wyoming Washington Oregon Utah Colorado Nevada Hawaii Nebraska North Carolina Iowa Wisconsin California Georgia Rhode Island New Mexico New York Michigan Indiana Maine Arkansas Connecticut Texas Florida Massachusetts Illinois Ohio Mississippi Pennsylvania Tennessee Delaware North Dakota Oklahoma Missouri New Jersey Arizona Maryland Montana Alabama Virginia Alaska Kentucky Louisiana

Corruption (Population) Idaho South Carolina New Hampshire Vermont Kansas Nevada Minnesota Washington Oregon Utah Colorado Hawaii North Carolina California Rhode Island Nebraska Wyoming Wisconsin Georgia Iowa Michigan Indiana New Mexico New York Arkansas Connecticut Maine Texas Florida Massachusetts Pennsylvania Illinois Tennessee Ohio Arizona Maryland Delaware Oklahoma Mississippi Missouri New Jersey Alabama Montana Virginia North Dakota Kentucky Louisiana Alaska

West Virginia not in the list. Yield column: from lowest to highest local government serial bond yields. Corruption columns: from least corrupt to most corrupt.


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공직자 부패가 지방정부의 채권 가격에 미치는 영향 류 철 · Tima T. Moldogaziev

국문초록 본 논문은 지방정부가 발행하는 채권의 가격 변동에 영향을 미치는 요인들에 관해 연구하였 다. 특별히 미국 지방정부채권의 가격에 대한 주정부의 다양한 영향력과 공직자의 부패의 악 영향에 대해 고찰한다. 장기 채권시장에서 지방정부채권의 가격을 결정할 때, 채권가격투자자 들은 지방채권 자체의 특성뿐만 아니라, 해당 주정부의 경제적ㆍ정치적 요인을 반영하는 것 으로 분석되었다. 무엇보다 부패가 심한 정부에서 발행되는 지방채권들일수록 높은 이자가격 을 부담하고 있음이 발견되었다. 이는 지방채권의 이자가격 산정에 있어서 “부패프리미엄”이 존재함을 의미한다. 󰋪 주제어: 부패, 지방정부 채권가격, 부패프리미엄