Corruption, credit ratings, and fiscal transfers

Credit ratings have an important impact on government's ability to finance its budget. Good credit ratings allow governments to borrow money on more favorable terms.Footnote ²¹ Credit ratings provide an external expectation on a state's perceived ability and willingness to repay its debts, which best represents our concept of creditworthiness. In particular, at the subnational level where information may be less available, bond credit ratings provide domestic and international investors key additional data used to assess investment strategies. Although they have received ample international critique from a host of critics and scholars,Footnote ²² these ratings remain highly salient to a state's reputation, and changes in ratings often warrant “front page news.”Footnote ²³ Rating agencies exercise a unique form of market based authority by assigning ratings that determine the creditworthiness of debt issuers, including sovereign governments, and the default risk associated with their bonds.Footnote ²⁴ They have been described as a market actor that combines “the normative market authority and the moral authority of the non-state, non-self interested referee,” exercising significant authority in the world economy.Footnote ²⁵ A great amount of scholarly attention has therefore been devoted to analyzing the determinants of credit ratings, and not least political determinants of credit ratings.Footnote ²⁶

The literature on the effect of the quality of institutions, e.g., factors such as corruption or rule of law, have provided evidence that better functioning institutions are strongly associated with higher credit ratings from international assessors.Footnote ²⁷ Scholars have noted that this relationship is due to the decrease in perceived risk of default among countries with better functioning institutions—leaders operating a strong rule of law system with lower corruption are more constrained from malfeasance, which in turn makes their commitments more credible.Footnote ²⁸ Moreover, the cost of publicly funded projects increases if civic servants receive graft, thus increasing debt and reducing a state's capacity to repay. Corrupt governments tend to direct public resources toward sectors that provide the greatest opportunities for rent seeking, such as large capital investments or infrastructure,Footnote ²⁹ rather than the ones that provide the greatest returns for the public good. Corruption also leads to tax evasion,Footnote ³⁰ which further undermines fiscal capacity. Several studies have found that corruption increases investors’ uncertainty, and thus leads to higher borrowing costs.Footnote ³¹

At the country level, the negative relationship between corruption and credit ratings has been consistent across multiple samples and model specifications.Footnote ³² Thus, our data and sample of US states presents an empirical puzzle, which is elucidated in figure 1. On the left side, we plot a measure of (control of) corruption risk on the x-axis (from International Country Risk Guide [ICRG]), and the Standard & Poor's sovereign credit rating on the y-axis. As per the previous literature, we observe a strong and positive relationship between these two variables, with the corruption measure explaining 75 percent of the variation in cross-national credit ratings. On the other hand, using data from the same year (2014), we find that our measure of corruption is essentially orthogonal to state-level credit ratings; only explaining 5 percent of the variation (p = 0.00). Our central argument in addressing this puzzle is that the effect of corruption on credit ratings is heterogeneous—conditional on a state's fiscal dependence to the central government.

Figure 1. Corruption and credit ratings at two levels of governance: National level (left) and in US states (right)

Thus, the relationship between corruption and credit ratings may not be as straightforward and direct as previous studies suggest. Although fewer studies investigate the effect of corruption on subnational credit ratings directly, a number of studies point to the fact that corruption may undermine credit ratings also at the subnational or municipal level.Footnote ³³ In the United States, both Depken and Lafountain (Reference Depken and Lafountain2006) and Butler et al. (Reference Butler, Fauver and Mortal2009) show that corruption is associated with lower subnational credit ratings.

However, despite the fact that claims related to the negative effect of corruption on a range of economic variables, including credit ratings, enjoy considerable empirical support and theoretical clout, these results do not necessarily hold across time and space at the subnational level. Building on the vast literature on fiscal dependence and decentralization, we suggest that one factor that may contribute to maintaining good credit ratings among corrupt subnational units is the relative level of fiscal transfers and fiscal dependence. A broad body of studies suggest that fiscal dependency fuels bailout expectations, and that government that co-finances the budget of subnational units will signal both ability and willingness to repay debts.Footnote ³⁴ Rodden (Reference Rodden2002, 670) highlights this key dilemma of fiscal federalism and contends that when “constitutionally or politically constrained central governments take on heavy co-financing obligations, they often cannot credibly commit to ignore the fiscal problems of lower level government.” Moreover, Rodden (Reference Rodden2006) notes explicitly that international ratings agencies grade lesser developed regions more favorably when the federal government has an established equalization system of transfers across subnational units, in that “they provide a safety net of varying importance during difficult times.”Footnote ³⁵ Other studies, similarly argue that fiscal dependence may create bailout expectations and implicitly guarantee debt repayment. This compensation effect is driven by federal governments’ wish to maintain their own ratings as well as avoid being punished by the electorate for failures in local public service delivery.Footnote ³⁶ Subnational creditworthiness becomes less about the solvency of the subnational borrower and more about the creditworthiness of the implicit guarantor—the federal government in this case.Footnote ³⁷ Thus, we suggest that a higher degree of federal dependence serves as a buffer mechanism against the negative effect of corruption on subnational credit ratings. In turn, the effect of corruption is anticipated to be heterogeneous—dependent on the level of federal dependence—not direct, as suggested by previous studies.

A simple two-state comparison with similar, high-profile corruption cases provides an illustrative example of our main point. On the one hand, the state of Illinois is, and has been, one of least dependent states on federal transfers, ranking usually in the bottom third of all states in terms of budget dependence annually. In 2008, former governor Rod Blagojevich was implicated in a corruption scandal where he attempted the sale of then Senator Obama's senate seat, along with extortion and lying to federal agents, which resulted in a conviction and prison sentence for Blagojevich. The corruption conviction in this case led to a near immediate downgrade from Standard & Poor's in the following year of 2009. On the other hand, Alabama is a state that is highly dependent on annual federal transfers, usually in ranking among the top 10 percent of states annually. However, a 2006 conviction of former governor Don Siegelman on several counts of bribery and obstruction led to no subsequent change in Alabama's credit rating.

This leads to the following two hypotheses:

Sample and data

Our empirical hypotheses are tested via a subnational analysis of US states from 2001–15. This sample offers several key advantages to test the hypotheses put forth in this study. First, the subnational analysis allows us to “naturally control for” unobserved cultural and institutional factors, which can lead to a more valid comparison.Footnote ³⁸ Second, as we show in this section, there is ample variation on our key variables both across states, as well as within with them. Third, with some notable exceptionsFootnote ³⁹ most of the literature on the relationship between institutional quality and credit ratings is at the country-level, our study provides a clear empirical compliment.

The dependent variable

The outcome variable of this study is bond credit rating of US states between 2001 and 2015. Rather than using actual debt or default or interest rates, the credit rating represents the expert, external expectation of a state's ability and willingness to repay its debt, which best represents our concept of creditworthiness. In particular, at the subnational level, where information may be less available, bond credit ratings provide domestic and international investors key additional data used to assess investment strategies. Although ratings have not been without controversyFootnote ⁴⁰ these ratings provide valuable information to the credit marketFootnote ⁴¹ and remain highly salient to a state's reputation.Footnote ⁴²

While all of the “big three” agencies (Standard & Poor, Moody's, and Fitch) rate some states in some years, only Standard & Poor provide data for all states over a significant time period annually, and thus we elect to use their measure to maximize our sample coverage.Footnote ⁴³ The corresponding ratings among the three agencies for available states is remarkably high, however.Footnote ⁴⁴ The Standard & Poor ratings are on a possible 23-point scale, with the coveted “AAA” rating being the max value (representing the highest assessment of creditworthiness) and “SD” being the lowest (the lowest assessment of creditworthiness), with higher scores resulting in a lower interest rate on debt. Similar to previous studies, we transform the ratings into an ordinal scale. As most state-years are at least a grade of “A” or above (save Illinois and California for two and three years, respectively), the “effective” range of our sample is a 6-point scale, with higher values equating to high levels of creditworthiness. Thus, while US municipalities may default on their debt, it is important to note that US states have not historically defaulted.

Although the grand mean of our outcome variable is relatively stable over time in our sample, we do observe temporal changes in ratings in 36 of the 50 states (72 percent) and the cross-sectional variation is noteworthy, with some states achieving and maintaining top ratings, while others are rated significantly lower. Figure 2 shows the average rating score by US state for our time period, with lighter shades equating to better average ratings:

Figure 2: Average state Standard & Poor credit ratings (2001–15)

Diagnostics tests and estimation strategy

In the literature, credit ratings are modeled in several ways. First, some scholars elect to use either tobit estimation,Footnote ⁵⁸ or ordered probit/logitFootnote ⁵⁹ due to the limited, ordered nature of the measure. Others choose to model variation in credit ratings continuously, via linear regression.Footnote ⁶⁰ While exact model predictions would be expected to vary based on the estimation choice, these approaches produce similar substantive effects irrespective of this decision, thus we present linear models for simplicity, with alterative estimations in appendix F.

As our data is time series cross-sectional (TSCS), we perform the standard diagnostic tests of problems commonly associated with such data. A Dickey-Fuller test for stationarity shows no unit root in the dependent variable, implying that the sample mean and variance do not significantly vary over time. However, we find the presence of positive serial correlation and heteroscedasticity in the data, and thus similar to most previous panel data studies,Footnote ⁶¹ we employ dynamic models with a lagged dependent variable and robust standard errors, clustered by states. To arrive at the proper lag structure for the dependent variable, we follow Hendry (Reference Hendry1995) and “test down” (via cs) to assess how many lags to include, which in our case is one lag.

We account for the unit heterogeneity of states over time and omitted, time-invariant variables from the model in several ways to test the consistency of our findings. First, a Hausman test favors fixed effects models, which account for the within-state variation of the dependent and explanatory variables. Our fixed effects estimation to test H1 is thus the following:

(1)$$CR_{it}-\overline {CR} _i = \beta _1( {C_{it}-{\bar{C}}_i} ) + \mathop \sum \limits_{k = 1}^k \beta _k( {\delta_{kit}-{\bar{\delta }}_{ki}} ) + u_i-\bar{u}_i + \epsilon _{it}-\overline {\epsilon _i} $$

Where credit ratings in each state-year (CR _it) are explained by corruption (C) and a ‘k’ number of covariates, δ _it, which includes a lagged dependent variable (CR _it−1).Footnote ⁶² u _i are the state-specific, time-invariant effects, and ε _it is our error term. The fixed effects transformation subtracts the within-unit average from each observation for each time point, and thus washes out unit specific effects, and also drops time invariant covariates.

However, as our effective time series is fifteen years, a somewhat short period to analyze relatively slow moving variables such as credit ratings and corruption, relying on solely within-state variation provides a “tougher test’ and oftentimes less efficient estimates.Footnote ⁶³ As the between-state variation in the dependent variable is more than twice that of the average within-state variation, we present partial-pooled and pooled models with alternative estimators to incorporate more directly this between-state variation. Thus, second, we report random intercept models. However, it is well understood that the inclusion of a lagged dependent variable in a random effects model violates a key assumption of the model, in that the random intercept (μ _i), which represents the unobserved state-level, static variables, is assumed to be orthogonal with the independent variables on the right side of the model. Yet the inclusion of a lag of our outcome variable, credit ratings, on the right side (CR _it−1) is necessarily correlated with μ _i and will bias CR _it−1 upward and estimates of other explanatory variables downward.Footnote ⁶⁴

To address this issue, we provide our random effects estimates based on a technique based on Anderson-Hsiao (Reference Anderson and Hsiao1981), which is developed for dynamic panel models via the use of an instrumental (IV) estimator. In this case, we employ the second lag of the dependent variable (CR _it−2), which is exogenous to μ _iFootnote ⁶⁵ when controlling for the first lag. The generalized, two-stage estimator (G2SLS) is particularly appropriate when the number of units is relatively large and the number of time units are relatively small, and no second-order autocorrelation, as in our case.

For example, our model specification to test H2 with this estimation is as follows:

(2)$$CR_{it} = \alpha + \beta _1( CR_{it-1}) + \beta _2( {C_{it}} ) + \beta _3( {T_{it}} ) + ( {\beta_4( {C\ast T_{it}} ) } ) + \delta k_{it} + \mu _i + \epsilon _{ij}$$

Where the following first stage estimates are:

(3)$$CR_{it-1} = \alpha + \beta _1( CR_{it-2}) + \theta k_{it} + \mu _i + \epsilon _{ij}$$

Where we run a first stage model in which the lag of our dependent variable (CR _it−1) is instrumented with the second lag (CR _it−2), along with the battery of “k” number of explanatory variables (θk _it) in the second stage model. In the second stage model, we then regress credit ratings CR _it on its first year lag (modeled as endogenous), along with the main variables of interest (corruption and transfers), and to test H2, we include β ₄ to test the interaction. δk _it represents our battery of control variables, while μ _i is the random intercept for each state and ε _ij is our model error term. Since credit rating agencies can react with a rating changes at any time (they do not have to wait to the end of a fiscal year, for example) we model our explanatory variables as in the same time as the outcome variable, with the exception of growth, which we expect to have a time lagged effect.

Finally, we use Prais-Winsten models, adjusting for first order autocorrelation and panel corrected standard errors,Footnote ⁶⁶ to provide further robustness checks of our FE and RE estimation.

Further robustness checks

In addition to checking for nonlinearity of our findings for H2, we explore further checks of robustness (see appendix D). We check for several alternative specifications. First, we check whether our results for table 2 hold if we include a measure of partisanship.Footnote ⁷² Namely, we include the partisan affiliation of the state's governor, the president's party affiliation, and whether the two are aligned. Similar to our theory regarding transfers, we would expect that partisan alignment would signal to external creditors that a state is more likely to receive financial backing if they are politically aligned with the federal executive. Table A4 (in appendix D) shows that the main results from table 2 hold in the main fixed effects models, while the effects of partisanship are negligible across the models.

Next, we re-run the models using an alternative dependent variable, namely the two-year moving average of a state's credit rating.Footnote ⁷³ We do this because ratings can be adjusted anytime during a given year, and therefore a yearly summary may lack precision in terms of effects from the explanatory variables. We re-run our tests of H2 in particular with this dependent variable across our three main specification, and find our original estimates to be robust (see appendix D, table A5).