Abstract:Do variables that measure economic base diversification, market expansion, population growth, and energy endowment affect municipal bond ratings? To examine this question, we specify and test a linear probability model of changes in municipal bond ratings related to changes in these variables. Using county data for 49 urban areas for 1970 and 1981, we find that changes in each of the variables strongly influence changes in municipal bond ratings . The results suggest that municipal officials and investors can … Show more
“…The differences may stem in part from their use of a sample of cities that includes those with populations of 10,000-25,000 people and no correction on self-selection bias. Consistent with our results, Wescott (1984) and Loviscek and Crowley (1988) find that the fiscal variables and debt variables are not the most important determinants of the ratings. The thresholds for both Moody's and S&P's are strongly significant, suggesting that the ratings represent clearly definable and different risk categories.…”
This paper extends previous studies on bond ratings by modeling as a system of equations the determinants of a municipality's decision to obtain a bond rating and the determinants of the municipality's rating for the two major rating agencies. Our model provides a framework to examine formally the differences between the two agencies in the determinants of the ratings. We estimate the four-equation system by smooth simulated maximum likelihood estimation and then construct minimum x 2 tests on cross-equation restrictions based on optimal minimum distance estimation. Self-selection is found to be important in Moody's ratings while not in those of S&P. Split ratings appear to reflect differences in both the weight attached to specific determinants of the ratings and differences in the way the bonds are classified.
52C.-G. MOON AND J. G . STOTSKY ratings. These studies have primarily employed cross-section analysis with several different statistical techniques. Although early research used ordinary least squares and multiple discriminant analysis techniques to investigate the determinants of bond ratings, some recent research has used univariate ordered probit and logit analysis. These techniques are appropriate in analyzing this issue because, unlike ordinary least squares, the dependent variable-the rating-is not constrained to lie along a linear scale but only along an ordinal scale, and the spacing of the categories is determined by the data.Several studies have used ordered categorical variable techniques to examine the determinants of municipal bond ratings. Farnham (1984, 1985) consider the determinants of Moody's and Standard & Poor's (S&P's) ratings using univariate ordered probit estimation and Wescott (1984) analyzes the determinants of Moody's ratings using cluster and probit analysis. Several studies have also applied ordered categorical variable techniques to the analysis of corporate debt ratings.The first econometric extension in this analysis is to account for self-selection. A selectivity problem may arise in this analysis from the practice of considering only observations in a sampIe for which a bond rating has been obtained rather than from considering all observations with outstanding debt in the sample. The selectivity problem arises in analyzing these ratings in that the rating is observed only for those municipalities that have chosen to get rated. Considering the sample of municipalities that issue general obligation debt, it is inappropriate to estimate the determinants of the ratings with only the municipalities that have a rating if there is some systematic reason that the municipalities that do not have a rating have chosen not to obtain one. In this case, the expectation of the error term of the regression equation explaining the determinants of the rating, conditional on that municipality having a bond rating, is not equal to zero. [See Heckman (1979).] The bias that results from neglecting the self-selection problem is indeterminate, as it depends on the nature of the relationship b...
“…The differences may stem in part from their use of a sample of cities that includes those with populations of 10,000-25,000 people and no correction on self-selection bias. Consistent with our results, Wescott (1984) and Loviscek and Crowley (1988) find that the fiscal variables and debt variables are not the most important determinants of the ratings. The thresholds for both Moody's and S&P's are strongly significant, suggesting that the ratings represent clearly definable and different risk categories.…”
This paper extends previous studies on bond ratings by modeling as a system of equations the determinants of a municipality's decision to obtain a bond rating and the determinants of the municipality's rating for the two major rating agencies. Our model provides a framework to examine formally the differences between the two agencies in the determinants of the ratings. We estimate the four-equation system by smooth simulated maximum likelihood estimation and then construct minimum x 2 tests on cross-equation restrictions based on optimal minimum distance estimation. Self-selection is found to be important in Moody's ratings while not in those of S&P. Split ratings appear to reflect differences in both the weight attached to specific determinants of the ratings and differences in the way the bonds are classified.
52C.-G. MOON AND J. G . STOTSKY ratings. These studies have primarily employed cross-section analysis with several different statistical techniques. Although early research used ordinary least squares and multiple discriminant analysis techniques to investigate the determinants of bond ratings, some recent research has used univariate ordered probit and logit analysis. These techniques are appropriate in analyzing this issue because, unlike ordinary least squares, the dependent variable-the rating-is not constrained to lie along a linear scale but only along an ordinal scale, and the spacing of the categories is determined by the data.Several studies have used ordered categorical variable techniques to examine the determinants of municipal bond ratings. Farnham (1984, 1985) consider the determinants of Moody's and Standard & Poor's (S&P's) ratings using univariate ordered probit estimation and Wescott (1984) analyzes the determinants of Moody's ratings using cluster and probit analysis. Several studies have also applied ordered categorical variable techniques to the analysis of corporate debt ratings.The first econometric extension in this analysis is to account for self-selection. A selectivity problem may arise in this analysis from the practice of considering only observations in a sampIe for which a bond rating has been obtained rather than from considering all observations with outstanding debt in the sample. The selectivity problem arises in analyzing these ratings in that the rating is observed only for those municipalities that have chosen to get rated. Considering the sample of municipalities that issue general obligation debt, it is inappropriate to estimate the determinants of the ratings with only the municipalities that have a rating if there is some systematic reason that the municipalities that do not have a rating have chosen not to obtain one. In this case, the expectation of the error term of the regression equation explaining the determinants of the rating, conditional on that municipality having a bond rating, is not equal to zero. [See Heckman (1979).] The bias that results from neglecting the self-selection problem is indeterminate, as it depends on the nature of the relationship b...
“…In addition to these self‐admitted statements from the rating firms, paying attention to the composition of one's economy makes practical sense. Loviscek and Crowley () argued that relative default risk is related to the ability to pay in major ways and, therefore, economic diversity is a more direct measure of tax base. They wrote that: “The diversification and expansion broaden the tax base and, therefore, increase ability to pay debt on time.…”
Section: Theoretical Framework and Hypothesesmentioning
confidence: 99%
“…Finally, credit ratings are important for investors. Securities' prices and trading volumes are affected by credit ratings and rating changes (e.g., Ederington et al 1987;Loviscek and Crowley 1990;Capeci 1991;Perry et al 1991;Micu et al 2004;Cohen 2014). Credit ratings help investors to match their risk preferences to securities in the market, diversify their investments by risk portfolios, and continuously monitor default risks of their investments (Ory and Raimbourg 2008;Hilscher and Wilson 2013).…”
Section: Literature Review: What's In the Rating?mentioning
confidence: 99%
“…Hildreth and Miller (2002) argue that undiversified economies are subject to unsystematic risks in the limited and narrow base and, hence, are vulnerable to greater default risks. Diversified economies, on the other hand, would not be excessively vulnerable to unsystematic risks in any given one area of economic activity (Loviscek and Crowley, 1990); barring major shocks such as the Great Depression that can bring down the entire economy and spare no "industry" from the crisis. At other times, however, a well-balanced portfolio should be able to spread the risks across the industries and result in lower levels of default risk.…”
Section: Theoretical Framework and Hypothesesmentioning
confidence: 99%
“…At other times, however, a well-balanced portfolio should be able to spread the risks across the industries and result in lower levels of default risk. Loviscek and Crowley (1990) argue that "Communities that have diversified economic bases should, ceteris paribus, have higher ratings than communities that do not. Diversification controls for unsystematic risk (e.g., plant closing due to loss of a government contract, an unexpected strike, or a fire).…”
Section: Theoretical Framework and Hypothesesmentioning
The study examines "through-the-cycle" stability of S&P and Moody's state credit ratings to national and state level business cycles during 1977-2010. Additionally, the study evaluates the associations between economic concentration and credit quality in a long panel of state credit ratings. The findings suggest that, with other temporal effects held constant, the ratings of S&P and Moody's are not procyclical and are robust to ups and downs in national or state-specific business cycles. Economic concentration is inversely associated with state credit quality and remains significant for the period of an average business cycle, controlling for national and state economic expansions and contractions.
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