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Title: Assessing Municipal Bond Default Probabilities
Citation Type: Miscellaneous
Publication Year: 2013
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Abstract: In response to a request from the California Debt and Investment Advisory Commission, we propose several approaches to explaining municipal default, and to estimating default likelihood for bonds issued by cities. The first approach relies on logistic regression analysis of major city financial statistics and socioeconomic variables from the Great Depression the last time a large number of cities defaulted to develop a statistical model. The model is used with contemporary figures, including data available in Comprehensive Annual Financial Reports (CAFRs) that cities are required to publish, to estimate default probabilities. For this study, we gathered data from CAFRs for 260 California cities with population over 25,000. Using CAFR data from the year 2011, and our Depression-era model, we estimate the default probability for California cities, and consider how well these estimates would have predicted the two defaults that actually occurred in California in 2012. Despite the fact that the model is based on historical events from over 80 years ago, it does a fairly impressive job of predicting the two 2012 defaults. The second approach relies on detailed case study evidence for recent historical defaults in California and other states. These case studies reveal that general fund exhaustion was associated with the most recent California municipal bond defaults. Thus our second approach for predicting default relies on a simple ranking of cities based on a standardized measure of general fund balance.This second approach, though highly simplistic, does an even better job of predicting the 2012 defaults than our Great Depression-era model. We continue this line of analysis by examining the determinants of general fund balance, and identify several variables that may provide a worthwhile departure for future research into the causes of municipal stress. Finally, we discuss the possibility of a hybrid model, which is informed by both the logistic regression analysis and case study evidence, as a third approach. Overall, this study sheds new light on the determinants and predictors of municipal default. By making both our findings and the data we gathered for this study available to scholars and the general public, this research will pave the way for better understanding this important topic.
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Authors: Joffe, Marc D.; Holian, Matthew J.
Publisher: California Debt and Investment Advisory Commission
Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure
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