Adriana S. Cordis

Measuring Public Corruption in the United States: Evidence From Administrative Records of Federal Prosecutions

A growing empirical literature examines public corruption in the United States, but most of these studies use survey data on federal corruption convictions that is aggregated, sparse, and of dubious quality and provenance. This article describes an alternative data source from administrative records that provides more detailed and reliable information on corruption prosecutions and convictions by type of state official and by lead charge. Contrary to conventional wisdom, corruption convictions are not increasing and mostly involve low-ranking federal or local officials. Administrative data on public corruption are not highly correlated with survey data in state-year observations. Consequently, prior empirical findings based on survey data may not be replicated when reexamined with administrative records on corruption prosecutions. The availability of more reliable and detailed data on corruption prosecutions is a boon to scholars interested in the causes and consequences of public corruption in the United States.

Corruption and the Composition of Public Spending in the United States

I investigate the relation between corruption and the composition of state government spending in the United States. The analysis reveals that the United States is not immune to the adverse effects of corruption documented in cross-country studies. Corruption lowers the share of state government spending devoted to higher education and raises the share of spending devoted to other and unallocable budget items. These results are robust to the use of political variables to instrument for corruption. There is also some evidence that corruption lowers the share of spending on corrections and public welfare and raises the share of spending on health and hospitals, housing and community development, and natural resources.

Discrete Stochastic Autoregressive Volatility

We use Markov chain methods to develop a flexible class of discrete stochastic autoregressive volatility (DSARV) models. Our approach to formulating the models is straightforward, and readily accommodates features such as volatility asymmetry and time-varying volatility persistence. Moreover, it produces models with a low-dimensional state space, which greatly enhances computational tractability. We illustrate the proposed methodology for both individual stock and stock index returns, and show that simple first- and second-order DSARV models outperform generalized autoregressive conditional heteroscedasticity and Markov-switching multifractal models in forecasting volatility.

Regime-Switching Factor Models in Which the Number of Factors Defines the Regime

We develop regime-switching factor models in which the number of factors determines the operative economic regime. To illustrate the proposed methodology, we analyze the covariance structure of a widely studied set of 25 equity portfolios.

Income Shifting as an Aspect of Tax Avoidance: Evidence from U.S. Multinational Corporations

We use jurisdiction-specific effective tax rates (ETRs) to investigate income shifting as an aspect of tax avoidance by U.S. firms. Our central prediction is that tax-based incentives for shifting income, as measured by the spread between domestic and foreign ETRs, should be reflected in the share of pre-tax income earned by U.S. firms in foreign jurisdictions. The data lend substantial support to this prediction. We find robust evidence of a positive correlation between the foreign share of pre-tax income and the ETR spread that is consistent with firms shifting income both into and out of the United States. The evidence also indicates that firms respond asymmetrically to positive and negative ETR spreads. Specifically, the response to a negative spread is stronger than to a positive spread of the same magnitude.

Capital Expenditures and Firm Performance: Evidence from a Cross-Sectional Analysis of Stock Returns

Using a simple two†period model of investment, we show that there should be a nonlinear relation between a firms investment†to†capital ratio and its subsequent stock returns. This prediction finds substantial empirical support. The evidence indicates that the slope of the investment function is negative at low investment levels, close to zero at intermediate investment levels and negative at high investment levels. Our results, which are robust to the use of narrowly†and broadly†defined measures of capital investment, pose a challenge to the hypothesis that the negative cross†sectional correlation between investment and stock returns is attributable to some sort of overinvestment phenomenon.

Accounting Ratios and the Cross-section of Expected Stock Returns: ACCOUNTING RATIOS AND EXPECTED STOCK RETURNS

Under clean�?surplus accounting, the log return on a stock can be decomposed into a linear function of the contemporaneous log return on equity, the contemporaneous log dividend–price ratio (if the stock pays a dividend), and both the contemporaneous and lagged values of the log book�?to�?market equity ratio. This paper studies the implications of this decomposition for the cross�?section of conditional expected stock returns. The empirical analysis reveals that the log accounting ratios capture cross�?sectional variation in both the conditional mean and conditional variance of log stock returns, which is consistent with the decomposition. It also brings fresh insights to the relation between firm size (market equity) and conditional expected stock returns. The evidence indicates that the conditional median return increases with firm size, while the conditional return skewness decreases with firm size. Empirically, the skewness effect outweighs the median effect, leading to the well�?documented inverse relation between size and average returns. The results of out�?of�?sample tests suggest that investors could use the information provided by the observed values of the log accounting ratios to formulate more effective portfolio strategies.