Comparison of Hypothesis Testing and Bayesian Model Selection

The main goal of both Bayesian model selection and classical hypotheses testing is to make inferences with respect to the state of affairs in a population of interest. The main differences between both approaches are the explicit use of prior information by Bayesians, and the explicit use of null distributions by the classicists. Formalization of prior information in prior distributions is often difficult. In this paper two practical approaches (encompassing priors and training data) to specify prior distributions will be presented. The computation of null distributions is relatively easy. However, as will be illustrated, a straightforward interpretation of the resulting p-values is not always easy. Bayesian model selection can be used to compute posterior probabilities for each of a number of competing models. This provides an alternative for the currently prevalent testing of hypotheses using p-values. Both approaches will be compared and illustrated using case studies. Each case study fits in the framework of the normal linear model, that is, analysis of variance and multiple regression.     

“Panelizing” Repeated Cross Sections

This paper considers the implementation of a non-stationary, heterogeneous Markov model for the analysis of binary dependent variables in a time series of repeated cross-sectional (RCS) surveys. The model offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how maximum likelihood estimates of the parameters can be obtained by Fishers method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch and West German women using ISSP (and other) data from 10 annual Dutch surveys conducted between 1987 and 1996 and 7 annual West German surveys conducted between 1988 and 1994. Some open problems concerning the application of the model are discussed.     

Casinos,casino size,and crime: A panel data analysis of Michigan counties

Growth in legal gaming in the United States over the past quarter century or so is well-documented. One important factor fueling this growth was the passage of the Indian Gaming Regulatory Act of 1988, which permitted Native American tribes to establish, under agreements or “compacts” with the states in which they are located, casinos offering what is known as Class III gaming: slot machines, blackjack, roulette, and other games. Since the passage of the Act, there have been 21 Native American casinos established in Michigan. Also, three non-Native American casinos opened in Detroit in 1999 and 2000. This growth in the number of casinos has sparked a wide-ranging debate over the social and economic impacts of casino development.The purpose of this research is to focus on the crime issue in the broader casino debate. We investigate the impact of these Michigan casinos on the rates of burglary, robbery, larceny and motor vehicle theft (property crimes) in casino host counties as well as in nearby counties. We employ a panel data set with annual observations on all 83 Michigan counties for the period 1994–2010. The dataset includes crime rates taken from the FBI crime data series, variables for the presence of a casino in a county or in a nearby county, the scale of a casino's operations as measured by revenues, and a variety of control variables suggested by the broader literature investigating the factors that determine crime rates generally.Our results suggest that in most cases the property crime rates studied are not affected by the presence or size of a casino in a county or in a nearby county. The largest such impact, which is negative, is for motor vehicle theft. The size of a casino does have a small positive effect on the motor vehicle theft rate.     

Information Disclosure as Environmental Regulation: A Theoretical Analysis

Governments around the world are beginning to embrace a new form of environmental regulation – mandatory disclosure of information. While information disclosure programs appear to have an impact on subsequent firm behavior – often resulting in lower levels of pollution – little is known about the costs and benefits of these programs and whether or not they enhance social welfare. This paper presents a simple bargaining model where mandatory information disclosure is used to overcome a lack of information on the part of the public. We characterize the conditions under which information disclosure will lead to a reduction in emissions, and ultimately, the conditions under which it will enhance social welfare. Several extensions of the model are briefly explored, including the effect of two sources of pollution – only one of which is subject to information disclosure. This paper was prepared while V. Santhakumar was a Visiting Scholar at the Vanderbilt Center for Environmental Management Studies, Vanderbilt University.     

The Optimal Timing of Adoption of a Green Technology

We study the optimal timing of adoption of a cleaner technology and its effects on the rate of growth of an economy in the context of an AK endogenous growth model. We show that the results depend upon the behavior of the marginal utility of environmental quality with respect to consumption. When it is increasing, we derive the capital level at the optimal timing of adoption. We show that this capital threshold is independent of the initial conditions on the stock of capital, implying that capital-poor countries tend to take longer to adopt. Also, country-specific characteristics, as the existence of high barriers to adoption, may lead to different capital thresholds for different countries. If the marginal utility of environmental quality decreases with consumption, a country should never delay adoption; the optimal policy is either to adopt immediately or, if adoption costs are “too high”, to never adopt. The policy implications of these results are discussed in the context of the international debate surrounding the environmental political agenda.      

Which Elasticity? Estimating the Responsiveness of Taxpayer Reporting Decisions

In this paper we present estimates of the responses of individuals to marginal tax rates in their reporting of income, using data from individual tax returns for the year 1995. One estimation method is ordinary least squares regression. A second method uses quantile regression, which provides evidence on behavioral responses at different points (or quantiles) in the distribution of income and so is relevant to the question of whether the responses of, say, the rich differ from those at other points in the income distribution. Our results clearly indicate that marginal tax rates affect the reporting decisions of individuals. However, there are significant differences in the marginal tax rate reporting responses for the various types of reported income, there are major differences across income classes, and there are notable differences in the estimated responses across estimation methods.

On the growth and welfare maximizing allocation of public investment

In this paper we present an endogenous growth model to analyze the growth maximizing allocation of public investment among N different types of public capital. Using this general model of public capital formation, we analyze the stability of the long-run equilibrium and we derive the growth-maximizing values of the shares of public investment allocated to the different types of public capital, as well as the growth-maximizing tax rate (amount of total public investment as a share of GDP). The empirical implication of the model is that both the effects of the shares of public investment and the tax rate on the long-run growth rate are non-linear, following an inverse U-shaped pattern. Our analysis is completed by showing that the growth-maximizing shares of public investment and the growth-maximizing tax rate also maximize welfare in the decentralized economy.