Donald J. Lacombe

The Effect of State Income Taxation on Per Capita Income Growth

This study examines the impact of changes in marginal state income tax rates on per capita income by comparing income growth in counties on state borders with income growth in adjacent counties across the state border. Compared to a standard cross-sectional analysis, this border-matching technique is a better way to hold constant many factors that can vary for geographical reasons, such as climate, culture, and proximity to markets. The results show that over the 30-year period from 1960 to 1990, states that raised their income tax rates more than their neighbors had slower income growth and, on average, a 3.4% reduction in per capita income.

Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote

One problem with describing election vote shares using ordinary least squares (OLS) is that it ignores the possible presence of spatial error correlation, whereby the errors are correlated in a systematic manner over space. This omission can bias OLS standard errors. We examine the 2004 presidential county vote outcome using OLS and a spatial error model (SEM) that accounts for spatial autocorrelation in the error structure. We find that spatial error correlation is present, that the SEM is superior to OLS for making inferences, and that several factors deemed important to the 2004 election outcome are not significant once the spatial error autocorrelation is taken into account.

Right-to-Work Laws and Manufacturing Employment: The Importance of Spatial Dependence

Using 2000 decennial census data, we estimate the relationships between right-to-work (RTW) laws and employment in manufacturing and other industries. Estimates that do not account for geographically correlated omitted factors dramatically overstate the positive relationship between RTW legislation and manufacturing employment. We estimate that RTW legislation is associated with an increase in manufacturings share of private wage and salary employment of 2.12%, an estimate almost 30% lower than the estimate that does not control for these spatially correlated omitted factors. Results for other industries indicate that RTW legislation is negatively associated with employment shares in the agriculture, forestry, fishing and hunting, and mining industries and some service industries, but is positively associated with employment shares in the information and professional, scientific, management, administrative, and waste management services industries. Improperly controlling for geographic factors can lead to incorrect inferences and misinform policy.

A spatial analysis of incomes and institutional quality: evidence from US metropolitan areas

We use the Stansel (2013) metropolitan area economic freedom index and 25 conditioning variables to analyze the spatial relationships between institutional quality and economic outcomes across 381 U.S. metropolitan areas. Specifically, we allow for spatial dependence in both the dependent and independent variables and estimate how economic freedom impacts both per-capita income growth and per-capita income levels. We find that while economic freedom and income levels are directly and positively related, increases in economic freedom in one area result in negative indirect effects on income levels in surrounding areas. In addition, we find that economic freedom has an insignificant relationship with economic growth.

Bayesian Estimation of the Spatial Durbin Error Model with an Application to Voter Turnout in the 2004 Presidential Election

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, the authors formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

An Introduction to Bayesian Inference in Spatial Econometrics

This tutorial is designed to introduce readers to Bayesian variants of the standard SAR and SEM models that are the most widely used and applied models in spatial econometrics. Particular attention is paid to the mathematical derivations required to obtain the full conditional distributions required for Gibbs sampling. The models are derived using diffuse as well as natural conjugate priors for the parameters.

THE GROWTH OF LOCAL GOVERNMENT IN THE UNITED STATES FROM 1820 TO 1870

As the United States became more urbanized in the nineteenth century, local-government expenditures increased as local governments expanded their services in response to their growing populations. Expenditures at all levels of government increased substantially in the nineteenth century, but local governments grew more than either the states or the federal government. Total local-government expenditures increased along with the growing urban population, but expenditures per urban resident also increased substantially. The major expansion in local-government expenditures began in the 1830s. This study examines the period of transition to local-government growth from 1820 to 1870.See Davis and Legler, “Government†; Legler, Sylla, and Wallis, “U.S. City Finances†; and Wallis, government expenditures only back to 1880, 1850, and 1840 respectively. The first two used less comprehensive data than the present note, and Wallis (“American Government Finance†) gives summary data on local expenditures without discussing how they were calculated.

Interests Versus Ideology in the Ratification of the 16th and 17th Amendments

The ideology of Progressivism that peaked in the early years of the 20th century brought with it the 16th amendment, which allowed the federal government to tax incomes, and the 17th amendment, which mandated direct elections of Senators. Both were ratified in 1913. The 16th amendment provided the financing for government to expand its scope, and the 17th amendment created more democratic accountability, which both were goals of the Progressive movement. An examination of House and Senate voting on these amendments suggests that those opposed to the amendments voted against them based on interests rather than ideology. Copyright 1998 Blackwell Publishers Ltd..

Use and Interpretation of Spatial Autoregressive Probit Models

Applications of spatial probit regression models that have appeared in the literature have incorrectly interpreted estimates from these models. Spatially dependent choices frequently arise in various modeling scenarios, including situations involving analysis of regional voting behavior, decisions by states or cities to change tax rates relative to neighboring jurisdictions, decisions by households to move or stay in a particular location. We use county-level voting results from the 2004 presidential election as an illustrative example of some issues that arise when drawing inferences from spatial probit model estimates. Although the voting example holds particular intuitive appeal that allows us to focus on interpretive issues, there are numerous other situations where these same considerations come into play. Past work regarding Bayesian Markov Chain Monte Carlo estimation of spatial probit models from LeSage and Pace (Introduction to spatial econometrics. Taylor and Francis, New York, 2009) is used, as well as derivations from LeSage et al. (J R Stat Soc Ser A Stat Soc 174(4):1007–1027, 2011) regarding proper interpretation of the partial derivative impacts from changes in the explanatory variables on the probability of voting for a candidate. As in the case of conventional probit models, the effects arising from changes in the explanatory variables depend in a nonlinear way on the levels of these variables. In non-spatial probit regressions, a common way to explore the nonlinearity in this relationship is to calculate “marginal effects” estimates using particular values of the explanatory variables (e.g., mean values or quintile intervals). The motivation for this practice is consideration of how the impact of changing explanatory variable values varies across the range of values encompassed by the sample data. Given the nonlinear nature of the normal cumulative density function transform on which the (non-spatial) probit model relies, we know that changes in explanatory variable values near the mean may have a very different impact on decision probabilities than changes in very low or high values. For spatial probit regression models, the effects or impacts from changes in the explanatory variables are more highly nonlinear. In addition, since spatial models rely on observations that each represent a location or region located on a map, the levels of the explanatory variables can be viewed as varying over space. We discuss important implications of this for proper interpretation of spatial probit regression models in the context of our election application.

Trends in NCAA athletic spending: arms race or rising tide?

We develop and empirically test a model of intercollegiate athletic department expenditure decisions. The model extends general dynamic models of nonprice competition and includes the idea that nonprofit athletic departments may simply set expenditure equal to revenues. Own and rival prestige are included in the athletic departments’ utility functions, generating rivalrous interaction. The model predicts that current own and rival investment has multi-period effects on prestige since investment is durable. We test the model using data from National Collegiate Athletic Association (NCAA) Division I athletic programs from 2006-2011, and the models incorporate spatial autocorrelation that captures dynamic rivalrous interaction. Results support the predictions of both models—NCAA Division I athletic programs appear to engage in dynamic nonprice competition in terms of expenditure and spend all revenues generated.