Daniel L. Millimet

Bio: Daniel L. Millimet is an academic researcher from Southern Methodist University. The author has contributed to research in topics: Childhood obesity & Estimator. The author has an hindex of 38, co-authored 159 publications receiving 5196 citations. Previous affiliations of Daniel L. Millimet include Virginia Tech & Binghamton University.

Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity-Score-Matching Estimator

Abstract: This study examines the effects of air quality regulation on economic activity. Anecdotal evidence and some recent empirical studies suggest that an inverse relationship exists between the stringency of environmental regulations and new plant formations. Using a unique county-level data set for New York State from 1980 to 1990, we revisit this conjecture using a seminonparametric method based on propensity score matching. Our empirical estimates suggest that pollution-intensive plants are responding to environmental regulations; more importantly, we find that traditional parametric methods used in previous studies may dramatically understate the impact of more stringent regulations.

Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous

Abstract: The validity of existing empirical tests of the Pollution Haven Hypothesis (PHH) is constantly under scrutiny due to two shortcomings. First, the issues of unobserved heterogeneity and measurement error in environmental regulation are typically ignored due to the lack of a credible, traditional instrumental variable. Second, while the recent literature has emphasized the importance of geographic spillovers in determining the location choice of foreign investment, such spatial effects have yet to be adequately incorporated into empirical tests of the PHH. As a result, the impact of environmental regulations on trade patterns and the location decisions of multinational enterprises remains unclear. In this paper, we circumvent the lack of a traditional instrument within a model incorporating geographic spillovers utilizing three novel identification strategies. Using state-level panel data on inbound U.S. FDI, relative abatement costs, and other determinants of FDI, we consistently find (i) evidence of environmental regulation being endogenous, (ii) a negative impact of own environmental regulation on inbound FDI in pollution-intensive sectors, particularly when measured by employment, and (iii) larger effects of environmental regulation once endogeneity is addressed. Neighboring environmental regulation is not found to be an important determinant of FDI.

The Environmental Kuznets Curve: Real Progress or Misspecified Models?

Abstract: We explore the importance of modeling strategies when estimating the emissions-income relationship. Using U.S. state-level panel data on nitrogen oxide and sulfur dioxide emissions, we estimate several environmental Kuznets curves using the standard parametric framework as well as a more flexible semiparametric alternative. Formal statistical comparisons of the results overwhelmingly reject the parametric approach. Moreover, the differences, particularly for sulfur dioxide, are economically significant.

Credit Programs for the Poor and the Health Status of Children in Rural Bangladesh

Abstract: The impact of participation in group-based credit programs, by gender of participant, on the health status of children by gender in rural Bangladesh is investigated. These credit programs are well suited to studies of how gender-specific resources alter intra-household allocations because they induce differential participation by gender. Women's credit is found to have a large and statistically significant impact on two of three measures of the healthiness of both boy and girl children. Credit provided to men has no statistically significant impact and the null hypothesis of equal credit effects by gender of participant is rejected.

Estimation and Inference in Econometrics

Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Regression Discontinuity Designs in Economics

Abstract: This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates. Concepts are discussed using examples drawn from the growing body of empirical research using RD.

Recent developments in the econometrics of program evaluation

Abstract: Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoreti- cal literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization, and other areas of empirical microeconomics. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical research- ers, as well as provide a historical overview of the area and give references to more technical research.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Abstract: We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.