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Title: ESSAYS ON APPLIED ECONOMETRICS

Citation Type: Dissertation/Thesis

Publication Year: 2020

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Abstract: This dissertation presents three independent essays in applied econometrics and macroeconomics. Chapter 1 studies the research question: How do firms’ productivity and markups respond to energy price shocks? Productivity and markups are two concepts closely related to the production process and welfare in economics. In general terms, these variables help us to understand: (i) how much a firm can produce given the scarcity of resources; and (ii) the gap between the marginal costs of producing goods and the final price that is charged to consumers. I exploit variations in costs due to, for instance, energy price shocks, to understand more about the behavior of these two variables. I proceed to answer this question as follows: First, I state two potential issues in the commonly used proxy-variable technique when employed as an intermediate step to recover markups: circularity and violation to the monotonicity/scalar unobservable assumption(s). I then present a novel structural estimator that overcomes these issues. Second, taking advantage of a panel of Chilean manufacturing plants, I study the relationship between energy prices, markups, and productivity using an instrumental variables research design. I explore a natural experiment, the 2004 Argentine crisis, as a potential source of exogenous variations for electricity prices. I also complement the research design by instrumenting for average variable cost constructing shift-share type instruments from variations in energy prices. Results suggest that a relevant channel through which firms adjust energy shocks is flexible markups. Specifically, my estimates suggest that a 10 percent average variable cost energy cost-shock increase leads to a roughly 3 percent decrease in markups. However, productivity remains unchanged. Chapter 2, focuses on the intersection of macroeconomics and labor economics and studies the following research question: What does hiring discrimination against black Americans imply for unemployment, job finding, and separation rates over the business cycle? Taking as given the empirical evidence suggesting discrimination against black Americans during the hiring process, I research the labor market consequences of such behavior. Because the question involves outcomes that we cannot directly measure or observe in the data, I rely on a the oretical model. I use the model to conduct a counterfactual experiment: whether the labor outcomes of black people would have been different had they had a relatively higher hiring probability. In 2010, Peter Diamond, Dale Mortensen, and Christopher Pissarides won the Nobel Prize in Economics “for their analysis of markets with search frictions.” They contributed to the development of a model that represents the workhorse in macroeconomics for understanding labor market outcomes. Based on this standard search-and-matching model, I introduce a novel modification that allows the model to generate heterogeneity among agents: the combination of endogenous separations with an urn-ball matching function. After calibrating the model to match the U.S. economy’s aggregate labor market statistics, I find that discrimination in the early stages of the hiring process leads to adverse labor outcomes over the business cycle: lower job finding probabilities and higher unemployment volatility for discriminated groups. Conversely, the model does not predict significant differences in separation rates. Finally, chapter 3 lies at the intersection of applied econometrics and the economics of crime. Here I propose an econometric approach to address two significant methodological challenges that emerge when studying aggregated crime figures: (i) allocation of weights, and (ii) underreporting. Thus, the chapter presents a way to aggregate crime variables into one non-linear index for which each relative importance or weight assigned to the different types of crime is determined from the relationship with potential covariates that may affect overall crime. Furthermore, the index deals with potential underreporting issues that characterize police crime datasets by implementing a zero-inefficiency stochastic frontier modeling approach. To illustrate the practical implementation of the crime index, I take advantage of two different datasets: (i) a yearly province-level panel of crime in Canada, during the period 2000-2010; and (ii) a detailed database of police-recorded crimes in Bogot´a, Colombia, over 2010-2018. The former allows researchers to access relevant socioeconomic information, while the latter is representative of a developing country in Latin America with serious crime problems. I find that the estimates in the econometric specification are a good match with previous results in the literature. Furthermore, the crime index provides a good unified global mapping of the evolution of different crime categories over time and locations in these two countries.

Url: https://www.proquest.com/docview/2449432722?pq-origsite=gscholar&fromopenview=true

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Authors: Chanci Arango, Luis David

Institution: Binghamton University

Department: Economics

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Data Collections: IPUMS CPS

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