Foreign Direct Investment and Political Uncertainty

Recent developments in the general equilibrium theory of multinationals emphasize the importance of multilateral considerations. Yet, existing explanations and corresponding estimations of FDI patterns have largely limited political and institutional investment impediments to a bilateral framework. Through the application of spatial econometric techniques, I demonstrate that the presence of both domestic and regional political uncertainty generate real options effects that lead to the delay or redirection of foreign direct investment. The magnitude and direction of these effects is conditional upon the host country regime type and the predominant multinational integration strategies in the region. Comparing these results with FDI of U.S. origin, I find evidence for divergent investment behavior by U.S. multinationals during regime changes in partner countries. Additionally, I find no evidence that multinationals from developing countries are more likely to complete cross-border deals in environments characterized by greater political risk or political uncertainty.

Essays on Uncertainty, Imperfect Information, and Investment Dynamics

Understanding how imperfect information affects firms' investment decision helps answer important questions in economics, such as how we may better measure economic uncertainty; how firms' forecasts would affect their decision-making when their beliefs are not backed by economic fundamentals; and how important are the business cycle impacts of changes in firms' productivity uncertainty in an environment of incomplete information. This dissertation provides a synthetic answer to all these questions, both empirically and theoretically. The first chapter, provides empirical evidence to demonstrate that survey-based forecast dispersion identifies a distinctive type of second moment shocks different from the canonical volatility shocks to productivity, i.e. uncertainty shocks. Such forecast disagreement disturbances can affect the distribution of firm-level beliefs regardless of whether or not belief changes are backed by changes in economic fundamentals. At the aggregate level, innovations that increase the dispersion of firms' forecasts lead to persistent declines in aggregate investment and output, which are followed by a slow recovery. On the contrary, the larger dispersion of future firm-specific productivity innovations, the standard way to measure economic uncertainty, delivers the ``wait and see" effect, such that aggregate investment experiences a sharp decline, followed by a quick rebound, and then overshoots. At the firm level, data uncovers that more productive firms increase investments given rises in productivity dispersion for the future, whereas investments drop when firms disagree more about the well-being of their future business conditions. These findings challenge the view that the dispersion of the firms' heterogeneous beliefs captures the concept of economic uncertainty, defined by a model of uncertainty shocks. The second chapter presents a general equilibrium model of heterogeneous firms subject to the real productivity uncertainty shocks and informational disagreement shocks. As firms cannot perfectly disentangle aggregate from idiosyncratic productivity because of imperfect information, information quality thus drives the wedge of difference between the unobserved productivity fundamentals, and the firms' beliefs about how productive they are. Distribution of the firms' beliefs is no longer perfectly aligned with the distribution of firm-level productivity across firms. This model not only explains why, at the macro and micro level, disagreement shocks are different from uncertainty shocks, as documented in Chapter 1, but helps reconcile a key challenge faced by the standard framework to study economic uncertainty: a trade-off between sizable business cycle effects due to changes in uncertainty, and the right amount of pro-cyclicality of firm-level investment rate dispersion, as measured by its correlation with the output cycles.

Trade Shocks and Local Labor Markets' Linkages: Theory and Evidence

In the past few years, there has been a concern among economists and policy makers that increased openness to international trade affects some regions in a country more than others. Recent research has found that local labor markets more exposed to import competition through their initial employment composition experience worse outcomes in several dimensions such as, employment, wages, and poverty. Although there is evidence that regions within a country exhibit variation in the intensity with which they trade with each other and with other countries, trade linkages have been ignored in empirical analyses of the regional effects of trade, which focus on differences in employment composition. In this dissertation, I investigate how local labor markets' trade linkages shape the response of wages to international trade shocks. In the second chapter, I lay out a standard multi-sector general equilibrium model of trade, where domestic regions trade with each other and with the rest of the world. Using this benchmark, I decompose a region's wage change resulting from a national import cost shock into a direct effect on prices, holding other endogenous variables constant, and a series of general equilibrium effects. I argue the direct effect provides a natural measure of exposure to import competition within the model since it summarizes the effect of the shock on a region's wage as a function of initial conditions given by its trade linkages. I call my proposed measure linkage exposure while I refer to the measures used in previous studies as employment exposure. My theoretical analysis also shows that the assumptions previous studies make on trade linkages are not consistent with the standard trade model. In the third chapter, I calibrate the model to the Brazilian economy in 1991--at the beginning of a period of trade liberalization--to perform a series of experiments. In each of them, I reduce the Brazilian import cost by 1 percent in a single sector and I calculate how much of the cross-regional variation in counterfactual wage changes is explained by exposure measures. Over this set of experiments, employment exposure explains, for the median sector, 2 percent of the variation in counterfactual wage changes while linkage exposure explains 44 percent. In addition, I propose an estimation strategy that incorporates trade linkages in the analysis of the effects of trade on observed wages. In the model, changes in wages are completely determined by changes in market access, an endogenous variable that summarizes the real demand faced by a region. I show that a linkage measure of exposure is a valid instrument for changes in market access within Brazil. By using observed wage changes in Brazil between 1991-2000, my estimates imply that a region at the 25th percentile of the change in domestic market access induced by trade liberalization, experiences a 0.6 log points larger wage decline (or smaller wage increase) than a region at the 75th percentile. The estimates from a regression of wages changes on exposure imply that a region at the 25th percentile of exposure experiences a 3 log points larger wage decline (or smaller wage increase) than a region at the 75th percentile. I conclude that estimates based on exposure overstate the negative impact of trade liberalization on wages in Brazil. In the fourth chapter, I extend the standard model to allow for two types of workers according to their education levels: skilled and unskilled. I show that there is substantial variation across Brazilian regions in the skill premium. I use the exogenous variation provided by tariff changes to estimate the impact of market access on the skill premium. I find that decreased domestic market access resulting from trade liberalization resulted in a higher skill premium. I propose a mechanism to explain this result: that the manufacturing sector is relatively more intensive in unskilled labor and I show empirical evidence that supports this hypothesis.

Essays on Matching Markets and Their Equilibria

Matching theory and matching markets are a core component of modern economic theory and market design. This dissertation presents three original contributions to this area. The first essay constructs a matching mechanism in an incomplete information matching market in which the positive assortative match is the unique efficient and unique stable match. The mechanism asks each agent in the matching market to reveal her privately known type. Through its novel payment rule, truthful revelation forms an ex post Nash equilibrium in this setting. This mechanism works in one-, two- and many-sided matching markets, thus offering the first mechanism to unify these matching markets under a single mechanism design framework. The second essay confronts a problem of matching in an environment in which no efficient and incentive compatible matching mechanism exists due to matching externalities. I develop a two-stage matching game in which a contracting stage facilitates subsequent conditionally efficient and incentive compatible Vickrey auction stage. Infinite repetition of this two-stage matching game enforces the contract in every period. This mechanism produces inequitably distributed social improvement: parties to the contract receive all of the gains and then some. The final essay demonstrates the existence of prices which stably and efficiently partition a single set of agents into firms and workers, and match those two sets to each other. This pricing system extends Kelso and Crawford's general equilibrium results in a labor market matching model and links one- and two-sided matching markets as well.

A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.