This paper attempts to show how fuzzy set theory can be used to weaken some of the stringent, rationality assumptions used in classical micro-economics. The objective of the paper is to see whether by introducing fuzziness we arrive to new results or just only generalizations of classical micro-economic results. We discover that the axiom of completeness is not needed anymore. Using fuzziness will also allow us to better explain the existing gap between delimiting possible choices and making the actual choice. We also introduce the notions of a fuzzy indifference set with a measurable area. The fuzzy utility surface is also discussed. The demand curve is now 'thick'. / In the producer area, the classical hypothesis that maximum profit entails maximum utility of profit is now substantially weakened when introducing fuzziness. / Finally, we consider revealed preference within a fuzzy context.

Thesis (Ph. D.)--Oregon State University, 1999. / Typescript (photocopy). Includes bibliographical references (leaves 78-81). Also available on the World Wide Web.

Thesis (D.A.)--Illinois State University, 1981. / Title from title page screen, viewed April 1, 2005. Dissertation Committee: Bernard J. McCarney (chair), John F. Chizmar, Robert D. Young, Ronald Halinski, L. Dean Hiebert. Includes bibliographical references (leaves 143-152) and abstract. Also available in print.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references. Also available in electronic version.

Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 2002. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Chicago, Irving B. Harris Graduate School of Public Policy Studies, June 2002. / Includes bibliographical references. Also available on the Internet.

This thesis includes three essays exploring some economic implications of queueing. A preliminary chapter introducing useful results from the literature which help contextualize the original research in the thesis is presented first. This introductory chapter starts by surveying queueing results from probability theory and operations research. Then it covers a few seminal papers on strategic queueing, mostly but not exclusively from the economics literature. These cover issues of individual and social welfare in the context of First Come First Served (FCFS) and Equitable Processor Sharing (EPS) queues, with one or multiple servers, as well as a discussion of strategic interactions surrounding queue cutting. Then an overview of some important papers on the impact of queueing on competitive behaviour, mostly Industrial Organization economists, is presented. The first original chapter presents a model for the endogenous determination of the number of queues in an M/M/2 system. Customers arriving at a system where two customers are being served play a game, choosing between two parallel queues or one single queue. Subgame perfect equilibria are obtained, varying with customer characteristics and game specifications. With risk neutrality and when jockeying is not permitted, a single queue is an equilibrium, as is two queues. With risk neutrality and jockeying allowed, there is a unique two queue equilibrium. With risk aversion and no jockeying, there is a unique single queue equilibrium, and with risk aversion and jockeying, the equilibrium depends on the magnitude of risk aversion. The second chapter analyses the individual decisions taken by consumers when deciding whether to join an M/M/1 queue where a subset of customers who interact repeatedly can both cut the queue and be overtaken once they join, by-passing occasional users. This is shown to be an equilibrium in repeated games for sufficiently patient customers. The expected sojourn time for customers under this discipline is described as a solution of a system of difference equations, and this is then used to obtain a threshold joining strategy for arrivals, which is independent of the number of regular customers in the queue, as regulars form a sub-queue under the LCFS discipline. Numerical methods are then employed to contrast sojourn times and thresholds with the equilibrium for a strict First Come First Served queueing discipline, and with the socially optimal joining rule. Finally, the third chapter describes a duopoly market for healthcare where one of the two providers is publicly owned and charges a price of zero, while the other sets a price so as to maximize its profit. Both providers are subject to congestion in the form of an M/M/1 queue, and they serve patient-customers with randomly distributed unit costs of time. Consumer demand (as market share) for both providers is obtained and described with its full complement of comparative statics. The private provider’s pricing decision is explored, and equilibrium existence is proven. Social welfare functions are described and the welfare maximizing condition obtained. Numerical simulations with uniform and Kumaraswamy distributions are performed for several parameter values, showcasing the pricing provider’s decision and its relationship with social welfare.

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Recent decades have seen great advances in the methods we use to understand cause and effect in the world of work. Building on that tradition, this dissertation explores two broad topics in econometrics as tools to address specific questions in labor economics. The main econometric contributions are to extend identification results for research designs based on bunching (Chapter 1) and those that make use of instrumental variables (Chapters 2 and 3). The empirical questions that compel them are described below. Chapter 1 examines the effect of overtime regulation on hours of work in the United States, extending a recently popularized technique that uses bunching observed at kinks in agents' choice sets for identification. In the U.S., most workers are required to be paid one-and-a-half times their typical rate of pay for any hours in excess of forty within a week. While prominent and long-standing, this policy has not been meaningfully reformed since it was first established at the federal level in 1938. As a result, few studies have been able to leverage causal research designs to assess its labor market impacts. I use bunching in the distribution of weekly hours at forty--where the policy introduces a convex "kink" in firms' costs--to estimate this effect. To do so, I develop a framework in which bunching at a choice-set kink is informative about causal effects under substantially weaker assumptions than those maintained in existing work. This allows the effect of the overtime policy to be partially identified without making parametric assumptions about firms' objective functions, or about the distribution of hours they would set in the absence of the policy. Using an administrative dataset of weekly hours derived from payroll records, I find that the bounds are informative and that covered hourly workers in the U.S. work an average of at least half an hour less as a result, in affected weeks. Chapter 2 turns to a still-more popular strategy in applied microeconomics: the instrumental variables research design. I propose a new method for estimating causal effects when a researcher has more than one such instrument, and apply it to reassess the labor market returns to college education. The method is motivated by the following issue. When treatment effects are heterogeneous, it is known that instruments can be used to identify local average treatment effects under an assumption known as "monotonicity”. However, when a researcher wishes to use multiple instruments together, this assumption can become quite restrictive, and empirical conclusions may be misleading if it is violated. I propose an alternative assumption that I call "vector monotonicity", which is quite natural in typical settings with multiple instruments. I show that vector monotonicity leads to identification of a useful class of treatment effect parameters, but the two-stage-least-squares estimator popular in applied work does not consistently estimate them. I propose an alternative estimator, and apply it to the classic question of the returns to schooling. I find that the approach based upon vector monotonicity reveals new patterns of heterogeneity in the earnings effect of college education. Chapter 3, with coauthors Ashna Arora and Jonas Hjort, considers the effects of a worker's first job on outcomes later in their career. This is typically a difficult question to answer empirically, as workers entering the labor force are not randomly assigned to employers. We make use of a unique opportunity to study this question in the context of medical residencies in Norway. For decades, medical school graduates in Norway were matched to residencies based on a random serial dictatorship mechanism, in which doctors could choose--in an order determined by lottery--among available positions in the country. We develop an econometric framework in which the random choice set a doctor is presented with provides a collection of instruments for their choice of residency hospital, and hence first job as a doctor. Because we only observe choices and not a doctor’s full preferences, this requires new methods--related to those of Chapter 2. We find persistent effects of a doctor’s first job on earnings, specializations, and mid-career moves. We use the estimates to assess the replacement of the serial-dictatorship by a decentralized labor market in 2013, which we find led to a small increase in resident welfare.

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