340 research outputs found
Interview with David M. Grether
No full textDavid Grether (1938–2021), professor of economics, emeritus, discusses his life and career and shares his thoughts and observations about the role and evolution of liberal arts studies at Caltech from his unique vantage point as two-time chair of Caltech’s Division of Humanities and Social Sciences (1982–1992, 2006–2007). The complex collegial and institutional relationship between Caltech’s lone liberal arts division and its five science/engineering divisions on the one hand, and between the humanists and social scientists grouped together within HSS, on the other, is a recurring theme of this retrospective.
Grether recalls his upbringing in Berkeley, California, and the roots of his early interest in social and economic problems, leading to undergraduate and graduate study in economics at UC Berkeley and Stanford respectively, and to several years as assistant professor of economics at Yale. Joining Caltech’s economics faculty in 1971, he gradually shifted much of his research focus from econometrics to behavioral economics and was instrumental in establishing the new field of experimental economics at Caltech in the 1970s. He talks about his impressions of the campus and numerous colleagues during these early years, and about the atmosphere in HSS during a time of significant transition, marked by the introduction of a PhD program in social science, differences over the future direction of the division, and the contrasting personal styles and academic agendas of consecutive HSS division chairs H. Smith, R. Huttenback, and R. Noll, whom he succeeded as chair in 1982. Grether’s detailed account of his experiences as HSS chair includes his interactions with a succession of Caltech provosts (J. Roberts, R. Vogt, B. Kamb, and P. Jennings), as well as faculty recruitment, fundraising, the expansion or introduction of research programs in the history of science, Asian studies, and neuroeconomics, the controversy surrounding the 1985 closure of Caltech’s Baxter Art Gallery, and a recap of related academic, administrative, and personnel issues. The oral history concludes with an overview of Grether’s later research work, his involvement in campus faculty committees, most notably his tenure as chair of the undergraduate admissions committee, and general reflections on his Caltech career
Does corruption relieve foreign investors of the burden of taxes and capital controls?
No full textIn a sample of fourteen source countries making bilateral investments in forty five countries, the author finds that taxes, capital controls, and corruption, all have large, statistically significant negative effects on foreign investment. Moreover, there is no robust support in the data for the"efficient grease"hypothesis - that corruption helps attract foreign investment by reducing firms'tax burden and the irritant of capital controls.International Terrorism&Counterterrorism,Capital Markets and Capital Flows,Decentralization,Fiscal&Monetary Policy,Economic Theory&Research,Economic Theory&Research,International Terrorism&Counterterrorism,Governance Indicators,National Governance,Capital Flows
The Effects of Total Sleep Deprivation on Bayesian Updating
No full textRecent evidence suggests that nearly 25% of U.S. adults (47 million) suffer from some level of sleep deprivation. The impact of this sleep deprivation on the U.S. economy includes direct medical expenses related to sleep deprivation and related disorders, the cost of accidents, and the cost of reduced worker productivity. Sleep research has examined the effects of sleep deprivation on a number of performance measures, but the effects of sleep deprivation on decision-making under uncertainty are largely unknown. In this article, subjects perform a decision task (Grether, 1980) in both a well-rested and experimentally sleep-deprived state. The experimental task allows us to explore the extent to which subjects weight prior odds versus new evidence (i.e., information) when forming subjective (posterior) beliefs of a particular event. Wellrested subjects display a tendency to overweight the evidence in forming subjective posterior probability estimates, which is inconsistent with Bayes rule but possibly consistent with use of a ‘representativeness’ heuristic. In his original Bayes rule experiment, Grether (1980) also found that typical student-subjects overweighted the evidence relative to the prior odds in making posterior assessments. Ironically, behavior following sleep-deprivation is more consistent with the use of Bayes rule, because this treatment significantly reduces the (over)weight that subjects place on the new evidence. Because choice accuracy is not significantly affected by sleep deprivation, the significant difference in estimated decision-model parameters may indicate that the brain compensates under adversity in certain risky choice decision environments.
David Dow—Lawyer, Teacher, Scholar
No full textDedication of this issue of the Nebraska Law Review to David Dow in appreciation of thirty-two years of service as professor and dean
Unknown heterogeneity, the EC-EM algorithm, and Large T Approximation
No full textWe study a panel structure with n subjects/entities being observed over T periods. We consider a class of models for each subject's data generating process, and allow for unknown heterogeneity. In other words, we do not know how many types we have, what the types are, and which subjects belong to each type. We propose a large T approximation to the posterior mode on the unknowns through the Estimation/Classification (EC) algorithm of El-Gamal and Grether (1995) which is linear in n, T , and the unknown number of types. If our class of models (likelihood functions) allows for a consistent asymptotically normal estimator under the assumption of homogeneity (number of types = 1), then the estimators obtained by our EC algorithm inherit those asymptotic properties as T " 1 and then as n " 1 (with a block-diagonal covariance matrix facilitating hypothesis-testing). We then propose a large T approximation to the EM algorithm to obtain posteriors on the subject classifications and diagnostic..
Creighton Law Review Board of Editors 2015/2016
No full textBack L-R: John Dunn, Justina Piatek, Nicole Northup, Joseph Eden
Middle L-R: Joseph Borghoff, Ronald Volkmer, Spencer Murphy, Kari Grether, Jerome David Sund
Front L-R: Jacqueline Schreurs, Mackenzie Angels, Rosemary Laughli
Unknown heterogeneity, the ec-em algorithm, and large t approximation. SSRI working paper #9622
No full textWe study a panel structure with n subjects/entities being observed over T periods. We consider a class of models for each subject's data generating process, and allow for unknown heterogeneity. In other words, we do not know how many types we have, what the types are, and which subjects belong to each type. We propose a large T approximation to the posterior mode on the unknowns through the Estimation/Classi cation (EC) algorithm of El-Gamal and Grether (1995) which is linear in n, T, and the unknown numberoftypes. If our class of models (likelihood functions) allows for a consistent asymptotically normal estimator under the assumption of homogeneity (number of types = 1), then the estimators obtained by our EC algorithm inherit those asymptotic properties as T "1and then as n "1(with a block-diagonal covariance matrix facilitating hypothesis-testing). We then propose a large T approximation to the EM algorithm to obtain posteriors on the subject classi cations and diagnostics for the goodness of the large T approximation in the EC stage. If the large T approximation does not seem to be appropriate, then we suggest the use of the more computationally costly EM algorithm, or the- even more costly- full Bayesian updating. We illustrate the procedure with two applications to experimental data on probability assessments within a class of Probit and a class of Tobit models
Is African manufacturing skill-constrained?
No full textTotal factor productivity has been low in most Sub-Saharan Africa. It is often said that the binding constraint on African industrial development is the inadequate supply of technologically capable workers. And many cross-country studies imply that the low level of human capital in Africa is an important source of low growth in per capita income. The results of the authors'study do not necessarily conflict with this view. They indicate that in non-competitive industrial sectors, with little inflow of new technology, the contribution of technological abilities, however it is measured, is limited. If liberalization of the economy generated greater competition, or if export growth were accelerated --permitting the import of inputs embodying new technology - local skills could contribute significantly more in raising output. The experience of other countries also suggests that as the economy opens to flows of international knowledge - whether through technology transfers or through informal transfers from purchasers of export - the technological capacity of local industry becomes important. The policy implications of this analysis are clear: Without the prospect of a more competitive environment, continued efforts to develop high-level industrial skills may be wasteful. But the absence of such skills may limit the benefits to the industrial sector from future liberalization, as a result of which the supply response toimproved incentives may be weak.Environmental Economics&Policies,Economic Theory&Research,Curriculum&Instruction,ICT Policy and Strategies,Small and Medium Size Enterprises,Economic Theory&Research,Environmental Economics&Policies,ICT Policy and Strategies,Curriculum&Instruction,Health Monitoring&Evaluation
Competition for scarce inputs: the case of airport takeoff and landing slots
No full textAn analysis of competition for scarce inputs, describing the outcome of an auction of takeoff and landing slots between two airline carriers and the possible outcomes from a merger or takeover wave. The results suggest that the concern over monopolization of airports may be misplaced.Airlines ; Competition
Intimidation or Impatience? Jump Bidding in On-line Ascending Automobile Auctions
No full textWe run a large field experiment with an online company specializing in selling used automobiles via ascending auctions. We manipulate experimentally the maximum amount which bidders can bid above the current standing price, thus affecting the ease with which bidders can engage in jump bidding. We test between the intimidation vs. costly bidding hypotheses of jump bidding by looking at the effect of these jump-bidding restrictions on average seller revenue. We find evidence consistent with costly bidding in one market (Texas), but intimidation in the other market (New York). This difference in findings between the two markets appears partly attributable to the more prominent presence of sellers who are car dealers in the Texas market.
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