This paper summarizes certain aspects of my research into the trade and employment effects in the United States of a significant multilateral reduction in tradedistorting measures by the world's major trading nations. The study differs from earlier investigations into this question such as those by Giorgio Basevi (1968), Stephen Magee (1973), Robert Stern (1964), and Beatrice Vaccara and Walter Salant (1960) in that the industry breakdown is much more detailed and the consequences of a multilateral tariff reduction on both U.S. export and import-competing industries is taken into account. By estimating not only the net trade and employment effects of a significant tariff reduction in over 350 industries but also in the 50 states and on some 14 occupational groups, it is hoped that the results will be useful for those who are now embarked on the so-called Tokyo round of trade negotiations within the framework of the General Agreement of Tariffs and Trade (GATT). Another novel feature of the study is the estimation of the net employment effects of multilateral tariff cuts under the assumption of flexible exchange rates.
There is growing evidence that inappropriate prescribing is harming patients and raising costs in the US health care system. Through a partnership between the federal government and academics, we seek to develop evidence on reducing this prescribing. We conduct several randomized letter interventions targeting high-volume prescribers of drugs that can harm patients. We take a continuous improvement approach, rapidly evaluating each round and using the results to inform subsequent work. The first round of letters yielded no effects, and we responded with new interventions that are now under evaluation. We discuss lessons our work provides for future government-academic partnerships.
John Formby, Terry Seaks, and W. Smith (hereafter FSS) argue that the P-Gini coefficient is affected by the arbitrary choice of the age to a degree which brings the validity of [the] age-related measure into question (FSS, 1989, p. 2). More pointedly, a sufficiently narrow age partition, the P-curve can always be driven to the L-curve. Convergence [of the P-Gini] to a nonzero estimate does not occur... (p. 4). These conclusions I believe result from a misapplication of Gastwirth's theorem on disaggregation, and a failure to observe statistical rules relating to sample size and sampling error. I will show that when these rules are observed, the value of the P-Gini does not converge to zero but properly reflects the relative importance of the nonlife-cycle factors affecting the distribution. When calculating the traditional L-Gini, the more disaggregation the better; the number and accuracy of the sample points are the only consideration since all are thrown into one conceptual box and compared in terms of income size. But if we try to identify the factors which account for income inequality in terms of age versus nonage related factors, we are setting up two conceptual boxes (the age-Gini and the P-Gini) and we are no longer simply dealing with a Gastwirth-type problem. Statistical considerations come into play; for example, we must have a sufficient number of sample points in each conceptual box in order to give a reliable estimate of the importance of each factor. The key-allocating device which I employ is the age-Gini, derived from the average age-income profile. The age-Gini shows the amount of inequality that would exist if all nonage-related sources of inequality were eliminated. When calculating this coefficient, the means of the age-groups are used in order to wash out all random and nonagerelated influences, but this separating device works well only if the means are based on large samples. Otherwise, sampling errors create spurious variation and impart an upward bias to the value of the age-Gini. FSS (p. 4) drive the age-Gini value up to the L-Gini by increasing the number of agegroups until they equal the number in the sample. Since the means of the age-groups are now based on samples of one, they become as erratic as the individual incomes, and impart the maximum upward bias to the age-Gini. It is true that the age-income profile (and the age-Gini) are conceptually refined by using smaller age intervals, but unless sample size is large compared to the number of age intervals, the gains from conceptual purification will be more than offset by the greater sampling errors of the age means. This kind of limitation is shared by many other statistical measures which do not thereby lose their validity or usefulness. Under what conditions will the true or limiting value of the P-Gini emerge? FSS in their footnote 3 state that there is no limiting value other than zero. Let us test this claim. Assume we have a scatter diagram of income (Y) and age (X), and wish to show average income in relation to age. We start with a finite number of age-groups and plot their mean incomes on the diagram. By continuously reducing the age interval and increasing sample size, we end up with a curve passing through the true means of infinitely small age intervals: this defines the average age-income profile. Since for each person we have data on income and age, we can with this curve (or an approximation of it) calculate the age-Gini and L-Gini without grouping for age or income. The age-income curve allows us to determine the mean income (u) at any given age and for all persons. *Department of Economics, Portland State University, P.O. Box 751, Portland, OR 97207.
Matching theory typically assumes that agents know their values for possible partners and confines attention to settings in which matching is either static, or driven by population dynamics. In many environments of interest, instead, dynamics originate in the agents learning their preferences through interactions with other agents. In this short paper, we illustrate how platforms can use appropriately designed auctions to account for the joint value of experimentation and cross-subsidization in dynamic matching markets. The model is a stylized version of the general one in Fershtman and Pavan (2016).
We conducted an experiment marketing microloans to farmers in the USA during Spring 2015 and found a simple direct mail letter increased borrowing from a government program. The subsequent spring, we built on this finding and enriched the design to test for information spillovers. The direct effect result did not replicate in the second year, thus lowering the likelihood that spillovers would be present and detectable. These results add to recent evidence on how (seemingly subtle) differences in context and treatment content affect consumer responses.
The relations between unobserved events and observed outcomes can be characterized by a bipartite graph. We propose an algorithm that explores the structure of the graph to construct the “exact Core Determining Class,” i.e., the set of irredudant inequalities. We prove that in general the exact Core Determining Class does not depend on the probability measure of the outcomes but only on the structure of the graph. For more general linear inequalities selection problems, we propose a statistical procedure similar to the Dantzig Selector to select the truly informative constraints. We demonstrate performances of our procedures in Monte-Carlo experiments.
Structural estimation of matching games with transferable utility, including matching games of trading networks and many-to-many matching, is increasingly popular in empirical work. I explore several modeling decisions that need to be made when specifying a structural model for a matching game. One decision is the choice of a game theoretic solution concept to impose in the structural model. I discuss pairwise stability, competitive equilibrium, and noncooperative games such as auctions. Another decision is whether to work with a continuum of agents or a finite number of agents. I explore other issues as well.