Knowledge that Transforms

American Economic Association Universal Academic Questionnaire Summary Statistics by Charles E. Scott and John J. Siegfried. Published in volume 107, issue 5, pages 678-80 of American Economic Review, May 2017

Report: Committee on the Status of Minority Groups in the Economics Profession (CSMGEP) by Cecilia Rouse and Gary A. Hoover. Published in volume 107, issue 5, pages 777-91 of American Economic Review, May 2017

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).

Maternal and neonatal mortality rates in the slums of Nairobi, Kenya are among the highest in the world. Mounting evidence suggests that delivering in a facility is not enough to ensure mortality reductions: women must deliver in high-quality facilities and arrive early enough for appropriate care if complications arise. We designed an RCT combining labeled cash transfers and pre-commitment incentives to encourage earlier and more effective delivery facility choice and to promote earlier facility arrival. We find that the intervention improves planning, increases delivery at the desired facility, and encourages more timely arrival at delivery facilities.

This paper introduces and analyzes a procedure called Testing-Based Forward Model Selection (TBFMS) in linear regression problems. This procedure inductively selects covariates that add predictive power into a working statistical model before estimating a final regression. The criterion for deciding which covariate to include next and when to stop including covariates is derived from a profile of traditional statistical hypothesis tests. This paper proves probabilistic bounds for prediction error and the number of selected covariates, which depend on the quality of the tests. The bounds are then specialized to a case with heteroskedastic data with tests derived from Huber-Eicker-White standard errors. TBFMS performance is compared to Lasso and Post-Lasso in simulation studies. TBFMS is then analyzed as a component into larger post-model selection estimation problems for structural economic parameters. Finally, TBFMS is used to illustrate an empirical application to estimating determinants of economic growth.

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.