Microeconomics & International Trade Seminars

Thursdays, 1:40 - 3:00 PM
via Zoom

Winter 2022

January 13
Vira Semenova, UC Berkeley
"Inference on weighted average value function in high-dimensional state space"
Host: Julian Martinez
This paper gives a consistent, asymptotically normal estimator of the expected value function when the state space is high-dimensional and the first-stage nuisance functions are estimated by modern machine learning tools. First, we show that value function is orthogonal to the conditional choice probability, therefore, this nuisance function needs to be estimated only at n-1/4 rate. Second, we give a correction term for the transition density of the state variable. The resulting orthogonal moment is robust to misspecification of the transition density and does not require this nuisance function to be consistently estimated. Third, we generalize this result by considering the weighted expected value. In this case, the orthogonal moment is doubly robust in the transition density and additional second-stage nuisance functions entering the correction term. We complete the asymptotic theory by providing bounds on second-order asymptotic terms.

January 27
Louis Putterman, Brown University
Host: Kristian Lopez Vargas

February 3
Severin Borenstein, UC Berkeley
Host: Natalia Lazzati

February 10
David Dillenberger, Penn State
Host: Gerelt Tserenjigmid

February 17
Steven Durlauf, University Chicago
Host: Gueyon Kim

February 24
Takuya Ura, UC Davis
Host: Julian Martinez

March 10
Paulina Oliva, USC
Host: Jeremy West

Spring 2022

April 7
Tavneet Suri, MIT
Host: Ariel Zucker

April 21
Frank Wolak, Stanford University
Host: Jessie Li

April 28
Jeff Smith, University of Wisconsin-Madison 
Host: Gueyon Kim

May 12
Matthew Gentzko, Stanford University
Host: Dong Wei

Fall 2021

September 30
Arun Chandrashekhar, Standford University
"Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization" 
Host: Ariel Zucker
We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India. The policies under consideration include the two most frequently discussed tools--reminders and incentives--as well as an intervention inspired by the networks literature. We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as "ambassadors" receiving regular reminders to spread the word about immunization in their community. By taking into account different versions (or "dosages") of each intervention, we obtain 75 unique policy combinations. We develop a new statistical technique--a smart pooling and pruning procedure--for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy. We proceed in two steps. First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective. Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner's curse. The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent. The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44% relative to the status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1%.

October 7
Ricardo Reyes-Heroles, Federal Reserve Board
"Escaping the Losses from Trade: The Impact of Heterogeneity and Skill Acquisition"
Host: Brenda Samaneigo
Future generations of workers can invest in education, acquire skill and avoid the negative consequences of trade openness for low-skilled workers. However, not all members of these future generations might have the resources required to make such investments. In this paper we exploit variation in exposure to import penetration shocks across space in the United States to show that greater import penetration increases college enrollment and that this increase is driven by future workers in richer households. To analyze the welfare implications of the effects of trade openness on college enrollment, we propose a dynamic multi-region model of international trade with heterogeneous agents. The model features incomplete credit markets and costly endogenous skill acquisition. We calibrate the model to match changes in aggregate trade data for the United States and differential import exposure across U.S. regions. Lower import barriers generate increased college enrollment and welfare gains for all workers in the long-run. However, these gains are concentrated on workers with a college education, whose welfare gains are twice as large as those of non-college workers. While all workers in the manufacturing sector lose from grater trade openness, a small number of college educated workers in manufacturing with low wealth experience the greatest losses. Increasing college enrollment for new cohorts over time plays a crucial role in allowing new generations of workers to escape the potential welfare losses form trade. However, poor dynasties take the longest to acquire skills. They are therefore the last to experience positive gains from trade openness, and entire generations may not realize any gains within a life-time.

October 14
Felipe Gonzalez, PUC - Chile
"The Economics of the Public Option: Evidence from Local Pharmaceutical Markets"
Host: Alonso Villacorta
We study the economic and political effects of competition by state-owned firms, leveraging the decentralized entry of public pharmacies to local markets in Chile around local elections. Public pharmacies sell drugs at a third of private pharmacy prices, because of a stronger upstream bargaining position and downstream market power in the private sector, but are also of lower quality. Exploiting a field experiment and quasi-experimental variation, we show that public pharmacies affected consumer shopping behavior, inducing market segmentation and price increases in the private sector. This segmentation created winners and losers, as consumers who switched to public pharmacies benefited, whereas consumers who stayed with private pharmacies were harmed. The countrywide entry of public pharmacies would reduce yearly consumer drug expenditure by 1.6 percent, which outweighs the costs of the policy by 52 percent. Mayors that introduced public pharmacies received more votes in the subsequent election, particularly by the target population of the policy.

October 21
Shaowei Ke, University of Michigan
"Learning from a Black Box"
Host: Gerelt Tserenjigmid
We study a decision maker’s learning behavior when she receives recommendations from a black box, i.e., the decision maker does not understand how the recommendations are generated. We introduce four reasonable axioms and show that they cannot be satisfied simultaneously. We analyze various relaxations of the axioms. In one relaxation, we introduce and characterize an updating rule, the contraction rule, which has two parameters that map each recommendation to a recommended belief and the trustworthiness of the recommendation, respectively. The decision maker’s posterior is formed by mixing her prior with the recommended belief according to the trustworthiness measure.

October 28
Seema Jayachandram, Northwestern University
"Using Machine Learning and Qualitative Interviews to Design a Five-Question Survey Module for Women's Agency"
Host: Brenda Samaneigo / Galina Hale
Open-ended interview questions elicit rich information about people's lives, but in large-scale surveys, social scientists often need to measure complex concepts using only a few close-ended questions. We propose a new method to design a short survey measure for such cases by combining mixed-methods data collection and machine learning. We identify the best survey questions based on how well they predict a "gold standard'' measure of the concept derived from qualitative interviews. We apply the method to create a survey module and index for women's agency. We measure agency for 209 women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we treat as her true agency. To determine the close-ended questions most predictive of the "truth," we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs.

November 18 - CANCELLED
Shachar Kariv, UC Berkeley
"Ever Since Allais"
Host: Kristian Lopez Vargas
The Allais critique of expected utility theory (EUT) has led to the development of theories of choice under risk that relax the independence axiom, but which adhere to the conventional axioms of ordering and monotonicity. Unlike many existing laboratory experiments designed to test independence, our experiment systematically tests the entire set of axioms, providing much richer evidence against which EUT can be judged. Our within-subjects analysis is nonparametric, using only information about revealed preference relations in the individual-level data. For most subjects we find that departures from independence are statistically significant but minor relative to departures from ordering and/or monotonicity.

December 2 - CANCELLED
Steven Durlauf, University of Chicago
Host: Gueyon Kim