Microeconomics & International Trade Seminars

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


Fall 2021


September 30
Arun Chandrashekhar, Standford University
"Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization" 
Host: Ariel Zucker
ABSTRACT: 
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
ABSTRACT: 
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
ABSTRACT: 
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
ABSTRACT: 
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
ABSTRACT: 
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
Shachar Kariv, UC Berkeley
"TBA"
Host: Kristian Lopez Vargas


December 2 
Steven Durlauf, University of Chicago
"TBA"
Host: Gueyon Kim


 


Winter 2022


January 13
Vira Semenova, UC Berkeley
"TBA"
Host: Julian Martinez


January 20
Andreas Kleiner, Arizona State
"TBA"
Host: Dong Wei


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


February 10
David Dillenberger, Penn State
"TBA"
Host: Gerelt Tserenjigmid


February 24
Takuya Ura, UC Davis
"TBA"
Host: Julian Martinez



Spring 2022


April 7
Tavneet Suri, MIT
"TBA"
Host: Ariel Zucker


April 21
Frank Wolak, Stanford University
"TBA"
Host: Jessie Li


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


May 12
Matthew Gentzko, Stanford University
"TBA"
Host: Dong Wei