Northwestern Events Calendar

Nov
11
2020

Economics Development Lunch

When: Wednesday, November 11, 2020
1:00 PM - 2:00 PM CT

Where: Online

Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students

Contact: Lola Ittner   (847) 491-5213

Group: Department of Economics: Development Economics Lunch Seminar

Category: Academic

Description:

Ritwika Sen and Hossein Alidaee (Northwestern University): 

First Presenter: Ritwika Sen

Title: Covid19 and the Value of Relationships in Informal Economies (with Vittorio Bassi, Tommaso Porzio and Esau Tugume)

Abstract: This project focuses on the value of employment relationships in informal economies, where there are usually no written labor or trade contracts. By studying the resilience of these relationships to the Covid19 lockdown, we seek to understand whether these relationships are valuable, and to clarify the sources of their value. We argue that in periods of normalcy inefficient firm-worker matches may persist in the presence of labor market frictions. However, these relationships will be disrupted if they hold little value and there is a cost to re-match (e.g. workers traveling back to the city) as managers will hire different workers once firms reopen after the lockdown. If instead relationships are valuable, these will restart despite any costs to re-match even in the absence of formal contracts. Our starting point is a representative survey of about 1,000 managers and their employees that we conducted in 2018-19. We are now re-surveying this sample through a phone survey to understand which relationships have been disrupted and why. To further examine the sources of relationship value we introduce a nudging experiment and plan to interpret our findings using an adaptation of the canonical Diamond-Mortensen-Pissarides (DMP) model of search and matching.

Second Presenter: Hossein Alidaee

Title: “Recovering Network Structure from Aggregated Relational Data using Penalized Regression”, joint w. E. Auerbach and M. Leung

Abstract: Social network data can be expensive to collect. Breza (2020) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model. Our main observation is that many economic network formation models produce networks that are effectively low-rank. As a consequence, network recovery from ARD is generally possible without parametric assumptions using a nuclear-norm penalized regression. We demonstrate how to implement this method and provide finite-sample bounds on the mean squared error of the resulting estimator for the distribution of network links. Computation takes seconds for samples with hundreds of observations. Easy-to-use code in R and Python can be found at https://github.com/mpleung/ARD.

*All fall lunches will take place via zoom

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