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  5. Econometrics of Network Models and Production Function Estimation
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Econometrics of Network Models and Production Function Estimation

Paper Session

Sunday, Jan. 3, 2021 12:15 PM - 2:15 PM (EST)

Hosted By: Korea-America Economic Association
  • Chair: Kyungchul (Kevin) Song, University of British Columbia

Spillovers in Social Programme Participation: Evidence from Chile

Pedro Carneiro
,
University College London
Aureo de Paula
,
University College London
Barbara Flores
,
University College London
Emanuela Galasso
,
World Bank
Rita Ginja
,
University of Bergen

Abstract

We analyze how peers affect the participation in a family allowance for poor families in Chile called Subsidio Unico Familiar (SUF) using a regression discontinuity
design. To identify this spillover effect, we exploit variation in the information about
social programs due to a home-visitation program for families in extreme poverty introduced in 2002 called Chile Solidario (CS). Conditional on an index of wealth, eligibility
to receive the home-visits are random around municipality level cutoffs. We find that
individual participation in CS increases the take-up of SUF by 30% and neighbors’ participation in CS also increases the take-up of SUF by 7%. We also study heterogeneity
by proximity to the municipality’s office given that the distance between households
and the municipality office might be a measure of participation costs. Effectively, we
find positive direct effect of CS (33%) and spillover effects (9%) on the take up of SUF
only for those families who are distant to the office, suggesting that neighbor s are an
important channel of information transmission.

Bootstrap with Cluster-Dependence in Two or More Dimensions

Konrad Menzel
,
New York University

Abstract

N/A

Estimating Network Spillovers via Fused Graphings

Eric Auerbach
,
Northwestern University
Max Tabord-Meehan
,
University of Chicago

Abstract

N/A

Nonparametric Identification of Production Function, Total Factor Productivity, and Markup from Revenue Data

Hiro Kasahara
,
University of British Columbia

Abstract

Abstract Commonly used methods of production function and markup estimation assume that a firm’s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function and markup from revenue data when a firm faces a general nonparametric demand function under imperfect competition. Under standard assumptions, we provide the constructive nonparametric identification of various firm-level objects: gross production function, total factor productivity, price markups over marginal costs, output prices, output quantities, a demand system, and a representative consumer’s utility function.
JEL Classifications
  • A1 - General Economics
  • L0 - General