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Peer Effects and Technology Adoption

Paper Session

Saturday, Jan. 4, 2020 10:15 AM - 12:15 PM (PDT)

Marriott Marquis, Balboa
Hosted By: American Economic Association
  • Chair: Theresa Kuchler, New York University

Competition in Network Industries: Evidence from the Rwandan Mobile Phone Network

Daniel Bjorkegren
,
Brown University

Abstract

This paper evaluates whether competition hinders or spurs investment in a network industry. When a network is split between competitors, potential network effects are foregone. However, a firm may invest in components that are not shared, to steal customers from its competitors. I structurally estimate the utility of adopting a mobile phone from its subsequent usage, using transaction data from nearly the entire Rwandan network over 4.5 years. I simulate the equilibrium choices of consumers and network operators, and consider Rwanda's decision to delay the introduction of competition. I show that there is a policy under which adding a competitor earlier would have reduced prices and increased incentives to invest in rural towers, increasing welfare by the equivalent of 1% of GDP. I analyze the effects of setting different interconnection rates, and reducing switching costs through number portability.

Financial Technology Adoption

Sean Higgins
,
Northwestern University

Abstract

How do the supply and demand sides of the market respond to financial technology adoption? In this paper, I exploit a natural experiment that caused exogenous shocks to the adoption of a financial technology over time and space. Between 2009 and 2012, the Mexican government disbursed about one million debit cards to existing beneficiaries of its conditional cash transfer program. I combine administrative data on the debit card rollout with a rich collection of Mexican microdata on both consumers and retailers. The shock to debit card adoption has spillover effects on financial technology adoption on both sides of the market: small retailers adopt point-of-sale (POS) terminals to accept card payments, which leads other consumers to adopt cards. Specifically, the number of other consumers with debit cards increases by 21 percent. Richer consumers respond to corner stores' adoption of POS terminals by substituting 12 percent of their supermarket consumption to corner stores. Finally, I use microdata on store prices, store geocoordinates, and consumer choices across store types to estimate the consumer gains from the demand-side policy's effect on supply-side POS adoption.

Knowledge Diffusion through Networks

Christopher Tonetti
,
Stanford University
Treb Allen
,
Dartmouth College
Kamran Bilir
,
University of Wisconsin

Abstract

How do geography and other barriers to the free flow of information shape the rate of knowledge diffusion? To address this question, we develop an empirical model of product discrete choice with Bayesian learning on a social network. Estimating this model using monthly data on the cholesterol-drug prescription decisions of over 50,000 U.S. physicians during January 2000 through December 2010, we find that the evolution of product choice efficiency is highly responsive to network structure changes, particularly targeted friction reductions that strengthen the strongest bilateral links.

Peer Effects in Product Adoption

Theresa Kuchler
,
New York University
Johannes Stroebel
,
New York University
Michael C. Bailey
,
Facebook
Arlene Wong
,
Princeton University
Drew Johnston
,
New York University

Abstract

We study the nature of peer effects in the market for new cell phones. Our analysis builds on de-identified data from Facebook that combines information on social networks with information on users' cell phone model usage. To identify peer effects, we use variation in friends' new phone acquisitions resulting from random phone losses or from carrier-specific contract terms. New phone acquisitions by a friend have a substantial positive and long-run effect on an individual's own demand for phones of the same brand, most of which is concentrated on the particular model purchased by the friend. While peer effects expand the overall market for cell phones, there are substantial negative demand spillovers across operating systems. We also find that stronger peer effects are exerted by more price-sensitive individuals. This correlation leads to larger social multipliers through peer effects, and larger differences between the elasticities of individual and aggregate demand. We provide evidence that most of the observed peer effects are the result of social learning.
JEL Classifications
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights