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on Network Economics |
| By: | Bailey, Michael; Johnston, Drew; Kuchler, Theresa; Ströbel, Johannes; Wong, Arlene |
| 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 combine information on social networks with information on users' cell phone models. To identify peer effects, we use variation in friends' new phone acquisitions resulting from random phone losses and carrier-specific contract terms. A new phone purchase by a friend has a substantial positive and long-term 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. We provide evidence that social learning contributes substantially to the observed peer effects. While peer effects increase the overall demand for cell phones, a friend's purchase of a new phone of a particular brand can reduce individuals' own demand for phones from competing brands---in particular those running on a different operating system. We discuss the implications of these findings for the nature of firm competition. We also find that stronger peer effects are exerted by more price-sensitive individuals. This positive correlation suggests that the elasticity of aggregate demand is substantially larger than the elasticity of individual demand. Through this channel, peer effects reduce firms' markups and, in many models, contribute to higher consumer surplus and more efficient resource allocation. |
| Keywords: | Demand Spillovers; peer effects; Social learning |
| JEL: | D4 L1 L2 M3 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13731&r=all |
| By: | Giulia Faggio; Olmo Silva; William C. Strange |
| Abstract: | This paper considers the heterogeneous microfoundations of agglomeration economies. It studies the co-location of industries to look for evidence of labor pooling, input sharing, and knowledge spillovers. The novel contribution of the paper is that it estimates single-industry models using a common empirical framework that exploits the cross-sectional variation in how one industry co-locates with the other industries in the economy. This unified approach yields evidence on the relative importance of the Marshallian microfoundations at the single-industry level, allowing for like-for-like cross-industry comparisons on the determinants of agglomeration. Using UK data, we estimate such microfoundations models for 97 manufacturing sectors, including the classic agglomeration cases of automobiles, computers, cutlery, and textiles. These four cases - as with all of the individual industry models we estimate - clearly show the importance of the Marshallian forces. However, they also highlight how the importance of these forces varies across industries - implying that extrapolation from cases should be viewed with caution. The paper concludes with an investigation of the pattern of heterogeneity. The degree of an industry's clustering (localization), dynamism, incumbent firm size, and worker education are shown to contribute to the pattern of heterogeneous microfoundations. |
| Keywords: | agglomeration, microfoundations, heterogeneity, industrial clusters |
| JEL: | R10 R12 L52 L60 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1619&r=all |