題目：Competition, Capacity Constraint and Price Dispersion: The case of seed market and beyond
主講人：Guanming Shi 副教授（University of Wisconsin, Madison）
時間：2015.6.25（星期四）14：00
地點：主樓510會議室
主講人簡介：
Professor Guanming Shi obtained her PhD degree in agricultural and resource economics from University of California, Berkeley in 2005. She has become an associated professor in agricultural and applied economics in University of Wisconsin-Madison since 2012. Her research focuses on firm’s strategic behavior including pricing and bundling in the biotech sees market; technology, innovation and intellectual property rights; and productivity, profitability and adoption of agricultural biotechnology. Professor Shi has published her research papers on the renowned international academic journals, including Nature Biotechnology, American Journal of Agricultural Economics, Agricultural Economics, and Journal of Agricultural and Resource Economics.
內容簡介：
The neoclassical theory predicts that increasing levels of competition among firms selling homogenous products will lead to the “law of one price”. Yet, the law of one price is not confirmed in practice. We present a simple clearinghouse model of price dispersion with one important additional assumption: firms have limited capacity to supply a homogenous product. The model generates “temporal” price dispersion due to exogenous differences in consumer preferences, loyal vs price sensitive shoppers, and heterogeneity of firms attributed to asymmetric loyal customer base and capacity-constraints. Thus, the equilibrium is characterized by mixed strategies in prices. We test the model’s comparative static predictions using a novel data set from the U.S. seed industry from 2004 to 2009. Our detailed analysis adds insight into the effect of competition on price dispersion and contributes to the scarce empirical evidence of the relationship between product availability and pricing. Unlike most previous empirical studies we measure price dispersion using a dispersion statistic and estimate the model using a relatively new methodology, the IV Quantile Regression.
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