The goal of this project is to quantify the sources of market segmentation, and to understand the implications of trade and investment frictions on the world automotive industry. The framework enables the PIs to assess the importance of trade costs, preferences for domestic brands, and demand heterogeneity. The PIs also quantify the impact of tariff and non-tariff barriers (e.g. arising from differences in fuel prices and environmental regulation) on market outcomes.
The project will be the first to develop consistent estimates of automobile demand and marginal costs using data across several continents. This geographical variation allows the PIs to estimate a variety of trade-related costs while accounting for model-level unobserved heterogeneity and home bias. This approach disentangles barriers set by policies from technological barriers such as transport costs. The scope of their analysis will advance the state of knowledge on the interactions between trade costs, preference heterogeneity, and government policies in shaping market outcomes in the automobile industry and in international trade more broadly.
The study will evaluate the importance of trade costs versus preference differences in the world trade in automobiles---a major manufacturing industry. This will lead to further insights into understanding the incentives of firms to conduct foreign direct investment and conduct market-specific model development. The dataset with assembly location included will be a valuable resource for analyzing the importance of FDI in the auto industry. The estimates on the importance of tariff and non-tariff barriers in the automotive industry will inform policy makers for the ongoing Trade and Investment Partnership negotiations between the U.S. and the EU.
To undertake this research, the PIs first construct a new data set by linking sales data from nine countries on three separate continents, collected by the consulting firm Polk Automotive, with a world census of assembly locations by model---collected by WardsAuto. They link these datasets to yield a model-level data on sales, prices, characteristics, assembly and headquarters location. Second, they use this data to estimate a structural model of international demand for automobiles. Extending techniques pioneered by Berry, Levinsohn, and Pakes (1995), they propose a model that accounts for inter and intra country heterogeneity in tastes for characteristics and income to flexibly estimate elasticities for automobiles in each country. This demand model will measure the importance of 'home bias' or the tendency of consumers to prefer their local manufacturers to foreign manufacturers. A key feature of their model is to separate these demand driven explanations from supply side drivers. Third, they use prices and demand elasticities to construct estimates of the marginal cost of supplying each automobile to each market, and use them to estimate the supply side of the model. Going forward, the cost and demand estimates can be used to analyze firms' location and marketing decisions and study the impact of potential policy interventions.
|Effective start/end date||5/1/15 → 4/30/19|
- National Science Foundation: $277,568.00