Understanding Multi-product Health Insurance Marketplaces: An Advancement in Aggregated Demand Estimation Using Bayesian Statistics
2020-11
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Understanding Multi-product Health Insurance Marketplaces: An Advancement in Aggregated Demand Estimation Using Bayesian Statistics
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2020-11
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Demand functions estimated by aggregated data used in economics and marketingoften employ the approach of Berry, Levinsohn and Pakes (Berry et al., 1995). To apply the method, researchers are required to collect market shares for each product of interest along with product-level attributes. Yet in many applications observed market shares are aggregated by firms or brands which sell multiple products. My thesis addresses this empirical issue by advancing existing BLP estimation procedure from Musalem et al. (2009) by using aggregated market shares at the firm level (port- folio market shares) and product-level attributes. I provide a solution to recovering the distributions of preference weights and price elasticities when researchers are limited to data containing only market shares at firm-level but consumers make choices over product-level attributes. The applications are specifically applied to the Health Insurance Marketplaces in the US.
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University of Minnesota Ph.D. dissertation. November 2020. Major: Health Services Research, Policy and Administration. Advisors: Bryan Dowd, Peter Huckfeldt. 1 computer file (PDF); 180 pages.
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Huang, Tsan-Yao. (2020). Understanding Multi-product Health Insurance Marketplaces: An Advancement in Aggregated Demand Estimation Using Bayesian Statistics. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/218047.
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