Using Panel Data to Easily Estimate Hedonic Demand Functions
本文利用面板数据，通过观察同一消费者在价格变化时的多次购买决策，估计出个体对清洁空气的线性需求函数，发现需求函数异质性对福利评估至关重要。
The hedonics literature has often asserted that if one were able to observe the same individual make multiple purchase decisions, one could recover rich estimates of preference heterogeneity for a given amenity. In particular, in the face of a changing price schedule, observing each individual twice is sufficient to recover a linear demand function separately for each individual, with no additional restrictions. Constructing a rich panel data set of buyers, we recover the full distribution of demand functions for clean air in the Bay Area of California. First, we find that estimating the full demand function, rather than simply recovering a local estimate of marginal willingness to pay, is important. Second, we find evidence of considerable heterogeneity, which is important from a policy perspective; our data-driven estimates of the welfare effects associated with a nonmarginal change in air quality differ substantially from those recovered using the existing approaches to welfare estimation.