论文标题

在非参数选择模型中估算福利效应:学校优惠券的情况

Estimating Welfare Effects in a Nonparametric Choice Model: The Case of School Vouchers

论文作者

Kamat, Vishal, Norris, Samuel

论文摘要

我们开发了新的强大离散选择工具,以了解为价格补贴支付的平均意愿及其对价格的影响的影响,而价格差异是在外源性的,价格变化的情况下。我们的起点是一种非参数,不可分割的选择模型。我们利用这样的见解,即我们在该模型中的福利参数可以表示为对不同替代方案的需求功能。但是,尽管数据的差异揭示了需求的价值,但这些参数通常取决于其价值超出这些价格的价值。我们展示了如何在指定需求完全非参数或以灵活的方式进行参数化时可以彻底表征我们可以学习的内容,这两者都暗示不一定会确定参数。我们使用工具来分析DC机会奖学金计划中学校优惠券提供的价格补贴的福利效应。我们发现,提供现状凭证和各种不同数量的反事实代金券可以具有积极且潜在的大笔成本净收益。积极的效果可以通过该计划中的低调学校的普及来解释;将它们从程序中删除可能会带来负面的净收益。我们还发现,相比之下,各种标准logit规范限制了对凭证需求较低的需求功能的关注,这些功能并不能与数据相符的较大福利幅度。

We develop new robust discrete choice tools to learn about the average willingness to pay for a price subsidy and its effects on demand given exogenous, discrete variation in prices. Our starting point is a nonparametric, nonseparable model of choice. We exploit the insight that our welfare parameters in this model can be expressed as functions of demand for the different alternatives. However, while the variation in the data reveals the value of demand at the observed prices, the parameters generally depend on its values beyond these prices. We show how to sharply characterize what we can learn when demand is specified to be entirely nonparametric or to be parameterized in a flexible manner, both of which imply that the parameters are not necessarily point identified. We use our tools to analyze the welfare effects of price subsidies provided by school vouchers in the DC Opportunity Scholarship Program. We find that the provision of the status quo voucher and a wide range of counterfactual vouchers of different amounts can have positive and potentially large benefits net of costs. The positive effect can be explained by the popularity of low-tuition schools in the program; removing them from the program can result in a negative net benefit. We also find that various standard logit specifications, in comparison, limit attention to demand functions with low demand for the voucher, which do not capture the large magnitudes of benefits credibly consistent with the data.

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