Estimating Equilibrium Choice Models with Social Interactions and Network Effects: Theoretical Foundations and Emprical Applications

估计具有社会互动和网络效应的均衡选择模型：理论基础和实证应用

• 批准号: 0137289
• 负责人: Christopher Timmins
• 金额: $ 17.59万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-03-01 至 2004-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0137289&HistoricalAwards=false
• 关键词: Estimating; Equilibrium; Choice; Models; Social

In many economic and social settings, the behavior or welfare of an individual is directly affected by the presence, characteristics, or behavior of other individuals in an associated reference group. Children might be affected by their classmates, home-owners by their neighbors, or commuters by their fellow motorists, to give just a few examples. In many of these settings, individuals are not randomly assigned to their reference groups but rather actively decide which group to join. This non-random sorting of individuals into groups leads to improper inferences about the influence of other group members both within the group and in the initial choice of group. A community with great amenities, for example, is likely to attract many high-income residents and have high housing prices. If the amenities are not seen in the data, a naive analysis is likely to attribute the high housing prices to the presence of high-income neighbors and suggest that high-income individuals have strong preferences to live with one another. This project develops a methodology for properly identifying the role of social interactions in the sorting of individuals into reference groups. This strategy draws on fact that each individual's choice of reference group is affected by the nature of their alternative options (the set of available neighborhoods in our earlier example). The research begins by developing a methodology that uses this source of variation to identify pure congestion and agglomeration interactions in sorting models. The methodology will then be extended to models with more complex forms of social interactions in which individuals are affected not only by the number but also the attributes of those in the same reference group. The scope of the currently identified applications of this methodology extends from urban, public, and environmental economics to models of non-price competition in industrial organization. Properly identifying social interactions in these settings is extremely important for a wide variety of policy considerations. We demonstrate the significance of this research with a series of empirical applications designed to both demonstrate the methodology and illustrate its practical importance. The first uses 1990 US Census data for the SF Bay Area to study neighborhood sorting. This analysis provides a complete picture of how households trade-off between important features of neighborhoods (location, schools, crime, socio-demographics, housing, and price), as well as how these trade-offs vary for households with different characteristics including income, race, education, employment, and family structure. Having properly identified the complex set of preferences that underlies the urban housing market, this project conducts a number of simulations designed to uncover the underlying causes of important aggregate urban phenomena such as racial segregation, crime patterns, school compositions, commuting patterns, and the geographic distribution of housing prices. This application will then be extended to focus more specifically on a series of education policy questions. The second set of applications address a very different policy issue: the consequences of global climate change in developing countries. While techniques are well developed to measure the marketed (agricultural) impacts of global warming, non-marketed impacts, such as those on climate amenities, have not been satisfactorily measured with traditional techniques owing to a lack of key data describing inter-location variation in the prices of housing and other geographically non-traded commodities. Specifying an equilibrium model for these commodities and estimating it with observed settlement patterns and our identification strategy yields a full measure of the amenity cost of global warming for Brazil, and its implications for post-Kyoto negotiations over greenhouse gas abatement efforts between Annex I countries and LDC's are discussed. This project will also fund the collection of data for the extension of this analysis to other developing countries in Central and South America.

在许多经济和社会环境中，个体的行为或福利直接受到相关参照群体中其他个体的存在、特征或行为的影响。孩子可能会受到同学的影响，房主可能会受到邻居的影响，通勤者可能会受到同行司机的影响，这只是几个例子。在许多这样的环境中，个人不是被随机分配到他们的参照组，而是主动决定加入哪个组。这种对个体的非随机分类导致了对群体内部和群体初始选择中其他群体成员的影响的不正确推断。例如，一个拥有良好设施的社区可能会吸引许多高收入居民，从而导致高房价。如果在数据中没有看到这些便利设施，一种天真的分析可能会将高房价归因于高收入邻居的存在，并认为高收入人群有强烈的偏好与他人住在一起。该项目开发了一种方法，用于正确识别社会互动在将个人分类为参考群体中的作用。该策略利用了这样一个事实，即每个个体对参考组的选择受到其可选选项的性质的影响（我们前面示例中的可用社区集）。该研究首先开发了一种方法，该方法使用这种变异源来识别分类模型中的纯拥堵和聚集相互作用。然后，该方法将被扩展到具有更复杂形式的社会互动的模型中，在这些模型中，个体不仅受到数量的影响，还受到同一参考群体中那些人的属性的影响。目前已确定的这种方法的应用范围从城市、公共和环境经济学扩展到工业组织中的非价格竞争模型。正确识别这些环境中的社会互动对于各种各样的政策考虑都是极其重要的。我们通过一系列实证应用来证明这项研究的意义，这些应用旨在证明该方法并说明其实际重要性。第一个使用1990年美国旧金山湾区的人口普查数据来研究社区分类。该分析提供了家庭如何在社区的重要特征（地点、学校、犯罪、社会人口、住房和价格）之间进行权衡的完整图景，以及这些权衡如何因家庭的不同特征（包括收入、种族、教育、就业和家庭结构）而变化。在正确识别了构成城市住房市场基础的复杂偏好之后，该项目进行了一系列模拟，旨在揭示重要的城市总体现象的潜在原因，如种族隔离、犯罪模式、学校构成、通勤模式和房价的地理分布。然后，该应用程序将扩展到更具体地关注一系列教育政策问题。第二组申请涉及一个非常不同的政策问题：全球气候变化对发展中国家的影响。虽然测量全球变暖的销售（农业）影响的技术已经发展得很好，但非销售影响，例如对气候便利的影响，由于缺乏描述住房和其他地理上非贸易商品价格的地点间变化的关键数据，用传统技术测量的结果并不令人满意。为这些商品指定一个均衡模型，并根据观察到的定居模式和我们的识别策略对其进行估计，从而全面衡量巴西全球变暖的舒适成本，并讨论其对附件一国家和最不发达国家之间关于温室气体减排努力的后京都谈判的影响。这个项目还将资助收集数据，以便将这种分析推广到中美洲和南美洲的其他发展中国家。