Estimating Neighborhood Choice Models: Lessons from a Housing Assistance Experiment

Author(s):  
Sebastian Galiani ◽  
Alvin Murphy ◽  
Juan Pantano
2015 ◽  
Vol 105 (11) ◽  
pp. 3385-3415 ◽  
Author(s):  
Sebastian Galiani ◽  
Alvin Murphy ◽  
Juan Pantano

We use data from a housing-assistance experiment to estimate a model of neighborhood choice. The experimental variation effectively randomizes the rents which households face and helps identify a key structural parameter. Access to two randomly selected treatment groups and a control group allows for out-of-sample validation of the model. We simulate the effects of changing the subsidy-use constraints implemented in the actual experiment. We find that restricting subsidies to even lower poverty neighborhoods would substantially reduce take-up and actually increase average exposure to poverty. Furthermore, adding restrictions based on neighborhood racial composition would not change average exposure to either race or poverty. (JEL I32, I38, R23, R38)


2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation


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