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Econometrics Dynamic discrete choice models

INRAE
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Working environment, missions and activities

Context

Although residential location is generally considered to be a choice based on individual preferences, it depends on the financial constraints of the agents and not all areas are accessible to all households. The foundations of urban economics are based on the idea that the choice of residential location is based on a trade-off between the cost of housing and the cost of transport to work. This raises the question of freedom of choice for individuals: what areas are accessible in terms of income, housing costs and transport costs? How do individuals divide up the areas accessible to them according to their preferences and their own (non-financial) constraints? Are the locations chosen by individuals optimal given these preferences and constraints? 

The internship is part of a research project (ECCLORE) involving five economists from INRAE, specializing in econometrics, behavioral economics, environmental economics and theoretical urban economics. 

Objectives of the internship

The objective of the intership is to examine the notion of freedom of choice in residential location and to determine the optimal location areas for individuals according to the constraints they face and their preferences. 

The first stage is to determine which areas of France are accessible at different income levels, based on the price of housing and transport. The trainee will then analyze a residential location survey carried out in 2024 among 2,000 residents of the Bourgogne-Franche-Comté region. This will enable him/her to determine the theoretical location of each individual, taking into account their income and place of work, and therefore their freedom of choice. 

Dynamic discrete choice models will be estimated to understand the actual locations of agents according to their constraints (particularly financial), their places of activity (particularly work) and spatial amenities (pollution, green spaces, etc.). 

The trainee will draw on the literature in urban economics (Gaigné et al., 2019) and property markets (Piazezzi et al., 2020) and on dynamic discrete choice models (Miao and Xing, 2024, Bayer et al., 2016).

More specifically, the internship will focus on the following objectives:

  • Calculation of the financial accessibility of areas for different income levels (Nativia queries with Python for transport times and costs; use of the DV3F database for housing costs); 
  • Descriptive and bivariate statistics on the survey (Territory: Bourgogne-France-Comté Region): socio-economic data, residential and work histories, characterization of family constraints; 
  • Matching with spatial data (DV3F for property, INERIS modelling for pollution, Cosia for land use); 
  • Estimation of theoretical location spaces and an optimal location for each individual profile, in relation to family and financial constraints; 
  • Estimation of dynamic discrete choice models (taking intertemporal utility into account) and explanation of the gap between the optimal location and the actual location. 
  • For different scenarios on individual preferences, inferred from latent variables constructed with survey indicators, comparison of actual and theoretical locations.

 

Supervisors Hélène Bouscasse (helene.bouscasse@inrae.fr), Tina Rambonilaza (tina.rambonilaza@inrae.fr)

 

Indicative bibliography

  • Bayer, P., McMillan, R., Murphy, A., & Timmins, C. (2016). A dynamic model of demand for houses and neighborhoods. Econometrica, 84(3), 893-942.
  • Gaigné, C., Koster, H. R., Moizeau, F., & Thisse, J. F. (2022). Who lives where in the city? Amenities, commuting and income sorting. Journal of Urban Economics, 128, 103394.
  • Miao, J., & Xing, H. (2024). Dynamic discrete choice under rational inattention. Economic Theory, 77(3), 597-652.
  • Piazzesi, M., Schneider, M., & Stroebel, J. (2020). Segmented housing search. American Economic Review, 110(3), 720-759. 

 

Training and skills required

  • Skills required: Proficiency in statistical analysis software (R or Python).
  • Education: Master's student or engineering school with skills in statistics and economics (environment, urban, health). 
  • Duration: 6 months full-time, from January or February to June or July 2025
  • The internship may lead to a PhD depending on the quality of the work carried out (ANR funding, research national agency).

 

Your quality of life at INRAE

When you join INRAE, you will benefit from (depending on the type and duration of your contract):

  • up to 30 days' holiday + 15 RTT per year (for full-time employees)
  • social support: advice and assistance, social aid and loans;
  • sports and cultural activities
  • collective restaurant.

How to apply

Please send us your CV and M1 or 2nd year school transcript.