Data and Code for "Spatial Externalities, Inefficiency, and Sufficient Statistics"

Principal Investigator(s):  View help for Principal Investigator(s) Gabriel Kreindler, Harvard University; Kartik Patekar, JPAL South Asia

Version:  View help for Version V1

Name File Type Size Last Modified
  data 05/10/2025 11:15:AM
  julia_env 05/10/2025 11:15:AM
01-runsims-main.jl text/x-common-lisp 13.2 KB 05/10/2025 07:15:AM
README.md text/x-web-markdown 5.5 KB 05/14/2025 06:52:AM
setup_julia.jl text/x-common-lisp 395 bytes 05/10/2025 07:15:AM
sims-functions-loadprep.jl text/x-common-lisp 40.7 KB 05/10/2025 07:15:AM
sims-functions-model.jl text/x-common-lisp 25.1 KB 05/10/2025 07:15:AM
sims-functions-rt.jl text/x-common-lisp 5.5 KB 05/10/2025 07:15:AM

Project Citation: 

Kreindler, Gabriel, and Patekar, Kartik. Data and Code for “Spatial Externalities, Inefficiency, and Sufficient Statistics.” Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-05-16. https://doi.org/10.3886/E229141V1

Project Description

Summary:  View help for Summary
The paper set up a simple spatial model to quantify the economic inefficiency generated by spatial externalities such as agglomeration and congestion. Using this model, we compute the expression of optimal charges and deadweight loss. The expressions highlight the importance of two empirical objects, an externality matrix, and an equilibrium elasticity matrix, and clarify how specific model assumptions may constrain the magnitude of deadweight loss. Using data and the model from an experiment studying Peak Hour Congestion Pricing (Gabriel Kreindler, 2024, Econometrica), we illustrate the use of our spatial externality model by computing optimal charges and deadweight loss.

Scope of Project

Subject Terms:  View help for Subject Terms externalities; welfare; sufficient statistics; spatial
JEL Classification:  View help for JEL Classification
      D60 Welfare Economics: General
      D62 Externalities
      R12 Size and Spatial Distributions of Regional Economic Activity
      R41 Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
Geographic Coverage:  View help for Geographic Coverage Bangalore, India
Time Period(s):  View help for Time Period(s) 2016 – 2017
Collection Date(s):  View help for Collection Date(s) 2016 – 2017
Universe:  View help for Universe Drivers in Bangalore
Data Type(s):  View help for Data Type(s) experimental data; observational data; survey data
Collection Notes:  View help for Collection Notes See Kreindler, G (2024) Replication package for "Peak-Hour Road Congestion Pricing: Experimental Evidence and Equilibrium Implications"
Zenodo [doi:10.5281/zenodo.10637043](https://doi.org/10.5281/zenodo.10637043)

Methodology

Data Source:  View help for Data Source
Data and code based on
Kreindler, Gabriel (2024) *Replication package for "Peak-Hour Road Congestion Pricing: Experimental Evidence and Equilibrium Implications"*  
Zenodo doi:10.5281/zenodo.10637043, https://doi.org/10.5281/zenodo.10637043
Unit(s) of Observation:  View help for Unit(s) of Observation driver level

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Published Versions

V1 [2025-05-16]

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