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README.md

Replication files for "Estimating Grouped Patterns of Heterogneity in Repeated Public Goods Experiments"

We used Python 2.7, Stata 14 and Matlab R2015b.

Replication Guide

Fischbacher and Gächter (2010) data setup

  • Obtain the dataset from Fischbacher and Gächter (2010) and unzip it into data/new data zip file.
  • Set the variable project_path in stata/paths.do to the project root directory.
  • Run fg2010_data_setup.do

deOliveira et al. (2015) data setup

  • Obtain the dataset from deOliveira et al. (2015) and unzip it into data/deOliveira2015.
  • Set the variable project_path in stata/paths.do to the project root directory.
  • Run deOliveira_data_setup.do

Grouped-Fixed-Effects in Python

  • Run FG2010.ipynb and deOliveira2015.ipynb.
  • The bootstrapped standard errors are computed inBootstrapped_Standard_Errors.ipynb.
  • Run csv_to_dta.to to convert the Python output to Stata *.dta files.

C-Lasso in Matlab

  • Setup the replication files by Zhentao Shi. Our code requires generic_functions/SSP_PLS_est.m and app_saving_PLS/hat_IC.m.
  • Run FG2010.m and Oliveira2015.m.
  • For the information criterion, see lambda.m.

Tables/Figures in Stata

  • Run csv_to_dta.do to convert the Python/Matlab output to Stata *.dta files.
  • Run regressions.do to obtain Tables 1 and 2.
  • Run compare_classifications.do to obtain Table 3 (Panel B requires data by Fallucci et al. (2018) ).
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