Data and Code for: "Vulnerability and Clientelism"

Principal Investigator(s):  View help for Principal Investigator(s) Gustavo Bobonis, University of Toronto; Paul Gertler, University of California-Berkeley; Marco Gonzalez-Navarro, University of California-Berkeley; Simeon Nichter, University of California-San Diego

Version:  View help for Version V1

Name File Type Size Last Modified
0. Master.do text/plain 3.5 KB 07/01/2022 11:21:AM
1. Cleaning.do text/plain 21.3 KB 06/28/2022 06:57:PM
2. Variable_Construction.do text/plain 86.4 KB 06/28/2022 07:00:PM
3. Voting_Analysis_Cleaning.do text/plain 39.1 KB 06/28/2022 01:34:PM
4. Main_Tables.do text/plain 33.8 KB 06/22/2022 05:11:AM
5. Appendix_Tables.do text/plain 62.9 KB 06/22/2022 07:42:AM

Project Description

Summary:  View help for Summary The code and data in this repository generate the results of the paper “Vulnerability and Clientelism” by Bobonis, Gertler, Gonzalez-Navarro, and Nichter published in the American Economic Review.

The archive folder “dofiles” contains the code that organizes the data and performs the data analysis.

The master do file is `0.Master.do'. This do file calls all the other do files in the folder in the required sequence. Executing this do file will perform the following steps:

`1.Cleaning.do' cleans up the raw datasets. `2.Variable_Construction.do' creates the analysis variables from the raw household survey, `3.Voting_Analysis_Cleaning.do' does the same for the electoral outcomes dataset. These steps result in the workhorse datasets:
"data/final_data/clientelism_household_data.dta"
"data/final_data/clientelism_individual_data.dta"
"data/final_data/clientelism_individual_data_stacked.dta"
"data/final_data/voting_data.dta"

Next, the do file `4.Main_Tables.do' performs the statistical analysis and generates all the tables in the paper in Tex format found in folder "output".  Lastly, the do file `5.Appendix_Tables.do' performs the statistical analysis and generates all the figures and tables reported in the online appendix. These figures and tables in pdf and tex format can be found in folders "output/figures" and "output/appendix_tables", respectively. 


Scope of Project

Subject Terms:  View help for Subject Terms Vulnerability; clientelism; voting
JEL Classification:  View help for JEL Classification
      O10 Economic Development: General
      O12 Microeconomic Analyses of Economic Development
      O54 Economywide Country Studies: Latin America; Caribbean
      P16 Capitalist Systems: Political Economy
Geographic Coverage:  View help for Geographic Coverage Brazil, Latin America
Universe:  View help for Universe
Rural households in Brazil’s semi-arid zone without reliable access to drinking water in 2011. More specifically, households eligible for the study met the following inclusion criteria: (a) they had no piped drinking water or cistern, (b) they had physical space on their property to build a cistern, and (c) their roofs were at least 40 m2 and composed of metal sheeting or tile (to facilitate rainfall collection).
Data Type(s):  View help for Data Type(s) administrative records data; experimental data; survey data
Collection Notes:  View help for Collection Notes Data and analysis is performed using Stata 17.

Methodology

Response Rate:  View help for Response Rate
From the 1,308 households identified for study participation, 91 percent were successfully interviewed during the baseline survey (Wave 1). During the election year survey (Wave 2), the response rate was higher, at 95 percent of households identified for study participation. In the post-election survey (Wave 3), the response rate decreased to 86 percent of households identified for study participation.
Sampling:  View help for Sampling
The sample selection of households involved two steps. First, 40 municipalities were randomly selected using weights proportional to the number of households without access to piped water and cisterns, according to the most recent administrative data from the federal government’s Cadastro Único. In the second step, clusters of neighboring rural households (i.e., logradouros in the Cadastro Único) were selected at random within the sample municipalities. Up to six eligible households were interviewed in each cluster. In order to ensure independence of observations across neighborhood clusters, we imposed  a restriction that clusters be located at least two kilometers away from each other. Our surveys were conducted in 425 neighborhood clusters in 40 municipalities, located in all nine states of the semi-arid region. 
Data Source:  View help for Data Source -Household surveys - Dedicated household surveys administered by authors 2011-2013.

-Electoral outcomes data -Tribunal Superior Eleitoral. 

-Rainfall data - Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,.. ., Michaelsen, J. (2015). The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Scientific Data 2(150066). Retrieved from doi:10.1038/sdata.2015.66 (accessed September 22, 2020.)
https://data.chc.ucsb.edu/products/CHIRPS-2.0/
Collection Mode(s):  View help for Collection Mode(s) computer-assisted personal interview (CAPI)
Scales:  View help for Scales See database and variable descriptors file.
Weights:  View help for Weights None.
Unit(s) of Observation:  View help for Unit(s) of Observation Adult household members present during visit.
Geographic Unit:  View help for Geographic Unit Sub-municipal neighborhood clusters.

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V1 [2022-10-19]

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