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
codebooks			06/27/2022 05:36:PM
data			06/27/2022 05:39:PM
dofiles			07/01/2022 03:21:PM
output			06/27/2022 05:49:PM
README_BGGN.pdf	application/pdf	169.3 KB	09/05/2022 02:56:PM

Project Citation:

Bobonis, Gustavo, Gertler, Paul , Gonzalez-Navarro, Marco, and Nichter, Simeon. Data and Code for: “Vulnerability and Clientelism.” Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-10-19. https://doi.org/10.3886/E173341V1

Project Description

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.

Funding Sources: Agencia Española de Cooperación Internacional para el Desarrollo; Social Sciences and Humanities Research Council (Canada) (488989); Social Sciences and Humanities Research Council (Canada) (493141); Canada Research Chairs Program; Canadian Institute for Advanced Research (CIFAR); Ontario Work-Study program

Scope of Project

Subject Terms: Vulnerability; clientelism; voting

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: Brazil, Latin America

Time Period(s): 5/2011 – 12/2013 (Before, during, and after the 2012 municipal elections)

Collection Date(s): 5/2011 – 12/2013

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): administrative records data; experimental data; survey data

Collection Notes: Data and analysis is performed using Stata 17.

Methodology

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:

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: -Household surveys - Dedicated household surveys administered by authors 2011-2013.

-Electoral outcomes data -Tribunal Superior Eleitoral.

https://dadosabertos.tse.jus.br/dataset/resultados-2008

https://dadosabertos.tse.jus.br/dataset/resultados-2012

-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): computer-assisted personal interview (CAPI)

Scales: See database and variable descriptors file.

Weights: None.

Unit(s) of Observation: Adult household members present during visit.

Geographic Unit: Sub-municipal neighborhood clusters.

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Published Versions

V1 [2022-10-19]

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