This is the code and data to replicate the analysis in Knorre, Kuchakov, Skougarevskiy, and Zhizhin (2023).
To replicate the analysis you need to clone this repository to your local machine. Then you need to install docker and build image, using following command:
$ docker build --rm -t "cost_of_crime_image" .This image is then used by an annotated Makefile that documents the data analysis in our paper.
To build the paper run make paper when in the repository folder.
Please note that those commands will not necessarily produce any publication-ready output files (e.g. tables or figures). Our intention is to make the analysis pipeline transparent to the readers with the aid of make.
The replication on a server-grade 16GB RAM with 2 vCPUs with Ubuntu 20.04 takes approx. 2 hours. Potential issues: you might need to install cmake. Also, in R >=4.2.0, stargazer <= 5.2.3 returns an error for objects with multiple models with long names. A quick fix for this is here.
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├─data/ # Data used in this study
| ├ rcvs2021_crimecost.rdata # Russian Crime Victimization Survey - 2021 individual-level data
| ├ rcvs2021_crimecost_imputed.rdata # RCVS-21 data after multiple imputation
| ├ rcvs2021_raking_fit.rdata # RCVS-21 after raking weights
| ├ kouzh_2020_data.rdata # 2020 round of the Comprehensive Monitoring of Living Conditions household survey
| └ yearly_total_crime_cost.rds # Yearly figures of official crime and calculated total costs
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├─code/
| └─helper_functions/
| | └ install_dependencies.r # Installs R dependencies used in the project
| |
| ├ 1_rcvs_vs_domestic_household_survey.r # Compare RCVS-21 and KOUZH, compute raking weights
| ├ 2_estimate_regression_models.r # Fit all regression models
| ├ 3_multiple_imputation_regressions.r # Compute multiply imputed models
| └ 4_extract_official_crime_counts # Create figures with official crime counts
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└─estimates/
└ costofcrime_estimates_19aug21.csv # Paper estimates under different scenarios
Creative Commons License Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Copyright © the respective contributors, as shown by the AUTHORS file.
Dmitriy Skougarevskiy, Ph.D.