Campaign-year data from Girod, Stewart and Walters (2018) on the intensity of state repression against nonviolent anti-government campaigns (0 = none to 3 = extreme). The top category holds 77 percent of observations and mixes targeted repression of campaign activity with indiscriminate repression of non-campaign actors: the top-inflated ordered outcome analyzed in the appendix of Bagozzi, Joo and Mukherjee (2024). Rows are campaign-years with nonviolent campaign activity and complete cases of that specification.
Usage
data(repression)Format
A data frame with 367 rows and 8 variables:
- campaign
Campaign name.
- country
Country.
- year
Year.
- repression
Repression intensity, 0–3 (the ordered outcome).
- negxpol
Authoritarianism (negative Polity score).
- oilrent
Lagged log oil rents per capita.
- dom_media
Domestic media salience of the campaign.
- civil_war
1 if an internal armed conflict is ongoing.
Source
Girod, D.M., Stewart, M.A. and Walters, M.R. (2018). Mass protests and the resource curse: The politics of demobilization in rentier autocracies. Conflict Management and Peace Science, 35, 503-522 (replication data); as analyzed in Bagozzi, B.E., Joo, M.M. and Mukherjee, B. (2024), Foreign Policy Analysis, 20, orae006, appendix Table A.5.
Provenance and terms
Taken from the public replication archive of the cited article and
redistributed here, with the variables renamed and recoded as documented in
data-raw/make_data.R, so that the published results can be reproduced;
the archive states the original terms of use.
See also
iop(); vignette("iop") analyzes these data.
Examples
data(repression)
table(repression$repression)
#>
#> 0 1 2 3
#> 38 12 34 283
m <- iop(repression ~ negxpol * oilrent | dom_media + civil_war,
data = repression, inflate = "top")
summary(m)
#>
#> Inflated ordered probit (inflated category: 3)
#> Call: iop(formula = repression ~ negxpol * oilrent | dom_media + civil_war,
#> data = repression, inflate = "top")
#> Response levels (in order): 0 < 1 < 2 < 3
#> N = 367 inference: analytic
#>
#> Outcome equation (ordered probit):
#> Estimate Std. Error z value Pr(>|z|)
#> negxpol -0.117683 0.040469 -2.908 0.00364 **
#> oilrent -0.123165 0.047257 -2.606 0.00915 **
#> negxpol:oilrent 0.039438 0.009427 4.184 2.87e-05 ***
#>
#> Cutpoints:
#> Estimate Std. Error z value Pr(>|z|)
#> 0|1 -1.07879 0.20794 -5.188 2.13e-07 ***
#> 1|2 -0.80044 0.21091 -3.795 0.000148 ***
#> 2|3 -0.05037 0.27218 -0.185 0.853187
#>
#> Inflation equation (P(ordered regime); inflated category "3", observed share 0.771, mean fitted P(ordered regime) 0.386):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.9874 0.3089 -3.196 0.00139 **
#> dom_media 0.8327 0.1893 4.398 1.09e-05 ***
#> civil_war -0.8720 0.1530 -5.699 1.21e-08 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> logLik = -232.65 AIC = 483.31 BIC = 518.45 df = 9