Country-year data on political violence used by Bagozzi, Hill, Moore and Mukherjee (2015) to introduce the zero-inflated ordered probit in conflict research. The outcome is ordered – no violence, repression, civil war – and its bottom category mixes countries that are structurally at peace with countries that are at risk but happened not to experience violence that year: a zero-inflated ordered outcome. Rows are the complete cases of the published specification.
Usage
data(bp)Format
A data frame with 1984 rows and 8 variables:
- country
Country name.
- year
Year.
- violence
Ordered factor:
none<repression<civil war.- loggdppc
Log real GDP per capita.
- parliament
1 if a parliamentary democracy.
- disaster
Number of natural disasters in the year.
- major_oil
1 if a major oil exporter.
- major_primary
1 if a major primary-commodity exporter.
Source
Besley, T. and Persson, T. (2009). Repression or civil war? American Economic Review: Papers and Proceedings, 99, 292-297 (replication data); as analyzed in Bagozzi, B.E., Hill, D.W., Moore, W.H. and Mukherjee, B. (2015). Modeling two types of peace: The zero-inflated ordered probit (ZiOP) model in conflict research. Journal of Conflict Resolution, 59, 728-752.
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") and vignette("quantities") analyze
these data.
Other datasets:
pta,
repression
Examples
data(bp)
table(bp$violence)
#>
#> none repression civil war
#> 1421 387 176
m <- iop(violence ~ loggdppc + parliament + disaster + major_oil + major_primary |
loggdppc + parliament + disaster + major_oil + major_primary,
data = bp, inflate = "bottom")
#> The inflation equation contains no covariate that is excluded from the outcome equation; the split is then identified by functional form alone. An exclusion restriction is advisable.
summary(m)
#>
#> Inflated ordered probit (inflated category: none)
#> Call: iop(formula = violence ~ loggdppc + parliament + disaster + major_oil +
#> major_primary | loggdppc + parliament + disaster + major_oil +
#> major_primary, data = bp, inflate = "bottom")
#> Response levels (in order): none < repression < civil war
#> N = 1984 inference: analytic
#>
#> Outcome equation (ordered probit):
#> Estimate Std. Error z value Pr(>|z|)
#> loggdppc 0.001604 0.057695 0.028 0.9778
#> parliament -0.095934 0.168983 -0.568 0.5702
#> disaster 0.273732 0.032817 8.341 < 2e-16 ***
#> major_oil 1.900762 0.450193 4.222 2.42e-05 ***
#> major_primary -0.562976 0.255867 -2.200 0.0278 *
#>
#> Cutpoints:
#> Estimate Std. Error z value Pr(>|z|)
#> none|repression 0.5017 0.4151 1.209 0.226854
#> repression|civil war 1.3985 0.4244 3.296 0.000982 ***
#>
#> Inflation equation (P(ordered regime); inflated category "none", observed share 0.716, mean fitted P(ordered regime) 0.793):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 20.6179 4.1970 4.913 8.99e-07 ***
#> loggdppc -2.2461 0.4425 -5.076 3.85e-07 ***
#> parliament -0.4388 0.4236 -1.036 0.300
#> disaster -0.1591 0.1700 -0.936 0.349
#> major_oil -4.7601 42.0348 -0.113 0.910
#> major_primary 3.9884 42.0309 0.095 0.924
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> logLik = -1384.26 AIC = 2794.51 BIC = 2867.22 df = 13