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add_creg_fractionalization() allows you to add information about the fractionalization/polarization of a state's ethnic and religious groups to your data.

Usage

add_creg_fractionalization(data)

Arguments

data

a data frame with appropriate peacesciencer attributes

Value

add_creg_fractionalization() takes a dyad-year, leader-year, leader-dyad-year, or state-data frame, whether the primary state identifiers are from the Correlates of War system or the Gleditsch-Ward system, and returns information about the fractionalization and polarization of the state(s) in a given year. The function returns four additional columns when the data are state-year and returns eight additional columns when the data are state-year (or leader-year). The columns returned are the fractionalization of ethnic groups, the polarization of ethnic groups, the fractionalization of religious groups, and the polarization of religious groups. When the data are dyad-year (or leader-dyad-year), the return doubles because it provides information for both states in the dyad.

Details

Please see the information for the underlying data creg, and the associated R script in the data-raw directory, to see how these data are generated.

The creg data have a few duplicates. When standardizing to true CoW codes, the duplicates concern Serbia/Yugoslavia in 1991 and 1992 as well as Russia/the Soviet Union in 1991. When standardizing to true Gleditsch-Ward codes, the duplicates concern Serbia/Yugoslavia in 1991 and Russia/Soviet Union in 1991. In those cases, the function does a group-by arrange for the more fractionalized/polarized estimate under the (reasonable, I think) assumption that these are estimates prior to the dissolution of those states. If this is problematic, feel free to consult the underlying data and merge those in manually.

The underlying data have both Gleditsch-Ward codes and Correlates of War codes. The merge it makes depends on what you declare as the "master" system at the top of the pipe (i.e. in create_dyadyears() or create_stateyears()). If, for example, you run create_stateyears(system="cow") and follow it with add_gwcode_to_cow(), the merge will be on the Correlates of War codes and not the Gleditsch-Ward codes. You can see the script mechanics to see how this is achieved.

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

References

Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Wacziarg. 2003. "Fractionalization". Journal of Economic Growth 8: 155-194.

Montalvo, Jose G. and Marta Reynal-Querol. 2005. "Ethnic Polarization, Potential Conflict, and Civil Wars" American Economic Review 95(3): 796--816.

Nardulli, Peter F., Cara J. Wong, Ajay Singh, Buddy Petyon, and Joseph Bajjalieh. 2012. The Composition of Religious and Ethnic Groups (CREG) Project. Cline Center for Democracy.

Author

Steven V. Miller

Examples


# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_creg_fractionalization()
#> # A tibble: 2,101,440 × 11
#>    ccode1 ccode2  year ethfrac1 ethpol1 relfrac1 relpol1 ethfrac2 ethpol2
#>     <dbl>  <dbl> <dbl>    <dbl>   <dbl>    <dbl>   <dbl>    <dbl>   <dbl>
#>  1      2     20  1920       NA      NA       NA      NA       NA      NA
#>  2      2     20  1921       NA      NA       NA      NA       NA      NA
#>  3      2     20  1922       NA      NA       NA      NA       NA      NA
#>  4      2     20  1923       NA      NA       NA      NA       NA      NA
#>  5      2     20  1924       NA      NA       NA      NA       NA      NA
#>  6      2     20  1925       NA      NA       NA      NA       NA      NA
#>  7      2     20  1926       NA      NA       NA      NA       NA      NA
#>  8      2     20  1927       NA      NA       NA      NA       NA      NA
#>  9      2     20  1928       NA      NA       NA      NA       NA      NA
#> 10      2     20  1929       NA      NA       NA      NA       NA      NA
#> # … with 2,101,430 more rows, and 2 more variables: relfrac2 <dbl>,
#> #   relpol2 <dbl>

create_stateyears() %>% add_creg_fractionalization()
#> Joining, by = c("ccode", "year")
#> # A tibble: 16,926 × 7
#>    ccode statenme                  year ethfrac ethpol relfrac relpol
#>    <dbl> <chr>                    <dbl>   <dbl>  <dbl>   <dbl>  <dbl>
#>  1     2 United States of America  1816      NA     NA      NA     NA
#>  2     2 United States of America  1817      NA     NA      NA     NA
#>  3     2 United States of America  1818      NA     NA      NA     NA
#>  4     2 United States of America  1819      NA     NA      NA     NA
#>  5     2 United States of America  1820      NA     NA      NA     NA
#>  6     2 United States of America  1821      NA     NA      NA     NA
#>  7     2 United States of America  1822      NA     NA      NA     NA
#>  8     2 United States of America  1823      NA     NA      NA     NA
#>  9     2 United States of America  1824      NA     NA      NA     NA
#> 10     2 United States of America  1825      NA     NA      NA     NA
#> # … with 16,916 more rows

create_stateyears(system = "gw") %>% add_creg_fractionalization()
#> Joining, by = c("gwcode", "year")
#> # A tibble: 18,463 × 7
#>    gwcode statename                 year ethfrac ethpol relfrac relpol
#>     <dbl> <chr>                    <dbl>   <dbl>  <dbl>   <dbl>  <dbl>
#>  1      2 United States of America  1816      NA     NA      NA     NA
#>  2      2 United States of America  1817      NA     NA      NA     NA
#>  3      2 United States of America  1818      NA     NA      NA     NA
#>  4      2 United States of America  1819      NA     NA      NA     NA
#>  5      2 United States of America  1820      NA     NA      NA     NA
#>  6      2 United States of America  1821      NA     NA      NA     NA
#>  7      2 United States of America  1822      NA     NA      NA     NA
#>  8      2 United States of America  1823      NA     NA      NA     NA
#>  9      2 United States of America  1824      NA     NA      NA     NA
#> 10      2 United States of America  1825      NA     NA      NA     NA
#> # … with 18,453 more rows
# }