add_nmc() allows you to add the Correlates of War National Material
Capabilities data to your data.
Value
add_nmc() takes a (dyad-year, leader-year, leader-dyad-year,
state-year) data frame and adds information about the national material
capabilities for the state or two states in the dyad in a given year. If the
data are dyad-year (or leader-dyad-year), the function adds 12 total columns
for the first state (i.e. ccode1) and the second state (i.e.
ccode2) for all estimates of national military capabilities provided
by the Correlates of War project. If the data are state-year (or leader-year),
the function returns six additional columns to the original data that contain
that same information for a given state in a given year.
Details
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.
The keep argument must include one or more of the capabilities estimates
included in cow_nmc. Otherwise, it will return an error that it cannot
subset columns that do not exist.
References
Singer, J. David, Stuart Bremer, and John Stuckey. (1972). "Capability Distribution, Uncertainty, and Major Power War, 1820-1965." in Bruce Russett (ed) Peace, War, and Numbers, Beverly Hills: Sage, 19-48.
Singer, J. David. 1987. "Reconstructing the Correlates of War Dataset on Material Capabilities of States, 1816-1985." International Interactions 14(1): 115-32.
Examples
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>% add_nmc()
#> # A tibble: 2,214,930 × 17
#> ccode1 ccode2 year milex1 milper1 irst1 pec1 tpop1 upop1 cinc1 milex2
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 20 1920 1657118 343 42809 743808 106461 27428 0.290 10755
#> 2 2 20 1921 1116342 387 20101 622541 108538 28210 0.253 10209
#> 3 2 20 1922 860853 270 36173 641311 110049 29013 0.256 10028
#> 4 2 20 1923 678256 247 45665 834889 111947 29840 0.272 13316
#> 5 2 20 1924 570142 261 38540 762070 114109 30690 0.254 12824
#> 6 2 20 1925 589706 252 46122 790029 115829 31565 0.254 12984
#> 7 2 20 1926 558004 247 49069 852304 117397 32464 0.263 13936
#> 8 2 20 1927 596501 249 45656 842978 119035 33389 0.239 16745
#> 9 2 20 1928 678100 251 52371 833446 120509 34340 0.240 18862
#> 10 2 20 1929 701300 255 57339 903141 121767 35318 0.240 21058
#> # ℹ 2,214,920 more rows
#> # ℹ 6 more variables: milper2 <dbl>, irst2 <dbl>, pec2 <dbl>, tpop2 <dbl>,
#> # upop2 <dbl>, cinc2 <dbl>
create_stateyears() %>% add_nmc()
#> Joining with `by = join_by(ccode, year)`
#> # A tibble: 17,511 × 10
#> ccode statenme year milex milper irst pec tpop upop cinc
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 United States of Ame… 1816 3823 17 80 254 8659 101 0.0397
#> 2 2 United States of Ame… 1817 2466 15 80 277 8899 106 0.0358
#> 3 2 United States of Ame… 1818 1910 14 90 302 9139 112 0.0361
#> 4 2 United States of Ame… 1819 2301 13 90 293 9379 118 0.0371
#> 5 2 United States of Ame… 1820 1556 15 110 303 9618 124 0.0371
#> 6 2 United States of Ame… 1821 1612 11 100 321 9939 130 0.0342
#> 7 2 United States of Ame… 1822 1079 10 100 332 10268 136 0.0329
#> 8 2 United States of Ame… 1823 1170 11 110 345 10596 143 0.0331
#> 9 2 United States of Ame… 1824 1261 11 110 390 10924 151 0.0330
#> 10 2 United States of Ame… 1825 1336 11 120 424 11252 158 0.0342
#> # ℹ 17,501 more rows