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.
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: 2,139,270 × 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 2,139,260 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: 17,121 × 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 17,111 more rows