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whittle_conflicts_reciprocation() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will keep the observations that are reciprocated (i.e. have militarized actions on both sides of the conflict).

Usage

whittle_conflicts_reciprocation(data)

wc_recip(...)

Arguments

data

a data frame with a declared conflict attribute type.

...

optional, only to make the shortcut work

Value

whittle_conflicts_reciprocation() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have militarized actions on both sides of the conflict. This will not eliminate all duplicates, far from it, but it's a sensible cut later into the procedure (after whittling onsets in whittle_conflicts_onsets()the extent to which dispute-level reciprocation is a heuristic for dispute-level severity/importance (after some other considerations).

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

Scholars are free to use this as a heuristic for whittling conflict-year data to be coerced into true dyad-year data, but I would be remiss if I did not offer a caveat about the reciprocation variable in inter-state dispute data. Namely, it is noisy and is not doing what scholars often think it's doing in the inter-state dispute data. Reciprocation is observed only when there is a militarized action on both sides of the conflict. By definition, someone on Side A will have a militarized action. Not every state on Side B does. However, scholars should not interpret that as the absence of militarized responses. In a forthcoming article in Journal of Conflict Resolution, Doug Gibler and I make the case that reciprocation isn't a useful variable to maintain at all because it can only invite errors (as is often the case in the CoW-MID data) and will obscure the fact that states that are attacked by another side routinely fight back. On many occasions, they also successfully repel the attack. Scholars who uncritically use this variable, certainly for hypothesis-testing on audience costs, are borrowing trouble with this measure.

wc_recip() is a simple, less wordy, shortcut for the same function.

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Author

Steven V. Miller

Examples


# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_reciprocation()
#> # A tibble: 9,640 × 39
#>    dispnum ccode1 ccode2  year midongoing midonset sidea1 sidea2 revstate1
#>      <dbl>  <dbl>  <dbl> <dbl>      <dbl>    <dbl>  <dbl>  <dbl>     <dbl>
#>  1    2968      2     20  1979          1        1      0      1         0
#>  2    3900      2     20  1989          1        1      0      1         0
#>  3    3972      2     20  1991          1        1      1      0         1
#>  4    4183      2     20  1997          1        1      0      1         0
#>  5    1665      2     40  1921          1        1      1      0         1
#>  6    1677      2     40  1933          1        1      1      0         1
#>  7    1677      2     40  1934          1        0      1      0         1
#>  8     246      2     40  1960          1        1      1      0         1
#>  9     246      2     40  1961          1        0      1      0         1
#> 10      61      2     40  1962          1        1      1      0         1
#> # … with 9,630 more rows, and 30 more variables: revstate2 <dbl>,
#> #   revtype11 <dbl>, revtype12 <dbl>, revtype21 <dbl>, revtype22 <dbl>,
#> #   fatality1 <dbl>, fatality2 <dbl>, fatalpre1 <dbl>, fatalpre2 <dbl>,
#> #   hiact1 <dbl>, hiact2 <dbl>, hostlev1 <dbl>, hostlev2 <dbl>, orig1 <dbl>,
#> #   orig2 <dbl>, hiact <dbl>, hostlev <dbl>, mindur <dbl>, maxdur <dbl>,
#> #   outcome <dbl>, settle <dbl>, fatality <dbl>, fatalpre <dbl>, stmon <dbl>,
#> #   endmon <dbl>, recip <dbl>, numa <dbl>, numb <dbl>, ongo2010 <dbl>, …

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_reciprocation()
#> Joining, by = "dispnum"
#> # A tibble: 10,590 × 19
#>    dispnum ccode1 ccode2  year dispongoing disponset sidea1 sidea2 fatality1
#>      <dbl>  <dbl>  <dbl> <dbl>       <dbl>     <dbl>  <dbl>  <dbl>     <dbl>
#>  1    2968      2     20  1979           1         1      0      1         0
#>  2    3900      2     20  1989           1         1      0      1         0
#>  3    3972      2     20  1991           1         1      1      0         0
#>  4    4183      2     20  1997           1         1      0      1         0
#>  5    1665      2     40  1921           1         1      1      0         0
#>  6    1677      2     40  1933           1         1      1      0         0
#>  7    1677      2     40  1934           1         0      1      0         0
#>  8     246      2     40  1960           1         1      0      1         0
#>  9     246      2     40  1961           1         0      0      1         0
#> 10      61      2     40  1962           1         1      1      0         0
#> # … with 10,580 more rows, and 10 more variables: fatality2 <dbl>,
#> #   fatalpre1 <dbl>, fatalpre2 <dbl>, hiact1 <dbl>, hiact2 <dbl>,
#> #   hostlev1 <dbl>, hostlev2 <dbl>, orig1 <dbl>, orig2 <dbl>, recip <dbl>


# }