Bayesian Improved Surname Geocoding (BISG) is a simple model thatpredicts individual race based off last names and addresses. Whilepredictive, it is not perfect, and measurement error in thesepredictions can cause problems in downstream analyses.
Bayesian Instrumental Regression for Disparity Estimation (BIRDiE) isa class of Bayesian models for accurately estimating conditionaldistributions by race, using BISG probabilities as inputs. This packageimplements BIRDiE as described inMcCartan, Fisher,Goldin, Ho, and Imai (2025). It also implements standard BISG and animproved measurement-error BISG model as described inImai,Olivella, and Rosenman (2022).
Installation
You can install the latest version of the package from CRAN with:
install.packages("birdie")You can also install the development version with:
# install.packages("remotes")remotes::install_github("CoryMcCartan/birdie")Basic Usage
A basic analysis has two steps. First, you compute BISG probabilityestimates with thebisg() orbisg_me()functions (or using any other probabilistic race prediction tool). Then,you estimate the distribution of an outcome variable by race using thebirdie() function.
library(birdie)data(pseudo_vf)head(pseudo_vf)#> # A tibble: 6 × 4#> last_name zip race turnout#> <fct> <fct> <fct> <fct>#> 1 BEAVER 28748 white yes#> 2 WILLIAMS 28144 black no#> 3 ROSEN 28270 white yes#> 4 SMITH 28677 black yes#> 5 FAY 28748 white no#> 6 CHURCH 28215 white yesTo compute BISG probabilities, you provide the last name and(optionally) geography variables as part of a formula.
r_probs=bisg(~nm(last_name)+zip(zip),data=pseudo_vf)head(r_probs)#> # A tibble: 6 × 6#> pr_white pr_black pr_hisp pr_asian pr_aian pr_other#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 0.956 0.00371 0.0103 0.000674 0.00886 0.0202#> 2 0.162 0.795 0.0122 0.00102 0.000873 0.0292#> 3 0.943 0.00378 0.0218 0.0107 0.000386 0.0202#> 4 0.569 0.365 0.0302 0.00114 0.00108 0.0339#> 5 0.971 0.00118 0.0131 0.00149 0.00118 0.0125#> 6 0.524 0.315 0.0909 0.00598 0.00255 0.0610Computing regression estimates requires specifying a model structure.Here, we’ll use a Categorical-Dirichlet regression model that lets therelationship between turnout and race vary by ZIP code. This is the“no-pooling” model from McCartan et al. We’ll use Gibbs sampling forinference, which will also let us capture the uncertainty in ourestimates.
fit=birdie(r_probs, turnout~proc_zip(zip),data=pseudo_vf,family=cat_dir(),algorithm="gibbs")#> Using weakly informative empirical Bayes prior for Pr(Y | R)#> This message is displayed once every 8 hours.print(fit)#> Categorical-Dirichlet BIRDiE model#> Formula: turnout ~ proc_zip(zip)#> Data: pseudo_vf#> Number of obs: 5,000#> Estimated distribution:#> white black hisp asian aian other#> no 0.293 0.34 0.372 0.569 0.685 0.499#> yes 0.707 0.66 0.628 0.431 0.315 0.501Theproc_zip() function fills in missing ZIP codes,among other things. We can extract the estimated conditionaldistributions withcoef(). We can also get updated BISGprobabilities that additionally condition on turnout usingfitted(). Additional functions allow us to extract a tidyversion of our estimates (tidy()) and visualize theestimated distributions (plot()).
coef(fit)#> white black hisp asian aian other#> no 0.2934753 0.3403649 0.3720582 0.5687325 0.6847874 0.4994076#> yes 0.7065247 0.6596351 0.6279418 0.4312675 0.3152126 0.5005924head(fitted(fit))#> # A tibble: 6 × 6#> pr_white pr_black pr_hisp pr_asian pr_aian pr_other#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 0.961 0.00349 0.0101 0.000523 0.00577 0.0195#> 2 0.0765 0.893 0.00814 0.00102 0.00106 0.0207#> 3 0.932 0.00542 0.0287 0.00538 0.000384 0.0286#> 4 0.587 0.352 0.0260 0.000833 0.000783 0.0335#> 5 0.945 0.00224 0.0219 0.00368 0.00334 0.0238#> 6 0.528 0.324 0.0895 0.00379 0.00143 0.0538tidy(fit)#> # A tibble: 12 × 3#> turnout race estimate#> <chr> <chr> <dbl>#> 1 no white 0.293#> 2 yes white 0.707#> 3 no black 0.340#> 4 yes black 0.660#> 5 no hisp 0.372#> 6 yes hisp 0.628#> 7 no asian 0.569#> 8 yes asian 0.431#> 9 no aian 0.685#> 10 yes aian 0.315#> 11 no other 0.499#> 12 yes other 0.501plot(fit)
A more detailed introduction to the method and software package canbe found on theGetStarted page.
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