This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). “Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.” Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001.
Here is a simple example that predicts the race/ethnicity of voters based only on their surnames.
The above produces the following output, where the last five columns are probabilistic race/ethnicity predictions (e.g., ‘pred.his’ is the probability of being Hispanic/Latino):
"Proceeding with surname-only predictions ..."
VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth
1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.0676000 0.00430000 0.00820000 0.86680000 0.05310000
2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.0812000 0.00240000 0.06890000 0.73750000 0.11000000
3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.0594000 0.00260000 0.82270000 0.10510000 0.01020000
4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.9355936 0.00220022 0.02850285 0.00780078 0.02590259
5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.0098000 0.00180000 0.00065000 0.98200000 0.00575000
6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.9187000 0.01083333 0.01083333 0.01083333 0.04880000
7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.5897000 0.34630000 0.02360000 0.00540000 0.03500000
8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.0486000 0.00570000 0.92920000 0.01020000 0.00630000
9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000
10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000In order to predict race/ethnicity based on surnames AND geolocation, a user needs to provide a valid U.S. Census API key to access the census statistics. You may request a U.S. Census API key here. Once you have an API key, you can use the package to download relevant Census geographic data on demand and condition race/ethnicity predictions on geolocation (county, tract, block, or place).
The following example predicts the race/ethnicity of voters based on their surnames, Census tract of residence (census.geo = “tract”), and which party registration (party = “PID”). Note that a valid API key must be provided in the input parameter ‘census.key’ in order for the function to download the relevant tract-level data.
library(wru)
data(voters)
predict_race(voter.file = voters, census.geo = "tract", census.key = "...", party = "PID")The above returns the following output.
VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth
1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621
6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455
4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895620643 0.0022078678 0.0139457411 0.023345853 0.064879895
5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.003164229 0.0006092345 0.0001072684 0.991261466 0.004857802
2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.029936354 0.0009275220 0.0129831039 0.850040743 0.106112277
8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.231046860 0.0016485574 0.6813780115 0.053180270 0.032746301
9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.817841573 0.0063677130 0.0258733496 0.107254103 0.042663261
3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.223924118 0.0002913000 0.4451163607 0.313431417 0.017236805
7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073
10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042In predict_race, the census.geo options are “county”, “tract”, “block” and “place”. Here is an example of prediction based on census statistics collected at the level of “place”:
data(voters)
predict_race(voter.file = voters, census.geo = "place", census.key = "...", party = "PID")It is also possible to pre-download Census geographic data, which can save time when running predict_race(). The example dataset ‘voters’ includes people in DC, NJ, and NY. The following example subsets voters in DC and NJ, and then uses get_census_data() to download Census geographic data in these two states (input parameter ‘key’ requires valid API key). Census data is assigned to an object named census.dc.nj. The predict_race() statement predicts the race/ethnicity of voters in DC and NJ using the pre-saved Census data (census.data = census.dc.nj). This example conditions race/ethnicity predictions on voters’ surnames, block of residence (census.geo = “block”), age (age = TRUE), and party registration (party = “PID”).
Please note that the input parameters ‘age’ and ‘sex’ must have the same values in get_census_data() and predict_race(), i.e., TRUE in both or FALSE in both. In this case, predictions are conditioned on age but not sex, so age = TRUE and sex = FALSE in both the get_census_data() and predict_race() statements.
library(wru)
data(voters)
voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset
census.dc.nj <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE) # create Census data object covering DC and NJ
predict_race(voter.file = voters.dc.nj, census.geo = "block", census.data = census.dc.nj, age = TRUE, sex = FALSE, party = "PID")The last two lines above are equivalent to the following:
Using pre-downloaded Census data may be useful for the following reasons: * You can save a lot of time in future runs of predict_race() if the relevant Census data has already been saved; * The machines used to run predict_race() may not have internet access; * You can obtain timely snapshots of Census geographic data that match your voter file.
Downloading data using get_census_data() may take a long time, especially at the block level or in large states. If block-level Census data is not required, downloading Census data at the tract level will save time. Similarly, if tract-level Census data is not required, county-level data may be specified in order to save time.
library(wru)
data(voters)
voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset
census.dc.nj2 <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE, census.geo = "tract")
predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE)
predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County)
predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract)
predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party)
predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party)Or you can also use the census_geo_api() to maually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter ‘state’ that requires a two-letter state abbreviation to proceed.
censusObj2 = list()
county.dc <- census_geo_api(key = "...", state = "DC", geo = "county", age = TRUE, sex = FALSE)
tract.dc <- census_geo_api(key = "...", state = "DC", geo = "tract", age = TRUE, sex = FALSE)
censusObj2[["DC"]] <- list(state = "DC", county = county.dc, tract = tract.dc, age = TRUE, sex = FALSE)
tract.nj <- census_geo_api(key = "...", state = "NJ", geo = "tract", age = TRUE, sex = FALSE)
county.nj <- census_geo_api(key = "...", state = "NJ", geo = "county", age = TRUE, sex = FALSE)
censusObj2[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE)Note: The age and sex parameters must be consistent when creating the Census object and using that Census object in the predict_race function. If one of these parameters is TRUE in the Census object, it must also be TRUE in the predict_race function.
After saving the data in censusObj2 above, we can condition race/ethnicity predictions on different combinations of input variables, without having to re-download the relevant Census data.
predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County)
predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract)
predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party)
predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party)Watch this!