ethnicolr: Predict Race and Ethnicity From Name

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We exploit the US census data, the Florida voting registration data, and the Wikipedia data collected by Skiena and colleagues, to predict race and ethnicity based on first and last name or just the last name. The granularity at which we predict the race depends on the dataset. For instance, Skiena et al.’ Wikipedia data is at the ethnic group level, while the census data we use in the model (the raw data has additional categories of Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites, Non-Hispanic Blacks, Asians, and Hispanics.

DIME Race

Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here

Caveats and Notes

If you picked a random individual with last name ‘Smith’ from the US in 2010 and asked us to guess this person’s race (measured as crudely as by the census), the best guess would be based on what is available from the aggregated Census file. It is the Bayes Optimal Solution. So what good are last name only predictive models for? A few things—if you want to impute ethnicity at a more granular level, guess the race of people in different years (than when the census was conducted if some assumptions hold), guess the race of people in different countries (again if some assumptions hold), when names are slightly different (again with some assumptions), etc. The big benefit comes from when both the first name and last name is known.

Install

We strongly recommend installing ethnicolor inside a Python virtual environment (see venv documentation)

pip install ethnicolr

Or

conda install -c soodoku ethnicolr
Notes:
  • The models are run and verified on TensorFlow 2.x using Python 3.7 and 3.8 and lower will work. TensorFlow 1.x has been deprecated.
  • If you are installing on Windows, Theano installation typically needs admin. privileges on the shell.

General API

To see the available command line options for any function, please type in <function-name> --help

# census_ln --help
usage: census_ln [-h] [-y {2000,2010}] [-o OUTPUT] -l LAST input

Appends Census columns by last name

positional arguments:
  input                 Input file

optional arguments:
  -h, --help            show this help message and exit
  -y {2000,2010}, --year {2000,2010}
                        Year of Census data (default=2000)
  -o OUTPUT, --output OUTPUT
                        Output file with Census data columns
  -l LAST, --last LAST  Name or index location of column contains the last
                        name

Examples

To append census data from 2010 to a file without column headers and the first column carries the last name, use -l 0

census_ln -y 2010 -o output-census2010.csv -l 0 input-without-header.csv

To append census data from 2010 to a file with column header in the first row, specify the column name carrying last names using the -l option, keeping the rest the same:

census_ln -y 2010 -o output-census2010.csv -l last_name input-with-header.csv

To predict race/ethnicity using Wikipedia full name model, if the input file doesn’t have any column headers, you must using -l and -f to specify the index of column carrying the last name and first name respectively (first column has index 0).

pred_wiki_name -o output-wiki-pred-race.csv -l 0 -f 1 input-without-header.csv

And to predict race/ethnicity using Wikipedia full name model for a file with column headers, you can specify the column name of last name and first name by using -l and -f flags respectively.

pred_wiki_name -o output-wiki-pred-race.csv -l last_name -f first_name input-with-header.csv

Functions

We expose 6 functions, each of which either take a pandas DataFrame or a CSV. If the CSV doesn’t have a header, we make some assumptions about where the data is:

  • census_ln(df, namecol, year=2000)
    • What it does:
      • Removes extra space
      • For names in the census file, it appends relevant data of what probability the name provided is of a certain race/ethnicity
Parameters  
  df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
  namecol : {string, list, int} string or list of the name or location of the column containing the last name
  Year : {2000, 2010}, default=2000 year of census to use
  • Output: Appends the following columns to the pandas DataFrame or CSV: pctwhite, pctblack, pctapi, pctaian, pct2prace, pcthispanic. See here for what the column names mean.

    >>> import pandas as pd
    
    >>> from ethnicolr import census_ln, pred_census_ln
    
    >>> names = [{'name': 'smith'},
    ...         {'name': 'zhang'},
    ...         {'name': 'jackson'}]
    
    >>> df = pd.DataFrame(names)
    
    >>> df
          name
    0    smith
    1    zhang
    2  jackson
    
    >>> census_ln(df, 'name')
          name pctwhite pctblack pctapi pctaian pct2prace pcthispanic
    0    smith    73.35    22.22   0.40    0.85      1.63        1.56
    1    zhang     0.61     0.09  98.16    0.02      0.96        0.16
    2  jackson    41.93    53.02   0.31    1.04      2.18        1.53
    
  • pred_census_ln(df, namecol, year=2000, num_iter=100, conf_int=0.9)

    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list, int} string or list of the name or location of the column containing the last name
      year : {2000, 2010}, default=2000 year of census to use
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), api (percentage chance asian), black, hispanic, white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above)

    >>> census_ln(df, 'name')
          name pctwhite pctblack pctapi pctaian pct2prace pcthispanic
    0    smith    73.35    22.22   0.40    0.85      1.63        1.56
    1    zhang     0.61     0.09  98.16    0.02      0.96        0.16
    2  jackson    41.93    53.02   0.31    1.04      2.18        1.53
    
    >>> census_ln(df, 'name', 2010)
          name   race pctwhite pctblack pctapi pctaian pct2prace pcthispanic
    0    smith  white     70.9    23.11    0.5    0.89      2.19         2.4
    1    zhang    api     0.99     0.16  98.06    0.02      0.62        0.15
    2  jackson  black    39.89    53.04   0.39    1.06      3.12         2.5
    
    >>> pred_census_ln(df, 'name')
          name   race       api     black  hispanic     white
    0    smith  white  0.002019  0.247235  0.014485  0.736260
    1    zhang    api  0.997807  0.000149  0.000470  0.001574
    2  jackson  black  0.002797  0.528193  0.014605  0.454405
    
  • pred_wiki_ln( df, namecol, num_iter=100, conf_int=0.9)

    • What it does:
    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list, int} string or list of the name or location of the column containing the last name
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable — category with the highest probability), “Asian,GreaterEastAsian,EastAsian”, “Asian,GreaterEastAsian,Japanese”, “Asian,IndianSubContinent”, “GreaterAfrican,Africans”, “GreaterAfrican,Muslim”, “GreaterEuropean,British”,”GreaterEuropean,EastEuropean”, “GreaterEuropean,Jewish”,”GreaterEuropean,WestEuropean,French”, “GreaterEuropean,WestEuropean,Germanic”,”GreaterEuropean,WestEuropean,Hispanic”, “GreaterEuropean,WestEuropean,Italian”,”GreaterEuropean,WestEuropean,Nordic”. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
    >>> import pandas as pd
    
    >>> names = [
    ...             {"last": "smith", "first": "john", "true_race": "GreaterEuropean,British"},
    ...             {
    ...                 "last": "zhang",
    ...                 "first": "simon",
    ...                 "true_race": "Asian,GreaterEastAsian,EastAsian",
    ...             },
    ...         ]
    >>> df = pd.DataFrame(names)
    
    >>> from ethnicolr import pred_wiki_ln, pred_wiki_name
    
    >>> odf = pred_wiki_ln(df,'last')
    ['Asian,GreaterEastAsian,EastAsian', 'Asian,GreaterEastAsian,Japanese', 'Asian,IndianSubContinent', 'GreaterAfrican,Africans', 'GreaterAfrican,Muslim', 'GreaterEuropean,British', 'GreaterEuropean,EastEuropean', 'GreaterEuropean,Jewish', 'GreaterEuropean,WestEuropean,French', 'GreaterEuropean,WestEuropean,Germanic', 'GreaterEuropean,WestEuropean,Hispanic', 'GreaterEuropean,WestEuropean,Italian', 'GreaterEuropean,WestEuropean,Nordic']
    
    >>> odf
       last  first  ... GreaterEuropean,WestEuropean,Nordic_ub                              race
    0  Smith   john  ...                               0.004559           GreaterEuropean,British
    1  Zhang  simon  ...                               0.004076  Asian,GreaterEastAsian,EastAsian
    
    [2 rows x 57 columns]
    
    >>> odf.iloc[0,:8]
    last                                                       Smith
    first                                                       john
    true_race                                GreaterEuropean,British
    rowindex                                                       0
    Asian,GreaterEastAsian,EastAsian_mean                   0.004554
    Asian,GreaterEastAsian,EastAsian_std                    0.003358
    Asian,GreaterEastAsian,EastAsian_lb                     0.000535
    Asian,GreaterEastAsian,EastAsian_ub                     0.000705
    Name: 0, dtype: object
    
  • pred_wiki_name(df, namecol, num_iter=100, conf_int=0.9)

    • What it does:
    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable—category with the highest probability), “Asian,GreaterEastAsian,EastAsian”, “Asian,GreaterEastAsian,Japanese”, “Asian,IndianSubContinent”, “GreaterAfrican,Africans”, “GreaterAfrican,Muslim”, “GreaterEuropean,British”,”GreaterEuropean,EastEuropean”, “GreaterEuropean,Jewish”,”GreaterEuropean,WestEuropean,French”, “GreaterEuropean,WestEuropean,Germanic”,”GreaterEuropean,WestEuropean,Hispanic”, “GreaterEuropean,WestEuropean,Italian”,”GreaterEuropean,WestEuropean,Nordic”. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above)

    >>> odf = pred_wiki_name(df, 'last', 'first')
    ['Asian,GreaterEastAsian,EastAsian', 'Asian,GreaterEastAsian,Japanese', 'Asian,IndianSubContinent', 'GreaterAfrican,Africans', 'GreaterAfrican,Muslim', 'GreaterEuropean,British', 'GreaterEuropean,EastEuropean', 'GreaterEuropean,Jewish', 'GreaterEuropean,WestEuropean,French', 'GreaterEuropean,WestEuropean,Germanic', 'GreaterEuropean,WestEuropean,Hispanic', 'GreaterEuropean,WestEuropean,Italian', 'GreaterEuropean,WestEuropean,Nordic']
    
    >>> odf
       last  first  ... GreaterEuropean,WestEuropean,Nordic_ub                              race
    0  Smith   john  ...                               0.000236           GreaterEuropean,British
    1  Zhang  simon  ...                               0.000021  Asian,GreaterEastAsian,EastAsian
    
    [2 rows x 58 columns]
    
    >>> odf.iloc[1,:8]
    last                                                                Zhang
    first                                                               simon
    true_race                                Asian,GreaterEastAsian,EastAsian
    rowindex                                                                1
    __name                                                        Zhang Simon
    Asian,GreaterEastAsian,EastAsian_mean                            0.890619
    Asian,GreaterEastAsian,EastAsian_std                             0.119097
    Asian,GreaterEastAsian,EastAsian_lb                              0.391496
    Name: 1, dtype: object
    
  • pred_fl_reg_ln(df, namecol, num_iter=100, conf_int=0.9)

    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list, int} string or list of the name or location of the column containing the last name
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
    >>> import pandas as pd
    
    >>> names = [
    ...             {"last": "sawyer", "first": "john", "true_race": "nh_white"},
    ...             {"last": "torres", "first": "raul", "true_race": "hispanic"},
    ...         ]
    
    >>> df = pd.DataFrame(names)
    
    >>> from ethnicolr import pred_fl_reg_ln, pred_fl_reg_name, pred_fl_reg_ln_five_cat, pred_fl_reg_name_five_cat
    
    >>> odf = pred_fl_reg_ln(df, 'last')
    ['asian', 'hispanic', 'nh_black', 'nh_white']
    
    >>> odf
       last first true_race  rowindex  asian_mean  asian_std  ...  nh_black_ub  nh_white_mean  nh_white_std  nh_white_lb  nh_white_ub      race
    0  Sawyer  john  nh_white         0    0.004004   0.004483  ...     0.015442       0.908452      0.035121     0.722879     0.804443  nh_white
    1  Torres  raul  hispanic         1    0.005882   0.002249  ...     0.005305       0.182575      0.072142     0.074511     0.090856  hispanic
    
    [2 rows x 21 columns]
    
    >>> odf.iloc[0]
    last               Sawyer
    first                john
    true_race        nh_white
    rowindex                0
    asian_mean       0.004004
    asian_std        0.004483
    asian_lb         0.000899
    asian_ub          0.00103
    hispanic_mean    0.034227
    hispanic_std      0.01294
    hispanic_lb      0.017406
    hispanic_ub      0.017625
    nh_black_mean    0.053317
    nh_black_std     0.028634
    nh_black_lb      0.010537
    nh_black_ub      0.015442
    nh_white_mean    0.908452
    nh_white_std     0.035121
    nh_white_lb      0.722879
    nh_white_ub      0.804443
    race             nh_white
    Name: 0, dtype: object
    
  • pred_fl_reg_name(df, namecol, num_iter=100, conf_int=0.9)

    • What it does:
    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above)

    >>> odf = pred_fl_reg_name(df, 'last', 'first')
    ['asian', 'hispanic', 'nh_black', 'nh_white']
    
    >>> odf
       last first true_race  rowindex       __name  asian_mean  ...  nh_black_ub  nh_white_mean  nh_white_std  nh_white_lb  nh_white_ub      race
    0  Sawyer  john  nh_white         0  Sawyer John    0.001196  ...     0.005450       0.971152      0.015757     0.915592     0.918630  nh_white
    1  Torres  raul  hispanic         1  Torres Raul    0.004770  ...     0.000885       0.066303      0.028486     0.022593     0.024143  hispanic
    
    [2 rows x 22 columns]
    
    >>> odf.iloc[1]
    last                  Torres
    first                   raul
    true_race           hispanic
    rowindex                   1
    __name           Torres Raul
    asian_mean           0.00477
    asian_std           0.002943
    asian_lb            0.000904
    asian_ub            0.001056
    hispanic_mean         0.9251
    hispanic_std        0.032224
    hispanic_lb         0.829494
    hispanic_ub           0.8385
    nh_black_mean       0.003826
    nh_black_std        0.002735
    nh_black_lb         0.000838
    nh_black_ub         0.000885
    nh_white_mean       0.066303
    nh_white_std        0.028486
    nh_white_lb         0.022593
    nh_white_ub         0.024143
    race                hispanic
    Name: 1, dtype: object
    
  • pred_fl_reg_ln_five_cat(df, namecol, num_iter=100, conf_int=0.9)

    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list, int} string or list of the name or location of the column containing the last name
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above)

    >>> odf = pred_fl_reg_ln_five_cat(df,'last')
    ['asian', 'hispanic', 'nh_black', 'nh_white', 'other']
    
    >>> odf
       last first true_race  rowindex       __name  asian_mean  asian_std  ...  nh_white_lb  nh_white_ub  other_mean  other_std  other_lb  other_ub      race
    0  Sawyer  john  nh_white         0  Sawyer John    0.142867   0.046145  ...     0.203204     0.221313    0.235889   0.023794  0.192840  0.193671  nh_white
    1  Torres  raul  hispanic         1  Torres Raul    0.101397   0.028399  ...     0.090068     0.100212    0.238645   0.034070  0.136617  0.145928  hispanic
    
    [2 rows x 26 columns]
    
    >>> odf.iloc[0]
    last                  Sawyer
    first                   john
    true_race           nh_white
    rowindex                   0
    __name           Sawyer John
    asian_mean          0.142867
    asian_std           0.046145
    asian_lb            0.067382
    asian_ub            0.073285
    hispanic_mean       0.068199
    hispanic_std        0.020641
    hispanic_lb          0.02565
    hispanic_ub         0.030017
    nh_black_mean       0.239793
    nh_black_std        0.076287
    nh_black_lb         0.084239
    nh_black_ub         0.085626
    nh_white_mean       0.313252
    nh_white_std        0.046173
    nh_white_lb         0.203204
    nh_white_ub         0.221313
    other_mean          0.235889
    other_std           0.023794
    other_lb             0.19284
    other_ub            0.193671
    race                nh_white
    Name: 0, dtype: object
    
  • pred_fl_reg_name_five_cat(df, namecol, num_iter=100, conf_int=0.9)

    • What it does:
    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic, or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above)

    >>> odf = pred_fl_reg_name_five_cat(df, 'last','first')
    ['asian', 'hispanic', 'nh_black', 'nh_white', 'other']
    
    >>> odf
       last first true_race  rowindex       __name  asian_mean  asian_std  ...  nh_white_lb  nh_white_ub  other_mean  other_std  other_lb  other_ub      race
    0  Sawyer  john  nh_white         0  Sawyer John    0.194250   0.120314  ...     0.126987     0.167742    0.259069   0.030386  0.142455  0.177375  nh_white
    1  Torres  raul  hispanic         1  Torres Raul    0.081465   0.038318  ...     0.019312     0.020782    0.158614   0.039180  0.081994  0.083105  hispanic
    
    [2 rows x 26 columns]
    
    >>> odf.iloc[1]
    last                  Torres
    first                   raul
    true_race           hispanic
    rowindex                   1
    __name           Torres Raul
    asian_mean          0.081465
    asian_std           0.038318
    asian_lb            0.032789
    asian_ub            0.034667
    hispanic_mean       0.646059
    hispanic_std        0.144663
    hispanic_lb         0.188246
    hispanic_ub         0.219772
    nh_black_mean       0.037737
    nh_black_std        0.045439
    nh_black_lb         0.006477
    nh_black_ub         0.006603
    nh_white_mean       0.076125
    nh_white_std        0.059213
    nh_white_lb         0.019312
    nh_white_ub         0.020782
    other_mean          0.158614
    other_std            0.03918
    other_lb            0.081994
    other_ub            0.083105
    race                hispanic
    Name: 1, dtype: object
    
  • pred_nc_reg_name(df, namecol, num_iter=100, conf_int=0.9)

    • What it does:
    Parameters  
      df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
      namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
      num_iter : int, default=100 number of iterations to calculate uncertainty in model
      conf_int : float, default=0.9 confidence interval in predicted class
    • Output: Appends the following columns to the pandas DataFrame or CSV: race + ethnicity. The codebook is here. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
    >>> import pandas as pd
    
    >>> names = [
    ...             {"last": "hernandez", "first": "hector", "true_race": "HL+O"},
    ...             {"last": "zhang", "first": "simon", "true_race": "NL+A"},
    ...         ]
    
    >>> df = pd.DataFrame(names)
    
    >>> from ethnicolr import pred_nc_reg_name
    
    >>> odf = pred_nc_reg_name(df, 'last','first')
    ['HL+A', 'HL+B', 'HL+I', 'HL+M', 'HL+O', 'HL+W', 'NL+A', 'NL+B', 'NL+I', 'NL+M', 'NL+O', 'NL+W']
    
    >>> odf
          last   first true_race            __name  rowindex  HL+A_mean  HL+A_std       HL+A_lb       HL+A_ub  HL+B_mean  ...   NL+M_ub  NL+O_mean  NL+O_std   NL+O_lb   NL+O_ub  NL+W_mean  NL+W_std   NL+W_lb   NL+W_ub  race
    0  hernandez  hector      HL+O  Hernandez Hector         0   0.000054  0.000354  5.833132e-10  4.291366e-09   0.009606  ...  0.000416   0.090123  0.036310  0.000705  0.003757   0.021228  0.021222  0.000368  0.001230  HL+O
    1      zhang   simon      NL+A       Zhang Simon         1   0.000603  0.002808  1.988648e-07  2.766486e-07   0.000026  ...  0.000086   0.125159  0.042818  0.050547  0.057208   0.003149  0.005437  0.000210  0.000225  NL+A
    
    [2 rows x 54 columns]
    
    >>> odf.iloc[0]
    last                hernandez
    first                  hector
    true_race                HL+O
    __name       Hernandez Hector
    rowindex                    0
    HL+A_mean            0.000054
    HL+A_std             0.000354
    HL+A_lb                   0.0
    HL+A_ub                   0.0
    HL+B_mean            0.009606
    HL+B_std             0.040739
    HL+B_lb                   0.0
    HL+B_ub              0.000003
    HL+I_mean            0.001605
    HL+I_std             0.004569
    HL+I_lb                   0.0
    HL+I_ub                   0.0
    HL+M_mean            0.147628
    HL+M_std             0.215733
    HL+M_lb              0.001253
    HL+M_ub              0.001297
    HL+O_mean             0.36902
    HL+O_std             0.132249
    HL+O_lb              0.002289
    HL+O_ub              0.019187
    HL+W_mean            0.264246
    HL+W_std             0.090536
    HL+W_lb              0.001782
    HL+W_ub              0.015628
    NL+A_mean            0.012004
    NL+A_std             0.010873
    NL+A_lb              0.000121
    NL+A_ub              0.000281
    NL+B_mean            0.010891
    NL+B_std              0.01404
    NL+B_lb              0.000094
    NL+B_ub              0.000383
    NL+I_mean            0.005182
    NL+I_std             0.008259
    NL+I_lb              0.000009
    NL+I_ub              0.000068
    NL+M_mean            0.068412
    NL+M_std              0.08564
    NL+M_lb              0.000172
    NL+M_ub              0.000416
    NL+O_mean            0.090123
    NL+O_std              0.03631
    NL+O_lb              0.000705
    NL+O_ub              0.003757
    NL+W_mean            0.021228
    NL+W_std             0.021222
    NL+W_lb              0.000368
    NL+W_ub               0.00123
    race                     HL+O
    Name: 0, dtype: object
    

Application

To illustrate how the package can be used, we impute the race of the campaign contributors recorded by FEC for the years 2000 and 2010 and tally campaign contributions by race.

Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here

Data

In particular, we utilize the last-name–race data from the 2000 census and 2010 census, the Wikipedia data collected by Skiena and colleagues, and the Florida voter registration data from early 2017.

Evaluation

  1. SCAN Health Plan, a Medicare Advantage plan that serves over 200,000 members throughout California used the software to better assess racial disparities of health among the people they serve. They only had racial data on about 47% of their members so used it to learn the race of the remaining 53%. On the data they had labels for, they found .9 AUC and 83% accuracy for the last name model.
  2. Evaluation on NC Data: https://github.com/appeler/nc_race_ethnicity

Authors

Suriyan Laohaprapanon, Gaurav Sood and Bashar Naji

Contributor Code of Conduct

The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.

License

The package is released under the MIT License.