Code
timestamp()##------ Wed Apr 23 11:37:59 2025 ------##Organize Data Notebook
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# this should anyone to access the data
drive_deauth()Introduction
This notebook documents the process of reading American Community Survey data from IPUMS and converting it to a set of “marriage market” data that can be used to model marriage choices, using the fakeunion package. To do that I need three datasets:
- A dataset of actual recent marriages with symmetrical information about each spouse. I am only looking at opposite-sex unions for this analysis.
- A dataset of potential male partners that is made up of those recently married (from 1) and currently single people (those not in cohabitation or less recent marriages). This dataset should contain all the same information about these men as the husbands from (1).
- A dataset identical to (2) but for potential female partners.
I set up a few key parameters right at the top of the document
Code
set.seed(39)
years_mar <- 5 # limit on the duration of marriages that will be identified
n_samples <- 5 # number of separate marriage markets to construct
n_fakes <- 7 # number of fake unions to generate for each real oneNext, I set up several functions that will be useful for coding the data. Since I have to repeat the same coding for several variables on both partners in a union, it makes sense to put that coding into a function to minimize code and the risk of error.
Code
# since I have to code the same variable for both spouses, it makes sense to put
# this coding into a function
code_race <- function(raced, hispand) {
# We want to take the raced and hispand variables and code them into a combined
# race variable. I am going to use the fullest possible coding here.
race <- case_when(
# Latino trumps everything, because combined question, sigh
# However, we will split them into Latino, Single Race and Latino,
# Multiple Race - CORRECTION, we will do this as a sensitivity analysis
# but its not showing any real differences, so we will leave as one to
# simplify main analysis
#hispand>0 & hispand<900 & raced>=800 ~ "Latino, Multiple Race",
#hispand>0 & hispand<900 & raced<800 ~ "Latino, Single Race",
hispand>0 & hispand<900 ~ "Latino",
# white and black are easy
raced==100 ~ "White",
raced==200 ~ "Black",
# for indigenous we get all AIAN, Pacific Islanders, and the one multiracial
# combo of them (855)
(raced>=300 & raced<400) | (raced==630 | (raced>=680 & raced<=699)) |
raced==855 ~ "Indigenous",
# Asian - also need to grab a couple of multiracial response which are
# really multiethnic Asian
((raced>=400 & raced<=679) | raced %in% c(869, 887)) ~ "Asian",
# Biracial groups
raced==801 ~ "White-Black",
(raced>=810 & raced<=819) ~ "White-Asian",
(raced>=831 & raced<=838) ~ "Black-Asian",
# for all part Indigenous groups we need to get the part AIAN and part PI
raced==802 | (raced>=820 & raced<=825) ~ "White-Indigenous",
raced==830 | (raced>=840 & raced<=842) ~ "Black-Indigenous",
(raced>=850 & raced<=854) | (raced>=860 & raced<=868) ~ "Asian-Indigenous",
TRUE ~ NA_character_
)
race <- factor(race,
levels=c("White","Black","Indigenous","Asian", "Latino",
"White-Black","White-Indigenous","White-Asian",
"Black-Indigenous","Black-Asian","Asian-Indigenous"))
return(race)
}
code_educ <- function(educd) {
# We want to re-code the educd variable into the following simple
# categories:
# - Less than high school diploma
# - High school diploma
# - Some college, but less than a four year degree
# - Four year college degree or more
educ <- ifelse(educd<60, "LHS",
ifelse(educd<=65, "HS",
ifelse(educd<=90, "SC",
ifelse(educd<=999, "C",
NA))))
educ <- factor(educ,
levels=c("LHS","HS","SC","C"),
ordered=TRUE)
return(educ)
}
code_bpl <- function(bpld) {
#recode any one born in the US (99 or less) as a single number. Otherwise
#we will be fitting state level endogamy. also code in missing values
# The general codes are way to general. The detailed codes have some
# overspecifity in the codes, but not in the actual data used here.
return(ifelse(bpld>=95000, NA,
ifelse(bpld<10000,1,bpld)))
}
code_language <- function(language) {
#I need to use the detailed language codes as the general language codes
#are too general. However the detailed language codes are too detailed in
#some places, particularly in translating between the two time periods. Thus
#I make some corrections to the detailed codes for consistency between the
#two time periods.
# We don't need to code language as a factor variable with labels because
# ultimately all we need to know is whether the two partners speak the same
# language. We should however code in any missing values (0), although
# there do not appear to be any.
lang_recode <- language
#the following cases will be collapsed to the their top two digit codes
#(starting at the 100 levels)
#1:27 - European language groups (e.g. English, French, German)
#35: Uralic
#37: Other Altaic
#43: Chinese
#47: Thai/Siamese/Lao (not separable in 1980)
#52: Indonesian
#53: Other Malay
#58: Near East Arabic Dialect
collapsed_cases <- c(1:24,35,37,43,47,52,53,58)
collapsed_language <- floor(language/100)
lang_recode <- ifelse(collapsed_language %in% collapsed_cases,
collapsed_language*100, lang_recode)
#A couple of cases need to be put back into there other categories
#420: Afrikaans
#1140: Haitian Creole
#1150: Cajun
#2310: Croatian
#2320: Serbian
uncollapsed_cases <- c(420,1140,1150,2310,2320)
lang_recode <- ifelse(language %in% uncollapsed_cases,
language, lang_recode)
#put malay and other malay together
lang_recode <- ifelse(language==5270, 5300, lang_recode)
#collapse Hindi and Urdu into 3101 (Hindustani)
lang_recode <- ifelse(language>3101 & language<=3104, 3101, lang_recode)
#For 1980 consistency put all American Indian languages in one group
lang_recode <- ifelse(lang_recode>7000 & lang_recode<=9300, 7000,
lang_recode)
#A few cases are "other" or "nec". These will be recoded as -1 and
#not treated as endogamy with each other
nec_codes <- c(3140,3150,3190,5290,5590,6200,6390,6400,9400,9410,9420,9500,
9600,9601,9602,9999)
lang_recode <- ifelse(lang_recode %in% nec_codes, -1, lang_recode)
#code any missing values
lang_recode <- ifelse(lang_recode==0, NA, lang_recode)
return(lang_recode)
}
code_generation <- function(bpl, age, year, yr_usa) {
# first, calculate age of entry into the US, NA for all non-migrants
age_usa <- age-yr_usa
# check
#tapply(age_usa, bpl>1, mean)
#summary(age_usa)
# some of these responses are as negative as -5. -1 is expected because of
# the crudeness of calendar year vs birthdays. Almost all of the values
# greater than -1 are for year of migration prior to 1940, which is top-coded
# in the data, so I think those make sense as well. In those cases, we will
# just replace with zero.
age_usa <- ifelse(age_usa<0, 0, age_usa)
# now we can code our generations. All native-borns are 2nd+.
generation <- case_when(
is.na(bpl) ~ NA_character_,
bpl==1 ~ "2nd+",
age_usa<6 ~ "1.75",
age_usa<13 ~ "1.5",
age_usa<18 ~ "1.25",
!is.na(age_usa) ~ "1st"
)
generation <- factor(generation,
levels=c("1st","1.25","1.5","1.75","2nd+"))
# checks
#summary(generation)
#table(bpl>1, generation, exclude=NULL)
#tapply(age_usa, generation, min)
#tapply(age_usa, generation, max)
return(generation)
}Read in the data
The data are ACS data from 2010-2019, extracted via IPUMS. Below, I show all variables and their position in the files.
Variable Columns Len 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
YEAR H 1-4 4 X X X X X X X X X X
SAMPLE H 5-10 6 X X X X X X X X X X
SERIAL H 11-18 8 X X X X X X X X X X
CBSERIAL H 19-31 13 X X X X X X X X X X
HHWT H 32-41 10 X X X X X X X X X X
CLUSTER H 42-54 13 X X X X X X X X X X
STATEFIP H 55-56 2 X X X X X X X X X X
COUNTYFIP H 57-59 3 X X X X X X X X X X
PUMA H 60-64 5 X X X X X X X X X X
MET2013 H 65-69 5 X X X X X X X X X X
STRATA H 70-81 12 X X X X X X X X X X
GQ H 82 1 X X X X X X X X X X
PERNUM P 83-86 4 X X X X X X X X X X
PERWT P 87-96 10 X X X X X X X X X X
SEX P 97 1 X X X X X X X X X X
AGE P 98-100 3 X X X X X X X X X X
MARST P 101 1 X X X X X X X X X X
MARRNO P 102 1 X X X X X X X X X X
YRMARR P 103-106 4 X X X X X X X X X X
RACE P 107 1 X X X X X X X X X X
RACED P 108-110 3 X X X X X X X X X X
HISPAN P 111 1 X X X X X X X X X X
HISPAND P 112-114 3 X X X X X X X X X X
BPL P 115-117 3 X X X X X X X X X X
BPLD P 118-122 5 X X X X X X X X X X
YRIMMIG P 123-126 4 X X X X X X X X X X
LANGUAGE P 127-128 2 X X X X X X X X X X
LANGUAGED P 129-132 4 X X X X X X X X X X
EDUC P 133-134 2 X X X X X X X X X X
EDUCD P 135-137 3 X X X X X X X X X X
PERNUM_SP P 138-141 4 X X X X X X X X X X
SEX_SP P 142 1 X X X X X X X X X X
AGE_SP P 143-145 3 X X X X X X X X X X
RACED_SP P 146-148 3 X X X X X X X X X X
HISPAND_SP P 149-151 3 X X X X X X X X X X
BPLD_SP P 152-156 5 X X X X X X X X X X
YRIMMIG_SP P 157-160 4 X X X X X X X X X X
LANGUAGED_SP P 161-164 4 X X X X X X X X X X
EDUCD_SP P 165-167 3 X X X X X X X X X X Now, I am ready to read the data in. Because the dataset is very large, the data are currently stored on Google Drive and accessed using the googledrive library. A local copy is downloaded, read in, and then deleted.
Code
# where the downloaded data will live (temporarily)
file_path <- here("data","data_raw","acs.dat.gz")
# download the data file
drive_download(as_id("15jz02cevyTvLvGoyivrhQ5AU_lN8Z6Xu"), path=file_path)File downloaded:• 'usa_00139.dat.gz' <id: 15jz02cevyTvLvGoyivrhQ5AU_lN8Z6Xu>Saved locally as:• '/home/aarong/projects/research/multiracial_intermar/data/data_raw/acs.dat.gz'Code
acs <- read_fwf(file_path,
col_positions = fwf_cols(
YEAR = c(1, 4),
SERIAL = c(11, 18),
HHWT = c(32, 41),
STATEFIP = c(55, 56),
COUNTYFIP = c(57, 59),
PUMA = c(60, 64),
MET2013 = c(65, 69),
PERNUM = c(83, 86),
SEX = 97,
AGE = c(98, 100),
MARST = 101,
MARRNO = 102,
YRMARR = c(103, 106),
RACED = c(108, 110),
HISPAND = c(112, 114),
BPLD = c(118, 122),
YRIMMIG = c(123, 126),
LANGUAGED = c(129, 132),
EDUCD = c(135, 137),
PERNUM_SP = c(138, 141),
SEX_SP = 142,
AGE_SP = c(143, 145),
RACED_SP = c(146, 148),
HISPAND_SP = c(149, 151),
BPLD_SP = c(152, 156),
YRIMMIG_SP = c(157, 160),
LANGUAGED_SP = c(161, 164),
EDUCD_SP = c(165, 167)
),
col_types = cols(.default = "i", HHWT="d"),
progress = FALSE)
# now that the data are loaded, delete
file.remove(file_path)[1] TRUECode variables
Code
table(acs$COUNTYFIP != 0, acs$MET2013 != 0)
FALSE TRUE
FALSE 5985337 4136095
TRUE 629571 14112354The table above shows the cases where county and/or metro area are identifiable in the full data. We have considerably more cases where metro area is identifiable than county is identifiable.
I recode, create, and clean all of the variables that I need in one mutate call. Embedded comments should provide more details. I will check my coding below.
Code
acs <- acs |>
mutate(
## unique ids for each respondent ##
id = SERIAL*1000000+PERNUM*10000+YEAR,
id_sp = ifelse(is.na(PERNUM_SP),NA,
SERIAL*1000000+PERNUM_SP*10000+YEAR),
## sex of respondent and spouse ##
sex = ifelse(SEX==1, "Male", ifelse(SEX==2, "Female", NA)),
sex_sp = ifelse(SEX_SP==1, "Male", ifelse(SEX_SP==2, "Female", NA)),
## age of respondent and spouse ##
age=AGE,
age_sp=AGE_SP,
## marital status of respondent ##
mar_stat=factor(MARST, levels=1:6,
labels=c("Married, spouse present", "Married, spouse absent",
"Separated","Divorced","Widowed","Never married")),
## race of respondent and spouse - see details in code_race function ##
race = code_race(RACED, HISPAND),
race_sp = code_race(RACED_SP, HISPAND_SP),
## education of respondent and spouse - see code_educ function for details ##
educ = code_educ(EDUCD),
educ_sp = code_educ(EDUCD_SP),
## language of respondent and spouse - see code_lang for details ##
lang = code_language(LANGUAGED),
lang_sp = code_language(LANGUAGED_SP),
## marriage market id ##
# we do marriage market with a cascading logic. First, we check for
# met2013. If this is identifiable, then we assign the metro area. Second,
# for cases without met2013, we look for county and if identiable, then
# we assign it. Third, if we have neither metro area or county, we assign
# PUMA
mar_market = case_when(
MET2013 != 0 ~ paste("M", MET2013, YEAR, sep = "."),
COUNTYFIP != 0 ~ paste("C", STATEFIP, COUNTYFIP, YEAR, sep = "."),
TRUE ~ paste("P", STATEFIP, PUMA, YEAR, sep = ".")
),
# a simpler alternative would be to use state
#mar_market = paste(STATEFIP, YEAR, sep=""),
## marriage duration ##
dur_mar = ifelse(YRMARR==0, NA, YEAR - YRMARR),
## country of birth of respondent and spouse - see code_bpl for details ##
bpl = code_bpl(BPLD),
bpl_sp = code_bpl(BPLD_SP),
## Years in the USA - missing for natives ##
yr_usa = ifelse(YRIMMIG==0, NA, YEAR-YRIMMIG),
yr_usa_sp = ifelse(YRIMMIG_SP==0, NA, YEAR-YRIMMIG_SP),
## immigrant generation - see code_generation for details ##
immig_gen = code_generation(bpl, age, YEAR, yr_usa),
immig_gen_sp = code_generation(bpl_sp, age_sp, YEAR, yr_usa_sp)
)Ok, now I check to make sure all of my coding worked as expected. This is a long section because the output is often quite large.
Code
options(max.print=10000)
# Do we have any duplicates in our ids?
sum(duplicated(acs$id))[1] 0Code
sum(duplicated(na.omit(acs$id_sp)))[1] 0Code
# check sex
table(acs$sex, acs$SEX, exclude=NULL)
1 2
Female 0 12881384
Male 11981973 0Code
table(acs$sex_sp, acs$SEX_SP, exclude=NULL)
1 2 <NA>
Female 0 7111457 0
Male 7109258 0 0
<NA> 0 0 10642642Code
# check mar_stat
table(acs$mar_stat, acs$MARST, exclude=NULL)
1 2 3 4 5 6
Married, spouse present 12859808 0 0 0 0 0
Married, spouse absent 0 550033 0 0 0 0
Separated 0 0 463526 0 0 0
Divorced 0 0 0 2800373 0 0
Widowed 0 0 0 0 1737851 0
Never married 0 0 0 0 0 6451766Code
# check age
summary(acs$age) Min. 1st Qu. Median Mean 3rd Qu. Max.
18.00 34.00 50.00 49.49 63.00 97.00 Code
summary(acs$age_sp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
15 39 52 52 63 97 10642642 Code
# check educ
table(acs$EDUCD, acs$educ, exclude=NULL)
LHS HS SC C
2 353763 0 0 0
11 4264 0 0 0
12 5102 0 0 0
14 11695 0 0 0
15 24418 0 0 0
16 53713 0 0 0
17 42663 0 0 0
22 61174 0 0 0
23 219247 0 0 0
25 95859 0 0 0
26 309624 0 0 0
30 348129 0 0 0
40 461405 0 0 0
50 629232 0 0 0
61 0 482760 0 0
63 0 5970021 0 0
64 0 1015313 0 0
65 0 1827631 0 0
71 0 0 3846057 0
81 0 0 1954892 0
101 0 0 0 4424265
114 0 0 0 1922591
115 0 0 0 480758
116 0 0 0 318781Code
table(acs$EDUCD_SP, acs$educ_sp, exclude=NULL)
LHS HS SC C <NA>
2 165054 0 0 0 0
11 2054 0 0 0 0
12 2135 0 0 0 0
14 5521 0 0 0 0
15 12541 0 0 0 0
16 27633 0 0 0 0
17 21641 0 0 0 0
22 30916 0 0 0 0
23 127555 0 0 0 0
25 43718 0 0 0 0
26 142836 0 0 0 0
30 167398 0 0 0 0
40 204473 0 0 0 0
50 224437 0 0 0 0
61 0 242380 0 0 0
63 0 3198790 0 0 0
64 0 520402 0 0 0
65 0 981161 0 0 0
71 0 0 1944654 0 0
81 0 0 1236390 0 0
101 0 0 0 2943171 0
114 0 0 0 1377987 0
115 0 0 0 360889 0
116 0 0 0 236979 0
<NA> 0 0 0 0 10642642Code
# check race
table(acs$RACED, acs$race, exclude=NULL)
White Black Indigenous Asian Latino White-Black
100 17415446 0 0 0 2053988 0
200 0 2451888 0 0 64288 0
302 0 0 4506 0 1708 0
303 0 0 2148 0 139 0
304 0 0 23095 0 1029 0
305 0 0 817 0 50 0
306 0 0 2011 0 45 0
307 0 0 10075 0 289 0
308 0 0 7961 0 233 0
309 0 0 730 0 178 0
310 0 0 3602 0 67 0
311 0 0 926 0 36 0
312 0 0 3872 0 101 0
314 0 0 6057 0 49 0
315 0 0 39512 0 1133 0
317 0 0 326 0 24 0
318 0 0 1408 0 187 0
319 0 0 1785 0 59 0
320 0 0 6398 0 707 0
321 0 0 1271 0 43 0
323 0 0 9054 0 420 0
324 0 0 1634 0 31 0
325 0 0 1681 0 207 0
328 0 0 1035 0 115 0
350 0 0 121 0 1 0
352 0 0 2058 0 94 0
353 0 0 114 0 4 0
354 0 0 1065 0 1018 0
355 0 0 181 0 7 0
356 0 0 128 0 1 0
357 0 0 125 0 3 0
358 0 0 238 0 23 0
359 0 0 131 0 1476 0
360 0 0 253 0 6102 0
361 0 0 20445 0 4688 0
362 0 0 23897 0 2196 0
370 0 0 1913 0 13 0
371 0 0 1421 0 11 0
372 0 0 1785 0 6 0
374 0 0 3090 0 73 0
375 0 0 4534 0 19 0
379 0 0 1072 0 63 0
398 0 0 668 0 227 0
399 0 0 19190 0 12629 0
400 0 0 0 313140 1869 0
410 0 0 0 12075 32 0
500 0 0 0 72378 1430 0
600 0 0 0 219272 5669 0
610 0 0 0 238569 965 0
620 0 0 0 111735 704 0
630 0 0 13565 0 1365 0
640 0 0 0 127187 584 0
641 0 0 0 1019 2 0
642 0 0 0 840 17 0
643 0 0 0 6302 17 0
660 0 0 0 17276 117 0
661 0 0 0 13506 37 0
662 0 0 0 12924 167 0
663 0 0 0 15683 148 0
664 0 0 0 10178 11 0
665 0 0 0 6078 24 0
666 0 0 0 5494 67 0
667 0 0 0 1500 13 0
669 0 0 0 26718 116 0
670 0 0 0 3728 11 0
671 0 0 0 8709 357 0
672 0 0 0 2460 64 0
673 0 0 0 2369 43 0
674 0 0 0 3028 333 0
675 0 0 0 5543 11 0
676 0 0 0 3028 32 0
677 0 0 0 1409 95 0
678 0 0 0 3481 60 0
679 0 0 0 3155 58 0
680 0 0 6184 0 367 0
682 0 0 2072 0 40 0
684 0 0 142 0 7 0
685 0 0 4738 0 825 0
689 0 0 1306 0 7 0
695 0 0 1851 0 15 0
697 0 0 380 0 1 0
698 0 0 27 0 0 0
699 0 0 3825 0 400 0
700 0 0 0 0 781221 0
801 0 0 0 0 15944 63828
802 0 0 0 0 18670 0
811 0 0 0 0 1206 0
812 0 0 0 0 1162 0
813 0 0 0 0 2618 0
814 0 0 0 0 332 0
815 0 0 0 0 294 0
816 0 0 0 0 151 0
817 0 0 0 0 110 0
818 0 0 0 0 592 0
819 0 0 0 0 79 0
821 0 0 0 0 595 0
822 0 0 0 0 93 0
823 0 0 0 0 194 0
824 0 0 0 0 164 0
825 0 0 0 0 4 0
826 0 0 0 0 47367 0
830 0 0 0 0 1716 0
832 0 0 0 0 114 0
833 0 0 0 0 63 0
834 0 0 0 0 173 0
835 0 0 0 0 84 0
836 0 0 0 0 50 0
837 0 0 0 0 84 0
838 0 0 0 0 83 0
841 0 0 0 0 91 0
842 0 0 0 0 71 0
845 0 0 0 0 6585 0
851 0 0 0 0 144 0
852 0 0 0 0 87 0
854 0 0 0 0 125 0
855 0 0 80 0 28 0
856 0 0 0 0 5798 0
861 0 0 0 0 98 0
862 0 0 0 0 104 0
863 0 0 0 0 50 0
864 0 0 0 0 213 0
865 0 0 0 0 153 0
867 0 0 0 0 108 0
868 0 0 0 0 27 0
881 0 0 0 0 84 0
882 0 0 0 0 69 0
883 0 0 0 0 1271 0
884 0 0 0 0 287 0
885 0 0 0 0 1921 0
886 0 0 0 0 71 0
887 0 0 0 623 10 0
890 0 0 0 0 1141 0
892 0 0 0 0 255 0
893 0 0 0 0 207 0
901 0 0 0 0 3319 0
902 0 0 0 0 476 0
903 0 0 0 0 11 0
904 0 0 0 0 1070 0
905 0 0 0 0 434 0
906 0 0 0 0 21 0
907 0 0 0 0 1549 0
911 0 0 0 0 206 0
912 0 0 0 0 147 0
913 0 0 0 0 61 0
914 0 0 0 0 140 0
915 0 0 0 0 67 0
916 0 0 0 0 180 0
917 0 0 0 0 84 0
920 0 0 0 0 278 0
921 0 0 0 0 182 0
922 0 0 0 0 15 0
923 0 0 0 0 168 0
925 0 0 0 0 647 0
930 0 0 0 0 13 0
931 0 0 0 0 5 0
932 0 0 0 0 48 0
933 0 0 0 0 8 0
934 0 0 0 0 17 0
935 0 0 0 0 9 0
940 0 0 0 0 17 0
941 0 0 0 0 19 0
942 0 0 0 0 11 0
943 0 0 0 0 207 0
944 0 0 0 0 49 0
950 0 0 0 0 244 0
951 0 0 0 0 1 0
952 0 0 0 0 51 0
953 0 0 0 0 6 0
954 0 0 0 0 5 0
955 0 0 0 0 2 0
960 0 0 0 0 223 0
961 0 0 0 0 9 0
962 0 0 0 0 4 0
963 0 0 0 0 914 0
964 0 0 0 0 31 0
970 0 0 0 0 6 0
971 0 0 0 0 3 0
972 0 0 0 0 1 0
973 0 0 0 0 243 0
974 0 0 0 0 410 0
976 0 0 0 0 1137 0
980 0 0 0 0 8 0
981 0 0 0 0 10 0
982 0 0 0 0 303 0
983 0 0 0 0 313 0
984 0 0 0 0 1 0
985 0 0 0 0 326 0
990 0 0 0 0 770 0
White-Indigenous White-Asian Black-Indigenous Black-Asian
100 0 0 0 0
200 0 0 0 0
302 0 0 0 0
303 0 0 0 0
304 0 0 0 0
305 0 0 0 0
306 0 0 0 0
307 0 0 0 0
308 0 0 0 0
309 0 0 0 0
310 0 0 0 0
311 0 0 0 0
312 0 0 0 0
314 0 0 0 0
315 0 0 0 0
317 0 0 0 0
318 0 0 0 0
319 0 0 0 0
320 0 0 0 0
321 0 0 0 0
323 0 0 0 0
324 0 0 0 0
325 0 0 0 0
328 0 0 0 0
350 0 0 0 0
352 0 0 0 0
353 0 0 0 0
354 0 0 0 0
355 0 0 0 0
356 0 0 0 0
357 0 0 0 0
358 0 0 0 0
359 0 0 0 0
360 0 0 0 0
361 0 0 0 0
362 0 0 0 0
370 0 0 0 0
371 0 0 0 0
372 0 0 0 0
374 0 0 0 0
375 0 0 0 0
379 0 0 0 0
398 0 0 0 0
399 0 0 0 0
400 0 0 0 0
410 0 0 0 0
500 0 0 0 0
600 0 0 0 0
610 0 0 0 0
620 0 0 0 0
630 0 0 0 0
640 0 0 0 0
641 0 0 0 0
642 0 0 0 0
643 0 0 0 0
660 0 0 0 0
661 0 0 0 0
662 0 0 0 0
663 0 0 0 0
664 0 0 0 0
665 0 0 0 0
666 0 0 0 0
667 0 0 0 0
669 0 0 0 0
670 0 0 0 0
671 0 0 0 0
672 0 0 0 0
673 0 0 0 0
674 0 0 0 0
675 0 0 0 0
676 0 0 0 0
677 0 0 0 0
678 0 0 0 0
679 0 0 0 0
680 0 0 0 0
682 0 0 0 0
684 0 0 0 0
685 0 0 0 0
689 0 0 0 0
695 0 0 0 0
697 0 0 0 0
698 0 0 0 0
699 0 0 0 0
700 0 0 0 0
801 0 0 0 0
802 118238 0 0 0
811 0 10449 0 0
812 0 19255 0 0
813 0 19198 0 0
814 0 4505 0 0
815 0 9034 0 0
816 0 3383 0 0
817 0 3808 0 0
818 0 19169 0 0
819 0 433 0 0
821 5383 0 0 0
822 1020 0 0 0
823 1079 0 0 0
824 2163 0 0 0
825 34 0 0 0
826 0 0 0 0
830 0 0 18158 0
832 0 0 0 1138
833 0 0 0 1142
834 0 0 0 2169
835 0 0 0 1368
836 0 0 0 1020
837 0 0 0 1696
838 0 0 0 588
841 0 0 1484 0
842 0 0 594 0
845 0 0 0 0
851 0 0 0 0
852 0 0 0 0
854 0 0 0 0
855 0 0 0 0
856 0 0 0 0
861 0 0 0 0
862 0 0 0 0
863 0 0 0 0
864 0 0 0 0
865 0 0 0 0
867 0 0 0 0
868 0 0 0 0
881 0 0 0 0
882 0 0 0 0
883 0 0 0 0
884 0 0 0 0
885 0 0 0 0
886 0 0 0 0
887 0 0 0 0
890 0 0 0 0
892 0 0 0 0
893 0 0 0 0
901 0 0 0 0
902 0 0 0 0
903 0 0 0 0
904 0 0 0 0
905 0 0 0 0
906 0 0 0 0
907 0 0 0 0
911 0 0 0 0
912 0 0 0 0
913 0 0 0 0
914 0 0 0 0
915 0 0 0 0
916 0 0 0 0
917 0 0 0 0
920 0 0 0 0
921 0 0 0 0
922 0 0 0 0
923 0 0 0 0
925 0 0 0 0
930 0 0 0 0
931 0 0 0 0
932 0 0 0 0
933 0 0 0 0
934 0 0 0 0
935 0 0 0 0
940 0 0 0 0
941 0 0 0 0
942 0 0 0 0
943 0 0 0 0
944 0 0 0 0
950 0 0 0 0
951 0 0 0 0
952 0 0 0 0
953 0 0 0 0
954 0 0 0 0
955 0 0 0 0
960 0 0 0 0
961 0 0 0 0
962 0 0 0 0
963 0 0 0 0
964 0 0 0 0
970 0 0 0 0
971 0 0 0 0
972 0 0 0 0
973 0 0 0 0
974 0 0 0 0
976 0 0 0 0
980 0 0 0 0
981 0 0 0 0
982 0 0 0 0
983 0 0 0 0
984 0 0 0 0
985 0 0 0 0
990 0 0 0 0
Asian-Indigenous <NA>
100 0 0
200 0 0
302 0 0
303 0 0
304 0 0
305 0 0
306 0 0
307 0 0
308 0 0
309 0 0
310 0 0
311 0 0
312 0 0
314 0 0
315 0 0
317 0 0
318 0 0
319 0 0
320 0 0
321 0 0
323 0 0
324 0 0
325 0 0
328 0 0
350 0 0
352 0 0
353 0 0
354 0 0
355 0 0
356 0 0
357 0 0
358 0 0
359 0 0
360 0 0
361 0 0
362 0 0
370 0 0
371 0 0
372 0 0
374 0 0
375 0 0
379 0 0
398 0 0
399 0 0
400 0 0
410 0 0
500 0 0
600 0 0
610 0 0
620 0 0
630 0 0
640 0 0
641 0 0
642 0 0
643 0 0
660 0 0
661 0 0
662 0 0
663 0 0
664 0 0
665 0 0
666 0 0
667 0 0
669 0 0
670 0 0
671 0 0
672 0 0
673 0 0
674 0 0
675 0 0
676 0 0
677 0 0
678 0 0
679 0 0
680 0 0
682 0 0
684 0 0
685 0 0
689 0 0
695 0 0
697 0 0
698 0 0
699 0 0
700 0 37456
801 0 0
802 0 0
811 0 0
812 0 0
813 0 0
814 0 0
815 0 0
816 0 0
817 0 0
818 0 0
819 0 0
821 0 0
822 0 0
823 0 0
824 0 0
825 0 0
826 0 8130
830 0 0
832 0 0
833 0 0
834 0 0
835 0 0
836 0 0
837 0 0
838 0 0
841 0 0
842 0 0
845 0 4341
851 455 0
852 566 0
854 527 0
855 0 0
856 0 593
861 1714 0
862 684 0
863 918 0
864 1560 0
865 3173 0
867 1813 0
868 1074 0
881 0 28
882 0 21
883 0 613
884 0 1930
885 0 5525
886 0 88
887 0 0
890 0 418
892 0 130
893 0 609
901 0 11568
902 0 2119
903 0 45
904 0 699
905 0 1411
906 0 63
907 0 340
911 0 2503
912 0 650
913 0 980
914 0 1000
915 0 588
916 0 433
917 0 370
920 0 208
921 0 584
922 0 346
923 0 152
925 0 1028
930 0 77
931 0 10
932 0 58
933 0 55
934 0 85
935 0 15
940 0 32
941 0 12
942 0 1
943 0 909
944 0 1097
950 0 818
951 0 21
952 0 42
953 0 11
954 0 15
955 0 2
960 0 834
961 0 4
962 0 0
963 0 4530
964 0 545
970 0 4
971 0 4
972 0 0
973 0 559
974 0 1110
976 0 2224
980 0 33
981 0 7
982 0 259
983 0 673
984 0 2
985 0 656
990 0 1120Code
table(acs$HISPAND, acs$race, exclude=NULL)
White Black Indigenous Asian Latino White-Black
0 17415446 2451888 246503 1249407 0 63828
100 0 0 0 0 1886193 0
200 0 0 0 0 292507 0
300 0 0 0 0 145997 0
411 0 0 0 0 9048 0
412 0 0 0 0 68131 0
413 0 0 0 0 40602 0
414 0 0 0 0 25873 0
415 0 0 0 0 12802 0
416 0 0 0 0 110583 0
417 0 0 0 0 2000 0
420 0 0 0 0 18697 0
421 0 0 0 0 7050 0
422 0 0 0 0 10263 0
423 0 0 0 0 70355 0
424 0 0 0 0 37962 0
425 0 0 0 0 1632 0
426 0 0 0 0 39439 0
427 0 0 0 0 4126 0
428 0 0 0 0 21175 0
431 0 0 0 0 1499 0
450 0 0 0 0 54710 0
460 0 0 0 0 91254 0
498 0 0 0 0 124602 0
White-Indigenous White-Asian Black-Indigenous Black-Asian
0 127917 89234 20236 9121
100 0 0 0 0
200 0 0 0 0
300 0 0 0 0
411 0 0 0 0
412 0 0 0 0
413 0 0 0 0
414 0 0 0 0
415 0 0 0 0
416 0 0 0 0
417 0 0 0 0
420 0 0 0 0
421 0 0 0 0
422 0 0 0 0
423 0 0 0 0
424 0 0 0 0
425 0 0 0 0
426 0 0 0 0
427 0 0 0 0
428 0 0 0 0
431 0 0 0 0
450 0 0 0 0
460 0 0 0 0
498 0 0 0 0
Asian-Indigenous <NA>
0 12484 100793
100 0 0
200 0 0
300 0 0
411 0 0
412 0 0
413 0 0
414 0 0
415 0 0
416 0 0
417 0 0
420 0 0
421 0 0
422 0 0
423 0 0
424 0 0
425 0 0
426 0 0
427 0 0
428 0 0
431 0 0
450 0 0
460 0 0
498 0 0Code
table(acs$RACED_SP, acs$race_sp, exclude=NULL)
White Black Indigenous Asian Latino White-Black
100 10714385 0 0 0 1098622 0
200 0 836008 0 0 23774 0
302 0 0 1950 0 834 0
303 0 0 963 0 58 0
304 0 0 12431 0 451 0
305 0 0 356 0 14 0
306 0 0 1147 0 18 0
307 0 0 4805 0 111 0
308 0 0 4365 0 111 0
309 0 0 380 0 80 0
310 0 0 1964 0 31 0
311 0 0 405 0 15 0
312 0 0 1966 0 47 0
314 0 0 3045 0 18 0
315 0 0 16411 0 500 0
317 0 0 132 0 8 0
318 0 0 454 0 75 0
319 0 0 985 0 27 0
320 0 0 2528 0 336 0
321 0 0 555 0 18 0
323 0 0 3623 0 167 0
324 0 0 764 0 16 0
325 0 0 492 0 83 0
328 0 0 411 0 51 0
350 0 0 66 0 0 0
352 0 0 896 0 37 0
353 0 0 55 0 1 0
354 0 0 380 0 461 0
355 0 0 83 0 3 0
356 0 0 80 0 0 0
357 0 0 64 0 0 0
358 0 0 89 0 9 0
359 0 0 69 0 751 0
360 0 0 105 0 3181 0
361 0 0 8838 0 2242 0
362 0 0 10737 0 981 0
370 0 0 860 0 7 0
371 0 0 753 0 5 0
372 0 0 824 0 3 0
374 0 0 1350 0 33 0
375 0 0 2068 0 7 0
379 0 0 406 0 24 0
398 0 0 300 0 114 0
399 0 0 8071 0 6071 0
400 0 0 0 185058 931 0
410 0 0 0 7401 14 0
500 0 0 0 42926 728 0
600 0 0 0 129904 2900 0
610 0 0 0 167624 494 0
620 0 0 0 66535 357 0
630 0 0 6700 0 582 0
640 0 0 0 73551 270 0
641 0 0 0 694 1 0
642 0 0 0 510 7 0
643 0 0 0 4152 13 0
660 0 0 0 8876 62 0
661 0 0 0 6786 24 0
662 0 0 0 7126 89 0
663 0 0 0 9289 84 0
664 0 0 0 6818 3 0
665 0 0 0 3828 12 0
666 0 0 0 3471 34 0
667 0 0 0 811 6 0
669 0 0 0 17092 60 0
670 0 0 0 2384 5 0
671 0 0 0 4664 146 0
672 0 0 0 1421 30 0
673 0 0 0 1093 16 0
674 0 0 0 1653 189 0
675 0 0 0 2803 7 0
676 0 0 0 1789 18 0
677 0 0 0 692 43 0
678 0 0 0 2051 31 0
679 0 0 0 1589 26 0
680 0 0 2982 0 113 0
682 0 0 1177 0 13 0
684 0 0 70 0 4 0
685 0 0 2712 0 371 0
689 0 0 732 0 4 0
695 0 0 1114 0 5 0
697 0 0 216 0 1 0
698 0 0 12 0 0 0
699 0 0 1959 0 157 0
700 0 0 0 0 398008 0
801 0 0 0 0 6576 21423
802 0 0 0 0 8492 0
811 0 0 0 0 570 0
812 0 0 0 0 490 0
813 0 0 0 0 1112 0
814 0 0 0 0 141 0
815 0 0 0 0 112 0
816 0 0 0 0 56 0
817 0 0 0 0 50 0
818 0 0 0 0 236 0
819 0 0 0 0 38 0
821 0 0 0 0 257 0
822 0 0 0 0 27 0
823 0 0 0 0 76 0
824 0 0 0 0 76 0
825 0 0 0 0 0 0
826 0 0 0 0 23059 0
830 0 0 0 0 583 0
832 0 0 0 0 49 0
833 0 0 0 0 20 0
834 0 0 0 0 57 0
835 0 0 0 0 29 0
836 0 0 0 0 10 0
837 0 0 0 0 24 0
838 0 0 0 0 19 0
841 0 0 0 0 28 0
842 0 0 0 0 23 0
845 0 0 0 0 2140 0
851 0 0 0 0 67 0
852 0 0 0 0 39 0
854 0 0 0 0 52 0
855 0 0 30 0 14 0
856 0 0 0 0 2594 0
861 0 0 0 0 53 0
862 0 0 0 0 52 0
863 0 0 0 0 26 0
864 0 0 0 0 101 0
865 0 0 0 0 72 0
867 0 0 0 0 49 0
868 0 0 0 0 9 0
881 0 0 0 0 44 0
882 0 0 0 0 34 0
883 0 0 0 0 514 0
884 0 0 0 0 140 0
885 0 0 0 0 904 0
886 0 0 0 0 28 0
887 0 0 0 278 5 0
890 0 0 0 0 528 0
892 0 0 0 0 103 0
893 0 0 0 0 84 0
901 0 0 0 0 1468 0
902 0 0 0 0 186 0
903 0 0 0 0 3 0
904 0 0 0 0 385 0
905 0 0 0 0 167 0
906 0 0 0 0 12 0
907 0 0 0 0 711 0
911 0 0 0 0 108 0
912 0 0 0 0 55 0
913 0 0 0 0 32 0
914 0 0 0 0 56 0
915 0 0 0 0 15 0
916 0 0 0 0 83 0
917 0 0 0 0 31 0
920 0 0 0 0 128 0
921 0 0 0 0 71 0
922 0 0 0 0 3 0
923 0 0 0 0 84 0
925 0 0 0 0 269 0
930 0 0 0 0 4 0
931 0 0 0 0 4 0
932 0 0 0 0 13 0
933 0 0 0 0 0 0
934 0 0 0 0 6 0
935 0 0 0 0 2 0
940 0 0 0 0 6 0
941 0 0 0 0 9 0
942 0 0 0 0 3 0
943 0 0 0 0 77 0
944 0 0 0 0 21 0
950 0 0 0 0 112 0
951 0 0 0 0 0 0
952 0 0 0 0 20 0
953 0 0 0 0 1 0
954 0 0 0 0 5 0
955 0 0 0 0 0 0
960 0 0 0 0 81 0
961 0 0 0 0 4 0
962 0 0 0 0 1 0
963 0 0 0 0 343 0
964 0 0 0 0 18 0
970 0 0 0 0 0 0
971 0 0 0 0 1 0
973 0 0 0 0 74 0
974 0 0 0 0 196 0
976 0 0 0 0 496 0
980 0 0 0 0 1 0
981 0 0 0 0 2 0
982 0 0 0 0 120 0
983 0 0 0 0 115 0
984 0 0 0 0 1 0
985 0 0 0 0 109 0
990 0 0 0 0 284 0
<NA> 0 0 0 0 0 0
White-Indigenous White-Asian Black-Indigenous Black-Asian
100 0 0 0 0
200 0 0 0 0
302 0 0 0 0
303 0 0 0 0
304 0 0 0 0
305 0 0 0 0
306 0 0 0 0
307 0 0 0 0
308 0 0 0 0
309 0 0 0 0
310 0 0 0 0
311 0 0 0 0
312 0 0 0 0
314 0 0 0 0
315 0 0 0 0
317 0 0 0 0
318 0 0 0 0
319 0 0 0 0
320 0 0 0 0
321 0 0 0 0
323 0 0 0 0
324 0 0 0 0
325 0 0 0 0
328 0 0 0 0
350 0 0 0 0
352 0 0 0 0
353 0 0 0 0
354 0 0 0 0
355 0 0 0 0
356 0 0 0 0
357 0 0 0 0
358 0 0 0 0
359 0 0 0 0
360 0 0 0 0
361 0 0 0 0
362 0 0 0 0
370 0 0 0 0
371 0 0 0 0
372 0 0 0 0
374 0 0 0 0
375 0 0 0 0
379 0 0 0 0
398 0 0 0 0
399 0 0 0 0
400 0 0 0 0
410 0 0 0 0
500 0 0 0 0
600 0 0 0 0
610 0 0 0 0
620 0 0 0 0
630 0 0 0 0
640 0 0 0 0
641 0 0 0 0
642 0 0 0 0
643 0 0 0 0
660 0 0 0 0
661 0 0 0 0
662 0 0 0 0
663 0 0 0 0
664 0 0 0 0
665 0 0 0 0
666 0 0 0 0
667 0 0 0 0
669 0 0 0 0
670 0 0 0 0
671 0 0 0 0
672 0 0 0 0
673 0 0 0 0
674 0 0 0 0
675 0 0 0 0
676 0 0 0 0
677 0 0 0 0
678 0 0 0 0
679 0 0 0 0
680 0 0 0 0
682 0 0 0 0
684 0 0 0 0
685 0 0 0 0
689 0 0 0 0
695 0 0 0 0
697 0 0 0 0
698 0 0 0 0
699 0 0 0 0
700 0 0 0 0
801 0 0 0 0
802 58798 0 0 0
811 0 4592 0 0
812 0 9886 0 0
813 0 9212 0 0
814 0 2146 0 0
815 0 4517 0 0
816 0 1639 0 0
817 0 2244 0 0
818 0 10819 0 0
819 0 194 0 0
821 2783 0 0 0
822 496 0 0 0
823 567 0 0 0
824 1142 0 0 0
825 14 0 0 0
826 0 0 0 0
830 0 0 5135 0
832 0 0 0 477
833 0 0 0 463
834 0 0 0 793
835 0 0 0 613
836 0 0 0 414
837 0 0 0 706
838 0 0 0 244
841 0 0 576 0
842 0 0 174 0
845 0 0 0 0
851 0 0 0 0
852 0 0 0 0
854 0 0 0 0
855 0 0 0 0
856 0 0 0 0
861 0 0 0 0
862 0 0 0 0
863 0 0 0 0
864 0 0 0 0
865 0 0 0 0
867 0 0 0 0
868 0 0 0 0
881 0 0 0 0
882 0 0 0 0
883 0 0 0 0
884 0 0 0 0
885 0 0 0 0
886 0 0 0 0
887 0 0 0 0
890 0 0 0 0
892 0 0 0 0
893 0 0 0 0
901 0 0 0 0
902 0 0 0 0
903 0 0 0 0
904 0 0 0 0
905 0 0 0 0
906 0 0 0 0
907 0 0 0 0
911 0 0 0 0
912 0 0 0 0
913 0 0 0 0
914 0 0 0 0
915 0 0 0 0
916 0 0 0 0
917 0 0 0 0
920 0 0 0 0
921 0 0 0 0
922 0 0 0 0
923 0 0 0 0
925 0 0 0 0
930 0 0 0 0
931 0 0 0 0
932 0 0 0 0
933 0 0 0 0
934 0 0 0 0
935 0 0 0 0
940 0 0 0 0
941 0 0 0 0
942 0 0 0 0
943 0 0 0 0
944 0 0 0 0
950 0 0 0 0
951 0 0 0 0
952 0 0 0 0
953 0 0 0 0
954 0 0 0 0
955 0 0 0 0
960 0 0 0 0
961 0 0 0 0
962 0 0 0 0
963 0 0 0 0
964 0 0 0 0
970 0 0 0 0
971 0 0 0 0
973 0 0 0 0
974 0 0 0 0
976 0 0 0 0
980 0 0 0 0
981 0 0 0 0
982 0 0 0 0
983 0 0 0 0
984 0 0 0 0
985 0 0 0 0
990 0 0 0 0
<NA> 0 0 0 0
Asian-Indigenous <NA>
100 0 0
200 0 0
302 0 0
303 0 0
304 0 0
305 0 0
306 0 0
307 0 0
308 0 0
309 0 0
310 0 0
311 0 0
312 0 0
314 0 0
315 0 0
317 0 0
318 0 0
319 0 0
320 0 0
321 0 0
323 0 0
324 0 0
325 0 0
328 0 0
350 0 0
352 0 0
353 0 0
354 0 0
355 0 0
356 0 0
357 0 0
358 0 0
359 0 0
360 0 0
361 0 0
362 0 0
370 0 0
371 0 0
372 0 0
374 0 0
375 0 0
379 0 0
398 0 0
399 0 0
400 0 0
410 0 0
500 0 0
600 0 0
610 0 0
620 0 0
630 0 0
640 0 0
641 0 0
642 0 0
643 0 0
660 0 0
661 0 0
662 0 0
663 0 0
664 0 0
665 0 0
666 0 0
667 0 0
669 0 0
670 0 0
671 0 0
672 0 0
673 0 0
674 0 0
675 0 0
676 0 0
677 0 0
678 0 0
679 0 0
680 0 0
682 0 0
684 0 0
685 0 0
689 0 0
695 0 0
697 0 0
698 0 0
699 0 0
700 0 17789
801 0 0
802 0 0
811 0 0
812 0 0
813 0 0
814 0 0
815 0 0
816 0 0
817 0 0
818 0 0
819 0 0
821 0 0
822 0 0
823 0 0
824 0 0
825 0 0
826 0 3546
830 0 0
832 0 0
833 0 0
834 0 0
835 0 0
836 0 0
837 0 0
838 0 0
841 0 0
842 0 0
845 0 1494
851 220 0
852 334 0
854 285 0
855 0 0
856 0 244
861 911 0
862 358 0
863 478 0
864 843 0
865 1545 0
867 971 0
868 501 0
881 0 14
882 0 12
883 0 325
884 0 1326
885 0 3413
886 0 64
887 0 0
890 0 213
892 0 58
893 0 282
901 0 3793
902 0 772
903 0 23
904 0 216
905 0 628
906 0 30
907 0 139
911 0 1344
912 0 286
913 0 490
914 0 504
915 0 272
916 0 220
917 0 114
920 0 84
921 0 291
922 0 156
923 0 68
925 0 425
930 0 23
931 0 3
932 0 14
933 0 13
934 0 53
935 0 4
940 0 16
941 0 3
942 0 0
943 0 452
944 0 503
950 0 306
951 0 7
952 0 18
953 0 6
954 0 8
955 0 1
960 0 373
961 0 1
962 0 0
963 0 2222
964 0 269
970 0 2
971 0 3
973 0 221
974 0 532
976 0 1180
980 0 14
981 0 2
982 0 76
983 0 275
984 0 2
985 0 207
990 0 455
<NA> 0 10642642Code
table(acs$HISPAND_SP, acs$race_sp, exclude=NULL)
White Black Indigenous Asian Latino White-Black
0 10714385 836008 113930 762869 0 21423
100 0 0 0 0 1009338 0
200 0 0 0 0 132986 0
300 0 0 0 0 77676 0
411 0 0 0 0 5141 0
412 0 0 0 0 34551 0
413 0 0 0 0 20142 0
414 0 0 0 0 13509 0
415 0 0 0 0 6228 0
416 0 0 0 0 57621 0
417 0 0 0 0 991 0
420 0 0 0 0 11641 0
421 0 0 0 0 3955 0
422 0 0 0 0 6068 0
423 0 0 0 0 39184 0
424 0 0 0 0 20690 0
425 0 0 0 0 855 0
426 0 0 0 0 22059 0
427 0 0 0 0 2584 0
428 0 0 0 0 12696 0
431 0 0 0 0 754 0
450 0 0 0 0 29443 0
460 0 0 0 0 39248 0
498 0 0 0 0 53751 0
<NA> 0 0 0 0 0 0
White-Indigenous White-Asian Black-Indigenous Black-Asian
0 63800 45249 5885 3710
100 0 0 0 0
200 0 0 0 0
300 0 0 0 0
411 0 0 0 0
412 0 0 0 0
413 0 0 0 0
414 0 0 0 0
415 0 0 0 0
416 0 0 0 0
417 0 0 0 0
420 0 0 0 0
421 0 0 0 0
422 0 0 0 0
423 0 0 0 0
424 0 0 0 0
425 0 0 0 0
426 0 0 0 0
427 0 0 0 0
428 0 0 0 0
431 0 0 0 0
450 0 0 0 0
460 0 0 0 0
498 0 0 0 0
<NA> 0 0 0 0
Asian-Indigenous <NA>
0 6446 45899
100 0 0
200 0 0
300 0 0
411 0 0
412 0 0
413 0 0
414 0 0
415 0 0
416 0 0
417 0 0
420 0 0
421 0 0
422 0 0
423 0 0
424 0 0
425 0 0
426 0 0
427 0 0
428 0 0
431 0 0
450 0 0
460 0 0
498 0 0
<NA> 0 10642642Code
# check birthplace
summary(acs$bpl) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 1 1 5430 1 71047 1615 Code
summary(acs$bpl_sp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 1 1 6021 1 71047 10643539 Code
# check language
summary(acs$lang) Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.0 100.0 100.0 477.3 100.0 7000.0 Code
sum(acs$lang==-1)[1] 26634Code
summary(acs$lang_sp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-1 100 100 486 100 7000 10642642 Code
sum(acs$lang_sp==-1, na.rm=TRUE)[1] 14032Code
# check dur_mar
summary(acs$dur_mar) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0 12 24 26 39 83 6451766 Code
# check immigrant generation
table(acs$bpl>1, acs$immig_gen, exclude=NULL)
1st 1.25 1.5 1.75 2nd+ <NA>
FALSE 0 0 0 0 21079605 0
TRUE 2582497 412471 362296 424873 0 0
<NA> 0 0 0 0 0 1615Code
table(acs$bpl_sp>1, acs$immig_gen_sp, exclude=NULL)
1st 1.25 1.5 1.75 2nd+ <NA>
FALSE 0 0 0 0 11856380 0
TRUE 1717835 240075 186011 219517 0 0
<NA> 0 0 0 0 0 10643539Code
# check marriage market
acs |>
mutate(mar_market_type = str_sub(mar_market, 1, 1)) |>
group_by(mar_market_type) |>
summarize(n = n()) |>
mutate(percent = 100 * n/sum(n))# A tibble: 3 × 3
mar_market_type n percent
<chr> <int> <dbl>
1 C 629571 2.53
2 M 18248449 73.4
3 P 5985337 24.1 Code
options(max.print=99999)Everything is coming up green lights at the moment.
Make restrictions
Now that I have coded and checked the ACS dataset, lets just restrict to the variables that I need to keep things tidy. I will also remove cases that are missing on race or race_sp because those are individuals who had a race outside of what I am considering here (other single or in combination and more than two races). I will also drop the relatively few cases that were missing birthplace. Dropping on spouse race_sp and bpl_sp has to first check to make sure its a valid case otherwise (given by a non-missing id).
Code
acs <- acs |>
filter(!is.na(race) & !is.na(bpl)) |>
filter(is.na(id_sp) | (!is.na(race_sp) & !is.na(bpl_sp))) |>
select(mar_market, mar_stat, dur_mar,
id, sex, age, race, educ, lang, bpl, immig_gen, yr_usa,
id_sp, sex_sp, age_sp, race_sp, educ_sp, lang_sp, bpl_sp, immig_gen_sp,
yr_usa_sp)I also want to eliminate individuals from the analysis who:
- have been in the USA less than the marriage window.
- Made an interstate move in the last five years
- Are currently married longer than the marriage window.
- Are married individuals in same-sex relationships
- Are cohabitors - these are identified by a valid spouse id but not married, spouse present on
mar_stat
Therefore, the only remaining people in the dataset are heterosexual married couples married for five years or less and single individuals, all of whom have been in the same “marriage market” for the last five years.
Code
acs <- acs |>
filter((is.na(yr_usa) | yr_usa>years_mar) &
(is.na(yr_usa_sp) | yr_usa_sp>years_mar) &
((mar_stat!="Married, spouse present" &
mar_stat!="Married, spouse absent") | dur_mar<=years_mar) &
!(!is.na(sex_sp) & sex==sex_sp) &
!(mar_stat!="Married, spouse present" & !is.na(id_sp)))
# checks
summary(acs$yr_usa) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
6 14 22 26 36 95 10359274 Code
summary(acs$yr_usa_sp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
6 12 18 20 27 92 11454972 Code
tapply(acs$dur_mar, acs$mar_stat, max)Married, spouse present Married, spouse absent Separated
5 5 79
Divorced Widowed Never married
82 83 NA Code
table(acs$sex, acs$sex_sp, exclude=NULL)
Female Male <NA>
Female 0 829320 5469011
Male 829485 0 4532824Code
acs |>
group_by(mar_stat, !is.na(id_sp)) |>
summarize(n=n())`summarise()` has grouped output by 'mar_stat'. You can override using the
`.groups` argument.# A tibble: 6 × 3
# Groups: mar_stat [6]
mar_stat `!is.na(id_sp)` n
<fct> <lgl> <int>
1 Married, spouse present TRUE 1658805
2 Married, spouse absent FALSE 148067
3 Separated FALSE 411033
4 Divorced FALSE 2326503
5 Widowed FALSE 1666182
6 Never married FALSE 5450050Create marriage market data
Now, I am ready to create the union and male/female alternate datasets that I will feed into fakeunion to get my marriage market data.
Code
# create actual union data
unions <- acs |>
filter(sex=="Male" & sex_sp=="Female" & !is.na(id_sp) &
mar_stat=="Married, spouse present")
# check that they are all hetero
table(unions$sex, unions$sex_sp, exclude=NULL)
Female
Male 829485Code
# check the marital status
table(unions$mar_stat)
Married, spouse present Married, spouse absent Separated
829485 0 0
Divorced Widowed Never married
0 0 0 Code
# check marriage duration
summary(unions$dur_mar) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 3.00 2.71 4.00 5.00 Code
# check years in the USA is past marriage duration
summary(unions$yr_usa) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
6 12 18 21 27 92 726622 Code
# ok now limit it to only necessary variables and rename
unions <- unions |>
select(mar_market, id, age, race, educ, lang, bpl, immig_gen,
id_sp, age_sp, race_sp, educ_sp, lang_sp, bpl_sp, immig_gen_sp) |>
rename(idh=id, ageh=age, raceh=race, educh=educ, langh=lang, bplh=bpl,
immig_genh=immig_gen,
idw=id_sp, agew=age_sp, racew=race_sp, educw=educ_sp, langw=lang_sp,
bplw=bpl_sp, immig_genw=immig_gen_sp)
# create alternate partner data
female_alt <- acs |>
filter(sex=="Female") |>
select(mar_market, id, age, race, educ, lang, bpl, immig_gen) |>
rename(idw=id, agew=age, racew=race, educw=educ, langw=lang, bplw=bpl,
immig_genw=immig_gen)
male_alt <- acs |>
filter(sex=="Male") |>
select(mar_market, id, age, race, educ, lang, bpl, immig_gen) |>
rename(idh=id, ageh=age, raceh=race, educh=educ, langh=lang, bplh=bpl,
immig_genh=immig_gen)Now, that I have these datasets, I can feed them into the generateCouples function to create my marriage market data. I use the replicate function to create multiple marriage markets so I can pool results across multiple random imputations of fake partners.
Note that the generateCouples data cannot handle tibbles properly so I am casting these back to vanilla data.frames and then re-tibblizing the end product.
Code
# fakeunion does not handle tibbles well so convert to vanilla dfs
mar_markets <- replicate(n_samples,
generateCouples(n_fakes, as.data.frame(unions),
as.data.frame(male_alt),
as.data.frame(female_alt),
geo="mar_market",
verbose=FALSE),
simplify=FALSE)Loading required package: wrswoRCode
mar_markets <- lapply(mar_markets, as_tibble)Lets do a summary on the first one just to make sure it all looks good.
Code
summary(mar_markets[[1]]) mar_market sampled choice group
Length:6635880 Length:6635880 Mode :logical 2012010 : 8
Class :character Class :character FALSE:5806395 4012010 : 8
Mode :character Mode :character TRUE :829485 5012014 : 8
8012016 : 8
9012010 : 8
14022014: 8
(Other) :6635832
idh ageh raceh educh
Min. :2.012e+06 Min. :18.00 White :4695007 LHS: 627805
1st Qu.:3.493e+11 1st Qu.:28.00 Latino : 888651 HS :2510322
Median :6.980e+11 Median :35.00 Black : 654990 SC :1615682
Mean :6.986e+11 Mean :39.27 Asian : 223321 C :1882071
3rd Qu.:1.061e+12 3rd Qu.:49.00 Indigenous : 68666
Max. :1.428e+12 Max. :97.00 White-Indigenous: 39504
(Other) : 65741
langh bplh immig_genh idw
Min. : -1.0 Min. : 1 1st : 371083 Min. :1.012e+06
1st Qu.: 100.0 1st Qu.: 1 1.25: 124613 1st Qu.:3.488e+11
Median : 100.0 Median : 1 1.5 : 118683 Median :6.975e+11
Mean : 381.8 Mean : 3756 1.75: 140756 Mean :6.985e+11
3rd Qu.: 100.0 3rd Qu.: 1 2nd+:5880745 3rd Qu.:1.061e+12
Max. :7000.0 Max. :71047 Max. :1.428e+12
agew racew educw langw
Min. :15.00 White :4693313 LHS: 534456 Min. : -1.0
1st Qu.:27.00 Latino : 869926 HS :2131725 1st Qu.: 100.0
Median :35.00 Black : 635904 SC :1737409 Median : 100.0
Mean :41.21 Asian : 263183 C :2232290 Mean : 402.7
3rd Qu.:52.00 Indigenous : 67570 3rd Qu.: 100.0
Max. :97.00 White-Indigenous: 39616 Max. :7000.0
(Other) : 66368
bplw immig_genw
Min. : 1 1st : 416026
1st Qu.: 1 1.25: 107242
Median : 1 1.5 : 116966
Mean : 4017 1.75: 141057
3rd Qu.: 1 2nd+:5854589
Max. :71047
It should be all good to go.
I have also found that fitting the fullest model of multiracial-to-multiracial intermarriage is proving difficult and takes a long time. So, we are going to estimate this model using a reduced marriage market of just multiracial individuals. That model will only be different than a full model because the control variables will be estimated differently, but should be otherwise quite close.
Code
unions_multi <- unions |>
filter(str_detect(raceh, "-") & str_detect(racew, "-")) |>
mutate(raceh=fct_drop(raceh), racew=fct_drop(racew))
male_alt_multi <- male_alt |>
filter(str_detect(raceh, "-")) |>
mutate(raceh=fct_drop(raceh))
female_alt_multi <- female_alt |>
filter(str_detect(racew, "-")) |>
mutate(racew=fct_drop(racew))
mar_markets_multi <- replicate(n_samples,
generateCouples(n_fakes,
as.data.frame(unions_multi),
as.data.frame(male_alt_multi),
as.data.frame(female_alt_multi),
geo="mar_market",
verbose=FALSE),
simplify=FALSE)Warning in generateCouples(n_fakes, as.data.frame(unions_multi),
as.data.frame(male_alt_multi), : The following areas had less than the required
sample for men and women and the entire list was drawn: C.12.91.2018,
C.13.51.2013, C.13.51.2014, C.16.19.2018, C.19.13.2011, C.19.153.2011,
C.19.163.2012, C.19.163.2015, C.23.11.2016, C.24.43.2012, C.37.49.2015,
C.37.49.2017, C.37.85.2013, C.37.85.2017, C.38.17.2017, C.41.19.2010,
C.45.51.2010, C.47.179.2012, C.48.441.2010, C.48.441.2015, C.48.441.2017,
C.48.441.2018, C.55.25.2017, C.55.9.2015, C.6.23.2012, M.11500.2015,
M.12020.2010, M.12100.2019, M.12220.2015, M.12620.2014, M.12620.2016,
M.12700.2017, M.12980.2010, M.13980.2013, M.14020.2010, M.15540.2016,
M.15980.2012, M.16580.2011, M.16620.2015, M.16620.2017, M.16860.2013,
M.17660.2015, M.17660.2018, M.17780.2010, M.17780.2019, M.18580.2018,
M.19460.2011, M.19460.2012, M.19500.2017, M.20100.2013, M.20940.2012,
M.21140.2014, M.22220.2010, M.22500.2017, M.22500.2018, M.22660.2017,
M.23580.2013, M.24140.2017, M.24780.2012, M.25220.2010, M.25260.2016,
M.25860.2010, M.25940.2012, M.27180.2017, M.27900.2019, M.28420.2010,
M.28700.2011, M.29180.2010, M.29180.2011, M.29700.2015, M.31700.2013,
M.31900.2015, M.33780.2014, M.33860.2019, M.34740.2011, M.35660.2011,
M.37620.2016, M.37620.2018, M.39380.2011, M.39540.2017, M.40980.2015,
M.40980.2016, M.46220.2018, M.49700.2017, M.49740.2015, P.1.2500.2010,
P.1.500.2012, P.1.500.2013, P.1.500.2014, P.12.500.2017, P.12.6300.2012,
P.13.100.2012, P.13.100.2016, P.13.1200.2017, P.13.1500.2014, P.13.200.2011,
P.13.2200.2012, P.13.2500.2015, P.13.2800.2011, P.13.2800.2014, P.13.3200.2011,
P.13.3200.2013, P.13.4100.2011, P.13.4200.2012, P.13.700.2012, P.13.800.2013,
P.16.1300.2014, P.16.200.2011, P.17.200.2011, P.17.300.2016, P.17.600.2015,
P.17.700.2011, P.17.700.2012, P.17.900.2011, P.17.900.2014, P.18.1300.2019,
P.18.2600.2011, P.18.3100.2013, P.18.3200.2013, P.18.3400.2019, P.18.600.2014,
P.18.700.2013, P.18.800.2016, P.19.1300.2018, P.19.500.2010, P.19.700.2017,
P.19.800.2017, P.20.100.2014, P.20.100.2019, P.20.1200.2013, P.20.1200.2015,
P.20.1200.2018, P.20.900.2010, P.20.900.2016, P.21.100.2011, P.21.100.2015,
P.21.1400.2014, P.21.2000.2015, P.21.400.2019, P.21.600.2011, P.22.1200.2010,
P.22.300.2018, P.22.600.2016, P.24.100.2016, P.24.1300.2015, P.24.1500.2014,
P.26.100.2019, P.26.1700.2010, P.26.1702.2012, P.26.1703.2014, P.26.2200.2016,
P.26.2200.2018, P.26.600.2019, P.26.700.2011, P.27.100.2016, P.27.200.2014,
P.27.400.2018, P.27.600.2011, P.27.800.2018, P.28.500.2013, P.28.700.2015,
P.29.100.2018, P.29.1200.2010, P.29.1400.2018, P.29.2200.2011, P.29.2200.2019,
P.29.2300.2015, P.29.2400.2017, P.29.300.2012, P.29.700.2010, P.29.700.2015,
P.30.200.2019, P.30.300.2017, P.30.400.2019, P.30.500.2014, P.30.700.2019,
P.31.100.2014, P.31.500.2012, P.32.400.2010, P.33.500.2014, P.35.100.2017,
P.35.100.2019, P.35.1100.2012, P.35.1200.2012, P.36.200.2013, P.36.2402.2019,
P.36.2500.2018, P.36.2702.2012, P.36.2702.2018, P.37.1000.2015, P.37.2400.2013,
P.37.2400.2017, P.37.3300.2013, P.37.3500.2011, P.37.3900.2011, P.37.3900.2016,
P.37.4400.2016, P.37.4500.2017, P.37.5100.2012, P.37.5100.2017, P.37.5200.2015,
P.37.700.2017, P.38.200.2016, P.38.300.2011, P.39.100.2013, P.39.2700.2017,
P.39.3500.2015, P.39.3700.2012, P.39.4500.2016, P.39.4700.2010, P.39.4900.2015,
P.39.5100.2017, P.40.100.2010, P.40.601.2014, P.41.300.2013, P.41.800.2011,
P.42.1100.2018, P.42.1900.2013, P.42.300.2016, P.42.3702.2014, P.42.600.2010,
P.45.1200.2011, P.45.1300.2015, P.45.400.2018, P.45.800.2012, P.45.800.2019,
P.46.200.2011, P.46.300.2010, P.46.300.2018, P.46.400.2011, P.46.400.2013,
P.46.600.2015, P.47.1100.2011, P.47.1100.2013, P.47.1700.2010, P.47.1900.2013,
P.47.1900.2015, P.47.200.2010, P.47.200.2011, P.47.200.2016, P.47.2200.2014,
P.47.2800.2011, P.47.2800.2013, P.47.2800.2018, P.48.100.2018, P.48.1000.2019,
P.48.1100.2010, P.48.1200.2016, P.48.1700.2017, P.48.1700.2018, P.48.2600.2012,
P.48.3601.2012, P.48.3601.2015, P.48.3900.2018, P.48.4000.2012, P.48.4000.2015,
P.48.5000.2014, P.48.600.2015, P.48.6200.2010, P.48.6200.2013, P.49.21001.2014,
P.5.1300.2010, P.5.1300.2013, P.5.1400.2011, P.5.1400.2018, P.5.1500.2017,
P.5.1500.2019, P.5.1700.2019, P.5.1800.2011, P.5.2000.2012, P.5.300.2012,
P.5.300.2017, P.5.400.2017, P.5.600.2015, P.5.700.2011, P.5.800.2012,
P.50.200.2012, P.50.300.2011, P.51.1600.2011, P.51.400.2011, P.51.51084.2012,
P.51.51084.2013, P.51.51085.2013, P.51.51097.2012, P.51.51105.2013,
P.51.51125.2015, P.53.10702.2013, P.53.10702.2015, P.53.10702.2016,
P.53.11200.2012, P.53.11200.2018, P.53.11900.2015, P.53.300.2010,
P.54.400.2017, P.54.600.2013, P.54.900.2010, P.55.1300.2014, P.55.1400.2015,
P.55.55101.2018, P.56.300.2012, P.56.400.2012, P.56.500.2018, P.6.3300.2013,
P.6.3300.2016, P.8.600.2014, P.8.802.2011, P.8.802.2017
Warning in generateCouples(n_fakes, as.data.frame(unions_multi),
as.data.frame(male_alt_multi), : The following areas had less than the required
sample for men and women and the entire list was drawn: C.12.91.2018,
C.13.51.2013, C.13.51.2014, C.16.19.2018, C.19.13.2011, C.19.153.2011,
C.19.163.2012, C.19.163.2015, C.23.11.2016, C.24.43.2012, C.37.49.2015,
C.37.49.2017, C.37.85.2013, C.37.85.2017, C.38.17.2017, C.41.19.2010,
C.45.51.2010, C.47.179.2012, C.48.441.2010, C.48.441.2015, C.48.441.2017,
C.48.441.2018, C.55.25.2017, C.55.9.2015, C.6.23.2012, M.11500.2015,
M.12020.2010, M.12100.2019, M.12220.2015, M.12620.2014, M.12620.2016,
M.12700.2017, M.12980.2010, M.13980.2013, M.14020.2010, M.15540.2016,
M.15980.2012, M.16580.2011, M.16620.2015, M.16620.2017, M.16860.2013,
M.17660.2015, M.17660.2018, M.17780.2010, M.17780.2019, M.18580.2018,
M.19460.2011, M.19460.2012, M.19500.2017, M.20100.2013, M.20940.2012,
M.21140.2014, M.22220.2010, M.22500.2017, M.22500.2018, M.22660.2017,
M.23580.2013, M.24140.2017, M.24780.2012, M.25220.2010, M.25260.2016,
M.25860.2010, M.25940.2012, M.27180.2017, M.27900.2019, M.28420.2010,
M.28700.2011, M.29180.2010, M.29180.2011, M.29700.2015, M.31700.2013,
M.31900.2015, M.33780.2014, M.33860.2019, M.34740.2011, M.35660.2011,
M.37620.2016, M.37620.2018, M.39380.2011, M.39540.2017, M.40980.2015,
M.40980.2016, M.46220.2018, M.49700.2017, M.49740.2015, P.1.2500.2010,
P.1.500.2012, P.1.500.2013, P.1.500.2014, P.12.500.2017, P.12.6300.2012,
P.13.100.2012, P.13.100.2016, P.13.1200.2017, P.13.1500.2014, P.13.200.2011,
P.13.2200.2012, P.13.2500.2015, P.13.2800.2011, P.13.2800.2014, P.13.3200.2011,
P.13.3200.2013, P.13.4100.2011, P.13.4200.2012, P.13.700.2012, P.13.800.2013,
P.16.1300.2014, P.16.200.2011, P.17.200.2011, P.17.300.2016, P.17.600.2015,
P.17.700.2011, P.17.700.2012, P.17.900.2011, P.17.900.2014, P.18.1300.2019,
P.18.2600.2011, P.18.3100.2013, P.18.3200.2013, P.18.3400.2019, P.18.600.2014,
P.18.700.2013, P.18.800.2016, P.19.1300.2018, P.19.500.2010, P.19.700.2017,
P.19.800.2017, P.20.100.2014, P.20.100.2019, P.20.1200.2013, P.20.1200.2015,
P.20.1200.2018, P.20.900.2010, P.20.900.2016, P.21.100.2011, P.21.100.2015,
P.21.1400.2014, P.21.2000.2015, P.21.400.2019, P.21.600.2011, P.22.1200.2010,
P.22.300.2018, P.22.600.2016, P.24.100.2016, P.24.1300.2015, P.24.1500.2014,
P.26.100.2019, P.26.1700.2010, P.26.1702.2012, P.26.1703.2014, P.26.2200.2016,
P.26.2200.2018, P.26.600.2019, P.26.700.2011, P.27.100.2016, P.27.200.2014,
P.27.400.2018, P.27.600.2011, P.27.800.2018, P.28.500.2013, P.28.700.2015,
P.29.100.2018, P.29.1200.2010, P.29.1400.2018, P.29.2200.2011, P.29.2200.2019,
P.29.2300.2015, P.29.2400.2017, P.29.300.2012, P.29.700.2010, P.29.700.2015,
P.30.200.2019, P.30.300.2017, P.30.400.2019, P.30.500.2014, P.30.700.2019,
P.31.100.2014, P.31.500.2012, P.32.400.2010, P.33.500.2014, P.35.100.2017,
P.35.100.2019, P.35.1100.2012, P.35.1200.2012, P.36.200.2013, P.36.2402.2019,
P.36.2500.2018, P.36.2702.2012, P.36.2702.2018, P.37.1000.2015, P.37.2400.2013,
P.37.2400.2017, P.37.3300.2013, P.37.3500.2011, P.37.3900.2011, P.37.3900.2016,
P.37.4400.2016, P.37.4500.2017, P.37.5100.2012, P.37.5100.2017, P.37.5200.2015,
P.37.700.2017, P.38.200.2016, P.38.300.2011, P.39.100.2013, P.39.2700.2017,
P.39.3500.2015, P.39.3700.2012, P.39.4500.2016, P.39.4700.2010, P.39.4900.2015,
P.39.5100.2017, P.40.100.2010, P.40.601.2014, P.41.300.2013, P.41.800.2011,
P.42.1100.2018, P.42.1900.2013, P.42.300.2016, P.42.3702.2014, P.42.600.2010,
P.45.1200.2011, P.45.1300.2015, P.45.400.2018, P.45.800.2012, P.45.800.2019,
P.46.200.2011, P.46.300.2010, P.46.300.2018, P.46.400.2011, P.46.400.2013,
P.46.600.2015, P.47.1100.2011, P.47.1100.2013, P.47.1700.2010, P.47.1900.2013,
P.47.1900.2015, P.47.200.2010, P.47.200.2011, P.47.200.2016, P.47.2200.2014,
P.47.2800.2011, P.47.2800.2013, P.47.2800.2018, P.48.100.2018, P.48.1000.2019,
P.48.1100.2010, P.48.1200.2016, P.48.1700.2017, P.48.1700.2018, P.48.2600.2012,
P.48.3601.2012, P.48.3601.2015, P.48.3900.2018, P.48.4000.2012, P.48.4000.2015,
P.48.5000.2014, P.48.600.2015, P.48.6200.2010, P.48.6200.2013, P.49.21001.2014,
P.5.1300.2010, P.5.1300.2013, P.5.1400.2011, P.5.1400.2018, P.5.1500.2017,
P.5.1500.2019, P.5.1700.2019, P.5.1800.2011, P.5.2000.2012, P.5.300.2012,
P.5.300.2017, P.5.400.2017, P.5.600.2015, P.5.700.2011, P.5.800.2012,
P.50.200.2012, P.50.300.2011, P.51.1600.2011, P.51.400.2011, P.51.51084.2012,
P.51.51084.2013, P.51.51085.2013, P.51.51097.2012, P.51.51105.2013,
P.51.51125.2015, P.53.10702.2013, P.53.10702.2015, P.53.10702.2016,
P.53.11200.2012, P.53.11200.2018, P.53.11900.2015, P.53.300.2010,
P.54.400.2017, P.54.600.2013, P.54.900.2010, P.55.1300.2014, P.55.1400.2015,
P.55.55101.2018, P.56.300.2012, P.56.400.2012, P.56.500.2018, P.6.3300.2013,
P.6.3300.2016, P.8.600.2014, P.8.802.2011, P.8.802.2017
Warning in generateCouples(n_fakes, as.data.frame(unions_multi),
as.data.frame(male_alt_multi), : The following areas had less than the required
sample for men and women and the entire list was drawn: C.12.91.2018,
C.13.51.2013, C.13.51.2014, C.16.19.2018, C.19.13.2011, C.19.153.2011,
C.19.163.2012, C.19.163.2015, C.23.11.2016, C.24.43.2012, C.37.49.2015,
C.37.49.2017, C.37.85.2013, C.37.85.2017, C.38.17.2017, C.41.19.2010,
C.45.51.2010, C.47.179.2012, C.48.441.2010, C.48.441.2015, C.48.441.2017,
C.48.441.2018, C.55.25.2017, C.55.9.2015, C.6.23.2012, M.11500.2015,
M.12020.2010, M.12100.2019, M.12220.2015, M.12620.2014, M.12620.2016,
M.12700.2017, M.12980.2010, M.13980.2013, M.14020.2010, M.15540.2016,
M.15980.2012, M.16580.2011, M.16620.2015, M.16620.2017, M.16860.2013,
M.17660.2015, M.17660.2018, M.17780.2010, M.17780.2019, M.18580.2018,
M.19460.2011, M.19460.2012, M.19500.2017, M.20100.2013, M.20940.2012,
M.21140.2014, M.22220.2010, M.22500.2017, M.22500.2018, M.22660.2017,
M.23580.2013, M.24140.2017, M.24780.2012, M.25220.2010, M.25260.2016,
M.25860.2010, M.25940.2012, M.27180.2017, M.27900.2019, M.28420.2010,
M.28700.2011, M.29180.2010, M.29180.2011, M.29700.2015, M.31700.2013,
M.31900.2015, M.33780.2014, M.33860.2019, M.34740.2011, M.35660.2011,
M.37620.2016, M.37620.2018, M.39380.2011, M.39540.2017, M.40980.2015,
M.40980.2016, M.46220.2018, M.49700.2017, M.49740.2015, P.1.2500.2010,
P.1.500.2012, P.1.500.2013, P.1.500.2014, P.12.500.2017, P.12.6300.2012,
P.13.100.2012, P.13.100.2016, P.13.1200.2017, P.13.1500.2014, P.13.200.2011,
P.13.2200.2012, P.13.2500.2015, P.13.2800.2011, P.13.2800.2014, P.13.3200.2011,
P.13.3200.2013, P.13.4100.2011, P.13.4200.2012, P.13.700.2012, P.13.800.2013,
P.16.1300.2014, P.16.200.2011, P.17.200.2011, P.17.300.2016, P.17.600.2015,
P.17.700.2011, P.17.700.2012, P.17.900.2011, P.17.900.2014, P.18.1300.2019,
P.18.2600.2011, P.18.3100.2013, P.18.3200.2013, P.18.3400.2019, P.18.600.2014,
P.18.700.2013, P.18.800.2016, P.19.1300.2018, P.19.500.2010, P.19.700.2017,
P.19.800.2017, P.20.100.2014, P.20.100.2019, P.20.1200.2013, P.20.1200.2015,
P.20.1200.2018, P.20.900.2010, P.20.900.2016, P.21.100.2011, P.21.100.2015,
P.21.1400.2014, P.21.2000.2015, P.21.400.2019, P.21.600.2011, P.22.1200.2010,
P.22.300.2018, P.22.600.2016, P.24.100.2016, P.24.1300.2015, P.24.1500.2014,
P.26.100.2019, P.26.1700.2010, P.26.1702.2012, P.26.1703.2014, P.26.2200.2016,
P.26.2200.2018, P.26.600.2019, P.26.700.2011, P.27.100.2016, P.27.200.2014,
P.27.400.2018, P.27.600.2011, P.27.800.2018, P.28.500.2013, P.28.700.2015,
P.29.100.2018, P.29.1200.2010, P.29.1400.2018, P.29.2200.2011, P.29.2200.2019,
P.29.2300.2015, P.29.2400.2017, P.29.300.2012, P.29.700.2010, P.29.700.2015,
P.30.200.2019, P.30.300.2017, P.30.400.2019, P.30.500.2014, P.30.700.2019,
P.31.100.2014, P.31.500.2012, P.32.400.2010, P.33.500.2014, P.35.100.2017,
P.35.100.2019, P.35.1100.2012, P.35.1200.2012, P.36.200.2013, P.36.2402.2019,
P.36.2500.2018, P.36.2702.2012, P.36.2702.2018, P.37.1000.2015, P.37.2400.2013,
P.37.2400.2017, P.37.3300.2013, P.37.3500.2011, P.37.3900.2011, P.37.3900.2016,
P.37.4400.2016, P.37.4500.2017, P.37.5100.2012, P.37.5100.2017, P.37.5200.2015,
P.37.700.2017, P.38.200.2016, P.38.300.2011, P.39.100.2013, P.39.2700.2017,
P.39.3500.2015, P.39.3700.2012, P.39.4500.2016, P.39.4700.2010, P.39.4900.2015,
P.39.5100.2017, P.40.100.2010, P.40.601.2014, P.41.300.2013, P.41.800.2011,
P.42.1100.2018, P.42.1900.2013, P.42.300.2016, P.42.3702.2014, P.42.600.2010,
P.45.1200.2011, P.45.1300.2015, P.45.400.2018, P.45.800.2012, P.45.800.2019,
P.46.200.2011, P.46.300.2010, P.46.300.2018, P.46.400.2011, P.46.400.2013,
P.46.600.2015, P.47.1100.2011, P.47.1100.2013, P.47.1700.2010, P.47.1900.2013,
P.47.1900.2015, P.47.200.2010, P.47.200.2011, P.47.200.2016, P.47.2200.2014,
P.47.2800.2011, P.47.2800.2013, P.47.2800.2018, P.48.100.2018, P.48.1000.2019,
P.48.1100.2010, P.48.1200.2016, P.48.1700.2017, P.48.1700.2018, P.48.2600.2012,
P.48.3601.2012, P.48.3601.2015, P.48.3900.2018, P.48.4000.2012, P.48.4000.2015,
P.48.5000.2014, P.48.600.2015, P.48.6200.2010, P.48.6200.2013, P.49.21001.2014,
P.5.1300.2010, P.5.1300.2013, P.5.1400.2011, P.5.1400.2018, P.5.1500.2017,
P.5.1500.2019, P.5.1700.2019, P.5.1800.2011, P.5.2000.2012, P.5.300.2012,
P.5.300.2017, P.5.400.2017, P.5.600.2015, P.5.700.2011, P.5.800.2012,
P.50.200.2012, P.50.300.2011, P.51.1600.2011, P.51.400.2011, P.51.51084.2012,
P.51.51084.2013, P.51.51085.2013, P.51.51097.2012, P.51.51105.2013,
P.51.51125.2015, P.53.10702.2013, P.53.10702.2015, P.53.10702.2016,
P.53.11200.2012, P.53.11200.2018, P.53.11900.2015, P.53.300.2010,
P.54.400.2017, P.54.600.2013, P.54.900.2010, P.55.1300.2014, P.55.1400.2015,
P.55.55101.2018, P.56.300.2012, P.56.400.2012, P.56.500.2018, P.6.3300.2013,
P.6.3300.2016, P.8.600.2014, P.8.802.2011, P.8.802.2017
Warning in generateCouples(n_fakes, as.data.frame(unions_multi),
as.data.frame(male_alt_multi), : The following areas had less than the required
sample for men and women and the entire list was drawn: C.12.91.2018,
C.13.51.2013, C.13.51.2014, C.16.19.2018, C.19.13.2011, C.19.153.2011,
C.19.163.2012, C.19.163.2015, C.23.11.2016, C.24.43.2012, C.37.49.2015,
C.37.49.2017, C.37.85.2013, C.37.85.2017, C.38.17.2017, C.41.19.2010,
C.45.51.2010, C.47.179.2012, C.48.441.2010, C.48.441.2015, C.48.441.2017,
C.48.441.2018, C.55.25.2017, C.55.9.2015, C.6.23.2012, M.11500.2015,
M.12020.2010, M.12100.2019, M.12220.2015, M.12620.2014, M.12620.2016,
M.12700.2017, M.12980.2010, M.13980.2013, M.14020.2010, M.15540.2016,
M.15980.2012, M.16580.2011, M.16620.2015, M.16620.2017, M.16860.2013,
M.17660.2015, M.17660.2018, M.17780.2010, M.17780.2019, M.18580.2018,
M.19460.2011, M.19460.2012, M.19500.2017, M.20100.2013, M.20940.2012,
M.21140.2014, M.22220.2010, M.22500.2017, M.22500.2018, M.22660.2017,
M.23580.2013, M.24140.2017, M.24780.2012, M.25220.2010, M.25260.2016,
M.25860.2010, M.25940.2012, M.27180.2017, M.27900.2019, M.28420.2010,
M.28700.2011, M.29180.2010, M.29180.2011, M.29700.2015, M.31700.2013,
M.31900.2015, M.33780.2014, M.33860.2019, M.34740.2011, M.35660.2011,
M.37620.2016, M.37620.2018, M.39380.2011, M.39540.2017, M.40980.2015,
M.40980.2016, M.46220.2018, M.49700.2017, M.49740.2015, P.1.2500.2010,
P.1.500.2012, P.1.500.2013, P.1.500.2014, P.12.500.2017, P.12.6300.2012,
P.13.100.2012, P.13.100.2016, P.13.1200.2017, P.13.1500.2014, P.13.200.2011,
P.13.2200.2012, P.13.2500.2015, P.13.2800.2011, P.13.2800.2014, P.13.3200.2011,
P.13.3200.2013, P.13.4100.2011, P.13.4200.2012, P.13.700.2012, P.13.800.2013,
P.16.1300.2014, P.16.200.2011, P.17.200.2011, P.17.300.2016, P.17.600.2015,
P.17.700.2011, P.17.700.2012, P.17.900.2011, P.17.900.2014, P.18.1300.2019,
P.18.2600.2011, P.18.3100.2013, P.18.3200.2013, P.18.3400.2019, P.18.600.2014,
P.18.700.2013, P.18.800.2016, P.19.1300.2018, P.19.500.2010, P.19.700.2017,
P.19.800.2017, P.20.100.2014, P.20.100.2019, P.20.1200.2013, P.20.1200.2015,
P.20.1200.2018, P.20.900.2010, P.20.900.2016, P.21.100.2011, P.21.100.2015,
P.21.1400.2014, P.21.2000.2015, P.21.400.2019, P.21.600.2011, P.22.1200.2010,
P.22.300.2018, P.22.600.2016, P.24.100.2016, P.24.1300.2015, P.24.1500.2014,
P.26.100.2019, P.26.1700.2010, P.26.1702.2012, P.26.1703.2014, P.26.2200.2016,
P.26.2200.2018, P.26.600.2019, P.26.700.2011, P.27.100.2016, P.27.200.2014,
P.27.400.2018, P.27.600.2011, P.27.800.2018, P.28.500.2013, P.28.700.2015,
P.29.100.2018, P.29.1200.2010, P.29.1400.2018, P.29.2200.2011, P.29.2200.2019,
P.29.2300.2015, P.29.2400.2017, P.29.300.2012, P.29.700.2010, P.29.700.2015,
P.30.200.2019, P.30.300.2017, P.30.400.2019, P.30.500.2014, P.30.700.2019,
P.31.100.2014, P.31.500.2012, P.32.400.2010, P.33.500.2014, P.35.100.2017,
P.35.100.2019, P.35.1100.2012, P.35.1200.2012, P.36.200.2013, P.36.2402.2019,
P.36.2500.2018, P.36.2702.2012, P.36.2702.2018, P.37.1000.2015, P.37.2400.2013,
P.37.2400.2017, P.37.3300.2013, P.37.3500.2011, P.37.3900.2011, P.37.3900.2016,
P.37.4400.2016, P.37.4500.2017, P.37.5100.2012, P.37.5100.2017, P.37.5200.2015,
P.37.700.2017, P.38.200.2016, P.38.300.2011, P.39.100.2013, P.39.2700.2017,
P.39.3500.2015, P.39.3700.2012, P.39.4500.2016, P.39.4700.2010, P.39.4900.2015,
P.39.5100.2017, P.40.100.2010, P.40.601.2014, P.41.300.2013, P.41.800.2011,
P.42.1100.2018, P.42.1900.2013, P.42.300.2016, P.42.3702.2014, P.42.600.2010,
P.45.1200.2011, P.45.1300.2015, P.45.400.2018, P.45.800.2012, P.45.800.2019,
P.46.200.2011, P.46.300.2010, P.46.300.2018, P.46.400.2011, P.46.400.2013,
P.46.600.2015, P.47.1100.2011, P.47.1100.2013, P.47.1700.2010, P.47.1900.2013,
P.47.1900.2015, P.47.200.2010, P.47.200.2011, P.47.200.2016, P.47.2200.2014,
P.47.2800.2011, P.47.2800.2013, P.47.2800.2018, P.48.100.2018, P.48.1000.2019,
P.48.1100.2010, P.48.1200.2016, P.48.1700.2017, P.48.1700.2018, P.48.2600.2012,
P.48.3601.2012, P.48.3601.2015, P.48.3900.2018, P.48.4000.2012, P.48.4000.2015,
P.48.5000.2014, P.48.600.2015, P.48.6200.2010, P.48.6200.2013, P.49.21001.2014,
P.5.1300.2010, P.5.1300.2013, P.5.1400.2011, P.5.1400.2018, P.5.1500.2017,
P.5.1500.2019, P.5.1700.2019, P.5.1800.2011, P.5.2000.2012, P.5.300.2012,
P.5.300.2017, P.5.400.2017, P.5.600.2015, P.5.700.2011, P.5.800.2012,
P.50.200.2012, P.50.300.2011, P.51.1600.2011, P.51.400.2011, P.51.51084.2012,
P.51.51084.2013, P.51.51085.2013, P.51.51097.2012, P.51.51105.2013,
P.51.51125.2015, P.53.10702.2013, P.53.10702.2015, P.53.10702.2016,
P.53.11200.2012, P.53.11200.2018, P.53.11900.2015, P.53.300.2010,
P.54.400.2017, P.54.600.2013, P.54.900.2010, P.55.1300.2014, P.55.1400.2015,
P.55.55101.2018, P.56.300.2012, P.56.400.2012, P.56.500.2018, P.6.3300.2013,
P.6.3300.2016, P.8.600.2014, P.8.802.2011, P.8.802.2017
Warning in generateCouples(n_fakes, as.data.frame(unions_multi),
as.data.frame(male_alt_multi), : The following areas had less than the required
sample for men and women and the entire list was drawn: C.12.91.2018,
C.13.51.2013, C.13.51.2014, C.16.19.2018, C.19.13.2011, C.19.153.2011,
C.19.163.2012, C.19.163.2015, C.23.11.2016, C.24.43.2012, C.37.49.2015,
C.37.49.2017, C.37.85.2013, C.37.85.2017, C.38.17.2017, C.41.19.2010,
C.45.51.2010, C.47.179.2012, C.48.441.2010, C.48.441.2015, C.48.441.2017,
C.48.441.2018, C.55.25.2017, C.55.9.2015, C.6.23.2012, M.11500.2015,
M.12020.2010, M.12100.2019, M.12220.2015, M.12620.2014, M.12620.2016,
M.12700.2017, M.12980.2010, M.13980.2013, M.14020.2010, M.15540.2016,
M.15980.2012, M.16580.2011, M.16620.2015, M.16620.2017, M.16860.2013,
M.17660.2015, M.17660.2018, M.17780.2010, M.17780.2019, M.18580.2018,
M.19460.2011, M.19460.2012, M.19500.2017, M.20100.2013, M.20940.2012,
M.21140.2014, M.22220.2010, M.22500.2017, M.22500.2018, M.22660.2017,
M.23580.2013, M.24140.2017, M.24780.2012, M.25220.2010, M.25260.2016,
M.25860.2010, M.25940.2012, M.27180.2017, M.27900.2019, M.28420.2010,
M.28700.2011, M.29180.2010, M.29180.2011, M.29700.2015, M.31700.2013,
M.31900.2015, M.33780.2014, M.33860.2019, M.34740.2011, M.35660.2011,
M.37620.2016, M.37620.2018, M.39380.2011, M.39540.2017, M.40980.2015,
M.40980.2016, M.46220.2018, M.49700.2017, M.49740.2015, P.1.2500.2010,
P.1.500.2012, P.1.500.2013, P.1.500.2014, P.12.500.2017, P.12.6300.2012,
P.13.100.2012, P.13.100.2016, P.13.1200.2017, P.13.1500.2014, P.13.200.2011,
P.13.2200.2012, P.13.2500.2015, P.13.2800.2011, P.13.2800.2014, P.13.3200.2011,
P.13.3200.2013, P.13.4100.2011, P.13.4200.2012, P.13.700.2012, P.13.800.2013,
P.16.1300.2014, P.16.200.2011, P.17.200.2011, P.17.300.2016, P.17.600.2015,
P.17.700.2011, P.17.700.2012, P.17.900.2011, P.17.900.2014, P.18.1300.2019,
P.18.2600.2011, P.18.3100.2013, P.18.3200.2013, P.18.3400.2019, P.18.600.2014,
P.18.700.2013, P.18.800.2016, P.19.1300.2018, P.19.500.2010, P.19.700.2017,
P.19.800.2017, P.20.100.2014, P.20.100.2019, P.20.1200.2013, P.20.1200.2015,
P.20.1200.2018, P.20.900.2010, P.20.900.2016, P.21.100.2011, P.21.100.2015,
P.21.1400.2014, P.21.2000.2015, P.21.400.2019, P.21.600.2011, P.22.1200.2010,
P.22.300.2018, P.22.600.2016, P.24.100.2016, P.24.1300.2015, P.24.1500.2014,
P.26.100.2019, P.26.1700.2010, P.26.1702.2012, P.26.1703.2014, P.26.2200.2016,
P.26.2200.2018, P.26.600.2019, P.26.700.2011, P.27.100.2016, P.27.200.2014,
P.27.400.2018, P.27.600.2011, P.27.800.2018, P.28.500.2013, P.28.700.2015,
P.29.100.2018, P.29.1200.2010, P.29.1400.2018, P.29.2200.2011, P.29.2200.2019,
P.29.2300.2015, P.29.2400.2017, P.29.300.2012, P.29.700.2010, P.29.700.2015,
P.30.200.2019, P.30.300.2017, P.30.400.2019, P.30.500.2014, P.30.700.2019,
P.31.100.2014, P.31.500.2012, P.32.400.2010, P.33.500.2014, P.35.100.2017,
P.35.100.2019, P.35.1100.2012, P.35.1200.2012, P.36.200.2013, P.36.2402.2019,
P.36.2500.2018, P.36.2702.2012, P.36.2702.2018, P.37.1000.2015, P.37.2400.2013,
P.37.2400.2017, P.37.3300.2013, P.37.3500.2011, P.37.3900.2011, P.37.3900.2016,
P.37.4400.2016, P.37.4500.2017, P.37.5100.2012, P.37.5100.2017, P.37.5200.2015,
P.37.700.2017, P.38.200.2016, P.38.300.2011, P.39.100.2013, P.39.2700.2017,
P.39.3500.2015, P.39.3700.2012, P.39.4500.2016, P.39.4700.2010, P.39.4900.2015,
P.39.5100.2017, P.40.100.2010, P.40.601.2014, P.41.300.2013, P.41.800.2011,
P.42.1100.2018, P.42.1900.2013, P.42.300.2016, P.42.3702.2014, P.42.600.2010,
P.45.1200.2011, P.45.1300.2015, P.45.400.2018, P.45.800.2012, P.45.800.2019,
P.46.200.2011, P.46.300.2010, P.46.300.2018, P.46.400.2011, P.46.400.2013,
P.46.600.2015, P.47.1100.2011, P.47.1100.2013, P.47.1700.2010, P.47.1900.2013,
P.47.1900.2015, P.47.200.2010, P.47.200.2011, P.47.200.2016, P.47.2200.2014,
P.47.2800.2011, P.47.2800.2013, P.47.2800.2018, P.48.100.2018, P.48.1000.2019,
P.48.1100.2010, P.48.1200.2016, P.48.1700.2017, P.48.1700.2018, P.48.2600.2012,
P.48.3601.2012, P.48.3601.2015, P.48.3900.2018, P.48.4000.2012, P.48.4000.2015,
P.48.5000.2014, P.48.600.2015, P.48.6200.2010, P.48.6200.2013, P.49.21001.2014,
P.5.1300.2010, P.5.1300.2013, P.5.1400.2011, P.5.1400.2018, P.5.1500.2017,
P.5.1500.2019, P.5.1700.2019, P.5.1800.2011, P.5.2000.2012, P.5.300.2012,
P.5.300.2017, P.5.400.2017, P.5.600.2015, P.5.700.2011, P.5.800.2012,
P.50.200.2012, P.50.300.2011, P.51.1600.2011, P.51.400.2011, P.51.51084.2012,
P.51.51084.2013, P.51.51085.2013, P.51.51097.2012, P.51.51105.2013,
P.51.51125.2015, P.53.10702.2013, P.53.10702.2015, P.53.10702.2016,
P.53.11200.2012, P.53.11200.2018, P.53.11900.2015, P.53.300.2010,
P.54.400.2017, P.54.600.2013, P.54.900.2010, P.55.1300.2014, P.55.1400.2015,
P.55.55101.2018, P.56.300.2012, P.56.400.2012, P.56.500.2018, P.6.3300.2013,
P.6.3300.2016, P.8.600.2014, P.8.802.2011, P.8.802.2017Code
mar_markets_multi <- lapply(mar_markets_multi, as_tibble)Check that we have no monoracial people here:
Code
summary(mar_markets_multi[[1]]$raceh) White-Black White-Indigenous White-Asian Black-Indigenous
2586 6625 3578 1079
Black-Asian Asian-Indigenous
403 673 Code
summary(mar_markets_multi[[1]]$racew) White-Black White-Indigenous White-Asian Black-Indigenous
2486 6802 3529 978
Black-Asian Asian-Indigenous
425 724 Code
save(unions, male_alt, female_alt,
file = here("data", "data_constructed", "unions_alts.RData"))
save(mar_markets, mar_markets_multi,
file = here("data", "data_constructed", "markets.RData"))Code
timestamp()##------ Wed Apr 23 12:35:14 2025 ------##