1 + 1[1] 2Dissertation & GRA research
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##load libraries
library(car,quietly = T)
library(stargazer,quietly = T)
Please cite as: Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.3. https://CRAN.R-project.org/package=stargazer library(survey,quietly = T)
Attaching package: 'survey'The following object is masked from 'package:graphics':
dotchartlibrary(questionr,quietly = T)
library(dplyr,quietly = T)
Attaching package: 'dplyr'The following object is masked from 'package:car':
recodeThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tableone,quietly = T)
library(haven,quietly = T)
library(ggplot2, quietly = T)
library(dplyr)
library(gt)
library(gtsummary)
library(flextable)
Attaching package: 'flextable'The following objects are masked from 'package:gtsummary':
as_flextable, continuous_summarylibrary(rio)
library(tidyverse)── Attaching packages
───────────────────────────────────────
tidyverse 1.3.2 ──✔ tibble 3.1.8 ✔ purrr 0.3.4
✔ tidyr 1.2.0 ✔ stringr 1.4.1
✔ readr 2.1.2 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ purrr::compose() masks flextable::compose()
✖ tidyr::expand() masks Matrix::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ tidyr::pack() masks Matrix::pack()
✖ dplyr::recode() masks car::recode()
✖ purrr::some() masks car::some()
✖ tidyr::unpack() masks Matrix::unpack()##load data
usa2 <-import ("usa_00010.dta.gz")
usa2<-zap_labels(usa2)
#summary(usa2)##recode variables
##Citizen
usa2$mycitizen<-Recode (usa2$citizen, recodes =" 1:2='Citizen'; 3:4= 'Not citizen';else=NA" ,as.factor=T)
usa2$ mycitizen<-relevel(usa2$mycitizen, ref='Citizen')
#English speaking
usa2$english <-Recode (usa2$speakeng, recodes =" 1='No'; 2:5='Speaks English'; 6='Not well'; else=NA" ,as.factor=T)
##educ
usa2$education<-Recode(usa2$educd, recodes="000:002='noschool';010:026='0Prim'; 030:061='1somehs'; 062:063='2hsgrad'; 065:090='3somecol'; 100:113='4colgrad';114:116='5masteranddoc'; else= NA", as.factor=T)
##Employed
usa2$employed<-Recode(usa2$empstat, recodes ="1= 'Employed';2:3='Unemployed';else=NA")
##sex
usa2$male<-as.factor(ifelse(usa2$sex==1, "Male", "Female"))
usa2$male<-relevel(usa2$male, ref='Male')
#Age cut into intervals
usa2$agec<-cut(usa2$age, breaks = c(18, 30, 40, 50, 60, 70, 80, 100), include.lowest = T)
#usa$agec<-cut(usa$age, breaks=c(0,24,39,59,79,99))
##race
usa2$raceg<-Recode(usa2$race, recodes="1='White'; 2='Black';4:6='Asian'; else='other'", as.factor=T)
usa2$raceg<-relevel(usa2$raceg, ref='White')
##born
usa2$born<-Recode(usa2$bpl, recodes=" 200='Mexico' ; 210='Central America'; 300='South America';400:499='Europe'; 548:599= 'Asia';547='Middle East'; 600='Africa'; else= NA", as.factor=T)
usa2$ born<-relevel(usa2$born, ref='Europe')
##Poverty
usa2$poor<- Recode(usa2$poverty, recodes = "000= 'NA'; 001= 'Below poverty threshold';501= 'Above poverty threshold'", as.factor=T)
##income grouping
#usa1$inc<-Recode(usa1$incwage, recodes = "9= NA;1='1_lt15k'; 2='2_15-25k';3='3_25-35k';4='4_35-50k';5='5_50kplus'", as.factor = T)
#usa1$inc<-Recode(usa1$incwage, recodes = "1:1='1_lt10k'; 1960:1970='2_25-50k';1980='3_75-80k';1990:2002='4_100kplus';else= NA", as.factor = T)
#usa1$wage<- car::recode(usa1$incwage, recodes = "1:10000= '1_lt10k';10000:25000='2_10-25k';25000:35000='3_25-35k';35000:50000='4_35-50k';50000:200000= '5_50kplus'", as.factor = T)
#wage/income using quantile
usa2<-filter(usa2, incwage>0)
usa2$incwage <- ifelse(usa2$incwage>999998, NA, usa2$incwage)
brks = quantile(usa2$incwage, probs = seq(0, 1,length.out=6), na.rm=T)
usa2$wage <- cut(usa2$incwage, breaks = brks, include.lowest = T)
##Occupation
usa2$occupation<- Recode(usa2$ind1990, recodes = "010:032= 'Agriculture'; 040:050= 'Mining';060= 'Construction'; 100:392='Manufacturing';400:472 ='Transportaion/Communication/Publicutilities';500:691= 'Wholesale&RetaiTrade';700:712= 'Finance/Ins/RealEst.'; 721:760= 'Business/Repair'; 761:791='PersonalServices'; 800:810= 'Ent.&Rec';812:893= 'Prof.services';900:932= 'Pub. Admin'; 940:960= 'Military'; else= NA ", as.factor=T)#summary(usa2)library(dplyr)
usa3<-usa2%>%
select(poor,occupation,
agec,raceg, employed, mycitizen, born, education, english,
male, perwt, strata) %>%
filter( complete.cases(.))##gt summary descriptive table
usa3%>% select(agec,occupation,born,raceg,employed,english,education,mycitizen)%>%
tbl_summary()| Characteristic | N = 627,6721 |
|---|---|
| agec | |
| [18,30] | 123,654 (20%) |
| (30,40] | 151,843 (24%) |
| (40,50] | 161,872 (26%) |
| (50,60] | 128,335 (20%) |
| (60,70] | 52,133 (8.3%) |
| (70,80] | 8,671 (1.4%) |
| (80,100] | 1,164 (0.2%) |
| occupation | |
| Agriculture | 35,602 (5.7%) |
| Business/Repair | 49,836 (7.9%) |
| Construction | 63,986 (10%) |
| Ent.&Rec | 8,987 (1.4%) |
| Finance/Ins/RealEst. | 30,073 (4.8%) |
| Manufacturing | 75,541 (12%) |
| Military | 2,213 (0.4%) |
| Mining | 2,893 (0.5%) |
| PersonalServices | 27,828 (4.4%) |
| Prof.services | 147,560 (24%) |
| Pub. Admin | 17,041 (2.7%) |
| Transportaion/Communication/Publicutilities | 40,522 (6.5%) |
| Wholesale&RetaiTrade | 125,590 (20%) |
| born | |
| Europe | 161,093 (26%) |
| Africa | 50,788 (8.1%) |
| Asia | 2,036 (0.3%) |
| Central America | 77,056 (12%) |
| Mexico | 258,472 (41%) |
| South America | 78,227 (12%) |
| raceg | |
| White | 421,092 (67%) |
| Asian | 7,790 (1.2%) |
| Black | 47,506 (7.6%) |
| other | 151,284 (24%) |
| employment status [general version] | |
| Employed | 582,674 (93%) |
| Unemployed | 44,998 (7.2%) |
| english | |
| No | 44,783 (7.1%) |
| Not well | 106,730 (17%) |
| Speaks English | 476,159 (76%) |
| education | |
| 0Prim | 83,247 (13%) |
| 1somehs | 70,816 (11%) |
| 2hsgrad | 130,733 (21%) |
| 3somecol | 145,790 (23%) |
| 4colgrad | 95,565 (15%) |
| 5masteranddoc | 74,766 (12%) |
| noschool | 26,755 (4.3%) |
| mycitizen | |
| Citizen | 318,192 (51%) |
| Not citizen | 309,480 (49%) |
| 1 n (%) | |
##
usa3%>% select(agec,occupation,born,raceg,employed,education,english,mycitizen)%>%
tbl_summary(
by= employed,
missing= "no")%>%
add_n()%>%
add_overall()%>%
add_p()| Characteristic | N | Overall, N = 627,6721 | Employed, N = 582,6741 | Unemployed, N = 44,9981 | p-value2 |
|---|---|---|---|---|---|
| agec | 627,672 | <0.001 | |||
| [18,30] | 123,654 (20%) | 109,223 (19%) | 14,431 (32%) | ||
| (30,40] | 151,843 (24%) | 142,874 (25%) | 8,969 (20%) | ||
| (40,50] | 161,872 (26%) | 154,051 (26%) | 7,821 (17%) | ||
| (50,60] | 128,335 (20%) | 121,606 (21%) | 6,729 (15%) | ||
| (60,70] | 52,133 (8.3%) | 46,669 (8.0%) | 5,464 (12%) | ||
| (70,80] | 8,671 (1.4%) | 7,303 (1.3%) | 1,368 (3.0%) | ||
| (80,100] | 1,164 (0.2%) | 948 (0.2%) | 216 (0.5%) | ||
| occupation | 627,672 | <0.001 | |||
| Agriculture | 35,602 (5.7%) | 31,454 (5.4%) | 4,148 (9.2%) | ||
| Business/Repair | 49,836 (7.9%) | 45,863 (7.9%) | 3,973 (8.8%) | ||
| Construction | 63,986 (10%) | 60,071 (10%) | 3,915 (8.7%) | ||
| Ent.&Rec | 8,987 (1.4%) | 8,016 (1.4%) | 971 (2.2%) | ||
| Finance/Ins/RealEst. | 30,073 (4.8%) | 28,464 (4.9%) | 1,609 (3.6%) | ||
| Manufacturing | 75,541 (12%) | 71,264 (12%) | 4,277 (9.5%) | ||
| Military | 2,213 (0.4%) | 2,105 (0.4%) | 108 (0.2%) | ||
| Mining | 2,893 (0.5%) | 2,724 (0.5%) | 169 (0.4%) | ||
| PersonalServices | 27,828 (4.4%) | 25,668 (4.4%) | 2,160 (4.8%) | ||
| Prof.services | 147,560 (24%) | 137,343 (24%) | 10,217 (23%) | ||
| Pub. Admin | 17,041 (2.7%) | 15,886 (2.7%) | 1,155 (2.6%) | ||
| Transportaion/Communication/Publicutilities | 40,522 (6.5%) | 38,281 (6.6%) | 2,241 (5.0%) | ||
| Wholesale&RetaiTrade | 125,590 (20%) | 115,535 (20%) | 10,055 (22%) | ||
| born | 627,672 | <0.001 | |||
| Europe | 161,093 (26%) | 148,671 (26%) | 12,422 (28%) | ||
| Africa | 50,788 (8.1%) | 46,849 (8.0%) | 3,939 (8.8%) | ||
| Asia | 2,036 (0.3%) | 1,853 (0.3%) | 183 (0.4%) | ||
| Central America | 77,056 (12%) | 72,153 (12%) | 4,903 (11%) | ||
| Mexico | 258,472 (41%) | 240,467 (41%) | 18,005 (40%) | ||
| South America | 78,227 (12%) | 72,681 (12%) | 5,546 (12%) | ||
| raceg | 627,672 | <0.001 | |||
| White | 421,092 (67%) | 390,729 (67%) | 30,363 (67%) | ||
| Asian | 7,790 (1.2%) | 7,233 (1.2%) | 557 (1.2%) | ||
| Black | 47,506 (7.6%) | 43,736 (7.5%) | 3,770 (8.4%) | ||
| other | 151,284 (24%) | 140,976 (24%) | 10,308 (23%) | ||
| education | 627,672 | <0.001 | |||
| 0Prim | 83,247 (13%) | 77,171 (13%) | 6,076 (14%) | ||
| 1somehs | 70,816 (11%) | 65,012 (11%) | 5,804 (13%) | ||
| 2hsgrad | 130,733 (21%) | 121,566 (21%) | 9,167 (20%) | ||
| 3somecol | 145,790 (23%) | 133,800 (23%) | 11,990 (27%) | ||
| 4colgrad | 95,565 (15%) | 89,674 (15%) | 5,891 (13%) | ||
| 5masteranddoc | 74,766 (12%) | 70,900 (12%) | 3,866 (8.6%) | ||
| noschool | 26,755 (4.3%) | 24,551 (4.2%) | 2,204 (4.9%) | ||
| english | 627,672 | <0.001 | |||
| No | 44,783 (7.1%) | 40,428 (6.9%) | 4,355 (9.7%) | ||
| Not well | 106,730 (17%) | 99,206 (17%) | 7,524 (17%) | ||
| Speaks English | 476,159 (76%) | 443,040 (76%) | 33,119 (74%) | ||
| mycitizen | 627,672 | <0.001 | |||
| Citizen | 318,192 (51%) | 297,244 (51%) | 20,948 (47%) | ||
| Not citizen | 309,480 (49%) | 285,430 (49%) | 24,050 (53%) | ||
| 1 n (%) | |||||
| 2 Pearson's Chi-squared test | |||||
#usa3%>% select(agec,occupation,born,raceg,employed,english,mycitizen)%>%
#tbl_summary(
# by= employed,
#label= list(agec~ "Age", occupation~ "occupation", born~ "place of birth", raceg~"race", english~"Speaks English", mycitizen~"citizenship status"),
# percent= "row",
# digits= list(agec~2),
#statistic= list(agec~"{mean} ({sd})"),
# response~ "{n}/{N} ({p}%)",
# type= list(response~ "catergorical"),
#missing= "always",
# missing_text = "Missing",)##
usa3%>% select(agec,occupation,education,male,born,raceg,employed,english,mycitizen)%>%
tbl_summary(
by= mycitizen,
missing= "no")%>%
add_n()%>%
add_overall()%>%
add_p()| Characteristic | N | Overall, N = 627,6721 | Citizen, N = 318,1921 | Not citizen, N = 309,4801 | p-value2 |
|---|---|---|---|---|---|
| agec | 627,672 | <0.001 | |||
| [18,30] | 123,654 (20%) | 46,813 (15%) | 76,841 (25%) | ||
| (30,40] | 151,843 (24%) | 59,480 (19%) | 92,363 (30%) | ||
| (40,50] | 161,872 (26%) | 82,359 (26%) | 79,513 (26%) | ||
| (50,60] | 128,335 (20%) | 83,279 (26%) | 45,056 (15%) | ||
| (60,70] | 52,133 (8.3%) | 38,460 (12%) | 13,673 (4.4%) | ||
| (70,80] | 8,671 (1.4%) | 6,832 (2.1%) | 1,839 (0.6%) | ||
| (80,100] | 1,164 (0.2%) | 969 (0.3%) | 195 (<0.1%) | ||
| occupation | 627,672 | <0.001 | |||
| Agriculture | 35,602 (5.7%) | 7,141 (2.2%) | 28,461 (9.2%) | ||
| Business/Repair | 49,836 (7.9%) | 22,608 (7.1%) | 27,228 (8.8%) | ||
| Construction | 63,986 (10%) | 19,042 (6.0%) | 44,944 (15%) | ||
| Ent.&Rec | 8,987 (1.4%) | 5,006 (1.6%) | 3,981 (1.3%) | ||
| Finance/Ins/RealEst. | 30,073 (4.8%) | 20,479 (6.4%) | 9,594 (3.1%) | ||
| Manufacturing | 75,541 (12%) | 36,109 (11%) | 39,432 (13%) | ||
| Military | 2,213 (0.4%) | 2,018 (0.6%) | 195 (<0.1%) | ||
| Mining | 2,893 (0.5%) | 1,330 (0.4%) | 1,563 (0.5%) | ||
| PersonalServices | 27,828 (4.4%) | 12,696 (4.0%) | 15,132 (4.9%) | ||
| Prof.services | 147,560 (24%) | 99,312 (31%) | 48,248 (16%) | ||
| Pub. Admin | 17,041 (2.7%) | 13,255 (4.2%) | 3,786 (1.2%) | ||
| Transportaion/Communication/Publicutilities | 40,522 (6.5%) | 24,024 (7.6%) | 16,498 (5.3%) | ||
| Wholesale&RetaiTrade | 125,590 (20%) | 55,172 (17%) | 70,418 (23%) | ||
| education | 627,672 | <0.001 | |||
| 0Prim | 83,247 (13%) | 19,426 (6.1%) | 63,821 (21%) | ||
| 1somehs | 70,816 (11%) | 23,604 (7.4%) | 47,212 (15%) | ||
| 2hsgrad | 130,733 (21%) | 59,723 (19%) | 71,010 (23%) | ||
| 3somecol | 145,790 (23%) | 91,763 (29%) | 54,027 (17%) | ||
| 4colgrad | 95,565 (15%) | 64,637 (20%) | 30,928 (10.0%) | ||
| 5masteranddoc | 74,766 (12%) | 50,058 (16%) | 24,708 (8.0%) | ||
| noschool | 26,755 (4.3%) | 8,981 (2.8%) | 17,774 (5.7%) | ||
| male | 627,672 | <0.001 | |||
| Male | 356,712 (57%) | 165,357 (52%) | 191,355 (62%) | ||
| Female | 270,960 (43%) | 152,835 (48%) | 118,125 (38%) | ||
| born | 627,672 | <0.001 | |||
| Europe | 161,093 (26%) | 115,062 (36%) | 46,031 (15%) | ||
| Africa | 50,788 (8.1%) | 32,567 (10%) | 18,221 (5.9%) | ||
| Asia | 2,036 (0.3%) | 1,319 (0.4%) | 717 (0.2%) | ||
| Central America | 77,056 (12%) | 31,896 (10%) | 45,160 (15%) | ||
| Mexico | 258,472 (41%) | 89,378 (28%) | 169,094 (55%) | ||
| South America | 78,227 (12%) | 47,970 (15%) | 30,257 (9.8%) | ||
| raceg | 627,672 | <0.001 | |||
| White | 421,092 (67%) | 224,864 (71%) | 196,228 (63%) | ||
| Asian | 7,790 (1.2%) | 5,586 (1.8%) | 2,204 (0.7%) | ||
| Black | 47,506 (7.6%) | 30,157 (9.5%) | 17,349 (5.6%) | ||
| other | 151,284 (24%) | 57,585 (18%) | 93,699 (30%) | ||
| employment status [general version] | 627,672 | <0.001 | |||
| Employed | 582,674 (93%) | 297,244 (93%) | 285,430 (92%) | ||
| Unemployed | 44,998 (7.2%) | 20,948 (6.6%) | 24,050 (7.8%) | ||
| english | 627,672 | <0.001 | |||
| No | 44,783 (7.1%) | 4,951 (1.6%) | 39,832 (13%) | ||
| Not well | 106,730 (17%) | 26,296 (8.3%) | 80,434 (26%) | ||
| Speaks English | 476,159 (76%) | 286,945 (90%) | 189,214 (61%) | ||
| 1 n (%) | |||||
| 2 Pearson's Chi-squared test | |||||
usa3%>% select(agec,occupation,education,male,born,raceg,employed,english,mycitizen)%>%
tbl_summary(
by= born,
missing= "no")%>%
add_n()%>%
add_overall()%>%
add_p()There was an error in 'add_p()/add_difference()' for variable 'agec', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, : FEXACT error 40.
Out of workspace.| Characteristic | N | Overall, N = 627,6721 | Europe, N = 161,0931 | Africa, N = 50,7881 | Asia, N = 2,0361 | Central America, N = 77,0561 | Mexico, N = 258,4721 | South America, N = 78,2271 | p-value2 |
|---|---|---|---|---|---|---|---|---|---|
| agec | 627,672 | ||||||||
| [18,30] | 123,654 (20%) | 28,278 (18%) | 10,610 (21%) | 511 (25%) | 17,025 (22%) | 52,910 (20%) | 14,320 (18%) | ||
| (30,40] | 151,843 (24%) | 32,074 (20%) | 13,046 (26%) | 490 (24%) | 21,662 (28%) | 66,909 (26%) | 17,662 (23%) | ||
| (40,50] | 161,872 (26%) | 37,097 (23%) | 12,880 (25%) | 464 (23%) | 19,070 (25%) | 72,950 (28%) | 19,411 (25%) | ||
| (50,60] | 128,335 (20%) | 38,484 (24%) | 9,816 (19%) | 390 (19%) | 13,605 (18%) | 48,238 (19%) | 17,802 (23%) | ||
| (60,70] | 52,133 (8.3%) | 19,996 (12%) | 3,881 (7.6%) | 134 (6.6%) | 5,014 (6.5%) | 15,466 (6.0%) | 7,642 (9.8%) | ||
| (70,80] | 8,671 (1.4%) | 4,425 (2.7%) | 516 (1.0%) | 40 (2.0%) | 606 (0.8%) | 1,836 (0.7%) | 1,248 (1.6%) | ||
| (80,100] | 1,164 (0.2%) | 739 (0.5%) | 39 (<0.1%) | 7 (0.3%) | 74 (<0.1%) | 163 (<0.1%) | 142 (0.2%) | ||
| occupation | 627,672 | <0.001 | |||||||
| Agriculture | 35,602 (5.7%) | 1,539 (1.0%) | 289 (0.6%) | 16 (0.8%) | 4,066 (5.3%) | 28,831 (11%) | 861 (1.1%) | ||
| Business/Repair | 49,836 (7.9%) | 13,230 (8.2%) | 3,716 (7.3%) | 179 (8.8%) | 7,001 (9.1%) | 18,835 (7.3%) | 6,875 (8.8%) | ||
| Construction | 63,986 (10%) | 8,183 (5.1%) | 948 (1.9%) | 82 (4.0%) | 11,517 (15%) | 37,739 (15%) | 5,517 (7.1%) | ||
| Ent.&Rec | 8,987 (1.4%) | 3,031 (1.9%) | 547 (1.1%) | 25 (1.2%) | 918 (1.2%) | 3,169 (1.2%) | 1,297 (1.7%) | ||
| Finance/Ins/RealEst. | 30,073 (4.8%) | 12,166 (7.6%) | 2,569 (5.1%) | 124 (6.1%) | 2,895 (3.8%) | 6,654 (2.6%) | 5,665 (7.2%) | ||
| Manufacturing | 75,541 (12%) | 17,683 (11%) | 4,060 (8.0%) | 139 (6.8%) | 8,657 (11%) | 38,110 (15%) | 6,892 (8.8%) | ||
| Military | 2,213 (0.4%) | 1,103 (0.7%) | 226 (0.4%) | 9 (0.4%) | 216 (0.3%) | 373 (0.1%) | 286 (0.4%) | ||
| Mining | 2,893 (0.5%) | 620 (0.4%) | 130 (0.3%) | 7 (0.3%) | 149 (0.2%) | 1,722 (0.7%) | 265 (0.3%) | ||
| PersonalServices | 27,828 (4.4%) | 5,273 (3.3%) | 2,197 (4.3%) | 70 (3.4%) | 4,519 (5.9%) | 11,531 (4.5%) | 4,238 (5.4%) | ||
| Prof.services | 147,560 (24%) | 54,938 (34%) | 20,765 (41%) | 606 (30%) | 13,140 (17%) | 35,443 (14%) | 22,668 (29%) | ||
| Pub. Admin | 17,041 (2.7%) | 6,500 (4.0%) | 2,194 (4.3%) | 71 (3.5%) | 1,626 (2.1%) | 4,337 (1.7%) | 2,313 (3.0%) | ||
| Transportaion/Communication/Publicutilities | 40,522 (6.5%) | 10,801 (6.7%) | 4,462 (8.8%) | 151 (7.4%) | 5,042 (6.5%) | 14,103 (5.5%) | 5,963 (7.6%) | ||
| Wholesale&RetaiTrade | 125,590 (20%) | 26,026 (16%) | 8,685 (17%) | 557 (27%) | 17,310 (22%) | 57,625 (22%) | 15,387 (20%) | ||
| education | 627,672 | <0.001 | |||||||
| 0Prim | 83,247 (13%) | 1,876 (1.2%) | 745 (1.5%) | 34 (1.7%) | 15,936 (21%) | 62,105 (24%) | 2,551 (3.3%) | ||
| 1somehs | 70,816 (11%) | 5,051 (3.1%) | 2,063 (4.1%) | 85 (4.2%) | 11,282 (15%) | 48,155 (19%) | 4,180 (5.3%) | ||
| 2hsgrad | 130,733 (21%) | 26,096 (16%) | 7,641 (15%) | 285 (14%) | 17,979 (23%) | 62,573 (24%) | 16,159 (21%) | ||
| 3somecol | 145,790 (23%) | 44,732 (28%) | 15,150 (30%) | 496 (24%) | 16,400 (21%) | 45,602 (18%) | 23,410 (30%) | ||
| 4colgrad | 95,565 (15%) | 40,284 (25%) | 13,482 (27%) | 545 (27%) | 6,939 (9.0%) | 16,058 (6.2%) | 18,257 (23%) | ||
| 5masteranddoc | 74,766 (12%) | 41,626 (26%) | 10,688 (21%) | 540 (27%) | 3,178 (4.1%) | 6,551 (2.5%) | 12,183 (16%) | ||
| noschool | 26,755 (4.3%) | 1,428 (0.9%) | 1,019 (2.0%) | 51 (2.5%) | 5,342 (6.9%) | 17,428 (6.7%) | 1,487 (1.9%) | ||
| male | 627,672 | <0.001 | |||||||
| Male | 356,712 (57%) | 84,012 (52%) | 28,429 (56%) | 1,202 (59%) | 44,792 (58%) | 158,908 (61%) | 39,369 (50%) | ||
| Female | 270,960 (43%) | 77,081 (48%) | 22,359 (44%) | 834 (41%) | 32,264 (42%) | 99,564 (39%) | 38,858 (50%) | ||
| raceg | 627,672 | <0.001 | |||||||
| White | 421,092 (67%) | 151,281 (94%) | 12,125 (24%) | 1,221 (60%) | 42,101 (55%) | 162,907 (63%) | 51,457 (66%) | ||
| Asian | 7,790 (1.2%) | 1,986 (1.2%) | 1,579 (3.1%) | 601 (30%) | 372 (0.5%) | 440 (0.2%) | 2,812 (3.6%) | ||
| Black | 47,506 (7.6%) | 3,913 (2.4%) | 36,040 (71%) | 5 (0.2%) | 2,500 (3.2%) | 978 (0.4%) | 4,070 (5.2%) | ||
| other | 151,284 (24%) | 3,913 (2.4%) | 1,044 (2.1%) | 209 (10%) | 32,083 (42%) | 94,147 (36%) | 19,888 (25%) | ||
| employment status [general version] | 627,672 | <0.001 | |||||||
| Employed | 582,674 (93%) | 148,671 (92%) | 46,849 (92%) | 1,853 (91%) | 72,153 (94%) | 240,467 (93%) | 72,681 (93%) | ||
| Unemployed | 44,998 (7.2%) | 12,422 (7.7%) | 3,939 (7.8%) | 183 (9.0%) | 4,903 (6.4%) | 18,005 (7.0%) | 5,546 (7.1%) | ||
| english | 627,672 | <0.001 | |||||||
| No | 44,783 (7.1%) | 790 (0.5%) | 285 (0.6%) | 20 (1.0%) | 8,913 (12%) | 32,518 (13%) | 2,257 (2.9%) | ||
| Not well | 106,730 (17%) | 6,444 (4.0%) | 2,351 (4.6%) | 143 (7.0%) | 19,379 (25%) | 69,147 (27%) | 9,266 (12%) | ||
| Speaks English | 476,159 (76%) | 153,859 (96%) | 48,152 (95%) | 1,873 (92%) | 48,764 (63%) | 156,807 (61%) | 66,704 (85%) | ||
| mycitizen | 627,672 | <0.001 | |||||||
| Citizen | 318,192 (51%) | 115,062 (71%) | 32,567 (64%) | 1,319 (65%) | 31,896 (41%) | 89,378 (35%) | 47,970 (61%) | ||
| Not citizen | 309,480 (49%) | 46,031 (29%) | 18,221 (36%) | 717 (35%) | 45,160 (59%) | 169,094 (65%) | 30,257 (39%) | ||
| 1 n (%) | |||||||||
| 2 Pearson's Chi-squared test | |||||||||