In caseybreen/wcensoc:
[^updated]: Last updated: r format(Sys.time(), '%d %B, %Y')
\captionsetup[table]{labelformat=empty}
| Page| Variable | Label |
|----:|:-----------------------|:---------------------------------------------|
| \hyperlink{page.2}{2} | \hyperlink{page.2}{HISTID} |Historical Unique Identifier |
| \hyperlink{page.3}{3} | \hyperlink{page.3}{byear} |Year of Birth |
| \hyperlink{page.4}{4} | \hyperlink{page.4}{bmonth} |Month of Birth |
| \hyperlink{page.5}{5} | \hyperlink{page.5}{dyear} |Year of Death |
| \hyperlink{page.6}{6} | \hyperlink{page.6}{dmonth} |Month of Death |
| \hyperlink{page.7}{7} | \hyperlink{page.7}{death_age} |Age at Death (Years) |
| \hyperlink{page.8}{8} |\hyperlink{page.8}{sex} |Sex |
| \hyperlink{page.9}{9} |\hyperlink{page.9}{race_first} |Race on First Application |
| \hyperlink{page.10}{10} |\hyperlink{page.10}{race_first_cyear} |First Race: Application Year |
| \hyperlink{page.11}{11} |\hyperlink{page.11}{race_first_cmonth}|First Race: Application Month |
| \hyperlink{page.12}{12} |\hyperlink{page.12}{race_last} |Race on Last Application |
| \hyperlink{page.13}{13} |\hyperlink{page.13}{race_last_cyear} |Last Race: Application Year |
| \hyperlink{page.14}{14} |\hyperlink{page.14}{race_last_cmonth} |Last Race: Application Month |
| \hyperlink{page.15}{15} |\hyperlink{page.15}{bpl} |Place of Birth |
| \hyperlink{page.16}{16} |\hyperlink{page.16}{zip_residence} |ZIP Code of Residence at Time of Death |
| \hyperlink{page.17}{17} |\hyperlink{page.17}{socstate} |State where Social Security Number Issued |
| \hyperlink{page.18}{18} |\hyperlink{page.18}{age_first_application} |Age at First Social Security Application |
| \hyperlink{page.19}{19} | \hyperlink{page.19}{weight} |CenSoc Sample Weight |
| \hyperlink{page.20}{20} | \hyperlink{page.20}{Additional IPUMS variables}| Additional 1940 Census variables, including: pernum, perwt, age, mbpl, fbpl, educd, educ_yrs, empstatd, hispan, incwage, incnonwg, marst, nativity, occ, occscore, ownershp, race, rent, serial, statefip, and urban.|
\vspace{100pt}
Summary: The CenSoc-Numident Version 3 Demo dataset (N = 64,686) links the IPUMS 1940 Census 1% sample to the National Archives' public release of the Social Security Numident file. Records were linked using a conservative variant of the ABE method developed by Abramitzky, Boustan, and Eriksson (\textcolor{blue}{2012}, \textcolor{blue}{2014}, \textcolor{blue}{2017}).
We note that this demo dataset is not conducive to high-resolution mortality research. We recommend using this file for exploratory and demonstrative purposes. To best conduct research with CenSoc data, researchers may download the full CenSoc-Numident from the CenSoc website, obtain an extract of the full-count 1940 Census from IPUMS-USA, and merge data using on the individual-level, unique identifier HISTID variable. Please adhere to CenSoc and IPUMS citation guidelines when using this file.
\newpage
\huge HISTID
\normalsize
\vspace{12pt}
Label: Historical Unique Identifier
Description: HISTID is a unique individual-level identifier. It can be used to merge the CenSoc-Numident file with the 1940 Full-Count Census from IPUMS.
\newpage
\huge byear
\normalsize
\vspace{12pt}
Label: Birth Year
Description: byear reports a person's year of birth, as recorded in the Numident death records.
## Library Packages
library(tidyverse)
library(data.table)
library(kableExtra)
## read in censoc_numident_v3 data file
censoc_numident_v3<-read_csv("/data/censoc/censoc_data_releases/censoc_numident_demo/censoc_numident_demo_v3/censoc_numident_demo_v3.csv")
byear_plot <- censoc_numident_v3 %>%
group_by(byear) %>%
summarise(n = n()) %>%
ggplot(aes(x = byear, y = n)) +
geom_line() +
geom_point() +
theme_minimal(base_size = 15) +
ggtitle("Year of Birth") +
theme(legend.position="bottom") +
xlab("title") +
labs(x = "Year",
y = "Count") +
# scale_y_continuous(labels = scales::comma, limits = c(0, 5000)) +
scale_x_continuous(breaks = scales::pretty_breaks(n=5))
\vspace{75pt}
byear_plot
\newpage
\huge bmonth
\normalsize
\vspace{12pt}
Label: Birth Month
Description: bmonth reports a person's month of birth, as recorded in the Numident death records.
## run in the console and copy and paste into documentation
bmonth_tabulated <- censoc_numident_v3 %>%
group_by(bmonth) %>%
tally() %>%
mutate(freq = signif(n*100 / sum(n), 2)) %>%
mutate(label = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) %>%
select(bmonth, label, n, `freq %` = freq) %>%
knitr::kable(format = "pipe")
\vspace{30pt}
bmonth_tabulated
\newpage
\huge dyear
\normalsize
\vspace{12pt}
Label: Death Year
Description: dyear reports a person's year of death, as recorded in the Numident death records.
dyear_plot <- censoc_numident_v3 %>%
group_by(dyear) %>%
summarise(n = n()) %>%
ggplot(aes(x = dyear, y = n)) +
geom_line() +
geom_point() +
theme_minimal(base_size = 15) +
ggtitle("Year of Death") +
theme(legend.position="bottom") +
xlab("title") +
labs(x = "Year",
y = "Count") +
scale_y_continuous(labels = scales::comma) +
scale_y_continuous(labels = scales::comma, limits = c(0, 7000)) +
scale_x_continuous(breaks = scales::pretty_breaks(n=5))
#The minimum y is 3000 deaths a year when the scale is removed
\vspace{75pt}
dyear_plot
\newpage
\huge dmonth
\normalsize
\vspace{12pt}
Label: Death Month
Description: dmonth reports a person's month of death, as recorded in the Numident death records.
dmonth_tabulated <- censoc_numident_v3 %>%
group_by(dmonth) %>%
tally() %>%
mutate(freq = signif(n*100 / sum(n), 2)) %>%
mutate(label = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) %>%
select(dmonth, label, n, `freq %` = freq) %>%
knitr::kable(format = "pipe")
\vspace{30pt}
dmonth_tabulated
\newpage
\huge death_age
\normalsize
\vspace{12pt}
Label: Age at Death (Years)
Description: death_age reports a person's age at death in years, calculated using the birth and death information recorded in the Numident death records.
death_age_plot <- censoc_numident_v3 %>%
group_by(death_age) %>%
summarise(n = n()) %>%
ggplot(aes(x = death_age, y = n)) +
geom_line() +
geom_point() +
theme_minimal(base_size = 15) +
ggtitle("Age at Death") +
theme(legend.position="bottom") +
xlab("title") +
labs(x = "Age at Death",
y = "Count") +
scale_y_continuous(labels = scales::comma) +
scale_x_continuous(breaks = scales::pretty_breaks(n=5))
\vspace{75pt}
death_age_plot
\newpage
\huge sex
\normalsize
\vspace{12pt}
Label: Sex
Description: sex reports a person's sex, as recorded in the Numident death, application, or claim records.
sex_tabulated <- censoc_numident_v3 %>%
group_by(sex) %>%
tally() %>%
mutate(freq = signif(n*100 / sum(n), 3)) %>%
mutate(label = c("Men", "Women")) %>%
select(sex, label, n, `freq %` = freq) %>%
knitr::kable(format = "pipe")
\vspace{30pt}
sex_tabulated
\newpage
\newpage
\huge race_first
\normalsize
\vspace{12pt}
Label: Race First
Description: race_first reports a person's race, as recorded on their first Social Security application entry.
Note: Before 1980, the race schema in the Social Security application form contained three categories: White, Black, and Other. In 1980, the SSA added three categories: (1) Asian, Asian American, or Pacific Islander, (2) Hispanic, and (3) North American Indian or Alaskan Native. The Other category was also removed.
race_first_tabulated <- censoc_numident_v3 %>%
group_by(race_first) %>%
tally() %>%
mutate(freq = signif(n*100 / sum(n), 3)) %>%
mutate(label = c("White", "Black", "Other", "Asian", "Hispanic", "North American Native", "Missing")) %>%
select(race_first, label, n, `freq %` = freq) %>%
knitr::kable(format = "pipe")
\vspace{30pt}
race_first_tabulated
\newpage
\huge race_first_cyear
\normalsize
\vspace{12pt}
Label: First Race: Application Year
Description: race_first_cyear is a numeric variable reporting the year of the application on which a person reported their first race.
\vspace{12pt}
\newpage
\huge race_first_cmonth
\normalsize
\vspace{12pt}
Label: First Race: Application Month
Description: race_first_cmonth is a numeric variable reporting the month of the application on which a person reported their first race.
\newpage
\huge race_last
\normalsize
\vspace{12pt}
Label: Race Last
Description: race_last reports a person's race, as recorded on their most recent Social Security application entry.
Note: Before 1980, the race schema in the Social Security application form contained three categories: White, Black, and Other. In 1980, the SSA added three categories: (1) Asian, Asian American, or Pacific Islander, (2) Hispanic, and (3) North American Indian or Alaskan Native. They also removed the Other category.
race_last_tabulated <- censoc_numident_v3 %>%
group_by(race_last) %>%
tally() %>%
mutate(freq = signif(n*100 / sum(n), 3)) %>%
mutate(label = c("White", "Black", "Other", "Asian", "Hispanic", "North American Native", "Missing")) %>%
select(race_last, label, n, `freq %` = freq) %>%
knitr::kable(format = "pipe")
\vspace{30pt}
race_last_tabulated
\newpage
\huge race_last_cyear
\normalsize
\vspace{12pt}
Label: Last Race: Application Year
Description: race_last_cyear reports the year of the application on which a person reported their last race.
\newpage
\huge race_last_cmonth
\normalsize
\vspace{12pt}
Label: Last Race: Application Month
Description: race_last_cmonth is a numeric variable reporting the month of the application on which a person reported their last race.
\newpage
\huge bpl
\normalsize
\vspace{12pt}
Label: Birthplace
Description: bpl is a numeric variable reporting a person's place of birth, as recorded in the Numident application or claims records. The accompanying bpl_string variable reports the person's place of birth as a character string. The coding schema matches the detailed IPUMS-USA birthplace coding schema.
For a complete list of IPUMS Birthplace codes, please see: \textcolor{blue}{https://usa.ipums.org/usa-action/variables/BPL}
bpl_tabulation <- censoc_numident_v3 %>%
filter(bpl < 10000 | is.na(bpl)) %>%
group_by(bpl, bpl_string) %>%
tally() %>%
ungroup() %>%
mutate(freq = round(n*100 / sum(n), 2)) %>%
select(bpl, bpl_string, n, `freq %` = freq)
rows <- seq_len(nrow(bpl_tabulation) %/% 2)
knitr::kable(list(bpl_tabulation[rows,1:4],
matrix(numeric(), nrow=0, ncol=1),
bpl_tabulation[-rows, 1:4]),
caption = "BPL Tabulation (Native born only)",
label = "tables", format = "latex", booktabs = TRUE) %>%
kableExtra::kable_styling(latex_options = c("HOLD_position"))
\newpage
\huge zip_residence
\normalsize
\vspace{12pt}
Label: ZIP Code of Residence at Time of Death
Description: zip_residence is a string variable (9-characters) reporting a person's ZIP code of residence at time of death, as recorded in the Numident death records.
\newpage
\huge socstate
\normalsize
\vspace{12pt}
Label: State where Social Security Number Issued
Description: socstate is a numeric variable reporting the state in which a person's Social Security card was issued. It is determined by the first three (3) digits of a person's Social Security number, as recorded in Numident death records. The accompanying socstate_string variable reports the state in which a person's Social Security card was issued as a character string. The coding schema matches the detailed IPUMS-USA birthplace coding schema.
The list of codes is also available at: \textcolor{blue}{https://usa.ipums.org/usa-action/variables/BPL}
\vspace{30pt}
socstate_tabulation <- censoc_numident_v3 %>%
#filter(socstate < 10000 | is.na(socstate)) %>%
group_by(socstate, socstate_string) %>%
tally() %>%
ungroup() %>%
mutate(freq = round(n*100 / sum(n), 2)) %>%
select(socstate, socstate_string, n, `freq %` = freq)
rows <- seq_len(nrow(socstate_tabulation) %/% 2)
knitr::kable(list(socstate_tabulation[rows,1:4],
matrix(numeric(), nrow=0, ncol=1),
socstate_tabulation[-rows, 1:4]),
caption = "Tabulation of socstate",
label = "tables", format = "latex", booktabs = TRUE) %>%
kableExtra::kable_styling(latex_options = "HOLD_position")
\newpage
\huge age_first_app
\normalsize
\vspace{12pt}
Label: Age at First Social Security Application
Description: age_first_application reports the age at which a person submitted their first Social Security application.
age_first_app_plot <- censoc_numident_v3 %>%
group_by(age_first_application) %>%
filter(age_first_application %in% c(0:110)) %>%
summarise(n = n()) %>%
ggplot(aes(x = age_first_application, y = n)) +
geom_point() +
geom_line() +
theme_minimal(15) +
theme(legend.position="bottom") +
labs(title = "Age of First Application",
x = "Age of First Application",
y = "Count") +
scale_y_continuous(labels = scales::comma) +
scale_x_continuous(breaks = scales::pretty_breaks(n=5))
\vspace{75pt}
age_first_app_plot
\newpage
\huge weight
\normalsize
Label: CenSoc Sample Weight [^1]
[^1]: The IPUMS-USA 1940 1% sample also includes a weight (perweight) to account for the 1940 sampling procedure (thus no weights for the 100% complete count 1940 census). For analysis, we recommend using both sets of weights. A final weight can be constructed by multiplying the two weights together.
Description: weight is a post-stratification person-weight to National Center for Health Statistics (NCHS) totals for persons (1) dying between 1988-2005 (2) dying between ages 65-100. Weights are based on age at death, year of death, sex, and race, and place of birth. Please see the \textcolor{blue}{technical documentation on weights} for more information.
weights_tabulated <- censoc_numident_v3 %>%
filter(!is.na(weight)) %>%
summarize('Min Weight' = round(min(weight),2), 'Max Weight' = round(max(weight), 2)) %>%
mutate(id = 1:n()) %>%
pivot_longer(-id, names_to = "Label", values_to = "Value") %>%
select(Value, Label) %>%
add_row(Label = "No Weight Assigned", Value = NA) %>%
knitr::kable(format = "markdown")
weights <- censoc_numident_v3 %>%
filter(!is.na(weight)) %>%
group_by(death_age, dyear) %>%
summarize(weight = mean(weight))
## plot mortality sex ratio Lexis surface
weights_lexis <- weights %>%
ggplot() +
geom_raster(aes(x = dyear, y = death_age,
fill = weight)) +
## Lexis grid
geom_hline(yintercept = seq(65, 100, 10),
alpha = 0.2, lty = "dotted") +
geom_vline(xintercept = seq(1985, 2005, 10),
alpha = 0.2, lty = "dotted") +
geom_abline(intercept = seq(-100, 100, 10)-1910,
alpha = 0.2, lty = "dotted") +
scale_fill_viridis_c(option = "magma") +
scale_x_continuous("Year", expand = c(0.02, 0),
breaks = seq(1988, 2005, 5)) +
scale_y_continuous("Age", expand = c(0, 0),
breaks = seq(65, 100, 10)) +
guides(fill = guide_legend(reverse = TRUE)) +
# coord
coord_equal() +
# theme
theme_void() +
theme(
axis.text = element_text(colour = "black"),
axis.text.y = element_text(size = 10),
axis.text.x = element_text(size = 10, angle = 45, hjust = .5),
plot.title = element_text(size = 10, vjust = 2),
legend.text = element_text(size = 10),
axis.title=element_text(size = 10,face="bold")
) +
labs(X = "Year",
Y = "Age",
title = "Average weight by age at death and year of death")
\vspace{30pt}
weights_tabulated
\vspace{30pt}
weights_lexis
\newpage
\huge IPUMS 1940 Census Variables
\normalsize
\vspace{12pt}
The variables below are from the IPUMS-USA 1940 Census 1% sample. We recommend looking at the terrific documentation on the IPUMS-USA website: \textcolor{blue}{https://usa.ipums.org/usa/index.shtml}
| Variable | Label |
|:-------------|:---------------------------------------------|
| pernum |Person number in household|
|perwt |IPUMS person weight[^2] |
| age |Age on April 1st, 1940|
| mbpl |Mother's place of birth[^3]|
| fbpl |Father's place of birth[^4]|
| educd |Educational attainment (detailed IPUMS codes)|
| educ_yrs |Educational attainment in years (constructed)[^5]|
| empstatd |Employment status (detailed)|
| hispan |Hispanic/Spanish/Latino origin (imputed)[^6]|
| incwage | Wage and salary income in 1939 |
| incnonwg |Had non-wage/salary income over $50 in 1939|
| marst |Marital status|
| nativity |Foreign birthplace or parentage|
| occ |Occupation|
| occscore |Occupational income score|
| ownershp |Ownership of dwelling (tenure)|
| race |Race[^7]|
| rent |Monthly contract rent|
| serial |Household serial number|
| statefip |State of residence 1940 (FIPS codes)|
| urban |Urban/rural status|
[^2]: The IPUMS perweight accounts for the 1940 sampling procedure to construct the 1% sample, and thus is only available in the 1940 1% sample. For analysis, we recommend using both the IPUMS perweight and the CenSoc weight. A final weight can be constructed by multiplying the two weights together
[^3]: This variable is only available for sample-line persons (a one-in-twenty sample asked additional questions in the 1940 Census) or those living with their mother.
[^4]: This variable is only available for sample-line persons (a one-in-twenty sample asked additional questions in the 1940 Census) or those living with their father.
[^5]: educ_yrs is constructed from the IPUMS educd variable but not directly available from IPUMS.
[^6]: The 1940 Census did not directly inquire about Hispanic ethnicity or origin. This variable is determined by IPUMS using information such as one's birthplace or a parent's birthplace.
[^7]: The IPUMS race variable reports race as recorded in the 1940 Census. In contrast, the race_first and race_last variables in this dataset contain race as self-reported on Social Security applications.
caseybreen/wcensoc documentation built on Nov. 21, 2024, 5:15 a.m.
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[^updated]: Last updated: r format(Sys.time(), '%d %B, %Y')
\captionsetup[table]{labelformat=empty}
| Page| Variable | Label | |----:|:-----------------------|:---------------------------------------------| | \hyperlink{page.2}{2} | \hyperlink{page.2}{HISTID} |Historical Unique Identifier | | \hyperlink{page.3}{3} | \hyperlink{page.3}{byear} |Year of Birth | | \hyperlink{page.4}{4} | \hyperlink{page.4}{bmonth} |Month of Birth | | \hyperlink{page.5}{5} | \hyperlink{page.5}{dyear} |Year of Death | | \hyperlink{page.6}{6} | \hyperlink{page.6}{dmonth} |Month of Death | | \hyperlink{page.7}{7} | \hyperlink{page.7}{death_age} |Age at Death (Years) | | \hyperlink{page.8}{8} |\hyperlink{page.8}{sex} |Sex | | \hyperlink{page.9}{9} |\hyperlink{page.9}{race_first} |Race on First Application | | \hyperlink{page.10}{10} |\hyperlink{page.10}{race_first_cyear} |First Race: Application Year | | \hyperlink{page.11}{11} |\hyperlink{page.11}{race_first_cmonth}|First Race: Application Month | | \hyperlink{page.12}{12} |\hyperlink{page.12}{race_last} |Race on Last Application | | \hyperlink{page.13}{13} |\hyperlink{page.13}{race_last_cyear} |Last Race: Application Year | | \hyperlink{page.14}{14} |\hyperlink{page.14}{race_last_cmonth} |Last Race: Application Month | | \hyperlink{page.15}{15} |\hyperlink{page.15}{bpl} |Place of Birth | | \hyperlink{page.16}{16} |\hyperlink{page.16}{zip_residence} |ZIP Code of Residence at Time of Death | | \hyperlink{page.17}{17} |\hyperlink{page.17}{socstate} |State where Social Security Number Issued | | \hyperlink{page.18}{18} |\hyperlink{page.18}{age_first_application} |Age at First Social Security Application | | \hyperlink{page.19}{19} | \hyperlink{page.19}{weight} |CenSoc Sample Weight | | \hyperlink{page.20}{20} | \hyperlink{page.20}{Additional IPUMS variables}| Additional 1940 Census variables, including: pernum, perwt, age, mbpl, fbpl, educd, educ_yrs, empstatd, hispan, incwage, incnonwg, marst, nativity, occ, occscore, ownershp, race, rent, serial, statefip, and urban.|
\vspace{100pt}
Summary: The CenSoc-Numident Version 3 Demo dataset (N = 64,686) links the IPUMS 1940 Census 1% sample to the National Archives' public release of the Social Security Numident file. Records were linked using a conservative variant of the ABE method developed by Abramitzky, Boustan, and Eriksson (\textcolor{blue}{2012}, \textcolor{blue}{2014}, \textcolor{blue}{2017}).
We note that this demo dataset is not conducive to high-resolution mortality research. We recommend using this file for exploratory and demonstrative purposes. To best conduct research with CenSoc data, researchers may download the full CenSoc-Numident from the CenSoc website, obtain an extract of the full-count 1940 Census from IPUMS-USA, and merge data using on the individual-level, unique identifier HISTID variable. Please adhere to CenSoc and IPUMS citation guidelines when using this file.
\newpage
\huge HISTID \normalsize \vspace{12pt}
Label: Historical Unique Identifier
Description: HISTID is a unique individual-level identifier. It can be used to merge the CenSoc-Numident file with the 1940 Full-Count Census from IPUMS.
\newpage
\huge byear \normalsize \vspace{12pt}
Label: Birth Year
Description: byear reports a person's year of birth, as recorded in the Numident death records.
## Library Packages library(tidyverse) library(data.table) library(kableExtra) ## read in censoc_numident_v3 data file censoc_numident_v3<-read_csv("/data/censoc/censoc_data_releases/censoc_numident_demo/censoc_numident_demo_v3/censoc_numident_demo_v3.csv")
byear_plot <- censoc_numident_v3 %>% group_by(byear) %>% summarise(n = n()) %>% ggplot(aes(x = byear, y = n)) + geom_line() + geom_point() + theme_minimal(base_size = 15) + ggtitle("Year of Birth") + theme(legend.position="bottom") + xlab("title") + labs(x = "Year", y = "Count") + # scale_y_continuous(labels = scales::comma, limits = c(0, 5000)) + scale_x_continuous(breaks = scales::pretty_breaks(n=5))
\vspace{75pt}
byear_plot
\newpage \huge bmonth \normalsize \vspace{12pt}
Label: Birth Month
Description: bmonth reports a person's month of birth, as recorded in the Numident death records.
## run in the console and copy and paste into documentation bmonth_tabulated <- censoc_numident_v3 %>% group_by(bmonth) %>% tally() %>% mutate(freq = signif(n*100 / sum(n), 2)) %>% mutate(label = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) %>% select(bmonth, label, n, `freq %` = freq) %>% knitr::kable(format = "pipe")
\vspace{30pt}
bmonth_tabulated
\newpage
\huge dyear \normalsize \vspace{12pt}
Label: Death Year
Description: dyear reports a person's year of death, as recorded in the Numident death records.
dyear_plot <- censoc_numident_v3 %>% group_by(dyear) %>% summarise(n = n()) %>% ggplot(aes(x = dyear, y = n)) + geom_line() + geom_point() + theme_minimal(base_size = 15) + ggtitle("Year of Death") + theme(legend.position="bottom") + xlab("title") + labs(x = "Year", y = "Count") + scale_y_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma, limits = c(0, 7000)) + scale_x_continuous(breaks = scales::pretty_breaks(n=5)) #The minimum y is 3000 deaths a year when the scale is removed
\vspace{75pt}
dyear_plot
\newpage
\huge dmonth \normalsize \vspace{12pt}
Label: Death Month
Description: dmonth reports a person's month of death, as recorded in the Numident death records.
dmonth_tabulated <- censoc_numident_v3 %>% group_by(dmonth) %>% tally() %>% mutate(freq = signif(n*100 / sum(n), 2)) %>% mutate(label = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) %>% select(dmonth, label, n, `freq %` = freq) %>% knitr::kable(format = "pipe")
\vspace{30pt}
dmonth_tabulated
\newpage
\huge death_age \normalsize \vspace{12pt}
Label: Age at Death (Years)
Description: death_age reports a person's age at death in years, calculated using the birth and death information recorded in the Numident death records.
death_age_plot <- censoc_numident_v3 %>% group_by(death_age) %>% summarise(n = n()) %>% ggplot(aes(x = death_age, y = n)) + geom_line() + geom_point() + theme_minimal(base_size = 15) + ggtitle("Age at Death") + theme(legend.position="bottom") + xlab("title") + labs(x = "Age at Death", y = "Count") + scale_y_continuous(labels = scales::comma) + scale_x_continuous(breaks = scales::pretty_breaks(n=5))
\vspace{75pt}
death_age_plot
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\huge sex \normalsize \vspace{12pt}
Label: Sex
Description: sex reports a person's sex, as recorded in the Numident death, application, or claim records.
sex_tabulated <- censoc_numident_v3 %>% group_by(sex) %>% tally() %>% mutate(freq = signif(n*100 / sum(n), 3)) %>% mutate(label = c("Men", "Women")) %>% select(sex, label, n, `freq %` = freq) %>% knitr::kable(format = "pipe")
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sex_tabulated
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\newpage
\huge race_first \normalsize \vspace{12pt}
Label: Race First
Description: race_first reports a person's race, as recorded on their first Social Security application entry.
Note: Before 1980, the race schema in the Social Security application form contained three categories: White, Black, and Other. In 1980, the SSA added three categories: (1) Asian, Asian American, or Pacific Islander, (2) Hispanic, and (3) North American Indian or Alaskan Native. The Other category was also removed.
race_first_tabulated <- censoc_numident_v3 %>% group_by(race_first) %>% tally() %>% mutate(freq = signif(n*100 / sum(n), 3)) %>% mutate(label = c("White", "Black", "Other", "Asian", "Hispanic", "North American Native", "Missing")) %>% select(race_first, label, n, `freq %` = freq) %>% knitr::kable(format = "pipe")
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race_first_tabulated
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\huge race_first_cyear \normalsize \vspace{12pt}
Label: First Race: Application Year
Description: race_first_cyear is a numeric variable reporting the year of the application on which a person reported their first race.
\vspace{12pt}
\newpage
\huge race_first_cmonth \normalsize \vspace{12pt}
Label: First Race: Application Month
Description: race_first_cmonth is a numeric variable reporting the month of the application on which a person reported their first race.
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\huge race_last \normalsize \vspace{12pt}
Label: Race Last
Description: race_last reports a person's race, as recorded on their most recent Social Security application entry.
Note: Before 1980, the race schema in the Social Security application form contained three categories: White, Black, and Other. In 1980, the SSA added three categories: (1) Asian, Asian American, or Pacific Islander, (2) Hispanic, and (3) North American Indian or Alaskan Native. They also removed the Other category.
race_last_tabulated <- censoc_numident_v3 %>% group_by(race_last) %>% tally() %>% mutate(freq = signif(n*100 / sum(n), 3)) %>% mutate(label = c("White", "Black", "Other", "Asian", "Hispanic", "North American Native", "Missing")) %>% select(race_last, label, n, `freq %` = freq) %>% knitr::kable(format = "pipe")
\vspace{30pt}
race_last_tabulated
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\huge race_last_cyear \normalsize \vspace{12pt}
Label: Last Race: Application Year
Description: race_last_cyear reports the year of the application on which a person reported their last race.
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\huge race_last_cmonth \normalsize \vspace{12pt}
Label: Last Race: Application Month
Description: race_last_cmonth is a numeric variable reporting the month of the application on which a person reported their last race.
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\huge bpl \normalsize \vspace{12pt}
Label: Birthplace
Description: bpl is a numeric variable reporting a person's place of birth, as recorded in the Numident application or claims records. The accompanying bpl_string variable reports the person's place of birth as a character string. The coding schema matches the detailed IPUMS-USA birthplace coding schema.
For a complete list of IPUMS Birthplace codes, please see: \textcolor{blue}{https://usa.ipums.org/usa-action/variables/BPL}
bpl_tabulation <- censoc_numident_v3 %>% filter(bpl < 10000 | is.na(bpl)) %>% group_by(bpl, bpl_string) %>% tally() %>% ungroup() %>% mutate(freq = round(n*100 / sum(n), 2)) %>% select(bpl, bpl_string, n, `freq %` = freq) rows <- seq_len(nrow(bpl_tabulation) %/% 2) knitr::kable(list(bpl_tabulation[rows,1:4], matrix(numeric(), nrow=0, ncol=1), bpl_tabulation[-rows, 1:4]), caption = "BPL Tabulation (Native born only)", label = "tables", format = "latex", booktabs = TRUE) %>% kableExtra::kable_styling(latex_options = c("HOLD_position"))
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\huge zip_residence
\normalsize
\vspace{12pt}
Label: ZIP Code of Residence at Time of Death
Description: zip_residence is a string variable (9-characters) reporting a person's ZIP code of residence at time of death, as recorded in the Numident death records.
\newpage \huge socstate
\normalsize
\vspace{12pt}
Label: State where Social Security Number Issued
Description: socstate is a numeric variable reporting the state in which a person's Social Security card was issued. It is determined by the first three (3) digits of a person's Social Security number, as recorded in Numident death records. The accompanying socstate_string variable reports the state in which a person's Social Security card was issued as a character string. The coding schema matches the detailed IPUMS-USA birthplace coding schema.
The list of codes is also available at: \textcolor{blue}{https://usa.ipums.org/usa-action/variables/BPL}
\vspace{30pt}
socstate_tabulation <- censoc_numident_v3 %>% #filter(socstate < 10000 | is.na(socstate)) %>% group_by(socstate, socstate_string) %>% tally() %>% ungroup() %>% mutate(freq = round(n*100 / sum(n), 2)) %>% select(socstate, socstate_string, n, `freq %` = freq) rows <- seq_len(nrow(socstate_tabulation) %/% 2) knitr::kable(list(socstate_tabulation[rows,1:4], matrix(numeric(), nrow=0, ncol=1), socstate_tabulation[-rows, 1:4]), caption = "Tabulation of socstate", label = "tables", format = "latex", booktabs = TRUE) %>% kableExtra::kable_styling(latex_options = "HOLD_position")
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\huge age_first_app
\normalsize
\vspace{12pt}
Label: Age at First Social Security Application
Description: age_first_application reports the age at which a person submitted their first Social Security application.
age_first_app_plot <- censoc_numident_v3 %>% group_by(age_first_application) %>% filter(age_first_application %in% c(0:110)) %>% summarise(n = n()) %>% ggplot(aes(x = age_first_application, y = n)) + geom_point() + geom_line() + theme_minimal(15) + theme(legend.position="bottom") + labs(title = "Age of First Application", x = "Age of First Application", y = "Count") + scale_y_continuous(labels = scales::comma) + scale_x_continuous(breaks = scales::pretty_breaks(n=5))
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age_first_app_plot
\newpage
\huge weight \normalsize
Label: CenSoc Sample Weight [^1]
[^1]: The IPUMS-USA 1940 1% sample also includes a weight (perweight) to account for the 1940 sampling procedure (thus no weights for the 100% complete count 1940 census). For analysis, we recommend using both sets of weights. A final weight can be constructed by multiplying the two weights together.
Description: weight is a post-stratification person-weight to National Center for Health Statistics (NCHS) totals for persons (1) dying between 1988-2005 (2) dying between ages 65-100. Weights are based on age at death, year of death, sex, and race, and place of birth. Please see the \textcolor{blue}{technical documentation on weights} for more information.
weights_tabulated <- censoc_numident_v3 %>% filter(!is.na(weight)) %>% summarize('Min Weight' = round(min(weight),2), 'Max Weight' = round(max(weight), 2)) %>% mutate(id = 1:n()) %>% pivot_longer(-id, names_to = "Label", values_to = "Value") %>% select(Value, Label) %>% add_row(Label = "No Weight Assigned", Value = NA) %>% knitr::kable(format = "markdown") weights <- censoc_numident_v3 %>% filter(!is.na(weight)) %>% group_by(death_age, dyear) %>% summarize(weight = mean(weight)) ## plot mortality sex ratio Lexis surface weights_lexis <- weights %>% ggplot() + geom_raster(aes(x = dyear, y = death_age, fill = weight)) + ## Lexis grid geom_hline(yintercept = seq(65, 100, 10), alpha = 0.2, lty = "dotted") + geom_vline(xintercept = seq(1985, 2005, 10), alpha = 0.2, lty = "dotted") + geom_abline(intercept = seq(-100, 100, 10)-1910, alpha = 0.2, lty = "dotted") + scale_fill_viridis_c(option = "magma") + scale_x_continuous("Year", expand = c(0.02, 0), breaks = seq(1988, 2005, 5)) + scale_y_continuous("Age", expand = c(0, 0), breaks = seq(65, 100, 10)) + guides(fill = guide_legend(reverse = TRUE)) + # coord coord_equal() + # theme theme_void() + theme( axis.text = element_text(colour = "black"), axis.text.y = element_text(size = 10), axis.text.x = element_text(size = 10, angle = 45, hjust = .5), plot.title = element_text(size = 10, vjust = 2), legend.text = element_text(size = 10), axis.title=element_text(size = 10,face="bold") ) + labs(X = "Year", Y = "Age", title = "Average weight by age at death and year of death")
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weights_tabulated
\vspace{30pt}
weights_lexis
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\huge IPUMS 1940 Census Variables \normalsize
\vspace{12pt}
The variables below are from the IPUMS-USA 1940 Census 1% sample. We recommend looking at the terrific documentation on the IPUMS-USA website: \textcolor{blue}{https://usa.ipums.org/usa/index.shtml}
| Variable | Label | |:-------------|:---------------------------------------------| | pernum |Person number in household| |perwt |IPUMS person weight[^2] | | age |Age on April 1st, 1940| | mbpl |Mother's place of birth[^3]| | fbpl |Father's place of birth[^4]| | educd |Educational attainment (detailed IPUMS codes)| | educ_yrs |Educational attainment in years (constructed)[^5]| | empstatd |Employment status (detailed)| | hispan |Hispanic/Spanish/Latino origin (imputed)[^6]| | incwage | Wage and salary income in 1939 | | incnonwg |Had non-wage/salary income over $50 in 1939| | marst |Marital status| | nativity |Foreign birthplace or parentage| | occ |Occupation| | occscore |Occupational income score| | ownershp |Ownership of dwelling (tenure)| | race |Race[^7]| | rent |Monthly contract rent| | serial |Household serial number| | statefip |State of residence 1940 (FIPS codes)| | urban |Urban/rural status|
[^2]: The IPUMS perweight accounts for the 1940 sampling procedure to construct the 1% sample, and thus is only available in the 1940 1% sample. For analysis, we recommend using both the IPUMS perweight and the CenSoc weight. A final weight can be constructed by multiplying the two weights together
[^3]: This variable is only available for sample-line persons (a one-in-twenty sample asked additional questions in the 1940 Census) or those living with their mother.
[^4]: This variable is only available for sample-line persons (a one-in-twenty sample asked additional questions in the 1940 Census) or those living with their father.
[^5]: educ_yrs is constructed from the IPUMS educd variable but not directly available from IPUMS.
[^6]: The 1940 Census did not directly inquire about Hispanic ethnicity or origin. This variable is determined by IPUMS using information such as one's birthplace or a parent's birthplace.
[^7]: The IPUMS race variable reports race as recorded in the 1940 Census. In contrast, the race_first and race_last variables in this dataset contain race as self-reported on Social Security applications.
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