| title | author | date | output | editor_options | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
A Multi-State Analysis of Debt Imprisonment:
Replication Materials |
Johann Gaebler |
May 14, 2023 |
|
|
These data represent:
- 4291977 individual jail bookings;
- 343684 bookings where one or more of the booking charges was failure to pay;
- 129173 bookings in which an individual was booked only on charges involving failure to pay;
- 2740744 cases from the state of Oklahoma;
- 195316 cases from Oklahoma involving failure to pay.
Our Texas analysis consists of 64 counties. The combined population of these counties is 14800000 people, or approximately 53% of Texas's population.
In Wisconsin, we have 28 counties, with a combined population 3100000 people. These counties make up 53% of Wisconsin's population.
d_tx_all_ftp <- d_tx %>%
group_by(geo_county) %>%
filter(any(all_ftp)) %>%
ungroup() %>%
filter(ymd(20050101) <= booking_date, booking_date <= ymd(20171231))
burglary_tx <- pop_tx %>%
group_by(year) %>%
summarize(pop = sum(pop), .groups = "drop") %>%
left_join(burglary, by = "year") %>%
mutate(burglary = 1e6 * burglary_tx / pop, year = make_date(year = year)) %>%
select(year, burglary) %>%
# NOTE: Texas did not report burglary data in 2015
drop_na()
# Generate montly counts by county
d_tx_all_ftp_per_cap <- d_tx_all_ftp %>%
group_by(geo_county, year = year(booking_date), month = month(booking_date),
all_ftp, any_ftp, booking_id) %>%
summarize(.groups = "drop") %>%
group_by(geo_county, year, month) %>%
summarize(
total_bookings = n(),
all_ftp_bookings = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(total_bookings > 0.05 * max(total_bookings)) %>%
# Add county population totals
ungroup() %>%
left_join(pop_tx, by = c("geo_county", "year")) %>%
# Group by month and year to calculate per capita rate
group_by(year, month) %>%
summarize(
total_pop_represented = sum(pop),
per_capita_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(total_bookings) / sum(pop),
per_capita_ftp_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(all_ftp_bookings) / sum(pop),
.groups = "drop"
) %>%
# Turn month and year into a date to order x-axis correctly
mutate(date = make_date(year = year, month = month))
d_wi_all_ftp <- d_wi %>%
group_by(geo_county) %>%
filter(any(all_ftp)) %>%
ungroup() %>%
filter(ymd(20050101) <= booking_date, booking_date <= ymd(20171231))
burglary_wi <- pop_wi %>%
group_by(year) %>%
summarize(pop = sum(pop), .groups = "drop") %>%
left_join(burglary, by = "year") %>%
mutate(burglary = 1e6 * burglary_wi / pop, year = make_date(year = year)) %>%
select(year, burglary)
# Generate montly counts by county
d_wi_all_ftp_per_cap <- d_wi_all_ftp %>%
group_by(geo_county, year = year(booking_date), month = month(booking_date),
all_ftp, any_ftp, booking_id) %>%
summarize(.groups = "drop") %>%
group_by(geo_county, year, month) %>%
summarize(
total_bookings = n(),
all_ftp_bookings = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(total_bookings > 0.05 * max(total_bookings)) %>%
# Add county population totals
ungroup() %>%
left_join(pop_wi, by = c("geo_county", "year")) %>%
# Group by month and year to calculate per capita rate
group_by(year, month) %>%
summarize(
total_pop_represented = sum(pop),
per_capita_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(total_bookings) / sum(pop),
per_capita_ftp_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(all_ftp_bookings) / sum(pop),
.groups = "drop"
) %>%
# Turn month and year into a date to order x-axis correctly
mutate(date = make_date(year = year, month = month))
combined_tot_pop <- bind_rows(
pop_wi %>%
group_by(year) %>%
summarize(pop = sum(pop), .groups = "drop") %>%
mutate(state = "Wisconsin"),
pop_tx %>%
group_by(year) %>%
summarize(pop = sum(pop), .groups = "drop") %>%
mutate(state = "Texas")
)
combined_all_ftp_per_cap <- bind_rows(
mutate(d_tx_all_ftp_per_cap, state = "Texas"),
mutate(d_wi_all_ftp_per_cap, state = "Wisconsin")
) %>%
left_join(combined_tot_pop, by = c("year", "state"))
burglary_combined <- bind_rows(
mutate(burglary_tx, state = "Texas"),
mutate(burglary_wi, state = "Wisconsin")
)
ftp_plot <- combined_all_ftp_per_cap %>%
ggplot(aes(x = date, y = per_capita_ftp_bookings)) +
geom_line(
aes(x = year, y = burglary, color = "Burglary"),
data = burglary_combined,
linetype = "dashed"
) +
geom_smooth(
aes(color = "Smoothed\naverage"),
method = "loess",
se = FALSE,
formula = y ~ x
) +
geom_line(aes(color = "FTP")) +
scale_x_date(
breaks = as_date(parse_date_time(2005:2018, "Y")),
labels = NULL
) +
expand_limits(y = 0) +
labs(
x = NULL,
y = "Annual bookings\n(per million pop.)"
) +
theme_bw() +
scale_color_manual(
name = "Booking\ntype",
values = c(
"Burglary" = "red",
"Smoothed\naverage" = "blue",
"FTP" = "black"
)
) +
facet_wrap(~state)
representation_plot <- combined_all_ftp_per_cap %>%
ggplot(aes(x = date, y = total_pop_represented / pop)) +
geom_area(alpha = 1/2, color = "black") +
scale_x_date(
breaks = as_date(parse_date_time(2005:2018, "Y")),
labels = as.character(2005:2018)
) +
scale_y_continuous(labels = label_percent(accuracy = 10)) +
labs(
y = "Pct. of state\npop. represented",
x = NULL
) +
coord_cartesian(ylim = c(0, 0.5)) +
theme_bw() +
theme(
strip.text.x = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
facet_wrap(~state)
g_1 <- ggplotGrob(ftp_plot)
# Add additional columns to second grobTable to account for missing legend
g_2 <- gtable_add_cols(ggplotGrob(representation_plot), g_1$widths[14:15])
final_plot <- rbind(g_1, g_2, size = "first")
final_plot$widths <- unit.pmax(g_1$widths, g_2$widths)
grid.draw(final_plot)
Out of the 64 counties providing data, 58 provided jail rosters that included enough information to rule out cases where an individual was booked on a failure to pay charge as well as a more serious charge. The combined population of these counties is 8800000 people, or approximately 31.6% of Texas's population.
In Wisconsin, 20 counties provided full jail rosters. The combined population of these counties is 1100000 people, or 18.2% of Wisconsin's population.
Extrapolating from these counties to the whole of Texas carries substantial uncertainty. These counties represent a convenience sample, rather than a random sample of Texas counties. Nevertheless, assuming they are roughly representative, we would expect around 38000 people per year to have been arrested for failure to pay alone in the state.
Similarly, if we extrapolate to the whole state of Wisconsin, we would expect 8000 bookings for failure to pay alone per year in the whole state.
While courts we spoke to indicated that an individual who came in to work on a payment plan would have their warrants recalled (cf. Tex. Crim. C. Sec. 425.045), it is possible that individuals who voluntarily reported directly to jail, rather than the municipal court, to work on a financial plan might, in the jail records, be “booked” without ever actually spending time in jail. To upper-bound the size of this subpopulation, we look, where data are available, at very short stays, and, in particular, at the percentage of stays that last less than an hour.
has_time <- d %>%
distinct(booking_id, length_of_stay, geo_state, geo_county) %>%
group_by(geo_state, geo_county) %>%
summarize(
p = mean(length_of_stay != round(length_of_stay), na.rm = TRUE),
.groups = "drop"
) %>%
arrange(desc(p)) %>%
filter(p > 0.5)
short_stays <- d %>%
filter(all_ftp) %>%
distinct(booking_id, length_of_stay, geo_state, geo_county) %>%
semi_join(has_time, by = c("geo_state", "geo_county")) %>%
with(ecdf(length_of_stay)) %>%
map_dbl(1:24 / 24, .) %>%
tibble(hours = 1:24, pct = .)We find that 8% of stays last an hour or less.
We turn to estimating the percentage of court debtors who are ultimately jailed for unpaid court debt.
cpf_per_person <- d_tx %>%
filter(any_ftp) %>%
mutate(
n_cpf = str_count(
str_c_na(charge, statute, warrant_type, note, severity, release_type,
sep = " "),
"(?i)CPR?F|CAP(IAS)? ?PRO ?(FINE)?"
)
) %>%
with(sum(n_cpf, na.rm = TRUE) / n_distinct(booking_id))On this basis, we estimate that 8% of individuals with an outstanding CPF warrant for unpaid court debts are jailed in a given year on that basis alone.
d_tx_all_ftp_los <- d_tx_all_ftp %>%
group_by(geo_county) %>%
summarize(
pct_usable = mean(! is.na(length_of_stay)),
pct_hms = mean(! is.na(booking_time)),
.groups = "drop"
) %>%
arrange(desc(pct_usable))
d_tx_all_ftp_los_red <- d_tx_all_ftp %>%
semi_join(d_tx_all_ftp_los, by = "geo_county") %>%
filter(! is.na(length_of_stay), all_ftp) %>%
group_by(geo_county, booking_id, length_of_stay) %>%
summarize(.groups = "drop") %>%
ungroup() %>%
filter(length_of_stay <= 100)
d_tx_all_ftp_los_red_quants <- d_tx_all_ftp_los_red %>%
with(tibble(
state = "Texas",
pct = c(0.5, 0.95, 0.99),
label = c("", "(95%)", "(99%)"),
length_of_stay = quantile(length_of_stay, pct)
))
d_wi_all_ftp_los <- d_wi_all_ftp %>%
group_by(geo_county) %>%
summarize(
pct_usable = mean(! is.na(length_of_stay)),
pct_hms = mean(! is.na(booking_time)),
.groups = "drop"
) %>%
arrange(desc(pct_usable))
d_wi_all_ftp_los_red <- d_wi_all_ftp %>%
semi_join(d_wi_all_ftp_los, by = "geo_county") %>%
filter(! is.na(length_of_stay), all_ftp) %>%
group_by(geo_county, booking_id, length_of_stay) %>%
summarize(.groups = "drop") %>%
ungroup() %>%
filter(length_of_stay <= 100)
d_wi_all_ftp_los_red_quants <- d_wi_all_ftp_los_red %>%
with(tibble(
state = "Wisconsin",
pct = c(0.5, 0.95, 0.99),
label = c("(50%)", "(95%)", "(99%)"),
length_of_stay = quantile(length_of_stay, pct)
))
d_wi_all_ftp_los_red %>%
mutate(state = "Wisconsin") %>%
bind_rows(mutate(d_tx_all_ftp_los_red, state = "Texas")) %>%
ggplot(aes(x = length_of_stay)) +
scale_y_log10(expand = expansion(mult = c(0, 1/8))) +
geom_vline(
aes(xintercept = length_of_stay),
data = bind_rows(d_tx_all_ftp_los_red_quants, d_wi_all_ftp_los_red_quants),
linetype = "dashed"
) +
geom_text(
aes(x = 10 + length_of_stay, y = 10^5.5, label = label),
data = bind_rows(d_tx_all_ftp_los_red_quants, d_wi_all_ftp_los_red_quants)
) +
geom_histogram(binwidth = 5, color = "black") +
labs(
x = "Length of stay (days)",
y = "Number of bookings"
) +
theme_bw() +
facet_grid(cols = vars(state))
Not all counties provided both booking and release dates. Therefore, we limit our analysis to the 57 counties for which we can meaningfully calculate the length of an individual's jail stay. These counties have a combined population of 5400000 people, or approximately 19.4% of Texas's population.
A small number of individuals, around 0.4%, have stays longer than 100 days. It's likely that there are record-keeping errors for these individuals, and so we drop them from the analysis.
In Wisconsin, for the same reason we limit our analysis to 18 counties. These counties have a combined population of 700000 people, 11.5% of Wisconsin's population. Likewise, of the FTP bookings represented, we drop the 2.1% in which the stays are longer than 100 days.
In Texas, the most frequent length of stay is 0 days, and the median length of stay is 1 day. Because the majority of individuals stay for a day or less, the mean length of stay is a relatively noisey measure. Unfortunately, only 6 counties provide the time of both booking and release. Among those six counties, the mean length of stay is 5.07 days, while the mean length of stay is 2.1 days among all counties. This discrepancy appears to be due more to systematic policy differences than to measurement error, as the the mean in counties with time information, ignoring time information, is 5.05 days.
In Wisconsin, the most frequent length-of-stay is 0 days. The median length of stay is 1 day. 8 counties provide the time of both booking and release. Among those them counties, the mean length of stay is 9.26 days. The mean length of stay is 6.2 days among all counties. (The the mean in counties with time information, ignoring time information, is 9.29 days.)
Most stays are very short, representing individuals who were arrested on an oustanding warrant and brought before a judge roughly within the next business day. To understand what might be driving longer stays, we examine how the proportion of "pay-or-stay" cases changes as we restrict our attention to longer and longer jail stays.
PAY_OR_STAY_REGEX <- glue(
"(LAY|L(AI)?D|PAY|P(AI)?D) ?(OUT)? (FINE|FEE)",
"(FINE|FEE)S? (PAY|P(AI)?D)",
"DAYS? TO PAY|D2P",
"PAY ?OR ?STAY",
"PAY.{{0,8}}OR.{{0,5}}DAYS?",
.sep = "|"
)
d_tx_all_ftp %>%
filter(all_ftp, length_of_stay < 100) %>%
mutate(
pay_or_stay = str_detect(
str_c_na(charge, statute, warrant_type, note, severity, release_type,
sep = " "),
PAY_OR_STAY_REGEX
)
) %>%
group_by(booking_id) %>%
summarize(
length_of_stay = round(mean(length_of_stay)),
pay_or_stay = any(pay_or_stay),
.groups = "drop"
) %>%
group_by(length_of_stay) %>%
summarize(
pay_or_stay = sum(pay_or_stay, na.rm = TRUE),
n = n(),
n_na = sum(is.na(pay_or_stay)),
.groups = "drop"
) %>%
with(tibble(
length_of_stay = c(-1, length_of_stay),
pay_or_stay = sum(pay_or_stay) - c(0, cumsum(pay_or_stay)),
n = sum(n) - c(0, cumsum(n)),
n_na = sum(n_na) - c(0, cumsum(n_na)),
pct = pay_or_stay / n,
std.err = sqrt(pct * (1 - pct) / n)
)) %>%
ggplot(aes(x = length_of_stay)) +
scale_y_continuous(labels = label_percent()) +
geom_ribbon(
aes(ymin = pct - 1.96 * std.err, ymax = pct + 1.96 * std.err),
color = "grey",
alpha = 0.3
) +
geom_line(aes(y = pct)) +
theme_bw() +
labs(
x = bquote("Stayed at least" ~ x ~ "days"),
y = "Bookings mentioning pay-or-stay"
) +
coord_fixed(ratio = 100, xlim = c(0,100), ylim = c(0,1))
d_ok_case_type <- d_ok %>%
mutate(
case_type = fct_explicit_na(case_type),
case_type = str_to_title(case_type),
case_type = fct_infreq(case_type),
case_type = fct_other(case_type, keep = c(
"Traffic", "Criminal Misdemeanor", "Criminal Felony"
))
)## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `case_type = fct_explicit_na(case_type)`.
## Caused by warning:
## ! `fct_explicit_na()` was deprecated in forcats 1.0.0.
## ℹ Please use `fct_na_value_to_level()` instead.
d_ok_case_type %>%
filter(case_type == "Traffic", total_cost < 2000, ftp) %>%
select(total_cost, ftp) %>%
ggplot(aes(x = total_cost)) +
geom_histogram(show.legend = FALSE, binwidth = 50, color = "black") +
labs(
title = NULL,
x = "Total court costs (dollars)",
y = "Number of cases"
) +
theme_bw()
The average fine in traffic cases is $221; in FTP cases, it is $483. In misdemeanor cases, it is $1011 and $1599 when an FTP warrant is issued. In felony cases, it is $2651 and $3706 when an FTP warrant is issued.
The average length of time between filing a case and the first issue of an FTP warrant is 1.6 years in all cases and 0.9 years in traffic cases.
d_all_ftp <- bind_rows(d_tx_all_ftp, d_wi_all_ftp) %>%
filter(all_ftp)
d_all_ftp_aug <- d_all_ftp %>%
drop_na(charge) %>%
mutate(
charge = str_to_lower(charge),
traffic = str_detect(
charge,
glue(
"license|nli|[^f]dl|ndl|nli|dwli|dwls|expired|revoked|suspended|valid|invalid|susp|exp",
"speeding|mph|limit|speed",
"responsibility|maintain|insurance|fmfr|ftmfr|mvi|fin resp|liability|liab ins",
"motor|defective|display|seat|belt|plate|seatbelt|light|red",
"signal|lane|yield|stop|turn|roadway|start|road|street",
"driving|operate|operating|traffic|driver|vehicle|drive|toll|veh|roadway|passenger",
.sep = "|"
)
),
intoxication = str_detect(
charge,
"public(?!.+(veh|motor))|intoxication|alcohol|furnish|furn alc|beer|wine|container(?!.+(veh|motor))|alco"
),
# Open container in vehicle is traffic
intoxication = intoxication & ! traffic,
possession = str_detect(
charge,
glue(
"poss(?!.+(alc|open))|posess(?!.+alc)",
"marijuana|mj|oz|\\bcs\\b(?! def)|controlled|dru?g",
.sep = "|"
)
),
theft = str_detect(
charge,
"theft|thft"
),
doc = str_detect(
charge,
"doc|disorder"
),
truancy = str_detect(
charge,
"attend|trua|traunt|truancy|(?<!trespass.{0,30}).+school(?! ?zone)"
),
other = ! (traffic | intoxication | possession | theft | doc | truancy),
category = extract_fct(
traffic = traffic,
intoxication = intoxication,
possession = possession,
theft = theft,
`disorderly conduct` = doc,
truancy = truancy,
`other/unknown` = other,
levels = c("traffic", "intoxication", "possession", "theft", "disorderly conduct", "truancy", "other/unknown")
),
category = fct_explicit_na(category),
category = fct_collapse(category, `other/unknown` = "(Missing)")
) %>%
group_by(booking_id) %>%
mutate(
any_traffic = any(traffic),
any_intoxication = any(intoxication),
any_possession = any(possession),
any_theft = any(theft),
any_truancy = any(truancy)
) %>%
ungroup()Of all of the FTP cases in Oklahoma, 37% are traffic cases.
This high percentage of traffic cases is mirrored by the charges represented in FTP jailings in Texas and Wisconsin. 50% of all cases are identifiably traffic. Adjusting for the fact that 22% of cases lack the underlying charge information, assuming these cases are missing at random, it would follow that 64% are traffic related.
For the remaining categories we have that:
6% (raw) and 7% (adjusted) of charges are for public intoxication, * 4% (raw) and 5% (adjusted) of charges are for posession of marijuana, * 3% (raw) and 4% (adjusted) of charges are for theft, * 1.3% (raw) and 1.7% (adjusted) of charges are for disorderly conduct, * 1.3% (raw) and 1.7% (adjusted) of charges are truancy-related.
d_tx_all_ftp_race <- d_tx_all_ftp %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
pct_usable = mean(! is.na(race)),
n = n(),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(n > 0.05 * max(n)) %>%
ungroup() %>%
select(-n) %>%
arrange(desc(pct_usable))
d_tx_all_ftp_race_red <- d_tx_all_ftp %>%
distinct(geo_county, booking_id, race, all_ftp, booking_date) %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_b = mean(race == "black", na.rm = TRUE),
ftp_pct_b = sum(race == "black" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_b = replace_na(ftp_pct_b, 0),
jail_pct_b = replace_na(jail_pct_b, 0)
) %>%
semi_join(
filter(d_tx_all_ftp_race, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_tx, by = c("geo_county", "year")) %>%
ungroup()
d_tx_all_ftp_eth <- d_tx_all_ftp %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
pct_usable = mean(! is.na(ethnicity)),
n = n(),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(n > 0.05 * max(n)) %>%
ungroup() %>%
select(-n) %>%
arrange(desc(pct_usable))
d_tx_all_ftp_eth_red <- d_tx_all_ftp %>%
distinct(geo_county, booking_id, ethnicity, all_ftp, booking_date) %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_h = mean(ethnicity == "hispanic", na.rm = TRUE),
ftp_pct_h = sum(ethnicity == "hispanic" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_h = replace_na(ftp_pct_h, 0),
jail_pct_h = replace_na(jail_pct_h, 0)
) %>%
semi_join(
filter(d_tx_all_ftp_eth, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_tx, by = c("geo_county", "year")) %>%
ungroup()
d_wi_all_ftp_race <- d_wi_all_ftp %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
pct_usable = mean(! is.na(race)),
n = n(),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(n > 0.05 * max(n)) %>%
ungroup() %>%
select(-n) %>%
arrange(desc(pct_usable))
d_wi_all_ftp_race_red <- d_wi_all_ftp %>%
distinct(geo_county, booking_id, race, all_ftp, booking_date) %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_b = mean(race == "black", na.rm = TRUE),
ftp_pct_b = sum(race == "black" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_b = replace_na(ftp_pct_b, 0),
jail_pct_b = replace_na(jail_pct_b, 0)
) %>%
semi_join(
filter(d_wi_all_ftp_race, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_wi, by = c("geo_county", "year")) %>%
ungroup()
d_wi_all_ftp_eth <- d_wi_all_ftp %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
pct_usable = mean(! is.na(ethnicity)),
n = n(),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(n > 0.05 * max(n)) %>%
ungroup() %>%
select(-n) %>%
arrange(desc(pct_usable))
d_wi_all_ftp_eth_red <- d_wi_all_ftp %>%
distinct(geo_county, booking_id, ethnicity, all_ftp, booking_date) %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_h = mean(ethnicity == "hispanic", na.rm = TRUE),
ftp_pct_h = sum(ethnicity == "hispanic" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_h = replace_na(ftp_pct_h, 0),
jail_pct_h = replace_na(jail_pct_h, 0)
) %>%
semi_join(
filter(d_wi_all_ftp_eth, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_wi, by = c("geo_county", "year")) %>%
ungroup()57 counties in Texas and 18 counties in Wisconsin provided sufficient race information to carry out this analysis. The Texas counties account for 8800000 residents in 2018, and the Wisconsin counties account for 900000 residents in 2018.
Among these counties in Texas, 24% of individuals booked for any reason are Black, 29% of individuals booked for FTP alone are Black, while 14% of the population of these counties living below the poverty line are Black. In Wisconsin, 9% of individuals booked for any reason are Black, 14% of individuals booked for FTP alone are Black, while 2% of the population of these counties living below the poverty line are Black.
Among these counties in Texas, 42% of individuals booked for any reason are Hispanic, 37% of individuals booked for FTP alone are Hispanic, while 54% of the population of these counties living below the poverty line are Hispanic. In Wisconsin, 6% of individuals booked for any reason are Hispanic, 4% of individuals booked for FTP alone are Hispanic, while 6% of the population of these counties living below the poverty line are Hispanic.
d_origin <- bind_rows(d_tx_all_ftp, d_wi_all_ftp) %>%
group_by(year = year(booking_date), geo_state, geo_county, zip, booking_id) %>%
summarize(.groups = "drop") %>%
group_by(year, geo_state, geo_county) %>%
summarize(pct_usable = mean(! is.na(zip)), n = n(), .groups = "drop") %>%
group_by(geo_state, geo_county) %>%
# Sometimes bookings include small numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typically,
# this is because their sentences are very long. Filter those years.
filter(n > 0.05 * max(n)) %>%
ungroup() %>%
select(-n)
d_origin_red <- bind_rows(d_tx_all_ftp, d_wi_all_ftp) %>%
filter(all_ftp) %>%
group_by(geo_state, geo_county, year = year(booking_date), booking_id, zip) %>%
summarize(.groups = "drop") %>%
ungroup() %>%
semi_join(filter(d_origin, pct_usable > 0.8), by = c("geo_state", "geo_county", "year")) %>%
left_join(ZIPS_TO_FIPS, by = "zip")
# Convert the county names to FIPS codes
origin_fips <- fips_codes %>%
mutate(
county = str_to_lower(county),
county = str_replace_all(county, "[^a-z]+", "_"),
state = str_to_lower(state)
) %>%
select(geo_state = state, geo_county = county, jail_county_code = county_code)
# Add the jail county codes to `d_origin_red`
d_origin_red %<>%
left_join(origin_fips, by = c("geo_state", "geo_county")) %>%
# Check if the booking could be out of county
mutate(in_county = (geo_state == state_abbrev) & (county_code == jail_county_code)) %>%
group_by(geo_state, geo_county, year, booking_id) %>%
summarize(
any_out_of_county = any(! in_county),
all_out_of_county = all(! in_county),
.groups = "drop"
) %>%
ungroup()
# Summarize and plot
d_origin_red %>%
group_by(geo_state, geo_county) %>%
summarize(
pct_all_out = mean(any_out_of_county, na.rm = TRUE),
pct_all_in = mean(any_out_of_county, na.rm = TRUE),
.groups = "drop"
) %>%
ungroup() %>%
mutate(
geo_county = str_replace_all(geo_county, "_", " "),
geo_county = str_to_title(geo_county),
geo_state = str_to_upper(geo_state),
nm = str_c(geo_county, ", ", geo_state),
nm = fct_reorder(nm, pct_all_out, .desc = TRUE)
) %>%
# NOTE: These two measures are viturally equal, so we ignore the difference.
arrange(desc(pct_all_out)) %>%
ggplot(aes(x = nm, y = pct_all_out)) +
geom_col(color = "black") +
scale_y_continuous(labels = label_percent()) +
labs(
y = "Proportion of FTP bookings of\nindividuals not living in county",
x = ""
) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
In 39% of cases where ZIP codes are available, the individual booked is from a ZIP code in a different county.
d_tx_any_ftp <- d_tx %>%
group_by(geo_county) %>%
filter(any(any_ftp)) %>%
ungroup() %>%
filter(ymd(20050101) <= booking_date, booking_date <= ymd(20171231))
# Generate montly counts by county
d_tx_any_ftp_per_cap <- d_tx_any_ftp %>%
group_by(geo_county, year = year(booking_date), month = month(booking_date),
any_ftp, any_ftp, booking_id) %>%
summarize(.groups = "drop") %>%
group_by(geo_county, year, month) %>%
summarize(
total_bookings = n(),
any_ftp_bookings = sum(any_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include smany numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typicanyy,
# this is because their sentences are very long. Filter those years.
filter(total_bookings > 0.05 * max(total_bookings)) %>%
# Add county population totals
ungroup() %>%
left_join(pop_tx, by = c("geo_county", "year")) %>%
# Group by month and year to calculate per capita rate
group_by(year, month) %>%
summarize(
total_pop_represented = sum(pop),
per_capita_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(total_bookings) / sum(pop),
per_capita_ftp_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(any_ftp_bookings) / sum(pop),
.groups = "drop"
) %>%
# Turn month and year into a date to order x-axis correctly
mutate(date = make_date(year = year, month = month))
d_wi_any_ftp <- d_wi %>%
group_by(geo_county) %>%
filter(any(any_ftp)) %>%
ungroup() %>%
filter(ymd(20050101) <= booking_date, booking_date <= ymd(20171231))
# Generate montly counts by county
d_wi_any_ftp_per_cap <- d_wi_any_ftp %>%
group_by(geo_county, year = year(booking_date), month = month(booking_date),
any_ftp, any_ftp, booking_id) %>%
summarize(.groups = "drop") %>%
group_by(geo_county, year, month) %>%
summarize(
total_bookings = n(),
any_ftp_bookings = sum(any_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(geo_county) %>%
# Sometimes bookings include smany numbers of individuals from other years,
# even if the rest of the data from those years are not available. Typicanyy,
# this is because their sentences are very long. Filter those years.
filter(total_bookings > 0.05 * max(total_bookings)) %>%
# Add county population totals
ungroup() %>%
left_join(pop_wi, by = c("geo_county", "year")) %>%
# Group by month and year to calculate per capita rate
group_by(year, month) %>%
summarize(
total_pop_represented = sum(pop),
per_capita_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(total_bookings) / sum(pop),
per_capita_ftp_bookings = (365 / days_in_month(first(month))) * 1e6 * sum(any_ftp_bookings) / sum(pop),
.groups = "drop"
) %>%
# Turn month and year into a date to order x-axis correctly
mutate(date = make_date(year = year, month = month))
combined_any_ftp_per_cap <- bind_rows(
mutate(d_tx_any_ftp_per_cap, state = "Texas"),
mutate(d_wi_any_ftp_per_cap, state = "Wisconsin")
) %>%
left_join(combined_tot_pop, by = c("year", "state"))
burglary_combined <- bind_rows(
mutate(burglary_tx, state = "Texas"),
mutate(burglary_wi, state = "Wisconsin")
)
alt_ftp_plot <- combined_any_ftp_per_cap %>%
ggplot(aes(x = date, y = per_capita_ftp_bookings)) +
geom_line(
aes(x = year, y = burglary, color = "Burglary"),
data = burglary_combined,
linetype = "dashed"
) +
geom_smooth(
aes(color = "Smoothed\naverage"),
method = "loess",
se = FALSE,
formula = y ~ x
) +
geom_line(aes(color = "FTP")) +
scale_x_date(
breaks = as_date(parse_date_time(2005:2018, "Y")),
labels = NULL
) +
expand_limits(y = 0) +
labs(
x = NULL,
y = "Annual bookings\n(per million pop.)"
) +
theme_bw() +
scale_color_manual(
name = "Booking\ntype",
values = c(
"Burglary" = "red",
"Smoothed\naverage" = "blue",
"FTP" = "black"
)
) +
facet_wrap(~state)
alt_representation_plot <- combined_any_ftp_per_cap %>%
ggplot(aes(x = date, y = total_pop_represented / pop)) +
geom_area(alpha = 1/2, color = "black") +
scale_x_date(
breaks = as_date(parse_date_time(2005:2018, "Y")),
labels = as.character(2005:2018)
) +
scale_y_continuous(labels = label_percent(accuracy = 10)) +
labs(
y = "Pct. of state\npop. represented",
x = NULL
) +
coord_cartesian(ylim = c(0, 0.5)) +
theme_bw() +
theme(
strip.text.x = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
facet_wrap(~state)
g_1 <- ggplotGrob(alt_ftp_plot)
# Add additional columns to second grobTable to account for missing legend
g_2 <- gtable_add_cols(ggplotGrob(alt_representation_plot), g_1$widths[14:15])
final_plot <- rbind(g_1, g_2, size = "first")
final_plot$widths <- unit.pmax(g_1$widths, g_2$widths)
grid.draw(final_plot)
Out of the 64 counties providing data, 60 provided jail rosters included in the figure above. The combined population of these counties is 13500000 people, or approximately 48.4% of Texas's population.
In Wisconsin, 25 counties provided full jail that allowed us to identify FTP. The combined population of these counties is 2600000 people, or 45.5% of Wisconsin's population.
In counties where individuals booked on any and all FTP charges can be distinguished, we see that there are 81 percent more bookings that include any FTP than include only FTP in Texas and 81 in Wisconsin.
d_tx_all_ftp_race_red_zip <- d_tx_all_ftp %>%
distinct(geo_county, booking_id, race, all_ftp, booking_date) %>%
# Remove individuals from out of county
semi_join(filter(d_origin_red, !any_out_of_county), by = "booking_id") %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_b = mean(race == "black", na.rm = TRUE),
ftp_pct_b = sum(race == "black" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_b = replace_na(ftp_pct_b, 0),
jail_pct_b = replace_na(jail_pct_b, 0)
) %>%
semi_join(
filter(d_tx_all_ftp_race, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_tx, by = c("geo_county", "year")) %>%
ungroup()
d_tx_all_ftp_eth_red_zip <- d_tx_all_ftp %>%
distinct(geo_county, booking_id, ethnicity, all_ftp, booking_date) %>%
# Remove individuals from out of county
semi_join(filter(d_origin_red, !any_out_of_county), by = "booking_id") %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_h = mean(ethnicity == "hispanic", na.rm = TRUE),
ftp_pct_h = sum(ethnicity == "hispanic" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_h = replace_na(ftp_pct_h, 0),
jail_pct_h = replace_na(jail_pct_h, 0)
) %>%
semi_join(
filter(d_tx_all_ftp_eth, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_tx, by = c("geo_county", "year")) %>%
ungroup()
d_wi_all_ftp_race_red_zip <- d_wi_all_ftp %>%
distinct(geo_county, booking_id, race, all_ftp, booking_date) %>%
# Remove individuals from out of county
semi_join(filter(d_origin_red, !any_out_of_county), by = "booking_id") %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_b = mean(race == "black", na.rm = TRUE),
ftp_pct_b = sum(race == "black" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_b = replace_na(ftp_pct_b, 0),
jail_pct_b = replace_na(jail_pct_b, 0)
) %>%
semi_join(
filter(d_wi_all_ftp_race, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_wi, by = c("geo_county", "year")) %>%
ungroup()
d_wi_all_ftp_eth_red_zip <- d_wi_all_ftp %>%
distinct(geo_county, booking_id, ethnicity, all_ftp, booking_date) %>%
# Remove individuals from out of county
semi_join(filter(d_origin_red, !any_out_of_county), by = "booking_id") %>%
group_by(geo_county, year = year(booking_date)) %>%
summarize(
jail_pct_h = mean(ethnicity == "hispanic", na.rm = TRUE),
ftp_pct_h = sum(ethnicity == "hispanic" & all_ftp, na.rm = TRUE) / sum(all_ftp, na.rm = TRUE),
n_bookings = n(),
n_ftp = sum(all_ftp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
ftp_pct_h = replace_na(ftp_pct_h, 0),
jail_pct_h = replace_na(jail_pct_h, 0)
) %>%
semi_join(
filter(d_wi_all_ftp_eth, pct_usable > 0.8),
by = c("geo_county", "year")
) %>%
left_join(pop_wi, by = c("geo_county", "year")) %>%
ungroup()After restricting to individuals from the county in question, among the counties in Texas providing enough information to analyze racial and ethnic disparities, 23% of individuals booked for any reason are Black, 22% of individuals booked for FTP alone are Black, while 10% of the population of these counties living below the poverty line are Black. In Wisconsin, 15% of individuals booked for any reason are Black, 15% of individuals booked for FTP alone are Black, while 2% of the population of these counties living below the poverty line are Black.
Among these counties in Texas, 61% of individuals booked for any reason are Hispanic, 61% of individuals booked for FTP alone are Hispanic, while 70% of the population of these counties living below the poverty line are Hispanic. In Wisconsin, 3% of individuals booked for any reason are Hispanic, 3% of individuals booked for FTP alone are Hispanic, while 4% of the population of these counties living below the poverty line are Hispanic.
combined_all_ftp_per_cap %>%
ggplot(aes(x = date, y = per_capita_ftp_bookings / per_capita_bookings)) +
geom_smooth(method = "loess", se = FALSE, formula = y ~ x) +
geom_line() +
scale_x_date(
breaks = as_date(parse_date_time(2005:2018, "Y")),
labels = as.character(2005:2018)
# labels = rep_along(2005:2018, "")
) +
scale_y_continuous(
labels = label_percent()
) +
expand_limits(y = 0) +
labs(
x = "Month of Booking",
# x = NULL,
y = "Bookings for failure to pay\n alone among all bookings"
) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_wrap(~state)
d %>%
select(
state = geo_state,
county = geo_county,
race,
ethnicity,
sex,
age,
zip,
booking_date,
booking_time,
release_date,
release_time,
charge,
statute,
severity,
release_type,
court,
warrant_type,
bond,
fine,
note,
length_of_stay,
ftp
) %>%
group_by(state, county) %>%
summarize_all(~ mean(! is.na(.)) > 0.8) %>%
ungroup() %>%
mutate(
state = case_when(
state == "tx" ~ "Texas",
state == "wi" ~ "Wisconsin"
),
county = str_replace_all(county, "_", " "),
county = str_to_title(county)
)## # A tibble: 92 × 22
## state county race ethnicity sex age zip booking_date booking_time
## <chr> <chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
## 1 Texas Anderson C… TRUE TRUE TRUE FALSE TRUE TRUE FALSE
## 2 Texas Angelina C… TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## 3 Texas Austin Cou… TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## 4 Texas Bell County TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## 5 Texas Blanco Cou… TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## 6 Texas Bosque Cou… TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## 7 Texas Bowie Coun… TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## 8 Texas Brazoria C… TRUE TRUE FALSE FALSE FALSE TRUE TRUE
## 9 Texas Brazos Cou… TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## 10 Texas Chambers C… TRUE TRUE TRUE FALSE FALSE TRUE TRUE
## # ℹ 82 more rows
## # ℹ 13 more variables: release_date <lgl>, release_time <lgl>, charge <lgl>,
## # statute <lgl>, severity <lgl>, release_type <lgl>, court <lgl>,
## # warrant_type <lgl>, bond <lgl>, fine <lgl>, note <lgl>,
## # length_of_stay <lgl>, ftp <lgl>
# Urbanicity
urbanicity <- read_csv(
path("data", "urbanicity.csv"),
show_col_types = FALSE
) %>%
filter(geo_state %in% c("tx", "wi")) %>%
mutate(
sample = geo_county %in% c(
d_tx_all_ftp$geo_county,
d_wi_all_ftp$geo_county
)
)
urbanicity <- bind_rows(
mutate(urbanicity, sample = FALSE),
filter(urbanicity, sample == TRUE)
) %>%
mutate(population = if_else(sample, "Sample", "Overall")) %>%
mutate(
urbanicity_code = factor(
urbanicity_code,
levels = c("Large central metro", "Large fringe metro", "Medium metro",
"Small metro", "Micropolitan", "Non-core")
),
state = if_else(geo_state == "tx", "Texas", "Wisconsin")
) %>%
select(-sample, -geo_state)
urbanicity %>%
count(state, population, urbanicity_code) %>%
group_by(state, population) %>%
mutate(p = n / sum(n)) %>%
ungroup() %>%
ggplot(aes(x = urbanicity_code, y = p)) +
scale_y_continuous(labels = label_percent(10)) +
geom_col(color = "black") +
facet_grid(rows = vars(state), cols = vars(population)) +
labs(x = "Urbanicity", y = "Proportion of counties") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
urbanicity %>%
group_by(state, population, urbanicity_code) %>%
summarize(pop = sum(pop), .groups = "drop_last") %>%
mutate(p = pop / sum(pop)) %>%
ggplot(aes(x = urbanicity_code, y = p)) +
scale_y_continuous(labels = label_percent(10)) +
geom_col(color = "black") +
facet_grid(rows = vars(state), cols = vars(population)) +
labs(x = "Urbanicity", y = "Proportion of population") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))