OPD Funding and Graphs
Robin Hardwick | Washington Defender Association
August 21, 2021
# Loading data
caseload_resources_2018 <- read_csv("Downloads/WDA Work/2018_wa_counties_caseload_resources.csv")
opd_10_101_over_time <- read_csv("Downloads/WDA Work/10.101 Funds Dispersed.csv")
calculating_10_101_2021 <- read_csv("Downloads/WDA Work/2021 OPD Funding Distributions.csv")
annual_pd_spending_by_county <- read_csv("Downloads/WDA Work/Annual PD Expenses Through 2019.csv")
county_stats_2020 <- read_csv("Downloads/WDA Work/County Statistics 2020.csv")
county_home_prices <- read_csv("Downloads/WDA Work/County_Home_Prices.csv")Introduction
The goal of the following report is to investigate the RCW 10.101.060 funding model Washington’s Office of Public Defense (henceforth referred to as OPD) and each county’s public defense spending. Through this investigation, we hope to identify trends in spending over time, as well as identify how the RCW 10.101 model could be improved. While the model is sufficient in ensuring counties receive equitable funding by population size and the number of cases filed (an approximation for how busy public defenders are in the county), it lacks consideration for other economic factors. For instance, with the two variables considered in RCW 10.101, the percentage of county residents who live in poverty, their incomes, and the costs of the housing market are not taken into account. We cannot deny the importance of considering a county’s caseload and population, but if a county has to fund more resources for its residents, they simply have less capacity than a richer county to dedicate funds to public defense. Thus, I push for the importance of beginning a conversation to reform the RCW funding model, wherein such socioeconomic complexities by county are considered.
Additionally, the report visualizes which courts by county have access to crucial resources, such as investigators and expert witnesses. Though not shown in conjunction with RCW funds, which counties have been accessing these resources should also inform and influence how funding is dispersed by county.
Total Public Defense Budget Over Time
To begin, I walk through how the public defense budget in Washington State (as recorded by OPD) has change over the years.
annual_pd_spending_by_county %>%
drop_na(Total_Public_Defense_Budget) %>%
group_by(Year) %>%
summarize(Yearly_Budget = sum(Total_Public_Defense_Budget)) %>%
mutate(Year = lubridate::ymd(Year, truncated = 2L)) %>%
ggplot(mapping = aes(x = Year, y = Yearly_Budget)) +
scale_y_continuous(labels=scales::dollar_format()) +
geom_col(fill = "aquamarine2", color = "white") +
robins_ggplot_theme() +
labs(y = "Total Public Defense Budget",
title = "Annual Public Defense Spending in Washington State Over Time",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
As the years continue, spending on public defense resources only seems to increase (and between 2005 and 2006 so too did the number of counties reporting their spending). This begs the question of what spending looked like prior to 2005, before data was able to be collected by a centralized entity. Were indigent defendants receiving adequate legal care, supervision, and resources? The lack of data here speaks volumes.
County Breakdown (Bar Chart)
annual_pd_spending_by_county_county <- annual_pd_spending_by_county %>%
drop_na(Total_Public_Defense_Budget) %>%
group_by(County) %>%
summarize(Total_Spent_Recorded = sum(Total_Public_Defense_Budget),
Average_Yearly_Spending = mean(Total_Public_Defense_Budget))
ggplot(data = annual_pd_spending_by_county_county, mapping = aes(x = fct_reorder(County,
Total_Spent_Recorded),
y = Total_Spent_Recorded)) +
geom_col(fill = "aquamarine2", color = "white") +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(x = "County",
y = "Total Reported Public Defense Budget",
title = "Total County Public Defense Funding as Reported to OPD (since 2005)",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
The above graph adds all of a county’s reported spending to OPD since 2005, and graphs it in comparison to other counties. We see a handful of counties clearly leading, King County the most so with spending over six hundred million. The counties to follow mostly do so in order of population, with some standouts—Yakima and Whatcom many spots above where they are in order of population. Other counties have such negligible differences between one another, raising the question should this be the case, given the diversity in socioeconomic factors between them? We will return to this question in the 10.101 model section.
ggplot(data = annual_pd_spending_by_county_county, mapping = aes(x =
fct_reorder(County,
Average_Yearly_Spending),
y = Average_Yearly_Spending)) +
geom_col(fill = "aquamarine2", color = "white") +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(x = "County",
y = "Average Yearly County Public Defense Funding",
title = "Annual Public Defense Spending on Average per Year as Reported to
OPD (since 2005)",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
In an attempt to look at spending on the yearly level (not overall since 2005), the above graph plots the average amount spent per year on public defense by county out of the data provided to OPD. County’s ordering has also not changed much, indicating that spending patterns have been very similar over time.
annual_pd_spending_by_county %>%
drop_na(Total_Public_Defense_Budget) %>%
mutate(County = reorder_within(County, Total_Public_Defense_Budget, Year)) %>%
ggplot(mapping = aes(x = County, y = Total_Public_Defense_Budget, fill = Year)) +
geom_col(color = "white", show.legend = FALSE) +
facet_wrap(~Year, scales = "free_y") +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
theme(strip.background = element_rect(color="white",
fill="lightgray",
size=1.5,
linetype="solid")) +
theme(strip.text.x = element_text(size = 10, colour = "gray4", family = "Times")) +
scale_x_reordered() +
robins_facet_theme() +
scale_fill_viridis(option = "D") +
labs(y = "Total Public Defense Budget",
title = "Annual Public Defense Spending Over Time in Washington State by County",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
Finally, we combine the above graphs to see change over time by county. As time has passed all counties hve grown gradually in terms of public defense spending except for King County, which has made astronomical leaps in their budget. These leaps completely exclude all other counties, whose budgets have barely changed if at all.
County Breakdown (Maps)
# For fill, we will want to do average and total! Could facet and have a map for every year as well
ggplot(data = pd_spend_geom, mapping = aes(geometry = geometry,
fill = Total_Spent_Recorded)) +
geom_sf() +
coord_sf() +
scale_fill_continuous(labels=scales::dollar_format()) +
scale_fill_viridis_c(direction = -1) +
theme_void() +
theme(plot.title = element_text(hjust = 0.5, family = "Times", face = "bold"),
plot.caption = element_text(face = "italic", family = "Times", hjust = 0),
legend.title = element_text(family = "Times"),
legend.text = element_text(family = "Times"),
plot.subtitle = element_text(hjust = 0.5, family = "Times", face = "italic")) +
labs(title = "Total County Public Defense Funding as Reported to OPD (since 2005)",
fill = "Total Reported Public Defense Budget
(in $)",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
ggplot(data = pd_spend_geom, mapping = aes(geometry = geometry,
fill = Average_Yearly_Spending)) +
geom_sf() +
coord_sf() +
scale_fill_continuous(labels=scales::dollar_format()) +
scale_fill_viridis_c(direction = -1) +
theme_void() +
theme(plot.title = element_text(hjust = 0.5, family = "Times", face = "bold"),
plot.caption = element_text(face = "italic", family = "Times", hjust = 0),
legend.title = element_text(family = "Times"),
legend.text = element_text(family = "Times"),
plot.subtitle = element_text(hjust = 0.5, family = "Times", face = "italic")) +
labs(fill = "Average Yearly County Public Defense Funding
(in $)",
title = "Annual Public Defense Spending on Average per Year as Reported to
OPD (since 2005)",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
This map visualizes two of our above bar charts in spatial form.
Controlling for Population
Briefly, we control for population and see how per capita measurements change the way counties spend money on public defense. This variable is not a perfect control, as it does not consider cases filed or other economic variables of interest, but is a good starting point for asking further questions. Our data looks at the total public defense spending in 2019.
county_stats_2020 %>%
mutate(County = fct_reorder(County, Amt_spent_per_capita_2019)) %>%
ggplot(mapping = aes(x = County, y = Amt_spent_per_capita_2019)) +
geom_col(fill = "aquamarine2", color = "white") +
coord_flip() +
scale_y_continuous(labels = scales::dollar_format()) +
robins_ggplot_theme() +
labs(y = "Amount Spend per Capita in 2019",
title = "Washington State Public Defense Spending per Capita by County in 2019",
subtitle = "Data from the Washington State Office of Public Defense",
caption = "© Robin Hardwick | Washington Defender Association")
When controlling for population, the order we saw before vastly shifts. Some higher spending counties (King, Whatcom) stay higher on the list while others have plummeted near to the bottom (Clark, Pierce, etc.). This is likely a result of the 6% base in RCW funding, which provides all counties a base amount of funding before considering other factors. This allows for smaller counties to receive funding that increases their per capita much higher than more populus counties.
pre_map <- full_join(wa_state_counties, county_stats_2020, by = c("id"))
pre_map[is.na(pre_map)] = 0
map_prop <- merge(wa_state_counties_geom, pre_map, by = "GEOID", all.x = TRUE)
ggplot(data = map_prop, mapping = aes(geometry = geometry,
fill = Amt_spent_per_capita_2019)) +
geom_sf() +
coord_sf() +
scale_fill_viridis_c(direction = -1) +
theme_void() +
theme(plot.title = element_text(hjust = 0.5, family = "Times", face = "bold"),
plot.caption = element_text(face = "italic", family = "Times", hjust = 0),
legend.title = element_text(family = "Times"),
legend.text = element_text(family = "Times"),
plot.subtitle = element_text(hjust = 0.5, family = "Times", face = "italic")) +
labs(fill = "$ Spent Per Capita",
title = "Public Defense Spending Per Capita in 2019",
subtitle = "Data from the Washington State Office of Public Defense",
caption = "© Robin Hardwick | Washington Defender Association")
The above graph depicts the same information as above, but in a spatial form. While the per capita amount varies, generally the westernmost counties have the highest spending.
10.101 Budget Over Time
Now, instead of looking at overall public defense spending over time, we zoom in on specifically on the 10.101 budget’s relationship with time.
opd_10_101_over_time %>%
drop_na(Funds_Dispersed_10_101) %>%
mutate(County = reorder_within(County, Funds_Dispersed_10_101, Year)) %>%
ggplot(mapping = aes(x = County, y = Funds_Dispersed_10_101, fill = Year)) +
geom_col(color = "white", show.legend = FALSE) +
facet_wrap(~Year, scales = "free_y", ncol = 3) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
theme(strip.background = element_rect(color="white",
fill="lightgray",
size=1.5,
linetype="solid")) +
theme(strip.text.x = element_text(size = 10, colour = "gray4", family = "Times")) +
scale_x_reordered() +
robins_facet_theme() +
scale_fill_viridis(option = "D") +
labs(y = "10.101 Funds Dispersed",
title = "10.101 Funds Distributed by OPD to WA Counties Over Time",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
With the exception of 2007 to 2008 (when the majority of counties, excluding Douglas, finally began to apply for funding), the amount of funds given does not seem to vary by much. The shape of the bars has stayed the same, making it difficult to determine if any counties have individually changed in the amount of 10.101 funding they have received over time. To take this closer look, I turn to animation in the below plot.
Animated!
opd_10_101_over_time <- opd_10_101_over_time %>%
mutate(Year = lubridate::ymd(Year, truncated = 2L))
animate <- ggplot(data = opd_10_101_over_time, mapping = aes(x = Year,
y = Funds_Dispersed_10_101,
color = County)) +
geom_line(show.legend = FALSE) +
facet_wrap(~County) +
scale_y_continuous(labels=scales::dollar_format()) +
robins_facet_theme() +
labs(y = "10.101 Funds Distributed by OPD",
title = "10.101 Funds Distributed Over Time by OPD",
subtitle = "Data from Katrin Johnson with OPD: Annual Public Defense Expenses thru 2019.xlsx",
caption = "
*29 counties did not report data in 2005. Douglas County is missing data in
2005—2008 and 2012
© Robin Hardwick | Washington Defender Association")
library(gganimate)
animate + transition_reveal(along = Year)
Here we are able to determine that Pierce, Snohomish, and Spokane have seen a slight increase over time in their amount of 10.101 funding, while King County has seen an up and down over the years. Feel free to compare the counties and their relationships over time with 10.101 using this figure.
10.101 Model
In the following section I begin our exploration of the specific facets of the 10.101 model, as well as visualizing the types of variables excluded and their impact on specific counties.
calculating_10_101_2021 %>%
mutate(County = fct_rev(County)) %>%
ggplot(mapping = aes(x = County, y = Total_Dist_Round,
size = `2019_Population`, color = `2019_Filings`)) +
geom_point() +
coord_flip() +
robins_ggplot_theme() +
scale_y_continuous(labels=scales::dollar_format()) +
scale_color_viridis(option = "C", direction = -1) +
labs(y = "Total 10.101 Funds Dispersed, Rounded",
size = "Population in 2019",
color = "Number of Case Filings in 2019",
title = "Factors Considered in the RCW 10.101 Model",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020 county disbursement estimates.xlsx",
caption = "© Robin Hardwick | Washington Defender Association")
Here we consider, in graph form, how the 10.101 formula decides to disperse its money. The more the county’s population and the more case filings in the previous year, the more funs allocated to them. This is shown through the transition from lighter colors to darker colors and smaller to larger points the farther to the right on the x-axis we look. With only these factors in mind, 10.101 seems to do an excellent job.
Poverty Levels
First, we introduce the poverty levels as recorded from 2015-2019 in OPD’s “County Statistics 2020” Excel sheet.
Total Public Defense Budget
county_stats_2020 %>%
mutate(County = fct_rev(County)) %>%
mutate(`Percent_Individuals_below_poverty_level_2015-2019` = parse_number(`Percent_Individuals_below_poverty_level_2015-2019`)) %>%
ggplot(mapping = aes(x = County,
y = `Amt_spent_on_public_defense_2019`,
color = `Percent_Individuals_below_poverty_level_2015-2019`)) +
geom_point() +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
scale_color_viridis(option = "C", direction = -1) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(color = "
Percentage of Individuals Below
the Poverty Level (2015-2019 estimate)",
y = "Total Funds Spent on Public Defense in 2020",
title = "Considering Povetry Level with on County's Public Defense
Budgets in 2020",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020 county
disbursement estimates.xlsx",
caption = "© Robin Hardwick | Washington Defender Association")
While not looking at 10.101 funds specifically but rather the county’s entire public defense budget, the above graph still makes multiple crucial connections. First of all, counties with a large amount of their population beneath the poverty level spend the least amount of money on public defense in 2020. Some counties spend the same amount as these with lower poverty levels, but the counties who are able to spend more on public defense (five million) have lower than 20% of their population living under the poverty level. The poverty level only continues to decline the higher the county’s public defense budget is. For instance, once it is over ten million, counties have less than 15% of their residents living under the poverty level.
10.101 Funds
county_stats_2020 %>%
mutate(County = fct_rev(County)) %>%
mutate(`2020_10.101_distribution` = parse_number(`2020_10.101_distribution`),
`Percent_Individuals_below_poverty_level_2015-2019` = parse_number(`Percent_Individuals_below_poverty_level_2015-2019`)) %>%
ggplot(mapping = aes(x = County,
y = `2020_10.101_distribution`,
color = `Percent_Individuals_below_poverty_level_2015-2019`)) +
geom_point() +
scale_color_viridis(option = "C", direction = -1) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(color = "
Percentage of Individuals Below
the Poverty Level (2015-2019 estimate)",
y = "10.101 Funds Distributed in 2020",
title = "Considering Povetry Level with the 10.101 Funding Distributions",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020
county disbursement estimates.xlsx",
caption = "© Robin Hardwick | Washington Defender Association")
Here we can directly look at how 10.101 funding discounts the percentage of a county’s residents who are below the poverty level. There are several counties who receive very similar funds (due to their similar population sizes and case filings), but have differing poverty rates, which impacts the amount of funds and resources on hand for public defense. Take Adams and Asotin, for instance. Many other counties have the same issue under the $150,000 mark, receiving similar funds despite their large difference in poverty levels.
Median Household Income
Now we turn to consider median household income and its impact on 10.101 funding.
Total Public Defense Budget
county_stats_2020 %>%
mutate(County = fct_rev(County)) %>%
mutate(`Median_household_income_2015-2019` =
parse_number(`Median_household_income_2015-2019`)) %>%
ggplot(mapping = aes(x = County,
y = `Amt_spent_on_public_defense_2019`,
color = `Median_household_income_2015-2019`)) +
geom_point() +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
scale_color_viridis(option = "C", direction = -1) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(color = "
Median Household Income
(2015-2019 estimate) in $",
y = "Total Funds Spent on Public Defense in 2020",
title = "Considering Income's Impact on a County's Public Defense
Budgets in 2020",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020 county disbursement estimates.xlsx",
caption = "© Robin Hardwick | Washington Defender Association")
Counties with larger median household income are able to put more money toward their county’s public defense budgets. The higher median incomes are, the more money the county collects in taxes, and thus the more resources at hand to allocate. Below we will look and see if OPD has been favoring counties with higher incomes to receive 10.101 funds that should be redistributed to counties with a lower median household income.
10.101 Funds
county_stats_2020 %>%
mutate(County = fct_rev(County)) %>%
mutate(`2020_10.101_distribution` = parse_number(`2020_10.101_distribution`),
`Median_household_income_2015-2019` =
parse_number(`Median_household_income_2015-2019`)) %>%
ggplot(mapping = aes(x = County,
y = `2020_10.101_distribution`,
color = `Median_household_income_2015-2019`)) +
geom_point() +
scale_color_viridis(option = "C", direction = -1) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(color = "
Median Household Income
(2015-2019 estimate) in $",
y = "10.101 Funds Distributed in 2020",
title = "Considering Income's Impact on the 10.101 Funding Distributions",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020 county disbursement estimates.xlsx",
caption = "© Robin Hardwick | Washington Defender Association")
While the trend is not as stark as above (counties with higher residents’ income always spending more on public defense), generally counties with higher household incomes are receiving the most 10.101 funds from OPD. Counties of note are King, Clark, Snohomish, and Pierce, all of whom have household incomes above all of the other counties yet still receive more funds than them (Spokane being an outlier).
Median Cost of a Home
Finally, we explore how the median cost of homes by county intersects with the county’s public defense spending and funds received by the OPD. This section uses the same data from OPD, but also data from the University of Washington’s State Housing Market Resources and Reports (specifically 2019, Quarter 4 to line up with the data obtained from 2019 from the past two sections).
Total Public Defense Budget
county_stats_2020 %>%
mutate(County = fct_rev(County)) %>%
ggplot(mapping = aes(x = County,
y = `Amt_spent_on_public_defense_2019`,
color = `Median_Home_Price_Q4_2019`)) +
geom_point() +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
scale_color_viridis(option = "C", direction = -1) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(color = "
Median Home Price
(2019, Quarter 4) in $",
y = "Total Funds Spent on Public Defense in 2020",
title = "Considering Median House Price's Impact on a County's Public Defense
Budgets in 2020",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020 county disbursement estimates.xlsx",
caption = "
* Gray points indicate an NA value.
© Robin Hardwick | Washington Defender Association")
This graph shows that generally, the higher the median home price, the more that county allocates on public defense. There is an argument to be made that more populated counties have more expensive housing markets, and thus of course would spend more on public defense due to more citizens and cases (both of which are included in the 10.101 model). We will see if this is true by comparing the median home price to the 10.101 allocations below.
10.101 Funds
county_stats_2020 %>%
mutate(County = fct_rev(County)) %>%
mutate(`2020_10.101_distribution` = parse_number(`2020_10.101_distribution`)) %>%
ggplot(mapping = aes(x = County,
y = `2020_10.101_distribution`,
color = `Median_Home_Price_Q4_2019`)) +
geom_point() +
scale_color_viridis(option = "C", direction = -1) +
coord_flip() +
scale_y_continuous(labels=scales::dollar_format()) +
robins_ggplot_theme() +
labs(color = "
Median Home Price
(2019, Quarter 4) in $",
y = "10.101 Funds Distributed in 2020",
title = "Considering Median House Price's Impact on the 10.101 Funding Distributions",
subtitle = "Data from Katrin Johnson with OPD: 10.101 2020 county disbursement estimates.xlsx",
caption = "
* Gray points indicate an NA value.
© Robin Hardwick | Washington Defender Association")
We see above that the higher the median home price is by county, the more that county spends on public defense. However, that trend is not reflected in the 10.101 funds. For the majority of counties (receiving below $150,000), they are receiving the same amount as other counties despite having very different median home prices. For instance, look at Wahkiakum, Kittitas, Jefferson, Island, and Douglas in comparison to Adams, Asotin, Columbia, and Stevens. Other counties, such as Clark, King, Pierce, and Skamania have the highest median home prices yet still receive the most 10.101 funds. Given how property taxes can provide local governments with more money, taking into consideration median house prices will allow counties who need more funds to receive an equitable amount.
Other Resources by County
The following section creates several spatial maps of resources available by county.
County’s Usage of Expert Witnesses
caseloads_2018_factors_tab <- caseload_resources_2018_factors %>%
select(Expert_Witnesses_Used_2018,
Expert_Witnesses_Used_2018_Factor) %>%
distinct(Expert_Witnesses_Used_2018, .keep_all = TRUE) %>%
arrange(Expert_Witnesses_Used_2018_Factor)
kable(caseloads_2018_factors_tab, format = "simple", col.names =
c("Courts Expert Witnessed Used In", "Legend Value"))| Courts Expert Witnessed Used In | Legend Value |
|---|---|
| Yes: all courts | 1 |
| No: District; Yes: Superior, Juvenile | 2 |
| No: Juvenile; Yes: Superior, District | 3 |
| No: District, Juvenile; Yes: Superior | 4 |
| No: Superior, Juvenile; Unanswered: District | 5 |
| No: Superior, Juvenile; Yes: District | 6 |
| No: all courts | 7 |
| NA | NA |
County’s Usage of Investigators
caseloads_2018_factors_tab2 <- caseload_resources_2018_factors %>%
select(Investigators_Use_2018,
Investigators_Use_2018_Factor) %>%
distinct(Investigators_Use_2018, .keep_all = TRUE) %>%
arrange(Investigators_Use_2018_Factor)
kable(caseloads_2018_factors_tab2, format = "simple", col.names =
c("Courts Investigators Used In", "Legend Value"))| Courts Investigators Used In | Legend Value |
|---|---|
| Yes: all courts | 1 |
| No: District; Yes: Superior, Juvenile | 2 |
| No: Juvenile; Yes: District, Superior | 3 |
| No: District, Juvenile; Yes: Superior | 4 |
| No: Superior, Juvenile; Unanswered: District | 5 |
| No: all courts | 6 |
| NA | NA |
Conclusion
In short, this report uncovers how economic variables (percentage of individuals under the poverty level, median income, and median home cost) informs public defense spending by county in Washington State. When comparing public defense spending overall with these three variables, we see generally that the less poverty and the higher resources (income/home cost), the more that county spends on public resources in total. However, when zooming in to look at RCW 10.101 funds in specific, many counties receive the same amount of funds as others despite how different poverty levels, income, and home costs are by counties.
The RCW 10.101 funding model must be revised to consider factors beyond population and case filings, as clearly there exist other influences on the amount a county is able to spend on public defense. Currently the author does not yet have the training to write such a model, but encourages OPD and the Washington State counties to take this information into consideration for funding purposes. After this fall the author will have taken a course on statistical modeling and should be able to present a revised model, alongside evidence and support for its robustness.
Other sections and graphs provide further context to the resources available in counties, alongside how public defense spending has changed (a slight increase) over time.
Bibliography
The author is thankful to both the Washington State Office of Public Defense (specifically Katrin Johnson for meeting with me to discuss OPD’s thoughts around RCW 10.101 and providing me with the data) and the University of Washington’s Center for Real Estate Research for publishing their 2019 reports of the housing market by county.
Please reach out to the author at robhardwi@reed.edu with any questions you may have.
This study was supported in part by the Paul K. Richter & Evalyn Elizabeth Cook Richter Memorial Fund.