County Dependence on
Monetary Sanctions:
Implications for
Women’s Incarceration
K at e K. O’N eill, T y ler Smi t h, a n d I a n K en n edy

Although men’s incarceration rates have declined in the United States, women’s have stayed steady and even
risen in some areas. At the same time, courts have increased their use of monetary sanctions, especially for
low-­level offenses. We propose that women’s incarceration trends can be partially explained by county dependence on monetary sanctions as a source of revenue. We suggest that monetary sanctions expose female
defendants to processes that increase their likelihood of incarceration, especially in counties more reliant on
monetary sanctions as a source of revenue, and where women’s poverty rates are high. Using data from
Washington State, we find county dependence on monetary sanctions is positively associated with rates of
women sentenced to incarceration. Although rural counties’ rates are higher, they depend on monetary sanctions no more than nonrural counties do.
Keywords: Incarceration; sex; gender; monetary sanctions; sentencing

Following several decades of persistent and exceptional growth, incarceration rates in the
United States began trending downward
around 2007 (Leigh 2020; Zeng 2017). These declines are certainly cause for optimism among
legal scholars and criminologists, who have
long noted not only that is such growth unsustainable (National Research Council 2014), but
also that the consequences of mass incarceration are far reaching, negative, and predomi-

nantly borne by poor, racialized communities
and individuals (Alexander 2010; Beckett 2018;
Clear 2009).
However, reductions in state prison populations have occurred almost entirely among men
(Sawyer 2018). White men’s incarceration rates
declined nationally by 11 percent from 2000 to
2016, White women’s increased by 44 percent,
and Black women remain twice as likely as
White women to be incarcerated (Sentencing

Kate K. O’Neill, Tyler Smith, and Ian Kennedy are Ph.D. students of sociology at the University of Washington,
United States.
© 2022 Russell Sage Foundation. O’Neill, Kate K., Tyler Smith, and Ian Kennedy. 2022. “County Dependence on
Monetary Sanctions: Implications for Women’s Incarceration.” RSF: The Russell Sage Foundation Journal of the
Social Sciences 8(2): 157–72. DOI: 10.7758/RSF.2022.8.2.08. We would like to thank Arnold Ventures for providing funding for this project (PI: Alexes Harris) and the Multi-­State Monetary Sanctions Study team and Research
Assistants who contributed to the data collection processes. Partial support for this research came from a Eunice
Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant, P2C
HD042828, to the Center for Studies in Demography & Ecology at the University of Washington. Direct correspondence to: Kate K. O’Neill, at oneillkk@uw.edu, 211 Savery Hall, Box 353340, Seattle, WA 98195-­340, United
States.
Open Access Policy: RSF: The Russell Sage Foundation Journal of the Social Sciences is an open access journal.
This article is published under a Creative Commons Attribution-­NonCommercial-­NoDerivs 3.0 Unported License.

158

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

Project 2018). In some states, including Washington State—the focus of this study—the
women’s prison population has continued to
grow even as men’s prison populations have decreased, and these sex differences are even
more pronounced when it comes to jail populations (Kang-­Brown and Subramanian 2017;
Sawyer 2018; Swavola, Riley, and Subramanian
2016).1 Although women’s incarceration rates
remain low relative to men’s, these diverging
patterns stress the importance of understanding the specific factors that explain why women
are becoming disproportionately affected by
criminal justice policies.
Largely missing from the research on women’s incarceration is the increasing use of monetary sanctions, also known as legal financial
obligations (LFOs), as a form of punishment in
the United States and its growing role in supplementing county budgets (Edwards 2020;
Martin 2018).2 Although LFOs are generally
touted as a kinder alternative to incarceration,
research indicates that people sentenced to
LFO debt suffer greatly for it (Harris 2016; Martin et al. 2018). The use of monetary sanctions
to supplement county budgets and pay for local
criminal legal systems shifts the literal costs of
doing justice from courts to defendants (Martin
et al. 2018) and incentivizes increased policing
of minor, misdemeanor offenses (Kohler-­
Hausmann 2013; Martin 2018). As local jurisdictions become increasingly dependent on LFO
revenue, it becomes even more important to
understand the potential impact on criminal
justice outcomes.
There is reason to believe women have been
especially hard hit by this shift. Although overall poverty rates have decreased substantially
since the 1970s, women remain disproportionately represented among America’s poor (McLanahan and Kelly 2006; Wagner 2019). People
who are poor are disproportionately represented in the criminal legal system in general
and among those carrying LFOs debt in par-

ticular (Bing, Pettit, and Slavinski 2022, this volume; Harris, Evans, and Beckett 2011; Hunt and
Nichol 2017; Peterson, Krivo, and Harris 2000).
Given women’s economically marginalized position, they may be more likely to request incarceration over LFOs during plea agreements or
sentencing arrangements, more likely to default on payments, and more likely to remain
under the supervision of the criminal legal system for extended periods of time. We may
therefore expect counties’ dependence on monetary sanctions to be associated with an increase in women sentenced to incarceration
because women may be more vulnerable to carceral sentencing as a result of their real or perceived inability to pay monetary sanctions.
Finally, there is reason to believe that the
relationship between LFO revenue and women’s incarceration will be stronger in rural
counties. Across the United States, rates of
women’s jail incarceration in small counties
have nearly doubled since 1970, but have remained relatively stable in mid-­size and large
counties (McCoy and Russo 2018). Reports produced by criminal justice nonprofits and research institutions have argued that upticks in
arrests for misdemeanor offenses and economic marginalization are key drivers of women’s incarceration in these spaces (Kajstura
2017; Kang-­Brown and Subramanian 2017; McCoy and Russo 2018). One potential reason is
that small and rural jurisdictions sentence defendants to monetary sanctions at higher rates
and with larger amounts than nonrural jurisdictions (Olson and Ramker 2001; Ruback and
Clark 2011). Given high poverty rates and growing jail populations, these increases are likely
associated with rural jurisdictions’ need to pay
for growing carceral and supervisory systems.
Thus the system of monetary sanctions places
rural counties in the precarious position of pursuing justice and revenue at the same time, potentially undermining the stated goals of the
criminal legal system (Martin 2018).

1. We use the term sex differences in our discussion of carceral trends because determinations regarding where
to house people sentenced to incarceration are generally made using biological sex (genital anatomy) criteria,
rather than gender identity criteria (National Center for Transgender Equality 2018).
2. As defined in the introduction to this volume, LFOs refer to “fines, fees, costs, restitution, surcharges, and
other financial penalties that are imposed on people when they encounter the United States criminal legal
system” (Harris, Pattillo, and Sykes 2022).

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coun t y dependence on moneta ry sa nctions

Considering these factors, we explore
whether women across the thirty-­nine counties
in Washington State are sentenced to incarceration at higher rates in counties that depend
more on monetary sanctions as a source of revenue. We also test whether these associations
are especially strong in rural counties. We combine data from 2007 to 2012 from the Washington State Administrative Office of the Courts
(AOC), the Washington State Auditor’s Office,
the Washington State Governor’s Office of Indian Affairs, and the U.S. Census Bureau to create a unique dataset used to trace associations
among county dependence on monetary sanctions, women’s sentencing rates, and county
characteristics. We then explore the effect of
rurality on monetary sanction dependence and
rates of women sentenced to incarceration, and
test whether women’s incarceration rates may
be explained by a county’s reliance on LFOs. We
find that as the percentage of county revenue
from fines and fees increases, so do rates at
which counties sentence women to incarceration. We find rural counties are no more dependent on monetary sanctions than are nonrural
counties, though they do sentence women to
incarceration at higher rates than nonrural
counties. Overall, our study indicates that rurality and county dependence operate as independent determinants of women’s incarceration
sentencing rates with potentially compounding
effects, but we do not find an interactive effect.
M o n e ta ry S a n c t i o n s a n d
Wo m e n ’ s I n c a r c e r at i o n

Monetary sanctions are an increasingly common and widespread form of punishment at all
levels of government and are imposed for all
manner of cases, including citations, traffic violations, misdemeanors, and felony charges
(Harris, Pattillo, and Sykes 2022, this volume).
Although it is difficult to estimate the number
of individuals who have outstanding legal debt,
a report by the National Center for Victims of
Crime (2011) estimates approximately ten million people across the country owe more than
$50 billion in restitution alone, and these already striking figures do not capture debtors or

159

debt attributable to fines, fees, or surcharges
associated with criminal legal contact. The rising use of monetary sanctions has been linked
to an increased dependence on fines and fees
for court revenue (Fernandes et al. 2019; Martin
et al. 2018). Karin Martin (2020) links this
change to the rising cost of criminal justice intervention and the fiscal pressures put on local
governments by the reduction of state budgets.
As jurisdictions across the country scrambled
to meet the financial burden of ballooning
criminal legal systems, the turn toward the collection of LFOs seemed like a straightforward
means of financial solvency. While recent research has demonstrated the inefficiency of
LFO collection systems and the negative consequences that these practices can have on indigent individuals, a majority of jurisdictions
use monetary sanction revenue as a portion of
their general funds (Fernandes et al. 2019;
Menendez et al. 2019).
Research on the system of monetary sanctions consistently finds it disproportionately
impacts economically marginalized people. In
their foundational work on monetary sanctions, Alexes Harris, Heather Evans, and Katherine Beckett (2010) demonstrate how LFOs increase social inequality by creating long-­term
debt for individuals who are unable to pay. Subsequent research has continued to outline the
various collateral consequences on people who
are poor, including housing instability, the loss
of driving and voting rights, continued entanglement in the criminal legal system, and damage to one’s credit (Cadigan and Kirk 2020; Colgan 2019; Link, Hyatt, and Ruhland 2020;
Pattillo et al. 2022, this volume). Thus the detrimental effects of LFOs on economically marginalized individuals and communities has become increasingly clear, an understanding
reiterated by a number of articles in this volume (Bing, Pettit, and Slavinski 2022; Boches et
al. 2022; Harris and Smith 2022; Pattillo et al.
2022; and Sykes et al. 2022).
Unfortunately, few studies have examined
whether monetary sanctions have differential
impacts on women, though this may be the
case.3 First, women are more likely to be repre-

3. Daniel Boches and his colleagues (2022, this volume) provide an interesting examination of how LFOs affect
family members indirectly. They point out that female partners and mothers of men who are imposed LFOs are

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160

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

sented in cases involving lower-­level offenses
and it is these types of offenses for which LFOs
are the predominant form of punishment.
Lindsay Bing, Becky Pettit, and Ilya Slavinski
(2022, this volume) demonstrate this directly,
showing that women made up between 10 and
14 percent more of the fine-­only misdemeanor
cases in Texas than cases involving more serious offenses. This means policy changes that
increase policing of misdemeanor offenses are
especially impactful on women (Chesney-­Lind
and Pasko 2013; Schwartz 2013), and attempts
to shift sentencing toward noncarceral punishments likely increase the number of women
carrying legal debt. Second, the financial hardships created and exacerbated by legal debt
may be especially pronounced among women
because women are more likely to experience
economic disadvantage than men (McLanahan
and Kelly 2006; Wagner 2019). If counties are
increasingly relying on monetary sanctions to
fund their legal systems, or are more likely to
impose monetary sanctions as a form of punishment, then the economic circumstances of
women become increasingly important for determining sentencing outcomes.
The fact of women’s economic precarity can
help us theorize why LFO sentencing rates
would be related to incarceration sentencing
rates. First, women may be more likely to opt
for short stints of incarceration than for legal
debt. If they are unable to afford the LFOs they
would accrue, then they may seek to serve their
punishment without the associated financial
burden. Second, women may be more likely to
default on legal debt and be incarcerated for
nonpayment. Finally, the imposition of LFOs
would increase their interaction with the courts
and leave them “tethered” to the criminal justice system (Harris 2016). This tethering may
make women debtors more visible to law enforcement, increasing their risk of arrest, even
as their newfound status as “repeat offenders”
places them at higher risk of receiving a carceral sentence. Thus inability to pay legal debt
is likely one of many mechanisms through
which low-­income women find themselves disproportionately represented among those in-

volved with the criminal justice system. We cannot observe these mechanisms directly with
our data, but the plausibility of these situations
motivated us to examine the linkage between
the use of LFOs as a form of punishment and
the number of women sentenced to incarceration.
Spat i a l Tr e n d s i n
M o n e ta ry S a n c t i o n i n g

Recent research on monetary sanctioning has
stressed the importance of analyzing the consequences of monetary sanction dependence
as a spatially contingent phenomenon (DOJ
2015; Edwards 2020). Studies on the spatial determinants and correlates of monetary sanctions provide a roadmap to understanding the
distribution of legal debt across the United
States. Research by Alexes Harris and her colleagues (Harris 2016; Harris, Evans, and Beckett
2011) suggests that particular counties and ethnic groups are disproportionately saddled with
LFO debt. Additional reporting from Governing
magazine finds that small towns and rural areas across the United States especially depend
on fines and fees as a source of county revenue,
some jurisdictions reporting upward of 90 percent of their general revenues in 2017 and 2018
as having been generated by monetary sanctions (Maciag 2019). Finally, Gabriela Kirk and
her colleagues (2022, this volume), show how
relationships between defendants and court actors in small jurisdictions can impact court
decision-­making. They found court actors in
rural settings often knew defendants more personally and were keenly aware of their financial
precarity, but that the same court actors felt restricted in their ability to apply discretion in
sentencing because of LFO laws and policies.
These studies, as well as broader examinations of LFO statutes, demonstrate the “decentralized, poorly coordinated, and inconsistent”
nature of LFO practices across jurisdictions
and the ways in which they “concentrate negative impacts among people with low incomes”
(Friedman et al. 2022, this volume). Given that
court systems are organized by geographic
area, dependence on monetary sanctions and

often coerced into paying the legal debt themselves. These arrangements can cause serious tension in familial
relationships.

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coun t y dependence on moneta ry sa nctions

what it means for both individual and county-­
level criminal justice outcomes cannot be
wholly disentangled from the structural characteristics of the spaces in which the system of
monetary sanctions operates.
These localized contingencies are especially
important when we consider their probable
impact on women’s incarceration rates. Although literature on the spatial determinants
and correlates of monetary sanctions is limited,
sociological and criminological literature on
the spatial distribution of incarceration and
criminal justice contact consistently find location to be an important predictor of carceral
trends (Clear 2009; Sampson 2012). Criminologists who study trends in women’s justice involvement in particular have long argued that
structural explanations of women’s offending,
arrest, and incarceration explain how these
phenomena are patterned across time and
space more effectively than do behavioral explanations (Chesney-­Lind and Pasko 2013;
Schwartz 2013; Steffensmeier and Allan 1996).
In addition to being leaders in LFO revenue
generation, rural counties are also implicated
in maintaining and even increasing women’s
incarceration rates in the face of men’s decelerating rates (McCoy and Russo 2018; Sentencing Project 2019). Feminist scholars have focused broadly on how and why tough-­on-­crime
policies have increased women’s visibility and
representation in the criminal legal system, but
few have explored how jurisdictional attempts
to pay for the rapid expansion of these systems
may themselves be tied to increases in women’s
criminal justice contact. Given the demonstrated economic precarity of women in rural
areas (Snyder and McLaughlin 2004), one might
expect these factors combine to make it more
difficult for women in rural areas to pay off
monetary sanctions than women in nonrural
areas, and more vulnerable to incarceration.

161

C u rr e n t S t u dy

Overall, we suggest that an association between
recent trends in women’s incarceration rates
(National Research Council 2014), the emergence of monetary sanctions as a sentencing
option (Harris, Evans, and Beckett 2010), and
rural areas’ alleged higher dependence on
monetary sanctions as a source of revenue (Olson and Ramker 2001; Ruback and Clark 2011).
In counties where the system of monetary sanctions is a significant revenue generator, LFOs
may take on a primary role in punishment. This
increased use of legal debt may leave women
particularly vulnerable to circumstances that
increase their likelihood of arrest and incarceration. These factors coalesce to increase rates
of women arrested, tried, and sentenced to incarceration while women’s economically marginalized position makes it harder for them to
pay off their legal debts and remove themselves
from criminal legal supervision. We expect that
as county dependence on monetary sanctions
increases, so will rates of women sentenced to
incarceration. Further, because these factors
are likely stronger in smaller, poorer jurisdictions, we expect this association to be stronger
in rural counties.
Data

We calculate county-­level women’s incarceration sentencing rates between 2007 and 2012
using data from the Washington Administrative Office of the Courts.4 The AOC is responsible for the supervision and information management for every court jurisdiction within the
state. The dataset includes case and defendant
information for thirty-­nine superior courts,
sixty-­one district courts, and ninety-­seven municipal courts.5 Each case has a coded indicator
of whether incarceration was imposed during
sentencing. In superior court cases, a dichotomous identifier indicates whether an individual

4. For more details about the collection of administrative data within Washington, and across all of the sites in
the larger study, see the introduction to this volume (Harris, Pattillo, and Sykes 2022). Our primary reason for
selecting Washington as our case study was that it was one of two states that contained full sentencing data
across a significant time period.
5. Unfortunately, the data from the Washington AOC does not include the Seattle Municipal court system, which
maintains its own separate database. We recognize that the omission of such a large municipal district could
bias our results. We therefore ran separate models that excluded the other courts in King County as a robustness
check. Differences in coefficients between the models were nominal and the substantive impact on our variables

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162

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

was sentenced to prison or jail. For district and
municipal courts, codes indicate whether the
defendant was sentenced to jail and the number of days that were sentenced. We used these
measures to generate two dichotomous variables tracking whether defendants were sentenced to jail (365 or fewer days of incarceration) or prison (366 or more days). We then
summed affirmative cases within counties, by
year, and generated women’s annual jail and
prison sentencing rates per every hundred
thousand adult women.
Although most cases had gender information for defendants, this information was unavailable for a subset of cases.6 For these, we
imputed defendant gender based on people’s
first names, using the gender package in the R
programming language. This package uses
birth year and first name to find the proportion
of people assigned male or female at birth with
that name in that year. It then imputes a gender
designation based on this information. The
process was 98 percent accurate when this information was available, and results were substantively identical in models comparing imputed versus provided gender designation data.
Information on county budgets came from
public records generated by the Office of the
Washington State Auditor, which is responsible
for overseeing the collection and usage of state
and local government funds. Information collected by the state auditor’s office includes an
accounting of all revenues and expenditures for
each county government within a given fiscal
year. This data indicated the total amount of
county revenue generated through the collec-

tion of financial penalties, including court-­
ordered obligations from both criminal and
traffic offenses. We calculated the annual percentages of county revenue derived from fines,
fees, and penalties by dividing revenue generated from these penalties by the total county
revenue and multiplying the resulting proportion by one hundred.7 This gave us a measure
of how much a county depended on monetary
sanctions for revenue generation each year, relative to other revenue sources. Given county
dependence on monetary sanctions may exert
both an immediate and lagged effect we control
for both contemporaneous and prior (t-­1) dependence on monetary sanctions in our final
models.
Demographic and population information
for each county comes from the Census Bureau.
Annual population estimates are provided by
the Census Bureau’s Population Estimates Program, which uses current data on births,
deaths, and migration to calculate population
change since the most recent decennial census
for federal, state, county, and local entities.8 Demographic information on the gender, age,
race, and poverty composition of each county
are fixed estimates pulled from the 2010 Decennial Census. Given our focus on adult incarceration sentencing rates, we exclude county residents under the age of eighteen from our
sentencing rate calculations. We estimate the
number of adult female residents by multiplying the estimated total population of the county
in a given year by the proportion of the population that is female, and then by the proportion
of the population age eighteen or older. We also

of interest was unchanged. This suggests that mechanisms operating within King County are similar in effect
to those operating in other counties, and that the omission of Seattle Municipal data does not substantially affect our findings.
6. We use the term gender here because our data may be more reflective of individual gender identities than
biological sex assigned at birth (National Center for Transgender 2018). Washington State lists “sex” (male,
female, or X) on its driver’s licenses, but allows residents to request a “change of gender designation” and display
their gender identity on their license in the “sex” field. In addition, residents can change their names and sex
designations with the Social Security Administration to correct their records and align them with their gender
identity.
7. Total county revenue was the sum of all revenue from fines and penalties, taxes, license and permits, intergovernmental grants, usage fees, rents and leases, investment interest, and other miscellaneous sources.
8. Census Bureau, “Population and Housing Unit Estimates,” https://www.census.gov/programs-surveys​/popest
.html (accessed August 19, 2021).

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coun t y dependence on moneta ry sa nctions

created a dichotomous variable to indicate
whether the county is considered a rural county
according to the standards set by the Washington State Office of Financial Management. In
accordance with this classification, counties
whose population densities were fewer than
one hundred people per square mile, or that
were smaller than 225 square miles, were
marked as rural in our analyses. One county
saw its status change from rural to nonrural
halfway through the six-­year data collection period given a gradual increase in its population
density. All other counties were consistently rural or nonrural throughout collection.
The racial and ethnic composition of Washington State’s rural counties differ from national norms and suggest the inclusion of separate controls for race and ethnicity. Nationally,
89 percent of rural residents are White, non-­
Hispanic (Parker et al. 2018). Only 76 percent
of rural Washingtonians in our analyses are
White, however, in spite of the fact that Washington is on average slightly whiter than the
rest of the country. An above-­average portion
of Washington’s non-­White population—particularly non-­White Hispanic persons—live in
rural areas; of the ten counties in Washington
with the largest Hispanic populations eight
are rural, and two of those rural counties are
majority Hispanic. Further, the demographics
of Washingtonians of color differ substantially
from national averages, particularly in regard
to its below-­average proportion of Black residents and above-­average proportion of Asian
residents. Given that the legal system in the
United States produces unequal outcomes
based on race and ethnicity, counties likely
vary in terms of their dependence on monetary sanctions and rates of women sentenced
to incarceration depending on the extent to
which their populations are racialized. We
therefore include both the percentage of people who are non-­White in the census, regardless of ethnic identification, and the percent-

163

age of people who are Hispanic, regardless of
racial identification, as two separate covariates
in our models. In doing so, we account for
both rural Washington’s unique demographic
composition and the discriminatory conditions experienced by non-­White and Hispanic
residents.
Finally, we include two controls for varying
jurisdictional arrangements within counties.
First, Washington State contains the seventh
largest Native American population in the
United States and about half of all Washington
State counties include federally recognized Native American reservations, officially referred to
as Indian Country by state.9 Because Native
Americans are overrepresented among those
who experience criminal justice contact (Males
2014; Nielsen and Silverman 1996), and sovereign reservations often maintain their own judicial systems, we considered it important to
capture the dynamics between Native American
populations, Tribal Court sovereignty, and sentencing rates. We generated a fixed, dichotomous variable measuring whether each county
contains Indian Country based on information
from the Washington State Governor’s Office of
Indian Affairs. This measure includes twenty-­
nine federally recognized tribes and their land.10
As a second jurisdictional control, we include a measure of the number of municipal
courts (sometimes called city courts) within a
county. Research has recognized the tendency
of municipal courts in particular to focus on
generating revenues through legal fines and
fees imposed for low-­level or misdemeanor
crimes (Fernandes et al. 2019). Although we include municipal-­level cases in our sentencing
rate, we recognize that these courts may have a
heavier influence on the relationship between
revenue generation and sentencing rates than
other court levels. We therefore include a fixed
count of the number of municipal courts operating within each county as indicated by the
Washington State Court Directory.11 Notably,

9. World Atlas, “States with the Largest Native American Populations,” https://www.worldatlas.com​/articles/us
-states-with-the-largest-native-american​-populations.html (accessed August 19, 2021).
10. Five of the thirty-­four tribes in Washington State are not eligible for inclusion in these analyses because they
are not federally recognized (see University of Washington 2020).
11. Washington Courts, “Washington State Court Directories,” https://www.courts.wa.gov/court_dir (accessed
August 19, 2021).

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16 4

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

Table 1. Descriptive Statistics
N
(Total
Observations)

n
(Counties)

Mean

Standard
Deviation

234

39

1,618.26

714.13

Focal variables
Female sentencing rate
County dependence

234

39

1.66

1.02

County dependence(t-1)

195

39

1.68

1.03

Rurality

234

39

0.78

0.41

Percent in poverty*

234

39

12.83

3.39

Percent under eighteen*

234

39

21.07

4.82

Percent non-White*

234

39

24.79

13.89

Percent Hispanic*

234

39

14.73

14.56

Indian Country*

234

39

0.51

0.50

Municipal courts

234

39

2.36

3.51

County population

234

39

171,822.70

338,420.10

County characteristics

Source: Author’s calculations.
*Denotes time-invariant (fixed) variable

the population size of a municipality and number of municipal courts are inextricably linked:
larger cities and towns have more municipal
courts. As a result, our municipal court count
is strongly associated with our measure of
county population (α = 0.82), and moderately
associated with our measure of rurality
(α = –­0.46): These associations are likely because as county population increases so does
the number of municipal courts, and rural
counties’ populations are generally smaller
than those of nonrural counties.
Methods

We posed three hypotheses in our analysis to
explore the associations between rurality, female sentencing and incarceration, and monetary sanctions.
H1: Rurality is positively associated with
county dependence on monetary sanctions.
We test the first hypothesis using a random-­
effects model. The random-­effects model is
more flexible than the fixed-­effects in that it
allows us to explore the potential effects of

time-­invariant variables such as percentage of
county residents in poverty. We use county-­
specific random intercepts to account for the
observation interdependence within counties
over time. These intercepts include a county-­
specific error term that is assumed to be uncorrelated to the other covariates in the model. Effects were estimated using generalized least
squares.
H2: Rurality is positively associated with female incarceration sentencing rates.
H3: County dependence on monetary sanctions is positively associated with female
sentencing rates.
We test our second and third hypotheses using random-­effects negative binomial models.
As with the first hypothesis, we use random effects to control for observation interdependence. In addition, most counties’ annual female sentencing rates are low, and we find that
our measure of counties’ annual female sentencing rates overdispersed (the variance exceeds the mean), indicating OLS and Poisson

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coun t y dependence on moneta ry sa nctions

models will produce biased coefficient estimates. Therefore, we use negative binomial
models, which include a parameter to account
for this overdispersion and are designed to
model count outcomes, such as a count of how
many women per hundred thousand in the
population are sentenced to incarceration each
year. Given strong associations between many
of our control variables with county population
size, we exclude county population from our
final negative binomial models.

Table 2. Random Effects: County Dependence on
Monetary Sanctions Regressed on Rurality
Observations

234

Counties
Intercept

39
11.47
(9.34)

Focal variables
Rurality
County characteristics
County population

R e s u lt s

Results generally support our hypothesis regarding the relationship between women’s carceral sentencing and county dependence on
monetary sanctions. We also find, however,
that rurality has little effect on monetary sanction dependence, despite its association with
women’s incarceration.

Percent in poverty

R e l at i o n s h i p s Am o n g
R u r a li t y, C o u n t y D e p e n d e n c e ,
and Sentencing

Percent Hispanic

Our first hypothesis explores the relationships
between rurality, sentencing, and county dependence on monetary sanctions.
As table 2 shows, our models show no significant difference between dependence on
monetary sanctions in rural and nonrural
counties. Notably, although rurality and the
percentage of residents in poverty is fairly
strongly correlated (α = 0.60 in 2012), the effect
of rurality is insignificant regardless of the inclusion of the percent in poverty variable.12 A 1
percent increase in residents in poverty, however, is associated with a 0.10 percent increase
in county dependence on monetary sanctions.
These findings indicate dependence on monetary sanctions may be more closely related to
poverty than to rurality, suggesting economic
motivations for counties’ turn to fines and fees
as a source of county revenue.
As table 3 shows, models support our second hypothesis, that rurality is positively associated with female incarceration. We find that
county rurality is associated with a 23 to 27 per-

165

Percent under eighteen
Percent female
Percent non-White

Indian Country
Municipal courts

–0.21
(0.17)
–8.05e-07
(7.98e-07)
0.10*
(0.05)
–0.005
(0.06)
–0.25
(0.18)
–0.01
(0.04)
0.03
(0.04)
–0.48
(0.32)
0.07
(0.07)

Source: Author’s calculations.
Note: Standard errors in parentheses.
*p≤.05, **p≤.01, ***p≤.001

cent increase in rates of women sentenced to
incarceration in models 1 and 2.13
In addition to supporting the second hypothesis, findings align with previous research
and arguments on geographic determinants of
incarceration rates across the United States
(McCoy and Russo 2018; Sentencing Project
2018). In fact, all county characteristics included in models 1 and 2 are associated, to varying degrees, with rates of women sentenced to
incarceration.
The third hypothesis, that county dependence on monetary sanctions is positively associated with female incarceration sentencing
rates, is supported for both contemporaneous

12. Notably, rural × percent in poverty interaction terms were statistically insignificant in supplementary analyses.
13. For interpretation, we convert negative binomial coefficients into percent changes in-­text, using the following
formula: (expβ-­1)×100=percent change in female sentencing rates

r sf: t he russell sage f ou n dat ion jou r na l of t he so ci a l sciences

166

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

Table 3. Random-Effects Negative Binomial Model: Rate of Women Sentenced to
Incarceration Regressed on County Dependence on Monetary Sanctions
Observations
Counties

Intercept
Focal variables
County dependence
County dependence(t-1)
Rurality
County characteristics
Percent in poverty
Percent under eighteen
Percent non-White
Percent Hispanic
Indian Country
Municipal courts
BIC

234
39

195
39

Model 1

Model 2

2.42***
(0.40)

2.70***
(0.42)

0.22***
(0.02)
—
0.21*
(0.09)

0.17***
(0.04)
0.11*
(0.04)
0.24**
(0.09)

–0.04*
(0.01)
0.08***
(0.02)
–0.04***
(0.01)
0.02
(0.01)
0.33***
(0.10)
0.06***
(0.02)
3,368.771

–0.05**
(0.02)
0.07***
(0.02)
–0.03**
(0.01)
0.02
(0.01)
0.35***
(0.10)
0.05**
(0.02)
2,798.741

Source: Author’s calculations.
Note: Standard errors in parentheses.
*p≤.05; **p≤.01; ***p≤.001

county dependence on monetary sanctions and
the prior year’s county dependence on monetary sanctions.14 Model 1 includes a contemporaneous measure of county dependence on
monetary sanctions; model 2 includes both
contemporaneous and the prior year’s dependence on monetary sanctions. In model 1, a 1
percent increase in the percentage of county
revenue derived from monetary sanctions is as-

sociated with a 23 percent increase in rates of
women sentenced to incarceration. In model 2,
we look at both current and prior dependence
on monetary sanctions, and find that a 1 percent increase in current county dependence is
associated with an 18 percent increase in rates
of women sentenced to incarceration. The prior
year’s dependence on monetary sanctions is
also associated with increases in rates of

14. In addition to main effects in both models, we tested interactions between our controls and our variables of
interest. None of the tested interactions were significant and the inclusion of interactions did not affect the
magnitude of our main effects. We also tested a measure of income inequality as an addition control but found
that its inclusion did not improve model fit. The interaction between rurality and income inequality was significant during these additional tests. This may indicate that although generally places with more income equality
tend to have fewer women incarcerated, that association is essentially not present in rural areas. However, because it did not contribute to explaining the variance in our model, we chose not to include it.

r sf: t he russell sage f ou n dat ion jou r na l of t he so ci a l sciences

coun t y dependence on moneta ry sa nctions

women sentenced to incarceration: a 1 percent
change in the previous year’s county dependence on monetary sanctions is associated with
a 12 percent increase in sentencing rates. These
results support the third hypothesis in that
county dependence on monetary sanctions is
positively associated with rates of women sentenced to incarceration.
Research on socioeconomic disparities in
incarceration leads us to expect that counties
with higher poverty should sentence women to
incarceration at higher rates, all else equal.
However, our regression results showed a negative coefficient for the relationship between
poverty and women’s incarceration sentencing.
This finding was initially puzzling, but we believe that it reflects the complex relationship
between county dependence on monetary
sanctions and poverty. We operationalize
county dependence as a measure of the percentage of LFOs collected by counties within a
given fiscal year relative to overall revenue. Unlike the sanctioning of LFOs, the collection of
LFOs is directly related to the economic circumstances of individuals who experience such
sanctions. Considering this, we would expect
counties with higher poverty rates to collect a
smaller proportion of sentenced LFO payments
than more affluent counties given residents’ inability to pay. Because our model shows a
strong positive effect between reliance on monetary sanctions and rates of women sentenced
to incarceration, it may be that the effect of poverty is captured by our county dependence variable. However, it is always possible that the
unique demographic or legal characteristics of
Washington could explain these unexpected
findings. Teasing out such associations is beyond the scope of this article.15 That said, we
leave poverty in our model as a control in order
to capture any potential residual effects and encourage future researchers to more formally
test the hypotheses implied by our results.

167

A second result standing in opposition to
findings in earlier studies is that the percentage
of non-­White residents per county is associated
with decreases in rates of women sentenced to
incarceration. However, a direct interpretation
of our race and ethnicity coefficients is complicated by their strong relationships with one another. Our results imply that, all else equal,
women’s incarceration rates are slightly lower
in counties with larger percentages of people
who are racialized as Black, indigenous, people
of color (BIPOC). However, changes in the percentage of BIPOC residents and percent of Hispanic residents both imply changes in percentages of people racialized as BIPOC. Still,
although the effect of the percentage of BIPOC
residents is small in magnitude, it is statistically significant and certainly surprising.
I n d i a n C o u n t ry, C o u n t y
D e p e n d e n c e o n M o n e ta ry
Sanctions, and Sentencing

In the course of validating our hypotheses, a
clear pattern regarding Indian Country, monetary sanctions, and sentencing emerged.
Counties that contain Indian Country have significantly higher sentencing rates than those
without Indian Country. The magnitude of
these effects varies slightly across models, but
on average Indian Country is associated with
rates of women’s incarceration being 40 percent higher than other areas of the state. Further, during our study period counties containing Indian Country generated less revenue
from monetary sanctions than other counties
did. Given the presence of sovereign criminal
legal systems and courts on many Native American reservations across the United States, this
result makes sense. Counties containing Indian Country have portions of their offending
populations ineligible for processing in the
courts captured in these analyses and thus are
unable to collect monetary sanctions from de-

15. Previous research has explored the association between macro-­economic factors and monetary sanctions.
Frank Edwards (2020) finds differences in the collection of LFOs by courts before and after the Great Recession
of 2008. However, he did not find a connection between local finances and low-­level criminal justice debt. Instead, differences were explained by increased caseloads and that “policing, traffic enforcement, and prosecutorial decision-­making are likely the drivers of these shifts.” This study demonstrates how criminal justice outcomes
vary not only by the individual characteristics of defendants, but also by physical location, timing, and jurisdictional characteristics.

r sf: t he russell sage f ou n dat ion jou r na l of t he so ci a l sciences

168

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

fendants processed in tribal courts. However,
the finding that rates of women sentenced to
incarceration are actually greater in counties
containing Indian Country cannot be explained
away by referencing the mechanics of tribal versus nontribal courts. We had anticipated that
counties containing Indian Country would
have lower sentencing rates because portions
of their populations are ineligible for sentencing in the courts captured in these analyses.
Instead, we find the opposite: counties containing Indian Country sentence women to incarceration at higher rates than do counties that
do not.
D i s c u s s i o n a n d C o n c lu s i o n

Our results highlight the importance of exploring the underlying mechanisms that drive
trends in women’s incarceration and how they
relate to the increasing use of monetary sanctions as a form of punishment. We find that the
more reliant counties in Washington State are
on monetary sanction revenue, the higher their
rate of sentencing women to incarceration.
This study thus takes the first step in establishing a relationship between monetary sanction
revenue and the punishment of women. Still,
the relationship between monetary sanctions,
local criminal legal practices, and the economic marginalization of women is multifaceted, and more work is needed to disentangle
these complex relationships. One possible explanation for the association between county
dependence on monetary sanctions and women’s incarceration is the increased policing of
low-­level crimes. Low-­level offenses have always
made up a larger share of women’s offending
than men’s (Chesney-­Lind and Pasko 2013).
Also, women may be especially likely to be
swept into the system in counties where revenues generated off the policing of these low-­
level offenses are used to keep penal systems
afloat. Our findings may also be explained by
what Franklin Zimring and Gordon Hawkins
(1991) call “the correctional free lunch.” From
this perspective, it may be that counties more
reliant on monetary sanctions are more likely
to sentence women to incarceration as a way to
shift the cost burden of punishment onto the
state. Of course, future studies will need to examine these potential links more concretely.

Specifically, qualitative research on jurisdictional policing and judicial practices could
shed light on how and why the system of monetary sanctions influences such practices. Statistical work could also be done to examine the
magnitude of low-­level offenses in moderating
this relationship.
Our findings in rural counties present a
more nuanced explanation of women’s carceral trends than those suggested in much of
the criminal justice reports and academic research. Although rurality is indeed associated
with higher rates of women sentenced to incarceration, it is not associated with higher levels of dependence on monetary sanctions as a
source of county revenue. It appears instead
that dependence on monetary sanctions and
rurality operate independently of one another
to increase rates of women sentenced to incarceration across the state. This suggests that
theories linking rural jurisdictions directly to
revenue-­generating policing and punishment
practices are overlooking important explanatory factors. Again, the economic marginalization of women in rural spaces may explain why
women in these areas are sentenced to incarceration at higher rates than their counterparts in nonrural areas (Barnett and Mencken
2002; McLanahan and Kelly 2006; Snyder and
McLaughlin 2004). Although we did not find an
association at the sentencing level, it may still
be possible that LFOs function as a bridge over
which the reduced economic means of rural
women leads to incarceration. Research at the
individual level is needed to validate this proposed relationship. We recommend that future
researchers explore the relationship between
individual women’s debt and likelihood of incarceration for infractions tied to its nonpayment and increased supervision.
These findings raise several directions of exploration in addition to those discussed. One
pressing question is whether the relationship
between rurality and dependence on monetary
sanctions for revenue operates at the city or
town level rather than county level. A recent
analysis by Governing magazine of city and
town budgets in 2017 and 2018 identified three
localities in Washington State that credited
more than 10 percent of their general revenues
to monetary sanctions (Maciag 2019). In our

r sf: t he russell sage f ou n dat ion jou r na l of t he so ci a l sciences

coun t y dependence on moneta ry sa nctions

analyses, no one county credited more than
5.25 percent of its annual revenues to monetary
sanction collections, suggesting variations in
measurement and jurisdictional focus may influence results and obfuscate the complicated
relationship between monetary sanction dependence and carceral trends. Further research
is needed to determine the true relationship
between rurality and local dependence on
monetary sanctions. We recommend that future researchers explore this relationship at the
city and town level.
Our findings regarding sovereign Native
American territories and women’s sentencing
rates suggest that intersections of race and
space shape the consequences of monetary
sanction dependence on carceral trends. Although counties containing Indian Country rely
less on LFOs for revenue, they tended to incarcerate women at a higher rate. This finding is
particularly striking given that tribal courts
likely remove many Native American women
from consideration for sentencing in the data
used in these analyses. Somewhat counter­
intuitively, we find higher rates of women being
incarcerated overall in counties where a segment of women are not under the jurisdiction
of state courts. These findings support the study
by Robert Stewart and his colleagues (2022, this
volume) who find that Native Americans in Minnesota were subject to larger amounts of LFOs
than White individuals and that these effects
were even greater when courts were near tribal
lands. A more direct and in-­depth analysis of
LFOs as they relate to tribal court sovereignty
and native populations is necessary.16
Finally, although our study does not directly
implicate sentencing or policing to generate
revenue, it clearly points to a link between
monetary sanctions and sentencing outcomes
for women. We agree with researchers that the
use of monetary sanctions as a source of juris-

169

dictional revenue creates tension with the
stated purpose of meting out justice (Martin
2020). We therefore recommend that counties
explore streams of funding unassociated with
the penalization of its residents to maintain
their criminal legal systems and that they take
steps to reduce the costs of implementing justice. Courts need to understand and address
the ramifications and downstream consequences of monetary sanctions on individuals—especially women—who have a limited
ability to pay. Even sentencing policies that appear lenient can lead to incarceration whenever
LFOs are involved, and a lack of data on defendants sentenced to incarceration for willful
nonpayment prevents a full accounting of the
scope of this social problem.17
Our findings complicate assertions that rural counties are both more punitive and more
dependent on monetary sanctions as a source
of revenue in that we find support only for the
former. Nonetheless, our results suggest county
dependence on monetary sanctions is one of
several drivers of women’s sentencing outcomes across Washington State. The finding
that monetary sanctions—a criminal legal punishment often touted as a kinder alternative to
incarceration—may in fact be associated with
increases in incarceration is alarming indeed.
If the system of monetary sanctions is meant
to act as a mechanism to recover the costs of
the criminal legal system through collections
and to provide an alternative to the seemingly
more costly option of incarceration, then it has
failed on both counts. This research contributes to a growing body of literature demonstrating that the primary goals of the system of
monetary sanctions are being subverted by the
system itself. The proliferate use of monetary
sanctions does not reduce rates of women sentenced to incarceration, and counties committed to reducing their carceral populations can

16. Such studies should also build on the growing literature related to settler colonialism, as Stewart and his
colleagues discuss: “Historically, settler governments extracted resources from tribal nations through coercion,
displacement, and assimilation; today, these same entities draw financial resources disproportionately from
American Indians through the criminal legal system. Put another way, settler colonial domination has transitioned
from collective to individualized extraction from Native subjects, and thus the structure of settler colonialism
persists” (153).
17. For a thorough qualitative exploration of the notion of willful nonpayment, see Fernandes, Friedman, and Kirk
(2022, this volume).

r sf: t he russell sage f ou n dat ion jou r na l of t he so ci a l sciences

17 0

state mon eta ry sa nctions a n d the costs of the cr imina l lega l system

and should consider alternative revenue
sources and sentencing options for women defendants.
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