Robbing Peter to Pay Paul: Public Assistance, Monetary Sanctions, and Financial Double-Dealing in America

RESEARCH ON MONETARY SANCTIONS

Labor extraction, convict leasing, monetary sanctions, and debtors’ prisons are examples of historical punishment practices that shape contemporary U.S. penal laws and sentences (Adamson 1983; Blackmon 2009; Wood and Trivedi 2007; Alexander 2010; Harris 2016). Today, monetary sanctions commonly accompany or replace formal imprisonment and function as a key mechanism of social control and surveillance of low-income communities and communities of color (Harris, Evans, and Beckett 2010; Kohler-Hausmann 2018).

The use of monetary sanctions varies considerably by jurisdiction because fines and fees are assessed based on unique guidelines established by local, state, and federal authorities (Bannon, Nagrecha, and Diller 2010; Gordon and Glaser 1991; Martin et al. 2018; O’Malley 2009; Ruback, Shaffer, and Logue 2004). Proponents of monetary sanctions claim that they promote a sense of accountability among justice impacted people, and that fines and fees are a socially efficient replacement for incarceration (Becker 1968; Becker 1976; for a review, see also Martin 2020). However, courts that impose fines and fees often fail to provide defendants the full extent of these sanctions at sentencing (Anderson 2008; Beckett and Harris 2011; Harris 2016; Martin et al. 2018). Some costs are not readily apparent, such as those related to public defenders (Colgan 2014), incarceration stays (Fernandes, Friedman, and Kirk 2022, this volume) and penalties for late payment (Harris 2016; Martin et al. 2018; Friedman et al. 2022, this volume). Even when people consistently make the minimum payments, the interest accrued on the principal debt can be crippling (Harris, Evans, and Beckett 2010). Furthermore, people under correctional supervision often have to agree to pay all legal financial obligations (LFOs) as a condition of release (Huebner and Shannon 2022, this volume).

The consequences of entwining money with punishment are numerous and well known. Monetary sanctions can affect nearly every facet of an individual’s economic, political, and social life (see Harris, Pattillo, and Sykes 2022, this volume). They thus often further marginalize those who are in precarious financial positions (Bing, Pettit, and Slavinski 2022, this volume; Harris 2016). Moreover, in some locales, until LFOs are paid in full, individuals’ criminal records cannot be cleared or expunged, and they may be sentenced to incarceration or community service or have their driver license suspended if they are unable to make payments (Harris 2016; Martin et al. 2018). These punishments for nonpayment may prevent individuals from finding or maintaining employment. In addition to documented racial discrimination in the job market (Bayot 2004; Pager 2003; Pager, Bonikowski, and Western 2009), civil judgments from monetary sanctions can damage one’s credit and is often listed on credit reports that some employers use as part of the background check process (Harris, Evans, and Beckett 2010). Thus monetary sanctions may place people and their families at increased risk of poverty if individuals are unable to secure employment.

THEORETICAL FRAMEWORK

Loïc Wacquant (2010) contends that increasing American insecurity is the result of changing state priorities in local and national spending on social welfare and punishment (see also Beckett and Western 2001). Simply put, he argues that the neoliberal push during the late twentieth century led to major structural changes in American life at the local, state, and national level, precipitating a spending shift from the “left hand” of the state (attending to issues of social welfare and public health) to the “right hand” (attending to matters of punishment and penality, national defense, and control over markets and the economy) (Wacquant 2010). As a result, the right hand of the state has disproportionately increased in power since the late twentieth century, and its penal arm has expanded to consume a greater proportion of public spending while also directly exacting social controls, incarceration, and violence against marginalized populations (Wacquant 2010).¹ A consequence of this expansion of power and resources is that social service systems have increasingly been underfunded, or in some cases, entirely defunded, as the criminal legal system expanded (Ingraham 2020; Saez and Zucman 2019; see Beckett and Western 2001).

The downsizing of public assistance programs and the expansion of the criminal legal system aligns with research on the neoliberalization of punishment in the United States. This scholarship explores how the negative externalities associated with economic and political changes shift ownership of social problems from the government to individuals, propagating “the neoliberal trope of personal responsibility” (Friedman and Pattillo 2019, 174). Neoliberal “responsibilization” in the criminal legal system is not merely ideological. Recent scholarship shows how local, state, and federal policies apply these logics to ensnare (Muñiz 2020), imprison (Simon 2007; Hinton 2016; Wacquant 2009), and apply monetary sanctions to individuals as revenue generation and extraction tools that ostensibly act as coercive financialization and predation of the poor (Fernandes et al. 2019; Kirk, Fernandes, and Friedman 2020; Fernandes, Friedman, and Kirk 2022, this volume; Pattillo and Kirk 2021; Friedman and Pattillo 2019; Page, Piehowski, and Soss 2019).

Although recent research on neoliberalism and monetary sanctions underscore the pervasiveness of state control in imposing monetary sanctions, supporting the underlying theoretical argument of Loïc Wacquant (2009), this conceptual framework is limited. Wacquant’s framing does not adequately theorize the integrated effects of monetary sanctions and welfare support on individuals, families, and communities because the macro approach to shifts in funding obscures the ways in which micro-level dispersions of public aid can recirculate into the penal arm of the state via the imposition of monetary sanctions.

We argue that, in the case of punishment and welfare, an economic sleight of hand emerges as a by-product of the poverty-punishment nexus; the state structures and participates in this legerdemain through financial double-dealing—disparate and contradictory economic entanglements that redistribute welfare resources from individuals to the criminal legal system and its institutional affiliates. These FDDs allow some government agencies to issue monetary benefits that foster Bryan Sykes and Becky Pettit’s (2015) findings of system inclusion: veterans benefits, Supplemental Nutrition Assistance Program (SNAP) benefits, Temporary Assistance for Needy Families (TANF) benefits, unemployment assistance, social security income or disability, and so on. Other state entities, however, issue monetary penalties that engender Sarah Brayne’s (2014) system avoidance: criminal legal system debt by way of monetary sanctions, wage-tax-commissary garnishments, child-support arrears, and so on. FDD allows local, state, and federal governments to draw from the same underlying source of economic support for poor individuals and families: the social safety net and other public-private benefits.

Figure 1 displays our theoretical model of micro and macro FDD in America. Wacquant (2009) argues, and other scholars find (see Ingraham 2020; Saez and Zucman 2019; Beckett and Western 2001), that state and federal governments allocate relative and disproportionate resources to social welfare (left hand) and criminal legal system functions (right hand). However, we contend that when social safety net resources are directly distributed to the poor and indigent, who may be ensnared in criminal court proceedings or probation supervision, the imposition of monetary sanctions (process #1 in figure 1) may facilitate an additional redistribution of social welfare funds via criminal courts, representing one form of FDD at the micro, individual level (process #2 in figure 1). An additional form of FDD takes place at the macro level (process #3 in figure 1), given that state statutes may direct the court or probation office to collect and allocate monetary sanctions to other criminal justice affiliates and agencies via a host of other costs, fees, special funds, and penalties (such as police unions, 911 operations, and emergency medical technician funds) (Harris, Evans, and Beckett 2010; Harris 2016; Friedman and Pattillo 2019; Pattillo and Kirk 2021, 2020; Verma and Sykes 2022, this volume). Taken together, the process of FDD can be theoretically interpreted as an economic backfilling of criminal justice operations, allowing the state to create an indirect feedback loop (process #4 in figure 1) that enables the right hand of the state to usurp additional social welfare resources from the left hand via people on public assistance who are caught in the criminal justice web. Because criminal justice operations have become increasingly hybridized and have made steady advancements beyond criminal law and into administrative and civil law (Verma and Sykes 2022, this volume), resulting in a shadow carceral state (Beckett and Murakawa 2012), the collection of monetary sanctions from people on public assistance is likely to result in macro-level FDD among a variety of institutions, with collected revenue going to a host of different state agencies and funds (see O’Neill, Smith, and Kennedy 2022, this volume).

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Figure 1.

A Theoretical Model of Micro and Macro Financial Double Dealing (FDD)

Source: Authors’ theoretical conceptualization.

Note: Dollar signs are not to scale of federal, state, or local fiscal apportionment, as well as individual payment amounts; instead, these dollar signs represent the disproportionate flow of financial revenue from governments to individuals to institutions (micro FDD), and the financial dynamics inherent in the collection and redistribution of monetary sanctions, inter- and intra-institutionally (macro FDD), given state statutes that govern the dispersal of collected LFOs to other agencies and institutions.

Although the analyses to come will focus on the redistribution of welfare resources to the criminal legal system through the imposition of legal financial debt, this process can be understood as one of multiple manifestations of FDDs by the state. The child-support system, for instance, is another institution that relies on the imposition of monetary penalties to recover welfare expenditures (Wacquant 2010; Gustafson 2012; Edin and Lein 1997). In the mid-1970s, the child-support system was restructured to mandate families receiving public benefits participate in programs aimed at recovering child-support payments to limit the government’s distribution of cash welfare (Cozzolino 2018; Farrell, Glosser, and Gardiner 2003; Turetsky and Waller 2020). The retooling of child support to collect payments often employed punitive strategies to recover remittances of these public benefits to the poor (Solomon-Fears, Smith, and Berry 2012; Turetsky 2005; Gustafson 2012; Edin and Lein 1997). Welfare and child-support reforms have been justified as an effort to refocus the financial responsibility of children toward their noncustodial fathers, who were seen as eschewing their financial obligations (Edin and Kefalas 2005; Edin et al. 2019; Edin and Nelson 2013; Haney 2018; Pleggenkuhle 2018; Turetsky and Waller 2020; Wilson 1987). Nonetheless, the use of state agencies tasked with dispersing social welfare benefits and relying on family courts to collect child support while using criminal courts to punish welfare fraud cases, represent but one form of FDD by the state.

The identification and analysis of FDD reveals how the left and right hands of the state work to magnify inequality by providing public assistance to those experiencing poverty while financially punishing people living at the economic margins. These programs are useful because they are fungible—a dollar not spent on food, health care, or other goods and services is a dollar a person at the economic margin may be able to spend elsewhere. However, the fungibility of inequality-reducing programs also constitutes a vulnerability; benefits can be offset by punishment costs and payments. FDD in turn deepens and extends the poverty and marginalization for people already living at the margins. Thus, social safety net contributions are funneled back to the state via punishment and surveillance mechanisms. In the following analyses, we demonstrate how micro-level FDD occurs for people and families receiving cash and noncash public assistance.

DATA

To explore the relationship between public assistance and monetary sanctions, we rely on data from two sources. First, we leverage data from the 2019 Survey of Household Economics and Decisionmaking (SHED). Developed by the Federal Reserve Board and collected by the U.S. Federal Reserve, this nationwide survey investigates how receipt of cash and noncash assistance from the social safety net is associated with court-ordered debt. A private consumer research firm, Ipsos, administered the survey to a nationally representative, probability-based online panel. After selecting respondents using address-based sampling techniques, Ipsos curated a final pool of potential SHED participants. From this pool, Ipsos obtained a final-stage completion (or response) rate of 61.2 percent, resulting in a final sample of 12,173 respondents.

The SHED survey contains detailed questions about income, retirement funds, wealth accumulation, demographic background, public assistance and social safety net participation, criminal justice contact, exposure to crime, and, principally for our purposes, a measure of monetary sanctions. The survey was completed online, and adults without a computer or internet were provided these tools. The median time to complete the survey was nineteen minutes (for additional detail, see Federal Reserve 2020). The survey item about court-ordered debt is limited to a dichotomous measure of whether the respondent or their immediate family member has unpaid legal expenses, fines, fees, or court costs. Unfortunately, numerical estimates of monetary sanctions imposed and paid were not collected.

To remedy this issue, we draw on novel data collected during the Multi-State Study of Monetary Sanctions (see Harris, Pattillo, and Sykes 2022, this volume). As a part of the study, survey data were collected from individuals for whom criminal legal system related debt was imposed. These surveys contained information on the amounts of monetary sanctions, whether a person has paid the debt, their criminal legal history, as well as whether the person receives public assistance. A total of 519 people were interviewed across our eight states, and after we removed missing data on key measures and data from Texas, due to Institutional Review Board regulations about matching these data with other sources, our analytic sample was reduced to 303 respondents.

Roughly half of the MSSMS sample is white (52.2 percent), and over a third of the sample is Black (34.7 percent). The sample is educationally advantaged, more than 83 percent having at least a high school diploma. Two-thirds of the sample is male, and the average age is approximately twenty-four. Half of the participants have never been married, and about 47 percent are parents to minor children. About 49 percent are employed, with considerable variation in their reported incomes. Approximately 95 percent report being incarcerated at some point in their lives, and have more than twice as many misdemeanor convictions (7.0), on average, than felony convictions (3.4).

We use these data, collectively, to investigate the risk of FDD among individuals and families that receive public assistance and were assessed a monetary sanction.

MEASURES AND METHODS

Given our use of two datasets to explore the association between public assistance and criminal justice debt, we rely on several different dependent measures to demonstrate the process of micro-FDD in America.

ANALYTIC STRATEGY

We use several statistical methods to estimate the association between receipt of public assistance and criminal legal system debt. First, to assess the scope of risk nationally, we use logistic regression models to estimate the odds that receipt of any public assistance is associated with outstanding monetary sanctions owed using the SHED data (figure 2). Additionally, we use these models to assess whether the type of public assistance benefits (cash receipt or noncash program participation) matters for one’s risk of owing monetary sanctions. If these two types of public assistance do not differ nationally, then collapsing these measures in the MSSMS data is valid for subsequent analyses.

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Figure 2.

Log-Odds of Having Any Public Assistance Associated with Criminal Justice Debt

Source: Authors’ tabulation based on 2019 SHED data (Federal Reserve 2020).

Note: Non-Hispanic Whites, married, less than a high school education, household income below $20,000, and New England region are the reference categories. All models include state fixed effects, and standard errors are clustered on state. For full-model estimates, see online appendices 1 and 2. N = 12,014.

***p < .001, **p < .01, *p < .05

Second, because our MSSMS measure of LFOs is a categorical, ordinal variable, we use ordered probit models to estimate the probability that individuals and families receiving public assistance are assessed at similar levels of LFOs (table 3). Furthermore, because we are also interested in whether any of the LFOs have been paid, we use probit models to estimate the relationship between receipt of public assistance and payment of LFOs (table 4). For both sets of models, we first run models controlling only for criminal legal measures and then additional models controlling for the suite of other criminal legal and social background measures. Last, given our interest in estimating the actual dollar values of LFO payments, we use ordinary least squares regression, where the dependent variable (LFO amount paid) adds $1 to all participants and then logs the measure (table 5) to ease interpretation of percentage differences.

FINDINGS

Table 1 (SHED) and table 2 (MSSMS) present descriptive statistics from each data source. Table 1 shows that, nationally, a little more than 6 percent of the U.S. population had criminal justice debt in 2019. Approximately 43 percent received some form of public assistance, approximately 35.2 percent receiving cash and 13.6 percent receiving noncash.

Comparatively, table 2 shows that nearly 60 percent of participants are on public assistance. Additionally, although the MSSMS measure of monetary sanctions is different from the SHED measure, we find considerable heterogeneity in the LFO amount assessed. Although fewer than seven-tenths of 1 percent of the participants say they were not assessed any LFOs, about half reported being assessed LFOs of $5,000 or less, and approximately one-quarter reported being assessed LFOs in excess of $10,000.

Although LFO assessments represent the court’s order for payment, two additional measures convey compliance with paying monetary sanctions. First, approximately 70 percent of participants indicate that they have paid something toward their LFOs. Second, the average amount paid is approximately $4,000.

To examine FDD, we rely on two measures of government assistance. First, about three in five participants in the MSSMS data reported being on public assistance with an average enrollment in at least one government program. Second, the family risk of FDD is based on the participant and family’s involvement with public programs. This measure is based on the coding of in-depth interviews, which reveal the context of public support. Approximately 41 percent of families do not receive public assistance (Risk Category 0), but the risk of FDD is heterogeneous; about 2 percent of participants belong to families that have applied for public assistance but were denied (Risk Category 1), and another 4 percent had recently lost their public assistance (Risk Category 2). Additionally, 4 percent of the participants said that they or their families applied for public assistance and were awaiting a decision (Risk Category 3), whereas another 4 percent rely on other family members who receive public assistance (Risk Category 4). More than 45 percent of families are in the highest category for risk of FDD, the participant reporting that they currently received some form of public assistance (Risk Category 5).

Figure 2 presents the log-odds of having any public assistance associated with criminal justice debt, as well as distinctions between cash and noncash public assistance. The first barplot shows that the odds of having criminal justice debt are 51 percent (= e^(0.415)-1) higher for people with any public assistance than for those who are not enrolled in safety net programs. Controlling for violent crime exposure and previous incarceration history moderates this association (barplot 2); however, including measures of socioeconomic and demographic background measures (barplot 3) strengthen this association, indicating that the odds of having criminal justice debt are 78 percent (= e^(0.579)-1) higher for people on any form of public assistance.

Next, we examine whether the odds of having criminal justice debt depends on the types of public transfers a person receives (that is, cash versus noncash). Barplots 4 through 6 present estimates of the association between criminal justice debt and cash support public transfers, whereas barplots 7 through 9 assess the association between criminal justice debt and enrollment in noncash government programs. Indeed, there appears to be few distinguishing differences between types of public assistance. For instance, after controlling for criminal justice contact, crime, and social background characteristics, barplot 6 shows that the odds of having criminal justice debt are 29 percent (= e^(0.255)–1) higher for people who receive cash support from the government (p < .05). Comparatively, the association is much stronger (p < .001) among people enrolled in noncash government programs (barplot 9), with their odds of owing criminal justice debt being 99 percent (= e^(0.688)–1) higher than people not enrolled in these programs. These findings from the SHED data suggest that distinguishing between cash and noncash public assistance may not be consequential when investigating the role of FDD within the criminal legal system.

Table 3 presents estimates from the ordered probit model that estimates the association between public assistance and the amount of LFOs imposed. Models 1 through 3 pertain to participants who report receiving public assistance. Models 4 through 6 are based on the family risk score. None of the first three models provide evidence that individuals who receive public assistance have been assessed LFOs significantly different from individuals without public assistance. This holds true whether we control for criminal legal only measures (model 2), or the full set of criminal legal and social background controls (model 3).

The family risk score, however, tells a slightly different story. We do find suggestive and strong evidence that families with a higher risk of FDD have lower probabilities of having the same LFO amounts imposed on them. For instance, in model 4, without any control variables, the negative, moderate association (B = –0.605, p < .1) in families where another family member receives public assistance (Risk Category 4) is associated with a lower probability of receiving the same LFO amount as someone who is not at risk of FDD. Controlling for criminal legal measures (model 5) provides moderate evidence for the risk of FDD among families that have lost public assistance (Risk Category 2) and among families where the participant is the public assistance recipient (Risk Category 5). Once criminal legal history and social background are controlled for, the strongest evidence for FDD is that in families where the participant receives public assistance, there is a strong, negative association (B = –0.443, p < .05). This indicates that these individuals and their family may be assessed smaller LFOs, but that reduced amount is nonzero.

Models 1 through 3 of table 4 display estimates from probit models of the association between participant receipt of public assistance and having paid any money toward one’s LFOs. Although models 1 and 2 do not show any clear association, model 3 presents evidence that participant receipt of public assistance is negatively associated (B = –0.345, p < .05) with having made any payments on their LFO, net of criminal legal history and social background.

Models 4 through 6 present a similar story for families at risk of FDD. Across all models, families with the highest risk of FDD have a lower probability (B = -0.408, p < .001, model 4) of making payments on their LFOs than families where there is no risk of FDD. In fact, across all three models, controlling for criminal legal history and social background further decreases the chances (B = –0.460, p < .001, model 6) that these families will make any payments on their LFOs.

Last, table 5 displays estimates from the ordinary least squares regression of the association between receipt of government assistance and the amount of money paid to the court. Confirming findings from table 3, models 3 and 4 of table 4 present moderate evidence (p < .1) that participants receiving public assistance, and families at higher risk of FDD, have paid less on their LFOs than individuals and families not receiving public assistance. Curiously, model 6 shows that, after controlling for criminal legal history and social background, families that have applications for public assistance under review have paid more (B = 1.326, p < .05) on their LFOs than people who do not receive public assistance. It could be that these individuals are concerned that their applications for assistance may be denied if they do not pay down their LFOs. Future research should explore this connection in more detail.

DISCUSSION AND POLICY IMPLICATIONS

Although many scholars have discussed the regressive interaction of monetary sanctions and public assistance, the motivation of this article is to move the conversation away from recognizing monetary sanctions as a collateral consequence of the penal system, and toward the recognition that, when financial sanctions are applied to the poor, particularly those who are the recipients of public assistance, these sanctions act as an exponent of punishment by depriving individuals and communities of social safety net provisions (see also Harris 2016). Recent scholarship has helped clarify the struggles of reentry (Western 2018), echoing qualitative (Macleod 2009) and quantitative (Pager 2003; Pager and Lincoln 2005; Petitt and Lyons 2009) findings that a criminal record is an “absorbing status” (Maroto 2015) that profoundly reduces labor access and wealth generation (Sykes and Maroto 2016; Maroto and Sykes 2020). Our work contributes to scholarship on the feedback effects of poverty and LFOs by specifying how money is, often unwittingly, transferred from assistance programs to penal institutions in the imposition of monetary sanctions.

Findings from our analysis are also relevant for other articles in this volume and have specific policy implications. First, Mary Pattillo and her colleagues (2022) examine the relationship between the criminal legal system and housing stability. Although previous research has focused on the criminalization of homelessness and the impact of incarceration on housing stability, Pattillo and her coauthors argue that financial penalties affect the affordability of life necessities, leading to poor housing outcomes (such as housing instability or homelessness), which the authors refer to as the “instability–LFO nexus” (p. 58). For example, they note that one participant, who could not afford housing, was expected to pay “7 percent of [their] net disability check, which was [their] only source of income,” to their LFOs (p. 64), illustrating the cycle of the housing instability–LFO nexus among the poor. Hence the concept of micro-level FDD captures the ways in which the criminal legal system maintains these social and economic inequalities by assessing LFOs on those who are economically fragile and receiving public assistance as their primary source of income.

Similarly, April Fernandes, Brittany Friedman, and Gabriela Kirk (2022) argue that the state’s arguments to recoup costs are imbued with legal moralism, which advocates that the law act as arbiter of morality. Considering the broader capitalist economic system and the state’s use of legal moralism, value and performance are tied to fiscal responsibility. Therefore, anyone using state resources (those incarcerated, people on public assistance, and so on) are considered morally reprehensible and a drain on the state. In this way, incarceration and involvement in the larger criminal justice apparatus becomes a fiscal burden created and sustained by those who are affected by the system. “Willful nonpayment” is used in pay-to-stay lawsuits because “incarcerated people with any modicum of assets then become willful nonpayers” (p. 89). The possession of assets in any form, including receipt of public assistance, can then be used to substantiate willful nonpayment of monetary sanctions. By framing the use of state resources as a moral failing, and the retention of any assets as willful nonpayment, the state not only justifies lawsuits to recoup costs from incarcerated persons, but also engages in macro-level FDD by imposing and collecting monetary sanctions from people receiving public assistance, only to redistribute those safety net resources to the criminal legal system and other state institutions.

Second, significant policy implications follow from FDDs. Colloquially referred to as the “anti-attachment statute,” Section 207 of the Social Security Act (42 USC §407) prevents Social Security benefits from being garnished, levied, or withheld by the federal government. Specifically, the statute makes clear that “none of the moneys paid or payable or rights existing under this subchapter shall be subject to execution, levy, attachment, garnishment, or other legal process, or to the operation of any bankruptcy or insolvency law” (42 USC §407(a), emphasis added). A few exceptions to this anti-attachment statute bear mention: child support and alimony obligations (42 USC§659); certain civil penalties under the Mandatory Victim Restitution Act (18 USC§3613); overdue federal taxes (26 USC §6334; Public Law 105–34); or withholding and paying another federal agency a nontax debt owed according to the Debt Collection Improvement Act of 1996 (Public Law 104-134). In other words, with only a few exceptions, the purpose of the statute is to protect a minimum standard of living for individuals with low income or disabilities by safeguarding Social Security benefits from paying other debt.

The anti-attachment statute for Social Security benefits has been used to prevent FDDs in at least one state in the United States. In State of Washington v. Catling, 193 Wn.2d 252, 438 3d 1174 (2019), the court held that LFOs “may not be satisfied out of any funds subject to the Social Security Act’s anti-attachment statute, 42 USC§407” (266). Although the defendant in this case was required to pay the $500 victim fund assessment as dictated by Section 207 of the Social Security Act (42 USC §407) (Section 207), the court held that such debt could not be settled with Social Security disability benefits. A series of recently filed cases raise similar issues in Washington since the State of Washington v. Catling ruling. All citing Catling, the courts conclude that although the imposition of fines and fees are legal, the judgment and sentences must be amended to specify that the mandatory LFOs may not be satisfied out of funds subject to the anti-attachment statute.³

Additionally, in a cursory review of recent case law, two lawsuits—one decided by the Montana Supreme Court and another in the Missouri Supreme Court—question the definition of “other legal process” in Section 207. In State of Montana v. Ingram, 2020 Mt. 327, the Supreme Court of Montana ruled that the District Court’s sentencing order—in which it imposed the statutory minimum fine of $5,000 for felony driving under the influence (DUI)—constitutes an “other legal process” and thus an improper attempt was made to attach the appellant’s Social Security benefits—which was Ingram’s sole source of income, $857 per month—in violation of 42 USC§ 407(a). Although the court ruled in Ingram’s favor, in its decision it makes clear that, just as was held in State v. Catling, 193 Wn.2d 252, 438 3d 1174 (2019), Ingram’s receipt of Social Security benefits does not exempt him from having to pay the mandatory fine. Instead, it prohibits the attachment of those benefits to pay off the fine.

In a second case, argued before the Supreme Court of Missouri on October 6, 2020, a similar question was raised as to whether payment of a monthly intervention fee as a condition of probation violates federal law by subjecting Mr. Graves’ supplemental Social Security benefits to “other legal process,” (Graves v. Missouri Department of Corrections, Mo. Ct. App. Mar. 31, 2020). In a 4-3 decision issued on October 5, 2021, the Missouri Supreme court decided that “Because the Division [of Probation and Parole] has not yet compelled Graves to pay monthly fees, his request for declaratory relief is not ripe for adjudication and the circuit court properly dismissed his claim,” (Graves v. Missouri Department of Corrections, Mo: Supreme Court 2021).⁴

These recent court decisions in Montana and Missouri underscore the confusion surrounding Section 207 of the Social Security Act and its meaning in the context of LFOs. Based on decided court cases, it seems that misunderstandings about Section 207 are largely driven by the need for clarity surrounding legal exceptions to the provision, as well as a need for an operational definition of “other legal process.” Although Section 207 protects financial resources from state extraction and FDD, in regard to Social Security benefits, our analysis reveals that similar laws and administrative policies are necessary to protect other forms of public benefits, cash and noncash alike, from being directly and indirectly redistributed from the social welfare to the penal hands of the state (on the payment of monetary sanctions using disability assistance, see Cadigan and Smith 2021). To do so, social policies must be carefully devised to consider whether specific types of public assistance receipt should be levied against the imposition and collection of LFOs. Noncash public assistance may pose a particular challenge for courts, as the loss of these benefits may require real economic expenditures by indigent individuals and their families. However, receipt of such noncash benefits in-and-of itself points to financial hardships that may well justify waived or expunged LFOs in criminal sentences. Therefore, policymakers should consider preventing the imposition and collection of monetary sanctions on all persons receiving any form of public assistance, especially those that are antipoverty or a means-tested public benefit.

CONCLUSION

The welfare reforms passed in the 1990s were an expression of the neoliberal logics of American public policy (Simon 2007; Hinton 2016; Wacquant 2009), through which the penal arm of the state became increasingly more powerful (Wacquant 2010). The efforts to dramatically tighten public assistance were framed as a way to hold individuals accountable and responsible for themselves and attend to what was perceived as the growing dependency on government resources (Edin and Lein 1997; Edin and Kefalas 2005; Edin et al. 2019; Edin and Nelson 2013; Gustafson 2012; Wilson 1987), which further marginalized people living in poverty. Our findings confirm this trend, in that individuals and families who receive public assistance are less likely to pay LFOs, even when the imposed LFOs are less than the amounts imposed on individuals and families who do not receive public assistance. Put simply, individuals and families at the highest risk of FDD are also at the highest risk of being further penalized for failing to pay LFOs. Even though people on public assistance may have lower monetary sanctions imposed, their poverty and economic hardships imply that they should not have these LFOs imposed at all, as their failure to pay even small amounts can trigger extended justice system involvement and surveillance. This finding is why some judges and probation officers rely on bench cards to assess indigence or public assistance receipt as a marker of poverty instead of relying on employment or income alone (see Harris, Pattillo, and Sykes 2022, this volume; Martin 2020).

Although we agree with Wacquant’s (2010) theoretical framework, we find that the transfer of resources from the state’s left to right hand (from social welfare to punishment) is a dynamic process that disproportionately harms individuals and families on public assistance who have criminal justice debt. Indeed, recent research on neoliberalism and punishment also calls into question the process that Wacquant theorizes (Lara-Millán 2021); interestingly, however, the process that Armando Lara-Millán (2021, 42) describes—where poor individuals “game the system” by committing crimes in order to receive health care in local jails because of underfunding to, and overcrowding in, public hospitals—speaks to the very mechanism of FDD at the micro and macro levels, as state resources allocated to one institution can diffuse downward through individuals and be redistributed to other state agencies. The only difference in this case of FDD, however, is that the liabilities are passed from one institution to another via individuals, as opposed to assets or resources derived from social safety net provisions, including Medicare.⁵

The neoliberal shift to decrease social safety net funding and to increase fiscal allocations for punishment and surveillance is not new. Families experiencing poverty have historically been affected by the underfunding of public assistance programs and the growing punitiveness of the criminal legal system. The restructuring of the child-support system in the 1970s mandated families receiving public benefits (cash welfare) participate in surveillance systems that impose the collection of financial resources from noncustodial parents (Cozzolino 2018; Farrell, Glosser, and Gardiner 2003; Turetsky and Waller 2020), and these child-support enforcement and collection methods have since been applied to the collection of monetary sanctions (Legler 1996). Although child-support programs have demanded large payments from fathers unable to meet these obligations (Ha, Cancian, and Meyer 2018; Sorensen and Oliver 2002), similar to criminal justice debt, child-support collections have been shown to intensify family hardship and erode family ties (Bartfeld 2003; Turner and Waller 2017; on criminal justice debt and family relations, see Boches et al. 2022, this volume). Within this context, the left and right hands of the state were always embedded in poverty management and the ascension of the punitive apparatus.

In short, contemporary research on poverty, inequality, and monetary sanctions misses a key dimension of predatory extraction: public assistance and social safety net resources. This omission is deeply consequential for the measurement of poverty and poverty mitigation (see also Fox et al. 2015), as judicial decisions about the imposition of monetary sanctions often consider resources associated with public assistance programs. Implications derived from our findings suggest that state fiscal budgets, and their reallocations and redistributions over time, paint a partial picture of the resources circulating to and through the criminal legal system. To capture more fully the totality of resources appropriated by the punishment sphere of the state through financial double-dealing, research needs to qualify and quantify the micro-level and macro-level redistribution of money from individuals and families receiving public assistance while paying down monetary sanctions. Policymakers and advocates also need to devise and enact new legislation that safeguards precious social safety net resources from being funneled back into the criminal legal system via monetary sanctions. Failure to do so will undoubtedly deepen the poverty, hardship, and misery of millions of individuals and families experiencing economic precarity in an age of growing social inequality.