A fundamental lesson from the social and the behavioral sciences is that the context of people’s lives can affect their behavior in profound ways. Poverty itself is a powerful context because of its economic, physical, social, and psychological dimensions. In Chapter 3, we documented the adverse consequences for children of living in poverty, as well as the severe constraints and stressors that inadequate financial resources place on families. Those constraints and stressors may in turn result in difficult choices and circumstances for both parents and children.
In this chapter, we consider a more general set of contextual factors that can promote or impede the effectiveness of anti-poverty policies and programs. For example, Supplemental Nutrition Assistance Program (SNAP) payments can best promote children’s nutrition and health when families have ready access to healthy and affordable food, and families can further benefit more from cash transfers when convenient and receptive banking institutions are available to help them manage their funds. Conversely, a job training program for parents may be less effective if there is racial discrimination in hiring, if there is an absence of employment opportunities, reliable transportation, or affordable child care options, or if parents are too disabled or sick to attend training.
Given the potential for such contextual factors to influence the effectiveness of programs and policies, it is surprising to our committee how little rigorous empirical research has been conducted to test these factors’ moderating influence. Nevertheless, a strong empirical case can be made
that these contextual factors influence decision-making in low-income families as well as the impact of consequential programs and policies. Note that because the committee’s charge is confined to a 10-year period, we refrain from addressing several structural factors—including race and gender attitudes, perceptions of the poor, and the formerly incarcerated—that might generate longer-run impacts on the success and equity of program administration.
Through internal discussions, public information-gathering sessions, and a review of the scholarly and policy literatures, the committee identified six major, often co-occurring contextual factors that policy makers and program administrators are advised to consider when designing and implementing anti-poverty programs of the sort discussed in Chapters 5, 6, and 7:
- Stability and predictability of income—Unstable and unpredictable income makes it difficult for families to juggle everyday challenges, diminishes the quality of everyday decisions, and renders the poor vulnerable to financial ruin.
- Equitable and ready access to programs—Because of cumbersome, inconsistent, or demeaning enrollment procedures, or because of other barriers, not all families who qualify for benefits from government programs receive them.
- Racial/ethnic discrimination—Our nation’s long and painful history of discrimination persists today in many forms and continues to influence differential access to opportunities and resources to overcome poverty, including employment, education, and housing opportunities.
- Equitable treatment by the criminal justice system—Unequal treatment in legal penalties and law enforcement has disproportionately affected low-income families, especially Black and Hispanic families, in ways that disrupt family and social networks and reduce the economic and psychological resources that people who have been incarcerated could otherwise provide to their families.
- Positive neighborhood conditions—Supportive, thriving social networks and neighborhood conditions enrich family life, personal connections, and access to opportunities, yet too frequently people who live in poverty are concentrated in neglected urban areas or are widely dispersed in rural areas with limited transportation or access to employment, poverty-reduction programs, or community resources.
- Health and well-being—Among parents, physical and mental ailments, substance abuse, and domestic violence can harm their ability to make sound decisions, care for their children, become educated, obtain and keep work, and support their households.
The chapter summarizes why each of these six contextual factors matters, how each of them might affect the administration of anti-poverty policies, and what conclusions the committee has reached. Research recommendations on these contextual factors are provided in the final chapter.
Why It Matters
An adequate and stable monthly family income enables parents to pay bills, meet basic needs, and engage in financial planning. When savings or access to affordable emergency resources are added to that, they can help buffer families against income shortfalls. But low-income families typically lack liquid assets and often pay high interest rates to obtain short-term credit (Barr, 2012). The resulting income instability can generate other kinds of instability—in housing and child care, for example—that in turn may limit families’ ability to work (Hahn et al., 2016; McKernan, Ratcliffe, and Vinopal, 2009) and compromise their children’s development (Hill et al., 2013). Because the savings and assets of Black and Hispanic families, at all income levels, are often considerably lower than those of White families, these populations are more vulnerable than White families to unpredictable changes in income (Kochhar and Cilluffo, 2017).
Research has provided ample evidence of these differences in financial stability, and of their consequences. For example, the incomes of low earners are more unstable than those of higher earners, and many lower-wage jobs offer little job security, fluctuating work hours, and no paid time off, which makes it difficult to budget and pay for dependable child care (Enchautegui, 2013; Gennetian and Shafir, 2015). Additionally, unexpected financial emergencies are ubiquitous among low-income households (Barr, 2009), and often require deferring bills or cutting spending on basic necessities, such as food. Approximately 9 percent of all children live in households in which one or more child is food insecure. Food security is defined as “access by all people at all times to enough food for an active, healthy life” (Coleman-Jensen et al., 2017, pg. 2).
Another important set of factors creating employment instability is the nature of the low-wage labor market and the difficulties many low-wage workers have in maintaining employment. Many low-wage jobs have high rates of turnover that create frequent periods of unemployment and require
looking repeatedly for new jobs. Low-wage jobs are also more likely to have irregular hours and require shift work that low-income parents have difficulty sustaining (Enchautegui, 2013; Enchautegui, Johnson, and Gelatt, 2015). Transportation can pose challenges for low-income parents if they do not live close to work and have to take public transportation, which is unreliable and often includes extremely long commutes (Enchautegui, 2013; Holzer and Wissoker, 2001). Compounding these problems are difficulties in obtaining reliable and flexible child care that can respond to these irregular shifts, long commutes, and high-turnover jobs (Enchautegui, 2013). Taken together, low-income families face a multitude of barriers to work that middle-income families do not face to the same degree (Enchautegui, 2013; Hill et al., 2013).
More than one-half of all low-income families are asset-poor, defined as lacking the liquid resources necessary to finance essential consumption for 3 months (Lusardi, Schneider, and Tufano, 2011). Related to this, in recent years, due to their limited financial reserves one in four U.S. households has used at least one alternative financial service, such as a payday, auto title, or refund anticipation loan, during the preceding year—services that are typically subject to very high interest rates (Burhouse et al., 2014; Caskey, 2006). Finally, more than one-half of all low-income families living in rental housing spend more than one-half of their income on housing costs (Desmond, 2016). Most of these problems are worse for racial/ethnic minority families, largely because of differences in wealth or assets minus debt (Kochhar and Cilluffo, 2017; Pew Charitable Trusts, 2015) and more limited options in terms of neighborhoods in which they can live.
The combination of unstable incomes, high fixed expenses, and low savings translates into persistent material hardship for many low-income families, as adverse events challenge their ability to meet basic living needs. These families have little “slack,” defined by Mullainathan and Shafir (2013) as the ease with which one can cut down on other expenses to satisfy an unexpected need. When better-off families experience a rough patch of income instability, they typically have discretionary expenses they can cut back on and savings or access to credit to tide them over. In contrast, when low-income families face unanticipated shocks, they first cut back on somewhat less urgent needs, such as certain foods and the bills that are least likely to produce dire consequences if left unpaid. They then must cut back on essentials, which means skipping payments and incurring costly late fees, utility or phone reconnection fees, and eviction threats, and consequently they face a new round of disruptions to work, child care, education, and family life (Barr, 2009; Edin and Lein, 1997; Shipler, 2004).
Relationship to Policy
The unstable circumstances faced by the families of children living in poverty have significant implications for the design of benefit programs. Programs such as SNAP and Housing Choice Vouchers aim to fulfill basic needs by providing monthly benefits. In the case of SNAP, the long, 4-week intervals between benefits, coupled with income instability, lead recipient families to overspend early in the benefit period and run short at the end (Hamrick and Andrews, 2016). Distributing SNAP benefits at weekly intervals might be more helpful to many families. For example, researchers have found lower achievement test scores among children of families receiving SNAP benefits when those tests were taken near the end of the benefit month (Castellari et al., 2017; Gassman-Pines and Bellows, 2018). Experimentation with weekly versus monthly benefit payments would help guide policy in this case. Moreover, although the Earned Income Tax Credit (EITC) can help families pay down debt or purchase needed durables by providing credits annually as a lump sum (Halpern-Meekin et al., 2015; Mendenhall et al., 2012), some families may need the credit to meet basic expenses and may therefore benefit from more frequent payments.1
Other program design features to consider are the ease of determining eligibility and the frequency with which renewal is required. For example, when the subsidy authorization period for child care subsidies was expanded from 6 to 12 months, families made use of the subsidies for which they were eligible for 2.5 months longer, on average (Michalopoulos, Lundquist, and Castells, 2010). Other studies have examined the administrative burden on families related to eligibility assessment, documentation, and scheduling and transportation issues. Research has shown that when these burdens are high, unpredictable (yet highly frequent) changes in family circumstances, such as job loss, moving, or a change in child care providers, can lead to a family abruptly losing its child care subsidy (Adams and Rohacek, 2010; Holcomb et al., 2006; Joshi et al., 2018). Abrupt subsidy losses of that kind can make finding or holding a job more difficult.
Programs that provide emergency assistance can help prevent low-income families from falling deeper into poverty when unexpected financial problems occur (Pavetti, Schott, and Lower-Basch, 2011). For example, the Temporary Assistance to Needy Families (TANF) program provides emergency grants so that families at risk of losing the ability to work can repair a vehicle or pay rent without having to turn to public assistance over the longer term. However, in 2013, only 2 percent of TANF spending was on “nonrecurrent short-term benefits” or emergency spending (Schott, Pavetti,
and Floyd, 2015). Moreover, the asset limits set on many government assistance programs prohibit parents from saving money for emergencies or purchasing items, such as a reliable car, that can facilitate work and help move their family out of poverty, without the risk of losing the benefit (Campbell, 2014). States have the flexibility to set asset limits for most programs, and across states there is considerable variation in this regard.
Public officials have a responsibility to ensure that families only receive benefits during the time period for which they are eligible, and short renewal periods for programs are a useful mechanism for carrying out that responsibility. However, low-income families’ eligibility may change rapidly with a loss or addition of a job or household member. Eligibility periods that are too short may leave families with such fluctuating circumstances more vulnerable than necessary and make it difficult for parents to move out of poverty.
School meal programs have moved to an annual eligibility determination, rather than requiring parents to report any time their income rises above the cutoff. This means that when children become eligible, they remain eligible for the whole school year. In addition, school districts have many options for directly certifying children who, for example, receive SNAP, so that they can also be eligible for the school lunch program without even applying. This sort of streamlining of eligibility requirements and using eligibility for one program as proof of eligibility for another could be a model for other programs (Currie, 2008).2
In the context of SNAP, longer periods between recertification have consistently been associated with higher rates of take-up and lower rates of drop-off, among eligible families (Hanratty, 2006; Ratcliffe, McKernan, and Finegold, 2007; Wilde et al., 2000). Research has also shown that simplification of the certification process increases the participation rate (Kaushal and Gao, 2011). Furthermore, replacing paper vouchers with Electronic Benefit Transfer (EBT) cards, which look and operate like prepaid debit cards and, in this way, feel quite mainstream and reduce potential stigma, increased participation (Kabbani and Wilde, 2003; Kaushal and Gao, 2011; Kornfeld, 2002; Wilde et al., 2000).
Another policy consideration related to instability is that participation in public programs can be hindered by income instability. To take maximal advantage of work supports like the EITC and child care subsidies, parents need to be able to sustain steady employment. The barriers to such employment, discussed above, also generate barriers to receiving the public program benefits of work-encouraging programs. Participation in child care
2 For other examples of steps that have been taken to improve access to school meals, see https://www.cbpp.org/research/key-steps-to-improve-access-to-free-and-reduced-price-school-meals.
programs is particularly problematic if child care usage is sporadic and unstable, which typically reduces take-up of child care subsidies.
CONCLUSION 8-1: Income instability, a paucity of savings, and little or no cushion for responding to unexpected financial difficulties are typical for many low-income families, and are more prevalent among Black and Hispanic families than among their White counterparts. Programs that provide regular income support, whether through tax credits, cash, or vouchers, may be more helpful to families if they provide adequate benefits at well-timed intervals. Further, programs that are easily accessible and that facilitate savings or provide emergency cash assistance or credit at a modest cost can help families cope with unexpected emergencies and may prevent them from falling deeper into poverty.
Why It Matters
Creating programs to reduce poverty through legislation does not, in itself, ensure equal program access to all families who qualify. If people are to participate in these programs, they need to understand them and then they need to be able to navigate the enrollment process. Often the bureaucratic systems that underpin enrollment are cumbersome, and they vary considerably both by program and even within the same program across different states. The receipt of benefits may even be more a function of where a family lives than of the family’s need.
For example, SNAP participation rates vary greatly across states, from an estimated low of 59 percent of eligible families to an estimated high of 100 percent (Gray and Cunnyngham, 2016). Some of this variation has been shown to be a function of enrollment requirements that are easier in some states than in others; 47 states allow families to apply for SNAP online, while the others require the recipient to fill out a paper application at a local office (Currie and Grogger, 2001).3 Such variation in administrative procedures can lead to considerable variation in participation rates among eligible families for anti-poverty programs across states, and even within states participation rates can differ markedly by the applicant’s race, ethnicity, and other characteristics (Moore, Perez-Lopez, and Hisnanick, 2017).
3 Information about SNAP benefits and enrollment requirements is provided on the United States Department of Agriculture’s Food and Nutrition Service website, see https://www.fns.usda.gov/snap/facts-about-snap.
The state in which a family lives may also determine the level of benefits families receive. For example, monthly TANF payments to a family vary from a low of $170 in Mississippi to a high of $1,021 in New Hampshire, and these differences are not fully accounted for by the variation in the cost of living across states (Floyd, 2017). Moreover, some states supplement federal programs, whereas others do not; 26 states have their own version of the EITC, increasing the benefit families receive (Internal Revenue Service, 2018b). A study comparing the availability of assistance programs across states following the 1996 federal welfare reforms found that states fell into one of five package-support clusters, which ranged from minimal (with low inclusion rates and below-average support) to integrated (with generous and highly inclusive support packages) (Meyers, Gornick, and Peck, 2001). Naturally, such “contextual” variation can have a profound influence on the potential success of federal programs. Furthermore, there is real concern that the application of programs can be biased—whether intentionally or unintentionally. For example, it has been argued that long-acting, reversible birth control methods like intrauterine devices and implants, as tools for fighting poverty are more likely to be recommended to Black and Latina women of low socioeconomic status than to White women of the same status (Dehlendorf et al., 2010)
In some cases, access for certain groups, such as immigrants or felons, is limited by a program’s design. An example can be found in the 1996 welfare reform legislation, the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), which was designed to create a separate eligibility regime for legal immigrants to limit their access to means-tested federal programs. Under this law, income-eligible documented immigrants who have been in the United States for less than 5 years are ineligible for the primary federal means-tested programs (SNAP, TANF, Supplemental Security Income [SSI], and Medicaid) unless they have 40 quarters of work history in the United States or have a military connection.
PRWORA also gave states discretion over immigrant eligibility after the 5-year period of ineligibility. Moreover, while citizen children of undocumented immigrants who are income-eligible can receive government benefits, when fears of deportation are high undocumented parents are hesitant to apply for benefits for their American children (Alsan and Yang, 2018; Capps et al., 2004). Even legal immigrants who are income-eligible may be reluctant to apply for anti-poverty programs for themselves or their children due to the fear of being deemed a “public charge,” which may jeopardize their ability to become permanent residents or become U.S. citizens (Batalova, Fix, and Greenberg, 2018; Perreira, Yoshikawa, and Oberlander, 2018). Hispanic families bear the brunt of these kinds of restrictions (Child Trends, 2014).
Even if access were not problematic, program participation is often limited because funding is insufficient to provide benefits to all eligible families. For example, the Housing Choice Voucher Program (often called the Section 8 program) is available to only about 15 percent of income-eligible families with children (Joshi et al., 2014). Moreover, the federal Child Care and Development Fund supports only 17 percent of eligible children.4
Relationship to Policy
As discussed above, state policies vary widely in the administrative burdens and requirements they impose on parents in anti-poverty programs, and states experience widely differing rates of participation in the programs (e.g., Holcomb et al., 2003). Data compiled by the Center on Budget and Policy Priorities indicate that many states are taking advantage of automated technology so that people can more easily apply for assistance, update relevant information (e.g., changes in earnings), and renew their eligibility online (Wagner and Huguelet, 2016). Florida’s public assistance program, Automated Community Connection to Economic Self-Sufficiency (ACCESS), provides an example of a program that increases efficiency in the enrollment process (Cody et al., 2010).5 Key features of the Florida program include automating the public assistance application process and providing for online submission of applications for TANF, SNAP, and Medicaid (Cody et al., 2010). Other states have also turned to automation to streamline eligibility processes and increase program access, but wide variation in application processes across states and counties remains a significant factor limiting the participation of eligible families in many places (Isaacs, Katz, and Amin, 2016; Loprest, Gearing, and Kassabian, 2016).
Work Support Strategies (WSS), a privately funded multistate initiative, is another example of how automation can improve the uptake of public assistance programs. The WSS initiative, which began in 2011, was developed to determine whether the implementation of technology improvements could better help qualifying families connect to work support programs (Isaacs, Katz, and Amin, 2016; Loprest, Gearing, and Kassabian, 2016). Evaluations suggest that using automated processes to streamline enrollment has resulted in time and money savings for both the applicants and the states. For example, in addition to reductions in lobby wait times in Colorado, Idaho, Illinois, and Rhode Island, individual participants gained an average of $195 annually in benefits, and one state, Idaho, reduced annual administrative costs by an estimated $53,500 (Isaacs, Katz, and Amin, 2016).
Training caseworkers to more effectively communicate and work with the families they serve may also improve the chances that parents will obtain steady employment. Caseworker training may also improve the chances that parents are informed about valuable services, such as child care subsidies (Strawn and Martinson, 2000). Federal rules have sought to establish minimum standards for access to information to help eligible families determine which benefits they qualify to receive. For example, SNAP regulations require applications and notices to be available in languages other than English when specific population thresholds are met.6 How effective such rules are in facilitating access to benefits is not known, however.
Other efforts to increase access to benefits and better coordinate and streamline services have been tried in many states (Annie E. Casey Foundation, 2010; Hoffman, 2006). Rigorous evaluations of the pilot programs would better inform states as to how to ensure that parents who are eligible for programs actually receive the benefits.
One program that has worked to minimize the administrative burden on eligible participants is the EITC. Because it is administered through the tax code rather than through a social services office, it does not require repeated sign-ups throughout the year or a lengthy and complicated application process. Eligible persons must simply fill out their tax returns. Take-up rates have improved over time as commercial tax preparers have increasingly served this market, and more organizations have begun to help lower-income workers file their taxes (Kopczuk and Pop-Eleches, 2007), although commercial tax preparers charge large fees and remove their fees before their clients receive their refunds. The Internal Revenue Service has also provided specific information for tax preparers to help reduce errors (Internal Revenue Service, 2018c).
CONCLUSION 8-2: Unnecessarily burdensome administrative procedures can discourage families from applying for, and thus prevent them from receiving, income assistance program benefits for which they are otherwise eligible. State-by-state variation in the implementation of federal policies can lead to inconsistencies in access among eligible families and to variation in the efficacy of anti-poverty programs.
CONCLUSION 8-3: Federal rules such as limits on the eligibility of documented immigrants and measures that discourage program use (e.g., “public charge” determination) reduce access to means-tested programs for entire groups, even for individuals who meet income-eligibility requirements. These rules may harm both citizen and immigrant children
6 Specifically, Code of Federal Regulations item 7CFR 272.4.
in such families by reducing the benefits available to them, with a disproportionate impact on racial and ethnic minority families.
Why It Matters
A substantial body of social science research shows that large racial/ethnic disparities persist in U.S. society in access to education, employment, housing, and health care, as well as in equitable treatment in the civil and criminal justice systems (Pager and Shepherd, 2008). Discrimination and unequal access to resources can lead to social policies being less effective for parents who are racial/ethnic minorities.
Employment and housing provide two examples. Discrimination in hiring makes it more difficult for parents from a racial/ethnic minority group to obtain employment and therefore to benefit from policies aimed at supporting low-wage workers or to maintain eligibility for programs that require beneficiaries to work (Bertrand and Mullainathan, 2004; Holzer, Raphael, and Stoll, 2006; Stoll, Raphael, and Holzer, 2004). Discrimination by landlords renders policies to expand housing less effective for parents who are members of racial/ethnic minorities and may expose these families to greater housing instability and the risk of homelessness (Desmond, 2016).
Even for individuals with similar levels of education, racial/ethnic minorities have higher rates of unemployment and lower earnings than Whites (Pew Research Center, 2016), with Black unemployment rates typically twice as high as White unemployment rates (U.S. Bureau of Labor Statistics, 2018a). Black and Hispanic employment is also more vulnerable to downturns in the economic cycle and takes longer to recover (U.S. Bureau of Labor Statistics, 2018b). Moreover, Black and Hispanic families have on average one-sixth of the wealth of their White counterparts (McKernan et al., 2013).
While not all of the racial/ethnic differences in employment, earnings, and asset accumulation can be attributed directly to discrimination, compelling evidence suggests that discrimination plays a continuing role, particularly for employment, and to a lesser degree for wages (Pager and Shepherd, 2008). For example, among job applicants, Whites receive 36 percent more requests to advance in the hiring process (callbacks), on average, than equally qualified Black applicants and 24 percent more callbacks than equally qualified Hispanic applicants (Quillian et al., 2017). Callback rates for Black and Hispanic males without a criminal record are lower than for Whites with a criminal record (Pager, Western, and Bonikowski, 2009).
Discrimination against racial/ethnic minorities also persists in housing. Rigorous studies sponsored by the U.S. Department of Housing and
Urban Development (HUD) find that while racial/ethnic discrimination in both rental and sales markets has declined over the past 40 years, Blacks, Hispanics, and Asians seeking housing continue to be informed of and shown fewer housing units than their White counterparts (Turner et al., 2013). For example, one paired-testing study, which sampled 8,000 qualified apartment home-seekers across 28 states, found that for every 25 visits, Black home-seekers were shown one unit fewer than White home-seekers, while Hispanic home-seekers were shown one unit fewer for every 14 visits (Turner et al., 2013).
As discussed above, provisions that limit legal immigrants’ eligibility for anti-poverty programs even when they would qualify based on income are discriminatory by program design.
Relationship to Policy
Discrimination against racial/ethnic minorities in the labor and housing markets can limit the effectiveness of anti-poverty programs in several ways. For example, the Housing Choice Voucher program sets a time limit on voucher-subsidized housing searches—typically 60 days (U.S. Department of Housing and Urban Development, 2015). Families unable to locate qualifying housing within that amount of time must return the vouchers. Consequently, if minority families seeking to move are shown fewer units than majority families, as Turner et al. (2013) found, this may result in lower levels of program take-up. Further, if minority families are steered toward housing in neighborhoods with access to fewer job opportunities, then housing subsidy programs will be less successful in promoting economic mobility.
Indeed, research has found that White families receiving Housing Choice vouchers are more likely to find rental units in low-poverty neighborhoods (those with poverty rates under 10%) with higher-performing schools than are Black and Hispanic families seeking the same (Horn, Ellen, and Schwartz, 2014; McClure, Schwartz, and Taghavi, 2015). Therefore, even when different families receive a housing subsidy that is comparable in monetary value, nonmonetary factors such as social ties, reliable information, and housing discrimination (associated with prevailing residential segregation patterns) may reduce the ability of Black and Hispanic families to translate their monetary benefit into better outcomes in employment and well-being.
Discrimination in the operation of anti-poverty programs themselves may also reduce the benefit these programs offer to people who belong to racial/ethnic minorities. In an examination of six types of federal programs (TANF, child care subsidies, Head Start, child support enforcement, programs for homeless and runaway youth, and adolescent pregnancy
prevention programs), McDaniel et al. (2017) concluded that employment discrimination, as well as the organization and delivery of the programs themselves, results in racial/ethnic inequities in access to the programs and, consequently, in program outcomes. For example, evidence indicates that TANF case workers are more likely to offer work supports, such as child care, to White TANF recipients than to Black or Hispanic recipients, which may make it more difficult for the latter to find or sustain employment (McDaniel et al., 2017).
CONCLUSION 8-5: Past and current racial/ethnic discrimination have contributed to substantial disparities in access to employment and housing. Discrimination in hiring and employment may undermine policies that aim to increase or subsidize wages and policies that require beneficiaries to work. Housing discrimination reduces racial/ethnic minority families’ access to and benefits from housing programs.
Why It Matters
As of 2015, some 2.8 percent of the U.S. adult population was either incarcerated (2.2 million adults) or on probation or parole (4.7 million adults) (Kaeble and Glaze, 2016; Kaeble and Bonczar, 2016). Although these figures have declined since their peak in 2007–2008, it remains the case that millions of Americans have close connections to people who are in prison or otherwise involved with the criminal justice system (Kaeble and Glaze, 2016; Kaeble and Bonczar, 2016; Lee et al. 2015).
In 2015, 7 percent of non-Hispanic White children had a parent who was ever incarcerated, compared with 16 percent of Black children and 8 percent of Hispanic children.7 Such racial/ethnic differences persist even after controlling for parents’ educational attainment. For example, among children born in 1990 whose fathers were high school dropouts, the cumulative risk of paternal incarceration by the time the child reached age 14 was 50.5 percent for Black children, but only 7.2 percent for White children (Wildeman, 2009).
Racial/ethnic differences in involvement with the criminal justice system can be attributed to several factors, including disproportionality in school discipline, differential involvement in delinquency, criminal case characteristics, and unequal treatment in the criminal justice system (Donnelly, 2018).
7 For more information, see https://datacenter.kidscount.org/data/tables/9734-children-whohad-a-parent-who-was-ever-incarcerated-by-race-and-ethnicity#detailed/1/any/false/1539/10,11,9,12,1,13/18995,18996.
In a recent quasi-experimental study, Arnold and colleagues (forthcoming) found that inexperienced and part-time judges in Miami and Philadelphia were more likely to make racially biased prediction errors when imposing bail amounts. Racial/ethnic disparities, however, can be seen throughout the various stages of the criminal justice process (National Research Council, 2014). An example from a recent consensus report issued by the National Research Council is that, for similar crimes, sentences issued for Blacks are more likely to be on the higher end of sentencing guidelines, whereas sentences for Whites tend to be toward the lower end. Further, the report committee found that both Blacks and Hispanics are more likely than Whites to be detained before trial, which has been shown to increase the chances that the defendant will receive a prison sentence (National Research Council, 2014).
According to a recent report by the U.S. Commission on Civil Rights (2017), the fines and fees levied against individuals for even minor crimes can cause low-income families to sink into debt, which can be difficult to escape. The same report also found that these fees were often targeted at communities of color and low-income individuals (U.S. Commission on Civil Rights, 2017).
The net effect of these disparities is that Black and Hispanic children are more vulnerable to the economic, social, and psychological adversities associated with having an incarcerated parent. Reviewing the most rigorous studies on the effect of parental incarceration on children’s behavioral problems, academic achievement, and delinquency, Wildeman, Wakefield, and Turney (2013) found that paternal incarceration has consistently negative effects on child well-being and that the effects are greater than if the father were merely absent from the household (e.g., due to divorce).
Research suggests that the effects of maternal incarceration are dependent on the behavior of the mother. For example, if a mother consistently placed her child in dangerous or stressful situations prior to being imprisoned, child outcomes may improve after incarceration. Children who were not exposed to dangerous or stressful situations may experience negative outcomes when the mother is incarcerated (Wildeman and Turney, 2014). Hagan and Foster (2015) have found higher rates of food insecurity and economic insecurity (inability to pay for rent or mortgage, telephone, and utilities) among families with adolescents who were experiencing or had experienced paternal or maternal imprisonment.
Relationship to Policy
Incarcerated parents face challenges in supporting their children economically and psychologically (Turney and Goodsell, 2018). Moreover, the incarceration of one parent puts added stress on the nonincarcerated parent
(National Research Council, 2014). However, the release of an incarcerated parent does not end the adverse effects, because a record of incarceration substantially reduces the parent’s ability to work (Looney and Turner, 2018) and to find housing (Keene et al., 2018) and reduces eligibility for public services (Sugie, 2012). The lower levels of educational achievement of parents who enter prison may also reduce their chances of gaining employment after release (Looney and Turner, 2018).
Accordingly, programs that aim to increase or supplement earnings or require beneficiaries to work, such as the EITC, may be less effective for families in which a parent has been incarcerated, unless efforts are made to reduce barriers to employment for these parents. Although the level of evidence in this area is slim, programs for which there is some evidence of effectiveness include training to recognize bias on the part of employers in the recruitment and hiring of staff (Carnes et al., 2015; Devine et al., 2012); readily accessible procedures to expunge records of criminal offenses committed as juveniles (Selbin, McCrary, and Epstein, 2018); and proactive assistance for newly released convicts in obtaining employment (Broadus et al., 2016).
Reforms directed at these problems can sometimes backfire, however. Recently, several states and municipalities have passed laws to “ban the box,” meaning they prohibit employers from asking about applicants’ criminal history. There is some evidence that employers undercut the effectiveness of such laws by discriminating against all of the applicants in the larger groups that are statistically more likely to have a criminal history. For example, employers may automatically screen out names that appear to be Black or Hispanic (Agan and Starr, 2018; Doleac and Hansen, 2016).
Social policies that exclude felons from receiving benefits may have developed with both punishment and deterrence in mind. A consequence of these policies, however, is that children in these families are (often unintentionally) denied benefits that are extended to other children in otherwise identical economic circumstances. The 1996 welfare reform imposed a lifetime ban on the receipt of TANF and SNAP for individuals with a drug felony conviction, except in states that opt out of the ban. The children of parents with a felony drug conviction are still eligible for SNAP benefits; however, by reducing the total amount of SNAP benefits a family receives as a result of these bans, families living in poverty may not be able to purchase the amount of food needed to maintain good health. To date, 37 states have implemented a full or modified ban on the receipt of TANF benefits for drug felons and 34 states have done so for SNAP benefits as well (Mauer and McCalmont, 2015).
Individuals with a drug conviction also lose their eligibility for college financial aid (U.S. Department of Education, 2019), and Housing Choice Voucher housing assistance is not available to ex-convicts (who are not
members of a protected class under anti-discrimination laws) unless local housing authorities choose to allow them to qualify (Curtis, Garlington, and Schottenfeld, 2013). Given the large racial disparity in criminal justice involvement (Lyons and Pettit, 2011), and specifically in drug-related convictions (National Research Council, 2014, pp. 91-97), these reductions in public benefits particularly penalize Black families and limit the ability of incarcerated and previously incarcerated parents to support their children (either privately or through the child support system) or to enable their children to rise out of poverty (Sugie, 2012, pp. 3-4).
CONCLUSION 8-6: Involvement of a parent or other relative with the criminal justice system harms significant numbers of low-income children, particularly minority children, both economically and in other ways. Prior incarceration may render some parents ineligible for benefits that could reduce child poverty and leave them unable to secure housing or work and thus provide for their children.
Why It Matters
Neighborhood conditions—particularly those associated with high concentrations of families living in poverty—are a potentially important context both for families and children and for the anti-poverty programs that serve them. As the county-based information presented in Chapter 2 makes clear, high-poverty areas—defined as census tracts (neighborhoods of about 4,000 people) with an official poverty rate of 20 percent or more—exist all over the United States. Census data show that the adult residents of these neighborhoods are more likely than residents of low-poverty areas to lack a high school diploma, to be unemployed, to be separated or never-married, to be single parents, and to rent rather than own a home (Bishaw, 2014, Table 2a).
Additionally, levels of child development, educational outcomes, psychological well-being, and health are all worse among children living in high-poverty neighborhoods than among other children (Leventhal, Dupere, and Shuey, 2015). At the same time, as noted concerning the associations between family-based poverty and child outcomes discussed in Chapter 3, it is difficult to disentangle correlation and causation in the associations between neighborhood-based poverty and child outcomes (Gennetian et al. 2012; Sanbonmatsu et al., 2006). Moreover, the effects of neighborhood poverty seem to depend on when children were exposed over the life course (Chetty, 2015).
Neighborhood conditions are associated with a person’s ability to move out of poverty. Areas with lower levels of intergenerational mobility are characterized by greater residential segregation by race and income, higher income inequality, poorer quality K–12 schools, weaker measures of social networks and community involvement, and weaker family structures (as measured by the prevalence of single parents) (Chetty et al., 2014).
Lack of intergenerational mobility is highest among Black families living in high-poverty neighborhoods. Chetty et al. (2018) found that White and Hispanic families are more likely than Black families to move up in the income distribution across generations. Moreover, the few geographic areas in which Black-White mobility gaps were found to be relatively small tended to be low-poverty neighborhoods where Whites had low levels of racial bias and Blacks grew up with their fathers present. However, fewer than 5 percent of Black children, as compared with 63 percent of White children, grow up in areas with poverty rates below 10 percent and where more than one-half of fathers are present.
The role of past de jure discrimination should not be overlooked. High levels of current racial/ethnic residential segregation have been shaped by historical discrimination in housing policy and lending, such as redlining in the mortgage market and segregation in public housing, as well as by current zoning regulations (Rothstein, 2017). Segregation in turn has led to a disproportionate share of racial/ethnic minority families living in high-poverty neighborhoods intergenerationally (Sharkey 2008, 2013).
Relationship to Policy
Policies that aim to increase access to nutrition, housing, or employment are likely to be less effective in places that lack the resources or social networks to support them. For example, families who live in high-poverty neighborhoods tend to eat substantially less nutritious food than their counterparts in low-poverty areas. Although socioeconomic status and limited access to nutritious food in high-poverty areas contribute to unhealthy eating, the vast part of this difference is explained by the concentration of lower levels of education and knowledge about the value of healthy eating in high-poverty neighborhoods as compared to lower-poverty neighborhoods (Allcott, Diamond, and Dube, 2018; Handbury, Rahkovsky, and Schnell, 2015). Thus, the SNAP program may be more effective at increasing nutritional outcomes for families who live in high-poverty neighborhoods if the program is coupled with counseling or education about how to choose and prepare healthy food. States have access to grant funding for such programs through the Supplemental Nutrition Assistance Program—Education
(SNAP-Ed) initiative.8 While not all states have chosen to implement nutrition education programs, a recent evaluation of the SNAP-Ed program by the Research Triangle Institute suggests that they have the potential to encourage low-income families to make healthier food choices (Hersey et al., 2014).
Geographic location can also play a significant role in creating environments that help break the cycle of intergenerational poverty. In particular, access to high-quality educational experiences, which integrate students from various socioeconomic backgrounds, can improve the likelihood of future success (Rothwell and Massey, 2014).
Housing programs can also have a considerable effect on the level of neighborhood poverty that families experience. The Moving to Opportunity (MTO) experiment, discussed in Chapter 3, demonstrated that offering housing vouchers to families to move to low-poverty neighborhoods (those with less than 10 percent of residents in poverty) led to a reduction in neighborhood poverty by 20 percentage points for families that took up the offer (Gennetian et al., 2012). Although the decrease in their experience of neighborhood poverty led to virtually no improvements in MTO-participating children’s well-being in the short term (Sanbonmatsu et al., 2006; Gennetian et al., 2012), the subset of children who moved to a lower-poverty neighborhood at a young age (before age 13) showed longer-term benefits in their college and labor market outcomes (Chetty, 2015). Thus housing programs may be made more effective by targeting families with younger children in high-poverty neighborhoods, as long as the program does not have enough funding to serve all families.
The MTO experiment and corresponding Three-City Study (Cove et al. 2008; Orr et al., 2003) also provide insight into the employment effects on parents of moving from a high-poverty neighborhood to a lower-poverty neighborhood. The effects of this experiment on employment were generally weak and showed that it was difficult for many families to integrate into lower-poverty neighborhoods and take advantage of new social networks and employment opportunities. Thus, policies that require a parent to work to receive benefits may be less effective for families with limited social networks or access to resources.
Rural areas have distinctly different needs where poverty is concerned. Low-income families in some of the nation’s rural areas face substantial burdens to employment because of extremely limited public transportation and child care options (Whitener, Duncan, and Weber, 2002). Families in these areas will not benefit from work-based policies in the same way that families with better access to employment will. These rural families may
benefit more from income supports that are not based on employment, such as child allowances or child support assurance.
CONCLUSION 8-7: Living in areas of concentrated poverty makes it difficult for parents to lift their children out of poverty; poor Black and Hispanic families face a considerably higher risk of concentrated neighborhood poverty and other forms of neighborhood disadvantage than poor White families.
Why It Matters
Across the United States, as in other countries, people living in poverty tend to have worse health than the rest of the population. In the case of U.S. children, this so-called health gradient grows steeper across childhood and adolescence (Case, Lubotsky, and Paxson, 2002), although the gradient has grown flatter in recent years with the expansion of Medicaid coverage for young children (Currie, Decker, and Lin, 2008). In adulthood, the gradient is steeper still: Adults (ages 18 and older) living in poverty in the United States were almost four times as likely in 2016 to report that they were in fair or poor health (28.2%)9 as adults with family incomes above twice the official poverty line (7.76%),10 and in 2014–2015 they were several times more likely to report serious psychological distress during the past 30 days (8% vs. less than 2%).11
According to the National Council on Disability, approximately 4.1 million parents in the United States live with disabilities, and their number is increasing (National Council on Disability, 2012). Of these 4.1 million parents, 52 percent receive SSI. The Social Security Administration maintains an extensive list of impairments12 that it has judged to be severe enough to limit or prevent an individual’s ability to work. Some examples include cystic fibrosis, multiple sclerosis, cerebral palsy, traumatic brain injury, and schizophrenia (Social Security Administration, 2018). Parents with work-limiting impairments such as these are twice as likely to be
9 National Center for Health Statistics, Summary Statistics: National Health Interview Survey, Table A-11a, https://ftp.cdc.gov/pub/Health_Statistics/NCHS/NHIS/SHS/2016_SHS_Table_A-11.pdf.
10 National Center for Health Statistics, Summary Statistics: National Health Interview Survey, Table A-11a, https://ftp.cdc.gov/pub/Health_Statistics/NCHS/NHIS/SHS/2016_SHS_Table_A-11.pdf.
11 National Center for Health Statistics, Health, United States, 2016, Table 46 (page 1 of 2).
12 For a complete list of qualifying impairments, see https://www.ssa.gov/disability/professionals/bluebook/listing-impairments.htm.
unemployed (48% compared to 22%) and three times as likely to live in poverty as those without disabilities (National Council on Disability, 2012).
Regardless of their own health, parents living below the federal poverty level may have to care for children with physical or mental health conditions or disabilities, which can affect the parents’ employability and increase stress on the family (Carlson, Keith-Jennings, and Chaudhry, 2017). According to a secondary analysis of data collected for the National Health Interview Survey (NHIS) from 2001 to 2011, children living in poverty are more likely than other children to have a disability. Results from this analysis also show that the number of children with disabilities living below 100 percent of the federal poverty level increased by 10.7 percent between 2001 to 2011 (Houtrow et al., 2014). Some family members may have to reduce the number of hours they work or stop work altogether to care for relatives with disabilities, which can place an additional strain on family finances (Rupp and Ressler, 2009). This difficult balance between work and caregiving can be especially challenging for single parents (Rupp and Ressler, 2009).
Furthermore, the costs related to caring for a family member with a disability may also create a significant financial burden (Carlson, Keith-Jennings, and Chaudhry, 2017; Stabile and Allin, 2012). An analysis of the period 1996 to 2004 found that people with disabilities had significantly higher health expenditures when compared to those without disabilities13 (Mitra, Findley, and Sambamoorthi, 2009). Moreover, a more recent examination of administrative and survey data suggests that families with children with disabilities are less likely than other families with children to visit the doctor, more likely to delay paying bills and rent, and more likely to require food assistance14 (Carlson, Keith-Jennings, and Chaudhry, 2017).
Impacts on the health, employability, and quality of life for persons living with a disability are often further exacerbated if they are Black or Hispanic, are older, have low educational attainment, or are living in poverty (Ross and Bateman, 2018). As an example, Blacks and Native Americans with disabilities have the lowest employment rates (McGrew, Scott, and Madowitz, 2018).
Mental health, developmental, and intellectual disabilities can also create significant barriers to employment (Luciano and Meara, 2014; National Council on Disability, 2012). An analysis of data from 2009 and 2010 found that individuals with a diagnosed mental illness were less likely to work, and 39 percent of those who identified as having a serious mental
13 This was a secondary analysis of the nationally representative Medical Expenditure Panel Survey (MEPS) collected from 1996 to 2004.
14 This was an analysis of SNAP administrative data and National Health Interview Survey data.
illness had incomes below $10,00015 (Luciano and Meara, 2014). However, this last analysis also found that when these individuals received employment services such as vocational counseling, their employment rates doubled (Luciano and Meara, 2014). Other studies suggest that families that care for ill or disabled members have an increased risk of emotional, mental, and physical health problems, including increased levels of depression and anxiety.16
Substance abuse is also linked to lower levels of employment and wages, although the causal pathway may work in several directions. Adults who abuse drugs or alcohol may seek less work or be less qualified for well-paying jobs (Terza and Vechnak, 2007). Alternatively, the loss of employment or stress of low wages may lead to greater use of substances as a coping mechanism (Badel and Greaney, 2013). Also, injuries that initially limit a person’s ability to work may lead to a growing dependency on pain medication (i.e., opioids) that further reduces the person’s ability to work or care for himself or herself (National Institute on Drug Abuse, 2018).
Relationship to Policy
A key question for policy is the extent to which poor health is the primary cause of lower employment and earnings, as opposed to poverty causing poor health.17 However, the fact that health and income are so highly correlated suggests that programs that condition receipt of benefits on employment or that are intended to increase or supplement earnings will not help poor parents who are unable to sustain stable employment due to poor health or a disability. According to a recent National Council on Disability (2012) report, additional family and work supports such as assistance for child care, transportation, and job training may help parents living with disabilities comply with TANF work requirements. Low-income families in which the parents have disabilities that prevent them from maintaining full-time, stable employment are also less likely to be eligible for family and medical leave and less likely to be able to afford to take leave when eligible (Mathur et al., 2017). Further, while the Family and Medical Leave Act guarantees job protection for eligible workers who need to take leave for up to 12 weeks, it does not include wage replacement.18 Lack of leave time places people with disabilities at extreme risk, because they may
15 Based on data collected from the National Survey on Drug Use and Health between 2009 and 2010.
experience sporadic health flare-ups or need time off for medical appointments (Vallas, Fremstad, and Ekman, 2015).
Vocational rehabilitation programs have been shown to be effective in helping adults with mental or physical health challenges find and maintain employment (Cullen et al., 2017; Graham et al., 2016; Suijkerbuijk et al., 2017). Despite this, little has been done to connect low-income parents to most of these programs (Farrell et al., 2013; Farrell and Walter, 2013). A recent study conducted by the Office of Planning, Research, and Evaluation at the U.S. Department of Health and Human Services found that there is often only limited collaboration between TANF agencies and Social Security Administration agencies (Farrell et al., 2013). One of the most successful programs included in the study—Families Achieving Success Today, in Ramsey County, Minnesota—found that participants were more likely to receive vocational rehabilitation services and obtain employment than members of the control group and that on average they earned $1,235 more in the first year (Farrell et al., 2013), although this amount may not be enough to enable a family to rise out of poverty.
CONCLUSION 8-8: Because parents who are in poor health or caring for a child in poor health may be less able to work and care for themselves or their children, anti-poverty programs that require employment to maintain eligibility or that have cumbersome eligibility requirements may be less effective for these families.
Adams, G., and Rohacek, M.H. (2010). Child Care Instability: Definitions, Context, and Policy Implications. Washington, DC: The Urban Institute.
Agan, A., and Starr, S. (2018). Ban the box, criminal records, and racial discrimination: A field experiment. The Quarterly Journal of Economics, 133(1), 191–235.
Allcott, H., Diamond, R., and Dube, J.P. (2018). The Geography of Poverty and Nutrition: Food Deserts and Food Choices Across the United States. Cambridge, MA: National Bureau of Economic Research.
Alsan, M., and Yang, C. (2018). Fear and the Safety Net: Evidence from Secure Communities. Cambridge, MA: National Bureau of Economic Research.
Annie E. Casey Foundation. (2010). Improving Access to Public Benefits: Helping Eligible Individuals and Families Get the Income Supports They Need. Baltimore, MD: Annie E. Casey Foundation.
Arnold, D., Dobbie, W., and Yang, C.S. (Forthcoming). Racial bias in bail decisions. Quarterly Journal of Economics.
Badel, A., and Greaney, B. (2013). Exploring the link between drug use and job status in the U.S. Regional Economist, January 7. Available: https://www.stlouisfed.org/publications/regional-economist.
Barr, M. (2009). Financial services, savings, and borrowing among low- and moderate-income households: Evidence from the Detroit Area Household Financial Services Survey. In R.M. Blank and M.S. Barr (Eds.), Insufficient Funds: Savings, Assets, Credit, and Banking Among Low-Income Households (pp. 66–96). New York, NY: Russell Sage Foundation.
Barr, M.S. (2012). Introduction. In M.S. Barr (Ed.), No Slack: The Financial Lives of Low-Income Americans (pp. 1–21). Washington, DC: Brookings Institution Press.
Batalova, J., Fix, M., and Greenberg, M. (2018). Chilling Effects: The Expected Public Charge Rule and Its Impact on Legal Immigrant Families’ Public Benefits Use. Washington, DC: Migration Policy Institute.
Bertrand, M., and Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), 991–1013.
Bishaw, A. (2014). Changes in Areas with Concentrated Poverty: 2000 to 2010. Washington, DC: U.S. Census Bureau.
Broadus, J., Muller-Ravett, S., Sherman, A., and Redcross, C. (2016). A Successful Prisoner Reentry Program Expands: Lessons from the Replication of the Center for Employment Opportunities. New York, NY: MDRC.
Burhouse, S., Chu, K., Goodstein, R., Northwood, J., Osaki, Y., and Sharma, D. (2014). 2013 FDIC National Survey of Unbanked and Underbanked Households. Washington, DC: Federal Deposit Insurance Corporation.
Campbell, A.L. (2014). Trapped in America’s Safety Net: One Family’s Struggle. Chicago, IL: University of Chicago Press.
Capps, R., Fix, M., Ost, J., Reardon-Anderson, J., and Passel, J.S. (2004). The Health and Well-Being of Young Children of Immigrants. Washington, DC: The Urban Institute.
Carlson, S., Keith-Jennings, B., and Chaudhry, R. (2017). SNAP Provides Needed Food Assistance to Millions of People With Disabilities. Washington, DC: Center on Budget and Policy Priorities.
Carnes, M., Devine, P.G., Baier Manwell, L., Byars-Winston, A., Fine, E., Ford, C.E., Forscher, P., Isaac, C., Kaatz, A., Magua, W., Palta, M., and Sheridan, J. (2015). The effect of an intervention to break the gender bias habit for faculty at one institution: A cluster randomized, controlled trial. Academic Medicine, 90(2), 221–230.
Case, A., Lubotsky, D., and Paxson, C. (2002). Economic status and health in childhood: The origins of the gradient. American Economic Review, 92(5), 1308–1334.
Caskey, J. (2006). Can Personal Financial Management Education Promote Asset Accumulation by the Poor? Terre Haute, IN: Networks Financial Institute.
Castellari, E., Cotti, C., Gordanier, J., and Ozturk, O. (2017). Does the timing of food stamp distribution matter? A panel data analysis of monthly purchasing patterns of U.S. households. Health Economics, 26(11), 1380–1393.
Chetty, R. (2015). Behavioral economics and public policy: A pragmatic perspective. American Economic Review, 105(5), 1–33.
Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States. Cambridge, MA: National Bureau of Economic Research.
Chetty, R., Hendren, N., Jones, M.R., and Porter, S.R. (2018). Race and Economic Opportunity in the United States: An Intergenerational Perspective. Cambridge, MA: National Bureau of Economic Research.
Child Trends. (2014). Immigrant Children: Indicators of Child and Youth Well-Being. Washington, DC: Child Trends.
Cody, S., Reed, D., Basson, D., Pedraza, J., Martin, E.S., Santos, B., and Smith, E. (2010). Simplification of Health and Social Services Enrollment and Eligibility: Lessons for California from Interviews in Four States: Final Report. Washington, DC: Mathematica Policy Research.
Coleman-Jensen, A., Rabbitt, M.P., Gregory, C.A., and Singh, A. (2017). Household Food Security in the United States in 2016. Washington, DC: U.S. Department of Agriculture, Economic Research Service.
Congressional Research Service. (2016). Green Book: Background Material and Data on the Programs within the Jurisdiction of the Committee on Ways and Means. Washington, DC: Congressional Research Service.
Cove, E., Turner, M.A., Briggs, X.D.S., and Duarte, C. (2008). Can Escaping from Poor Neighborhoods Increase Employment and Earnings? Washington, DC: The Urban Institute.
Cullen, K.L., Irvin, E., Collie, A., Clay, F., Gensby, U., Jennings, P.A., Hogg-Johnson, S., Kristman, V., Laberge, M., McKenzie, D., Newnam, S., Palagyi, A., Ruseckaite, R., Sheppard, D.M., Shourie, S., Steenstra, I., Van Eerd, D., and Amick, B.C. (2017). Effectiveness of workplace interventions in return-to-work for musculoskeletal, pain-related and mental health conditions: An update of the evidence and messages for practitioners. Journal of Occupational Rehabilitation, 28(1).
Currie, J. (2008). The Invisible Safety Net. Princeton: Princeton University Press.
Currie, J., and Grogger, J. (2001). Explaining recent declines in Food Stamp Program participation. Brookings-Wharton Papers on Urban Affairs, 203–244.
Currie, J., Decker, S., and Lin, W. (2008). Has public health insurance for older children reduced disparities in access to care and health outcomes? Journal of Health Economics, 27(6), 1567–1581.
Curtis, M.A., Garlington, S., and Schottenfeld, L.S. (2013). Alcohol, drug, and criminal history restrictions in public housing. Cityscape: A Journal of Policy Development and Research, 15(3).
Cutler, D.M., Lleras-Muney, A., and Vogl, T. (2008). Socioeconomic Status and Health: Dimensions and Mechanisms. Cambridge, MA: National Bureau of Economic Research.
Dehlendorf, C., Ruskin, R., Grumbach, K., Vittinghoff, E., Bibbins-Domingo, K., Schillinger, D., and Steinauer, J. (2010). Recommendations for intrauterine contraception: A randomized trial of the effects of patients’ race/ethnicity and socioeconomic status. American Journal of Obstetrics and Gynecology, 203(4), 319-e1–319-e8.
Desmond, M. (2016). Evicted: Poverty and Profit in the American City. New York, NY: The Crown Publishing Group.
Devine, P.G., Forscher, P.S., Austin, A.J., and Cox, W.T. (2012). Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. Journal of Experimental Social Psychology, 48(6), 1267–1278.
Doleac, J., and Hansen, B. (2016). Does “Ban the Box” Help or Hurt Low-skilled Workers? Statistical Discrimination and Employment Outcomes When Criminal Histories Are Hidden. Cambridge, MA: National Bureau of Economic Research.
Donnelly, E.A. (2018). Do disproportionate minority contact (DMC) mandate reforms change decision-making? Decomposing disparities in the juvenile justice system. Youth Violence and Juvenile Justice. doi: https://doi.org/10.1177%2F1541204018790667.
Edin, K., and Lein, L. (1997). Making Ends Meet: How Single Mothers Survive Welfare and Low-Wage Work. New York, NY: Russell Sage Foundation.
Enchautegui, M.E. (2013). Nonstandard Work Schedules and the Well-Being of Low-Income Families. Washington, DC: The Urban Institute.
Enchautegui, M.E., Johnson, M., and Gelatt, J. (2015). Who Minds the Kids When Mom Works a Nonstandard Schedule? Washington, DC: The Urban Institute.
Evans, W., Wolfe, B., and Adler, N. (2012). The SES and health gradient: A brief review of the literature. In The Biological Consequences of Socioeconomic Inequalities (pp. 1–37). New York, NY: Russell Sage Foundation.
Farrell, M., and Walter, J. (2013). The Intersection of Welfare and Disability: Early Findings from the TANF/SSI Disability Transition Project. OPRE Report 2013-06. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Farrell, M., Baird, P., Barden, B., Fishman, M., and Pardoe, R. (2013). The TANF/SSI Disability Transition Project: Innovative Strategies for Serving TANF Recipients with Disabilities. OPRE Report 2013-51. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Floyd, I. (2017). TANF Cash Benefits Have Fallen by More Than 20 Percent in Most States and Continue to Erode. Washington, DC: Center on Budget and Policy Priorities.
Gassman-Pines, A., and Bellows, L. (2018). Food instability and academic achievement: A quasi-experiment using SNAP benefit timing. American Educational Research Journal, 56(5),897–927.
Gennetian, L.A., and Shafir, E. (2015). The persistence of poverty in the context of financial instability: A behavioral perspective. Journal of Policy Analysis and Management, 1–33.
Gennetian, L.A., Sciandra, M., Sanbonmatsu, L., Ludwig, J., Katz, L.F., Duncan, G.J., Kling, J.R., and Kessler, R.C. (2012). The long-term effects of Moving to Opportunity on youth outcomes. Cityscape, 14(2), 137–168.
Graham, C.W., West, M.D., Bourdon, J.L., Inge, K.J., Seward, H.E., and Campbell, C. (2016). Employment interventions for return to work in working aged adults following traumatic brain injury (TBI): A systematic review. Campbell Systematic Reviews 2016:6. Oslo, Norway. Available: http://www.campbellcollaboration.org.
Gray, K.F., and Cunnyngham, K. (2016). Trends in Supplemental Nutrition Assistance Program Participation Rates: Fiscal Year 2010 to Fiscal Year 2014. Washington, DC: Mathematica Policy Research.
Hagan, J., and Foster, H. (2015). Mass incarceration, parental imprisonment, and the Great Recession: Intergenerational sources of severe deprivation in America. The Russell Sage Foundation Journal of the Social Sciences, 1(2), 80–107.
Hahn, H., Adams, G., Spaulding, S., and Heller, C. (2016). Supporting The Child Care and Workforce Development Needs of TANF Families. Washington, DC: The Urban Institute.
Halpern-Meekin, S., Edin, K., Tach, L., and Sykes, J. (2015). It’s Not Like I’m Poor: How Working Families Make Ends Meet in a Post-Welfare World (1st ed.). Oakland: University of California Press.
Hamrick, K.S., and Andrews, M. (2016). SNAP participants’ eating patterns over the benefit month: A time-use perspective. PLoS ONE, 11(7). Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158422.
Handbury, J., Rahkovsky, I., and Schnell, M. (2015). Is the Focus on Food Deserts Fruitless? Retail and Food Purchases Across the Socioeconomic Spectrum. Cambridge, MA: National Bureau of Economic Research.
Hanratty, M.J. (2006). Has the Food Stamp Program become more accessible? Impacts of recent changes in reporting requirements and asset eligibility limits. Journal of Policy Analysis and Management, 25(3), 603–621.
Hersey, J.C., Cates, S.C., Blitstein, J.L., and Williams, P.A. (2014). SNAP-Ed Can Improve Nutrition of Low-Income Amercians Across Life Span. Research Triangle Park, NC: RTI International.
Hill, H.D., Morris, P., Gennetian, L.A., Wolf, S., and Tubbs, C. (2013). The consequences of income instability for children’s well-being. Child Development Perspectives, 7(2), 85–90.
Hoffman, L. (2006). Improving Access to Benefits for Low-Income Families. Washington, DC: NGA Center for Best Practices.
Holcomb, P.A., Tumlin, K.C., Koralek, R., Capps, R., and Zuberi, A. (2003). The Application Process for TANF, Food Stamps, Medicaid and SCHIP: Issues for Agencies and Applicants, Including Immigrants and Limited English Speakers. Washington, DC: The Urban Institute.
Holcomb, P.A., Adams, G., Snyder, K., Koralek, R., Martinson, K., Bernstein, S., and Capizzano, J. (2006). Child Care Subsidies and TANF: A Synthesis of Three Studies on Systems, Policies, and Parents. Washington, DC: The Urban Institute.
Holt, S. (2015). Periodic Payment of the Earned Income Tax Credit Revisited. Washington, DC: The Brookings Institution. Available: https://www.brookings.edu/research/periodic-payment-of-the-earned-income-tax-credit-revisited.
Holzer, H.J., Raphael, S., and Stoll, M.A. (2006). Perceived criminality, criminal background checks, and the racial hiring practices of employers. The Journal of Law and Economics, 49(2), 451–480.
Holzer, H.J., and Wissoker, D. (2001). How Can We Encourage Job Retention for Welfare Recipients. Series No. A-49, October. Washington, DC: The Urban Institute.
Horn, K.M., Ellen, I.G., and Schwartz, A.E. (2014). Do Housing Choice Voucher holders live near good schools? Journal of Housing Economics, 23, 28–40.
Houtrow, A.J., Larson, K., Olson, L.M., Newacheck, P.W., and Halfon, N. (2014). Changing trends of childhood disability, 2001–2011. Pediatrics 134(3), 530–538.
Internal Revenue Service. (2018a). States and Local Governments with Earned Income Tax Credit. Available: https://www.irs.gov/credits-deductions/individuals/earned-income-taxcredit/states-and-local-governments-with-earned-income-tax-credit.
________. (2018b). Welcome to the Tax Preparer Toolkit: EITC and Other Refundable Credits. Available: https://www.eitc.irs.gov/tax-preparer-toolkit/welcome-to-the-tax-preparertoolkit.
Isaacs, J.B., Katz, M., and Amin, R. (2016). Improving the Efficiency of Benefit Delivery: Outcomes From the Work Support Strategies Evaluation. Washington, DC: The Urban Institute.
Joshi, P., Ha, Y., Schneider, K.G., and Hardy, E. (2018, January). Multiple and Interacting Sources of Child Care Subsidy Stability: Complexities of Administrative and Family-Level Factors. Paper presented at the Society for Social Work and Research Annual Meeting, Washington, DC.
Joshi, P.K., Geronimo, K., Romano, B., Earle, A., Rosenfeld, L., Hardy, E., and Acevedo-Garcia, D. (2014). Integrating racial/ethnic equity into policy assessments to improve child health. Health Affairs, 33(12), 2222–2229.
Kabbani, N.S., and Wilde, P.E. (2003). Short recertification periods in the U.S. Food Stamp Program. The Journal of Human Resources, 38, 1112-1138.
Kaeble, D., and Bonczar, T. (2016). Probation and Parole in the United States, 2015. Washington, DC: Bureau of Justice Statistics.
Kaeble, D., and Glaze, L. (2016). Correctional Populations in the United States, 2015. Washington, DC: Bureau of Justice Statistics.
Kaushal, N., and Gao, Q. (2011). Food Stamp Program and consumption choices. In M. Grossman and N. Mocan (Eds.), Economic Aspects of Obesity. Chicago, IL: University of Chicago Press.
Keene, D.E., Rosenberg, A., Schlesinger, P., Guo, M., and Blankenship, K.M. (2018). Navigating limited and uncertain access to subsidized housing after prison. Housing Policy Debate, 28(2), 199–214.
Kochhar, R., and Cilluffo, A. (2017). How wealth inequality has changed in the U.S. since the Great Recession, by race, ethnicity, and income. Fact Tank: News in the Numbers. Available: http://www.pewresearch.org/fact-tank/2017/11/01/how-wealth-inequality-haschanged-in-the-u-s-since-the-great-recession-by-race-ethnicity-and-income.
Kopczuk, W., and Pop-Eleches, C. (2007). Electronic filing, tax preparers and participation in the Earned Income Tax Credit. Journal of Public Economics, 91, 1351–1367.
Kornfeld, R. (2002). Explaining Recent Trends in Food Stamp Program Caseloads: Final Report. Washington, DC: U.S. Department of Agriculture, Economic Research Service.
Lee, H., McCormick, T., Hicken, M.T., and Wildeman, C. (2015). Racial inequalities in connectedness to imprisoned individuals in the United States. Du Bois Review: Social Science Research on Race, 12(2), 269–282.
Leventhal, T., Dupere, V., and Shuey, E.A. (2015). Children in neighborhoods. In H. Bornstein, T. Leventhal, and R.M. Lerner (Eds.), Handbook of Child Psychology and Developmental Science: Ecological Settings and Processes. Hoboken, NJ: John Wiley & Sons, Inc.
Looney, A., and Turner, N. (2018). Work and Opportunity Before and After Incarceration. Washington, DC: The Brookings Institution.
Loprest, P., Gearing, M., and Kassabian, D. (2016). States’ Use of Technology to Improve Delivery of Benefits: Findings From the Work Support Strategies Evaluation. Washington, DC: The Urban Institute.
Luciano, A., and Meara, E. (2014). The employment status of people with mental illness: National survey data from 2009 and 2010. Psychiatric Services, 65(10), 1201–1209.
Lusardi, A., Schneider, D., and Tufano, P. (2011). Financially Fragile Households: Evidence and Implications. Washington, DC: The Brookings Institution.
Lyons, C.J., and Pettit, B. (2011). Compounded disadvantage: Race, incarceration, and wage growth. Social Problems, 58(2), 257–280.
Mathur, A., Sawhill, I.V., Boushey, H., Gitis, B., Haskins, R., Holtz-Eakin, D., Holzer, H.J., Jacobs, E., McCloskey, A.M., Rachidi, A., Reeves, R.V., Ruhm, C.J., Stevenson, B., and Waldfogel, J. (2017). Paid Family and Medical Leave: An Issue Whose Time Has Come. Washington, DC: The American Enterprise Institute and The Brookings Institution.
Mauer, M., and McCalmont, V. (2015). A Lifetime of Punishment: The Impact of the Felony Drug Ban on Welfare Benefits. Washington, DC: The Sentencing Project.
McClure, K., Schwartz, A.F., and Taghavi, L.B. (2015). Housing Choice Voucher location patterns a decade later. Housing Policy Debate, 25(2), 215–233.
McDaniel, M., Woods, T., Pratt, E., and Simms, M. (2017). Identifying Racial and Ethnic Disparities in Human Services: A Conceptual Framework and Literature Review. OPRE Report No. 2017-69. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
McGrew, A., Scott, L., and Madowitz, M. (2018). The State of the U.S. Labor Market: PreMarch 2018 Jobs Release. Available: https://www.americanprogress.org/issues/economy/news/2018/04/05/448989/state-u-s-labor-market-pre-march-2018-jobs-release.
McKernan, S.M., Ratcliffe, C., and Vinopal, K. (2009). Do Assets Help Families Cope with Adverse Events? Washington, DC: The Urban Institute.
McKernan, S.M., Ratcliffe, C., Steuerle, C.E., and Zhang, S. (2013). Less than Equal: Racial Disparities in Wealth Accumulation. Washington, DC: The Urban Institute.
Mendenhall, R., Edin, K., Crowley, S., Sykes, J., Tach, L., Kriz, K., and Kling, J.R. (2012). The role of Earned Income Tax Credit in the budgets of low-income households. Social Service Review, 86(3), 367–400.
Meyers, M.K., Gornick, J.C., and Peck, L.R. (2001). Packaging support for low-income families: Policy variation across the United States. Journal of Policy Analysis and Management, 20(3), 457–483.
Michalopoulos, C., Lundquist, E., and Castells, N. (2010). The Effects of Child Care Subsidies for Moderate-Income Families in Cook County, Illinois. OPRE 2011–3. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Mitra, S., Findley, P.A., and Sambamoorthi, U. (2009). Health care expenditures of living with a disability: Total expenditures, out-of-pocket expenses, and burden, 1996 to 2004. Archives of Physical Medicine and Rehabilitation, 90(9), 1532–1540.
Moore, K., Perez-Lopez, D., and Hisnanick, J.J. (2017). Participation Rates and Monthly Payments From Selected Social Insurance Programs. Household Economic Studies. Current Population Reports, March. Washington, DC: U.S. Census Bureau.
Mullainathan, S., and Shafir, E. (2013). Scarcity: Why Having Too Little Means So Much. New York, NY: Times Books/Henry Holt.
National Council on Disability. (2012). Rocking the Cradle: Ensuring the Rights of Parents With Disabilities and Their Children. Washington, DC: National Council on Disability.
National Insitute on Drug Abuse. (2018). Opiod Overdose Crisis. Available: https://www.drugabuse.gov/drugs-abuse/opioids/opioid-overdose-crisis.
National Research Council. (2014). The Growth of Incarceration in the United States: Exploring Causes and Consequences. Washington, DC: The National Academies Press.
Orr, L., Feins, J.D., Jacob, R., Beecroft, E., Sanbonmatsu, L., Katz, L.F., Liebman, J.B., and Kling, J.R. (2003). Moving to Opportunity Interim Impacts Evaluation. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.
Pager, D., and Shepherd, H. (2008). The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets. Annual Review of Sociology, 34, 181–209.
Pager, D., Western, B., and Bonikowski, B. (2009). Discrimination in a low-wage labor market: A field experiment. American Sociological Review, 74, 777–799.
Pavetti, L., Schott, L., and Lower-Basch, E. (2011). Creating Subsidized Employment Opportunities for Low-Income Parents: The Legacy of the TANF Emergency Fund. Washington, DC: Center on Budget and Policy Priorities.
Perreira, K.M., Yoshikawa, H., and Oberlander, J. (2018). A new threat to immigrants’ health—the public-charge rule. New England Journal of Medicine, 379(10), 901–903.
Pew Charitable Trusts. (2015). What Resources Do Families Have for Financial Emergencies? The Role of Emergency Savings in Family Financial Security? Issue Brief. Available: http://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2015/11/emergency-savings-what-resources-do-families-have-for-financial-emergencies.
Pew Research Center. (2016). On Views of Race and Inequality, Blacks and Whites Are Worlds Apart. Washington, DC: Pew Research Center.
Quillian, L., Pager, D., Hexel, O., and Midtboen, A.H. (2017). Meta-analysis of field experiments shows no change in racial discrimination in hiring over time. Proceedings of the National Academy of Sciences, 114(41), 10870–10875.
Ratcliffe, C., McKernan, S.M., and Finegold, K. (2007). The Effect of State Food Stamp and TANF Policies on Food Stamp Program Participation. Washington, DC: The Urban Institute.
Ross, M., and Bateman, N. (2018). Disability Rates Among Working-Age Adults Are Shaped by Race, Place, and Education. Washington, DC: The Brookings Institution.
Rothstein, R. (2017). The Color of Law: A Forgotten History of How Our Government Segregated America. New York, NY: Liveright.
Rothwell, J.T., and Massey, D.S. (2014). Geographic effects on intergenerational income mobility. Economic Geography, 91(1), 83–106.
Rupp, K., and Ressler, S. (2009). Family caregiving and employment among parents of children with disabilities on SSI. Journal of Vocational Rehabilitation, 30, 153–175.
Sanbonmatsu, L., Kling, J.R., Duncan, G.J., and Brooks-Gunn, J. (2006). Neighborhoods and Academic Achievement: Results From the Moving to Opportunity Experiment. NBER Working Paper No. 11909. Cambridge, MA: National Bureau of Economic Research.
Schott, L., Pavetti, L., and Floyd, I. (2015). How States Use Federal and State Funds Under the TANF Block Grant. Washington, DC: Center on Budget and Policy Priorities. Available: https://www.cbpp.org/sites/default/files/atoms/files/4-8-15tanf_0.pdf.
Selbin, J., McCrary, J., and Epstein, J. (2018). Unmarked: Criminal record clearing and employment outcomes criminal law/criminology. The Journal of Criminal Law and Criminology, 108(1).
Sharkey, P. (2008). The intergenerational transmission of context. American Journal of Sociology, 113(4), 931–969.
________. (2013). Stuck in Place: Urban Neighborhoods and the End of Progress Toward Racial Equality. Chicago, IL: University of Chicago Press.
Shipler, D.K. (2004). The Working Poor: Invisible in America. New York, NY: Vintage.
Social Security Administration. (2018). Disability Evaluation under Social Security: Part III—Listing of Impairments. Available: https://www.ssa.gov/disability/professionals/bluebook/listing-impairments.htm.
Stabile, M., and Allin, S. (2012). The economic cost of childhood disability. Future of Children, 22(1), 65–96.
Stoll, M.A., Raphael, S., and Holzer, H.J. (2004). Black job applicants and the hiring officers’ race. Industrial and Labor Relations Review, 57(2), 267–287.
Strawn, J., and Martinson, K. (2000). Steady Work and Better Jobs: How to Help Low-Income Parents Sustain Employment and Advance in the Workforce. New York, NY: MDRC.
Sugie, N.F. (2012). Punishment and welfare: Paternal incarceration and families’ receipt of public assistance. Social Forces: A Scientific Medium of Social Study and Interpretation, 90(4), 1403–1427.
Suijkerbuijk, Y.B., Schaafsma, F.G., van Mechelen, J.C., Ojajarvi, A., Corbiere, M., and Anema, J.R. (2017). Interventions for obtaining and maintaining employment in adults with severe mental illness, a network meta-analysis. Cochrane Database System Review, 9, CD011867.
Terza, J.V., and Vechnak, P.B. (2007). The Effect of Substance Abuse on Employment Status. University Park: The Pennsylvania State University.
Turner, M.A., Santos, R., Levy, D.K., Wissoker, D., Aranda, C., and Pitingolo, R. (2013). Housing Discrimination Against Racial and Ethnic Minorities 2012. Washington, DC: U.S. Department of Housing and Urban Development.
Turney, K., and Goodsell, R. (2018). Parental incarceration and children’s wellbeing. The Future of Children, 28(1).
U.S. Bureau of Labor Statistics (2018a). Table A2. Employment Status of the Civilian Population by Race, Sex, and Age. Washington, DC: U.S. Bureau of Labor Statistics. Available: https://www.bls.gov/news.release/empsit.t02.htm.
________. (2018b). Labor Force Statistics from the Current Population Survey. August 30. Washington, DC: U.S. Bureau of Labor Statistics. Available: https://www.bls.gov/cps/demographics.htm#race.
U.S. Commission on Civil Rights. (2017). Targeted Fines and Fees Against Low-Income Communities of Color: Civil Rights and Constitutional Implications. Washington, DC: U.S. Commission on Civil Rights.
U.S. Department of Education. (2019). Federal Student Aid: Students with Criminal Convictions. Washington, DC: U.S. Department of Education. Available: https://studentaid.ed.gov/sa/eligibility/criminal-convictions#incarcerated.
U.S. Department of Housing and Urban Development. (2015). Housing search and leasing. Chapter 8 in Housing Choice Voucher Program Guidebook. Washington, DC: U.S. Department of Housing and Urban Development.
Vallas, R., Fremstad, S., and Ekman, L. (2015). A Fair Shot for Workers with Disabilities. Washington, DC: Center for American Progress. Available: https://www.americanprogress.org/issues/poverty/reports/2015/01/28/105520/a-fair-shot-for-workers-with-disabilities.
Wagner, J., and Huguelet, A. (2016). Opportunities for States to Coordinate Medicaid and SNAP Renewals. Washington, DC: Center on Budget and Policy Priorities.
Whitener, L., Duncan, G., and Weber, B. (2002). Reforming Welfare: What Does It Mean for Rural Areas? Washington, DC: U.S. Department of Agriculture.
Wilde, P., Cook, P., Gundersen, C., Nord, M., and Tiehen, L. (2000). The Decline in Food Stamp Program Participation in the 1990s. Washington, DC: U.S. Department of Agriculture, Economic Research Service.
Wildeman, C. (2009). Parental imprisonment, the prison boom, and the concentration of childhood disadvantage. Demography, 46(2), 265–280.
Wildeman, C., and Turney, K. (2014). Positive, negative, or null? The effects of maternal incarceration on children’s behavioral problems. Demography, 51(3), 1041–1068.
Wildeman, C., Wakefield, S., and Turney, K. (2013). Misidentifying the effects of parental incarceration? A comment on Johnson and Easterling (2012). Journal of Marriage and Family, 75(1), 252–258.