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A Roadmap to Reducing Child Poverty (2019)

Chapter: 2 A Demographic Portrait of Child Poverty in the United States

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Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
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Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
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Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
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Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
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Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
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Page 35
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 36
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 37
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 38
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 39
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 40
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 41
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 42
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 43
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 44
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 45
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 46
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 47
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 48
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 49
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 50
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 51
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 52
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 53
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 54
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 55
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 56
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 57
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 58
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 59
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 60
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 61
Suggested Citation:"2 A Demographic Portrait of Child Poverty in the United States." National Academies of Sciences, Engineering, and Medicine. 2019. A Roadmap to Reducing Child Poverty. Washington, DC: The National Academies Press. doi: 10.17226/25246.
×
Page 62

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Prepublication Copy Uncorrected Proofs 2 A Demographic Portrait of Child Poverty in the United States In light of the committee’s charge to identify programs that would reduce child poverty in the United States by half within a decade, and to set the stage for the program and policy proposals we make later in this report, in this chapter we provide an overview of child poverty in the United States. We begin with a brief explanation of how poverty is defined. Next we offer an overview showing which demographic subgroups of U.S. children suffer the highest poverty rates today and how child poverty rates have changed over time. The chapter’s final section compares the extent of child poverty in the United States and in peer English-speaking countries. The impacts of poverty on child development are discussed in Chapter 3, while contextual factors that reinforce poverty among low-income families are discussed in Chapter 8. MEASURING U.S. CHILD POVERTY “Poverty” typically refers to a lack of economic resources, but measuring it requires careful consideration of the types of economic resources to be counted as well as agreement on a minimum threshold below which a family’s economic resources may be considered insufficient. In the 1960s, the U.S. federal government developed a method for identifying a threshold amount of household cash income below which a given household, and all related individuals living in that household, would be designated as “poor.” (See Appendix D, 2-1 for a brief history of poverty measurement in the United States.) This official poverty measure (OPM) of income poverty is still being used to determine social program eligibility as well as to track long-term trends in poverty rates. Also available are poverty measures based on consumption instead of income. Nevertheless, the statement of task for our committee directed us to use the Supplemental Poverty Measure (SPM) of income poverty, which we adjusted for underreporting of some types of income in the survey data. Box 2-1 illustrates differences in estimated child poverty among these measures. For the reasons detailed in Appendix D, 2-2 (on income poverty) and Appendix D, 2-3 (on consumption poverty), we consider the adjusted SPM to be currently the best available approach to poverty measurement.1                                                              1 The large literature of poverty measurement, in the United States and abroad, addresses types of poverty measures and measurement issues that are not central to our charge—for example, the merits of deprivation indexes compared with income- or consumption-based indexes. We briefly note these other measures and measurement issues in Appendix D, 2-2.

BOX 2-1 How Much Child Poverty Is There? Disagreements over how poverty should be defined and how the definitions should be applied using data from the federal statistical system have generated a wide range of poverty estimates. The Official Poverty Measure (OPM), published by the U.S. Census Bureau, estimates that 20 percent of U.S. children were poor in 2015. The Supplemental Poverty Measure (SPM), also published by the Census Bureau, estimates for the same year that 16 percent of children were poor. Our report uses the SPM, corrected for underreporting of some kinds of income in the Annual Social and Economic Supplement to the Current Population Survey, resulting in an estimated 13 percent child poverty rate in 2015. The rationale for using the SPM corrected for underreporting rather than using the OPM is detailed in Appendix D, 2-2—importantly, the SPM takes account of taxes, in-kind benefits, and nondiscretionary expenses (e.g., child support payments) and so is suited for the kinds of policy analysis that we were charged to undertake, and the corrected SPM accounts more completely for families’ resources. Based on an alternative approach to poverty measurement, using consumption rather than income to determine poverty status, a 2018 Council of Economic Advisers (CEA) report declared that “our War on Poverty is largely over and a success” (Council of Economic Advisors, 2018, p. 29). This alternative measure (based on Consumer Expenditure Survey data) produced just a 5 percent poverty rate for children in 2015, dropping to 4 percent in 2016 (Meyer and Sullivan, 2017, Table 3). While many economists believe that consumption is theoretically a better measure than income in determining how families are actually faring, the committee considered the SPM to be superior to currently-available consumption-based poverty measures (see Appendix D, 2-3)—importantly, it is difficult to trace the effects of more generous assistance programs (e.g., a more generous child tax credit) on consumption, whereas it is straightforward to do so for income; also, it is difficult to evaluate the measure cited by the CEA given how its poverty thresholds were derived and updated, which resulted in contemporary thresholds and poverty rates that seem unrealistically low. There are sources of error in both federal income and expenditure statistics, as well as more work that is needed to improve both income-based and consumption-based poverty measures. The committee concludes that the corrected SPM is the preferred measure for its purposes but also recommends investment in better data and measures (see Chapter 9).   PREPUBLICATION COPY, UNCORRECTED PROOFS 2-2

Measuring Poverty with the Supplemental Poverty Measure For this report, the committee was directed to use the SPM, which bases poverty thresholds on the expenditures U.S. families must make for food, clothing, shelter, and utilities (FCSU) plus a small additional amount for other needs (such as personal care, transportation, and household supplies). Expenditures are measured using the average of five years of data from the Consumer Expenditure Survey, with the poverty threshold set at the level of FCSU expenditures for family units with two children that separates the bottom one-third of such families, ranked by FCSU expenditures, from the top two-thirds. For 2016, thresholds ranged between about $22,000 and $26,000 for two-adult, two-child families, depending on whether the family owned or rented its housing (Fox, 2017). The SPM thresholds are also adjusted for family size, using an equivalence scale, and for local cost-of-living differences in housing.2 The household resources considered are the sum of money income from all sources, including earnings and government cash benefits such as Social Security and Unemployment Compensation. A key difference between the OPM and SPM is that SPM-based household resources also include “near-cash” income benefits such as the Supplemental Nutrition Assistance Program (SNAP, formerly called food stamps) and housing subsidies, as well as benefits from many smaller programs. Deducted from household resources are child care and other work expenses, child support payments made, and out-of-pocket medical expenses (including insurance premiums). Taxes paid, most notably payroll taxes, are also deducted from household resources, while refundable tax credits from programs like the Earned Income Tax Credit, Child Tax Credit, and the Additional Child Tax Credit are added to resources. As we show below, because government spending on tax credits and programs that provide “near cash” (as opposed to cash) benefits have grown markedly over the past 50 years, conclusions about trends in child poverty largely depend on whether poverty is measured using the OPM or the SPM. Key differences between the official measure and the SPM are summarized in Table 2-1 and in Appendix D, 2-6.                                                              2 Appendix D, 2-4 provides a detailed explanation of how equivalence scales are used to adjust threshold levels. Appendix D, 2-5 discusses how cost-of-living adjustments (COLAs) are used in the SPM, including how geographic COLAs compensate for differences in the price of rental housing. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-3

TABLE 2-1 Key Differences in Poverty Measure Concepts Between the Official Poverty Measure (OPM) and the Supplemental Poverty Measure (SPM) Concept Official Poverty Measure Supplemental Poverty Measure Measurement Families (individuals Resource units (official family definition plus any co- units related by birth, marriage, resident unrelated children, foster children, or or adoption) or unrelated unmarried partners and their relatives) or unrelated individuals individuals (who are not otherwise included in the family definition) Poverty Three times the cost of a Based on expenditures for food, clothing, shelter, and threshold minimum food diet in utilities (FCSU), and a little more 1963 Threshold Vary by family size, Vary by family size and composition, as well as adjustments composition, and age of geographic adjustments for differences in housing householder costs by tenure Updating Consumer Price Index: 5-year moving average of expenditures on FCSU thresholds all items Resource Gross before-tax cash Sum of cash income, plus noncash benefits that measure income resource units can use to meet their FCSU needs, minus taxes (or plus tax credits), minus work expenses, out-of-pocket medical expenses, and child support paid to another household SOURCE: Fox (2017). The Census Bureau has published SPM-based poverty statistics every fall since 2011. Its most recent report (Fox, 2018) indicates that, in 2017, 15.6 percent of children lived in families with incomes below the SPM-based poverty line. That rate is lower than the 18.0 percent rate based on the OPM (Semega, Fontenat, and Koller, 2017), owing primarily to the SPM’s more comprehensive measure of household resources. For certain demographic groups other than children, poverty rates are higher when measured by the SPM as compared with the OPM. An example is the elderly, whose higher out-of-pocket medical payments are deducted from household resources in the SPM but not in the OPM. In addition, the 15.6 percent overall child poverty rate conceals considerable demographic and geographic variation, which we explore in subsequent sections of this chapter and Appendix D, 2-8 and 2-9. The committee’s statement of task directs it to identify programs and policies that reduce both SPM-based poverty and deep poverty by half in 10 years. To address deep poverty, the committee adopted a common definition, namely having resources below 50 percent of those PREPUBLICATION COPY, UNCORRECTED PROOFS 2-4

used to define poverty based on the SPM. We also provide data on “near poor” children by including those with household resources below 150 percent of poverty. These three sets of thresholds are used consistently throughout this report. Indirect Treatment of Health Care Needs and Benefits in the SPM One important family need that is difficult to incorporate into poverty measurement is health care—both households’ medical costs and the extent to which health insurance programs for low-income families help households afford them and shield families from falling into poverty as a result of health shocks. The importance of health care and health insurance has historically been recognized by making health insurance through Medicaid part of the package of benefits offered to low income families such as those who qualified for Aid to Families with Dependent Children (the program that preceded TANF). The OPM takes no account of health care needs or insurance benefits. It was developed before the life-extending advances of the past 50 years in medical treatments, such as treatment for childhood cancer, and before the expansion of health insurance to cover such treatments. However, for reasons detailed in National Research Council (1995; see also Remler, Korenman, and Hyson, 2017, and the discussion in Chapter 7), the SPM takes only a partial step forward. SPM thresholds do not include any estimated expenditure amounts for medical care, but the SPM definition of resources subtracts families’ medical out-of-pocket expenditures for any insurance premiums, copayments, deductibles, or bills for uncovered care.3 This deduction of medical out- of-pocket expenses puts some people below the SPM poverty line whom the OPM would not count as poor.4 Conversely, reductions in out-of-pocket medical care costs—through Medicaid expansion, for example—will reduce measured SPM poverty rates, all else equal (see, e.g., Summers and Oellerich, 2013). These adjustments in the SPM, despite being a step forward, still do not account for the full contribution of government health insurance programs to reducing poverty, particularly Medicaid and the Children’s Health Insurance Program (CHIP), to the well-being of low-income children and their parents. As we discuss in more detail in Chapter 7, one problem with the SPM approach is that families that defer medical care because they cannot afford it will appear to be better off than they really are. On the other hand, families who are covered by Medicaid but have little or no out-of-pocket expenses in a particular year will be appear to be worse off than they really are, because having insurance in case of future illness is much better than having no insurance at all. Nevertheless, both types of families are treated the same in this instance by the SPM. As we discuss in Chapter 7, a conceptually complete approach to the problem, one suggested by a paper by Korenman et al. (2017) commissioned by the committee is to include the value of a basic health insurance plan in the poverty threshold and to include in resources the amount of government subsidy received by a family for insurance coverage. Korenman et al.                                                              3 The reason for subtracting medical out-of-pocket costs is that, unless low-income families receive free care from providers or qualify for insurance (e.g., Medicaid) that does not require the family to contribute toward their care, then obtaining health care will require out-of-pocket expenditures. 4 See, e.g., U.S. Census Bureau, Table A-6: Effect of Individual Elements on SPM Poverty Rates: 2016 and 2015, September 21, 2017. Available: https://www.census.gov/library/publications/2017/demo/p60-261.html. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-5

report some new estimates of the impact of Medicaid on poverty using this approach (see Chapter 7).5 Adjusting the Supplemental Poverty Measure Using the TRIM3 Model Both the SPM and the OPM poverty rates are based on annual data from government surveys. To obtain these data, large national samples of households are chosen at random to participate in the ASEC supplement to the Current Population Survey, which is conducted by the U.S. Bureau of Labor Statistics. Consequently, both poverty rates are subject to bias when households misreport their incomes. The total amount of income that households report receiving from social programs in a given year can be checked against estimates of the total benefits that were paid out based on government administrative data. These comparisons often reveal large discrepancies, which have grown over time (Meyer, Mok, and Sullivan, 2009; Moffitt and Scholz, 2009; Wheaton, 2008). For example, household reports of food stamp income in the 1986 Current Population Survey accounted for 71 percent of administrative benefit totals, but in the 2006 Current Population Survey they accounted for only 54 percent of administrative benefit totals (Meyer, Mok, and Sullivan, 2009). To address this underreporting, the committee relied on the Transfer Income Model, Version 3 (TRIM3).6 TRIM3 is a microsimulation model that adjusts benefits from tax and transfer programs across households so that aggregated benefits reported by or assigned to households match the totals shown by administrative records.7 Imputing or modeling government transfers in this manner increases the estimated incomes of many low-income households, and in some cases it raises them above a poverty threshold. As a result, the SPM-based child poverty rates presented in this chapter and used in the policy simulations in Chapters 4, 5, and 6 are almost always lower than SPM rates reported in Census Bureau publications. The committee used the most recent version of the TRIM3 model that was available when the bulk of its simulation work was conducted. It is based on incomes in calendar year 2015 as reported in the 2016 ASEC. Importantly, that version of TRIM3 is based on program rules and federal and state tax codes that prevailed in 2015.8 Given the potential importance of changes in                                                              5 We discuss the benefits of Medicaid and CHIP in improving child health in Chapter 3. An alternative approach to valuing health care for poor families is to create a medical care financial risk index; this is discussed in National Research Council (2012). This is a useful perspective and adds extra information to how risk varies by income in the population, but it not easily incorporated into a poverty index (Korenman et al., 2018). 6 TRIM3 was developed and is managed by the Urban Institute with primary funding from the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. See http://trim3.urban.org/T3Welcome.php for more details about the TRIM3 model. 7 TRIM3 corrects underreporting of TANF, SSI, and SNAP only. In the 2001 CPS, just 52% of self- employment income was reported, 59 percent of dividends, 70 percent of retirement and disability benefits (excluding Social Security and worker’s compensation), and 73 percent of interest income. Unemployment compensation is also underreported and not corrected by TRIM3. Discussions of these and other estimates are provided in Winship (2016, Appendix D, 3). In contrast, earnings are actually overreported at the bottom of the CPS earnings distribution when compared to administrative data (Bollinger et al., 2018; Hoyakem Bollinger, and Ziliak, 2015). 8 TRIM3 baselines for a particular year always involve applying that year’s rules to that year’s data. The results are aligned and validated using the actual benefit and tax data for that year. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-6

federal income tax rules taking effect in 2018, the committee includes some data in later chapters showing that its key conclusions regarding child poverty reductions associated with program and policy proposals were largely unaffected by the recent changes in the tax code. Figure 2-1 compares child poverty rates using the OPM and SPM, as well as using our modification of the SPM—labelled “TRIM3-SPM” in the remainder of this report—which is adjusted for underreported income. Some of the differences are stark. Based on the conventional definition of OPM poverty (household income below 100 percent of the applicable poverty line, with no adjustment for underreporting of income), nearly a fifth (19.7 percent) of U.S. children—14.5 million children in all—were poor in 2015.9 The addition of tax credits, in-kind income, and other adjustments in the SPM drove the poverty rate down to 16.3 percent. Census Bureau publications use the “SPM, no adjustment for underreporting” poverty measure in their reports. But adjustments for underreporting reduced the SPM child poverty rate to 13.0 percent. Such large impacts from adjusting poverty rate estimates for underreporting of income—a 3.3 percentage-point reduction in the case of child poverty in 2015—led the committee to one of its research recommendations, presented in Chapter 9. Although it produces a poverty count that is one-third lower than the official OPM-based count reported by the Census Bureau, our adjusted SPM-based poverty rate of 13.0 percent still represents 9.6 million U.S. children living in households with economic resources judged by the SPM to be inadequate. The congressional charge to the committee is to identify programs that— either alone or in combination—would lift nearly 5 million of these 9.6 million children out of poverty within 10 years.                                                              9 The 19.7 percent figure for 2015 SPM-based poverty is considerably higher than the 18.0 percent figure reported above in Fox (2017), because the latter is based on 2016 incomes. Economic growth between 2015 and 2016 increased family income and decreased poverty rates among low-income families. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-7

FIGURE 2-1 Rates of Poverty, Deep Poverty, and Near Poverty for Children Using Three Alternative Poverty Measures, 2015. NOTE: OPM = Official Poverty Measure; SPM = Supplemental Poverty Measure SOURCE: Estimates from TRIM3, which include adjustment for underreporting, commissioned by the committee. A look at rates of deep poverty, defined by the percentage of children whose families’ resource levels are less than half the poverty line, shows even more measurement sensitivity to the inclusion of taxes, in-kind income, and adjustments for underreporting. According to the OPM, which makes none of those adjustments, some 8.9 percent of children lived in deep poverty in 2015. When all three adjustments are made, the deep-poverty rate drops by more than two-thirds, to 2.9 percent. This 2.9 percent rate translates into 2.1 million children living in households with grossly inadequate resources. The congressional charge to the committee regarding deep poverty is identifying programs and policies that reduce this 2.1 million figure by more than one million children. By contrast, when poverty is defined to include the “near poor”—those with incomes up to 150 percent of the poverty line—the 31.4 percent rate based on the OPM actually increases: it rises to 38.1 percent with no adjustments for underreporting and to 35.6 percent with adjustments. Substantial numbers of near-poor families pay more in taxes than they receive in tax credits, and they also incur additional work-related expenses. These factors combine to reduce net incomes and push some near-poor families below 150 percent of the SPM poverty line (Short and Smeeding, 2012). PREPUBLICATION COPY, UNCORRECTED PROOFS 2-8

CONCLUSION 2-1: The Supplemental Poverty Measure (SPM) has advantages over the Official Poverty Measure (OPM), the most important of which is that it includes government benefits, such as near-cash benefits and tax transfers, that are not included in the OPM. Current estimates of child poverty based on the SPM are substantially lower than those based on the OPM, and lower still when the SPM poverty estimate is adjusted for the underreporting of income in Census Bureau surveys. SPM-based estimates of poverty, combined with underreporting adjustments, indicate that 13.0 percent of U.S. children—more than 9.6 million children in all—were poor in 2015. In the case of deep poverty (defined by 50 percent of the SPM poverty thresholds), the corresponding rate is 2.9 percent, representing 2.1 million children. A DEMOGRAPHIC PORTRAIT OF U.S. CHILD POVERTY IN 2015 Policymakers and researchers share a broad interest in understanding the distribution of poverty as well as the impacts of poverty-reducing programs across demographic groups. In this section we therefore discuss how child poverty varies according to six demographic factors: race/ethnicity, maternal schooling, family structure, adult work, immigration status, and parent’s age. Throughout this section, except where defined otherwise, the poverty rates we cite are based on the TRIM3-SPM measure described in the previous sections. Note that a complete set of poverty-rate estimates for selected demographic groups and definitions, provided in Appendix D, Table 2-5 and Appendix E, includes demographic breakdowns not discussed in this chapter. Also note that American Indian and Alaska Native status is not included because the ASEC data did not provide a sufficient sample size to support reliable estimates for this group; a discussion of American Indian and Alaska Native child poverty using other sources of data is provided in Appendix D, 2-7 and in a research recommendation in Chapter 9. Race and Ethnicity The U.S. population is becoming more racially and ethnically diverse, and the diversity of the child population is increasing even more rapidly than that of the population as a whole. As detailed in Appendix D, 2-8, the proportion of racial- and ethnic-minority children in the total U.S. child population increased from less than one-third in 1990 to nearly half in 2017 (U.S. Census Bureau, 2018). The Hispanic child population has shown especially dramatic growth, increasing from 9 percent in 1980 to 25 percent in 2017 (U.S. Census Bureau, 2018). According to the Census Bureau, as of 2013 racial/ethnic minority groups combined comprised more than 50 percent of the population of children under age 1 (Pew Research Center, 2016). By 2020, the entire child population is projected to include more Hispanics, Blacks, Asians, and other minorities than non-Hispanic Whites (U.S. Census Bureau, 2018). PREPUBLICATION COPY, UNCORRECTED PROOFS 2-9

Concerns over varying rates of child poverty across racial and ethnic groups are long- standing (Eggebeen and Lichter, 1991; Lichter, Qian, and Crowley, 2008; Hill, 2018). These differences are readily apparent in our TRIM3-SPM-based estimates, as shown in Figure 2-2. Hispanic children experience the highest rates of poverty and deep poverty. The poverty rates for Black (17.8 percent) and Hispanic (21.7 percent) children were more than double those of non- Hispanic White (7.9 percent) children.10 Similar relative disparities are found for rates of deep poverty. If the line is drawn at 150 percent of SPM to include near-poverty, more than half of all Black (50.6 percent) and Hispanic (54.6 percent) children, but less than one in four (22.9 percent) non-Hispanic White children, are counted as poor or near-poor. FIGURE 2-2 TRIM3-SPM Rates of Poverty, Deep Poverty, and Near Poverty for Children by Race/Ethnicity in 2015. NOTE: Based on TRIM3-SPM measurement. SOURCE: Estimates from TRIM3, which include adjustment for underreporting, commissioned by the committee. Another way of describing poverty across racial and ethnic groups is by asking what share of a given poverty group comprises children from specific racial or ethnic categories. Such a breakdown of data is shown in Figure 2-3.11 Again using our TRIM3-SPM-based estimates,                                                              10 The TRIM3-SPM poverty rate for children in the Other Races (non-Hispanic) category, which includes American Indian and Alaska Native, Asian and Pacific Islander, and multiracial children, is 11.1 percent. 11 Figure 2-3 also show poverty shares for children living in persistently poor counties. These data are discussed below. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-10

non-Hispanic White children comprise a little more than half of all children but only about one- third of children in poverty or in deep poverty. The largest share of poor children are Hispanic. Similar shares of children in deep poverty are Hispanic and non-Hispanic White. FIGURE 2-3 TRIM3-SPM Estimates of the Share of Children by Race/Ethnic Category Comprising Poor, Deeply Poor, and Near-Poor Children, 2015. NOTE: Children in other race/ethnic groups not shown. Estimates based on TRIM3-SPM measurement. SPM = Supplemental Poverty Measure. SOURCE: Estimates from TRIM3, which include adjustment for underreporting and from the Census Bureau, commissioned by the committee. CONCLUSION 2-2: Poverty rates for children vary greatly by the child’s race/ethnicity. Based on our TRIM3 SPM poverty estimates, Black and Hispanic children have substantially higher rates of poverty and deep poverty than non-Hispanic White children. Hispanic children constitute the largest share of poor children and nearly as large a share of deeply poor children as non-Hispanic Whites. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-11

Education of Parents Adults’ educational attainment is a strong correlate of their poverty status (Wood, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Completing more schooling is associated with higher rates of employment, higher earnings, better health, and a greater chance of having a spouse or partner, all of which are in turn associated with higher household income (Child Trends Data Bank, 2016). Figure 2-4 shows that child poverty rates are inversely related to the education level of the parents. Based on the TRIM3 model, one-third of children whose parents dropped out of high school are living below the 100 percent SPM poverty line and more than two-thirds (70.7 percent) of these children are within 150 percent of the SPM poverty line. FIGURE 2-4 TRIM3-SPM Rates of Poverty, Deep Poverty, and Near-Poverty for Child by Education Level of Parents in 2015. NOTE: Fraction of all children in each group: Less than high school – 12.4%; HS grad or GED – 24.4%; Some college – 29.0%; BA+ – 33.9%; other – 0.2%. SPM = Supplemental Poverty Measure; BA = Bachelor’s degree; GED = General Educational Diploma. SOURCE: Estimates from TRIM3,, which include adjustment for underreporting, commissioned by the committee. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-12

Family Composition Family structure has grown increasingly diverse over recent decades (Furstenberg, 2014); for example, more than 40 percent of children today are born to unmarried parents (Martin et al., 2018) and more than half of children will spend some of their childhood not living with both of their biological parents (McLanahan and Jencks, 2015). Although most unmarried biological parents are living together when their child is born, nearly half of these couples will separate before that child’s fifth birthday (Kennedy and Bumpass, 2008). Children born to unmarried parents may experience several different family structures over the course of their childhoods, such as living with a step-parent, with a grandparent, or in single-parent households (Manning, Brown, and Stykes, 2014). The proportion of children in single female-headed households is substantially higher for Black children (57 percent) than for either White (18 percent) or Hispanic (32 percent) children (National Center for Education Statistics, 2018). For children living with both biological parents, our TRIM3 estimates find that poverty rates are less than half those of children with other family structures (Figure 2-5). But even given the economic advantages of having two potential earners in the household, more than one in four (27.5 percent) children living with their two biological parents have family incomes below the 150 percent (near-poor) poverty line. Children living with a single parent or with neither biological parent (including foster children) have the highest rates of poverty and deep poverty. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-13

FIGURE 2-5 TRIM3-SPM Rates of Poverty, Deep Poverty, and Near Poverty for Children, by Family Composition in 2015. NOTE: Fraction of all children in each group: No biological parent – 4.6%; Single parent – 23.6%; Two biological parents – 71.8 %; other – 0.1%. SPM = Supplemental Poverty Measure. SOURCE: Estimates from TRIM3,, which include adjustment for underreporting, commissioned by the committee. Workers in the Household Nearly four-fifths of all children live in families with at least one full-time working adult and, as shown in Figure 2-6, the TRIM3 SPM poverty rates for these children (6.5 percent) are correspondingly low. The poverty rates among children living with a part-time, as opposed to full-time, worker are correspondingly higher. By far the highest child poverty rates are observed for the relatively small fraction (6.3 percent) of children living in households with no workers: nearly a quarter (22.3 percent) of these children are in deep poverty, three-fifths (61.5 percent) are below the poverty line, and the vast majority (90.8 percent) are below the 150 percent near- poverty line.  PREPUBLICATION COPY, UNCORRECTED PROOFS 2-14

FIGURE 2-6 TRIM3-SPM Rates of Poverty, Deep Poverty, and Near-Poverty for Children, by Number of Working Adults in Household, 2015. NOTE: Fraction of all children in each group: No workers – 6.3%; 1+ part-time or part-year worker – 14.1%; 1+ full-year, full-time worker – 79.6%. SPM = Supplemental Poverty Measure. SOURCE: Estimates from TRIM3, which include adjustment for underreporting, commissioned by the committee. Immigration Status Children in immigrant families, defined as those with at least one foreign-born parent, represent about a quarter of all children (Woods and Hanson, 2016).12 The TRIM3 SPM poverty rate of children in immigrant families (20.9 percent) is twice as high as that of children in non- immigrant families (9.9 percent) (Appendix D, Table 2-6). The majority of children in immigrant families are U.S. citizens: some 88 percent of all children in all types of immigrant households are citizens, and 79 percent of children living in households with members who are unauthorized                                                              12 In the TRIM3 analyses, a child is considered to have an immigrant parent if he or she has at least one biological, adoptive, or step-parent that was born in another country. A recent immigrant is defined as a person entering as a legal permanent resident within the last 5 years. Children are classified by their own status. For example, in the case of an SPM unit containing unauthorized immigrant parents, an unauthorized immigrant child, and a native-born citizen child, the unauthorized immigrant child would be categorized as “Child is a noncitizen, unit contains unauthorized immigrant” and the native-born child would be classified as “Child is a citizen, unit contains unauthorized immigrant.” PREPUBLICATION COPY, UNCORRECTED PROOFS 2-15

immigrants are citizens. The immigrant status of their families is associated with a higher risk of poverty (Capps, Fix, and Zong, 2016; Migration Policy Institute, 2017; Woods and Hanson, 2016). The relationship between poverty, citizenship and immigration status is shown in Figure 2-7 and Appendix D, Table 2-6, again based on the TRIM3-SPM model. Children living in households in which all members are citizens have a poverty rate of 10.2 percent, nearly three points below the 13.0 percent overall child poverty rate. By contrast, living in households with noncitizens—particularly unauthorized immigrants—is associated with higher poverty rates, even for children who are themselves U.S. citizens. Child Citizenship When the household includes recent or unauthorized immigrants, the poverty rate among noncitizen children is even higher: 31.8 percent and 33.3 percent, respectively. Citizenship for the child appears to buy very little in the way of poverty reduction if other household members are unauthorized: 31.5 percent of citizen children whose households have at least one unauthorized resident are poor, as are 24.7 percent of citizen children whose households have at least one recent immigrant. However, child citizenship is associated with a much lower rate of deep poverty: 6.4 percent versus 15.2 percent, respectively, for citizen versus noncitizen children, in both cases living with unauthorized household members. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-16

FIGURE 2-7 TRIM3-SPM Rates of Poverty, Deep Poverty, and Near-Poverty for Children, by Citizenship Status of Child and Adults in Household, 2015. NOTE: Fraction of all children in each group: Child is not a citizen, some in household are unauthorized – 1.1%; Child is citizen, some in household are unauthorized – 6.9%; All household members are citizens – 81.5%; other – 10.0%. SOURCE: Estimates from TRIM3 commissioned by committee, which include adjustment for underreporting. Age of Parent Our final demographic dimension is the age of the parent, defined as the age of the biological parent, adoptive parent, or step-parent if present.13 Children born to younger mothers are more likely to live in poverty (Mather, 2010). On average, maternal age at first birth has been increasing (Mathews and Hamilton, 2016), and over the last three decades births to teen mothers have declined very significantly—by more than 64 percent (Martin et al., 2017). Despite these trends, in 2015 more than one-quarter of children were born to mothers under age 25, and race and ethnic minority children were more likely than their white counterparts to be born to young mothers (Martin et al., 2017).                                                              13 Age of parent is determined first by asking the mother, if present. If the mother is not present, then the biological, adoptive, or stepfather (if present) is asked. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-17

The poverty risk for living with a younger parent (which we define here as under age 25) is readily apparent in Figure 2-8; nearly one-quarter (23.8 percent) of children living with a young parent fall below the 100-percent-of-SPM poverty line.14 Nearly three-fifths of children with a young parent live in families with incomes less than 150 percent of the poverty line. FIGURE 2-8 TRIM3-SPM Rates of Poverty, Deep Poverty, and Near-Poverty for Children, by Age of Parent, 2015. NOTE: Fraction of all children in each group: Age 35+ – 64.4%; Age 25-34 – 30.8%; Age <25 – 4.5%; other – 0.2%. SOURCE: Estimates from TRIM3, which include adjustment for underreporting, commissioned by the committee. CONCLUSION 2-3: Poverty rates for children vary greatly depending on other characteristics of parents and households. Higher poverty rates are associated with low levels of parental schooling and with living with a single parent, no parent, or a young parent. Poverty is more prevalent when both children and other family members are not citizens, although these poverty rates improve only a little when children are U.S. citizens but living in households with family members who are unauthorized. Children in families with no workers have by far the highest rates of poverty and near-poverty,                                                              14 Note this is not the age at birth but the age of the parent at the time of the survey. As shown in the notes to Figure 2.8, only 4.5% of all parents of children less than 18 are to parents of age less than 25. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-18

but even full-time work is insufficient to lift one-quarter of children living with full-time workers above the 150% SPM poverty line. Geographic Distribution of Poverty Child poverty rates also vary across communities. As documented in Chapter 8, the experience of child poverty in a community with good schools, resources for families, and pathways for economic mobility may be different than the experience in a community that has suffered from persistent poverty for decades. To examine the geographic distribution of both point-in-time and persistent poverty, we use county data based on the OPM (official poverty measure), because SPM county-level estimates are not available (see Appendix D, 2-9).15 For the point-in-time analyses, we classified counties as poor if 20 percent or more of children under age 18 lived in families with incomes below poverty thresholds in 2015. As shown in Figure 2-9 and Appendix D, 2-9, nearly all counties in the South and Southwest and many counties in the West and the Appalachian region had child poverty rates of 20 percent or higher in 2015. Relative to the total number of children of a given race/ethnicity, the risk of residing in a point-in-time poor county is highest among Black children (70.8 percent), followed by American Indian and Alaskan Native (70.6 percent), Hispanic (65.0 percent), and non-Hispanic White children (46 percent).                                                              15 The Committee assessed the lowest geographic disaggregation level that can be achieved with the SPM and found that there are no county or other sub-state (besides metropolitan area) SPM estimates. This is primarily because the CPS ASEC is the primary dataset used for SPM, and its sample size does not allow estimates for such small geographic areas. Because of its larger sample size, the ACS is the most likely alternate dataset, but it is missing critical variables used in calculating SPM. While there has been some work, primarily Renwick (2015), has experimented with using the CPS ASEC to inform ACS imputations of missing variables, so that the ACS can hypothetically be used to estimate sub-state SPM, in the end those researchers created only state-level (single-year) estimates and reached no conclusions about sub-state level SPM estimates. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-19

FIGURE 2-9 Counties with OPM Point-in-Time Child Poverty Rates 20 Percent or Higher, 2015 NOTE: OPM = Official Poverty Measure. SOURCE: Estimates by the committee from United States Population Estimates, 2016 Vintage, Census Bureau; data as of July 1, 2015. 2015 county child poverty rates from Census Small Area Income and Poverty Estimates (SAIPE) Program data. We also examined the geographic distribution of persistently high child poverty. A county was classified as having persistently high child poverty if 20 percent or more of its children were classified as OPM-poor over four decades: in the 1980, 1990, and 2000 decennial censuses and in the 2007–2011 American Community Survey 5-year estimates (see Appendix D, 2-9). Some 10.2 million children (13.9 percent of all children) lived in persistently poor counties in 2015. The 10.2 million figure includes 3.6 million white children, 3.1 million Hispanic children and 2.7 million Black children (Figure 2-3). The risk of living in a persistently poor county is highest among American Indian and Alaska Native children (36 percent) followed by Black (27 percent), Hispanic (17.1 percent), non-Hispanic White (9.4 percent) and Asian and Pacific Islander (8.2 percent) children (Appendix D, Figure 2-7). Persistently high poverty is more geographically concentrated than point-in-time poverty (Figure 2-10). The South and Northeast regions have the highest proportion of children in persistently poor counties (22.1 percent and 17.3 percent, respectively; see Appendix D, Figure 2-9) and account for the vast majority of children (81.3 percent) living in those counties. Although not readily apparent in Figure 2-10, due to their small land mass the persistently poor counties in the Northeast, which include the cities of New York, Philadelphia, Newark, and Boston, account for 2.1 million children. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-20

FIGURE 2-10 Counties with OPM Child Poverty Rates 20% or Higher in 1980, 1990, 2000 and 2008-2012. NOTE: OPM = Official Poverty Measure. SOURCE: Estimates by Committee from United States Population Estimates, 2016 Vintage, Census Bureau; data as of July 1, 2015. 2015 county child poverty rates from Census Small Area Income and Poverty Estimates (SAIPE) Program data. CONCLUSION 2-4: Poverty rates for children vary considerably by geographic location. About one in seven children live in counties with persistently high child poverty (OPM child poverty rates always above 20 percent since 1980). The South and several large metropolitan areas in the Northeast regions have the highest proportions of children in counties with persistently high child poverty. HISTORICAL TRENDS IN CHILD POVERTY, 1967–2015 Historical trends in the OPM (official poverty measure) are published annually by the Census Bureau. As shown in Figure 2-11, they suggest that virtually no progress has been made in reducing child poverty between the late 1960s and today. If anything, child poverty rates as measured by the OPM were a little higher in 2016 (18.0 percent) than they had been 50 years before, in 1967 (16.6 percent, U.S. Census Bureau, 2018; Table 3). PREPUBLICATION COPY, UNCORRECTED PROOFS 2-21

Given the growth in near-cash benefits over this period, it is possible that child poverty rates based on the SPM, which counts most near-cash benefits as income, and the OPM, which does not, may show different trends. A first step in investigating whether this is the case is to construct a consistent time series of SPM-based rates, as shown in Figure 2-11 (Hardy, Smeeding, and Ziliak, 2018). FIGURE 2-11 Official (OPM) and Supplemental (SPM) Child Poverty Rates, 1967-2016. NOTE: The SPM poverty measure is anchored in 2012 living standards and adjusted back to 1967 using the Consumer Price Index. Income data are not adjusted for underreporting. SOURCE: Original analyses commissioned by the committee from Christopher Wimer (2017, October). Two complications arise. First, because some TRIM3 adjustments are not available for most of the years we examine, the analyses in this section are based on Current Population Survey data that are not adjusted for income underreporting. A second complication is the difficulty of defining SPM-based poverty in a consistent way across the half century between 1967 and 2016. Recall that the SPM uses a poverty threshold based on the 33rd percentile of the distribution of core living expenses. Thus, the poverty threshold in the SPM is tied to changes in the standard of living of this low-income group. In contrast, the OPM poverty thresholds are adjusted over time only by rates of inflation. Wimer et al. (2013) have estimated annual SPM thresholds going back in time to 1967, using available ASEC historical data. They have also constructed SPM thresholds that are anchored in current living standards and adjusted them backward in time only by inflation, as PREPUBLICATION COPY, UNCORRECTED PROOFS 2-22

well as thresholds that are anchored in 1967 and then adjusted forward only by inflation. Though the SPM was designed to be a relative measure, whether to measure poverty in relative or absolute terms for purposes of historical analysis is an unsettled question. We use an anchored SPM (an absolute measure) here and in our analysis in Chapter 4 of the effects of changes in the labor market, family structure, and government programs on child poverty over time, because this measure allows us to abstract from changes in living standards. We anchor the measure in recent (2012) living standards to make it as comparable as possible with the TRIM3-SPM poverty estimates presented elsewhere in this report, which focuses on the current period.16 Appendix D, 2-10 provides further discussion and illustration of child poverty trends using anchored and unanchored SPM measures. Figure 2-11 shows both OPM- and anchored SPM-based child poverty rates from 1967 to 2016. As noted before, over this period OPM-based child poverty rates increased from 16.6 percent to 18.0 percent, while the anchored SPM indicates that child poverty actually decreased by nearly half—from 28.4 percent to 15.6 percent.17 SPM poverty rates are higher than OPM poverty rates in the earlier years of the period in part because of the higher SPM threshold and (to a lesser extent) because during that period the tax system took more income from poor families with children than these families received from government as in-kind benefits. As we show in Chapter 4, much of the decline in SPM-based child poverty is due to increasingly generous government benefits. Because it does not count benefits from the Earned Income Tax Credit, Supplemental Nutrition Assistance Program, public housing, and housing vouchers, OPM-based child poverty rates include only cash transfers (like SSI and the cash portion of TAN) and therefore fail to consider the largest portion of the social safety net. Consequently, trends in the OPM are not useful for drawing conclusions regarding changes in the well-being of children in the United States. An alternative is to construct SPM poverty thresholds based on changes in living standards rather than inflation; this “historical SPM” also shows a substantial decrease in child poverty, but the decrease is only about half as large, or 25 percent (see Figure 2-15 in Appendix D, 2-10). The decrease in poverty is smaller because living standards at the 33rd percentile of the income distribution have increased over the last half-century by more than the cost of living. Figure 2-12 depicts historical trends in anchored SPM-based child poverty, near poverty, and deep poverty rates. As with the basic (under 100 percent) SPM poverty measure, shown in Figure 2-11, deep poverty rates had fallen by 2016 to nearly half of their 1967 levels. In the case of the line drawn at 150 percent of SPM, poverty rates fell by nearly 40 percent between 1967 and 2016. Strikingly, most of these three sets of declines occurred prior to the year 2000. It is also worth noting that SPM-based poverty rates declined for all three race-ethnic groups: for Whites, Blacks, and Hispanics. (Historical trends in OPM- and SPM-based child poverty rates by race-ethnicity between 1970 and 2016 are presented in Appendix D, 2-8.)                                                              16 These estimates were taken from a study (Wimer, 2017) commissioned by the committee for this report. Due to the relative nature of the SPM, historical changes in poverty could be at least partly due to changes in poverty thresholds (Wimer et al., 2013). Anchored measures of poverty apply current poverty thresholds to historic data by adjusting for inflation to isolate changes in family resources from changes in living standards. For more information, refer to Wimer et al., 2013. 17 As explained in Fox et al. (2015), an SPM poverty line anchored in 1967 living standards and subsequently adjusted for inflation annually yields estimates of poverty reduction that are similar to estimates anchored in current living standards and adjusted backwards for inflation, like those reported in the figures and text. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-23

FIGURE 2-12 Trends in SPM Rates of Poverty, Deep Poverty, and Near-Poverty for Children, 1967–2016. NOTE: The SPM poverty measure is anchored in 2012 living standards and adjusted back to 1967 using the Consumer Price Index. Income data are not adjusted for underreporting SOURCE: Original analyses commissioned by the committee from Christopher Wimer (2017, October). CONCLUSION 2-5: When measured by the official poverty measure, poverty rates changed very little between 1967 and 2016; by contrast, when measured by the anchored Supplemental Poverty Measure (SPM), they fell by nearly half over that period, due to the increases in government benefits. SPM-based rates of deep and near child poverty declined as well over the period, both overall and across subgroups of children defined by race and ethnicity. CHILD POVERTY IN THE UNITED STATES AND OTHER ENGLISH-SPEAKING DEVELOPED COUNTRIES Over the past several decades, researchers have developed the capacity to analyze child poverty across countries by using comparable microdata. The two most widely used sources of international data are the Luxembourg Income Study, which allows analysts to work with the PREPUBLICATION COPY, UNCORRECTED PROOFS 2-24

microdata, and the Organisation for Economic Co-operation and Development (OECD) poverty and income database, which is more up-to-date but provides only country-level statistics and relative poverty measures. Early staff and committee discussions with the sponsors of this report revealed a particular interest in comparing child poverty rates across a subset of OECD English-speaking nations: Australia, Canada, Ireland, the United States, and the United Kingdom. These countries have income support systems that differ from those found in central and northern Europe, including Scandinavia (Esping-Anderson, 1990). Three of them are large and diverse nations (Canada, Australia, and the United States), while the other two (Ireland and the United Kingdom), though smaller in size, still exhibit some geographic and ethnic heterogeneity. We gauge the comparative effectiveness of anti-poverty programs across these same countries in Chapter 4. Most published international poverty comparisons use a poverty line defined by a given fraction of each country’s median income, such as 40, 50, or 60 percent.18 This is a relative poverty concept because it measures the fraction of families who have income that is low relative to overall income in the country. Families in a high-income, industrialized country might all have incomes that are higher than the incomes of families in a low-income country, but relative poverty could still be high in the former if the lower-income families there were “farther away” from the country’s overall median income.19 OECD poverty statistics are typically based on a poverty line drawn at 50 percent of median income, a line we will call “OECD-50.” For this measure, household resources include money income and near cash benefits minus taxes (including tax credits). Estimates of child poverty using the OECD-50 for the United States and the four English-speaking comparison countries are shown in the top bars of Figure 2-13 (labeled “Relative Poverty (OECD-50)).” Rates of child poverty using this relative measure are much higher in the United States than in these peer countries—more than twice as high as in Ireland and nearly five percentage points higher than in Canada, the country with the second-highest child poverty rates.                                                              18 As explained in Appendix D, 2-2, the income data and thresholds are also adjusted for family size. 19 The SPM poverty measure is also relative, but it is based on the distribution of expenditures rather than income, and is set at a given (33rd) percentile of the expenditure distribution rather than at a fraction of the median. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-25

FIGURE 2-13 Child Poverty in the U.S. and Four Other Anglophone Countries, Using Three Alternative Measures, Various (Recent) Years. NOTE: OECD-50 = Poverty rate defined as 50 percent below each country’s median income; LIS SPM-40 = poverty rate defined as below the 40th percentile in each country’s income distribution, based on the Luxembourg Income Study (LIS); LIS-SPM-PPP = poverty rate defined following the SPM definition and adjusted for PPP (purchasing power parity). Data are not adjusted for underreporting. SOURCE: Original LIS analyses commissioned by the committee from the LIS Cross-National Data Center. To explore the sensitivity of cross-national child poverty rates to the specific definition of child poverty, Figure 2-13 also shows poverty rates using two other measures. The first uses LIS data to set the poverty threshold for each country at the same percentile of the country’s income distribution as the SPM threshold in the U.S. income distribution. Since that point is at the 40th percentile of the income distribution, we label this measure “Relative Poverty (LIS-SPM-40).” Drawing the line at the 40th percentile lowers child poverty rates, but the country rankings are similar to those found with the OECD-50 measure of relative poverty. The third measure is based on what is sometimes called “absolute” poverty. Absolute poverty measures the fraction of families in a country whose incomes fall below some fixed amount, regardless of how affluent the country is. For this reason, high-income countries will tend to have lower absolute poverty rates than lower-income countries. In our case, the dollar levels of the U.S. SPM poverty thresholds are translated into poverty thresholds in other PREPUBLICATION COPY, UNCORRECTED PROOFS 2-26

countries using the purchasing power of the dollar relative to other countries’ currencies.20 Because the translations are based on purchasing power parity (PPP) data, we label this measure “Absolute poverty (LIS-SPM-PPP).” Appendix D, 2-11 discusses these measures in more detail. As shown in the bottom panel of Figure 2-13, using an absolute poverty standard changes the country rankings somewhat. The United Kingdom now has the highest absolute poverty rate, followed by the United States, Ireland, Canada, and lastly Australia. The primary reason for this shift in rankings is that living standards are generally higher for U.S. children than for UK children, so a poverty line defined by U.S.-based income cuts the UK income distribution at a higher point than where it cuts the U.S. income distribution. Finally, we compare rates of deep poverty and near-poverty in the United States and these peer countries using the Luxembourg Income Study and the absolute SPM poverty measure (Figure 2-14). At 3.6 percent, the United States has by far the highest rate of deep child poverty, nearly twice the rate seen in the next-ranked nation (Australia, at 1.9 percent).21 By contrast, the United States is in the middle of the pack where near-poverty is concerned (defining near- poverty as 150 percent of the absolute SPM), with a rate of 29.2 percent. This near-poverty rate is considerably lower than what is seen in the United Kingdom (46.3 percent) and Ireland (37.2 percent), where the poverty line cuts their distributions at a much higher income level (see Appendix D Figure 2-3), but it is higher than in countries with absolute living standards most similar to those in the United States: Australia (21.6 percent) and Canada (27.1 percent). Poverty rates for children in single-parent families, in working families (except for the United Kingdom), and in immigrant families are higher in the United States than in the other comparison nations, even using the absolute Luxembourg Income Study SPM PPP poverty rates. (These rates are shown in Appendix D, 2-11 and Appendix D, Figure 2-5). CONCLUSION 2-6: How child poverty rates in the United States rank relative to those in peer English-speaking developed countries depends on how poverty is defined. The United States has much higher rates of child poverty than these peer countries using relative, within-country measures of poverty. However, when an absolute poverty measure is used, child poverty rates in the United States are more similar to rates in peer countries. Rates of deep poverty, by contrast, are considerably higher for children in the United States than for children in these peer countries, whether absolute or relative measures are used.                                                              20 The 2013 U.S. SPM translates into about $25,550 for two parents and two children. This amount is converted to other currencies using 2011 Purchasing Power Parities (PPP) and national consumer price changes when years differ. The SPM poverty line income, on a household basis, ignoring health care costs and work expenses and other adjustments for COLAS and housing status, is about 40-41 percent of the U.S. median adjusted income on a comparable basis (Fox, 2017; Wimer and Smeeding, 2017; Short, 2013). 21 These figures are not adjusted for underreporting in any nation. The comparisons by level and composition of poverty are shown in Appendix D Figures 2-3 and 2-4. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-27

FIGURE 2-14 Deep and Near Child Poverty in the U.S. and Four Other Anglophone Countries, LIS-SPM-40, Various (Recent) Years. NOTE: Poverty lines are absolute and based on the LIS SPM converted to other countries using purchasing power parities (PPPs). LIS-SPM-PPP = poverty rate defined following the SPM definition and adjusted for PPP (purchasing power parity). Data are not adjusted for underreporting. SOURCE: Original LIS analyses commissioned by the committee from the LIS Cross-National Data Center. PREPUBLICATION COPY, UNCORRECTED PROOFS 2-28

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Korenman, S., Remler, D. K., and Hyson, R. (2017). Accounting for the impact of Medicaid on child poverty. The National Academy of Sciences, Engineering, and Medicine. Lichter, D., Qian, Z., and Crowley, M. (2008). Poverty and Economic Polarization among Children in Racial Minority and Immigrant Families. In D.R. Crane and T. Heaton (Eds.), Handbook of Families & Poverty. Thousand Oaks, California: SAGE Publications, Inc. Manning, W., Brown, S., and Stykes, B. (2014). Family complexity among children in the United States. The ANNALS of the American Academy of Political and Social Science, 654(1), 48–65. Martin, J. A., Hamilton, B. E., and Osterman, M. J. (2017). Births in the United States, 2016. https://www.cdc.gov/nchs/products/databriefs/db287.htm Martin, J. A., Hamilton, B. E., Osterman, M. J., Driscoll, A. K., and Drake, P. (2018). Births: Final Data for 2016. https://www.cdc.gov/nchs/products/databriefs/db287.htm Mather, M. (2010). U.S. Children in Single-Mother Families. Washington, DC: Population Reference Bureau. Mathews, T. J., and Hamilton, B. E. (2016). Mean Age of Mothers is on the Rise: United States, 2000-2014. Hyattsville, MD: National Center for Health Statistics. McLanahan, S., and Jencks, C. (2015). Was Moynihan right? What happens to children of unmarried mothers. EducationNext, 15(2). Meyer, B. D. and Sullivan, J. X. (2017). Consumption and Income Inequality in the U.S. Since the 1960s. NBER Working Paper No. 23655. http://www.nber.org/papers/w23655 Meyer, B. D., Mok, W. K. C., and Sullivan, J. X. (2009). The Under-Reporting of Transfers in Household Surveys: Its Nature and Consequences. Working Paper No. 15181. Cambridge, MA: National Bureau of Economic Research. Available: www.nber.org/papers/w15181. [December 2017]. Migration Policy Institute. (2017). Children in U.S. Immigrant Families: Number and Share of the Total U.S. Child Population, by Age Group and State. Washington, DC: Migration Policy Institute. https://www.migrationpolicy.org/programs/data-hub/charts/children- immigrant-families [April 2018]. Moffitt, R. A., and Scholz, J. K. (2009, November). Trends in the Level and Distribution of Income Support, Working Paper 15488. Cambridge, MA: National Bureau of Economic Research. Available: www.nber.org/papers/w15488 [December 2017]. National Academies of Sciences, Engineering, and Medicine (2017). Promoting the Educational Success of Children and Youth Learning English: Promising Futures. Washington, DC: The National Academies Press. https://doi.org/10.17226/24677. National Center for Education Statistics. (2018). The Condition of Education: Characteristics Of Children's Families. Washington, DC: Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator_cce.asp. [June 2018]. National Research Council. (1995). Measuring Poverty: A New Approach. Washington, DC: National Academies Press. Pew Research Center. (2016). It’s official: Minority babies are the majority among the nation’s infants, but only just. http://www.pewresearch.org/fact-tank/2016/06/23/its-official- minority-babies-are-the-majority-among-the-nations-infants-but-only-just/ (accessed September 5, 2018) PREPUBLICATION COPY, UNCORRECTED PROOFS 2-30

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The strengths and abilities children develop from infancy through adolescence are crucial for their physical, emotional, and cognitive growth, which in turn help them to achieve success in school and to become responsible, economically self-sufficient, and healthy adults. Capable, responsible, and healthy adults are clearly the foundation of a well-functioning and prosperous society, yet America's future is not as secure as it could be because millions of American children live in families with incomes below the poverty line. A wealth of evidence suggests that a lack of adequate economic resources for families with children compromises these children’s ability to grow and achieve adult success, hurting them and the broader society.

A Roadmap to Reducing Child Poverty reviews the research on linkages between child poverty and child well-being, and analyzes the poverty-reducing effects of major assistance programs directed at children and families. This report also provides policy and program recommendations for reducing the number of children living in poverty in the United States by half within 10 years.

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