House poor in Los Angeles: examining patterns of housing-induced poverty by race, nativity, and legal status

Abstract

Housing affordability in the United States is generally operationalized using the ratio approach, with those allocating more than thirty percent of income to shelter costs considered to have housing affordability challenges. Alternative standards have been developed that focus on residual income, whether income remaining after housing expenditures is sufficient to meet non-housing needs. This study employs Los Angeles Family and Neighborhood Survey data to consider racial/ethnic, nativity and legal status differences in one residual income standard. Logistic regression analyses of housing-induced poverty focus on whether there are differences among five distinct groups: US born Latinos, Non-Hispanic Whites, and African Americans, authorized Latino immigrants, and unauthorized Latino immigrants. Results suggest that: (1) Latino natives are significantly more likely to be in housing-induced poverty than African Americans and Latino immigrants, and (2) unauthorized Latino immigrants are not more likely to experience the outcome than other groups. The present work extends previous research. First, the results provide additional evidence of the value of operationalizing housing affordability using a residual income standard. Alternatives to the ratio approach deserve more empirical attention from a wider range of scholars and policymakers interested in housing affordability. Second, housing scholarship to date generally differentiates among Latinos by ethnicity, nativity, and citizenship. The present study contributes to emerging research investigating heterogeneity among Latinos by nativity and legal status.

Keywords:

• affordability
• immigration
• minorities
• residual income

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Corrigendum

Acknowledgments

This research is supported by grant R03 HD058915-01A1 from the National Institute of Child Health and Development. The author appreciates the research assistance of Tun Lin Moe, suggestions by the editor and anonymous reviewers on earlier versions of the manuscript, the advice of Miguel Montiel and Richard A. Williams, and the information provided by Sylvia Allegretto, Lisa Manzer, and Michael E. Stone used in Table 1. All errors of fact and interpretation are the sole responsibility of the author.

Notes

¹See Thalmann (2003), Stone (1993, 2006b), Stone, Burke, and Ralston (2011), Pelletiere 2008; and Jewkes and Delgadillo (2010) for reviews of housing affordability standards in the US and elsewhere.

²Official poverty measures receive widespread attention by members of the media, researchers, and others (e.g. Censky 2011; Lopez and Velasco 2011; Roberts 2011) and are used by dozens of benefit programs to determine program eligibility (Congressional Research Service 2006).

³Latinos in this study are an ethnic group that can be of any race; Whites and Blacks/African Americans refer to those who are not Hispanic. Although this study focuses on these differences among Latinos, this population is heterogeneous along many other dimensions.

⁴See Stone (2006a) and Stone et al (2011) for through discussions of residual income approaches in the United States and other countries.

⁵Federal poverty thresholds provide different values depending on the size of the family unit, the number of children, and for family units of one or two persons differentiate by age of the householder. Federal poverty guidelines provide figures that vary by number of persons in the family unit and by residence in the contiguous states, Washington DC, Alaska, and Hawaii. Dollar amounts vary between thresholds and guidelines. For example, the poverty threshold for a family of 4 with 2 children is $17,463, and is $17,050 according to the poverty guidelines. See Fischer (1992) and Citro and Michael (1995) for more information about federal poverty measures.

⁶These and other concerns have led to the development of the Supplemental Poverty Measure (SPM). Estimates of the prevalence of poverty using the SPM and the poverty thresholds were first calculated for 2010 (Short 2011).

⁷For more information about EPI, see Allegretto (2005) and http://www.epi.org/resources/budget/ and for SSS budgets, see http://www.selfsufficiencystandard.org/.

⁸A similar table for the United States and Boston, MA appears in Stone (2006a). The two adult, two children household type is used for comparison purposes only; female female-headed households with children have higher risks of experiencing poverty than those headed by two adults (Snyder, McLaughlin, and Findeis 2006).

⁹Kutty's (2005) multivariate analyses did not include racial/ethnic variables, but her descriptive analyses suggest that Whites are the least likely to experience housing-induced poverty, compared with Latinos and African Americans. Stone's (1993) descriptive analyses of “shelter poverty” suggest that African American and Latino households are more likely to be shelter-poor than all households; however, the differences are minimized or eliminated when comparing households with similar tenure status, income, and household size.

¹⁰This gap in the literature is partially due to data limitations. Large data sources like the American Housing Survey or the American Community Survey collect information about citizenship, that is, whether immigrants are naturalized citizens or non-citizens (a broad category that includes legal permanent residents, temporary migrants, unauthorized immigrants, etc.) but not legal authorization.

¹¹As a very small number of respondents in L.A.FANS are immigrants who identify as non-Hispanic White or Black, only native-born members of either group are included in the study. Due to the small sample size and heterogeneity of US and foreign-born Asians and Pacific Islanders in L.A.FANS data, they are excluded from the analyses.

¹²In households with children under 18, the mother of a randomly selected child was designated the primary care giver (PCG) and completed a parent questionnaire. In most households, the PCG and the RSA (randomly selected adult) were the same person (RSA/PCG) or in the same nuclear family. In a small number of households, more than one nuclear family resided in the home, and the RSA and the PCG could be from different nuclear families and both families could have filled out the household survey depending on respondent selection criteria. Due to concerns about correlated errors and double-counting housing cost, income, and other information, this study includes only adults who filled out the adult module as the RSA or as the RSA/PCG and excludes respondents who were in a “second” nuclear family. The sample also excludes a small number of respondents who reported housing costs that are more than 100 percent of their income, due to concerns about the quality of their housing cost and/or income data.

¹³Imputed data for missing rent and mortgage payment (Bitler and Peterson 2004) were used when housing cost data were missing for renters or homeowners with mortgages. L.A.FANS asked homeowners with mortgages whether the mortgage amount included taxes or property insurance. For homeowners who reported that their mortgage payment did not reflect property taxes, their housing costs were increased to include annual property taxes of 1.16 percent, the average property tax rate for Los Angeles County (Christensen and Esquivel 2010) based on the self-assessed value of their home provided to L.A.FANS. Those whose mortgage payments do not reflect homeowners' insurance premiums include the average homeowners’ annual premium for California from US Census Bureau data for the year that the respondent was surveyed: $592 in 2000, $599 in 2001, and $660 in 2002 (US Census Bureau n.d.). Finally, housing costs for homeowners without mortgages include estimated property taxes based on the value of their home and homeowners insurance.

¹⁴Thus, the true costs of living in the residence may vary from the calculation of housing costs in this study. For example, eligible homeowners with mortgages who have enough income to itemize deductions for mortgage interest and property taxes have a tax savings that could be directed to housing expenditures.

¹⁵When L.A.FAN’s respondents are missing one or more components of income, data from the imputed income file (Bitler and Peterson 2004) are used instead.

¹⁶The L.A.FANS household module asked respondents surveyed in 2000 to provide their 1999 income and asked those surveyed in 2001 or 2002 to provide their income for the year 2000. Following US Census Bureau procedures, the 1999 federal poverty thresholds are referenced for those reporting 1999 income and the 2000 thresholds are used for respondents reporting 2000 income.

¹⁷Nearly three-quarters of the Latinos in the analytic sample identify as Mexican/Mexicano or Mexican American.

¹⁸Ancillary analyses indicate that respondents with no family members earning income from employment generally receive income from social security, supplemental security income, pensions/retirement, public assistance income, food stamps, or other sources.

¹⁹Native-born respondents and immigrants who have spent more than half of their life in the United States have a value of one on this variable. The advantage of this operationalization is that every respondent with valid data has a value. It is preferable to a continuous variable representing years in the country, as the percent of life variable is only moderately correlated with covariates and multicollinearity diagnostics are well within the acceptable ranges.

²⁰The general rule of thumb is that multicollinearity can be a serious problem when variance inflation factors (VIF) are 10 or higher (Menard 1995). Collinearity diagnostics indicate a mean VIF of 1.32 for the baseline model and 1.86 for the fully-specified model.

²¹Logistic regression analyses when authorized Latino immigrants are the reference group provide identical results.

²²Other tax breaks for homeowners include the deduction of local and state property taxes on owner-occupied residences and that homeowners do not declare as income the rent that they would otherwise pay if they were renting the residence (Subsidyscope 2011).