Liana Fox

Trends in Poverty with an Anchored Supplemental Poverty Measure

Note: We are grateful for funding support from the Annie E. Casey Foundation and from the National Institute of Child Health and Human Development (NICHD) through grant R24 HD058486-03 to the Columbia Population Research Center (CPRC). We benefited from research assistance from Nathan Hutto and Ethan Raker. We are also grateful to seminar participants at CPRC and Russell Sage Foundation, as well as many colleagues who provided helpful insights and advice, in particular,

Progress on Poverty? New Estimates of Historical Trends Using an Anchored Supplemental Poverty Measure

This study examines historical trends in poverty using an anchored version of the U.S. Census Bureau’s recently developed Research Supplemental Poverty Measure (SPM) estimated back to 1967. Although the SPM is estimated each year using a quasi-relative poverty threshold that varies over time with changes in families’ expenditures on a core basket of goods and services, this study explores trends in poverty using an absolute, or anchored, SPM threshold. We believe the anchored measure offers two advantages. First, setting the threshold at the SPM’s 2012 levels and estimating it back to 1967, adjusted only for changes in prices, is more directly comparable to the approach taken in official poverty statistics. Second, it allows for a better accounting of the roles that social policy, the labor market, and changing demographics play in trends in poverty rates over time, given that changes in the threshold are held constant. Results indicate that unlike official statistics that have shown poverty rates to be fairly flat since the 1960s, poverty rates have dropped by 40 % when measured using a historical anchored SPM over the same period. Results obtained from comparing poverty rates using a pretax/pretransfer measure of resources versus a post-tax/post-transfer measure of resources further show that government policies, not market incomes, are driving the declines observed over time.

Parental Wealth and the Black–White Mobility Gap in the U.S.

Utilizing longitudinal data from the Panel Study of Income Dynamics (PSID), this paper examines the relationship between parental wealth and intergenerational income mobility for black and white families. I find that total parental wealth is positively associated with upward mobility for low‐income white families, but is not associated with reduced likelihood of downward mobility for white families from the top half of the income distribution. Conversely, I find that total parental wealth does not have the same positive association for low‐income black families, while home ownership may have negative associations with the likelihood of upward mobility for these families. However, for black families from the top half of the income distribution, home equity is associated with a decreased likelihood of downward mobility, suggesting a heterogeneous relationship between home ownership and mobility for black families.

The Effect of Obesity on Intergenerational Income Mobility

Obesity has increasingly become a national health concern of epidemic proportions. Recent Centers for Disease Control (CDC) data indicate that the national age-adjusted obesity prevalence reached an all-time high of 34 % in 2008, with more than 16 % of children and adolescents obese (Flegal et al. 2010). Overall, 68 % of U.S. adults are overweight or obese. Overweight and obese individuals are at increased risk of a range of medical conditions, including coronary heart disease, type-2 diabetes, cancer, sleep apnea, and reproductive dysfunction. Medical costs associated with overweight and obesity were

Time for Children: Trends in the Employment Patterns of Parents, 1967-2009

92.6 billion in 1998 and accounted for at least 9 % of all national health care spending (Finkelstein et al. 2003). More than 10 years later, the costs have surely increased with obesity’s increased prevalence. In addition to health care spending, obesity has many socioeconomic costs, with vast research detailing correlates between socioeconomic status (SES) and weight.