Entitlement Gaps and Vulnerability in the New Economy

Global poverty is primarily concentrated within groups who depend upon their labour, as opposed to capital of any form, for the provision of income. The emergence of a global economy – Globalization – and the spread of neo-liberal ideology and policies, which forged new lines of social cleavage and widened the digital divide, has further entrenched poverty within existing vulnerable populations, as well as exposing new populations to economic and social vulnerability. This expansion of vulnerability results from a shortfall in entitlement: Entitlement not only to the ability to earn income, but also the ability (inability) to translate that income into material well-being. The formation of an inclusive and just society necessitates closing this entitlement gap. To close the gap, we must understand the dynamics of socio-economic exclusion in this climate, and the livelihood coping strategies of the marginalized before effective policy formulation can take place

The Ability of Various Measures of Fatness to Predict Application for Disability Insurance

This paper compares a variety of measures of fatness (e.g. BMI, waist circumference, waist-tohip ratio, percent body fat) in terms of their ability to predict application for Social Security Disability Insurance (DI). This is possible through a recent linkage of the National Health and Nutrition Examination Survey (NHANES) III to Social Security Administration (SSA) administrative records. Our results indicate that the measure of fatness that best predicts application for DI varies by race and gender. For white men, BMI consistently predicts future application for DI. For white women, almost all are consistently predictive. For black men, none predict application. For black women, waist circumference and waist-to-hip ratio are the only significant predictors of DI application. This variation across race and gender suggests that the inclusion of alternative measures of fatness in social science datasets should be considered, and that researchers examining the impact of fatness on social science outcomes should examine the robustness of their findings to alternative measures of fatness.

Three Essays on Obesity, Poverty, and the Labor Market

This dissertation broadly examines the economic and health-related consequences of individual behaviors, and their interaction with government programs. It is divided into three distinct chapters. The first chapter examines how changes in family income contribute to increasing obesity among low-income families. In the general adult female population, the prevalence of obesity decreases substantially as family income increases. However, this relationship is not necessarily causal, as numerous other factors could be driving this negative correlation. I make use of the expansion of the New York State Earned Income Tax Credit (EITC) program over the course of the 1990s as a source of exogenous variation in family income in order to estimate the causal effect of family income on obesity. I show that increasing family income has a positive effect on weight and obesity prevalence among the sample population. This effect is concentrated among those who are already obese. The second chapter simulates the effect on New York State residents of an expansion of the EITC on employment, hours worked, income, and poverty, and compares these results to a simulation which excludes labor supply effects. Relative to estimates excluding labor supply effects, the preferred behavioral results show that an expansion of the New York State EITC increases employment by an additional 14,244 persons, labor earnings by an additional $95.8 million, and family income by an additional$84.5 million; decreases poverty by an additional 56,576 persons; and increases costs to the State by $29.7 million. The third chapter is a co-authored work with Richard Burkhauser and John Cawley. It returns to the topic of obesity, and investigates which measures of fatness most accurately predict application for Social Security Disability (DI) benefits. Although the social science literature has wholly embraced the use of body mass index (BMI) as a measure of fatness, many medical researchers argue that BMI is a poor measure of a person?s true fatness. Our results indicate that despite the limitations of BMI, it is consistently a significant predictor of future application for DI, although more accurate measures of fatness occasionally perform better as predictors of application

The effects of female labor force participation on obesity

This paper assesses whether a causal relationship exists between recent increases in female labor force participation and the increased prevalence of obesity amongst women. The expansions of the Earned Income Tax Credit (EITC) in the 1980s and 1990s have been established by prior literature as having generated variation in female labor supply, particularly amongst single mothers. Here, we use this plausibly exogenous variation in female labor supply to identify the effect of labor force participation on obesity status. We use data from the National Health Interview Survey (NHIS) and replicate labor supply effects of the EITC expansions found in previous literature. This validates employing a difference-in-differences estimation strategy in the NHIS data, as has been done in several other data sets. Depending on the specification, we find that increased labor force participation can account for at most 19% of the observed change in obesity prevalence over our sample period. Our preferred specification, however, suggests that there is no causal link between increased female labor force participation and increased obesity.Women - Employment ; Obesity ; Tax credits

The Effects of Female Labor Force Participation on Obesity

This paper assesses whether a causal relationship exists between recent increases in female labor force participation and the increased prevalence of obesity amongst women. The expansions of the Earned Income Tax Credit (EITC) in the 1980s and 1990s have been established by prior literature as having generated variation in female labor supply, particularly amongst single mothers. Here, we use this plausibly exogenous variation in female labor supply to identify the effect of labor force participation on obesity status. We use data from the National Health Interview Survey (NHIS) and replicate labor supply effects of the EITC expansions found in previous literature. This validates employing a difference-in-differences estimation strategy in the NHIS data, as has been done in several other data sets. Depending on the specification, we find that increased labor force participation can account for at most 19% of the observed change in obesity prevalence over our sample period. Our preferred specification, however, suggests that there is no causal link between increased female labor force participation and increased obesity.female labor force participation, obesity, earned income tax credit

The Importance of State Anti-Discrimination Laws on Employer Accommodation and the Movement of their Employees onto Social Security Disability Insurance

The rate of application for Social Security Disability Insurance (SSDI) benefits, as well as the number of beneficiaries has been increasing for the past several decades, threatening the solvency of the SSDI program. One possible remedy is to promote continued employment amongst those experiencing the onset of a work limiting disability through the provision of workplace accommodations. Using the Health and Retirement Study data linked to Social Security administrative records and a state fixed effects model, we find that the provision of workplace accommodation reduces the probability of application for SSDI following disability onset. We estimate that receipt of an accommodation reduces a worker’s probability of applying for SSDI by 30 percent over five years and 21 percent over 10 years. We then attempt to control for the potential endogeneity of accommodation receipt by exploiting exogenous variation in the implementation of state and federal anti-discrimination laws to estimate the impact of workplace accommodation on SSDI application in an instrumental variables (IV) model. While our coefficients continue to indicate that accommodation reduces SSDI application, we obtain implausibly large estimates of this effect. Overall our results imply that increasing accommodation is a plausible strategy for reducing SSDI applications and the number of beneficiaries.Social Security Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/87957/1/wp251.pd

WP 2016-351

This paper examines how the extraction of home equity, including but not limited to equity extracted through reverse mortgages, affects credit outcomes of senior households. We use data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel, supplemented with our unique credit panel dataset of reverse mortgage borrowers. We track credit outcomes for seniors who extracted equity through cash-out refinancing, home equity lines of credit or home equity loans between 2008 and 2011, and a random sample of nonextractors. We estimate differences-in-differences by extraction channel using individual, fixed-effects panel regression. We find that seniors extracting equity through reverse mortgages have greater reductions in consumer debt, and are less likely to become delinquent or foreclose three years post origination relative to other extractors and nonextractors. These effects are greater among households who experienced a credit shock within the two years prior to loan origination. To help isolate the effect of the extraction channel on credit outcomes, we re-estimate our models with a matched sample of consumers at the time of extraction. We find that otherwise similar HECM borrowers have larger reductions in credit card debt post-extraction than other equity borrowers and non-borrowers, with no significant difference in the rates of delinquency on non-housing debt post extraction. For HECM borrowers, we find that increased initial withdrawal and increased monthly cash flow contribute to the reduction in credit card debt.Social Security Administration, RRC08098401, R0UM16-12http://deepblue.lib.umich.edu/bitstream/2027.42/134705/1/wp351.pdfDescription of wp351.pdf : Working pape

The Ability of Various Measures of Fatness to Predict Application for Disability Insurance

This paper compares a variety of measures of fatness (e.g. BMI, waist circumference, waist-to-hip ratio, percent body fat) in terms of their ability to predict application for Social Security Disability Insurance (DI). This is possible through a recent linkage of the National Health and Nutrition Examination Survey (NHANES) III to Social Security Administration (SSA) administrative records. Our results indicate that the measure of fatness that best predicts application for DI varies by race and gender. For white men, BMI consistently predicts future application for DI. For white women, almost all are consistently predictive. For black men, none predict application. For black women, waist circumference and waist-to-hip ratio are the only significant predictors of DI application. This variation across race and gender suggests that the inclusion of alternative measures of fatness in social science datasets should be considered, and that researchers examining the impact of fatness on social science outcomes should examine the robustness of their findings to alternative measures of fatness.Social Security Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/61810/1/wp185.pd

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions