The Experts below are selected from a list of 27168 Experts worldwide ranked by ideXlab platform
Dahlia K Remler - One of the best experts on this subject based on the ideXlab platform.
-
including health insurance in Poverty Measurement the impact of massachusetts health reform on Poverty
Journal of Health Economics, 2016Co-Authors: Sanders Korenman, Dahlia K RemlerAbstract:We develop and implement what we believe is the first conceptually valid health-inclusive Poverty Measure (HIPM) – a Measure that includes health care or insurance in the Poverty needs threshold and health insurance benefits in family resources – and we discuss its limitations. Building on the Census Bureau's Supplemental Poverty Measure, we construct a pilot HIPM for the under-65 population under ACA-like health reform in Massachusetts. This pilot demonstrates the practicality, face validity and value of a HIPM. Results suggest that public health insurance benefits and premium subsidies accounted for a substantial, one-third reduction in the health inclusive Poverty rate.
-
including health insurance in Poverty Measurement the impact of massachusetts health reform on Poverty
National Bureau of Economic Research, 2016Co-Authors: Sanders Korenman, Dahlia K RemlerAbstract:We develop and implement what we believe is the first conceptually valid health-inclusive Poverty Measure (HIPM)—a Measure that includes health care or insurance in the Poverty needs threshold and health insurance benefits in family resources—and we discuss its limitations. Building on the Census Bureau’s Supplemental Poverty Measure, we construct a pilot HIPM for the under-65 population under ACA-like health reform in Massachusetts. This pilot is intended to demonstrate the practicality, face validity and value of a HIPM. Results suggest that public health insurance benefits and premium subsidies accounted for a substantial, one-third reduction in the Poverty rate. Among low-income families who purchased individual insurance, premium subsidies reduced Poverty by 9.4 percentage points.
-
rethinking elderly Poverty time for a health inclusive Poverty Measure
National Bureau of Economic Research, 2013Co-Authors: Sanders Korenman, Dahlia K RemlerAbstract:Census's Supplemental Poverty Measure (SPM) nearly doubles the elderly Poverty rate compared to the "Official" Poverty Measure (OPM), a result of the SPM subtraction of medical out-of-pocket (MOOP) expenditures from income. Neither the SPM nor OPM counts health benefits or assets as resources. Validation studies suggest that subtracting MOOP from resources worsens a Poverty Measure's predictive validity and excluding assets exacerbates this bias, since assets fund MOOP. The SPM is based on a 1995 NAS report that recommended a health-exclusive Poverty Measure, despite considering it, conceptually, a "second best" to a Health-Inclusive Poverty Measure (HIPM). We analyze the reasons for the NAS recommendation and argue that constructing a HIPM is now feasible if we conceptualize health needs as a need for health insurance, and if plans with non-risk-rated premiums and caps on MOOP are universally available, a condition largely met by the Affordable Care Act and Medicare Advantage Plans. We describe four HIPM variants and present analyses that suggest the SPM treatment of MOOP results in a less valid Measure of elderly Poverty and an overstatement of the elderly Poverty rate (by up to 5.5 percentage points or 50 percent). Many elderly classified as poor by the SPM's unlimited MOOP deduction are not poorly insured persons with incomes near the Poverty line, but well-insured persons with incomes well above the Poverty line.
Sanders Korenman - One of the best experts on this subject based on the ideXlab platform.
-
including health insurance in Poverty Measurement the impact of massachusetts health reform on Poverty
Journal of Health Economics, 2016Co-Authors: Sanders Korenman, Dahlia K RemlerAbstract:We develop and implement what we believe is the first conceptually valid health-inclusive Poverty Measure (HIPM) – a Measure that includes health care or insurance in the Poverty needs threshold and health insurance benefits in family resources – and we discuss its limitations. Building on the Census Bureau's Supplemental Poverty Measure, we construct a pilot HIPM for the under-65 population under ACA-like health reform in Massachusetts. This pilot demonstrates the practicality, face validity and value of a HIPM. Results suggest that public health insurance benefits and premium subsidies accounted for a substantial, one-third reduction in the health inclusive Poverty rate.
-
including health insurance in Poverty Measurement the impact of massachusetts health reform on Poverty
National Bureau of Economic Research, 2016Co-Authors: Sanders Korenman, Dahlia K RemlerAbstract:We develop and implement what we believe is the first conceptually valid health-inclusive Poverty Measure (HIPM)—a Measure that includes health care or insurance in the Poverty needs threshold and health insurance benefits in family resources—and we discuss its limitations. Building on the Census Bureau’s Supplemental Poverty Measure, we construct a pilot HIPM for the under-65 population under ACA-like health reform in Massachusetts. This pilot is intended to demonstrate the practicality, face validity and value of a HIPM. Results suggest that public health insurance benefits and premium subsidies accounted for a substantial, one-third reduction in the Poverty rate. Among low-income families who purchased individual insurance, premium subsidies reduced Poverty by 9.4 percentage points.
-
rethinking elderly Poverty time for a health inclusive Poverty Measure
National Bureau of Economic Research, 2013Co-Authors: Sanders Korenman, Dahlia K RemlerAbstract:Census's Supplemental Poverty Measure (SPM) nearly doubles the elderly Poverty rate compared to the "Official" Poverty Measure (OPM), a result of the SPM subtraction of medical out-of-pocket (MOOP) expenditures from income. Neither the SPM nor OPM counts health benefits or assets as resources. Validation studies suggest that subtracting MOOP from resources worsens a Poverty Measure's predictive validity and excluding assets exacerbates this bias, since assets fund MOOP. The SPM is based on a 1995 NAS report that recommended a health-exclusive Poverty Measure, despite considering it, conceptually, a "second best" to a Health-Inclusive Poverty Measure (HIPM). We analyze the reasons for the NAS recommendation and argue that constructing a HIPM is now feasible if we conceptualize health needs as a need for health insurance, and if plans with non-risk-rated premiums and caps on MOOP are universally available, a condition largely met by the Affordable Care Act and Medicare Advantage Plans. We describe four HIPM variants and present analyses that suggest the SPM treatment of MOOP results in a less valid Measure of elderly Poverty and an overstatement of the elderly Poverty rate (by up to 5.5 percentage points or 50 percent). Many elderly classified as poor by the SPM's unlimited MOOP deduction are not poorly insured persons with incomes near the Poverty line, but well-insured persons with incomes well above the Poverty line.
Bezirgen Veliyev - One of the best experts on this subject based on the ideXlab platform.
-
functional sequential treatment allocation with covariates
arXiv: Machine Learning, 2020Co-Authors: Anders Bredahl Kock, David Preinerstorfer, Bezirgen VeliyevAbstract:We consider a multi-armed bandit problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or a socio-economic functional such as an inequality, welfare or Poverty Measure. We develop expected regret lower bounds for this problem, and construct a near minimax optimal assignment policy.
-
functional sequential treatment allocation
arXiv: Econometrics, 2018Co-Authors: Anders Bredahl Kock, David Preinerstorfer, Bezirgen VeliyevAbstract:Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best. However, the sequential nature of the problem permits learning about the effectiveness of the treatments, which we Measure by a functional of the associated outcome distributions, for example an inequality, welfare or Poverty Measure. In the present article, we evaluate assignment policies according to their regret, that is, the sum of all losses incurred due to assigning subjects to suboptimal treatments. We first study explore-then-commit (ETC) policies. These are policies, where one initially explores which treatment is of the highest quality, typically through a randomized controlled trial, and subsequently fully commits to the ``inferred superior'' (but potentially suboptimal) treatment. Then, we introduce and study the Functional Upper Confidence Bound (F-UCB) policy, which interweaves exploration and exploitation and is thus not of the ETC type. Our results show, in particular, that the F-UCB policy (i) performs much better than any ETC policy, and (ii) is near minimax optimal. We also show that a suitably adapted F-UCB policy is near minimax optimal under minimal assumptions when covariate information is available.
Christopher Wimer - One of the best experts on this subject based on the ideXlab platform.
-
a new method for measuring historical Poverty trends incorporating geographic differences in the cost of living using the supplemental Poverty Measure
Journal of economic and social measurement, 2016Co-Authors: Laura B Nolan, Irwin Garfinkel, Neeraj Kaushal, Jaehyun Nam, Jane Waldfogel, Christopher WimerAbstract:The U.S. Census Bureau and the Bureau of Labor Statistics recently developed a substantially improved Measure of Poverty, the Supplemental Poverty Measure (SPM). The SPM has only been released since 2009, and prior efforts by researchers to construct a historical SPM time series have not taken into account an essential element of the new Measure - geographical differences in the cost of living - which is necessary for accurately describing Poverty trends in important demographic and regional subgroups. We build the first historical SPM time series from 1967-2014 that adjusts Poverty thresholds for cost of living. We do so bringing together a constellation of data sources - the Current Population Survey, the Decennial Census, the Department of Housing and Urban Development's Fair Market Rents, and others. We find that geographically adjusting thresholds increases Poverty rates in metro areas, the Western states, and among Latinos, but decreases Poverty rates in non-metro areas and in the South. The geographic adjustment of Poverty thresholds is an impactful component of the SPM.
-
trends in deep Poverty from 1968 to 2011 the influence of family structure employment patterns and the safety net
RSF: The Russell Sage Foundation Journal of the Social Sciences, 2015Co-Authors: Liana Fox, Christopher Wimer, Irwin Garfinkel, Neeraj Kaushal, Jaehyun Nam, Jane WaldfogelAbstract:This paper examines the changing face of deep Poverty in the United States over the past fifty years and the role of family structure, employment patterns, and governmental taxes and transfers in explaining these trends. Using a newly developed historical Measure of Poverty based on the Census Bureau's supplemental Poverty Measure, we find that deep Poverty rates have been fairly constant over the past fifty years, both overall and for families with children. In view of changes in family structure and government policy over this period, the intransigence of deep Poverty is surprising. However, this overall stability obscures changes in the demographics of individuals and families in deep Poverty, as well as the role of government policy. Governmental transfers reduce the risk of deep Poverty for all subgroups examined, but the significance and the role of these programs have changed over time.
-
elderly Poverty in the united states in the 21st century exploring the role of assets in the supplemental Poverty Measure
Research Papers in Economics, 2015Co-Authors: Christopher Wimer, Lucas ManfieldAbstract:Official estimates of elderly Poverty do not take into account either the medical needs of the elderly, which can be quite extensive, or the assets at their disposal, which may also be extensive. The new Supplemental Poverty Measure (SPM) explicitly takes into account medical needs but has been criticized for not concomitantly taking into account asset portfolios. In this paper we consider both jointly, using an approach adapted from a recent National Academy of Sciences report recommending methods for measuring Poverty and medical risk while taking account of assets. We use longitudinal data from the Health and Retirement Study (HRS).
-
elderly Poverty in the united states in the 21st century exploring the role of assets in the supplemental Poverty Measure
Social Science Research Network, 2015Co-Authors: Christopher Wimer, Lucas ManfieldAbstract:Official estimates of elderly Poverty do not take into account either the medical needs of the elderly, which can be quite extensive, or the assets at their disposal, which may also be extensive. The new Supplemental Poverty Measure (SPM) explicitly takes into account medical needs but has been criticized for not concomitantly taking into account asset portfolios. In this paper we consider both jointly, using an approach adapted from a recent National Academy of Sciences report recommending methods for measuring Poverty and medical risk while taking account of assets. We use longitudinal data from the Health and Retirement Study (HRS). The paper found that: Confirming previously published research, the elderly have considerably higher Poverty rates under the SPM than under the official Measure, and this disparity is driven by the SPM’s treatment of medical out-of-pocket expenditures. SPM Poverty rates are considerably lower than actual SPM rates when an annuitized portion of liquid assets is incorporated into the Measure of resources. When both liquid and near-liquid assets are considered, trends and levels of SPM Poverty rates are extremely close to official Poverty rates. SPM Poverty rates would be even lower than official rates if all assets, including reverse mortgages on homes, were considered, though this might be considered a less “reasonable” approach by some observers. Incorporating assets has differing effects on pre-existing disparities in Poverty rates: it exacerbates inequalities by race and marital status but reduces inequalities by age and has little effect on inequalities by gender. The policy implications of the findings are: Government agencies should consider examining the role of assets when assessing the trends and levels of Poverty rates among older Americans. Promoting savings and asset ownership among younger Americans is likely to pay substantial dividends with respect to the economic well-being of older Americans in the future.
Lucas Manfield - One of the best experts on this subject based on the ideXlab platform.
-
elderly Poverty in the united states in the 21st century exploring the role of assets in the supplemental Poverty Measure
Research Papers in Economics, 2015Co-Authors: Christopher Wimer, Lucas ManfieldAbstract:Official estimates of elderly Poverty do not take into account either the medical needs of the elderly, which can be quite extensive, or the assets at their disposal, which may also be extensive. The new Supplemental Poverty Measure (SPM) explicitly takes into account medical needs but has been criticized for not concomitantly taking into account asset portfolios. In this paper we consider both jointly, using an approach adapted from a recent National Academy of Sciences report recommending methods for measuring Poverty and medical risk while taking account of assets. We use longitudinal data from the Health and Retirement Study (HRS).
-
elderly Poverty in the united states in the 21st century exploring the role of assets in the supplemental Poverty Measure
Social Science Research Network, 2015Co-Authors: Christopher Wimer, Lucas ManfieldAbstract:Official estimates of elderly Poverty do not take into account either the medical needs of the elderly, which can be quite extensive, or the assets at their disposal, which may also be extensive. The new Supplemental Poverty Measure (SPM) explicitly takes into account medical needs but has been criticized for not concomitantly taking into account asset portfolios. In this paper we consider both jointly, using an approach adapted from a recent National Academy of Sciences report recommending methods for measuring Poverty and medical risk while taking account of assets. We use longitudinal data from the Health and Retirement Study (HRS). The paper found that: Confirming previously published research, the elderly have considerably higher Poverty rates under the SPM than under the official Measure, and this disparity is driven by the SPM’s treatment of medical out-of-pocket expenditures. SPM Poverty rates are considerably lower than actual SPM rates when an annuitized portion of liquid assets is incorporated into the Measure of resources. When both liquid and near-liquid assets are considered, trends and levels of SPM Poverty rates are extremely close to official Poverty rates. SPM Poverty rates would be even lower than official rates if all assets, including reverse mortgages on homes, were considered, though this might be considered a less “reasonable” approach by some observers. Incorporating assets has differing effects on pre-existing disparities in Poverty rates: it exacerbates inequalities by race and marital status but reduces inequalities by age and has little effect on inequalities by gender. The policy implications of the findings are: Government agencies should consider examining the role of assets when assessing the trends and levels of Poverty rates among older Americans. Promoting savings and asset ownership among younger Americans is likely to pay substantial dividends with respect to the economic well-being of older Americans in the future.