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Discoveries in the Economics of Aging

Discoveries in the Economics of Aging

by David A. Wise (Editor)


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The oldest members of the Baby-Boomer generation are now crossing the threshold of eligibility for Social Security and Medicare with extensive and significant implications for these programs’ overall spending and fiscal sustainability. Yet the aging of the Baby Boomers is just one part of the rapidly changing landscape of aging in the United States and around the world.

The latest volume in the NBER’s Economics of Aging series, Discoveries in the Economics of Aging assembles incisive analyses of the most recent research in this expanding field of study. A substantive focus of the volume is the well-documented relationship between health and financial well-being, especially as people age. The contributors explore this issue from a variety of perspectives within the context of the changing demographic landscape. The first part of the volume explores recent trends in health measurement, including the use of alternative measurement indices. Later contributions explore, among other topics, alternate determinants of health, including retirement, marital status, and cohabitation with family, and the potential for innovations, interventions, and public policy to improve health and financial well-being.

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Product Details

ISBN-13: 9780226146096
Publisher: University of Chicago Press
Publication date: 06/30/2014
Series: National Bureau of Economic Research Conference Report
Pages: 528
Product dimensions: 6.40(w) x 9.10(h) x 1.50(d)

About the Author

David A. Wise is John F. Stambaugh Professor of Political Economy at the Kennedy School of Government at Harvard University and area director for Aging and Health Studies at the NBER.

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Discoveries in the Economics of Aging

By David A. Wise

The University of Chicago Press

Copyright © 2014 National Bureau of Economic Research
All rights reserved.
ISBN: 978-0-226-14612-6


Evidence for Significant Compression of Morbidity in the Elderly US Population

David M. Cutler, Kaushik Ghosh, and Mary Beth Landrum

Older Americans are living longer. Life expectancy at age sixty-five has increased about two years in the past two decades. But are we living healthier? This issue is vital for health policy and economic reasons. Longer life is valuable to people, but it is even more valuable if the additional years lived are in good health. For the public sector as well, the consequences of longer lives depend on their quality. Medical spending for healthy seniors is modest; spending for the severely disabled is much greater. Thus, if morbidity is being compressed into the period just before death, the impacts of population aging are not as severe as if additional life involves many years of expensive care.

This question of whether morbidity is being compressed into the period just before death has been at the center of health debates in the United States for some time. Fries (1980) first put forward the argument that the United States was undergoing a compression of morbidity. His work was provocative, and others took different views. Gruenberg (1977) argued that reduced disease mortality would extend unhealthy life, while Manton (1982) posited a dynamic equilibrium where both morbidity and mortality are falling, leading to indeterminate impacts on disability-free and disabled life expectancy.

Empirical evidence on trends in morbidity is also unclear. Some authors argue that morbidity is being compressed into the period just before death (Cai and Lubitz 2007; Manton, Gu, and Lowrimore 2008), while others believe that the period of disabled life is expanding (Crimmins and Beltrán-Sánchez 2010) or that the evidence is more mixed (Crimmins et al. 2009).

There are three reasons for this disagreement. First, there is not a single definition of morbidity. Some studies look at whether people report specific chronic conditions, which have increased over time, while other studies look at functioning. As a result, studies differ in the morbidity trends they incorporate.

Second, it is often difficult to link health to the stage of life of the individual. If people are reporting more chronic disease, is that in the period just before the end of life, in which case the additional disease does not encompass many years? Or is the disease occurring in periods of time far from the end of life, in which case it represents many years of poor health? To answer this question, one needs data on quality of life matched to time until death. Most cross-section data sources do not have such a link, however, and thus they need to make assumptions about the disease process to generate lifetime disease-prevalence estimates. These assumptions can have large impacts on the results.

Third, the data samples that tend to be used often focus on a particular subset of the population; for example, the noninstitutionalized. Since there are changes in the residential location of the elderly population over time, focusing on population subsets can give biased results.

In this chapter, we examine the issue of compression of morbidity, addressing these three concerns. Our primary data source is the Medicare Current Beneficiary Survey, or (MCBS). We have MCBS data for a representative sample of the entire elderly population between 1991 and 2009. The sample sizes are large, over 10,000 individuals annually. Further, the MCBS data have been linked to death records through 2008, and hence all deaths can be matched. Importantly, this includes deaths that occur after the person has left the survey. Thus, we can form morbidity measures by time until death for a large, representative share of the elderly population.

We use these data in two ways. First, we examine trends in various measures of morbidity by time until death. We consider a number of different metrics: the presence of disease, whether the person reports activities of daily living (ADL) or instrumental activities of daily living (IADL) disability, and various summary measures of functioning that draw together nineteen different dimensions of health (Cutler and Landrum 2012). We show trends overall and by time until death.

As is well known, the MCBS data from the 1990s and 2000s show a reduction in the share of elderly people who report ADL or IADL limitations (Freedman et al. 2004, 2013). Our first result is that this reduction in disability is most marked among those with many years until death. Health status in the year or two just prior to death has been relatively constant over time; in contrast, health measured three or more years before death has improved measurably.

We then translate these changes into disability-free life expectancy and disabled life expectancy. We show that disability-free life expectancy is increasing over time, while disabled life expectancy is falling. For a typical person age sixty-five, life expectancy increased by 0.7 years between 1992 and 2005. Disability-free life expectancy increased by 1.6 years; disabled life expectancy fell by 0.9 years. The reduction in disabled life expectancy and increase in disability-free life expectancy is true for both genders and for nonwhites as well as whites. Hence, morbidity is being compressed into the period just before death.

The chapter is structured as follows. We begin in the next section by defining the compression of morbidity and showing how disability and mortality changes jointly affect disability-free and disabled life expectancy. The second section describes the data we use. The third section presents simple trends in health status by time until death. The fourth section calculates disabled and disability-free life expectancy. The last section concludes.

1.1 The Compression of Morbidity

The question we wish to examine is whether morbidity has been compressed into the period just before death, or whether it is accounting for a greater part of the life of elderly individuals. While this goal is clear, the empirical implementation needs a more precise definition. We consider two definitions of a compression of morbidity. One definition, dating back to Fries (1980), is whether the life table is "rectangularizing"—that is, whether disabled life expectancy is falling over time. A second definition is more modest: the share of remaining life that is nondisabled is increasing over time. Note that in this latter formulation, disabled life expectancy may be increasing as well, just not as rapidly as nondisabled life expectancy.

In situations where only morbidity or mortality is changing, these two measures will always move together. In situations where both mortality and morbidity are changing, however, trends in the two measures of compression of morbidity may be different.

To see this, consider a simple example presented in table 1.1 The first column depicts a person who lives for five years, the first three of which are without disability, and the fourth and fifth are with a disability. To be concrete, suppose that the person has heart disease in the fourth year and develops chronic obstructive pulmonary disease in the fifth, which results in death six months later. The specific diseases do not matter, but as is typical in the data, we reflect disability as occurring progressively over life and generally do not consider recovery.

In forming life tables, people who die during a year are assumed to die halfway through the year. Thus, the baseline life expectancy is 4.5 years, of which the first 3.0 years is disability-free and the latter 1.5 years is disabled.

Now imagine that morbidity declines (column [2]). To be specific, suppose that because of improved medical treatment of cardiac risk factors, the person does not suffer a coronary event in the fourth year and thus is not disabled in that year. In year 5, however, the person still suffers lung disease and dies. As the last rows show, overall life expectancy is unchanged, but disability-free life expectancy has increased to 4.0 years and disabled life expectancy has fallen to 0.5 years. By either definition, disability has been compressed into the period before the end of life.

The third column shows the impact of a reduction in mortality. We imagine that the medical system gets better at treating the combination of heart disease and lung disease, and thus the person survives an additional year with both conditions, albeit they are still disabled. Total life expectancy has increased by one year in this example, all of which is associated with disability. Further, the share of life that is disabled has increased. Thus, there is an expansion of disability by either measure. Note that in this example, the person is still better off; it is just that the disabled part of life has increased.

The final column shows a combination of disability reductions (the person does not suffer the coronary event) and mortality reductions (the person survives an additional year with lung disease). Life expectancy has increased by one year, relative to the baseline. The increase is entirely in disability-free life; disabled life starts one year later but ends one year later. In this scenario, whether morbidity has been compressed depends on the definition employed: disabled life expectancy has not declined, but a greater share of life is spent in the nondisabled state.

In general, the impact of combined morbidity and mortality changes on disability-free and disabled life expectancy depends on how rapid each change is and when in the course of life it occurs. All of this we need to evaluate empirically.

1.2 Medicare Current Beneficiary Data

Our primary data source is the Medicare Current Beneficiary Survey (MCBS). The MCBS, sponsored by the Centers for Medicare and Medic-aid Services (CMS), is a nationally representative survey of aged, disabled, and institutionalized Medicare beneficiaries that oversamples the very old (age eighty-five or older) and disabled Medicare beneficiaries. Since we are interested in health among the elderly, we restrict our sample to the population age sixty-five and older.

A number of surveys have measures of disability in the elderly population (Freedman et al. 2004), including the National Health Interview Study and the Health and Retirement Study. Still, the MCBS has a number of advantages relative to these other surveys. First, the sample size is large, about 10,000 to 18,000 people annually. In addition, the MCBS samples people regardless of whether they live in a household or a long-term care facility, or switch between the two during the course of the survey period. Third, the set of health questions is very broad, encompassing health in many domains. Fourth, and most importantly, individuals in the MCBS have been matched to death records. As a result, we can measure death for over 200,000 people, even after they have left the survey window. Death data are available through 2008.

The MCBS started as a longitudinal survey in 1991. In 1992 and 1993, the only supplemental individuals added were to replace people lost to attrition and to account for newly enrolled beneficiaries. Beginning in 1994, the MCBS began a transition to a rotating panel design, with a four-year sample inclusion. About one-third of the sample was rotated out in 1994, and new members were included in the sample. The remainder of the original sample was rotated out in subsequent years. We use all interviews that are available for each person from the start of the survey in 1991 through 2009. We ignore the panel structure of the MCBS interviews and treat each survey year as a repeated cross section that has been linked to mortality information.

The MCBS has two samples: a set of people who were enrolled for the entire year (the Access to Care sample) and a set of ever-enrolled beneficiaries (the Cost and Use sample). The latter differs from the former in including people who die during the year and new additions to the Medicare population. The primary data that we use are from the health status questionnaire administered in the fall survey, which defines the Access to Care sample. We thus use the Access to Care data. We compute time until death from the exact date at which the Access to Care survey was administered to the person.

The MCBS population becomes older and less white over time, as the elderly population changes demographically. We do not want to show trends that are influenced by these demographic changes. We thus adjust survey weights so that the MCBS population in each year matches the population in the year 2000 by age, gender, and race. All of our tabulations are weighted by these adjusted weights.

Recall that our death dates are available through 2008. For each individual interviewed in 1991–2007, therefore, we can determine if they died in the next twelve months or survived that period. Similarly, we can categorize individuals through 2006 as dying between twelve and twenty-four months or not, and individuals through 2005 as dying between twenty-four and thirty-six months or not. Death at thirty-six months or beyond is also known for the population through 2005.

Trends in the distribution of time until death are shown in figure 1.1 The share of the population that is within one year of death is about 5 percent on average. Reflecting the overall reduction in mortality, this share is declining over time (this will be true of the population 1–2 years from death and 2–3 years from death as well). Between 1991 and 2007, the decline is 1 percentage point, or 18 percent. Correspondingly, the share of the population that is three or more years from death increased by about 3 percentage points, also shown in figure 1.1.

The MCBS asks extensive health questions. The first set of health questions are about medical events the person has experienced. These include cardiovascular conditions (heart disease, stroke), diseases of the central nervous system (Alzheimer's disease, Parkinson's disease), musculoskeletal problems (arthritis, broken hip), pulmonary disease, and cancer. For purposes of disability assessment, we divide these diseases into four groups, based on their likely association with death and disability (Lunney et al. 2003). The first disease is cancer. Once past the acute phase of cancer treatment, people with cancer tend to have a reasonably high quality of life until the last few months of life, when health deteriorates markedly. The second group is permanently disabling conditions that get progressively worse. Alzheimer's disease, Parkinson's disease, and pulmonary disease fall into this category. The third group is acute conditions for which recovery is possible but not assured. This includes heart disease, strokes, and hip fractures. Finally, we group diabetes and arthritis as commonly disabling but generally nonfatal conditions.

Table 1.2 shows the prevalence of these conditions across all years of the survey, the annual percentage point change in the prevalence over time, and the disability rate conditional on having the disease (defined as whether the person reports an ADL or IADL limitation; see following). Nonfatal conditions are the most common. Over half of the elderly population reports a prior diagnosis of arthritis, the prevalence of which is increasing by 0.3 percentage points annually. Nearly one in five elderly people has diabetes. Acute conditions for which recovery is possible are the next most common, ranging in prevalence from 4 percent of the population (hip fracture) to 26 percent (ischemic heart disease). Perhaps owing to better prevention, the prevalence of both heart disease and heart attacks is declining over time. About 18 percent of the elderly population has a history of cancer, which is increasing over time. Degenerative diseases are relatively less common, though pulmonary disease affects about one-seventh of the elderly population. People with these conditions are extremely likely to report having an ADL or IADL impairment.


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Table of Contents

David A. Wise and Richard Woodbury
I. Health and Disability
1. Evidence for Significant Compression of Morbidity in the Elderly US Population
David M. Cutler, Kaushik Ghosh, and Mary Beth Landrum
Comment: Daniel McFadden and Wei Xie
2. The Lifetime Risk of Nursing Home Use
Michael D. Hurd, Pierre-Carl Michaud, and Susann Rohwedder
Comment: David M. Cutler
3. A Comparison of Different Measures of Health and Their Relation to Labor Force Transitions atOlder Ages
Arie Kapteyn and Erik Meijer
Comment: Steven F. Venti
II. Health and Financial Well-Being
4. The Nexus of Social Security Benefits, Health, and Wealth at Death
James M. Poterba, Steven F. Venti, and David A.Wise
Comment: Jonathan Skinner
5. Understanding the SES Gradient in Health among the Elderly: The Role of Childhood Circumstances
Till Stowasser, Florian Heiss, Daniel McFadden, and Joachim Winter
Comment: Robert J. Willis
III. Determinants of Health
6. Early Retirement, Mental Health, and Social Networks
Axel Börsch-Supan and Morten Schuth
Comment: Elaine Kelly
7. Spousal Health Effects: The Role of Selection
James Banks, Elaine Kelly, and James P. Smith
Comment: Amitabh Chandra
8. Grandpa and the Snapper: The Well-Being of the Elderly Who Live with Children
Angus Deaton and Arthur A. Stone
Comment: David Laibson
9. Expectations, Aging, and Cognitive Decline
Gábor Kézdi and Robert J. Willis
Comment: John B. Shoven
IV. Interventions to Improve Health and Well-being
10. Nutrition, Iron Deficiency Anemia, and the Demand for Iron-Fortified Salt: Evidence from an Experiment in Rural Bihar
Abhijit Banerjee, Sharon Barnhardt, and Esther Duflo
Comment: Amitabh Chandra
11. The Diffusion of New Medical Technology: The Case of Drug-Eluting Stents
Amitabh Chandra, David Malenka, and Jonathan Skinner
Comment: Jay Bhattacharya
12. Who Uses the Roth 401(k), and How Do They Use It?
John Beshears, James J. Choi, David Laibson, and Brigitte C. Madrian
Comment: James M. Poterba
Author Index
Subject Index

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