CONCLUSION

This chapter has examined three propositions: health differences generate income inequality, income differences generate health inequality, and income inequality damages health. Grossly simplifying a host of arguments and a vast body of evidence, our verdicts on these three charges are “guilty,” “not proven,” and “not guilty,” respectively.^{[110] [111]} More cautious assessments of the weight of evidence are provided in the conclusions to Sections 17.3-17.5, respectively.

Chief among the multitude of mechanisms through which ill-health can impinge on income is the loss of earnings arising from reduced productivity combined with institu­tional inflexibilities that result in adjustment through employment rather than wages, or marginal changes in work intensity. In high-income countries, ill-health is a major cause oflabor-force withdrawal in middle age. On pure efficiency grounds, disability insurance (DI) is called for to weaken the dependence of income on health and thus compress the income distribution. But there is a strong moral hazard effect that makes employment even more sensitive to ill-health. Achieving the optimal balance between income replacement and work incentives is perhaps the greatest challenge for policy that seeks to constrain income inequality arising from ill-health. The task is made even more dif­ficult by increasing economic inequality itself, in the context of which DI can further weaken the labor market attachment of the low-skilled facing deteriorating opportuni­ties. Research needs to move beyond identifying the impact of ill-health on exiting from employment to the design of programs and incentives that can help individuals experiencing health problems remain in work.

Early-life experience might be another major route through which health impacts the distribution of income. Exposure to health risks in utero and ill-health in infanthood appear to impact earnings capacity both by interfering with the accumulation of human capital and skills and by triggering illnesses in adulthood that disrupt employment. The currently observed income distribution is, to some degree, the product of health events that occurred during the childhood of the current adult population. This contribution to economic inequality will be particularly strong if, as appears to be the case, disadvantaged children, who would have grown up to be poorer in any case, face greater health risks. Policies directed at childhood circumstances, including those intended to break the link between parental socioeconomic status and health, may not only be preferred norma­tively in pursuit of the goal of equal opportunity (see Chapter 4), but they might also be favored simply for their effectiveness in influencing the distribution of income among adults. However, much of this line of argument is still supposition. The evidence that childhood health is influenced by economic background and determines adult economic outcomes is persuasive but not yet concrete. Fortunately, the pace of progress in this field makes it unnecessary to call for more research on the contribution of early-life health to economic inequality.

Our “not proven” verdict on the contribution of income (and wealth) to health inequality arises from the potential difficulty in detecting an effect if one did, in fact, exist. At least in high-income countries with near-universal health insurance coverage and in which the burden of disease is mostly chronic, economic circumstances are likely to exert a toll on health, if at all, over a lifetime. The empirical strategies that have been employed, such as fixed effects and instrumenting with transitory financial shocks, are incapable of

identifying the long-run effects that may be operating.

Rather than further identifying a reduced form effect of income or wealth on health, we believe it is more fruitful to focus on plausible inputs to the health production function that can be influenced by economic status. For example, establishing the health effect of damp, squalid housing is more feasible than finding the health effect of the income that affords superior quality housing. This is not merely a call for empirical pragmatism. Pro­vided that redistribution policy is motivated, in part, by (health) specific egalitarianism— and we attribute the extensive involvement of governments in the provision of health insurance and medical care as being motivated not only by the correction of market fail­ures but also by concern for the distribution of health—it might be more efficient to enable poor people to live in less unhealthy conditions, rather than redistributing cash to them. Once basic nutritional needs are satisfied and access to medical care has been divorced from the ability to pay, the path leading from income to health seems a very long one.

If one switches attention from the distribution of health to that of well-being, then the association between income and health may be used to justify greater redistribution of income, even in the absence of any causal effect.

A still more effective redistribution policy might be one that operates through a factor, perhaps education, that exerts a causal impact on both income and health (Deaton, 2002).

irrespective of their consequences for earnings or living costs are consistent with these ethics. But government social policies typically are not. Transfers compensate for finan­cial losses, not reductions in other dimensions of welfare.

With respect to the charge that income inequality threatens health, a case could be made for revising the verdict from “not guilty” to “not proven.” It is fundamentally dif­ficult to separate any potential effect of income inequality on the individual’s health from that of physical, environmental, social, cultural, or economic determinants of health that operate on the level at which income inequality is measured. Identifying the impact of relative income on health is even more challenging than doing so for absolute income given the added complexity of defining and measuring the reference point.

The conclusions offered above are based on evidence from high-income countries. In low-income countries, in which a substantial fraction of the population may live close to subsistence and only the economically privileged can afford effective medical care, ill- health is not only an important cause of economic inequality but a consequence of it. But it is the absolute living conditions of the poor, and not their relative deprivation, that takes the toll on health.

ACKNOWLEDGMENTS

We thank the editors, Tony Atkinson and Francois Bourguignon, for their encouragement and detailed comments on drafts of this chapter. Thanks are also due to Hans van Kippersluis and participants of the NH Handbook of Income Distribution conference, particularly Andrew Clark, for suggestions. Hale Koc and Hao Zhang provided excellent research assistance. We are grateful to various organizations for col­lecting and providing data (see Appendix). We acknowledge support from the National Institute on Aging, under Grant R01AG037398.

APPENDIX

Table A1 Descriptions and means of variables used in analyses in Section 17.2

Table A1 DescriDtions and means of variables used in analyses in Section 17.2—cont'd

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^aIn the USA and Dutch surveys, respondents report their health in general as being excellent, very good, good, fair (USA)/ moderate (NL), or poor. In the Chinese survey, respondents report their health relative to others of theirown age as verygood, good, fair, or poor.

^bGross household income is before payment of taxes and social security contributions and after receipt of transfers. Annual income for USA and monthly income for NL and China.

^cEducation has been classified using the International Standard Classification of Education (ISCED) for the USA and the Netherlands, with low education referring to ISCED < 4, middle education to ISCED ¼ 4, and higher education to ISCED > 4 (UNESCO Institute for Statistics). For China, low education refers to a primary orjunior high school degree, middle education to a senior high school degree, and high education to vocational higher education and university higher education.

^dReference category in the least squares and interval regressions in Tables 17.1 and 17.2.

Table A2 Acronyms used in tables

Name/definition

Datasets

BHPS British Household Panel Study

BRFSS Behavioral risk factor surveillance system

CERRA Leiden University Center for Research on Retirement and Aging Panel

CHS US National Centre for Health Statistics

CIDJ Chinese Inequality and DistributiveJustice survey project

CMF Compressed Mortality File of the National Centre for Health Statistics

CNEF Cross-national equivalent file

ECHP European Community Household Panel

FORS Future of Retirement Survey

FSUH Financial Survey of Urban Housing

GHN Globalization-Health Nexus database

GHS General Household Survey

GSOEP German Socioeconomic Panel

GTD WHO Global Tuberculosis Database

HRS Health and Retirement Study

HSE Health Survey of England

IOT International Obesity Taskforce

LCMS Living Condition Monitoring Study

LLH Living Conditions, Lifestyle, and Health survey

NCD Swedish National Cause of Death Statistics

NHANES National Health and Nutrition Examination Survey

NITS Swedish National Income Tax Statistics

OECD OECD Health Data

PSID Panel Study of Income Dynamics

PUMS US Census Public Use Micro Sample

RHS Retirement History Study

SALDRU South African Labour & Development Research Unit survey

SEDLAC Socio-Economic Database for Latin America and the Caribbean

SHARE Survey of Health, Ageing and Retirement in Europe

SOCIOLD Socioeconomic and occupational effects on the health inequality of the older

workforce

STF US Census Summary Tape File 3C

ULF Statistics Sweden’s Survey of Living Conditions

UNHDR United Nations Development Report

WDID World Bank World Development Indicators

WHO Various databases

WIID WIDER World Income Inequality Database

Table A2 Acronyms used in tables—cont'd

Name/definition

Health measures

ADL Activities of Daily Living

U5MR Under-5 mortality rate

GHQ General health questionnaire (psychological health)

HAZ Height-for-age z-score

HSCL Hopkins Symptoms Checklist

IMR Infant mortality

LE Life expectancy

Major diagnosis Cancer, heart disease, lung disease (McClellan, 1998—minor)

Minor diagnosis Hypertension, diabetes, stroke (McClellan, 1998—major), arthritis, back pain

MR Mortality rate

SAH Self-assessed health

SB Stillbirth rate

WHZ Weight-for-height z-score

Estimators

DID Difference-in-differences

DP Dynamic programming

FD First difference

FE Fixed effects

GMM Generalized method of moments

GOP Generalized ordered probit

GPSM Generalized propensity score matching

IV Instrumental variables

LPM Linear probability model

MSM Method of simulated moments

OLS Ordinary least squares

QR Quantile regression

RE Random effects

SML Simulated maximum likelihood

2SLS Two-stage least squares

2SQR Two-stage quantile regression

WLS Weighted least squares

DATA SOURCES

We use data from the Chinese Health and Nutrition Survey (CHNS), the Netherlands Longitudinal Internet Studies for the Social Sciences (LISS), and the ALP. We thank the organizations that collected and provided these data.

The LISS panel data were collected by CentERdata (Tilburg University, The Netherlands) through its MESS project funded by the Netherlands Organization for Sci­entific Research.

RAND ALP are proprietary capabilities ofthe RAND Corporation. They were devel­oped by RAND with its own funds and with the support of numerous clients and grantors who have commissioned social science and economics research and analysis at RAND.

The National Institute of Nutrition and Food Safety, China Center for Disease Con­trol and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, and R.01-HD38700) and the Fogarty International Center, all contributed to the collection of the CHNS.

We thank the Equality Trust Fund for providing the data to reconstruct the first figure in Section 17.5.

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