Angus Deaton

Christina H. Paxson

Abstract: This paper examines issues of household saving, growth. and aging in Taiwan. The Taiwanese patterns of high income growth, declines in fertility, and increases in life expectancy all have implications for life-cycle saving. We use data from fifteen consecutive household income and expenditure surveys. from 1976 to 1990, to examine whether observed profiles of consumption and saving are consistent with life-cycle theory. The patterns of consumption and saving across households of different ages and cohorts appear to be broadly consistent with a life-cycle model. However. the data also indicate that household consumption tracks income closely. and this evidence casts doubt on simple life-cycle theory.

Saving, Growth, and Aging in Taiwan

Poverty counts are counts of individuals in poverty but are calculated from household or family data on income or expenditure. The transition from one to the other requires assumptions about intrahousehold allocation, about differences in needs across different people, and about the extent of economies of scale. The number of elderly in poverty, or the number of children in poverty, is sensitive to these assumptions and to differences in living arrangements across age groups. We explore the sensitivity of poverty counts to variations in assumptions about child costs and economies of scale using data from the United States and from six large Indian states. Because living arrangements of the elderly are so different in the United States and India, the use of the latter forces us to think about household structure and poverty in the United States. We argue that the official poverty counts in the United States are compromised by unrealistically high costs of children and by unrealistically high economies of scale. We provide a discussion of how economies of scale and child costs can be estimated from the data, using identifying assumptions that label private goods and adult goods, and we make calculations based on the 1990 Consumer Expenditure Survey. We obtain plausible estimates of child costs, together with a number of interesting but hard-to-explain anomalies when we try to estimate economies of scale.

Measuring Poverty Among the Elderly

We investigate age-specific mortality in Britain and the United States since 1950. Neither trends in income nor in income inequality provide plausible explanations. Britain and the US had different patterns of income growth but similar patterns of mortality decline. Patterns of income inequality were similar in both countries, but adult and elderly mortality rates declined most rapidly during the period when inequality increased. Changes in the rate of mortality decline in the US led changes in Britain by about four years, most notably for infant and older adult mortality where there have been significant technical improvements in treatment. British mortality is lower, but the schedules cross at around age 65. This pattern was established before Medicare, and most likely comes from rationing by age in Britain. Merged income, income inequality, and mortality data on an age/year (or cohort/year) basis show no evidence that income has any effect on mortality in Britain. Education is protective, but less so than in the US. Understanding the effect of income on mortality presents many puzzles, between countries, and between analyses at different levels of aggregation. Our results suggest an important role for medical technology in determining the rate of mortality decline since 1950.

Mortality, Income, and Income Inequality Over Time in Britain and the United States

Abstract: People whose family income was less than $5,000 in 1980 could expect to live about 25 percent fewer years than people whose family income was greater than $50,000. We explore this finding using both individual data and a panel of aggregate birth cohorts observed from 1975 to 1995. We assume that health status is determined by social status, defined as income relative to the mean income of a reference group. When reference groups are not observed, health is a function of income whose slope (the gradient) depends on the ratio of within to between-group inequality. We derive results on how this relationship changes at different levels of aggregation. Our results on individuals show that income reduces the risk of death, and does so even controlling for education. Only some of the effect of income can plausibly be attributed to the reduction in earnings of those about to die. The panel of cohorts also shows a strongly protective effect of income, but there is evidence that cyclical increases in income may raise mortality, even when the long-run effects of income are in the opposite direction. There is no evidence that recent increases in inequality raised mortality beyond what it would otherwise have been.

Mortality, Education, Income, and Inequality Among American Cohorts

This paper examines whether the observed cross-country correlation between aggregate saving rates and economic growth can be explained by models in which higher growth increases saving rates, rather than the other way around. The paper focusses on two explanations of why growth might increase saving. First, standard life-cycle theory implies that higher growth will increase the life- time wealth of younger savers relative to older dissavers, thereby increasing the aggregate saving rate. Second, models of consumption with habit formation imply that consumption responds slowly to unexpected income growth, and so unanticipated growth can produce a higher saving rate at least in the short run. I assess the validity of these explanations using time-series of cross-sections of household income and consumption surveys from four countries: the US, Britain, Taiwan and Thailand. I find that although in three out of the four countries there is evidence that saving behavior is consistent with life-cycle theory, there is simply too little life-cycle saving for higher growth to have a large effect on the aggregate saving rate. The habit formation model also implies very small effects of growth on saving rates. A large portion of the observed cross-country correlation between saving and growth cannot be explained by these models.

Christina H. Paxson

Books

Your next favorite book is out there