V. Demographic Change, Personal Saving, and Medical Expenditure¹

1. A variety of perspectives have been used to explain the decline in U.S. personal saving since the early 1960s.² Recently, one area of increased interest has been the effects of changes in demographics on saving. The life-cycle model of consumption suggests that such demographic shifts should contribute to a decline in saving as the population ages, but empirical studies thus far have not suggested that demographics alone have played a significant role in explaining the saving decline. However, there has been a substantial redistribution of income to the elderly through programs such as Social Security and Medicare, and it may be through such resource transfers from groups with higher propensities to save to groups with lower propensities that population aging has played an important role in reducing the personal saving rate. In particular, medical expenditure as a share of disposable income has risen sharply over this period, closely matching the decline in saving, and it can be shown that this rise in medical spending may be linked to demographic factors (Chart 1).

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UNITED STATES: PERSONAL SAVING RATES AND MEDICAL EXPENDITURES

Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A005

Source: Bureau of Economic Analysis, U.S. Department of Commerce, National Income and Product Accounts.

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2. The life-cycle model suggests that saving rates follow a “hump-shaped” profile over individuals’ lifetimes. Earnings are expected to rise with age up to retirement, and decline subsequently. The model predicts that individuals will borrow against future labor income when young, become net savers later in their earning years, and spend their accumulated savings (dissave) during retirement. In fact, estimated savings profiles by age group show just such a “hump shape,” with a peak in the individual’s saving rate at around age 60 (Table 1).³

Table 1.

United States: Savings Rates, by Age, Selected Years

(In percent)

Sources: Browning and Lusardi (1996); and Consumer Expenditure Survey.

Table 1.

United States: Savings Rates, by Age, Selected Years

(In percent)

Age	25-34	35-44	45-54	55-64	65+
Savings ratio, 1972/73	9.5	12.1	16.8	22.9	14.9
Savings ratio, 1982/85	9.6	8.6	10.5	15.8	11.5

Sources: Browning and Lusardi (1996); and Consumer Expenditure Survey.

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Table 1.

United States: Savings Rates, by Age, Selected Years

(In percent)

Age	25-34	35-44	45-54	55-64	65+
Savings ratio, 1972/73	9.5	12.1	16.8	22.9	14.9
Savings ratio, 1982/85	9.6	8.6	10.5	15.8	11.5

Sources: Browning and Lusardi (1996); and Consumer Expenditure Survey.

3. The life-cycle model clearly implies that an economy with a large proportion of households nearing the end of their life cycles would have a lower aggregate saving rate than one with a small proportion. Empirical research also points to the existence of different propensities to save across age groups. Nevertheless, beyond these two basic points, there is little agreement on precisely how saving may have been affected by demographic changes. The broad conclusion from the empirical literature is that population aging by itself cannot explain a significant part of the decline in saving, mainly because the shift in the age distribution of the population thus far has been too small and because average propensities to save do not differ sufficiently across age groups.⁴ Indeed, Gokhale, Kotlikoff and Sabelhaus (1996) show that applying the age distribution from earlier years to the data for 1987–90 could result in either a lower or higher saving rate, depending on the time period from which the age distribution is chosen.

4. Even though the increase in the relative number of the elderly is not enough to account for the decline in saving, Gokhale, Kotlikoff and Sabelhaus (1996) find that a redistribution of resources (in particular, in the form of transfers) toward the elderly may account for the decline in saving. The impact of this shift in resources from age groups with a high saving rate (the young) to those with a tow saving rate (the old) ran be understood by noting that the overall saving rate can be expressed as,

where Y denotes income and s the saving rate out of income. If S_young is greater than s_old, transferring a dollar of income from a young to an old person reduces the numerator, but does not reduce the denominator, and so reduces the overall saving rate.

5. This effect would be even larger for in-kind transfers such as Medicare payments, since the elderly cannot save out of such transfers.⁵ Chart 2 shows how Medicare transfers have grown in importance over time. Since 1970, transfers from those under age 65 in the form of Medicare taxes to those age 65 and over in the form of Medicare payments for medical services have grown substantially, both in real terms and in terms of disposable income.⁶ Over this period, these transfers have grown from about 1 percent to about 3–3½ percent of disposable personal income. Chart 3 shows the importance of these transfers in real income per person. The transfers have significantly raised the trend in real income for those aged 65 and over, and also raised the percentage of total income accounted for by the elderly by an increasing amount over time.

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UNITED STATES: MEDICARE GENERATIONAL TRANSFERS

Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A005

Source: The Medicare Trustees’ Annual Reports; Bureau of Economic Analysis, U.S, Department of Commerce; and Fund staff estimates.1/ Estimated as total Hospital Insurance (HI) and Supplementary Medical Insurance (SMI) benefits received by Medicare enrollees.2/ Estimated as the sum of total HI payroll taxes and SMI revenues taken from the federal government treasury (assuming those under 65 account for 90 percent of federal government tax revenues).

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UNITED STATES: INCOME AND THE MEDICARE GENERATIONAL TRANSFER ^1/

Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A005

Source: Current Population Survey, Bureau of the Census, U.S Department of Commerce; and Fund staff estimates.^1/ Total income by age group was found by multiplying national population estimates for each age group by the mean income for each age group, as estimated by the Current Population Survey.

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6. Examination of the components of medical expenditure can shed light on the sources of the increase in medical expenditure per capita (Chart 4). Over the period 1970–96, total expenditures on medical care as a percentage of disposable income more than doubled, from about 8 to more than 16 percent of disposable income (see Chart 1). Much of the increase was accounted for by an increase in hospital and nursing home expenses, although other categories such as expenditures on physicians’ services registered significant percentage increases. In addition, increases in the price of medical care have significantly raised current-dollar expenditures on medical care, in light of the low elasticity of such expenditures with respect to price.⁷

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UNITED STATES: SELECTED TYPES OF MEDICAL EXPENDITURES

Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A005

Source: Bureau of Economic Analysis, U.S. Department of Commerce, National Income and Product Accounts.

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7. There is empirical evidence suggesting that, aside from the effects of rising Medicare spending, population aging more generally has been an important factor driving the increased expenditure on health care. For example, Fuchs (1984) shows that real health care expenditures by those aged 65 or older rose by an average of 8 percent a year over the period 1965–81, compared with an average of 5.3 percent a year for those aged less than 65. Public health care expenditures per elderly person rose by an average of 10.5 percent a year over the same period, compared with an average of 7.2 percent for nonelderly persons. Fuchs also found that per-capita hospital expenditures by the elderly rose by 6.8 percent a year over that period, compared with 5.8 percent for the nonelderly. In a cross-country study, O’Connell (1996) found that population aging contributed significantly to the relatively high level of health expenditure in the United States.

8. To further explore whether demographic changes associated with population aging can explain the increase in medical expenditures, simple regressions were estimated. Table 2 shows the estimated relationships between real medical expenditures per capita, some economic determinants of medical expenditures (relative prices, real income, and real wealth), transfers (real Medicare expenditures per person aged 65 or older), and three demographic variables: the proportion of the population over 65 years of age, life expectancy, and the proportion of the elderly who are over 75 years of age. Transfers are included to measure the effects of intergenerational transfers. Analyses of this kind traditionally include the proportion of the population older than 65 to capture the effects of demographics on the demand for medical care, but other measures are included here since progress in medical technology makes it difficult to interpret the significance of the elderly ratio. In particular, the average 65-year old in 1996 was likely to be much healthier than his or her counterpart in 1960. For this reason, the proportion of the relatively old among the elderly and life expectancy are also included. In addition, the “older” elderly are more likely to suffer from long-term illnesses that are expensive to treat.

Table 2.

United States: Explaining Per-Capita Real Medical Expenditures

Dependent variable: log (real medical services expenditures per capita).

Annual data, 1959–96. Regressions estimated using Phillips-Hansen fully modified OLS (Phillips and Hansen (1990)). An asterisk denotes significance at the 5 percent level. Variables are: Relative price: price deflator for consumption of medical services divided by the GDP deflator Real disposable income per capita: based on NIPA data. Medicare generosity: real Medicare expenditures (deflated by NIPA price index for consumption of medical services) per person 65 and over. Life expectancy: expected remaining years of life for a person aged 65 (simple average of rates for male and female). Prop, of elderly 75+: number of persons aged 75 and older as a percentage of persons 65 and older. Real wealth per capita based on Flow of Funds data.

Table 2.

United States: Explaining Per-Capita Real Medical Expenditures

Dependent variable: log (real medical services expenditures per capita).

	Independent Variables
C	Log Relative Price	Log Real Disposable Income Per Capita	Log Real Medicare Generosity	Life Expectancy	Prop, of Elderly 75+	Log Real Wealth Per Capital	Prop, of Population 65+
−4.9*	−0.72*	0.60*	0.12*	0.12*	0.08*	0.004	…
−3.5*	−0.48*	0.85*	0.16*	0.12*	0.07*	…	−0.07
0.8	0.23	1.57*	0.24*	0.15*	…	…	−0.23*
−4.9*	−0.69*	0.62*	0.12*	0.11*	0.08*	…	…

Annual data, 1959–96. Regressions estimated using Phillips-Hansen fully modified OLS (Phillips and Hansen (1990)). An asterisk denotes significance at the 5 percent level. Variables are: Relative price: price deflator for consumption of medical services divided by the GDP deflator Real disposable income per capita: based on NIPA data. Medicare generosity: real Medicare expenditures (deflated by NIPA price index for consumption of medical services) per person 65 and over. Life expectancy: expected remaining years of life for a person aged 65 (simple average of rates for male and female). Prop, of elderly 75+: number of persons aged 75 and older as a percentage of persons 65 and older. Real wealth per capita based on Flow of Funds data.

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Table 2.

United States: Explaining Per-Capita Real Medical Expenditures

Dependent variable: log (real medical services expenditures per capita).

	Independent Variables
C	Log Relative Price	Log Real Disposable Income Per Capita	Log Real Medicare Generosity	Life Expectancy	Prop, of Elderly 75+	Log Real Wealth Per Capital	Prop, of Population 65+
−4.9*	−0.72*	0.60*	0.12*	0.12*	0.08*	0.004	…
−3.5*	−0.48*	0.85*	0.16*	0.12*	0.07*	…	−0.07
0.8	0.23	1.57*	0.24*	0.15*	…	…	−0.23*
−4.9*	−0.69*	0.62*	0.12*	0.11*	0.08*	…	…

Annual data, 1959–96. Regressions estimated using Phillips-Hansen fully modified OLS (Phillips and Hansen (1990)). An asterisk denotes significance at the 5 percent level. Variables are: Relative price: price deflator for consumption of medical services divided by the GDP deflator Real disposable income per capita: based on NIPA data. Medicare generosity: real Medicare expenditures (deflated by NIPA price index for consumption of medical services) per person 65 and over. Life expectancy: expected remaining years of life for a person aged 65 (simple average of rates for male and female). Prop, of elderly 75+: number of persons aged 75 and older as a percentage of persons 65 and older. Real wealth per capita based on Flow of Funds data.

9. The results of the regressions are presented in Table 2. In all cases, as would be expected, the demand for medical services is relatively price inelastic. Real income per capita is also important in determining the trend in medical expenditures (real wealth is not as important, possibly because of liquidity constraints). Real Medicare expenditures per person over age 65 are also significant in each case. Life expectancy and the proportion of the older elderly in the population appear to be more significant than the elderly ratio (percent of the population over 65), perhaps reflecting trends in medical technology and demands for medical services with age. It is clear in any caw that demographics and transfers appear to have played an important role in the increase in real medical expenditure per capita.

10. Although significant direct effects on personal saving resulting from the aging of the U.S. population are difficult to find in the data, there appears to be an indirect link through the effects of government transfers, which have shifted resources between demographic groups. In particular, in-kind-transfers from younger to older age groups through programs like Medicare may have played a key role in explaining the decline in the savings rate since the early 1960s. The sharp rise in medical expenditures over this period, which coincides with the decline in saving, is related both to increases in Medicare and more generally to shifts in the age distribution of the population. The approaching surge in the population eligible for benefits under the main government transfer programs for senior citizens as the baby-boom generation begins to retire provides added reasons for dealing with the financial problems of these programs to mitigate possible further substantial declines in personal savings.