Countries in Asia are at different points in the demographic transition. East Asian countries started earlier and are farther along, particularly Japan. The countries of South and Southeast Asia started later and are at a middle stage (Mason, Lee, and Lee, 2010). The changes in population growth rates and sizes over the transition are certainly important, but here we focus particularly on the changes in population age distributions and do not consider changes in the scale of the population.
Populations passing through the transition start with high proportions of children and low proportions of elderly and eventually move to the reverse situation: relatively few children and many elderly. In the earliest part of the transition, the proportions of children often increase because of declining infant and child mortality. In the middle of the transition, while fertility is declining, the proportions of the population in the working ages rise over a half-century or so and total dependency ratios fall. The resulting boost to per capita income growth is an important component of the “demographic dividend.” However, as fertility bottoms out and the growth of the working age populations slows, the population ages as the ratio of elderly to working age rises. In the end, the proportion
1 Research for this chapter was funded by parallel grants from the National Institutes of Health to Lee and Mason, NIA R37 AG025247 and R01 AG025488. We are grateful to Gretchen Donehower and Turro Wongkaren for their help and to all the country research teams in the National Transfer Account (NTA) project for use of their data. The researchers are identified and more detailed information is available for many countries in working papers on the NTA website: http://www.ntaccounts.org
of the population in the working ages may be close to its level before the transition began—but with the elderly traded for dependent children.
Children and the elderly are similar from an economic perspective, because both groups have labor income that is small relative to their consumption. They must rely on sources other than their labor to provide for their material needs. However, children rely almost exclusively on public and private transfers to provide for their net consumption needs, while the elderly, in addition to these sources, may also rely on accumulated assets to fund their consumption. These assets have an important bearing on economic performance because they are a source of non-labor income and, in addition, if invested in the domestic economy, they raise its labor productivity. To the extent that the elderly rely on assets to fund their old age consumption, the burden on workers (and taxpayers) is reduced, and actual and anticipated population aging and longer life can accelerate the accumulation of capital and boost economic growth. To summarize, population aging has a negative effect on per capita consumption through increased dependency and may also have a positive effect through increased capital accumulation.
We will also suggest that lower fertility, the most important source of population aging, is associated with higher human capital investments per child. Thus, over the demographic transition, the quality and productivity of workers rise at the same time that their numbers fall. This change and the effects of population aging on physical capital provide two powerful mechanisms for maintaining or increasing standards of living despite the deterioration in the support ratio.
Estimates in this chapter are based on National Transfer Accounts (NTAs), an international project that draws on the work of research teams in 36 countries on six continents to estimate age profiles of key economic flows across age (see Table 4-1, along with the dates to which the NTA estimates refer.
NATIONAL TRANSFER ACCOUNTS: DATA AND METHODS
NTAs are composed of four broad economic flows: labor income, consumption, transfers, and asset-based flows. Where appropriate, flows are distinguished by whether they are public or private and by their purpose (education, health, and other). The relationship among the flows is captured by the flow constraint: The gap between consumption and labor income must equal net transfers plus asset-based flows. Only a brief overview of methods can be provided here. They are documented fully in Lee and Mason et al. (2011) and Mason et al. (2009).
The age profiles of labor income and consumption provide a cross-
TABLE 4-1 Annual Rate of Change of Support Ratio, 2010 to 2050, Sorted by Rate
|Economies (Code) Year||Annual Change (%)|
|Austria (AT) 2000, Germany (DE) 2003, Japan (JP) 2004, Slovenia (SL) 2004, South Korea (KR) 2000, Spain (ES) 2000, Taiwan (TW) 1998||-0.82 to -0.60|
|China (CN) 2002, Finland (FI) 2004, Hungary (HU) 2005, Sweden (SE) 2003, Thailand (TH) 2004, US (US) 2003||-0.42 to -0.25|
|Brazil (BR) 1996, Chile (CL) 1997, Costa Rica (CR) 2004, Mexico (MX) 2004, Uruguay (UY) 2006||-0.21 to -0.01|
|India (IN) 2004, Indonesia (ID) 2005, Philippines (PH) 1999||+0.07 to 0.31|
|Kenya (KE) 1994, Nigeria (NG) 2004||+0.56 to 0.76|
sectional characterization of the economic lifecycle at a point in time.2 Labor income is a comprehensive measure that includes the pretax income of males and females, those in the labor force and those not (who enter as zeros), and unpaid family workers.3 It includes wages, salaries, and fringe benefits, as well as two-thirds of self-employment income, with the other one-third counted as asset income. Consumption consists of private consumption that is imputed to individuals within each household,4 as well as all public consumption including public education, publicly provided
2 These age profiles are cross-sectional and do not accurately represent longitudinal life cycle profiles. For example, it is often the case that the cross-sectional age profile rises over time with productivity growth. In this case, a longitudinal age profile might continue to rise throughout life rather than turning down at older ages as it does in the cross-section. Longitudinal profiles can be approximated by introducing an assumed rate of productivity growth. When data for calculating repeated cross-sections are available, as they are in a number of countries, empirical longitudinal profiles can be estimated. Here we restrict our attention to the cross-sectional age profiles.
3 The National Transfer Accounts are currently unisex, and all results presented here are averages across the sexes. It would be straightforward to estimate labor income separately for men and women, but because so much of women’s work is in home production that falls outside National Income and Product Accounts, doing so would give a misleading picture of production differences by gender and therefore of net transfers between men and women. In order to introduce gender into the accounts, it would be necessary to draw on time use studies to estimate and monetize home production. We have begun preliminary work along these lines, but cannot address these issues here.
4 For private consumption expenditures, the basic approach is to allocate health and education expenditures to household members using information in the survey, either directly or using regressions, while the remainder of household expenditures is allocated in proportion to equivalent adults consumption weights equal to .4 for ages 0-4, rising linearly until 1.0 at age 20, and 1.0 thereafter.
health care and long-term care, and other forms of public consumption (evaluated at cost of provision).
Transfers and asset-based reallocations are the complement of the economic lifecycle. Transfers consist of both public and private transfers. Public transfer inflows consist of cash transfers, such as pensions or family assistance, and in-kind transfers, equivalent to public consumption. Public transfer outflows are equal to the taxes paid by each age group to fund public transfer inflows. Net public transfers are the difference between public transfer inflows and outflows. Private transfers consist of inter-household transfers, including private transfers to and from the rest of the world, and intra-household transfers, in other words, transfers between co-resident household members.
Asset-based flows are defined as asset income less saving. Asset income includes the returns to capital held by corporations, partnerships, and individuals, including the return to owner-occupied dwellings. Asset income also includes property income—dividends, rent, and interest income and expense. Borrowing, lending, and repayment of loans are also included here. Saving is equivalent to net national saving. Public and private asset income and net saving are estimated, but only the combined values are presented here.
Per capita flows are calculated as population averages over all individuals at a given age, both males and females. For purposes of comparison, we divide each country’s age profile of labor income and consumption by the average level of labor income for that country at ages 30-49, ages chosen to avoid masking the effects on profiles of decisions about schooling and retirement. Aggregate flows are calculated as the product of per capita flows and population at each age.
NTAs are estimated using existing cross-sectional surveys, National Income and Product Accounts (NIPAs), and public-sector administrative data. The age profiles of private flows such as consumption and transfers are estimated using nationally representative household surveys of income, consumption, and the labor force. The profiles for Japan, for example, are based on the 2004 National Survey of Family Income and Expenditure, for India on the 2004 India Human Development Survey (IHDS), and for China on the 2002 China Household Income Project (CHIP). Public flows estimates are based on a combination of household surveys and public administrative records and reports. For example, age-specific estimates for public education make use of national public education spending for primary, secondary, and tertiary levels combined with age-specific enrollment rates.
Initial estimates of per capita NTA age profiles are not necessarily consistent with their macroeconomic counterparts from NIPAs. Thus, per capita profiles are scaled, adjusted by a factor that is constant across age,
to ensure that aggregate profiles are consistent with NIPAs. This procedure assumes that errors in estimates are proportionately the same at each age.
This mode of construction ensures that the general level of the NTA profiles, if not every detail of shape, will be as accurate as NIPAs. Of course, there may be quality problems with the NIPAs themselves, but it is beyond the scope of the NTA project to try to improve NIPAs. For some quantities, such as intrahousehold transfers, there is no counterpart in NIPAs, but by construction these transfers must sum to zero for each household. For interhousehold transfers, the control total is the NIPA number for net transfers with the rest of the world, which allows for remittances, for example.
We want to emphasize that these are cross-sectional profiles, and hence they are influenced by differences across cohorts, such as differences in educational attainment, wealth, and attitudes, as well as differences that can be attributed to aging per se.
ECONOMIC ACTIVITY OVER THE LIFECYCLE
The estimated profiles of consumption and labor income are shown in Figure 4-1. Panel A shows the per capita profiles for the four poorest NTA countries outside Asia and likewise for the four richest NTA countries outside Asia. Children begin productive economic activity at a younger age in the poor than in the rich countries, and their labor income is higher. At advanced ages, labor income begins to decline earlier in the poor countries. Near age 60, labor income drops precipitously in rich countries, due in part to the incentives and opportunities provided by public-sector pension programs (Costa, 1998; Gruber and Wise, 1999, 2001). Labor income is lower in the rich countries than the poor from the early 60s until age 85 or so. The greater labor income at the extremes of the age distribution is a characteristic feature of the poor countries.
There are also striking differences between the consumption age profiles in the poor and rich countries. In the rich countries, there is a characteristic bulge in consumption by children and youth, reflecting the heavy investment per child in human capital. It is equally striking that in the poor countries, consumption is quite flat from age 20 or so until the end of life. In the rich countries, by contrast, consumption rises from the early 20s on, and particularly after age 80 or so, when the costs of health and long-term care rise dramatically. Not all of this upward trend with age, however, is due to rising costs of health care and long-term care.
Putting together the age patterns of consumption and labor income, we see that population aging in rich countries is more costly than in poor ones, because the elderly in rich countries consume more and produce less than in poor countries.
FIGURE 4-1 Age profiles of consumption and labor income for countries and regions of the world, from National Transfer Accounts.
NOTE: See Table 4-1 for country codes.
SOURCE: Calculated from the National Transfer Accounts database. See Lee and Mason (2011).
Panel B compares the age profiles of Japan to the average for the rich non-Asian countries. Investment in human capital in Japan is higher per child, particularly in late secondary and tertiary education. The broad pattern of consumption at older ages is similar, but after age 60, consumption is even higher in Japan. The age pattern of labor income in Japan is strikingly different. It begins a few years later, a gap that persists until age 40
or so. Most interestingly, the whole distribution is then pushed to the right, with earnings relatively higher from the late 40s through the early 60s, apparently reflecting the influence of the seniority system in Japan. Labor income in old age is also higher in Japan, although broadly similar in shape to the other rich countries.
Panel C shows the average age profiles for South and Southeast Asia (combined) and for East Asia excluding Japan and includes the average for the poor countries for comparison. For the moment, we set aside the difference in level of consumption, which largely reflects the situation in China. The shapes of the consumption curves are very similar above the early 20s. However, at younger ages, East Asia stands out for the high level of investment in human capital of children, which is indeed an important feature of the region. As for labor income, the profile for South and Southeast Asia looks much like that for the other poor countries. However, the age profile for East Asia starts at older ages, reflecting the greater human capital investment, and then rises more rapidly, crossing the other profiles at around age 20. Thereafter, the East Asian age profile is shifted several years to the left of the other two, to higher values up until 40 or so and then lower values after the mid-40s. This early decline in labor income is striking, and we are not sure what explains it.
Finally, Panel D compares the age profiles for India (Ladusingh and Narayana, 2011) and China (Li, Chen, and Jiang, 2011). Labor income in the two countries moves in lockstep until around age 40, but then it drops rapidly in China while continuing at a high level in India until age 60, when it drops toward the Chinese level. As for consumption, the most striking difference is that the level of consumption in China is very low relative to labor income. This reflects the extraordinarily high level of saving in China (see Li, Chen, and Jiang, 2011).
We can use these age profiles of consumption and labor income to calculate the way that changing population age distribution would affect the ratio of producers to consumers, on the hypothetical assumption that the age profiles remain the same over time or shift at the same rate, for example due to productivity growth. Given the base-year age profile of consumption, we calculate the number of “effective consumers” in a year by multiplying the population at each age by the consumption profile for that age and then summing over all ages. The number of effective producers is calculated similarly. The support ratio for a year is the ratio of effective producers to effective consumers. In the base year, the number of effective consumers equals aggregate consumption in the economy and effective producers equals aggregate labor income. In most countries,
aggregate consumption exceeds aggregate labor income, so the support ratio is typically less than unity. Our interest is in the proportional changes in the support ratio, not in its level. Changes depend entirely on changing population age distributions as these interact with a country’s age profiles. For easier comparison of proportional changes across countries, we set average consumption at ages 30-49 equal to 60% of average labor income at these ages in the base year. This affects the level of the support ratio in every year, but it does not affect its proportional variations over time. Without this adjustment, China would have a very, very high support ratio because it has an exceptionally high aggregate saving rate.
Support ratios calculated in this way for 1950 to 2050 are shown in Figure 4-2 based on United Nations estimates and projections (United Nations Population Division, 2009). Panel A shows regional averages for Europe, Latin America, and Africa. Europe largely achieved replacement level fertility by the 1930s, and the trajectory of its support ratio in Panel A reflects its post-World War II baby boom and baby bust more than the transition itself. Population aging will reduce Europe’s support ratio to low levels starting around 2010. In Latin America, rising support ratios will boost growth rates of per capita income—an effect called “the demographic dividend”—until around 2030, after which population aging will reduce the support ratio less rapidly than in Europe. Africa, as represented here by only Kenya and Nigeria, is at the early stages of the demographic dividend phase and will benefit from it for many decades to come.
Panel B shows that China, Taiwan, and South Korea have quite similar support ratio trajectories, with deeper and more rapid variations than seen in Panel A. All three economies are about to end the dividend phase and to embark on declining support ratios, China a bit more gradually than the others. Japan’s trajectory is very different from Europe’s and from those of other East Asian economies.
In Panel C, Thailand’s trajectory resembles that of the emerging East Asian economies in Panel B, while the other South and Southeast Asian countries’ trajectories are similar to Latin America’s in Panel A.
Finally, Panel D shows China, Japan, and India together for comparison purposes. They are indeed very different. Simplifying, one might say that the trajectories are similar in shape, but Japan’s is 20 years ahead of China’s, which is, in turn, 30 years ahead of India’s. India has 30 more years to arrive at the dividend phase, while China has just exhausted it and Japan has already been aging for years.
We can also calculate the rates of change of these support ratios. In economies with exceptionally rapid fertility transitions, such as Japan, Mexico, South Korea, Taiwan, and Thailand, the support ratio sometimes changes by more than 1% per year. Table 4-1 shows the average
FIGURE 4-2 Support ratios for countries and regions.
NOTE: See Table 4-1 for country codes.
SOURCE: Calculated from National Transfer Accounts data and United Nations Population Division (2009).
rate of change of the projected support ratio between 2010-2050 for the NTA countries. We see that the majority (five out of eight) of the Asian NTA economies have declining support ratios over this period, indicating that population aging will reduce the rate of consumption growth, other things being equal. In the remaining three countries, projected support ratios rise over the next four decades.
HOW DO THE ELDERLY FUND THEIR CONSUMPTION?
As discussed earlier, children’s consumption is funded largely by public and private transfers, and partly by their own productive efforts, particularly in poorer countries. The elderly, however, may additionally use assets accumulated during their earlier working years through saving and/or inheritance. Where assets are an important source of funding for consumption in old age, an increase in the ratio of elderly to working age population will raise the ratio of assets to workers. This will be true whether asset accumulation takes place through funded government pension programs (as opposed to pay-as-you-go pensions), funded occupational pensions, or direct saving by individuals. If these assets are invested in domestic capital, then capital per worker will rise, raising the productivity of labor and offsetting to a greater or lesser degree the drop in support ratios (Lee, Mason, and Miller, 2003; Mason and Lee, 2007).
We first combine public and private transfers into the single measure “transfers” and consider how the funding of old age consumption is divided among transfers, labor income, and asset income. It is possible for an elderly person to own substantial assets but to save the asset income rather than use it to fund consumption. In this case, we will not count it as a funding source for consumption. On this principle, we can calculate the percentage of old age consumption that is funded by one’s own labor income, by net transfers, and by assets. Net transfers can have a negative value, indicating that net transfers are made to younger people. The percentages must add to 100%.
Figure 4-3 is a triangle graph. The proportion of each of these three funding components is indicated along one side of the triangle. If a country is situated at one of the vertices of the triangle, then elder consumption is funded 100% by the source that names that vertex. If a country is situated on an edge of the triangle, then support comes entirely from a mixture of the two sources naming the vertices that the edge connects. If a country is inside the triangle, the elderly are funded by a mix of all three sources. If a country is situated to the right of the triangle, the elderly are receiving negative net transfers, meaning that they contribute to others, rather than the reverse.
In Figure 4-3, the points cluster around a line from the transfer vertex (where Austria, Hungary, Slovenia, and Sweden are funded nearly 100% by transfers) to a point on the opposite edge (between India and Indonesia). Along this line, for a given share of transfers, about one-third of the balance of the funding comes from labor income and two-thirds from assets. Most of the action in the chart reflects the tradeoff between relying on transfers and on assets, with less systematic variation in labor income as a source. Nonetheless, in economies relying more on assets,
FIGURE 4-3 Shares of elder consumption funded from labor income, transfers, and asset-based reallocations in seventeen NTA countries.
NOTE: See Table 4-1 for country codes.
SOURCE: Mason et al. (2011).
people also rely more on labor income in old age. The Asian countries fall close to the line, with China, Japan, South Korea, and Taiwan closer to the transfers vertex and India, Indonesia, and the Philippines at the other end of the line. These are also the poorest NTA countries in the region, and poor countries generally have higher labor income in old age. Note also that India and Indonesia are well outside the triangle, indicating that the elderly make net transfers to others rather than receive them.
A surprising result at first glance is the extent to which the elderly are relying on assets in some relatively low-income countries like India, Mexico, and the Philippines. The imputed rent from owner-occupied housing is important for many elderly living in these countries, and if the elderly own a productive asset such as a farm used as part of a family enterprise, one-third of the income will be allocated to the asset and therefore to the elderly. Also it should be kept in mind that we are measuring the extent to which the elderly are relying on assets to fund their consumption. In many countries the elderly may save most or all asset income, rather than use it to fund their consumption.
The next step is to set labor income aside and consider how consumption net of labor income is funded. The advantage is that by looking at how elder consumption net of labor income is funded, we can distinguish public and private transfers as well as asset income. Figure 4-4 shows the result. Most countries are arrayed along the edge joining assets and public transfers, indicating that the elderly have zero net family transfers or, in most cases, they are making net familial transfers to younger people. Public transfers are a very important source of old age support for most countries. Asian countries, however, rely more prominently on the family as a source of old-age consumption. India, the Philippines, and Thailand lie on the edge joining assets and family transfers, indicating that public transfers play no role. Indonesia also lies on this edge-line, but so far above the asset vertex as to be outside the range of the chart. China, South Korea, and Taiwan are well inside the triangle, indicating that old age support is
FIGURE 4-4 How is elder consumption net of labor income funded? Shares of public transfers, family transfers, and asset-based reallocations for seventeen NTA countries.
NOTE: See Table 4-1 for country codes.
SOURCE: Calculated from data in Lee and Mason (2011).
drawn from all three sources, with Taiwan drawing quite equally on the three. Japan lies on the edge connecting assets and public transfers, indicating that on net, elders neither give nor receive family transfers.
In the countries located near the public transfers vertex, the elderly have little motivation to save for retirement on average, because their consumption needs are met by public pensions and healthcare. This does not mean that there is little saving, because there are certainly many other motives to save besides provision for old age. It does mean, however, that in these countries, population aging is less likely to produce a second demographic dividend by raising asset accumulation and making the economy more capital intensive and the labor more productive.
POPULATION AGING AND HUMAN CAPITAL ACCUMULATION
The main cause of population aging is low fertility, not longer life. East Asia has had exceptionally rapid and deep fertility declines, and the average total fertility rate in the region is now 1.5 births per woman, with Taiwan lowest at 1.0. East Asia is also known for its heavy investment in education per child, and it is sometimes suggested that this is one of the explanations for its rapid economic growth over recent decades. Might the very low fertility be linked to the high investment in human capital?
A well-developed theory in economics, originally due to Becker and Lewis (1973) and Willis (1973), asserts that parents derive utility from both the quantity and the average quality of their children, as well as from their own consumption. “Quality” indicates the amount spent to rear each child on average. Quantity and quality interact in the budget constraint multiplicatively, since the total amount spent by parents on their children is the number of children times the amount spent per child, quantity times quality. In the extreme case often used for expositional purposes, the parents first decide an amount to allocate to children in total, and then decide how to allocate it between quantity and quality. In this case the quantity-quality tradeoff is particularly stark: Quality = Total Funds Pre-Allocated to Children Divided by Quantity.
When income rises over time, the demand for quality, which is posited to have a larger income elasticity than the demand for quantity, rises, and this raises the shadow price of quantity. The net result is that parents opt to have lower fertility and to invest much more in each child. Other factors can also alter the balance between quantity and quality, such as the rate of return to human capital as perceived by parents, the availability of contraceptives, or improved transportation networks that reduce the cost to parents of sending their children to school.
Although the quantity-quality theory was developed for private expenditures, it may also characterize public spending on human capital.
The rise in the support ratio may ease fiscal constraints faced by governments, allowing them to invest more in human capital. Governments might be forward-looking and invest more in human capital so as to offset the coming decline in the number of workers.
In light of these various possibilities, it is interesting to look at the cross-national relationship between investment in human capital per child and the Total Fertility Rate (TFR). (See Lee and Mason, 2010a, for a more detailed analysis of the full set of NTA countries.) First, however, we explain how we measure human capital investment. The theoretical literature (Becker et al., 1973; Willis, 1973) counts all expenditure on children as quality. We choose to focus on the explicit spending on education and health, since this is more clearly discretionary (rather than being passively tied to parental consumption such as housing and meal choices) and is more clearly linked to later life labor productivity. We sum from the NTA estimates of public and private spending per child for each single year of age. For education, these sums go from 0 to 26 to include postgraduate education, while for healthcare they go from 0 through 17. This gives a synthetic cohort estimate of human capital investment per child, with public and private spending combined. We do not attempt to take into account the opportunity cost of the children’s time as a costly input. As elsewhere, we standardize the results for different countries by dividing the human capital measure by average labor income for ages 30-49. Our measure, then, can be interpreted as the number of years’ worth of prime age labor income that are invested in human capital per child. For fertility we use the average TFR in the five years preceding the NTA observation year. In the stylized story, the product of quantity and quality is constant, in which case the logs of quantity and of quality should be linearly related with a slope of −1.0. We will therefore plot the logarithm of our measure of human capital investment against the logarithm of fertility.
Figure 4-5 shows the result for the eight Asian countries, for both total and private human capital expenditures. For total human capital expenditures, there is a strong negative relationship, with an elasticity of −.91, quite close to the expository model prediction of −1.0. For private expenditures alone, the elasticity is similar at −.89. However, the scatter is much tighter to the line for the total investment than for the private investment, consistent with the idea that public and private investments substitute for one another.
The log of standardized public expenditure on human capital in a country is shown by the gap between the two markers (circle and cross) for that country. Thus Japan and Taiwan invest the same amount in total, but Taiwan has low public spending and high private spending, while Japan has the opposite. China and South Korea both have low public spending, whereas Thailand has unusually high public spending.
FIGURE 4-5 Investment in human capital in relation to total fertility rate in Asia.
NOTE: See Table 4-1 for country codes.
SOURCE: Calculated from data in Lee and Mason (2011).
The East Asian countries have far lower fertility than those in Southeast Asia or India, and they have correspondingly higher human capital investment per child. The per child human capital investment in Taiwan and Japan is approximately five years of prime age labor income, an amount comparable to that of Europe.
Elsewhere we have looked at the relation of changes over time in fertility and human capital in Japan, Taiwan, and the United States, and found similar results (Lee and Mason, 2010b).
We expect that as fertility falls in other Asian countries, spending per child will rise, with beneficial effects for future labor productivity and economic growth. To some degree, quality of labor will be substituted for quantity of labor, reducing the difficulties of the working ages in providing for the elderly population.
NATIONAL TRANSFER ACCOUNTS AND HEALTH AND RETIREMENT STUDIES (HRS)
National Transfer Accounts rely on survey data as a key source of information, in addition to National Income and Product Accounts and various kinds of administrative data. The richness and quality of HRS-type data (henceforth, HRSTD) can potentially improve the quality of the NTA estimates in a number of ways, despite its restriction to respondents who are at least 45 or 50 years of age. HRSTD can provide high-quality data on interhousehold transfers to and from the elderly. While NTAs require such data for the entire age range, HRSTD can be used to check the quality of the NTA estimates where they overlap. Preliminary work along these lines for the United States has so far found good agreement between the NTA and HRS data, after appropriate adjustments are made to bring the concepts into line.
HRSTD on bequests should be particularly valuable, because data on bequests, particularly smaller bequests, are hard to come by. In some countries, data on savings and asset holdings are also not readily available. Savings rates are necessary for NTA flow accounts. Ordinarily, they are calculated in NTA as a residual, so having high quality data to check against this residual is valuable, in part because matching the residual would provide a partial confirmation of the other estimates from which it is derived. Asset holdings are necessary to construct NTA wealth accounts. The difficulties with measures of asset holdings are particularly severe in East Asia, where many elderly transfer ownership of their assets to their co-residing adult son well before the time of death. This practice makes it very difficult to trace the accumulation of assets over the life course, which is necessary for understanding how population aging affects asset accumulation. It should be possible, using longitudinal HRSTD, to trace asset transfers of this sort. With the data now used to construct NTAs, we attribute ownership of assets to the head of the household, which may obscure the behavioral processes that lead to its original accumulation.
If HRSTD can help improve NTA estimates, NTAs also broaden the picture offered by HRSTD in certain respects. Most obviously, NTAs cover the entire age range, not just the elderly. But there is much more. NTAs estimate intrahousehold transfers such as transfers to co-resident elders. Since familial transfers are a very important source of support for the elderly in Asia, as we saw in Figure 4-5, it is important to develop information about their size absolutely and relative to other sources of support available to the elderly. Familial transfers to co-resident elderly also provide for intergenerational sharing that enables the elderly to share in the benefits of very rapid economic growth long after they have left the labor force.
NTAs also provide a natural interface with macroeconomic models and analyses, including overlapping generations models and Auerbach-Kotlikoff-Gohkale style Generational Accounting (Auerbach, Gokhale, and Kotlikoff, 1991; Auerbach, Kotlikoff, and Leibfritz, 1999). Estimates derived from HRSTD can be inserted in NTAs and used for this purpose. Similarly, NTAs lend naturally to long-term projections and assessments of fiscal sustainability of public-sector programs.
Finally, the exercise of formulating NTA estimates leads to questions that can be addressed using the microanalysis of HRSTD. The question raised above about the transfer of ownership of elders’ assets at the point of moving into an adult child’s house is a case in point.
Population aging in East Asia will be early and profound, due to early fertility declines to very low levels and high and rising life expectancy. In most of Southeast Asia (Thailand is an exception) and India, aging will come later and more gradually. What can be said about the economic effects that this population aging will have? The data presented in this chapter provide some insights and raise some questions.
Japan is the richest Asian country and had the earliest fertility transition. Unlike other Asian countries, Japan has also instituted public sector transfer programs for the elderly that are quite similar to those in Europe, with generous pensions, health care, and long-term care. As a result, Japan will face similarly severe long-term fiscal problems as its population ages. As in Europe and the United States, the consequences of population aging in Japan are exacerbated by a strong upward gradient in consumption by age, a pattern that has probably emerged in recent decades as the welfare state has grown.
In the rest of Asia, the public-sector transfers to the elderly are very low, and if they remain so, then population aging will not threaten fiscal sustainability. However, it would not be surprising if they followed Japan and other rich countries in coming decades and developed similar public transfers to the elderly.
Without public transfers for the elderly, one might wonder whether the family will instead bear the costs of population aging. Indeed, in East Asia and in Thailand, net familial support of the elderly is important. In India and Southeast Asia, however, neither public transfers nor net familial transfers go to the elderly. The elderly, who continue to earn labor income, also receive substantial asset income and use it not only for their own consumption, but also to make net transfers to their children. These downward transfers, funded by assets, may reflect residence by children’s families in homes owned by their elderly parents or may reflect familial
work on farms owned by the elderly parents, to whom our methods impute one-third of family farm output as the asset share. In any case, in these circumstances population aging would impose smaller costs on the working age population. Furthermore, outside of Japan, consumption is flat across age from the early twenties until death, which means that population aging will be less costly for families.
Population aging may also be associated with increased physical capital and increased human capital per worker. In countries where the elderly hold substantial assets that they accumulated through their savings out of their lifetime earnings rather than through inheritance, population aging will tend to raise asset holdings per capita, and if these are invested in the domestic economy, then the rising capital labor ratio will boost productivity and wages. In addition, the low and declining fertility that is the main cause of population aging is associated with increased investments in human capital per child, raising future productivity and earnings. This has been particularly so in Asia, both through public and private spending. In this way, quality of workers may be substituted for quantity, further reducing the adverse effects of population aging in this region.
The economic consequences of population aging in Asian countries will depend on whether they follow the path of Japan or instead retain the current features of their public sectors and private economic behaviors.
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