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Aging in Sub-Saharan Africa: Recommendations for Furthering Research 7 Labor Force Withdrawal of the Elderly in South Africa David Lam, Murray Leibbrandt, and Vimal Ranchhod INTRODUCTION The elderly in South Africa face a complex set of challenges. South Africans over age 50 have spent most of their lives under the system of apartheid. Levels of inequality in education between races and within races are far greater among these older cohorts than they are for younger South Africans. Elderly black South Africans have lived their most productive years under the restrictions on employment, residence, and other opportunities that apartheid imposed. As they now enter retirement, they face new pressures caused by the impact of HIV/AIDS and high unemployment on the next generation. At the same time, South Africa’s elderly have access to an old age pension system that is among the most generous in the developing world. The old age pension helps lift many older South Africans out of the most extreme forms of poverty, putting many of them in a position to support their children and grandchildren. Decisions of the elderly about work and retirement are made in this complex set of circumstances. Older workers face an increasingly competitive labor market characterized by high unemployment, with limited opportunities for those with poor education and training. They often live in large extended households in which their own resources may be an important source of economic support. The pension provides such a source without necessarily competing with work. The state old age pension program has spawned a considerable body of research. This research is reviewed in the next section of the paper. The review shows that the state old age pension is the key plank of South Africa’s
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research social safety net, that these pensions are well targeted to the poor, and, because of the large number of three-generation and skipped-generation households in South Africa, they reach many poor children. In addition, it seems that many of the unemployed survive through their links to related pensioners. More recent research has begun to explore the impact of these pensions on labor participation behavior. Given all of the above, the dearth of research on the elderly themselves is surprising. We know very little about the circumstances of the elderly, their health, and how they cope with the pressures placed on them by the importance of their pension income to their extended families. Two recent studies have begun to address these issues (Møller and Devey, 2003; Møller and Ferreira, 2003). The first of these studies compares older and younger households on the basis of data from 1995 and 1998 national surveys, defining older households as those that include at least one member age 60 or older. The second study is based on a 2002 survey of the living conditions and financial and health situations of 1,111 older nonwhite households in Cape Town and the rural Eastern Cape. Here, older was defined as households containing 1 person age 55 or older. The first study confirms that older households are larger and include larger numbers of dependents and unemployed members than younger households. In both 1995 and 1998, roughly half of older black households included three or more generations. Such households tend to be concentrated in rural areas. The Møller and Ferreira (2003) study shows that among black older households, the percentage of household members under age 25 was 58 percent in rural areas and 51 percent in urban areas. In contrast, less than a quarter of these household members were actually age 55 or older. Less than 10 percent of older people lived alone. Møller and Devey (2003) show that many older black households are poor. However, access to state old age pensions strongly decreases the probability that such households fall into the lowest expenditure quintile. Pensioner households have better access to services and express significantly higher levels of satisfaction with their living conditions than nonpensioner older households. Møller and Ferreira (2003) confirm the dominance of the state old age pension as the primary income source in older households. They found that pension income is often the sole income in these households, especially in rural areas. They describe three elements of a “gradient of disadvantage” that makes older rural households worse off than older urban households. First, household well-being as measured by income or expenditure per capita is lower in the rural households. Second, the drain on the resources of the rural elderly through expenditures on other members of the household is higher in rural areas. Third, urban households are far more successful at accessing other government grants, such as the child support grants and
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research disability grants for which the members of their household may be eligible. All in all this leads to a situation in which the elderly in urban areas are better placed to use their pensions for their own support. Aside from work on the old age pension, the magnitude of South Africa’s unemployment problem has spawned a growing body of work on labor force participation in the country. This work shows that South African participation rates are low by international standards, especially for women (Winter, 1998). However, participation rates, and female participation rates in particular, rose sharply in the 1990s, despite the fact that many of these new participants did not move into employment but joined the ranks of the unemployed (Casale and Posel, 2002; Klasen and Woolard, 2000). Research by Mlatsheni and Leibbrandt (2001) and Leibbrandt and Bhorat (2001) highlights the importance of education as a factor affecting female participation rates. However, this literature on participation has given very little specific attention to the labor market behavior of the elderly. Given the considerable focus on the impact of the old age pension on the work activity of the nonelderly, it is surprising that the labor force behavior of those who actually receive these pensions has received so little attention. The fact that it is the elderly who are facing a retirement decision as part of this participation behavior would seem to make their labor force behavior especially interesting. This paper provides a broad overview of the labor force activity of older workers in South Africa. We begin the paper with a discussion of important features of the social and economic environment that provide a background for the analysis. Drawing on excellent microdata, we then analyze the age profile of participation, focusing in particular on the possible effects of the old age pension on retirement. We look at several important variables that may affect the economic activity of the elderly, including marital status, living arrangements, the pension system, and education. We estimate probit regressions in order to look at key determinants of labor force activity. SOCIAL AND ECONOMIC BACKGROUND A number of features of South African society and economy are important to keep in mind in analyzing the economic activity of the elderly. In this section of the paper we discuss some of these important features, with particular focus on the old age pension system and patterns of household structure.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research South Africa’s Old Age Pension No analysis of the elderly in South Africa would be complete without a discussion of the country’s old age pension system. Social assistance in South Africa consists of three main programs: pensions for the elderly, grants for the disabled, and grants for the support of poor children. Old age and disability pensions are set at generous levels. The 2005 level is R780 per month, about $120. Relative to this amount, the child support grant is set at a meager R180 or about $28 per month and is payable for children up to and including the age of 14. All three grants are supposedly means-tested, but in practice the means test is rarely administered for applicants who appear poor. A total of between 4 and 5 million people (or 10 percent of the total population) receive one or the other grant. South Africa’s public welfare system is exceptional among developing countries and is a major pillar in its highly redistributive social policies (Seekings, 2002; Van der Berg, 2001; Van der Berg and Bredenkamp, 2002). The state old age pension system is unique and the most important aspect of the South Africa social assistance system. It has an interesting history, as it evolved from a grant that was paid exclusively to white South Africans to one that was paid to all South Africans regardless of racial categorization. The grant was first introduced in 1928 as a form of income support for poor elderly whites (Sagner, 2000). Only in 1944 did the preapartheid state extend the social pension to include members of other race groups, and, even then, pension payment size was legally determined by race at a ratio of 4:2:1 for whites, Indians/coloreds, and blacks/Africans, respectively. Beginning in the late 1970s, the racial gap in pensions was significantly reduced through the allocation of large additional funds to this scheme by the apartheid state. During the 1980s, the size of pensions more than doubled for Africans, while it declined by 40 percent (in real terms) for whites (Ferreira, 1999). By 1985, white pensions were only 2.5 times higher than those of blacks and 1.5 times higher than those for those categorized as coloreds and Indians (Schlemmer and Møller, 1997). Take-up rates, particularly among elderly black South Africans, increased markedly during this time. The Social Assistance Act of 1992 provided steps to deracialize pensions and achieve pension parity, which was finally achieved in 1993, just one year prior to the first democratic elections. By 1993, the take-up rate among eligible black South African men and women stood at 80 percent. Case and Deaton (1998) provide a comprehensive analysis of the workings of the South African pension system. The key features of the system are that it is paid to women age 60 and over and men age 65 and over, with a means test that allows 80 percent of age-eligible black South Africans to receive the pension. Most receive the maximum benefit. The benefit was
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research about 2.5 times the median per capita income by 2000. The 2005 pension of R780 per month can be compared to the minimum wage for domestic workers, which was set at R754 in rural areas in 2005. The means test for the pension in 2004, if enforced, would have applied at annual income levels of about R18,000 for single individuals and about R34,000 for married couples. This implies that many elderly could receive the full value of the pension while continuing to work full time, even with the means test (below we show that some elderly do in fact work while receiving the pension, although the percentages are low). Given the weak enforcement of the means test, the receipt of the pension does not have a direct negative incentive on work for many low-income elderly. The impact of the pension on labor supply is thus primarily an income effect. Considerable research has been done on the impact of the old age pension. Duflo (2003) found a positive effect of the pension on the health outcomes of young girls in the household. Jensen (2004) found that the pension tends to “crowd out” private transfers from family members living away from home. Looking at the impact of the pension on labor supply, Bertrand, Mullainathan, and Miller (2003) found that the pension tends to reduce the labor supply of working-age adults. Posel, Fairburn, and Lund (2004) found a more complex effect of the pension on labor supply, with the pension increasing the probability that prime-age adults migrate for work. Edmonds (2005) found a negative effect of the pension on the work activity of children. Given the attention focused on the pension’s impact on the labor supply and other outcomes of prime-age adults and children, it is surprising that little attention has been given to the pension’s impact on the labor supply of the elderly themselves. This is an issue we analyze in detail below. Household Structure As noted above, another dimension of South African society that is important in analyzing the economic activity of the elderly is the complex extended household structure that is common among black South African households. As noted by Case and Deaton (1998), one of the reasons the pension system is so effective in reducing poverty in South Africa is that the elderly recipients of the pension often live in households with young children. While these complex extended household patterns have long historical roots in South Africa, they have taken on new importance as HIV/AIDS and high unemployment have weakened the ability of prime-age adults to support their families. Edmonds, Mammen, and Miller (2005) remind us how unusual this situation is when they note that the standard literature on old age pensions focuses on how pensions enable the elderly to maintain their independence. They conclude that the arrival of a state old age pen-
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research sion into a black South African household leads to the departure of primeage working women and the arrival of children under age 5 and women of childbearing age. They see this behavior as evidence of the household’s making use of the pension to reshape itself according to the comparative advantage for work inside and outside the household. In line with this behavior, we would expect that the labor supply decisions of the elderly are therefore often being made simultaneously with decisions about living arrangements. We look at the links between household structure and the labor force activity of the elderly in some detail. Although we do not identify the causal links between household structure and the work activity of the elderly, we show that there appear to be important links between these variables. DATA We use two main data sets for our analysis, each with strengths and weaknesses. We use the 10 percent sample of the 2001 census for many of our estimates, taking advantage of the large sample size. With roughly 4 million total observations, the census gives us thousands of observations at single years of age, even at ages from 60 to 70. For example, the number of individuals in the 70-74 age group in the census sample is over 12,800 black South African men, 25,000 black South African women, 4,500 white men, and 6,000 white women. The census provides standard information on employment status, along with information on schooling, household structure, and marital status. For comparative purposes we also use the 10 percent sample of the 1996 census. The other important data set used in our analysis is the South Africa Labor Force Survey (LFS), a nationally representative household survey of about 30,000 households collected by Statistics South Africa. We use the September 2000 LFS for some of our analysis because it has greater detail than the census for such variables as work activity and pension receipt. The drawback of the LFS is the smaller sample size. For example, in the 50-79 age group there are roughly 4,000 black South African men and 6,000 black South African women, making it difficult to look at fine age detail and making it almost impossible to look at any population group other than black South Africans. For certain parts of our analysis, we pool the LFS data for September 2000 and September 2001, giving us a larger sample size. Although the LFS is designed with a rotating panel structure, a new sample was introduced in September 2001. There is therefore no overlap in the two waves that we pool. We also merge the September LFS with the 2000 Income and Expenditure Survey (IES), allowing us to look at the impact of pension income on total household income.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research AGE PROFILES OF LABOR FORCE PARTICIPATION Figure 7-1 shows the age profile of labor force participation and employment for men and women ages 45-75 who identify themselves as black/ African in the 1996 and 2001 census.1 The ages of eligibility for the state old age pension (65 for men and 60 for women) are indicated on the figure for reference. The measure of labor force participation used in Figure 7-1 follows standard international definitions, counting labor force participants as those who were either working, on vacation or sick leave from work, or looking for work. The “working” series includes those who were working and those who were on vacation or sick leave during the week before the census. Work is defined broadly, including any work for pay, profit, or family gain.2 One of the stark features of Figure 7-1 is the large gap between the “in labor force” and “working” series, confirming the high rates of unemployment for both men and women in South Africa. The unemployment rate at age 50 in 2001, for example, is 34 percent for men and 40 percent for women.3 Figure 7-1 shows a relatively rapid rate of withdrawal from the labor force for both men and women after age 50. In the 2001 census, male participation rates fall from around 75 percent at age 50 to 47 percent at age 60 and 10 percent at age 70. Participation rates for women in 2001 fall from around 55 percent at age 55 to about 20 percent at age 60 and below 5 percent at age 70. The percentage who are actually working is well below the participation rate for both men and women around age 50, but, like the participation rate, it falls steadily with age. The percentage of men working in 2001 is about 50 percent at age 50, falls to 30 percent at age 60, and is below 10 percent at age 70. Put another way, if we could interpret this cross-sectional relationship as the life-cycle work profile of a cohort of men, it would imply that over half of the men who were working at age 50 1 The census asked the following question for each household member: “How would (the person) describe him/herself in terms of population group?” The possible responses and proportions giving each response were “Black/African” (79%), “Colored” (9%), “Indian or Asian” (3%), and “White” (9%). Henceforth we refer to the black/African group, which is our main focus, as African. 2 The wording of the census questionnaire is the following: “Does (the person) work (for pay, profit, or family gain)? Answer yes for formal work for a salary or wage. Also answer yes for informal work such as making things for sale or selling things or rendering a service. Also answer yes for work on a farm or the land, whether for a wage or as part of the household’s farming activities. Otherwise answer no.” 3 The standard definition of the unemployment rate is the number of unemployed (actively searching) divided by the number in the labor force. In Figure 7-1 this is the difference between the “in labor force” and “working” values divided by the “in labor force” value. For men age 50 this is 0.25/0.74 = 0.34.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research FIGURE 7-1 Percentage working and participating in the labor force, 1996 and 2001 censuses. stopped working several years before they reached the age of eligibility for the state old age pension. Comparisons between 1996 and 2001 census data are interesting for a number of reasons. First, it helps to disentangle age and cohort effects that would be impossible to separate in a single cross-section. In this paper we often interpret the age patterns in labor force activity as indicating changes over the life cycle. But it is important to remember that the age patterns we observe may be affected by differences in the behavior of different cohorts. For example, as we document below, younger cohorts are considerably better educated than older cohorts, especially among Africans. This may lead to differences in life-cycle labor force behavior that will show up in the age profile of participation at any given point in time. By looking at two cen-
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research suses five years apart, we can be clearer about age and cohort effects and more certain about whether we are seeing life-cycle labor force behavior. The age profiles from the 1996 census shown in Figure 7-1 suggest that the basic shape of the age profile in work and labor force participation did not change much between 1996 and 2001, although both rates for men dropped at all ages. Looking at the figure for women, the 1996 and 2001 lines are almost indistinguishable for both labor force participation and work. Figure 7-1 shows that there are sharp drop-offs in labor force participation and work for both men and women around the age of eligibility for the old age pension (age 60 for women and age 65 for men). These declines around the pension age are larger in 2001 than they are in 1996. The decline in labor force participation rate for men between ages 64 and 66 is 13.6 percentage points in 1996 and 16.9 percentage points in 2001. This implies that 54.5 percent of those still in the labor force at age 64 have left it by age 66 in 2001. Exit from work is at a similar rate, with the percentage of men working at age 66 roughly half of the percentage working at age 64. The figure for women shows a similar discontinuity around the pension age in rates of labor force participation and employment. The decline in labor force participation rates for women between ages 59 and 61 is 18.4 percentage points, or 57 percent of the age 59 rate. The decline in the percentage working is 10.3 percentage points, or 52 percent of the age 59 rate. Figure 7-2 shows participation rates for all four of the major population groups. As this figure shows, there are relatively small racial differences in participation rates for men at all ages. Participation rates are somewhat lower for African and colored men from ages 45 to 55, with similar rates of decline in participation for all groups from ages 55 to 65. Participation rates for women are considerably lower at all ages, falling from around 60 percent at age 45 to 20 percent at age 60 and under 5 percent at age 70. Racial differences in participation rates are larger for women than for men, with Indian women having the lowest rates. Participation rates for African and white women are almost identical up to age 59, with the participation of African women showing a larger drop in participation at age 60. Participation rates above age 65 are between 10 and 20 percent for men, rates that are much lower than the 65 percent participation rate reported for Southern Africa in a cross-national analysis of 1980 data (Clark and Anker, 1993). Participation rates of older men in South Africa are somewhat higher than those for most European countries, however. As shown in the cross-national study of retirement in Organisation for Economic Co-operation and Development (OECD) countries coordinated by the National Bureau of Economic Research (NBER), participation rates for men ages 60-64 were below 20 percent in France, Belgium, and the Netherlands around 1995 (Gruber and Wise, 1999). We discuss comparisons to OECD countries in more detail below.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research FIGURE 7-2 Labor force participation rates by age and population group, 2001 South Africa census. COMPONENTS OF LABOR FORCE ACTIVITY Figure 7-3 shows the 2001 distribution of labor force activity in more detail for African men, white men, African women, and white women. Four categories of activity are shown. The two components of labor force participation—working and unemployed—are shown separately. In the 2001 census, respondents were asked to give the reason for not working for all those who were not working in the previous seven days. Two of these categories are included in Figure 7-3: the percentage reported as being a “pensioner or retired person/too old to work” and the percentage reported as “unable to work due to illness or disability.” Additional possible reasons
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research FIGURE 7-3 Distribution of labor force activity by age, African and white men and women, South Africa, 2001 census.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research FIGURE 7-8 Percentage receiving employer-provided pension, September 2000 South Africa LFS. ployment measure, we estimate employment rates of less than 10 percent for pension recipients. Schooling Schooling is an important determinant of employment at all ages, affecting both labor demand and labor supply. In many countries it is observed that better educated workers have later ages of retirement (Peracchi and Welch, 1994). There is a strong effect of schooling on both wages and the probability of employment for prime age workers in South Africa (Anderson, Case, and Lam, 2001; Mwabu and Schultz, 1996). It is there-
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research TABLE 7-3 Percentage Working by Pension Status, African Men and Women, September 2000, South Africa LFS Age Group Number Percentage Receiving Pension Not Receiving Pension Receiving Pension Percentage Working Percentage Working Number Broad Narrow Number Broad Narrow Men 50-54 1,253 1.6 1,233 67.2 62.3 20 13.6 7.1 55-59 884 2.4 857 66.7 61.0 27 20.8 9.1 60-64 776 14.6 653 53.8 47.1 123 13.0 6.8 65-69 548 63.8 201 46.6 41.0 347 21.9 6.6 70-74 415 84.4 66 26.3 17.0 349 18.7 7.9 75-79 228 86.9 31 20.2 20.2 197 17.6 3.3 Total 4,104 24.9 3,041 61.9 56.4 1,063 18.9 6.5 Women 50-54 1,520 1.8 1,487 55.3 49.5 33 28.2 13.1 55-59 1,136 6.8 1,064 46.4 40.7 72 14.0 4.3 60-64 1,253 62.9 459 43.1 33.7 794 20.7 7.5 65-69 859 85.2 127 27.4 22.5 732 16.7 5.5 70-74 768 90.4 75 11.3 7.7 693 11.8 3.0 75-79 380 93.1 25 21.3 2.0 355 9.1 1.4 Total 5,916 43.6 3,237 48.5 42.2 2,679 15.5 4.9 NOTE: Broad measure of work includes work on family plot; narrow measure does not.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research fore natural to look at the impact of schooling on the work activity of the elderly. Table 7-4 shows summary statistics for the distribution of schooling for African men and women by 5-year age groups. As the table clearly shows, levels of schooling among the elderly in South Africa are very low. Over 45 percent of men and over 50 percent of women age 60 and above in the 2001 census had 0 years of schooling. The percentage completing 7th grade is under 30 percent for those age 60 and older, and the percentage completing secondary school is below 5 percent for all age groups age 55 and older. Although men have more schooling than women in older age groups, the gender gap is relatively small compared with many African countries and narrows substantially at younger ages. As shown by Anderson and colleagues (2001), a female advantage in schooling has clearly emerged among younger cohorts. Table 7-4 shows the substantial improvements in schooling that have taken place in South Africa over time, with mean years of schooling more than doubling from the 60-64 to the 30-34 age group. Figure 7-9 shows the age profile of employment for African men and women, dividing the sample into those with less than 7 years of schooling and those with at least 7 years of schooling. The better educated group has higher rates of employment at all ages for both men and women. Less educated men begin to withdraw from employment at a faster rate in their late 50s, dropping to employment rates below 30 percent by age 60. The gap in employment between the schooling groups is much larger for African women, for whom there is about a 20 percentage point difference in employment rates between the education groups at ages up to 60. The better educated women appear to have a steeper rate of decline in employment beginning at age 60, with both groups falling to employment rates of below 10 percent by age 65. The combination of large improvements in schooling over time and the strong positive relationship between schooling and employment should create a tendency for increasing employment rates for older South Africans over time. This may be especially true for women, for whom the impact of schooling on employment is particularly large. PROBIT REGRESSIONS In order to get a clearer picture of the variables affecting the work activity of the elderly in South Africa, we use the 2001 census data to estimate probit regressions of the probability of employment. The dependent variable is one if the individual was working in the week prior to the census and zero for everyone else, whether or not they are in the labor force. The regressions are estimated for the sample of Africans ages 50-75, with separate regressions estimated for men and women. Two specifications are used in the regressions. The first includes years of schooling,
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research TABLE 7-4 Schooling Attainment of African Men and Women by Age Group, 2001 Census Age Group Mean Years of Schooling Percentage Completing Zero Schooling Women Men Women Men 30-34 8.20 8.33 14.4 11.9 35-39 7.03 7.38 20.1 15.9 40-44 6.06 6.54 25.7 20.2 45-49 5.18 5.70 30.9 25.2 50-54 4.42 4.86 37.2 31.9 55-59 3.97 4.29 41.7 37.0 60-64 3.04 3.56 53.1 45.8 65-69 2.66 3.16 58.0 51.5 70-74 2.12 2.64 65.4 57.9 dummy variables for marital status, a flexible parameterization of age, and dummy variables for province and urban residence. The second regression adds measures of household composition. As noted above, living arrangements are likely to be endogenous, determined jointly with decisions about labor supply, so these variables are included simply to indicate the association between living arrangements and the elderly labor supply and not as indicators of causation. Table 7-5 presents the estimates of these probit regressions. Regressions 1 and 2 present estimates of the first specification for women and men, respectively. As suggested by Figure 7-9, the coefficient on years of schooling is positive and highly significant for both men and women. The marginal effects (dF/dx) column indicates that 1 year of additional schooling is associated with about a 1 percentage point increase in the probability of working for both men and women, evaluated at the sample means of the independent variables. This translates into a similar percentage point increase in schooling for men and women, although it is a smaller proportional change for men given their higher levels of employment. The marital status dummies, with married as the omitted category, indicate that unmarried women are significantly more likely to work, controlling for all of the other variables in the regression, with the largest effect for divorced women. Evaluated at sample means, the percentage increase in the probability of work compared with married women is 0.5 percentage points for widows, 8 percentage points for divorced women, and 4 percentage points for women who have never married. The effects of marriage for men go in the opposite direction, with married men having significantly higher probabilities of employment than widowed, divorced, or never married men. Married men have probabilities of employment at least 10 percentage points
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research At Least 4 Years At Least 7 Years At Least 12 Years Women Men Women Men Women Men 82.0 83.7 70.8 71.1 30.5 31.4 74.7 78.5 60.0 62.7 20.6 22.4 67.7 72.5 50.6 54.6 14.7 16.6 61.1 66.1 42.8 46.9 9.8 12.0 54.1 58.5 35.5 39.0 6.6 8.2 49.6 52.8 31.7 33.9 5.1 6.3 38.6 44.0 23.0 27.1 3.5 4.9 33.8 39.0 19.9 24.0 2.9 4.5 27.0 32.4 15.2 19.3 2.2 3.8 higher than men in any of the other categories of marital status, even with very flexible controls for age. We use a cubic function of age to permit a flexible shape for the age-employment profile. We also include two dummy variables, permitting shifts in the age profile at ages 60 and 65. The age 60 dummy is equal to one for age 60 and above; the age 65 dummy is equal to one for age 65 and above. For women we estimate a decline of 3.4 percentage points in employment probabilities at age 60, the age at which women become eligible for the state old age pension. We also estimate a positive effect of the age 65 dummy for women, with a decline in the probability of employment of 2.9 percentage points. For men the coefficient on the age 60 dummy is not statistically significant, but we estimate a significant negative effect of the age 65 dummy. This suggests that men speed up their withdrawal from employment when they reach the age of pension eligibility, with a predicted drop of 7.2 percentage points in the probability of employment at age 65. The coefficient on the urban dummy indicates that both men and women are significantly more likely to work in urban areas, with a larger coefficient for women. We observe substantial differences in employment across provinces with significantly lower probabilities of employment for both women and men in Eastern Cape, Northern Cape, KwaZulu-Natal, and Northern Province when compared with Western Cape. Gauteng has higher rates of employment than Western Cape for women, but differences between Gauteng and Western Cape are not statistically significant for men. We caution that none of the locational variables can be considered exogenous, since the decision about where to live may be made jointly with the decision about whether to retire.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research FIGURE 7-9 Percentage working by years of schooling, 2001 census. NOTE: High schooling is 7 or more years of schooling; low schooling is under 7 years. Regressions 3 and 4 add three household composition variables to the regression—the number of household members under the age of 18, the number of men ages 18-59, and the number of women ages 18-59. The number of household members under age 18 is negatively associated with the employment of both men and women, with a larger coefficient for women. This may reflect a trade-off between labor market work and caring for grandchildren, especially for women. The number of adult men in the household is negatively associated with the employment of women but positively associated with the employment of men, although both effects are extremely small. The effect of the number of adult women in the household is slightly positive on the employment of women and statistically insignificant in its effect on the employment of men. Since we have controlled for the presence of children, the positive effect of women ages 18-59 on the employment of women may indicate that older women are less needed for child care responsibilities if the children’s mother is in the household. As with the locational variables, we caution again that household living arrangements are likely to be endogenous with respect to the labor supply decisions of potential household members. Unobserved variables, such as the health of elderly household members, are likely to affect both living
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research arrangements and work activity. Since the living arrangements of the elderly are likely to be influenced by many of the same unobservable variables that affect labor supply, the coefficients on these household composition variables should not be given a causal interpretation. CONCLUSIONS We have referred to the large literature showing the importance of the noncontributory old age pension for poor households in South Africa. Those elderly who do not have pension income are among the poorest South Africans. Many of those with a pension live in three-generation or skipped-generation households. Indeed, we have made reference to a literature arguing that this extended household structure may in part be a response to the pension. Leaving aside these difficult issues of endogenous household structure, the fact remains that the African elderly are usually in the minority in their own households, and their pension income is available to support large numbers of children and working-age adults. This makes the state old age pension a key element in South Africa’s social safety net and a central plank in overall social welfare policy. It also implies that an unusual burden is placed on South Africa’s elderly. Given this, it strikes us as an omission that the existing literature has devoted so much attention to the impact of the pension on labor supply and other outcomes of the nonelderly without interrogating the impact on the elderly themselves. This paper has sought to fill in this gap by examining the labor supply behavior of the elderly. Our analysis of South African census and survey data indicates that withdrawal from the labor force occurs at a fairly rapid rate above age 45. According to the 2001 census, male participation rates fall from around 80 percent at age 45 to 50 percent at age 60 and 10 percent at age 70, with only modest differences across the four main population groups. Participation rates for women are lower at all ages, with participation rates for African women falling from around 60 percent at age 45 to 20 percent at age 60 and 5 percent at age 70. Using the metric of unused productive capacity developed by Gruber and Wise (1999) this pattern of labor force withdrawal leads to somewhat less unused capacity than most European countries and slightly more unused capacity than the United States. For black South Africans, the noncontributory old age pension system is triggered almost entirely by simple age eligibility rules, with women becoming eligible at age 60 and men becoming eligible at age 65. The fraction of women receiving the pension jumps from under 10 percent at age 59 to almost 70 percent at age 61, with the pension becoming almost 50 percent of household income for women age 61. Although the pension does not necessarily imply a tax on work, especially for low-wage workers, we find that the age of pension eligibility is associated with increased rates of retire-
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research TABLE 7-5 Probit Regressions for Employment, Africans Ages 50-75, 2001 Census Variable Probit Regression Coefficients and Robust Standard Errors Female Regression 1 Male Regression 2 b SE dF/dX b SE dF/dX Years of schooling 0.064 (0.001)*** 0.010 0.032 (0.001)*** 0.011 Widowed 0.030 (0.010)*** 0.005 −0.329 (0.019)*** −0.098 Divorced 0.388 (0.021)*** 0.080 −0.426 (0.031)*** −0.120 Never Married 0.203 (0.011)*** 0.036 −0.655 (0.015)*** −0.178 Age 60 –0.207 (0.023)*** –0.034 –0.017 (0.022) –0.005 Age 65 –0.185 (0.030)*** –0.029 –0.228 (0.027)*** –0.072 Age-50 0.023 (0.008)*** 0.004 0.001 (0.008) 0.000 (Age 50) squared −0.008 (0.001)*** −0.001 −0.005 (0.001)*** −0.002 (Age 50) cubed 0.000 (0.000)*** 0.000 0.000 (0.000)*** 0.000 Urban 0.265 (0.011)*** 0.045 0.038 (0.011)*** 0.012 Eastern Cape −0.355 (0.028)*** −0.050 −0.650 (0.026)*** −0.186 Northern Cape −0.258 (0.050)*** −0.035 −0.080 (0.043)* −0.026 Free State −0.035 (0.029) −0.006 −0.117 (0.027)*** −0.037 KwaZulu-Natal −0.204 (0.027)*** −0.031 −0.402 (0.026)*** −0.121 Northwest −0.161 (0.028)*** −0.024 −0.144 (0.026)*** −0.046 Gauteng 0.115 (0.026)*** 0.020 0.023 (0.024) 0.008 Mpumulanga 0.049 (0.029)* 0.008 0.024 (0.027) 0.008 Northern Province −0.096 (0.029)*** −0.015 −0.144 (0.027)*** −0.104 Number < 18 Men 18-59 Women 18-59 Constant −0.943 (0.031)*** 0.108 (0.029)*** Sample size 172,241 118,858 Pseudo R-squared 0.208 0.156 Log likelihood −56986 −62038 NOTES: Robust standard errors in parentheses. Significance levels: *** = 0.01, ** = 0.05, * = 0.1. Omitted categories: Married, Western Cape. ment. Reaching the age of pension eligibility leads to an increase in the hazard rate of leaving employment for women from 5 percent at age 58 to over 40 percent at age 60. Men also retire at a faster rate when they reach the pension-eligibility age of 65, with the hazard rate rising to over 30 percent at ages 65 and 66. While this is a sharp jump in retirement, it is not as large as observed in many European countries, where hazard rates can be as high as 60 percent at key program eligibility ages. We found large effects of schooling on employment of the elderly. Our probit regressions imply about a 1 percentage point increase in the probability of employment for each year of schooling for both men and women. Since schooling levels rise rapidly from older to younger ages, especially for
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research Female Regression 3 Male Regression 4 b SE dF/dX b SE dF/dX 0.062 (0.001)*** 0.010 0.031 (0.001)*** 0.010 0.030 (0.011)*** 0.005 −0.345 (0.019)*** −0.102 0.370 (0.021)*** 0.074 −0.463 (0.031)*** −0.129 0.181 (0.012)*** 0.032 −0.678 (0.015)*** −0.182 −0.206 (0.023)*** −0.034 −0.011 (0.022) −0.004 −0.188 (0.030)*** −0.029 −0.228 (0.027)*** −0.072 0.021 (0.008)*** 0.003 0.001 (0.008) 0.000 −0.008 (0.001)*** −0.001 −0.005 (0.001)*** −0.002 0.000 (0.000)*** 0.000 0.000 (0.000)*** 0.000 0.244 (0.011)*** 0.041 0.025 (0.011)** 0.008 −0.344 (0.028)*** −0.048 −0.647 (0.026)*** −0.182 −0.256 (0.050)*** −0.035 −0.079 (0.043)* −0.025 −0.035 (0.029) −0.005 −0.117 (0.027)*** −0.037 −0.170 (0.027)*** −0.026 −0.377 (0.026)*** −0.114 −0.158 (0.028)*** −0.023 −0.156 (0.026)*** −0.049 0.104 (0.026)*** 0.017 0.012 (0.025) 0.004 0.064 (0.030)** 0.011 0.031 (0.027) 0.010 −0.083 (0.029)*** −0.013 −0.325 (0.027)*** −0.098 −0.058 (0.007)*** −0.009 −0.037 (0.004)*** −0.012 −0.009 (0.005)* −0.002 0.003 (0.000)*** 0.000 0.004 (0.001)*** 0.001 0.000 (0.003) 0.001 −0.807 (0.036)*** 0.174 (0.030)*** 172,241 118,858 0.214 0.160 −56569 −61759 Africans, this implies that employment rates at older ages may increase in the future. Employment rates at older ages may also be pushed upward by the fact that younger cohorts are more likely to live in urban areas, since we estimate a substantial positive effect of urban residence on employment. It is beyond the scope of this paper to fully explain the patterns in labor force withdrawal that we have documented. While some factors, such as the age eligibility for the old age pension, appear to play an important role, many questions remain. For example, even if the pension helps explain sharp drops in labor force participation immediately around the pension age, it presumably cannot explain the steady declines in participation that begin around age 45. Health may be important in these declines, but existing data
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research sources provide limited information on health and disability. The lack of panel data also complicates interpretation of our results, especially as they relate to the high level of unemployment. Does the decline in participation result from individuals retiring from their jobs, or from individuals losing their jobs and deciding not to search for a new one? Further research on the labor supply of the elderly in South Africa would clearly benefit from longitudinal data that includes data on health and disability. REFERENCES Anderson, K., Case, A., and Lam, D. (2001). Causes and consequences of schooling outcomes in South Africa: Evidence from survey data. Social Dynamics, 27(1), 1-23. Bertrand, M., Mullainathan, S., and Miller, D. (2003). Public policy and extended families: Evidence from pensions in South Africa. World Bank Economic Review, 17(1), 27-50. Casale, D.M., and Posel, D.R. (2002). The continued feminization of the labor force in South Africa: An analysis of recent data and trends. South African Journal of Economics, 70(1), 156-184. Case, A., and Deaton, A. (1998). Large cash transfers to the elderly in South Africa. Economic Journal, 108, 1330-1361. Clark, R.L., and Anker, R. (1993). Cross-national analysis of labor force participation of older men and women. Economic Development and Cultural Change, 41(3), 489-512. Duflo, E. (2003). Grandmothers and granddaughters: Old age pension and intra-household allocation in South Africa. World Bank Economic Review, 17(1), 1-25. Edmonds, E. (2005). Child labor and schooling responses to anticipated income in South Africa. Journal of Development Economics. Available: http://188.8.131.52/search?q=cache:I0wPhNpkmh4J:www.dartmouth.edu/~eedmonds/liquidity.pdf+child+labor+and+schooling+responses+to+anticipated+income+in+South+Africa&hl=en&gl=us&ct=clnk&cd=1 [accessed September 2006]. Edmonds, E., Mammen, K., and Miller, D. (2005). Rearranging the family? Household composition responses to large pension receipts. Journal of Human Resources, 40(1), 186-207. Ferreira, M. (1999). The generosity and universality of South Africa’s pension system. The EU Courier, 176. Gruber, J., and Wise, D.A. (1999). Social security and retirement around the world. Chicago, IL: University of Chicago Press. Kapteyn, A., and de Vos, K. (1999). Social security and retirement in the Netherlands. In J. Gruber and D.A. Wise (Eds.), Social security and retirement around the world. Chicago, IL: University of Chicago Press. Klasen, S., and Woolard, I. (2000). Unemployment and employment in South Africa, 1995-1997. Report to the Department of Finance, South Africa. Jensen, R.T. (2004). Do private transfers displace the benefits of public transfers? Evidence from South Africa. Journal of Public Economics, 88(1-2), 89-112. Leibbrandt, M., and Bhorat, H. (2001). Modeling vulnerability in the South African labor market. In B. Haroon, M. Leibbrandt, M. Maziya, S. van der Bberg, and I. Woolard (Eds.), Fighting poverty: Labor markets and inequality in South Africa. Cape Town, South Africa: University of Cape Town Press. Mlatsheni, C., and Leibbrandt, M. (2001, September). The role of education and fertility in the participation and employment of African women in South Africa. (DPRU Working Paper No 01/54). Cape Town, South Africa: School of Economics, University of Cape Town.
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Representative terms from entire chapter: