Poverty among the elderly in Sub-Saharan Africa and the role of social pensions

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Poverty among the elderly inSub-Saharan Africa and the roleof social pensionsNanak Kakwani a & Kalanidhi Subbarao ba Faculty of Economics and Business, The University ofSydney , Sydney, Australiab Consultant, World BankPublished online: 05 Jun 2008.

To cite this article: Nanak Kakwani & Kalanidhi Subbarao (2007) Poverty amongthe elderly in Sub-Saharan Africa and the role of social pensions, The Journal ofDevelopment Studies, 43:6, 987-1008, DOI: 10.1080/00220380701466476

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Poverty among the Elderly inSub-Saharan Africa and theRole of Social Pensions

NANAK KAKWANI* & KALANIDHI SUBBARAO***Faculty of Economics and Business, The University of Sydney, Sydney, Australia

**Consultant, World Bank

Final version received July 2006

ABSTRACT Drawing on household survey information, this study delineates the poverty profileof the elderly in 15 low-income sub-Saharan African countries which include countries with ahigh and low prevalence of the HIV-AIDS pandemic. The study shows that the povertysituation of the elderly living with children and the elderly-headed households is much worsethan the average in many countries. The impact of providing a social pension to the elderly ongroup-specific and national poverty head-count ratios and poverty gap ratios, and its fiscalimplications, are analysed. Simulating various plausible eligibility criteria and benefit levels, thestudy concludes that while the case for an universal (untargeted) social pension is weak,substantial welfare gains can be obtained at a low cost with a social pension targeted to thepoor among the elderly.

I. Introduction

Recent changes in demographic structures in some sub-Saharan African countriesare placing the elderly at the risk of poverty. Conflicts and HIV/AIDS haveincreased the probability of death among prime age adults, generating apparentperversities in life expectancies at different ages (Disney, 2003). Some prime ageadults have migrated from rural to urban areas in search of livelihoods. Theresult has been that some of the elderly have become prime earning membersfor families and/or caregivers for grandchildren, either because prime age adultshave died (or are sick and dying) or migrated. The region has also witnessed anunprecedented increase in the number of orphans who have lost either one orboth parents. The welfare consequences of the growing number of orphansand vulnerable children have been analysed (Subbarao and Coury, 2004). An

Correspondence Address: 51/3 Wulumay Close, Rozelle NSW 2039, Australia. Email: n.kakwani@

econ.usyd.edu.au

Journal of Development Studies,Vol. 43, No. 6, 987–1008, August 2007

ISSN 0022-0388 Print/1743-9140 Online/07/060987-22 ª 2007 Taylor & Francis

DOI: 10.1080/00220380701466476

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analysis of poverty among the elderly and the impact of social pensions is donefor South Africa and Brazil (Case and Deaton, 1998; Barrientos and Lloyd-Sherlock, 2002; Barrientos, 2004), for India (Deaton and Praxon, 1995; Pal andPalacios, 2005) and for Nepal (Palacios and Rajan, 2004). Most of these studiesexamine the case for, and impacts and effectiveness of, a non-contributorypension for the elderly regardless of the household types in which the elderlyreside. Moreover, the economic and welfare consequences of the growing burdenon the elderly in low income sub-Saharan countries have not been systematicallyanalysed from the perspective of the role of non-contributory social pensions.This study aims to fill these gaps.The study is organised as follows. The next section (II) describes data sources

and the methodology. The poverty profile of the elderly in 15 low-incomesub-Saharan African countries is delineated in section III. Two aspects of welfareare discussed: poverty incidence and the poverty gap ratio. In section IVthe implications for poverty reduction of a social pension to the elderly living indifferent household arrangements are analysed in terms of impacts and costs. Inparticular, the section examines the short-run impacts of providing assistanceto different groups of the elderly on a reduction in poverty among the group-specific elderly population, as well as for national poverty reduction. The last sectiondraws some inferences for the role of non-contributory social pensions for theelderly.

II. Data and Methodology

Household Survey Information

The study utilises the standardised unit record household datasets from 15 Africancountries compiled by the World Bank. The standardisation is done for the purposeof comparing social welfare across a wide range of countries in sub-Saharan Africa.Each standardised file consists of a common set of standard variables, extracted fromthe nationally representative household surveys. In compiling these datasets,consistency checks were also performed. Any inconsistencies that were present inthe original surveys were corrected.Each standardised file contained a variable called weighting coefficient (WTA),

which is the number of population households that are represented by each samplehousehold.1 The sum of WTA for all sample households provided us the totalnumber of households in the country. Since our analysis is based on individuals (nothouseholds), we constructed a population weight variable (POP), which is obtainedby multiplying the weighting coefficient (WTA) by the household size. The sum totalof the (POP) variable for all sample households provided an estimate of the totalpopulation in the country. The total number of elderly in the country could also beestimated from the POP variable.With the exception of three countries, the datasets belong to 1998–2001.2

Although the choice of the 15 selected countries is governed by the availability ofhousehold survey information, the sample includes both western and eastern Africancountries, Francophone and Anglophone countries, and countries with a highincidence of HIV-AIDS and others.

988 N. Kakwani & K. Subbarao

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Poverty Lines

National poverty lines were obtained from various World Bank poverty assessmentreports. These poverty lines do not take account of different needs of householdmembers by age and sex. They also do not take account of the economies of scale,which operate in large households. We modified these poverty lines using thefollowing common methodology:

1. In many countries, the poverty lines were not available for the survey years. Weused the consumer price index to adjust these lines so that they correspond to thesurvey years.

2. The national poverty lines obtained from poverty assessment reports were singlepoverty lines and thus made no allowance for different needs of householdmembers, which do vary with age and sex. We decided that different needs ofindividuals can adequately be approximated by the calorie requirements, whichare estimated for individuals of different age and sex. We obtained the calorierequirements that are widely used in Africa. These requirements are given inTable 1. The household surveys in each country had information on the age and sexof each household member. We allocated the calorie requirements as given inTable 1 to each household member. Adding up the calorie requirements of eachmember and dividing by household size, we obtained the per calorie requirement ofeach household. We could then calculate the per capita calorie requirement of thewhole population by the weighted average of the per capita calorie requirementswith weights proportional to population of individuals represented by thesample households. These average calorie requirements presented in Table 2 varyacross countries because of differences in countries’ population composition.

3. Average poverty lines in the survey years as obtained in (1) were allocated toeach household in proportion to their per capita calorie requirements so thataverage poverty line for the country as a whole is the same.

4. Finally we adjusted for the economies of scale. A larger household will have alower per capita poverty line than a smaller household. The economies of scaleparameter was assumed to be equal to 0.7, so that larger households will incur

Table 1. Calorie requirements by age and sex

Age Requirement Kcal

Children 0 to 1 8001 to 3 13004 to 6 18007 to 10 2000

Males 11 to 14 250015 to 18 300019 to 50 290051þ 2300

Females 11 to 50 220051þ 1900

Elderly Poverty in Sub-Saharan Africa 989

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about 30 per cent less expenditure than smaller households but still will enjoy thesame utility level. Thus, the per capita poverty line for the ith household will be:

ðplineÞi ¼ kðaplineÞn0:7i =ni

where k is the constant of proportionality and (apline) is the average povertyline. The parameter k is determined such that mean of (pline) across allhouseholds is equal to the average poverty line (apline). This ensures that theadjustment for economies of scale does not change the mean of the poverty line.

Individual and Household Classifications

The living conditions of the elderly will be assessed in relation to the average andother household types. For purposes of this study, children and the elderly areclassified as follows:

1. Children from 0 to 14 years;2. Elderly males and females 60 years and older;3

3. Elderly males and females 65 years and older.

The household type classification will be:

1. Households with no elderly persons;2. Households with only elderly persons;3. Households with only children and elderly persons;4. Mixed households with children, working-age persons and elderly;5. Households headed by elderly persons;6. Households headed by working-age (15–59) males or females.

Households in groups 5 and 6 are sub-groups of household group 4.

Table 2. Average calorie requirements and poverty lines

Calorie requirement Poverty line in local currency

Burundi 2150 63760Burkina Faso 2140 47736Cote d’Ivoire 2266 166758Cameroon 2164 139186Ethiopia 2164 862Ghana 2192 680270Guinea 2140 291386Gambia 2191 2607Kenya 2198 13277Madagascar 2178 766139Mozambique 2165 1859424Malawi 2188 3829Nigeria 2253 11285Uganda 2139 223118Zambia 2193 428305


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Poverty Measures

Suppose we want to establish a social assistance programme (either targeted to theelderly as a social pension or a conditional grant to children or to any otheridentified vulnerable group) in a poor country. Since the resources available to thecountry’s government are limited, so our scheme should be such that it should leadto a maximum reduction in aggregate (national-level) poverty. To achieve thisobjective, we first need to fix a poverty measure, which we want to reduce. In theliterature, there exist many poverty measures, which indicate different facets ofpoverty. Focusing on a single poverty measure may not be desirable. Our study willfocus on two poverty measures: (1) head-count ratio; (2) poverty gap ratio. Thesetwo measures are more than adequate to capture different facets of poverty amongvulnerable groups including the elderly. All the measures belong to a single class ofadditive separable poverty measures:4

y ¼Z z

0

Pðz; xÞfðxÞdx ð1Þ

where x is the income or expenditure of a person, which is a random variable withdensity function f(x) and z is the poverty line. P(z, x) is a homogenous function ofdegree zero in z and x such that

Pðz; xÞ ¼ 0 if x ¼ z

@Pðz; xÞ@x

< 0

@2Pðz; xÞ@x2

> 0

Foster et al. (1984) proposed a class of poverty measures that is obtained bysubstituting

Pðz; xÞ ¼ z� x

z

� �að2Þ

in (1), where a is the parameter of inequality aversion. For a¼ 0, y¼H, that is, thehead-count ratio. This measure gives equal weight to all poor irrespective of theintensity of poverty suffered by them. For a¼ 1, each poor is weighted by his or herdistance from the poverty line relative to z. This measure is called the poverty gapratio.

Policy Simulations

The study analyses alternative scenarios for targeting assistance to the elderly. Weneed an objective in order to be able to assess various scenarios. We decided that ourobjective will be to achieve a maximum reduction in the national poverty with a givenfixed budget. Thus our focus will be not only on the impact of social pension onpoverty incidence among the elderly but also on the poverty incidence at the nationallevel. We used the two alternative scenarios. In one we fixed the total budget


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(excluding administrative cost) equal to 0.5 per cent of GDP in local currency. In thesecond scenario, we fixed benefit level equal to 70 per cent and 35 per cent of theaverage national poverty threshold expenditure.The study will consider the following alternatives:

1. Perfect targeting (filling the gap) and universal targeting (this is purely to serveas a benchmark, recognising such perfect targeting is unrealistic in practice).

2. Targeting by household types:. Targeting all elderly (regardless of household structure in which they live);. Targeting elderly living alone;. Targeting elderly living with children;. Targeting only the poor among the elderly (regardless of household structure

in which they live);

The purpose of these simulations is to measure the impact of providing a socialpension to a particular group of the elderly on total poverty as well as on povertyamong the chosen group of the elderly and assess trade-offs to alternative optionsincluding fiscal (budgetary) implications. For example, a programme of socialpension (with a given budget) aimed at all poor households regardless whether or nothousing the elderly may have a significant poverty reduction impact but may nothave a big dent on poverty among the elderly. On the other hand a social pensionprogramme aimed at the elderly living alone may substantially reduce the incidenceof poverty of that particular group but may not contribute significantly to areduction in the incidence of poverty at the national level.

III. Profile of the Elderly in Africa

The Setting: Characteristics of Sample Countries

The study is based on recent household survey information for 15 low-income sub-Saharan countries. Table 3 provides some background information on basiccharacteristics of sample countries. The sample countries include very low incomecountries with per capita incomes of $100 to slightly better off countries with per capitaincomes close to $300. Two countries in the sample have per capita incomes higher than$500. The incidence of head-count poverty ranges from a low 36.7 per cent to a high68.9 per cent. The sample includes countries with a low incidence of HIV-AIDSpandemic among young adults in the age group 15–24 (such as Guinea, Gambia andMadagascar) to countries with a high incidence (Malawi and Zambia). There is also awide range of variationwith respect to primary-school completion rates. Thus althoughall 15 countries are sub-Saharan countries, there is much heterogeneity across thesecountries with respect to levels of both economic and human capital development.

Where Are the Elderly?

The elderly (defined as those above 60 years of age) range from a low 3.5 per cent ofpopulation in Zambia to about 7 per cent in Guinea (Figure 1).5 The livingarrangements of the elderly are shown in Table 4. The single elderly (i.e. the elderly


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Table

3.Characteristics

ofsample

countries

Country

GDP

per

capita($)

Population

(millions)

Head-count

poverty

(%)

HIV

prevalence

rate

15–24(%

)

Life

expectancy

atbirth

Primary

school

completion

rate

(%)

MF

MF

12

34

56

78

9

Burundi

100

761.2

511

41

42

43

BurkinaFaso

220

12

52.0

49.7

43

44

25

Cote

d’Ivoire

630

16

36.7

2.9

8.3

45

46

40

Cameroon

580

15

60.9

5.4

12.7

48

50

43

Etiopı́a

100

66

40.9

4.4

7.8

41

43

24

Ghana

290

20

43.6

1.4

3.0

55

57

64

Guinea

410

838.1

0.6

1.4

46

47

34

Gambia

320

162.2

0.5

1.4

52

55

70

Kenya

350

31

49.7

6.0

15.6

46

47

63

Madagascar

260

16

62.0

0.1

0.2

54

57

26

Mozambique

210

18

68.9

6.1

14.7

41

43

36

Malawi

160

11

63.9

6.3

14.9

38

39

64

Nigeria

290

130

63.4

35.8

45

47

67

Uganda

260

23

48.2

24.6

43

43

65

Zambia

132

10

66.7

8.1

21

37

38

73

Note:Data

forallcolumnsexceptcolumn4belongto

theyear2001.Column4provides

thelatest

available

estimate

oftheincidence

ofpoverty,

calculatedbyauthors.

Source:

WorldBank:WorldDevelopmentIndicators,2003,andauthors’calculations.


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living alone) constitute a very small percentage of the population in sample countries.While the share of the elderly in total population is high in somewest African countries,the proportion of the elderly living alone is very small in these countries. By contrast inmany east African countries, the share of the elderly in total population is low(presumably because life expectancies are low), but the proportion of the elderlyliving alone is somewhat higher than for west African countries, again presumablydue to relatively high AIDS-induced mortality of the middle-aged population in eastAfrica.One of the consequences of high adult mortality (either due to AIDS or due to

conflicts or both) is that the elderly may have become caregivers for children, in whichcase a household type of ‘elderly with children’ becomes important. Column 4 in Table4 presents the percentage of population living in such households. The proportionranges from a low 0.06 per cent in Gambia to a high 1.34 per cent in Uganda, 1.38 percent in Malawi, and 1.30 per cent in Burundi.6 It is worth noting that the householdtype ‘elderly with children’ existed even prior to the AIDS pandemic with working-ageadults migrating to cities, leaving children behind with elders in rural areas.Another household type that is of interest is ‘households headed by the elderly’.

The head of the household is defined in the surveys as any person, male or female, atleast 15 years old, who is regarded by other members of the household as their head,and who is generally the main breadwinner in the household.7 These are householdsin which the elderly are the breadwinners with or without prime age adults living.This is shown in the last column of Table 4. Nearly a quarter of all households areheaded by the elderly in Burkina Faso, Guinea and Gambia. This proportion isbetween 11 to 15 per cent in Madagascar, Mozambique, Malawi and Zambia. In theremaining countries the proportion varied between 16 and 20 per cent.The above findings show much heterogeneity across countries with respect to the

proportion of elderly population, the living arrangements of the elderly and householdheadship by age. The variations in household types and living arrangementspresumably reflect the variations in, and the changing character of, the traditionalextended family system and household coping strategies across countries in the wakeof the HIV-AIDS pandemic, regional conflicts and migration patterns.

Figure 1. Population share of elderly, in %. Source: Authors’ calculations from householdsurveys


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Poverty among the Elderly (Head-count Ratio)

The varying living arrangements of the elderly have prompted us to consider four ques-tions pertaining to differences in the incidence of poverty. Is the incidence of poverty:

(a) higher in households where the elderly are living, compared with the average?(b) higher in households where the elderly and children are living, compared with

the average? and(c) higher in households headed by the elderly than the national average/(d) Are the patterns similar for the incidence of the poverty gap ratio?(e) How statistically significant are these difference?

These questions are explored in Table 5. In 11 out of 15 countries, the incidence ofpoverty among households in which the elderly are living (we call them ‘mixedhouseholds’) is higher than the average; in nine countries the differences arestatistically significant. In Malawi and Zambia, where the incidence of the HIV-AIDS is very high, the differences are very large and statistically significant.8

In 10 out of 15 countries, the incidence of poverty in households where the elderlyare living with children (usually grandchildren) is higher than the average; thedifferences are statistically significant in eight countries, which include Malawi,Uganda and Zambia where the HIV-AIDS prevalence rates are high. The findingseems to confirm the generally held impression that the incidence of poverty amongthe elderly is exacerbated when they become caregivers for children. In Malawi,Uganda and Zambia, households in which the elderly are living with children is 20percentage points higher than the average and statistically significant.

Table 4. Population share by household type

CountryNo elderlypersons

Elderlypersonsonly

Elderly andchildrenonly

Mixedhouseholds

Not headedby elderly

Headed byelderly

1 2 3 4 5 6 7

Burundi 85.21 0.57 1.30 12.92 86.46 13.54Burkina Faso 58.86 0.26 0.43 40.46 74.38 25.62Cote d’Ivoire 74.93 0.40 0.39 24.49 82.07 17.79Cameroon 69.72 0.41 0.47 29.40 81.13 18.83Etiopı́a 79.78 0.50 0.88 19.03 83.99 16.12Ghana 75.11 1.22 1.23 22.70 81.69 18.04Guinea 60.44 0.36 0.98 38.22 74.70 25.30Gambia 53.80 0.11 0.06 46.02 72.87 27.13Kenya 82.62 1.36 0.98 15.25 84.98 15.23Madagascar 84.89 0.67 0.64 13.78 88.08 11.61Mozambique 81.30 0.77 0.84 17.32 86.34 13.90Malawi 83.46 0.84 1.38 14.33 86.46 13.54Nigeria 79.41 1.27 0.80 18.61 83.30 16.82Uganda 78.16 0.89 1.34 19.83 83.16 17.08Zambia 83.83 0.46 0.39 15.33 87.52 12.48

Source: Authors’ calculations.


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Table

5.Theincidence

ofpoverty

forallpersons,andfordifferenthousehold

types,headcountratio(%

)

Country

Noelderly

persons

Elderly

persons

only

Elderly

and

children

only

Mixed

households

withelderly

Notheaded

byelderly

Headed

by

elderly

All

persons

Burundi(1998)

61.6

67.5

58.6

59.0

61.4

59.8

61.2

BurkinaFaso

(1998)

48.9

47.0

59.3

58.1*

51.0

57.5*

52.6

Cote

d’Ivoire(1998)

33.1

46.1

60.7*

47.1

34.5

46.9*

36.7

Cameroon(1996)

58.7

45.9

32.8

66.7*

58.6

70.8*

60.9

Ethiopia

(2000)

40.3

42.4

42.3

43.5*

40.1

45.2*

40.9

Ghana(1998)

40.9

32.4

57.7*

52.1

42.2

49.6*

43.6

Guinea

(1994)

34.8

37.5

58.4*

42.8*

37.0

41.1

38.1

Gambia

(1998)

53.9

58.6

31.7

72.0*

58.5

72.0*

62.2

Kenya(1997)

47.9

45.4

55.2*

59.3*

48.1

58.8*

49.7

Madagascar(2001)

62.3

50.0

62.1

60.5

62.1

61.1

62.0

Mozambique(1996)

69.2

53.8

75.5*

67.6

68.8

69.8*

68.9

Malawi(1997)

62.3

62.9

82.3*

71.8*

62.4

73.8*

63.9

Nigeria

(1996)

61.6

34.3

64.2

72.8*

62.5

67.9*

63.4

Uganda(1999)

48.6

56.4*

65.2*

45.1

48.3

47.4

48.2

Zambia

(1998)

64.4

72.0*

90.2*

78.6*

64.8

80.1*

66.7

*represents

thattheratioisstatisticallysignificantlydifferent(at5%

level

or10%

level)from

theaverageforallpersons.

Source:

Authors’calculations.


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In 12 out of 15 countries the incidence of poverty in households headed by theelderly is higher than the average; the differences are statistically significant in 11countries.

An interesting finding is that the ‘elderly persons only’ (i.e. elderly living alone) arenot worse off than the average except in Uganda and Zambia. In fact, in mostcountries the incidence of poverty among the elderly living alone is lower than theaverage. In Uganda and Zambia not only is the proportion of elderly living alonehighest among sample countries but also this group depicts a higher than averageincidence of poverty. It is worth noting, however, that while the incidence of head-count poverty among the elderly living alone is not very different from the average inmost countries, the depth of poverty among the elderly living alone is much higherthan the average (see the discussion on poverty gap ratio below).

Poverty Gap Ratio

From the perspective of an individual or household’s deprivation, poverty gap ratiois more instructive than head count poverty.9

Cross-countrypatterns inpovertygap ratioare similar to thoseobserved for thehead-count ratio (Table 6). Households with elderly and children showmuch higher povertygap ratios than the average in 11 countries, and the differences (from the average) arestatistically significant in eight countries. As with the head-count ratio, the elderlydisadvantage furtherworsenswhenwe consider households headed by the elderly. In 13out of 15 countries, households headed by the elderly exhibit higher than averagepoverty gap ratios, and the differences are statistically significant in 11 countries.

IV. Social Pensions for the Elderly: Impacts and Costs

Fiscal Cost of Filling the Poverty Gap among the Elderly

We now examine the pros and cons of assisting the elderly with a non-contributorysocial pension programme. We proceed with the analysis as follows. First, we look atthe fiscal implications of the best of all options from the perspective of the elderly,viz. filling the poverty gap among different household types housing the elderly fortypical low-income countries of Africa. The analysis in later sections is done with afixed (hard) budget constraint (assuming a level of spending of 0.5 per cent of GDP),and a fixed benefit level (70 per cent of the national average poverty threshold). Wethen consider four different categories of potential beneficiaries: a social pension to(a) all elderly individuals regardless of their income/wealth status; (b) elderly withchildren but with no prime age adults, (c) poor among the elderly, i.e. those elderlywho are living in households below the national poverty line, and (d) all householdsheaded by the elderly. The main purpose is to assess which household group is a goodcandidate for a social pension programme, i.e. yields the maximum possible gains innational poverty reduction, with a given budget and with a given benefit level.

Once we recognise the fact that the elderly live in extended families, and allow forthe possibility that any assistance to the elderly will be shared by all in the family,eliminating the poverty gap of households in which the elderly live would requireconsiderable budgetary resources (Table 7). For example, in Burkina Faso, while


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Table

6.Poverty

gapbyhousehold

types

Country

Noelderly

persons

Elderly

persons

only

Elderly

and

childrenonly

Mixed

households

Notheaded

byelderly

Headed

byelderly

Allpersons

Burundi

26.2

27.0

33.6

23.1

26.2

24.3

25.9

BurkinaFaso

14.6

12.2

18.8

18.3

15.4

18.6

16.7

Cote

d’Ivoire

10.0

16.0

25.1

14.3

10.5

13.9

11.1

Cameroon

22.6

23.8

21.1

25.3

22.5

27.3

23.4

Ethiopia

9.9

12.1

10.7

11.0

9.9

11.4

10.2

Ghana

14.4

12.0

22.3

19.8

14.9

18.9

15.7

Guinea

10.2

13.0

21.7

14.0

10.9

14.3

11.8

Gambia

20.9

24.7

11.8

31.0

23.7

30.6

25.6

Kenya

17.1

15.9

21.6

21.0

17.1

21.2

17.7

Madagascar

27.1

17.6

25.1

26.1

27.1

25.1

26.9

Mozambique

29.4

19.2

31.9

29.8

29.2

31.3

29.4

Malawi

26.5

25.6

33.7

29.6

26.5

30.5

27.1

Nigeria

28.3

12.1

26.8

38.1

29.0

34.1

29.9

Uganda

16.7

20.1

22.9

15.9

16.6

17.2

16.7

Zambia

32.8

41.6

59.3

44.1

33.0

46.5

34.7

Source:



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individual poverty gap among the elderly can be eliminated with only 0.2 per cent ofGDP, it would require twice as much for filling the poverty gap among the elderly withchildren, and 13 times more resources for filling the poverty gap among elderly headedhouseholds. Results are similar for other countries. In 10 out of 15 countries 2 to5 per cent of GDP would be required to fill the entire poverty gap among householdsheaded by the elderly – clearly not affordable for most countries. Even to fill thepoverty gap among the elderly with children – a small proportion of the population inall countries – the resources required ranged from 0.1 to 0.5 per cent of GDP.

Considering that any programme of social pension to fill the poverty gap ofhouseholds with the elderly is fiscally unaffordable, we examine the implications of asocial pension with a fixed budget constraint, and with a fixed benefit level, in thenext section.

Simulation Results with a Fixed Budget Constraint and Benefit Level

In deciding on the threshold level of the budget constraint, we relied on internationalexperience. Thus in advanced OECD countries, total public spending on socialsecurity amounted to 2 to 3 per cent of GDP. In India, the total expenditure onvarious safety net programmes including old age pensions amounted to 1.5 to 2 percent of GDP.10 Brazil, Namibia and South Africa spend 1, 2 and 1.4 per cent of GDPrespectively on old age pensions. An expenditure level of 0.5 per cent of GDP fornon-contributory social pension may be considered the upper boundary for low-income sub-Saharan countries based on three considerations: (a) most sub-Saharancountries have incomes much lower than low-income countries of south Asia andLatin America; (b) there may be groups poorer and more vulnerable than the elderlycompeting for social safety net expenditures; and (c) the demands on public spending

Table 7. Budgetary cost as % of GDP required to eliminate poverty gap by household types

Country

Elderlypersonsonly

Elderly andchildren only

Mixedhouseholds

Not headedby elderly

Headed byelderly

1 2 3 4 5 6

Burundi 0.24 0.51 3.24 23.71 3.72Burkina Faso 0.01 0.02 1.28 2.40 0.81Cote d’Ivoire 0.04 0.04 1.21 3.26 0.90Cameroon 0.06 0.05 2.89 7.53 2.02Etiopı́a 0.09 0.11 2.24 8.82 2.05Ghana 0.16 0.21 3.01 8.36 2.39Guinea 0.04 0.13 2.85 4.44 2.01Gambia 0.03 0.01 9.10 11.71 5.51Kenya 0.18 0.14 1.83 8.27 1.92Madagascar 0.07 0.07 1.39 9.39 1.14Mozambique 0.19 0.27 4.49 22.94 3.92Malawi 0.28 0.46 3.90 21.53 3.97Nigeria 0.09 0.10 2.83 10.34 2.39Uganda 0.15 0.19 1.73 7.52 1.71Zambia 0.19 0.17 4.49 19.77 3.99



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from other priority sectors such as health and education may be quite high: forexample, in our sample 15 African countries, the total public expenditure on healthranged from 1.5 to 2 per cent of GDP.As for the absolute level of the benefit, there is much variation across countries,

and international experience is less helpful as a guide. We work with 70 per cent ofthe national average poverty threshold, given the large income gap (from the povertyline) for some critical vulnerable groups such as the elderly with children. Thesethresholds are meant to be illustrative, to understand the implications of targetingvarious categories of the elderly. Simulations can be done with other thresholds aswell, depending upon the prevailing country situation with respect to poverty, fiscalaffordability and competing demands from other sectors.With a hard budget constraint of 0.5 per cent of GDP, the impacts of providing a

social pension to the elderly aged 60 and above living in various living arrangementsare shown in Table 8. The simulation assumes that though the pension is given to theelderly, it is shared within households. In 11 out of 15 countries, the impact onnational poverty reduction of targeting social assistance pensions to households withelderly and children (column 4) is higher than what could be obtained by targetingit to the elderly only group (column 2). The poverty reduction impacts of targetingthe household type ‘households with elderly and children’ are particularly large intwo countries devastated by AIDS, viz. Uganda and Malawi.11

We now compare households headed by the elderly with those not headed by theelderly (the last four columns of Table 8). The reduction in head-count povertyaccomplished by targeting the elderly headed households is certainly very impressivefor that particular group. As for impacts on national poverty reduction, the impact isgreater than targeting all of the elderly in 10 out of 15 countries (comparing column8 and column 2). Comparing the impacts of targeting by household headship, wefind that in five out of 15 countries the reduction in national head-count poverty isgreater if the programme is targeted to households headed by the elderly than forthose not headed by the elderly. The opposite is the case for eight countries; and fortwo countries the differences in reduction of national poverty between targeting thetwo groups are small.Table 9 reports results of the simulations with respect to impacts on the poverty

gap ratio for households headed by the elderly, and those not headed by the elderly.Unlike in the case of head-count poverty, targeting all elderly headed households fora social pension results in a much more pronounced reduction in the nationalpoverty gap ratio than if it were targeted to non-elderly-headed households. Whatthis implies is that most elderly headed households have higher poverty gap ratios(i.e. their welfare condition is much worse) and so any assistance targeted to themreduces the poverty gap ratio substantially even if it does not enable them to crossthe poverty line.In summary, simulations suggest that the gains in poverty reduction (both group-

specific and national) obtained by targeting a social pension to elderly-headedhouseholds and elderly and children groups are much stronger than the gains inpoverty reduction obtained by an universal pension to all the elderly regardless ofhousehold types and poverty status. However, implementing a pension programmetargeted to specific household types may be operationally difficult. The potential foradverse incentive effects can be large. For example, if ‘single’ elderly are targeted for


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Table

8.Per

centchangein

head-countpoverty

ratioandnationalhead-countratiowithsocialpensionexpenditure

of0.5

percentofGDP,by

household

types

Country

Elderly

only

Elderly

andchildrenonly

Householdsnotheaded

by

theelderly

Householdsheaded

bytheelderly

Group-specific

National

Group-specific

National

Group-specific

National

Group-specific

National

12

34

56

78

Burundi

769.7

70.4

723.1

70.3

70.5

70.4

71.9

70.2

BurkinaFaso

7100.0

70.2

7100.0

70.5

72.1

71.5

73.6

71.0

Cote

d’Ivoire

7100.0

70.5

7100.0

70.7

72.5

71.9

722.1

75.0

Cameroon

7100.0

70.3

7100.0

70.3

71.2

71.0

75.9

71.3

Etiopı́a

793.4

70.5

792.2

70.8

71.7

71.4

75.5

71.0

Ghana

758.8

70.5

771.8

71.2

71.4

71.1

74.0

70.8

Guinea

7100.0

70.4

797.7

71.5

72.4

71.7

72.8

70.8

Gambia

7100.0

70.1

7100.0

0.0

71.4

71.0

72.3

70.7

Kenya

766.3

70.8

799.1

71.1

71.4

71.2

77.5

71.4

Madagascar

7100.0

70.5

7100.0

70.6

71.3

71.2

78.5

71.0

Mozambique

776.4

70.5

777.8

70.7

70.3

70.3

73.4

70.5

Malawi

760.9

70.5

746.0

70.8

70.6

70.5

72.1

70.3

Nigeria

791.3

70.6

7100.0

70.8

70.6

70.5

76.9

71.2

Uganda

792.8

71.0

788.3

71.6

71.5

71.3

75.5

70.9

Zambia

799.8

70.5

7100.0

70.5

70.6

70.5

72.7

70.4

Source:



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a social pension, it is just possible an elderly person (a grandma or a grandpa)currently living in an extended family household setting might be ‘kicked out’ tofetch a pension. If a household type such as ‘elderly and children’ is targeted, it is justpossible extended family households might ‘adjust’ their family structure to fit inwith the category for which a pension is eligible. Thus, while some of the householdtypes with the elderly identified above are undoubtedly prone to higher levels ofpoverty than the average, targeting those specific categories might be difficult inpractice.Bearing the above practical difficulties in mind, one could consider only two

categories of the elderly regardless of which household setting they are living: allelderly persons, and all poor elderly persons (i.e. the elderly living in householdsliving below the national poverty line.) In the first case the only requirement fortargeting is an assessment of the ‘elderly status’ of the person living in the household,and in the second case an assessment of the elderly status as well as the poverty statusof the household would be required. The household characteristics, including thecategories identified above, may be good candidates for a social pension, and can beused to design a proxy means test to determine the poverty situation of thehousehold.The poverty reduction impacts of targeting to all elderly persons, all poor elderly

persons, are shown in Table 10 for head count poverty and for the poverty gapratio.12 With budgetary resources equal to 0.5 per cent of GDP, a social pensiontargeted only to poor elderly leads to almost twice as much reduction in nationalpoverty ratio than if the resources were spent on social pension to all the elderly. Forexample, in Burundi, whereas extending social pension to all the elderly (60þ) leads

Table 9. Per cent change in poverty gap at national level with a social pension expenditure of0.5 per cent of GDP, by household type

Country

Not headed by the elderly Headed by the elderly

Impact onthe group

Nationalimpact

Impact onthe group

Nationalimpact

1 2 3 4

Burundi 71.3 71.2 78.3 71.1Burkina Faso 74.0 72.9 712.5 73.4Cote d’Ivoire 75.8 74.5 726.6 75.9Cameroon 74.1 73.2 717.8 73.9Ethiopia 72.3 71.9 711.0 72.0Ghana 72.6 72.0 710.5 72.3Guinea 74.2 72.9 710.4 73.2Gambia 72.5 71.7 76.7 72.2Kenya 73.0 72.4 715.3 72.8Madagascar 73.4 73.0 726.4 72.9Mozambique 71.5 71.3 78.9 71.3Malawi 71.5 71.3 79.5 71.5Nigeria 73.1 72.5 714.0 72.7Uganda 73.3 72.7 714.1 72.5



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to 0.42 per cent reduction in national poverty, it leads to 0.92 per cent reduction if itwere targeted to poor elderly. Moreover, in the same country, targeting theprogramme to all of the elderly (60þ) seems (column 2) to be an inferior option (interms of national poverty reduction impacts) compared with the option of devotingthe same resource to all households whether or housing the elderly (column 4). Theresults are similar for all countries, and for the poverty gap ratio.

We now replace the fixed budget constraint with a fixed benefit level. If theobjective were to provide a social pension equal to 70 per cent of the nationalaverage poverty threshold to all elderly, and to poor elderly, (a) how much does thiscost? and (b) what are its poverty reduction impacts/The results are summarised inTable 11.

A universal social pension to all elderly is a very expensive policy and mostcountries of Africa may not afford it (column 2, Table 11). It is very doubtful if thislevel of spending on an universal social pension programme for the elderly isjustifiable on welfare grounds, especially in countries such as Madagascar,Mozambique, Malawi and Nigeria where, as shown in Table 5, mixed householdsare poorer on average than the elderly. Means-testing the social pension to the pooramong the elderly would substantially lower the budgetary cost (column 3), providedthe administrative cost of means-testing is not large.

If the pension is targeted to the poor among the elderly, a pension of even a lowerbenefit level of 35 per cent of poverty threshold will lead to a reduction of about twoto three percentage points in the national head-count poverty. Though we have notshown the results, the impacts on the reduction in the national poverty gap ratio arealso substantial and in the same direction.

Table 10. Per cent change in national head-count poverty, and in the national poverty gapratio, with a social pension expenditure equal to 0.5% of GDP, by household types

Country

% change in head-count poverty % change in poverty gap ratio

All elderly60 years þ

Poor elderly60 years þ

Allpersons



Allpersons

1 2 3 4 5 6 7

Burundi 70.42 70.92 70.48 70.92 71.46 71.14Burkina Faso 71.63 72.99 71.44 72.78 74.25 73.08Cote d’Ivoire 73.17 74.88 71.82 73.99 75.34 74.90Cameroon 70.98 72.32 71.33 72.92 74.23 73.38Ethiopia 71.00 72.32 71.55 71.71 73.34 71.92Ghana 70.78 72.07 71.01 71.88 73.75 72.06Guinea 71.44 73.49 71.15 72.97 75.89 73.01Gambia 70.73 71.13 70.81 71.87 72.65 71.85Kenya 71.08 72.15 71.31 72.16 73.55 72.50Madagascar 71.18 72.45 71.24 72.06 73.00 72.99Mozambique 70.44 70.66 70.37 71.10 71.62 71.32Malawi 70.53 70.76 70.33 71.27 71.73 71.29Nigeria 71.03 71.66 70.70 72.05 73.18 72.54Uganda 71.11 72.38 71.06 72.26 73.76 72.70Zambia 70.41 70.54 70.59 71.52 71.88 71.45



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Caveats and Limitations

While the above simulations do cast doubts on the fiscal affordability anddesirability (on welfare grounds) of universal social pensions for the elderly, andunderscore the gains in targeting social pensions to those most in need, more work isneeded to assess the administrative costs and feasibility of implementing a means-testedsocial pension. Second, the potential adverse incentive effects also need to be takeninto account while designing a targeted social pension programme. From theperspective of avoiding such adverse incentive effects and potential (artificial)changes in household types and compositions in response to a social pensionprogramme provided to a specific household type, it appears preferable to providethe pension to all poor elderly i.e., all poor households in which the elderly live, for thepension benefit.13 In brief, while there is a case for targeted non-contributory socialpensions for the elderly from a (national) poverty reduction standpoint, caution isneeded in selecting the right household group in which the elderly are living whileimplementing a social pension policy.

Universal Versus Means-tested Social Pensions

The choice between keeping the eligibility for a social pension universal versuskeeping it means-tested is much debated in the literature.14 This study extends thedebate by introducing the poverty reduction impacts of various options. The mainfinding is that with a budget limit of 0.5 per cent of GDP – a fiscally sustainable cost

Table 11. Cost of social pension benefit equal to 70% of poverty threshold expenditure, as% of GDP, and per cent change in poverty ratio among the poor elderly

Country

Cost of social pension equalto 70% of poverty

threshold, as % of GDPPercent change in head count poverty

ratio among poor elderly



Pension¼ .70%poverty threshold

Pension¼ .35% ofpoverty threshold

1 2 3 4 5

Burundi 3.05 1.81 72.98 71.49Burkina Faso 2.11 1.19 75.72 72.86Cote d’Ivoire 1.11 0.52 74.92 72.46Cameroon 1.55 0.97 74.59 72.29Ethiopia 3.74 1.63 75.16 72.58Ghana 3.30 1.50 76.06 73.03Guinea 2.66 1.17 77.28 73.64Gambia 2.83 1.93 75.56 72.78Kenya 2.06 1.11 74.68 72.34Madagascar 1.15 0.64 73.13 72.56Mozambique 2.92 1.93 73.66 72.33Malawi 3.13 2.24 74.41 72.20Nigeria 1.68 1.00 74.07 72.03Uganda 1.86 0.97 74.45 72.22Zambia 1.68 1.33 72.43 71.21



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for most countries – if one were to choose 60þ as the cut-off point for eligibility,targeting (and means-testing) the pension to the poor among the elderly, rather thanrendering the eligibility universal, appears to yield the best possible results in povertyreduction of both the elderly in need and for national poverty reduction. In otherwords, bearing all factors into consideration, the case for universal untargeted socialpensions for the elderly appears rather weak. Perhaps with a higher age cut-off point,say 75þ, it might be possible to render eligibility universal and also keep the fiscalcost within the 0.5 per cent of GDP for most countries, but then the number ofbeneficiaries (surviving up to 75 years) will be so few and the overall benefits of thesocial pension will be so limited in scope as to be almost inconsequential in terms ofpoverty reduction at the national level, while leaving the majority of the poor amongthe surviving elderly in the age group 60–75 untouched by the pension programme.On the other hand, keeping it universal with an eligibility cut-off at 60þ will beunjustifiable on welfare grounds, apart from being fiscally so expensive as to beunsustainable for most low-income countries with relatively high incidence ofaverage poverty and poverty gap ratios.

It is often asserted that considerations of fiscal sustainability are far-fetched becausethey ignore the potential for economic growth in these low-income countriesSimulations done by Smith and Subbarao (2003) show that typical low-incomecountries, even to keep the absolute number of the poor constant, need to grow at 5 to7 per cent per annum, whereas the actual (realised) growth of GDP for most low-income African countries was less than 3 per cent per annum in the recent past. Thissuggests that the fiscal leverage from economic growth is likely to be extremely limited,if not nil, for low-income countries of Africa in the medium term, and so the argumentthat universal social pensions can sustained in a ‘growth scenario’ is tenuous at best.

In sum, it appears desirable, in the larger interest of the elderly themselves, totarget the pension to the poor among the elderly keeping the age cut-off at 60þ (or65þ), and encourage country-specific work on the feasibility of creative and cost-effective approaches to targeting. As pointed out by Coady et al. (2004) in theirsurvey of approaches to targeting, often a single cross-section household surveywould be enough to assess the costs and benefits of various approaches to targeting(simple means tests, proxy means tests, categorical, self-selection, communitytargeting) in a given country situation. This survey also notes that often it should bepossible to combine different approaches: in this case a social pension to the elderlyabove 60 or 65 years of age (categorical) could be combined with means testing(individual assessment) or proxy means testing, and if the benefit level is kept lowenough to be unattractive to the non-poor as was done in Nepal,15 it could induceself-selection as well. In addition, recent innovations in delivery such as imposingconditions for the receipt of transfer can avoid adverse incentive effects and leakages.

V. Conclusions and Implications for Policy

The main objective of the study is to delineate the poverty among the elderly in 15low-income sub-Saharan countries and to assess the role of social pensions for theelderly. The study finds that, when defined by household structure, the elderly-only,elderly with children and elderly-headed households are poorer than others in 11 outof 15 sample countries. The findings suggest that even in the 11 countries where certain


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categories of the elderly happen to be at a higher risk of poverty, the case for anuniversal social pension for all of the elderly is weak both on welfare grounds, and onconsiderations of fiscal affordability. For example, for a typical country consideredin our sample, an universal social pension for all the elderly above 60 years of agewould cost about 2 per cent of GDP, a level comparable to, or higher than, thecurrent levels of spending on healthcare. Increasing the age cut-off to 70þ or 75þmight lower costs, but few would be eligible for the pension, and it would have littleimpact on poverty reduction at the national level.The study finds, however, that there is a case for a non-contributory social pension

to some of the elderly in all countries. Further detailed analysis and simulations suggestthat from the perspective of maximum impacts on reduction in poverty among thepoor elderly, and for national poverty reduction, there appears to be a need for a non-contributory pension programme restricting the eligibility to the poor among theelderly. Considerations of affordability and fiscal sustainability suggest that it is best tolimit the benefit level to about one-third of the poverty threshold, for the the eligibilityage threshold to be 60þ or 65þ and to explore alternative non-income-based methodsof targeting to restrict the pension only to the poor among the eligible elderly.Poverty-targeting of the pension programme has undoubtedly some limitations.

Even the best targeted approach entails an irreducible element of randomness thatleads to inclusion and exclusion errors.16 Fortunately, several recent innovativeapproaches in targeting, including proxy means tests and community-driventargeting coupled with conditional transfers, offer much scope for reducing theerrors in targeting, especially since most African countries now have at least onelarge, nationally representative household survey in the post-HIV-AIDS period(around the year 2000).17 Community-driven approaches can also be successful inreaching the needy in Africa.18 Given the heterogeneity of the situation of the elderlyacross the 15 countries, more country-specific work is needed to exploreadministratively feasible, cost-effective and non-income-based targeting options toreach the poor among the elderly with a social pension.

Acknowledgements

For excellent comments and suggestions, the authors wish to express their gratefulthanks to Arvil Van Adams, Louise Fox, Marito Garcia, Margaret Grosh, RobertHolzmann, Valerie Kozel, Robert Palacios, Menahem Prywes and Anita Schwarzand two anonymous referees and the editor of this journal. This study would nothave been possible without the generous assistance of Christophe Rockmore, PascalHeus and Rose Mungai in data management. The findings, interpretations andconclusions expressed in this study are entirely those of the authors. They do notnecessarily represent the views of the UNDP or the World Bank, its executivedirectors or the countries they represent.

Notes

1. The household as defined in the surveys does not include migrants.

2. The household survey information for Guinea belongs to 1994, and that of Cameroon and

Mozambique to 1996.


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3. The estimates of the welfare profile in section III, and costing and targeting simulations in Section IV,

are done with the elderly defined as 60þ.4. This class of measures exclude Sen’s (1976) and Kakwani’s (1980) poverty measures, which are based

on interdependent utility function and therefore are not additively separable.

5. There is no accepted definition of the ‘elderly’ in terms of the age limit. In this paper we use an age cut-

off of 60 years and above to define the elderly. Given the current very low levels of life expectancy in

most sub-Saharan countries, an age cut-off limit of 60 years appears reasonable.

6. We are presenting only national level aggregates. It is just possible that there are significant intra-

country differences. An analysis of intra-country differences is beyond the scope of this paper.

7. It is difficult to verify whether actually this definition has been followed in all surveys.

8. The figures of head count poverty reported in Tables 3 and 5 are not consistent with the published

national poverty incidence figures because we adjusted the poverty lines in this study for economies of

scale.

9. Poverty gap ratio is defined as the mean income shortfall below the poverty line as a proportion of the

poverty line, with non-poor having zero income shortfalls.

10. It is difficult to estimate the amount spent on the elderly because the government runs both a pension

programme and also an in-kind Annapoorna (food distribution) programme.

11. Targeting this particular household type for social pension cannot solve the wider problem of orphans

and vulnerable children because, as noted by Subbarao and Coury (2004), these children live in other

household types as well.

12. For the purposes of this simulation, we are abstracting from the administrative costs of targeting

which undoubtedly will be incurred, both for identifying the elderly and the poor among the elderly.

13. Bearing in mind fiscal affordability and sustainability over time, it is not surprising that countries such

as India have restricted the old age pension only to the poor among the elderly.

14. There is only limited experience with respect to social pension programmes in Africa, and

evaluations of such experience are even scarcer. Two notable exceptions are South Africa and

Namibia. The eligibility for the South African social pension programme is determined by age. So

it is simple to administer, though the requirement of birth certificates might exclude some eligible

individuals. An evaluation by Case and Deaton (1998) has shown that the programme, though

universal, is largely pro-poor, and women benefited more than men due to the higher life

expectancies of the former over the latter. Their analysis of behavioral responses is not definitive.

Subbarao (1998) noted that the eligibility for the Namibian social pension programme is also

determined by age, and is largely pro-poor. However, exclusion errors were pervasive due to its

complex and documentation-intensive registration procedures. The experience thus far in Africa is

limited to middle-income countries where affordability is less of a concern. There are many who

recommend old age pensions to be universal. However, in low-income African countries where

competition for scarce resources is fierce, it is doubtful if universal (untargeted) social pensions are

affordable on fiscal grounds or desirable on welfare grounds. For an overview of targeting

approaches, see Coady et al. (2004).

15. See Palacios and Rajan, 2004.

16. As Subbarao et al. (1997) noted, ‘screening out the poorest (exclusion errors) is a bigger problem than

including the non-poor (inclusion errors) in the targeting of any safety net transfer program; too much

fine tuning in targeting may actually hurt the poor if the program loses political support’.

17. See Coady et al., 2004.

18. For an overview of community-driven approaches to targeting social assistance transfers, see Conning

and Kevane, 2001.

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Barrientos, A. (2004) Cash transfers for older people reduce poverty and inequality. Draft background

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Poverty among the elderly in Sub-Saharan Africa and the role of social pensions

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Transcript of Poverty among the elderly in Sub-Saharan Africa and the role of social pensions