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Self-assessed well-being and economicrank in South AfricaDorrit Poselaa Professor, Development Studies, Howard College Campus,University of KwaZulu-Natal, Durban 4041, South Africa.Published online: 17 Dec 2013.

To cite this article: Dorrit Posel (2014) Self-assessed well-being and economic rank in South Africa,Development Southern Africa, 31:1, 51-64, DOI: 10.1080/0376835X.2013.851020

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Self-assessed well-being and economicrank in South Africa

Dorrit Posel1

This study explores subjective measures of well-being in South Africa collected in the first two

waves of the National Income Dynamics Study. These subjective measures include individual

life satisfaction, current self-assessed economic rank and expected economic rank in the future.

The paper describes how the distributions of these measures have changed over the course of

the panel and it investigates the relationship between life satisfaction and perceived economic

rank in a multivariate context, controlling for individual fixed effects. The panel data suggest a

leftward shift in the distribution of life satisfaction over the two waves. Moreover, the majority

of adults did not perceive their economic rank as having improved and they reported lower

expectations of future upward economic mobility. Perceptions of current and future economic

rank are key correlates of life satisfaction, findings that remain robust to controls for

unobserved individual heterogeneity.

Keywords: subjective well-being; life satisfaction; relative income; perceived economic rank

JEL codes: D31; D63; I31

1. Introduction

Most nationally representative household surveys in South Africa collect data on money-

metric measures of well-being, which are then used to generate statistics on poverty and

inequality. However, these measures may be limited in several ways. First, they typically

are not able to identify differences in economic well-being within the household when all

resources in the household are not equally shared. Second, income received or spent

captures only one aspect of economic status specifically and of well-being more

generally, and a wide range of other factors will also affect an individual’s quality of life.

In recent decades, subjective indicators increasingly have been used to complement

money-metric indicators of well-being. There is by now a substantial international

literature that explores the measurement and correlates of self-reported happiness

(or life satisfaction) and perceived economic status (for reviews, see Stutzer & Frey,

2010; Ravallion, 2012). However, although research on income poverty and inequality

in South Africa is very well established, there has been little work on subjective

indicators, at least partly because this information was not regularly available in

national household survey data. Data collected in the National Income Dynamics

Study (NIDS) provide a unique opportunity to augment income measures of well-

being for South Africa because NIDS is the first household panel survey that includes

a range of questions asking respondents to provide subjective assessments of their

individual well-being, in addition to collecting detailed information on income and

expenditure.

1Professor, Development Studies, Howard College Campus, University of KwaZulu-Natal, Durban4041, South Africa. Correspondence: posel@ukzn.ac.za

Development Southern Africa, 2014

Vol. 31, No. 1, 51–64, http://dx.doi.org/10.1080/0376835X.2013.851020

# 2013 Development Bank of Southern Africa

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This study analyses two sets of self-assessed measures of well-being: life satisfaction and

perceived economic status. Section 2 outlines the nature of information collected in

NIDS and considers how the distributions of life satisfaction and perceived economic

status have changed across the two waves. The section also probes how self-assessed

economic status differs from economic status measured using reported income.

Section 3 estimates the correlates of self-assessed life satisfaction using pooled and

fixed-effects regression analysis. A key objective is to explore the relationship

between life satisfaction and self-assessed economic status. Section 4 summarises the

main findings of the study.

2. Subjective measures of well-being

2.1 Subjective well-being or life satisfaction

Many international studies have found that when people are asked to assess how satisfied

or happy they are with their lives, their responses provide meaningful and useful measures

of their quality of life (for reviews of these studies, see Kahneman & Krueger, 2006;

Stutzer & Frey, 2010). Self-reported happiness or life satisfaction, commonly referred

to as subjective well-being (SWB) in the literature, is well correlated with a range of

other characteristics or factors that would be expected to influence an individual’s

quality of life, such as unemployment or ill-health, and follow-up studies have found

that low levels of subjective well-being predict mortality, particularly in men, and

suicide (Koivumaa-Honkanen et al., 2000, 2001; Stutzer & Frey, 2010).

In South Africa, data on subjective well-being have been collected in a few nationally

representative household surveys, but the question on life satisfaction typically has

been asked about the household: ‘how satisfied is the household with how it lives

these days’.2 This framing of the question assumes not only that a respondent would

be able to report objectively on a household’s level of satisfaction, but more

fundamentally that there is a unified SWB function at the household level.3

In contrast, NIDS collects information on life satisfaction at the level of the individual. In

both waves 1 and 2 of NIDS, all adults were asked the following question: ‘Using a scale

of 1 to 10 where 1 means “very dissatisfied” and 10 means “very satisfied” how do you

feel about your life as a whole right now?’ (See Question M5 of the adult questionnaire,

Southern Africa Labour and Development Research Unit, 2008:23.)

Figure 1 compares responses among all adults (aged 17 years and older) in the two waves of

NIDS. Only those adults who were present in both waves as resident household members are

included in the sample for each wave. In both years, the modal level of reported satisfaction

was five. However, the distribution in reported life satisfaction has clearly shifted to the left

over the two-year period. The percentage of all adults who report being dissatisfied increased

from 2008 to 2010, while the percentage who report being satisfied has fallen. For example,

whereas 21% of all adults reported a satisfaction level of three or lower in 2008, this increased

by 10 percentage points to 31% in 2010.

The overall distribution in reported life satisfaction masks sharp cleavages by race, as

illustrated in Figure 2. Although the modal level of satisfaction reported by African

2See, for example, the 1993 Project for Statistics on Living Standards and Development and the1998 October Household survey.3Most of the earlier literature using these data overlooked this latter concern, and instead dealt withwhether a single respondent was able to report reliably on the household’s subjective well-being(Bookwalter et al. 2006; Kingdon & Knight 2006, 2007).

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adults was five (mirroring the national distribution), it was eight among white adults.

Furthermore, race differences widened over the two-year period. Both whites and

Africans reported lower levels of satisfaction overall in 2010 than in 2008, but the

decline was more pronounced among Africans. By 2010 the majority of African

adults in the panel (53%) reported a satisfaction level of four or lower, compared with

39% in 2008. The comparable figures among whites are 9% in 2010 and 7% in 2008.

The data presented in Figures 1 and 2 consider the two waves of NIDS as individual

cross-sections. Table 1 takes advantage of the panel nature of the data and describes

changes in subjective well-being for each individual. Of the 8870 adults who provided

information on their subjective well-being in both waves of the panel, only about 15%

reported the same level of satisfaction in 2010 as in 2008, although the percentage

among whites specifically is considerably higher (25%). Over one-half of all adults

reported lower levels of subjective well-being in 2010 than in 2008, a finding that is

driven particularly by the decline in satisfaction among Africans. Slightly more than

one-half (53%) of all African adults in the panel reported being less satisfied in 2010

than in 2008, compared with 35% of white adults.

2.2 Subjective measures of economic status

In addition to collecting subjective measures of life satisfaction, the adult module in

waves 1 and 2 of NIDS includes a number of questions asking individuals to assess

their economic status. Using a six-step ladder, with the bottom step representing the

poorest people in South Africa and the top step representing the richest, adults were

asked to identify on what step they thought their household ranked today. They were

also asked on what step they thought they ranked when they were aged 15, and on

Figure 1: Subjective well-being in South Africa, 2008 and 2010

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The data are

weighted.

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what step they expected to rank two years (and five years) in the future. (See questions

M1 to M4 of the adult questionnaire.) These questions capture information on perceived

relative economic status – relative to others in South Africa, or relative to where the

individual ranked in the past or expects to rank in the future.

Figure 3 compares the distribution of current perceived economic ranking in South Africa,

in each of the waves of the panel. In 2008, the modal ladder step was two and a little under

one-half of all adults (48%) thought that they ranked among the poorest third (steps 1

Figure 2: Subjective well-being among Africans and whites: (a) 2008 and (b) 2010

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The data are

weighted.

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and 2) of South Africans. By 2010, the modal ladder step had increased to three, and a

smaller share of adults (44%) assessed their economic status as being on steps 1 and 2

of the economic ladder. The percentage of adults who perceived their economic status

as being in the middle of the economic ladder (steps 3 and 4) also rose slightly (by three

percentage points) to 52%. However, in both years, less than 4% of adults in the sample

thought that they were among the richest third of South Africans.

Of course it is not possible for less than 4% of all adults to be among the richest one-third of

South Africans, and for more than 40% to be among the poorest third. Rather, the statistics

presented in Figure 3 suggest that a considerable share of South African adults

underestimates their relative economic position – in comparison with others in South

Africa, a sizeable group of people are actually better off than they perceive themselves to be.

Table 2 explores this further by comparing the perceived economic rank of adults with

how adults would rank using a measure of economic well-being derived using reported

income.4 To identify an income rank, the distribution of per-capita household income is

Table 1: Differences in subjective well-being among individuals in 2008 and 2010

Difference in satisfaction

level (2010 value – 2008

value)

Percentage of all

adults

Percentage of African

adults

Percentage of White

adults

–9 0.62 0.71 –

–8 0.84 1.01 0.08

–7 2.12 2.43 0.71

–6 2.78 3.10 1.74

–5 4.51 4.86 1.17

–4 6.01 6.81 1.89

–3 9.57 10.08 7.90

–2 11.60 12.18 10.45

–1 11.77 11.86 10.71

0 14.60 12.76 24.93

1 10.05 9.18 14.92

2 8.63 8.03 9.84

3 6.58 6.15 9.09

4 4.65 5.06 3.04

5 2.96 2.91 3.52

6 1.29 1.31 –

7 0.68 0.76 –

8 0.24 0.25 –

9 0.48 0.56 –

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The statistics in bold represent the

percentage of adults whose measure of life satisfaction was unchanged across the two waves of NIDS. The data

are weighted.

4In NIDS, detailed information is collected on both labour and non-labour income received byindividuals (including earnings, social grants, remittances and private pensions). Non-labourincome is collected as point values. Wages and earnings are also reported as point values exceptwhere respondents did not or would not provide this information, in which case earnings were

Self-assessed well-being and economic rank 55

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divided into thirds. Similarly the six ladders steps are grouped into thirds (with the

bottom two steps representing the lowest third, for example) (see Posel & Casale, 2011).

The table describes considerable divergence between perceived relative status and income

ranking. For example, in 2008 only 7% of adults who ranked among the richest one-third in

terms of reported income perceived their economic status as corresponding to the upper

two steps of the economic ladder. The majority (65%) perceived their relative

economic status to be in the middle of the economic ladder (steps 3 and 4). The largest

correspondence between the income rank of individuals and their perceived economic

rank occurs among adults in the bottom third of the income distribution. In 2008, 66%

of adults who were placed in the bottom third of the income distribution also perceived

that they ranked on the bottom two steps of the economic ladder.

The table suggests further that among adults in the upper third of the income distribution,

the divergence between income rank and perceived rank is considerably larger among

Africans than among whites. In 2008, for example, only 5% of Africans in the upper

third of the income distribution perceived their relative economic status as

corresponding to the richest third of South Africans; while 34% ranked their economic

status on the lowest two steps of the ladder (the comparable percentages for whites in

the upper third of income are 10% and 17% respectively).

What explains this poor match between where individuals think they rank on the

economic ladder and where they actually rank in the income distribution, and why is

the divergence larger among richer Africans than whites? One explanation is that the

Figure 3: Perceived economic ranking in South Africa, 2008 and 2010

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The data are

weighted.

reported in parentheses. To generate a continuous income variable, earnings in parentheses wereassigned the mid-point of the parenthesis.

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level of income associated with a ‘middle-class’ lifestyle may far exceed the middle of

South Africa’s income distribution, leading respondents to underestimate their ranking

on the income ladder. It is also possible that people base their assessments in the

ladder question on accumulated income and expected future income (or permanent

income), while the income rank is based on current monthly income. In the context of

large historical inequalities in access to resources, current monthly income may not be

a good predictor of permanent income, and particularly among Africans. Even though

Africans may rank in the upper third of the income distribution, their economic status

in terms of permanent income may be lower.

A further explanation is that people do not have complete or accurate information about

the economic status of others. Given racially differentiated opportunities in the past,

whites have higher levels of education and are more likely to be proficient in English,

the dominant language of business, politics and communication in the country (Casale

Table 2: Income versus perceived economic rank in South Africa, 2008 and 2010

Income rank Perceived richest Perceived middle Perceived lowest Total

2008

All

Richest third 0.07 (0.07) 0.65 (0.01) 0.28 (0.01) 1.00

Middle third 0.03 (0.00) 0.45 (0.01) 0.53 (0.01) 1.00

Lowest third 0.01 (0.00) 0.33 (0.01) 0.66 (0.01) 1.00

Africans

Richest third 0.05 (0.01) 0.61 (0.02) 0.34 (0.02) 1.00

Middle third 0.02 (0.00) 0.45 (0.01) 0.53 (0.01) 1.00

Lowest third 0.01 (0.00) 0.33 (0.01) 0.65 (0.01) 1.00

Whites

Richest third 0.10 (0.02) 0.73 (0.03) 0.17 (0.02) 1.00

Middle third 0.02 (0.02) 0.50 (0.11) 0.48 (0.11) 1.00

Lowest third 0 (0) 0.57 (0.23) 0.43 (0.23) 1.00

2010

All

Richest third 0.08 (0.01) 0.68 (0.01) 0.25 (0.01) 1.00

Middle third 0.02 (0.00) 0.46 (0.01) 0.52 (0.01) 1.00

Lowest third 0.01 (0.00) 0.39 (0.01) 0.60 (0.01) 1.00

Africans

Richest third 0.05 (0.01) 0.64 (0.02) 0.31 (0.02) 1.00

Middle third 0.02 (0.00) 0.46 (0.01) 0.53 (0.01) 1.00

Lowest third 0.01 (0.00) 0.39 (0.01) 0.60 (0.01) 1.00

Whites

Richest third 0.14 (0.02) 0.74 (0.03) 0.12 (0.02) 1.00

Middle third 0.0 (0.0) 0.22 (0.13) 0.78 (0.13) 1.00

Lowest third 0 (0) 0.08 (0.09) 0.92 (0.09) 1.00

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The data are weighted. Standard

errors are in parentheses. An individual’s income rank is calculated with reference to average per-capita total

household income. The statistics in bold represent the proportion of adults for whom perceived economic rank

coincided with income rank.

Self-assessed well-being and economic rank 57

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& Posel, 2011). Consequently whites may have access to more information when

assessing their relative economic status, helping to explain why the divergence is

smaller for this sub-sample. In addition, one of the legacies of apartheid may be that

even relatively rich Africans still perceive their economic status as being inferior,

particularly when compared with whites (Posel & Casale, 2011).

A comparison of the two years of data presented in Table 2 suggests that, over the two waves

of NIDS, perceived economic status increased primarily among adults in the bottom third of

the income distribution. In particular, a growing share of adults in the lowest income third

thought that they ranked in the middle of the economic ladder (39% in 2010, compared with

33% in 2008). To explore changes in self-assessed economic status further, Figure 4

describes the difference in reported ladder steps in 2010 and 2008, for each individual in

the panel. In 2010, a little over one-third of all adults reported being on the same ladder

step as in 2008; 31% reported being on a lower step, and 35% on a higher step. However,

Figure 5 shows that whites were more likely than Africans to perceive their economic

ranking in South Africa as unchanged across the waves, and Africans were more likely

to view their economic ranking as having declined.

One of the more striking changes in the responses to questions about perceived economic

status concerns expectations of future mobility. In 2008, 72% of adults anticipated being

on a higher rung in two years time compared with at the time of the survey; by 2010 this

had fallen to 51%. Table 3 shows that expectations of future mobility declined

particularly among Africans (from 77% in 2008 to 50% in 2010). In comparison with

other groups, whites are distinctive – although they were the least likely to anticipate

being on a higher ladder rung in the future, their expectations of future mobility did

not decline across the waves and rather increased.

In sum, descriptive statistics identify three broad changes in the subjective assessments

of well-being among adults in South Africa. First, over one-half of the resident adults

Figure 4: Differences in perceived economic rank (2010–2008)

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The data are

weighted.

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reported being less satisfied with their lives in 2010 than in 2008; and Africans were

considerably more likely than whites to report lower levels of satisfaction. Second,

almost two-thirds of the adults did not perceive that their economic ranking in South

Africa had improved over the period, although there is also evidence that richer

individuals considerably underestimate their relative class position. Third,

expectations of future upward mobility declined markedly among adults, and

particularly among Africans. These changes in subjective perceptions of economic

rank may partly reflect the effects of the economic recession in South Africa (2008–

09), where economic output contracted and employment levels declined significantly

(Verick, 2012). However, they suggest further that the effects of this recession have

been experienced more strongly by Africans than by whites. The next section explores

the relationship between changes in self-assessed economic rank and changes in

satisfaction among Africans between the two waves of the panel.

3. Predicting life satisfaction: the role of perceived economic status

There is a large literature from both psychology and economics that investigates what

makes people more or less satisfied with their lives. One of the main themes to

Figure 5: Differences in ladder steps (2010–2008), Africans and whites

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1. The data are

weighted.

Table 3: Anticipated upward mobility (two years in the future)

2008 2010

African 0.77 (0.01) 0.50 (0.01)

Coloured 0.71 (0.02) 0.56 (0.03)

Indian 0.69 (0.05) 0.55 (0.07)

White 0.40 (0.03) 0.53 (0.03)

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include adults who were older than 16 years in wave 1.The data are weighted. Upward

mobility is defined as an individual expecting to be on a higher ladder step in two years time compared with at

the time of the survey.

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emerge from the economics literature in particular is how the economic status of

individuals affects their subjective well-being. Several studies have shown that self-

assessed satisfaction is influenced not simply by how rich or poor individuals are, but

also by how their economic status compares relative to others (cf. Easterlin, 1974,

1995; McBride, 2001; Ferrer-i-Carbonell, 2005; Luttmer, 2005; Kingdon & Knight,

2007; Bookwalter & Dalenberg, 2009). To identify this relative status, or relative

income, studies have commonly used the individual’s position in the distribution of

reported income (or expenditure), or compared the individual’s reported income with

the mean or median income of a selected comparison group.

However, as the discussion in the previous section has illustrated, there can be

considerable divergence between an individual’s actual and perceived ranking in the

income distribution. In particular, richer South Africans typically underestimate how

well off they are in comparison with others. If relative standing affects individual

subjective well-being because of perceptions of relative deprivation or relative

advantage, then a subjective measure of relative standing may provide a better

measure of relative income than where individuals are actually rank based on their

income.

An earlier study explored the relationship between relative economic standing and an

individual’s level of satisfaction in South Africa, using wave 1 of the NIDS data

(Posel & Casale, 2011). The results from this study suggest that, in addition to

absolute income, relative economic standing, measured by the individual’s rank in the

income distribution, is a significant predictor of how satisfied individuals are with

their lives. However, individual perceptions of relative standing are an even stronger

predictor. For example, individuals in the richest third of the income distribution

reported far higher levels of satisfaction if they also thought that they ranked among

the richest third of South Africans.

One of the limitations of this study is that with data from only one wave of NIDS, it

was not possible to control adequately for an individual’s personal traits or attitudes to

life. If these unobserved individual characteristics are correlated with both reported

levels of satisfaction and perceptions of relative standing, then this endogeneity will

produce bias in the estimates. With the release of the second wave of NIDS, there

is now information for each individual at two points in time, and it is therefore

possible to control for those unobserved individual characteristics that are time

invariant using fixed effects (or first differencing) estimation techniques, and in so

doing to test whether the earlier findings from Posel & Casale (2011) are robust to

heterogeneity bias.

To estimate the predictors of subjective well-being, I estimate three regressions:

SWBit = a+ gYi1 + bXi1 + di + nit (1)

SWBit = a+ gYit + bXit + Wt + di + nit (2)

SWBit − SWB∗i = gFE Yit − Y∗

i

( )+ bFE Xit − X∗

i

( )+ nit − ni (3)

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where SWBit is individual i’s reported level of satisfaction in time t; Yi is a vector of

income variables; Xi is a vector of individual demographic and employment-related

variables; SWBi∗, Yi

∗ and Xi∗ represent the mean values of the variables over the two

waves; di is the time-invariant error capturing unobserved individual-specific

characteristics; and nit is the idiosyncratic or time-varying error.

The first regression (Equation 1) is an ordinary least squares (OLS) regression estimated

only for wave 1 of NIDS.5 The second regression (Equation 2) is a pooled OLS regression

for both waves of NIDS, and includes a wave dummy variable, Wt for t ¼ 1. The third

regression (Equation 3) is the fixed-effects estimation, which exploits differences in the

independent and explanatory variables for each individual across the two waves.

The income variables measure an individual’s income rank, as described in the previous

section (whether in the upper or middle third of the income distribution, with the poorest

third as the omitted category); an individual’s perceived economic rank (on the upper

two steps of the economic ladder, or the middle two steps, with the lowest steps as the

omitted category); whether an individual expects to be on a higher step of the

economic ladder in the future (upward mobility); and the individual’s absolute income

(measured as the log of per-capita household income). Individual characteristics

include: age; education (completed secondary or tertiary education, with less than

completed secondary as the omitted category); marital status (married, cohabiting,

widowed or divorced, with never married as the omitted category); employment status

(employed or not economically active [including students, housewives and

pensioners], with unemployed as the omitted category); health status (measured by

whether the individual reports being in very good health); and whether the individual

reports difficulty with daily activities such as dressing, bathing or eating.

Table 4 reports the results of the estimations for all African adults (17 years and older) in

the NIDS panel.6 Overall, the fixed-effects estimation confirms earlier findings in Posel

& Casale (2011), based only on wave 1 of NIDS. Absolute income is a positive and

significant correlate of subjective well-being among Africans in both the pooled and

panel regressions. In addition to absolute income, an increase in the individual’s

relative economic standing is also associated with significantly higher satisfaction

levels. However, it is specifically perceived economic rank rather than actual income

rank that matters. In fact, actual income rank is not a significant predictor of

satisfaction levels in the fixed effects or pooled regressions. In comparison with the

coefficients in the OLS pooled regression, the coefficients for perceived economic

rank fall,7 but the relationship between the estimated coefficients remains unaltered:

an increase in perceived economic rank has an increasing effect on reported levels of

satisfaction. Positive expectations about future relative economic standing also

positively affect self-assessed satisfaction. Part of the explanation for falling levels of

satisfaction, particularly among Africans, may therefore lie with the decline in

perceived economic rank and the fall in positive expectations of upward mobility in

the future.

5Life satisfaction is often also estimated using ordered probit regressions. Because the cut-offpoints in the probit regressions for wave 1 and for the pooled sample are relatively equallyspaced, OLS regressions are used (treating the dependent variable as a linear measure ofsubjective well-being).6A Hausman test rejected the null hypothesis of no systematic differences between the coefficientsfrom a random and fixed-effects model (x2 ¼ 57.56), suggesting that a fixed-effects model is moreappropriate.7A fall in the estimated coefficients may derive from both endogeneity bias and measurement error.

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In addition to subjective measures of current and future economic status, changes in the

individual’s demographic characteristics also significantly affect satisfaction in ways that

are consistent with many other studies (for reviews of findings in the subjective well-

being literature, see Dolan et al., 2008; Blanchflower, 2009). Life satisfaction

Table 4: Predicting subjective well-being among Africans: OLS and fixed effects

NIDS Wave 1 (2008)NIDS Panel (2008–10)

OLS OLS pooled data Fixed effects

Perceived richest third 2.405∗∗∗ 1.870∗∗∗ 1.683∗∗∗

(0.185) (0.117) (0.211)

Perceived middle third 0.969∗∗∗ 0.821∗∗∗ 0.718∗∗∗

(0.050) (0.032) (0.058)

Richest income third 0.263∗∗ –0.038 –0.091

(0.119) (0.082) (0.154)

Middle income third 0.239∗∗∗ –0.005 0.059

(0.072) (0.048) (0.091)

Expect upward mobility 0.356∗∗∗ 0.267∗∗∗ 0.212∗∗∗

(0.055) (0.032) (0.058)

Log (per-capita household income) 0.166∗∗∗ 0.277∗∗∗ 0.136∗∗

(0.045) (0.031) (0.062)

Age2 –0.000 0.000∗∗∗ 0.001∗∗∗

(0.000) (0.000) (0.000)

Matric (Grade 12) 0.052 0.019 –0.131

(0.071) (0.044) (0.133)

Diploma or degree 0.195∗∗ 0.285∗∗∗ 0.181

(0.097) (0.061) (0.186)

Married 0.201∗∗∗ 0.114∗∗∗ 0.557∗∗∗

(0.064) (0.042) (0.205)

Cohabiting –0.019 –0.014 0.013

(0.084) (0.058) (0.202)

Divorced or widowed 0.044 –0.031 0.409∗

(0.092) (0.061) (0.245)

Employed 0.110∗ 0.089∗ 0.327∗∗∗

(0.066) (0.047) (0.089)

Not economically active 0.200∗∗∗ 0.135∗∗∗ 0.272∗∗∗

(0.066) (0.044) (0.083)

Daily activities difficult –0.628∗∗∗ –0.504∗∗∗ –0.448∗∗∗

(0.133) (0.079) (0.146)

Very good health 0.446∗∗∗ 0.549∗∗∗ 0.495∗∗∗

(0.050) (0.034) (0.061)

Wave 1 dummy – 0.743∗∗∗ 0.926∗∗∗

(0.033) (0.092)

R2 0.098 0.108 0.077 (within)

Source: Author’s own calculations, NIDS 2008 and 2010.

Notes: The samples include African adults who were older than 16 years in wave 1. Standard errors are in

parentheses. All estimations also included a quadratic in age and a wave dummy variable. ∗∗∗Significant at the

1% level ∗∗Significant at the 5% level ∗Significant at the 10% level.

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increases significantly among individuals who acquire tertiary education, who marry,

become employed or not economically active (rather than unemployed), and who

report being in very good health.8 In contrast, individuals who experience difficulty

with basic daily activities become significantly less satisfied.9

4. Conclusion

Similar to the distribution of income in South Africa, the distribution of life satisfaction

is highly skewed by race. Whites are not only richer than Africans, but they also report

higher levels of life satisfaction. Differences in access to resources help explain these

differences in subjective well-being. However, satisfaction levels are influenced not

only by the level of absolute income, but also by relative income, and in particular by

how individuals think their income compares with the income of others.

There is considerable divergence between where individuals think they rank on an

income ladder for South Africa and where they are actually ranked based on their

reported income. Self-assessed rankings typically are far lower than ‘actual’ income

rankings. This disparity may arise if perceived rankings incorporate assessments of

income flows over time, while income rankings are based only current monthly

income. Richer individuals probably also underestimate how well-off they are in

comparison with other South Africans, and associate what they perceive to be a

‘middle-class’ lifestyle with being in the middle of South Africa’s income

distribution. Perceptions of relative income status therefore provide a more

meaningful measure of relative deprivation or relative advantage, and perceived

ranking is a stronger predictor of subjective well-being than a ranking based on

reported income.

The period spanned by waves 1 and 2 of NIDS coincides with an economic recession in

South Africa. Over this period, reported levels of life satisfaction declined significantly

among adults, but by considerably more among Africans than among whites. More than

one-half of all African adults in the longitudinal sample reported lower levels of

satisfaction in 2010 than in 2008 (compared with less than 40% of whites). Key to

explaining these trends in life satisfaction is that Africans were also more likely than

whites to perceive their economic standing relative to others in the country as having

fallen over the two-year period, and to lower their expectations of future upward

mobility.

References

Blanchflower, DG, 2009. International evidence on well-being. In Krueger, AB (Ed.), Measuring

the Subjective Well-being of Nations: National Accounts of Time. University of Chicago

Press, Chicago, pp. 115–226.

Bookwalter, JT & Dalenberg, DR, 2009. Relative to what or whom? The importance of norms and

relative standing to well-being in South Africa. World Development 38(3), 345–55.

Bookwalter, JT, Fuller, BS & Dalenberg, DR, 2006. Do household heads speak for the household?

A research note. Social Indicators Research 79, 405–19.

8Although not reported in Table 4, a quadratic in age is significant in both the Wave 1 and pooledregressions, with subjective well-being initially decreasing with age and then increasing, acommon finding documented across a wide range of empirical studies on subjective well-being.9The positive and significant coefficient for the wave 1 dummy variable captures the aggregatedecline in reported subjective well-being across the waves.

Self-assessed well-being and economic rank 63

Dow

nloa

ded

by [

Cor

nell

Uni

vers

ity L

ibra

ry]

at 2

2:48

12

Nov

embe

r 20

14

Casale, D & Posel, D, 2011. English language proficiency and earnings in a developing country:

The case of South Africa. Journal of Socio-Economics 40, 385–93.

Dolan, P, Peasgood, T & White, M, 2008. Do we really know what makes us happy? A review of

the economic literature on the factors associated with subjective well-being. Journal of

Economic Psychology 29, 94–122.

Easterlin, R, 1974. Does economic growth improve the human lot? Some empirical evidence. In

David, PA & Reder, MW (Eds.) Nations and Households in Economic Growth: Essays in

Honor of Moses Abramovitz. Academic Press, New York, pp. 89–125.

Easterlin, R, 1995. Will raising the incomes of all increase the happiness of all? Journal of

Economic Behavior and Organization 27, 35–47.

Ferrer-i-Carbonell, A, 2005. Income and well-being: an empirical analysis of the comparison

income effect. Journal of Public Economics 89, 997–1019.

Kahneman, D &. Krueger, AB, 2006. Developments in the measurement of subjective well-being.

The Journal of Economic Perspectives 20(1), 3–24.

Kingdon, GG & Knight, J, 2006. Subjective well-being poverty vs. income poverty and capabilities

poverty? Journal of Development Studies 42(7), 1199–224.

Kingdon, GG & Knight, J, 2007. Community, comparisons and subjective well-being in a divided

society. Journal of Economic Behavior and Organization 64, 69–90.

Koivumaa-Honkanen, H, Honkanen, R, Viinamaki, H, Heikkila, K, Kaprio, J, & Koskenvuo, M,

2000. Self-reported life satisfaction and 20-year mortality in healthy Finnish adults.

American Journal of Epidemiology 152(10), 983–91.

Koivumaa-Honkanen, H, Honkanen, R, Viinamaki, H, Heikkila, K, Kaprio, J, & Koskenvuo, M,

2001. Life satisfaction and suicide: A 20-year follow-up study. American Journal of

Psychiatry 158(3), 433–9.

Luttmer, EFP, 2005. Neighbors as negatives: Relative earnings and well-being. The Quarterly

Journal of Economics 120(3), 963–1002.

McBride, M, 2001. Relative-income effects on subjective well-being in the cross-section. Journal

of Economic Behavior and Organization 45, 251–78.

Posel, D & Casale, D, 2011. Relative standing and subjective well-being in South Africa: The role

of perceptions, expectations and income mobility. Social Indicators Research 104(2), 195–

223.

Ravallion, M. 2012. Poor, or just feeling poor? On using subjective data in measuring poverty.

Policy Research Working Paper No. 5968. World Bank, Washington, DC.

Southern Africa Labour and Development Research Unit, 2008. National Income Dynamics

Study: Adult Questionnaire. http://www.datafirst.uct.ac.za/catalogue3/index.php/catalog/428

Accessed 29 October 2013.

Stutzer, A & Frey, BS, 2010. Recent advances in the economics of individual subjective well-

being. Social Research 77(2), 679–714.

Verick, S, 2012. Giving up job search during a recession: The impact of the global financial crisis

on the South African labour market. Journal of African Economies 21(3), 373–408.

64 D Posel

Dow

nloa

ded

by [

Cor

nell

Uni

vers

ity L

ibra

ry]

at 2

2:48

12

Nov

embe

r 20

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