INSTITUTIONAL RACISM MAKES (ALMOST) EVERONE LESS
HAPPY: EVIDENCE FROM THE END OF APARTHEID IN SOUTH
AFRICA+
Ben Fitch-Fleischmann*
University of Oregon
Jeffrey T. Bookwalter
University of Montana
Douglas R. Dalenberg
University of Montana
October, 2014
Abstract
In addition to getting utility from outcomes, people may also get “procedural” utility
from the process by which these outcomes arise. This paper examines the impact of a
large social regime change on well-being independently from the change’s direct impact
on observable outcomes. We use a Blinder-Oaxaca style decomposition for models of life
satisfaction before and after the end of apartheid in South Africa, and find that the
observed increase in life satisfaction should not be attributed to improvements in socioeconomic conditions, but to changes in the life satisfaction derived from given levels of
these conditions. These results suggest that institutions, specifically institutionalized
racism, can have important impacts on life satisfaction independently from any direct
influence they have on outcomes.
JEL Codes: D60, D63, I31
Keywords: procedural utility, outcome utility, subjective well-being, apartheid
+
For useful comments and suggestions, we would like to thank, without implicating, Richard Barrett and
participants of the Southern Africa Labour and Development Research Unit seminar series at the University
of Cape Town and the applied microeconomics workshop at the University of Oregon.
*
Corresponding Author. Email: [email protected].
1
I. Introduction
Traditional utility theory considers outcomes—such as tangible goods and
services, and leisure—as the only inputs into utility functions. However, the processes
that lead to these outcomes are also likely to influence utility. Frey et al. (2004) give a
theoretical introduction to this notion of procedural utility. Frey and Stutzer (2005) show,
as evidence of the existence of procedural utility, that having participation (voting) rights
corresponds to increased life satisfaction whereas actual participation through use of
these rights does not. Economic models of subjective well-being typically consider social
and economic measures, but how much might be missed by failing to account for
procedural utility? In this paper we consider the potential magnitude of procedural utility
relative to outcome utility by comparing models of life satisfaction between different
social regimes.
Utility is neither observable nor directly measurable and empirical analysis of
well-being requires a proxy. Survey-based measures of subjective well-being, through
reports of life satisfaction or happiness, are increasingly used as proxies for utility.1
Though economists often prefer to consider revealed preferences, there are a number of
reasons to study subjective well-being.2
For policy makers, the knowledge of what makes their constituents happiest, or
contributes the most to their life satisfaction, ought to guide the allocation of scarce
1
Kahneman and Krueger (2006) point out that over 100 papers were written analyzing self-reported
subjective well-being data over the period 2001-2005, including seminal work on the Moving To
Opportunity for Fair Housing program and the Rand Health Insurance experiments (Kling et al. 2007).
2
A deep body of literature has examined the relationship between stated happiness or life satisfaction and
other outward individual characteristics, finding that self-reported happy people are likelier to be rated as
happy by family or friends (Sandvik et al. 1993) and spouses (Costa and McRae, 1988), to smile more
(Fernandez-Dols and Ruiz-Belda, 1995), and subjective well-being measures are reasonably stable within
individuals, though sensitive to changes in life situations (Ehrhardt et al. 2000).
2
public resources. Understanding the varying degrees to which employment and other
social and economic conditions impact individual life satisfaction should be an important
policy consideration. For example, determining the optimal level of unemployment
insurance should account for the fact that the subjective cost of unemployment is far
greater than the associated loss of income (Clark and Oswald, 1994; Oswald, 1997; and
Di Tella et al. 2001). A large economic literature provides in-depth discussion of how the
study of subjective well-being can inform policy decisions and, consequently, improve
the lives of people around the world (see, e.g., Ferrer-i-Carbonell and Frijters (2004),
Helliwell (2003), Kahneman et al. (2004), Kahneman and Krueger (2006), Ng (1997),
Sen (2000), and Veenhoven (2002), among many others).
Given evidence that the procedures through which outcomes are determined
indeed influence utility (e.g. Frey and Stutzer, 2005), it seems natural that societal
institutions or social regimes might play a role in life satisfaction beyond the impact they
have on outcomes. How large might this role be, relative to outcomes such as socioeconomic conditions? This paper presents evidence that societal and governmental
structures can have a large influence on life satisfaction independently of resource
allocations or material, educational, health, and other outcomes. We compare models of
life satisfaction under different social regimes to address this question. To avoid crosscultural comparisons, which are complicated by differences in language and culture, we
compare models estimated for the same country five years apart. We use the significant
changes in South African society brought about by the end of apartheid to examine the
potential magnitude of the impact that social regimes may have on utility, controlling for
the social and economic measures typically considered in models of subjective well-
3
being.
II. Background
South Africa presents a particularly interesting case to understand the
determinants of subjective well-being. In the 1980s and 1990s, South Africa experienced
wrenching social and political change as apartheid came to a negotiated end and was
replaced with an admittedly flawed multi-racial democracy. Through this period and still
today, South Africa has one of the most unequal distributions of material well-being, with
the juxtaposition of first-world levels of wealth alongside some of the poorest areas in the
world.
South Africa’s apartheid history began when Dutch sailors arrived at the Cape of
Good Hope in 1652. Between then and the British takeover of the Cape in 1806, black
South Africans found themselves increasingly disadvantaged at the hands of the colonial
powers. After the discovery of gold and diamonds in the late 1800s, there began a
decades long process of designating ‘homelands’ to which indigenous groups were
forcibly moved. While these homelands bore some relationship to the historic ranges of
indigenous people, they generally only included the least economically desirable lands.
The National Party came to power in 1948 and marked the beginning of the
official apartheid era. The rhetoric surrounding apartheid was that separate racial groups
should be allowed to develop separately. In practice, apartheid prohibited black South
Africans from participating in most parts of the economy and society. Economic failure,
increasingly onerous international sanctions, and a rising tide of violence forced the
apartheid government to consider some reforms in the 1980s.
The first steps in
dismantling apartheid began in the final years of the 1980s and, under State President
4
F.W. De Klerk, apartheid was slowly rolled back. The De Klerk administration sought
public support for dismantling apartheid and in a whites-only ballot referendum in
March, 1992, an overwhelming majority of the population voted to negotiate the end of
apartheid. In November of 1993 an interim constitution was drafted, outlining the process
by which the first non-racial elections would take place. In April of 1994, Nelson
Mandela’s African National Congress received nearly two-thirds of the votes and took
over as the leading party in the Government of National Unity.
In the four years following the end of apartheid, the percentage of South Africans
reporting neutral or better life satisfaction increased for every racial group, with the
largest increase found, not surprisingly, among black South Africans. Figure 1 depicts the
change in life satisfaction over this period by racial groups. Only 33 percent of black
South Africans reported neutral or better life satisfaction in 1993, but 77 percent reported
so in 1998. Over this same period, reports of neutral or better life satisfaction increased
for Coloureds (mixed black-white heritage), Indians/Asians, and whites by 30, 17, and 8
percentage points, respectively.
Figure 1. Percentage of respondents reporting neutral or better life satisfaction
Note: Data are from the South African Labour and Development Research Unit (1994) and Statistics
South Africa (1998).
5
Did the end of apartheid provide so many new opportunities, so much new
economic and social wealth for black South Africans, that it could cause a 44 percentage
point increase in the number no longer dissatisfied with their lives? Could this rising tide
of economic inclusion also improve the lives of white South Africans, whose previously
favored status had been removed? We argue against this. The end of apartheid indeed
brought many changes, but we argue that economic and social conditions did not change
enough in the five years following the end of apartheid to explain these substantial
changes in life satisfaction. We make the assumption that variation in a nation’s social
and governmental structures are exogenous to current life satisfaction, at least in the short
run. Since large, explicit changes in government structures are relatively rare,
constructing a direct test of the causal impact of institutional changes on life satisfaction
is challenging. Despite this, we argue that the substantial improvement in life satisfaction
in South Africa, which accompanies a large procedural change, suggests that process
alone can have enormous impacts on utility. The rest of this paper lays out our argument.
Section III describes our strategy for measuring the portion of the life satisfaction change
due to changes in observable conditions. Section IV describes the data we use, and
Section V presents our models and discusses their connection to procedural utility.
Section VI concludes.
III. Modeling life satisfaction
Comparisons of subjective well-being, perhaps more than cross-country
comparisons along other lines, are likely complicated by language and cultural
6
differences. Thus, we compare models within the same country. To examine the change
in reported levels of subjective well-being across different social regimes we estimate
models of life satisfaction for black and white South Africans separately for 1993 and
1998, controlling for factors commonly considered in models of subjective well-being.
We focus on blacks and whites because they are the racial groups for whom apartheid
had the most distinct consequences. These groups together comprise 88 percent of our
total sample.
If the gain in life satisfaction over the study period is due primarily to changes in
economic and social outcomes, we expect that the 1998 levels of social and economic
characteristics could be used in the 1993 model to form a reasonable prediction of the life
satisfaction reported in 1998. We use a decomposition technique introduced by Oaxaca
(1973) to identify the portion of the life satisfaction increase attributable to changes in
levels of the conditions we include in our models.
III. A. Estimating the impact of observable characteristics on life satisfaction
The large subjective well-being literature in economics suggests many
contributors to life satisfaction, including income, wealth, employment, housing,
education, and family characteristics, to name just a few. In our models we incorporate
observable conditions covering all of these areas, including household and family
characteristics and the household’s surrounding environment.
The dependent variable is the response, by the head of the household, to the
question, “Taking everything into account, how satisfied is this household with the way it
7
lives these days?”3 Responses are on a five-point scale that we have ordered: very
dissatisfied (1), dissatisfied (2), neutral (3), satisfied (4), and very satisfied (5).4 The
results from an ordered probit model are qualitatively similar to a simpler linear model,
so we focus on the linear model to ease interpretation. Ferrer-i-Carbonell and Frijters
(2004) examine the effects of modeling methodology on estimates of the determinants of
life satisfaction and find that the estimates from a linear model differ surprisingly little
from an ordered model, and thus assuming cardinality “makes little difference to the
results” (p 642). Helliwell (2003) finds that the change from one “level” of happiness to
the next is the same across the entire scale and that “the results do not depend importantly
on whether the measures of subjective well-being are treated as ordinal or cardinal” (p
354). The primary advantage of the OLS model is that the estimated coefficients are
easily interpreted as the change on the five-point life-satisfaction scale resulting from a
one-unit change in the respective independent variable.
III. B. Life satisfaction changes not captured by observable characteristics
After estimating life satisfaction models for 1993 and 1998, we use the time gap
between the surveys and apply the decomposition technique developed by Oaxaca (1973)
to determine how much of the increase in life satisfaction can be attributed to observable
economic and social outcomes, and how much can be attributed to factors unrelated to
these outcomes. We include a comprehensive set of economic outcome variables in our
3
Bookwalter et al. (2006) show a stronger link between subjective well-being and household characteristics
than between subjective well-being and individual head of household characteristics, suggesting that
household heads do represent the entire household in their answers.
4
The original data codes the responses to the question in descending order, from very dissatisfied (5)
through very satisfied (1). We reordered them so that increasing response values correspond to increasing
life satisfaction.
8
models, and any changes in life satisfaction left unexplained by observable conditions we
attribute to changes in South African society and institutions—processes—that do not
affect outcomes. That is, the increase in life satisfaction is decomposed into two parts:
one part due to procedural utility and a remaining portion attributable to changes in
outcome utility. An alternative explanation for the increases in life satisfaction which
cannot be attributed to changes in outcomes might be optimism. This is unlikely, though,
because any optimism felt by South Africans, especially blacks, due to the end of
apartheid seems likelier to influence reports of life satisfaction in 1993 than it would in
1998, four years after apartheid's end. In our results we consider decompositions
separately by groups that might be expected to differ in the degree to which they are
forward-looking.
The Oaxaca decomposition was originally used as a method of decomposing
wage differentials into one part attributed to differences in mean characteristics of the
groups (male and female in Oaxaca’s original article) and another part attributed to
differences in the payoffs to those characteristics to tease out evidence of discrimination.
This tool has proven useful for a wider set of topics, including the decomposition of
student test scores (Krieg and Storer, 2006, and McEwan and Marshall, 2004), home
ownership rates (DeSilva and Elmelech, 2012), and changes in subjective well-being
(Knight and Gunatilaka, 2010). For further discussion of the Oaxaca decomposition, see
Oaxaca (1973), Blinder (1973) or Borjas (2008)5.
5
See Bauer and Sinning (2008) and Sinning et al. (2008) for application of the Oaxaca decomposition to
nonlinear regression models.
9
To apply the Oaxaca decomposition to differences in subjective well-being over
time, suppose that life satisfaction in each year is determined only by income. A simple
model of life satisfaction in year t can be written:
(1)
Where S is the level of life satisfaction, I is a measure of income, t denotes the year and
is a constant. Following Oaxaca’s wage example, the average difference in life
satisfaction between two years can then be written:
.
By adding and subtracting the term
(2)
from the right side, equation (2) can be written:
.
(3)
If income influences life satisfaction the same in each year, then
.
Similarly, if life satisfaction levels for people with no income are the same in both years,
then
. Thus the first term in square brackets in equation (3) is the portion of the
change in life satisfaction due to changes in the way that income influences life
satisfaction (the coefficient effect), and the second term in square brackets is the portion
of the life satisfaction difference due to changes in the average levels of income in 1993
and 1998 (the endowment effect).
If changes in utility are indeed due to differences in processes, rather than
outcomes, we would expect to see that only a small portion of the life-satisfaction
increase is attributable to changes in outcomes. The change in process in this study—the
end of institutional discrimination of black South Africans—affected all South Africans.
However, it could be that changes in process affect only the utility of those who perceive
that the changes in processes have been in their favor. We consider decompositions
10
separately by race, as well as for different demographic groupings, to explore this
possibility.
IV. Data
We use data from representative cross-sectional household surveys conducted in
South Africa in 1993-1994 and 1998. The earlier survey was conducted by the Southern
African Labour and Development Research Unit at the University of Capetown and
supported by the World Bank’s Project for Statistics on Living Standards and
Development (SALDRU, 1994). A sample of approximately 9,000 households was
surveyed in late 1993 and early 1994. The 1998 data are from Statistics South Africa’s
October Household Survey, conducted each year from 1995 to 1999 (Statistics South
Africa, 1998). Slightly less than 20,000 households were surveyed. We use the data from
1998 because they provide the largest temporal gap from 1993 for which the majority of
variables, particularly the life satisfaction reports, are comparable to the 1993 data.
During the study period there were huge improvements in reported life
satisfaction across all South African racial groups. For black South Africans, not only did
the percentage reporting neutral or better life satisfaction increase dramatically, the entire
distribution of responses to the life satisfaction question, shown in Figure 2, shifted in a
positive direction.
11
Figure 2. Reported life satisfaction for black South Africans in 1993 and 1998.
Many variables across the two data sets are directly comparable and we have
made adjustments where the original answer codes were not. Appendix A provides a
concordance of our coding.
The dependent variable reflects household-level life
satisfaction, so we collapse characteristics of individual household members to the
household level.
Household characteristics include household size, children per adult, whether the
household head is male, and whether the household is in an urban environment. The
income measure we use is the real monthly income per adult equivalent unit measured in
1,000 Rand (base year 2008). Employment is measured as the percentage of household
members over 18 years old that were employed. We also include variables indicating
whether any household member was the recent victim of a crime, or was ill or injured in
the two weeks prior to the survey. Household levels of education are captured through a
measure of the highest level of education achieved by the most educated household
member. Housing characteristics include dwelling type, ownership, rooms per person,
12
cooking energy, and access to water. Descriptive statistics are presented in Table 1.
[Table 1 about here]
The changes in observable conditions for black South Africans were mixed
between 1993 and 1998, but generally showed improvement. On average, employment
fell but income increased. For blacks, average monthly income increased by 270 Rand
($54 at the 1998 Dollar/Rand exchange rate), though household employment fell by 4
percentage points. Whites experienced an increase in monthly income of 580 Rand ($116
USD) per household member, and a 12 percentage point drop in employment.
Despite similar trends for blacks and whites in income (positive) and employment
(negative), education levels appear to have increased for blacks but decreased for whites.
The number of black households with the most educated member attending some high
school increased by 6 percentage points, with a corresponding fall (4 percentage points)
in those with little or no education. White South Africans also experienced an increase in
the number of households with the most educated member having some high school
education, but this comes from a fall in the number having some college education rather
than improvements for those at lower levels. For blacks, there was also a 12 percentage
point increase in dwelling ownership, and a 13 percentage point increase in the number
living in houses (the highest dwelling category). These are modest improvements overall,
though they do not appear as dramatic as might have been expected given the end of
apartheid and the jump in life satisfaction.
13
V. Changes in the determinants of life satisfaction from 1993 to
1998
The coefficient estimates from OLS models of life satisfaction for black and white
South Africans are presented in Table 2. The coefficients represent the expected change
on the five-point life satisfaction scale given a change in the independent variable. In
1993, for example, living in an urban environment, relative to rural, corresponds to a fall
in expected life satisfaction of 0.25 points. Overall, the estimation results in this study are
similar to past models of subjective well-being. Having a household member who is ill,
injured, or the victim of a crime corresponds to a lower life satisfaction. Improved
housing conditions, better cooking energy, and easier access to water all correspond to
higher levels of life satisfaction. Higher levels of education are associated with increased
life satisfaction, though the life satisfaction “payoffs” from higher education decrease
(from 1993 to 1998) relative to those from basic education. Residing in an urban area has
large negative effects in both years. For black South Africans, being categorized as urban
often corresponds to living in a township adjacent to one of South Africa’s large urban
areas (such as Soweto or Alexandra).
These townships are very poor, have little
infrastructure and high rates of crime and violence.
[Table 2 about here]
The estimated impact of income on life satisfaction is insignificant, though
employment has a large positive effect for blacks. Compared to the larger effects of other
variables, our results suggest that income plays a surprisingly small role in determining
life satisfaction in South Africa during the period considered. Bookwalter and Dalenberg
14
(2004) argue that income, at the margin, does little to improve the factors most important
to South African households, many of which have a large public good component.
There is a noticeable pattern of lower level improvements having smaller effects
in 1998 than in 1993. For example, living in a hut (relative to a shack) corresponds to an
increase of 0.48 points in expected life satisfaction in 1993, but only a 0.13 point increase
in 1998. A possible interpretation is that as housing and infrastructure improve from
1993 to 1998, it takes higher order improvements to get the equivalent increase in
subjective well-being. Similarly, the negative impact on life satisfaction due to a
household member’s illness, injury, or crime victimization is much bigger in 1998 than
1993. These patterns are consistent with the idea of hedonic adaptation: rising income or
improved living conditions may generate additional subjective well-being in the short
run, but over time households adapt to these new conditions and require further
improvements to achieve and maintain a corresponding gain in subjective well-being.
Can the changes in education, income, unemployment, and living conditions
explain the increase in life satisfaction? The results of the Oaxaca decompositions for
whites and blacks are presented in Table 3. We find that only 9.4 percent of the increase
in life satisfaction for black South Africans can be explained by improvements in the
levels of income, education, and other outcomes. In other words, if we were to use the
1993 model to predict life satisfaction based on the 1998 levels, we would predict only a
9.4 percent increase in the average reported life satisfaction for black households. A
larger portion of the life satisfaction increase experienced by whites—36 percent—is
attributable to changes in levels, but this still leaves nearly two-thirds of the increase
attributable to non-outcome sources of utility.
15
[Table 3 about here]
Over 90 percent of the improvement in life satisfaction for blacks, and over 60
percent for whites, is not attributable to differences, over time, in the levels of socioeconomic outcomes. In other words, controlling for outcomes, a change in processes has
brought about an enormous increase in life satisfaction. This is true for both blacks and
whites. It seems reasonable that a change in procedural utility would be largest for the
group that the procedural change was expected to benefit the most—even if these
expectations were not realized in the form of outcomes—and blacks indeed show the
larger increase not attributable to changes in outcomes. The non-outcome increase in life
satisfaction for whites, though, is far from trivial. In Table 4 we separate out the
decompositions along demographic lines representing groups that we might expect to be
differentially affected by the end of apartheid. (The models on which these
decompositions are based are available upon request.) To get a sense of how the degree to
which a household is forward-looking might relate to changes in procedural utility, we
look at households with and without children, as well as across different age bins. We
also differentiate households by gender composition.
[Table 4 about here]
The decomposition results done separately by different demographic groups
suggest two stories. The first is that, among black South Africans, the outcome
(endowment) increase in life satisfaction is remarkably consistent across all groups,
staying between 6 and 12 percent. This suggests no differences in the degree to which
optimism might be driving the increase in life satisfaction. As we mentioned before, we
16
do not put much stock in this idea, since optimism should be reflected at least as much in
1993 as in 1998. The consistency of the decomposition results for blacks suggests no
difference across groups in the degree to which we might expect optimism, in the form of
forward-looking behavior, to influence life satisfaction.
The second story revealed by these results is that there is a set of white
households whose life satisfaction increase is in fact due mostly to changes in outcomes.
White households with children are quite similar to black households in that their life
satisfaction increase was not driven much by changes in outcomes. However, the life
satisfaction increases in older, white households, and white households without children,
were driven mostly by changes in the levels of outcome variables. Whites with and
without children experienced about the same increase due to changes in levels, but white
households with children experienced an additional increase in life satisfaction
independent from changes in outcomes. The same is true when looking separately at
households by gender—both male dominated and female dominated white households
experienced a life satisfaction increase driven by outcomes, but female dominated
households saw an additional, non-outcome-related increase roughly twice as big as that
experienced by male dominated households.
These results suggest that the change in utility brought about by a procedural
change is closely tied to how much certain groups might expect the change to benefit
them, even if the expected improvements in outcomes are not realized. The older, male,
white households, and those without children, show the least change in what determines
their happiness, while the white households with children and the younger white
households, similar to black households, show a substantial increase in life satisfaction
17
that is not due to changes in outcomes. These results are consistent with a story of
integration led by the younger generations.
VI. Concluding Remarks
In this paper, we consider the magnitude of the effect that processes or institutions
might have on utility independently from their effects on outcomes. We analised the
change in life satisfaction experienced by South Africans that accompanied the end of
apartheid, and decomposed the increase in life satisfaction into one portion attributable to
changes in the levels of outcomes (income, education, health, etc.), and a second portion
which we attribute to changes in the life satisfaction “payoff” associated with a given
level of these outcomes. Over the study period, which begins just before the end of
apartheid, South Africans of all races experienced substantial increases in life
satisfaction. Some indicators of material well-being, education, health, housing and
infrastructure improved over this time period, but the results presented in this paper
suggest that these changes can explain only about ten percent of the increase in life
satisfaction experienced by black South Africans. A very dynamic relationship between
outcomes and life satisfaction is a possible explanation. However, there was an enormous
shift during this time period from a severely discriminatory institutional structure and
social regime to a system with much fairer processes. This suggests that the utility
derived from processes can be substantially larger than the utility derived from the
outcomes these processes generated. For black South Africans, this result is remarkably
consistent across ages, genders, and households with and without children.
We also consider the increase in life satisfaction for white South Africans, who
experience, on average, a life satisfaction increase of which only about one-third is driven
18
by changes in outcomes. However, when we look within whites across demographic
groups, we find that white households with more males, with older members, and without
children do not experience nearly as large of an increase in non-outcome related life
satisfaction. The fact that younger white households and those with children share in the
improvements due only to procedural changes is consistent with a story of racial
integration led by the young.
Taken altogether, our results suggest that all individuals governed by a particular
institution or process do not equally experience procedural utility. However, all South
Africans in this period experienced some life satisfaction increase driven by procedural
changes. It seems reasonable that those whom a procedural change is most expected to
benefit would experience greater increases in life satisfaction, but our main point remains
that these increases were in fact driven by the procedural changes themselves and not by
any subsequent outcomes.
Blanchflower and Oswald (2005) state directly that in
countries where people are starving, “food comes first and philosophizing second.” They
argue that richer economies should question the need for further riches. We agree, but our
results show that, even in developing countries, substantial well-being improvements can
be achieved independently of economic growth.
19
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countries? Evidence from Cuba and Mexico’, Education Economics, vol. 12(3),
pp. 205-217.
Ng, Y. (1997). ‘A case for happiness, cardinalism, and interpersonal comparability’, The
Economic Journal, vol. 107(445), pp. 1848-1858.
Oaxaca, R. (1973). ‘Male-female wage differentials in urban labor markets’,
International Economic Review, vol. 14(3), pp. 693-709.
Oswald, A. (1997). ‘Happiness and economic performance’, The Economic Journal, vol.
107(445), pp. 1815-1831.
Sandvik, E., Diener, E. and Seidlitz, L. (1993). ‘Subjective well-­‐being: The convergence
and stability of self-­‐report and non-­‐self-­‐report measures’, Journal of
Personality, vol. 61(3), pp. 317-342.
Sen, A. (2000). Development as Freedom, New York: Anchor Books.
21
Sinning, M., Hahn, M. and Bauer, T. (2008). ‘The Blinder-Oaxaca decomposition for
nonlinear regression models’, Stata Journal, vol. 8(4), pp. 480-492.
South Africa Labour Development Research Unit (1994). South Africans Rich and Poor:
Baseline Household Statistics, University of Cape Town, South Africa.
Statistics South Africa (1998). ‘October Household Survey (South Africa), 1998’, South
African Data Archive, Pretoria, South Africa.
Veenhoven, R. (2002). ‘Why social policy needs subjective indicators’, Social Indicators
Research, vol. 58(1/3), pp. 33-45.
22
Table 1: Descriptive Statistics
Dependent Variable
Satisfied? 1=very dissatisfied,
5=very satisfied
Household characteristics
Household size
Children per adult
Male head of household
Urban dweller
Income and income potential
Real monthly income in 1000 rand
Pct. of household adults employed
Injury or illness
Crime victim
No or less than High School
Some High School (Base Case)
Completed High School or beyond
Housing Characteristics
Shack (Base Case)
Hut or other
House or flat
Owns dwelling
Rooms per person
Cook with other material (Base case)
Cook with wood or dung
Cook with electricity or gas
Water at distance
Water outside but close (Base case)
Water piped into house
n
Blacks
1993/4
Blacks
1998
Whites
1993/4
Whites
1998
2.31
(1.19)
3.43
(1.09)
3.75
(1.08)
3.93
(0.89)
5.39
(3.57)
0.82
(0.96)
0.69
(0.46)
0.42
(0.49)
4.57
(2.81)
0.90
(1.05)
0.57
(0.49)
0.45
(0.50)
3.16
(1.58)
0.40
(0.55)
0.85
(0.36)
0.95
(0.22)
2.91
(1.47)
0.35
(0.54)
0.79
(0.41)
0.92
(0.27)
0.66
(1.16)
0.41
(0.38)
0.30
(0.46)
0.08
(0.27)
0.52
(0.50)
0.41
(0.49)
0.07
(0.25)
0.93
(2.54)
0.37
(0.38)
0.29
(0.45)
0.04
(0.20)
0.47
(0.50)
0.47
(0.50)
0.07
(0.25)
4.69
(8.04)
0.65
(0.37)
0.27
(0.45)
0.19
(0.39)
0.05
(0.22)
0.45
(0.50)
0.50
(0.50)
5.27
(9.54)
0.53
(0.40)
0.19
(0.39)
0.10
(0.30)
0.04
(0.19)
0.58
(0.49)
0.38
(0.49)
0.14
(0.35)
0.39
(0.49)
0.47
(0.50)
0.66
(0.48)
0.94
(0.99)
0.35
(0.48)
0.37
(0.48)
0.28
(0.45)
0.28
(0.45)
0.55
(0.50)
0.17
(0.38)
6,332
0.13
(0.34)
0.27
(0.44)
0.60
(0.49)
0.78
(0.42)
1.08
(0.98)
0.29
(0.46)
0.28
(0.45)
0.43
(0.49)
0.36
(0.48)
0.43
(0.50)
0.21
(0.41)
13,607
0.002
(0.04)
0.002
(0.04)
0.996
(0.06)
0.70
(0.46)
2.48
(1.43)
0.004
(0.06)
0.003
(0.05)
0.99
(0.08)
0.01
(0.04)
0.001
(0.04)
1.00
(0.05)
1,345
0.001
(0.03)
0.05
(0.22)
0.95
(0.22)
0.71
(0.45)
2.49
(1.44)
0.001
(0.04)
0.001
(0.03)
0.997
(0.05)
0.02
(0.12)
0.00
(0.04)
0.98
(0.13)
2,269
23
Note: Sample means with standard deviations in parenthesis.
24
Table 2: OLS estimation
Dependent Variable
Satisfied? 1=v.dis. 5=v.sat.
Household characteristics
Household size
Children per adult
Male head of household
Urban dweller
Income and income potential
Real monthly income in 1000 rand
Pct. of household adults employed
Injury or illness
Crime victim
No or less than High School
Completed High School or beyond
Blacks
1993/4
Blacks
1998
Whites
1993/4
Whites
1998
0.001
(0.005)
-0.037*
(0.017)
0.099**
(0.032)
-0.251**
(0.045)
-0.023**
(0.004)
0.031**
(0.011)
-0.010
(0.020)
-0.164**
(0.025)
0.024
(0.032)
-0.116
(0.074)
0.180*
(0.091)
-0.047
(0.132)
-0.019
(0.021)
-0.009
(0.048)
-0.009
(0.050)
-0.201**
(0.067)
-0.021
(0.017)
0.389**
(0.052)
0.008
(0.033)
-0.114*
(0.057)
-0.078*
(0.033)
0.151*
(0.066)
0.006
(0.004)
0.162**
(0.030)
-0.143**
(0.021)
-0.324**
(0.050)
-0.060**
(0.021)
0.082*
(0.039)
0.000
(0.004)
-0.048
(0.090)
-0.162*
(0.069)
-0.076
(0.075)
0.075
(0.131)
0.276**
(0.060)
-0.002
(0.002)
0.000
(0.055)
-0.079
(0.050)
-0.390**
(0.066)
-0.073
(0.112)
0.111**
(0.039)
Housing Characteristics
Hut or other
0.481**
0.133**
1.451
0.519**
(0.053)
(0.035)
(0.877)
(0.166)
House or flat
0.263**
0.248**
1.799**
0.211
(0.049)
(0.033)
(0.540)
(0.154)
Own dwelling
0.035
0.047
0.225**
0.084
(0.037)
(0.026)
(0.067)
(0.045)
Rooms per person
-0.037*
0.044**
0.095**
-0.014
(0.015)
(0.010)
(0.026)
(0.016)
Cook with wood or dung
-0.174**
0.040
1.036
-1.022
(0.041)
(0.028)
(0.541)
(0.661)
Cook with electricity or gas
0.150**
0.245**
1.101*
0.628
(0.046)
(0.025)
(0.495)
(0.538)
Water at distance
-0.060
-0.139**
-0.740
-0.226
(0.042)
(0.024)
(1.391)
(0.306)
Water piped into house
0.021
0.004
-0.098
-0.160
(0.053)
(0.028)
(0.406)
(0.129)
Constant
2.018**
3.279**
0.379
3.482**
(0.083)
(0.054)
(0.569)
(0.567)
n
6,332
13,607
1,345
2,269
R-square
0.053
0.058
0.079
0.037
F test
18.22**
46.58**
7.834**
5.579**
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and highest education level is some high school. * p<.05, ** p<.01.
25
Table 3: Oaxaca Decomposition results
Blacks
Whites
OLS
Estimates
Percentage
Estimates
Percentage
Raw Differential
1.12
0.17
Endowments
0.10
9.4%
0.06
36.3%
Coefficients
1.02
90.6%
0.11
63.7%
Note: n is 6332 for Blacks in 1993 and 13607 in 1998; n is 1345 for Whites in 1993 and 2269 in 1998.
Table 4: Oaxaca Decompositions by different demographic groups
Est.
Pct.
Est.
Pct.
Est.
Pct.
Blacks without kids Blacks with kids
White without kids
Raw Differential 1.08
1.14
0.10
Endowments
0.07
6.1%
0.12
10.3%
0.06
62.7%
Coefficients
1.01
93.9%
1.02
89.7%
0.04
37.3%
Est.
Pct.
Whites with kids
0.25
0.06
24.3%
0.19
75.7%
Raw Differential
Endowments
Coefficients
Blacks Age 0-20
1.13
0.13
12.0%
1.00
88.0%
Blacks Age 21-27
1.10
0.10
8.9%
1.00
91.1%
Blacks Age 28-35
1.13
0.09
7.7%
1.04
92.3%
Blacks Age 35+
1.13
0.09
7.5%
1.04
92.5%
Raw Differential
Endowments
Coefficients
Whites Age 0-20
0.26
0.03
11.3%
.023
88.7%
Whites Age 21-27
0.23
0.03
12.4%
0.20
87.6%
Whites Age 28-35
0.15
0.12
81.7%
0.03
18.1%
Whites Age 35+
0.11
0.06
54.4%
0.05
45.6%
Blacks
Blacks
Whites
Whites
<=1/2 female
>1/2 female
<=1/2 female
>1/2 female
Raw Differential 1.10
1.17
0.14
0.23
Endowments
0.09
8.2%
0.12
10.0%
0.06
42.8%
0.05
22.3%
Coefficients
1.01
91.8%
1.05
90.0%
0.08
57.4%
0.18
77.7%
Note: Categories are based on household composition (average household age and percent of household
members that are female). For Blacks, n ranges from a low of 1235 to a high of 9636, while for Whites, n
ranges from a low of 203 to a high of 1228.
26
Appendix A: Survey coding concordance for housing and infrastructure
SALDRU
OHS
Shack (Base Case)
Shack.
Informal dwelling / shack, in
backyard / shack not in back
yard, e.g. in an informal
squatter settlement, caravan /
tent.
Hut or other
Hut / traditional dwelling
hostel, outbuilding,
combination of buildings,
other.
Traditional dwelling / hut /
structure made of traditional
materials, dwelling / house /
flat / room in backyard,
room / flatlet, unit in
retirement village, other.
House
House / part of a house
flat, maisonette.
Dwelling / house or brick
structure on a separate stand or
yard, flat or apartment in a
block of flats, town / cluster /
semi-detached house.
Cook with other
Charcoal / coal, paraffin.
material (Base case)
Coal, paraffin.
Cook with wood or
dung
Wood, dung.
Wood, dung.
Cook with
electricity or gas
Electricity from grid,
electricity from generator,
town gas (piped), gas from
bottle.
Electricity, gas.
Water at distance
Public tap (free), public tap
(paid for), borehole, flowing
river / stream, dam / stagnant
water, well (non-borehole),
protected spring, other.
Public tap, borehole off site /
commercial, flowing water
stream, dam / pool / stagnant
water, well, spring, other.
Water outside but
close (Base case)
Piped - yard tap,
water carrier / tanker,
rainwater tank.
Piped water, on site or in yard
water carrier / tanker,
borehole on site, rainwater
tank on site.
Water piped into
house
Piped – internal.
Piped water in dwelling.
27
Appendix B: Estimation Results by Sub-groups
(Available from Authors - Not to be included in paper)
Table B1: OLS estimation for Black households with and without children
Dependent Variable
Blacks
Blacks
Satisfied? 1=v.dis. 5=v.sat.
w/o kids
w/o kids
1993/4
1998
Household characteristics
Household size
-0.006
-0.028
(0.031)
(0.019)
Children per adult
Male head of household
Urban dweller
Income and income potential
Real monthly income in 1000 rand
Pct. of household adults employed
Injury or illness
Crime victim
No or little schooling
Completed High School or beyond
Blacks
w/ kids
1993/4
Blacks
w/ kids
1998
-0.019**
(0.005)
0.029*
(0.012)
-0.017
(0.024)
-0.173**
(0.032)
0.007
(0.008)
0.191**
(0.039)
-0.177**
(0.024)
-0.372**
(0.062)
-0.078**
(0.025)
0.070
(0.045)
-0.167*
(0.067)
-0.350**
(0.078)
0.025
(0.038)
-0.121**
(0.041)
0.018**
(0.006)
-0.003
(0.019)
0.182**
(0.036)
-0.156**
(0.054)
-0.061*
(0.024)
0.540**
(0.094)
0.156*
(0.073)
-0.129
(0.112)
-0.031
(0.073)
0.627**
(0.161)
0.006
(0.005)
0.128*
(0.051)
-0.049
(0.042)
-0.225**
(0.086)
0.001
(0.038)
0.076
(0.080)
0.037
(0.028)
0.246**
(0.063)
-0.020
(0.036)
-0.107
(0.065)
-0.098*
(0.038)
0.008
(0.072)
Housing Characteristics
Hut or other
0.844**
0.124*
0.228**
0.139**
(0.094)
(0.062)
(0.064)
(0.044)
House or flat
0.404**
0.230**
0.133*
0.242**
(0.090)
(0.060)
(0.059)
(0.040)
Own dwelling
0.108
0.109*
0.035
-0.008
(0.076)
(0.044)
(0.046)
(0.036)
Rooms per person
-0.085**
0.022
0.205**
0.118**
(0.017)
(0.012)
(0.047)
(0.029)
Cook with wood or dung
-0.412**
0.127*
-0.089
0.004
(0.093)
(0.058)
(0.046)
(0.033)
Cook with electricity or gas
0.137
0.422**
0.045
0.163**
(0.076)
(0.043)
(0.058)
(0.031)
Water at distance
-0.112
-0.071
-0.013
-0.159**
(0.080)
(0.046)
(0.050)
(0.028)
Water piped into house
-0.004
0.028
-0.001
-0.011
(0.083)
(0.045)
(0.068)
(0.036)
Constant
2.094**
3.068**
1.765**
3.326**
(0.169)
(0.100)
(0.105)
(0.074)
n
1769
3968
4563
9639
R-square
0.146
0.066
0.033
0.058
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. * p<.05, ** p<.01.
28
Table B2: OLS estimation for White households with and without children
Dependent Variable
Whites
Whites
Satisfied? 1=v.dis. 5=v.sat.
w/o kids
w/o kids
1993/4
1998
Household characteristics
Household size
-0.045
-0.013
(0.060)
(0.035)
Children per adult
Male head of household
Urban dweller
Income and income potential
Real monthly income in 1000 rand
Pct. of household adults employed
Injury or illness
Crime victim
No or little schooling
Completed High School or beyond
Whites
w/ kids
1993/4
Whites
w/ kids
1998
0.001
(0.032)
-0.019
(0.064)
0.008
(0.090)
-0.307*
(0.125)
0.001
(0.003)
0.054
(0.100)
-0.086
(0.082)
-0.488**
(0.116)
0.018
(0.457)
0.026
(0.061)
0.157
(0.102)
0.176
(0.194)
-0.005
(0.060)
-0.150
(0.078)
0.110*
(0.049)
-0.103
(0.097)
0.315
(0.199)
-0.301
(0.174)
-0.003
(0.006)
-0.059
(0.114)
-0.206*
(0.104)
-0.133
(0.100)
0.021
(0.161)
0.286**
(0.083)
-0.003
(0.002)
-0.041
(0.070)
-0.060
(0.062)
-0.308**
(0.079)
-0.057
(0.117)
0.148**
(0.051)
0.006
(0.003)
-0.028
(0.168)
-0.092
(0.094)
0.025
(0.115)
0.166
(0.237)
0.226**
(0.087)
Housing Characteristics
Hut or other
2.279**
0.690**
0.256
(0.716)
(0.200)
(0.158)
House or flat
2.879**
0.380
1.444**
0.092
(0.224)
(0.196)
(0.307)
(0.065)
Own dwelling
0.094
-0.047
0.321**
0.227**
(0.095)
(0.060)
(0.104)
(0.070)
Rooms per person
0.054
-0.013
0.307**
0.075
(0.030)
(0.018)
(0.091)
(0.049)
Cook with wood or dung
-0.121
-1.594**
2.338**
(0.224)
(0.418)
(0.366)
Cook with electricity or gas
-0.237
-0.039
1.801**
1.734**
(0.216)
(0.084)
(0.358)
(0.521)
Water at distance
-0.832
-0.530
0.522
(1.405)
(0.297)
(0.266)
Water piped into house
-0.059
-0.292
0.066
(0.249)
(0.154)
(0.221)
Constant
0.820*
4.135**
-0.799
2.033**
(0.373)
(0.142)
(0.673)
(0.518)
n
720
1358
625
911
R-square
0.068
0.039
0.116
0.062
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended is some high school. * p<.05, ** p<.01.
29
Table B3: OLS estimation for Black households with average household age less than 21 and between 21
and 27
Dependent Variable
Blacks age
Blacks age
Blacks age
Blacks age
Satisfied? 1=v.dis. 5=v.sat.
category 1
category 1
category 2
category 2
1993/4
1998
1993/4
1998
Household characteristics
Household size
0.011
-0.022**
0.013
-0.001
(0.010)
(0.008)
(0.009)
(0.008)
Children per adult
-0.007
0.031
-0.061
0.036
(0.027)
(0.016)
(0.040)
(0.031)
Male head of household
0.275**
-0.004
0.100
-0.041
(0.061)
(0.039)
(0.056)
(0.038)
Urban dweller
-0.200*
-0.183**
-0.213**
-0.147**
(0.088)
(0.050)
(0.083)
(0.050)
Income and income potential
Real monthly income in 1000 rand
0.081
0.018
0.010
-0.008
(0.058)
(0.010)
(0.034)
(0.010)
Pct. of household adults employed
0.304**
0.037
0.334**
0.364**
(0.099)
(0.054)
(0.093)
(0.061)
Injury or illness
0.043
-0.217**
-0.026
-0.126**
(0.061)
(0.039)
(0.058)
(0.040)
Crime victim
-0.137
-0.443**
-0.064
-0.337**
(0.117)
(0.095)
(0.103)
(0.097)
No or little schooling
-0.145*
-0.071
-0.026
-0.053
(0.064)
(0.039)
(0.059)
(0.043)
Completed High School or beyond
0.008
0.182*
0.079
0.013
(0.143)
(0.074)
(0.109)
(0.069)
Housing Characteristics
Hut or other
0.176
0.071
0.468**
0.132
(0.102)
(0.067)
(0.096)
(0.070)
House or flat
0.046
0.231**
0.249**
0.267**
(0.095)
(0.060)
(0.089)
(0.065)
Own dwelling
0.010
-0.093
0.036
0.083
(0.076)
(0.052)
(0.068)
(0.052)
Rooms per person
0.184*
0.061
-0.020
0.116**
(0.079)
(0.044)
(0.032)
(0.028)
Cook with wood or dung
-0.172*
0.023
-0.134
-0.067
(0.077)
(0.049)
(0.074)
(0.055)
Cook with electricity or gas
-0.124
0.188**
0.218*
0.194**
(0.100)
(0.048)
(0.086)
(0.050)
Water at distance
-0.114
-0.145**
-0.032
-0.132**
(0.084)
(0.042)
(0.075)
(0.047)
Water piped into house
-0.131
0.044
0.043
-0.069
(0.123)
(0.063)
(0.101)
(0.054)
Constant
2.012**
3.491**
1.859**
3.041**
(0.175)
(0.106)
(0.152)
(0.109)
n
1576
3936
1940
3700
R-square
0.046
0.066
0.050
0.062
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. Age category 1 is average household age less than 21, age
category 2 is 21 up to 27. * p<.05, ** p<.01.
30
Table B4: OLS estimation for Black households with average household age 27 up to 35 and 35 and over
Dependent Variable
Satisfied? 1=v.dis. 5=v.sat.
Household characteristics
Household size
Children per adult
Male head of household
Urban dweller
Income and income potential
Real monthly income in 1000 rand
Pct. of household adults employed
Injury or illness
Crime victim
No or little schooling
Completed High School or beyond
Blacks age
category 3
1993/4
Blacks age
category 3
1998
Blacks age
category 4
1993/4
Blacks age
category 4
1998
0.002
(0.012)
0.054
(0.070)
-0.013
(0.067)
-0.268**
(0.088)
-0.032**
(0.011)
0.176**
(0.046)
-0.032
(0.042)
-0.175**
(0.051)
-0.020
(0.025)
-0.022
(0.119)
0.029
(0.077)
-0.297**
(0.103)
-0.035*
(0.016)
0.156*
(0.075)
0.040
(0.043)
-0.124*
(0.049)
0.040
(0.032)
0.359**
(0.115)
0.072
(0.067)
-0.296**
(0.102)
-0.019
(0.066)
0.082
(0.124)
0.012
(0.009)
0.255**
(0.072)
-0.168**
(0.047)
-0.365**
(0.102)
-0.116*
(0.046)
0.067
(0.078)
-0.124**
(0.028)
0.554**
(0.116)
-0.103
(0.082)
0.052
(0.132)
-0.203*
(0.098)
0.410*
(0.173)
0.003
(0.006)
0.094
(0.062)
-0.072
(0.044)
-0.099
(0.106)
-0.058
(0.048)
0.022
(0.097)
Housing Characteristics
Hut or other
0.638**
0.172*
0.686**
0.153*
(0.108)
(0.077)
(0.126)
(0.074)
House or flat
0.407**
0.214**
0.354**
0.240**
(0.097)
(0.072)
(0.121)
(0.071)
Own dwelling
0.031
0.081
0.086
0.091
(0.073)
(0.057)
(0.092)
(0.057)
Rooms per person
-0.044
0.049*
-0.087**
0.010
(0.027)
(0.023)
(0.024)
(0.014)
Cook with wood or dung
-0.213*
0.101
-0.198
0.137*
(0.084)
(0.065)
(0.104)
(0.062)
Cook with electricity or gas
0.259**
0.282**
0.076
0.325**
(0.089)
(0.054)
(0.097)
(0.050)
Water at distance
-0.012
-0.130*
-0.155
-0.127*
(0.083)
(0.052)
(0.103)
(0.053)
Water piped into house
-0.055
-0.023
0.114
0.079
(0.097)
(0.056)
(0.109)
(0.054)
Constant
1.816**
3.212**
2.275**
3.218**
(0.167)
(0.119)
(0.204)
(0.121)
n
1581
2988
1235
2983
R-square
0.078
0.073
0.099
0.051
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. Age category 3 is 27 up to 35, and age category 4 is 35 and over.
* p<.05, ** p<.01.
31
Table B5: OLS estimation for White households with average household age less than 21 and between 21
and 27
Dependent Variable
Whites age
Whites age
Whites age
Whites age
Satisfied? 1=v.dis. 5=v.sat.
category 1
category 1
category 2
category 2
1993/4
1998
1993/4
1998
Household characteristics
Household size
-0.110
0.013
0.032
-0.076*
(0.136)
(0.073)
(0.066)
(0.038)
Children per adult
-0.112
-0.077
-0.116
-0.049
(0.242)
(0.131)
(0.169)
(0.093)
Male head of household
0.522
0.278
-0.143
-0.076
(0.487)
(0.205)
(0.179)
(0.113)
Urban dweller
-0.354
-0.451*
-0.023
-0.169
(0.341)
(0.181)
(0.367)
(0.217)
Income and income potential
Real monthly income in 1000 rand
0.013
0.001
0.000
-0.005
(0.022)
(0.007)
(0.017)
(0.003)
Pct. of household adults employed
-0.252
-0.093
0.388
0.096
(0.277)
(0.180)
(0.274)
(0.159)
Injury or illness
-0.110
-0.256
0.103
0.034
(0.153)
(0.161)
(0.140)
(0.124)
Crime victim
-0.255
-0.155
-0.057
-0.425**
(0.175)
(0.194)
(0.160)
(0.142)
No or little schooling
-0.032
0.205
-0.078
-1.883**
(0.330)
(0.163)
(0.370)
(0.104)
Completed High School or beyond
0.144
0.044
0.238
0.034
(0.152)
(0.110)
(0.122)
(0.095)
Housing Characteristics
Hut or other
-0.090
0.327
(0.223)
(0.206)
House or flat
0.291
0.157
(0.731)
(0.084)
Own dwelling
0.461**
0.306**
0.251
0.235*
(0.167)
(0.117)
(0.145)
(0.101)
Rooms per person
0.128
0.114
0.158**
0.029
(0.187)
(0.068)
(0.046)
(0.051)
Cook with wood or dung
Cook with electricity or gas
2.634**
(0.387)
1.987**
(0.302)
Water at distance
Water piped into house
0.275
0.984
(0.247)
(0.874)
Constant
1.181
3.633**
-0.357
(0.630)
(0.494)
(0.781)
n
203
284
313
R-square
0.175
0.090
0.116
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. Age category 1 is average household age less than 21, age
category 2 is 21 up to 27. * p<.05, ** p<.01.
32
1.136*
(0.519)
0.734
(0.471)
3.398**
(0.566)
370
0.096
Table B6: OLS estimation for White households with average household age 27 up to 35 and 35 and over
Dependent Variable
Whites age
Whites age
Whites age
Whites age
Satisfied? 1=v.dis. 5=v.sat.
category 3
category 3
category 4
category 4
1993/4
1998
1993/4
1998
Household characteristics
Household size
0.129
0.062
0.016
-0.007
(0.070)
(0.051)
(0.059)
(0.037)
Children per adult
-0.114
0.005
-0.327
-0.108
(0.212)
(0.131)
(0.340)
(0.217)
Male head of household
0.173
-0.078
0.235
0.002
(0.210)
(0.126)
(0.132)
(0.065)
Urban dweller
-0.253
-0.296
0.061
-0.146
(0.170)
(0.186)
(0.193)
(0.084)
Income and income potential
Real monthly income in 1000 rand
0.001
0.004
0.002
-0.004
(0.006)
(0.004)
(0.007)
(0.003)
Pct. of household adults employed
-0.063
0.044
-0.231
-0.034
(0.313)
(0.150)
(0.135)
(0.080)
Injury or illness
-0.342*
0.042
-0.185
-0.097
(0.172)
(0.133)
(0.111)
(0.064)
Crime victim
0.152
-0.534**
-0.184
-0.345**
(0.150)
(0.173)
(0.126)
(0.093)
No or little schooling
-0.344
-1.093**
0.156
-0.011
(0.433)
(0.325)
(0.165)
(0.116)
Completed High School or beyond
0.226
0.025
0.337**
0.154**
(0.141)
(0.099)
(0.099)
(0.055)
Housing Characteristics
Hut or other
0.577*
0.765**
(0.228)
(0.204)
House or flat
2.588**
0.440*
(0.371)
(0.195)
Own dwelling
0.205
0.162
0.218
-0.076
(0.146)
(0.123)
(0.125)
(0.065)
Rooms per person
0.246**
0.048
0.055
-0.013
(0.074)
(0.059)
(0.039)
(0.019)
Cook with wood or dung
0.399
-0.005
-0.860
(0.482)
(0.381)
(0.714)
Cook with electricity or gas
-0.365
0.022
0.667
(0.489)
(0.371)
(0.591)
Water at distance
1.069**
-0.596*
(0.327)
(0.288)
Water piped into house
2.307**
-0.210
-0.055
-0.382**
(0.198)
(0.180)
(0.288)
(0.136)
Constant
0.757
3.985**
0.665
3.456**
(0.526)
(0.394)
(0.498)
(0.606)
n
304
387
525
1228
R-square
0.107
0.073
0.096
0.047
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. Age category 3 is 27 up to 35, and age category 4 is 35 and over.
* p<.05, ** p<.01.
33
Table B7: OLS estimation for Blacks by percentage of household that is female
Dependent Variable
Blacks female
Blacks female
Blacks female
Blacks female
Satisfied? 1=v.dis. 5=v.sat.
pct <=50%
pct <=50%
pct >50%
pct >50%
1993/4
1998
1993/4
1998
Household characteristics
Household size
-0.006
-0.019**
0.012
-0.030**
(0.008)
(0.006)
(0.008)
(0.006)
Children per adult
-0.012
0.038*
-0.050
0.026
(0.024)
(0.016)
(0.025)
(0.015)
Male head of household
0.064
-0.032
0.165**
0.050
(0.051)
(0.031)
(0.046)
(0.031)
Urban dweller
-0.307**
-0.148**
-0.183**
-0.175**
(0.057)
(0.032)
(0.070)
(0.040)
Income and income potential
Real monthly income in 1000 rand
-0.010
0.009
-0.019
-0.001
(0.020)
(0.005)
(0.037)
(0.007)
Pct. of household adults employed
0.363**
0.209**
0.406**
0.102*
(0.070)
(0.040)
(0.078)
(0.045)
Injury or illness
0.028
-0.096**
-0.014
-0.205**
(0.044)
(0.029)
(0.048)
(0.031)
Crime victim
-0.081
-0.230**
-0.164*
-0.453**
(0.076)
(0.064)
(0.082)
(0.080)
No or little schooling
-0.101*
-0.047
-0.032
-0.078*
(0.045)
(0.028)
(0.050)
(0.032)
Completed High School or beyond
0.276**
0.109*
-0.046
0.053
(0.090)
(0.053)
(0.094)
(0.056)
Housing Characteristics
Hut or other
0.537**
0.119*
0.408**
0.165**
(0.069)
(0.047)
(0.081)
(0.055)
House or flat
0.232**
0.203**
0.282**
0.310**
(0.066)
(0.043)
(0.073)
(0.051)
Own dwelling
0.085
0.076*
-0.049
-0.004
(0.049)
(0.034)
(0.058)
(0.042)
Rooms per person
-0.061**
0.034**
0.074
0.062**
(0.017)
(0.013)
(0.039)
(0.017)
Cook with wood or dung
-0.208**
0.070
-0.137*
0.001
(0.055)
(0.039)
(0.063)
(0.041)
Cook with electricity or gas
0.114*
0.262**
0.176*
0.215**
(0.058)
(0.033)
(0.076)
(0.039)
Water at distance
-0.086
-0.159**
-0.025
-0.117**
(0.057)
(0.032)
(0.064)
(0.036)
Water piped into house
-0.076
0.009
0.200*
-0.010
(0.066)
(0.036)
(0.087)
(0.044)
Constant
2.132**
3.227**
1.802**
3.358**
(0.115)
(0.074)
(0.129)
(0.082)
n
3822
7665
2510
5942
R-square
0.061
0.055
0.059
0.067
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. * p<.05, ** p<.01.
34
Table B8: OLS estimation for Whites by percentage of household that is female
Dependent Variable
White female
White female
White female
White female
Satisfied? 1=v.dis. 5=v.sat.
pct <=50%
pct <=50%
pct >50%
pct >50%
1993/4
1998
1993/4
1998
Household characteristics
Household size
0.003
-0.027
0.083
0.024
(0.045)
(0.026)
(0.053)
(0.039)
Children per adult
-0.112
0.007
-0.112
-0.030
(0.099)
(0.058)
(0.117)
(0.082)
Male head of household
0.230
0.037
0.102
-0.169
(0.189)
(0.085)
(0.165)
(0.094)
Urban dweller
-0.041
-0.227**
-0.067
-0.140
(0.141)
(0.073)
(0.327)
(0.152)
Income and income potential
Real monthly income in 1000 rand
-0.001
-0.003
0.002
0.001
(0.006)
(0.002)
(0.005)
(0.003)
Pct. of household adults employed
-0.036
0.058
-0.039
-0.103
(0.111)
(0.063)
(0.165)
(0.111)
Injury or illness
-0.148
-0.103
-0.205
-0.012
(0.083)
(0.062)
(0.125)
(0.083)
Crime victim
-0.114
-0.483**
0.014
-0.117
(0.088)
(0.078)
(0.148)
(0.125)
No or little schooling
0.024
0.090
0.179
-0.240
(0.171)
(0.143)
(0.211)
(0.168)
Completed High School or beyond
0.292**
0.084
0.215
0.184*
(0.071)
(0.047)
(0.116)
(0.071)
Housing Characteristics
Hut or other
2.755**
0.417*
0.439**
(0.720)
(0.164)
(0.128)
House or flat
3.079**
0.213
-0.008
(0.215)
(0.147)
(0.165)
Own dwelling
0.312**
0.111*
0.042
0.019
(0.081)
(0.055)
(0.123)
(0.081)
Rooms per person
0.088**
-0.009
0.118*
-0.019
(0.034)
(0.020)
(0.046)
(0.027)
Cook with wood or dung
1.171
-2.321**
0.151
(0.635)
(0.084)
(0.801)
Cook with electricity or gas
0.890
-0.124
2.318**
1.067
(0.624)
(0.070)
(0.214)
(0.780)
Water at distance
-2.595
0.027
-0.617
(1.340)
(0.171)
(0.489)
Water piped into house
-1.944**
-0.108
0.309
-0.320
(0.279)
(0.144)
(0.414)
(0.273)
Constant
1.115
4.152**
0.501
3.338**
(0.756)
(0.184)
(0.438)
(0.807)
n
914
1535
431
734
R-square
0.096
0.050
0.064
0.041
Note: Robust standard errors in parenthesis. Base case is: Shack, cook with other material, water outside
but close, and attended some high school. * p<.05, ** p<.01.
35

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