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Informal sector employment and poverty in South Africa: identifying the contribution of 'informal' sources of income on aggregate poverty measures

March 2017

Authors:

Paul Cichello

Boston College

Michael Rogan

Rhodes University

Informal employment definitions

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Poverty estimates (Pα) for South Africa, 2008

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Poverty-effectiveness ratios for the decomposition of the poverty headcount (P0), 2008

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Decomposition of change in poverty (Z = 594) by income source.

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Figures - uploaded by Michael Rogan

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Content uploaded by Michael Rogan

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REDI3x3 Working paper 34 March 2017

Informal sector employment and poverty in

South Africa: identifying the contribution of

‘informal’ sources of income on aggregate

poverty measures

Paul Cichello and Michael Rogan

Abstract

We examine the role that informal sector employment plays in poverty reduction using data

from the National Income Dynamics Study (NIDS). Using a Shapley decomposition approach,

we find that government transfers and formal sector jobs are the dominant drivers of

aggregate poverty reduction. Informal sector jobs currently play a limited role in poverty

reduction at the national level. This is primarily driven by the fact that there are relatively

few informal sector jobs compared to formal sector jobs. On a per-job basis, the poverty

reduction associated with formal sector jobs and informal sector jobs is quite similar. The

poverty reduction associated with one informal sector job is generally between 50 to 100 per

cent of the poverty reduction associated with one formal sector job (depending on the

poverty measure, poverty line and year chosen). Therefore, from a poverty reduction

standpoint, policy makers are encouraged to view job gains and losses in the informal sector

approximately on par with gains and losses of formal sector jobs.

The Research Project on Employment, Income Distribution

and Inclusive Growth is based at SALDRU at the University of

Cape To wn and supported by the National Treasury. Views ex-

pressed in REDI3x3

Working Papers are those of the authors

and are not to be attributed to any of these institutions.

Informal sector employment and poverty in South Africa: identifying the

contribution of ‘informal’ sources of income on aggregate poverty measures

Paul Cichello

Department of Economics, Boston College, Boston, Massachusetts

Michael Rogan

Neil Aggett Labour Studies Unit (NALSU), Institute of Social and Economic Research (ISER),

Rhodes University

1. Introduction

Against the backdrop of very high rates of unemployment in post-apartheid South Africa, the

role of the informal sector, the informal economy or informal employment, more broadly1, in

employment creation and overall development has been marginalised. Former state president

Thabo Mbeki famously, and somewhat controversially, identified informal workers as part of

the ‘second economy’ which is characterised by poverty and under-development and which is

structurally disconnected from the formal economy (see Devey et al., 2006; Valodia &

Devey, 2012). Even where there has been some degree of recognition of the importance of

the informal sector to employment creation and livelihoods, policy responses are often

unsupportive. For example, the government’s principle policy document, the National

Development Plan (NDP), has projected that between 1.2 and two million new informal

sector ‘jobs’ (including domestic work) will be needed by 2030 if the country is to meet its

targets in reducing unemployment (National Planning Commission, 2012: 121). The

document is almost completely silent, however, in terms of how the informal sector will be

supported or how current policies can be extended to ensure that the informal sector grows in

line with overall employment growth.

Policy gaps and the lack of recognition of the importance of the informal sector are not

unique to the South African context and two of the key contrasting views of the informal

sector have often suggested that, on the one hand, the sector is an indicator of a ‘backward’

and unproductive economy while, on the other hand, it is understood as a critical source of

employment and earnings for workers on the margins of the labour market. In this paper we

explore the case for supporting informal types of employment by considering the extent to

which earnings from informal sector self-employment (and informal employment, more

broadly) contribute to a reduction in income poverty. We argue that, by using a popular,

intuitive and widely understood indicator of development, namely the poverty headcount rate,

1 See the ILO (2013) or Hussmanns (2004) for an overview of these terms.

it is possible to make the case that the informal sector should be an important part of

government’s strategy to reduce income poverty.

The remainder of the paper is structured as follows. Section two provides a brief description

of the informal sector in South Africa. In linking the sector to income poverty, the section

also offers a brief review of the post-apartheid poverty literature as well as recent work on

low earnings and ‘working poverty’. Section three identifies the sources of data which are

suitable for measuring both informal sector employment and income poverty. It then

describes the decomposition method and the simulation techniques which we use to measure

the contribution of various employment types to poverty reduction. Section four presents our

findings of the decomposition, highlighting poverty reduction of various types of

employment on both an aggregate and a per-job basis. We place special emphasis on

comparing income from informal self-employment, where jobs are exclusively informal to

income from our formal employment income, where jobs are almost exclusively from formal

sector employers but also present results from other employment types that are either ‘mixed’

(i.e. incorporating both informal sector employers and formal sector employers) or other

employment (i.e. neither formal nor informal sector employers). Section five presents

alternative approaches. First, we decompose the change in poverty reduction over the 2008

and 2012 period. Second, we show the results of a simple simulation of poverty reduction

following the addition of 1 million new informal self-employment jobs. Section six concludes

by considering the case for supporting or developing the informal sector as a way of

contributing to the reduction of poverty.

2 The informal sector and poverty in South Africa

2.1 The informal sector in South Africa

The informal sector constitutes a small share of the total workforce in South Africa, relative

to other sub-Saharan African countries (ILO, 2013; Kingdon & Knight, 2004). Nonetheless,

the sector still accounts for about 17 per cent of total employment or about 2.4 million jobs

according to Statistics South Africa’s official estimates (Statistics South Africa, 2015). A

number of stylised facts about the broad characteristics of employment in the informal sector

in South Africa are now widely accepted. For example, activities in the informal sector are

concentrated largely in the wholesale, retail and trade sector (44 per cent), services (16 per

cent), and construction (16 per cent) (Statistics South Africa, 2015). In terms of status in

employment, most of those working in the informal sector (61 per cent) are self-employed

while 36 per cent are employees (ILO, 2013).

While the informal sector remains a crucial livelihood source for many workers who exist at

the margins of the labour market, it is vulnerable in a number of ways. An analysis (Verick,

2010) of the South African labour market during the 2008 global financial crisis suggested

that, instead of absorbing workers who were displaced from the formal sector, the majority

(64 per cent) of job losses during the immediate crisis period were actually in the informal

sector. Moreover, the informal sector is often ignored in policy documents and, in some

cases, policy responses are openly hostile towards workers in the sector. Further evidence

that the informal sector is vulnerable is seen in work which has shown that informal self-

employment is not a free-entry sector (Kingdon & Knight, 2004) and that there are a number

of barriers to entry (Cichello et al. 2011). A more nuanced analysis, moreover, has suggested

that there is significant segmentation within the South African informal sector itself (Heintz

& Posel, 2008). The informal sector in South Africa, therefore, should not be characterised as

a homogenous sector which can provide free-entry to the unemployed.

2.2 Poverty and the informal sector in South Africa

The main link between the informal sector and income poverty, in broad terms, is through its

contribution to employment creation and earnings. According to a World Bank firm survey in

Johannesburg, informal enterprises were found to generate an average of three jobs- the same

number as small formal firms. While about 44 per cent of these jobs were allocated to

household members, the vast majority (93 per cent) were full-time, paid jobs (Chandra et al.,

2002). With respect to informal traders, in particular, there is additional evidence that

opportunities to trade on a greater scale in concentrated areas (i.e. city centres and markets)

creates the possibility of new opportunities, additional service industries and products (Philip,

2010).

Although earnings tend to be low in the informal sector, an estimate of the contribution by

informal self-employment to total income earned from all employment in South Africa is

about five per cent (Wills, 2009a). In terms of the wider economy, one measure suggests that

the informal sector contributes about 26 per cent of total value added in South Africa. The

same study found that, the sector contributes between 7-12 per cent of South Africa’s total

gross domestic product (GDP) (Budlender et al., 2001; Ligthelm, 2006). In terms of

expenditure, an estimated R51.7 billion (or 6.3 per cent of total household expenditure) was

spent at informal businesses in 2004 (Ligthelm, 2006).

Turning now to income poverty specifically, the main finding from the poverty literature in

South Africa is that the increase in government transfers (in the form of means tested social

grants) during the early 2000s has been the main driver of the well-documented decrease in

income poverty over the past decade (Leibbrandt et al., 2010; Posel & Rogan, 2012; van der

Berg et al., 2008). To some extent, this large impact of government transfers on poverty

reduction has tended to overshadow the contribution of other income sources to reducing

aggregate poverty levels. Nonetheless, a handful of studies (Posel & Casale, 2006; Rogan &

Reynolds, 2015; Vermaak, 2010, 2012) have investigated the link between labour market

earnings and income poverty, specifically. However, most of this work does not distinguish

between formal and informal sector earnings in their respective analyses. One exception is a

study by Rogan and Reynolds (2015) which found that about 41 per cent of workers (both the

self-employed and employees) in the informal sector were below the poverty line in 2012

(compared with 17 per cent of workers in the formal sector) and that about 37 per cent of the

working poor in South Africa are from the informal sector (Rogan & Reynolds, 2015). To the

best of our knowledge, however, there is no research on the contribution of the informal

sector to poverty reduction in South Africa.

3. Data and methods

3.1 Data options: Statistics South Africa’s household surveys

To estimate the contribution of informal-sector earnings to poverty reduction, two types of

information are required. First, the survey must collect information on employment status

which can be used to measure informal-sector employment. Second, the same survey needs to

have comprehensive information on total household income with which to identify

households that are below the poverty line. Despite the availability of many household

surveys in South Africa, there are surprisingly few data sources that capture comprehensive

and well-defined information on both employment and total household income.

Perhaps the logical starting point would be the official Labour Force Surveys (LFSs)

(collected bi-annually between 2000 and 2007 and then quarterly from 2008). Both the LFSs

and the Quarterly Labour Force Surveys (QLFSs) capture comprehensive information on

labour-market status and earnings, and can clearly identify employment in the informal

sector. Estimates of income poverty, however, are difficult to undertake when using the LFSs

since they do not capture comprehensive information on total household income (e.g. income

from social grants and remittances are not measured by most LFSs). The QLFSs are even less

appropriate for poverty analyses since they only capture information on labour-market

earnings (i.e. not total household income). There is, therefore, no possibility for using these

data to analyse household poverty.

In terms of Stats SA’s national household surveys, the 2008/09 Living Conditions Survey

(LCS) and the Income and Expenditure Surveys (IES) are arguably the ideal sources for the

analysis of poverty. The LCS is the source of Stats SA’s official reports on both poverty lines

and poverty levels; it was originally designed as a tool to monitor changes in living standards

and poverty risks over time. In addition to comprehensive information on household income

and expenditure (from which poverty can be measured), the LCS also captures some

potentially useful data on employment. In practice, however, the LCS cannot be used to link

informal-sector earnings to poverty reduction because Stats SA released an aggregated

income variable only, therefore it is not possible to link income with specific types of

employment.2 The IES is, of course, the other main source of poverty estimates in South

Africa but it captures very little information on self-employment and wage-employment.

This leaves the annual General Household Surveys (GHSs) as the last possible source of

‘official’ data on the link between informal employment and poverty. However, the module

on employment is not very detailed and the surveys, therefore, are fairly blunt tools with

which to measure both poverty and informal-sector earnings.

3.2 Preferred data option: The National Income Dynamics Study (NIDS)

Given the limitations of Stats SA’s official surveys in terms of linking informal-sector

earnings with household poverty status, we turn now to an alternative source of data. The

National Income Dynamics Study (NIDS) is a nationally representative household panel

survey which is conducted by the Southern Africa Labour and Development Research Unit

(SALDRU) at the University of Cape Town every two years (2008, 2010, 2012, 2014). The

NIDS data currently offer the best possible way to link informal earnings with poverty

reduction in South Africa, since a wide range of income sources are captured and the survey

collects detailed information on employment.

However, in terms of the measurement of informal-sector employment there are two

limitations associated with using the NIDS data. These relate to the important distinction

between (1) informal-sector employment and (2) informal employment, a broader and

different concept developed by the International Labour Organization (ILO) and the

International Conference of Labour Statisticians (ICLS). The latter concept is concerned

mainly with working conditions and specifically also includes employees in the formal sector

that work ‘informally’ without regular contracts and/or benefits (also called ‘unprotected

workers’).

Unfortunately, analyses of the NIDS data are not able to provide estimates of employment

numbers for the informal sector as such. This is because the questionnaire does not allow for

a distinction between informal employees in the informal sector and those in the formal

sector. Thus, some formal-sector employees that work ‘informally’ are included in the NIDS

‘informal wage-employment’ numbers. A second limitation is that, when using the NIDS

data, it is not possible to distinguish, among the informally self-employed, between own-

account workers (enterprise owners without employees) and employers (enterprise owners

with employees).

2 Stats SA itself no longer has access to the raw earnings data from the survey so it is not possible to attempt to

reconstruct the aggregate income variable in any way (personal correspondence with Stats SA).

The questionnaire is also different from Stats SA’s labour market and household surveys in

that it includes a separate section of questions for ‘casual workers’. According to the

fieldworker instructions, this category includes a range of occupations such as ‘construction

work, waitressing, gardening or paid domestic work’. Based on the definition3 of casual

work, it is likely that most workers identified in this category would be informal employees

but, again, it is not possible to distinguish whether this employment is in the informal sector

or in the formal sector. Moreover, it is not possible to identify the domestic workers within

this category because ‘domestic work’4 is not an occupational code in the NIDS and there is

no industry-code variable linked with casual work. (In any case, domestic workers are not

part of the standard definition of the informal sector – see ILO 2013.) Consequently, the

‘casual worker’ category is problematic as well.

Therefore, the NIDS data leave us with a potentially useful but constrained data source in

terms of analysing employment in the informal sector. Still, it appears to be the best source of

data for the complex task at hand.

3.3 Definitions

We use the NIDS data to define five distinct employment categories (see Table 1). The two

categories of primary use to this analysis are informal self-employment and informal regular

wage-employment. The former includes only the self-employed in enterprises that are not

registered for income tax or VAT. The latter category corresponds to the broad informality

definition of the 17th ICLS. It includes a subset of regular5 wage employees (from both

formal- and informal-sector firms) that are deemed to have informal employment. For our

purposes, we would like this category to be limited to only those workers hired by informal-

sector firms. Therefore, the category adds an additional criterion, ‘non-payment of UIF6

contributions’, to the standard (worker-based) definition of informal wage-employment. This

will improve the likelihood that workers captured in this category are working in the informal

sector.7 (See Table 1 for definitions of the five categories.) As we still cannot eliminate all

3 The actual definition for casual work is given as ‘work that is irregular and short-term, or any work that the

respondent does in addition to any work that she/he had described in the previous questions’.

4 Only domestic workers with regular (i.e. not casual) wage-employment can be identified as a specific

occupational category in the NIDS.

5 The NIDS is unique in that it distinguishes between ‘regular’ employment and ‘casual’ employment and, as

outlined earlier, the questionnaire has two separate modules for regular and casual employees. This makes the

NIDS questionnaire somewhat different from the LFS and QLFS questionnaires.

6 The Unemployment Insurance Fund (UIF) is a government-administered fund to which employees contribute,

through monthly deductions from their wages. Upon involuntarily losing a job, the employee can claim benefits

for a period, an amount that depends on the period of employment and ending wage/salary.

7 This specific criterion has not been used in the South African literature (compare Heintz & Posel 2008 and

Wills 2009). UIF payment is a potentially useful proxy for employment in the formal sector since all employers

and workers are required by law to contribute to the UIF. The only exemptions are for workers working less

than 24 hours a month for an employer; public servants; foreigners working on contract; workers who get a

monthly state (old age) pension; or workers who earn only commission.

those employees who are working for formal-sector firms, this category is recognised as

coming from ‘mixed’ sectors.

Table 1: Informal employment definitions

INFORMAL SECTOR

Informal self-

employment

Self-employed in enterprises that are not registered for income tax or VAT

MIXED: INFORMAL SECTOR PLUS FORMAL SECTOR

Informal

(regular) wage

employment

(augmented)

Employees with regular employment who do not receive both pensions and

medical aid from their employers, and do not contribute to the Unemployment

Insurance Fund (UIF) and who do not have written employment contracts

Casual

employment

Employees with work that is irregular and short-

term, or any work that the

respondent does in addition to their first two wage jobs/self-employment

businesses

FORMAL SECTOR

Formal sector

employment

Self-employed in VAT or tax-registered enterprises;

and

Employees with regular employment who have a written contract or who pay

UIF contributions or who receive both employer-based pensions and medical

aid

OTHER EMPLOYMENT CATEGORIES

Domestic work Employees with regular employment who work in ‘private households’

Subsistence

agriculture Individuals engaged in subsistence agriculture. This is a relatively small

component in the data and is often omitted in the analysis that follows

We suggest that the addition of the UIF criterion to the worker-based definition of informal

wage-employment (i.e. the one frequently used in the South African literature) is a potentially

useful way of narrowing the definition and measurement of ‘informal employees’ to increase

the proportion of those inside the informal sector (i.e. since it is unlikely that informal-sector

employers would deduct UIF payments from their employees). This might be particularly

important when analysing the NIDS data since these data preclude the possibility of

replicating the estimates of informal wage-employment inside the informal sector, as reported

by Stats SA (2015) and the ILO (2013).

Despite the substantial differences in the way in which the NIDS questionnaire measures

employment (compared to the QLFSs), 20088 estimates from the NIDS of the categories of

employment in Table 1 are broadly in line with those from the QLFS. For example, we

estimate that there were roughly 1.3 million workers in informal self-employment in wave 1

of the NIDS (2008), compared with about 1.5 million in the LFS of the second quarter of

2008 (own calculations from wave 1 of the NIDS and 2008 Q2 of the QLFS).

8 We make this comparison with the first (2008) wave because NIDS is a longitudinal survey and the 2008 wave

is the only one that is not affected by any type of survey attrition.

Notwithstanding the important limitations of using the NIDS data to measure informal

employment and the informal sector, we therefore have some confidence that our estimates

are broadly in line with those derived from the official QLFS.

3.4 Methods

In order to estimate how formal and informal sector employment impact poverty, we must

first define and calculate poverty. In this paper, we use the Foster-Greer-Thorbecke (FGT)

class of poverty measures. These measures include the popular poverty headcount ratio (P0),

as well as the poverty gap index (P1) and the severity of poverty (P2) which place increasing

importance on reductions in poverty that occur further below the poverty line. We will

construct these estimates using three possible poverty lines.9

The extent to which (formal or) informal sector employment reduces national poverty rates

depends on three things: the number of jobs the sector creates, the earnings that these jobs

bring to households and the extent to which that increased income reduces poverty in

households below the poverty threshold. We begin our analysis by identifying how income

from various income sources- both labour income and non-labour income- reduce national

poverty levels in both absolute and relative terms, where the relative values are a per cent of

total poverty reduction as compared to a counterfactual with no income. We use the Shapley

decomposition approach (see Appendix A) to estimate the average marginal effect of

individual income sources on the reduction of aggregate poverty rates (Shorrocks 2013). In

order to estimate the decomposition, our analysis makes use of the Distributional Analysis

STATA Package (DASP) module developed by Araar and Duclos (2007). The decomposition

(Araar & Duclos, 2009a) identifies the contribution of each income source to the elimination

of poverty by comparing what the FGT measures would have been without each respective

source of income. By making use of the Shapley values, the model estimates the average

marginal effect of each income source over all possible combinations of income sources.

Next, we consider the total number of jobs classified under each employment category (from

Table 1) and estimate the per-job ‘impact’ on poverty for each type of employment. For ease

of interpretation, we consider the per million jobs impact on national poverty rates. We then

create ratios using formal employment as the numeraire so that we can estimate the impact of

a typical informal self-employment job on poverty levels relative to a typical formal

employment job.

We then use a similar approach to examine the change in poverty over the 2008 to 2012

period. We decompose the change in poverty rates over the 2008 to 2012 period based on the

9 These poverty lines are as follows: R307, R424, and R594 monthly per capita household income in March

2010 (see Statistics South Africa 2014, Table 2).

changes in income from various income sources. In this case, the relative contributions

represent the change in poverty rates due to an income source relative to the total change in

poverty rates observed of the 2008 to 2012 period, i.e. the poverty reduction since 2008 is

accounted for rather than poverty reduction compared to a counterfactual scenario with no

income whatsoever. These poverty dynamics offer a different perspective and emphasise the

recent changes in the economy. For example, using this approach, even a very valuable sector

of the economy, if stagnant, will automatically offer no impact on the change in poverty,

despite the fact that the income source may well have different poverty reduction levels

attributed to it in the two cross-sectional periods. In practice, we estimated these changes over

time by modifying the Araar and Duclos decomposition coding slightly to allow for an initial

level of income and to list results more suitable for analysing changes over time.

Finally, we demonstrate a simple simulation which illustrates a ‘back of the envelope’

assessment of the impact that adding one million new informal self-employment jobs would

have on national poverty rates. This simulation assumes that all jobs would be given to

randomly selected unemployed individuals (either searching or non-searching) and that no

other household income would be affected. We assume the new earnings would come from

random draws from the distribution of earnings from current informal self-employment

jobs.10

4. Findings

4.1 Poverty and informal employment

We begin by presenting aggregate poverty estimates for the South African population as a

whole at Statistics South Africa’s three official poverty lines. In line with the broader post-

apartheid poverty literature, the table shows that, at the official upper-bound poverty

threshold (R594 per capita monthly household income) roughly half of the population is

identified as income poor in the first round (2008- wave 1) of NIDS. At the food poverty line

(R307) about 30 per cent of South Africans live in poor households and therefore are not

likely to be able to meet even their basic food and nutritional needs. While much more could

be discussed in relation to the findings presented in the table, the key point is that the

aggregate estimates of income poverty based on the NIDS data are closely in line with

estimates from Statistics South Africa’s household surveys (in particular the LCS and the

GHSs). As such, the analysis can now be narrowed to a focus on poverty and informal

employment.

10 In practice, we identify the mean (µ) and standard deviation (s) of log earnings for existing informal self-

employment jobs. For each unemployed individual that is randomly selected to get one of these new jobs, we

create pay by taking e(µ+zs) where z is a randomly generated number from the standard Normal distribution.

Table 2 Poverty estimates (Pα) for South Africa, 2008

Z = 307

Z = 424

Z = 594

0.287

0.406

0.521

(0.004)

(0.005)

0.122

0.183

0.264

(0.002)

(0.003)

0.073

0.112

0.168

(0.002)

Source: Own calculations from NIDS (2008)

Notes: Standard errors are in brackets. All poverty lines and income are

measured in March 2010 prices. Household well-being is estimated as average

per capita total household monthly income.

Income from the formal sector is of unparalleled importance when it comes to overall income

received by South Africans. This is immediately apparent in the first column of Table 3,

which shows that such income comprises 56.7% of total per capita income received by

households. In comparison, informal self-employment adds just 3.1% and our two mixed

categories- informal wage employment and casual employment- comprise just 2.4 and 2.2 per

cent of income, respectively. Non-labour income categories are also important but none,

other than imputed rental income (15.6%), garner more than a 7 per cent share of income.

However, the relative importance of income sources to poverty reduction11 looks markedly

different. When we decompose the contribution of income sources to poverty using, for

example, the food poverty line of 307 Rand, income from formal sector employment

accounted for a 26.9 percentage point reduction in the 2008 poverty headcount (see column

3). This 26.9 percentage point reduction in poverty represents 37.7 per cent of the total

reduction in poverty (see column 2). While still the largest single contributing income source

to poverty reduction, formal sector earnings now account for just 37.7 per cent of poverty

reduction even though it accounted for 56.7 per cent of total income received. In contrast,

social grants account for 20.7 per cent of overall poverty reduction despite accounting for just

6.6 per cent of all income received by households.

The reason for this is that social grant income is well targeted to households that would

otherwise be below the poverty line. Additionally, the grant income is not so large that

households who receive it end up well above the poverty line. Much more of the money is

having a poverty reducing impact. In contrast, formal sector earnings are either not going to

households that would otherwise be poor or are adding so much to those household incomes

that the household ends up far beyond the poverty level (i.e. much of the income is not

poverty-reducing).

11 For the full poverty decomposition results for P1 and P2 see Appendix B.

Table 3 Decomposition of the poverty headcount (P0) by income source, 2008

In Table 4 we present the share of poverty reduction from each income source divided by the

overall share of income. These ‘poverty-effectiveness’ ratios fall between a range of 0.4 for

investment income to 3.21 for social grants. The key finding from the table is that income

from types of employment other than the formal sector is relatively effective in reducing

poverty even if their absolute contribution to aggregate reductions in poverty was small. For

example, the ratio of poverty reduction shares to income shares is greater than two (at all

three poverty lines) for domestic work. This suggests, as outlined above, that income from

domestic work is particularly well targeted to workers from poor households and that it is

important in lifting domestic workers and their households above the poverty line. In terms of

earnings from our two mixed categories of informal employment, the poverty-reducing

effectiveness of these types of jobs is also relatively high. The ratios for informal regular

wage jobs are just under two at all three poverty lines. Interestingly, the ratios for informal

sector employment are somewhat lower (1.14, 1.13 and 1.16 at the three poverty lines,

respectively). This suggests that income from informal sector enterprises is either less well

targeted to workers below the poverty line or that, where it is received, it is not enough to

move households out of poverty (relative to income from other types of informal

employment). More broadly though, the income from informal sector self-employment still

Income Source

Income

Relative Absolute Relative Absolute Relative Absolute

0.031 -0.036 -0.025 -0.035 -0.021 -0.036 -0.017

(0.005) (0.002) (0.001) (0.002) (0.001) (0.002) (0.001)

0.024 -0.044 -0.031 -0.045 -0.027 -0.043 -0.020

(0.002) (0.002) (0.001) (0.002) (0.001) (0.003) (0.001)

0.022 -0.038 -0.027 -0.038 -0.023 -0.033 -0.016

(0.002) (0.002) (0.001) (0.002) (0.001) (0.002) (0.001)

0.010 -0.026 -0.018 -0.024 -0.014 -0.021 -0.010

(0.001) (0.001) (0.001) (0.002) (0.001) (0.002) (0.001)

0.567 -0.377 -0.269 -0.438 -0.260 -0.500 -0.239

(0.012) (0.005) (0.004) (0.005) (0.004) (0.006) (0.004)

0.066 -0.207 -0.147 -0.148 -0.088 -0.099 -0.047

(0.002) (0.003) (0.002) (0.003) (0.002) (0.003) (0.001)

0.070 -0.028 -0.020 -0.033 -0.020 -0.037 -0.018

(0.007) (0.001) (0.001) (0.002) (0.001) (0.002) (0.001)

0.048 -0.056 -0.040 -0.054 -0.032 -0.048 -0.023

(0.008) (0.002) (0.001) (0.002) (0.001) (0.002) (0.001)

0.156 -0.180 -0.129 -0.179 -0.106 -0.176 -0.084

(0.004) (0.003) (0.002) (0.003) (0.002) (0.004) (0.002)

Total 11 -0.713 1 -0.594 1 -0.479

(0) (0) (0.004) (0) (0.004) (0) (0.005)

Remittance

income

Imputed rental

income

Casual

employment

Domestic work

Formal sector

employment

Social grant

income

Investment

income

Informal regular

wage emp.

Z = 307

Z = 424

Z = 594

Informal self-

employment

has a substantially higher ‘poverty-effectiveness’ ratio than earnings from the formal sector

(0.66, 0.77 and 0.88, respectively).

Before moving on, a few points are worth noting. First, using this simple approach, social

grant income appears well targeted and it is clearly a critical lynchpin in aggregate poverty

reduction. Second, formal sector employment- in aggregate- is crucial to overall poverty

reduction in South Africa. Formal sector income may not be particularly well distributed (i.e.

among those residing in poor households), but it is such a dominant portion of the overall

income that it is a vital component to poverty reduction overall (even though the ratio of

relative poverty reduction to overall income share is only 0.66). Third, policymakers should

also distrust notions that increases in informal sector employment, alone, would solve

national poverty issues. Despite a moderate level of poverty-effectiveness (the ratio of

poverty reduction to the overall income share derived from informal self-employment, for

example, is 1.14 at the food poverty line), the total income provided by this sector is too

small to eliminate national poverty.

Table 4 Poverty-effectiveness ratios for the decomposition of the poverty headcount (P0), 2008

Income Source Income Share Z = 307 Z = 424 Z = 594

Poverty-effectivenss ratios

Informal self-

employment

0.03

1.14

1.13

1.16

1.81

1.48

Informal regular

wage emp.

0.02

1.86

1.90

2.13

Casual

employment

0.02

1.74

1.72

Domestic work

0.01

2.59

2.41

0.88

Formal sector

employment

0.57

0.66

0.77

Social grant

income

0.07

3.12

2.24

1.49

0.53

Remittance

income

0.05

1.18

1.14

1.02

Investment

income

0.07

0.40

0.47

1.12

Imputed rental

income

0.16

1.15

1.14

Tables 5 and 6 present the results of the same decomposition analysis for each of the FGT

indicators based on 2012 data (using the official upper-bound poverty threshold of R594)12.

These results demonstrate how the poverty-effectiveness of social grant income becomes

more prominent for P1 and P2 measures as compared to the simple poverty headcount ratio

(P0). Likewise, formal sector earnings perform worse in poverty effectiveness as the alpha

increases to 1 and 2. This is again intuitive. As the alpha increases, our poverty measure

places increasing emphasis on income that draws households closer to the poverty line even if

they don’t reach it outright. Well-targeted social grant income, which flows into poor

households that still don’t get above the poverty line, will now receive more weight in

poverty reduction measures, whereas a sizeable portion of formal sector earnings would have

zero poverty impact once the household has crossed the poverty line.

The change in poverty-effectiveness of income from informal self-employment also tends to

decline but only slightly as the p-alpha measures increase. For example, the decline in 2008

for the R 307 poverty line is from 1.14 to 1.06 as compared to a decline from 0.66 to 0.44 for

formal sector employment. This small decline holds up across all our various poverty lines in

each year. The same is true for informal wage employment. For casual employment, the

12 Appendix C shows the results at the two lower poverty lines.

Income Source

Income

Relative Absolute Relative Absolute Relative Absolute

0.026 -0.037 -0.022 -0.037 -0.030 -0.036 -0.032

(0.003) (0.002) (0.001) (0.001) (0.001) (0.001) (0.001)

0.024 -0.037 -0.022 -0.037 -0.030 -0.036 -0.032

(0.004) (0.002) (0.001) (0.001) (0.001) (0.001) (0.001)

0.013 -0.027 -0.016 -0.029 -0.024 -0.030 -0.026

(0.001) (0.002) (0.001) (0.001) (0.001) (0.001) (0.001)

0.012 -0.023 -0.014 -0.029 -0.024 -0.028 -0.025

(0.001) (0.002) (0.001) (0.002) (0.001) (0.001) (0.001)

0.635 -0.543 -0.323 -0.390 -0.317 -0.339 -0.301

(0.013) (0.006) (0.004) (0.004) (0.004) (0.004) (0.004)

0.071 -0.116 -0.069 -0.226 -0.184 -0.259 -0.230

(0.003) (0.003) (0.002) (0.003) (0.002) (0.003) (0.002)

0.057 -0.027 -0.016 -0.023 -0.019 -0.023 -0.020

(0.007) (0.002) (0.001) (0.001) (0.001) (0.001) (0.001)

0.024 -0.034 -0.020 -0.042 -0.034 -0.043 -0.039

(0.002) (0.002) (0.001) (0.001) (0.001) (0.001) (0.001)

0.135 -0.150 -0.089 -0.181 -0.147 -0.200 -0.178

(0.005) (0.004) (0.002) (0.002) (0.002) (0.002) (0.002)

Total 1 1 -0.595 1 -0.813 1 -0.888

(0) (0) (0.005) (0) (0.002) (0) (0.002)

Investment

income

Remittance

income

Imputed rental

income

Social grant

income

Table 5 Decomposition of poverty (Z = 594) by income source, 2012

Informal self-

employment

Informal regular

wage emp.

Casual

employment

Domestic work

Formal sector

employment

poverty-effectiveness ratios actually increase or hold almost identical. This is in stark contrast

to the consistent large increases for social grants and decreases in formal sector earnings.

We now turn our focus to the per-job impact on poverty rather than the aggregate impacts.

While the NIDS survey does not necessarily capture every job an individual has, it does

capture the vast majority of jobs. For example, self-employment jobs are captured separately

from regular (formal or informal) wage employment and casual employment. So if an

individual has two jobs of different types, the survey captures both. Additionally, the NIDS

questionnaire captures information on the first two of each respondent’s regular wage

employment jobs. The survey also captures whether the individual has more than one self-

employment job but does not collect any information on this second job. In the analysis

below, we assume the second self-employment job is in the same sector (formal or informal)

as the first self-employment job.

Table 7 again shows that the formal sector employment is, by far, the largest source of

employment, providing more than 10 million jobs in 201213. Formal employment is also

dominant with respect to the income it provides per job. The income from one million formal

(sector) jobs would constitute a 6.1 per cent share of total income, while other types of

employment provide no more than 2.0 per cent of aggregate income per million jobs. For the

13 Appendix D shows the same estimates for the depth and severity of poverty. Similarly, Appendix E shows the

full range of relative poverty impacts at all three poverty lines for P0-P2.

Poverty-effectiveness ratios

Income Source Income Share P0P1P2

Table 6 Poverty-effectiveness ratios for the poverty (Z = 594) by income source, 2012

Informal self-

employment

0.03

1.40

1.39

1.36

1.53

Casual

employment

0.01

2.10

2.30

2.35

Informal regular

wage emp.

0.02

1.56

1.57

2.43

Formal sector

employment

0.63

0.86

0.61

0.53

Domestic work

0.01

1.97

2.48

3.64

Investment

income

0.06

0.48

0.41

0.40

Social grant

income

0.07

1.64

3.19

1.78

Imputed rental

income

0.14

1.11

1.34

1.48

Remittance

income

0.02

1.38

1.71

reasons highlighted previously, namely the relatively low poverty effectiveness of income

from formal sector employment, the total poverty reduction per million jobs from formal

sector employment is not exceptionally larger than the reduction from the other employment

categories shown in the table. For example, the per-job impact on poverty from informal

sector self-employment is approximately 63 per cent of that of a formal job if one uses the

food poverty line (see column 4). Informal regular wage employment is even higher at 81 per

cent. In other words, the decomposition analysis suggests that the loss of 100 informal sector

self-employment jobs and the loss of 63 formal jobs have a similar impact in terms of overall

poverty reduction.

Across all three poverty lines and p-alpha values, the per-job impact on poverty reduction,

relative to formal jobs, ranges from 39 per cent (for casual work when measuring P0 at the

R594 poverty line) to 108 per cent (for both informal regular wage employment and domestic

work when measuring P2 at the R307 food poverty line). In other words, the relative

contribution of different types of ‘informal’ jobs to poverty reduction varies considerably

depending on the poverty line and the FGT measure (p-alpha values). More specifically,

however, the decompositions show that, at the food poverty line, informal regular wage jobs

and domestic work actually have a larger relative impact on the severity of poverty (P2) per

job than formal types of employment (as shown in Table E-3 in Appendix E). In terms of the

impact of informal sector self-employment, the relative per-job impact on poverty reduction

ranges from 48 per cent (P0 at the R594 line) to 88 per cent (P2 at R307 line). The key

conclusion here, therefore, is that the importance of informal sector self-employment to

poverty reduction is greater at the lowest poverty line and particularly for workers who live in

households further below the poverty threshold. This particular finding is critically important

for policymakers since it demonstrates that, for the poorest households, the impact of

earnings from informal sector self-employment are almost as important as earnings from

formal jobs, even though earnings are considerably lower in the informal sector. This again

points to the conclusion that informal sector jobs are ‘well targeted’ to poor households and

particularly to households relatively far below the poverty line.

Income Source

Number of

jobs

Share of

income per

million jobs

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Informal self-

employment

1,462,314 0.018 -0.021 0.63 -0.018 0.56 -0.015 0.48

Informal regular

wage emp.

1,185,124 0.020 -0.027 0.81 -0.023 0.71 -0.019 0.60

Casual

employment

1,358,512 0.009 -0.018 0.54 -0.015 0.45 -0.012 0.38

Domestic work 923,511 0.013 -0.028 0.85 -0.024 0.74 -0.015 0.48

Formal sector

employment

10,400,000 0.061 -0.033 1-0.033 1-0.031 1

Table 7 Relative impact of jobs on poverty headcount (P0) by income source, 2012

Z = 307

Z = 424

Z = 594

5. Additional approaches

5.1 Change in poverty over time

Next we examine the changes in poverty over time during the 2008 to 2012 period using a

panel of respondents in both the 2008 and 2012 survey and weights that adjust for attrition

over time.14 Table 8 shows the considerable decline in poverty over the 2008 to 2012 period

is visible under all measures of poverty regardless of the poverty line.

Table 8 Change in poverty estimates (P

) for South

Africa, 2008-2012

Z = 307

Z = 424

Z = 594

-0.077

-0.092

-0.104

(0.004)

-0.038

-0.049

-0.064

(0.002)

(0.003)

-0.024

-0.034

-0.045

(0.002)

Growth in earnings from formal employment accounted for nearly all the change in income

over the 2008 to 2012 period (See Table 9). Some income categories, such as informal self-

employment, saw little change in aggregate income while the change in other categories

counterbalanced each other, with some categories gaining income (informal regular wage

employment and social grant income) and others suffering significant losses (remittances).

Formal sector employment accounts for the majority (60 per cent) of the reduction in the

poverty headcount ratio in the 2008 to 2012 period. Social grants, again, play a vital role at

18 per cent. Despite a large decline in remittances, this had little to no impact on poverty

rates. Informal wage employment seems to play a larger role on poverty reduction (8.0 per

cent) than in the cross-sectional results while informal self-employment income has a similar

relative effect on poverty reduction, accounting for 3.2 per cent of the change in poverty.

14 One important caveat is that the aggregate poverty changes shown in Table 8 are much larger than those

reported in the broader literature. Since NIDS is the only nationally representative panel survey in South Africa,

the poverty results presented here are not easily comparable with repeated cross-sectional surveys. Nonetheless,

caution should be exercised when interpreting the decompositions over time since the decreases in poverty are

not in line with aggregate estimates from other studies. The results presented in Tables 9 and 10 are therefore

only broadly illustrative of how informal sources of income contributed to poverty reduction in the NIDS

sample.

Table 10 reminds us that the aggregate impact on poverty from one particular income source

depends on both the size of the change in income and the resulting per-Rand reduction in

poverty. As before, the per-Rand impact of formal sector income on poverty reduction

appears to be quite low compared to many other sources of income. Informal self-

employment and domestic work stand out for their high per-Rand effects on poverty

reduction.

There is some caution in these results, however. The odd sign on the casual employment

figures and the extremely large relative ratios for informal self-employment are likely signals

that some of these results are driven by the change in the composition of households that are

engaged in these activities over time, which this method does not model. For example,

informal self-employment accounts for 3.2 per cent of poverty reduction despite only

accounting for 0.2 per cent of income growth. It’s unlikely that the same households were

taking part in informal self-employment in both years and that this small increase in income

suddenly moved a large number of them out of poverty. Instead, there was likely a shift in the

composition of households that were engaged in informal self-employment, with a higher

proportion of households who were just over the poverty line taking part. These small

compositional shifts, and even measurement error problems, can have large impacts on our

results in Table 10 as these are ratios where we use the change in the share of income over

time in the denominator rather than the share of income at a point in time. These ratios are

particularly susceptible if the ratio is near zero, as is the case with informal self-employment

income (.002). Thus, while the results actually amplify the importance of informal

Income Source

Income

Relative Absolute Relative Absolute Relative Absolute

0.002 0.032 -0.003 0.035 -0.002 0.031 -0.001

(0.042) (0.011) (0.001) (0.012) (0.001) (0.013) (0.001)

0.064 0.080 -0.008 0.064 -0.004 0.058 -0.003

(0.03) (0.011) (0.001) (0.012) (0.001) (0.014) (0.001)

-0.016 0.030 -0.003 0.031 -0.002 0.032 -0.001

(0.011) (0.011) (0.001) (0.013) (0.001) (0.016) (0.001)

0.022 0.053 -0.005 0.048 -0.003 0.035 -0.002

(0.011) (0.008) (0.001) (0.009) (0.001) (0.01) (0)

0.971 0.596 -0.062 0.558 -0.036 0.542 -0.025

(0.113) (0.025) (0.003) (0.027) (0.002) (0.032) (0.002)

0.081 0.178 -0.019 0.258 -0.016 0.288 -0.013

(0.017) (0.014) (0.002) (0.018) (0.001) (0.022) (0.001)

0.014 0.005 0.000 0.018 -0.001 0.022 -0.001

(0.038) (0.006) (0.001) (0.006) (0) (0.007) (0)

-0.171 -0.007 0.001 -0.016 0.001 -0.011 0.000

(0.108) (0.012) (0.001) (0.013) (0.001) (0.015) (0.001)

0.043 0.043 -0.004 0.017 -0.001 0.016 -0.001

(0.034) (0.012) (0.001) (0.014) (0.001) (0.018) (0.001)

Total 1 1 -0.104 1-0.064 1-0.045

(0) (0) (0.004) (0) (0.003) (0) (0.002)

Investment

income

Remittance

income

Imputed rental

income

Social grant

income

Table 9 Decomposition of change in poverty (Z = 594) by income source.

Informal self-

employment

Informal regular

wage emp.

Casual

employment

Domestic work

Formal sector

employment

employment relative to formal employment compared to our earlier cross-sectional analysis,

they are likely more prone to errors.

Our dynamic analysis also looked at the change in employment over time for each type of

employment (results not shown). Despite the stagnant income, there was a considerable loss

of informal self-employment jobs. The decline in informal self-employment jobs appears to

be a lost opportunity for increased poverty reduction during this period.

5.2 Simulated increase of one million new informal self-employment jobs

The previous analysis offered one approach to understanding the impact of informal-sector

jobs on poverty. The Shapley decomposition demonstrated a way to look at the observed

aggregate poverty reduction at a point in time (or change across time) and attribute the

influence of each income source to this poverty reduction. From the results of that

decomposition, we could calculate the implied per-job impact on poverty reduction of an

informal self-employment job or an informal wage-job. We could also use this information to

identify the poverty reduction per one million informal-sector jobs.

In this section, we take an entirely different approach. We simulate a situation with additional

informal self-employment jobs directly. Then, we see how much the poverty rates decline

once the simulated additional earnings are added to household income per capita.

Specifically, we simulate the impact that adding one million new, informal, self-employment

jobs would have on national poverty rates using the 2012 data. We assume that the new jobs

Income Source

Income

Relative

Ratio

Absolute

Ratio

Relative

Ratio

Absolute

Ratio

Relative

Ratio

Absolute

Ratio

0.37

0.02

Imputed rental

income

0.04

0.99

0.10

0.39

0.03

1.60

0.07

Remittance

income

-0.17

0.04

0.00

0.09

0.01

0.06

0.00

Investment

income

0.01

0.32

0.03

1.29

0.08

Social grant

income

0.08

2.22

0.23

3.20

0.20

3.58

0.16

0.56

0.03

0.04

Formal sector

employment

0.97

0.61

0.06

0.57

0.14

1.57

0.07

Casual

employment

-0.02

-1.86

0.19

-1.96

0.12

Domestic work

0.02

2.37

0.25

2.16

0.06

0.91

0.04

-2.00

0.09

Informal regular

wage emp.

0.06

1.25

0.13

0.99

Table 10 Ratios for the decomposition of the change in poverty (Z = 594) by income source.

Informal self-

employment

0.00

14.70

1.54

15.75

1.00

14.01

0.63

would be given to randomly selected unemployed individuals (either searching or non-

searching) and that no other household income would be affected. Likewise, we take

household formation as exogenous. We also assume that the new earnings would come from

random draws from the distribution of earnings from current informal self-employment jobs.

Given that we start at the current household income level, there may be considerably lower

impact on poverty rates than that found in earlier decomposition results. However, income

levels in households with unemployed individuals could be lower than that of the current self-

employed, resulting in a larger poverty impact.

In Table 11 we report results from this simple simulation by presenting the change in poverty

for all the three poverty measures. For example, if one million more of these informal self-

employment jobs were added to the economy, we estimate that the poverty rate at the R307

threshold would decrease from 19.2% to 17.5% (or decrease by 8.5%). At the upper-bound

poverty line, the relative decrease in the poverty headcount after ‘adding’ these jobs would be

about 6%. Again, this demonstrates that income from self-employment in the informal sector

is particularly important for individuals in the poorest households. The fact that relative

decreases in the severity of poverty are even greater after simulating one million new jobs,

reinforces this point (e.g. the severity of the poverty index would decrease by 12.5% as

compared to the 8.5% decline in the poverty headcount at the lowest poverty line under this

simulated scenario). Across the various poverty lines and p-alpha measures, the simulation

results in a reduction of poverty of between 6.0 and 12.5 per cent, with an average reduction

of 9.6 per cent.

One of the initial findings of this chapter was that formal employment is the dominant factor

in explaining aggregate poverty reduction, with informal-sector employment offering

relatively little impact on aggregate poverty rates. On the other hand, we found that, in terms

of relative poverty-reducing effectiveness, changes in informal-sector income are more potent

than formal-sector income – as also illustrated in the per-million-jobs impact analysis earlier.

The simulation results here also suggest that a massive surge in informal self-employment

jobs would lead to a significant reduction in national poverty figures. Therefore, this should

be considered as an element of addressing poverty; however, even with such a massive

growth in informal-sector jobs, there would still be vast swathes of poverty remaining. Thus,

it cannot be the only strategy in the fight to reduce poverty.

2012 Simulated % change 2012 Simulated % change 2012 Simulated % change

P(0) 0.192 0.175 -8.5% 0.294 0.269 -8.5% 0.405 0.381 -6.0%

P(1) 0.074 0.065 -11.5% 0.121 0.109 -10.0% 0.187 0.171 -8.5%

P(2) 0.041 0.036 -12.5% 0.068 0.061 -11.1% 0.112 0.101 -9.8%

Z = 594

Z = 424

Z = 307

Table 11 Simulation: Change in poverty headcount if add 1 million new informal self-employment jobs

6. Concluding remarks

Formal sector earnings and social grants are, by far, the most important sources of income in

explaining the total amount of poverty alleviation in South Africa in 2008 and in 2012. Yet,

the reasons are quite distinct. A large amount of formal sector earnings flows into non-poor

households but the earnings form such a dominant share of total income in South Africa,

63.5% in 2012, that formal sector earnings accounts for 42.6% of poverty reduction using the

food-poverty line. Social grants, on the other hand, are targeted overwhelmingly towards

poor households. Thus, while they comprise just 7.1% of total income, they account for

20.9% of overall poverty reduction.

Similarly, earnings from formal sector jobs account for 24.1 times the amount of overall

income as informal self-employment jobs but just 11.4 times the amount of aggregate poverty

reduction as informal self-employment jobs. Still, this large disparity may make it tempting

for policymakers to focus very heavily on improving the number of formal sector jobs in an

attempt to ease poverty. We believe that is a faulty interpretation of reality.

The large disparity in aggregate poverty reduction is primarily driven by the fact that there

are many more formal sector jobs not by the difference in poverty impact of a given job.

When considering poverty impacts on a per-job basis, the relative worth of informal sector

jobs is greatly amplified. For example, in 2012, there were 7.1 times as many formal sector

jobs as informal self-employment jobs. Thus, on a per-job basis, in 2012, formal sector jobs

were providing just 1.60 times the poverty reduction as informal self-employment jobs. Put

differently, on average, an informal self-employment job had 63% of the poverty reduction

impact of a formal employment job in 2012.

So what is a policy maker to learn from these results? First, do not shut down any informal

sector jobs unless there is a dire reason to intervene. If you are considering a policy that

would eliminate 100 typical informal self-employed jobs, ask yourself the following, “Would

I be willing to lose 63 typical formal sector jobs to implement this policy?” Our

decomposition analysis suggests that the poverty effects associated with those two scenarios

is the same. Obviously poverty is not the only consideration, but we hope this puts the stark

nature of the decision making in perspective.

Second, we believe the potential poverty reduction from growing informal sector jobs has

been understated in policy discussions. While we long for the day when all South Africans

can enjoy jobs with earnings levels well beyond the poverty line, we should not denigrate

work that brings people in very low incomes closer to or just past the poverty line.

Government should pride itself on helping these jobs to exist and should search for ways to

promote such jobs. The search for cost-effective strategies of promoting such employment

should cover the entire spectrum of options, from street lighting or other infrastructure

changes that can be provided, to improved regulatory environments for the informal sector, to

provision of social protection, to helping informal sector firms bargain with formal firms, to

effective training of such workers. A period of exploratory approaches, ideally accompanied

by proper evaluations of effectiveness, could greatly improve the number and quality of

informal sector jobs.

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van der Berg, S.,Louw, M.,Yu, D. (2008). Post-transition poverty trends based on an alternative data

source, South African Journal of Economics,Vol. 76, No. 1, 59-76.

Verick, S. (2010). Unravelling the impact of the global financial crisis on the South African labour

market. Employment Working Paper No. 48. Geneva, International Labour Organization.

Vermaak, C. (2010). The impact of multiple imputation of coarsened data on estimates of the working

poor in South Africa. UNU-WIDER Working Paper No. 2010/86. Helsinki, World Institute

for Development Economics Research, United Nations University.

Vermaak, C. (2012). Tracking poverty with coarse data: evidence from South Africa, Journal of

Economic Inequality,Vol. 10, No. 239-265.

Wills, G. (2009a). South Africa’s informal economy: a statistical profile. Wiego Working Paper

(Urban Policies) No 6. Manchester, Women in Informal Employment: Globalizing and

Organizing (WIEGO).

Wills, G. (2009b). South Africa’s Informal Economy: A Statistical Profile. Urban Policies Research

Report No. 7, WIEGO.

Appendix A:

The poverty measures used in this decomposition are the p-alpha measures:



;;= 



  = 



 1









where y is income, z is the poverty line, there are K income sources and Sk represents income

from source k. Wi is the weight given to individual i and n is the sample size.

The Shapley decomposition approach calculates the average marginal reduction in poverty

when adding an income source over all possible combinations of income sources and all

possible orderings. The method starts at zero income (Pα = 1) and adds in one income source

at a time, assessing the marginal poverty reduction at each step, until all income sources are

included. The approach repeats this procedure over all possible orderings for our 11 different

income sources. Conveniently, the Araar and Duclos coding reweights outcomes so that we

can reduce this to 2k orderings and poverty calculations, rather than completing the procedure

using k! orderings (Araar & Duclos, 2009a, 2009b; Duclos & Araar, 2006).

Appendix B:

Table B-1 Decomposition of the poverty gap (P1) by income source

Z = 307

Z = 424

Z = 594

Income Source Income

Share Relative Absolute Relative Absolute Relative Absolute

Informal self-

employment

0.031

-0.034

-0.030

-0.034

-0.028

-0.034

-0.025

(0.005)

(0.001)

Informal regular

wage emp.

0.024

-0.041

-0.036

-0.042

-0.034

-0.042

-0.031

(0.002)

(0.001)

(0.002)

(0.001)

(0.002)

(0.001)

Casual

employment

0.022

-0.039

-0.034

-0.039

-0.032

-0.038

-0.028

(0.002)

(0.001)

Domestic work

0.010

-0.026

-0.023

-0.026

-0.021

-0.025

-0.019

(0.001)

Formal sector

employment

0.567

-0.278

-0.244

-0.306

-0.250

-0.340

-0.250

(0.012)

(0.003)

(0.004)

(0.003)

(0.004)

(0.003)

Agricultural

income

0.001

-0.004

-0.003

-0.002

(0)

Other income

0.005

-0.006

-0.005

-0.006

-0.005

-0.006

-0.004

(0.001)

(0)

Social grant

income

0.066

-0.264

-0.232

-0.245

-0.200

-0.219

-0.161

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

Investment

income

0.070

-0.025

-0.022

-0.026

-0.021

-0.028

-0.021

(0.007)

(0.001)

Remittance

income

0.048

-0.056

-0.049

-0.056

-0.046

-0.055

-0.040

(0.008)

(0.002)

(0.001)

(0.002)

(0.001)

(0.002)

(0.001)

Imputed rental

income

0.156

-0.228

-0.200

-0.217

-0.178

-0.209

-0.154

(0.004)

(0.002)

Total

-0.878

-0.817

-0.736

(0)

(0.002)

(0)

(0.002)

(0)

(0.003)

Source:

Own calculations from NIDS using the DASP module developed by Araar and Duclos (2007)

Notes:

The data are weighted.

Standard errors in brackets.

Income sources are expressed in monthly per capita terms (2010 prices).

Table B-2 Ratios for the decomposition of the poverty gap (P1) by income source

Z = 307

Z = 424

Z = 594

Income Source Income

Share Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio

Informal self-

employment 0.03 1.09 0.95 1.09 0.89 1.10 0.81

Informal

regular wage

emp. 0.02 1.74 1.53 1.77 1.45 1.79 1.32

Casual

employment 0.02 1.76 1.54 1.75 1.43 1.72 1.26

Domestic work 0.01 2.63 2.31 2.61 2.13 2.52 1.85

Formal sector

employment 0.57 0.49 0.43 0.54 0.44 0.60 0.44

Agricultural

income 0.00 3.88 3.41 3.43 2.80 3.18 2.34

Other income 0.00 1.37 1.20 1.32 1.08 1.24 0.92

Social grant

income 0.07 3.99 3.50 3.70 3.02 3.31 2.44

Investment

income 0.07 0.35 0.31 0.37 0.30 0.40 0.30

Remittance

income 0.05 1.17 1.03 1.17 0.95 1.15 0.85

Imputed rental

income 0.16 1.46 1.28 1.39 1.14 1.34 0.99

Table B-3 Decomposition of the squared poverty gap (P2) by income source

Z = 307

Z = 424

Z = 594

Income Source Income

Share Relative Absolute Relative Absolute Relative Absolute

Informal self-

employment

0.031

-0.033

-0.031

-0.034

-0.030

-0.034

-0.028

(0.005)

(0.001)

Informal regular

wage emp.

0.024

-0.039

-0.036

-0.040

-0.036

-0.041

-0.034

(0.002)

(0.001)

(0.002)

(0.001)

(0.002)

(0.001)

Casual

employment

0.022

-0.038

-0.036

-0.039

-0.034

-0.038

-0.032

(0.002)

(0.001)

Domestic work

0.010

-0.025

-0.023

-0.026

-0.023

-0.026

-0.021

(0.001)

Formal sector

employment

0.567

-0.248

-0.230

-0.268

-0.238

-0.294

-0.244

(0.012)

(0.003)

Agricultural

income

0.001

-0.004

-0.003

(0)

Other income

0.005

-0.006

-0.005

-0.006

-0.005

(0.001)

(0)

Social grant

income

0.066

-0.273

-0.253

-0.265

-0.235

-0.249

-0.207

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

Investment income

0.070

-0.024

-0.022

-0.025

-0.022

-0.026

-0.021

(0.007)

(0.001)

Remittance income

0.048

-0.055

-0.051

-0.055

-0.049

-0.055

-0.046

(0.008)

(0.001)

(0.002)

(0.001)

Imputed rental

income

0.156

-0.254

-0.236

-0.240

-0.213

-0.227

-0.189

(0.004)

(0.002)

Total

-0.927

-0.888

-0.832

(0)

(0.002)

(0)

(0.002)

(0)

(0.002)

Source:

Own calculations from NIDS using the DASP module developed by Araar and Duclos (2007)

Notes:

The data are weighted.

Standard errors in brackets.

Income sources are expressed in monthly per capita terms (2010 prices).

Table B-4 Ratios for the decomposition of the squared poverty gap (P2) by income source

Z = 307

Z = 424

Z = 594

Income Source Income

Share Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio

Informal self-

employment 0.03 1.06 0.99 1.08 0.96 1.09 0.90

Informal

regular wage

emp. 0.02 1.66 1.54 1.70 1.51 1.74 1.45

Casual

employment 0.02 1.73 1.61 1.74 1.55 1.74 1.45

Domestic work 0.01 2.53 2.34 2.57 2.28 2.57 2.13

Formal sector

employment 0.57 0.44 0.41 0.47 0.42 0.52 0.43

Agricultural

income 0.00 4.45 4.13 4.08 3.62 3.71 3.08

Other income 0.00 1.36 1.27 1.36 1.21 1.32 1.10

Social grant

income 0.07 4.13 3.83 4.00 3.55 3.77 3.13

Investment

income 0.07 0.34 0.32 0.35 0.31 0.37 0.31

Remittance

income 0.05 1.14 1.06 1.16 1.03 1.16 0.96

Imputed rental

income 0.16 1.63 1.51 1.53 1.36 1.45 1.21

Appendix C:

Table C-1 Decomposition of poverty (Z = 307) by income source

Income Source Income

Share Relative Absolute Relative Absolute Relative Absolute

Informal self-

employment

0.026

-0.038

-0.030

-0.036

-0.033

-0.035

-0.033

(0.003)

(0.002)

(0.001)

Informal regular

wage emp.

0.024

-0.039

-0.032

-0.036

-0.034

-0.035

-0.033

(0.004)

(0.002)

(0.001)

Casual

employment

0.013

-0.030

-0.024

-0.030

-0.028

-0.030

-0.029

(0.001)

Domestic work

0.012

-0.032

-0.026

-0.028

-0.026

-0.027

-0.026

(0.001)

(0.002)

(0.001)

Formal sector

employment

0.635

-0.426

-0.344

-0.320

-0.297

-0.286

-0.274

(0.013)

(0.005)

(0.004)

(0.003)

Agricultural

income

0.001

-0.003

-0.004

-0.003

-0.004

(0)

Other income

0.002

-0.003

-0.002

(0)

Social grant

income

0.071

-0.209

-0.169

-0.275

-0.255

-0.286

-0.274

(0.003)

Investment

income

0.057

-0.023

-0.019

-0.022

-0.021

-0.022

-0.021

(0.007)

(0.001)

Remittance

income

0.024

-0.042

-0.034

-0.044

-0.041

-0.044

-0.043

(0.002)

(0.001)

Imputed rental

income

0.135

-0.155

-0.125

-0.202

-0.187

-0.228

-0.219

(0.005)

(0.003)

(0.002)

Total

-0.808

-0.926

-0.959

(0)

(0.003)

(0)

(0.002)

(0)

(0.001)

Source:

Own calculations from NIDS using the DASP module developed by Araar and Duclos (2007)

Notes:

The data are weighted.

Standard errors in brackets.

Income sources are expressed in monthly per capita terms (2008 prices).

Table C-2 Ratios for the decomposition of poverty (Z = 307) by income source

Income Source Income

Share Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio

Informal self-

employment 0.03 1.42 1.15 1.35 1.25 1.32 1.27

Informal regular

wage emp. 0.02 1.65 1.33 1.52 1.41 1.45 1.39

Casual

employment 0.01 2.36 1.90 2.36 2.19 2.41 2.31

Domestic work 0.01 2.76 2.23 2.42 2.24 2.30 2.20

Formal sector

employment 0.63 0.67 0.54 0.50 0.47 0.45 0.43

Agricultural

income 0.00 2.68 2.17 3.06 2.83 3.58 3.43

Other income 0.00 1.29 1.04 1.13 1.05 1.15 1.10

Social grant

income 0.07 2.95 2.38 3.88 3.59 4.03 3.86

Investment

income 0.06 0.40 0.33 0.39 0.36 0.39 0.37

Remittance

income 0.02 1.72 1.39 1.82 1.68 1.82 1.75

Imputed rental

income 0.14 1.15 0.93 1.49 1.38 1.69 1.62

Table C-3 Decomposition of poverty (Z = 424) by income source

Income Source Income

Share Relative Absolute Relative Absolute Relative Absolute

Informal self-

employment

0.026

-0.038

-0.027

-0.036

-0.032

-0.035

-0.033

(0.003)

(0.002)

(0.001)

Informal regular

wage emp.

0.024

-0.039

-0.028

-0.037

-0.033

-0.036

-0.033

(0.004)

(0.002)

(0.001)

Casual

employment

0.013

-0.028

-0.020

-0.030

-0.026

-0.030

-0.028

(0.001)

(0.002)

(0.001)

Domestic work

0.012

-0.032

-0.022

-0.029

-0.026

-0.028

-0.026

(0.001)

(0.002)

(0.001)

Formal sector

employment

0.635

-0.483

-0.341

-0.353

-0.310

-0.289

(0.013)

(0.005)

(0.004)

Agricultural

income

0.001

-0.002

-0.003

-0.004

(0)

Other income

0.002

-0.003

-0.002

(0)

(0.001)

(0)

Social grant

income

0.071

-0.160

-0.113

-0.253

-0.222

-0.276

-0.257

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

Investment

income

0.057

-0.025

-0.017

-0.023

-0.020

-0.022

-0.021

(0.007)

(0.002)

(0.001)

Remittance

income

0.024

-0.038

-0.027

-0.043

-0.038

-0.044

-0.041

(0.002)

(0.001)

Imputed rental

income

0.135

-0.151

-0.106

-0.190

-0.167

-0.213

-0.199

(0.005)

(0.003)

(0.002)

Total

-0.706

-0.879

-0.932

(0)

(0.004)

(0)

(0.002)

(0)

(0.002)

Source:

Own calculations from NIDS using the DASP module developed by Araar and Duclos (2007)

Notes:

The data are weighted.

Standard errors in brackets.

Income sources are expressed in monthly per capita terms (2008 prices).

Table C-4 Ratios for the decomposition of poverty (Z = 424) by income source

Income Source Income

Share Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio Relative

Ratio Absolute

Ratio

Informal self-

employment 0.03 1.44 1.02 1.37 1.21 1.34 1.25

Informal

regular

wage emp. 0.02 1.64 1.15 1.56 1.37 1.49 1.39

Casual

employment 0.01 2.25 1.59 2.34 2.06 2.38 2.22

Domestic work 0.01 2.72 1.92 2.49 2.19 2.37 2.21

Formal sector

employment

0.63 0.76 0.54 0.56 0.49 0.49 0.45

Agricultural

income 0.00 2.08 1.46 2.87 2.52 3.29 3.07

Other income 0.00 1.72 1.21 1.20 1.06 1.16 1.08

Social grant

income 0.07 2.25 1.59 3.57 3.13 3.88 3.62

Investment

income 0.06 0.43 0.30 0.40 0.35 0.39 0.36

Remittance

income 0.02 1.57 1.11 1.78 1.56 1.81 1.69

Imputed rental

income 0.14 1.12 0.79 1.41 1.24 1.58 1.47

Appendix D:

Income Source

Numbe r of

jobs

Share of

income per

million jobs

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Informal self-

employment

1,462,314 0.018 -0.023 0.79 -0.022 0.73 -0.020 0.67

Informal regular

wage emp.

1,185,124 0.020 -0.028 1.00 -0.028 0.93 -0.026 0.84

Casual

employment

1,358,512 0.009 -0.020 0.71 -0.019 0.64 -0.017 0.57

Domestic work 923,511 0.013 -0.028 0.99 -0.028 0.93 -0.026 0.84

Formal sector

employment

10,400,000 0.061 -0.029 1.00 -0.030 1.00 -0.030 1.00

Agricultural

income

298,937 0.004 -0.011 0.39 -0.010 0.33 -0.009 0.28

Table D-1 Jobs Stuff of the poverty headcount (P

) by formal income source

Z = 307

Z = 424

Z = 594

Income Source

Numbe r of

jobs

Share of

income per

million jobs

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Informal self-

employment

1,462,314 0.018 -0.023 0.87 -0.023 0.81 -0.022 0.75

Informal regular

wage emp.

1,185,124 0.020 -0.028 1.07 -0.028 1.01 -0.027 0.94

Casual

employment

1,358,512 0.009 -0.021 0.81 -0.021 0.74 -0.019 0.67

Domestic work 923,511 0.013 -0.028 1.06 -0.028 1.01 -0.027 0.95

Formal sector

employment

10,400,000 0.061 -0.026 1.00 -0.028 1.00 -0.029 1.00

Agricultural

income

298,937 0.004 -0.014 0.51 -0.012 0.44 -0.011 0.37

Table D-2 Jobs Stuff of the poverty headcount (P

) by formal income source

Z = 307

Z = 424

Z = 594

Appendix E:

Income Source

Numbe r of

jobs

Share of

income per

million jobs

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Informa l self-

employment

1,504,590 0.021 -0.017 0.49 -0.014 0.42 -0.011 0.37

Informal regular

wage emp.

1,201,757 0.020 -0.026 0.75 -0.022 0.66 -0.017 0.55

Casual

employment

1,474,585 0.015 -0.019 0.54 -0.015 0.46 -0.011 0.35

Domestic work 769,514 0.013 -0.024 0.70 -0.019 0.56 -0.013 0.43

Formal sector

employment

7,788,926 0.073 -0.034 1-0.033 1-0.031 1

Agricultural

income

1,261,647 0.001 -0.001 0.03 -0.001 0.03 -0.001 0.03

Table E-1 Jobs Stuff of the poverty headcount (P

) by formal income source

Z = 307

Z = 424

Z = 594

Income Source

Numbe r of

jobs

Share of

income per

million jobs

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Informa l self-

employment

1,504,590 0.021 -0.020 0.63 -0.019 0.58 -0.017 0.52

Informal regular

wage emp.

1,201,757 0.020 -0.030 0.96 -0.028 0.88 -0.026 0.81

Casual

employment

1,474,585 0.015 -0.023 0.74 -0.021 0.67 -0.019 0.59

Domestic work 769,514 0.013 -0.030 0.96 -0.028 0.86 -0.024 0.75

Formal sector

employment

7,788,926 0.073 -0.031 1.00 -0.032 1.00 -0.032 1.00

Agricultural

income

1,261,647 0.001 -0.003 0.08 -0.002 0.07 -0.002 0.06

Table E-2 Jobs Stuff of the poverty headcount (P

) by formal income source

Z = 307

Z = 424

Z = 594

Income Source

Numbe r of

jobs

Share of

income per

million jobs

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Change in

poverty per

million jobs

Change in

poverty

relative to

formal job

Informa l self-

employment

1,504,590 0.021 -0.020 0.69 -0.020 0.65 -0.019 0.60

Informal regular

wage emp.

1,201,757 0.020 -0.030 1.02 -0.030 0.97 -0.028 0.90

Casual

employment

1,474,585 0.015 -0.024 0.82 -0.023 0.76 -0.022 0.69

Domestic work 769,514 0.013 -0.030 1.03 -0.030 0.97 -0.028 0.88

Formal sector

employment

7,788,926 0.073 -0.029 1.00 -0.031 1.00 -0.031 1.00

Agricultural

income

1,261,647 0.001 -0.003 0.11 -0.003 0.09 -0.002 0.08

Table E-3 Jobs Stuff of the poverty headcount (P

) by formal income source

Z = 307

Z = 424

Z = 594

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(REDI3x3) is a multi-year collaborative national research initiative. The project seeks to address

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Contribution of the informal sector towards sustainable livelihoods: evidence from Khayelitsha Township, Cape Town

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Full-text available

May 2023

Purpose-This study aims to investigates the contribution of the informal sector towards secure livelihoods. Using a case study design, the study focusses on Mandela park, situated in Khayelitsha Township, Cape Town, in the Western Cape province of South Africa. Khayelitsha is predominantly an old township established by the apartheid government using unjust segregation laws to foster spatial planning that isolated people of colour in areas with insufficient infrastructure and informal economic activities. Therefore, informal trading became a survival strategy in Khayelitsha, attracting an increasing number of informal traders in public spaces within the township in pursuit of livelihoods. Informal activities are generally conducted to generate income and secure sustainable livelihoods. Design/methodology/approach-This study uses a qualitative research design, incorporating structured interviews instrumental in data collection and in-depth thematic analysis. Findings-The study findings reveal that the informal sector positively contributes to the sustainable livelihoods of those involved in the informal sector and the relatives of those through income generation, family support, wealth creation, source of employment, business incubation and innovation and creativity. Originality/value-The study concludes that given the increasing unemployment rate in South Africa, caused by the stagnant economic growth rate, policymakers should rethink their policies on the informal economy, acknowledge the sector's relevance and support the sector.

An Evil to be Extinguished or a Resource to be harnessed-Informal Sector in Developing Countries: A Case of Zimbabwe

Article

Full-text available

Jul 2021

The informal sector remains a permanent feature of all economies. It is a more pronounced component of developing country economies where it is an integral part. Literature on economic development offers diverging viewpoints on the sector’s contribution to economic growth. On the one hand, researchers view the sector with positivity as a resource to be harnessed, capable of growth and innovation and on the other hand with negativity as a pure survivalist strategy and a stumbling block to effective policy formulation and economic development. Therefore as an inconvenience to be extinguished. The ambivalent view is further compounded by the sector’s paltry contribution to national tax revenues and as such, the IS has remained unappreciated and largely on the peripheral of economic reviews and policy responsiveness. The former view suggests that the latter is a misconception and narrow recognition of the sector as merely survivalist and contributing negligibly to employment creation and poverty alleviation efforts. Failure to recognize a coexistence of both street traders and firms with potential for growth, improved productivity and innovation, has consequently resulted in misguided policies. Policymakers pay little or no attention to the potential of informal sector firms, the challenges they face, the vulnerability they are exposed to and the appropriate support they require. This gap motivates this paper, as it seeks to explore the role played by the IS in developing countries with a special focus on Zimbabwe. The study employs a mixed-method research design. The research established that the sector remains unequivocally a major source of employment for many developing countries. It also established that a single-minded focus on formalizing the sector, linked to registration for tax purposes, ignores the other arm to the equation that is the creation of job opportunities, hospitable working conditions, improved productivity and increased profits which are possible with adequate policy support. The research recommends improved policy research and support which is development focused to aid informal firms to grow and be independent institutionally and financially from their owners as well as skills capacitation to improve operational and financial management.

The Spatial Evolution of Spaza Shops since the Inception of the Special Economic Zone (SEZ) in Saldanha

Article

Aug 2021

Lesley Welman

Learning Financial and Business Skills for the Sustainability of Female Necessity Entrepreneurs in the Informal Street Trade in South Africa

Article

Full-text available

Mar 2023

Western-centric studies on opportunity-based entrepreneurship have dominated the literature on female entrepreneurs. Existing research has also tended to focus on the experiences of women in the formal rather than the informal sector. In this article, we examine female necessity entrepreneurs' experiences of learning financial and business skills in the informal street trade in South Africa. The theoretical framework for this research, which was located within a critical feminist paradigm and used a feminist phenomenological design, drew on African feminisms, resilience, and informal learning. Phenomenological, in-depth, face-to-face interviews were conducted with a convenience sample of twelve women in the informal street trade in the Durban area of KwaZulu-Natal, to capture the essence of their experiences of running their businesses. The main findings showed that poor black women, especially those trading without permits in the informal street trade, faced multiple challenges, but were able to learn some of the financial and business skills necessary for survival. However, this learning was reactive, problem-centred, observation based, imitative, and partial. These findings emphasised the need for these entrepreneurs to upgrade their financial and business skills in order for their businesses to remain sustainable. The study recommended various targeted policy initiatives to support female necessity entrepreneurs.

A critical review of the taxation of the informal sector in Zimbabwe

Thesis

Full-text available

Feb 2022

Favourate Sebele

Catching up, falling behind: the need to build upgrading coalitions for innovation and inclusive development in South Africa

Article

Full-text available

Jan 2020
Nova Econ

Glenda Kruss

Through analysis of the South African case, a country stalled in a middle income trap, the paper aims to add to the literature on catch-up. It uses Albuquerque’s (2019) model of the vicious cycles arising from inequality and income concentration, together with the notion of ‘upgrading coalitions’ (Doner and Schneider 2016) required to challenge these vicious cycles, to analyse the persistence of lock-ins. It then analyses a global astronomy project, a ‘window of opportunity’ building on historically grown capabilities, promoted by ‘upgrading coalitions’ operating in the national interest. In contrast, it proposes a ‘detour’ to build domestic capabilities, driven by an upgrading coalition centred on local economic development and livelihoods in the informal economy. The paper aims to reinforce the evidence on how inequality is both a cause and consequence of a middle income trap, and open debate on how upgrading coalitions may be a critical strategy for breaking lock-ins.

The Informal Economy in Comparative Perspective: Theory, Policy and Reality

Article

Jul 2023

Martha Alter Chen

Sustaining the Township Economy: An Investigation into the Factors Influencing the Shopping Experience of Spaza Shop Customers in South Africa

Article

Full-text available

Oct 2022

The township economy supports many South Africans by creating employment and reducing the socio-economic challenges facing the country. To ensure sustainable growth of the sector, research is required on key businesses within the informal economy. This study empirically investigated the factors influencing the shopping experience of spaza shop customers in South Africa. Spaza shops, an under-researched South African context, continue to expand in various townships and serves as retail outlet where consumers purchase their daily products and services. The study embraced a quantitative research design. A self-administered questionnaire was employed to collect the data for the study. The target respondents include customers who were older than 18 years and had previously purchased from spaza shops. Respondents were selected using convenience sampling. The questionnaires were distributed to six townships in South Africa. 185 usable questionnaires were included in the data analysis. The empirical results provided evidence that supports the existence of statistically significant positive correlations between five independent experience factors, namely: esthetic, distinctiveness, cognitive, affective and social; with customer experience (dependent variable). Additionally, three of the five experience factors (social, affective and cognitive) are significant predictors of customer experience in the context of this study. Based on the identified factors, several recommendations are made for improving customer experience in a spaza shop context. Recommendations made are expected to enhance the competitiveness of spaza shops thereby enabling them to increase their contribution to employment creation and national Gross Domestic Product.

Shadow men: The third wheel of public transport in Ghana

Article

Feb 2022

The literature on urban transport in Ghana focus exclusively on the state of buses, drivers and transport unions, much to the neglect of a group of workers referred to as “shadow men.” Their function is to help fill these buses with passengers. This paper explores the activities of these shadow men, their origin, and their contributions to the growth of urban transport. The researchers selected a total of 47 respondents for the study. The researchers conducted interviews with the Head of Department of Urban Transport of the Kumasi Metropolitan Assembly and the Head of Ghana Private Road Transport Union, and then purposely selected 25 of these shadow men, 4 trotro drivers, 6 trotro mates, and 10 passengers in Kumasi, Ghana’s second largest city. In addition to these interviews, the researchers held two focus group discussions that involved drivers and drivers’ mates (conductors) in one group, and passengers in another group. Thematic analysis was used to analyze the data. The research found that there are two broad categories of shadow men: those engaged in it as a full-time business, and out-of-work drivers and drivers’ mates who rely on it for income until they have a new bus to work with. The findings also underscore the lucrative nature of the work and the city authorities’ indifference to the shadow men. Shadow men are essential to the urban transport sector in Ghana and should therefore be given considerable attention by academics, and included in the plans of city authorities.

The Informal Sector and Economic Growth of South Africa and Nigeria: A Comparative Systematic Review

Article

Full-text available

Nov 2020

The informal sector is an integral part of several sub-Saharan African (SSA) countries and plays a key role in the economic growth of these countries. This article used a comparative systematic review to explore the factors that act as drivers to informality in South Africa (SA) and Nigeria, the challenges that impede the growth dynamics of the informal sector, the dominant subsectors, and policy initiatives targeting informal sector providers. A systematic search of Google Scholar, Scopus, ResearchGate was performed together with secondary data collated from grey literature. Using Boolean string search protocols facilitated the elucidation of research questions (RQs) raised in this study. An inclusion and exclusion criteria became necessary for rigour, comprehensiveness and limitation of publication bias. The data collated from thirty-one (31) primary studies (17 for SA and 14 for Nigeria) revealed that unemployment, income disparity among citizens, excessive tax burdens, excessive bureaucratic hurdles from government, inflationary tendencies, poor corruption control, GDP per capita, and lack of social protection survival tendencies all act as drivers to the informal sector in SA and Nigeria. Several challenges are given for both economies and policy incentives that might help sustain and improve the informal sector in these two countries.