csae CENTRE FOR THE STUDY OF AFRICAN ECONOMIES

CENTRE FOR THE STUDY OF AFRICAN ECONOMIESDepartment of Economics . University of Oxford . Manor Road Building . Oxford OX1 3UQT: +44 (0)1865 271084 . F: +44 (0)1865 281447 . E: csae.enquiries@economics.ox.ac.uk . W: www.csae.ox.ac.uk

Reseach funded by the ESRC, DfID, UNIDO and the World Bank

Centre for the Study of African EconomiesDepartment of Economics . University of Oxford . Manor Road Building . Oxford OX1 3UQT: +44 (0)1865 271084 . F: +44 (0)1865 281447 . E: csae.enquiries@economics.ox.ac.uk . W: www.csae.ox.ac.uk

CSAE Working Paper WPS/2018-06

‘The Contribution of Formal and Non-formal Finance to Household Welfare: Evidence

from South Africa’

Lwanga Elizabeth Nanziri*

Abstract

Access to finance has been identified as a tool in the fight against poverty and inequality. While efforts

have been made to ensure that affordable formal financial services are accessible, the use of alternative

non-formal mechanisms persists in many developing economies and thus compromises the potential

gains from financial inclusion. Using a dataset from the FinScope surveys on South Africa, this paper

investigates whether welfare outcomes of users of formal financial services and users of alternative

non-formal financial services differ. Results, based on panel and treatment effect techniques show that

the use of formal and semi-formal financial services leads to positive and significant welfare outcomes

which are measured using an asset and well-being index. While these positive outcomes persist beyond

the immediate period following the use of formal financial services, there is no such effect when one

uses non-formal financial services. An attempt is made to contextualise these results for financial

inclusion.

Keywords: Financial Inclusion; Recentered Influence Function; Social Grants; South Africa; Welfare

JEL Codes: G2, I3

*Newton International Fellow, Department of Economics, University of Oxford. Tel: 01865 271074, Email: elizabeth.nanziri@economics.ox.ac.uk The author is grateful for the technical support received from Prof. Stefan Dercon, Director of the Centre for the Study of African Economies, Department of Economics, University of Oxford.

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1. Introduction

There is an increasing focus on inclusive finance given evidence that on average, broad-based access

to financial services can lead to better livelihoods for households by promoting income equality

(Burgess and Pande, 2005; Yunus, 2006) and reducing poverty (Beck, Demirgüç-Kunt and Levine,

2007; Honohan, 2008; Cull, Ehrbeck, and Holler, 2014; Financial Inclusion 2020–FI2020). According

to economic theory (see for instance Modigliani and Blumberg, 1954), households need to smooth

life-cycle consumption, i.e. save and invest when young and dis-save in old age; to mitigate risks and

financial shocks and; to take advantage of economic opportunities. Financial inclusion thus provides

the mechanisms to capture opportunities, reduce vulnerability and improve welfare (Cull et al., 2014),

although the short and long-run benefits are an empirical debate. What has received less attention

empirically is the heterogeneity in terms of the sources of financial services available to individuals,

especially in developing countries. The existence of intermediate but not very highly regulated financial

service providers can lead to the use of non-formal financial alternatives, if they can meet the needs

of users.1 Thus, if the use of financial services improves welfare, then the sources of these services

should not matter. Yet proponents of financial inclusion advocate for the use of formal financial

services from regulated financial institutions, defining financial inclusion as the holding of formal bank

accounts (see Allen et al., 2012). This definition inadvertently makes the formal financial mechanisms

appear superior to non-formal mechanisms. Indeed, governments in developing countries have

pursued financial inclusion initiatives (such as the provision of low cost or no-fees bank accounts) as

a tool for fighting poverty and working towards sustainable development following the universal

access to finance drive by the G20 summit in Pittsburg (see G20 2009). But in countries which have

experienced financial exclusion for a long period, the divide between formal and non-formal financial

inclusion might be too strict a definition to measure progress in poverty eradication. These issues are

of relevance in developing economies, raising the need for more empirical work, to establish the

relative welfare gains of using financial services from various sources. This would complement

governments’ efforts in facilitating the transition from financial exclusion to financial inclusion.

1 Formal financial products in this study, refer to services that are obtained from institutions that are regulated by the monetary authorities (such as mainstream banks, insurance companies); Semi-formal products are obtained from institutions that are primarily providers of merchandise (such as retail stores or cellular telephone companies) but can offer some financial services such as loyalty benefits and insurance, in line with their businesses; and Informal products are obtained from non-regulated vendors or individuals, based on a community arrangement such as informal lenders

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This paper therefore, investigates the difference in welfare outcomes when using

formal financial services and when using non-formal alternatives in South Africa. In this regard,

financial inclusion is expanded to refer to the use of formal or semi-formal financial services, and the

base category are the informal financial services.2 The argument is that, the use of financial services

allows individuals to acquire durable items and to meet household needs such as medical care, shelter,

education for children and energy for cooking or heating, which improve welfare. For instance,

durable items can be sold during times of liquidity constraint, insurance products can be used to

mitigate shocks while savings products allow for continued consumption during times of negative

income shocks. These benefits are likely to differ depending n the source of the financial services.

South Africa provides a unique place to examine differences in welfare outcomes following the use of

formal and/or non-formal financial services, once we control for race and geographical location.

These characteristics were the basis of discrimination in basic services (including financial services) in

Apartheid South Africa. A few papers including the financial diaries of the poor, have acknowledged

the contribution of semi-formal and informal financial sectors to access to finance (see for instance

Collins, Morduch, Rutherford, and Ruthven 2009; Johnson and Nino-Zarazua, 2011; Klapper and

Singer, 2013; Singer, 2014), yet none has explicitly examined the contribution of services from these

sectors to the welfare of the users. Also, despite a sophisticated banking system acknowledged by

Collins and Morduch (2011), Porteous and Hazelhurst (2004) and Ardington et al. (2003), a large

proportion of the South African population still use non-formal financial services as shown in Table

1. The paper makes use of a rich cross-section dataset, the FinScope surveys on access and use of

financial services for the period 2006 to 2011. We then mimic experimental literature to split the

sample into treatment and control and use the inverse probability of using formal financial services to

weight the welfare distributions of the two groups. We then estimate a recentered influence function

(RIF) of financial inclusion on welfare outcomes. To minimize the effect of possible selection into

use of financial services, and the possible reverse causality between use and welfare, panel techniques,

2 Informal financial services are obtained from informal arrangements (e.g. mashonisas, funeral parlours or burial societies) and stokvels. The word ‘stokvel’ comes from ‘stock fairs’ which referred to rotating cattle auctions by English settlers in the Eastern Cape in the early nineteenth century, characterized by interactive socializing. The practice was later adopted by black communities but not necessarily with respect to trading. The term now refers to groups of people who meet regularly and contribute money to meet each other’s financial needs in a rotating manner. The term stokvel is normally used to refer to all types of informal savings schemes to contribute to basic or family needs, for investments, parties, and burial societies (Wits Business School – WBS, 2009)

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incorporating initial welfare conditions, are employed on a synthetic panel constructed from the cross-

sections. The analysis is also conducted on a sample of social grant recipients who were involuntarily

included into the formal financial sector. Their welfare outcomes are compared to those of their

excluded counterparts. Two measures of welfare are constructed—an asset index (a quantitative

measure constructed from the possession of durable items) and a well-being index (a qualitative

measure constructed from indicators of deprivation).

Results show that there are positive and significant welfare outcomes from using formal and

semi-formal financial services. Welfare benefits persist beyond the immediate period from using

formal financial services while benefits from the use of semi-formal financial services are only

significant in the immediate period. This result is robust in both estimation strategies, in the sub-

sample of social grant recipients as well as a sample of only users of credit services. This builds on

studies such as Khandker and Samad (2013); Bauchet et al. (2011); Ashraf et al. (2010); and Karlan

and Zinman (2010). We also find that users of formal financial services are more resilient to financial

shocks. We conclude that in terms poverty eradication, formal financial services offer more sustainable

gains, given the accumulation of assets and a positive and significant wellbeing outlook following the

use of formal credit. In terms of financial inclusion however, the semi-formal financial sector would

appear to provide an alternative mechanism for individuals who might not be able to participate in

the formal sector, giving them short-term welfare gains. The bundling of different financial services

in a single credit contract, akin to the semi-formal financial sector, might account for the short-term

welfare outcomes observed. This seemingly attractive feature to consumers does not motivate good

financial discipline especially in the presence of say, temptation to acquire consumer items or

instantaneous goods. Despite a lack of data on the explicit characteristics of the various financial

sectors in our data, we argue that based on the evidence presented in this study, the use of formal

financial services can lead to long-term financial sustainability, while the semi-formal financial services

can be used during the transition from the informal sector. The assumption here is that formal and

semi-formal financial services are substitutes and not necessarily complimentary for all consumers.

The rest of the paper is organized as follows. Section 2 provides the context of the study while

Section 3 draws on the relevant theoretical and empirical literature. The methodological approach and

data are discussed in Section 4, while the results are discussed in Sections 5. Section 6 concludes.

2. Financial Inclusion in South Africa

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The post-Apartheid government of South Africa inherited a highly unequal society as a result of the

discriminatory apartheid policies. These policies excluded non-Whites (Blacks, Coloureds, and

Indians) from basic services such as the use of mainstream financial services. These individuals then

found non-formal mechanisms to manage their finances, such as through stokvels. Under the post-

Apartheid Reconstruction and Development Programme of 1994 (RDP, 1994), the Broad Based Black

Economic Empowerment (B-BBEE) strategy, a precursor for the B-BBEE Act of 2003, was

developed and gave rise to the Financial Sector Charter of 2003. The ‘Charter’ committed its

participants (which included banks and insurance companies) to ‘actively promote a transformed,

vibrant and globally competitive financial sector that reflected the demographics of South Africa. As

a result, affordable financial services and products were extended to the previously excluded, notably

the low-cost transactional/Mzansi account. This account boosted inclusion between 2003 and 2008

by six million account holders, many of whom were first time users (FinScope surveys 2003-2012). By

2012, all social grant recipients were required to receive their funds through a South African Social

Security Account (SASSA), a form of Government-to-People (G2P) payment system. This was a way

of including individuals at the lower end of the income spectrum in the formal financial system. Money

transfer mechanisms also became available through grocery and retail stores.

In a series of reforms, the Usury Act (which excluded borrowers of small loans from the

mainstream financial sector) was repealed, and the National Credit Act of 2005 was adopted. This

made provision for credit repayments in instalments, thus enabling many individuals to take up

merchandise credit. This provided a form of semi-formal credit. But earlier studies such as Ardington

and Leibbrandt (2004) show that by 2004, the use of formal financial services in South Africa was

closely associated with households at the higher end of the income distribution and more linked to

formal employment. Srinivasan (2006) also found that, even though more Blacks in South Africa got

access to formal credit between 1993 and 2004, the increase was not statistically significant. The

FinScope survey of 2012 further found that the extent of formal credit access was indeed as small as

26% of the population. Over the period 2006 and 2012, household debt-to-disposable income almost

reached 80 percent from formal and semi-formal credit, while household savings sunk as low as–0.2

percent (South Africa Reserve Bank, 2012). This has implications for household welfare and inequality.

Indeed, the country’s Gini coefficient has been relatively high by international standards, oscillating

between 57.7 and 70.0 during the period 2003 and 2012.

On the other hand, the informal financial mechanisms continue to thrive. For example, by

2012 the informal financial sector had a registered membership of up to 12 million South Africans

-

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pooling together up to R44 billion per year (Africa Response, 2012). While it is possible that individuals

use services across sectors, it would appear that the sectors are considered substitutes rather than

complements. So, how does a choice of financial sector impact the welfare of individuals?

3. Methodology and Data

We are interested in establishing how the welfare outcomes differ following the use of financial

services from different sources. The outcome variable can be denoted as W(1) for individuals who use

financials services from formal sources (financially included or treated) and W(0) for the financially

excluded or control, those who use financial services from non-formal sources (semi-formal or

informal).3 The causal parameter of interest is the average treatment effect on the treated ATT =

E[W(1) – W(0)|USE = 1]. This is the difference in the average welfare realized by the treated group

and the welfare they would have realized had they not used formal financial services.

In the absence of panel data, we use a pooled dataset from repeated cross-sections of the

FinScope surveys of South Africa. The Re-centered Influence Function (RIF) approach by Firpo,

Fortin and Lemieux (2009) is adopted, which uses the inverse probability as a weighting algorithm for

the welfare outcomes of the treatment, control, and counterfactual groups. The first step in this

approach is to estimate the conditional probability of using formal financial services given one’s

observable characteristics, that is, 𝑋 = 𝑥 given by 𝑝(𝑥) = Pr[𝑈𝑆𝐸 = 1|𝑋 = 𝑥]. This is similar to

propensity score matching, assuming ‘ignorability’ and ‘common support’.4 The three weights that are

required are 𝑤�̂� =𝑇

𝑝, 𝑤�̂� =

1−𝑇

1−𝑝 and 𝑤𝑐�̂� =

1−𝑇

𝑝∗ (

𝑝(𝑋)

1−𝑝(𝑋)), which correspond to 'treatment', 'control',

and the 'counterfactual' respectively. T is the proportion of users of formal financial services and �̂� is

the true probability of using formal financial services given the observable covariates. The welfare

function of each group is weighted using these weights and then recentered. Recentering is done by

estimating the influence of a marginal change of the covariates on the welfare distribution (FW) across

quantiles (which is our preferred statistic). For each observation, the sample quantile 𝑞𝜏 is estimated,

3 Since the sectors are not mutually exclusive, we follow Klapper and Singer (2013) and rank the sectors such that formal is superior to semi-formal, which is in turn superior to informal. Individuals who use multiple products are then allocated to the superior category. 4 Ignorability assumes that welfare, use of formal services and the unobservables have a joint distribution such that selection into treatment is not influenced by unobservables once we control for observables. Common support assumes that there is no exclusion to the use of some products by some groups.

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plus a density function 𝑓𝑤(𝑞𝜏) at the quantile using kernel methods,5 and a dummy variable 1{𝑊 ≤

𝑞𝜏} is formed with one indicating that the value of the outcome variable is below 𝑞𝜏 and zero otherwise

(second term of expression (1) which is also the Influence Function). Adding back the statistic to the

influence function yields the RIF given by expression (1).

𝑅𝐼𝐹(𝑊; 𝑞𝜏, 𝐹𝑊) = 𝑞𝜏 +𝜏−1{𝑊≤𝑞𝜏}

𝑓𝑤(𝑞𝜏) (1)

All variables are re-centered so that ordinary least squares regressions can be estimated for the

new dependent variables (recentered Wellbeing and Wealth indices) on the covariates by quantiles.

The point estimates are then used to compare the within-group and between-group variation in the

welfare of the included and the excluded across quantiles. If indeed financial inclusion is welfare

enhancing, then we should expect positive and significantly larger point estimates for the included

individuals (those using either formal or semi-formal services) than for the excluded individuals (those

usin informal services).

Given the nature of the subject under review however, the use of financial services is not

necessarily random, and perhaps not based purely on observable characteristics. This violates the strict

assumptions on which the RIF approach is based. It is also possible that one’s welfare state might

drive the use of financial services from a particular source. To deal with these selection and

endogeneity issues, a synthetic panel is constructed from the cross-sections in the spirit of Deaton

(1985). The idea behind this strategy is to control for time-invariant observed, and most importantly,

unobserved heterogeneities amongst individuals. Reverse causality between welfare status and the

choice of the source of financial services is mitigated through the use of lagged values of welfare. All

regressions include regional dummies to control for region-specific differences in financial

infrastructures, as well as time and cohort fixed effects. The synthetic panel is constructed using a

single year of birth from 18 years to 65 years. According to Deaton (1985), beyond 65 years, the sample

ceases to be representative in terms of economic activity, which is also crucial for financial sector

participation. The estimates are conducted on group averages. They provide the average treated effect

of the treatment captured by parameter β in the estimable model (2) given below:

�̅�𝑐𝑡 = 𝛽𝑈𝑆𝐸̅̅ ̅̅ ̅̅𝑐𝑡 + 𝛾�̅�𝑐𝑡

′ + �̅�𝑐 + �̅�𝑡 + 𝜀�̅�𝑡, c =1, 2, …., C; t = 1, 2, …, T (2)

where �̅�𝑐𝑡 is the average welfare value of all observed w𝑖𝑡’s in cohort c in period t. The independent

variables on the other hand consist of the mean of the financial inclusion (USE) for each sub-group,

5 The Epanechnikov kernel is used in this case and a band-width of 0.06 which yield consistent estimates.

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a vector of covariates (x) comprising of mean of income, mean of education, mean of marital status

in each sub-group, mean regional dwelling, etc. �̅�𝑐 and �̅�𝑡 are cohort and time fixed effects

respectively, and 𝜀�̅�𝑡 is the mean of cohort unobserved characteristics.

To further mitigate the selection bias resulting from the endogenous decision to use or not to use

formal financial services, we repeat the analytical exercise on a sample of social grant recipients.

Following the Social Assistance Act in 2004, the South African government mandated the South

African Social Security Agency (SASSA) to administer seven different welfare grants. These include

the care dependency grant (1500 ZAR/month), the child support grant (3500 ZAR/month), the foster

care grant (890 ZAR/month), the disability grant (1510 ZAR/month), the grant in aid (350

ZAR/month), the old age pension (1510 ZAR/month), and the war veterans grant (1520

ZAR/month). This group was gradually introduced to the formal sector through a no-cost

transactions account, the SASSA Account, through which to access their social welfare support. Grant

recipients were not given an option to use these bank accounts per se, but they were rather drawn into

the system based on a predetermined criterion by authorities. The SASSA account came into effect in

2007. This sub-sample should provide a cleaner identification of testing the hypothesis that financial

inclusion leads to better welfare.

Measuring welfare

The two dependent variables used in this study are the wellbeing index and the asset index. Indicators

of deprivation are used to construct a well-being index. In many poverty and inequality studies, the

extent of deprivation has been used as an indication of welfare, measured by a 'multi-dimensional

poverty index' (MPI).6

If being poor is synonymous with low well-being, then a measure of deprivation

can be a proxy for one's well-being. In the FinScope surveys used by this study, respondents were

asked on a scale of 1-5, where 1= 'Often'; 2= 'Sometimes'; 3= 'Rarely'; 4= 'Never' and; 5= 'Don't

know', to respond to the question: In the past 12 months, how often have you or your family gone without: food;

shelter; medication or medical treatment; cash income; electricity or energy to cook food or heat your home’? Following

the framework advanced by Noble et al. (2000) and Finn et al. (2013), the well-being index (WB) is

constructed according to the expression below:

6 Motivated by the work of Nussbaum and Sen (1993) and used in studies such as Noble et al., 2000; Woolard and Leibbrandt, 2009; Finn et al., 2013; Noble, Zembe, and Wright, 2014.

9

𝑊𝐵𝑖 = ∑ 𝑥𝑘𝐾𝑘=1 (3)

Where 𝑖 = 1, 2, … , 𝑁 (the number of individuals); 𝑘 is the number of deprivation questions asked and

𝑥 is the actual response given by each respondent to question 𝑘. The higher the score for an individual,

the more ‘well-off’ an individual is.

The asset index is constructed from an individual's possession of durable assets. This is based

on the argument that assets might be better at capturing the long-term welfare of individuals or

households, compared to income or expenditure which often exhibit substantial measurement errors.

Assets, as a measure of welfare are also widely used such as in the Demographic and Health Surveys

(see Gwatkin et al., 2000; McKenzie, 2003). An asset index is constructed using a weighting method

and individuals are ranked according to their scores. Simple summing across assets is often criticized

on the basis of assuming equal weights for the items considered, while such items often differ in value

as well as in their distribution in the population. This difference is a function of the utility derived by

individual consumers, thus, a weighting procedure such as factor analysis is preferred because it takes

into account the underlying correlation between these items and their distribution in the population.7

This study makes use of Banerjee’s (2010) Uncentered Principal Component (PC) in which every

variable is divided by its mean and then extracting the first principal component of the cross-product

matrix.8 Thus the first principal component of this "Uncentered PC" procedure is considered to be

the asset index, which is constructed according to the following expression:

𝑊𝐿𝑇(𝑆) = [1 − ∑ ((2𝑟𝑖−1)

𝑛2 )𝑛𝑖=1 ] 𝑦𝑖 (4)

where WLT is the asset index, S is an n X m matrix such that A = A(S) is a scaled version of S obtained

by dividing each member of S by the mean of the relevant column. 𝑦𝑖 = (𝐴𝑥)𝑖, 𝑖 = 1,2, … , 𝑛 and x

is the first Eigen vector associated with the maximal Eigen value of the non-negative square matrix

𝐴′𝐴 normalized so that its components sum to 1; 𝑟𝑖(𝑖 = 1,2, … , 𝑛) is the rank of individual i in the

re-arrangement of the vector 𝑦 = (𝑦1, 𝑦2, … , 𝑦𝑛) in non-increasing order. A higher score is preferred.

Finally, financial inclusion is measure as a binary variable equal to one if an individual uses at

least one formal and/or semiformal financial service, and zero if he uses informal financial services.

3.1 The Data

7 See Gwatkin et al., 2000; Vyas and Kumaranayake, 2006. 8 See Filmer and Pritchett (1998) for the conventional principal component approach to index construction.

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This study uses cross-sectional data from the FinScope repeated surveys. The surveys are part of the

formal financial sector’s efforts to establish consumers’ perceptions and use of financial services. The

financial services considered are from the formal, semi-formal and informal sectors, while services are

grouped into transactional, credit, insurance and savings, or investments. Data was also collected on

indicators of well-being including possession of durable items. For each cross-section, enumeration

areas (EAs) were selected from the census sampling areas, ensuring provincial, rural, and urban

representation. Households were then randomly selected from the EAs. Finally, one individual (aged

16 or above) was selected using a Kish table, for a face-to-face interview during June–July, using a

structured questionnaire. The initial survey for South Africa was conducted in 2003, then in 2005 and

thereafter annually. However, the variables of interest to this study were only consistent for the period

2006 to 2011. Each survey has over 3,000 observations that are nationally representative.9

Appendix A1 shows the summary of the data per cross-section and the pooled data. Based on

the pooled data, there are slightly more Black females, with at least primary level of education, below

45 years of age, urban dwelling and earning less than 6,000 ZAR/month. In the first instance, the

proportion of users of formal and non-formal financial services is almost the same for Coloured males,

with, high school education attainment and on average, cross age groups. Exclusion on the other hand

is skewed towards Blacks in rural areas, and with no monthly income. (see pooled sample statistics in

bold in Columns (1) and (2)). Incorporating semi-formal financial services in the inclusion definition,

statistics in Columns (3) and (4) shows a slightly higher proportion of inclusion for Black women,

with up to primary school education, social grant recipients and earning less than 1,000 ZAR. There

is not much variation by region (urban/rural or by province).

Table 1: Panel A shows the use of financial services from the financial sectors available in the

data, along with the related average welfare scores in each sector. Up to 63% of South Africans

reported using at least one formal financial product, which is the strict measure of financial inclusion

in the literature (see Allen et al., 2012). Using the weaker definition however, this figure increases to

about 76%. This shows some evidence of increased participation in the regulated financial sector

following the financial inclusion policies, from just over 40% in 1994 (Srinivasan, 2006).

Disaggregating this usage into product categories shows that the bulk of usage is for formal

transactional products. This would include the Mzansi low-cost account that was embraced by many

9 For more detail on these surveys see FINSCOPE Financial Access Surveys: https://www.finmark.org.za/finscope/

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first-time formal financial services users. Indeed, for the rest of the categories there is a higher pro-

portion of non-users with marginal use of semi-formal credit products and informal insurance. There

is also evidence of higher welfare for use of services from the formal and semi-formal financial sources

compared to informal services.

Table 1: Use of Financial Services and Welfare outcomes in South Africa, 2006-2011

Panel A: Use of Financial Services and Welfare Outcomes Mean Use (%) Well-being Index Asset Index

Use by financial sector Non-users 16.8 3.59 1.52

Informal 7.3 3.54 1.34

Semi-formal 12.5 3.57 1.97

Formal 63.3 4.35 5.11

Use by product category Transactional Non-use 38.4 3.61 1.79

Formal 61.6 4.11 5.14

Credit Non-use 47.6 3.97 2.61

Informal 5.2 3.53 1.80

Semi-formal 33.1 4.01 8.21

Formal 14.1 4.74 8.60

Insurance Non-use 67.6 3.99 3.60

Informal 11.8 3.76 1.85

Semi-formal 4.5 4.07 6.14

Formal 16.1 4.65 6.87

Savings and Investment Non-use 73.1 3.98 3.22

Informal 11.2 4.01 3.01

Formal 15.7 4.65 7.64

Panel B: Mean Formal Products Use by Racial Group

N Transactions Credit Insurance Savings

Blacks 10 602 0.60 0.18 0.10 0.40 Coloureds 3 309 0.64 0.19 0.14 0.38 Asians 1 344 0.77 0.23 0.15 0.58 Whites 3 760 0.94 0.32 0.25 0.88

Note: The table shows mean use of financial services as categorised by product type and major financial sectors in South Africa, and the mean welfare score in each sector using the two measures of welfare. The data is weighted to be nationally representative using survey weights aligned to Statistics South Africa. The well-being index ranges from 1 to 5 with an average of 4.11 while the asset index ranges from 0 to 21.1 with an average of 4.1. The welfare scores are not year weighted by the weights referred to in the RIF approach. Source: Author’s computation from the FinScope surveys 2006-2011.

Disaggregated use of financial services and welfare outcomes by race is reported in Table 1: Panel B.

Almost all Whites had a bank account, 94% owned a transactions account, while 88% owned a savings

account. Additionally, a cross-tabulation of welfare outcomes by race and bank account holding

revealed that, the average welfare scores of non-banked Whites were far higher than those of banked

non-Whites. For instance, the average well-being for Whites who were not banked was 4.4 compared

to 4.1 for banked non-Whites. This figure rose to 5.8 versus 3.5 respectively, when the wealth index

was considered. For this reason, we excluded the ‘Whites’ from the analysis as it was evident that they

12

would most likely bias the results. Moreover, Whites were not considered to have been marginalized

and as such were not beneficiaries of the inclusion initiatives. We also excluded individuals who did

not use any financial services from any of the 3 sectors.

4. Econometric Results

4.1. RIF regressions estimates

In this section, we present the pooled OLS estimates of welfare on inclusion status after the

transformation in expression (1). These results provide the between group comparisons, that is, the

contribution of financial inclusion on the welfare of the included compared to their excluded

counterparts. Results are reported for both measures of welfare and financial inclusion.

Table 2: Panel A shows a positive and significant relationship between inclusion and welfare.

The effect is monotonically increasing for the wealth index (Columns 1–3) while it is declining, albeit

positive and significant, for the wellbeing index (Columns 4 – 6). The effect is also larger in the lower

quantiles regardless of the measure of welfare used. For instance, wealth increases by a factor of two

between the 10th and the 50th quantile (Columns 1 and 2) while the increase between the 50th and 90th

quantile is only by a factor of 0.1 (Columns 2 and 3). Even the decline in wellbeing is larger in the

lower quantiles, i.e. by 0.5 between the 10th and the 50th quantiles of wellbeing, compared to no change

between the 50th and the 90th quantiles. In fact, there is no significant change in one’s wellbeing beyond

the median, and this pattern is consistent for other individual characteristics as shown in Appendix

A3a.

In Table 2: Panel B, the positive and significant relationship between financial inclusion and

welfare is maintained. While the monotonic increase in the wealth scores persists, as well as the relative

increases across quantiles, the point estimates are larger than in Panel A (see Panel B: Columns 1 – 3).

However, the effect of inclusion is not significant in quantiles below the median wellbeing as shown

in Panel B: Column 4. These results point to higher welfare gains when we relax financial inclusion to

refer to the use of formal or semi-formal financial services. The contribution of other covariates

(shown in Appendix A3a and Appendix A3b) is mixed is depending on the measure of welfare used

and on the position of the individual on the welfare distribution. Within group differences also follow

a similar pattern, but they show that the gap in welfare outcomes is larger across quantiles of the

excluded than across similar quantiles for the included. The latter results are available on request.

13

Overall, these results can be interpreted as the average effect of financial inclusion in a cross-sectional

setting. In the next sections, we conduct further analysis to test the robustness of the observed

treatment effect, and to establish the possible transmission mechanism.

Table 2: Unconditional quantile estimates of financial inclusion on welfare outcomes

Panel A: Financial inclusion = use of formal financial services

(1) (2) (3) (4) (5) (6)

Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

USE1 0.159*** 0.534*** 0.571*** 0.050*** 0.021*** 0.021***

(0.0187) (0.046) (0.117) (0.011) (0.003) (0.003)

Constant -0.456*** -0.713*** 3.099*** 1.889*** 4.890*** 5.005***

(0.058) (0.110) (0.322) (0.029) (0.008) (0.008)

N 12,591 12,591 12,591 12,591 12,591 12,591

R-squared 0.150 0.280 0.167 0.078 0.136 0.136

Panel B: Financial inclusion = use of formal/semi-formal financial services

Variables (1)

Wq10 (2)

Wq50 (3)

Wq90 (4)

WBq10 (5)

WBq50 (6)

WBq90

USE2 0.223*** 0.681*** 0.997*** 0.019 0.022*** 0.022***

(0.032) (0.068) (0.136) (0.016) (0.005) (0.005)

Constant -0.470*** -0.573*** 2.851*** 1.881*** 4.891*** 5.005***

(0.066) (0.125) (0.340) (0.034) (0.009) (0.009)

N 11,326 11,326 11,326 11,326 11,326 11,326

R-squared 0.149 0.293 0.181 0.078 0.144 0.144

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. W = wealth index, WB = wellbeing index. Covariates include gender, age, education, marital status, trust in banks, travel time to banks, personal monthly income, rural/urban region, province and year dummies.

4.2. The pseudo-panel estimates

The pseudo-panel estimates are based on expression (2). They provide an insight into the static and

the dynamic effect of financial inclusion on welfare. Models (1), (3), (5) and (7) in Table 3 provide the

average treatment effect on welfare outcomes conditional on being financially included in the current

period. In Panel A, we notice that the effect of formal inclusion is a positive welfare outcome as shown

in Columns (1) and (3). The magnitude increases even further when we account for initial inclusion

status in a dynamic framework. For instance, formal inclusion in the current period leads to an increase

in wellbeing by 0.931 points on the wellbeing index and 3.135 on the wealth index as shown in

Columns (2) and (4) respectively. However, there is no significant effect after one year of inclusion,

14

and the effect becomes significantly lower welfare after two years. Nonetheless, the cumulative effect

is positive at 0.186 and 0.804 for wellbeing and welfare respectively.10

Turning to the relaxed definition of financial inclusion, Columns (6) and (8) in Panel B show that

current use of formal or semi-formal financial services increases welfare. However, there is no

significance within a year or two of inclusion under this definition. It appears that the benefits accrue

in the immediate period and nothing beyond this period especially for the wealth index in Column (8).

The cumulative wellbeing resulting from Column (6) is a significant increase in wellbeing by 0.935

points, almost the same as the first period results el in Column (2), when inclusion is restricted to the

use of formal financial services. Recall that wellbeing in this study was captured by questions linked

to deprivation (or not) in ‘the past 12 months’ in terms of access to food, shelter, medication, cash,

and energy to cook or heat people’s homes. This being a subjective measure, it is not necessarily the

case that the wealth outcomes should move in tandem with wellbeing. Karlan and Zinman (2010)

observe that individuals can report subjectively that they are doing well even when there is no

quantitative outcome that matches that subjective self-assessment.

In terms of the key covariates, we argue that decisions are made sequentially such that previous

income levels will determine one’s use of financial services in the next period. In this regard, we

incorporate the lag of income in all regressions. One point to note is the argument we advanced for

constructing an asset index, that they provide a long-term measure of use of income. Lagged income

is thus a proxy for initial welfare. While it did not significantly contribute to welfare outcomes in the

regressions, using lagged income led to larger and significant coefficients of inclusion than using

current income.

10 The cumulative effect is obtained by adding the coefficients on the contemporaneous and lagged values of the inclusion variable.

15

Table 3: The pseudo-panel estimates of the use of financial services on welfare outcomes

Panel A: Formal services Panel B: Formal and semi-formal services

(1) (2) (3) (4) (5) (6) (7) (8)

Variables Wellbeing Index Wealth Index Wellbeing Index Wealth Index

Inclusion2011 0.776*** 0.931*** 2.247*** 3.135*** 0.846*** 1.340*** 0.716 3.583***

(0.145) (0.153) (0.388) (0.402) (0.258) (0.335) (0.623) (0.923)

Inclusion2010 -0.127 -0.613 -0.073 -1.520

(0.169) (0.444) (0.294) (0.809)

Inclusion2009 -0.618*** -1.718*** -0.332* -0.493

(0.155) (0.409) (0.190) (0.523)

0 - R9992009 -1.829 -1.256 -2.587 -1.119 -2.242 -1.595 -3.527 -2.355

(1.425) (1.384) (3.809) (3.645) (1.374) (1.475) (3.311) (4.066)

R1000-59992009 -1.816 -1.171 -3.164 -1.397 -2.247 -1.546 -3.682 -2.542

(1.424) (1.384) (3.807) (3.647) (1.367) (1.480) (3.295) (4.080)

R6000-99992009 -2.054 -0.984 -2.088 1.218 -2.448* -1.320 -3.848 0.318

(1.453) (1.435) (3.886) (3.778) (1.382) (1.520) (3.331) (4.192)

R10000-240002009 -2.087 -1.013 -1.544 2.548 -2.542* -1.562 -3.571 0.715

(1.527) (1.523) (4.082) (4.011) (1.460) (1.605) (3.519) (4.425)

Province FE Yes Yes Yes Yes Yes Yes Yes Yes

Covariates Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

Cohort FE Yes Yes Yes Yes Yes Yes Yes Yes

Constant 5.328*** 5.101*** 4.024 3.275 3.975*** 4.852*** 0.770 3.889

(1.422) (1.366) (3.803) (3.599) (1.403) (1.475) (3.382) (4.066)

Observations 260 208 260 208 260 208 260 208

R-squared 0.240 0.329 0.272 0.427 0.410 0.238 0.541 0.287

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

Two interpretations can be advanced for these results. First, the analysis in this study is

undertaken over a period of the global financial crisis. Thus, the negative result after two years of

inclusion coincides with the year 2009 during which South Africa’s financial sector was affected. What

is impressive however, is that the negative results for the following year (2010) are not statistically

significant. We can therefore argue that being included in the formal financial sector provided a

mechanism of resilience against this financial shock compared to using semi-formal financial services.

This supports arguments and empirical work stating that access and use of finance enables users to

mitigate shocks (see for instance Gash and Gray, 2016; Brune et al., 2011; Ashraf et al., 2006).. This

resilience is partly through the accumulation of assets (Cohen and Young, 2007), which can be

liquidated to smooth consumption during and after a shock. The second interpretation of the above

16

results is that these are group or cohort averages since this is a synthetic panel constructed out of

repeated cross-sections. We are therefore unable to conduct robust individual level analysis as would

have been the case with a natural panel. Even though we control for cohort effects, these results at

most provide a sense of an insight into the direction and significance of the effect of using financial

services from different sources (formal or non-formal) and not necessarily the magnitude, if compared

with the results in Table 2. In this regard, we see that the positive effect of financial inclusion is

preserved.

These results have a novel component and a component that conforms to empirical literature.

Welfare gains from formal inclusion have been reported in empirical work such as Khandker and

Samad (2013), Burgess and Pande (2005), etc. Group dynamics in Khandker and Samad (2013) can be

likened to cohort dynamics in this study. The novelty in this study relates to the short-term welfare

benefits associated with the use of semi-formal financial services compared to using formal financial

services. Burgess and Pande (2005) report short-term welfare outcomes but in relation to the use of

banking services (considered to be formal services in this study). We investigate the robustness of this

finding in the sections that follow.

4.3. Social grants sub-sample

This section reports results based on a sample of social grant recipients. As mentioned earlier, it is

assumed that this sub-sample does not suffer from selection into the use of especially formal financial

services. The characteristics of this sub-sample are provided in Appendix A2. The pooled sample

shows that a high proportion of recipients are Black women in rural areas, with less than high school

level of education across all age categories. There is almost equal representation I the two groups in

terms of observables characteristics, even when the definition of financial inclusion is adjusted to

incorporate the use of semi-formal financial services.

In Table 4, we see evidence of slightly higher mean welfare for the included social grants

recipients (treatment group) compared to their excluded counterparts (the control group). We also

notice that on average, welfare outcomes are lower when semi-formal financial services are

incorporated into financial inclusion. We also observe that, going by the expanded definition of

financial inclusion, the number of financially excluded social grant recipients declines (up to zero for

2010 and 2011), while it increases for the financially included across the period of study.

17

Table 4: Trend of social grant recipients by inclusion station status and welfare outcomes

Inclusion status 2006 2007 2008 2009 2010 2011

Excluded N 170

115 162

134 145

130 162

43 268

-- 280

--

Mean wellbeing Mean wealth

3.34 0.97

3.89 0.82

3.40 1.15

3.37 1.01

3.58 1.18

3.65 1.17

3.71 1.14

3.50 1.09

3.49 1.03

-- --

3.32 1.34

-- --

Included N 322

288 409

379 412

378 463

523 407

569 490

652

Mean wellbeing Mean wealth

3.81 2.54

3.79 2.42

3.77 1.99

3.72 1.89

3.73 2.08

3.69 1.98

3.86 1.94

3.82 1.73

3.65 2.18

5.55 1.70

3.68 2.46

3.54 2.00

Total 492 403 568 513 557 557 625 566 675 569 770 652

Note: The table shows grant recipients per year and the average welfare scores. 1997 receive child support grants, 2343 receive old age grants, 270 receive disability grants and 77 receive a combination of the three types of grants. The well-being index ranges between 1 – 5 while the wealth index ranges between 0 – 21 both with an average of 4.1. welfare outcomes are raw scores and they are not yet weighted by the weights referred to in the RIF approach. Figures in bold are statistics for the strict definition of financial inclusion (use of formal financial services).

Table 5 reports the results of the RIF estimates while Table 6 reports the dynamic estimates

from the pseudo-panel. In Table 5: Panel A, we notice the familiar monotonic increase in welfare

across wealth quantiles (Columns 1–3). The difference in welfare is about the same, i.e., 2.08 versus

1.98 for the 10th – 50th quantiles and the 50th – 90th quantile respectively. On the other hand, the effect

on wellbeing quantiles is an inverted u-shape across quantiles, with a turning point at the median.

Although wellbeing at the 10th quantile is only significant at 10%, wellbeing at the median is very

significant, before tapering off slightly as shown in Columns (4) – (6).

Turning to the more relaxed definition of financial inclusion, Table 5: Panel B shows that

results are only consistent for the wealth measure. That is, inclusion has a positive and significant

effect on welfare across wealth quantiles (Columns 1 – 3). The effect on wellbeing is insignificant

(Columns 4 – 6). These results suggest that in a cross-section setting, formal financial inclusion enables

one to acquire durable items and to maintain a positive outlook on one’s wellbeing. The use of semi-

formal services on the other hand, adds little value to the possession of durables and it has no

significant effect on one’s wellbeing. It should be noted that the SASSA account is for transaction

purposes only, but account holders can acquire other types of financial services if the financial

institutions deem them eligible. In our dataset, we find that indeed social grant recipients do own

credit, insurance, and savings products in addition to their SASSA accounts. There is also empirical

18

evidence that they use their meagre income to engage in economically viable ventures which boosts

their welfare (Neeves et al., 2009; Case and Deaton, 1998).

Table 5: Unconditional quantile estimates of financial inclusion on welfare outcomes of social grant recipients

Panel A: Financial inclusion = use formal financial services

(1) (2) (3) (4) (5) (6) Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

Inclusion1 0.144*** 0.444*** 1.325*** 0.014* 0.055*** 0.019***

(0.020) (0.046) (0.196) (0.009) (0.015) (0.007) Constant -0.167*** -0.016 -1.688*** 1.094*** 3.826*** 4.985***

(0.052) (0.107) (0.527) (0.018) (0.037) (0.017) N 3,390 3,390 3,390 3,390 3,390 3,390 R-squared 0.131 0.289 0.162 0.012 0.106 0.116

Panel B: Financial inclusion = use of formal or semi-formal financial services

(1) (2) (3) (4) (5) (6) Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

Inclusion2 0.138*** 0.397*** 1.351*** -0.000 0.037 0.014 (0.032) (0.068) (0.311) (0.012 (0.024) (0.011) Constant -0.225*** -0.153 -1.349** 1.084*** 3.822*** 4.987*** (0.065) (0.125) (0.635) (0.025) (0.044) (0.020) N 2,958 2,958 2,958 2,958 2,958 2,958 R-squared 0.122 0.266 0.139 0.010 0.100 0.106

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Treatment is ’Inclusion’, defined for each panel. Covariates include gender, age, education, marital status, trust in banks, travel time to banks, income, region and year dummies. Appendix Tables A4a and A4b show the full set of results.

In Table 6: Panel A, we do not see a statistically significant effect of use of formal financial

services on wellbeing in the current period (Columns 1 and 2). In fact, current inclusion is insignificant

for wellbeing, and it significantly contributes to lower wellbeing after one or two years. The cumulative

effect of inclusion is to lower wellbeing by 0.979. On the other hand, immediate inclusion increases

wealth as shown in Column (4), but this is once again accompanied by a decline in wealth after a year

or two of inclusion. The cumulative effect is to reduce wealth by 1.189 as shown in Column (3). Given

the scales of these two indices (1-5 versus 0-21 for wellbeing and for wealth respectively), the decline

in wellbeing is far greater than that in wealth. If we consider the effect of the global financial shock,

we can see once again that the negative effect declines steadily between 2009 (the second lag) and

2011(the current period). This confirms the resilience to financial shocks as was the case in the

previous section on the full sample. We cannot say the same for use of semi-formal services where

there is no significant effect even in the static panel analysis in Columns (5) and (7). There were no

observations for informal services users during 2010 and 2011 (see Table 4) hence the missing values

in the dynamic model in Columns (6) and (8). However, considering the second lag alone, the effect

19

is to reduce welfare. Yet social grant recipients have often been targeted by non-formal money lenders

in the unsecured money market for as long as they show evidence that they are still receiving social

support.11

Table 6: Pseudo-panel estimates of the effect of financial inclusion on welfare outcomes of social grant recipients

Panel A: formal services Panel B: formal or semi-formal services

(1) (2) (3) (4) (5) (6) (7) (8)

Variables Wellbeing Index Wealth Index Wellbeing Index Wealth Index

Inclusion2011 -0.158 -0.157 0.338 0.701** 0.222 -- 0.457 --

(0.196) (0.199) (0.254) (0.280) (0.281) (0.365) Inclusion2010 -0.365* -0.670** -- --

(0.195) (0.273) Inclusion2009 -0.457** -1.220*** -0.360* -0.846***

(0.183) (0.257) (0.216) (0.315)

Covariates Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

Cohort FE Yes Yes Yes Yes Yes Yes Yes Yes

Constant 10.75 16.33** -7.690 -1.035 11.10 15.14* -0.0004 -3.738

(7.363) (7.616) (9.561) (10.71) (7.360) (7.758) (0.672) (11.33)

N 260 208 260 208 260 208 260 208

R-squared 0.299 0.173 0.421 0.242 0.299 0.134 0.419 0.145

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. the wellbeing index ranges between 1 and 5 while the wealth index ranges between 0 and 21. Covariates include gender, age, education, marital status, trust in banks, travel time to banks, income, region and year dummies.

In the context of social grant recipients, these results are plausible. Any effort to acquire assets

by cash or on credit is likely to create a liquidity constraint in the current period (for cash purchases)

or in the subsequent periods (for credit purchases). The latter also implies that subsequent purchases

might not be possible. However, remaining in the formal financial sector seems to provide a

mechanism of mitigating the negative effects of any financial shocks as seen in the dynamics model in

Column (4) in Panel A. This is also dependent on continued receipt of the social grant.

4.4. Extension of the analysis

11 See http://www.kayafm.co.za/money-lender-targets-social-grant-beneficiaries/

20

In this section, we repeat the analysis on credit services for a few reasons. First, credit regulations were

relaxed as part of increasing financial access to the previously marginalized in South Africa. Second,

credit products are heterogeneous and are provided in all three types of financial sectors (formal, semi-

formal, and informal financial sectors) which makes the analysis consistent. Third, the durable assets

considered for the wealth index can be acquired on credit. Thus, this analysis should also assist in

interrogating the effect on the wealth index and in disentangling the results obtained on the aggregated

use of financial services.

Table 7: RIF Estimates of the effect of the use of credit services on welfare outcomes

Panel A: Credit use = formal credit

(1) (2) (3) (6) (7) (8)

Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

USEcr1 0.108*** 1.204*** 2.716*** 0.036*** 0.035*** 0.035***

(0.016) (0.08) (0.372) (0.010) (0.004) (0.004)

Constant -0.673*** -0.991*** 4.459*** 1.865*** 4.901*** 5.015***

(0.103) (0.189) (0.516) (0.044) (0.010) (0.010)

N 6,629 6,629 6,629 6,629 6,629 6,629

R-squared 0.169 0.318 0.179 0.076 0.176 0.176

Panel B: Credit use = formal or semi-formal credit

(1) (2) (3) (6) (7) (8)

Wcrq10 Wcrq50 Wcrq90 WBcrq10 WBcrq50 WBcrq90

USEcr2 0.131*** 1.256*** 1.632*** 0.021 0.048*** 0.048***

(0.039) (0.093) (0.171) (0.018) (0.005) (0.005)

Constant -0.688*** -1.383*** 3.149*** 1.888*** 4.885*** 4.998***

(0.110) (0.206) (0.552) (0.045) (0.011) (0.011)

N 6,629 6,629 6,629 6,629 6,629 6,629

R-squared 0.171 0.324 0.171 0.077 0.184 0.184

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. treatment is use of credit (USEcr), defined in each panel. The dependent variables are wealth index (W) and wellbeing index (WB). Covariates include gender, age, education, marital status, trust in banks, travel time to banks, income, and region. Appendix Tables A5a and A5b show the full set of results.

Results in Table 7 mimic what was observed at the aggregate level of financial services, that

inclusion has a positive and significant effect across quantiles of welfare (see Table 3). The use of

formal credit has the effect of monotonically increasing welfare across wealth quantiles (Columns 1 –

3) while the effect is the same across quantiles of wellbeing (Columns 4 – 6). The inclusion of semi-

formal services slightly attenuates the wealth outcomes in upper quantiles, but it slightly increases

wellbeing in these quantiles (Columns 7 – 8) as was the case in Table 3: Panel B. It is therefore safe to

21

conclude that in a cross-sectional analysis, the use of semi-formal financial services has a positive and

significant effect on the user’s welfare.

The dynamic results reported in Table 8: Panel A show an overall positive effect of formal

credit as shown in Columns (1) – (4). Immediate use of formal credit increases wellbeing by 0.255 as

shown in Column (2), while credit use of up to one year reduces wellbeing by 0.343 and by 0.190 for

credit use of up to two years. The cumulative effect on wellbeing is a decline by 0.032. In terms of the

wealth index, which should have a direct relationship with the use of credit, we see a positive and

significant effect in both the static and in the dynamic models in Columns (3) and (4). The effect is

greater when we incorporate use beyond the current period. For instance, while immediate credit

increases wealth by 1.550, the cumulative effect after two years is 2.144, which is a much higher effect

than that recorded in Table 3 when financial services are aggregated. A possible interpretation of the

wellbeing outcomes is that in the immediate term, formal credit can smooth liquidity constraints which

allows individuals to access food, shelter, medication, and electricity/energy for cooking (these are

variables used to construct this index). However, it is possible that interrupted repayment of the cash

loan or overdraft could affect continued benefits from the formal sector. An individual will still hold

the credit account over a period while servicing the loan and not necessarily taking newer loans. This

could negatively impact some of the indicators of wellbeing used in this study. On the other hand,

assets can be owned for a longer period. Even when a repossession occurs, it is not instantaneous. In

our case, the cumulative effect is positive even in the post-financial crisis period. It is therefore possible

that these assets were fully paid for, or that the repayments were not affected by the financial crisis.

Similar short-term wellbeing gains have been reported by Banerjee et al. (2013).

Incorporating semi-formal credit in Table 8: Panel B, we do not find any significant effect

except when previous credit use is incorporated in the wealth index in Column (8). Even then, the

coefficients of the lags are not significant (up to five lags were considered), implying that the benefit

of semi-formal credit is only in the immediate term. Again, the short-term benefits from semi-formal

services are evident.

22

Table 8: Pseudo-panel estimates of the effect of credit use of welfare outcomes

Panel A: Formal credit Panel B: Formal and semi-formal credit

(1) (2) (3) (4) (5) (6) (7) (8)

Variables Wellbeing Index Wealth Index Wellbeing Index Wealth Index

USEcr2011 0.139 0.255** 1.267*** 1.550*** 0.084 0.112 0.093 0.809***

(0.092) (0.102) (0.238) (0.277) (0.101) (0.106) (0.245) (0.288)

USEcr2010 -0.343*** 0.308 -0.247** 0.174

(0.0971) (0.263) (0.106) (0.287)

USEcr2009 -0.190** 0.286**

(0.085) (0.032) 0-R9992009 -1.374 -1.472 -0.428 0.070 -1.722 -0.970 -3.008 -1.503

(1.453) (1.396) (3.779) (3.787) (1.348) (1.462) (3.254) (3.967)

R1000-59992009 -1.277 -1.310 -0.861 -0.344 -1.615 -0.827 -3.051 -1.957

(1.451) (1.396) (3.775) (3.787) (1.338) (1.463) (3.231) (3.970)

R6000-99992009 -1.349 -0.833 0.063 0.605 -1.997 -0.725 -3.430 -0.420

(1.476) (1.424) (3.839) (3.863) (1.356) (1.494) (3.273) (4.055)

R10000-240002009 -1.344 -0.701 0.700 0.999 -1.961 -0.655 -3.016 0.409

(1.550) (1.513) (4.033) (4.104) (1.431) (1.572) (3.455) (4.265)

Constant 5.386*** 5.532*** 3.067 2.062 4.137*** 5.123*** 1.028 3.831

(1.454) (1.399) (3.784) (3.793) (1.387) (1.458) (3.348) (3.958)

N 259 205 259 205 259 258 259 258

R-squared 0.169 0.259 0.266 0.352 0.399 0.179 0.542 0.205

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

Overall, results in Section 4 confirm previous studies insofar as the positive welfare benefits

of financial inclusion are concerned (see Burgess and Pande, 2005; Khandker and Samad, 2013; Ashraf

et al., 2010), while providing newer evidence of the contribution of non-formal financial services like

semi-formal financial services, albeit in the short-run. Our data shows that even those individuals

drawn into the formal financial sector with only a transactions account (the social grants recipients),

are able to acquire other financial services such as credit, insurance, and savings. This would explain

the positive welfare outcomes observed at least in the immediate period. We can therefore argue that

the formal financial services assist in liquidity constraints as well as in the accumulation of assets for

up to two periods in this study at the very least. A possible explanation is that formal financial products

and contracts are used in a stand-alone framework (for instance a transactions account is often a stand-

alone product from a credit or an insurance product). Semi-formal financial products on the other

hand, are often bundled under one contract (for instance, a clothing credit contract can have an

insurance product and a revolving credit facility which one can use as some form of a savings plan as

23

well). However, in order for one to enjoy these services, the main credit account has to be properly

repaid, which makes this arrangement less resistant to financial shocks, subsequently compromising

the user’s long-term welfare outlook.

5. Conclusions

This paper has investigated the relative welfare outcomes of using formal and non-formal financial

services, making use of a cross-sectional dataset from the FinScope surveys of South Africa for the

period 2006 – 2011. The dataset has enabled us to go beyond the common definition of financial

inclusion as the ‘use of formal financial services’ (Allen et al., 2012) to accommodate substitute

financial services provided by non-formal financial service providers, and to compare the welfare

benefits therefrom. We have looked beyond the immediate welfare gains using subjective and

quantitative welfare measures, thus building on and broadening studies on financial inclusion.

There are two key results. First, there is evidence of long-term welfare benefits associated with

the use of formal financial services controlling for both observable and potential unobservable

confounders. Incorporating the use of semi-formal services in the inclusion definition reveals the

short-term nature of the benefits associated with such products. If the choice of semi-formal is based

on maximizing utility subject to some characteristics, then this utility does not translate into long-term

welfare benefits, a notion that is elaborated by Coleman (1980), that utility maximization is not

necessarily welfare maximization. We therefore confirm earlier findings that financial inclusion has

long-term benefits (Khandker and Samad, 2013). This result further builds on studies that recognize

the role of non-formal financial sectors such as Johnson and Nino-Zarazua (2011) and Klapper and

Singer (2013), and offer evidence that in the immediate term, this sector provides welfare benefits for

individuals who are not able to use the formal financial services. This argument is based on the

assumption that formal and semi-formal financial services are substitutes and not necessarily

complements, the choice of which depends on both personal and sector-specific characteristics.

Unfortunately, the dataset did not have explicit characteristics of these sectors and all inference is

made on the basis of the observed choice made by the individual. A second result is that the use of

formal financial services provides resilience to financial shocks compared to semi-formal financial

services. This confirms earlier studies such as Brune et al. (2011), Ashraf et al. (2006), etc., that

inclusion provides a mechanism for mitigating shocks and for planning ahead.

24

From a policy perspective, and to facilitate uptake of formal financial services, there is a need

to develop financial services that are akin to those in the semi-formal sector to facilitate the transition

from informality. Regulation should also be tightened to monitor the types of services, and the terms

and conditions, that consumers bundle up in a given credit contract in the semi-formal financial sector.

Finally, consumers should be educated on the pros and cons of using formal and non-formal financial

services with respect to welfare outcomes.

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28

Appendix A1: Summary of the data

Variable 2006

n=2783 2007

n=3016 2008

n=3122 2009

n=3015 2010

n=3335 2011

n=3744 Pooled

N=19015 (1)

Excluded1

(2) Included1

(3) Excluded2

(4) Included2

Male 0.492 0.465 0.459 0.458 0.471 0.468 0.468 0.415 0.485 0.399 0.475 Blacks 0.715 0.727 0.727 0.729 0.758 0.765 0.739 0.882 0.695 0.884 0.725 Coloureds 0.103 0.093 0.100 0.098 0.092 0.095 0.097 0.095 0.097 0.102 0.096 Asians 0.027 0.033 0.034 0.033 0.030 0.028 0.031 0.011 0.037 0.007 0.033 Whites 0.156 0.148 0.139 0.140 0.119 0.112 0.134 0.012 0.172 0.006 0.146 No education 0.049 0.039 0.018 0.026 0.037 0.043 0.036 0.095 0.017 0.127 0.027 Primary school 0.148 0.111 0.0970 0.101 0.118 0.120 0.115 0.233 0.078 0.259 0.101 High school 0.637 0.680 0.713 0.700 0.652 0.687 0.679 0.650 0.688 0.597 0.687 Post-high school 0.166 0.171 0.172 0.173 0.193 0.150 0.170 0.022 0.217 0.017 0.185 18 – 34 years 0.293 0.331 0.349 0.341 0.352 0.374 0.343 0.404 0.324 0.328 0.345 35 – 44 years 0.367 0.407 0.401 0.394 0.398 0.316 0.378 0.279 0.409 0.310 0.385 45 – 59 years 0.203 0.124 0.127 0.132 0.120 0.182 0.148 0.131 0.153 0.127 0.150 60+ years 0.136 0.137 0.123 0.133 0.131 0.128 0.131 0.186 0.114 0.235 0.121 Social grant 0.239 0.271 0.240 0.270 0.218 0.225 0.242 0.348 0.209 0.446 0.223 Eastern Cape 0.120 0.114 0.120 0.110 0.128 0.133 0.122 0.138 0.117 0.129 0.121 Free State 0.053 0.047 0.049 0.048 0.059 0.057 0.053 0.065 0.049 0.058 0.052 Gauteng 0.262 0.292 0.251 0.237 0.242 0.239 0.252 0.130 0.291 0.122 0.265 Kwa-Zulu Natal 0.141 0.181 0.177 0.199 0.197 0.198 0.184 0.187 0.183 0.145 0.188 Limpopo 0.112 0.087 0.107 0.106 0.104 0.100 0.102 0.181 0.078 0.209 0.092 Mpumalanga 0.063 0.063 0.068 0.065 0.075 0.074 0.069 0.091 0.062 0.078 0.068 Northern Cape 0.015 0.018 0.021 0.022 0.024 0.022 0.021 0.027 0.019 0.024 0.020 North West 0.097 0.067 0.083 0.087 0.066 0.067 0.077 0.094 0.071 0.126 0.072 Western Cape 0.137 0.131 0.124 0.126 0.105 0.111 0.121 0.089 0.131 0.108 0.122 Urban 0.674 0.704 0.661 0.657 0.649 0.666 0.668 0.461 0.733 0.425 0.691 Never married 0.425 0.447 0.532 0.539 0.717 0.518 0.536 0.587 0.520 0.499 0.539 Divorced 0.032 0.017 0.026 0.018 0.177 0.037 0.054 0.068 0.049 0.026 0.057 Widowed 0.080 0.071 0.061 0.068 0.019 0.074 0.061 0.089 0.053 0.119 0.056 Married 0.464 0.465 0.380 0.375 0.087 0.370 0.349 0.256 0.378 0.356 0.348 Monthly income Up to R 999 0.520 0.507 0.460 0.383 0.431 0.431 0.453 0.759 0.348 0.869 0.406 R 1000 - 5999 0.381 0.414 0.438 0.504 0.419 0.445 0.434 0.239 0.502 0.130 0.469 R 6000 - 9999 0.058 0.044 0.062 0.069 0.078 0.057 0.062 0.001 0.082 0.001 0.068 R 10000 - 24999 0.037 0.032 0.036 0.040 0.060 0.059 0.045 0.001 0.060 0 0.050 R 25000+ 0.004 0.003 0.005 0.005 0.011 0.009 0.006 0 0.008 0 0.007 Less than 30 minutes 0.042 0.044 0.697 0.715 0.706 0.721 0.514 0.422 0.543 0.182 0.546 30 – 60 minutes 0.113 0.111 0.210 0.193 0.250 0.225 0.189 0.208 0.183 0.067 0.201 Trust in banks 0.671 0.532 0.487 0.451 0.237 0.216 0.411 0.267 0.456 0.426 0.409

29

Table A2: Characteristics of the social grant recipients

Pooled Excluded1 Included1 Excluded2 Included2

variable mean mean mean mean mean

Male 0.205 0.232 0.190 0.254 0.195

Blacks 0.897 0.907 0.892 0.914 0.894

Coloureds 0.086 0.085 0.087 0.084 0.087

Asians/Indians 0.017 0.008 0.021 0.002 0.02

No education 0.087 0.184 0.036 0.205 0.064

Primary school 0.233 0.328 0.184 0.296 0.221

High school 0.643 0.480 0.729 0.488 0.674

Post-high school 0.036 0.008 0.051 0.011 0.041

18 – 34 years 0.254 0.186 0.290 0.224 0.260

35 – 44 years 0.335 0.281 0.364 0.270 0.349

45 – 59 years 0.113 0.122 0.108 0.132 0.109

60+ years 0.298 0.411 0.238 0.374 0.282

Eastern Cape 0.197 0.173 0.210 0.183 0.200

Free State 0.056 0.068 0.049 0.053 0.056

Gauteng 0.160 0.078 0.202 0.055 0.180

Kwa-Zulu Natal 0.200 0.217 0.191 0.187 0.203

Limpopo 0.133 0.211 0.092 0.244 0.111

Mpumalanga 0.059 0.057 0.059 0.049 0.061

Northern Cape 0.024 0.030 0.021 0.022 0.025

North West 0.093 0.084 0.097 0.109 0.090

Western Cape 0.079 0.080 0.078 0.099 0.075

Urban 0.523 0.365 0.606 0.337 0.560

Single 0.457 0.387 0.494 0.372 0.474

Divorced 0.069 0.092 0.057 0.024 0.078

Widowed 0.134 0.178 0.111 0.193 0.122

Married/with partner 0.340 0.343 0.338 0.411 0.325

Up to R 9999 0.642 0.688 0.618 0.904 0.587

R 1000 – 5 999 0.349 0.311 0.369 0.0950 0.402

Trust banks 0.381 0.292 0.428 0.397 0.378

Source: Author’s compilation from the FinScope surveys for the period 2006 - 2011.

30

Appendix A3a: Unconditional (average) effect of financial inclusion on welfare

(1) (2) (3) (4) (5) (6)

Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

Formal use 0.159*** 0.534*** 0.571*** 0.050*** 0.021*** 0.021***

(0.019) (0.046) (0.117) (0.011) (0.003) (0.003)

Male 0.058*** 0.114*** 0.086 -0.013* -0.003 -0.003

(0.012) (0.0357) (0.131) (0.007) (0.002) (0.002)

Primary school 0.181*** 0.144 0.083 0.005 0.023*** 0.023***

(0.056) (0.0936) (0.162) (0.027) (0.007) (0.007)

High school 0.397*** 0.957*** 1.094*** 0.044* 0.055*** 0.055***

(0.0532) (0.0924) (0.185) (0.026) (0.006) (0.006)

Post-high school 0.461*** 1.865*** 4.421*** 0.082*** 0.089*** 0.0892***

(0.054) (0.105) (0.363) (0.027) (0.007) (0.007)

35 – 44 years -0.061*** -0.136*** -0.204 -0.017* -0.007** -0.007**

(0.015) (0.0447) (0.159) (0.009) (0.003) (0.003)

45 – 59 years 0.007 0.249*** 0.427** 0.007 -0.002 -0.002

(0.018) (0.0531) (0.200) (0.010) (0.004) (0.004)

60+ years 0.047** 0.712*** 1.005*** 0.020 0.023*** 0.023***

(0.023) (0.0631) (0.243) (0.014) (0.004) (0.004)

R1000 - 5999 0.070*** 0.211*** -0.172 0.030*** 0.027*** 0.027***

(0.014) (0.0414) (0.132) (0.008) (0.003) (0.003)

R6000 - 9999 0.088*** 1.212*** 4.261*** 0.063*** 0.069*** 0.069***

(0.015) (0.0636) (0.450) (0.010) (0.005) (0.005)

R10000 - 24000 0.082*** 1.303*** 9.421*** 0.048*** 0.077*** 0.077***

(0.015) (0.0672) (0.668) (0.012) (0.006) (0.006)

Urban area 0.392*** 1.406*** 1.744*** 0.108*** 0.042*** 0.042***

(0.020) (0.0457) (0.125) (0.011) (0.003) (0.003)

Eastern Cape -0.210*** -0.890*** -0.964*** -0.233*** -0.049*** -0.049***

(0.024) (0.0675) (0.243) (0.017) (0.005) (0.005)

Free State 0.063*** -0.665*** -1.564*** 0.024** 0.026*** 0.026***

(0.019) (0.0784) (0.244) (0.012) (0.005) (0.005)

Kwa-Zulu Natal -0.024 -0.168*** 0.850*** -0.012 0.007 0.007

(0.017) (0.0618) (0.279) (0.011) (0.004) (0.004)

Limpopo -0.036 -0.454*** -0.335 0.072*** -0.003 -0.003

(0.034) (0.0778) (0.293) (0.016) (0.006) (0.006)

Mpumalanga 0.039* -0.179** -0.119 0.004 -0.009 -0.009

(0.023) (0.0765) (0.302) (0.013) (0.005) (0.005)

Northern Cape 0.083*** -0.516*** -0.930*** 0.027** 0.025*** 0.025***

(0.022) (0.0821) (0.273) (0.013) (0.005) (0.005)

North West 0.103*** -0.448*** -0.855*** 0.014 -0.011** -0.011**

(0.023) (0.0768) (0.255) (0.013) (0.005) (0.005)

Western Cape 0.051*** 0.0655 0.457 0.004 0.018*** 0.018***

(0.015) (0.0691) (0.278) (0.010) (0.005) (0.005)

Constant -0.456*** -0.713*** 3.099*** 1.889*** 4.890*** 5.005***

(0.058) (0.110) (0.322) (0.029) (0.008) (0.008)

Observations 12,591 12,591 12,591 12,591 12,591 12,591

R-squared 0.150 0.280 0.167 0.078 0.136 0.136

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 W = wealth index, WB = wellbeing index. The table shows RIF estimates of financial inclusion on welfare outcomes. The missing categories are the base categories for each covariate.

31

Appendix A3b: Unconditional (average) effect of formal/semi-formal financial inclusion on welfare

(1) (2) (3) (4) (5) (6) Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

Formal or semi-formal use 0.223*** 0.681*** 0.997*** 0.019 0.022*** 0.022***

(0.032) (0.068) (0.136) (0.016) (0.005) (0.005) Urban 0.375*** 1.317*** 1.608*** 0.107*** 0.036*** 0.036***

(0.021) (0.049) (0.133) (0.012) (0.003) (0.003) Males -0.052*** -0.153*** -0.257* 0.012 0.001 0.001

(0.013) (0.037) (0.138) (0.008) (0.003) (0.003) Primary school 0.181*** 0.154 0.166 0.018 0.027*** 0.027***

(0.060) (0.103) (0.185) (0.030) (0.007) (0.007) High school 0.414*** 1.003*** 1.079*** 0.065** 0.060*** 0.060***

(0.058) (0.100) (0.200) (0.029) (0.007) (0.007) Post-high school 0.483*** 1.939*** 4.424*** 0.109*** 0.097*** 0.097***

(0.058) (0.111) (0.382) (0.029) (0.008) (0.008) 30 – 44 years -0.044*** -0.028 0.015 -0.010 -0.003 -0.003

(0.015) (0.046) (0.167) (0.010) (0.003) (0.003) 45 – 59 years 0.022 0.318*** 0.630*** 0.014 0.001 0.001

(0.019) (0.055) (0.207) (0.011) (0.004) (0.004) 60+ years 0.059** 0.687*** 1.189*** 0.023 0.025*** 0.025***

(0.025) (0.069) (0.266) (0.015) (0.005) (0.005) R1000 – 5999 0.091*** 0.338*** -0.041 0.046*** 0.034*** 0.034***

(0.015) (0.043) (0.138) (0.010) (0.003) (0.003) R6000 – 9999 0.114*** 1.313*** 4.622*** 0.079*** 0.076*** 0.073***

(0.015) (0.063) (0.462) (0.010) (0.005) (0.005) R10000 – 24999 0.104*** 1.349*** 9.873*** 0.062*** 0.085*** 0.085***

(0.016) (0.069) (0.682) (0.013) (0.006) (0.006) Eastern Cape -0.193*** -0.944*** -0.883*** -0.238*** -0.050*** -0.050***

(0.025) (0.069) (0.254) (0.018) (0.005) (0.005) Free State 0.051** -0.680*** -1.673*** 0.013 0.022*** 0.022***

(0.020) (0.082) (0.249) (0.013) (0.005) (0.005) Kwa-Zulu Natal -0.031* -0.211*** 0.984*** -0.015 0.006 0.006

(0.018) (0.063) (0.292) (0.011) (0.004) (0.004) Limpopo -0.033 -0.556*** -0.246 0.063*** -0.005 -0.005

(0.036) (0.080) (0.308) (0.017) (0.006) (0.006) Mpumalanga 0.019 -0.271*** -0.350 0.003 -0.015*** -0.015***

(0.025) (0.079) (0.310) (0.014) (0.006) (0.005) Northern Cape 0.077*** -0.535*** -0.773*** 0.019 0.022*** 0.022***

(0.022) (0.085) (0.291) (0.013) (0.006) (0.006) North West 0.112*** -0.396*** -0.665** 0.016 -0.011** -0.011**

(0.023) (0.079) (0.270) (0.014) (0.005) (0.005) Western Cape 0.044*** -0.002 0.579** -0.001 0.016*** 0.016***

(0.016) (0.071) (0.291) (0.011) (0.005) (0.005) Constant -0.470*** -0.573*** 2.851*** 1.881*** 4.891*** 5.005***

(0.066) (0.125) (0.340) (0.034) (0.009) (0.009) Observations 11,326 11,326 11,326 11,326 11,326 11,326 R-squared 0.149 0.293 0.181 0.078 0.144 0.144

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. W = wealth index, WB = wellbeing index. The table shows RIF estimates of financial inclusion on welfare outcomes. The missing categories are the base categories for each covariate.

32

Appendix A4a: RIF estimates of the effect of financial inclusion on welfare quantiles of social grant recipients

(1) (2) (3) (4) (5) (6)

Variables Wq10 Wq50 Wq90 WBq10 WBq50 WBq90

Formal use 0.144*** 0.444*** 1.325*** 0.014* 0.055*** 0.019***

(0.020) (0.046) (0.196) (0.009) (0.015) (0.007)

Males -0.023 -0.152*** -0.156 0.005 0.016 0.013

(0.022) (0.049) (0.286) (0.007) (0.016) (0.008)

Primary school 0.107** 0.075 0.642** -0.005 0.095*** 0.042***

(0.045) (0.076) (0.283) (0.013) (0.026) (0.011)

High school 0.202*** 0.434*** 2.574*** 0.005 0.162*** 0.076***

(0.042) (0.077) (0.328) (0.014) (0.026) (0.012)

Post-high school 0.230*** 0.866*** 5.474*** 0.007 0.264*** 0.122***

(0.044) (0.135) (1.001) (0.023) (0.046) (0.023)

35 – 44 years -0.037 -0.088 0.260 0.011 -0.032 -0.001

(0.023) (0.061) (0.233) (0.012) (0.020) (0.009)

45 – 59 years 0.006 0.266*** 1.843*** 0.018 0.018 0.023**

(0.029) (0.072) (0.352) (0.014) (0.024) (0.011)

60+ years 0.002 0.520*** 4.025*** 0.031** 0.129*** 0.073***

(0.025) (0.064) (0.390) (0.012) (0.022) (0.010)

R1000 - 5999 0.019 0.109** 0.091 0.006 0.053*** 0.015**

(0.017) (0.043) (0.247) (0.007) (0.014) (0.007)

R6000 - 9999 -0.018 0.476** 6.077*** 0.013 0.179*** 0.076**

(0.056) (0.189) (2.002) (0.009) (0.054) (0.037)

R10000 - 24999 -0.035 0.346 14.08*** 0.018 0.204* 0.149***

(0.052) (0.488) (2.764) (0.021) (0.113) (0.054)

Urban area 0.228*** 0.922*** 1.586*** 0.015 0.052*** 0.028***

(0.023) (0.052) (0.218) (0.009) (0.017) (0.007)

Eastern Cape -0.136*** -0.617*** -0.800* -0.021** -0.185*** -0.071***

(0.027) (0.077) (0.410) (0.010) (0.026) (0.012)

Free State 0.053** -0.151* -0.695 -0.003 0.060** 0.054***

(0.021) (0.090) (0.455) (0.011) (0.029) (0.014)

Kwa-Zulu Natal -0.035 -0.272*** 1.371*** -0.033** -0.019 0.007

(0.025) (0.077) (0.492) (0.013) (0.026) (0.013)

Limpopo 0.101*** -0.309*** 0.453 -0.004 0.022 -0.011

(0.039) (0.094) (0.451) (0.015) (0.033) (0.015)

Mpumalanga 0.016 -0.143 0.455 -0.012 0.012 -0.005

(0.034) (0.103) (0.540) (0.014) (0.033) (0.016)

Northern Cape 0.057** -0.239** -0.820* -0.002 0.048* 0.035**

(0.027) (0.094) (0.441) (0.010) (0.029) (0.014)

North West 0.035 -0.288*** -0.100 0.002 -0.056* -0.023*

(0.030) (0.090) (0.454) (0.011) (0.030) (0.014)

Western Cape 0.035** 0.049 2.325*** -0.004 0.031 0.038***

(0.017) (0.083) (0.543) (0.009) (0.027) (0.013)

Constant -0.167*** -0.016 -1.688*** 1.094*** 3.826*** 4.985***

(0.052) (0.107) (0.527) (0.018) (0.037) (0.017)

Observations 3,390 3,390 3,390 3,390 3,390 3,390

R-squared 0.131 0.289 0.162 0.012 0.106 0.116

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. W = wealth index, WB = wellbeing index. The table shows RIF estimates of ‘strict’ financial inclusion on the welfare outcomes of social grant recipients. The missing categories are the base categories for each covariate.

33

Appendix A4b: RIF estimates of the effect of formal/semiformal financial inclusion on the welfare of social grant recipients

(4) (5) (6) (1) (2) (3) Variables Wq10 W50 W90 WBq10 WBq50 WBq90

USE2 0.138*** 0.397*** 1.351*** -0.000 0.037 0.014

(0.032) (0.068) (0.311) (0.012) (0.024) (0.011) Urban area 0.232*** 0.964*** 2.133*** 0.016 0.068*** 0.034***

(0.024) (0.055) (0.273) (0.010) (0.018) (0.008) Males -0.009 -0.004 0.550 0.007 0.028 0.017**

(0.022) (0.052) (0.352) (0.008) (0.018) (0.008) Primary school 0.111** 0.087 0.688** 0.001 0.088*** 0.043***

(0.049) (0.084) (0.341) (0.016) (0.030) (0.013) High-school 0.215*** 0.370*** 2.183*** 0.012 0.135*** 0.063***

(0.045) (0.083) (0.359) (0.016) (0.029) (0.012) Post-high school 0.252*** 0.841*** 5.207*** 0.013 0.238*** 0.111***

(0.046) (0.135) (1.205) (0.025) (0.049) (0.024) Divorced -0.009 0.115 1.579*** 0.008 0.039 0.031**

(0.033) (0.074) (0.489) (0.013) (0.027) (0.012) Widowed 0.015 0.266*** 2.522*** 0.020* 0.061*** 0.023**

(0.026) (0.064) (0.444) (0.010) (0.022) (0.010) Married 0.028 0.198*** 1.756*** 0.015* 0.088*** 0.042***

(0.019) (0.050) (0.303) (0.009) (0.017) (0.008) R1000 - 5999 0.016 0.196*** 1.327*** 0.012 0.100*** 0.039***

(0.017) (0.046) (0.302) (0.008) (0.016) (0.007) R6000 - 9999 -0.010 0.671*** 10.04*** 0.021*** 0.245*** 0.104***

(0.057) (0.160) (2.370) (0.008) (0.044) (0.036) R10000 - 24999 -0.022 0.486** 17.30*** 0.026* 0.237*** 0.166***

(0.035) (0.241) (1.266) (0.015) (0.037) (0.023) Eastern Cape -0.121*** -0.415*** -1.086** -0.003 -0.181*** -0.057***

(0.041) (0.098) (0.534) (0.017) (0.033) (0.015) Free State 0.039 0.026 -1.202* 0.010 0.060* 0.063***

(0.037) (0.116) (0.632) (0.017) (0.037) (0.018) Gauteng -0.012 0.128 -0.249 0.015 0.012 0.018

(0.035) (0.107) (0.666) (0.016) (0.035) (0.017) Kwa-Zulu Natal -0.048 -0.120 1.515** -0.017 0.001 0.023

(0.041) (0.100) (0.638) (0.019) (0.034) (0.016) Limpopo 0.113** -0.182* 0.001 0.006 0.040 0.010

(0.047) (0.110) (0.569) (0.021) (0.038) (0.017) Northern Cape 0.036 -0.066 -1.300** 0.013 0.048 0.047***

(0.041) (0.119) (0.607) (0.017) (0.037) (0.018) North West 0.057 -0.067 -0.200 0.021 -0.035 -0.007

(0.042) (0.111) (0.598) (0.017) (0.036) (0.017) Western Cape 0.024 0.135 1.577** 0.015 0.011 0.036**

(0.035) (0.112) (0.734) (0.016) (0.035) (0.017) Constant -0.225*** -0.153 -1.349** 1.084*** 3.822*** 4.987***

(0.065) (0.125) (0.635) (0.025) (0.044) (0.020) Observations 2,958 2,958 2,958 2,958 2,958 2,958 R-squared 0.122 0.266 0.139 0.010 0.100 0.106

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. W = wealth index, WB = wellbeing index. The table shows RIF estimates of ‘weaker’ financial inclusion on the welfare outcomes of social grant recipients. The missing categories are the base categories for each covariate.

34

Appendix A5a: RIF Estimates of credit use on welfare outcomes

(1) (2) (3) (6) (7) (8) Variables Wcrq10 Wcrq50 Wcrq90 WBcrq10 WBcrq50 WBcrq90

USEcr1 0.108*** 1.204*** 2.716*** 0.036*** 0.035*** 0.035***

(0.016) (0.081) (0.372) (0.010) (0.004) (0.004) Urban area 0.554*** 1.651*** 1.551*** 0.103*** 0.038*** 0.038***

(0.037) (0.081) (0.224) (0.016) (0.004) (0.004) Males -0.079*** -0.157** -0.139 0.001 -0.005 -0.004

(0.022) (0.063) (0.230) (0.010) (0.003) (0.003) Primary school 0.211** 0.189 -0.0119 0.004 0.017* 0.017*

(0.102) (0.159) (0.314) (0.041) (0.009) (0.009) High school 0.601*** 1.400*** 0.539 0.055 0.048*** 0.048***

(0.097) (0.156) (0.336) (0.039) (0.009) (0.009) Post-high school 0.694*** 2.440*** 4.320*** 0.108*** 0.085*** 0.085***

(0.097) (0.178) (0.598) (0.040) (0.010) (0.010) 35 – 44 years -0.049* -0.009 -0.183 -0.003 -0.000 -0.000

(0.027) (0.080) (0.280) (0.013) (0.004) (0.004) 45 – 59 years 0.051 0.350*** 0.195 0.015 -0.001 -0.001

(0.031) (0.093) (0.336) (0.015) (0.005) (0.005) 60+ years 0.076* 0.902*** 1.356*** 0.026 0.020*** 0.020***

(0.046) (0.122) (0.451) (0.022) (0.007) (0.007) R1000 – 5999 0.151*** 0.248*** -0.736*** 0.055*** 0.035*** 0.035***

(0.028) (0.075) (0.229) (0.013) (0.004) (0.004) R6000 – 9999 0.178*** 1.300*** 2.615*** 0.094*** 0.072*** 0.072***

(0.027) (0.112) (0.629) (0.014) (0.006) (0.006) R10000 – 24999 0.145*** 1.415*** 8.614*** 0.064*** 0.074*** 0.074***

(0.028) (0.115) (0.935) (0.016) (0.007) (0.007) Eastern Cape -0.312*** -0.995*** -0.963** -0.219*** -0.060*** -0.060***

(0.043) (0.115) (0.392) (0.024) (0.006) (0.006) Free State 0.051 -0.739*** -1.538*** 0.027 0.005 0.005

(0.035) (0.138) (0.388) (0.018) (0.007) (0.007) Kwa-Zulu Natal -0.093*** -0.042 0.913** 0.008 0.002 0.002

(0.029) (0.103) (0.441) (0.014) (0.005) (0.005) Limpopo 0.137** -0.472*** -0.354 0.028 -0.016** -0.016**

(0.060) (0.144) (0.541) (0.026) (0.008) (0.008) Mpumalanga 0.084** -0.120 0.171 -0.033 -0.015** -0.015

(0.039) (0.131) (0.528) (0.021) (0.007) (0.007) Northern Cape -0.095** -0.641*** -0.823* 0.0205 0.018** 0.018**

(0.046) (0.137) (0.461) (0.0191) (0.007) (0.007) North West 0.097** -0.350** -0.594 0.040** -0.009 -0.009

(0.042) (0.137) (0.447) (0.019) (0.007) (0.007) Western Cape 0.007 -0.110 0.600 0.002 0.008 0.008

(0.028) (0.118) (0.437) (0.015) (0.006) (0.006) Constant -0.673*** -0.991*** 4.459*** 1.865*** 4.901*** 5.015***

(0.103) (0.189) (0.516) (0.044) (0.010) (0.010) Observations 6,629 6,629 6,629 6,629 6,629 6,629 R-squared 0.169 0.318 0.179 0.076 0.176 0.176

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. W = wealth index, WB = wellbeing index. The table shows RIF estimates of formal credit use on welfare outcomes. The missing categories are the base categories for each covariate.

35

Appendix 5b: RIF estimates of credit inclusion on welfare outcomes

(2) (3) (4) (7) (8) (9) Variables Wltcrq10 Wltcrq50 Wltcrq90 WBcrq10 WBcrq50 WBcrq90

USEcr2 0.131*** 1.256*** 1.632*** 0.021 0.048*** 0.048***

(0.0391) (0.093) (0.171) (0.018) (0.005) (0.005) Urban area 0.526*** 1.471*** 1.622*** 0.091*** 0.031*** 0.031***

(0.038) (0.083) (0.230) (0.017) (0.004) (0.004) Males -0.071*** -0.064 0.055 0.003 -0.001 -0.001

(0.022) (0.063) (0.232) (0.010) (0.003) (0.003) Primary school 0.215** 0.241 0.099 0.004 0.019** 0.019**

(0.102) (0.157) (0.311) (0.041) (0.009) (0.009) High school 0.607*** 1.491*** 0.809** 0.067 0.051*** 0.051***

(0.096) (0.153) (0.334) (0.039) (0.009) (0.009) Post-high school 0.706*** 2.625*** 4.943*** 0.113*** 0.089*** 0.089***

(0.096) (0.174) (0.598) (0.039) (0.010) (0.010) 35 – 44 years -0.042 0.062 -0.032 -7.27e-05 0.002 0.002

(0.027) (0.080) (0.281) (0.013) (0.004) (0.004) 45 – 59 years 0.057* 0.421*** 0.377 0.018 0.001 0.001

(0.031) (0.092) (0.340) (0.015) (0.005) (0.005) 60+ years 0.072 0.852*** 1.247*** 0.027 0.018*** 0.018***

(0.046) (0.122) (0.451) (0.022) (0.007) (0.007) R1000 – 5999 0.157*** 0.320*** -0.505** 0.059*** 0.036*** 0.036***

(0.027) (0.074) (0.223) (0.013) (0.004) (0.004) R6000 – 9999 0.202*** 1.602*** 3.469*** 0.105*** 0.079*** 0.079***

(0.026) (0.107) (0.619) (0.014) (0.006) (0.006) R10000 – 24999 0.180*** 1.840*** 9.734*** 0.080*** 0.085*** 0.085***

(0.027) (0.107) (0.921) (0.016) (0.007) (0.009) Eastern Cape -0.315*** -1.038*** -1.074*** -0.219*** -0.062*** -0.062***

(0.042) (0.114) (0.395) (0.024) (0.006) (0.006) Free State 0.039 -0.874*** -1.810*** 0.023 0.001 0.001

(0.035) (0.137) (0.393) (0.018) (0.007) (0.007) Kwa-Zulu Natal -0.085*** 0.018 0.952** 0.012 0.004 0.004

(0.029) (0.103) (0.445) (0.014) (0.005) (0.005) Limpopo 0.128** -0.549*** -0.453 0.026 -0.019** -0.019**

(0.059) (0.141) (0.545) (0.026) (0.008) (0.008) Mpumalanga 0.079** -0.181 0.054 -0.034 -0.017** -0.017**

(0.039) (0.131) (0.528) (0.021) (0.007) (0.007) Northern Cape -0.096** -0.653*** -0.843* 0.020 0.017** 0.017**

(0.046) (0.138) (0.461) (0.019) (0.007) (0.007) North West 0.103** -0.298** -0.569 0.041** -0.006 -0.006

(0.042) (0.135) (0.450) (0.019) (0.007) (0.007) Western Cape 0.007 -0.091 0.695 0.001 0.008 0.008

(0.028) (0.117) (0.440) (0.015) (0.006) (0.006) Constant -0.688*** -1.383*** 3.149*** 1.888*** 4.885*** 4.998***

(0.110) (0.206) (0.552) (0.045) (0.011) (0.011) Observations 6,629 6,629 6,629 6,629 6,629 6,629 R-squared 0.171 0.324 0.171 0.077 0.184 0.184

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. W = wealth index, WB = wellbeing index. The table shows RIF estimates of ‘formal and/or semi-formal’ credit on the welfare outcomes. The missing categories are the base categories for each covariate.