Empirical results

The independent variables, along with household savings as a dependent variable, are regressed and their results are presented in a table. Having various regression models and presenting the results in a table is an analysis reporting method used by several studies (Esson, 2003:13; Neves et al., 2009:48-49; Paxton, 2009:225;

Seguino & Floro, 2003:159-160). Four regression models – tobit, normal OLS, robust OLS and probit – are ran concurrently in this study in order that each would mitigate the weaknesses of the others, which were discussed in the previous chapter. The dummy variables in the four models are gender and pooled income. The reference categories for the dummies are male and households that do not pool income, respectively. The results of the regression models are summarised in Table 5.4 below.

Table 5.4: Tobit, Normal OLS, Robust OLS and Probit regression models

Tobit Normal OLS Robust OLS Probit

82

Probability 0.0077*** 0.0159** 0.0230** 0.0151**

VIF 1.02 1.26 - 1.18

Note: The dependent variable (constant) in each model is savings. The absolute vales of t-statistics and z-statistics are in parentheses. The t-statistics are for the tobit, normal OLS and robust OLS models.

The z-statistics are for the probit model. All the regression models include gender and pool income as dummies. *, **, *** denote significance at 10 per cent, 5 per cent and 1 per cent levels, respectively.

The null hypothesis for all models is that an independent variable has a significant impact on household savings. The alternative hypothesis is that an independent variable does not significantly impact household savings.

Three of the four models, normal OLS, Robust OLS, and probit are statistically significant at 5% significance level, with probability values (p-values) of 0.03, 0.02 and 0.02 respectively. The tobit model is statistically significant at 1% significance level with a probability value of 0.0077. Models that are statistically significant imply that a combination of all the independent variables used in the respective regression models do have an impact on household savings. The results of this study imply that social grant income, other income, income pooling, expenditure, gender of a household financial handler, age of a household financial handler, household size collectively have an influence on household savings as argued by Chakrabarty and Hildenbrand

83 (2009:649), Erfe (2007:22), Gough (2011:30), Keynes (1936:20), Paxton (2009:211) and Sarantis and Stewart (2001:24). However, on an individual basis, some of the independent variables within the models are statistically insignificant.

Statistically insignificant variables found across all four models are necessity expenditure, age squared and household size. The implication of the statistically insignificant variables is that the variables do not have an influence on the savings of the surveyed household. This means that this study rejects the null hypothesis at 1%, 5% and 10% significance levels that the independent variables individually have an impact on household savings of the sample. There is only one independent variable that is statistically significant in all four models, namely other income. It is significant at 1% significance level in tobit, normal OLS and robust OLS, and 10% significance level in probit model. Notwithstanding, there are independent variables that were significant in at least one of the four models, and these are social grants, pool income, normal expenditure, luxury expenditure and age.

Social grants, as one of the key variables of this study, is statistically significant at 5%

level in the tobit and normal OLS regression models, and significant at 1% level in the robust OLS regression model. Pooled income is significant in the probit regression model only at 5% significance level. Normal expenditure is statistically significant in the tobit regression model and statistically significant at 10% level. Luxury expenditure is statistically significant at 5% significance level in the probit regression model. Age is statistically significant at 10% level in the robust OLS model. The statistically significant independent variables imply that the variable do indeed have an influence on household savings.

Two independent variables are weakly statistically significant, that is, the variables were almost statistically significant at 10% level. The independent variables were almost significant because their t-statistics are close to the benchmark of 1.65. They are normal expenditure in the normal OLS model, and age squared in the robust OLS model. Similarly, Esson’s (2003:13) empirical evidence shows that some of the independent variables of savings were statistically insignificant. So also are the empirical results of Paxton’s (2009:210) study of the savings behaviour of poor households in Mexico, which revealed that within the study’s model, some of the

84 independent variables were statistically insignificant although the overall model was significant. These findings are similar to those of this study.

The R-squared or chi-squared presented in Table 5.4 are used as an indicator of how much the normal OLS, probit and tobit models explain the variation in household savings. The tobit model has the highest chi-squared of 23.97, interpreted as 23.97 per cent of the variation in household savings of the truncated sample, which is explained by the tobit regression model. The normal OLS model indicate that 12 per cent of the variation in household savings is explained by the model. The probit regression model explains 20.50 per cent of the variation in household savings. The results are in line with the claims of Kochhar and Cohn (2011:10), Mok and Sullivan (2009:43), and Sharma (2010:565) that these independent variables have an influence on household savings, although some of the independent variables were statistically insignificant.

This study argues that the best regression model despite having several statistically insignificant variables is the tobit regression model, followed by the probit regression model and lastly, the normal OLS regression model. The rating of the tobit regression model is based on the fact that, as the dependent variable is censored, the tobit regression provides consistent estimates that approach the ‘true’ population parameters as the surveyed households (sample size). However, it is worth mentioning that the probit regression model has more significant and weakly significant variables than the tobit regression model. Additionally, the probit model takes into account the fact that the truncated sample has two groups, households that save and households that do not save.

It is important to ask whether the expected signs of the independent variables presented in Table 5.3 have been met. The actual signs of the variables in the models presented in Table 5.4 would indicate how the independent variables impact household savings. Independent variables that meet the expected sign in all models are social grant, other income, female and age, thus implying that they positively influence household savings. Sarantis and Stewart (2001:24) claim that, usually, the age of a household financial handler, as an independent variable, influences household savings positively. Paxton (2009:210-211) revealed that poor households

85 that have female financial handlers have positive savings. Similarly, all the four models in this study indicate that female household financial handlers contribute positively to household savings.

The actual signs of some of the independent variables met the expected signs in at least one model and had contradictory signs in other models. For instance, necessity expenditure has a negative sign in tobit, normal OLS and robust OLS. The implication is that households tend to consume necessity goods and services irrespective of their prices and household incomes. Necessity expenditure has an inverse relationship with household savings, hence the negative sign in three of the four models. Luxury expenditure also has a negative sign in three of the four models, indicating that households that indulge in luxury goods and services do not save. Normal expenditure has a positive sign in the tobit and normal OLS regression models and a negative sign in the robust OLS and probit regression models. The signs of the normal expenditure are not necessarily surprising as normal expenditure tends to adhere to the demand theory. Therefore, the fluctuating signs indicate the flexibility of households in terms of consuming normal goods and services.

An observation made is that of household size with a negative expected sign but in all four models the sign is positive, thus implying that an increase in household size would lead to higher household savings. For instance, according to the probit regression model, a one point increase in the household size brings about 0.02 point higher likelihood of a household being a saver. The negative expected sign is due to the expectation that a large household has high consumption levels, which leads to less saving or dissaving (Paxton, 2009:211). Consumption in this study has been controlled by sub-classifying it into three, necessity, normal, and luxury consumption. As a result, household size in the regression models tend to have a contradictory sign, implying that an increase in household size would lead to an increase in household savings.

The contradictory sign across all four models is not surprising given the fact that consumption has been controlled in the models.

Another possible explanation for the positive sign of household size in all four models is that a large household is highly likely to receive multiple social grants and possibly have other income sources resulting in a positive correlation between household size

86 and household saving. However, this argument is weaker than the previous argument of controlled consumption. It is however not advisable for poor households to be large as a means of increasing their probability of receiving social grants as doing so could increase dependency on government social grants and safety nets.

Another observation regarding the contradictory signs between the expected signs and signs found in the regressions is that of age squared. Although age squared had a positive expected sign, it is negative while age is positive in the four models. This can be explained by the fact that household financial handlers save less as they get older as the life-cycle hypothesis holds (Di Falco & Bulte, 2011:1128; Sarantis &

Stewart, 2001:24).

It is indeed, quite common for independent variables to have contradictory signs. For instance, studies into the savings patterns of poor households in Mexico and in different parts of South Africa (Neves et al., 2009:48-49; Esson, 2003:13; Paxton, 2009:211) had several independent variables with unexpected coefficient signs.

Despite the contradictory signs of some of the independent variables’ t-statistics and z-statistics, it is important to note that all the signs of the statistically significant independent variables (i.e. social grants, other income, pooled income and luxury expenditure) matched the expected signs. Such an outcome enabled the researcher to find the utilised models and significant independent variables reliable.

These findings have important implications that are discussed here. Using the tobit regression model, a 1 unit change in social grants, measured in South African rands (ZAR), brings about an 0.05 times increase in household savings. In the robust OLS regression model, a 1 unit change in social grants brings about 0.05 times increase in household savings. The implication of both interpretations is that for every rand received from social grants, households are likely to save 0.05 cents. It is therefore recommended that the government continues offering social grants to poor households in an informal settlement as the households are likely to save some of this income.

In the tobit regression model, a 1 unit change in other income, measured in ZAR, brings about an 0.06 times increase in household savings. With the probit regression

87 model, a 1 unit change in other income brings neither an increase nor decrease in the likelihood of a household saving. However, using the robust OLS, a 1 unit change in other income brings about 0.03 times increase in household savings. It can therefore be recommended that informal settlement households with social grant recipients should be involved in economic activities that generate additional income besides the social grants as additional income increases household savings.

In the probit regression model, a 1 unit increase in pooled income, measured in ZAR, brings about 0.61 point higher likelihood of a household being a saver. The policy recommendation is that households should be encouraged to pool their household income as that leads to household savings.

The tobit regression model indicates that a 1 unit change in normal expenditure, measured in ZAR, brings about an 0.08 times increase in household savings. For its part, the probit regression model indicates that a 1 unit increase in luxury expenditure, measured in ZAR, brings about 0.002 point less likelihood of a household being a saver. The government could set measures that restrict spending on luxury goods and services by households with social grant recipients as such expenditure tends to hinder saving.

The robust OLS regression model shows that a 1 unit change in age, measured in years, brings about a 16.50 times increase in household savings. This confirms that savings do increase with age as posited in the life cycle hypothesis (Sarantis &

Stewart, 2001:24). However, the robust OLS regression model indicates that a 1 unit change in age squared, measured in years, brings about 0.15 times decrease in household savings. These show that the life-cycle hypothesis is correct in stating that household savings decrease as household financial handlers reach their retirement age. Households with social grants recipients in an informal settlement could be warned about this trend and advised to take precautionary measures when a household financial handler approaches retirement.

88 5.2.2.3. Multicollinearity and heteroscedasticity

Arguably, all four regression models used in this study are reliable as they have tested negative for multicollinearity using the Variation Inflation Factors (VIF) that measures

‘the inflation of the variance of a slope estimate caused by the non-orthogonality of the predictors over and above what the variance would be with orthogonality’ (Liao &

Valliant, 2012:53). In general, reliable models either have tolerable or no multicollinearity (Grewal, Cote & Baumgartner, 2004:526). Tolerable multicollinearity is indicated a VIF value that is higher than one but is equal to or less than 10 (O’Brien, 2007:674). The VIF formula is:

VIF = _1−𝑅¹ ₂ (5.1)

Where 𝑅² is the goodness of fit of the model, goodness of fit that is measured by R-squared or chi-R-squared. All regression models used in this study do not have multicollinearity. The tobit, normal OLS and probit regression models have VIF values of 1.02, 1.26 and 1.18 respectively. In the probit regression model, gender was omitted as a precautionary measure because of its multicollinearity.

In addition to the multicollinearity test, the models tested negative for heteroscedasticity using the White test and thus proved reliable. Chi-squares greater than the alpha of p-values indicate that the null hypothesis of homoscedasticity is not rejected.

5.3. Summary and conclusions

The aim of this chapter was to analyse the results of the data collected from households with social grant recipients in Freedom Park, Soweto. The analysis included descriptive and econometric regression analyses, with the former including economic structure, saving instruments and motives analysis. The socio-economic structure analysis comprised both demographic and socio-economic analyses.

Demographic factors were the age, gender and ethnicity of the households’ financial handlers, as well as their household size. The age of the household financial handlers surveyed in this study ranged between 16 and 89 years. A dominant age group within

89 this study was the adult group between 36 and 59 years. Majority of the surveyed households’ financial handlers were African females. In terms of size, the surveyed households had a minimum of one and a maximum of 14 members. While the household that had a maximum of 14 household members saves, the one with a maximum of 12 members does not.

The economic analysis in this chapter focused on social grant income received, additional income earned from labour market and entrepreneurial activities, remittances received and savings. Out of the seven common social grants offered by the South African government, the surveyed households had members that received four social grants –the child support grant, old age grant, foster child grant and disability grant. The number of households that received additional income within the study were 139 (64.65 per cent) while 42 (19.53 per cent) received remittances from friends and family. Out of the 139 households that received additional income, 32 (14.88 per cent) indicated that their additional income fluctuates and often are unpredictable.

The income of the surveyed households is mainly spent on necessity goods and services. However, a certain portion is also spent on normal and luxury goods and services. In this study, saving instruments used by households that save were burial societies, stokvels, bank accounts, a post office bank account, an investment account and cash hoarding. The saving motives for these households were precautionary, transactional, liquidity, housing, education and bequest.

Data was analysed using four regression models, namely tobit, normal OLS, robust OLS, and probit. Household savings was a dependent variable in all four regression models, which were based on the independent variables identified in the literature.

These were expected to have either a positive or negative impact on the savings of poor households. Pool income, necessity expenditure, normal expenditure and luxury expenditure were independent variables, which had contradictory signs, as opposed to the expected signs. Among the four regression models, independent variables that were statistically significant in at least one regression model were social grant, other income, pooled income and luxury goods expenditure.

90 Social grants as a key variable of this study was statistically significant in the tobit, normal OLS and robust OLS models. This confirms that social grants do indeed have a positive impact on household savings. The other independent variables, necessity expenditure, normal expenditure, gender, household size and age, were statistically insignificant in all four regression models.

However, although some of the independent variables were statistically insignificant, the overall four regression models were statistically significant, confirming that the independent variables do indeed have an impact on savings. The tobit regression model was statistically significant at 1% significance level and normal OLS, robust OLS and probit regression models were statically significant at 5 % significance level.

Furthermore, the models were tested for multicollinearity and heteroscedasticity to ensure the reliability of the models.

The next chapter summarises, concludes and offer policy recommendations.

91 CHAPTER 6

SUMMARY, CONCLUSIONS AND POLICY RECOMMENDATIONS 6.1. Introduction

The aim of the study was to analyse the savings behaviour of households with social grant recipients in Freedom Park, Soweto and its objectives are set out in details in chapter one. Chapters two and three presents detailed surveys of the related literature on the savings behaviours of poor households while chapter four describes the research methods adopted in the study. In chapter five, the results of the collected data are presented and analysed in detail. This chapter offers summaries of the main findings of this study in the light of the research question and its sub-questions provided in chapter one. Finally, this chapter discusses the implications and caveats suggested by the findings of this study and offers recommendations for further research.