Panel estimates: Pooled ordered probit and Random Effect Ordered Probit

The impact of remittances on SWB are further analysed by using pooled ordered probit and REOP models – shown in Table 8. The results for the pooled ordered probit and REOP are very similar in terms of sign and statistical significance.

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More specifically, we found that most of the variable coefficients of remittances, age, age squared, population group, marital status (except for divorced separated individuals), BMI, religion, smoking, no exercise, education, province and urban informal areas that were statistically significant in in the pooled ordered probit were also significant in the REOP models, and they had the same signs. Thus, we will only report on the results based on the REOP model.

Columns 5 and 6 of Table 8 present the results from the REOP model. The results seem to support the results of the pooled ordered probit model. The variable of interest, remittances, still positively influences SWB at a 1% significance level. Likewise, the estimates of education dummies remain positive but increase in magnitude and become statistically significant at the 1% level of significance. Other variables such as gender (female), whether the respondent smokes, does any exercise as well as his or her age still enter with its predicted negative sign the highly statistically significant level.

Overall (as noted earlier), the vast majority of the estimated coefficients seem robust for the type of data used whether cross-sectional or panel data. Furthermore, the estimated coefficients seem robust for the type of estimation method employed i.e. pooled ordered probit for cross-sectional data and a pooled ordered probit and REOP model for panel data.

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Table 8: Results for cross-sectional and panel analyses

Cross-Section Panel

Ordered Probit Ordered Probit REOP

Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.

Remittances 0.1503*** (0.0379) 0.0766*** (0.0222) 0.0789*** (0.0226) Age -0.0091** (0.0047) -0.0159*** (0.0026) -0.0165*** (0.0027) Age squared 0.0001** (0.0000) 0.0002*** (0.0000) 0.0002*** (0.0000) Female -0.1037*** (0.0370) -0.0556*** (0.0208) -0.0558*** (0.0215) Population Group Coloured 0.5745*** (0.0647) 0.4511*** (0.0386) 0.4602*** (0.0402) Asian/Indian 1.0752*** (0.1329) 1.0822*** (0.0777) 1.1029*** (0.0812) White 0.6822*** (0.1097) 0.6496*** (0.0628) 0.6628*** (0.0655) Marital Status

Living with partner -0.0655 (0.0581) -0.1046*** (0.0326) -0.1033*** (0.0335)

Widow/Widower -0.0825 (0.0509) -0.0886*** (0.0306) -0.0888*** (0.0316) Divorced/Separated -0.0029 (0.0862) -0.0807 (0.0502) -0.0800 (0.0517) Never Married -0.0615* (0.0370) -0.1056*** (0.0224) -0.1076*** (0.0232) Height -0.0029 (0.0019) 0.0004 (0.0010) 0.0004 (0.0011) BMI 0.0737** (0.0309) 0.0523*** (0.0185) 0.0526*** (0.0190) Religion 0.3508*** (0.0484) 0.2804*** (0.0275) 0.2815*** (0.0280) Smoking -0.0966** (0.0403) -0.0741*** (0.0235) -0.0740*** (0.0242) Illness/Disability -0.1488 (0.0964) 0.0520 (0.0397) 0.0542 (0.0407) No exercise -0.0840** (0.0330) -0.0839*** (0.0191) -0.0838*** (0.0194) Education Primary School 0.0139 (0.0476) 0.0879*** (0.0276) 0.0894*** (0.0287) Secondary 0.0776 (0.0515) 0.1815*** (0.0298) 0.1820*** (0.0310) Matric 0.1213** (0.0604) 0.2250*** (0.0348) 0.2262*** (0.0362) Tertiary 0.3232*** (0.0615) 0.3918*** (0.0375) 0.3938*** (0.0389) Province Eastern Cape -0.6451*** (0.0751) -0.5232*** (0.0444) -0.5289*** (0.0463) Northern Cape -0.4160*** (0.0695) -0.2205*** (0.0408) -0.2238*** (0.0425) Free State -0.1802** (0.0849) -0.1298*** (0.0501) -0.1289*** (0.0522) KwaZulu-Natal -0.5152*** (0.0734) -0.4985*** (0.0436) -0.5043*** (0.0454) North West -0.4355*** (0.0829) -0.1374*** (0.0489) -0.1377*** (0.0509) Gauteng -0.2476*** (0.0758) -0.1801*** (0.0451) -0.1812*** (0.0470) Mpumalanga -0.3149*** (0.0815) -0.2751*** (0.0480) -0.2773*** (0.0500) Limpopo -0.3281*** (0.0805) -0.4250*** (0.0478) -0.4299*** (0.0498) Geo-type

Tribal Authority Areas _{0.0388 (0.0485)} -0.0014 (0.0282) 0.0004 (0.0293)

Urban Formal -0.0610 (0.0488) -0.0208 (0.0285) -0.0194 (0.0296)

Urban informal -0.2914*** (0.0674) -0.2170*** (0.0397) -0.2182*** (0.0413)

Note: Standard errors in parentheses

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4.4 Conclusion

This chapter was aimed at giving the reader an overview of the data that would ultimately be used in the model specification. This section also discussed the reason why data that was made available by a statistical agency such as Stats SA was not sufficient for the analysis. The biggest shortfall identified was that none of the surveys that were conducted by Stats SA covered a question on SWB, and that the questions on remittances were limited or absent. Due to this shortfall it was decided that data from NIDS would be more sufficient.

The next step in giving the reader an overview of the data was the section on descriptive stats. Most of the results shown in Table 5 are what would be expected. There are, however, some results that stand out. For one, the proportion of remittances received declined between the first two waves and then picked up again in 2012. This could be due to the financial crisis that occurred within this period. The other variables that were noticeable include BMI, exercise and education.

Thereafter remittances and SWB were studied in more detail. Remittances were also compared with the population groups to show which population group received the most remittances throughout all three waves. The results indicated that African respondents received the largest percentage of remittances between 2008 and 2012. Here, again there was a significant decline between 2008 and 2010 in the proportion of Black respondents that reported remittance receipts.

When the SWB variable was analysed the graphical depiction illustrated that the modal level of satisfaction was 5 which meant that the largest proportion of respondents reported a satisfaction level of 5. The comparison of SWB and remittances indicated that the level of satisfaction corresponded with the proportion of remittances received and most people reported satisfaction level of 5 and the modal level of remittances received was also 5.

The aim of this study was to investigate the impact of remittances on SWB in South Africa, and this was analysed in Section 4.3. A large part of the existing literature on the subject

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of SWB is based on cross-sectional data. However, our study is different from existing literature due to the panel nature of the NIDS data set and the use of remittances as the focus explanatory variable. In this study we made use of both methods (cross-sectional and panel). We regard the panel method as more reliable but employed a cross-sectional analysis for comparability to current literature.

The results from the cross-sectional analysis reveal that remittances positively influenced SWB at a significance of 1.0%. Moreover, all the personal characteristics and location variables (with the exception of tribal areas and urban formal areas, illness, height and primary and secondary levels of education) significantly affect SWB. The results also show that age (negative) and age squared (positive) are significant at 5.0% and that individuals who have never been married report a lower level of SWB than married individuals. The results also confirm the importance of education on SWB, and surprisingly, show that BMI positively and significantly influences SWB.

We then further analysed the impact of remittances on SWB by using pooled ordered probit and REOP models. In terms of sign and statistical significance, the results of these two models are very similar.

The results of the REOP seem to support the results of the pooled ordered probit model. Remittances still positively influence SWB at a 1.0% significance level. Furthermore, the estimates of education dummies remain positive but increase in magnitude and become statistically significant at the 1.0% level of significance. Lastly, other variables, which include gender (female), whether the respondent smokes, does any exercise as well as his or her age still enter with predicted negative signs and are statistically significant.

Our findings overall show that the majority of coefficients seem robust for the type of data used (i.e. cross-sectional or panel), while also seeming robust for the type of estimation method used (i.e. pooled ordered probit for cross-sectional data and a pooled ordered probit and REOP model for panel data).

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