Measuring human capital in South Africa using a socioeconomic status human capital index approach

ABSTRACT

The Human Capital Index (HCI) developed by the [World Bank, 2018a. The human capital project. World Bank. https://hdl.handle.net/10986/30498 Accessed 26 February 2019] provides a measure which can be used to study human capital (HC) productivity gaps between countries. The HCI uses measures of survival, education and health to estimate, at a country level, the HC ‘a child born today can expect to attain by her/his 18th birthday, given the risks of poor health and poor education where she lives’ [World Bank, 2018a. The human capital project. World Bank. https://hdl.handle.net/10986/30498 Accessed 26 February 2019, 2]. The socioeconomic disaggregated human capital index (SES-HCI), an extension of the HCI, provides a means for analysing HC inequalities within countries. This study estimates SES-HCIs for South Africa by income quintiles, school quintiles, geographical area, gender and race. The main driver of HC inequalities in all the SES indicators is found to be the quality of schooling. Factors to address the inequalities and the limitations of the measuring instruments are identified.

KEYWORDS:

• Human capital
• inequality
• human capital index

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 It is the risks faced based on an assumption that ‘they experience currently-prevailing risks of poor health and poor education faced by children aged 0–17’ (D’Souza et al., 2019)

2 The National Income Dynamics Study (NIDS) calculates household income as the sum of labour market income (net of taxes), government grants, other government income, investment income, remittances received, subsistence agriculture income and imputed rent for owner-occupied housing (Chinhema et al., 2016). For the purpose of calculating inequality, the household income is divided by the number of household residents to obtain a household per capita income, which is assigned to each household member (Leibbrandt et al., 2012).

3 HC was not included in the measure of wealth (Orthofer, 2017)

4 Wealth (also known as ‘net worth’) is calculated as the difference between the market value of all assets and liabilities (Orthofer, 2017) and in NIDS household wealth is calculated by summing net financial wealth, net business equity, net real estate equity, value of vehicles, total value of pension/retirement annuities and livestock wealth (Chinhema et al., 2016).

5 The total numbers of individuals successfully interviewed in Wave 1 and Wave 5 were 26776 and 37 368 respectively (Brophy et al., 2018).

6 The Demographic Household Survey (DHS) program has fielded over 400 surveys across 90 countries, collecting household demographic data including birth histories. In South Africa, three surveys have been conducted: in 1998, 2003 and 2016. The SADHS reviewed 11 083 households (NDoH et al., 2019).

7 In the South African context, a standard 8% earnings return to education may underestimate the returns, with Depken et al. (2019) highlighting approximately 18% as an accurate estimate of returns per year of education attained in South Africa. The estimation approach included by Depken et al. (2019) included ordinary least squares (OLS) and instrumental variables (IV). Furthermore, in comparison to primary school attainment, van Broekhuizen (2011) estimated high and increasing rates of return to higher education, noting that secondary schooling, matric, bachelor’s degree and post-graduate degree attainment had 13%, 35%, 115% and 180% average earnings returns relative to primary schooling respectively.

8 Trends in International Mathematics and Science Study (TIMSS) is an IEA assessment which was administered to Grade 9s in South Africa in 1995, 1999, 2003, 2011 and 2015 (van der Berg and Gustafsson, 2019). To calculate harmonised test scores, TIMSS 2015 results available from Reddy et al., 2016, were used.

9 Progress in International Reading Literacy Study (PIRLS) is an IEA assessment which was administered to Grade 4s in South Africa in 2006, 2011 and 2016 (van der Berg and Gustafsson, 2019). To calculate the harmonised test scores, PIRLS Literacy 2016 results available from Howie et al. (2017) were used.

10 See Kraay (2018: 41–42) for details on the literature used to calculate the returns used for health, and Kraay (2018: 34–36) for education.

11 The 8% rate of return to schooling suggested by Kraay (2018) is deliberately chosen to be on the lower range, given a vast majority of the returns to education estimated do not control for health.

12 Age 18 is the expected age at which a child will complete grade 12 (Matric) in South Africa. Enrolment is measured until age 17 for the HCI but, given that enrolment at age 17 would result in attainment at age 18, age 18 is included when using the attainment approach.

13 The HCI definition would need to be adjusted from expected HC at age 18 to age 25.

14 School quintile (1 to 5) is the school quality variable used in the NIDS data. For each income quintile, gender, race and geographical area a harmonised test score was calculated using a score per school quintile calculated by averaging the values for the TIMSS Maths and Science scores and PIRLS scores to provide a score per school quintile as found in Reddy et al. (2016) and Howie et al. (2017). Thereafter the percentage of the school going population per disaggregated variable attending that particularly school quintile (available from the NIDS data) was calculated and a test score estimated. See the results in Table 1. The national figure obtained calculated from this method was 345 and the figure provided by the World Bank (2018b) for South Africa was 343. Thus the scores calculated are expected to provide a reliable estimate given the available data.

15 Kraay (2018) defined ASR as the fraction of 15-year olds surviving until age 60.

16 The NIDS dataset provides a derived household income variable which was divided by household size to calculate a household income per capita which was used to create 5 data samples, one for each quintile.

17 Traditional and farm options were combined to generate a rural variable.

18 The descriptive statistics for household income per capita quintile 5 are: Observations (5761), mean (13 512.55); Standard deviation (25 101.16), minimum value 4600 and maximum 868 507.80.

19 Van Broekhuizen (2011) used the wave 1 2008 NIDS dataset.