Data

The data used for this minor dissertation is obtained from four waves of the National Income Dynamics Study (NIDS), covering the period of 2008-2015. The NIDS data set is the first national representative longitudinal survey that tracks individuals over time to explore any changes in their lifestyle and wage levels. The NIDS data is a national panel study that is conducted by the Southern Africa Labour and Development Research Unit (SALDRU) based at the University of Cape Town’s School of Economics. The aim of the NIDS is to understand and also investigate possible reasons why some individuals are making progress, while others are not (Yu, 2012).

The first wave of the NIDS data was conducted in 2008 with a sample of about 28,226 individuals and 7,296 households across South Africa (SALDRU, 2009). The individuals who were interviewed in 2008, are re-interviewed every two years. This implies that the survey continues to be repeated with the same individuals. According to SALDRU (2009) the project adopted a stratified two-stage sample design. The second wave was then conducted in 2010, successful interviews were obtained for 6,787 households, with a total of 28,551 individuals successfully completing interviews. In wave three 8,040 households with a total of 32,633 individuals were successfully interviewed. For wave four 11,895 households with a total of 37,396 individuals were successfully interviewed (SALDRU, 2014).

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The NIDS collects data on a wide range of indicators4 to be able to build a critical information base for evidence based policy-making. For the purpose of this minor dissertation data at individual level that provide information on labour market variables such as earnings/wages; education attainment; occupation; sector; age; gender; race and location, will be used.

The NIDS data set includes the distribution of net earnings in the various employment categories, such as regular wage workers, self-employment, casual workers and subsistence farming. This minor dissertation will then restrict the sample to the working population employed in the formal sector or regular wage workers aged 15-64 years. This is because the informal economy stands outside the industrial relations system in South Africa (The Department of Labour, 1996). The mean earnings amongst regular wage workers is roughly R4, 284 per month. Observations with missing data on any variable included in the wage functions were disregarded.

Based on the restriction applied, that the sample used in the analyses will be the working population employed in the formal sector between the ages of 15-64 years, this minor dissertation will briefly summarise the characteristics of the South African labour market for all formally employed workers. According to the second quarter report of the labour force survey of Statistics South Africa (2017) 16.1 million people are employed in the formal sector. Of the 16.1 million formally employed workers, 24% of these individuals are union members and 57% are not unionised, as presented in Appendix 1. This suggests that there are fewer workers who are members of a trade union in the formal sector. The table in Appendix 1 also indicates that the majority of employers decide for themselves what annual salary increments their employees should receive, then followed by union negotiations.

In terms of demographics more men (9 million) are employed, compared to their female counterparts (7 million). This is despite the fact that there are more women in South Africa than men (Statistic South Africa population survey, 2017). The largest race group that is employed is African workers. This is due to the fact that the majority of the population in South Africa is African people (Statistic South Africa population survey, 2017). The table in Appendix 1 also shows that middle-aged individuals are the largest group who are employed in the formal sector.

4 _{These indicators include, labour outcome, household composition, education, health, economic activities, welfare} participation, and household income and expenditure.

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Appendix 1 further indicates that labour markets in South Africa are characterised by a working population that is less educated, since only 3.3 million members of the working population have tertiary qualifications. These findings suggest that the labour market is characterised by a working class that is less skilled. The biggest sector that employed people in 2017 was the community and social services, followed by the trade sector. More individuals are employed in elementary occupations, illustrating again that the South African labour market is less skilled. The province that employs more people is Gauteng, followed by Kwa-Zulu Natal.

This brief summary gives a perspective of the South African labour market. The inclusion of this summary is important because it gives information on the underlying characteristics of the labour market.

4.2.1 Data preparation

This minor dissertation merged and appended the four waves into one data set that resulted in 142, 870 observations. The waves were merged to attain a full sample instead of running estimations on single waves. Individuals were identified across waves by their unique identifier (PID) for all the merged waves. The merging in this minor dissertation followed the guidelines provided in the NIDS user manual section 2.7. The manual explained the process for matching respondents across waves through merging. Most of the selected variables had to be recoded for better referencing of the selected variables used.

Moreover, the majority of variables compiled in the NIDS data set are presented as categorical and are not ready to be used. For instance, race, gender, marriage, education, province, rural and urban areas, sector and occupation are included. These variables were individually transformed into appropriate dummy variables so that their different groups could be quantified. This minor dissertation had to individually generate the following variables: job tenure; job tenure squared; age squared; old men; old women and size of the firm, since the NIDS data did not provide this information.