An analysis of underemployment amongst young people in South Africa : the case of university graduates

Abstract

Labour markets around the world are undergoing significant changes which has seen the rate of secure and stable employment decline significantly. South Africa has also not been spared from this wave of change, where besides the country’s high unemployment rate, both at national level and among the youth by global standards, non-standard employment such as casual and part-time work has been on the rise. At the same time, higher education has also proven to no longer be a guarantee of employment, let alone secure/stable and full-time employment. Such rising labour market instability has therefore necessitated the exploration of alternative measures of labour underutilisation, over and above the typical measures of unemployment. Hence the primary objective of the study was to analyse underemployment among university graduates in South Africa. The empirical process followed in the study was based on the post-positivism paradigm which necessitated the use of a quantitative research approach which entailed primary data collection through a survey. The overall sample size after data cleaning was 576 in total. The survey data was subject to a three stage analysis. Firstly, descriptive statistics of the frequency distribution for discrete data. Secondly, a binary logistic regression to determine factors that contribute or influence underemployment status. Lastly, an OLS regression model was employed where three different types of regressions were used to analyse the determinants of the three types of underemployment. The regressions were conducted based on the three main perceptions of underemployment status as indicated in the newly-validated scale called the Subjective Underemployment Scale (i.e. underpayment, hours’ discrepancy and field). The results of the study showed that 82.6 percent of the sampled graduates were employed (part-time or full time) and that only 17.4 percent were unemployed. Of those that were unemployed, more than 26 percent had been so for more than a year which is indicative of long-term unemployment. The employment findings by gender showed that female graduates constituted a large share of the employed (61.6%) compared to 38.4 percent male graduates. At the same time and as expected, females assumed a large percentage of the unemployed at 63.3 percent compared to males (36.7%). In terms of underemployment, approximately 45 percent of the participants considered themselves underemployed. Concerning the various categories of underemployment, the study found that 36.4 percent were underemployed by income, which entailed workers whose earnings were 20 percent less than what they earned in their previous job1 or 20 percent less than the occupational average income for those in their first jobs. Only 5.1 percent of the sampled graduates were time-underemployed, inter alia, working less than 35 hours a week. About 58.5 percent considered themselves to be underemployed in terms of skills

which was defined as those who perceived themselves to be in jobs where they were overqualified or had the most skills and experience that their counterparts in the same jobs. South Africa’s higher education institutions have a history of being dualistic which can be divided into historically advantaged institutions (HAI - white) and historically disadvantaged institutions (HDI - black). The study found that out of 351 young people who graduated from HAI, 42.7 percent were underemployed while of the 124 that graduated from HDI, 52.9 percent were underemployed. The study found that in terms of the specific age categorisation, 45.6 percent of the underemployed participants are between the ages of 25 and 29, 31.6 percent are in the 15-24 age category while 22.8 percent are in the age category of 30-34 years. In addition, despite the findings reporting that more female participants are employed relative to their male counterparts, the study’s findings on gender show that in all the underemployment types, young females are overrepresented, which raises concerns of the kinds of employment they are in. About 56 percent of those that were income-underemployed are females compared to 44 percent males. Also, in terms of skills, 64.6 percent of those that were underemployed by skills are females relative to only 35 percent males. These findings are also mirrored in the time-underemployment status suggesting that typical employment indicators do not provide a true picture of employment or rather kinds of jobs that workers are in. Surprisingly, the study found that out of the total number of underemployed participants, 59.9 percent were White, followed by 35.8 percent Blacks, 3.8 percent Coloured and 0.5 percent Asian/Indians. However, within each race group, the findings still mirror themes to emerge from literature that underemployment is more prevalent among non-white workers. In terms of the perceptions, the results show that a larger share of the participants seems to be in agreement with the underpayment factor where the perception seems to be that their jobs were not paying them sufficient income, relative to their counterparts. Similar findings were also observed with regards to the status factor where a substantial number of the participants perceived themselves as being in positions that were inferior to their level of skills and abilities they possess. The results, however, did indicate a disagreement among the participants when it comes to perceiving themselves as being underemployed in terms of working hours. Perceptions relating to involuntary temporary employment similarly to the hours’ discrepancy factor show that a higher number of graduates did not perceive themselves to be involuntary and temporary employment, suggesting that a higher share is in fact in permanent positions of a full-time nature. In terms of the fifth factor which relates to skills, more than 30 percent of the participants in each scale item strongly disagreed with this factor (68.4%). Last but not least, the perception regarding poverty wage employment showed very small differences between those that identified themselves with this factor (48.3%) and those that did not (51.5%).

The results of the binary logistic regression confirmed what is already in the literature that the younger graduates were more likely to be underemployed compared to their more mature counterparts, that is, those in the 30-34 age category. With regards to marital status, those that were unmarried have a higher likelihood of being underemployed when contrasted with their married counterparts. In terms of geographical location, the regression results found that urban graduates were less vulnerable to underemployment as compared to their counterparts, which in other words imply that rural graduates were more vulnerable to underemployment. Career guidance was also statistically significant in the model where the study found that graduates who received career guidance were less likely to fall in underemployment and that the odds of this was 0.596 higher than participants who did not receive career guidance. Lastly, in regards with race, Blacks and Coloured youth have an increased probability of being underemployed compared to their White and Asian/Indian counterparts. The second part of the regression analysis entails the use of the OLS model to measure income underemployment, skills underemployment and time underemployment. Pertaining to the first OLS regression on income underemployment, participants who were not married tended to be underpaid which again corresponds with the findings of the logistic regression. Participants who received career guidance perceived themselves as being underemployed due to the income they were receiving at the time of the survey. More findings in terms of age and race show that respondents who were below the age of 24 perceived themselves as underemployed due to the income they were receiving similarly Black youth perceived themselves as being underpaid. The findings of the second OLS regression on skills underemployment showed that again the unmarried graduates were most likely to agree with the perception that they were employed in jobs which were outside their field of studies. In terms of graphical location, the regression analysis found that graduates situated in urban areas tended to agree with the perception that they were underemployed by skills. Lastly, Black graduates relative to white graduates seem to agree with the perception of being underemployed by skills. The third OLS regression found that again, young people in the 15-24 age group are most likely to identify with time underemployment compared to their mature counterparts. In terms of race, Asians/Indians relative to graduates of other races were most likely to be in agreement with perception that they are working less hours than they would like to. From these findings, a model was conceptualised in Figure 7.2 which contributes to a broader understanding of the dynamics present in the South African graduate labour market and may explain the predictors of underemployment among young graduates. Figure 7.3 also provides a framework for reducing graduate underemployment. Government needs to work with higher education institutions and the private sector to try and better prepare graduates for work. It is crucial to go beyond just getting graduates into employment but ensuring that they are in stable and sustainable employment. The main aim should not just be about reducing youth unemployment numbers through any kinds of jobs but good jobs which utilises an

individual’s time and skills while at the same time earning decently. There is also a need for graduates to play their part in their career prospects by ensuring that they also are soft-skills ready, and reduce their unrealistic expectations in their initial entry into the labour market.