The effect of unemployment rate in South Africa

1. Abstract
Unemployment is a serious economic issue that affects people in many different and challenging
ways. This investigation looks at how unemployment affects South Africa's GDP (Pasara &
Garidzirai, 2020).
The persistence of individual unemployment is greatly influenced by structural factors such as the
length of unemployment and job security. The length of unemployment affects employment
chances, with longer periods of unemployment resulting in lower employment rates (Pasara &
Garidzirai, 2020).
People stay unemployed longer on average because unemployment exits are recorded at lower
rates. The longer someone is unemployed, the worse their human capital becomes, making them
less employable (Pasara & Garidzirai, 2020). The results compare several articles' stances on South
Africa's unemployment rate.
Introduction
South Africa has long struggled with systemic issues like joblessness, poverty, poor growth, and
disparity. The nation encountered similar troubles while operating under sanctions in the 1980s
(Afolayan et al., 2019). Outstanding issues in South African economy include unemployment and
slow economic growth (Afolayan et al. 2019). For those who were willing to work, the growth in
creating employment even after independence was not totally adequate. This hindered economic
growth even more, which had been at a stall for almost a decade (Afolayan et al., 2019).
Approximately 204,000 jobs were created in the fourth quarter of 2022, which resulted in a 0.1%
decline in the official unemployment rate to 32.9%. According to the quarterly labor force survey
released on Tuesday by the South African Bureau of Statistics ( South African statistics, 2022)
According to Statistics South Africa (2022), there were 7.7 million unemployed people in the third
quarter of 2022, down from 7.8 million the previous quarter, while there were 3.5 million
discouraged job seekers, down from 3.5 million.
Between the two quarters, there was a 264,000 increase in the total number of people who left the
workforce for reasons other than disappointments, adding 210,000 to the population who were
not working (Statistics South Africa,2022). The presence of unemployment is connected with
adverse structural and economic conditions.
Slowing economic growth, which thus slows the demand for labor, is the key economic condition
linked to unemployment. Most economies experience rising unemployment as a result of slow
growth, and employment levels increase when business circumstances improve (Nonyana & Njuho,
2018).
The rates of unemployment in South Africa is high despite the fact that economy is performing
well. These results indicates that structural rather than economic issues are responsible for South
Africa's unemployment. The unemployment rate in South Africa is high despite the fact that
economy is doing well. This research found that institutional rather than fiscal matters are
responsible for South Africa's unemployment (Nonyana & Njuho, 2018).
Modern South Africa's unemployment is mostly a result of structural factors related to
technological developments and skill mismatches (Statistics South Africa, 2022). Unskilled

2. employees make up a sizable segment of the workforce (those with education levels below a high
school diploma), which implies that many of them have never had a job. The workforce with lower
skill levels suffers because of new technologies that drive job expansions toward higher-skilled
industries. As the sector changes to include new technology, The number of low-skilled workers
being absorbed has decreased. Modern South Africa's unemployment is mostly caused by
structural factors related to technological developments and skill mismatches (Statistics South
Africa, 2022).
Unskilled workers make up a sizable segment of the workforce (those with education levels below a
high school diploma), which suggests that many of them have never had a job. The workforce with
lower skill levels suffers as a result of technological advancements that drive job development
toward higher-skilled industries. The industry's adoption rate of new technologies has decreased
the rate at which low-skilled individuals are hired (Statistics South Africa, 2022).
Methodology
According to Pasara and Garidzirai (2020), the study employed quantitative analysis known as the
Vector Autoregressive (VAR) model to investigate the relationship between unemployment,
economic expansion, and overall fixed capital development in South Africa. Use the VAR model to
test for interdependencies amongst elements, as it not only demonstrates the interactions
between variables but also provides some useful insights on causality (Garidzirai, 2020). However,
unit root testing using the Augmented Dickey Fuller (ADF) test were carried out prior to
implementing the VAR model. To reduce the number of possible degrees of freedom for all three
variables—Gross Domestic Product (GDP), Gross Investment (GCF), and Unemployment Rate
(UNEMP)—the best delay length, k, was identified (Garidzirai, 2020). The authors' designated
Akaike Information Criterion (AIC) and Schwartz Criterion (SC), indicated by the writers, presuppose
that the lag is significant even though it is feasible if the two criteria do not match. Models with
incorrect or excessive parameters are at risk (Pasara & Garidzirai, 2020).
In contrast to the experiments above, the researchers assessed connections between study
variables using quarterly time series data from Quantec EasyData for the years 2005 to 2019.
(Habanabakize, 2020).
The availability of data led to the selection of the sampling period (Habanabakize, 2020).
Employment in the social welfare sector (domestic labour) is the dependent variable, and the
factors are interest payments and revenues in the hotel and fast-food sectors (Habanabakize,
2020). Autoregressive variance lag was used to test the long-term associations between the
variables (ARDL). Based on three key qualities and related benefits, this strategy was chosen. First,
the ARDL model is free from the integration order issue that is present in conventional techniques
like the Johansen likelihood approach (Habanabakize, 2020). Second, whereas the boundary test

3. process is appropriate for both small and big sample sizes, many conventional multivariate
cointegration methods only yield reliable results for high sample sizes (Habanabakize, 2020). Third,
ARDL gives accurate t-statistics and unbiased long-term estimates even when inherent explanatory
variables are included (Habanabakize, 2020)
The ARDL model below was developed to assess cointegration between variables (Habanabakize,
2020).
Using quarterly data from 1994 to 2016, Makaringe and Khobai (2018) investigated the relationship
between unemployment and economic development in South Africa. The study uses the ARDL
regression model to generate the regression coefficients and regression analysis's findings indicate
that unemployment hinders South Africa's economic expansion (Makaringe and Khobai 2018).
According to Makaringe and Khobai (2018), the study investigates the connection between job
growth and economic expansion in South Africa and Toda-Yamamoto causality tests are used in the
study to gauge the relationship. From 2000-Q1 through 2012-Q3, the paper uses quarterly data, t
The findings demonstrate that GDP drives occupation rather than occupation causing economic
growth (Makaringe and Khobai 2018).
The impact of the budget deficit, real effective exchange rates, labor productivity, and log of output
on unemployment in South Africa was already researched (Banda et al., 2016). The regression
model's parameters are estimated by the study using the error correction model (ECM). The
findings demonstrate that rising unemployment is a result of rising labor productivity, budget
deficit, and log of GDP (Banda et al., 2016).

4. Results
Makaringe & Khobai (2018) state that the study uses time series data for the years 2012 to 2018 to
analyze how unemployment affects South Africa's economic progress. The growing significance of
the connection between unemployment and growth in South Africa served as the inspiration for
this study (Makaringe & Khobai, 2018).. The relationship between unemployment and economic
growth has been the subject of numerous research in industrialized nations, and the study time
and country all influenced the conclusions drawn. However, little research has been done to study
the relationship between unemployment and economic growth, particularly in South Africa. There
are currently too few jobs in South Africa, and the unemployment rate has been fluctuating
recently. Leaders and economists have come up with a variety of explanations for why order levels
are reversing the pattern of volatile unemployment because of this. These suggestions are
anticipated to significantly contribute to the growth of the labor force in South Africa (Makaringe &
Khobai, 2018).
According to Jeke & Wanjuu. (2021), the findings in Table 6 indicate the log of capital stock and
investment in human capital (HUCAP) have a significant long-term stimulating influence on the log
of South African output. The findings indicate that over the long run, a 1% increase in capital stock
causes a 0.154% increase in real gross domestic output. Keeping everything else constant (Jeke &
Wanjuu, 2021). If all other factors remained unchanged, an increase in human capital investment
per unit might boost log real GDP by 0.003%. On the other hand, over time, inflation dampens
South Africa's real GDP (Jeke & Wanjuu, 2021). The results also reveal that the unemployment rate
(UNEMPL) does not significantly affect the log of real GDP in the long run, with a 1% increase in
inflation expected to diminish real GDP by 0.004% over time, everything else being equal. This is
due to the probability value exceeding 0.05. (Jeke & Wanjuu. 2021).
Jeke & Wanjuu. (2021) state that the short-term findings demonstrate that more than 32%
recovery occurs in a year when the real GDP logarithm diverges from the long-run one. The
economy will probably need three years to fully recover from a systemic shock. Additionally, the
log of capital stock (lnKAPSTC) only influences the log of real GDP, according to the short-run
regression coefficient results (lnRGDP) (Jeke & Wanjuu. 2021). The short-term results demonstrate
when the long-run and short-run logarithms of real GDP diverge. recovery of more than 32% after a
year. Jeke & Wanjuu. (2021) also state that the economy's restoration from an institutionalized
disruption will most likely take three years. The quick regression coefficient results further
demonstrate that the log of real GDP is only affected by the log of invested capital (lnKAPSTC)
(lnRGDP).

5. In this study, Jeke & Wanjuu. (2021) state that the logarithm of capital stock and human capital are
used as control variables to assess the impacts of unemployment and inflation on economic
production in South Africa. The main goal of this study is to determine whether, as suggested by
previous studies in this field, inflation and unemployment have an impact on the logarithm of real
GDP in South Africa after controlling for the aforementioned factors. is. For instance, just a few
articles report that inflation lowers actual GDP (Muryani and Pamungkas, 2018). According to
Muryani and Pamungkas (2018), unemployment boosts real GDP. As demonstrated by Mohseni and
Jouzaryan (2016) and Makaringe and Khobai (2018), unemployment lowers real GDP.
In conclusion, unemployment occasionally lingered for a while. According to Banda et al., the
technical manufacturing processes used in the South African economy require more capital (Banda
et al.,2016). Instead of increasing labor intensity, qualified people are needed. Since most
unemployed groups are comprised of unskilled workers and workers, this is typically a difficult
situation. Work involvement-intensive industries should prioritize sector formation policies that use
these groups (Banda et al.,2016).
Recommendations
For these three macroeconomic variables, Sa'idu and Muhammad (2015) state that there must be
significant institutional coordination and interagency communication. Economic growth, inflation,
and unemployment in the nation. As a result, the report offers the following policy
recommendations to the government:
1. An economy that is being restructured internally Growth that is inconsistent with ideals
imported from other nations
2. Increased actual salaries for workers; smart advanced technology to generate more sustainable
jobs.
3. Make sure pricing volatility is managed macroeconomically.
4. Investing in infrastructure, particularly in power, can lead to job growth.
5. Lastly, data for the study and VAR models should be used in future research to examine the long-
term dynamic characteristics of these variables.

6. References
1. Afolayan, O., T., Okodua, H., Matthew, O., & Osabohien, R. (2019) 2019. Reducing
unemployment Malaise in Nigeria: The role of electricity consumption and human capital
development. International Journal of Energy Economics and Policy,9(4),63-73.
https://www.econjournals.com/index.php/ijeep/article/view/7590/4400 .
2. Banda, H., Ngirande H., & Hogwe F. (2016). The impact of economic growth on
unemployment in South Africa: 1994–2012. Corporate Ownership & Control, 12(4),699-707.
http://repository.nwu.ac.za/bitstream/handle/10394/25719/2015The_impact.pdf?sequen
ce=1&isAllowed=y.
3. Habanabakize, T. (2020) . Assessing the impact of interest rate, catering, and fast-food
income on employment in the social services industry. International journal of economics
and finance studies,12(2),534-550.
https://www.sobiad.org/eJOURNALS/journal_IJEF/archieves/IJEF-2020-2/t-
habanabakize.pdf
4. Jeke, L., & Wanjuu, L., W. (2021). The economic impact of unemployment and inflation on
output growth in South Africa. Journal of Economics and International Finance, 13(3), 117-
126. https://academicjournals.org/journal/JEIF/article-full-text-pdf/868A8F867220
5. Makaringe, S. C., & Khobai, H. (2018). The effect of unemployment on economic growth in
South Africa (1994-2016). (MPRA paper No. 85305). Munich Personal RePEc Archive.
https://mpra.ub.uni-muenchen.de/85305/1/
6. Nonyana, J. Z., & Njuho, P. M. (2018). Modelling the length of time spent in an
unemployment state in South Africa. South African Journal of Science, 114(11-12), 1-7.
http://www.scielo.org.za/scielo.php?pid=S0038-
23532018000600016&script=sci_arttext&tlng=es .
7. Pasara, M. T., & Garidzirai, R. (2020). A causality effects among gross capital formation:
Unemployment and economic growth in South Africa.
https://sciendo.com/downloadpdf/journals/subboec/64/1/article-p33.pdf.
8. Sa’idu, B, M., & Muhammad, A., A. (2015). Do unemployment and inflation substantially
affect economic growth? Journal of Economics and Development Studies. 3(2),132-139.
http://jedsnet.com/journals/jeds/Vol_3_No_2_June_2015/13.pdf
9. Statistics South Africa. (2022). Post-Enumeration Survey (PES) 2022. Republic of South
Africa http://www.statssa.gov.za/