Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

Assignment resubmission.docx

Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Chargement dans…3
×

Consultez-les par la suite

1 sur 6 Publicité
Publicité

Plus De Contenu Connexe

Plus récents (20)

Publicité

Assignment resubmission.docx

  1. 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. 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. 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. 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. 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. 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/

×