Macroeconomic Determinants of Unemployment in Africa:

A Panel Data Analysis Approach

Kunofiwa Tsaurai¹

Abstract: The study explored the macroeconomic determinants of unemployment in Africa using panel data analysis (fixed effects, random effects, pooled ordinary least squares, dynamic generalized methods of moments), with data spanning from 2001 to 2015. Several empirical studies on the determinants of unemployment have been done, results of which are convergent, divergent and quite mixed. What is clearly coming out of this empirical research is that there is no universal agreed list of the determinants of unemployment. Across all the four econometric estimation methods used, the variables which were found not to be significant determinants of unemployment include information and communication technology, human capital development and infrastructural development. The dynamic GMM method observed that the lag of unemployment exacerbated unemployment in Africa. All the four econometric approaches used produced results which show that FDI increased unemployment in the African continent. African authorities are therefore urged to ensure that they implement policies which ensures that the inflow of foreign direct investment (FDI) translates into easing unemployment woos in the continent. The random effects and pooled OLS noted that financial development had a significant positive effect on unemployment in the case of Africa. The relevant African authorities should therefore implement programmes and policies that enhances the poor people’s financial inclusion so that they can benefit from developed financial markets. As expected and justified by literature, the pooled OLS shows that trade openness and population growth had a significant positive impact on unemployment in Africa. However, the interaction between information and communication technology (ICT) and human capital development was found to have had a significant negative influence on unemployment across all the four econometric estimation approaches. Africa is therefore urged to enhance human capital development if they intend to economically benefit from new technologies especially during the present day 4^th industrial revolution otherwise their people becomes redundant. As expected, fixed effects and pooled OLS methods shows that economic growth had a significant negative influence on unemployment in Africa. It is against this backdrop that the study urges African authorities to implement growth-oriented policies if they intend to reduce unemployment in the region.

Keywords: Unemployment; Africa; Determinants; Panel Data

JEL Classification: E24; N17; C23

1. Introduction

Background of the study, research gaps and problem statement

The rapidly rising rate of unemployment in developing countries and Africa has been a major source of concern not only to international developmental bodies such as United Nations, African Development Bank and the African Union, but to scholars and policymakers (Folawewo and Adeboje. 2017). The unsettling concern is understandable given that unemployment gives rise to other socially related problems such as drug abuse and increase in general crime rate, both of which slows down economic growth in the continent. Due to this reason, it has become imperative for every government especially in the African continent to come up with policies and strategies that ensures that there is unemployment reduction in the continent, consistent with Belabbas et al (2018). Consistent with Riaz and Zafar (2018), upping efforts to reduce unemployment begins with a thorough understanding of the determinants of unemployment. The argument motivated the author to undertake this study to lay bare determinants of unemployment’s policy implications to the African authorities so that they can have a starting point.

The intricate relationship between unemployment, poverty and inequality is one of the reasons why the author undertook this study. The Millennium Development Goal of poverty eradication cannot be achieved if unemployment rates continue to rise therefore as a scholar, the author is of the view that further empirical tests on unemployment determinants are imperative if the academic community is going to contribute or make any difference in our society.

What is worth noting is that the impact of economic growth on unemployment is now a settled issue in finance and economics. What has not yet been satisfactorily or finalized in the literature is the agreeable list of variables that determine unemployment as can be seen by divergent, convergent and mixed findings established by prior empirical research on determinates of unemployment (see Table 2). What further shows that the issue of determinants of unemployment is still an unsettled issue is that even the empirical research work which agrees on the same list, they cannot concur on how each of that variable influence unemployment. For example, Gaber (2018), Maqbool et al (2013), Folawewo and Adeboje (2017), Riaz and Zafar (2018), Baah-Boateng (2014), Baah-Boateng (2016), Ebaidalla (2016) noted economic growth reduced unemployment whilst Kerckhoffs et al (1994) and Alrayes and Wadi (2018) could not find any clear relationship between economic growth and unemployment. Whilst Folawewo and Maqbool et al (2013), Adeboje (2017) and Gaber (2018) noted that inflation was a significant determining factor on unemployment, Alrayes and Wadi (2018) observed no significant impact of inflation on unemployment. Such divergent views on the subject matter triggered the current study to add a voice on the determinants of unemployment in an African context.

Even though Africa has high unemployment rates and face quite similar unemployment problems, majority empirical studies have shied away from exploring the determinants of unemployment in Africa as a whole. Majority of the available empirical research on unemployment determinants in Africa are single country studies (Kyei and Gyekye. 2011; Raifu. 2017; Khumalo and Eita. 2015; Baah-Boateng; 2014; Eita and Ashipala. 2010; Baah-Boateng. 2016; Dagume and Gyekye. 2016; Batu. 2016; Fila et al. 2016; Batel and Choga. 2018; Mbekeni and Phiri. 2019). The scant empirical research work that explored the determinants of unemployment as a bloc were done by Ebaidalla (2016) which focused on Sub-Saharan Africa and Folawewo and Adeboje (2017) which studied on Economic Community of West African States (ECOWAS). Still, these empirical studies were not representative of Africa enough and suffered from the following methodological weaknesses: (1) They ignored the fact that unemployment data is characterised by dynamic features, just like poverty and (2) they did not consider the endogeneity problem. The current study fills in these gaps.

Structure of the paper:

The remaining part of the study is organized into five different components. Section 2 explains the theoretical literature on various factors that determines unemployment. Section 3 is a discussion of empirical literature on the determinants of unemployment. Section 4 is the research methodology, which focuses on data description, general and econometric estimation models, pre-estimation diagnostics, diagnostic tests, main data analysis, results discussion and interpretation. Section 5 concludes the paper whilst Section 6 is the reference list.

2. Determinants of Unemployment -Theoretical Literature Review

The Determinants of Unemployment, from a Theoretical Point of View are Summarized in Table 1.

Table 1. Theory Intuition and a Priori Expectation

Source: Author compilation

3. Determinants of Unemployment -Empirical Literature Review

Table 2 below is a discussion of the empirical literature on the determinants of unemployment.

Table 2. The Determinants of Unemployment – An Empirical View

Source: Author compilation

As can be seen in Table 2, several empirical studies on the determinants of unemployment have been done, results of which are convergent, divergent and quite mixed. What is clearly coming out of this empirical research is that there is no universal agreed list of the determinants of unemployment. It is also clear that despite high rates of unemployment in Africa, most empirical research on unemployment determinants have shied away from carrying out such a research on Africa. It is against this backdrop that further empirical tests on determinants of unemployment in an African context is required.

4. Research Methodology

Data used in the study: 2001 to 2015 panel data extracted from African Development Bank, World Bank Indicators and International Monetary Fund databases were used for the purposes of this study. Stratified purposeful sampling was used to choose the African countries which formed part of this study. Three African countries from each African region was chosen depending on the availability of data of the variables. For example, Burundi, Kenya and Rwanda are the Eastern African included as part of the sample. Algeria, Morocco and Tunisia were the North African nations included in the sample whereas West African nations include Nigeria, Ghana and Senegal. Central African group of countries which formed part of the sample include Gabon, Democratic Republic of Congo and Cameroon whilst Namibia, Mozambique and South Africa are the Southern African group of countries included in the sample.

Pre-estimation diagnostics

Correlation analysis and descriptive statistics are the two forms of pre-estimation diagnostics that were done in this study (see results in Table 3 and 4).

Table 3. Correlation Analysis

Note: ***/**/* denotes statistical significance at the 1%/5%/10% level respectively.

Source: Author compilation from E-Views

Where UNEMPL, FIN, ICT, FDI, OPEN, POP, GROWTH, HCD and INFR respectively stands for unemployment, financial development, information and communication technology, foreign direct investment, trade openness, population growth, economic growth, human capital development and infrastructural development.

Significant positive relationships that were observed in line with the theme of the study include the following: (1) Financial development and unemployment, (2) Information and communication technology and unemployment, (3), foreign direct investment and unemployment, (4) trade openness and unemployment, (5) economic growth and unemployment, (6) human capital development and unemployment and (7) infrastructural development and unemployment. As expected, population growth and unemployment are negatively but significantly related. The weakness of these correlation analysis results is that they do no show the direction of causality hence they are not useful for policy making decision purposes. Following Stead (1996), there is no multi-collinearity problem in the data set since the maximum size of the correlation is 76% (between financial development and infrastructural development).

Table 4. Descriptive Statistics

Note: ***/**/* denotes statistical significance at the 1%/5%/10% level respectively.

Source: Author compilation from E-Views

Apart from trade openness data, all other data sets have abnormal values since the probability of their Jarque-Bera criteria is equal to zero. Abnormal values are also found in the economic growth data set, considering that the standard deviation of economic growth data is way above 1 000. The fact that economic growth data’s range is more than 10 000 further proves that economic growth data is characterized by abnormal values. To deal away with these problems, the author converted the data into natural logarithms to improve the overall quality of results for decision making, following Nor et al (2015) and Aye and Edoja (2017).

Panel unit root tests: Table 5 summarized results of the panel unit root tests.

Table 5. Panel Unit Root Tests –Individual Intercept

Note: LLC, IPS, ADF and PP stands for Levin, Lin and Chu (2002); Im, Pesaran and Shin (2013); ADF Fisher Chi Square and PP Fisher Chi Square tests respectively. *, ** and *** denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation from E-Views

All the data sets were stable at first difference.

Panel co-integration tests: The study used Kao (1999) approach to test whether there is a co-integration relation between and among the variables.

Table 6. Kao Residual Co-integration Test - Individual intercept

Source: Author’s compilation from E-Views

The results in Table 6 indicates that there is a long run relationship between the variables used in the study. This is because the no relationship null hypothesis was rejected at 1% significance level.

General model description

UNEMPL =f(ICT, HCD, FIN, FDI, OPEN, POP, GROWTH, INFR) [1]

Two factors were considered in coming up with equation 1, namely data availability and prior empirical work on the similar subject matter. Specifically, the earlier research considered include Patel and Choga (2018), Khatun (2017), Mbekeni and Phiri (2019), Alrayes and Wadi (2018), Morrisroe et al (2016), Ductor and Grechyna (2019) and Feriyanto (2018). These variables and their proxies are clearly described in Table 7.

Table 7. Abbreviation, Variables and Proxies

Source: Author compilation

The econometric representation of equation 1 looks as follows:

UNEMPL_it = ₀ + ₁ICT_it + ₂HCD_it + ₃FIN_it + ₄FDI_it + ₅OPEN_it + ₆POP_it +₇GROWTH_it +₈INFR_it +Ɛit [2]

Table 8 describes the meaning of econometric terms included in equation 2.

Table 8. Description of Econometric Meaning of the Equation 2 Terms

Source: Author compilation

The second objective of this study is to find out if human capital development is a channel through which ICT influences unemployment in Africa hence the introduction of the interaction term (ICT_it . HCD_it) into equation 2 to give rise to equation 3.

UNEMPL_it = ₀ + ₁ICT_it + ₂HCD_it + ₃ (ICT_it . HCD_it) + ₄FIN_it + ₅FDI_it + ₆OPEN_it + ₇POP_it +₈GROWTH_it +₉INFR_it +Ɛit [3]

A co-efficient ₃ in equation 3 which is negative and significant means that the complementarity between ICT and HCD would have enhanced unemployment reduction in Africa. Pooled ordinary least squares (OLS), fixed and random effects are the panel econometric methods used to estimate the results for equation 3.

Table 9. Determinants of Unemployment in Transitional Economies

***, ** and * denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation from E-Views

Under the fixed effects, random effects and pooled OLS methods, ICT was found to have an insignificant negative influence on unemployment. The results resonate with Vivarelli (2007) whose study noted that ICT generate new jobs in the capital sector of the economy and that new technologies create new investments thereby opening new opportunities for the unemployed people. On the other hand, human capital development had a non-significant positive influence on unemployment, a finding which contradicts with available literature which says that human capital development improves productivity and enhances employability of the workforce (Samiullah. 2014; Massingham and Leona Tam. 2015).

The interaction between ICT and human capital development was found to be negative and significant, meaning the complementarity between these two variables reduced unemployment in Africa. These results apply across all the three econometric estimation approaches. The finding supports the views by Samiullah (2014) and Massingham and Leona Tam (2015) that once the people are equipped with key technical skills, they can easily take advantage of new ICT technologies to create employment for themselves or to be better placed to take up new challenging roles in the new 4^th industrial revolution.

Whilst fixed effects show a non-significant positive relationship running from financial development towards unemployment, random effects and pooled OLS indicates that financial development had a significant positive influence on unemployment. The results are in line with the International Monetary Fund (2015) report which noted that developed financial markets exclude small businesses which are the engine for job creation efforts in the economy.

Across all the panel estimation methods used, FDI was found to have had a significant positive effect on unemployment, a finding which agrees with Irpan (2016:3) whose study observed that some foreign investors prefer to bring their own unskilled labour force hence contributing to increased unemployment rates in the host country.

Fixed effects show that trade had a non-significant negative influence on unemployment, a finding which resonates with Mitra and Ranjan (2010) whose study is of the view that high trade openness enhances the international competitiveness of local businesses to generate more employment. Random effects indicate that trade openness had a non-significant positive effect on unemployment whilst pooled OLS approach noted the existence of a significant positive relationship running from trade openness towards unemployment. These results agree with Helpman and Itskhoki (2010) whose study argued that trade openness leads to more unemployment because it makes large local firms easily and cheaply purchase materials from abroad and setting up their manufacturing branches in other countries thus exporting jobs to those countries.

According to the fixed and random effects, population growth had a non-significant negative impact on unemployment, a finding which generally agrees with Aiyedogbon (2012) whose study was of the view that high levels of population growth cause a rapid supply of the job seekers in the labour market to grow at a pace which outpace the rate at which jobs are being created in the economy hence leading to unemployment. On the other hand, pooled OLS shows a significant positive relationship running from population growth towards unemployment, a finding which resonates with Jorgenson’s (1963) market size hypothesis in that high levels of population enlarge the market size, attracts FDI brings along with it other advantages such as employment creation.

Fixed effects and pooled shows that economic growth significantly reduced unemployment in Africa, a finding which resonates with Thirwall (1989) whose study explained that a larger economic size is better capable of employing more people into the workforce. In line with Abdul-Khaliq et al (2014) whose study revealed that an increase in economic growth reduced employment prospects in Arab countries, random effects approach produced results which show that economic growth had an insignificant positive impact on unemployment.

The study noted that infrastructural development had a non-significant negative impact on unemployment across the three panel data analysis methods. The results generally agree with the labour markets theory which was supported by Smith (2003) in that transport and communications infrastructure investment and development reduces unemployment.

Borrowing from the vicious cycle of poverty hypothesis argued by Azher (1995), that poverty leads to more poverty, unemployed people are likely not be able to afford quality education for their children, itself a contributor to unemployment among their children. This lag of unemployment as a source of unemployment is captured in equation 4 which is estimated using the Arellano and Bond’s (1991) dynamic GMM approach.

UNEMPL_it = ₀ + ₁UNEMPLLAG_it + ₁ICT_it + ₂HCD_it + ₃ (ICT_it . HCD_it) + ₄FIN_it + ₅FDI_it + ₆OPEN_it + ₇POP_it +₈GROWTH_it +₉INFR_it +Ɛit [4]

Table 10. Dynamic Generalised Methods of Moments (GMM) Results

***, ** and * denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation from E-Views

The dynamic GMM approach found out that the lag of unemployment had a significant positive impact on unemployment, in line with the argument put forward in the vicious cycle of poverty that poverty leads to more poverty and unemployment causes even more unemployment (Azher. 1995).

ICT had an insignificant negative influence on unemployment (reduced unemployment), a view which was shared by Vivarelli (2007) that new technologies new technologies create new investments thereby opening new opportunities for the unemployed people. Consistent with Samiullah (2014) who opined that human capital development improves the productivity of the people by equipping them with the necessary job skills and thereby improving their employability, the dynamic GMM observed a non-significant negative relationship running from human capital development towards unemployment. The dynamic GMM also shows that the interaction between ICT and human capital development had a significant negative influence on unemployment, in line with an argument by Samiullah (2014) who noted that once the people are well equipped with necessary skills to carry out production, they can easily use the new developed technologies to their advantage for unemployment reduction purposes.

A non-significant positive relationship running from financial management towards unemployment was also detected under the dynamic GMM method, a finding which support International Monetary Fund’s (2015) argument that developed financial markets can have a deleterious effect on employment. In line with Irpan’s (2016) argument that certain foreign investors might contribute towards unemployment by demanding to bring into the host country their own unskilled labour force, the dynamic GMM method found out that financial development had a significant positive effect on unemployment.

The dynamic GMM noted that trade openness had a non-significant negative effect on unemployment. The finding resonates with Mitra and Ranjan (2010) whose study noted that trade openness enables easy participation of local firms on the international arena hence enabling them to enjoy advantages such as local, regional and international expansion. Population growth was also found to have an insignificant positive influence on unemployment, in agreement with Aiyedogbon (2012) whose study observed that high population growth exacerbate unemployment.

In contradiction to theory but in line with Abdul-Khaliq et al’s (2014) findings, the dynamic GMM noted the existence of a non-significant positive relationship running from economic growth towards unemployment in Africa. Finally, infrastructural development was found to have had an insignificant negative impact on unemployment in the case of Africa. Such a finding is generally consistent with the labour markets theory as argued by Smith (2003).

5. Summary of the Study

The main aim of this study is to explore the macroeconomic determinants of unemployment in Africa using panel data analysis with data spanning from 2001 to 2015. For comparison purposes, panel data analysis methods include fixed effects, random effects, pooled OLS and the dynamic GMM, a method which is superior in that it takes care of the dynamic features of unemployment data and the endogeneity problem. Across all the four econometric estimation methods used, the variables which were found not to be significant determinants of unemployment include information and communication technology, human capital development and infrastructural development. The dynamic GMM method observed that the lag of unemployment exacerbated unemployment in Africa. All the four econometric approaches used produced results which show that FDI increased unemployment in the African continent. African authorities are therefore urged to ensure that they implement policies which ensures that the inflow of FDI translates into easing unemployment woos in the continent. The random effects and pooled OLS noted that financial development had a significant positive effect on unemployment in the case of Africa. The relevant African authorities should therefore implement programmes and policies that enhances the poor people’s financial inclusion so that they can benefit from developed financial markets. As expected and justified by literature, the pooled OLS shows that trade openness and population growth had a significant positive impact on unemployment in Africa. However, the interaction between ICT and human capital development was found to have had a significant negative influence on unemployment across all the four econometric estimation approaches. Africa is therefore urged to enhance human capital development if they intend to economically benefit from new technologies especially during the present day 4^th industrial revolution otherwise their people becomes redundant. As expected, fixed effects and pooled OLS methods shows that economic growth had a significant negative influence on unemployment in Africa. It is against this backdrop that the study urges African authorities to implement growth-oriented policies if they intend to reduce unemployment in the region.

References

Abdul-Khaliq. S. Soufan. T. & Shihab. R. A. (2014). The relationship between unemployment and economic growth rate in Arab country. Journal of Economics and Sustainable Development, 5(9), 56-59.

Abshoko, A. D. (2016). Determinants of youth unemployment: Evidence from Ethiopia. Global Journal of Human-Social Science: Arts and Humanities-Psychology, 16 (4), 10-20.

Aiyedogbon, B. O. (2012). Poverty and youth unemployment in Nigeria. International Journal of Business and Social Science, 3 (20), 269-279.

Alrayes, S. E. & Wadi, R.M. A. (2018). Determinants of unemployment in Bahrain. International Journal of Business and Social Science, 9 (12), 64-74.

Arellano, M. & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.

Aye, G. C. & Edoja, P. E. (2017). Effect of economic growth on C02 emission in developing countries: Evidence from a dynamic panel threshold model. Cogent Economics and Finance, 5 (1), 1-22.

Aysit, T. & Mehmet, T. (2004). Determinants of unemployment duration for men and women in Turkey. Turkish Economic Association Discussion Paper Number 2004/6.

Azher, B.A. (1995). Rural savings: Their magnitude, determinants and mobilization. Pakistan Development Review, 34(4), 779-786.

Baah-Boateng, W. (2014). Determinants of unemployment in Ghana. African Development Review, 25 (4), 385-399.

Baah-Boateng, W. (2016). The youth unemployment challenge in Africa: What are the drivers? The Economic and Labour Relations Review, 27 (4), 413-431.

Baccaro, L. & Rei, D. (2007). Institutional determinants of unemployment in OECD countries: Does the deregulatory view hold water? International Organization, 61 (3), 527-569.

Batu, M. M. (2016). Determinants of youth unemployment in urban areas of Ethiopia. International Journal of Scientific and Research Publications, 6 (5), 343-350.

Belabbas, R. Bellatreche, R. and Zaghba, T. (2018). Analysis of unemployment determinants in North Africa countries: A panel data co-integration approach. 36^th International Academic Conference, London, 15-25.

Cheema, A. R. & Atta, A. (2014). Economic determinants of unemployment in Pakistan. International Journal of Business and Social Science, 5 (3), 209-221.

Dagume, M. A. & Gyekye, A. (2016). Determinants of youth unemployment in South Africa: Evidence from the Vhembe district of Limpopo province. Environmental Economics, 7 (4), 59-67.

Ductor, L. & Grechyna, D. (2019). Determinants of in and outs of unemployment. Applied Economic Letters, DOI: 10.1080/13504851.2019.1646858, 1-8.

Ebaidalla, E.M. (2016). Analysis of youth unemployment in Sub-Saharan Africa: Determinants and possible ways forward. African Journal of Economic and Sustainable Development, 5 (4), 302-317.

Eita, J. H. & Ashipala, J. M. (2010). Determinants of unemployment in Namibia. International Journal of Business and Management, 5 (10), 92-104.

Feriyanto, N. (2018). Determinants of unemployment in Regency/City in Special Province Yogyakarta. European Research Studies Journal, XX1 (3), 367-380.

Fila, D. T.; Mansingh, J.P. & Legesse, W. (2016). Determinants of youth unemployment: The case of Ambo Town, Oromia, Ethiopia. International Journal of Economics and Business Management, 2 (2), 162-169.

Folawewo, A. O. & Adeboje, O.M. (2017). Macroeconomic determinants of unemployment: Empirical evidence from Economic Community of West African States. African Development Review, 29 (2), 197-210.

Gaber, A. (2018). Determinants of unemployment: Empirical evidence from Palestine. Munich Personal RePEc Archive paper number 89424, 1-10.

Helpman, E. & Itskhoki. O. (2010). Labour market rigidities, trade and unemployment. Review of Economic Studies, 77(3), 1100-1137.

Im, K.S. Pesaran, M. H. & Shin, Y. (2003), Testing unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.

International Monetary Fund. (2015). World Economic Outlook: Adjusting to Lower Commodity Prices. Washington (October).

Irpan, H. M.; Saad, R.M.; Nor, A.H.S.M. & Ibrahim, N. (2016). Impact of foreign direct investment on the unemployment rate in Malaysia. Journal of Physics: Conference Series, 710 (1), 1-10.

Johnny, N.; Timipere, E.T.; Krokeme, O. & Markjackson, D. (2018). Impact of foreign direct investment on unemployment rate in Nigeria. International Journal of Academic Research in Business and Social Sciences, 8 (3), 57-69.

Jorgenson, D.W. (1963). Capital theory and investment behaviour. The American Economic Review, 53(2), 247-259.

Kamran, A. Shujaat, S. Syed, N.A. and Ali, S.N. (2013). A study on determinants of unemployment in Pakistan. Proceedings of Seventh International Conference on Management Science and Engineering Management, pp. 1337-1348.

Kao, C. (1999). Spurious regression and residual-based tests for co-integration in panel data. Journal of Econometrics, 90 (1999), 247-259.

Kargbo, A. A. Ding, Y. & Kargbo, M. (2016). Financial development, human capital and economic growth: New evidence from Sierra Leone. Journal of Finance and Bank Management, 4(1), 49-67.

Kerckhoffs, C.; Neubourg, C. D. & Palm, F. (1994). The determinants of unemployment and jobsearch duration in the Netherlands. De Economist, 142 (1), 21-42.

Khatun, M.M. (2017). Macroeconomic determinants of youth unemployment and inactivity rates in Bangladesh. Journal of Economics and Sustainable Development, 8 (4), 117-122.

Khumalo, Z. Z. & Eita, J.H. (2015). Determinants of unemployment in Swaziland. Journal of Applied Sciences, 15 (9), 1190-1195.

Kyei, K. A. & Gyekye, K. B. (2011). Determinants of unemployment in Limpopo province in South Africa: Exploratory studies. Journal of Emerging Trends in Economics and Management Sciences, 2 (1), 54-61.

Levin, A.; Lin, C. F. & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108 (1), 1-24.

Maqbool, M. S.; Mahmood, T. Sattar & Bhali, M. N. (2013). Determinants of unemployment: Empirical evidences from Pakistan. Pakistan Economic and Social Review, 51 (2), 191-208.

Massingham, O. R. and Tam, L. (2015). The relationship between human capital, value creation and employee reward, ‘Journal of Intellectual Capital’, 16 (2), 390-418.

Mbekeni, L. & Phiri, A. (2019). South African unemployment in the post-financial crisis era: What are the determinants? Munich Personal RePEc Archive Paper Number 94159.

Mitra, D. & Ranjan, P. (2010). Offshoring and unemployment: The role of search frictions labour mobility. Journal of International Economics, 81(2), 219-229.

Morrisroe, K.; Huq, M.; Stevens, W.; Rabusa, C.; Proudman, S. & Nikpour, M. (2015). AB0607 determinants of unemployment amongst Australian systemic sclerosis patients. Annals of the Rheumatic Diseases, 75 (2), 1-12.

Nor, N. H. H. M.; Ripain, N. & Ahmad, N. W. (2015). Financial development and FDI-Growth nexus: Panel Analysis. Proceeding of the 2^nd International Conference on Management and Muamalah, pp. 435-446.

Patel, D. C. & Choga, I. (2018). Determinants of youth unemployment in South Africa. Proceedings of Economics and Finance Conferences 7108913, International Institute of Social and Economic Sciences.

Raifu, I. A. (2017). On the determinants of unemployment in Nigeria: What are the roles of trade openness and current account balance? Review of Innovation and Competitiveness, 3 (4), 5-30.

Riaz, A. & Zafar, F. (2018). Determinants of unemployment in less developing countries. SHS Web of Conferences, 48 (15), 1-6.

Sabir, G. M. (2015). Economic determinants of unemployment: Empirical result from Pakistan. Journal of Economics and Sustainable Development, 6 (9), 150-154.

Samiullah. (2014). Relationship between unemployment and human capital. Journal of Resources Development and Management, Vol. 3 (2014), 1-11.

Smith, S. (2003). Labour Economics (2^nd edition).

Stead, R. (1996). Foundation quantitative methods for business. England: Prentice Hall.

Thirwall, A. (1989). Growth and development, Macmillan Education, pp. 306.

Trimurti, C. P. & Komalasari, Y. (2014). Determinants of unemployment: Empirical evidences from 7 Province in Indonesia. Scientific Research Journal, 2 (8), 5-9.

Vivarelli, M. (2007). Innovation and employment: A survey: IZA. Discussion paper number 2621, Germany: University of Bonn.