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Fostering Labour Productivity Growth for Productive and Decent Job Creation in Sub-Saharan African Countries: the Role of Institutional Quality

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Abstract

The majority of jobs created in Africa are informal, low-paying jobs that do not allow employees to live decently. An improvement in labour productivity will contribute to the creation of productive and decent jobs. The objective of this work is to study the effect of institutional quality on labour productivity in sub-Saharan Africa (SSA). To do this, we consider a panel of 31 countries over the period from 1996 to 2016. Thus, we construct an empirical model based on the stochastic frontier production function developed by Battese and Coelli (1995), to which we apply panel estimation techniques (static and dynamic), particularly with system generalized method of moments (System-GMM) and within estimators. Our results show that institutional quality indicators have a positive and significant influence on labour productivity. Political stability, government effectiveness and the rule of law are the indicators that contribute most to increasing labour productivity in sub-Saharan Africa. A series of robustness tests are performed to confirm our results. Specific indicators that contribute to labour productivity growth in sub-Saharan Africa are identified for the different sectors of the economy (agriculture, industry, services). These results enable us to identify several policy implications relevant to African decision-makers in order to create productive and decent jobs, especially for young people.

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Notes

  1. Governance here refers to the quality of institutions. Both terms will be used interchangeably in this work.

  2. The studies identified in Africa, dealing with this link, are generally microeconomic studies. For example, Goedhuys et al. (2008), using data on Tanzanian firms, show that the business environment positively affects their labour productivity. Similarly, Eifert et al. (2005) find that high indirect costs—due to high costs of transport and utilities, bribes, security, etc.—are a major cause of poverty, and losses related to the business environment reduce the productivity of African firms.

  3. In 2008, according to Africa’s Pulse, a biannual World Bank report, global labour productivity remained below 10% of that of the USA over the past 50 years for SSA, compared to 31.7% for developing countries except sub-Saharan Africa and 77.8% for advanced economies.

  4. The Global Competitiveness Index covering 140 countries measures the national competitiveness of economies, which is defined as all the institutions, policies and factors that determine the level of productivity. The competitiveness index for SSA is 45.2 out of 100 (below the global average of 60%). According to the report’s authors, this low score for African economies is due to a weakness in the institutions set up by states, as well as the inadequacy of their public policies. The average institutional score for sub-Saharan Africa is 47.5, which is lower than the global average of 55.3.

  5. Following Benjamin and Mbaye (2012), our study focuses on labour productivity, rather than total factor productivity (TFP). Indeed, three criticisms are generally addressed to the estimation of TFP: (i) it is calculated by assuming constant returns to scale, which may lead to the effect of scale on input efficiency being unduly attributed to technological variation; (ii) it assumes that factor shares in total costs are identical across sectors, which is not always the case, as technology can vary from one firm to another and from one sector of activity to another; (iii) the capital stock used in this method is generally calculated using the perpetual inventory method, generally based on sound assumptions regarding the depreciation rate and the initial capital ratio (Harrigan, 1997; Mbaye, 2002).

  6. Accounting breaks down differences in production per worker (i.e. labour productivity) into differences in factor allocations and Solow’s residue, which is TFP (TFP represents technological or efficiency differences).

  7. Economic freedom is the fundamental right of every human being to control his own work and property. In an economically free society, individuals are free to work, produce, consume and invest as they see fit. In economically free societies, governments allow factors of production (labour and capital) and goods to move freely and refrain from any coercion or constraint of freedom beyond what is necessary to protect and maintain freedom itself (Gwartney et al., 2019).

  8. The authors distinguish between these policies and political stability itself

  9. The stochastic frontier production function is initially used to study the technical efficiency of individual firms and then generalized to macroeconomic research (Aigner et al., 1977; Battese & Coelli, 1988; Meeusen and Van den Broeck, 1977). Several studies on the effect of institutional quality on labour productivity have used this model (Méon & Weill, 2005; Klein & Luu, 2003).

  10. The policy measures and institutional changes that most readily come to his mind are an improvement in government regulation and an increase in the ability of unions to influence production conditions.

  11. The policy measures and institutional changes that most readily come to his mind are an improvement in government regulation and an increase in the ability of unions to influence production conditions.

  12. This function has been used by many authors (Moroney & Lovell, 1997; Dawson, 1998; Méon & Weill, 2005).

  13. Endogeneity is a situation where an explanatory variable is correlated with the error term

  14. The dynamic dimension involves integrating the lagged dependent variable among the explanatory variables in the model; since the dependent variable is correlated with the error term, so is its lagged value; this also raises an endogeneity problem; in this case, the estimate within is no longer appropriate.

  15. This method suits the structure of our data (a non-cylindrical panel with T=21 < N=31)

  16. These values are obtained by multiplying the respective coefficients of the six institutional quality indicators by the standard deviations of the latter.

  17. By myopic policies, the author refers to the increase in the taxation of capital for redistribution purposes, the increase in government consumption for compensational purposes, the reduction of investment in the legal system, the delay (or reversal) of structural reforms and the renunciation on previous made commitments.

  18. These values are obtained by multiplying the respective coefficients of the six institutional quality indicators by the standard deviations of the latter

  19. These values are obtained by multiplying the respective coefficients of the six institutional quality indicators by the standard deviations of the latter

  20. This indicator comes from Fraser institute; it varies from 0 to 10 with higher values reflecting greater economic freedom.

  21. This is the arithmetic mean of the sub-indicators: government stability, internal conflict, external conflict and ethnic tensions.

  22. We did not multiply the coefficients estimated by the values of the standard deviations of the variables in order to obtain the coefficients that have a greater impact on labour productivity, because these indicators take different values. Indeed, indicators such as bureaucratic quality range from 0 to 4, corruption, voice and accountability and law and order from 0 to 6 and political stability from 0 to 12. A higher value reflects better governance. The correlation between these indicators and those of the WGI is quite high (Table 13); this allows us to use them as alternative measures of institutional quality.

  23. These values are obtained by multiplying the respective coefficients of the six institutional quality indicators by the standard deviations of the latter

  24. These values are obtained by multiplying the respective coefficients of the six institutional quality indicators by the standard deviations of the latter

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Correspondence to Koffi Délali Kpognon.

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Appendix

Appendix

List of countries included in our sample:

Benin, Botswana, Burundi, Cameroon, Central African Republic, Congo, Côte d'Ivoire, Democratic Republic of Congo, Gabon, Gambia, Ghana, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Togo, Uganda, Tanzania, Zimbabwe.

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Kpognon, K.D., Atangana Ondoa, H., Bah, M. et al. Fostering Labour Productivity Growth for Productive and Decent Job Creation in Sub-Saharan African Countries: the Role of Institutional Quality. J Knowl Econ 13, 1962–1992 (2022). https://doi.org/10.1007/s13132-021-00794-x

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