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The productivity cost of power outages for manufacturing small and medium enterprises in Senegal

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Abstract

This paper investigates on the productivity effects of power outages on manufacturing small and medium enterprises (SMEs) in Senegal, using a panel data on manufacturing firms. Productivity is estimated using stochastic frontier models, and power outages measured by their frequency or their duration. We controlled for firms owning a generator, relevant covariates as data availability permits as well. The main results are drawn from random effects linear panel model. Nonetheless, the results remain consistent to the robustness checks using different models: a double-sided truncated data model and a generalized linear model, and different productivity measures, using data envelopment analysis. We find that power outages have negative significant effects on the productivity of SMEs in Senegal. Further, firms with a generator were successful in countering the adverse effect of power outages on productivity, this make the negative effect bore only by SMEs, which in some cases cannot afford to own a generator. As a matter of fact, the manufacturing sector lost up to around 15% of the actual productivity due to power outages in 2012, and small and median firms have lost, respectively, around 4.7 and 4.2%. Besides, another important finding is the significant positive effect of access to credit on productivity. At last, it is confirmed that productivity increases with firms’ size.

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Fig. 1

Source: By author, from World Bank data and the 2012 survey

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Notes

  1. We acknowledge the financial support of TrustAfrica and Institute for Research Communication and Development in funding the survey of the power outages effect for firms in Senegal (2012).

  2. In the literature very small enterprises, also referred to as micro firms, have been analyzed separately from small and medium enterprises. For recent references see Reinl and Kelliher (2014), Fajnzylber et al. (2011), Alves and de Medeiros (2015), Westhead et al. (2002), Baumann and Kritikos (2016) and Khan and Quaddus (2018).

  3. Originally developed by Durbin (1954), extended further by Wu (1973) and Hausman (1978), and known as Durbin–Wu–Hausman tests (DWH tests), this procedure is about comparing consistent estimates to some more efficient ones. In the case in hand in this paper, if endogeneity is not an issue, the panel estimates are consistent, and are preferable to panel-IV estimates. We test the null hypothesis that the error terms are uncorrelated with all the regressors against the alternative that they are correlated with some of the regressors. The regression extended version of the test is used here (Davidson and MacKinnon 2003). We first run the suspected variable against all other covariates, and save the residuals; afterwards, we run the main equation with the saved residuals as an explanatory variable, alongside the other covariates. If the residuals come out significant, therefore, endogeneity is an issue; in the opposite case, the suspected variable is free of endogeneity.

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Acknowledgements

I wish to express my deep appreciation to African Economic Research Consortium (AERC) for the financial support to carry out this research. I am also grateful to the resource persons and members of AERC’s thematic group D for various comments and suggestions that helped the evolution of this study from its inception to completion. Thanks are due to TrustAfrica and Institute for Research Communication and Development in funding the survey of the power outages effect for firms in Senegal (2012). I am indebted to the anonymous referees who reviewed the paper and provided comments and suggestions that helped in shaping and improving the overall quality of the paper. Many thanks to Jonathan Haughton and Darlene Chisholm at Suffolk University, and Babacar Séne at Université Cheikh Anta Diop for commenting early draft of this article.

Funding

This research was funded by the African Economic Research Consortium (AERC) (Grant No. RT15508) in Nairobi (Kenya).

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  1. Faculte des Sciences Economiques et de Gestion, Université Cheikh Anta Diop, BP 5683, Dakar Fann, Senegal

    Lassana Cissokho

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Appendix

Appendix

See Tables 9, 10, 11, 12, 13 and 14.

Table 9 Power Outages and related issues in Senegal
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Table 10 Production frontier estimation
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Table 11 Endogeneity test, second stage results
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Table 12 The effects of power outages on productivity: comparing different specifications (random effect)
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Table 13 The effects of power outages on productivity (random effect. log DEA scores as dependent variable)
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Table 14 The effects of power outages on productivity, generalized linear model
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Cissokho, L. The productivity cost of power outages for manufacturing small and medium enterprises in Senegal. J. Ind. Bus. Econ. 46, 499–521 (2019). https://doi.org/10.1007/s40812-019-00128-8

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