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Remittances and value added across economic sub-sectors in Sub-Saharan Africa

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Abstract

This research assesses the relevance of enhancing remittances on value added across economic sectors in sub-Saharan Africa for the period 1980–2014 using the Generalised Method of Moments. First, no significant net effects on added value to the agricultural sector are apparent. Second, enhancing remittances engenders a positive net effect on added value to the manufacturing sector. Third, there are negative net effects on added value to the service sector. Given that the unfavourable net incidence of remittances to the service sector is associated with a positive marginal or conditional effect, the analysis is extended by computing thresholds at which remittances induce net positive effects on added value to the service sector. The extended analysis shows that a remittance threshold of 48.5% of GDP is the critical mass needed for further enhancement of remittances to engender positive net effects on value added to the service sector.

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Notes

  1. It is important to note that remittances as a percentage of GDP is used to present remittances in relative terms. This is a form of normalization used in most studies and widely accepted in the presentation of datasets in scholarly and policy circles (Akobeng 2016). The World Bank from which the data is sourced also presents the variable in this manner. However, remittance as a percentage of GDP can also be misleading if an economy is small or GDP is stalling (or not growing) because the same levels of remittance could be misinterpreted as increasing, whereas, it is the denominator that is constant or decreasing.

  2. The countries selected on data availability constraints are: Benin; Botswana; Burkina Faso; Burundi; Cameroon; Central African Republic; Cote d'Ivoire; Gabon; Kenya; Lesotho; Mauritania; Mauritius; Mozambique; Namibia; Niger; Nigeria; Rwanda; Senegal; Sierra Leone; South Africa; Sudan; Eswatini; Tanzania; Togo and Zimbabwe.

  3. Other studies on output and economic development supporting the relevance of adopted control variables are: Nyasha and Odhiambo (2015a, 2015b); Okafor, Piesse and Webster (2017); Muazu and Alagidede (2017); Kumi, Muazu and Yeboah (2017);Kreuser and Newman (2018); Maryam and Jehan (2018) and Yaya and Cabral (2017).

  4. “Our justification for employing two control variables in the GMM specification is very solid because employing more than two variables will lead to findings that do not pass all post-estimation diagnostic tests owing to instrument proliferation, even when the option of collapsing instruments is taken on board in the estimation exercise. There is a choice here between having valid estimated models and avoiding variable omission bias. Hence, adding more control variables will produce invalid estimations (Bruno et al. 2012; Osabuohien and Efobi 2013…)” (Asongu and Odhiambo 2020, p. 678).

Abbreviations

GDP:

Gross domestic product

SSA:

Sub-Saharan Africa

ICT:

Information and communication technology

GMM:

Generalised method of moments

WDI:

World development indicators

UNCTAD:

United Nations conference on trade and development

FDSD:

Financial development and structure database

FDI:

Foreign direct investment

DHT:

Difference in Hansen test

TSLS:

Two-stage least squares

IV:

Instrumental variable

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  1. Department of Economics, University of South Africa, P. O. Box 392, Pretoria, UNISA 0003, South Africa

    Simplice A. Asongu & Nicholas M. Odhiambo

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SAA participated in the writing of the manuscript and data analysis. SAA and NMO participated in the revision of the manuscript. Both authors read and approved the final manuscript.

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Appendix

Appendix

1.1 Appendix 1

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Table 2 Definitions and sources of variables
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1.2 Appendix 2

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Table 3 Summary statistics
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1.3 Appendix 3

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Table 4 Correlation matrix (uniformsample size:123)
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Asongu, S.A., Odhiambo, N.M. Remittances and value added across economic sub-sectors in Sub-Saharan Africa. Qual Quant 56, 23–41 (2022). https://doi.org/10.1007/s11135-021-01110-0

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