ICTs and labour productivity growth in sub‐Saharan Africa

• Author: Bruno SERGI,Evelyn WAMBOYE,Abel ADEKOLA
• DOI: http://doi.org/10.1111/j.1564-913X.2014.00021.x
• Published date: 01 June 2016
• Date: 01 June 2016

International Labour Review, Vol. 155 (2016), No. 2

Copyright © The authors 2016

Journal compilation © International Labour Organization 2016

* Pennsylvania State University, email: efw10@psu.edu. ** Jay S. Sidhu School of

Business and Leadership, Wilkes University, PA, email: abel.adekola@wilkes.edu. *** Davis

Center for Russian and Eurasian Studies, Harvard University, Cambridge, MA, and University

of Messina, Italy, email: bsergi@fas.harvard.edu. The authors would like to thank the managing

editor of the International Labour Review and one anonymous referee for their helpful comments

and suggestions.

Responsibility for opinions expressed in signed articles rests solely with their authors, and

publication does not constitute an endorsement by the ILO.

ICTs and labour productivity growth

in sub-Saharan Africa

Evelyn WAMBOYE,* Abel ADEKOLA** and Bruno SERGI***

Abstract. This article investigates the effect of various information and commu-

nication technologies (ICTs) on labour productivity growth, using a sample of

43 sub-Saharan African countries. The authors’ ndings show signicant increasing

returns for labour productivity growth from xed-telephone and mobile-cellular

penetration, conrming the presence of network effects. Specically, doubling the

current proliferation rate of xed and mobile-cellular telephones increases labour

productivity growth by approximately 0.12–0.15 per cent, and 0.05 per cent, respect-

ively. Furthermore, the results point to nancial inclusion as one of the possible

channels through which mobile-cellular subscriptions affect labour productivity

growth in sub-Saharan Africa.

Sub-Saharan African countries started registering consistent positive eco-

nomic growth in the mid-1990s (see gure 1). In 1995, the region’s GDP

per capita growth rate averaged around 1 per cent, and by 2 004 had peaked at

3.58 per cent – the highest growth rate ever recorded for the region since 1970.

While the 2008 global nancial crisis temporarily interrupted this momentum,

the positive trend was not reversed. This notable economic performance was

supported primarily by robust domestic demand, higher commodity prices

and investment in the service sector (World Bank, 2013). Increasing political

stability in previously unstable countries and external debt relief through the

Heavily Indebted Poor Countries (HIPC) initiative also contributed to a fa-

vourable growth environment (World Economic Forum, 2013; Yonazi et al.,

2012; Wamboye and Tochkov, 2014).

The period of robust growth in sub-Saharan Africa coincided with the rapid

proliferation of information and communication technologies (ICTs) in the

region. For example, between 200 0 and 2011, the number of mobile-cellular

International Labour Review232

subscriptions per 100 people rose by more than 3,000 per cent and that of

Internet users per 100 people by more than 2,400 per cent. In 20 00, less than

2 per cent of the population had mobile-cellular subscriptions or used the In-

ternet; however, by 2011, this had increased to more than half of the popu-

lation, and roughly 13 per cent of the population, respectively (see gure 2).

Internet bandwidth also grew almost 20-fold between 20 08 and 2012, rising

from 80 Gbps in 200 8 to roughly 15.7 Tbps in 2012 (ITU, 2010). The increase in

mobile-cellular and Internet penetration rates was accompanied by increased

speed of Internet access and the declining cost of mobile telephone calls.1

Nonetheless, the ICT penetration rate in sub-Saharan Africa still lags behind

that of the Middle East and North Africa (gure 3).

In this article, we evaluate the impact of a number of ICT indicators on

labour productivity growth, i.e. real GDP per worker growth, in a sample of

43 sub-Saharan African countries over the period 1975–2010.2 The indicators

serve as proxies for the underlying causal factors that are either not observ-

able or for which no appropriate data exist in the context of African economies.

Using non-linear parametric analysis in a dynamic framework, we estimate the

1 Statistics are from the World Bank Africa Development Indicators database.

2

Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cabo Verde, Central African

Republic, Chad, Comoros, Democratic Republic of the Congo, the Congo, Côte d’Ivoire, Equatorial

Guinea, Ethiopia, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia,

Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda,

Senegal, SierraLeone, SouthAfrica, Sudan, Swaziland, United Republic of Tanzania, Togo, Uganda,

Zambia and Zimbabwe. Because of inadequate data for the specied sampling periods, Eritrea,

Sao Tome and Principe, Seychelles, Somalia and South Sudan are not included in the sample.

1

2

3

Figure 1. Sub-Saharan Africa (all income levels), annual GDP per capita growth

4

5

–2

–1

0

–4

–3

6

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

Source: World Bank Africa

Development Indicators database, 2016.

2003

2005

2007

2009

2011