Africa has been experiencing fast economic growth in the past decade. Between 2001 and 2010,
six of the world’s ten fastest-growth economies were in sub-Saharan Africa (SSA) (AfDB et al.,
2012). In 2011, the economy of SSA grew by 5.2 per cent, down from 5.4 per cent in 2010 (ILO,
2012). Economic growth in Ghana, for instance, reached 14.2 per cent in 2011 while Ethiopia’s
economy expanded by 11.4 per cent in 2010. Rwanda and Mozambique managed growth rates of
7.2 per cent and 6.8 per cent respectively in 2010, while Uganda and Kenya recorded growth of
6.1 per cent and 5.6 per cent respectively. Benin, however, managed a moderate growth of only
2.6 per cent in 2010. The generally robust growth performance in SSA is expected to translate
into productive and decent employment for the growing working-age population. The challenge
however has been the lack of consistent and reliable labour market data at country level to
facilitate efficient monitoring of the employment generation impact of economic growth. There
are different sources of labour market data including population and housing censuses, living
conditions and labour force surveys, administrative surveys and advertised job vacancies. The last
two sources only capture indicators in the formal segment of the labour market, which accounts
for less than one-fifth of the labour market of most countries in SSA. Indeed, the dominance of
informal sector employment in SSA implies that labour market performance can best be
monitored through regular nationwide surveys.
A comprehensive analysis of employment and unemployment implications of economic growth at
country level in SSA has not been carried out in many countries. As a result, policy makers and
donor partners in Africa are compelled to rely on anecdotal sources to pursue policies to address
employment and earnings challenges. Comprehensive analyses of the labour market for the
assessment of employment and unemployment implications of country economic development
policies are therefore needed. This mapping exercise of relevant labour market indicators and
employment related policies in selected countries is a start towards responding to this need. It is
an initiative of the Knowledge Platform Development Policies, sponsored by the Netherlands, and
aiming to promote inclusive development and transformation. There is a growing consensus that
employment should be viewed as a central objective of development policy as the lack of
productive employment remains a major obstacle to reducing poverty [ODI, Development
Progress 2013]. Despite remarkable growth rates in many sub-Saharan African countries over the
past decade, the high level of self-employment, the large informal sectors, the prevalence of lowpaying
work and the general persistence of underemployment for large segments of the
population are symptoms of lack of productive employment and hence of persisting poverty.
The need for the mapping of employment and unemployment trends using country case studies
emerged out of the Platform’s meeting in October 2012. Two themes were selected at this
meeting: Promoting Productive and Sustainable Employment and Strategic Actors for Inclusive
Development. The mapping exercise is linked to the former and is intended to provide
information and knowledge base in preparation to the Platform’s meeting in Accra, Ghana, in
April 2013.
The report of the mapping exercise covers seven SSA countries, namely Benin, Ethiopia, Ghana,
Kenya, Mozambique, Rwanda and Uganda, and focuses on key labour market indicators and on employment policies in selected African countries.2 The mapping includes for each of the
selected countries data on (and identification of sources for) the following eight indicators:
1. Employment-to-population ratio
2. Breakdown of employment by age and gender
3. Sectoral distribution of employment (agriculture, manufacturing sectors, services
sectors, public sector, informal sector)
4. Urban and rural employment (unemployment)
5. Hours worked if available
6. Labour remuneration in different activities if available
7. Hours worked and remuneration in informal sector if available
8. Size and nature of unemployment by age and gender.