HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress

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“Unemployment, joblessness or informality: Tracking jobs under SDG”
William Baah-Boateng
Senior Research Fellow, African Centre for Economic Transformation (ACET)
Senior Lecturer, Department of Economics, University of Ghana
Ela Bhatt Guest Professor, University of Kassel, Germany
Introduction
Until recently, employment issues were not featured prominently in the global discourse
on Africa’s development. In the design of the Millennium Development Goals (MDG),
employment was not considered as an issue to measure and monitor the development
effort of developing countries. It was until 2008 when employment was finally
recognized as the vehicle through which economic growth is translated into sustainable
poverty eradication and women empowerment. One target and five indicators were
introduced in Goal 1 and one indicator in Goal 3. This was probably triggered by rising
joblessness particularly, among the youth against the backdrop of the strong and
impressive growth performance of many countries in Africa.
In the UN Sustainable Development Goals (SDG) that seeks to complete the job of the
MDG has devoted one out of 17 goals to focus on employment and economic growth.
Goal 8 of the SDG seeks to promote inclusive and sustainable economic growth,
employment and decent work for all. This goal recognizes that growth is not an end in
itself but can impact on people’s livelihood through better and well-remunerated
employment creation. Essentially, the consideration of inclusive growth in the SDG is
informed by the fact that poverty reduction is only possible through well-paid and stable
jobs. Over a period of five years between 2007 and 2012, global unemployment increased
by about 32 million to 202 million out of which over a third were young people (ILO,
2014). It is estimated that 470 million jobs are needed globally for new entrants to the
labour market between 2016 and 2030 (UNDG, 2013).
Goal 8 of the SDG has 12 targets including sustain per capita economic growth, achieve
full productive and productive employment and decent work for all, and substantially
reduce the proportion of youth not in employment, education or training. The main thrust
of this paper is to discuss the conceptual and measurement issues within the framework
of the SDG with the focus on unemployment and informality.
Unemployment and Informality dichotomy
Unemployment and informality are two key labor market concepts that have been widely
discussed in the labor market discourse in Africa amidst measurement concerns. The

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relationship between the two has attracted some discussions in the empirical literature of
labor economics in recent times. The high (or low) degree of informality has been cited
as one major reason behind low (or high) rate of unemployment in countries on the
continent (see Baah-Boateng, 2015). Thus, a clear trade-off exists between
unemployment and informality in Africa.
Figure 1 presents a scatter chart of unemployment rates and informal sector employment
as a proportion of total employment of 30 African countries between 1997 and 2012
showing a clear trade-off between informality and unemployment rate. The upper left
corner of Figure 1 shows a list of countries (mostly in the West, East and Central Africa)
with low unemployment rates below 10% and informal sector employment of at least
75% of total employment. On the other hand, countries in the lower right corner of Figure
1 mostly from the southern Africa are characterised by low degree of informality and
high rates of unemployment
Figure 1: Unemployment-Informality trade-off
Source: Baah-Boateng (2015)
The negative association between unemployment and informality is also established
quantitatively (see Table 1). From a simple Spearman’s and Pairwise correlation covering
30 countries shows a significantly negative association (with negative correlation of 0.7)
between informal sector employment and unemployment (Table 1), confirming the trade-
off observed in Figure 1.
Rwanda
Benin
Niger
Uganda
B.Faso
M'gascar
S.Leone
Liberia
CameroonC.Ivoire
Tanzania
Ethiopia
Ghana
Malawi
Mauritius
Mali
Morocco
Egypt
Kenya
Senegal
Algeria
Zimbabwe
Zambia
Namibia
Botswana
Tunisia
Swaziland
S.Africa
Lesotho
Réunion
0
20406080
100
0 10 20 30
Unemployment rate

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Table 1: Results of Simple Correlation between Unemployment Rates and Informality &
informality and working poverty rate
Spearman Correlation Pairwise Correlation
Unemployment Rate and Informal
employment
Number of Observations = 30
Spearman’s rho = -0.7371
Test of Null hypothesis: unemployment rates
and informality are independent
Prob>|t| = 0.00000
Pairwise correlation between informality and
unemployment rates = -0.7206
Number of observations = 30
Prob>|t| 0.0000
Source: Baah-Boateng (2015)
The informality-unemployment trade-off is strongly linked to the structure of the labour
markets of African countries. The high unemployment rate and low informality are
reported in countries characterized largely by better-structured and better regulated labour
market with majority of jobs created in the formal sector and limited opportunity for
informality to flourish. The inability of the formal sector to absorb the increasing
jobseekers coupled with limited informal sector to absorb the surplus labour implies that
unemployment rate will be high. These countries include South Africa, Namibia, Tunisia,
and Botswana.
On the other hand, countries with poorly structured labour market where formal jobs are
difficult to come by create environment for informality to thrive. These countries are also
characterized by the absence of well-structured safety net such as unemployment
insurance, which compels most of the unemployed to seek refuge in the informal sector
as survival strategy. For example, given two economies with one having a comprehensive
social protection scheme and the other little or no social protection scheme, individuals in
the former can afford to remain unemployed while those in the latter countries with no or
limited unemployment insurance coverage must do something to make a living; no matter
how inadequate. Thus, a high degree of informality provides a safety net for many
jobseekers that are unable to endure the challenges of remaining unemployed for a long
time.
Indeed, many jobseekers especially those with limited employable skills required in the
formal labour market are compelled to seek refuge in the informal economy as a survival
strategy. Increasingly, many of them, especially the less educated, are settling for survival
jobs in order to sustain themselves. The flow from unemployment to informality
produces low unemployment rate but high degree of informality. As noted by some
research work (Cling et al. 2007; Fares et al. 2006; World Bank 2006, inter alia),
unemployment as defined by ILO is increasingly seen as inadequate to characterize low-
income countries’ labour markets.

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Definition and Measurement Concerns
Unemployment
The concern about the application of the concept of unemployment from the ILO
perspective in Africa and many other developing regions has not been in doubt. The ILO
definition of unemployment which has been adopted by countries considers individual to
be unemployed if individual, within seven days is available for work, has no work and
actively looking for work (ILO, 1982). Baah-Boateng (2015) argues that many people fail
to make effort to seek work even though they have no work and are available for work
for various reasons including perception of no work or seasonality of work. In effect,
besides the high informality that tends to mask the extent of unemployment in less
regulated labor markets, is the high discouraged-worker effect arising out of the failure of
many jobless people who are available to make effort to seek work.
Source: Computed from Countries’ Household Surveys
The high discouraged-worker effect may be the underlying justification for the use of the
target of “reducing substantially the proportion of youth Not in Employment Education
and Training (NEET” rather than youth unemployment in Goal 8 of the SDG. Thus, the
target captures youth joblessness that includes the ILO defined unemployment and
discouraged workers. This can also be looked at from the perspective of increasing the
proportion of youth in employment without considering the quality of employment.
Generally, the representation of youth in well-remunerated jobs is lower than adults thus
undermining the effectiveness of using the NEET indicator to monitor access of youth to
quality jobs.
0
10
20
30
40
50
60
70
Figure 2: Unemployment Rates varrying definitions
Jobless, available &
seeking
Jobless & Available

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Informality
Informality is viewed from different perspectives by different schools of thoughts. ILO
(2012) defines informal economy as all economic activities by workers and economic
units that are in law or in practice not covered or insufficiently covered by formal
arrangements. In the view of the World Bank (2004), informal sector refers to activities
and income that are partially or fully outside government regulation, taxation, and
observation. Generally, the ILO (1999) proposed informal workforce to include the
following
- owner-employers of micro enterprises, which employ a few paid workers, with or
without apprentices;
- own-account workers, who own and operate one-person business, who work alone
or with the help of unpaid workers, generally family members and apprentices;
- dependent workers, paid or unpaid, including wage workers in micro enterprises,
unpaid family workers, apprentices, contract labor, homeworkers and paid
domestic workers.
This categorization however excludes informal engagement in the formal sector including
casualization and those with temporary employment in the formal sector. This is informal
employment in formal labor market settings making the operationalization of the concept
of informality for the purpose of measurement complex. In 2003, International
Conference of Labor Statisticians (ICLS) adopted a broader definition and measurement
to include informality outside the informal enterprises and referred to it as “informal
employment”. Statistics on informality published by countries largely cover the
workforce in the informal sector while many workers in the formal sector who are
engaged informally are captured as formal sector workers but informally employed. This
does not give a true picture of informal and formal employment in many countries in
Africa.
Data Challenges and Options
The dominance of non-wage employment in many African countries implies that data for
measuring and tracking employment performance of countries can largely be sourced
from household surveys and census. Essentially, most of non-wage employment is
informal. In Africa, labor market indicators are computed and constructed from survey
datasets, mostly welfare and living standards survey datasets. Notable among the surveys
are Core Welfare Indicators Questionnaires (CWIQ), population census and household
living condition survey. These surveys and population census are not conducted regularly
and thus makes it difficult to track employment and labor market performance, at least on
annual basis. In addition, these surveys are conducted for different reasons and purposes
with different sampling frame and questions, making it difficult to harmonise them for

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trend analysis. For example, while household surveys have wage employment as one
option of employment type, population census accounts for regular employment but the
two are different conceptually.
Ideally, labor force survey is the primary and appropriate means of sourcing employment
and labor market data for informality dominated labor market. However, many countries
in Africa have never conducted labor force survey with many others having had one or
two labor force surveys. This tends to impede regular and effective tracking and
monitoring of employment and labor market performance of countries within the global
development agenda. Administrative survey, which largely covers formal sector, is one
good source of regular labor market data but limited in an informal sector dominated
labor markets. The best option of ensuring regular production of labor market data is
conscious effort of government to invest more in the conduct of labor force survey at
least every 3 years.
Reference:
AfDB, OECD, UNDP, and UNECA (2012) Promoting Youth Employment, African
Economic Outlook 2012, www.africaneconomicoutlook.org
Aryeetey, E., Baah-Boateng W, Ackah C, Mbiti, I and Lehrer, K. (2014) “Ghana” in Hino and
Ranis (ed.) Youth and Employment in Sub-Saharan Africa: Working but Poor, Rutledge
Publication, pp. 233-302, ISBN: 9780415859387
Baah-Boateng W (2015) “Unemployment in Africa: how appropriate is the global definition and
measurement for policy purpose?” International Journal of Manpower, Vol. 36, Issue. 5,
pp. 650-667 Emerald, ISSN: 0143-7720
Cling, J. P., Gubert, F. N., Christophe J., and Anne-Sophie R. (2007) ‘Youth and Labour
Markets in Africa: A Critical Review of Literature’ Document de Travail No 49,
Agence Française de Développement, Paris,
Fares, J. M., Claudio E., and Orazem, F. P (2006) ‘How are Youth Faring in the Labour
Market? Evidence from around the World’, Policy Research Working Paper
Series 4071, The World Bank, Washington DC
ILO (2014) “Risk of a Jobless Recovery”, Global Employment Trends 2014,
International Labor Office, Geneva
ILO (2013) “Key Indicators of the Labor Market (KILM)” 7th
Edition, International Labor Office,
available at www.ilo.org
ILO (2012) “Measuring Informality: A Statistical Manual on the informal sector and
informal employment. International Labor Office Geneva

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ILO (1982) Thirteenth International Conference of Labour Statisticians, International
Labour Office, Geneva,
UNDG (2013) “Growth and Employment in the Post–2015 Agenda”, Messages from a
global consultation, United Nations Development Group,
www.worldwewant2015.org/employment
World Bank (2006) Labour Diagnostics for Sub-Saharan Africa: Assessing Indicators
and Data Available World Bank, Washington, DC