United Nations Economic Commission for Africa
African Centre for
Statistics
Key Gender Issues in the Labour
Force: African Experiences
Workshop on Household Surveys and Measurement of Labour
Force
14-18 April 2008, Maseru, Lesotho
Dimitri Sanga, Ph.D.
Senior Statistician
Outline
 Labour
force surveys
 The need for engendering labour force
surveys
 Key gender issues emerging from
selected African labour force surveys
 Mainstreaming gender into labour force
surveys
 Conclusions
2
Labour force surveys
3
Objectives
 Overall
objective: Collect information on
the characteristics of employment and
the distribution of the population based
on their position in the labour market
4
Main categories
Population of a given age group
Active
Employed
Inactive
Unemployed
5
Information

Keeps track of the labour market and the
economy in general

Provides regular information on indicators:
Activity rate
Average work schedule
Proportion of temporary/permanent jobs
Proportion of part/full time jobs
Unemployment rate
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Information(Cont’d)

May provide information on:
Sector: public sector and private enterprises
Socio-professional category: employee, family
helper, head of an enterprise…
Others: employment contract, access to social
security, the right to paid leave, sick leave, etc

Employment characteristics can be used to:
Identify those who are in the informal economy
Those who belong to an informal production unit
Those holding an informal job in a formal
enterprise or in a household
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Labour force surveys in Africa

LFS in developed and medium-income
countries: on an infra-annual basis

In Africa:
Some LFS
Equivalent tools: employment surveys or
employment segment of household surveys
Frequency may be non-defined (because the
operation may depend on external financing)
In some countries: frequency is at best annual
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The need for
engendering labour
force surveys
9
The case for engendered LFS
 Engendered
labour force surveys like
other statistical operations help:
Conduct unbiased evidence-based policy
formulation and decision making
Address issues of inequalities and
empowerment of women
Raise consciousness, persuade policy
makers and other stakeholders to take into
account the gender dimension in policy
formulation and decision making processes
10
Key gender issues
emerging from selected
African LFS
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Ethiopia
12
Activity rate by age and sex
Whatever
the age
group:
women are
less active
than men
Activity Rate
65+
35.13
73.38
58.65
60 - 64
92.82
73.76
55 - 59
96.07
77.6
50 - 54
95.28
85.33
45 - 49
86.59
40 - 44
84.46
35 - 39
30 - 34
85.77
25 - 29
85.62
81.9
20 - 24
97.67
98.1
97.87
97.76
95.91
88.7
69.07
73.16
15 - 19
54.47
10-14
66.57
73.46
All ages
Male
Source: Ethiopian LFS, 10+
84.75
Female
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Employment to population ratio
100
90
80
70
60
%
50
40
30
20
Male
Female
10
0
10 - 14
15 - 19
20 - 24 25 - 29
Male
70.1
73.2
86.4
Female
53.4
62.7
73.8
30 - 34
35 - 39
40 - 44 45 - 49
93
96.4
96.5
97.8
78.2
80.2
79.1
82.5
50 - 54
55 - 59
60 - 64
65+
97.3
95.5
94.5
92.8
75.2
81.2
76.4
72.9
61
38.9
Source: Ethiopian LFS,
Men are more employed than women in all age groups
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Employment rates by age group and sex
120
100
Percentage
80
60
40
20
0
All ages
10-14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
Male
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65+
Female
Source: Ethiopia LFS 2005 (10+)
15
Employment by industry and sex
16,000,000
Some
industries
are female
dominated
while
others are
male
dominated
14,000,000
12,000,000
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000
0
Agriculture, Hunting
Forestry & Fishing
Manufacturing
Male
Female
Whole sale & Retail
Trade,Repair of
Vehicles,Personal &
Household goods
Source: Ethiopian LFS, 2005 (10+)
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Employment in formal/informal sector
1,400,000
Men are
mainly
employed in
the formal
sector while
female
dominate
the informal
sector
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
Formal sector
Informal sector
Not Identified
Male
1,189,349
563,664
8,935
Female
629,966
761,517
2,364
Source: Ethiopian LFS, 2005 (10+)
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Unemployment vs. literacy
700,000
600,000
500,000
400,000
300,000
200,000
100,000
0
All Illiterate
All Literate
Male
Female
Source: Ethiopian LFS, 2005 (10+)
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Unemployment vs. Education level
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0
Male
Female
Source: Ethiopian LFS, 2005 (10+)
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Current female employment by marital status
50
45
40
35
30
25
20
15
10
5
0
Married/Living together
Divorced/Separated/Widowed
Never married
Source: Ethiopian LFS, 2005 (10+)
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Zambia
21
Proportion of currently unemployed
population by sex and age group
30
25
Percentage
20
15
10
5
0
All
Zambia
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
Age-Group
Male
Female
Source: Zambia LFS 2005 (15+)
22
Unemployment by education level
30
25
Percentage
20
15
10
5
0
Grade 1-7
Grade 8-9
Grade 10-12
Male
A Level
Degree
Female
Source: Zambia LFS 2005 (15+)
23
Distribution of employment by occupation and
sex
Service
Production and related
Agriculture, forestry and
fisheries
Sales
Clerical and related
Professional, technical and
related
Administrative, managerial
0
10
20
30
40
50
60
70
80
Percentage
Female
Male
Source: Zambia LFS 2005 (15+)
24
Distribution of employment by status
60
53
51
50
40
Percentage
34
30
26
22
20
12
10
1
1
0
Self-Employed
Employer
Paid Employee
Male
Unpaid Family Worker
Female
Source: Zambia LFS 2005 (15+)
25
Informal/formal sector
•The
proportion of
informal
sector
employees is
higher among
females than
males
94
100
83
90
80
70
60
50
40
30
20
17
6
10
Male
0
Formal
Informal
Female
•More jobs in
the informal
sector than in
the formal
sector
Source: Zambia LFS 2005 (15+)
26
Employment in the informal sector by industry
and sex
120
100
Percentage
80
60
40
20
0
Male
Source: Zambia LFS 2005 (15+)
Female
27
Employment status in the informal sector by
sex
80
70
60
Percentage
50
40
30
20
10
0
Self Employed
Employer
Paid Employee
Male
Unpaid Family
Worker
Other Status
Female
Source: Zambia LFS 2005 (15+)
28
Employment status in the informal sector
80
70
60
50
40
30
20
10
0
Employer
Paid Employee
Male
Female
Unpaid Family
Worker
Other Status
Source: Zambia LFS 2005 (15+)
Women are mainly unpaid family workers in the informal
sector
29
Economically active population
80
70
60
50
40
30
20
10
0
Grade 1-7
Grade 8-9
Grade 10-12
A level
Degree
Ma le
Fe ma le
Source: Zambia LFS 2005 (15+)
Primary education: no imbalance
Above primary education: more are more likely to be active
30
Mainstreaming gender
into LFS
31
Different stages of the survey
 Gender
issues should be taken into
account at different stages of the survey:
Planning and design
Methodology
Data collection and processing
Data analysis and dissemination
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Planning and design
Study the societal needs in data on the labour
force
 Selection of topics:

Should reflect ways in which men and women
view, perform, control, benefit from their work
activities

Coverage:
 As many topics and types of productive activities
 Work in widest sense, working time
 Job-seeking behaviour
 Moonlighting or combined activities
 Casual work, subsistence
 Informal employment
33
Methodology

Concepts and definitions:
Employment: does it include or not women in
long maternity leave?
Head of he household: is it always a man?

Questionnaire design:
 Adequate detail: on pots occupied by the respondent
 Formulation: avoid biased questions:



Do you work? (interpreted as formal sector)
Are you engaged in any work paid in money or in kind?
Do you sell products on the street or at the market?
Type and place of work
 Reflect income components (salaries, benefits,
overtime…)
34
Data collection and processing

Training of statistician and field staff: an good
understanding of gender sensitive statistics

Respondent’s choice (proxy or not)

Unit: household or individual?

Treatment of the collected information:
Imputation methods should take into account the
gender perspective
Missing values
35
Data analysis and dissemination
Use effectively gender blind data through
appropriate data analysis
 Have a tabulation policy in support of gender
sensitive analysis
 Relevant dissagregation:

 By sex (a minimum)
 Marital status
 Family/personal characteristics
 Job characteristics
 Family context
 Personal circumstances

Gender statistics database
36
Conclusions
 There
is a need to engender statistical
processes including LFS
 Mainstreaming
gender into LFS should
be done at every step of the survey
process
 There
is a need for sensitisation of survey
statisticians through training
37
Thank you!
African Centre for Statistics
Visit us at http:www.uneca.org/statistics/
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