Transcript 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.

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
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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
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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
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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
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Key gender issues
emerging from selected
African LFS
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Ethiopia
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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+)
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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
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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+)
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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+)
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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+)
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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+)
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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+)
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Employment in the informal sector by industry
and sex
120
100
Percentage
80
60
40
20
0
Male
Source: Zambia LFS 2005 (15+)
Female
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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+)
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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
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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
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Mainstreaming gender
into LFS
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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
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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…)
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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
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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
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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
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Thank you!
African Centre for Statistics
Visit us at http:www.uneca.org/statistics/
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