Engendering Labour Statistics

1
Engendering Labour Statistics

• UNECE Statistical Division

2
Segregation
3
Activity rate
Unemployment Rate
4
Employment Rate
5
Labour Force Surveys, Census, Surveys
Enterprise surveys, LFS, Census
LFS, Census, Registers
6
Engendering Labour Statistics

• How can we make the process of collecting,
  processing and disseminating employment
  statistics more gender sensitive?

7
Engendering Labour Statistics

• Where is the gender bias?
• Man-biased data collection (question wording)
• Inadequate definitions and concepts
• Gender-biased responses
• Gender-biased enumerators
• Gender-blind content of the data collection

8
Engendering Labour Statistics

• Question wording
• Formally there is a clear distinction between
  employed and non employed population
• ILO definition a person is currently employed if
  he/she has worked at least one hour the week
  previous the survey
• Work for income (cash or kind) or unpaid
  production of goods

9
Engendering Labour Statistics

• Question wording
• Prior 1994, US Labour Force Survey (LFS) What
  were you doing most of last weekworking, keeping
  house, or something else?
• For women who primarily kept house but also did
  some paid work, this question appears to have led
  to some underreporting of work
• Now, US LFS Last week, did you do any work for
  pay or profit?
• Following the redesign, the survey found an
  increase in the number of workers, primarily
  women, who usually worked fewer than 10 hours per
  week

10
Engendering Labour Statistics

• Question wording
• Can the LFS questions be improved to include all
  women and men who do work according to the ILO
  definition?
• Do the current questions capture persons who have
  atypical jobs?

11
Engendering Labour Statistics

• Question wording
• Concepts should be operationalized in a way that
  respondents can understand it.
• What does work mean?
• What does child care mean?
• Cognitive testing, focus groups help to make sure
  that the concepts used are interpreted correctly

12
Engendering Labour Statistics

• Concepts and definitions
• Womens work tend to be more heterogeneous than
  mens work, but standard classifications are more
  one-dimensional
• Some unit of analysis hide the individual (and
  therefore the gender) dimension
• household, farm, economic unit

13
Engendering Labour Statistics

• Gender-biased responses
• Male respondents may fail to report women
• Respondents may not understand the content of the
  questionnaire
• Respondent give wrong answers to meet social
  norms

14
Meeting social norms US Survey Example
Engendering Labour Statistics

• The following questions and results were obtained
  in an American survey

'Yes'
Have you ever heard the word AFROHELIA? (no such
word!)
8
Have you ever heard of the famous writer, John
Woodson? (no such writer!) Have you ever heard
of the Midwestern Life Magazine? (no such
magazine!) Do you recall that, as a good citizen
you voted last December in the special election
for your state representative? (no election!)
16 25
33
Have you ever heard of the Taft-Pepper Bill
concerning veteran's housing (no such bill!)
53
Sometimes this type of bias is called prestige
error
15
Engendering Labour Statistics

• Gender-biased enumerators
• Enumerators may introduce his/her personal view
  (norm) in the interview
• Poor training
• Social pressure
• Lack of interest
• Enumerators may establish poor relationship
• Not gender-correct language
• Body language

16
Enumerator-bias Example Australian Survey
Engendering Labour Statistics

• Average number of sex partners reported
• By women who were watched as they filled in their
  survey answers 2.6
• By women who knew they were completely anonymous
  3.4
• By women who thought they were attached to a lie
  detector 4.4

Sydney Morning Herald, August 31, 2003
17
Engendering Labour Statistics

• Gender-blind content
• What are the gender issues in employment?
• .Next activity.

18

• Thank you !