Availability and Quality of Data

Angela MeUNECE Statistics Division

Issues in looking at the quality of the data

For users: do not leave the issue of quality and availability only to statisticians. There are issues that are not too “statistically technical” that affect the Message and need to be addressed

Issues in looking at the availability of the data

Do the available data provide evidence for gender analysis?

Under-use of existing data Gender is not properly considered in

the existing sources There are no sources available Miss-use of existing data

Under-use of existing data: ex. Wages

Activity branch Male Female

Transport/Communication

23450 15670

Education 20670 18970

Health 27690 22840

Public administration

15460 13250

No total pay gap presented (needed for advocacy)

No pay gap included in the gender publication despite the data are available

Under-use of existing data: ex. Wages

In gender statistics publications do you include data on:

Employment by occupation

Employment by status in employment (self-employers)

Employment by family composition

These data are available if a census and/or a labour force survey was carried out and usually show large gender disparities

Gender not properly considered

Sex is not included in the data collection Business registers, disease reporting,

voting registers

Sex is not included in the dissemination of the data

Issues that reflect an unequal participation of women an men are not properly collected Quality of work, informal employment,

leading positions

Data collection not available

Surveys on gender attitudes

Surveys on time-use

Surveys on reproductive health

Surveys on violence against women

Miss-use of data: Example of Monetary Poverty

Data on income poverty are based on household income or consumption

• Difficult to disaggregate by sex (transfers within households are unknown)

• It is not relevant to use the concept of head of household: it is only a statistical concept that does not reflect the income distribution in households

Miss-use of data: Example of Monetary Poverty

Data based on income of the head of households (HH) give a biased picture

• Women who declare as HH are usually:• the most educated• The ones that do not live with a partner

• Statistics of men HH are usually based on the largest proportion of households and women HH are a not representing minority

Miss-use of data: Example of Monetary Poverty

To be measured considering income or consumption by type of household

One-single-person households by sex

One-single-parent households by sex One-income-earning-person

households by sex Others…

Issues in looking at the availability of the data

How to improve?

Inquiry about all the data available Try to influence the existing sources to

make them more gender-sensitive Advocate for the development of new

gender-sensitive data collection (within the national statistical masterplan)

Avoid the miss-use of data

Issues in looking at the quality of the data

Why data quality is important?

Wrong data give wrong messages Wrong messages lead to wrong political

interventions or no intervention Advocacy needs to be backed up by

solid data to be credible in the long run

Issues in looking at the quality of the data

Some of gender related issues

Inadequate definitions and concepts

Man-biased data collection (question wording)

Gender-biased responses Gender-biased enumerators

Inadequate definitions and concepts

Data collection is based on: Households or farm and not on

individual The concept of the head of economic

activity Classifications are men-oriented (ex:

occupation -ISCO) Concept definitions (ex: in some

countries employment may include women in long maternity leave)

Biased question wording

Example: Do you work?

“Work”=interpreted as formal work

People engaged in informal activities are undercounted. Usually women are

engaged more than men in informal sector (particularly agriculture) and

therefore are undercounted

Biased question wording

More women-sensitive…..

o Are you engaged in any work paid in money or in kind?

o Do you sell products on the street or at the market?

o Are you engaged in agriculture activities to produce goods for the household consumption?

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

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

Gender-biased enumerators

Issues in looking at the quality of the data

Different sources may provide different data

Entrepreneurship

An issue of definition and data availability

Entrepreneurship: definitions

Own-account workers

Employers

Owners

Managers

Self-employed

Members of Ex. Boards

Entrepreneurship

Labour Force Surveys (LFS)self-employedmanagersemployersown-account workers

Enterprise Surveysemployersmanagersownersmembers of ex. boardsaccess to credit

Registers (Business, taxes, …)SME ownersaccess to credit

Entrepreneurship

Different sources different perspectives different concepts and definitions

Data from different sources may not

be comparable

Entrepreneurship: Data Availability