SESSION 4 - World Bank

Report
Workshop on Improving Gender Statistics in Rwanda
Session 4
Mainstreaming Gender into Statistics
Collection and Reporting
Serena Lake Kivu Hotel, Rubavu District
March 25-27, 2014
Session Objectives
The main objectives of this session are to:
•
Identify areas in the data collection process where gender bias
and other gender-related measurement errors may occur and
provide guidance on how to avoid them;
•
Raise awareness of the importance and usefulness of different
types of data sources for producing gender statistics; and
•
Show how a gender perspective can be integrated into
particular types of data collection and how the collected data
can be improved for gender statistics purposes.
Primary references:
UNSD 2013, Integrating a Gender Perspective in Statistics, Chapter 3;
UNECE and WBI 2010, Developing Gender Statistics: A Practical Tool, Chapter 3.
2
Gender statistics production process
Data collection is an integral part of the process of producing
gender statistics, as shown in the diagram below.
Identify gender issues
Identify data needed, assess data sources,
address conceptual and measurement issues
Undertake data collection
Analyse and interpret the collected data
Produce and disseminate statistics
3
I. Data collection stages
Planning
Design
Development
Collection of data
Processing of data
4
Planning
• The objectives need to identify and reflect the gender topics and policy
issues to be addressed by the collection.
 Objectives are typically determined following consultations between data
producers and data users.
 Include analysts and statisticians with a gender focus in such consultations.
• Because the objectives usually take account of findings from reviews of
previous data collections in the same program, a gender perspective
should be incorporated in such reviews.
 Analysis of surveys from previous census round can show,
̶ whether there was sex-selective underreporting of some characteristics
̶ whether errors were due to poorly phrased questions or instructions, proxy
response, sex of the interviewers, shortcomings in interviewers’
qualifications or to coding or data entry mistakes
• User groups and advisory committees can provide valuable input when
planning and developing collections.
 Include stakeholders concerned with gender issues among their members.
5
Collection design
Sample designs should:

Cover all groups of population or economic units known to have distinct gender
patterns; for example,
 Agricultural censuses and surveys should include small holdings which are typically
owned and worked by women

Ensure that reliable statistics can be produced for both females and males in
sufficient detail,

Allow disaggregation by other characteristics as required by meaningful gender
analysis

The units of data collection should be as disaggregated as possible to reveal
gender-based inequalities in household or holdings.
 For example, sex-disaggregated data on property ownership can be collected at the
individual level, while data on decision-making in agricultural activities can be collected
at sub-holding level, for plots of land and types of livestock.

The sample size should take account of gender statistics needs.
For example, the sample size of a survey measuring status in employment should be
large enough to allow data to be analysed separately for female and male employers
or other categories of self-employed, and further disaggregated by age group,
rural/urban areas and educational attainment.
6
Collection development
 Gender issues and gender-specific conceptual and measurement
issues should be taken into account in
– design of collection questionnaires and formulation of questions
– preparation of collection manuals preparation, and
– training of interviewers and supervisors
 Questionnaires should be field-tested to ensure both women and
men understand the questions in the same way and to detect any
potential gender bias in reporting.
 Collection manuals’ preparation should:
 Include detailed information about each question, and
 instructions and procedures to be followed when using the questionnaire
 Use general language free of gender biases or other stereotypes related
to the characteristics measured
 Use examples that do not reinforce gender stereotypes
7
Collection development (continued)
• Practices to help to avoid gender bias include:
 Make questions as simple as possible, unambiguous, and using
everyday terms that all respondents can understand
 Use probing questions to reduce gender-related under-reporting and
assist response coding
̶
E.g., prompt respondents to remember something they may have
forgotten
 Give the same importance to answers related mainly to women than
to those related mainly to men, when potential answers are
categorised and pre-coded
 Avoid using keywords that apply exclusively to one sex (e.g.,
housewife, or fisherman) or language that reinforces gender
stereotypes
 Include short explanatory notes for the interviewer when needed
8
Data Collection
 Field staff should be selected on the basis of competence, with both
women and men recruited.
 Training of interviewers and supervisors should equip them with the
skills needed to handle, consistently, the gender-related issues likely to
be encountered in the field.
 Training should address:
 gender-related measurement issues and gender stereotypes;
 gender concerns associated with topics in the collection; and
 how data collected will address those concerns.
 Certain types of surveys may require more careful selection and more
extensive training of interviewers.
 Sex of the interviewer can play an important part in obtaining certain
types of sensitive information from respondents.
 Women may be more likely to disclose information on reproductive health to
women interviewers than men interviewers.
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Data Processing
 It is important to avoid gender bias in data coding and data
editing, including imputations for non-response and
misreporting.
 Subject matter specialists with training in gender issues
should be involved in formulating rules for these processes
so that the coders or processing assumptions are not based
on gender stereotypes.
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II. Bringing a gender perspective into
selected types of data collection
• The next part of this session illustrates the importance of each
of the following types of collection for gender statistics.
• It also provides examples of practices that can improve the data
collected from a gender perspective.
Population censuses and
surveys
Population and
housing censuses
Business censuses and
surveys
Administrative
data collections
Agricultural censuses and
surveys
Labour force surveys
Time use surveys
Personal violence
surveys
11
What data sources are used in producing gender statistics?
• Many data sources are used by countries to produce gender statistics. The main
types are:
– Population and housing censuses
– Population sample surveys
– Business censuses and surveys
– Administrative records
• To construct some gender indicators, data from more than one source may need
to be combined.
• Some data sources provide more sex-disaggregated or gender-relevant data than
others—e.g., demographic and health, or education, surveys.
• However, most data sources could improve the collection, coverage and quality of
gender statistics by integrating a gender perspective in their planning, design,
development and data collection.
• Handout 2.1 has the data sources used for producing gender statistics in Rwanda
(from the NISR website)
12
Quality of data sources used in producing gender statistics
• The quality of gender-relevant data from each source depends on many
factors, including:
– concepts, definitions and classifications used
– collection design and coverage
– the way questions are asked
– the collection methods used
• Gender bias can be present in any type of data collection and arise at any
stage of the statistical production process.
• It is important to understand the value and limitations of the particular
sources available in a country for purposes of producing gender statistics.
13
How can a gender perspective be brought into data collection?
• For each individual about whom data is collected, sex should be
recorded plus other characteristics (e.g., age, marriage status,
number of children) important for gender analysis.
• All stages of the data collection process should be examined to:
 ensure user needs for gender statistics are taken into account
 avoid gender bias in data obtained
14
Assessing the existing data sources: suggested questions
When reviewing the different types of data collection, think of:
•
How adequate are the existing sources for gender statistics purposes?
•
What are the main gaps or deficiencies in covering gender differences , and what
action is being taken to address them?
•
Are metadata provided for all statistics, including information on data quality?
Where can it be found?
•
Are the statistics consistent with international standards? Are there any notable
departures that would affect international comparability?
•
What are the main conceptual and measurement issues associated with existing
sources? How have they been addressed? Does more need to be done?
•
What improvements to data sources (including potential sources) are most needed
from a gender perspective? Are there any plans to make these improvements?
•
Are there any other developments expected to improve the quality and availability
of gender statistics?
15
Population and housing censuses
• Population censuses are typically the largest statistical collections undertaken by a
country and are conducted relatively infrequently (e.g., every 10 years).
• They obtain data on each person in the population, including their sex, age and
other characteristics.
• Generally a range of other topics are also covered. For example:
–
–
–
–
–
–
–
female and male labour force participation;
current occupation;
paid and unpaid work;
income;
educational participation and attainment;
aspects of health and disability;
living arrangements.
• They provide:
– a rich source of information for examining differences between females and males across
many dimensions of life and in fine geographical detail.
– information for studying families, households and population sub-groups from a gender
perspective.
– population benchmarks for constructing indicators and other analytic measures for
studying gender issues.
• Handout 4.2 provides checklists for integrating a gender perspective into
census questionnaires, manuals and training.
16
Population and housing censuses: importance for gender statistics
• Primary source of benchmark gender statistics on various topics, both for the
whole population and for specific population groups.
• Reliable, and sometimes the only, source of statistics on small geographic
areas and small population groups disaggregated by various characteristics
required for gender analysis.
• Source of data for population denominators needed to calculate various
gender indicators based on data provided by administrative records.
• Where civil registration systems have incomplete coverage, census data along
with household surveys data are vital for providing gender statistics on
fertility, mortality, marriages and migration.
• Rwanda censuses collect information disaggregated by sex.
– Do they collect other gender-relevant information?
– What gender-relevant information do they not collect and why?
17
Population sample surveys
• Household and other population surveys collect information directly from a
representative sample of households or individuals.
• They can cover a very wide range of topics in depth.
• Data collected invariably includes sex and age of each individual in the sample.
• Such surveys are very flexible and may have:
• a multipurpose focus, with many discrete topics
• a more general social focus, with a range of topics for analysis of cross-cutting issues
• a primary focus on a particular topic or population group (e.g. labour force, education,
literacy, health, disability, time use, domestic violence, migrants)
• attached sessions on separate topics (e.g. sessions on discrete topics may be attached to
regular labour force surveys)
• The implications of sampling error (as well as non-sampling error) need to be
considered when using data from these surveys to produce gender statistics.
• In Rwanda, the following surveys collect sex-disaggregated and gender-relevant data:
• DHS
• EICV
• Which other surveys collect gender-relevant data?
18
Labour force surveys: importance for gender statistics
• The surveys provide data on the structure and composition of the labour force,
disaggregated by sex, age group and other individual characteristics.
• This information is essential for design and evaluation of government policies aimed at
employment creation and equal opportunity in employment.
• Labor surveys can provide data on employment and unemployment trends for
particular sub-groups, which are crucial for assessing the social affects of government
employment policies or structural adjustment policies.
• Labour force data can be collected at the same time as data on other topics. The
combined information can be useful for understanding gender differences in labour
force participation.
• Recurrent surveys can show the extent to which seasonal variations in labour force
participation differ between women and men
• Does Rwanda collect labour force surveys?
• How does Rwanda collect sex-disaggregated and gender-relevant data on labour
force and employment?
19
Business censuses and surveys
• These collections obtain data from businesses and other organisations, as
well as from the registers on which the collections are based.
• The focus of the collections may be:
̶
̶
particular industries or activities (e.g., manufacturing, agriculture, education
services), or
economy-wide
• They can provide gender-relevant information if sex-disaggregated data are
collected for individuals associated with the organisation. For example:
̶
̶
̶
earnings of different categories of employees;
characteristics of owners or managers of businesses and agricultural holdings,
including type, size and location;
numbers of students and staff in different fields at educational and research
institutions.
• In Rwanda, the Civil Servant census, establishment/enterprise surveys, and
Manpower surveys also collect sex-disaggregated data.
20
Agricultural censuses and surveys
• Agricultural censuses cover all agricultural holdings. They collect structural and
operational data related to each holding.
• Agricultural surveys (a type of business survey) cover only a sample of
agricultural holdings. They are usually conducted more often than censuses
and can accommodate more detailed questions.
Examples of topics they can be cover
are:
• size of holding
• ownership
• management
• economic activity
•
•
•
•
production
income
employment
agricultural practices
• The role of these collections in obtaining statistics on gender and agriculture
should be considered within an integrated system of producing gender
statistics.
21
Improving gender coverage in agricultural censuses and surveys
• Information on the composition and organisation of farm labour can be
provided by recording sex and other characteristics of the household
members and hired labourers working on the agricultural holding.
• Gender differences in management of agricultural holdings can be provided
by obtaining data on the characteristics of holders and sub-holders and
combining the data with other information, such as size and types of crops or
livestock.
Agricultural censuses and surveys are important for gender statistics:
• They can identify gender differences in
– ownership of agricultural assets,
– access to agricultural services and credit, and
– access and use of agricultural practices.
• For Rwanda, where a large majority of employed work as small-scale farmers
women (76%, compared to 53% of men, EICV3), agricultural censuses and
surveys can reveal much about women’s economic roles, and their time use
and unpaid work (which contributes to their household’s wellbeing)
22
Specialized Surveys: Time use surveys
• Time use surveys collect data on how individuals allocate their time to
specific activities over a specified period.
• They typically use diaries to collect time use data, together with household
and individual questionnaires to collect demographic and socio-economic
data, including sex and age of individuals.
• They may be conducted in stand-alone mode or as a session attached to
another population survey (such as a labour force survey, a living standards
survey, or a multi-topic survey).
• Many countries have conducted such surveys, either as part of a regular
survey cycle or on a periodic or ad hoc basis.
• Ethiopia has recently completed a time use survey
23
Time use surveys: importance for gender statistics
• Time use survey data reveal the extent to which time allocation patterns differ
between women and men.
• The data are essential for estimating participation of women and men in unpaid work
and its value for the economy.
• The data from time use surveys can:
 improve labour force survey estimates, for both women and men, on:
̶ participation in all forms of work (e.g., through more extensive capturing of relevant
non-market activities)
̶ working time, work locations and scheduling of economic activities
 provide valuable insights into work/family balance for women and men,
including the division of labour within the family, and
 contribute to understanding gender differences in:
̶ investment of time in education and health
̶ welfare and quality of life, including availability and use of leisure time and extent of
time poverty
̶ intra-household inequality in terms of the distribution of household chores among
female and male members of a household
• How has Rwanda measured women’s and men’s time use?
24
Specialized surveys: Personal violence surveys
• Surveys on personal violence measure the extent, nature and
consequences of all types of violence against individuals.
• The focus may be on violence against women, or more broadly on
violence against women and men.
• The surveys may be conducted in stand-alone mode or as a session
attached to another population survey (such as a health survey, or a crime
victimisation survey).
• Many countries have conducted such surveys, either as part of a regular
survey cycle or on a periodic or ad hoc basis.
• How does Rwanda collect data about personal violence?
25
Personal violence surveys: importance for gender statistics
• Can provide reliable measures of prevalence of violence against women
and men, including physical, sexual, psychological and economic abuse.
̶ Administrative sources of data on violence against individuals, especially
women--are characterised by extensive underreporting.
• Groups most at risk of violence can be identified, as data typically include
characteristics of those who experience violence and those who do not.
• Data on the characteristics of perpetrators as well as victims can be used
to develop strategies of violence prevention and early intervention.
• Can provide representative and comprehensive data on the impact of
violence, e.g., injuries, and the need for medical attention.
• Such surveys may be the only source of data on the proportion of victims
seeking help and accessing support and other services.
̶ This information is crucial for developing strategies to respond to the needs
of victims, such as emergency housing, counselling etc.
• Can provide data on the extent of tolerance of violence in the wider
community.
26
Administrative records: importance for gender statistics
• These records contain data routinely collected through administrative processes by
sectoral or line ministries and other national and local public entities.
• Where records hold information on individuals, including their sex, they can be a
valuable and cost effective source of gender statistics.
• A wide range of topics may be covered.
– For example: school enrolments; registered unemployed; registered births, deaths and
marriages; registered diseases; use of health services; provision of income support;
arrests for criminal activity.
• They have the potential to provide more frequent, reliable and finely disaggregated
data than sample surveys, and to provide insights into gender issues not well
covered by census or survey data.
• But, their usefulness may be limited because their primary focus is administration
not statistics. They may be problems for example, because
– Their coverage, completeness of records, definitions, classifications and collection
methods may be deficient for purposes of gender statistics, and
– The details they hold may not be current.
• Handouts 2.1 and 4.1 have examples of administrative data for Rwanda with the
topics they cover
27
Improving administrative records data from a gender perspective
• Statistical agencies should develop and implement strategies to
ensure that custodians of relevant administrative records understand
the importance of gender statistics and the potential of those
records to enhance the statistics.
• Statistical agencies may need to negotiate with those responsible for
relevant administrative records in order to:
– improve the existing quality of administrative data for purposes of
gender statistics;
– ensure the data are updated regularly; or
– gain access to gender-relevant data not currently used for statistical
purposes.
• Inter-agency agreements and inter-agency steering committees can
facilitate progress on actions arising from such negotiations and help
to achieve timely completion.
28
Summary of main themes of presentation
• Many types of data collection are important for gender statistics.
• Data collectors should integrate a gender perspective into all stages
of the collection process for all relevant collections.
• Gender bias and other gender-relevant measurement errors can
occur in any type of data collection and at any stage of the
collection process.
• Such measurement issues need to be identified as early as possible
to ensure they are adequately addressed.
• There are many practices that can be adopted to avoid gender bias
and improve reported data for gender statistics purposes.
• Improvements to reported data benefit all statistics sourced from
the collection, not only gender statistics.
29
Exercise 4
1. Choose a census or survey that you are familiar with from the list
in Handout 2.1:
– Does it collect sex-disaggregated or gender-relevant statistics? What are
they?
– Which steps or activities could have been taken to better integrate a
gender perspective and minimize gender problems or biases?
2. To what extent is a gender perspective integrated in the
production of statistics in Rwanda? What is working well in this
regard, and what needs more attention?
– Please give specific examples of data collection instruments or
processes that integrate a gender perspective
3. What data collection census or survey in Rwanda has the highest
priority for improvement from a gender perspective? Why? What
needs to be done and by whom to improve it?
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