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Workshop on Improving Gender Statistics in Rwanda Session 3

From Gender Issues to Gender Statistics

Serena Lake Kivu Hotel, Rubavu DistrictMarch 25-27, 2014

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Learning Objectives

By the completion of the session, participants should know and understand:

• The steps to bring gender issues into statistics;

• The role of gender issues in guiding gender statistics production and use;

• How gender statistics can inform gender issues in several key areas;

• How to identify and assess data needed, sources and data gaps; and

• How to address the conceptual and measurement factors that can affect the usefulness of the statistics.

Primary references: UNSD 2013, Integrating a Gender Perspective in Statistics, Chapter 2;UNECE and WBI 2010, Developing Gender Statistics: A Practical Tool, Chapter 3.National Gender Statistics report 2013, published by NISR and the GMO with support from UN Women and UN Rwanda.

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Key steps in bringing gender issues into statistics

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How can the priorities for gender statistics be identified??

1. Start by identifying and understanding the critical gender issues or country priorities in Rwanda and the data needed to address them

– Use country gender assessment or profile, policies and strategies to identify the key gender issues

– Describing the issues in terms of policy-relevant questions (e.g. ‘do women earn less than men’) can help in determining what data are needed.

2. Then assess existing sources in terms of data availability and quality.– Population censuses or surveys, administrative data, special studies

3. Based on this information, identify the data gaps and their statistical implications.

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How can the priorities for gender statistics be identified? (continued)

4. The statistical implications may include:- Better utilisation of existing data, such as through recoding, re-tabulation or

re-analysis of micro data;

- Improvement in methodology of existing data collection efforts;

- New data collection, such as a new collection instrument –for example, a time use survey; or additions to an existing collection instrument—for example, a population census;

- Improvement in data dissemination.

5. Set the priorities for developing gender statistics based on this information and on available human and economic resources. - For example, the Gender Statistics Framework, GSF- The monitoring role of the GMO

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Country priorities in Sub-Saharan Africa

A 2012 UN Global Review of Gender Statistics Programmes in 33 countries in the Africa region (including Rwanda) found that:

Most UNECA countries had national priorities related to gender statistics generation/collection, compilation and dissemination

Most common thematic areas covered by the programs were:- Sexual and reproductive health- Mortality- Unemployment- Poverty- Morbidity- Education and training

Some examples of priorities were:- Formulate better gender-sensitive policies

and monitor their progress- Better knowledge of gender issues for better

integration of women- Integrate gender into statistics in order to

adequately meet the need of data users- Mainstreaming gender in socio-economic

and demographic surveys

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Country priorities in gender statistics for Rwanda

• National priorities for gender statistics depend on current policy goals and plans as well as current statistical capacity. Different countries have different priorities.

• Some gender statistics are produced by all countries. But there is often a gap between statistics needed to address national gender goals and statistics currently produced.

In Rwanda, the National Gender Policy defines the institutional framework and mechanisms within which gender equality and equity policies and programmes will be designed, implemented, monitored and evaluated, and coordinated, and guides the integration of a gender perspective into all sectors and institutions.

The Gender Statistics Framework (GSF) was developed in 2012 to encourage the use of sex-disaggregated data for evidence-based planning, monitoring progress and policy formulation. The GSF has around 300 indicators in 13 thematic areas compiled from surveys, censuses and administrative data.

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Key steps in bringing gender issues into statistics

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What data sources are used in producing gender statistics?

• Countries use many data sources to produce gender statistics. The main types are:– Population and housing censuses– Population sample surveys– Business censuses and surveys – Special topic surveys, e.g., time use or gender violence surveys– Administrative records

• Constructing some gender indicators may require combining data from more than one source.

• 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 and quality of gender statistics by integrating a gender perspective in their planning, design, development and data collection.

• Data sources will be covered in depth in session 4 on Production of gender statistics

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Bringing gender issues into specific fields of statistics

The next part of this presentation illustrates some of the gender issues, data needs and measurement challenges in four specific fields: - Population, households and families - Education;- Health and Nutrition; and - Work/employment and time use

Keep in mind that the key steps in bringing gender issues into statisticsapply in these as well as all other fields.

Each of the four fields is represented by a number of indicators in Rwanda’s Gender Statistics Framework (or other data sources). For each indicator, the system provides various disaggregations, the source and the responsible agency.

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Key steps in bringing gender issues into statistics

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POPULATION, HOUSEHOLDS and FAMILIES

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POPULATION, HOUSEHOLDS and FAMILIESWhat are some of the gender issues?

A few examples relevant to Rwanda ....

• Is the sex ratio of the population (males per 100 females) moving towards equality and how does it differ by age groups?

• Who is more likely to ever having been married?

• To what degree does age at first marriage differ between women and men?

• Are there more households headed by women or by men?

• Which households are more likely to be poor?

• To what extent does life expectancy at birth differ between women and men?

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POPULATION, HOUSEHOLDS and FAMILIESWhat can statistics tell us about these issues?

For Rwanda, the 2013 National Gender Statistics Report reported that:

• The sex ratio (number of males per 100 females) shows more females than males (2012 Population and Housing Census Provisional Results).

• The sex ratio is particularly eschewed against males among men particularly at 25 to 65 years old, but also at 15 to 24 years old (2012 Population and Housing Census).

• Women 15-49 years old are more likely to ever have been married than men (2010 DHS).

• Median age at first marriage is higher for men (24.9) than women (21.4) (2010 DHS).

• There are more households headed by men than by women: 66.5% versus 33.5% (EICV3).*

• Households headed by women (including de facto female headed households) are more likely to be poor than those headed by men (EICV3). *

• Women have a longer life expectancy than men: 55 years versus 51 years on average (2009, Rwanda Population Projection).*

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POPULATION, HOUSEHOLDS and FAMILIES: What types of data do countries generally need to inform gender issues?

Data needed:• Live births, • Children ever born to a woman• Population composition, including age and sex• Marriages and divorces, duration of marriage• Marital status, consensual unions• Contraceptive use*• Females in reproductive age group*• Household type by type of household head:

single, married, widowed/divorced, male, female• Young persons by household type• Older persons by household type• Older persons living in institutions• Family nuclei of lone parents with young children* Reported also in health statistics

Disaggregation:• By sex, and generally by

age as well

Also important may be:• geographic area, • urban/rural area, • migration status, • wealth status, • educational attainment, • other variables relevant to

understanding living arrangements.

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POPULATION, HOUSEHOLDS and FAMILIES: Conceptual and Measurement Issues

Some common issues that may arise in producing gender statistics ...

• Household members. In some population censuses and surveys, female members of the household may be more likely to be underreported than male members. Collection methods may need to be improved and special promotional

material to reach women developed.

• Household type. Classifications of household type may need adjustment to identify certain types of living arrangements that are most relevant from a gender perspective, such as households where the male head has migrated temporarily or permanently for work, or all-adult households Special survey questions or reporting instructions may be needed to

capture these situations

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POPULATION, HOUSEHOLDS and FAMILIES: Conceptual and Measurement Issues

• Informal unions. These may not be adequately covered in statistics, as the marital status of an individual is usually recorded in relation to the marriage laws or customs of the country. Special survey questions or reporting instructions may be needed to

capture data on informal unions in countries where they are common.

• Non-marital fertility. Data may not be available or detailed enough to understand trends. Additional or differently worded survey questions may be needed.

• Family planning. Unmet need for family planning has often not been calculated using a comparable methodology over time. Use of contraceptive methods may be under-reported. It may be necessary to standardise methodology and assess reporting

accuracy.

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HEALTH and NUTRITION

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HEALTH: What are some of the gender issues?

A few examples relevant to Rwanda ....

• What are the trends in infant mortality among boys and girls? Do the trends differ between urban and rural areas? Is the gender gap narrowing?

• Is the pregnancy-related maternal mortality decreasing?

• What is the prevalence of malnutrition among boys and girls? Is there an association between child malnutrition and a mother’s level of education?

• Is the HIV prevalence higher among women or among men? • Do females have as much knowledge about AIDS? What groups have least

knowledge?

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HEALTH: What can statistics tell us about these issues?

Statistics in Rwanda show that (DHS 2010):

• The infant mortality rate decreased from 86 deaths per 1000 live births in 2005 to 50 in 2010. It is higher for boys than for girls, and higher in rural areas (2010 DHS).

Year 2005 Year 20100

102030405060708090

10086

50

Infant Mortality Rate

Deaths per 1000 live births

Males Females0

10

20

30

40

50

60

70

80

67

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IMR, Males versus Females, 2010

Deaths per 1000 live births

• Malnutrition among children under 5 years old is still quite high (2010 DHS): – 44 % of children under age 5 are stunted or too short for their age. – More boys than girls are stunted: 47% of boys compared to 41% of girls. – Stunting is most common among children of less educated mothers and those from

poorer families.

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HEALTH: What can statistics tell us about these issues (continued)?

Statistics in Rwanda show that:• The pregnancy-related maternal mortality rate among women 15-49, although still

high, fell sharply between 2000 and 2011 (2010 DHS).

• HIV prevalence (for the population aged 15-49 years old) is higher among women than men: 3.7% to 2.2% (2010 DHS).

• Knowledge of HIV prevention is better among young females than young males (2010 DHS).– 53% to 47% among 15-24 years old females and males respectively.– Women with no education are less exposed to knowledge about HIV prevention. – Rural residents are less likely than urban residents to know about HIV prevention

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HEALTH: What types of data do countries generally need to inform gender issues?

Data needed:• Live births, place of delivery, delivery attendance• Deaths and causes of death, abortions• Children ever born and children surviving• Weight and height of persons, vaccinations

received, selected health conditions and treatments received

• Health expenditure of households • Pre natal care, contraceptive use• HIV/AIDS: prevalence, tests done, deaths,

knowledge, access to retroviral drugs, condom use• Health risk factors, e.g. alcohol, tobacco, obesity,

physical activity, high risk sexual behaviour• Unintentional and occupational injuries• Population by sex and age for calculation of rates

Disaggregation:• By sex, and generally

by age as well

Also important may be • small areas, and • small population

groups

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HEALTH: Conceptual and measurement issues

Some common issues that may arise in producing gender statistics ...

• Understanding gender gaps may require a distinction between biological and social factors and how they may be entangled. Certain measures and indicators (e.g. of child mortality or nutrition) may make biological factors less relevant than social factors, or vice versa.

• Estimates of sex differentials in infant and child mortality rates based on household surveys may have large standard errors and wide confidence intervals. Information on quality of the available data should be provided to users.

• Data on maternal mortality may be unreliable due to underreporting and misclassifications of deaths. Estimates obtained from household surveys have wide confidence intervals. Where data on maternal mortality are suspected to be inadequate, it is

important to interpret the data within the context of other maternal health indicators.

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HEALTH: Conceptual and measurement issues (continued)

• Data on births and deaths from censuses or surveys, as well as from civil registrations, may have coverage and accuracy deficiencies. For example,– Female births may be more severely underreported than male births in countries

where women have a lower status. – Births and deaths may also be underreported due to premature death or

omissions as a result of proxy responses or recall errors. Data quality should be assessed using multiple sources. Users should be

informed about quality deficiencies. Adjustments may be needed for underreporting and for distortions in

the age structure. A data improvement strategy may be needed.

• Causes of death are often not reported, or misreported, for both females and males in civil registration systems. Systematic and targeted efforts to improve reporting may be needed.

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HEALTH: Conceptual and measurement issues (continued)

• Reliable statistics on abortions may not be readily available. Research may be needed to improve estimation methods.

• Sex differentials in nutrition may be clearer if data on weight and height of girls and boys under 5 are disaggregated also by age.

• HIV prevalence. Sex bias may occur in estimates based on population surveys due to gender differences in participation in testing.

• Sexual behaviour. Sex bias in reporting may occur due to normative reporting of sexual behaviour, e.g. condom use and high risk sex. Use of contraceptive methods may be underreported. Data sources may need to be reviewed, or existing methods adjusted.

• Alcohol consumption. Type and frequency of consumption may vary by gender and surveys may not adequately distinguish relevant risk behaviours. Survey questionnaires may need to be revised.

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EDUCATION

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EDUCATION: What are some of the gender issues?

A few examples relevant to Rwanda ....

• Do women have lower literacy rates than men? What are the trends? How are gender differences in adult literacy distributed across geographic areas and population groups?

• Is the male rate of secondary school enrolment much different to that of females? What impact does poverty have on female and male enrolments?

• To what extent does school attendance vary between females and males of differing ages?

• Are women under-represented among teaching staff? Have patterns changed over time?

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EDUCATION: What can statistics tell us about these issues?

For Rwanda, according to the 2013 National Gender Statistics Report and EICV3:

• The literacy rate for 15 years and over increased from 65% in 2006 to 70% in 2010 (EICV3). – The female literacy rate is lower than the male rate: 65% compared to 76% (EICV3) – Literacy is higher in urban areas than in rural areas, and lowest among the poorest

(EICV3 estimates).

• Girls’ net enrolment rates are higher than boys’ in both primary and secondary school: 98% and 27% for girls compared to 94% and 24% for boys

• Girls’ school attendance rates for both primary and secondary levels are higher and accelerating faster than for boys

• These indicators point to an advantage for girls over boys

• The majority of teachers in primary education are women, but the pattern reverses for secondary, tertiary and vocational levels, where men are the large majority of teachers.

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EDUCATION: What types of data do countries generally need to inform gender issues?

Data needed:• Number of students in primary, secondary and

tertiary education, e.g. enrolments, completions etc• School attendance and reasons for not attending• Tertiary education graduates• Education expenditure of households• Number of teachers and researchers • Number of schools with particular facilities• Literacy • Highest level of education attained• Participation in non-formal education and training• Participation in continuing vocational training• Use of information services (e.g. for farmers)• Users of the internet, computers, mobile phones etc

Disaggregation:• By sex, and often by

age as well• Also important may

be level of education, field of study, small areas, and small population groups

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EDUCATION: Conceptual and measurement issues

Some common issues that may arise in producing gender statistics ...

• Educational participation. Using only enrolment data may overstate the educational participation of girls or boys to different degrees, since they include persons enrolled in but not necessarily attending school. Both enrolment and attendance statistics need to be considered.

• Population groups. Some groups with distinct gender differences in educational participation may not be covered in statistics on enrolment or attendance, resulting in biased estimates. Excluded groups may be those outside the regular education system in the case

of administrative records; living in remote areas or institutions in the case of household surveys; or studying abroad.

The magnitude of under-coverage should be assessed and users should be informed of the implications for gender studies.

Using a combination of sources (censuses, surveys, administrative records) may improve coverage.

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EDUCATION: Conceptual and measurement issues (continued)

• Literacy. Statistics based on self-reporting or proxy-reporting may overestimate literacy rates, particularly for persons considered dependant. Direct assessment can provide more objective measures. The most accurate measurement uses a direct test of 3 criteria to assess the

literacy and numeracy of female and male heads of households: • Ability to read a simple written note • Ability to write a simple letter • Ability to do a written calculation

– This method was used by the DHS 2010– The EICV3 used proxy-reporting to track literacy rates

• Researchers. The proportion of researchers that are female or male may differ between institutional sectors or settings. All sectors and settings should be covered for a complete picture.

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WORK

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WORK: What are some of the gender issues?

A few examples relevant to Rwanda ....

• Do females and males have different employment rates?

• Are there gender differences in types of employment (jobs) across different population groups?

• Are paid hours worked similar for males and females?

• Do females spend more time on housework than males?

• Who works more hours, women or men?

• Lack of data about many economic and employment statistics is also an important gender issue—including on wages or earnings, employment status and informal employment, unemployment, underemployment

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WORK: What can statistics tell us about these issues?

For Rwanda, available statistics reported the following (EICV3) (see also next slide): • The female employment rate is slightly higher than the male rate: 85% compared to 83%. • Most women are employed as small-scale farmers in family farmers (72%), and many men also

are employed as small-scale farmers (49%), – In urban areas, the majority of men are employed in wage non-farm jobs (58%).

• The number of men working in agriculture has fallen while the number of women has increased since 2006. – Almost 2 million women are small-scale farm workers compared to over 1.1 million men.

• Men’s average paid working time per week on all jobs was larger than women’s, yet women’s average time doing housework per week was three times that of men.

• Women’s average total number of hours worked (paid and domestic) per week was much larger than men’s.

Average hours per weekType of work Men Women

Paid work 31 24Domestic work* 9 27

Total work time 40 51* Does not include unpaid agricultural work, only domestic chores

35Source: NISR, EICV3 Thematic Report Gender, 2013

%

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WORK: What types of data do countries generally need to inform gender issues?

Data needed:• Labour force participation• Employment, unemployment, underemployment• Status in employment and informal employment • Number of hours worked• Wages or earnings from work • Ownership and management of agricultural

resources, use of agricultural inputs• Time use by type of activity• Access to and use of flexible working arrangements• Availability and use of formal childcare services• Maternity and paternity leave benefits• Children in employment and in unpaid housework

Disaggregation:• By sex, and often by

age, occupation, and industry of activity.

• Also important may be small areas, and small population groups.

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WORK: Conceptual and measurement issues

Some common issues that may arise in producing gender statistics ...• Labour force participation and employment. Women’s participation in labour

force and employment may be underreported due to: • difficulty in separating activities that should or should not be included; • gender-based stereotypes of women and their work roles; • difficulty of capturing seasonal and intermittent activity; and • various coverage limitations when data are sourced from business surveys.

Collection methods and training may need to be improved. An alternative data source may need to be considered.

• Unemployment. Women’s unemployment may be underreported because:• they may be perceived or define themselves as not seeking work;• they are more likely to be discouraged or to be seasonal workers;• There may be coverage limitations when data come from administrative records.

Collection methods and training may need to be improved. An alternative data source may need to be considered.

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WORK: Conceptual and measurement issues (continued)

• Occupation and status in employment.• Often not recorded with enough detail to properly assess gender differences in types of

work and employment conditions. • Women’s employment status may be misclassified in employment categories due to

misclassification of jobs.

Collection methods and training may need to be improved.

• Gender pay gap. May be higher or lower depending on the concept used—for example,• wages of paid employees,• earnings of paid employees including overtime and regular bonuses, or • income related to employment of all workers, including all bonuses and social security

benefits.

Users should be informed of the particular concept used: various concepts may be useful.

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WORK: Conceptual and measurement issues (continued)

• Small agricultural holdings. Excluding small holdings from agricultural censuses and surveys induces a gender bias in the statistics as women holders tend to concentrate in this sub-sector.

The extent of the bias should be quantified and users informed.

• Productive agricultural resources. Comprehensive coverage of gender issues in access to productive resources in agriculture requires:

Use of data collection units and data analysis more disaggregated than the holding level.

• Head of agricultural holdings. Many agricultural holdings classified as ‘male-headed’ may in fact be headed jointly by women and men, and incorrectly recorded due to omissions and interviewers and/or respondents’ gender bias.

Interviewer training and/or respondent instructions may need improvement.

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WORK: Conceptual and measurement issues (continued)

• Work on own account production of services. • Not usually covered by conventional labour force statistics, which are limited to

activities that contribute to the production of goods and services as defined by the SNA. • In particular, own account production of services, which is mostly carried out by women,

is excluded.

Time use statistics can shed light on both the included and excluded activities performed by women and men, such as• Getting fuel, water or forage,• preparing meals, cleaning and other housework,• caring for children and others in the household, • directly-provided volunteer services, and• working without pay on own-account or family farms or businesses.

In particular, time use statistics can provide measures of unpaid housework undertaken by women and men, provided that • contextual information is collected--e.g. whether the work was paid or unpaid and

for whom it was performed, and• simultaneous activities are recorded.

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SUMMARY: How to address conceptual and measurement issues for gender statistics in different sectors

• Understand the issue - impact on identifying gender gap or not? (every data source has limitations)- incomplete coverage? sex bias in questions? proxy respondents? non-response?- inadequately-trained interviewers? data coding, data entry or editing errors?

• Improve future data collection activities

• Adjust the estimates- imputation? benchmarking?

• Use multiple data sources?- confront the data?- combine the data?

• Use different methods

• Communicate with the users- ensure users understand the quality of the data, including possible gender biases- provide reliability measures, e.g. statistical significance, confidence intervals

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Exercise 3.1

1. How would you go about identifying the priorities in gender statistics in Rwanda? Please give specific examples

2. Prepare a table-using the table shell provided--showing gender issues, data needed and sources of data for the topic of educational participation.

3. What gender issues in Rwanda are most in need of better data? What improvements to data sources or methods would be required to deliver better data?

Exercise 3.1 Day 1: Prepare a table - using the mock-up provided - showing gender issues, data needed and sources of data for the topic of educational participation. (Session 3)

Educational Participation

Gender issues Data needed Sources of DataEXAMPLE

Do the same proportions of girls and boys enter the first grade of school?

New entrants in first grade of school by sex and age, and population by sex and age

School administrative records, combined with data from population census and (specify) household surveys