Learning Objectives

Workshop on Improving Gender Statistics in RwandaSession 6Analysis and Presentationof Gender StatisticsSerena Lake Kivu Hotel, Rubavu DistrictMarch 25-27, 2014

Learning Objectives At the completion of this session, participants should understand and become familiar with: • how analysis and presentation of gender statistics can enhance the usefulness of the statistics; • the main types of analytic measures and analytic tools that can add value to basic data; and • tools and techniques for presenting statistics in ways that ensure the visibility of meaningful differences and similarities between women and men. Primary references: UNSD 2013, Integrating a Gender Perspective in Statistics, Chapter 4 UNFPA 2013, Guide on Gender Analysis of Census Data UNSD and UNFPA presentations at the April 2013 UNSD Workshop in Japan

Analysis of gender statistics Analysis is an integral part of the statistical production process. In broad terms, analysis of gender statistics involves: • Identifying the gender issues to be informed by the analysis. • Obtaining statistics and other relevant data from available sources. • all variables of interest need to be disaggregated by sex as a primary classification; • many variables may also need to be cross-tabulated, e.g. labour force participation by sex by age group by geographic area. • Analysing and interpreting the data, including derivation of indicators and other analytic measures. • Reporting the findings, including presenting the statistics in easy-to-use formats that are appropriate to the statistical product in which they will be disseminated.

Key steps in analysing gender statistics

I. Gender analysis • The purpose of the analysis determines the type and level used. • The type and level of analysis usually vary by the type of statistical product to be used in reporting results. • Disseminating basic data collected in censuses and surveys typically involves tables with minimum data processing and analysis. • More analytical reports or articles typically conduct additional processing and analysis. • For most types of analysis, indicators and other analytic measures play an important role. • Use the basic data to select and construct relevant indicators and other analytic measures. • Apply more complex analytic tools and techniques to the basic data to better understand some issues.

Analytic measures • In analysing data from a gender perspective, use measures of composition and distribution of particular variables by sex. • Such measures include: • proportions and percentages; • ratios and rates; • medians and quantiles, means and standard deviations. • They provide the basis for constructing many of the gender indicators used to monitor progress towards gender equality.

Proportions and percentages In gender statistics, proportions and percentages can be calculated as relative measures of: • Distributions of each sex across the categories of a characteristic--e.g., • Proportion or percentage of women who are employed--compared to women that are not employed or unemployed; • Labour force participation rate of women of the total female population; • Literacy rate of women—literate versus illiterate women. • Sex distributions within the categories of a characteristic-e.g., • Proportion or percentage of the employed who are women or men; • Proportion or percentage of parliament members who are women or men; • Share of women (or men) among older persons living alone. The following 2 slides illustrate these two types of measures

(a) Distribution of each sex across the categories of a characteristic Distribution of employed males and females by occupational group Rwanda. Occupational group by sex and urban/rural (percentages) Source: Rwanda NISR, 2012, The Third Integrated Household Living Conditions Survey (Eicv3) Main Indicators report

(b) Sex distribution within the categories of a characteristic Female and male distribution within marital status and education categories Rwanda: Percent distribution of women and men age 15-49 by selected background characteristics, 2010 Source: Rwanda NISR, 2012, Rwanda Demographic and Health Survey 2010, Final Report

Rates • Rates of incidence can be used to study the dynamics of change over time. • They are a special type of ratio obtained by dividing number of events during a period by number of population exposed to the events during the period—for example: • Fertility rates – an average based on number of births-- in Rwanda, the total fertility rate (in the last 3 years) was 4.6 (2010 DHS) • Mortality rates, such as • Infant mortality rates (under 1 year) per 1000 live births – in Rwanda, 50 (2010 DHS) • Females: 55 • Males: 67 (2010 DHS), • Under five mortality rate per 1000 births – in Rwanda, 76 (2010, DHS) • Females: 97 • Males: 107 (2010 DHS) • By convention, some percentage measures are also called rates–for example: • Literacy rate (percentage of population that is literate) – in Rwanda, • Literacy rate among population 15 and older in 2010 was 70% (EICV3): • Among women 15 and older: 65% (EICV3) • Among men 15 and order: 76% (EICV3)

Ratios • Particular compositional aspects of a population can be made explicit by the use of ratios, where a single number expresses the relative size of two numbers– for example: • Sex ratio (number of males per 100 females): 93 for Rwanda (2012 Census) • Sex ratio at birth (number of male live births per 100 female live births): 109.4 for Rwanda (2010 DHS); • Maternal mortality ratio: 496 for Rwanda (2010 DHS) • For some sex ratios, standardisation of the variables used may be necessary to adequately reflect gender differences– for example: • The gender parity index for primary gross enrolment, calculated as the ratio of the gross enrolment rate for girls to the enrolment rate for boys, • This rate controls for the sex composition of the school age population. • For Rwanda, the gender parity index for gross primary enrolment: 1.03 (2011, Rwanda Education Statistics)

Measures of central tendency and dispersion • Medians and quantiles • Often used in gender statistics to show the distribution of income or wealth across the population; • They can be useful in studying gender issues associated with poverty or in analysing the economic resources of different household types (such as single mother households). • Means (averages) -- Examples of gender-relevant indicators: • Average (mean) time use on unpaid work • Average (mean) size of land owned • Mean age at first marriage • Mean age of mother at first child • Standard deviations, coefficient of variation, etc. • These measures are important for measuring the degree of association between variables and making population inferences based on sample data. • Although not often presented in gender statistics • Projections • An example relevant to gender statistics is the projection of the male and female populations to a specified date in the future.

Understanding gender differences using analytic measures It often may be necessary to disaggregate simple summary measures or combine them with other data to adequately inform gender issues. This is illustrated in the following example which explores poverty among female or male headed households in Rwanda. • Data from the 2012 EICV3 revealed little difference in poverty incidence among female-headed and male-headed households: • 47% of female-headed households (which comprise 28% of all households), were poor compared to • 44% of male-headed households (which comprise 66% of all households). • However, the data revealed a higher poverty rate for de facto female-headed households (51% compared to 47% for de jure female-headed and 44% for male-headed households). In these households, which are only 6% of all households, male heads were absent for more than 3 months in the previous 12 months mainly because of detention or compulsory service (41%) or for work (28%). • For households in extreme poverty, the difference by household head is much larger: 34% of de facto female-headed households are extremely poor, compared to 26% of de jure female-headed and only 23% of male-headed households. • Defacto female-headed households heads tend to be more similar to male than to other female heads in terms of marriage, age, education and literacy, but they are larger (6 members on average compared to 5 for male-headed and 4 for de jure female-headed households) and more likely to have younger children or grandchildren at home, and more likely to work as wage workers (32%) than de jure female-household heads (17%). • De jure female-headed households tend to be headed by older, widowed, less educated and literate women (almost 70% are 45 or older), who are much more likely to be small-scale farmers. These characteristics explains in part why they are different from male and de facto female-headed households. • Source: NISR. EICV3 Thematic Report on Gender, 2013.

Use and value of standardisation In some situations it can be useful to standardise a measure to better understand gender differences or to avoid it being misleading (or biased). Examples where standardisation may be important for the analysis: • Risk of renewed divorce of men or women in second or third marriages. • Standardisation by order of marriage can take account of the fact that more men than women remarry after a first divorce or widowhood. • Literacy rates of women and men. • Age standardisation can take account of the fact that literacy rates are lower at higher ages, were women predominate. • Incidence of disability in women and men. • Age standardisation can take account of the fact that there are more women than men in the population and that the excess of women over men is concentrated in the oldest ages where disabilities are most common.

A country example showing effect of age standardisation Un-standardised and Age Standardised Prevalence of Selected Types of Disabilities in Mexico, based on 2010 Population Census Source: UNFPA Guide on Gender Analysis of Census Data

Use and value of multivariate analysis Multivariate analysis can • assist in disentangling variability and understanding interrelationships within a population group • provide a more comprehensive view of different relationships, making it easier to identify situations where, for example, the relationship between two variables can be accounted for by their common dependence on a third factor. Examples of its use in the context of gender statistics are: • Understanding the relationship between women’s educational attainment and their economic level in rural and urban areas and at varying ages; • Investigating whether the relationship between two characteristics that are highly correlated, such as lower education and early marriage, is caused by another factor, such as belonging to a certain ethnic group; • Understanding whether the marital status of a woman has a direct effect on her labour force participation after controlling for other intervening factors. • Understanding the various factors that affect age of marriage;

A country example: A study using multivariate analysis in Rwanda • Measuring the Success of Family Planning Initiatives in Rwanda: • A Multivariate Decomposition Analysis. • Contraceptive use in Rwanda has increased far more than the Ministry of Health projected for the year 2010. Moreover, other indicators of progress, such as women delivering in health facilities and reductions in infant and maternal mortality have also exceeded expectations. • This study described the family planning initiatives in Rwanda and analyzed the 2005 and 2010 RDHS data to identify factors that contribute to the increase in contraceptive use, by decomposing the contributions of women’s characteristics and their effects. • The study found a mean predicted increase of 0.342 in contraceptive prevalence rate between 2005 and 2010. • The largest increase (77 percent) results from changes in the effects of women’s characteristics compared with changes in these characteristics (17 percent). • The variables showing significant contribution in effects are women’s education, experience of child mortality, and place of residence. • As for compositional differences, woman’s education, exposure to family planning messages in the media or at health facilities, husband’s desire for children compared with wife’s, and woman’s child mortality experience had relatively greater effects. • Additional research is needed to assess the contribution of supply side factors that would have been also important for the increased contraceptive use in Rwanda. • Source: Muhoza, DieudonnéNdaruhuye, Pierre Claver Rutayisire, and AlineUmubyeyi. 2013. Measuring the Success of Family Planning Initiatives in Rwanda: A Multivariate Decomposition Analysis. DHS Working Papers, 2013, No. 94. Washington, DC: USAID.

Some tips for analysing gender statistics • Assess data quality to avoid misinterpretation of results. • Use appropriate analytic measures and techniques to construct indicators that reflect the gender issues to be studied. • Consider the value of using multivariate analysis to assist in understanding gender inequality in its many dimensions. • Interpret the results of analysis with careful consideration of the different factors that may be involved (such as distinguishing the between the impact of socio-economic and biological factors on health outcomes). • Take care when combining data from different sources and use appropriate techniques.

Concerns when integrating data from different sources • When different sources need to be combined to calculate a particular analytic measure (e.g., a rate), check the sources for consistency and comparability. • Comparability issues can arise because of: • differences in concepts, definitions, coverage or time period; • errors or variations in classification or data processing procedures; or • variations in concepts or practices in different years within the same source. • In most cases comparability checks can be made by reviewing each source’s documentation. • Consider consulting also the specialists who supply or use the data from that source.

Some further considerations ... • Be aware of the different implications, for gender analysis, of data produced at different levels of statistical unit. • Statistics on poverty may be produced at household level and/or individual person level but the concepts used are not the sameand thus not comparable • Using sex of ‘head of household’ (or household headship) to analyse gender differences can be problematic. • ‘Head of household’ can refer to many different concepts; it does not capture intra-household gender inequalities; and it can reinforce gender stereotypes. • There is no uniformity in country practices concerning the concept or its use. • Comparing households with different characteristics can provide useful insights into gender issues. • It can be useful to disaggregate households by • size and composition (sex and age of each member), • type (one person, couples with/without children, single mother/father with or without children, etc.) and • other characteristics

Exercise 6.1: Data analysis • You are asked to prepare an article analysing a topical gender issue—on health, education, employment or violence--based on results of the 2012 Population Census. • How would you go about the task? • What analytic measures and tools would you expect to use? • Does the analysis of gender statistics need to be improved in any of the fields (topic areas) with which you are familiar? What are the specific field(s)? What needs to be done and why?

II. Presentation of Gender Statistics

Presentation of gender statistics • How the statistics are presented will influence understanding and use of the data for program or policy making • Gender data presentation seeks to: • Highlight key gender issues • Facilitate comparisons between women and men • Convey the main messages resulting from data analysis • Reach a wide audience • Encourage further analysis • Stimulate demand for more information • Tables, graphs and charts are the key forms of statistics’ presentation.

Graphs and Charts • These are powerful ways to present data. They can: • Summarize trends, patterns and relationships between variables; • Illustrate and amplify the main messages of a publication, and inspire the reader to continue reading; • Give a quick and easy understanding of the differences between women and men. • A graph or chart should: • Be simple, not too cluttered • Show data without changing the data’s message • Clearly show any trend or differences in the data • Be accurate in a visual sense—for example • If one value is double another, it should appear to be double in the graph or chart.

Types of Graphs and Charts • There are many types of graphs and charts. It is important to select the right type for data being analysed. • The selection may also be influenced by the message to be conveyed and the method of dissemination (e.g. printed or electronic). • Some of the main types of graphs and charts used in presenting gender statistics are: • Line charts • Bar charts: vertical, stacked and horizontal • Age pyramids • Dot charts • Pie charts • Scatter plots • Maps

Line charts • Line charts can give a clear picture of trends over time—examples: • Trends in sex ratios; • Literacy rates over time; • Labour force participation rates by age group over time. Source: NISR & Measure DHS. 2012. Rwanda Demographic and Health Survey (RDHS) 2010

Line charts (continued) Rwanda: Age-specific employmentrate in the last 12 months, by sex, 2010 (RDHS) Line charts also can give a clear picture of differences across age groups. This chart shows that in Rwanda in 2010: • Labor force participation rates were lower for women than for men at all ages. • The largest gender gap is at 15-19 years of age. Source: NISR & Measure DHS: Rwanda Demographic and Health Survey, 2010

Vertical bar charts Figure 1.1.2 Sex Ratios: Number of males per 100 females by age group (EICV3) • Both vertical and horizontal bar charts are common for presenting gender statistics. • A key feature is that the greater the value, the greater the length of the bar. • Examples of use: • total fertility rate by region; • antenatal care by urban/rural area; • proportion of women having third and higher order birth by education level. Source: NISR. 2012. EICV3 Main Indicators Report

Vertical bar charts (continued) Grouped (or clustered) bar charts can present a particular characteristic forwomen and men at the same time, facilitating comparisons between them. Rwanda: Lower secondary education Gross and Net Enrolment Rate from 2008 to 2012 Source: Ministry of Education, MINEDUC. 2013. 2012 Education Statistics Yearbook

Stacked bar charts • Stacked bar charts illustrate data sets containing two or more categories • Most effective for categories that add up to 100 per cent. • Common problems: • Bars with more than three segments are difficult to compare from one bar to another; • one or more categories may be too short to be visible on the scale. Source: NISR. 2013. EICV Thematic Report Gender

Stacked bar charts (continued) Rwanda: Poverty levels, by sex of household head, EICV3 • Stacked bar charts are also used to present the distribution of a variable within the female and male population • Examples • the distribution of female and male deaths by cause of death; • the distribution of female and male school attendance. Source: NISR. 2013. EICV Thematic Report Gender

Horizontal bar charts Rwanda: Household composition (% household members) by sex of head • Horizontal bar charts are often preferred when • many categories need to be presented (e.g. regions of a country), or • where categories have long labels. • May be preferred for showing some type of time use data, because the left-to-right motion on the x-axis generally implies the passage of time Source: EICV3. Note: composition estimates for de facto female-headed households include the absent head. Other relation or in-law includes parent, sister/brother, adopted child; Other includes servant and unrelated

Age pyramids • Age pyramids are useful for describing the age structure of a population and its changes over time. • Pyramids can use percentages instead of absolute numbers to highlight the age groups where women or men are over-represented. Source: NISR. 2013. EICV3 Thematic Report Gender

Dot charts Primary school net attendance rate for girls and boys by wealth quintile and urban/rural areas Yemen, 2006 • Preferred to bar charts when presenting many categories or data points, because bars can become too thin and difficult to interpret. • Can convey a lot of information in a simple way without clutter. • May be vertical or horizontal. Source: UNSD presentation “From raw data to easily understood gender statistics,” at Workshop on Integrating a Gender Perspective into National Statistics, Kampala, Uganda 4 - 7 December 2012. Data from Yemen Ministry of Health and Population, and UNICEF, 2008.

Pie charts • Used for simple comparisons of a small number of categories that make up a total. • Can illustrate the percentage distributions of qualitative variables and as an alternative to bar charts. • Using more than five categories will generally make a pie chart difficult to read. Rwanda: % of type of earnings for currently married women and men, 15-49, 2010 Source: NISR & Measure DHS. 2012. Rwanda Demographic and Health Survey 2010

Scatter plots School attendance rates for 6-17 years old by sex and state, India, 2005-06 • Often used to show the relationship between two variables. • Useful when many data points need to be displayed, such as a large number of regions, sub-regions or countries. • Also useful for identifying outliers in the data. • Design note: the four states where girls have significantly lower school attendance rates than boys have been highlighted. Source: UNSD presentation “From raw data to easily understood gender statistics,” at Workshop on Integrating a Gender Perspective into National Statistics, Kampala, Uganda 4 - 7 December 2012. Data from India Ministry of Health and Family Welfare, Government of India, 2007

Maps • Maps are used to show spatial patterns and geographic distributions for a particular variable. • They can increase the visibility of regional clusters within a country and highlight regional pockets that deviate substantially from the norm. Source: NISR. 2012. EICV3 Main Indicators Report

Interactive graphs and charts • A range of data visualization tools can be employed to enhance on-line dissemination of graphs and charts. • These tools can animate presentations, provide other interactive features, and display three or four dimensions of data simultaneously. For example: • A moving image can show transitions in a variable over time—changing the shape of an age pyramid; • Actual values and other details underlying a particular point in a graph or chart can be displayed instantly on request--by hovering over the point; • Bubble charts (a variation of the scatter plot) can be used • to visualize three or four dimensions of data and also • be animated to show changes over time.

Tables • Tables are a necessary form of presentation of data. • Types of tables: • Large comprehensive tables, often placed in a separate part of a publication (e.g., in an annex). • Text tables, which are smaller and part of the main text of a publication. They often support a point made in the text. • Always preferable to presenting many numbers in the text itself, as they allow more concise explanations. • Selection of data to present should focus on most striking differences or similarities between women and men. • Some data may be more easily conveyed in a table than in a graph--For example, • when data do not vary much across categories of a characteristic, or • when data vary too much.

Text tables with one column Rwanda: Fertility by province, 2010 DHS • Can be used to present data with not much variation between categories, • Often listed in ascending or descending order. Source: NISR & Measure DHS. 2012. RDHS 2010.

Text tables with two or more columns • Used to present data for females and males side by side sodifferences are clearly visible. Source: NISR. 2013. EICV3 Thematic Report Gender.

Text tables with two or more columns (continued) • Can be used as a form of presentation when the focus of analysis is a breakdown variable • in this case, mother’s age--associated with a number of related indicators expressed in different units Source: NISR & Measure DHS. 2012. RDHS 2010

Some tips for user-friendly presentation of gender statistics • Focus on a limited number of messages for each table, graph or chart. • The messages should generally relate to a specific gender issue. • Adopt good design practices; for example: • Ensure charts have clear, simple headings; labels are clear and accurate; axes are clear and divided consistently; a key is provided; data sources are acknowledged. • Facilitate comparisons between women and men; for example: • Present data for women and men side by side; • Ensure consistency in the way data for women and men are presented (e.g., use the same color for women and men in all charts in a presentation). • Consider the audience; for example: • Rounded numbers may communicate a message more easily to the general public. • Ensure simplicity of the visual layout; for example: • Labels for values presented inside a graph or chart can be distracting and often may be redundant; • Including a third dimension on a two-dimensional graph/chart can be misleading.

Exercise 6.2 1. You are asked to prepare an article presenting the results for a topical gender issue based on the most recent Population Census—such as boys’ increasing dropout rates compared to girls’ permanency or women’s much lower access to and use of credit for agriculture • How would you go about the task? • What presentational tools would you expect to use? 2. Does the presentation of gender statistics need to be improved in any of the fields (topic areas) with which you are familiar? What are the specific field(s)?How would you improve the presentation and why?

Learning Objectives