Objectives of Session

1. Gender Statistics Training Workshops: VietnamSESSION 4Analysis and Presentationof Gender StatisticsFebruary 18-20, 2014: MocChau – Son LaFebruary 25-27, 2014: Danang

2. Objectives of Session The main objectives of this session are to: • provide insights into how analysis and presentation of gender statistics can enhance the usefulness of the statistics; • examine the main types of analytic measures and analytic tools that can add value to basic data; and • describe 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 DataUNSD and UNFPA presentations to April 2013 UNSD Workshop in Japan

3. 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.

4. Key steps in analysing gender statistics

5. Type and level of analysis • Type and level of analysis usually varies by type of statistical product to be used in reporting results. • Tables constructed to disseminate basic data collected in censuses and surveys typically involve minimum data processing and analysis. • Additional processing and analysis typically occurs when more analytical reports or articles are produced. • For most types of analysis, indicators and other analytic measures play an important role. • Using the basic data to select and construct relevant indicators and other analytic measures is a critical activity. • Applying more complex analytic tools and techniques to the basic data may also be necessary to better understand some issues.

6. Analytic measures • There are a number of measures that can be very useful when analysing data from a gender perspective. • Such measures include: • proportions and percentages; • ratios and rates; • quantiles and medians; • means (averages); • standard deviations; and • projections. • They provide the basis for constructing many of the gender indicators used to monitor progress towards gender equality.

7. 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.Examples of gender indicators: proportion or percentage of women who are employed; labour force participation rate of women; literacy rate of women. • Sex distributions within the categories of a characteristic.Examples of gender indicators: proportion or percentage of the employed who are women; proportion or percentage of parliament members who are women; share of women among older persons living alone.

8. (a) Distribution of each sex across the categories of a characteristic Distribution of unemployed males and females by educational attainment Vietnam: Number and structure of unemployed by sex and educational attainment, 2009 Source: Vietnam GSO 2010 , The 2009 Vietnam Population and Housing Census, Part II Major Findings

9. (b) Sex distribution within the categories of a characteristic Female distribution within the categories of educational attainment Vietnam: Number and structure of unemployed by sex and educational attainment, 2009 Source: Vietnam GSO 2010 , The 2009 Vietnam Population and Housing Census, Part II Major Findings

10. 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. Examples of gender indicators: sex ratio (number of males per 100 females); sex ratio at birth (number of male live births per 100 female live births); maternal mortality ratio; gender pay gap (ratio of women’s to men’s average earnings). • For some sex ratios, standardisation of the variables used may be necessary to adequately reflect gender differences.Example of gender indicator: gender parity index for educational participation calculated as ratio of education enrolment rate for girls to that for boys.

11. Rates • Rates of incidence can be used to study the dynamics of change. They are a special type of ratio, in that they are obtained by dividing number of events during a period by number of population exposed to the events during the period.Examples of gender indicators: fertility rates; morality rates: infant morality rates. • By convention, some percentage measures are also called rates.Example of gender indicator: literacy rate (percentage of population that is literate).

12. Other measures • Quantiles and medians These measures are often used to describe the distribution of income or wealth across the population (quantiles) and to identify the mid point of the distribution (median). 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 indicators: average time use on unpaid work; average size of land owned; mean age at first marriage; mean age of mother at first child; ratio of average earnings of women employed in manufacturing to those of men in manufacturing. • Standard deviations, coefficient of variation, etc. Although not often presented in gender statistics, these measures have an important role in measuring the degree of association between variables and in making population inferences based on sample data. • ProjectionsAn example relevant to gender statistics is the projection of the male and female populations to a specified dare in the future.

13. Understanding gender differences using analytic measures Simple summary measures may often need to be further disaggregated or combined with other data to adequately inform gender issues. This is illustrated in the following example relating to the sex ratio at birth in Vietnam. Based on its 2009 Census, Vietnam ‘s sex ratio at birth was 110.6, well above the expected range of 104-106. This information (births over the last 12 months disaggregated by sex) was then combined with data on children ever born. This provided a classification of births by sex and birth order. Analysis of the combined data showed that the sex ratio for first births was 110.2, second births 109.0 and third births 115.5. This lead to the finding that couples without sons among their first two children tended to be highly motivated to have a third child and to make sure it was a boy. A further finding was that sex selection was almost non-existent among the poor. This underscored the importance of considering income, or a proxy for income such as educational attainment, when interpreting findings. Source: UNFPA presentation to UNSD workshop in Japan, April 2013

14. Usefulness of standardisation by age or other characteristics In some situations it can be useful to standardise a measure to make it more informative for understanding gender differences or to avoid it being misleading. Examples where standardisation may be important: • analysing the 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. • analysing literacy rates of women and men. Age standardisation can take account of the fact that literacy rates are lower at higher ages in which women predominate. • analysing the 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 the excess of women over men is concentrated in the oldest ages where disabilities are most common.

15. A country example showing effect of age standardisation Unstandardised 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

16. Usefulness of multivariate analysis Multivariate analysis can assist in disentangling variability and understanding interrelationships within a population group. It can provide a more comprehensive view of different relationships, thereby 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 ( e.g. lower education and early marriage) is caused by another factor (e.g. 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.

17. A country example: A study using multivariate analysis in Vietnam • Based on its 2009 Population and Housing Census, Vietnam undertook a series of logistic regressions of different marital status categories. • One of the issues studied was delayed marriage, defined as being unmarried among the population aged 40-69. The study found that: • delayed marriage was most correlated to low educational attainment, disability, religious adherence, in-migration status, and residence in the Southeast and the Mekong River Delta; and • there were some significant differences between females and males in the likelihood of delayed marriage for a number of the variables examined, including level of educational attainment, work status, type of disability and region of residence. • Source: GSO 2011, Vietnam Population and Housing Census 2009: Age-sex structure and marital status of the population in Vietnam

18. Integrating data from different sources • When different sources are to be combined to calculate a particular analytic measure (eg a rate), it is essential to check the sources for consistency and comparability. For example, 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. It may also be worthwhile consulting specialists who supply or use the data from that source.

19. 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 usefulness of 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 impact of socio-economic and biological factors on health outcomes). • Take care when combining data from different sources and use appropriate techniques.

20. Some further considerations ... • Be aware of the different implications, for gender analysis, of data produced at different levels of statistical unit. • For example, statistics on poverty may be produced at household level and/or individual person level but concepts used are not the same. • Using sex of ‘head of household’ to analyse gender differences is problematic. • For example, ‘head of household’ can refer to a number of 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. • For example, disaggregating households by size and composition (sex and age of each member), type (one person, couples with/without children, single mother/father, etc) and other characteristics can be illuminating.

21. Presentation of gender statistics • The general goals for presentation are: • highlight key gender issues • facilitate comparisons between women and men • reach a wide audience • convey the main messages resulting from data analysis • encourage further analysis • stimulate demand for more information • Tables, graphs and charts are the key forms of presentation.

22. 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 and 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 (e.g. If one value is double another, it should appear to be double in the graph or chart).

23. 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 • age pyramids • dot charts • pie charts • scatter plots • maps

24. Line charts • Line charts can give a clear picture of trends over time. • Examples of their use in gender statistics: trends in sex ratios; literacy rates over time; labour force participation rates by age group over time. Vietnam: Trend and projection of sex ratio (males/100 females), 1989 - 2059 Source: GSO 2011, Vietnam Population and Housing Census 2009,Age-sex structure and marital status of the population of Vietnam.

25. Line charts (continued) Vietnam: Age-specific labour force participation rates, 2011 Line charts can also give a clear picture of differences across age groups . For example, this chart shows that in Vietnam in 2011: • At all ages, labour force participation rates were lower for women than for men. • The gender gap reaches its maximum at age group 55-59 years. This is related to women’s retirement age being set at 55 years. Source: GSO 2012, Report on 2011 Vietnam Labour Force Survey

26. Bar charts: vertical bars • Bar charts may be vertical or horizontal. Both are common in presenting gender statistics. A key feature of these charts is 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. Vietnam: Percentage of women aged 15-49 years having third and higher order births by education level, 1/4/2012 Source: GSO 2012, The 1/4/12 time-point population change and family planning survey, major findings

27. Bar charts: vertical grouped bars Grouped (or clustered) bar charts can present a particular characteristic forwomen and men at the same time, so facilitating comparisons between them. The following chart illustrates this using two sets of differently colored bars for women and men. Vietnam: Proportion of the labour force with technical qualifications by urban/rural residence and sex 2009 Source: Vietnam GSO 2010 , The 2009 Vietnam Population and Housing Census, Part II Major Findings

28. Bar charts: vertical stacked bars Vietnam: Property titles by sex of the owner and urban/rural areas, 2006 • Stacked bar charts illustrate data sets containing two or more categories. They are most effective for categories adding up to 100 per cent. • Common problems: more than three segments of the bar are difficult to compare from one bar to another; one or more categories may be too short to be visible on the scale. Source: Viet Nam MOCST and others, 2008, Results of nationwide survey of the family in Vietnam 2006, Key findings

29. Bar charts: vertical stacked bars (continued) Vietnam: Proportion of population 5 years and older by school attendance, sex and urban/rural residence, 2009 • Sometimes stacked bar charts are used to illustrate the distribution of a variable within the female and male population. • Examples are: the distribution of female and male deaths by cause of death; the distribution of female and male school attendance. Source: Vietnam GSO 2010 , The 2009 Vietnam Population and Housing Census, Part II Major Findings

30. Bar charts: horizontal bars Vietnam: Infant mortality rate and under five mortality rate by occupation and industry of the mother, 2009 • 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. Source: GSO 2011, Vietnam Population and Housing Census 2009,Fertility and mortality in Vietnam: Patterns, trends and differentials

31. Age pyramids • Age pyramids are useful tools for describing the age structure of a population and changes in it over time. They include pyramids that use percentages instead of absolute numbers to highlight the age groups where women or men are over-represented. Vietnam: Population age group (years) and sex pyramid, 2012 Source: Vietnam GSO 2012, The 1/4/12 time-point population change and family planning survey, major findings

32. Dot charts • Dot charts can convey a lot of information in a simple way without clutter. They may be vertical or horizontal. If many categories or data points have to be illustrated, dot charts may be preferred over bar charts as barscan become too thin and difficult to interpret. Gender differential in life expectancy at birth (years), selected countries 2005-2010 Source: UNSD presentation Integrating a gender perspective into health statistics, made to April 2013 UNSD workshop in Japan on improving the integration of a gender perspective into official statistics:

33. Pie charts Vietnam: Frequency of injuries among women who were ever injured due to physical or sexual violence by husbands, 2010 • Pie charts are used for simple comparisons of a small number of categories that make up a total. They can illustrate the percentage distributions and are an alternative to bar charts. • Using more than five categories will generally make a pie chart difficult to read. Source: GSO 2010, Results from the National Study on domestic violence against women in Vietnam, Summary report

34. Scatter plots Female share of total tertiary graduates relative to female share of graduates in education field of study by country, 2008 • Scatter plots are often used to show the relationship between two variables. • They are useful when many data points need to be displayed, e.g., a large number of regions, sub-regions or countries. • They are also useful in identifying outliers in the data. Source: UNESCO Global Education Digest 2010

35. Maps Vietnam: Child sex ratio by province, 2009 • Maps are often used to show spatial patterns and geographic distributionsin respect of 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: GSO 2011, Vietnam Population and Housing Census 2009, Sex ratio at birth in Vietnam, New evidence on patterns, trends and differentials

36. Interactive graphs and charts (electronic on-line) • A range of data visualisation 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 be presented showing transitions in a variable over time (e.g. changing 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 (e.g. by hovering over the point); • bubble charts (a variation of the scatter plot) can be used to visualise three or four dimensions of data and they can also be animated to show changes over time.

37. Tables • Tables may not have the wide appeal of graphs, but are they 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. • Text tables are always preferable to presenting many numbers in the text itself, as they allow more concise explanations.

38. Tables (continued) • As with graphs, selection of data to be presented in text tables depends on the findings of analysis in terms of most striking differences or similarities between women and men. • Some data to be presented 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.

39. Text tables with one column • These can be used, for example, to present data with not much variation between categories. Data are often listed in ascending or descending order. Vietnam: Total fertility rate by socio-economic region, 2009 Source: GSO 2011, Vietnam Population and Housing Census 2009,Fertility and mortality in Vietnam: Patterns, trends and differentials

40. Text tables with two or more columns • These can be used, for example, to present data for females and males side by side data so that differences are clearly visible. Vietnam: Migration rate of population aged 15 years and over in 12 months preceding the survey by sex and marital status, 1/4/2012 (Unit:per thousand) Source: GSO 2012, The 1/4/12 time-point population change and family planning survey, major findings

41. Text tables with two or more columns (continued) • These can also be used when the focus of analysis is a breakdown variable (ethnic group of mother in the example below) that is associated with a number of related indicators expressed in different units. Vietnam: Some indicators of mortality by ethnic group of mother, 2009 Source: GSO 2011, Vietnam Population and Housing Census 2009,Fertility and mortality in Vietnam: Patterns, trends and differentials

42. 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 colour for women in all charts in a presentation, and likewise for men). • Consider the audience. For example: • rounded numbers may communicate a message more easily to 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.