1. Gender Statistics Training Workshops: Vietnam SESSION 4 Analysis and Presentation of Gender Statistics February 18-20, 2014: Moc Chau – Son La February 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 Data UNSD and UNFPA presentations to April 2013 UNSD Workshop in Japan
UNSD: United Nations Statistical Division
UNFPA: United Nations Population Fund

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
Identify gender issues
Obtain relevant data from available sources
Analyse and interpret the data
Report the findings

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. Such measures include:
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.
These two types of measures are illustrated in the table on economic activity status shown in the following two slides.

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
Distribution of unemployed males and females by educational attainment:
Female and male totals are used as denominators, proportions calculated by columns
Used for comparison of unemployed women and men with regard to their educational attainment
The basis for calculating gender gap: for example, the proportion of unemployed women that have never attended school is 3.5 percentage points higher than the proportion of unemployed men with this characteristic.
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
Female distribution within the categories of educational attainment:
Categories of the characteristic are used as denominators; proportions are calculated by row.
Used to show the under- or over-representation of women or men in selected population groups: for example, females account for 60.7% of the 85,982 unemployed persons that have never attended school.
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.
For some gender indicators based on sex ratios, standardisation of the variables used in calculating the ratio may be needed to adequately reflect gender differences. For example, calculating the gender parity index for participation at various levels of education as the ratio of girls enrolled to boys enrolled gives a poor measure of gender differences in access to education because differences in the school age population of girls and boys are not taken into account. An alternative calculation that controls for the sex composition of the school age population uses the ratio of the 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 Means (averages)
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.
Projections An example relevant to gender statistics is the projection of the male and female populations to a specified dare in the future.
The size of the standard variation relative to that of the mean is called the coefficient of variation.

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
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 and third births
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
Notes on table:
Column 2
Percentage of persons with a disability who are women 50.1%.
Columns 3-4
Percentage of women who have a disability is 4.00%, i.e. the prevalence of disability among women. This compares with 4.17% for men.
Columns 5-6
These show the hypothetical results from age standardisation.
Age standardisation involves applying the percentage of males, and of females, with a disability by age to the same age distribution. In other words, rather than applying each percentage to the corresponding male or female population, each percentage is applied to a common population which, in this case, consists of all individuals of both sexes.
The age-standardised results vary between sexes because of the different proportions of disabilities among women and men, but not because of the different numbers of women and men in the base population.
The age standardised percentage of women with a disability is lower (3.87%), while the corresponding percentage for men is higher (4.29%). For further details, see the UNFPA Guide on Gender Analysis of Census Data, Chapter 12 Disability.
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.
Two types of multivariate analyses which have proved useful in social studies are multiple linear regression and logistic regression. Multiple classification analysis (MCA) is another useful technique, closely related to linear regression.

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 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
GSO: General Statistical Office of Vietnam.
The source publication provides details of the logistic regression model used to study delayed marriage, including the regression coefficients for each independent variable in the model, and discusses the results for each variable in some depth (see section Delayed marriage, pages ).

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.
Poverty statistics at household level and individual level are discussed in UNSD Integrating a Gender Perspective in Statistics (Poverty section of Chapter 2).
Conceptual and practical issues associated with ‘head of household”, and types of household disaggregation that can provide insights into gender issues, are discussed in detail in UNFPA Guide on Gender Analysis of Census Data (Chapter 7: Households and Families).

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. These are powerful ways to present data. They can:
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),
Generally recommended that line charts start from zero at the y-axis of a variable. However, in this case, starting from a ratio of 92 makes more sense as it facilitates observation of movements in the ratio over the period.
It is important to use a consistent scale on each axis, otherwise the line’s shape can give incorrect impressions about the information.
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 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 years having third and higher order births by education level, 1/4/2012
The proportions on which the chart are based are calculated as the number of women who had a third or higher order birth in the 12 months prior to the survey per 100 women who gave birth during that period. In total, those having a third or higher order birth represented 14.2% of all the women who gave birth. As shown in the chart, when all the women who gave birth are grouped by education level, the higher the education level the lower the proportion having third and higher order births.
Design note:
3-D visual effect will not change the main story, but it will make the graph unnecessarily complicated and potentially misleading.
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 for women 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
The chart shows that, in 2009, the proportion of the male labour force that had received training was higher than that for women, and was higher in urban areas than in rural areas.
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.
Design note: Category/categories of most interest should generally be placed at the bottom of the bars to facilitate the comparison.
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.
The chart indicates that the proportion of people who have never attended school is higher among females than males overall, and in both urban and rural areas.
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.
The chart shows that women who work in less manual employment, or in the service sector, have children with a lower mortality rate.
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
The narrowing of the three bars at the base of the pyramid for both females and males is evidence that fertility in Vietnam has seen continuous and rapid declines.
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 bars can become too thin and difficult to interpret.
Gender differential in life expectancy at birth (years), selected countries
Design note: As the focus is on the gender gap, total life expectancy is not shown in the illustrative chart. Also the dots are not connected visually to the x axis as only the relevant years on the y axis are shown.
Source: UNSD presentation Integrating a gender perspective into health statistics, made to April 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.
Of women who had been physically or sexually abused by husbands, 26% reported having been injured as a direct result of the violent act. Among these, as shown in the chart, 60% reported that they had been injured more than once.
Design note: A common error is to show too many categories in pie charts, resulting in labels that are hard to read or shares that are too narrow.
Source: GSO 2010, Results from the National Study on domestic violence against women in Vietnam, Summary report

34. Source: UNESCO Global Education Digest 2010
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.
The chart shows that, among tertiary graduates, the education field of study is dominated by women in most countries, including Vietnam. The dots that are close to the diagonal represent the countries where the proportion of female graduates in all fields of study is similar to the proportion of female graduates in the education field of study.
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 distributions in 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.
The chart shows that most of the 63 provinces in Vietnam had a child sex ratio (males per 100 females, calculated from the population aged under 5 years) that was abnormally high. Only 17 had ratios that did not differ substantially from 105 (ie. a balanced sex ratio at birth). The most distinct cluster of sex ratio imbalance was in the northern plains of the country.
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.
Bubble charts visualise the first two dimensions of data using coordinates on the x and y axes and represent the relationship by bubbles. The size and colour of the bubble can then be used to represent two further dimensions of data. Such a chart can display, for example, a country’s fertility rate and life expectancy at birth (through the x/y co-ordinates), as well as its population size (through the size of the bubble) and its region in the world (through the colour of the bubble).

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.
Many statistical publications have as a main objective the dissemination of data collected and have to be specific about the values observed for the characteristics measured. This can be achieved in large, comprehensive tables. For example, a single table may contain information on several characteristics and indicators and provide several breakdowns of variables.

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)
The migration data refers to migration between areas within Vietnam, not international migration.
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.
Presentation standards and practices differ across countries, particularly on issues where some subjectivity is involved. The tips presented in this slide are ones that are unlikely to be contentious.
In Vietnam, eight examples of “correct presentation” are listed in the draft (22/11/2013) Guidance on the Collection and Use of Gender Statistics for Reporting on Gender Equality, prepared by Vietnam’s Ministry of Labour, Invalids and Social Affairs. Two of the examples raise issues that are worth noting here as the suggested approach is contrary what the UNSD publication Integrating a Gender Perspective in Statistics recommends in Chapter 4.
The first issue concerns terminology. The Vietnamese guide states that words such as male/female and boy/girl should be used instead of women/men. The UNSD recommends that the words women/men and girls/boys should be used instead of females/males. The approach in Australia differs from both of these: statistical standards published by the Australian Bureau of Statistics (ABS catalogue number ) state that the terms men, women, boys and girls etc should not be used as labels for output categories as these terms do not have universally accepted or agreed upon definitions and thus may confuse the user. For purposes of data integration and compatibility, it is preferable that all data by sex be labelled males and females in output.
The second issue concerns the ordering of data by sex. The Vietnamese guide states that male and female data should be presented consistently in a report, with female data either always before or always after male data. The UNSD recommends that women should always be presented before men. The approach in Australia in ABS output is to consistently show males before females, with the term persons used in preference to total.