Vietnam Module 4

Gender Statistics Training Workshops: Vietnam
Analysis and Presentation
of Gender Statistics
February 18-20, 2014: Moc Chau – Son La
February 25-27, 2014: Danang
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
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
– 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
Key steps in analysing gender statistics
Identify gender issues
Obtain relevant data from
available sources
Analyse and interpret the data
Report the findings
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.
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
• 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:
(a) Distributions of each sex across the categories of a
Examples of gender indicators: proportion or percentage of
women who are employed; labour force participation rate of
women; literacy rate of women.
(b) 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.
(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
(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
• 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.
• 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
• By convention, some percentage measures are also called rates.
Example of gender indicator: literacy rate (percentage of population that is
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
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.
An example relevant to gender statistics is the projection of the male and female
populations to a specified dare in the future.
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
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
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
• 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.
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
Type of disability
Walking or moving
Speaking or communicating
Personal care
Paying attention or learning
Mental disabilities
Male Female
Male Female
Source: UNFPA Guide on Gender Analysis of Census Data
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.
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
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
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
Take care when combining data from different sources and use
appropriate techniques.
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
– 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
– 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.
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
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).
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
• 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
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.
Line charts (continued)
Line charts can also give a
clear picture of differences
across age groups .
Vietnam: Age-specific labour force participation rates,
For example, this chart shows
that in Vietnam in 2011:
• At all ages, labour force
participation rates were
lower for women than for
• The gender gap reaches its
maximum at age group 5559 years. This is related to
women’s retirement age
being set at 55 years.
Source: GSO 2012, Report on 2011 Vietnam Labour Force Survey
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
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
Source: Vietnam GSO 2010 , The 2009 Vietnam Population and Housing Census, Part II Major Findings
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.
Per cent
House and
residential land
Farm and
forest land
Women and men
Source: Viet Nam MOCST and others, 2008, Results of nationwide
survey of the family in Vietnam 2006, Key findings
Bar charts: vertical stacked bars (continued)
Sometimes stacked
bar charts are used
to illustrate the
distribution of a
variable within the
female and male
Vietnam: Proportion of population 5 years and older by
school attendance, sex and urban/rural residence, 2009
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
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
need to be
presented (e.g.
regions of a
or where
categories have
long labels.
Source: GSO 2011, Vietnam Population and Housing Census 2009, Fertility and mortality
in Vietnam: Patterns, trends and differentials
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
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 2005-2010
Sri LankaThailand
Viet Nam
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:
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
distributions and are
an alternative to bar
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
Scatter plots
Scatter plots are often
used to show the
relationship between
two variables.
Female share of total tertiary graduates relative to female share
of graduates in education field of study by country, 2008
They are useful when
many data points need to
be displayed, e.g., a large
number of regions, subregions or countries.
They are also useful in
identifying outliers in the
Source: UNESCO Global Education Digest 2010
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
Source: GSO 2011, Vietnam Population and
Housing Census 2009, Sex ratio at birth in
Vietnam, New evidence on patterns, trends and
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.
• 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.
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.
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
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
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
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:
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:
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.
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.

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