1. Analysis and Presentation of Gender Statistics
3 October 2007
Republic of Moldova
UNECE Statistical Division

2. Analysis of Gender Statistics
Why do Gender Analysis?
Improve design of policies, projects and programs
Measure impact of interventions
Understand differences between genders

3. For Example…
In many countries, men have higher labour force participation rates than women
Sex-disaggregated data shows us this, but we don’t know why
So, we need more information…..

4. Percent of Economically Active People Aged 20-29 by Sex
Men
Women
This graph presents the sex-disaggregated data only
The reasons for the difference are not clear
Source: United Nations Economic Commission for Europe, 2000.

5. Percent Economically Active People Aged 20-29 by Sex and the Presence of a Pre-school Child: 1998
By introducing another variable, such as the presence of a pre-school child in the family, differences between men and women are more apparent.

6. Presenting Data Presentation is crucial Should attract readers
Encourage further analysis
A range of formats
Tables
Graphs
Diagrams
Maps
Crucial for correct interpretation
Point about only presenting statistically relevant information
Data must be accurate
There are a range of different formats that can be used for presentation, such as tables, graphs, diagrams and maps.
a bit more about maps or GIS – the reality is that not everyone understands a table, or graph but everyone seems to be able to understand a map - so if we are trying to show regional differences then a map is the way to go & this is a rapidly expanding area of data presentation
GIS software is now readily & cheaply available & the subject is being taught in schools

7. Tips for Good Presentation
Clear visual message
Appropriate heading
Convey one finding or a single concept
Simple
A common mistake is to over-complicate presentation with too much information
The reader should be able to quickly gauge the key message of the presentation
Always provide a link or source of further information

8. A Good Graph Accurately shows facts Grabs the readers attention
Shows trends or changes
Is clear and easy to read
Has a title and minimal labels
Uses colours or patterns to show differences

9. How many statisticians present data

10. Make it Easy to Understand
Graphic presentation of data makes it easier to understand
Easier to see the differences between men and women

11. Percentage Married at Older Ages by Sex in the US: 2003
Using the data in the table we just saw, a simple message is conveyed with this graph

12. How we present sex-disaggregated data influences the analyses we make

13. This graph shows the mean age at first marriage in these countries using sex-disaggregated data

14. Using the same data, this graph provides a different message

15. Both graphs give important, yet different, information

16. Life Expectancy at Birth for Select Countries: 2003
32.3
37.9
73.0
74.6
64.4
73.3
77.6
79.6
80.1
83.1
82.5
84.4
84.2
32.2
40.1
41.0
59.6
62.5
62.9
67.9
70.1
71.9
72.9
74.4
75.6
76.5
78.9
Botswana
Zimbabwe
Swaziland
Russia
Belarus
India
Egypt
China
Mexico
Chile
United States
France
Italy
Japan
Singapore
Male
Female
Life Expectancy at Birth for Select Countries: 2003
Source: U.S. Census Bureau, International Programs Center, International Data Base.
Again, this graph shows sex-disaggregated data for another statistical indicator: life expectancy at birth

17. Female Advantage in Life Expectancy at Birth in Select Countries: 2003
0.1
-2.2
-3.2
13.4
12.1
1.5
5.1
3.2
5.6
6.7
5.7
7.5
6.1
6.8
5.3
Botswana
Zimbabwe
Swaziland
Russia
Belarus
India
Egypt
China
Mexico
Chile
United States
France
Italy
Japan
Singapore
Female Advantage in Life Expectancy at Birth in Select
Countries: 2003
Source: U.S. Census Bureau, International Programs Center, International Data Base.
By presenting the data differently, the reader gets another message. Again, both graphs give important and different information.

18. From ‘raw data’ to easily understood gender statistics
Tables and graphs from ‘raw data’
Gender concern here is Poverty
Underlying cause is the lack of means of economic support
Closer analysis requires reasons for not being economically active
Sources: labour force surveys or population censuses
Example from Tanzania

19. Population ages 10 and over by economic activity status and reasons for not economically active in Tanzania Mainland 1990/91
Raw data
What can we tell from this information?
Too many messages – let’s see what can be extracted.

20. Basic Table 1 Population ages 10 and over by economic activity status
Exact numbers
Focuses only on economic activity rate
Exact numbers rounded to 1,000’s and percentages to integers

21. Population ages 10 and over by economic activity status
Exact numbers
Further simplified
Deleted two columns of numbers and included total in 1,000’s

22. Basic Table 2 Not economically active ages 10 and over by reasons
Exact numbers
Focuses only on reasons for being not economically active
Exact numbers rounded to 1,000’s and percentages to integers

23. Not economically active ages 10 and over by reasons
Exact numbers
Further simplified
Deleted two columns of numbers and included total in 1,000’s

25. Acknowledgements Victoria Velkoff, US Census Bureau
Statistics Sweden Engendering Statistics: A Tool for Change
Statistics New Zealand
Office of National Statistics UK
Statistics Denmark
Russian Federal State Statistics Office
UNECE Gender Statistics Database
That concludes this presentation. I would like to acknowledge the following people and organisations for providing the source material.