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

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

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…..

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.

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.

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

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

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

How many statisticians present data

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

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

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

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

Using the same data, this graph provides a different message

Both graphs give important, yet different, information

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

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.

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

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.

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

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

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

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

Acknowledgements Victoria Velkoff, US Census Bureau Statistics Sweden Engendering Statistics: A Tool for Change Statistics New Zealand http://www.stats.govt.nz/NR/rdonlyres/A1892BF2-6E4A-4D08-9667-BC5EE45B99F4/0/GraphicsGuidelines.pdf 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.