Presentation of gender statistics in tables

Modified on 2015/05/26 15:34 by Sean Zheng — Categorized as: Chapter 4 - Analysis and presentation of gender statistics

Although they may not have the wide appeal of graphs, tables are necessary forms of presentation of data. Many statistical publications have as their main objective the dissemination of data and have to be specific about the values observed for the characteristics measured, which can be achieved through the use of large, comprehensive tables. Such tables are often placed in the annex to a publication and are therefore called “annex tables”. Annex tables may present information on several characteristics and indicators, covering several breakdown variables in a single table. In comparison, text tables are smaller tables that are referred to in and are part of the main text in a publication. They are often needed in support of a point made in the text. In that regard, tables are always a better alternative than the presentation of many numbers in a text, making the explanation in the text more concise. The selection of the data to be presented in small tables depends upon the findings of the analysis in terms of the most interesting groups or most striking differences or similarities between women and men.

Lastly, some of the data that need to be presented may be more easily conveyed by a table than in a graph. Most often, when data do not vary much across categories of a characteristic, or when they vary too much, tables are a better choice of presentation than graphs. List tables (tables with only one column of data) can be used, for example, to present data that does not have much variation between categories. List tables can show, for example, the regions of a country that have the minimum values observed for fertility rates or the lowest proportion of women married before the age of 18. For instance, table IV.2 shows the states in India that have the lowest proportion of women aged 15 to 19 who have had a live birth. Lists are often in ascending or descending order of the variable, rather than in alphabetical order.

Table IV. 2

States with the lowest percentages of women aged 15-19 who have had a live birth, India, 2005-2006

  Women aged 15 to 19 who have had a live birth (per cent)
Himachal Pradesh 2
Jammu & Kashmir 3
Kerala 3
Goa 3
Delhi 4
Uttaranchal 4
Punjab 4

Source: International Institute for Population Sciences and Macro International, 2007.

List tables or tables with two or more data columns can be used when the values observed for some categories vary widely compared to the rest of categories. For example, table IV.3 shows adult mortality rates by cause of death that vary widely from one cause of death to another.

Table IV. 3

Estimated adult crude death rates by cause of death, South Africa, 2008

Selected top causes of death

Crude death rates (per 10,000 persons age 15-59)
Causes of death Women Men
HIV/AIDS 81 65
Respiratory infections 8 11
Diarrhoeal diseases 7 5
Malignant neoplasms 6 7
Cardiovascular diseases 5 7
Injuries 3 12
Maternal conditions 3 ..
Nutritional deficiencies 2 1
Tuberculosis 2 7

Source: WHO, 2012.

Tables are an interesting form of presentation when the focus of analysis is a breakdown variable that is associated with a number of related indicators expressed in different units. Table IV.4 shows, for example, that in India more years of schooling of women are associated with a lower incidence of teenage pregnancies, lower total fertility rates and lower under-5 mortality rates for their children.

Table IV. 4

Demographic indicators by mother's years of schooling, India, 2005-2006

Women age 15-19 who have had a live birth (per cent) Total fertility rate (live births per 1000 women) Under-five mortality rate(deaths per 1000 live births)
No education 26 3.55 81
Less than 5 years completed 16 2.45 59
5-7 years completed 15 2.51 55
8-9 years completed 6 2.23 36
10-11 years completed 4 2.08 29
12 or more years completed 2 1.80 28

Source: International Institute for Population Sciences and Macro International, 2007.

Tables may be a better alternative than graphs when presenting changes in the values of multiple indicators (or one indicator disaggregated by a multi-categorical variable) between two points in time. Table IV.5, for example, shows the increase in women’s participation in most of the parliamentary committees in Sweden between 1985 and 2010. Similar tables may be constructed to present, for example, changes over time in the participation of women in managerial positions within regions of a country, or changes in sex ratio in the youth labour force within the largest cities of a country.

Table IV. 5

Women in parliamentary committees, Sweden, 1985 and 2010

Per cent women in total members in each committee
Committee 1985 2010
Labour market 27 65
Taxation 13 59
Health and Welfare 47 59
Education 27 59
Housing/Interior 20 53
Traffic 13 53
Finance 20 47
Justice 27 47
Constitution 20 47
Environment and Agriculture 20 47
Foreign Affairs 27 47
Cultural Affairs 60 41
Defense 20 35
Social Insurance 60 35
Industry 20 29
All committees 28 48

Source: Statistics Sweden, 2010.