Guide on Gender Analysis of Census Data Full Draft of 6 December 2012 Contents


Appendix 6: How to Apply this Guide in a Country Context



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Appendix 6: How to Apply this Guide in a Country Context

While there are many ways in which this guide can be used, the following provides four key steps for carrying out a gender analysis project at the country level. It emphasizes collaboration between the producers and users of gender statistics by involving NSO and gender experts from the government, academia and civil society. As such, these steps will help ensure that gender statistics are more meaningful, user-friendly and address the key gender issues relevant to that country.


Step One: Selection of Gender Issues:
Participants: NSO, Gender experts from government and civil society, including research institutions

Format: Workshop

Documentation: 10 Key Question Tool, Census Questionnaire, this manual (table of contents: chapter 3)

Purpose: To identify the key gender issues that can be analysed with the census data obtained in country X.

Roles: Gender Experts provide an evidence-based overview of the key gender issues in the country, ideally using the 10 Key Question Tool below and considering the 10 gender issues. Statisticians explain what can and cannot be measured with census data on the basis of the country’s census questionnaire.

Expected Outcome: Consensus on what statisticians should compute
Box : The 10 Key Questions Tool

1. Who does what? [activities]

2. How? With what? [access to resources]

3. Who owns what? [ownership of assets]

4. Who is responsible for what? [obligations]

5. Who is entitled to what? [claims, rights]

6. Who controls what? [income, spending]

7. Who decides what? [power]

8. Who gets what? [distribution]

9. Who gains and who loses? [redistribution]

10. Why - What is the basis for the situation? [rules, norms,

customs]


(Questions 1-9 can be combined with the additional question, "And With Whom?' in order to capture the social relations involved)
Step Two: Census Analysis – Preparation of Tabulations and Computation of Sex-Disaggregated Indicators
Participants: NSO, Gender experts from research institutions, external consultants

Format: Desktop study/in-depth statistical analysis

Documentation: “Principles and Recommendations”, this manual (sections 4 and 5 of each chapter in Part II)

Purpose: To provide the raw data, tabulations and indicators needed for answering the key gender questions identified in Step One.

Roles: Statisticians and researches perform high quality data analysis

Expected Outcome: Tabulations and indicators are available and of high quality
Step Three: Interpretation of Data, Suggesting further Analyses
Participants: NSO, Gender experts from government and civil society, including research institutions

Format: Workshop

Documentation: Tabulations and indicators produced by NSO, this manual (sections 2 and 6)

Purpose: To make sense of the data and suggest further analyses going into more depth with some key findings

Roles: Statisticians walk participants through the analyses carried out, outline problems encountered and summarise the gender differences identified; Gender Experts discuss what may be underlying the gender differences documented

Expected Outcome: Consensus on additional variables that need to be taken into consideration (and technically can be) in order to shed light upon the findings
Step Four: Advocacy Material is devised
Participants: Gender experts from government and civil society

Format: Workshop

Documentation: Key national policies, this manual (section 8)

Purpose: To identify how the indicators, tabulations and results of multivariate analysis can be used to inform and advocate for current and future national gender equality policies and initiatives, or for reporting purposes

Roles: Experts provide an overview of the key gender policies and initiatives currently on-going and planned in-country and select critical data for evidence-based advocacy
Expected Outcome: An advocacy plan with clearly defined roles/responsibilities/timeline (cite key references)

1 http://unstats.un.org/unsd/demographic/sources/census/2010_PHC/censusclockmore.htm; last accessed on 3 December 2012. The data indicates that the majority of countries is succeeding in their census planning and taking. However, as conducting a census is a complex and costly process that requires great efforts in capacity building, some countries and regions have been forced to delay or even cancel their censuses. Some of the challenges that countries are facing include: administrative organization, funding constraints, post-conflict situations, humanitarian crisis, natural hazards, etc.

2 If the cost is too large for the NSO, it may opt for distributing the information through the IPUMS programme of the University of Minnesota, which designs user samples for release to the public, guided by the specifications provided by NSOs.

3 In addition, economists often use the term “equity” in a sense that is completely distinct from the one explained in this section, namely to refer to debt-free assets in the form of real estate, bonds or particularly stocks.

4 This discussion parallels the one of “equality of opportunities” versus “equality of outcomes” in regard to the role of the school system. While some consider schools as the great equalizers of opportunities between children of different social backgrounds (or, in this case, different sexes), others (e.g. Jencks, 1972) have demonstrated that, even in societies like the United States, which places a lot of emphasis on the principle of equal opportunity, only a relatively small proportion of the inequality of outcomes can be explained in terms of differences in access to education.

5 Note that in French and Spanish, the usage of the terms is the opposite: fécondité or fecundidad for actual reproductive outcomes and fertilité or fertilidad for biological capacity.

6 The update is being undertaken to incorporate new developments, take account of new trends such as those brought about by the advent of AIDS, and adapt the older techniques to the possibilities created by more modern computational tools such as EXCEL. For more information, see http://demographicestimation.iussp.org/.

7 Nevertheless, omission and displacement of births in DHS data are not trivial, and of course, as in any survey, one has to account for sampling errors, which limits the possibility of using DHS data for small sub-groups.

8 For example, Argentina, Azerbaijan, Bahamas, Costa Rica, Kazakhstan, Mexico, Palau, Peru, the Seychelles and Thailand ask the traditional fertility and child survival questions, but do not disaggregate them by sex.

9 For a common definition of live birth, see: http://unstats.un.org/unsd/demographic/sconcerns/natality/ natmethods.htm.

10 Brazil, Botswana, Burundi, Cayman Islands, China, Djibouti, Dominican Republic, Ecuador, El Salvador, Fiji, Iran, Liberia, Malawi, Maldives, Occupied Palestinian Territories, Republic of Congo, St. Lucia, Samoa, Sudan, Swaziland, Tokelau, Trinidad and Tobago.

11 Note, however, that the adolescent birth rates used for monitoring MDG 5.B are estimated independently and not based on the UN population projections.

12 The same problem affects the Own Children Method for fertility estimation.

13 In the 2010 census round, however, two censuses have asked a question how many additional children women intended to have, namely Republic of Korea (2005) and Kazakhstan (2009).

14 definition adapted from WHO website: http://www.who.int/healthinfo/statistics/indmaternalmortality/en/index.html

15 It is quite easy to show a strong correlation between maternal mortality and selected gender indicators but this does not imply a causal relationship, as both are correlated with the overall level of development.

16 Note that in India the sex ratio is computed the other way around, as the number of girls over the number of boys.

17 Where “date of birth of last live-born child born” is not disaggregated by sex, one needs to look at the age and sex of the youngest child in the household and – if under 1 year old – verify if its age/birthday is compatible with the declared date of last birth.

18 Some anomalies in sex ratios at birth can be explained in biological terms. For example, a study by the Arctic Monitoring Assessment Program in 2007 found abnormally low sex ratios, in the order of 50, in some arctic communities in Russia, Greenland and Canada, which it attributed to high levels of endocrine disruptors in the blood of inhabitants, particularly PCBs and DDT. Other studies (e.g. Rocheleau et al., 2011), however, have contested the effect of PCBs on human sex ratios at birth. There is also some discussion among geneticists as to whether sex ratios vary naturally according to race, maternal and paternal age and birth order (e.g. Erickson, 1976; Imaizumi and Murata, 1979; Ruder, 1985; Chahnazarian, 1988). Historical data from Europe suggest considerable heterogeneity between families, with boys predominating in some and girls in others, in proportions that differ from what one would expect if the process were purely random (Garenne, 2008 b). In the case of Africa, Garenne (2008 a) found that sex ratios declined with maternal age and birth order. Due to the fact that he used DHS data, no information on paternal age was available. He concluded that these findings are consistent with James’s (1989, 1996) theories about the biological factors of the sex ratio, in particular, the effect of concentrations of sex hormones (e.g. progesterone, gonadotropin, estrogen, testosterone). Higher levels of gonadotropin and progesterone were found to be associated with more female births (lower sex ratios). Conversely, higher concentrations of male hormones (e.g. testosterone) seem to favour high sex ratios. The African data do not seem to suggest any deliberate sex selection. Oster (2005) has argued, based on existing medical literature and analysis of cross country data and vaccination programmes, that parents who are carriers of hepatitis B have a higher offspring sex ratio (more boys) than non-carrier parents. Since China and some other countries have high hepatitis B carrier rates, she suggested that hepatitis B could explain up to 50 per cent of Asia’s “missing women”. However, Lin and Luoh (2008), using data from a large cohort of births in Taiwan, found only a very small effect of maternal hepatitis carrier status on offspring sex ratio, a conclusion which was later endorsed by Oster as well (Oster et al., 2008).


19 When sex ratios began to rise in Armenia in the 1990s, for example, at first the tendency was to attribute this to the aftermath of conflict in the region. It was not until further analysis established that the imbalance was limited to second and third birth orders that the sex selection process was recognized for what it was.

20 This is based on Article 5a) of the original CEDAW text, which states that “States parties shall take all appropriate measures to modify the social and cultural patterns of conduct of men and women, with a view to achieving the elimination of prejudices and customary and all other practices which are based on the idea of the inferiority or the superiority of either of the sexes or on stereotyped roles for men and women.” Similarly, General Comment 14 of the CEDAW states that “States parties' reports also disclose that polygamy is practiced in a number of countries. Polygamous marriage contravenes a woman’s right to equality with men, and can have such serious emotional and financial consequences for her and her dependants that such marriages ought to be discouraged and prohibited.”

21 This difference in enumeration methods can have implications from the viewpoint of a gender analysis. For example, if the census coincides with a period of the year in which many of the men are temporarily absent from their homes because of seasonal activities (temporary harvesting labour) and if the census is conducted according to the de facto criterion, more women than usual will be classified as living without their husbands. If the census is a de jure census, the missing men will be counted in their households of usual residence, but they may not be considered heads of households, even if they normally act as such.

22 PPP refers to purchasing price parity, which measures the relative purchasing power of different countries’ currencies over the same types of goods and services, adjusting for inflation. PPP helps provide an accurate comparison of standards of living across countries (World Bank, 2011).

23 The change from USD 1 to USD 1.25 was introduceed to correct for inflation of the US dollar.

24 In a hot deck imputation information from other respondents with similar characteristics is used to make imputed that are best suited for the missing information. See: United Nations, (2008 a) Principles and Recommendations for Population and Housing Censuses Revision 2: 70.

25 http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK: 20242879~menuPK:435055~pagePK:148956~piPK:216618~theSitePK:430367~isCURL:Y~isCURL: Y,00.html.

26 http://unstats.un.org/unsd/methods/poverty/chapters.htm.

27 http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK: 22405907 ~menuPK:6626650~pagePK:148956~piPK:216618~theSitePK:430367,00.html.

28 http://mdgs.un.org/unsd/mdg/Host.aspx?Content=indicators/officiallist.htm.

29 Washington Group on Disability Statistics (WG) (s.d.), The Measurement of Disability

Recommendations for the 2010 Round of Censuses. p.1.



30 Based on UNSD/CAPMAS (2000). Gender and Development: An Information Kit I. Cairo, CAPMAS.

31 The World Plan of Action of the First Conference also stated that regional action should include regional standing committees of experts from countries in order to “give leadership in the methods of reporting on the situation of women and in the development of indicators for assessing the progress made towards the goals of this Plan in conjunction with regional statistical bodies and international efforts to this end” (World Conference of the International Women’s Year, World Plan of Action, Paragraph 207, 1975).

32 The Beijing Platform for Action made reference to the need to develop and strengthen statistical systems in several issues, such as labor and economic activity (including female contribution in the unremunerated and domestic sectors), health of girls and women of all ages, incidence of violence (including domestic violence, sexual harassment and other different forms of violence against women and girls), and sharing of power and decision-making.

33 http://unstats.un.org/unsd/demographic/products/Worldswomen/WW_full%20report_color.pdf

34 Activities no longer considered as ‘household activities’ include: production of agricultural produce, gathering of fruits etc. and their storage; processing of primary products (produced or bought) and the collection of water; other processing activities, sold or not, like weaving, dress making and furniture making. (Tempelman, 1999).

35 http://www.gender.no/Facts_figures/1322


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