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


Chapter 2: Conceptual Clarifications on Gender Equality and Gender-Responsive Data Analysis



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Chapter 2:
Conceptual Clarifications on Gender Equality and Gender-Responsive Data Analysis

17. The following reviews some core concepts that sometimes lead to misunderstandings between producers and users of data. By pointing out where differences in meaning exist between the common statistical usage and that of the gender literature and by offering a shared definition for the purpose of this manual, dialogue will be enhanced in view of a shared goal: Making statistics reflect all the national population and measuring progress towards gender equality.



A. Sex and Gender

18. In its most basic meaning, the concept of gender helps us understand how biological differences between men and women (sex) acquire cultural and social meanings and produce identities, differences, and inequalities in a given setting (gender). Sex characteristics at birth are universal. By contrast, gender refers to socio-cultural differences and social relationships between women and men that can change, over time for the same individual, and differ within and among societies. In the English language, it is helpful to think of the terms female and male as referring to sex differences, and feminine and masculine as referring to gender differences. Something is “gendered” when socially and culturally defined gender differences intervene in constructing it. Integrating gender analysis into development work means analysing the various forms gender differences take and the ways they intersect with other social markers (race, class, caste, religion, ethnicity, sexuality, etc.).


Text Box 4: Definitions – Sex vs. Gender
Sex: Refers to the classification of people as male or female, based on biological and physiological characteristics such as chromosomes, hormones, and reproductive organs.
Gender: is a social and cultural construct, which values men’s and women’s, girls’ and boys’ attributes differently. Accordingly, it assigns socially acceptable and often stereotypical roles and responsibilities to men and women. Gender-based roles and other attributes, therefore, change over time and vary with different cultural contexts. The concept of gender includes the expectations held about the characteristics, aptitudes and likely behaviours of both women and men (femininity and masculinity). This concept is also useful in analysing how commonly shared practices legitimise discrepancies between sexes.
19. Gender is a social organising principle that influences people’s roles and responsibilities in the context of other social variables including ethnicity, culture, economic and social class, religion. It is also a social structure that places women and men in different positions, roles, and responsibilities. Finally, it is a social stratification that values what women and men do differently. As a result, there are vast differences in the resources that women and men are able to access, in the value attributed to their respective contributions, and in their ability to effectively act on the world and on their own behalf (Lorber, 1994; Kabeer, 2002).
20. In terms of policy, three interrelated points are crucial to understanding the way gender works; gender affects peoples’ lives with regards to needs, access to power/resources and differential effects a policy may have on women and men.

a) Needs: One must be aware that women, men, girls and boys may have different needs as a function of their socio-cultural and economic situations in a given context. For instance, to enhance the schooling of girls, an increase in the number of female teachers and separate girls’ bathrooms in mixed schools may be instrumental. Girls have specific needs in this context as sexual violence may be a real problem in many places and parents need to be reassured so that they are willing to send their daughters to a school.

b) Power/Resources: Access to, and control of, power and resources including decision-making is gender-differentiated. For instance, the role of household head is more often ascribed to men than women due to social bias that men are family leaders, regardless of whether they are the main income earners. Similarly, the fact that parliaments and local governments are strongly male-dominated may translate into a bias toward spending municipal resources on larger roads that support trade and create jobs for men, whereas less consideration is given for instance to street lighting and police services that increase safety for women.

c) Effects: Population policies and social programmes may have (unintended) differential effects on women and men, and girls and boys. For instance, health policies that do not consider the different – and often lower – income status of women-headed households may inadvertently restrict poor women from accessing user-paid services and consequently reinforce poor health outcomes for population subgroups such as households headed by single women. Women may also not have the opportunity to participate in decision-making as their time is limited by double or triple roles in productive and reproductive work.
As a consequence, conceptualizing gender in statistics - beyond the simple disaggregation of data by sex - is complex. Much of the remainder of this guide will suggest ways of doing this that are meaningful and consistent – or valid and reliable.
21. The terms gender and sex are often used synonymously. Indeed, the terms “gender disaggregation” or “disaggregated by gender” continue to be widely used and to confuse those who were taught during gender training that sex and gender are quite different concepts. Although the English language (unlike most others) does allow the use of the word “gender” in the sense of “sex”, there is now broad international consensus that gender is not a useful category for defining statistical variables: gender statistics are actually disaggregated by sex and not by gender.
Text Box 5: What is Sex Disaggregated Data?
Sex-disaggregated data is data that is cross-classified by sex, presenting information separately for men and women, boys and girls. When data is not disaggregated by sex, it is more difficult to identify real and potential inequalities. Sex-disaggregated data is necessary for effective gender analysis.

Source: “Gender Equality, UN Coherence & You – Glossary, July 2010”
22. Interpretation is needed to make sense of sex-disaggregated statistics in terms of gender. Sex-disaggregated data are an indispensible starting-point and should be routinely available. However, socio-cultural and economic analysis on sex-differences (i.e. gender analysis) needs to be carried out on those data in order to make them meaningful in terms of gender. This guide will show you how to do this.

B. Measuring sex/gender differences, gender inequality and gender inequity through gender analysis

23. Gender Statistics is a policy-oriented approach to data, thus focusing on gender (the socio-cultural construct) rather than sex (the biological marker) as an analytical category. (Appendix 4 provides a more detailed overview of the evolution of gender statistics). Gender Statistics is about producing and disseminating statistics that reflect the realities of women and men of all ages, with a view to informing gender equality initiatives and policies. Current challenges in gender statistic include financial shortages and capacity gaps that lead to low data quality, or insufficient data analysis and dissemination, as well as a lack of normative frameworks in-country, and coherent definitions and methods worldwide that lead to shortcomings in political will and in implementation on the ground.


24. It is customary to distinguish between two categories of gender statistics, namely sex-disaggregated statistics and other gender-relevant statistics. The latter refer to data that provide information on the situation of either sex or the gender relations between men and women, but that cannot be meaningfully compared between the sexes. For example, maternal mortality is an inherently female phenomenon, whereas the incidence of prostate cancer is an inherently male phenomenon. Although fertility can, in principle, be quantified for both men and women, the former is much more difficult than the latter. In addition, men and women have inherent biological differences with respect to the way in which they are affected by fertility. Therefore, fertility is probably better thought of as a gender-related statistic, rather than in terms of sex disaggregation.
25. Statisticians use words like “variance”, “variation”, “disparity”, “dispersion”, “inequality” or “differentiation” descriptively, without much regard for the inherent fairness or injustice of the differences observed between individuals. Sociologists make distinctions between such concepts as “inequality of opportunities” and “inequality of outcomes”. And economists may distinguish between kinds or degrees of inequality that are functional or dysfunctional to economic growth.3 The gender area has its own terminology for describing disparities between the sexes in terms of their fairness or lack thereof. Sex/gender difference is a descriptive, value-neutral concept. Sex/gender difference refers to disparities or lack of similarities between men and women – as social groups – in their respective status and livelihood conditions. For instance, if women and men have different consumption preferences, needs and aspirations, this creates differences in the way they spend their money. Similarly, they may have different inclinations with respect to their career paths or ways to spend their leisure time. Preferences, needs and aspirations are generally influenced by representations of femininity and masculinity and can therefore be described as “gender differences”. Where differences are related to biological traits – think of reproductive health issues for instance – one can speak of “sex differences.” Where no differences exist, there is a situation of “parity”. But parity is not the ultimate goal of gender equality as some differences between the sexes are acceptable. For example, the goal that men and women should have the same distribution of occupations may not be a pertinent one, while it is pertinent to require the occupational status and incomes associated with these occupations to be similar.

26. Gender inequality is a human-rights-related, normative concept; it implies an assessment of a given gender difference as unfair. Gender inequality refers to women’s and men’s, girls’ and boys’ unequal “conditions, treatment and opportunities for realizing their full potential, human rights and dignity, and for contributing to (and benefiting from) economic, social, cultural and political development” (Gender Equality, UN Coherence & You – Glossary 2010). Unequal valuing by society of the similarities and the differences between men and women, and of the roles they play, also constitutes a form of inequality. Achieving gender equality in turn requires women's empowerment to ensure that decision-making at private and public levels and access to and control of resources are no longer weighted in men's favour. This will result in women’s and men’s ability to participate as equal partners in productive and reproductive spheres of life.


27. To illustrate the concepts of gender difference and inequality, think about a village where the girl’s school starts and finishes after the boy’s school. As long as the variation in time is not too large, this constitutes a “difference” between them, to be taken into account when planning transportation systems for example. This difference may or may not be due to discrimination or unfairness in the way society values men and women. By calling it a “difference”, the fact that times vary is acknowledged but not judged. Now consider a village with equal numbers of girls and boys of schooling age but with two boys’ schools and only one school for girls. Here, girls and boys do not have the same opportunities for realising their potential as access to education is harder for girls. Hence, there is gender inequality - it is unfair that on average girls have less educational resources than boys which may negatively affect their educational achievement.
28. Gender inequity is a policy-oriented concept that considers whether women and men, girls and boys have the same chances of reaching expected outcomes such as literacy or decent work. Gender equitable policies “ensure that women and men, girls and boys have equal chance not only at the starting point but also when reaching the finish line” (Gender Equality, UN Coherence & You – Glossary, 2010). They cannot be limited to providing equal access but must also consider the fact that individuals start from unequal positions and hence may require different degrees of policy intervention in order to achieve the same end result. In practice, this will often involve some sort of positive discrimination to level the playing field, such as special scholarships for girls to counter unequal access to secondary or tertiary education or quotas to promote a balance of women and men in senior management positions or parliaments.
29. The means to achieve gender equality and gender equity may therefore differ. For example, regardless of the time girls and boys leave for school – which is a random inequality –girls on average are more vulnerable to sexual assault and/or sexual harassment than boys both at and on their way to school. Equity policies would try to ensure that schools have facilities needed by girls so that they are allowed and willing to attend (i.e. private and separate toilets) and that the education system aims to mitigate risk factors by providing safe passages for girls. Equality policies, on the other hand, would try to ensure that girls continue to be sent to school and that those schools provide them equal opportunity to learn so they have the potential to achieve equal outcomes.4
30. ‘Gender mainstreaming’ is the chosen approach of the United Nations system and international community toward realising gender equality. The key concern with regard to gender equity, on the other hand, is fairness of opportunities, and the chosen approach is the empowerment of women and girls through targeted interventions, or special measures. Equality and equity are inter-related concepts. Discrimination based on sex/gender is both the root for needing equity policies and the barrier for achieving gender equality.
Text Box 6: What is Gender Mainstreaming?
Gender Mainstreaming: "Mainstreaming a gender perspective is the process of assessing the implications for women and men of any planned action, including legislation, policies or programmes, in any area and at all levels. It is a strategy for making the concerns and experiences of women as well as of men an integral part of the design, implementation, monitoring and evaluation of policies and programmes in all political, economic and societal spheres, so that women and men benefit equally, and inequality is not perpetuated. The ultimate goal of mainstreaming is to achieve gender

Source: United Nations Economic and Social Council (ECOSOC), 1997
31. While sex/gender difference can be measured fairly easily, measuring gender equality and gender inequity poses greater challenges. To define sex/gender difference, variance, or the degree to which the objects or individuals being described are different from the mean (average), can be used. Similarly, to detect inequity and inequality, variance can show where parity has not been achieved between women and men, girls and boys. Nevertheless, much caution has to be employed in the construction of gender indicators because, as was pointed out earlier, parity may not always be relevant or desirable nor does it describe the equitable access to or fairness of opportunities. Rather what is more typically measured is a government’s stated commitment to equality and the existence of equity interventions.
32. In most cases, in addition to mainstreaming statistical analysis, qualitative and policy research is needed in order to assess whether the opportunities of women and men are unequal. While gender differences in literacy, for example, is fairly easy to pinpoint on the basis of variance by sex, an investigation of gender inequality in literacy would need to consider opportunity factors such as the access to scholarships or stipends and explicit criteria for admission to schools and literacy programmes. In addition, the ability to take advantage of the opportunities being offered may differ between population groups. For example, even if boys and girls have access to the same schools, girls may be restricted by the fact that they need to care for their younger siblings part of the time, or in some other cases boys may be restricted by the fact that parents count on them from an early age to generate some monetary income. The field of gender statistics is devoted to holistically assessing such gender inequalities through gender analysis.
33. Gender analysis is not about women and cannot be carried out on women as an isolated group. Rather, gender analysis involves looking at power relations between men and women and it may even focus specifically on men and boys to analyse how certain behaviours come to be socially perceived as “masculine behaviours”. In this vein, many countries have underscored the need to improve the quality and dissemination of their gender analysis of human development indicators and promoted the use of gender-responsive indicators. Such indicators need to capture and reflect the potentially different impacts of development strategies and actions on men and women, and boys and girls. This will require going beyond simply disaggregating data by sex and/or socioeconomic group, ethnicity, race and generation (i.e. age). It will entail, among other things, efforts to select indicators that are sensitive to possible difference – such as control of and access to resources such as schools– from the outset of the analysis and mainstreaming gender perspectives into the entire statistical system.
Text Box 7: Conceptual Differences between Statistics on Women, Sex-Disaggregated Data and Gender Analysis
Statistics on women: Women-only statistics produced mainly to report on the situation of women in different countries and regions. They are historically connected to the Women in Development (WID) approach. One limitation is that they do not allow for comparison between men and women and thus cannot provide data on gender gaps.
Sex-disaggregated data: Describe gender ratios of a certain phenomenon and are a crucial tool for quantifying differences and inequities between men and women. Historically connected to the Gender and Development (GAD) approach, sex-disaggregated data, although crucial, are not sufficient for the development of adequate gender analyses.
Gender analysis is an intellectual effort that involves at least the following fundamental aspects:

- Sex-disaggregated data for measuring gender differences and different cultural and socioeconomic realities faced by women and men;

- Multivariate analysis for capturing and interpreting relations that may not be visible if using sex-disaggregated data only;

- Gender-specific indicators for topics that may be of greater relevance to one sex than the other;

- In-depth examination and interpretation in order to get a fuller, more valid picture of what is occurring in context and which are the social constraints that lead to inequality;

- Identifying areas where new data need to be collected in order to fully grasp elements of inequality;

- Translating data into policy and planning to provide the evidence-based for strategy formulation.

Text Box 8: Evidence-Based Advocacy
Advocacy is the pursuit of influencing outcomes – including public policy and resource allocation decisions within political, economic, and social systems and institutions – that directly affect people’s lives. In practice, it includes the continuous and adaptive process of gathering, organising and formulating information and data into an effective argument, which is then communicated to policy-makers through various interpersonal and mass media communication channels. In the context of gender analysis, advocacy seeks to influence policymakers, political and social leaders to create an enabling policy and legislative environment and allocate resources equitably.
Evidence-based advocacy can be contrasted with normative-based advocacy. While normative-based advocacy draws its legitimacy from national legislation and/or international norms and standards such as Human Rights conventions, evidence-based advocacy is based on demonstrable facts and measurable data and information from official statistics, surveys, experiments, evaluations, direct observation, etc. In terms of presentation, evidence-based advocacy uses graphs, tables and charts and includes citations to show the strength of the evidence on which a particular argument is based.
In order to be persuasive, evidence must be reliable and relevant to the interests of the decision-maker or audience targeted. Therefore, different types of evidence have to be organised and presented differently for different audiences and part of effective advocacy is understanding and taking into account the interests, needs and prejudice of the various target groups. Often, evidence-based advocacy provides evidence about the problem, the likely impact of change, the feasibility of possible solutions, and about who is responsible to make the change.
UNFPA (2002). Advocacy: Action, Change and Commitment. Distance Learning Courses on Population Issues, Course 4. United Nations Population Fund (UNFPA) and United Nations System Staff College (UNSSC), New York and Turin.
UNICEF (2009). Evidence-Based Advocacy for Gender in Education. A Learning Guide. UNICEF, Bangkok.
Websites consulted:

  • Gender Equality, UN Coherence & You – Glossary: Definitions A-Z on http://www.unicef.org/gender/training/content/scoIndex.html

  • 2010 World Population and Housing Census Programme (UN Statistics Division) on http://unstats.un.org/unsd/demographic/sources/census/censusquest.htm

  • UNFPA Census Portal on http://www.unfpa.org/public/op/edit/home/sitemap/pid/6734



C. Some issues in data analysis and the construction of indicators



A basic typology of indicators
34. Indicators that compare the situation of women to that of men can be constructed in a variety of ways and the results may vary according to the specifics of the definitions used. For some purposes, a particular indicator definition may be ideal, while it may be highly misleading when used for other ends. Take the example of teenage pregnancy. Reproductive health providers often use the percentage of deliveries in which the mother is under age 20 as an indicator for the user profile of maternity clinics. To the extent that young mothers may require special care for which clinics need to be prepared, this is a perfectly adequate indicator. However, the same indicator is also often used to quantify the incidence of teenage pregnancy, a use for which it is ill-suited. This is because the percentage of women under age 20 who become mothers may be stationary or even diminish, but as the fertility rates of older women decline faster than those of adolescents (as is often the case), the result will be an increase of the percentage of deliveries in which the mother is under age 20. This is due to older women having less children, not to younger women having more.


Percentage distribution indicators
35. Problems similar to the ones outlined above often characterize the use of indicators in which the comparison between men and women is made in terms of absolute numbers. For example, one might quantify the degree to which unemployment is a problem for men and women by constructing an indicator consisting of the percentage of the unemployed that are women. If the objective of this indicator is to define the profile of users of particular services available to the unemployed (e.g. to know if the unemployment agency should make a greater effort to provide information on job openings or training courses that usually attract a lot of female applicants), this indicator may be entirely reasonable. But if the objective is to quantify if women have a higher or a lower risk of becoming unemployed, it is inadequate, at least in countries where female labour force participation is lower than male labour force participation, as is usually the case. The absolute number of unemployed women may be smaller than that of unemployed men, but when computed as a percentage of the female and male labour forces, the picture may be entirely different as the percentage of economically active women that are unemployed is often higher than that of men.
36. The same applies to indicators such as school enrolment. The percentage of primary school students who are girls may be a perfectly adequate indicator for planning purposes, e.g. to know how many toilet facilities schools need to have for boys or girls, respectively, but as an indicator for the propensity of boys or girls to enroll in secondary education, it may be flawed by the fact that the base population of boys and girls in the relevant age group is not the same, particularly at the local level. An alternative indicator, which quantifies the relationship not in terms of percentages, but as a ratio between the number of boys and girls has the same disadvantage. This is why the MDG indicator that deals with differential school enrolment is not stated in terms of absolute numbers, but rather in terms of Gross Enrolment Ratios, i.e. the number of girls enrolled (regardless of age) as a proportion of the population of the relevant age group, divided by the equivalent proportion of boys in the same age group (gender parity index). This corrects for the problem of unequal base populations. It does not correct, however, for the the fact that the larger numbers of students of one sex may be due not to a higher propensity to receive education, but to high repetition rates. In Brazil, for instance, Gross Enrollment Ratios for girls are higher than those for boys at all levels, except primary because boys tend to stay longer in primary school, due to their lower educational performance. The use of the Net Enrollment Ratio, rather than the Gross Enrollment Ratio, would prevent this bias (Leonardo Athias, of IBGE, personal communication).
Ratio indicators
37. The previous paragraph shows how a ratio indicator based on rates is an improvement over a ratio indicator based on absolute numbers or a percentage distribution indicator. Nevertheless, indicators of this type do have one major limitation, namely that the same result can be brought about either by high rates in the numerator and the denominator or by low rates in both. Thus, it is difficult to decide whether an enrolment ratio of 1 represents a good or a bad outcome from the viewpoint of female education. Nor does it provide a clear idea on how easy or how difficult it would be to correct it. An enrolment ratio of 0.9 is much easier to correct when it results from a female rate of 18 per cent, compared to a male rate of 20 per cent than when it results from a female rate of 81 per cent compared to a male rate of 90 per cent.

Standardized and unstandardized indicators
38. Despite the drawbacks outlined in the previous paragraph, ratio indicators are sufficiently detailed for many purposes. In those cases in which they are not, there is always the option of computing the male and female indicators separately, so that one can evaluate their individual values, rather than just their relative size. However, there are situations in which ratios or even individual male and female indicators can be uninformative or even misleading if no account is taken of intervening factors.
39. As an example, take the proportion of men and women that experience multiple divorces. This proportion will usually be higher for men. But how to interpret this ? Is it because men in their second or third marriage are more likely to divorce again than women in the same situation ? That may be, but a more likely explanation is that more men than women remarry after a first divorce or widowhood and consequently, even if the divorce rate for men and women in second or third marriages is the same, there will still be more men than women with multiple divorces. In order to take account of this fact, one should either break down the information by second, third, fourth etc. marriages, or one should weight the divorce rates by some uniform distribution of second, third, fourth etc. marriages, which does not vary by sex. This so-called standardization by order of marriage will ensure that the result can be used to compare the actual propensity of men and women to experience additional divorces in later unions, rather than depending on an intervening factor (in this case, remarriage). Of course, if the only objective of the analysis is to estimate the proportion of men and women that ever experience a multiple divorce, rather than to estimate the risk of a renewed divorce of men or women in higher order marriages, standardization is not necessary.
40. It is partly for the same reason that the literacy rate in the MDG indicators has been defined in relation to men and women aged 15-24, rather than 15 and over. Literacy rates are lower at higher ages and since it is at these ages that women predominate, the male-female differential is disproportionally weighted in favour of males. Again, this is not a problem if the only objective is to know how many illiterates there are in the population, by sex. But in order to get a realistic imagine of male-female differentials, it is better to compute the indicator by age group, or to standardize. An additional reason for using the 15-24 age category is that it provides a better measure of the recent performance of the educational system, rather than a historical assessment of something that is more difficult to correct by regular educational policies.
41. Standardization is particularly relevant in the case of disability, which is why the chapter on that subject illustrates it in some detail. A short example out of that chapter may serve to further clarify the issue. One way to express the differential incidence of disability in men and women is to compute the number of years that men and women of a certain age can expect to live with a disability in the future. This number tends to be higher for women. But the number of years that they can expect to live without a disability is also higher for women. One solution to this ambiguous relationship is to compute the percentage of the expected number of remaining years of life that men and women should expect to live with a disability. But an alternative is to compute the expected number of years with a disability in a mortality-standardized way, by using the same life table (e.g. an average for both sexes) for men and women. The latter removes the differential impact of the intervening variable (mortality) from the indicator of interest.
42. In February of 2012, the UN Statistical Commission approved a Minimum Set of Gender Indicators, consisting of 52 quantitative indicators and 15 policy indicators related to the existence of national norms. In the substantive chapters that follow, systematic reference will be made to this set of indicators and the degree to which they can be estimated from census data. The operationalization of the indicator set is still being discussed at the time of publication of this guide, so the the fact that this guide indicates that a certain indicator can be estimated from census data is no guarantee that the methodology that will ultimately be approved will actually endorse this option.
Multivariate analysis to disentangle intra-group variability and interrelationships
43. Men and women are not homogeneous groups. While women as a group may have lower educational attainment than men, some sub-groups of women may have higher educational attainment than some sub-groups of men or even men in general. The relationship may vary in terms of other intervening socio-economic and demographic factors such as economic level, rural or urban residence, and age. Therefore, one may want to know if this relationship of women’s lower educational attainment holds at different economic levels, in both rural and urban places of residence, and at varying ages. Additionally, when two variables are correlated, such as lower education and early marriage, the next step is to ascertain whether one causes the other. Two characteristics may be correlated without being causally related. In this example, early marriage could be highly correlated with lower education, yet their relationship could be spurious, i.e. caused by another factor, such as belonging to a certain ethnic group with prescribed cultural norms regarding both education for girls and early marriage.
44. Multivariate analysis – meaning analysis with multiple predictor variables – allows, inter alia, for the measurement of the effects of two or more independent (also called predictor or explanatory) variables on a dependent or outcome variable. It makes it possible to measure the effect of each separate explanatory variable on the dependent variable, while controlling for (i.e. keeping constant, as in the famous condition “ceteris paribus” – all other factors being equal) the effect of all other explanatory variables being considered. While multivariate analysis cannot, strictly speaking, demonstrate the existence of causal relationships, it can approximate the analysis to a causal interpretation in that it provides a more comprehensive view of the different relationships, thereby making it easier to identify situations in which, for example, the relationship between two variables can be accounted for by their common dependence on a third factor.
45. Two types of multivariate analyses which have proved very useful in social studies are multiple linear regression and logistic regression. Multiple classification analysis (MCA) is another useful technique, closely related to linear regression.



            1. Multiple linear regression uses several explanatory variables (which may be continuous or discrete) to predict a dependent variable, which has to be continuous (interval level, meaning that differences between values have a true numerical interpretation). The relationship has to be linear, but rather than using the original independent variables, one may transform or combine them, as long as the transformations do not introduce any non-linear parameters to be estimated. For example, if the original explanatory variables include age a) and educational attainment e), one may introduce other variables equal to A2 or A*E or even E/(A-6), but not E/(A-k), where k has to be estimated from the data. In the end, what is being estimated is a set of coefficients – one for each independent variable – which quantify the influence of each of them on the dependent variable.

b) Another attractive technique, which is closely related to linear regression, is Multiple Classification Analysis (MCA). MCA (also named Factorial ANOVA) quantifies the interrelationship between a set of predictors and a dependent variable in a linear, additive model. As is the case with linear regression, the dependent value should be at the interval scale. It is one of the nice features of MCA that it so easily handles discrete explanatory variables that do not allow a true numerical interpretation (e.g. ethnic group or level of agreement with a statement). In the MCA-analysis these categorical independent variables are called factors. MCA also allows the inclusion of continuous explanatory variables, called covariates. MCA basically produces the same results as a multiple linear regression with a set of discrete variables expressed as dummies (e.g. economically active or not, completed high school or not, etc.). However, the advantage of MCA over linear regression lies in the way the results are presented. The constant in the MCA analysis is simply the overall mean of the dependent variable. Each coefficient of the categorical, independent variables is presented as a deviation from the overall mean. First, unadjusted deviations are given, and thereafter, adjusted deviations are presented, i.e. after controlling for the effect of all other independent variables (factors and covariates).


c) Logistic regression also uses several explanatory variables to predict a dependent variable, but in this case the dependent variable is discrete, taking two dichotomous values (e.g. yes or no, attending or not attending school). A variant of logistic regression is multinomial regression, in which the outcome has more than two alternatives (e.g. marital status). The actual outcome of the logistic or multinomial regression equation is a number between 0 and 1, which describes the probability that a given outcome will happen. The same considerations about linearity mentioned above apply here as well, with the difference that the entire linear expression is linked to this probability through a logistic function which, by definition, can only take on values between 0 and 1. The slope coefficients (B) in a logistic regression are so-called log odds ratios, which are hard to interpret. Therefore, the exponential of the log odds ratio is calculated (eB) which gives the odds ratio, i.e. the probability that something will happen divided by the probability that it will not.
46. Multivariate analysis relies upon a set of assumptions about the variables in the model. Before applying a certain technique, researchers should always test whether these assumptions hold. For instance, in the case of linear regression, assumptions that are regularly violated by researchers are: a) The assumption of linearity between the predictor and the dependent variable, b) The assumption of constant variance of the error terms (homoscedasticity) c) The assumption of no correlation between the error term and the predicted variable, and d) The assumption of absence of high correlation between the predictor variables (multicollinearity). A violation of homoscedasticity would, for instance, be ‘age’ as a predictor of children ever born by women. The variance of the number of children ever born among younger women is much smaller than among older women. An example of multicollinearity would be the use of both weight and height as predictors in a regression model, as the two are highly correlated. For more detailed information on multivariate methods, their variations and the ways to deal with the problems that can arise when applying them, there are numerous standard texts that one can consult, among them Hoyle, Harris and Judd (2001), Knoke, Bohmstedt and Potter Mee (2002), Linneman (2011) and Stock and Watson (2010).
47. Some examples of the use of multivariate analysis include:
a) Nguelebe (2005) using 2003 census data on early marriage and schooling in the Central African Republic (CAR) found that just over one-fourth (26.3 per cent) of girls between the ages of 12 and 19 were already in a union, compared with just 4.2 per cent of boys in the same age group. Census data can examine if there are bivariate relationships (using cross-tabulations) between early marriage and such factors as attending school, literacy, urban/rural residence, regional of origin, ethnic group, religious affiliation, and employment status of the mother. But this leaves open the possibility of misinterpretations, due to the kinds of problems pointed out earlier, e.g. that both early marriage and low schooling may be the result of belonging to a certain ethnic group or religion. A multiple regression model to predict early marriage might take the age of first marriage as an outcome variable or one might use logistic regression if the outcome variable is whether a given girl is married or not by the time she reaches a certain age. Factors that share bivariate relationships with early marriage would be included as independent or predictor variables. Qualitative (discrete, categorical) explanatory variables, such as ethnic group or religion, have to be broken down into a series of binary choices (called dummy variables), which are then treated as separate variables, e.g. Catholic (yes/no), Protestant (yes/no), Muslim (yes/no), etc.
b) Census data could be used to examine the relative effects of the independent variables of racial or ethnic composition and rural or urban residence on poverty, a dependent variable. Persons of a specific ethnicity may make up a majority of those residing in the rural area of a country. Multivariate analysis allows us to determine the relative effects of living in a rural area and belonging to a specific ethnici group on poverty, while accounting for the possible interrelations amongst the three variables: 1) place of residence, 2) ethnicity and 3) poverty. A possible result of this analysis might be that ethnicity does not have an effect onpoverty outcomes once place of residence is controlled for.
c) Census data could be used to examine the relative effects of living in a certain region, type of household (i.e. with or without children), sex and educational attainment of the head of household or whether the household head is employed full time or not. With this, the researcher is able to use multivariate analysis to understand to what extent, if any, being employed full time can be explained by region of residence, the sex of the head of household, whether the household has children or not, and the educational attainment of the head of household.
With each of these above examples, the estimation of the effects of the dependent variables is done simultaneously, so the results show the effect of each independent variable on the dependent variable, while controlling for the intervening effects of all other independent variables. The next section focuses on a concrete example using a gendered perspective.
48. A gendered analysis using both bivariate tabulations and multivariate methods can be found in the work of Snyder, McLaughlin and Findeis (2006) using a 5-percent sample of the 2000 US Census data to examine race and residence as independent variables affecting poverty prevalence of female-headed households with children. This study first cross-tabulates census data to learn that cohabiting and grandmother, female-headed households with children comprise over 25 percent of all female-headed households with children. Using cross-tabulations again, they then find that household poverty is highest for female-headed households with children that do not have other adult household earners. They also note the relative difference in income between these female-headed households 1) with and 2) without other earners or income, and the average difference between these two groups is substantial; those with other earners or income are lifted out of poverty.
49. Then, multivariate models are used to provide validation to these tabular results. Because this is done at the level of individual households, where poverty is a categorical variable (poor versus non-poor households), logistic regression is the method of choice. If it were done at the level of census tracts or other geographical units, conventional multiple regression would be more appropriate. Poverty is found to be highest among racial/ethnic minorities and female-headed households with children in rural areas compared to central cities and suburban areas. The authors are also able to estimate the relative effects of the independent variables of race and residence and family type, while controlling for the effects of other factors, such as region, educational attainment, age, marital status, number of hours worked last year, and public assistance receipt. These findings – that ethnic minorities and rural female-headed households have even higher rates of poverty than female-headed households in general – are net effects and independent of these other factors.
50. Building on other research that finds a steady rise in female-headed households with children since 1970 (Casper and Bianchi, 2002) and that over one-half of children will live in a female-headed household (Graefe and Lichter, 1999) during childhood, the tabulations establishing the poverty link take on real life course implications for women and children. Understanding the relative effects of defining characteristics, such as race and rural residence, in predicting poverty can then be useful for policy makers and advocates wishing to ameliorate the possible outcome of poverty, especially for ethnic minorities and those in rural areas. Household composition is an important component of the economic support that a family has. Census data can be used to monitor trends in family household composition on the one hand, and even control for family household composition while examining income, poverty status and public assistance receipt on the other.
Geo-spatial analysis
51. With the ever more ubiquitous availability of Geographic Information Systems (GIS), census data are increasingly being represented and analysed in connection with their underlying geography. In the case of gender studies, Bosak and Schroeder (2005) discuss some of the advantages, as well as the pitfalls, of using these techniques. Although geo-spatial analysis can be done without them, maps are a frequent companion to such endeavors. Poverty maps, for example, have been around for many years, as a visually appealing tool that makes it possible for governments to pinpoint the areas where poverty is most acute (see Chapter 8).
52. A distinction must be made, however, between the mere representation or visualization of data in the format of a map and the analytical use of geospatial analysis to advance the understanding of the processes involved. The first merely uses maps as an alternative to the presentation of data in tables, with the advantage that some characteristics of the data are more easily grasped that way. For example, it may be that the education gap between boys and girls is geographically clustered in certain zones of the country that comprise several geographic base units. This may suggest that the problem has to do with factors that are common to these base units, maybe the fact that a certain ethnic group predominates in this part of the country or the fact that a certain type of agriculture is practiced there. Visualizing the data in the format of a map may help to develop an intuition for such explanations. The studies of sex ratios (Ebenstein and Leung, 2010) in Chapter 5 and of female enrollment in Gansu, China (Cao and Lei, 2008) discussed in Chapter 9 are examples of this type of use of spatial data. For more details on these kinds of maps, see Schultz (2009), among others.
53. Geo-spatial analysis, in the strict sense of the word, goes beyond such intuitions by explicitly linking data that affect each other in ways that allow a spatial interpretation. A certain district may have a large gender gap in education because of something that is not characteristic of the district itself, but of a district situated nearby, e.g. the presence of a large textile factory that employs lots of young girls. Unless the data are analysed taking into account their spatial structure, such relationships may be missed. In the case of employment and mobility, there is already a substantial literature in the more developed countries which links gender factors to the availability of opportunities across space. Some of these analyses (e.g. Hoogstra, 2012; Tkocz and Kristemen, 2010) can be quite complex, involving methodological tools such as autoregressive and cross-regressive spatial lags to detect relations both within and among groups and spatial weights matrices to represent travel times. These econometric techniques are, unfortunately, beyond the scope of this manual. For more information in this regard, one may consult a variety of basic (e.g. Kalkhan, 2011) or more advanced (e.g. Mitze, 2012) texts.
54. Other geo-spatial applications, however, are much simpler, like demonstrating that the education of women in Lesotho and Ethiopia tends to be higher as they live closer to main roads (Walker and Vajjhala, 2009). In this case, basically all that is required is overlaying the population data from the census or (in this case) DHS with a geographical data base showing the location of roads, elaborating the respective distances and computing their correlation with population characteristics. There are also several studies that – with or without the use of maps - relate the ratio of school enrollment rates between boys and girls with the distance to the nearest school, to explore the assumption that the school enrollment of girls might (or not) be more negatively affected by a large distance from school than the school enrollment of boys (see Chapter 9).
Life course approach
55. The life course approach provides a way to examine a person’s life as it is enmeshed within a social, cultural and structural context over time. In this way, it is possible to understand a person’s life history as it relates to decisions and actions that lead to later events. As an example, the experience of poverty as a child may affect educational outcomes at that point in time, but also later in life and may trigger a trajectory of lowered opportunity for that individual due to this earlier exposure. Scholars have formally defined life course as “a sequence of socially defined events and roles that the individual enacts over time” (Giele and Elder 1998). This approach takes a person’s lived experiences and connects them with a particular historical context and a specific socioeconomic level over time.
56. A gendered analysis that utilizes the life course approach examines how a woman’s life experiences are shaped differently from those of men because they are female. For example, in Table 2, Heise (1994) puts forward a gendered life course analysis of women’s disadvantaged position as it may explain violence against women at each phase of life – from Prenatal, Infancy, Childhood, Adolescence, Reproductive and Old Age phases. At each phase, the social, structural and historical context come together to present women with a lowered level of health and heightened level of violence against them, ranging from sex selection at the Prenatal Phase, to female infanticide at the Infancy Phase, to genital cutting at the Childhood Phase, to dating and courtship violence at the Adolescence Phase, to marital rape at the Reproductive Phase to abuse of widows at the Old Age Phase.
Table 2: Gender discrimination throughout a woman’s life course


Phase

Type

Prenatal

Prenatal sex selection, battering during pregnancy, coerced pregnancy (rape during war)

Infancy

Female infanticide, emotional and physical abuse, differential access to food and medical care

Childhood

Genital cutting; incest and sexual abuse; differential access to food, medical care, and education; child prostitution

Adolescence

Dating and courtship violence, economically coerced sex, sexual abuse in the workplace, rape, sexual harassment, forced prostitution

Reproductive

Abuse of women by intimate partners, marital rape, dowry abuse and murders, partner homicide, psychological abuse, sexual abuse in the workplace, sexual harassment, rape, abuse of women with disabilities

Old Age

Abuse of widows, elder abuse (which affects mostly women)

Source: Heise (1994).


57. A gendered life course approach considers that disparities may become more pronounced or attenuated and even reversed in direction as the life cycle progresses. While there may not be major differences for girls and boys in education in Early Childhood, as a child ages and becomes a young adult, any minor differences become more and more apparent. UNICEF (2007) reports in the State of the World’s Children that while the gap between girls’ and boys’ enrolment in primary school has been declining, nearly 20 per cent of girls will drop out of primary school by level 5. Thus, for every 100 boys out of school, 115 girls are in the same situation, and 43 per cent of girls are at the appropriate grade level for their age. Gender parity in primary enrolment does not carry over into parity in secondary school and overall educational attainment. Indeed, by the time these girls reach adulthood, as women they will comprise two-thirds of the world’s illiterate population.
58. Being cross-sectional in scope, census data present a methodological challenge for the life course approach, which generally uses longitudinal survey data of persons’ social, cultural and structural contexts. However, using US census data Stevens (1990) creates synthetic cohorts from the same census year (i.e. computing mean educational attainment for women and men by specific age groups, 20-29 year olds, 30-39 year olds, 40-40 year olds, etc. to know if educational attainment is increasing or decreasing over time for women and men), and Fussell and Furstenberg (2002) follow American cohorts over time using census data gathered at 10-year increments. This method of following cohorts over time is limited in use for countries with irregular census data collections or where question wording has changed. Overall, these cohort construction methods work for some research questions and not others.

MEMORY CARD: SUMMARY PART 1
59. In summary, gender and census data are linked in many ways: Gender advocates need robust and reliable data to sustain their claims and produce more convincing advocacy materials. Data producers need to understand gender issues to make sure the data they generate is fully representative of the entire population, including of vulnerable women such as widows and disabled girls, and of the entire spectrum of issues pertaining to both sexes. Census data has many limitations as a basis for gender analysis, many of which are linked to its focus on breadth (full geographical coverage) rather than depth (few questions). The following chapters will discuss gender issues that can nevertheless be analysed with census data.
Figure 1: Gender Equality: Normative Ideal and Development Objective



Gender refers to the social roles and relations between women and men. This includes the different responsibilities of women and men in a given culture or location, and the allocation of power and resources based on gender-based social constructions.
Vision of Gender Equality



Gender analysis helps to frame questions about women and men's roles and rela-tions in order to avoid making assump-tions about who does what, when and why. The aim of such an analysis is to formulate development interventions that

are better targeted to meet both women’s

and men’s needs and constraints.




Gender mainstreaming means

that attention to gender equality is

given a central part in all development interventions, including analyses, policy advice, advocacy, legislation, research and planning, implementation, monitoring and evaluation of programmes and projects.




Gender sensitive indicators

demonstrate changes in women

and men’s access to, and control of, different types of resources in a given society or location over a period of time. They are useful in monitoring and evaluating progress towards gender equality.



(Source: DLPI, Course 3, module 3)




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