Guide on Gender Analysis of Census Data Full Draft of 6 December 2012 Contents
Table 14: South Africa (2008) - Population by sex and marital status
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- 4. Tabulations
- Table 15: Percentages of never married women by age group for consecutive censuses in East and Southeast Asia
- Table 16: Cambodia (2008) - Age of the (male) head of household and the (female) spouse
- Country Example 7: Gender and Long-term Shifts in Rates of Marriage and Divorce in the United States
Table 14: South Africa (2008) - Population by sex and marital status
223. Censuses can reveal patterns of widowhood in a country. Indeed, often widows are enumerated as heads of households in census data. Remarriage, on the other hand, cannot be determined as censuses normally do not ask if a person has been married before. While some censuses ask about the data of first marriage and current marital status, the “married” category is not split into “married to 1st, 2nd, 3rd, … spouse”. The 2006 census of the Maldives is one of the few that asked how many times each individual had been married and to how many people, but the quality of the information obtained was not very good. This is a major obstacle for the identification of gender differences in marriage behaviour. 224. To measure child marriage, “age at first marriage” is the key census variable to analyse. However, relatively few censuses include it (e.g. Algeria, Azerbaijan, Bermuda, China, Democratic People’s Republic of Korea, Guinea-Bissau, India, Israel, Kazakhstan, Lesotho, Malta, Maldives, Occupied Palestine Territories, Republic of Korea, St. Lucia, Sudan, Swaziland). Where age at first marriage is not collected in a census, a proxy, called Singulate Mean Age at Marriage (SMAM), can be calculated from the population distribution by marital status (see the Indicators section below for details). However, the SMAM only provides an aggregate measure for the entire population or sub-groups thereof, but not for individuals. It is also possible to compare successive censuses, to see how the distribution of marital status among individuals aged (x,x+n) years in the first censuses has changed t years later, when these same individuals are aged (x+t,x+n+t). 4. Tabulations 225. Marital status should be tabulated for persons of all ages, irrespective of the national minimum legal marriageable age. In this way, persons who were married below the minimum age, persons who married in another country with a different minimum marriageable age and persons who were permitted to marry below the legal minimum age because of special circumstances are not excluded. Even then, there is still a risk that child marriages will be underdeclared or even purged from the data by the census authorities. 226. The Principles and Recommendations for Population and Housing Censuses Rev. 2 (United Nations, 2008 a) recommends basic and additional tabulations with regard to marital status. The latter can only be applied where the relevant information is available. The most important basic table is the following: P4.2-R Population, by marital status, age and sex 227. When compiled for successive censuses, this table can help to detect important trends. In the case of several East and South-east Asian countries, for example, there has been a dramatic increase in the proportions of women remaining single in their 30s and 40s, especially in the big cities. In 2000, 17 per cent of women aged 45-49 in Bangkok remained single, 13 per cent in Singapore and 10 per cent of Chinese women in Kuala Lumpur. The 2000 census data also show sharp increases in the proportions of non-married among women in their 30s in some countries where non-marriage rates were traditionally low, notably the Republic of Korea and Indonesia (Jones, 2003). The following table illustrates this with data from the 1960-2000 censuses of several countries in the region. Table 15: Percentages of never married women by age group for consecutive censuses in East and Southeast Asia 1960 1970 1980 1990 2000 Thailand 30-34 6.7 8.1 11.8 14.1 16.1 35-39 4.2 5.2 7.3 9.6 11.6 40-44 3.1 3.9 5.3 7.0 9.3 45-49 2.6 3.0 4.1 5.2 8.0 Peninsular Malaysia Chinese 30-34 3.8 9.5 13.3 15.8 18.2 35-39 2.7 5.7 7.6 9.1 10.5 40-44 2.6 3.4 5.8 6.4 8.4 45-49 2.5 2.4 4.6 5.7 7.2 Peninsular Malaysia Malays 30-34 1.1 3.3 7.9 10.2 9.7 35-39 0.8 1.9 3.8 5.8 6.0 40-44 0.6 1.1 2.2 4.1 4.4 45-49 0.6 0.7 1.7 2.3 3.2 Japan
30-34 9.6 7.2 9.1 13.9 26.6 35-39 5.6 5.8 5.5 7.5 13.8 40-44 3.1 5.3 4.4 5.8 8.6 45-49 1.9 4.0 4.4 4.6 6.3 Republic of Korea 30-34 0.5 1.4 2.7 5.3 10.7 35-39 0.2 0.4 1.0 2.4 4.3 40-44 0.1 0.2 0.5 1.1 2.6 45-49 0.1 0.1 0.3 0.6 1.7 Singapore Chinese 30-34 4.7 11.1 17.8 22.4 21.9 35-39 4.3 5.8 9.3 15.6 16.2 40-44 5.2 3.8 6.7 12.3 14.1 45-49 6.2 3.3 4.6 7.9 12.6 Hong Kong (1996) 30-34 6.0 5.6 11.0 24.8 26.5 35-39 5.0 3.0 4.5 11.6 14.6 40-44 5.9 2.9 2.7 7.3 9.0 45-49 7.4 3.8 2.3 3.9 5.9 Source: Jones (2003): Tables 1 and 2 Non-marriage has also increased among males, although the age pattern and the timing of the increase in the various countries have been different from the patterns found in women. 228. Very high percentages of single women aged 35-39 can be found not only in East Asia, but also in Europe, Australia and the Caribbean region. In these cases, the reason is that women in this part of the world often do not marry, but live together with their partners without a formal marriage contract. In Jamaica (2001), according to the World Marriage Data Base of the UN Population Division (2008), 64.5 per cent of women aged 35-39 had never been formally married; in Dominica, this was 58.1 per cent. In Sweden (2006), the number was 40.6 per cent. Compared to these percentages, the number of older single women in East Asia seems relatively low, but it must be borne in mind that its interpretation is quite different as unmarried women in East Asia are unlikely to be cohabiting with their partners. 229. Marital status by sex and age should also be tabulated in combination with religion, school attendance, educational attainment, fertility levels, life expectancy, migratory status, employment status and disability status. The Principles and Recommendations for Population and Housing Censuses Rev. 2 – while not explicitly recommending the above tabulations for religion, fertility, life expectancy and educational variables – suggest the following four as “additional” under the respective chapter headings:
P7.7-A Usually (or currently) active population, by main occupation, marital status and age P7.8-A Usually (or currently) active population, by main status in employment, marital status and age P8.3-A Total population 15 of age years and over, by disability status, cross-classified by marital status, urban/rural residence, age and sex Table 16: Cambodia (2008) - Age of the (male) head of household and the (female) spouse
230. Table 16 shows the simultaneous distribution of the ages of heads of household and their spouses, in the case where the head of household is a man, for the 2008 census of Cambodia. An analogous table can be constructed for the opposite case, where the head of household is female and the spouse male. On the whole, this particular table does not show any major age differences between heads and their spouses, with the possible exception of the first column (spouses under age 20), whose husbands are, on average, 24.70 years old. The mean ages of the husbands (head of households) for the other age categories are remarkably close to those of the spouses. On the whole, the difference even tends to diminish with age, which indicates a relatively low incidence of remarriages of older men with (much) younger women. Spouses over age 75 tend to have husbands slightly younger than themselves, due to the higher mortality of men compared to women at higher ages. There are, of course, exceptions (the numbers most distant from the diagonal), but 82.4 per cent of the spouses have ages within the same 5-year age bracket as their husbands or the one adjacent to that. Cases of young women living with much older men (the lower left corner of the table) do occur, but are not very common. The results in other countries may, of course, be quite different. 231. The study by Teachman, Tedrow and Crowder (2000) in the Country Example below illustrates the usefulness of census data for investigating long-term trends and change in marriage and divorce.
232. Marital status by sex, age, religion and/or ethnicity can indicate a relation between belief and nuptiality, which reflects attitudes towards marriage and divorce as well as different legal provisions, especially in countries where a “Personal Status Law” - and not a civil code - regulates marriage, divorce, custody, inheritance and so forth (Israel, most Muslim-majority countries, with the notable exception of Tunisia). Such tabulations have been used to diffuse stereotypes, e.g. to show that in the US ‘born-again’ Christians, despite their emphasis on family values, actually have similar divorce rates as other Christians or non-affiliated persons (Lehrer et al., 1993). The same information can also be used to determine typical differences in the age at marriage between men and women according to religion, using the concept of SMAM referred to earlier. WE WILL DO A VISUALIZATION HERE OF AGE AT FIRST MARRIAGE OR MAYBE THE DIFFERENCE IN AGE AT FIRST MARRIAGE BETWEEN MEN AND WOMEN FOR MALAWI, MAYBE DIFFERENTIATED BY CURRENT AGE GROUPS, TO SEE TRENDS. MAYBE WE CAN SPATIALLY CORRELATE THAT WITH EDUCATION OR SOME OTHER EXPLANATORY VARIABLE. 233. To understand the impact of education in a context culturally favourable to polygamy, one may further disaggregate table P4.2-R above, i.e. “Population, by marital status, age and sex,” by educational attainment and/or literacy. If this does not yield clear results on polygamy, one may select areas where polygamy is prevalent and compare, for women of the same age group, ethnic group and religion, if the proportion of women polygamously married is negatively related to educational attainment/literacy. Past research suggests that women who have received no formal education are more likely to be in polygamous unions than women who have received primary or secondary education. Where both spouses have not received formal education, polygamy is most widespread (UNICEF, 2005). Comparing the educational level of women in polygamous unions to women in monogamous unions can confirm these findings in-country. It is necessary to compare women of the same age group to control for the effect of changes in education. 234. Understanding widowhood can contribute to explaining certain social phenomena such as poverty. The widowed population can be tabulated by age and crossed with variables such as household headship, socio-economic level (using the Basic Needs Approach) and receipt of state benefits (where such data are available). In addition to their larger numbers, widows are more likely than widowers to co-reside with their children. In Vanuatu (2011), for example, 33.9 per cent of widowed women over age 60 are living with their children, compared to 23.9 per cent of widowed men and 18.5 per cent of women over age 60 who are not widowed. It is important to remember that marriage data uncontrolled for age will give a distorted image. While there are usually many more widows than widowers this is partially due to the fact that there are, numerically speaking, more older women than older men in a population. When cross-classified by age, the proportions are less disparate. 235. Although there is a statistical relationship between the age at first marriage or the marital status of women at any given age and their level of education, it is generally impossible to demonstrate the causal direction of this relationship, at least with census data. It may be that early marriage is an obstacle to further schooling, but it is equally possible that the early marriage is a consequence of having dropped out of school at an early age. It takes longitudinal data of a kind that is normally not available in censuses to disentangle the causality of the relationship. At the very least one should know when the woman got married and when she left school, but not many censuses ask the former and almost none ask the latter. One could try to estimate the age of leaving school from the highest grade attended, but this can be deceptive as it is exactly those women who are most delayed in their education who are most likely to drop out. Download 4.06 Mb. Share with your friends:
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