Repensar la inmanencia y la trascendencia

The UN provides recommendations to ascertain the quality of data on age and sex as these are critical variables on demographic studies (Poston, 2005). This section therefore highlights the recommendations and methodologies to be used in research that deals with these variables.

2.3.1 Recommendations on Age and Sex

Age is a more complex demographic characteristic than sex. The United Nations recommendations define age as the interval between the date of birth and the date of census expressed in completed years (Poston, 2005). Age data collected from census or survey can be tabulated in a single year of age, 5-years age groups, or broader groups (Shryock et al., 1980).

The UN recommendations for population and housing censuses insists on the tabulation of the national total, urban and rural population, for each civil division, for each principal locality, in single years of age to 100. However if any particular geographical area is not possible to tabulate in single age, the age data should at least be tabulated in 5-year age groups, by sex. Data on sex are collected by asking each person to report either being female or male. The UN also

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recommends that the principal tabulations of birth statistics for geographic areas within countries be made according to place of usual residence (Becht et al., 2003).

However, to process direct reports on age is easy to highlight inaccurate information on age than reports on date of birth, hence no valuable prove (authentic document). Questions on age are rarely fully addressed because most answers are approximations. Women often forget births that occurred in the distant past and make systematic errors when estimating events (Dandona et al., 2006). Prior to 2009, the missing date of birth information is not imputed for GHS in South Africa. It practices limit age imputation based on the reported age of the partner or spouse whether married or living together in the household. This is done using the age of the parents in the case of the missing child age information (Stats SA, 2009).

In Sub-Saharan countries, the proportion of population which unfilled age reports is high because of the existence of illiterate population. These errors may result from incorrect recording of responses during enumerations; misunderstanding of questions on age, mistakes during data entry, or in our context, respondents not knowing their exact age signifying that under enumeration in censuses is largely caused by mis-reporting (Adam, 2007). Misreporting has serious implications on the estimates in demographic issues, as well as heaping age of respondents. Froen et al. (2009) prove that in South Africa, only 50 per cent of deaths and also 18 per cent of births in the first year of life are registered, for example. This reveals the non-consistency of the civil registration system in the country.

The United Nations recommends that attention must be given to the date of birth of children reported as 1 year of age by asking normally their date of birth and the rest of the population have to complete the question on age. Respondents may also be assigned to form age groups on the basis of birth or some historical events affecting the population; this is the case in South Africa (Joubert et al., 2012). The major problem relating to the quality of data on sex collected in censuses reside in the difference observed in the completeness of the coverage of the two sexes.

Malley et al. (2007) and Lester et al. (2010) assert that parents may report boys as girls so they may avoid the attention to be overlooked when their cohort is called up for military purposes.

These factors contribute to differentials in the enumeration of both sexes. The problem of identification is acute where the coverage of annual industrial surveys in Africa has been extended to small-scale industrial activities (engaging 10 or less persons). These activities

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exclude from the coverage of industrial surveys mainly because they are difficult to locate.

Techniques like area sampling could be used to solve the lack of information and technique for managing the survey especially in small-scale activities (Adam, 2007). Some countries in Africa experience that a sampling unit may have different names when used in different contexts and the names may change over time or differ in spelling. When evaluating estimates of omissions and counting errors for the whole nation or for large geographic areas, many of these errors cancel out a significant number of people who are included in the number of omissions because they are missing at the correct location. They are also included in the number of counting errors because they are counted but at the wrong location. Anderson & Fienberg (2002) argue that about 40 to 50 percent of counting errors represent people who are also counted as omissions or duplications for which the two classifications balance out. However, for small areas, these kinds of geographic location errors may make a difference.

Household mobility or migration of sampling units is a complicating factor in the execution of African censuses and surveys. Mobility tends to group into three categories as follows:

The first group is that of Nomads: they are people who move together with their animals in search of new pastures and water points. It presents serious problem of enumeration of the people in many countries of Africa (Sahel), Sudan, Ethiopia, Mauritania, Somalia, Lesotho, northern Cameroon, etc. The United Nations have brought different methods to capture the data but the result is not satisfactory and the problem remains unsolved (Zeleza, 1997; Adam, 2007).

The second group represents international and internal migrants (mobility in search of opportunities; education, health and work). Notably rural-urban, rural-rural, and urban-rural movements of people pose great problems; even seasonal labourers contribute to errors in counting of people. These are the characteristics in countries like South Africa, Botswana, and Malawi. Statistics SA suggests that this pattern of under enumeration reflects different levels of urbanization and difficulties in achieving comprehensive coverage in rural areas (Stats SA, 1998a).

Shifting cultivation experienced among sedentary population: In countries facing this situation, it is easy for holders to abandon old and non-productive fields for new ones so that

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sampling frames of fields become outdated. This problem occurs in the northern parts of Uganda and in the northern parts of Kenya.

A post enumeration survey in South Africa in November 1996 indicates that the undercount in the census varies by province. According to Statistics SA, infants and young adult men are particularly prone to under enumeration. On the other hand young women are more geographically mobile than younger girls or older women (Wright et al., 2009) and as a result they are also often missed in censuses. Arnab & Serumaga (2006) argues that new migration patterns develop as a result of HIV/AIDs where adult children succumbing to the disease move from urban areas to rural areas to be cared for by their parents and families. In the same vein, Richter & Desmond, (2008) postulate that HIV/AIDs related deaths are contributing to the disintegration of households, resulting in orphaned children being forced to relocate in rural areas. People tend to be undercounted in population census all over the world; but undercounting is not the only problem in population censuses. People are also erroneously included, but the net effect generally is undercounting.

In the 1996 census, South Africa experienced patterns of undercount by age that is common in other parts of the world. One may have the true age of a respondent reported. However, in other situations, it is hard to define. For example a sick person may rate himself/herself fit depending on the circumstances. Under-enumeration is also linked to census fieldwork and procedures related to complexity in social transactions among individuals (Welzel & Kligemann, 2003). In reality, the value reported or observed is always irrespective of who reports it and under what circumstances it is obtained. In some category, there are omitted people from the census in their residences, enumerated people are more than once, babies born after census, people whose existence are ascertained but whose characteristics are missing and so are taken from another enumerator records (substitutions). The combination of erroneous and substitutions can be added together to produce the total number of counting errors, for example, people who miss the census or are counted at the wrong location (omissions). Citro & Kalton (2007) estimate the net undercount in the 1990 census of USA to be about 4 million which is quite close to the estimates of 4.7 million on the basis of demographic analysis.

According to the UN, the first population census in the USA for instance was taken in 1770, but it was in 1960 that demographic analysis is applied to verify the results of the population census.

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Strong digit preference occurs in the reporting of ages (UN, 1964). Phillips et al. (2003) set out that the quality of the 1970 census data for blacks is not as good as in the 1996 census in South Africa. Arnold (1990) notes errors in the coverage based on the wrong declaration of children ages. He also observes that many women ages have on digits ending in 0 and 5 which signals that they tend to round their ages. Blacks and Coloureds are less likely than Whites and Indians to be enumerated. Statistics SA suggests that this pattern of under enumeration reflects different levels of urbanization and difficulties in achieving comprehensive coverage in rural areas (Stats SA, 1998a).

Wittenberg (2004) claims that the surveys are badly designed to pick up mining employment.

The fact that the census picks up much more employment in construction and in private households creates more problems for the quality of the household surveys than for the census. It is also hard to prove how workers in the manufacturing sector could erroneously place into these categories (Robbins, 2005). Instead, one can envisage how the household surveys may miss some domestic workers and small-scale construction workers. Robbins (2005) discovers that individuals with stable jobs are more likely to start families and become head of households than other individuals. This should increase rather than decrease the probability of these individuals becoming captured in the census or in standard household surveys. When a worker is illegal in the country, the employer tends to keep him informally. However this alone cannot explain the large differences between the survey and the population census. In a study by Klassen &

Wooland (2001) cited in Wittenberg (2004), manufacturing workers are captured in the census but not recorded as being manufacturing workers.

Roberts (2005) demonstrates that the unemployment issue that dominates in the apartheid era continues to affect demographic data and also explains the overlaps occurring between census data and survey data. Moller (2007) investigates in South African shack settlements on the periphery of urban centre populate by jobseekers increase while job opportunities do not keep swiftness with demand.

For the 1973 population census of Sudan, the census cartographic units use data on listings which is incorrectly done; hence, the demarcation of zones is also inaccurate (Taha, 2001). It is common experience that African census fails to provide adequate addresses of sampling units especially in the rural areas where housing units are not numbered and where any numbering

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during a census operation is invariably too temporary (Adam, 2007). An adequate or accurate sampling frame in Africa cannot be over emphasized because it is rare to find adequate lists of households as sampling frames, even updating old frames (Session IV UN, 2007). Cartographic work is inaccurate in many cases either because maps are not accurate or the existing documents are not reflecting the reality. In some countries in Africa settlements are dispersed; for example, in forests in central Africa and in mountains of Ethiopia where physical difficulties are recurrent, settlements become removed from communication networks and other social services so that it turn out to be difficult to construct complete sampling frame and to investigate enumeration areas systematically (Adam, 2007).

The recruitment of interviewers and supervisors require a selective method. In the context of Africa, the interviewers are limited or some of them have no prior experience. Jones et al. (2010) suggest that appropriate training has to be achieved with particular challenges in various settings;

every single question must be explained to avoid difficulties on the field of duty. Most of sub-Saharan Africa countries adopt the metropolis language as their official language (English, French, Spanish or Portuguese) on attainment of independence. The majority of the people however do not understand the official language as it is acquired through formal education to which the majority of the people are not exposed. The questionnaire is printed in English or French, responses are recorded in the language of the questionnaire. There are errors involved in leaving the work translation in the hands of field staff, not to mention lack of consistency and uniformity in such a way questions are translated into local language by different enumerators (UN, 2005). For example, Tanzania experiences this situation; its report mentions some perplexities (Kiregyera, 1982). Feskens et al. (2006) suggest that it is more useful to make questionnaire more understandable to reduce the bias in the response.

McCarty et al. (2007) demonstrate that some political issues have a significant role on the informant and they can influence the response rate. Storms & Loosveldt (2004) as cited in Graham et al. (2006) explain that different cultural backgrounds present challenges because of cultural relevance which also affect the response rate.