Sideline demographic methods and data

A classic problem in life table construction is the calculation of the probability of death in an age interval (x,x+n) from the observed age-specific death rate _n M_x. For period life tables one typically makes the assumption that _n M_x= _n m_x and proceeds to apply conversion formula _n q_x=(n* _n m_x)/(1+n*(1- _n a_x )* _n m_x ). However, the application of this formula derived from a cohort perspective to estimation for life tables calculated from period data can be problematic. Cohort measure are different from their period counterparts in that cohort measures are concerned with all events that occurred to the cohort members while period measures are only concerned with the events that occurred to individuals within a given period and age interval. Three issues might preclude the direct application of the formula above to period life table construction: the ambiguity in the definition of _n a_x; the definition of _n M_x; the conversion formula between _n m_x and _n q_x. In this paper, we give clear definition for _n a_x and _n M_x from period perspectives, and derive the conversion formula between _n m_x and _n q_x for the construction of period life tables. We use simulated data to illustrate the problems with traditional indicators and asses the performance of our revised definitions.

There is acknowledgement among social and behavioral science researchers that metadata are important for data sharing. Researchers however seem not to see any usefulness or investing resources into adequately documenting the primary data they produce. They may create traditional codebooks to accompany the data but they almost invariably do not provide any structured documentation of their data using an internationally accepted structured data documentation standard designed for social and behavioral science data called DDI. They normally leave most of the work of proper documentation of data to archivists. There are however, clear benefits that could be derived from proper documentation of research data. In this paper, we attempt to provide examples of these benefits. This paper aims to show case an example of the usefulness of investing time into data documentation using an international standard called DDI. This study uses the TAZAMA data as an example, it looks at the process used to extract some data from the TAZAMA source database and transforming them into specified requirements using a framework created by DDI documentation of the data. This example will hopefully provide concrete evidence of the benefits to be derived from structured documentation of research data.

The low birth weight is an important factor explaining the child mortality in India. Low birth weight occurs because of poor maternal health and nutrition. However, there is no reliable estimate of low birth weight is available in India. Information on birth weight available in large scale demographic surveys in India are not complete. Looking at the importance of the issue the current study aims: (i) to study pattern of reporting system of birth weight in India and states and (ii) to examine the heaping at certain digits in reporting of birth weight in India and states by mother memory recall and health card. Data from the third round of National Family Health Survey (NFHS-3) conducted during 2005-06 is used. Bi-variate and multivariate analysis will be used for analysis purpose. As per NFHS-3 data set birth weight is reported only for 34 percent births and birth weights are missing for 66 percent births. Regional variation is observed in reporting of birth weight in India. It varies highest 72 percent in Southern India and lowest 12 percent reported in Central region. Incidence of low birth weight also varies by region. Highest 27 percent low birth weight was reported in Northern region and lowest 18 percent reported in Southern region. Reporting/Incidence of low birth weight varies by residence, education and wealth index.

This paper aims to compare the evolution of demographic indicators of 196 countries in the context of epidemiological and demographic transition in the world between 1960 and 2010. In this paper we consider to analyze some characteristics: fertility, migration, mortality and epidemiological transition. Such analyses were done using the data published by the Department of Economic and Social Affairs of the United Nations.

The methodology consists in a factorial analysis of the indicators provided by DESA-UN. We standardized the entire period data such that the 1980s has become the reference decade. With the standardize indicators we constructed a factorial analysis aiming reduce it into two dimensions. We applied the factor loading of the 1980s to all years analyzed comparing the evolutions of continents and its countries.

The conclusions strengthen the remarks pointed in Kirk (1996) and Bongaarts (2002) with regard to the rapid transition occurred during the lasts decades, mostly in the underdeveloped countries. As we show it is possible to conclude that epidemiological and demographical transitions have been finished or are near to finish in South America and Asia. In Africa’s countries the situation is different, only 25% (14 countries) are in the same situation of the Asia and South America countries.

A standard methodological assumption in social and demographic surveys is that interviewers should be strangers to respondents. Bias is feared should the interviewer and respondent know each other prior to the interview. We refer to this assumption as the “stranger-interviewer norm” and note that it has never been systematically tested. In order to subject it to scrutiny, we fielded an experimental survey in a town of the Dominican Republic countryside in the summer of 2010. We employed a mix of local and outsider interviewers and hired enough locals to produce an adequate number of “insider” interviews, that is, interviews where interviewer and respondent knew each other. Systematic randomization in our sampling design gives our survey the rigor of an experiment. In this paper we use these data to evaluate how the accuracy of responses varies by the level of familiarity between interviewer and respondent, as we were able to validate a number of survey questions by checking official documents that confirmed (or disconfirmed) respondents’ reports. At the time of this submission our analyses have failed to find support for the stranger-interviewer norm. There is instead some evidence that respondents are more cooperative with interviewers when these are not outsiders.

This paper analyzes links between declines in the family size of women and declines in the family size of children during the demographic transition. We extend Preston’s (1976) model in two ways. First, we derive the relationship between the variance of women’s family size and children’s family size, a relationship that has important implications for inequality in children’s family size. Second, we analyze family size from the perspective of children of a given age rather than women of a given age. We apply the framework to 310 data sets from the IPUMS-International census project and the Demographic and Health Surveys, representing 101 countries. Consistent with Preston’s conjecture, we find that mean family size of children tends to fall more slowly than mean family size of women as fertility declines. The increase in resources per child is 5%-20% smaller than it would be if children’s family size decreased at the same rate as women’s family size. We show that inequality in children’s family size increases substantially as fertility declines, the result of increasing skewness in women’s family size.

DHS surveys are the main source of estimates and analyses of fertility and under-five mortality in developing countries. All of these estimates and analyses are based on assumptions about the underlying accuracy of the events and dates in the birth histories. The main concerns of the proposed paper are whether the assumptions are valid and the sensitivity of various measures and analyses to deviations from the assumed accuracy. Three potential weaknesses in the data can be identified: the representativeness of the respondents and the accuracy of their reported ages; possible omission of births in the birth history; and systematic displacement of events. Each of these possible sources of distortion will be examined with statistical methods, building primarily on the leverage provided by multiple surveys in the same country. The second part of the assessment concerns the impact of errors on key outcomes. Those outcomes will be specified in advance and will include measures of levels and trends in rates such as the TFR and neonatal mortality rate and coefficients in statistical models.

One of the Millenium Development Goals (MDG5) is to reduce the maternal mortality ratio in all developing countries by 75% between 1990 and 2015. Progress toward this goal has been documented by the World Health Organization (WHO) and the Institute for Health Metrics and Evaluation (IHME), using similar models to estimate levels and trends in virtually all countries. The Demographic and Health Surveys (DHS) are the principal source of data for the WHO and IHME models. Approximately 120 DHS surveys have included the maternal mortality module, using the direct sisterhood method; all of those surveys will be included. Estimates of adult mortality are also produced from these data, sometimes including information about brothers of the respondent. This will be the first such assessment since 1997, when only 14 surveys had included the module. Several strategies will be used, including comparisons of the reports from related members of the same household and comparisons across successive surveys in the same country. The paper will include recommendations for interpreting the estimates, will discuss potential adjustments (although DHS will continue to publish only unadjusted estimates) and will describe potential implications for the WHO and IHME models.

The quality of the information in household surveys is always of concern to researchers and to policy makers who depend on their results. Although considered the “gold-standard” of household surveys in developing countries, the Demographic and Health Surveys (DHS) are not immune to errors in data, either due to respondent mis-reporting, interviewer mis-recording or deliberate falsification. These errors are thought to be more common in surveys where basic data are not known well, such as that of the dating of events and ages.
In quite a few households, more than one respondent is interviewed and many of these consist of mother-daughter pairs. Indeed, in the 2011 Benin DHS, there were 1488 mother-daughter pairs. A relatively unused technique in assessing data quality is to compare the reports of mothers and daughters. Direct comparison of birth dates, age, and educational attainment are available. When a sibling history is asked, the mother’s birth history information can be compared with the daughter’s sibling history to determine omission of births by either mother or daughter by survival status.

In many developing countries, vital statistics are non-existent, in those where they exist, the data are inadequate and defective that can not be relied upon as a true measure of vital events (births, deaths, marriages, etc.). It is expected that all under-5 children births covered in 2008 Nigeria NDHS ought to have been registered; but bared 30% of them was registered.
The study will employ secondary data. Secondary data for the study will be extracted from the 2008 Nigeria Demographic and Health survey (NDHS). Data on women aged 15-49 years that have had at least a child within the last five years will be extracted for 33,385 women sampled from the NDHS (2008). Data will be analyzed using appropriate descriptive and inferential statistics.
It is expected that there will be relationship between mothers’ demographic characteristics and under-5 birth registration.
The findings from the study are expected to give more insight to the influence of the mothers’ demographic characteristics (parity, proportion of dead children, birth interval and marital status) on under-5 birth registration.. It will also help in building a policy frame work to improve quality and quantity of vital registration, especially birth registration