31 research outputs found
How Many Persons in East Timor Went 'Missing' During the Indonesian Occupation?: Results from Indirect Estimates
Indonesias occupation of East Timor from 1975 to 1999 caused an unknown number of deaths due to violence and starvation. In the first few years alone casualty estimates range from 60,000 (Houk 1978) to over 300,000 (Defert 1992). Recent statistical work done for the Commission for Reception, Truth and Reconciliation in East Timor (CAVR) concludes that there were at least 102,800 (+/- 12,000) conflict-related deaths during the period (Silva and Ball 2006). However, this is a conservative estimate. This paper complements the existing estimate by calculating a reasonable high-end estimate, using standard demographic methods of indirect estimation to census data from before, during, and after the Indonesian occupation. My results suggest that a reasonable upper bound on excess deaths during the period is 204,000 (+/-51,000). Since technically these are "missing persons" some could have emigrated, been overlooked by a later census, or, in the case of children, not been born. Great care is taken to ensure that these sources of error are minimized as much as possible. Sensitivity tests were conducted on these results using a variety of different assumptions, yielding estimates as low as 100,000 and as high as 360,000. Previous efforts to indirectly compute the "missing" population in East Timor simply extrapolated earlier-period population growth rates in order to surmise the number of missing persons. The estimates computed here are much more sophisticated: they indicate the age and sex distribution behind these totals, and account for changing migration, fertility, and baseline mortality during the period in order to help isolate the population that went missing due to excess mortality alone
The Young and the Restless: Population Age Structure and Civil War
Three months after the attacks of September 11, 2001, the New York Times asked, Is the Devil in the Demographics? (Sciolino, 2001). The article examined the vulnerability of large cohorts of unemployed youth to extremist ideology and political recruitment, and speculated about the hazards created by future youth cohorts in the Middle East. In the post-9/11 era, however, there has been very little academic research on the relationship between youthful age structure and warfare (three notable exceptions: Urdal, 2002; Hammel & Smith, 2002; Cincotta et al., 2003). Literature on civil war and insurgency has instead highlighted the role of other causal factors such as the presence of valuable resources, the degree of ethnic fractionalization, and type of political regime, while downplaying the importance of population age structure (see, e.g., Collier & Hoeffler, 2001; Fearon & Laitin, 2003; Elbadawi & Sambanis, 2002).
While these factors likely play an important role in the onset of civil war, the importance of youthful age structure - particularly in insurgency-based civil wars - should not be ignored. The relationship between large youth cohorts and civil war appears to have held throughout history. For example, Herbert Moller (1968) suggests that wars in pre-modern and present-day Europe, including the rise of the Nazi party in Germany, corresponded with surges in the proportion of young men in the population. Yale historian Paul Kennedy (1993) argues that revolutions occur more often in countries with large populations of "energetic, frustrated, young men." Even after controlling for the fact that more youthful countries are less developed and have more vulnerable political regimes, my research finds that a large difference in the number of young adults compared to the number of older adults - "relative cohort size" - can help predict civil war, particularly insurgent-based civil wars...
Tuberculosis mortality and the male survival deficit in rural South Africa:An observational community cohort study
BACKGROUND: Women live on average five years longer than men, and the sex difference in longevity is typically lower in populations with high mortality. South Africa-a high mortality population with a large sex disparity-is an exception, but the causes of death that contribute to this difference are not well understood. METHODS: Using data from a demographic surveillance system in rural KwaZulu-Natal (2000-2014), we estimate differences between male and female adult life expectancy by HIV status. The contribution of causes of death to these life expectancy differences are computed with demographic decomposition techniques. Cause of death information comes from verbal autopsy interviews that are interpreted with the InSilicoVA tool. RESULTS: Adult women lived an average of 10.4 years (95% confidence Interval 9.0-11.6) longer than men. Sex differences in adult life expectancy were even larger when disaggregated by HIV status: 13.1 (95% confidence interval 10.7-15.3) and 11.2 (95% confidence interval 7.5-14.8) years among known HIV negatives and positives, respectively. Elevated male mortality from pulmonary tuberculosis (TB) and external injuries were responsible for 43% and 31% of the sex difference in life expectancy among the HIV negative population, and 81% and 16% of the difference among people living with HIV. CONCLUSIONS: The sex differences in adult life expectancy in rural KwaZulu-Natal are exceptionally large, atypical for an African population, and largely driven by high male mortality from pulmonary TB and injuries. This is the case for both HIV positive and HIV negative men and women, signalling a need to improve the engagement of men with health services, irrespective of their HIV status