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Bayesian population reconstruction of female populations for less developed and more developed countries
- Source :
- Population Studies. 70:21-37
- Publication Year :
- 2016
- Publisher :
- Informa UK Limited, 2016.
-
Abstract
- We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works for data of widely varying quality. Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data, while formally accounting for measurement error. As inputs, Bayesian reconstruction uses initial bias-reduced estimates of standard demographic variables. We reconstruct the female populations of three countries: Laos, a country with little vital registration data where population estimation depends largely on surveys; Sri Lanka, a country with some vital registration data; and New Zealand, a country with a highly developed statistical system and good quality vital registration data. In addition, we extend the method to countries without censuses at regular intervals. We also use it to assess the consistency of results between model life tables and available census data, and hence to compare different model life table systems.
- Subjects :
- History
Total fertility rate
Population Dynamics
Bayesian probability
Population
Developing country
01 natural sciences
Article
010104 statistics & probability
Bayes' theorem
0502 economics and business
Econometrics
Humans
Bayesian hierarchical modeling
050207 economics
0101 mathematics
education
Developing Countries
Demography
education.field_of_study
Developed Countries
05 social sciences
1. No poverty
Bayes Theorem
Censuses
Emigration and Immigration
Census
Population reconstruction
Geography
Research Design
Female
Subjects
Details
- ISSN :
- 14774747 and 00324728
- Volume :
- 70
- Database :
- OpenAIRE
- Journal :
- Population Studies
- Accession number :
- edsair.doi.dedup.....2510a2fe6e15062c8a7ed7b89a459bda
- Full Text :
- https://doi.org/10.1080/00324728.2016.1139164