Abstract
In countries with well-developed data reporting systems, demographic estimation is based on data collected by censuses and vital registration systems. In countries with inadequate or inaccurate data reporting systems, demographic estimation must rely on methods that are more “indirect.” Such estimation techniques usually adopt model schedules—parameterized functions describing collections of age-specific rates that are based on patterns observed in populations other than the one being studied—selecting one of them on the basis of some incomplete data on the observed population. The justification for such an approach is that age profiles of observed schedules of rates vary within predetermined limits for most human populations. Rates for one age group are highly correlated with those of other age groups, and expressions of such interrelationships form the basis of model schedule construction.
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References
Rogers, A., & Raymer, J. (1999). Fitting observed demographic rates with the multiexponential model schedule: An assessment of two estimation programs. Review of Urban and Regional studies, 11(1), 1–10.
Rees, P. H., & Willekens, F. J. (1986). Data and accounts. In A. Rogers & F. J. Willekens (Eds.), Migration and settlement: A multiregional comparative study (pp. 19–58). Dordrecht: D. Reidel.
Wetrogan, S., & Long, J. (1990). Creating annual state-to-state migration flows with demographic detail. In United States. Bureau of the Census (Ed.), Perspectives on migration analysis. Current population reports, P-23, No. 166. Washington, DC: U.S. Department of Commerce, Bureau of the Census.
Rogers, A., Raymer, J., & Newbold, K. B. (2003). Reconciling and translating migration data collected over time intervals of differing widths. Annals of Regional Science, 37(4), 581–601.
Citro, C. F. (1998). Model-based small-area estimates: The next major advance for the federal statistical system for the 21st century. Chance, 11(3), 40–41, 50.
Potrykowska, A. (1986). Modelling inter-regional migrations in Poland, 1977–1981. Papers of the Regional Science Association, 60, 29–40.
George, M. V. (1994). Population projections for Canada, provinces and territories, 1993–2016. Ottawa: Statistics Canada, Demography Division, Population Projections Section.
Bennett, R. J., & Haining, R. P. (1985). Spatial structure and spatial interaction: Modeling approaches to the statistical analysis of geographical data. Journal of the Royal Statistical Society. Series A, 148(1), 1–36.
Rogers, A., & Castro, L. J. (1981). Model migration schedules. Research report 81–30. Laxenburg, Austria: International Institute for Applied Systems Analysis.
Isard, W. (1960). Methods of regional analysis: An introduction to regional science. Cambridge: MIT Press.
Nair, P. S. (1985). Estimation of period-specific gross migration flows from limited data: Bi-proportional adjustment approach. Demography, 22(1), 133–142.
Rogers, A., Willekens, F., & Raymer, J. (2003). Imposing age and spatial structures on inadequate migration-flow datasets. Professional Geographer, 55(1), 56–69.
Willekens, F., Por, A., & Raquillet, R. (1981). Entropy, multiproportional, and quadratic techniques for inferring patterns of migration from aggregate data. In A. Rogers (Ed.), Advances in multiregional demography. Research report 81–6. Laxenburg, Austria: International Institute for Applied Systems Analysis.
Hofmeyr, B. E. (1988). Application of a mathematical model to South African migration data, 1975–1980. Southern African Journal of Demography, 2(1), 24–28.
Raymer, J., & Rogers, A. (2008). Applying model migration schedules to represent age-specific migration flows. In J. Raymer & F. Willekens (Eds.), International migration in Europe: Data, models and estimates (pp. 175–192). Chichester: Wiley.
Wilson, A. G. (1970). Entropy in urban and regional modelling. London: Pion.
Rogers, A., & Little, J. S. (1994). Parameterizing age patterns of demographic rates with the multiexponential model schedule. Mathematical Population Studies, 4(3), 175–195.
Muhidin, S. (2002). The population of Indonesia: Regional demographic scenarios using a multiregional method and multiple data sources. Amsterdam: Rozenberg Publishers.
Kawabe, H. (1990). Migration rates by age group and migration patterns: Application of Rogers’ migration schedule model to Japan, The Republic of Korea, and Thailand. Tokyo: Institute of Developing Economies.
Liaw, K.-L., & Nagnur, D. N. (1985). Characterization of metropolitan and nonmetropolitan outmigration schedules of the Canadian population system, 1971–1976. Canadian Studies in Population, 12(1), 81–102.
Rogers, A., & Jordan, L. (2004). Estimating migration flows from birthplace-specific population stocks of infants. Geographical Analysis, 36(1), 38–53.
Willekens, F. J. (1983b). Specification and calibration of spatial interaction models: A contingency-table perspective and an application to intra-urban migration in Rotterdam. Tijdschrift voor Economische en Sociale Geografie, 74(4), 239–252.
Stillwell, J. C. H. (1986). The analysis and projection of interregional migration in the United Kingdom. In R. Wood & P. Rees (Eds.), Population structures and models: Developments in spatial demography (pp. 160–292). London: Allen and Unwin.
Rogers, A., & Raymer, J. (2005). Origin dependence, secondary migration, and the indirect estimation of migration flows from population stocks. Journal of Population and Research, 22(1), 1–19.
Bogue, D. J., Hinze, K. E., & White, M. J. (1982). Techniques of estimating net migration. Chicago, Ill.: Community and Family Study Center, University of Chicago.
Rogers, A. (1968). Matrix analysis of interregional population growth and distribution. Berkeley: University of California Press.
Aufhauser, E., & Fischer, M. M. (1985). Log-linear modelling and spatial analysis. Environment and Planning A, 17(7), 931–951.
Engels, R. A., & Healy, M. K. (1981). Measuring interstate migration flow: An origin destination network based on Internal Revenue Service records. Environment and Planning A, 13(11), 1345–1360.
Willekens, F., & Baydar, N. (1986). Forecasting place-to-place migration with generalized linear models. In R. I. Wood & P. Rees (Eds.), Population structures and models: Developments in spatial demography (pp. 203–244). London: Allen and Unwin.
Rogers, A. (1975). Introduction of multiregional mathematical demography. New York: Wiley.
Good, I. J. (1963). Maximum-entropy for hypothesis formulation, especially for multidimensional contingency tables. Annals of Mathematical Statistics, 34(3), 911–934.
Willekens, F. J. (1980). Entropy, multiproportional adjustment and the analysis of contingency tables. Systemi Urbani, 2, 171–201.
Rogers, A., & Watkins, J. (1987). General versus elderly interstate migration and population redistribution in the United States. Research on Aging, 9(4), 483–529.
Bates, J., & Bracken, I. (1982). Estimation of migration profiles in England and Wales. Environment and Planning A, 14(7), 889–900.
Yano, K. (1991). The integration of spatial interaction models using generalized linear modeling. Geographical Review of Japan, 64(6), 367–387.
O’Brien, L. (1992). Introducing quantitative geography: Measurement, methods and generalised linear models. London: Routledge.
Rogers, A., & Liu, J. (2005). Estimating directional migration flows from age-specific net migration data. Review of Urban and Regional Development, 17(3), 177–196.
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Rogers, A., Raymer, J., Little, J. (2010). Introduction. In: The Indirect Estimation of Migration. The Springer Series on Demographic Methods and Population Analysis, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8915-1_1
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DOI: https://doi.org/10.1007/978-90-481-8915-1_1
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