International forensic population estimations: challenges and analytical strategies to reconstruct national demographic trends and levels since 1950 for 230 countries/areas

Leontine AlkemaNational University of Singapore

Needs Assessment Conference on Census Analysis in Asia (NACCA)23-25 November 2011, Bali

Joint work with A.E.Raftery, P. Gerland, S. Clark, F. Pelletier, T. Buettner and G.Heilig University of Washington and United Nations Population Division

The 7 Billion Mark, Uncertainty& New Methods for Total Fertility Projections17,000,000,000,000October 31, 2011 was 7-billion dayHow (un)certain are we of the timing of this event?UN Population Divison, Patrick Gerland:At least one or two percent error in population estimate 1% means 70 million people (e.g. population of Thailand)2% means 140 million (e.g. population of Russia)WPP2010: net growth of about 78 million people in 20111% error in measuring the world population => roughly +/- 1 year uncertainty in timingLikely to be even greater than thatIASSA:http://www.iiasa.ac.at/Admin/PUB/Documents/IR-11-002.pdfTotal Fertility (TF) ProjectionsNew TF projection methods incorporated in World Population Prospects WPP 2010Key differences with projection methods from WPP 2008:More country-specificNo floor of 1.85Uncertainty assessment includedTF projections: WPP 2008 (blue) and 2010 (red)

TF projections: WPP 2008 (blue) and 2010 (red)

TF projections: WPP 2008 (blue) and 2010 (red)

Introduction of new TF methodMotivation:To understand UN projectionsTo use yourself on subnational levelTF projections are informed by the past

8

3-phase model TFR time series since 1950 can be described with 3 phases:Pre-transition high fertilityFertility transitionPost-transition low fertilityUse this information to make projections:1. Find out which phase a country is in (Phase II or III)2. Use observed changes during that phase to find the range of expected changes in TFR9Is your country in Phase III yet?We defined the start of Phase III in observation period (before 2005-2010): midpoint of earliest two subsequent increases below 2Observed in 21 countries(Belgium, Bulgaria, Channel Islands, Czech Republic, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Luxembourg, Netherlands, Norway, Russian Federation, Singapore, Spain, Sweden, United Kingdom, United States of America)

10Phase II: Observed declines

TF projection for high fertility countriesExtension of 2008 UN method (5-year periods): TF(t+1) = TF(t) - 5year decline5year decline given by decline curveFor each country, estimate decline curve d(c ,fc,t)fc,t= TF for country c, 5-year period tParameter vector c determines its shape12Estimating decline curves: How?More formally: use a Bayesian hierarchical modelBayesian inference: unknown parameters have probability distributions, which are updated with new information Unknown decline parameters are distributed around a world averageUse Markov Chain Monte Carlo (MCMC) algorithm to get many samples of the set of model parameters, e.g. cs

Exchange information between countries: For a specific country, its parameter estimates are determined by its observed declines, as well as the world level experienceExample: maximum 5-year decline (height) for Mozambique

13Country-specific TF decline curves

14

Country-specific TF decline curves 15Each decline curve gives a future TF trajectory:Decline curve gives expected change in next periodAdd random distortion/errorRepeat until start of Phase III:when increase has been observed below a threshold parameter

Result:Each set of model parameters gives a future TFR trajectoryMany sets Many TFR trajectories Median projection and projection intervals

TF projection16Example: Indonesia

How did we project the TF in Phase III, after the turn-around?What happens post-fertility-transition?Again, use observed changes in Phase III to inform expected changes

Assume TF will eventually fluctuate around 2.1, e.g. we expectIncrease if TF 2.1Allow for uncertainty in future outcomes by adding distortion/errors/random fluctuations

Post-transition projections continued

Probabilistic projection model for 5-year changes during and after the fertility transitionDuring the fertility transition:the 5-year decreases are modeled as a function of TF level and decline parameters, with random distortions added to itthe decline parameters are estimated with a Bayesian hierarchical modelAfter the fertility transition the TFR will converge to/fluctuate around 2.1, using an AR(1) modelResults: Set of country-specific projectionsModel was validated using out-of-sample validation (projecting from 1980 and 1995 onwards, to compare projections with observations)Summary TF Projection Model20Population projectionsWPP2010 population projections are based on median TF projections, and show +/- 0.5 child scenariosResults from fully probabilistic projections as well!Based on probabilistic projections of life expectancyNathan Keyfitz (1981):"Demographers can no more be held responsible for inaccuracy in forecasting population 20 years ahead than geologists, meteorologists, or economists when they fail to announce earthquakes, cold winters, or depressions 20 years ahead.What we can be held responsible for is warning one another and our public what the error of ourestimates is likely to be."Probabilistic population projections

Probabilistic population projections

Subnational TF projectionsTF projection model can be applied to any subpopulation, e.g. provinces/statesMethod implemented in software R:free statistical software (http://cran.r-project.org/)Approach:Collect subnational TF (census) time series data and construct 5-year estimatesImport data into RInstall packages bayesTFR and bayesDem Run software directly in R, or use GUI

Example: TF projections for states in Brazil By Patrick Gerland, UN Population Division

Resulting subnational projectionsbased on experience of the fertility transition through the world in the past 60 yearscombined with the experience of subnational units

Additional informationReferences (next slide)More information:UN Population Division: http://esa.un.org/unpd/wpp/Bayespop: Leontine Alkema: alkema@nus.edu.sgTF project funded by NICHD grant number 1 R01 HD054511 01 A1

ReferencesAlkema L, Raftery AE, Gerland P, Clark SJ, Pelletier F, Buettner T , Heilig GK: Probabilistic Projections of the Total Fertility Rate for All Countries. in: Demography (2011), 48:815-839White Paper: Probabilistic Projections of the Total Fertility Rate for All Countries for the 2010 World Population ProspectsAdrian E. Raftery, Leontine Alkema, Patrick Gerland, Samuel J. Clark, Francois Pelletier, Thomas Buettner, Gerhard Heilig, Nan Li, Hana Sevckova. (United Nations population Division, Expert Group Meeting on Recent and Future Trends in Fertility, New York, 2-4 December 2009)Sevcikova H, Alkema L, Raftery AE, (2011): bayesTFR: An R Package for Probabilistic Projections of the Total Fertility Rate. Journal of Statistical Software, 43(1), 1-29.Sevcikova H., Alkema L, Raftery AE (2011). bayesTFR: Bayesian Fertility Projection. R Package and documentation: http://cran.r-project.org/web/packages/bayesTFR/index.htmlSevcikova H. (2011). bayesDem: Graphical User Interface for bayesTFR and bayesLife. R Package and documentation: http://cran.r-project.org/web/packages/bayesDem/index.htmlPopulation Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2010 Revision - http://esa.un.org/unpd/wpp/27WPP 2010 findings (compared to 2008)Variability among countries increases in 2045-2050For high-fertility countries in 2005-2010, the fertility decline is slowerFor countries with 2 < TF < 3, decline is fasterFor countries with TF < 2.1, slower rise to 2.1More countries fall below 1.85 child by 2045-2050 and return toward 1.85 by 2095-2100Slower convergence toward 1.85 child (reached in 2095-2100 instead of 2045-2050)TF 95% intervals are generally wider than one child, especially for countries with TF > 2.1

Overview SE Asia

Overview Southern Asia

Overview Eastern Asia