Uncertain population forecasts
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Presentation Transcript
Uncertain population forecasts Nico Keilman Department of Economics, University of Oslo
Main points • Uncertainty in forecasts of certain population variables surprisingly large • Forecasts for the young and the old age groups are the least reliable • Forecast errors increase as forecast interval lengthens • European forecasts have not become more accurate during the past 2-3 decades • Traditional forecasts with their high and low scenarios do not give a correct impression of uncertainty probabilistic forecasts
Focus National forecasts in industrialized countries (to a large extent)
Where does uncertainty manifest itself? Forecasts of: • Total population • Age structure • Fertility • Mortality • Migration
Measuring uncertainty Empirical findings – historical forecasts evaluated against actual population numbers (ex post facto)
Total population size fairly accurate Forecasts of population size • all countries of the world • made by the UN, the World Bank, and the US Census Bureau between 1972 and 1994 were too high by, on average, • 0.8 %, 5 years ahead • 2.4 %, 15 years ahead • 3.5 %, 25 years ahead
Young age groups fertility • Old age groups mortality
Uncertain Population of Europe (UPE)Joint work with Juha Alho, Harri Cruijsen, Maarten Alders, Timo Nikander, Din Quang Pham Evaluated historical accuracy of population forecasts • national agencies in 14 European countries • 1950-2000 One (of several) source of information for probabilistic forecasts
European forecasters have under-predicted gains in life expectancy: - by 2.3 years of life for forecasts 15 years ahead- by 4.5 years of life for forecasts 25 years ahead
European forecasters have predicted too high fertility:- by 0.2 children per woman for 15 years ahead- by 0.4 children per woman for 25 years ahead
European forecasters have predicted too low levels of migration:- by 1 per thousand of population for 6-8 years ahead - by 3 per thousand of population for 18-25 years ahead
Why uncertain? • Data quality • Social science predictions • No accurate behavioural theory • Rely on observed regularities instead • Problems when sudden trend shifts occur assumption drag
Error indicator for TFR forecasts, 14 countries The graph shows estimated forecast effects in a model that also controls for period, duration, country, and forecast variant. Log of absolute error in TFR is dependent variable. Estimates in black, 95% confidence intervals in red. Launch years 1950-54 are reference category for the forecast effects. R2 = 0.704, N = 4847
Error indicator for e0 forecasts, 14 countries The graph shows estimated forecast effects in a model that predicts the log of absolute error in e0. The model controls for period, duration, country, sex, and forecast variant. Estimates in black, 95% confidence intervals in red. Launch years 1950-54 are reference category for the forecast effects. R2 = 0.722, N = 5562. NB No data for launch years 1955-59
Three problems related to deterministic population forecasts 1. Wide margins for some variables, narrow margins for others
Example: Old Age Dependency Ratio (OADR) for Norway in 2060Source: 2005-based forecast of Statistics Norway High Middle Low |H-L|/M millions % POP67+ 1.55 1.33 1.13 31 POP20-66 4.03 3.39 2.83 36 OADR 0.38 0.39 0.40 4 (!)
Problems … (cntd) 2. Too narrow margins in the short run,too wide margins in the long run
Problems … 3. A limited number of variants, without probability statements, leave room for politically motivated choices.
TFR assumptions for 18 EEA+ countries, 2045-2049Averages across countries
Life expectancy assumptions for 18 EEA+ countries, 2045-2049 Men Averages across countries
Net migration assumptions for 18 EEA+ countries, 2045-2049 Averages across 18 EEA+ countries (UN, UPE), across 15 EU-15 countries (Eurostat)
Implications • Forecast users should be informed about the reliability of the future population numbers • Historical errors just a first step • Expected errors for the current forecast probabilistic forecasts UPE: probabilistic forecasts for 18 European countries. See http://www.stat.fi/tup/euupe/
Forecast users should be prepared for the unexpected - use buffers? - flexibility? - risk aversion?
Users should check whether overpredictions are more costly, or less costly, than underpredictions Loss function Forecasters should educate the users, cf. • weather forecasts: EPS (Ensemble Prediction System) Meteograms: series of Box plots • inflation and interest rate forecasts: uncertainty fans
30% 50% 70% 90% Bank of Norway’s forecast of future interest rate (%) with uncertainty fan Source: Norges Bank
