Sub-replacement Fertility: Why Pure Postponement Models are Inadequate

1. Sub-replacement Fertility: Why Pure Postponement Models are Inadequate Elizabeth Sowers & Ron Lesthaeghe, UC Irvine Background and Research Question Analysis of Cohort Fertility Schedules by Age Regression Analysis • Period Total Fertility Rates (PTFRs) for Western Europe from 1960-2003 have have fallen to below replacement level. • Period measures of fertility can be substantially deflated by a shift in the timing of childbirth to older ages1 (“postponement effect”). • Previous research has tried to estimate the magnitude of a TFR without the birth tempo effect. • Both the Bongaarts-Feeney model (1998) and the Kohler-Philipov adjustment (2001) models assume full recuperation of delayed births, and do not account for situations where postponed fertility is not being made up, or for the disparity in “catching up” among countries. • Research Question: Do pure postponement models have any predictive value for period TFRs? • The graphs below show there is great postponement of births, but that the extent to which fertility declines at young ages is made-up later in life varies greatly. • The cohort analysis is based on the comparison of cumulated age-specific fertility schedules for each cohort to that of the benchmark cohort born 1940-44. • “Catching up” countries have reduced the deficit at age 40 (def (40)) compared to the deficit at age 30 (def(30)), whereas countries that are not catching up have values of def(40) that are equal or larger than d(30) (bottom graphs). • Clearly, postponement is occurring, but not all countries are recuperating from the deficit of births at early ages. • The regression analysis uses 3 factors to predict the PTFRs 30 years after the birth of a cohort: CTFR for the baseline cohort, the trough parameter def (30), and the gap reduction parameter def(40)-def(30). • Model 3 explains 79.3% of the variance by regressing the CTFRs of the baseline cohort, the level of the trough parameter (postponement effect), and the recuperation factor on the PTFRs. • The graphs above plot the PTFR against the value predicted by the model, showing that each model is a substantial improvement over the previous one. “Catching Up” Countries like Belgium, Denmark, Finland, France, the Netherlands, Sweden Model 1 Model 2 Model 3 Not “Catching Up” Countries like Austria, Greece, Italy, Portugal, Spain Conclusion • Fertility models based on pure postponement are inadequate, and this analysis has shown that the factor of differential catching up is essential to explain the differences in PTFRs. • Countries with lowest-low fertility have not exhibited any catching up after age 30. Countries with PTFRs that have not fallen below 1.50 either have high baseline CTFRs or clearly exhibit catching up. Data and References 1 Bongaarts & Feeney: 1998; Frejka & Sardon: 2006). Fertility data source: Council of Europe (2004) Recent Demographic Developments in Europe – 2004 Edition (CD). (Council of Europe: Strasbourg, France). For more information on postponement and catching up, see Lesthaeghe & Willems (1999), Lesthaeghe (2001), Frejka & Calot (2001), and Sobotka (2003).