Demographers have known since the 1940s that standard measures of period fertility, such as the widely used total fertility rate, are distorted by changes in the timing of childbearing. Period fertility rates are depressed during years in which women delay childbearing and inflated in years when childbearing is accelerated. This problem is usually ignored because there has been no generally accepted method for solving it. This study proposes a method for removing the tempo distortions from the total fertility rate. The key assumption of the method is that period effects, rather than cohort effects, are the primary force in fertility change, an assumption supported by past research. An application of the adjustment procedure to fertility trends in United States shows that concern over below-replacement fertility in the past 25 years has been largely misplaced. Without the distortion induced by the rising age at childbearing, the underlying level of fertility was essentially constant at close to two children per woman throughout this period. Below-replacement fertility in Taiwan since the mid-1980s is also largely attributable to tempo effects.
Period life expectancy is calculated from age-specific death rates using life table methods that are among the oldest and most widely employed tools of demography. These methods are rarely questioned, much less criticized. Yet changing age patterns of adult mortality in countries with high life expectancy provide a basis for questioning the conventional use of life tables. This article argues that when the mean age at death is rising, period life expectancy at birth as conventionally calculated overestimates life expectancy. Estimates of this upward bias, ranging from 1.6 years for the United States and Sweden to 3.3 years for Japan for 1980-95, are presented. A similar bias in the opposite direction occurs when mean age at death is falling. These biases can also distort trends in life expectancy as conventionally calculated and may affect projected future trends in period life expectation, particularly in the short run. Copyright 2002 by The Population Council, Inc..
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. This content downloaded from 128.235.251.160 on Tue
The life expectancy implied by current age-specific mortality rates is calculated with life table methods that are among the oldest and most fundamental tools of demography. We demonstrate that these conventional estimates of period life expectancy are affected by an undesirable ''tempo effect.'' The tempo effect is positive when the mean age at death is rising and negative when the mean is declining. Estimates of the effect for females in three countries with high and rising life expectancy range from 1.6 yr in the U.S. and Sweden to 2.4 yr in France for the period 1980 -1995.W hen a group of persons is observed from birth to death, mean lifetime may be calculated simply and directly as mean age at death. This statistic is problematic, however, for studying trends in mean lifetime. Mean lifetime for Swedish females born in 1850, for example, reflects mortality conditions from the mid-19th to the mid-20th centuries, a period of historically unprecedented increases in human survival. The study of these changes requires a different approach.Period life expectancy at birth calculated by life table methods has been the standard solution to this problem since the mid-19th century (1). This paper argues that it is an imperfect solution, because life expectancy at birth calculated in this way is distorted whenever it is changing.Conventional life expectancy depends solely on the force of mortality function for time t. We propose an alternative measure that depends both on the force of mortality function and on the rate of change in the standardized mean age at death. Our alternative is based on the assumption that the observed force of mortality function at any given time has the same shape as the force of mortality function inherent in the standardized population age distribution at time t, which reflects the history of mortality in the population. We demonstrate that this assumption is realistic in contemporary societies with high life expectancy and also that the proposed measure is consistent with well-established measures used in other demographic contexts. MethodsCohort Mean Lifetime. The distribution of lifetimes for a group of persons born during any given time period (a ''birth cohort'') may be described in three different ways. The survival function,gives the proportion of individuals who survive to exact age a. It is nonincreasing, with l(0) ϭ 1.0 and l( ) ϭ 0 for some advanced age . The death density function,gives the distribution of deaths by age. The force of mortality function,gives the risk of dying at each age. These functions are formally equivalent in the sense that any two may be derived from the third. The force of mortality function (a) may be derived from d(a) or l(a) by using Eq. 1c, for example, and l(a) may be derived from (a) or d(a) by using Fig. 1 plots l(a), d(a), and (a) for the cohort of females born in Sweden in 1850. The survival function declines to zero at around age 100 yr. The density function is broadly bimodal with peaks at age 0 and Ϸ80 yr. The force of mortality exhibits a U-s...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.