Academic journal article Demographic Research

Reverse Survival Method of Fertility Estimation: An Evaluation

Academic journal article Demographic Research

Reverse Survival Method of Fertility Estimation: An Evaluation

Article excerpt

Abstract

BACKGROUND

For the most part, demographers have relied on the ever-growing body of sample surveys collecting full birth history to derive total fertility estimates in less statistically developed countries. Yet alternative methods of fertility estimation can return very consistent total fertility estimates by using only basic demographic information.

OBJECTIVE

This paper evaluates the consistency and sensitivity of the reverse survival method - a fertility estimation method based on population data by age and sex collected in one census or a single-round survey.

METHODS

A simulated population was first projected over 15 years using a set of fertility and mortality age and sex patterns. The projected population was then reverse survived using the Excel template FE_reverse_4.xlsx, provided with Timæus and Moultrie (2012). Reverse survival fertility estimates were then compared for consistency to the total fertility rates used to project the population. The sensitivity was assessed by introducing a series of distortions in the projection of the population and comparing the difference implied in the resulting fertility estimates.

RESULTS

The reverse survival method produces total fertility estimates that are very consistent and hardly affected by erroneous assumptions on the age distribution of fertility or by the use of incorrect mortality levels, trends, and age patterns. The quality of the age and sex population data that is 'reverse survived' determines the consistency of the estimates. The contribution of the method for the estimation of past and present trends in total fertility is illustrated through its application to the population data of five countries characterized by distinct fertility levels and data quality issues.

CONCLUSIONS

Notwithstanding its simplicity, the reverse survival method of fertility estimation has seldom been applied. The method can be applied to a large body of existing and easily available population data - both contemporary and historical - that so far has remained largely under-exploited, and contribute to the study of fertility levels and trends.

(ProQuest: ... denotes formulae omitted.)

1. Introduction

In order to study changes in fertility levels and trends in less statistically developed countries, demographers have developed a series of estimation techniques based on data from census counts and household surveys (Brass 1975, Moultrie et al. 2012, United Nations 1983). Since the launch of the World Fertility Surveys (WFS) program in the late 1970s (and especially since the implementation of the Demographic and Health Surveys (DHS) program), population specialists have mostly relied on the ever-growing body of household surveys that have collected full birth history to derive fertility estimates in these countries. Yet, as a recent study has shown (Avery et al. 2013), alternative methods of fertility estimation can return very consistent fertility estimates using only basic demographic information.

Among the existing methods of fertility estimation the reverse survival method is one of the most parsimonious. Based on population data by age and sex collected in one census or single-round survey, the method consists in 'reverse surviving' those no longer present in the population of a given age in order to derive the number of births that occurred n years ago, using a set of probabilities of child and adult survivorship and age-specific fertility rates (ASFRs). The reverse survival method of fertility estimation is very similar to the own-children method of fertility estimation (Cho et al. 1986), but its data requirement is even lower.

Notwithstanding its simplicity, the reverse survival method of fertility estimation has seldom been applied. However, the method can be applied to a large body of existing and easily available population data, which has remained largely under- exploited. In contexts where limited demographic data are available the sole reliance of the method on age and sex distribution makes it of prime interest. …

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