Modeling the fertility impact of the proximate determinants: Time for a tune-up

Abstract

(ProQuest: ... denotes formulae omitted.)1. IntroductionThe proximate determinants (PD) of fertility are the biological and behavioral factors through which the background determinants (social, economic, and environmental variables) affect fertility. The distinguishing feature of a proximate determinant is its direct connection to fertility. If a proximate determinant, such as contraceptive use, changes, then fertility necessarily changes also (assuming the other proximate determinants remain constant). This is not necessarily true for a background determinant of fertility such as income or education. Consequently, fertility differences among populations and trends in fertility over time can always be traced to variations in one or more of the proximate determinants. If accurately measured and modeled, the proximate determinants should explain 100% of variation in fertility.These relationships were first recognized in the mid-1950s when Kingsley Davis and Judith Blake (1956) defined a large set of proximate determinants which they called the intermediate fertility variables. This set was quite comprehensive and included some biological factors that differ little among populations2. In the late 1970s Bongaarts (1978, 1982) defined a somewhat different and smaller set of proximate determinants, thus simplifying the task of constructing models of human reproduction. His analysis indicated that four proximate determinants - marriage/cohabitation, contraception, induced abortion, and postpartum infecundability - are the most important for the analysis of fertility levels and trends. The identification of this smaller set of proximate determinants (PDs) led to the development of a relatively simple model that quantifies the fertility effect of each of these PDs.The objective of this paper is to fine-tune this PD model in light of new evidence, research, and data that have become available over the past three decades. The model as originally conceived remains conceptually sound, and there is no reason to change the general multiplicative nature of the main equation. However, in recent decades reproductive behavior has changed substantially and certain original simplifying assumptions have become less accurate over time. In addition, new research allows some features of the model to be improved. A few revisions of features of the model are therefore desirable.After a brief overview of the original PD model, the proposed revisions will be discussed and implemented. The revised model will then be applied to data from 36 developing countries using data collected in DHS surveys and compared with earlier models.2. BackgroundAt the core of the original PD model is the following multiplicative equation for a population at a given point in time...whereTFR = Observed total fertility rateCm = Marriage indexCc = Contraception indexCi = Postpartum infecundability indexCa = Abortion indexTF = Total fecundity rate.The model treats each PD as a factor that inhibits fertility. Each index has values that range from 1 to 0 depending on the degree of inhibition. The index of marriage measures the impact of the proportion of women in a marital union (i.e., formal marriage or consensual union). The index equals one when all women are cohabitating, and zero when no women are in a union. Only women in a union are assumed to be at risk of childbearing. The index equals one when no contraception is used, and zero when all fecund women use 100% effective contraception. The index equals one in the absence of lactational amenorrhea or postpartum abstinence and declines in size as the period of postpartum infecundability rises. The index of abortion equals one in the absence of abortion and declines as the incidence of abortion rises.The total fecundity rate is the hypothetical total fertility rate that would be observed in a population in which all inhibiting effects of the proximate variables are absent, i. …