The Proximate Determinants Framework of Human Fertility

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  • 1:10 Bongaarts' Proximate…
  • 3:15 Proximate Determinant Equation
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Instructor: Christopher Muscato


In this lesson, you will explore how various factors influence human fertility rates in populations, and discover how we measure that with the proximate determinants framework. Then test your understanding with a brief quiz.

The Proximate Determinants Framework

Look at these two populations. Notice anything different about them? One of them is full of babies! Although all human societies are made up of, you know, humans, we have very different rates of fertility, or the rates of conception and childbirth. Some populations have high fertility rates, some have lower ones. The question of why fertility rates change by population is one that has fascinated many researchers.


One possible explanation is the proximate determinants framework, which studies the relationship between biological and behavioral factors that directly impact fertility, including social, cultural, economic, and environmental variables. So these external variables influence personal biological and behavioral factors, and those, in turn, influence fertility. This framework was designed by John Bongaarts, a Dutch demographer, in the 1970s and 1980s, and it remains one of the most influential models to this day.

Bongaarts' Proximate Determinants

There are many variables than can influence fertility. In fact, some social scientists have identified as many as nine proximate determinants. Bongaarts himself claimed that there were a total of seven, but he defined four specific determinants as the most important factors in explaining fertility levels.

The first proximate determinant, the first factor really affecting fertility rates, is the proportion of women who are married or in a sexual relationship. This one's pretty basic; higher rates of sexually active women will equal a population with a higher fertility rate. Obviously, this factor can be greatly influenced by cultural and social expectations. What is the average age of marriage? Are women primarily raised to prepare for careers or to be mothers?

The second of the main proximate determinants is contraceptive use. Are contraceptives available, are they used correctly, and are they effective? Again, this is very often a matter of external factors, such as social pressures, expectations, and in this case, even things like government health care and sexual education programs.

The third proximate determinant is postpartum infecundability, the period after childbirth before a woman is again able to conceive. Although this is very largely biological, it can also be influenced by external factors and the use of things like wet nurses or nannies.

The final of the main four proximate determinants is induced abortion. This can include unintended and intentional termination of a pregnancy. Miscarriages would be an example of an unintended termination and a biological factor that impacts fertility. Medical abortions, of course, are not biological, and whether or not that is even a realistic option is largely due to social and cultural pressures.

The Proximate Determinant Equation

So those are the four main proximate determinants, the behavioral and biological factors that can influence fertility rates in a population. In order to figure out exactly how these proximate determinants impact fertility rates, Bongaarts developed an equation. How great is that? Here it is:

TFR = TF x Cm x Ci x Ca x Cc

Got it? Okay, maybe we should break this down a bit. TFR is the total fertility rate. That's easy enough. TF is the total fecundity rate, or the potential maximum rate of fertility. Let's put this rate at 15, an average number.

The other four parts of this equation are the proximate determinants, and each one reduces fertility. Cm in this equation is the index of marriage, which shows the reduction in fertility in terms of time past sexual maturity when a woman is not sexually active. Ci is postpartum infecundability, measured as a decrease in fertility due to time spent either unable to conceive or not being sexually active after a birth. Ca is the measurement of induced abortions in terms of loss of fertility from terminating a pregnancy. Finally, we've got Cc, the reduction of fertility from contraceptives.

Each of these four determinants ranges from 1, having no negative impact on fertility, to 0, completely inhibiting fertility. Here's an example of what a basic completed equation may look like:

TFR = 15 x 0.8 x 0.83 x 0.96 x 0.7

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