Catálogo de publicaciones - libros

The widespread opinion that demography is lacking in theory is based in part on a particular view of the nature of scientific theory, logical empiricism. A newer school of philosophy of science, the or view, provides a different perspective on demography, one that enhances its status as a scientific discipline. From this perspective, much of formal demography can be seen as a collection of substantive models of population dynamics (how populations and cohorts behave), in short, theoretical knowledge. And many theories in behavioral demography – often discarded as too old or too simplistic – are perfectly good scientific theories, useful for many purposes, although often in need of more rigorous statement.

This chapter looks at the methodological thinking of two leading North American demographers in the second half of the twentieth century, Ansley J. Coale and Nathan Keyfitz. Their ideas are compared to the model-based view of science, which is then used to discuss the interrelationships among the various elements in demographic knowledge.

Twenty-first century computing has given us new ways of doing science. Old notions of elegance and simplicity are not completely outmoded. But they need to be complemented by a greater awareness of the complexity of social, economic and demographic systems, and the realization that simple models, while tractable and intellectually satisfying, often are not adequate to the task at hand – explanation, prediction, or policy guidance. In the words of one biologist, ‘It is only now that we have the ability to do complex calculations and simulations that we are discovering that a great many systems seem to have an inherent complexity that cannot be simplified…’ (Rowe GW, . Oxford University Press, Oxford, 1994).

The model-based view of science encourages a ‘toolbox’ approach to theory, models, methods, and techniques. Some tools are multipurpose. Some purposes can be served by more than one tool. Some standardization in the use of tools is inevitable, but it is important to avoid stylized analysis, or the rote use of a tool for a given purpose. These ideas help clarify the relationship of two kinds of quantitative analysis, simulation and statistical modeling (what Adrian Raftery, paraphrasing C. P. Snow, has termed ‘the two main cultures of quantitative research’), notably the lack of interaction between them in day-to-day research.

The starting point for this essay is the observation, partly impressionistic, that demography has tended to neglect the predator-prey equations in courses, textbooks, compendia, and research papers. This is surprising, since the equations bear the name of A. J. Lotka, one of the acknowledged founders of modern demography. This relative neglect is unfortunate also, since a central fact about the human species is that we are deeply implicated in nature as both predator and prey. Possible explanations for this situation are discussed, including a general neglect of systematic theory, and of differential equations, a branch of mathematics especially suited to the statement and exploration of theories of demographic processes.

1972 saw the publication of two mathematical models of the first marriage process, one by Ansley J. Coale and D. R. McNeil (in the ), the other by Gudmund Hernes (in the ). The former went on to become well-known by demographers, as the standard or canonical model. The latter was largely ignored for many years, despite its obvious merits and its publication in a leading sociological journal. This chapter compares the two models and considers possible explanations for their different receptions.

As is often the case in demography, Goodman . (, 5:1–27, 1974) developed their theory of the interrelationships of fertility, mortality and kinship numbers by means of continuous mathematics [integrals], but resorted to finite approximations for calculating results. Recent developments in computer software now provide an alternative procedure that avoids extensive programming of finite approximation algorithms: (1) continuous functions are found to represent discrete data on fertility and mortality; (2) the resulting functions and parameter estimates are then inserted directly into the kinship equations, and the integrals evaluated numerically. This procedure has the potential for use in many other areas of population mathematics, where theory is given by integrals and other continuous expressions, but data are for discrete age groups.

Generally viewed in demography as a stylized technique for the measurement of mortality, the life table also can be seen as a general theoretical construct or abstract model, with many applications and empirical interpretations, of which current or past mortality measurement is only one. It is a general model that depicts the effect on a cohort, real or imaginary, of some attrition event. The abstract life-table model is demographic theory in the same sense that Newton’s law of falling bodies is physical theory.

In two stimulating papers, Anatole Romaniuc (United Nations  29:16–31, 1990; 30:35–50, 2003) puts the cohort-component projection model in a broader perspective, viewing it as ‘prediction, simulation, and prospective analysis.’ The projection algorithm can serve several different analytic aims (see Chap. above).

The cohort-component population projection algorithm has generally been viewed as having one purpose, namely population forecasting. And it has been ‘canonized’ as the one best method for this purpose. A more fruitful view might be to see it first and foremost as a theoretical model of population dynamics, useful for many different purposes. At the same time, other approaches to population forecasting should be given greater attention, approaches with both advantages and disadvantages compared to the cohort-component approach (See Chap. above).