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with support from the UN Population Fund (UNFPA)
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in collaboration with the European Association for Population
Studies and the IUSSP. For further information please contact
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EUROPEAN POPULATION CONFERENCE
CONGRES EUROPEEN DE DEMOGRAPHE
Milano, 4-8 settembre 1995
Plenary VI
®God has chosen to give the easy problems to the physicists¯
or why demographers need theory*
by Guillaume Wunsch
®We ought not think of the social world as a
system of phenomena in which we can expect to find
a strong underlying order¯
1. The need for theory
The two quotations which open this paper, respectively drawn
from C. A. Lave and J. G. March (1975) (two social
scientists) and D. Little (1993) (a philosopher), reflect the
bewildering nature of social phenomena and the difficulties
demographers are confronted with, when they want to make sense of
the phenomena they study. To be sure, demographers have developed
sophisticated methods of analysis and benefited from the
availability of large and diverse data sets pertaining to their
field, but in how many instances can we say with confidence that we
have explained and understood the demographic processes which are
studied, that we are able to make reliable forecasts, and that we
can recommend efficient policy measures?
For example, suppose we want to determine if a woman's number
of children influences her labor participation, or more generally
find an answer to a ®Why?¯, ®What-is?¯, or ®What-if?¯ question
concerning the occurrence of a particular event or relationship.
For example, why is male mortality higher than female mortality in
Europe? What is the impact of family
allowances on French fertility? What would be the impact on
internal migration if one decreased the price of public transport
in Italy? Explanation is the search for suitable answers (research
hypotheses) to these questions; plausible answers then have to be
tested in order to be either confirmed or falsified by facts.
To come back to our original problem, other determinants of
labor participation might be the woman's age and other
household income [1]. This leads firstly to the following simple
causal structure: Household income Number of children Labour
participation Age of female
This causal structure is then tested using, say, a logit
regression model applied to survey data, and the logit
coefficients are computed. It is seen that the presence of young
children does have a significant impact on female's labor
participation, controlling for household income and age of female.
Have we however truly explained the relationship between the
presence of children and labor participation, controlling for
possible confounding factors? For example, instead of the simple
causal system tested above, the relationship might be in the form
of a causal chain such as
Age Children Income Labor participation
as one's number of children might be a determinant of other
household income through e.g. family allowances. In this case,
income should not be controlled as it is an intervening variable
between presence of children and labor participation. Similarly,
presence of children and income should not be controlled if one
wishes to evaluate the impact of woman's age. More complex causal
structures, taking account of other confounding variables (such as
social category and education) and translated by a system of
simultaneous equations, can also be proposed and the subsequent
results might be quite different from those obtained in the former
cases.
This rather trivial example shows that explanation in
demography does not only entail pointing out the variables or
possible risk factors associated with the effect one is studying,
through the use of a more or less sophisticated statistical
technique. Epidemiological studies have discovered for example
some 280 or more variables associated with coronary heart disease
(CHD), including snoring, grey hair, wifely love, premature
baldness, etc. (W. E. Stehbens, 1993). All these factors have been
shown to be associated with CHD, but as one knows good correlations
are not necessarily good explanations! Further data possibly
derived from other types of studies (non-epidemiological studies,
in this case) should confirm the statistical evidence, and
especially a plausible explanation linking causes and effects
should be provided, so that the phenomenon becomes intelligible or
comprehensible. Statistical techniques are moreover not always the
best tools for certain purposes, such as determining the reasons
underlying a specific behavior; qualitative analyses could be
preferred in those cases.
A good explanation therefore requires uncovering the
generative process, the causal history or mechanism, or finding the
®reasons¯ of the causes as R. Franck (1994a) would say, such that
we can understand why a cause produces an effect. In the social
sciences, this causal process is usually based on the actions of
individual agents, taking account of the impact of groups, social
structures, and institutions, and on the
relationships between man and his context or environment. This is
the ®causal mechanism¯ aspect of explanation. Other views
consider that explanation should also lead to making a phenomenon
expectable, to unifying different phenomena, or to resolve
curiosity and reduce contingency taking into account the context in
which the ®Why? or What-is/if? question¯ is asked (see D. M.
Hausman, 1993a). I believe that, at the minimum, explanation
should state the ®reasons¯ of the causes, as pointed out above;
from this viewpoint, the other characteristics are important but
not necessary.
Finally, the causal system must be related to its
environment or context. May we for example assume the closure of
the causal system, such that no outside (implicit) variable has an
impact on more than one explicit variable in the system?
Furthermore, does a cause(s) and effect(s) approach necessarily
lead to a true understanding of the relationships involved, such
that our results form the basis of possible predictions and policy
actions? These are some of the topics which will be examined in the
present paper, taking population as an example. Finally, I have
intentionally included many references to the non-demographic
literature especially, to which the interested reader is referred
for further information.
2. Theories, laws, and explanation
Demographers do not usually proceed by asking for example
what are all the causes, both proximate, intermediate, and
ultimate, of mortality in contemporary Europe. This is the view of
modern science, however. In the past, scientists have on the
contrary searched (unsuccessfully) for a global interpretation of
society in its total manifoldness, hoping to develop a synthesis of
all social sciences … la Talcott Parsons. Max Weber, among others,
has been a strong opponent of this aim. Indeed, a ®total¯
explanation of mortality is practically impossible, to say the
least. Causes are so numerous and so interrelated that we would
end up with a fuzzy explanation, a huge system of variables, and an
untestable theory. It has indeed been conjectured (J. F. Traub and
H. Wozniakowski, 1994) that there exist unanswerable
scientific questions. Such would be the case of many problems
(especially those which are discrete) with a large number of
variables, as well as those concerning the behavior of chaotic
systems (systems extremely sensitive to initial conditions). From
this perspective, it would probably be unwise to develop very large
models in a causal or a systems approach, as their solution would
possibly be intractable due to their computational
complexity.
In most cases therefore, we are interested in knowing the
impact of one (or a small number of) possible cause(s) on an effect
(or response). If we want to see for example if there are
differences in mortality according to income, we must first delimit
the period of time and the area we are interested in, then define
what we mean by mortality and by income, find suitable indicators
of these two concepts, obtain relevant data and measure the
variables, and check if the relation observed is statistically
significant. Theories employed in making observations, i.e. the
so-called measurement theories, should be distinguished from
explanatory theories dealing with the possible causal mechanism
producing these observations. It is the latter type of theories
which is the subject of this paper.
We must also control possible confounding variables, i.e.
those which on the one hand have an impact on mortality and on the
other hand are associated with income. If confounding variables
are not controlled, the relationship between income and mortality
cannot be adequately determined. Suppose there is a variable, such
as education, which is associated with income and has an influence
on mortality. Controlling for education could therefore
significantly change the relationship between income and
mortality. Furthermore, why is income associated with education?
If one time-orders the variables, education could be a cause of
income, higher educated people earning more money. Another reason
would be that, on the contrary, income is a cause of education, as
higher income enables one to take better advantage of the
educational system. Or education and income could be the effects
of a common cause, such as the social class of one's parents. All
these possible reasons explain the association between education
and income, and all are a priori equally valid interpretations of
this relationship.
In the first case, the causal model is:
education income mortality
One could also have the following model, where there is both
a direct and an indirect path leading from education to mortality:
income education mortality
The second case is represented by the following causal chain:
income education mortality
Here too one can postulate a ®direct¯ effect of income on
mortality (through possibly a better use of the health care
system), and an indirect effect through education, leading to the
causal graph:
education income mortality
Finally, in the third case, the causal structure would be:
income social mortality class education
As the relationship between cause and effect usually
depends upon their role in the wider causal structure, it is
necessary to specify the interrelations between possible causes and
confounding variables, that is to develop a causal theory linking
the variables together while specifying their order, causes
preceding effects in time. This theory, among other things, helps
us to determine which variables have to be controlled and those
which should not be controlled. The theory is developed according
to the prior knowledge, experience, and imagination of the
scientist, taking account of the state of the art at that time. In
this respect as in many others, scientific theories are hardly
different from everyday inference or judgement under uncertainty,
i.e. the psychology of causation or how people make judgements of
probable cause; see e.g. H. J. Einhorn and R. M. Hogarth (1986).
Theories can therefore change over time and space, and differ from
one scientist to the other; as always, the proof of the pudding is
in the eating, i.e. are the theories confirmed by the data on hand
or not?
In particular, a population theory [2] should not only take
into account the state of knowledge of demographers at the present
time, but it should also consider the state of the other sciences
dealing with individuals and groups. For example, a theory of
demographic behavior cannot ignore all the work done on human
behavior in cognitive psychology, in decision theory, in artificial
intelligence, and in the philosophy of mind.
Demographic propositions on behavior should therefore not
contradict findings in other fields; if they do, there should be
very good reasons. To give another example, a population theory
developed at the micro (individual) level should not neglect the
impact of institutions and groups, at the macro level, on the
actions of the human agent; demography must therefore integrate the
results of sociological research or, at the very least, cannot
ignore them. In this sense, demographic explanations should
necessarily be multidisciplinary, but the same could be said for
the other social sciences too, such as e.g. economics.
Going back to our example, to complicate things further, the
impact of income on mortality is not necessarily the same at all
levels of education. In this case, we would also have to take into
account the interaction between these two causes, interaction being
the joint impact of the two causes (income and education) in
addition to their individual action on the response variable
(mortality). As we see, even with a very small number of
variables, the possible causal structures are numerous. Theory
enables the scientist to select a small sub-set of possible
structures or ®possible worlds¯ (usually only one) from the many
which can be proposed with the variables on hand. For an
interesting discussion concerning the selection of one or a small
handful of theories among the many possible alternatives, see C.
Glymour et al. (1987). The model thus chosen is then submitted to
empirical testing, in order to be either confirmed or falsified
[3]. If the plausible explanation is strong and if there are no
good alternatives, it will be kept as the best theory under
uncertainty till a better explanation is proposed.
Finally, we must say something about the rest of the
world, i.e. the environment of our explanatory system. Can we
assume that there are no other implicit or hidden variables which
could act as confounders (®strong¯ closure of the explanatory
system), or can we consider that the latter affect at the most one
explicit variable and are not interrelated (®weak¯ closure of the
system)? If not, are we prepared to accept the possibility of
hidden confounders which could play havoc with our results? For
example, many statistical models actually assume the weak closure
of the system, but this hypothesis can only be tested by the
progress of science. Further research may indeed show that some
hidden confounders were not taken into account in previous
studies. This is the process of accumulation of knowledge, one of
the main characteristics of science.
If one has followed these guidelines, and if our hunches are
empirically confirmed, one can at the most provide a good
description of the possible relationship between income and
mortality, controlling for various parasitic variables such as
education. This exercise does not lead us however to understand
why there exists such an association, because we have not yet
provided an explanation of this relationship. Even if we know that
there is a mortality gradient from the lower to the higher income
classes, we have not uncovered why these differentials exist. We
cannot furthermore generalize our results to other times and other
places, nor predict the future, nor come up with effective policy
measures. For example, one could suggest that to eliminate
mortality differentials in the UK, it is sufficient to move
everyone from the lower to the upper income classes, but this
proposed policy is not very illuminating for the British health
authorities! We must therefore find better answers to
®explanation-seeking-why-questions¯, as Carl Hempel (1965) has
coined them, and this is the purpose of an explanatory theory.
As I have stressed at the very beginning of this paper,
theory-development in the social sciences is beset with
difficulties. Firstly, in most cases we cannot have recourse to
controlled experiments in order to check our hypotheses, for
ethical or practical reasons. We cannot, for example, randomly
distribute people among income classes and then see how and why
they die! In addition, though randomization is usually considered
an efficient method for controlling possible hidden confounders,
this is however not absolutely true if there are interaction
effects between explicit and implicit causes (see G. Wunsch 1994,
section II).
Secondly, mortality has numerous causes or determinants, and
the interrelations between these causes are not well-known.
Furthermore, many causes probably do not act in a deterministic
way; probabilistic explanations are therefore required. There are
also probably numerous risk factors specific to each individual;
the theory we develop will therefore explain perhaps only a small
part of the total variance. There are other problems too. Most
concepts in the social sciences are abstract constructs, such as
social class. These concepts must therefore be defined and
operationalized in order to be measurable, and all demographers do
not necessarily agree on a joint meaning and do not reach
consensus on a common set of indicators or operational
definitions.
Finally, the social sciences deal with people and not only
with things as in the natural sciences. Things such as stellar
objects have no beliefs, desires, intentions, motives, reasons, and
goals, contrary to human beings. Explaining human deeds therefore
requires not only observing human actions (meaning here voluntary
behavior) but also unravelling the reasons which cause these
actions, i.e. understanding purportful (intentional) and purposeful
(goal-oriented) behavior. This implies that it is not always
sufficient to find the neurobiological, psychological, or social
conditions which determine human actions, but that it can be
necessary to discover too the purposes and reasons of the agent's
actions, even though these reasons may grow up in many cases out of
specific socio-cultural contexts [4]. For example (J. D.
Greenwood, 1988), an aggressive action can be a self-determined act
of revenge, independent of all internal or external causes such as
aggressive drives or environmental stimuli.
On the other hand, as one's knowledge and value-system
(determinants of many behaviors) are both acquired through
observational learning and imitation, education in a broad sense is
a causal factor explaining many human actions. I cannot therefore
agree with the reductionist viewpoint of methodological
individualism, as social contexts (inter- personal relations and
institutions) affect individual behaviors. In particular, human
beings do not act only on the basis of pure rationality; any theory
of behavior neglecting the social context of human action is
therefore incomplete and inadequate.
Explanation therefore needs to determine the causes of the
generation of the action, including the agent's reasons which have
lead to this action as well as the social determinants of his
action. Some of the things we do can be causally explained by the
reasons we have for doing them (on this topic, see F. Dretske,
1988). However, as often in causation, the same causes may lead to
quite different effects: people having similar reasons do not
necessarily undertake similar actions, though their decisions are
probably not arbitrary . Actions can therefore be predictable in
various degrees, taking account among others of the pressure
(constraints) of social institutions. This does not rule out the
possibility that some actions might be truly undetermined, as the
result of free agency, but we should nevertheless not give up too
soon in our search for explanation.
In fact, to some extent it is easier for us to understand
human actions than plate tectonics for example, as we can in the
first case draw upon our personal experience (see e.g. T. Abel,
1953). Many human actions are therefore expected or familiar, on
the basis of our own behavior. This capacity to understand the
behavior of someone else is, however, not per se a method of
analysis; it helps in making assumptions or hunches, and in
developing a plausible theory of human action, but it cannot be
applied to verify a theory. Understanding through introspection or
empathy is therefore no substitute for a good explanation, pace
some sociologists who believe otherwise.
Formally, a causal theory is a set of variables and of
relations between these variables, developed in order to provide a
possible answer to an explanation-seeking- why-question (Why?,
What-if?, What-is?) concerning some particular domain in space and
time, in our case a particular demographic phenomenon such as the
fall in fertility in Western Europe during the latter half of the
sixties.
In addition, according to the practice of scientists (A.
Hohenester et al., 1988), a theory should satisfy various criteria
of simplicity and applicability. It should, for example, have
relatively few parameters compared to the actual situation which it
tries to explain, as a theory is a simplification or
idealization of reality. A theory should be internally consistent
or valid, e.g. not contain contradictory propositions. It should
also be able to make new predictions. Finally, a theory should be
clearly testable and externally valid, i.e. confirmed by the data,
if we want to distinguish a scientific theory from a purely
speculative one.
In particular, as A. Piaser (1994) has stressed, a theory
should ®mime¯ the process one is studying, i.e. translate into
one's language the essential characteristics of the production
mechanism. This language should be structurally homologous or
similar to the process one is studying. Ideally, there should
therefore be some sort of isomorphism or correspondence between the
elements x in the real world and the elements x* of the language
being used. For many philosophers, such as R. N. Giere (1994),
mature scientific theories are formulated in the language of
mathematics; such is the case with physics for example. Piaser
criticizes this viewpoint, rightly I believe; mathematics is the
language of physics not so much because physics is a ®mature¯
science but because mathematics corresponds so well to many
physical phenomena. In this case, mathematics can be used not only
to represent existing phenomena but also to predict new results.
Moreover, what is a mature science? One which uses
mathematics as its language? Is economics a mature science because
its theories are often expressed mathematically? I believe the
maturity of a science is not measured by the language it uses but
by its capacity to explain its domain. Even in physics,
mathematics cannot always be applied in such cases as many-
variable, non-linear, stochastic problems, which are best
expressed e.g. in statistical terms or by simulation.
In the social sciences and in the humanities, mathematics is
not necessarily the most adequate language available: try
explaining in formulas the process of demographic transition in
China, the fall of the Roman Empire, or the success of New Age
music! The complexity of the social world is probably better
convened in statistical terms, in graphs and graphics, and in
ordinary language, than in the highly formalized language of
mathematics. The latter would be useful nevertheless if the
phenomena observed are simple and regular, rare cases in our field.
This does not mean, I insist, that mathematics are useless in
demographic theory, but that the quality of a theory in the
population sciences is definitely not proportional to its
mathematical content. Furthermore, this conclusion does not
obviously condemn the use of mathematics in developing methods of
demographic analysis, nor in demographic modelling; what would
these fields be without mathematics!
The use of ordinary language does not necessarily sanction
however sloppy theorizing, fuzzy definitions, and faulty
reasoning. Clear definitions, logic, and careful deductions (or
inductions) are a prior quality of theories expressed in ordinary
language, just as they are required as well in mathematical
theories. In this respect however, a scientific theory expressed
in a mathematical language is less prone to slipshod reasoning and
to ill-defined concepts, due to the axiomatic nature of
mathematics. Theories expressed in ordinary language require
therefore an even greater effort in precision and in deductive
reasoning. In particular, care should be taken not to take
ordinary words for granted: concepts in demographic theories should
therefore be clearly defined and made distinct, if
necessary, from their ordinary meaning. For example, ®parity¯ in
fertility theory does not mean ®equal status or pay¯, as defined in
my Oxford Paperback Dictionary!
In addition to interpreting an actual process in one's mind
using an homologous language, another important
characteristic of a theory is the property of generalization. Take
the following case: Nancy had a traffic accident because she was
dreaming of John and did not see the car coming from her right.
This explanation is a reason of Nancy's car crash, but it is not a
scientific theory ... except if day- dreaming is a common
characteristic of female drivers. On the other hand, demographers
have shown that driving a motorcycle is more dangerous than driving
a car, that the young and the old have greater risks of accidents
than the middle- age groups, that some months of the year, some
days of the week, and some hours of the day are more accident-prone
than others. Reasons have been given for these general findings,
and though they do not obviously apply to all times and to all
regions of the world, one can consider that demographers have
provided a good general picture of traffic accidents and their
causes. These relations are not
deterministic, as some young drivers are more careful than their
fathers for example, but they apply on average to the population of
drivers.
Do explanations therefore lead to laws (or lawlike
statements) in the social sciences? All depends upon the
definition of a ®law¯. If a law must be exceptionless and
universally valid, as in physics, there are few cases of laws in
the social sciences and in demography in particular, except for
laws having a biological basis. For example, a woman's fertility
is located between menarche and menopause; and the span of life of
the human species is limited and equal to more or less 115 years.
Otherwise, higher education does not always lead to lower
fertility or infant mortality, and labor participation is not
always reduced by the presence of young children. Even though
these relationships are common, they are not true in each and every
situation, and the strength and direction of the relation is not
always the same.
On the other hand, if by ®law¯ one means what should
usually happen if our theory is correct, then social and
population theories can indeed lead to laws valid for a certain
place and time. Good explanations do not have to yield completely
general laws. In this sense, belonging to an upper social class in
the UK does usually lead to a better health status and a lower
mortality. Wearing a safety belt in Italy is usually a sound
behavior when driving a car. There are exceptions to these rules,
but they nevertheless apply in most circumstances. What social
laws formulate are typical actions, relations, or events, and not
necessities.
Social laws hold only ceteris paribus, and several social
scientists have claimed that the ceteris paribus clause
disqualifies laws in our domain, as all other things never remain
equal in the real world [5]. I believe this position is incorrect
to some degree. In the long run, things obviously do change and
the ®law¯ on safety-belts proposed above cannot be applied to the
Roman Empire nor even to the contemporary world before the
invention of that device! I have stressed however that
explanations and laws in the social sciences are never completely
general or universal; they are only valid for a certain time and
place, i.e. for a specific niche in history. Moreover, laws do not
have to be exceptionless. This is due to the fact that
explanations and scientific laws are abstractions, idealizations,
and generalizations of the real world; exceptions therefore can and
do occur, but this does not necessarily disprove the
explanation nor the law.
A theoretical law states what would happen if various
simplified conditions hold. If these conditions do not perfectly
hold, i.e. if the ceteris paribus clause is not completely
satisfied, laws can still cite factors, aspects, and tendencies of
a complex situation, as H. Kincaid (1990) has stressed. To quote
Kincaid more or less freely, here are six practices which lend
credence to ceteris paribus laws, even though they nevertheless do
not waive all criticism:
1) It can sometimes be shown that in some narrower range of cases,
the ceteris paribus law is satisfied.
2) We can sometimes show that although other things do not remain
equal, it makes little difference in practice.
3) We can sometimes explain away the failure of the ceteris paribus
law, invoke the relevant reasons thereof, and give at least an
approximate prediction of the combined effect of the counteracting
factors and relevant laws.
4) Sometimes, we can provide inductive evidence for a ceteris
paribus law, by showing that as conditions approach those required
by the ceteris paribus clause, the law becomes more predictively
accurate.
5) The law may still make some predictions which are borne out and
which are unexplained by alternative hypotheses.
6) A ceteris paribus law may provide a single explanation for some
set of diverse phenomena, even though we cannot fully specify the
ceteris paribus clause or explain away all counteracting factors.
It must be stressed that the ceteris paribus clause does not
imply that all causes except one (the risk factor of interest) must
remain equal. Once one has estimated for example the
structural parameters of the causal model, it is possible to
determine the impact of, say, Xk on Xn taking account of the
changes in the other Xi. The clause means that the parameters
themselves have to remain stable and not change significantly, due
to a modification in context that would alter the causal structure
by e.g. adding new variables or changing the relationships between
existing variables.
It is often assumed that a law is any true generalization
which supports prediction. A law is however not a statistical
regularity, as regularities are not explanatory by themselves.
Indeed, regularities are neither necessary nor sufficient
conditions for causation. Social regularities are only
associations, for example a positive relation between income and
health, and as such they may lead to wrong causal inferences. On
the other hand, the observation of an empirical regularity is often
a first step towards understanding the causal mechanism producing
the association, that is discovering a plausible explanation, such
as Durkheim's theory of suicide. To give another example, if
health is positively correlated with income, this might be due to
particular reasons such as a better access to the health care
system in the absence of an adequate social security system.
Regularities may therefore lead to explanations; if the latter are
confirmed or not falsified by the data, explanations in turn may
lead to lawlike statements. Laws derive from explanatory theories,
and not vice versa!
As D. Little (1993) has pointed out, once we have
discovered and confirmed the causal mechanism involved, we no
longer even need to use the empirical regularity in order to
explain. In the above example, access to health care becomes the
prime determinant of health and not income any more: a good social
security system would be just as valid as a higher income. All
regularities do not however lead to a satisfactory explanation (see
E. Sober, 1987); for example, two time-series can be highly
correlated without one being the cause of the other or both having
a common cause. This is the case for example if both variables
have increased over time, such as the increase during the past
centuries in average temperature and the world population; no one
I hope believes this is a true causal relationship, at least during
the pre-industrial era!
A causal theory usually leads to the type of proposition ®If
X then Y because Z¯, where X represents causes, Y effects, and Z
reasons. In the causal process, causes can be internal or external
to the agent. For example, education of the mother is an external
cause of her lower infant mortality, but the reasons thereof relate
to the human agent and not to the external cause, as Caldwell's
explanation of the relationship between infant mortality and
education has exemplified so well. To give another example, drawn
from R. Franck (1994b), if I pour water on the flowers in my
garden, they will grow; but the reason of their growth is mainly
contained in the flowers themselves and not in the water: try
pouring water on pebbles instead and see the result! The same
external cause can therefore produce different results, taking
account of the internal causes particular to each agent. In other
words, a good explanation implies not only determining the external
causes of an agent's action, but also the agent's reasons leading
to this voluntary behavior.
The ®If...then¯ statement must be qualified however.
Consider the following two examples: ®If fire then smoke¯ and ®If
smoke then fire¯. Both of these statements are correct in
English. As one sees in this case, common-sense reasoning in
natural language may yield contradictory propositions. As J. Pearl
(1988a) has stressed, it is therefore necessary to
distinguish, from a causal viewpoint, between expectation-evoking
(If fire then smoke) and explanation-evoking (If smoke then fire)
propositions, the first being a causal rule and the second an
evidential rule. In this paper, we deal mainly with causal rules
or propositions leading from the cause to the effect (if ®cause¯
then ®effect¯), and not vice-versa.
Evidential rules are however not devoid of explanation: they
point from evidence to hypothesis or from effect to cause, such as
in the case of symptoms Y suggesting disease X (or smoke suggesting
fire). Explanation-evoking propositions are currently used in
medical practice for example, but they cannot combine diagnosis
with prediction. On the contrary, causal propositions predict the
effect. For example, smoking causes lung cancer (among other
diseases), and therefore one predicts for Nancy (who smokes) a
higher probability of developing eventually a lung cancer than if
she did not smoke. On the other hand, Joe's lung cancer suggests
or is evidence for his addiction to smoking, but in fact his
disease could be due to asbestos pollution in his factory
(actually, Joe is a non-smoker). This is the essence of what is
sometimes called ®causal asymmetry¯ (not to be confused with causal
priority): two or more causes (smoking and asbestos) can have a
common effect (lung cancer), while two or more consequences (lung
cancer and bronchitis) can have a common cause (smoking). Common
causes compete with each other for the effect, while common
consequences support each other; for an impressive and thorough
discussion of the logic of causal dependencies, see J. Pearl
(1988b).
In two recent articles, D. M. Hausman (1993a and 1993b) has
persuasively shown why causes explain their effects while effects
do not explain their causes, by establishing a link between causal
asymmetry and explanatory asymmetry. Briefly, the argument goes as
follows. Suppose X(3) is dependent upon X(1) and X(2). If the
value of X(3) doubles, this has no implication for the values of
the causal variables: all one can say is that at least one of them
increased by enough to make X(3) double. To quote Hausman (1993a):
®One cannot make causes wiggle in any definite way by manipulating
their effects¯. On the contrary, if X(1) is a cause of X(3) and
X(4), and if one knows the relationship between these variables, a
change in X(1) can predict the changes in the two effects, under
the assumption of the weak closure of the system of variables. The
same result is valid in the first example: if X(1) is independent
of X(2), a change in X(1) can be used to predict the change in
X(3), in a deterministic or in a stochastic situation. Note that
in these examples, X(1) and X(2) are independent of each other,
while X(3) and X(4) are not as they have a common cause X(1); this
is the essence of causal asymmetry, as stated above. To sum up, why-
questions are requests for causes and not for effects, as
knowledge of causes is more valuable in many cases than knowledge
of effects. As one knows, policy measures are therefore based on
the manipulation of causes and not of effects!
3. Do demographers have theories?
The answer to this question is obviously positive, since
demography during the past decades has become less descriptive and
more explicative. In fact, theories abound in demography, even to
the extent that it is difficult to extract from the mass (or mess)
of research hypotheses, more or less confirmed, some strong
conclusions and some definite explanations. Demography has never
had a grand explanatory paradigm, such as the postulate of
rationality and the concept of utility in microeconomics; the
postulate, in sociology, of the dependence of conduct on social
forces outside the individual; structuralism in linguistics or in
social anthropology; Gestaltism or behaviorism in psychology.
Some demographers have however suggested, from an
anthropological viewpoint, that population processes lead to the
adjustment of population size to the capacities of the environment
(see B. de Bruijn, 1993). A central paradigm in demography could
therefore be based on the following postulate: every population
adopts social and individual conducts leading to the eventual
adaptation of its numbers to the environment's carrying capacity,
the latter varying across time and space according to material,
social, and individual constraints and needs. Fertility,
mortality, and migration changes, brought about by individual and
social conducts, would then be seen as strategies designed to adapt
population numbers to the changing environment, taking account of
constraints, needs, and values.
The lack of a grand unified approach is not necessarily a
disadvantage, as the break between Freudian and Jungian
psychoanalysts shows for example. A general paradigm, such as
Freudism, can become canonized and it can then stifle research. If
Freud's work stands as a bible for psychoanalysts, how could this
field progress beyond and possibly in contradiction to what Freud
has written? To take another example, can one develop Marxism
without Marx, if someone is still interested? Luckily, demography
has always avoided canonizing its founding fathers, and contrary to
sociology or to philosophy we are fortunate to have few doctoral
theses devoted to the thought and writings of e.g. L. Henry, A. J.
Coale, or J. C. Caldwell, even though these
demographers, among others, have had a profound impact on
population research. Their work has indeed become integrated in
the mainstream of demographic thinking, and that is the best
compliment we can pay to them.
On the other hand, the fact that the population sciences lack
a basic demographic paradigm implies that demographers have to
cannibalize other fields of inquiry, in order to found their
explanations. For example, as two recent general reviews of the
field of mortality have shown [6] and to which the reader is
referred, explanations have drawn upon findings in history,
epidemiology, public health, nutrition, sociology, anthropology,
economics, and political science. The spectrum is probably wider
still, as findings from e.g. the psychology of health could also be
considered in a micro approach. Though social facts, and
demographic phenomena in particular, should be explained in a
multidisciplinary approach, one can raise nevertheless the
following related questions. These questions have an impact, among
others, on the training of demographers: in addition to demographic
modes of observation and data analysis, what indeed should be
taught to burgeoning demographers?
What is specifically demographic in the explanation of
mortality levels and trends? Are all explanations in demography
necessarily drawn from other fields? Do demographers therefore need
to be at the same time specialists in these different fields, a
hopeless task indeed? Is population thus to be studied only by
multidisciplinary teams, and what would be the specific role of
demographers in these teams? Is the specificity of demography only
to gather together the results of various approaches to the
explanation of population trends and levels? Would a demographer
therefore be the conductor of a multidisciplinary orchestra in
which there are no demographic instruments?
If one looks at the demographic literature, too many
papers still do not define the concepts used, nor explain why such
and such indicators are taken instead of others. Very often too,
the explanatory framework (if there is one) is a mere sketch, and
frequently one does not learn the reasons for postulating
relationships between some variables and not between others.
Population journals have their part of responsibility here: if
concepts are not defined, if the link between indicators and
concepts are not clarified, if the conceptual framework is
unsubstantiated, the paper should be rejected for publication.
Actually, as experience shows, most population journals apply these
selection criteria none too often.
For example, O. Lopez Rios, A. Mompart, and G. Wunsch (1992)
have related regional mortality differentials in Spain to the
demand and supply of health care, controlling, among others, for
the level of social development and of economic development in the
Spanish provinces. Are these macro-concepts relevant for causal
analysis, as they are so wide and fuzzy? Though the authors have
given a definition of ®social development¯ and of ®economic
development¯ in their paper, have they really pointed out all their
dimensions and justified the battery of indicators they use in
their LISREL model? Are all the relations postulated between the
latent variables based on sound theoretical grounds and prior
research findings? The answers to these questions are most
probably negative.
Too many studies are still confined to pointing out the most
relevant determinants, controlling for possible confounding
factors. We thus know that having a car or being a tenant is
strongly related to mortality in the UK, that toilets outside the
house are a significant determinant of mortality in Norway, etc.
These factors are actually indicators of social class
differentials; what we need instead is the knowledge of the causal
mechanism (the intervening variables and paths) leading from social
class to mortality, i.e. the reasons of the causes. To give
another example, if education of the mother has a strong impact on
the survival of her children, what are the reasons thereof, and
have we confirmed or falsified the postulated
mechanisms?
In an excellent recent review of studies dealing with social
class differentials in mortality, G. Davey Smith et al. (1994) have
shown that the usual explanations of differential mortality by
social class do not hold: the persistence of social class
differences in mortality cannot be explained solely by artefacts,
social selection, or behavioral factors. They stress the need for
relating social selection and behavioral factors to life
circumstances, and for taking into account the pattern of inter-
relations between the various possible causes of social class
differentials in mortality. For example, in the British Regional
Heart Study, when social class differentials in ischemic heart
disease (IHD) risk were considered, they were adjusted for smoking
behavior which was said to largely account for them. In the same
study, when the relationship between smoking and IHD risk was
examined, socio-economic position was not taken into account.
Using the Whitehall data, the authors have however demonstrated
that a significant part of the association between smoking and
mortality appears to be due to the relationship between smoking and
socio- economic position. A life course analysis of the link
between social class, morbidity, and mortality has therefore to be
performed, using longitudinal data. Ideally, this study should
also take into account familial background and household
characteristics, as stressed in M. Salhi et al. (1995).
In the field of family formation and fertility, the
situation is probably not better, though I lack the expertise in
this domain in order to be a good judge [7]. Take however a recent
example, drawn from an otherwise excellent paper by R. Lesthaeghe
and G. Moors (1994). These authors postulate that one's system of
values (SV) determines to some extent decisions on family formation
(the ®selection¯ effect) and/or that the choice of one's family and
domestic type (FT) reinforces or weakens some of one's values (the
®affirmation¯ effect). This leads to the temporal causal chain SV
FT SV which, ideally, should be confirmed or falsified with
longitudinal data.
The authors test this theory with period data drawn from the
1990 European survey on values in four EU countries. Firstly, as
the authors themselves acknowledge, cross- sectional data cannot
discriminate between the two effects, as the data are not ordered
in time. Secondly, as the survey was not conducted for the purpose
of testing this particular theory, do the available indicators tap
all the relevant attitudes and values? Along the same line of
argument, the authors have named their latent
variables (dimensions of conservatism) by exploratory principal
components analysis applied to the available set of items, in an
inductive approach. A sounder way would probably be to use a
deductive approach, starting with the selection of possible
relevant concepts, constructing scales for each attitude or value,
and checking the validity of the items by confirmatory analysis.
This approach would however require the collection of original
data, and naturally be much more expensive than solely using
secondary data already obtained for other purposes.
Thirdly, and more importantly for the quality of a theory,
the authors do not test the ®reasons¯ of the causes: what is the
mechanism through which SV influences FT, or vice versa? Finally,
though Lesthaeghe and Moors take account too, as independent
variables, of the level of formal education, of the type of
religious upbringing, and of current socio-economic status, can the
authors assume the closure of their system? There might be e.g. a
common cause (socialisation itself?) influencing both SV and FT,
which should be controlled in order to derive the true relationship
between the two sets of variables. Moreover, what are the
relationships between the independent variables themselves? One can
assume, for example, that formal education and religious upbringing
influence one's current attitudes towards conservatism, and that
the level of formal education has an impact on one's socio-economic
position. The residuals approach, adopted by the authors for
dealing with multicollinearity between independent variables in a
logistic regression, does not adequately take account of a possible
order among causes. As Lesthaeghe and Moors are specifically
concerned with explanatory theory, these issues cannot I believe be
lightly dismissed, however interesting their results.
4. Discussion and conclusions
Demographers, like many other social scientists, often
complain about the lack of theory in their discipline. In fact, as
I have previously pointed out, demography has developed
numerous conceptual frameworks and theories explaining fertility,
mortality, and migration levels and trends. We are indeed
confronted in each field with a mosaic of theories from which no
general Gestalt emerges. This is partly due, as we have seen, to
the absence of a central paradigm of explanation in demography. It
is also due to the fact that many theories are so local in scope,
so time and space dependent, that cumulation of knowledge is
difficult to achieve. Having shown for example that the recent
decrease in Syldavian mortality is due to less smoking and alcohol
intake, and that the fall in Bordurian mortality results from
better access to the health care system, how do we put these two
explanations together? There are probably many Humpty-Dumpties
similar to this example in demography.
A more troublesome problem related to the diversity of
theories is the level of explanation. Some theories in the
population sciences are developed at the macro (societal) level and
others at the micro (individual) level; combining both types is
once again difficult to achieve. Like all social sciences,
demography deals with individuals in a social context, i.e. with
people behaving in interaction with others and with institutions.
As Emile Durkheim or Max Weber have already stressed a long time
ago, individual actions are therefore to a large extent socially
and culturally determined. Population theories thus have to take
account of this multi- level determination: theories developed at
one level only are bound to be inadequate.
In particular, I doubt if demographic theories (and other
social theories) can legitimately be grounded on the so-called
rationality principle, in at least the traditional sense of
utility-maximizing behavior, such as in the micro-economic
theories of fertility (see B. de Bruijn, 1992). If this is the
case, demographic theories are firmly rooted in the individual;
society only intervenes as constraining individual decisions.
Firstly, many people do not act rationally, as many studies in the
decision and behavioral sciences have shown, due to e.g. the
ambiguity of the context, a lack of logical competence, their
unconscious, their emotions, or individual and social habits. In
fact, as W. Berkson (1989) has suggested, the social sciences can
perhaps dispense with rational behavior: what we are probably much
more interested in is predictable action, whether it is governed by
rationality or not. For example, young and drunk late night
irresponsible male drivers are not acting rationally, but they are
acting predictably as mortality statistics have unfortunately
shown. Rationality is in the mind of the scientist, who tries to
make sense of the data, and not necessarily in the minds of the
actors themselves!
Secondly, and more importantly perhaps, the rationality
principle focuses the theory, as I have said, at the micro level.
The latter cannot show, I believe, how we move from individual
decisions to social conducts, from personal characteristics to
social structures, or from lower level to higher level
organisation. Collectivities do not act like individuals, and
their ®behavior¯ cannot be considered solely as the aggregation of
the actions of their members. This possibly implies moving from
the individual to interpersonal networks and systems, as in the
study of the behavior of small groups such as families. Much still
needs to be done however in the study of large groups, with which
demographers are usually concerned.
To conclude, good explanations of the causes and
consequences of population trends require theories taking account
of both the micro and the macro levels, of individuals and
society. Multi-level theories cannot therefore be based on a
rationality principle which would be firmly grounded at the
individual level only. Individuals are members of a family and a
household, of a neighbourhood, of a workplace, of a community, of
a social class, etc. Like Russian dolls, individuals are
therefore imbricated in a succession of wider more or less
overlapping entities, which transcend to some extent the
individual decision-making process.
Demographic theories thus have to tell us how and why society
influences individual behaviors concerning birth, death, and
migration, but also how and why individual behaviors are themselves
organised into higher-level social conducts. In other words,
population theories must integrate individual behavior and social
structure. Demography is, after all, a Social Science; it should
therefore be both social and science!
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_______________________________
* The comments and suggestions of Robert Franck and Hubert
G‚rard, among others, are gratefully acknowledged.
1. For a recent discussion of the possible determinants of female
labour supply, see e.g. K. Henkens et al. (1993).
2. I make no distinction, in this text, between population and
demographic theories, and use both as synonyms.
3. Proving one's theory is however a very difficult task! See G.
Wunsch (1994), section VII.
4. This point has been stressed by Max Weber; see M. Ekstr”m
(1992).
5. For an exponent of this view, see M. Loriaux (1994).
6. E. ThiltgŠs et al. (1995); D. Tabutin (to be published). It
seems that migration theories are also based mostly on non-
demographic explanations, economics being the prime source in this
case.
7. For an interesting overview, see B. de Bruijn (1993); T. K.
Burch (1994) also provides a stimulating discussion of various
issues related to theories of fertility decline.