UNITED NATIONS POPULATION INFORMATION NETWORK (POPIN)
UN Population Division, Department of Economic and Social Affairs,
with support from the UN Population Fund (UNFPA)

VI: Why demographers need theory, by G. Wunsch

*****************************************************************

This document is being made available by the Population Information

Network (POPIN) Gopher of the United Nations Population Division,

Department for Economic and Social Information and Policy Analysis,

in collaboration with the European Association for Population

Studies and the IUSSP.  For further information please contact

Professor G.C. Blangiardo, Local Organizer, EAPS Conference, Milan,

University of Milan, Istituto di Statistica, V. Visconti di Modrone

21, Milan, Italy.

*******************************************************************



                 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!





5. References



Abel T. (1953), The Operation Called Verstehen, in H. Feigl, M.

Brodbeck (eds.), Readings in the Philosophy of Science, Appleton-

Century-Crofts, New York, 677-687.



Berkson W. (1989), Testability in the Social Sciences, Philosophy

of the Social Sciences, 19, 157-171.



Burch T. K. (1994), Icons, Strawmen and Lack of Precision:

Reflexions on Current Demographic Theorizing about Fertility

Decline, Paper presented at the 1994 Meeting of the Population

Association of America, Miami.



Davey Smith G., D. Blane, M. Bartley (1994), Explanations for

Socio-economic Differentials in Mortality: Evidence from Britain

and Elsewhere, European Journal of Public Health, 4(2), 131-144.



de Bruijn B. (1992), The Concept of Rationality in Social

Sciences, Pdod-Paper n. 9, Population Research Centre, University

of Groningen.



de Bruijn B. (1993), Interdisciplinary Backgrounds of Fertility

Theory, Pdod- Paper n. 16, Population Research Centre, University

of Groningen.



Dretske F. (1988), Explaining Behavior.  Reasons in a World of

Causes, Mit Press, Cambridge, Mass.



Einhorn H. J., R. M. Hogarth (1986), Judging Probable Cause,

Psychological Bulletin, 99(1), 3-19.



Ekstr”m M. (1992), Causal Explanation of Social Action, Acta

Sociologica, 35, 107-122.



Franck R. (1994a), Introduction g‚n‚rale, in R. Franck (sous la

dir.), Faut-il chercher aux causes une raison?, Librairie

Philosophique J. Vrin, Paris, 24-40.



Franck R. (1994b), Deux approches inattendues de la causalit‚:

Aristote et les Sto‹ciens, in R. Franck (sous la dir.), Faut-il

chercher aux causes une raison?, Librairie Philosophique J. Vrin,

Paris, 166-182.



Giere R. N. (1994), The Cognitive Structure of Scientific

Theories, Philosophy of Science, 61, 276-296.



Glymour C., R. Scheines, P. Spirtes, K. Kelly (1987), Discovering

Causal Structure, Academic Press, Orlando.



Greenwood J. D. (1988), Agency, Causality, and Meaning, Journal for

the Theory of Social Behaviour, 18(1), 95-115.



Hausman D. M. (1993a), Why Don't Effects Explain Their Causes?,

Synthese, 94, 227-244.



Hausman D. M. (1993b), Linking Causal and Explanatory Asymmetry,

Philosophy of Science, 60, 435-451.



Hempel C. G. (1965), Aspects of Scientific Explanation, The Free

Press, New York.



Hohenester A., L. Mathelitsch, M. J. Moravcsik (1988), The Usage of

®Theory¯ and ®Model¯ in Scientific Conceptualization,

Scientometrics, 14 (5-6), 411-420.



Kincaid H. (1990), Defending Laws in the Social Sciences,

Philosophy of the Social Sciences, 20(1), 56-83.



Lave C. A., J. G. March (1975), An Introduction to Models in the

Social Sciences, Harper and Row, New York.



Lesthaeghe R., G. Moors (1994), Expliquer la diversit‚ des formes

familiales et domestiques: th‚ories ‚conomiques ou dimensions

culturelles?, Population, 49(6), 1503-1526.



Little D. (1993), On the Scope and Limits of Generalizations in the

Social Sciences, Synthese, 97, 183-207.



Lopez Rios O., A. Mompart, G. Wunsch (1992), SystŠme de soins et

mortalit‚ r‚gionale: une analyse causale, European Journal of

Population, 8, 363-379.



Loriaux M. (1994), Des causes aux systŠmes: la causalit‚ en

question, in R. Franck (sous la dir.), Faut-il chercher aux causes

une raison?, Librairie Philosophique J. Vrin, Paris, 41-86.



Pearl J. (1988a), Embracing Causality in Default Reasoning,

Artificial Intelligence, 35, 259-271.



Pearl J. (1988b), Probabilistic Reasoning in Intelligent Systems:

Networks of Plausible Inference, Morgan Kaufmann Publishers, San

Mateo.



Piaser A. (1994), Entre causalit‚ et processus. L'explication en

sociologie historique, in R. Franck (sous la dir.), Faut-il

chercher aux causes une raison?, Librairie Philosophique J. Vrin,

Paris, 381-419.



Salhi M., G. Caselli, J. Duchˆne, V. Egidi, A. Santini, E.

ThiltgŠs, G. Wunsch (1995), Assessing Mortality Differentials Using

Life Histories: a Method and Applications, in A. Lopez, G. Caselli,

T. Valkonen (eds.), Premature Adult Mortality in

Developed Countries: from Description to Explanation, Clarendon

Press, Oxford.



Sober E. (1987), Explanation and Causation, British Journal for the

Philosophy of Science, 38, 243-257.



Stehbens W. E. (1993), The Quality of Epidemiological Data of

Coronary Heart Disease and the Lipid Hypothesis of Atherogenesis,

Journal of Clinical Epidemiology, 46(12), 1359-1364.



Tabutin D. (to be published), Transitions et theories de la

mortalit‚, in H. G‚rard, V. Pich‚ (sous la dir.), Sociologie de la

population.



ThiltgŠs E., J. Duchˆne, G. Wunsch (1995), Causal Theories and

Models in the Study of Mortality, in A. Lopez, G. Caselli, T.

Valkonen (eds.), Premature Adult Mortality in Developed Countries:

from Description to Explanation, Clarendon Press, Oxford.



Traub J. F., H. Wozniakowski (1994), Breaking Intractability,

Scientific American, Jan., 90B-93.



Wunsch G. (1994), L'analyse causale en d‚mographie, in R. Franck

(sous la dir.), Faut-il chercher aux causes une raison?, Librairie

Philosophique J. Vrin, Paris, 24-40.



_______________________________



  * 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.


For further information, please contact: popin@undp.org
POPIN Gopher site: gopher://gopher.undp.org/11/ungophers/popin
POPIN WWW site:http://www.undp.org/popin