Population and Development : High and Low Fertility in Poorer Countries [1 ed.] 9780203839201, 9780415592833

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ROUTLEDGE LIBRARY EDITIONS: DEVELOPMENT

POPULATION AND DEVELOPMENT

POPULATION AND DEVELOPMENT High and Low Fertility in Poorer Countries

Edited by GEOFFREY HAWTHORN

Volume 20

Routledge Taylor & Francis Group LONDON AND NEW YORK

First published in 1978 This edition first published in 2011 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Simultaneously published in the USA and Canada by Routledge 270 Madison Avenue, New York, NY 10016 Routledge is an imprint of the Taylor & Francis Group, an informa business © 1978 Frank Cass & Co. Ltd. Printed and bound in Great Britain All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 13: 978-0-415-58414-2 (Set) eISBN 13: 978-0-203-84035-1 (Set) ISBN 13: 978-0-415-59283-3 (Volume 20) eISBN 13: 978-0-203-83920-1 (Volume 20) Publisher's Note The publisher has gone to great lengths to ensure the quality of this reprint but points out that some imperfections in the original copies may be apparent. Disclaimer The publisher has made every effort to trace copyright holders and welcomes correspondence from those they have been unable to contact.

Population and Development

Edited by

Geoffrey Hawthorn

FRANK CASS

First published in 1978 in Great Britain by

FRANK CASS AND COMPANY LIMITED Gainsborough House, Gainsborough Road, London E11 1RS and in the United States of America by

FRANK CASS AND COMPANY LIMITED c/o Biblio Distribution Centre 81 Adams Drive, P.O. Box 327, Totowa, N.J. 07511

Copyright © 1978 Frank Cass & Co. Ltd. ISBN 0 7146 3102 7

This collection of essays first appeared in a Special Issue on Population and Development of the Journal of Development

Studies,

Volume 14

No. 4, published by Frank Cass and Company Limited.

All rights reserved. No part of this publication may be reproduced in any form, or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of Frank Cass and Company Limited in writing

Printed in Great Britain by Billing & Sons Ltd., Guildford, London and Worcester

Contents

Geoffrey Hawthorn

1

Robert Repetto

22

T. P. Dyson, C. L. G. Bell and R. H. Cassen

40

INTRODUCTION THE INTERACTION OF FERTILITY AND THE SIZE DISTRIBUTION OF INCOME FERTILITY, MORTALITY AND INCOME— CHANGES OVER THE LONG R U N : SOME SIMULATION EXPERIMENTS POPULATION AND DEVELOPMENT: OUTLINES FOR A STRUCTURALIST APPROACH MODES OF REPRODUCTION

Geoffrey McNicoll

79

Alan Macfarlane 100

PARENTHOOD, MARRIAGE AND FERTILITY IN WEST AFRICA

Meyer Fortes

FAMILY SIZE PREFERENCES OF SPOUSES IN RURAL EASTERN NIGERIA

Alfred O. Ukaegbu 150

PRODUCTION RELATIONS AND POPULATION: RAMDAUA

121

Vinod K. Jairath 165

PRODUCTION RELATIONS AND POPULATION: RAMPUR

Monica Das Gupta

ECONOMIC CHANGE, SOCIAL DIFFERENTIATION AND FERTILITY: ALUTHGAMA

W. M. Tilakaratne 186

ON SOCIAL NORMS AND FERTILITY DECLINE

177

Nigel R. Crook 198

Introduction by Geoffrey Hawthorn* Neither this introduction nor the collection of papers that follows is intended to be a summary and review of recent work on the relations between fertility and 'development' or 'under-development' in poor societies or of the implications of these for economic and social policy. This is partly because good reviews already exist [Cassen, 1976; Ridker, 1976: Birdsall, 7977]; partly because in the collection itself McNicoll briefly describes what he calls the 'consensus' of opinion in this work; and partly because the criticisms of this 'consensus' are now becoming so frequent and familiar as perhaps to make it more important to think about how one might improve on it. Accordingly, the collection is intended briefly to review some of these criticisms, to make some programmatic remarks about how one might proceed, and most importantly to try to show by example what doing so could look like and achieve. I

In principle, a science (and economic and social demography does have some of the pretensions and character of a science) should be at once general, realistic and precise [Levins, 1968: 6–9]. In practice, of course, none is. Levins suggests that in his own, which is population biology, the more applied ecologists have sacrificed generality for realism and precision and that those who have come to it from physics have sacrificed realism for generality and precision. Much the same can be said of the analysis of the conditions, causes, correlates and consequences of fertility in poor societies, except that here, the balance is less even. Here, there has until very recently been a more marked disposition to sacrifice realism for generality and precision. There are two very reasonable (or at least intelligible) explanations for this. One is practical, the other, although not unrelated, more academic. Practically, the initiative for research on fertility in poor societies has come from various international agencies: to begin with, the World Health Organisation, and later, among others, the Ford Foundation, the Population Council, the United States Agency for International Development and various branches of the United Nations. These organisations have taken a global view. The consequence, observed even by some of those who are themselves involved [David and Armar, 1978; *Lecturer in Sociology, University of Cambridge. I am indebted to Robert Cassen for advice and grateful to all those who have contributed, especially when it meant accepting a delay in publication. None is responsible for the interpretation I make of his or her paper in this introduction.

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Demeny, 1977; Zeidenstein, 7977], has been a somewhat uneasily-combined emphasis upon general models of long-term change and general policies of short-term ambition, policies that have been designed (such are the financial and other administrative constraints of large bureaucracies working on annual budgets and perpetually having to make a case for these budgets being renewed) yearly to increase rates of deliberate contraception. Academically, most of the thinking and a good deal of the research other than that of a purely demographic kind which has been done on fertility in poor societies in the last two decades has been done by economists. This is for several reasons. 'Development' is by definition a fundamentally if not exhaustively economic matter. Those who commission the research on 'development' and fertility find it easy to talk to professional economists and professional economists find it easy to talk to them. This is partly because the agencies' global perspective demands either the elaboration of very general theoretical models or the coherent assembly of large amounts of immediately comparable information from a number of very heterogenous countries both of which, and especially the first, economists are readily able to provide or to assemble; partly because agencies with a well-developed commitment to administrative order find it convenient to talk to people with a predictable and well-developed commitment to intellectual order; and partly because in the United States (where much of this work is commissioned and done) there is an established yet relatively idiosyncratic tradition of looking at all kinds of social policy in utilitarian terms. Last, but not least, economists have simply been more energetic in this field of inquiry than other social scientists. These tendencies to generality and precision, as McNicoll explains below, have led those doing the work to assume a certain homogeneity in the units they study and related to this to neglect what might be described as 'intermediate' factors, factors of a contextual, organisational, cultural kind, which stand analytically and empirically between the conventional indices of neo-classical macro-economics and the conventional units of neo-classical micro-economics. Accordingly, there is much work on the relationships between aggregate indicators of 'development' and fertility, and more recently, on the (largely hypothetical) relationships between domestic production and consumption and fertility. There is less work of what McNicoll describes as a 'structuralist' kind. Two examples make the point. The first is from a set of arguments about the conditions of economic growth in poor societies, the second from a set of arguments about the 'rationality' of high fertility in poor households. Both are very familiar to those who work in the area. As Cassen [1976: 804–5] and others make clear, much of the argument about the more specifically demographic conditions of economic growth in poor societies has developed from the model proposed by Coale and Hoover [1958]. This made three general assumptions: that output is a function of capital alone; that savings, the source of capital, will be adversely affected by high fertility; and that with a higher fertility, and so a higher ratio of non-productive to potentially productive workers, more capital will have to be diverted to less directly productive investments in health, education and other services. These three assumptions, with others,

INTRODUCTION

3

led to the conclusion that a higher fertility was a net disadvantage both in the short term and in the long, even though in the long-term total (but not per capita) product would increase. The most crucial of the three is clearly the first. If it can be shown that output is not a direct and simple function of capital, the validity of the others becomes much less relevant. This has indeed been claimed [Kuznets, 1966]. Kuznets suggests that less than ten per cent of the growth of the now developed economies can in the statistical sense be explained by the theoretically conventional inputs. He would be the first to agree that too much weight should not be put upon the exact proportion, but even if the 'true' figure is nearer to fifty per cent [Leibenstein, 1971: 177], the plausibility of any model which supposes that it must effectively be one hundred is much reduced. Exactly what the 'residual' factors are, of course, is not altogether clear. Kuznets himself suggests 'the improved quality of . . . resources', 'the effects of changing arrangements' and 'the impact of technological change'. The first and last of these would doubtless be conceded by many economists and could even if only as exogenous factors be incorporated into their models without too much difficulty. Such a concession and such incorporation would not threaten the conventional presumption that changes in fertility tend to follow rather than to precede economic growth. But if one also concedes the second, 'the effects of changing arrangements', and several economic historians do [see Weber, 1972; North and Thomas, 1973; contrast Hicks, 1969], then one begins not only to concede a factor or set of factors which may only be 'exogenous' more by definition than in fact but also to concede that changes in fertility may be intrinsic to and even precede the growth which they are conventionally supposed to follow. Macfarlane, in this collection, argues just this. England, for so long to so many the classical case of self-generated growth, may, Macfarlane argues, have been a comparatively eccentric and perhaps even unique society in the millenium or more before industrialisation. It may have been eccentric in its combination of a bilateral kinship system (if indeed a bilateral system can be said to be a 'system' at all) and of a long-established disposition to individual contract labour. It may therefore have stood in sharp contrast to the standard picture (a picture, as Macfarlane says, still held by many of England itself) of a poor and largely agrarian society of separate and partly self-sufficient peasant households towards the bottom of a set of relatively enduring strata between which there were long-established and strenuously-maintained relations of economic obligation of a non-market kind and within which kin were 'extended'. As such, it may well have been (Macfarlane argues that it was) a society of late marriage and unusually low fertility in which a large number of surviving children was actually regarded as a nuisance. If Macfarlane is right (the details of his argument remain to be published and reviewed) there has been a double mistake. The conventional neo-classical models of growth have here at least ignored a set of factors which may be intrinsic rather than incidental both to growth and to a relatively low level of fertility. And to the extent that they have taken the English case as typical, they may have sacrificed some not only desirable but also and arguably crucial realism. As many reviewers have explained [IUSSP, 1977; Leibenstein, 1974,

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1977; and references therein], the argument about the 'rationality' of relatively high fertility in poor households has developed from a set of arguments about the economic advantage of relatively low fertility in rich ones. For rich households and for poor, these arguments make the standard suppositions of neo-classical micro-economics. They suppose that households or individuals within households have a determinate and determinable set of preferences and constraints both for the present and for the future which they rationally assess so as to produce an (arguably unique) optimal outcome. Previously, the arguments supposed that households consume and were subject only to the constraint of income. More recently, they have supposed that households also produce and are subject also to constraints of time and other less definite costs. As the reviewers explain, each of these suppositions is in doubt. Nevertheless, the argument persists. It does seem to make some sense of some actual events. This is nowhere clearer than in the debate about the findings of the Khanna Study [Wyon and Gordon, 1971]. This study is interesting and important for at least three reasons. It remains one of the most thorough and honestly reported of its kind [McGreevy and Birdsall, 1974]. It prompted a clear and vigorous criticism which ironically (in view of the critic's Marxist inclinations) did much to support the neo-classical view of household rationality [Mamdani, 1972]. And it has stimulated or at least given point to some undeniably important research, some of the preliminary results of some examples of which are reported below by Das Gupta, Jairath and Tilakaratne [see also Cain, 1977; Epstein and Jackson, 1977; Hull 1975; Nag, Peet and White, 1977; White, 1976]. Wyon and Gordon carried out an extended field experiment in the Indian Punjab, in Ludhiana District, to test the hypothesis that in a poor society with a high and recently increased rate of natural increase couples would realise the value of a reduced fertility and so be willing to resort to more 'modern' and efficient means to achieve it. Wyon and Gordon introduced vaginal foam tablets into one set of villages but not into another and waited to see what happened. In essence, little did, and what did could not plausibly be attributed to the experimental trial. The hypothesis was rejected. Wyon and Gordon themselves were puzzled. At one point [1971: 313] they suggested that the increases in agricultural production brought about by the Intensive Agricultural District Programme in Ludhiana had increased its carrying capacity. At another [1971: 313] they attributed what decline there had been (partly as a result of a rising age at marriage, partly as a result of slightly declining age-specific marital fertility) to a recurrence of what they believed to have happened in Europe, to a net decline in fertility with an expanding resource base and a consequent increase in prosperity. Mamdani made a brief visit to one of the test villages two years after Wyon and Gordon had finished their study. From the conversations that he had there with a mysteriously selected and seemingly small number of men, he claimed to be able to show that all except the numerically insignificant Brahmins and the largest farmers (those farmers who had been able to take advantage of the artificially cheap provision to the already credit-worthy of labour-saving inputs) had and believed themselves to have an interest in a large number of children. Wyon and Gordon had

INTRODUCTION

5

themselves noted such beliefs [1971: 83, 146–7, 309] but had not elaborated. Mamdani did, and suggested that far from Wyon and Gordon's implicit Malthusianism what one might describe as a reverse Malthusianism more accurately explained what had happened. For the moderately prosperous and for the poor, the distributionally skewed 'development' taking place under the IADP had put a premium on cheap, that is to say, family labour, and had therefore put a premium on its reproduction. Mamdani's book was impressionistic and even tendentious [Cassen, 1976: 793–5]. But despite its unconventional method and its moral and intellectual distance from the University of Chicago and the microeconomic models of fertility developed there and elsewhere in the United States, it did lend some weight to the arguments behind such models, and it was helped in this by the prevailing view of the delegates from the poor countries at the World Population Conference in 1974, a view that in a more moderate form has become part of the conventional wisdom [McNamara, 1977], the view that 'development is the best contraceptive' and by implication that uneven development or progressive 'underdevelopment', the kind of change taking place in the so-called Green Revolutions in places like Ludhiana, is the very worst. This argument continues. Some continue to stress the net benefits of children to poorer people in poor societies [McNamara, 1977: 170]. Some question these benefits [Mueller, 1976; IUSSP, 1977: 11]. Others hesitate [Cassen, 1976; forthcoming]. Yet it is not at all clear that there is much point in continuing to pursue the argument in general terms. First, even within the Indian Punjab, the effects of agricultural modernisation and the available supply of labour from outside are so complex and variable [Agarwal, 1977] that Mamdani's generalisations may even be too general for one small area. Second, the three empirical papers on South Asia in this collection, by Das Gupta, Jairath and Tilakaratne, show that under different conditions much the same kind of 'development' can have quite opposite effects on the felt demand for children (regardless of whether or not that demand can be said to be 'rational'). If what Mamdani says about the poor in the village he went to is correct, it stands in some contrast to what Jairath says about the poor in Ramdaua; and if what he says about the moderately prosperous Punjabi Jats is correct, that stands in some contrast to what Das Gupta says about the Haryana Jats in Rampur. A fortiori, and third, it seems that it must be the case that when one moves to other societies with other kinds of production, other kinds of kinship system and other conditions, to Taiwan, for instance, from which Mueller draws some of her general conclusions [Mueller, 1976], or to West Africa, from which Caldwell draws some of his [Caldwell, 1976; Fortes, below], any generalisations (and certainly any precise generalisations) are going to have to be so severely qualified as to begin to cease to look like generalisations at all. One can draw one or other of two clear morals from examples such as these and from the arguments (like McNicoll's below) which they imply. The first and less radical is that the putative science of economic and social demography is following a reassuringly standard course. To begin with (and it is admittedly a long beginning, stretching at least from Malthus to the present day [Eversley, 1959; Keyfitz, 1972]) the priority has been to get a

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very general grip on the most fundamental sets of relations. It has been to develop general models and to give some bite to their largely hypothetical parameters by assigning to these parameters an often confessedly artificial precision. But now, and not least because of the acceleration in this enterprise in the 1950s, 1960s and 1970s brought about by the interest and the funds of the international agencies, the point has come at which there are rapidly diminishing analytical and practical returns from it. Accordingly, a premium, it might be said, should now be put upon realism, upon specifying the particular conditions under which the generalisations do and do not hold. The models are necessary but clearly no longer sufficient. On this view, an optimistic analogy might be with the progression from inchoate natural histories through the general theory of natural selection and the elaborations of formal population genetics and back to more limited but also more theoretically informed studies in the field. The second and more radical moral is that the putative science of economic and social demography is following a much less reassuringly standard course. That is to say, the rough and ready approximations of the neo-classical models, both macro and micro, have shown themselves to be so rough and ready, so approximate, as to make one wonder whether they do not actually constitute a systematic distortion of the facts and of the mechanisms which connect them. Perhaps the time has come for a change, if not for a complete collapse into natural histories, then certainly for a reconsideration of what kinds as well as what levels of generality might do instead. Such a distinction and the choice it suggests may seem excessively grand. It may smack of that moral posturing which so often passes for thought in the social sciences. But others who have looked at recent work on the macro-and micro-economics of fertility have asked a similar question [IUSSP, 1977: 5: Ben-Porath, 1977; Cassen, 1976: 820]. If there is a 'consensus' on what we do understand, there are also the beginnings of an agreement about what we do not [Ridker, 1976]. Disavowing the view that changes of direction in a science are always the result of non-rational switches (a view which is always more plausible for fields other than one's own), the remaining parts of this introduction consider the evidence from within this collection and elsewhere for each of the two morals, and how any consequent decision about them could and should affect future research and policy. II The strategy suggested by the first and more cautious of the two morals, the strategy of specifying the conditions under which the prevailing models may be expected to hold, has proved easier and proceeded further at the micro level than at the macro. This is for two reasons. The first and more purely theoretical of these is that the neo-classical micro-economic theories of individual consumption and household production are clearer and less variable than those of the neo-classical macro-economic theories of aggregate production and consumption. The second, more practical, is that

INTRODUCTION

7

whereas it is possible to argue that the performance of an economy cannot best be explained in terms of the actions of individual economic agents it is not so possible similarly to argue that the performance of a population of potentially reproducing couples (or their equivalent) cannot best be explained in terms of the actions of individual couples. One has at some point to go to the micro level. To be consistent with neo-classical assumptions, one has to distinguish at that level between conditions of constraint and conditions of taste, conditions of supply and conditions of demand. More than twenty years ago, Davis and Blake [1956] proposed an exhaustive set of 'intermediate variables', of variable factors affecting sex, conception and gestation, which could be seen to stand between a society and its births. These have proved useful, especially in the field [see Macfarlane, 1976: 214–47], but the authors did not distinguish between factors of constraint and factors of taste. This was true also of the several discussions of so-called 'natural fertility', of the fertility one would expect from a population whose women were not deliberately trying to control their reproduction [BourgeoisPichat, 1965], discussions which did not ask whether different rates of nuptiality and sex were discretionary. Later, however, in what remains an extremely interesting paper, Tabbarah [1971] used the available information on natural fertility to ask how many surviving children different populations of women could have, and then set his answers against the available evidence on how many they appeared to want. He suggested that of the populations he looked at, the two figures more or less coincided in North Africa and most of South and South-East Asia, that the second exceeded the first in Sub-Saharan Africa, and that the first only exceeded the second (thereby creating a demand for the control of births) in Korea and Taiwan and Western Europe and North America. Finally, Easterlin [1975] has elaborated the distinction and redescribed it in terms of the neoclassical notions of supply and demand. This literature tells one much more about supply, however, than it does about demand. This is partly because we simply know much more about what affects the 'supply' of births. But it is also partly because economists conventionally treat tastes as exogenous and so determined outside their models. And with the exception of those who distinguish between the first two births and subsequent ones [Leibenstein, 1974: 460], economists also treat tastes, including the taste for children, as discretionary. It remains largely true, as Duesenberry put it nearly twenty years ago, that economists believe that 'economics is all about how people make choices' (even if only selectively [Leibenstein, 1976, 1977]) whereas 'sociology is all about how they don't have any choices to make' [1960: 233]. And since anthropologists, sociologists and psychologists have not until recently been very forthcoming about what it is that different kinds of people may not be making choices between and why, the economists have in general tended to work with the assumptions that Leibenstein made in 1957 and has since repeated [1974: 460], that children can be useful for consumption, for production and for security in the parents' old age, and have asked about the importance of these 'utilities' and the 'costs' of pursuing them in the face of other tastes and other constraints. But the pressing question remains: do

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people have tastes for children, and if they do, what are these tastes and why? If people are to have such tastes, two other things must also probably be true. They must be able to entertain the possibility of having a different taste, for more children or for less, and they must entertain the possibility of it mattering that they could. Otherwise, the question will simply not occur to them. They will simply assume that 'maximum is optimum', as Fortes puts it in his paper here, or conversely, as seems possible in conformist societies like those in parts of Western Europe now and in the United States, that the acceptable number is low and effectively identical to what everyone else wants too [United Nations, 1976: 97–147; Whelpton, Campbell and Patterson, 1966: 32–124; Ryder, 1973b]. Unfortunately, even some of the recent and deliberate research on the 'value of children' in different societies seems to have presumed discretion and so to have presumed the existence of these two conditions [Bulatao and Arnold, 1977: 149 and passim]. But there is reason to believe that one should not [Ryder, 1973a]. Theoretically, as Crook suggests in his paper here, one can imagine a small and homogeneous population of potential parents in which the idea of a counterfactual fertility simply does not exist. The possibility will not have been aired, and although a questionnaire from outside may suggest it and elicit what may seem to be an intelligible and discretionary response, there might be good grounds for doubting whether such a response is valid. It does not of course follow that all such communities will not in fact have a counterfactual idea. One well-known one, for example, the Hutteries, must do if its members regard contraception as a sin [Bennett, 1967: 127]. But among the Hutterian brethren as much as among the members of the wider society in which they live the counterfactual may have receded so far as to make it true for all practical purposes, that is to say, at the moments of marriage and sex, that no alternative is considered. This, though, is speculation. What is not is the evidence provided here by Ukaegbu, Fortes and Macfarlane. Ukaegbu implicitly (by showing that some of the standard indicators of 'modernisation' make virtually no difference to desired fertility) and Fortes quite explicitly suggest that high fertility actually constitutes a person in the societies they discuss. It constitutes his or her status in the lineage which (whether one regards it as a 'lineage' or with Macfarlane and after Fox [1967] as an 'ancestor-focused descent group') is more or less exhaustively constitutive of the society itself. Fortes' argument, his significant exception of the Gonja, and Macfarlane's comparison between pre-industrial England and other more stereotypically 'peasant' societies all suggest that it may only be in a bilateral system with cognatic descent that a person is able to regard him or herself as a discretionary individual and so able to choose his fertility and choose a low one without putting his status, in the broadest sense, at risk. And if one then puts this together with Goody's comparison of some of the crucial differences in these respects between 'Eurasia' and Sub-Saharan Africa [1976], which include the presence of economically constituted strata in the one and their absence in the other, then the structural conditions of discretion and thus of the applicability of the assumptions about discretion

INTRODUCTION

9

in the micro-economic theories begin to become clear: they are of a population related bilaterally, reckoning descent and status more generally from the individual, cognatically rather than agnatically, and living in a society of economically constituted strata between which, and thus between the members of which, there is by definition some economic competition. Conversely, the conditions of the non-applicability of the theories also become clear: they are of a population related unilineally, reckoning descent and status more generally from its ancestors, agnatically, for example, rather than cognatically, and living in a society without economically constituted strata and so without the kind of economic competition between individuals and households that exists elsewhere. England and the unequivocally unilineal areas of West Africa, however, are each exceptional. Most societies outside Western Europe, North America and white Australasia, with the exception of shrivelled and disappearing bands of hunter-gatherers, are both in some sense unilineal and stratified, and in these, as one can see for instance in some of the literature on Asia [Mandelbaum, 1974; Hull, 1975], there can be often very complicated and unpredictable tensions between the dictates of kin and the promptings of material interest. Moreover, these tensions and the unpredictability of their outcomes can be exacerbated in the course of economic change. For West Africa in general, for example, and for the Nigerian Yoruba in particular, Caldwell has argued [1976: 338–50] that what he regards as the sudden and dramatic change there at least among the elite from an 'extended' to a more 'nucleated' pattern of family life and the associated change to a lower fertility is a function of the reversal of the flow of wealth between the generations (now going from parents to children instead of from children to parents) which is itself a function of 'Westernisation', of the adoption of European and North American ideas of what constitutes a proper family life. Yet both a weakness in his own account and evidence from elsewhere [especially Oppong, 1974, 1977, 1978, forthcoming] suggests that it might not be so straightforward. From his own account, in which he admits that the flow of goods from children to parents is actually greatest amongst the more prosperous and secure, it is difficult to see how a mere change of fashion could induce prosperous prospective parents to renounce the possibility of future benefits. Oppong's work on the Akan in southern Ghana (a group which was, and outside the elite largely still is, matrilineal) shows that a similar change in the elite there is caused by men wishing to assert some control over their wives, and although the reckoning of descent and its economic consequences have not in the past been so clear among the Yoruba (Caldwell does not mention them at all), there seems to have been sufficient ambiguity between cognatic and agnatic tendencies [Krapf-Askari, 1969: 63–79] to make it reasonable to believe that it would also be in the interest of Yoruba men to try to do the same. 'Westernisation' may have stimulated them to do so. It seems unlikely, both intuitively and from the evidence, to have been the sole or even the most important cause. Moreover, the kind of transformation in African societies that Caldwell is talking about, in so far as it is associated with economic change or 'modernisation' rather than with

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'Westernisation', is a transformation that might be expected to bring with it (and is perhaps already bringing with it 1 ) a new economic stratification which may erode some of the established ties of kinship and at its lower levels at least create the alternative conditions of high fertility so clear in many parts of Asia. Nevertheless, although such changes might be complicated in their nature and in their outcomes, they might be thought to lead eventually to states of affairs more consistent with the premises of the neo-classical models. But this does not follow. First, at the one extreme, in exactly the way in which both the move from cottage to factory production in England between the end of the eighteenth century and the middle of the nineteenth and the rise in their numbers (so affecting the overall supply of labour) may have increased the dependence and seclusion of women and, by a no doubt complicated route, brought them to the state so familiar from accounts of life on housing estates in the first two decades after the Second World War, so the move towards nucleation (whether or not it is part of that struggle for gentility that Caldwell describes as 'Westernisation'), in West Africa and elsewhere, like Sri Lanka in Tilakaratne's account below, might also actually increase the dependence and seclusion and so the discretion of women in such places now. And if this is true, then it does not perhaps make sense, for situations like this, to abandon the household for the individual as the unit for micro-economic analysis [compare IUSSP, 1977: 6] or even to assume except in the most hypothetical sense that wives are suffering an 'opportunity cost' in staying at home. Second, at the other extreme, although the dictates of a strictly economic rationality in households towards the bottom of a society which is becoming increasingly unequal in its allocation both of the factors of production and of income may become more apparent and more apparently unequivocal [Sen, 1966], it does not follow that the importance of other kin will disappear. On the contrary, as patterns of so-called 'involution' in Asia suggest [Geertz, 1963; Hull, 1975, 1977; White, 1976], the increasing economic importance of kin may actually serve to reinforce connections and social pressures of a noneconomic kind. Considerations such as these suggest that in specifying the conditions under which the standard neo-classical assumptions might reasonably be thought to hold one has at least to remember that 'exchanges' may differ in different societies [Ben-Porath, 1977] to an extent to which many of them may not be able to be accommodated in even the most ambitious of models [Stigler and Becker, 1977]. At most, one has to consider the possibility of there being no effective discretion over exchange at all, and this propels one towards the second and less reassuring of the two models. But before that, there is the question of the conditional nature of the conventional macroeconomic models. It is important to make an elementary distinction between these. On the one hand there are those like Coale and Hoover's which assume one or more functions (Coale and Hoover themselves took a Harrod-Domar function for their short-term analysis and a Cobb-Douglas function for their longer-term one) and from these calculate the different determinate outcomes consequent upon estimating different values and rates of change in the relevant parameters. On the other hand there are

11

INTRODUCTION

those like Dyson, Bell and Cassen's below, which assume more modest and less far-reaching functions (in this case of inverse relations between income and both mortality and fertility and of a reciprocal effect of declining fertility on the rate of increase of GNP) which are in part a priori and in part, with reservations, based upon existing cross-sectional analyses of the kind presented here by Repetto. The difference is one of degree, of course, not of kind, but it is an important one. In the case of the first, the assumptions are theoretically more complete and so less directly connected to observable trends. In the case of the second, the assumptions are theoretically less complete and more directly connected to observable (if somewhat imperfectly observed) trends. Each is confessedly unrealistic, but the second less so. And despite the objections that can fairly be made even to the simpler and less ambitious models [Arthur and McNicoll, 1975; McNicoll, 1976], it is still interesting to ask what would follow from attempts to engineer changes in any one or more of the plausibly relevant and accessible factors. What does seem clear, though, from past work is that there is less to be gained from elaborating stronger and less realistic models of the kind developed by Coale and Hoover (however useful these may initially be to concentrate the mind) and more to be gained from elaborating weaker but more realistic ones incorporating information which, even if imperfect, does at least have the merit of being a probable approximation to the facts, whatever the inconsistency between these facts and the assumptions and expectations of more abstract and general theories. III If neither the stronger assumptions of the macro-economic theories of growth nor the crucial assumptions of the micro-economic models of decision are often or even ever true in poorer societies (and it is worth recalling that there is as yet no good evidence anywhere for the truth of all the assumptions of any of the micro-economic theories of fertility), the case for drawing the second and more drastic of the two morals, the moral that one should reconsider the entire analytical basis of inquiries into fertility in poor societies, might seem to be overwhelming. The difficulty is in seeing where, theoretically, one could start instead. This is once again more evident at the micro level. Despite the fact that there are both intuitive and evidential grounds for doubting whether people ever do behave as the utilitarians imagine they do, and despite the fact that there would be good grounds for regarding them as incoherent if they did [Williams, 1976: 96–112], it is at first sight difficult to see how else one could characterise actions of people who do appear to have (and believe themselves to have) discretion. Even Mamdani, for example, and in this collection Jairath, each of whom gives a marxisant account of the economic and social relations in an Indian Village, explain the villagers' fertility in broadly utilitarian terms. But it does not follow from this that there is anything much theoretically to be gained from pursuing an economic theory of fertility. The promise of such a theory is generality, precision and predictability; the price, realism. The price would be tolerable if the

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promise were promising. But it is not. There is an important theoretical distinction between the weak and uncontentious claim that people may make choices between various ends and various means to those ends in the light of various constraints and the strong and far from uncontentious claim that these ends and means are comparable, calculable and additive. The second, if true, would permit prediction and so control. It would permit a proper theory. The first, on the other hand, could permit only post hoc description or at most some tentative guesses. Unlike the second, it has no distinctively theoretical power at all. This is not to say that economic ends and means and constraints do not play any part at all in decisions about fertility. As they are conventionally conceived and measured, they may certainly explain much less than was previously thought: the historic decline in fertility in Europe, for example, does not seem to have been consistently or in many places even intelligibly related to changes in economic preferences and resources [Coale, 1973; Livi-Bacci, 1971; Knodel, 1974; Sweezy, 1976]. They may certainly not be able to explain the finer variations between fertilities at any one point in time: income differentials in Europe, for example, are still considerable, but differences in fertility now are not [OPCS, 1974: 45–6; United Nations, 1976: 51–2]. Nevertheless, there is a range of circumstantial evidence, on income distribution and fertility of the aggregative cross-sectional crossnational kind, presented here by Repetto, and on the economic contributions (in the broadest sense) made by children, presented here by Das Gupta, Jairath and Tilakaratne and also elsewhere [Cain, 1977; Hull, 1975; Nag, Peet and White, 1977; White, 1976], which suggests that prospective parents or other sets of individuals [Caldwell, 1976; Hull, 1977] may take economic factors into account in deciding (if they are actually deciding) to have more children or less. But the argument does imply that in addition to the empirical qualifications that have to be made to the pure models of household decision-making (III above), there are strictly theoretical grounds for doubting the strictly theoretical plausibility and use of these models. If they are possibly incoherent, possibly false and unable to predict, it is not at all clear what they add to post hoc accounts of the kind provided here, for example, by Repetto, Das Gupta, Jairath, Tilakaratne and Macfarlane. They are at best 'an appropriate way to face the question' [IUSSP, 19777: 5; compare Demeny, 1972]. They cannot answer it. The same is true at the macro level. The practical impotence as much as the theoretical emptiness of the neo-classical models of economic development has led the international agencies to emphasise the importance of re-distribution and the satisfaction of 'basic needs' rather than growth, but there are intellectual as well as political and institutional reasons for these agencies not having proposed an alternative theory. McNicoll here suggests that the kind of association shown below by Repetto between greater equality of income and lower fertility might be extended to connect with 'dependency theory' and so implicitly with other non-neoclassical models of 'development' and 'under-development'. It is a refreshing suggestion, but not an altogether promising one if it is a distinctively theoretical advance that he has in mind. There is an important theoretical distinction, rather different from that which one has to draw in

INTRODUCTION

13

trying to explain the actions of individuals and households, between the weak and by now surely uncontentious claim that standard neo-classical theories (for instance [Hicks, 1969]) do not explain growth or its absence [Kuznets, 1966, 1974] and the strong and highly contentious claim that others, Marxist, 'neo-Marxist' or 'Keynesian', do [Brown, 1974]. To 'explain' in the theoretical sense is to be able to predict conditionally specific, approximately true and non-tautologous outcomes from a clear and preferably limited set of premises. One weakness of the Marxist view in any of its forms, in Marx himself or Lenin or Luxemburg or the more recent 'neo-Marxist' accounts of 'dependent development' [Lenin, 1933; Luxemburg, 1951; Cardoso, 1972], is that apart from the difficulty it has in satisfactorily characterising and predicting the operations of a fullydeveloped capitalism, it offers no clear and consistent account of the nature and degree of 'correspondence' or 'contradiction' between capitalist and pre-capitalist 'modes of production'. The confusion is clear from a comparison of just two recent and influential papers [Laclau, 1971; Alavi, 1975], and the difficulty is compounded by several other accounts which suggest that the extensions of capitalist 'relations of production' can at least in the medium term actually reinforce relations (most evidently relations of patronage) of a pre-capitalist kind [Allum, 1973; Carter, 1974; Gellner and Waterbury, 1977]. Since these two modes or sets of relations coexist almost everwhere in the Third World (and as Allum's account of Naples and some of the essays collected by Gellner and Waterbury reveal, in parts of the First World too), the weakness is a crucial one. A second, related to this, is that no marxism has a clear and consistent account of the State. There is a presumption in Marx himself (a presumption ironically similar to some liberal theories of the State as broker) that whereas the State in capitalist societies is constituted by the relations of production and passive, in pre-capitalist societies it itself (where it existed at all) constituted those relations and was active. The first is now energetically if somewhat ambiguously challenged by many European Marxists [Offe, 1975; Habermas, 1976; Poulantzas, 1976]. The second, however, which is also clearly true of many if not most States in the Third World now (for some interesting and illuminating, if also rather extreme, examples see the papers in [Dunn, 1978]), presents any putative theorist of 'development' or 'underdevelopment' with serious difficulties, for the 'development' or 'underdevelopment' that is taking place in these societies (as other parts of the theory insist) is largely of a capitalist kind, and in some cases, for example India, cannot simply be explained as an extension of western capitalism. This is the merest sketch of the nature and possibilities of an alternative to the standard neo-classical accounts of the course and causes of economic and social 'development'. But even were it to be extended, the conclusion would be the same. Marxist and neo-Marxist theories of development and under-development are doubly deficient: they do not produce a satisfactory theory of the more strictly economic relations, and (which is their more distinctive ambition) they do not produce a satisfactory theory of more broadly social and political (or 'political economic') relations either. Much the same, in a strictly theoretical sense, can be said of 'Keynesian' models too. The implication of this is that just as there are no strictly theoretical

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alternatives to the neo-classical models of decision so there are no strictly theoretical alternatives, certainly no valid theoretical alternatives, to the neo-classical models of growth. There is instead a set of often very promising suggestions (taken up in an interesting and undogmatic way by Goody [1976] and here by Jairath and Macfarlane) about how to begin to look at what McNicoll calls the 'structural' concomitants of economic change. At both the macro level and the micro, therefore, the second and more drastic of the two morals is largely empty. The assumptions of these models are certainly in doubt, and the inferences one can make from them either vacuous or false. But there is no readily available alternative of a properly theoretical kind.

IV This discussion has so far supposed that a distinctively theoretical understanding of the conditions, correlates, causes and consequences of different rates of fertility in poor societies is indeed what is needed. But quite apart from the fact that if 'ought' implies 'can' and if we cannot then perhaps we ought not to continue to pursue this aim, there is at least one other powerful reason for doubting whether it is a sensible one. It is practical. We have explained that the impetus for general and precise theories in this field has come from the international agencies and from the economists from whom these agencies have sought advice. The agencies have a global perspective, and the economists are appropriately disposed to look for general, that is to say cross-national, truths. The point has been to devise a general solution to a global problem. We may reasonably accept with Cassen [1976: 821] and now even the government of the People's Republic of China, the most populous but for so long one of the most resistant of poor societies, that 'there seem to be few convincing arguments disturbing the conclusion that rapid population growth slows down the improvement of average living standards' and so reasonably accept that high rates of population growth are indeed a 'global' problem. But it does not follow that this problem can have any general solution. The balance between fertility and infant mortality is very different in different poor societies. The other conditions of high fertility are equally different. And the political, administrative and more generally social facilities for reducing it are more different still. Practically, it makes little sense to try to devise a general solution. This is coming to be agreed [Zeidenstein, 1977]. McNicoll, for example, has elsewhere argued [1975] that for these three reasons, and also because even within one country the distance between views of the public good and views of private goods and between the formulators of a policy and its intended beneficiaries can be so great, it makes sense to consider policies at what he calls a 'community level. There, the rational connection between two possibly conflicting views of the good and the social connection between leaders and led are both more likely to be made. He makes his point from the successful examples of Japan and China. It can also be made from a host of less successful ones. Arguably the best detailed account of

INTRODUCTION

15

the workings of a less successful family planning programme is Blaikie's [1975] study of northern Bihar. Blaikie reaffirms in some detail many of the already known differences in India, between higher castes and low, between literates and illiterates, between men and women, and so forth, and sheds some light on others, for example, on the differential success of the provision of intra-uterine devices and vasectomies. But he also shows that the main reason for the failure of the programme in the area lay in its actual organisation. Posts remained unfilled, where they did not co-ordination between their occupants was poor, resources and people were often in different places and neither were very conspicuous in the more remote villages with larger proportions of low-caste people and high fertilities. If one puts this picture together with accounts of local social relations elsewhere in India (two of the most illuminating are Breman's [1974] study of southern Gujarat and Carter's [1974] study of western Maharastra), the real difficulties become clear. From Blaikie it is plain that it is not money that is at stake. The programme in Bihar was reasonably well financed. It is organisation and the indifference and among some even the contempt for the poor. From Breman it is plain that these attitudes are more deeply rooted and are actually being exacerbated in what passes for 'development'. And from Carter it is plain that at least one reason for this is that the local 'political class', as Carter calls it, which consists of the largest landlords, has an interest in maintaining as extensive a class as possible of poor and dependent clients from whom to extract appropriate votes in the elections. One has of course to be careful in building a case upon three different studies from three different areas, but if one accepts that together they describe at least the common outlines of the situation in many parts of rural India, then it becomes clear that what may have been possible in Japan or China is not now possible at all in many areas there.2 A similar case could no doubt be made for parts of Africa and Latin America. But this is not to say that McNicoll's case collapses. On the contrary, evidence such as this suggests that it is absolutely crucial to pay attention to more local social facts. The failures as much as the successes suggest what the conditions of success might be. Consider again the cases of China, Taiwan and India. Each began the present period of its political history at the end of the 1940s with high rates of infant mortality and fertility and a large and relatively impoverished and at its lower levels relatively exploited proportion of rural households living in conditions of considerable insecurity and in kinship systems which constrained women and put a high premium on reproduction. Now, however, it seems clear (however obscure the details may be) that in China and Taiwan infant mortality and fertility have fallen as far and as fast as they have in any large population outside Japan whereas in India, although there has been a slight decline, they have not. Why? The first and most obvious difference between China, Taiwan and India is that two are inhabited by Chinese and one is not. What effects this may have had, independently of any associated differences of social organisation, are not known. A second difference is that unlike the two Chinas India has been crippled by trying to take democracy seriously. This has led the Congress Party (and is now leading the Janata coalition) to accede to the interests of

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the dominant groups in the countryside, to allow them low taxes and effective freedom from real agrarian reform in return for delivering the votes. A third difference, related to this, is that whereas for different reasons and in different ways the two Chinas (Taiwan, by putting a low and effective ceiling on landholding, increasing the proportion of owners and successfully encouraging a large amount of non-agricultural activity in rural areas, the People's Republic by eventually abolishing private landholding and guaranteeing a decent subsistence in a consequently collectivised agriculture) have each created a relatively secure present and a relatively predictable future for a majority of rural people, India has not. One consequence of this has been to make poor rural Indians more persistently dependent upon the economic and social security provided by kin. A fourth difference is that China and to a lesser extent Taiwan have brought more unmarried and married women into gainful employment outside the home. A fifth difference is that in China and to a lesser extent Taiwan, family planning and the means of improving child health have been made readily available to people in rural areas and connected to the supply of other services, whereas in India, they have not. The consequences are clear. One of the causes, again related to the political differences between the countries, differences that cut across the conventional distinction between 'capitalism' and 'socialism', is the greater freedom that has been allowed to the medical profession in India. These comparisons, although they are of course at the most general level (the local complexities are best described for India) and even then only a sketch, nevertheless make McNicoll's point. Attempts to reduce fertility have to connect to local interests, and whether they do so or not is directly, if in a complicated way, related to 'structural' factors in a nation as a whole. Whether these 'structural' factors can be changed, and if so how, is obviously as difficult to decide as it is important. Disregarding the vertiginous improbability of attempting (let alone wanting and actually managing) to replicate the People's Republic anywhere else, and concentrating instead upon the politically more sensible prospect of attempting to replicate, say, some of the features of Taiwan in India, the arguments are perhaps necessarily inconclusive. Ranis, for example [1977], suggests that redistribution after growth cannot be expected to achieve the kinds of result that a balanced and in part deliberately equitable growth from the beginning has achieved in Taiwan. 3 Lipton [7977], in a diagnosis based largely upon the recent economic history of India, and consistent in its insistence upon the deleterious consequences of what he calls 'urban bias' with much of Ranis' summary of the differences between Taiwan on the one hand and Colombia and the Philippines on the other, suggests that a re-direction of investment towards agriculture could still in India redress both more evidently economic and less evidently social blockages and inequities. But Lipton largely ignores the political reasons for the imbalance he describes and so fails to ask what seems to be a crucial and perhaps indeed primary question, of the prospects of the appropriate political will in the country. The Janata coalition's recent declaration of intent, to try to move towards a greater self-sufficiency in each small rural area, gives little hope, since it too for the same comprehensible and

17

INTRODUCTION

depressing reasons fails to address itself to the more strictly social inertia in the countryside. If Taiwan does represent one set of conditions for success, however, what might be held to follow for the analysis (as distinct from the engineering) of the conditions, correlates, causes and consequences of different rates of fertility in poor societies? First and more obviously, it seems more illuminating to concentrate on relatively particular political, economic and social histories than on trying to refine more abstract and general (and spuriously precise) theoretical models. In the terms with which we began this introduction, it seems more illuminating to concentrate on realism than on generality or precision. But second, and almost as obviously, it does not follow that these histories should be strictly particular. Quite apart from the simple but nevertheless important point that it is not in principle possible even to begin such a history without having some real or imaginary counterfactuals in mind, it is clear from the comparison here between China, Taiwan and India and even clearer from other more systematic work, like Goody's exciting contrast of the conditions of 'production and reproduction' in 'Eurasia' and Sub-Saharan Africa, that although theories in any very strong sense of the term may not be able to explain very much even quite ambitious empirical generalisations may. In exactly the way in which Goody, making a comparison at the most general level between societies in which it is land that is scarce and societies in which it is people who are, suggests a fairly fundamental explanation of different patterns of kinship and stratification and even fertility and the status of women (although fertility itself was not his first concern), so others, making geographically and socially less extensive comparisons, might be expected to provide good explanations as well. 4 Realism does require some generality. V But it is pointless as well as empty to recommend one orthodoxy over another. The best social science, like the best history (and perhaps the best population biology too), proceeds on a variety of methods. Together, the contributors to this collection show what can be gained by qualifying, extending and even ignoring some of the narrow if elegant conceptions by which this particular field has recently been constrained.

NOTES 1. This observation depends upon so far unpublished research in northern Ghana by Andrew Shepherd. 2. An exception, as Dyson, Bell and Cassen mention below, is Kerala. An indication of why this might be so is given by Gulati [1976]. 3. For two other reasonably representative accounts of the Taiwanese experience, one consistent with that of Ranis and more plausible, the other more straightforwardly neoclassical and less plausible, see respectively Griffin [1976] and Mueller [1977]. 4. It is more than incidentally interesting to compare Goody's more general account of 'Eurasia' with Macfarlane's insistence on the uniqueness or at least the eccentricity of England.

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REFERENCES Agarwal, Bina, 1977, Mechanisation in Farm Operations: Choices and their Implications, Ph.D thesis, University of Delhi. Alavi, Hamza, 1975, 'India and the Colonial Mode of Production', Ralph Miliband and John Saville, eds., The Socialist Register 1975, London: Merlin. Allum, Percy, 1973, Politics and Society in Post-War Naples, Cambridge: Cambridge University Press. Arthur, W. Brian and Geoffrey McNicoll, 1975, 'Large-scale Simulation Models in Population and Development: What Use to Planners?', Population and Development Review, Vol. 1, no. 2. Birdsall, Nancy, 1977, 'Analytical Approaches to the Relationship of Population and Development', Population and Development Review, Vol. 3, nos. 1/2. Ben-Porath, Yoram, 1977, 'The Economic Value and Costs of Children in Different Economic and Social Settings', International Union for the Scientific Study of Population, International Population Conference 1977, Vol. 4, Liege: IUSSP. Bennett, John W., 1967, Hutterian Brethren: the Agricultural Economy and Social Organisation of a Communal People, Stanford: Stanford University Press. Blaikie, Piers M., 1975, Family Planning in India: Diffusion and Policy, London: Edward Arnold. Bourgeois-Pichat, J., 1965, 'Les facteurs de la fécondité non-dirigée', Population, Vol. 20, no. 2.

Breman, Jan, 1974, Patronage and Exploitation: Changing Agrarian Relations in South Gujarat, India, Berkeley and Los Angeles: University of California Press. Brown, Michael Barratt, 1974, The Economics of Imperialism, London: Penguin. Bulatao, Rodolfo A. and Fred Arnold, 1977, 'Relationships Between the Value and Cost of Children and Fertility: Cross-cultural Evidence', International Union for the Scientific Study of Population, International Population Conference 1977, Vol. 1, Liège: IUSSP. Cain, Mead T., 1977, 'The Economic Activities of Children in a Village in Bangladesh', Population and Development Review, Vol. 3, no. 3. Caldwell, John C., 1976, 'Toward a Restatement of Demographic Transition Theory', Population and Development Review, Vol. 2, nos. 3/4. Cardoso, Fernando Henrique, 1972, 'Dependency and Development in Latin America', New Left Review, no. 74. Carter, Anthony, 1974, Elite Politics in Rural India: Political Stratification and Alliances in Western Maharastra, Cambridge: Cambridge University Press. Cassen, Robert H., 1976, 'Population and Development: a Survey', World Development, Vol. 4, nos. 10/11. Cassen, Robert H., forthcoming, India: Population, Society, Economy, London: Macmillan. Coale, Ansley J., 1973, 'The Demographic Transition', International Union for the Scientific Study of Population, International Population Conference 1973, Vol. 1, Liège: IUSSP. Coale, A. J. and E. M. Hoover, 1958, Population Growth and Economic Development in Low Income Countries, Princeton: Princeton University Press. David, A. S. and A. A. Armar, 1978, 'Foreign aid in population programmes: the Ghanaian experience', paper given at Population Dynamics Programme seminar, University of Ghana, Legon, January 1978. Davis, Kingsley and Judith Blake, 1956, 'Social Structure and Fertility: an Analytic Framework', Economic Development and Cultural Change, Vol. 4, no. 3. Demeny, Paul, 1972, 'Economic Approaches to the Value of Children: an Overview', James T. Fawcett, ed., The Satisfactions and Costs of Children, Honolulu: East-West Center, mimeo. Demeny, Paul, 1977, 'Population Policy and the International Donor Community: a Perspective on the Next Decade', Population and Development Review, Vol. 3, nos. 1/2. Duesenberry, James S., 1960, 'Comment', Universities-National Bureau Committee for Economic Research, Demographic and Economic Change in Developed Countries, Princeton: Princeton University Press. Dunn, John, ed., 1978, West African States: Failure and Promise, Cambridge: Cambridge University Press. Easterlin, Richard A., 1975, 'An Economic Framework for Fertility Analysis', Studies in Family Planning, 6, 54–63.

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Epstein, T. Scarlett and Darrell Jackson, eds., 1977, The Feasibility of Fertility Planning: Micro-perspectives, Oxford: Pergamon. Eversley, D. E. C., 1959, Social Theories of Fertility and the Malthusian Debate, Oxford: Clarendon Press. Fox, Robin, 1967, Kinship and Marriage, London: Penguin. Geertz, Clifford, 1963, Agricultural Involution: the Process of Ecological Change in Indonesia, Berkeley and Los Angeles: University of California Press. Gellner, Ernest and Waterbury, John, eds., 1977, Patrons and Clients in Mediterranean Societies, London: Duckworth. Goody, Jack, 1976, Production and Reproduction, Cambridge: Cambridge University Press. Griffin, Keith, 1976, 'An Assessment of Development in Taiwan', Land Concentration and Rural Poverty, London: Macmillan. Gulati, Leela, 1976, 'Age at Marriage of Women and Population Growth: the Kerala Experience', Economic and Political Weekly, Annual Number, August. Habermas, Jürgen, 1976, Legitimation Crisis, London: Heinemann. Hicks, John, 1969, A Theory of Economic History, London: Oxford University Press. Hull, Terence H., 1975, Each Child Brings its own Fortune: and Inquiry into the Value of Children in a Javanese Village, Ph.D thesis, Australian National University. Hull, Terence H., 1977, 'Units of analysis in the study of fertility decision-making (with examples drawn from research in Java)' International Union for the Scientific Study of Population, op. cit, below. International Union for the Scientific Study of Population, 1977, Household Models of Economic-Demographic Decision-Making, Liège: IUSSP. Keyfitz, Nathan, 1972, 'Population Theory and Doctrine: a Historical Survey', William Petersen, ed., Reader in Population, New York: Macmillan. Knodel, John E., 1974 The Decline of Fertility in Germany 1871–1939, Princeton: Princeton University Press. Krapf-Askari, Eva, 1969, Yoruba Towns and Cities, Oxford: Clarendon Press. Kuznets, Simon, 1966, Modern Economic Growth: Rate, Structure and Spread, New Haven: Yale University Press. Kuznets, Simon, 1974, Population, Capital and Growth: Selected Essays, London: Heinemann. Laclau, Ernesto, 1971, 'Feudalism and Capitalism in Latin America', New Left Review, no. 67. Leibenstein, Harvey, 1971, 'The Impact of Population Growth on Economic Welfare: Nontraditional Elements', National Academy of Sciences, Rapid Population Growth: consequences and policy implications, Baltimore and London: Johns Hopkins University Press. Leibenstein, Harvey, 1974, 'An Interpretation of the Economic Theory of Fertility: Promising Path or Blind Alley?', Journal of Economic Literature, Vol. 12, no. 2. Leibenstein, Harvey, 1976, Beyond Economic Man, Cambridge, Mass.: Harvard University Press. Leibenstein, Harvey, 1977, 'The Economic Theory of Fertility: Survey, Issues and Considerations', International Union for the Scientific Study of Population, International Population Conference 1977, Vol. 4, Liege: IUSSP. Lenin, V. I., 1933, Imperialism: the Highest Stage of Capitalism, London: Little Lenin Library. Levins, Richard, 1968, Evolution in Changing Environments, Princeton: Princeton University Press. Lipton, Michael, 1977, Why Poor People Stay Poor: Urban Bias in World Development, London: Temple Smith. Livi-Bacci, Massimo, 1971, A Century of Portuguese Fertility, Princeton: Princeton University Press. Luxemburg, Rosa, 1951, The Accumulation of Capital, London: Routledge and Kegan Paul. Macfarlane, Alan, 1976, Resources and Population: a Study of the Gurungs of Nepal, Cambridge: Cambridge University Press. McGreevy, William P. and Nancy Birdsall, 1974, The Policy Relevance of Recent Social Science Research on Fertility, Washington, D.C.: Smithsonian Institution Interdisciplinary Communications Program. McNamara, Robert, 1977, 'Possible Interventions to Reduce Fertility', Population and Development Review, Vol. 3, nos. 1/2.

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McNicoll, Geoffrey, 1975, 'Community-level Population Policy: an Exploration', Population and Development Review, Vol. 1, no. 1. McNicoll, Geoffrey, 1976, 'Economic-demographic Models', Leon Tabah, ed., Population Growth and Economic Development in the Third World, Dolhain: IUSSP and Ordina Editions. Mamdani, Mahmood, 1972, The Myth of Population Control: Family, Caste and Class in an Indian Village, New York and London: Monthly Review Press. Mandelbaum, David G., 1974, Human Fertility in India, Berkeley, Los Angeles: University of California Press. Mueller, Eva, 1976, 'The Economic Value of Children in Peasant Agriculture', Ronald G. Ridker, ed., Population and Development: the Search for Selective Interventions, Baltimore and London: Johns Hopkins University Press. Mueller, Eva, 1977, 'The Impact of Demographic Factors on Economic Development in Taiwan', Population and Development Review, Vol. 3, nos. 1/2. Nag, Moni, Robert Creighton Peet, and Benjamin White, 1977, 'Economic Value of Children in Two Peasant Societies', International Union for the Scientific Study of Population, International Population Conference 1977, Vol. 1, Liège: IUSSP. North, Douglas C. and R. P. Thomas, 1973, The Rise of the Western World: a New Economic History, Cambridge: Cambridge University Press. Offe, Claus, 1975, 'The Theory of the Capitalist State and the Problem of Policy Formation', 'Introduction to Part III', Leon N. Lindberg, et al., eds., Stress and Contradiction in Modern Capitalism: Public Policy and the Theory of the State, Lexington, Mass.: D. C. Heath. Office of Population Censuses and Surveys, 1974, The Registrar General's Quarterly Return for England and Wales, Quarter ended 30 June 1974, London: HMSO. Oppong, Christine, 1974, Marriage among a Matrilineal Elite: a Family Study of Ghanaian Senior Civil Servants, Cambridge: Cambridge University Press. Oppong, Christine, 1977, 'Changing family structure and conjugal love: the case of the Akan of Ghana', paper given at the International Conference on Love and Attraction, University of Wales, Swansea, September 1977. Oppong, Christine, 1978, 'The Crumbling of High Fertility Supports', John C. Caldwell, ed., The Persistence of High Fertility: Population Prospects in the Third World, Canberra: Australian National University Press. Oppong, Christine, forthcoming, Changing Sex Roles and Family Relations in Contemporary Ghana. Poulantzas, Nicos, 1976, L'Etat, le pouvoir et le socialisme, Paris: Presses Universitaires de France. Ranis, Gustav, 1977, 'Equity with growth in Taiwan: how "special" is the "special case"?', paper given at Workshop on Analysis of Distributional Issues in Development Planning, Bellagio, April 1977. Ridker, Ronald G., 1976, Population and Development: the Search for Selective Interventions, Baltimore and London: Johns Hopkins University Press. Ryder, Norman B., 1973a, 'Comment', Journal of Political Economy, Vol. 81, no. 2, Part II. Ryder, Norman B., 1973b, 'The Future Growth of the American Population', C. F. Westoff et al., Toward the End of Growth: Population in America, Englewood Cliffs, N.J.: PrenticeHall. Sen, A. K., 1966, 'Peasants and Dualism with or without Surplus Labor', Journal of Political Economy, Vol. 74, no. 5. Stigler, G. J. and G. S. Becker, 1977, 'De Gustibus non est Disputandum', American Economic Review, Vol. 67, no. 1. Sweezy, Alan, 1976, 'Economic Development and Fertility Change', Smithsonian Institution Interdisciplinary Communications Program Occasional Monograph Series no. 4, New Perspectives on the Demographic Transition, Washington, D.C.: Smithsonian Institution ICP. Tabbarah, Riad B., 1971, 'Toward a Theory of Demographic Development', Economic Development and Cultural Change, Vol. 17, no. 3. United Nations, 1976 Fertility and Family Planning in Europe around 1970: a Comparative Study of Twelve National Surveys, ST/ESA/SER. A/58, New York: United Nations. Weber, Max, 1927, General Economic History, London: Allen and Unwin. Whelpton, Pascal K., Arthur A. Campbell, and John E. Patterson, 1966, Fertility and Family Planning in the United States, Princeton: Princeton University Press.

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White, Benjamin, 1976, Production and Reproduction in a Javanese Village, Ph.D. thesis, Columbia University. Williams, Bernard, 1976, Morality, Cambridge: Cambridge University Press. Zeidenstein, George, 1977, 'Strategic Issues in Population', Population and Development Review, Vol. 3, no. 3.

The Interaction of Fertility and the Size Distribution of Income by Robert I

Repetto*

THE EFFECT OF INCOME DISTRIBUTION ON FERTILITY

The idea that the onset of a broad fertility decline is associated with the exposure of a substantial fraction of the population to modern systems of social, economic and value organisation has extensive antecedents in the 'threshold hypothesis' popular in the 1950s and 1960s [United Nations, 1963]. From this idea several authors have drawn the implication that development strategies which promote the participation of the largest possible fraction of the people in the process of modernisation will best accomplish the goal of rapid reduction in the birth rate [Rich, 1973; Kocher, 1974]. William Rich has succintly stated this idea: The shift in attitude toward reduced births is, rather, a function of a combination of environmental changes that affect the orientation of families enough to alter fertility decisions. In a developing country, this appears to occur when families begin to participate significantly in the modern social, political, and economic systems. Thus nations in which only a small elite constitutes the modern sector while the majority of the population continues to live at the subsistence level and to maintain its traditional way of life are not likely to experience reduced national fertility as readily as those countries which bring about mass participation in the development process [Rich, 1973: 9]. From this point it is not far to the hypothesis that the distribution of income within a country will be closely related to its fertility rate. The missing step is only the recognition that to hold modern attitudes and values it is necessary that people participate in modern systems, and that if people do so participate they will acquire the corresponding attitudes [Inkeles and Smith, 1974]. Participation in modern life, or even sustained aspirations to do so, requires that people have the means to partake of a variety of consumption activities, to invest in themselves and their children, to be concerned with more than day-by-day subsistence, to come in contact with a broader range of experience. And, conversely, rising living standards draw people into contact with a wider range of modern systems from which at deeper levels of poverty they had been effectively excluded. In other words, over certain ranges at least, rising income levels might be both necessary and sufficient for the process of modernisation underlying fertility declines. At least, changes in income might play a key role in

*Associate Professor of Population and Economics, Department of Population Sciences, Harvard School of Public Health, Boston, Massachusetts.

FERTILITY A N D DISTRIBUTION OF INCOME

23

facilitating and stimulating the process of modernisation. This notion is fully consistent with the idea that among households within the modern sector, those already extensively participating in modern social and economic systems, changes in income might play a negligible or even reversed role in inducing fertility changes. It would indeed be surprising if the distribution of income within a population were irrelevant to its fertility rate. The literature on the aggregation of economic relationships implies that, to the extent that income affects fertility at all, the distribution of income among households will matter except in the very special and improbable case that the response of fertility to a change in household income is linear, with an identical slope coefficient at all levels of income. That is, the distribution of income will affect the level of fertility unless the response of a household's fertility to an increased $1,000 per year (say) in income is the same no matter whether the household's initial income be $500 per year, $5,000 per year, or $50,000 per year. However, the meaning of that additional income, in terms of increased opportunities and resultant changes in life patterns, should differ tremendously between poor, middle and rich households. There is no reason to expect that the impact on fertility decisions should not differ as well. Indeed, a large number of studies based on various kinds of data have noted that the relation between income and fertility is not linear. Typically, it assumes a 'U' or reversed 'J' shape. This implies that changes in income can have different effects on fertility depending on the initial incomes of the recipients. Summarising a large body of recent econometric research into the socio economic determinants of fertility, T. Paul Schultz has concluded in a recent report, 'Finally, the character of family size suggests that linear demand models are too restrictive for the study of fertility. Both theoretical and empirical evidence have been presented for non-linearity between explanatory variables and fertility,' [Schultz, 1974]. Empirical studies of the micro-economic demand for fertility [Willis, 1973], based on disaggregated household survey data, demonstrate the non-linearity in the income-fertility relation [Bernhardt, 1972; Simon, 1974; 62–65]. Time series study of changes in fertility rates and their relationships to social and economic change have also found the same non-linearity is not more widely recognised is that in most research, the relationships have been specified to be linear, a priori, and the exploration of departures from linearity has been ruled out. This has ruled out also the consideration of distributional issues such as those discussed in this paper. When authors have admitted possible non-linearities, they have generally found them [Sanderson and Willis, 1971; Ben-Porath, 1973]. The implication, which follows directly from the familiar theory of the aggregation of economic relations, is that the size distribution of income will affect the overall fertility rate. This goes considerably beyond the mere methodological problem of finding a suitable functional form for econometric estimation of a macro-economic fertility function. If households at different income levels respond differently in their fertility behaviour to changes in household income, then, in terms of the overall birth rate, it matters which households receive the increments to income. Since for most of the world

24

POPULATION A N D DEVELOPMENT

fertility reduction is an important objective, these facts have considerable policy significance. One plausible explanation of the origin of this non-linearity arises out of the interaction of supply and demand factors in the determination of fertility rates. Recent econometric work on fertility, such as that discussed above, presupposes that fertility is determined by parental demand, with the generation of children either completely controlled by parents or capable of representation by a random error term around the true demand uncorrelated with household demographic and socio-econometric status. This formulation has been forcefully criticised by demographers and sociologists. In his comments on the econometric theory, Ryder stated, 'I think the results of a generation of fertility research suggest that variations in parity [among U.S. women] are more likely to reflect variations in the efficacy of fertility regulation than variations in intention,' [Ryder, 1973]. Moreover, the use and efficiency of contraception have been shown to be systematically related to education, income, and similar socio-economic variables [Michael, 1973]. For low-income countries, there is also evidence that fecundity, the 'supply' side of fertility, may be substantially affected by nutritional and health status, both of which are strongly related to income levels [Frisch, 1974]. For developing countries especially, these supply forces are probably of considerable importance. The economist's demand model of fertility also really refers to the number of children alive in the family, a stock concept, rather than to the number of births during a period, a flow concept [Tobin, 1973]. A more complete formulation would relate the fertility of the household to the interplay between the number of children alive in the household, including perhaps their characteristics, and the number of children desired. The fertility rate would represent a means of adjustment of actual to desired family size; at least, a means of limiting the disparity between them. In such a stock adjustment model, the adjustment rate, depending both on fecundability and the efficiency of fertility control, would vary systematically with socio-economic status. So would desired family size and the survival rate of children out of prior births. That is, if Bit and Dit represent the number of births and infant or child deaths in a family in an interval of time t, while Nit* and Nit represent the numbers of children desired and alive at the start of the interval, the model of household fertility might be depicted as follows:

1.

Bit = s.f.(N i t *–N i t )

2.

N i t + 1 = N i t + B i t -D i t

3.

N i t *=N*(Y i t , Ait)

4.

D it =D(Y it )

5. sit=s(Yit, Ait)

f≥0

25

FERTILITY AND DISTRIBUTION OF INCOME

Here, the parameter represents the adjustment factor, Ait represents the age of the woman of the household in the tth period, and Yit represents household income per capita. The resultant representation of the fertility process can be summarised as 6.

B it =s it (Y it , Ait). f[N*(Yit, Ait) — N it (Y it )]

f≥0

In such a model, the interaction of supply factors affecting s and D, and the demand factors affecting N*, lead to the presumption of non-linearity in the response of fertility to income changes. Whether the determinants of family size, the efficiency of fertility control, and the infant death rate include other socio-economic characteristics of the household than income—education, labour force participation of the woman, residence, consumption aspirations, individual modernity, or others—is not central to the point. Most of these are also strongly influenced by household living standards, except perhaps in the very short run. What is important is the presumption of non-linearity. This implies that some measure of the variance of income among households will be a factor influencing the overall fertility rate in that population. Suppose, for example, that (6) can be approximated by a function which is quadratic in household income. 7.

B i t =aA i t + bYit + cY2it

Summing across the population of households and dividing by the total number, it is apparent that the population birth rate will depend both on the mean income per capita among households, and the population variance of household income per capita. 8.???B t =aĀ t+ b

t

+c

2 t

+ cS 2 (Y)

This explains the hypothesis of dependence on the overall fertility rate on the distribution of income within the population. II

THE EFFECT OF FERTILITY ON THE DISTRIBUTION OF INCOME

A number of mechanisms have also been pointed out through which the level of fertility might be expected to influence the distribution of income. Some of these have to do with the way in which income distribution is conventionally measured, as the variation in income among all households at a particular date. It has no close relationship to the difference among households in total lifetime income. As both Kuznets and Paglin have recently made clear [Kuznets, 1975; Paglin, 1975] changes in the age distribution of the population will affect the current distribution of income, due to the life -cycle of earnings, without necessarily affecting the distribution of lifetime incomes. Lower fertility and mortality rates, by elongating the age pyramid and increasing the variation in age among earners, would tend to increase the current inequality in the distribution of earned income. Other demographic effects on income distribution have been pointed out by Kuznets [1975]. The fact that larger households tend to

26

POPULATION A N D DEVELOPMENT

have larger incomes but smaller incomes per household member gives rise to an important distinction between the distribution of incomes among households and the distribution of household income among individuals. The identity of the poor tends to be masked within the distribution of household income by the inclusion of most of the poor in large household units with substantial total income. If the basis of the distribution is made the number of individuals rather than the number of household units, and household income is converted to a per capita basis, then the degree of inequality is comparatively higher. When household size is falling rapidly over time due to changes in the birth rate, this distinction is important in understanding changes in the measured distribution of household income, which fail to capture the effects of narrowing differentials in family size. Narrowing differentials in family size imply narrowing differentials in income per household member, the distribution of which would show a greater trend toward equality, or a lesser trend toward inequality, than the commonly calculated distribution of income among households. In addition to these measurement effects, fertility levels should be expected to influence the transmission of human and physical assets. For example, high-parity children, especially in closely spaced families, tend to receive less care and attention from parents and often less schooling as well [Wray, 1971; Leibenstein, 1971]. Consequently, they tend to achieve and earn less than low-parity children. Also, high fertility implies not only a large number of high-parity children, but usually also substantial fertility differentials within the population. Differential fertility can be expected to affect income distribution not only because it tends to widen differentials in human capital formation, but also because it tends to widen disparities in the accumulation of material wealth when, as is usually the case, the wealthier have fewer children than the poor [Pryor, 1973]. Thus, differences among households in the location of demand and supply curves for human capital tend, as a consequence of high fertility, to be positively correlated and the curves themselves more unequally distributed among households. Both effects increase inequalities in the distribution of income [Becker, 1967]. Fertility levels in the long run are likely to exert more macro-economic effects on distribution as well. High fertility and rapid population growth will probably be associated with lower wages relative to returns on human and physical capital. Since the ownership of capital is more concentrated by far than the ownership of labour services, higher relative returns to capital with relatively inelastic factor substitutability would tend to increase the concentration of income. Both macro- and micro-economic mechanisms suggest that higher fertility will lead to inequality in the income distribution. III

SPECIFICATION OF THE MODEL

A. The Fertility Equation In line with the preceding discussion, the current fertility rate is specified as dependent on both the level of average income per capita and the distribution of income. It is hypothesised that the more equally is income distributed, the lower will be average fertility. Since, in the typical country,

FERTILITY A N D DISTRIBUTION OF INCOME

27

the poorer sixty per cent of household receive only about one-quarter of total personal income, while accounting for something closer to threequarters of total fertility, such an hypothesis would seem plausible. Although there are strong theoretical and empirical reasons to believe that the 'pure' wealth effects of higher permanent income on fertility are positive, pure wealth effects are difficult to isolate, since higher income tends to be associated with higher aspirations, greater investments in human capital, a higher relative price of children, greater contraceptive efficiency, and a number of other relevant social and economic changes conducive to lower fertility. The preliminary research on this topic found a consistently negative association between average fertility and average income per capita when income distribution, mortality, and educational attainment were taken into account. The infant mortality rate is expected to be positively related to fertility through the stock adjustment mechanism: the higher is mortality, the lower will actual family size relative to that desired, and the higher should be current fertility. There is considerable evidence of this association [Schultz, 1974: 15–18]. However, since high fertility and closely spaced births also increase infant mortality, the relative strengths of the causal mechanisms are still unsettled, along with the speed and magnitude of the fertility response, and the role of choice and physiological mechanisms in creating the response. The female literacy rate, as a measure of female educational attainment, is hypothesised to be negatively related to fertility, since more widespread literacy is indicative of more widespread contraceptive knowledge and also of broader opportunities for women outside the home. There is overwhelming support for these effects. Perhaps, in the more advanced countries where female literacy is very high, it might not be a sensitive indicator of variations in women's educational attainment, but for the mass of the world's population, literacy is still not high. The mean literacy rate in the sample is only 57 per cent. Also, there is evidence from relatively advanced countries that the early years of schooling have a much stronger impact on subsequent fertility than the later years [Ben-Porath, 1973]. However, in some regressions, an alternative measure, newspaper circulation per thousand of population, which was used in previous analysis, was retained. This variable more effectively captures differences in educational levels and access to information in the more advanced countries, and is possibly a less error-prone measure than reported literacy as obtained through census interviews. B. The Income Distribution Equation Recent theoretical and empirical research has attributed a good deal of the dispersion in earned incomes to differentials in the stock of human capital within the population, including that acquired through schooling and that acquired through on-the-job training [Mincer, 1970]. Another important determinant of dispersion in earned income has been found to be differences in employment. In addition, many social scientists point to underlying structural aspects of a society which influence who will be given educational opportunities and favourable career openings. These

28

POPULATION A N D DEVELOPMENT

structural aspects determine the power and class relationships in a society and are responsible for much of the inter-generational persistence in income levels and inequality. The basis for the hypothesis that high fertility tends to increase income inequality has already been presented. In addition, the effect of human capital stock distribution is captured by a measure of the dispersion of educational attainment in the adult population. An attempt has been made also to introduce the effect of fundamental structural aspects of the society by a measure of the concentration of agricultural land ownership. This variable has a strict economic interpretation. Since, in most of the world's economies, agriculture is a major, if not the main, source of income and land is the principal non-labour input into agriculture, the concentration of land ownership is a measure of the distribution of non-human capital. However, beyond this, the concentration of land ownership is also a reasonable indicator of the concentration of political power and influence in most countries, and so probably represents a reasonably good measure of undelrying structural elements in the society. The final variable introduced in the explanation of variations in income distribution is the level of average per capita income. This captures several effects. Firstly, as Chiswick and Mincer have pointed out [1972], the mean values of the educational stock and the rate of return on capital should be related to the distribution of earnings, and these are systematically related to the level of average income per capita. Secondly, through Engel's Law, higher income per capita is associated with a lower share of agriculture in total output, and this reduces the seasonality of employment and a major source of variation in hours worked among participants in the labour force. Thirdly, higher per capita income is associated with a larger share of government in the economy, and government action is usually redistributive in intent and in effect. Fourthly, higher income per capita is associated with a higher value of capital per worker, and this can in most cases be expected to lead to a larger share of capital in total output, and hence to a more concentrated distribution of income. It should be noted, however, that many of these tendencies run in opposite directions, and may be partially offsetting. C. The Infant Mortality Equation There is no body of comparable theory on the determinants of infant mortality. There are large numbers of empirical studies, however. A recent survey has identified the factors most closely related to high infant mortality [Russell, 1974]. One of these is fertility. High fertility implies high parity and closely spaced births, and a high proportion of births to quite young or older mothers. The consequences are a greater probability of maternal malnourishment, birth complications and abnormalities, low birth weights, and earlier weaning. In addition to the level of fertility, the average calorie intake per capita was introduced as a measure of the nutritional status of the population. Since few cases of protein deficiency are found when there is adequate calorie intake, while protein deficiency is almost always found when calorie intake is found to be deficient, the latter is the best single measure of general

FERTILITY AND DISTRIBUTION OF INCOME

29

malnutrition. Malnutrition of the mother during and after pregnancy is associated with low birth weights and early weaning, while malnourishment of the child is strongly synergistic with respiratory and intestinal infections and other diseases, substantially raising mortality rates [Scrimshaw, Taylor and Gordon, 1968]. Finally, the female literacy rate has been used as a measure of mothers' education, which has been found substantially to influence the quality of care given the child, and to have a close association with the infant mortality rate [Sloan, 1971]. D. Inter-relationships Among the Variables The current fertility rate, the distribution of income, and the infant mortality rate have been treated as mutually determining and endogenous in this model. The last two influence fertility, while fertility influences both of them in turn. N o direct interaction is postulated between the distribution of income and the infant mortality rate, although they will be currently related through the effects of fertility. The distribution of land and the distribution of educational attainment in the adult population are predetermined variables, and therefore exogenous within the structure of this model. Female literacy is also predetermined, since it depends on schooling decisions made well prior to the beginning of the reproductive years. Somewhat less clearly, the level of average income per capita is a predetermined variable, since it depends on a sequence of past savings and investment decisions which determine the current capital stock, past births and infant deaths which determine the current labour force, and a largely exogenous flow of productivity changes. Nutritional intake is almost entirely a function of income, and so is also exogenous. Consequently, all three equations in the system are identifiable, and are actually over-identified. IV

THE DATA

The data for the empirical testing of this model consist of a cross-national sample of 68 developed and less-developed countries for which observations on all the variables could be taken for a single period in the mid1960s. Inevitably, the quality of the data vary widely from country to country, and there are some inconsistencies in the definitions or differences in the measurement of certain variables among countries, although care was taken to obtain the most consistent set of observations available. Given these limitations, the use of a cross-national sample requires some explanation. The strongest justification is that it permits observation of populations which have been living under very different income distributions for considerable periods of time, and permits an exploration of the long-run effects of differences in income distribution. Within most economies, changes in income distribution tend to be slow, and the effects difficult to distinguish from those of other trend-like variables A crossnational sample provides wide variation in the variables of interest. Furthermore, the probability that there are substantial errors of measurement in the principal variables does not by any means imply that the validity of tests of association are weakened. In general, with errors of measurement affecting the variables of interest, any observed association

30

POPULATION A N D DEVELOPMENT

can be taken as the floor above which the true association in absolute value is likely to lie. The errors of measurement create 'noise' which reduces the strength of the association. Measurements of correlation or covariance tend to be biased toward zero. Therefore, if a close association is found to exist in data of poor quality, there is reason to suspect that the true association between variables is even closer. This is only true, of course, if the errors of measurement in the different variables are independent across the sample of countries. However, this condition is likely to be fulfilled by measurements of the birth rate and the distribution of income. These statistics are estimated from entirely different sources of information, by independent methodologies, and usually by different agencies. There seems no reason to suspect that if, in a sample of countries, the birth rate is overestimated, the degree of inequality in the distribution of income among households should also be over-estimated; and, if the birth rate is under-estimated, so should be the inequality in the income distribution. A similar set of considerations explains the inclusion of both more developed and less developed countries in the same sample. One can be sure that the differences in fertility rates, levels of living and the degree of income inequality between countries at widely different levels of development are not simply or primarily errors of measurement. They represent real and systematic differences among those populations. Inclusion of a wide sweep of systematic variation in the variables of interest within the sample permits more reliable estimation of the relationships of the system. Restriction of the sample, on the other hand, to a sub-set of more similar countries, such as the less developed countries alone, would tend to increase the 'noise' level relative to the true signal. It is well known, for example, that the distribution of birth rates among countries is strongly bimodal, with the less developed countries distributed around a modal value of about forty, and the more developed countries around a value of about twenty. Few would suspect that the inter-modal differences represented primarily measurement error. However, the difference between a crude birth rate of 44 in one LDC and a measured rate of 41 in another might indeed be just measurement error. It seems obvious that better understanding of the underlying forces determining long-term fertility differentials would be obtained if the broader systematic differences were explored along with the narrower ones. Two measures of fertility have been used. One consists of the annual number of births divided by one-half the population aged 15 to 64. It is an approximation to the general fertility rate, which is usually defined as the annual number of births divided by the number of women in the age group 15–44. This variable, available for 112 countries for years in the mid-1960s, was computed from data published in Frederick H. Harbison [1970]. An alternative, widely used measure of fertility is the gross reproduction rate, a period rate measuring the number of daughters a woman would have if she lived through her reproductive period manifesting the same age-specific fertility rates as the current population of women. This measure was available for all the countries of the sample from the U N [1974]. These data refer to the period 1965–70, a somewhat later period than the other data,

FERTILITY A N D DISTRIBUTION OF INCOME

31

most of which refers to the period 1960–65. Consequently, this variable is less suitable as an explanatory variable in regressions of the distribution of income and the infant mortality rate, and perhaps somewhat more suitable for use in ordinary least squares regressions in which fertility is the dependent variable, since the implicit lag should reduce the degree of simultaneity between the dependent variable and the regressors. In earlier research on this topic, two measures of income distribution were used: the share of total personal income received by the poorest forty per cent of the population, and the Gini coefficient. Those data have been published in the World Bank volume referred to above, along with a discussion of the sources and methods of compilation. It was found that these two measures were correlated to the extent of 0·95 across the sample, and gave very similar results in the analysis. Consequently, only one measure, the Gini coefficient, which is somewhat more general, has been used here. Since the number of countries for which data on income distribution are available is a limit on the overall size of the sample, an additional effort has been made to gather more data, yielding distribution data for a half-dozen more countries. The sources for these are presented in the accompanying footnote 1 . The primary source of data on the infant mortality and female literacy rates was the U N publication, but other sources were used to fill in missing observations for a number of countries. 2 Data on average daily caloric intake were obtained from Harbison [1970] along with per capita income figures (on GNP at factor cost expressed in US dollars at 1964 prices). Data on the distribution of land holdings were obtained from the FAO [1971]. The concept of holding refers to use rather than ownership, and holdings are undoubtedly more equally distributed than ownership, due to the widespread practices of renting and sharecropping. Data were available on the percentage of total agricultural area and the percentage of total holdings, by size class of holdings. From these were estimated the share of the total area held by the smallest forty, fifty, and sixty per cent of holders. These are highly correlated, and only the last was subsequently used in the analysis. Apart from the distinction between landowners and landholders, there are other deficiencies in this variable. Holdings under one acre were excluded from the figures. For many countries, especially in Asia, a very substantial fraction of all holdings are thus excluded. Also, no distinction is made in the data between different kinds of agricultural land: irrigated vs. unirrigated, or cropped vs. grazed land. Consequently, this variable can be taken only as a very approximate measure of the concentration of land ownership, subject to substantial measurement error. A crude measure of the dispersion of the educational attainment of the adult population was also the best that could be calculated from available data. The UNESCO Statistical Yearbook in various years since 1965 presents information on the proportions of the adult population (25 years old and older, with some exceptions) who have less than a primary school education, and who have completed primary, secondary, and tertiary education. There are some differences among countries in the number of school years included in each level; and, in addition, the year of reference varies from country to country, mostly within the period 1960–65. Some

32

POPULATION A N D DEVELOPMENT

missing observations were filled in from E. Dennison [1967]. The measure of dispersion was formed by weighting the percentages in each educational category by the number of categories separating it from the modal category, which thus received a zero weight. For example, in Algeria the percentages in each category were 91·8, 6·0, 1·8, and 0·4, so the measure of dispersion was (1 × 6·0) + (2 × 1·8) + (3 × 0·4) = 10·8. The availability of data on the dispersion of the educational stock and of landholdings limited the size of the sample used for estimation of the complete model. Forty-five observations were available for this purpose, whereas 68 country data points could be used to estimate the fertility equation alone by ordinary least squares. V

THE FINDINGS

The results of ordinary and two-stage least squares regressions of both fertility measures on income distribution and the other variables suggested by the model are presented in Table 1. With one exception, over 60 per cent of the total variation in fertility is accounted for by the model, and the regression lines are highly significant statistically. All regression coefficients have the expected sign. There is a consistently close relationship between more equitable income distribution and lower fertility. When the simultaneous influence of fertility on income distribution is taken into account, the effect of distribution on fertility appears larger. In each case, the size of the regression coefficient is larger in the TSLS estimates than in the corresponding OLS regression. From the first regression in Table 1, covering the entire sample, a reduction of the Gini coefficient of 0·10 is associated with a gross reproduction rate lower by 0·21. At the sample means of 0·4415 and 2·41 respectively, this TABLE 1 REGRESSIONS OF FERTILITY ON INCOME DISTRIBUTION AND OTHER VARIABLES

Explanatory Variables Dependent Variable

Method

Income Distrib.

1. Regressions Using the Entire 2-14 GRR OLS (3-46) FERT OLS 137-3 (4-03)

Infant Mort.

Female Liter.

Income/ Capita

Sample of 68 Countries •0006 -1-09 —0004 (0-34) (3-21) (318) •140 —012 (201) (1-32)

News Circ.

R2 •69

—20 (4-40)

•76

2. Regressions Using the Sample of 45 Countries Available for Simultaneous Estimation GRR

OLS

GRR

TSLS

FERT

OLS

FERT

TSLS

3-20 (3-76) 7-17 (2-63) 184-4 (3-82) 204-0 (1-43)

•0029 (1.44) —0093 (0-63) •138 (1-52) •68 (1-63)

The figures in parentheses are t-statistics.

-1-23 (206) -3.02 (1-48)

—0003 (3-07) —00006 (0-21) •012 (1-39) •009 (0-73)

•73 •26 — 19 (3-87) —07 •(0-58)

•78 •60

33

FERTILITY A N D DISTRIBUTION OF INCOME

implies an elasticity of 0·39. The elasticity as calculated from the regression over the whole sample using the alternative measure of fertility is 0·47. Of course, no false precision should be read into these estimates. They are formed from crude, cross-national, cross-sectional data. Also, the Gini coefficient itself is an ambiguous summary statistic of the distribution of income. Yet, they indicate that the association seems to be a strong one. The increased size of the coefficient when estimated as part of a wider model indicates that the association cannot be attributed to the effects of high fertility on the distribution of income, or to the effects on both of other underlying variables encompassed by the model. Female literacy and the alternative measure of educational attainment are consistently inversely related to fertility. The effects of increases in average per capita income on fertility, ceteris paribus, are negative but small and often statistically insignificant. The infant mortality rate is positively related to fertility, both in the simple and the simultaneous model. From the first equation, the elasticities at the sample means of fertility with respect to the infant mortality rate, average income per capita, and the female adult literacy rate are 0·02, –0·10, and –0·25 respectively. Table 2 presents the results of the TSLS regression of the Gini coefficient on fertility, as measured by the approximate general fertility rate, income per capita, the dispersion of educational attainment, and the share of the smallest 60 per cent of holdings in total area. TABLE 2 REGRESSION OF INCOME DISTRIBUTION ON FERTILITY AND OTHER VARIABLES

Explanatory Variable Fertility Dispersion of Education Share of Smallest 60% of Holdings in Total Area Average Income per Capita R2 = 0 4 5

Regression Coefficient

t-statistic

00007 0088

1-77 1-51

-0-216 -0154

2-22 0-57

All coefficients have the expected sign, and, with the exception of average income per capita, are of reasonable size relative to their standard errors. The coefficient of determination is 0·45. N o strict probability statements are possible, since tests of significance under this estimation procedure are asymptotically valid, and in this case the sample size is quite small, only 45 observations. The estimated elasticity of the Gini coefficient with respect to the fertility rate is 0·20, which, is substantial, but only one-half that of income distribution on fertility. The finding of only a weak association between the level of per capita income and its distribution is not unexpected, in view of the offsetting influences identified in the discussion of the specification of the equation. In Table 3, the TSLS regression of infant mortality on fertility and other variables indicates that high fertility is indeed a substantial factor behind high infant mortality, along with nutritional intake and the female literacy

34

POPULATION A N D DEVELOPMENT

rate, which appears to have the closest association with infant mortality of all the three. TABLE 3 REGRESSION OF THE INFANT MORTALITY RATE ON FERTILITY AND OTHER VARIABLES

Explanatory Variable

Regression Coefficient

Fertility Female Literacy Rate Average Calorie Intake

049 -125-60 0-48

t-statistic 1-25 3-71 1-42

R 2 =0-52

The elasticities of infant mortality with respect to fertility and female literacy are 0·77 and –0·88 respectively. The influence of high fertility on infant mortality seems to be much stronger than the reverse influence. Given these inter-relationships, it is interesting to consider the total impact of a change in an exogenous variable subject to policy manipulation, female literacy for example, on birth, death and papulation growth rates. It is clear that the total effect of an increase in female literacy on fertility and infant mortality rates is greater than the direct effects. Illustrative calculations using the first and last regression equations of Table 1 suggest that the total effect of a rise in literacy on infant mortality might be thirty per cent greater than the direct effect. An analogous calculation indicates that the total effect on fertility would be about ten per cent greater than the direct impact, since the resultant fall in infant mortality would also tend to lower fertility. At the mean of the current sample, represented by a gross reproduction rate of 2·4 and an infant mortality rate of 80 per thousand, only about one-sixth of the fall in fertility following a rise in female literacy would be offset demographically by a fall in infant mortality, so the impact on the rate of population growth would be substantial. A similar process of reinforcement results from the positive interaction of fertility and income distribution. As an illustration, using the comparable TSLS regressions in Tables 1 and 2 as a base, the total effects of a redistribution of landholdings on income distribution is twenty per cent larger than the direct effect, due to the positive feedback from fertility. At the same time, each increase of ten percentage points in the share of the smallest sixty per cent of holdings in the total agricultural area is estimated to reduce fertility by about five per cent, other things remaining equal. VI

ADDITIONAL FINDINGS

Reasons have already been given for inclusion of countries at very different levels of development in the sample. On the other hand, there are considerable cultural and other differences between Chad and the United States, as an example, which are not captured by the model, and these could distort the results. An attempt was made to restrict the sample to 41 less developed countries only. The explanatory power of the model fell

35

FERTILITY A N D D I S T R I B U T I O N OF INCOME

considerably, as had been feared, and the regression coefficient of income distribution became statistically insignificant although still positive in sign. Less defensibly, the fertility regression was again run on a sample excluding the East European countries, Bulgaria, Czechoslovakia, Hungary, Poland and Yugoslavia. These countries are all extreme observations, with the lowest fertility and the most equitable income distribution in the sample. Removing them reduced the size of the coefficient of income distribution and its significance to slightly below conventional levels. These results are reported in Table 4. TABLE 4 FERTILITY REGRESSIONS ON SELECTED SUBSAMPLES

Dependent I/zti-iViA/zj

Explanatory Variables

R2

AAntUnsI

Income Distrib.

Infant Mort.

Female Literacy

Income/ Capita

-0-95 (2-81)

-00005 (3-97)

0-67

-0-57 (1-61)

-00009 (214)

0-26

1. Excluding Five East European Countries GRR

OLS

1 14 (1-58)

00005 (0-29)

2. Excluding 27 More Developed Countries GRR

OLS

0-52 (0-67)

-00019 (012)

All the findings presented so far refer to static cross-sectional comparisons, while what is really of interest is the response of fertility to changes in the distribution of income over time. This is difficult to establish. Generally, in those countries in which the statistical basis for the construction of time series is more adequate, changes in the distribution of income have been slow and small. The effect of these changes, if any, is indistinguishable from those of other trend variables. Moreover, for most less advanced countries, credible comparisons of the distribution of income between two periods are extremely difficult, because of shifting data sources, definitions, and computational procedures. Nonetheless, for the less developed countries embarked or about to embark on the demographic transition, what statistical evidence is available has been summarised in Table 5. Changes in income distribution, measured by the Gini coefficient, are compared to changes in the crude birth rate. Countries have been grouped into (a) those in which the distribution of income very likely improved, (b) those in which it probably did not change any substantial amount, and (c) those in which it very likely deteriorated. Only those countries were included for which definitionally comparable distribution data of some authority could be found. These were selected from the World Bank compilation by S. Jain and A. Tiemann [1973] and R. Weisskopf [1969]. Crude birth rates were taken from U N Demographic Yearbooks and from the U N Statistical Office, Statistical Papers Series A, Vol. XXVI, No. 1, with the exception of data for the Philippines. Despite all the deficiencies in the data, one point stands out clearly. In those developing countries in

36

POPULATION A N D DEVELOPMENT

TABLE 5 CHANGES IN INCOME DISTRIBUTION AND CHANGES IN THE CRUDE BIRTH RATE

Country

Year

Source

Gini Coeff.

Year

CBR

(A) Countries in which Income Distribution Apparently Improved Costa Rica (households)

1961 1971

IBRD IBRD

0-5064 0-4287

1960-65 1971

44-46 31-6

Sri Lanka (households)

1953 1969-70

IBRD IBRD

0-4503 0-3730

1953 1971

38-7 29-9

Taiwan (households)

1953 1961 1964

IBRD IBRD IBRD

0-5542 0-4500 0-3180

1953 1961 1964

45-2 38-3 34-5

(B) Countries in which Income Distribution was Substantially Unchanged Mexico (households)

1963 1969

IBRD IBRD

0-5509 0-5580

1960-65 1969

44—45 41-6

Peru (Econ. Act. Population)

1961 1970/1

IBRD IBRD

0-5886 0-5714

1960-65 1965-70

44—45 41-8

Philippines (households)

1956 1965

IBRD IBRD

0-4742 0-4891

1950-55 1960-65

45-47 43-45

Yugoslavia (households)

1963 1968

IBRD IBRD

0-3304 0-3309

1963 1968

21-4 19-0

(C) Countries in which Income Distribution Apparently Deteriorated Brazil (Income Recipients)

1960 1970

IBRD IBRD

0-5578 0-6135

1960-65 1965-70

41-43 37-8

India (Income Recipients)

1953-5 1961-4

IBRD IBRD

0-3955 0-4610

1953 1963

41 38

1953 1963

Weisskopf Weisskopf

0-417 0-441

1953 1963

35 31

Puerto Rico (households)

which income distribution improved substantially, birth rates fell considerably more rapidly than in those in which it did not. Further, we know that in these three countries, only a small part of the fertility decline can be attributed to the impact of family planning programmes; most, operating through rising age at marriage and lower marital fertility in the later reproductive years, stemmed from reduced demand for children and greater control over fertility achieved without benefit of official support. Finally, overall rates of growth in income were high in Taiwan but low in Sri Lanka, and also high in Brazil, Mexico, and Puerto Rico, but low in India. The rate of growth of average per capita income is not a good distinguishing variable.

FERTILITY A N D DISTRIBUTION OF INCOME

VII

37

SOME IMPLICATIONS

These findings, if borne out by more detailed investigations [Repetto, 1976] are important for the design of development and population policies. Disillusionment with conventional family planning programme as the main policy instrument with which to induce fertility declines is already widespread, as the evidence accumulates of their limited coverage and low demographic effectiveness. At the same time, especially in the poorest and most populous countries, the prospects for rapid increases in the level of living are not bright. However, the findings reported above suggest that, from the demographic standpoint, the pattern of income growth is at least as important as the pace. Typically, at least half of any increment to income accrues to the upper fifteen per cent of households. If this income is largely irrelevant to the decline in fertility, then the problem can be narrowed to that of raising the other half of income, that which accrues to the remaining 85 per cent of households. It may even be that only the thirty per cent of income which goes to the poorest sixty per cent of households is really crucial in determining the pace of fertility decline. As a consequence, redistributive measures and a selective strategy of growth designed to raise the incomes of the relatively poor would be more successful in lowering the rate of population growth than the present mix of development policies in most less developed countries. The interaction of fertility and income distribution also adds another reason to doubt the conventional dichotomy between growth and equity. The impact of greater equality on the fertility level would permit a faster increase in the accumulation of physical and human capital per worker, and so a faster increase in output per worker. A reduced dependency ratio would lead to a faster increase in output per capita, while both of these would tend to raise the savings ratio. The positive feedback from fertility to greater equality in income distribution, operating through the microeconomic and macro-economic channels described in section II, would tend to sustain and accelerate the process, producing further increases in the rate of growth of income per capita. On these grounds, greater equality seems compatible with faster rates of growth.

NOTES 1. The sources for this additional country data are as follows: Australia—N. Podder, 'Distribution of Household Income in Australia', Economic Record, June, 1972. Bolivia, Morocco, Nigeria, Surinam, Trinidad and Tobago—I. Adelman, and C. T. Morris, An Anatomy of Patterns of Income Distribution in Developing Nations. Final Report, AID Grant/csd-226, Northwestern Univ., Evanston, Ill., 1971. Ghana—K. Ewasi, 'Notes on the Relative Distribution of Income in Developing Countries'. Review of Income and Wealth, December, 1971. 2. Other general sources included C. L. Taylor and M. C. Hudson, World Handbook of Political and Social Indicators, New Haven: Yale University Press, 1972, and the Population Reference Bureau. Additional Sources for particular countries included L. J. Cho, 'The Demographic Situation in Korea', mimeo, East-West Center, Honolulu, April. 1973; Country Profiles; Iran, The Population Council, October, 1972, and Country Profiles; Ghana, The Population Council, October, 1970. For certain advanced countries in which literacy rates are quite high, including Australia, Canada, Czechoslovakia, Denmark, Finland, West Germany, the Netherlands, Norway, Sweden, and the U.K., the female adult illiteracy rates were

38

POPULATION A N D DEVELOPMENT

estimated by the percentage of adult women with no schooling. For lack of alternative sources, the same method was used for Ghana, Guyana, Kenya, South Africa, and Trinidad and Tobago. Adult schooling rates were taken from UNESCO Statistical Yearbooks. Other country data sources were, for Burma, UNESCO, Progress of Education in the Asian Region, Bangkok; UNESCO, 1972, Table 14; and, for Taiwan, Country Profiles: Taiwan, The Population Council, February, 1970. REFERENCES Becker, Gary S., 1967, 'Human Capital and the Size Distribution of Income: an Analytical Approach', W. S. Woytinsky Lecture 1, Ann Arbor: Institute of Public Administration, University of Michigan. Ben-Porath, Yoram, 1973, 'Fertility in Israel: Point and Counterpoint', Journal of Political Economy, Vol. 81, no. 2, Part II. Bernhardt, Eva, 1972, 'Fertility and Economic Status in Sweden: Some Recent Findings on Differentials in Sweden', Population Studies, Vol. 26, no. 2. Chiswick, Barry, and Jacob Mincer, 1972, 'Time Series Changes in Personal Income Inequality in the U.S. from 1939, with Projections to 1985', Journal of Political Economy, Vol. 80, no. 3, Part II. Dennison, Edward, 1967, Why Growth Rates Differ, Paris: Organisation for Economic Cooperation and Development. Food and Agriculture Organisation, 1971 Report on the 1960 World Census of Agriculture, Vol. 5, Rome: FAO. Frisch, Rose, 1974, 'Demographic Implications of the Biological Determinants of Female Fecundity', Cambridge, Mass.: Center for Population Studies, Harvard University. Harbison, Frederick, et al., 1970 Quantitative Analyses of Modernisation and Development, Princeton: Princeton University Press. Hashimoto, Masanori, 1974, 'Economics of Post-War Fertility in Japan: Differentials and Trends', Journal of Political Economy, Vol. 82, no. 2, Part II. Inkeles, Alex, and David H. Smith, 1974, Becoming Modern, London: Heinemann. Jain, S., and A. Tiemann, 1973, Size Distribution of Income: Compilation of Data', Development Research Center Discussion Paper No. 4, New York: IBRD, August. Kocher, James E., 1974, Rural Development, Equity and Fertility Decline, New York: Population Council. Kuznets, Simon, 1975, 'Demographic Components in the Size Distribution of Income', New Haven: Economic Growth Center, Yale University. Leibenstein, Harvey, 1971, 'The Impact of Population Growth on Economic Welfare: Nontraditional Elements', National Academy of Sciences, Rapid Population Growth: consequences and policy implications, Baltimore and London: Johns Hopkins University Press. Michael, Robert, 1973, Education and the Derived Demand for Children', Journal of Political Economy. Vol. 81, no. 2, Part II. Paglin, Morton, 1975, The Measurement and Trend of Inequality: a Basic Revision', American Economic Review, Vol. 65. Pryor, Frederick, 1973, Simulation of the Impact of Social and Economic Institutions on the Size Distribution of Income and Wealth'. American Economic Review, Vol. 63. Rich, William, 1973, Smaller Families through Social and Economic Progress, Washington, D.C.: Overseas Development Council. Russell, Louise, 1974, Determinants of Infant and Child Mortality: Report of a Feasibilty Study, Washington, D.C.: National Planning Association. Ryder, Norman B., 1973, 'Comment', Journal of Political Economy. Vol. 81, no. 2, Part II. Sanderson, W. and R. Willis, 1971, Economic Models of Fertility: Some Examples and Implications', Annual Report, Washington, D.C.: National Bureau of Economic Research. Schultz, T. Paul. 1974, Fertility Determinants: a Theory, Evidence and an Application to Policy Evaluation, Santa Monica: Rand Corporation. Scrimshaw, N., C. Taylor, and J. Gordon, 1968, Interactions of Nutrition and Infection. Monograph Series no. 57, Geneva: World Health Organisation. Simon, Julian, 1974, The Effects of Income on Fertility, Monograph no. 19, Chapel Hill: Population Center. University of North Carolina.

FERTILITY A N D DISTRIBUTION OF INCOME

39

Sloan. F.. 1971. Survival of Progeny in Developing Countries, Santa Monica: Rand Corporation. Tobin, James, 1973, Comment", Journal of Political Economy. Vol. 81, no. 2, Part II. United Nations, various years, Demographic Yearbook, New York: United Nations. United Nations, 1963, Population Bulletin no. 7, New York: United Nations. United Nations Statistical Office, 1974, Population and Vital Statistics Report, Statistical Papers Ser. A, Vol. 26, no. 1, New York: United Nations. United Nations Educational, Scientific and Cultural Organisation, various years since 1965, Statistical Yearbook, Paris: UNESCO. Weisskopf, Richard, 1969, 'Income Distribution and Economic Growth: an International Comparison', Cambridge, Mass.: Center for International Affairs, Harvard University, May. Wilkinson, Maurice, 1973, 'An Econometric Analysis of Fertility in Sweden 1870–1965', Econometrica. Willis, Robert, 1973, 'A New Approach to the Economic Theory of Fertility Behaviour", Journal of Political Economy. Vol. 81, no. 2, Part II. Wray, Joe D., 1971, 'Population Pressure on Families: Family Size and Child Spacing'. Reports on Populationi Family Planning. no. 9, New York: Population Council.

Fertility, Mortality and Income – Changes over the Long Run: Some Simulation Experiments By

I

T. P.

Dyson,

C.L.G.

Bell,

R.

H.

Cassen*

INTRODUCTION

Several economic-demographic models of a whole economy have been developed since Coale and Hoover first published their Economic Development in Low Income Countries [1958]. Coale and Hoover simulated an economy under alternative assumptions of high and low fertility. In their short-term model, total product grew more slowly under high fertility than it did under low fertility. On the other hand, in the long-run model, although total product was higher under high fertility than under low fertility, per capita income levels were considerably reduced. Partly in answer to the criticism levelled at Coale and Hoover and those who followed in their footsteps, a number of increasingly complex models have been produced. 1 While most modellers have been concerned with the economic consequences of different demographic trajectories (particularly fertility), another approach has been to make population growth endogenously determined and to explore the consequences of different economic growth paths. For example, assuming a homogenous population, Casetti [1977] attempts to shed some light on how much economic growth is needed to 'defuse' the population explosion of an ideal-typical LDC. 2 The concerns underlying the simulations presented in this paper stem from emerging themes in the field of development economics. There, the well-known Redistribution with Growth [Chenery et al., 1974] reflects the increasing concern of economists that distributional objectives should be treated as an integral part of overall development strategy. In that volume Ahluwalia and Chenery present a model of a segmented closed economy (i.e. one consisting of 'rich', 'middle' and 'poor' income groups) which, in a general way, is used to explore the effects of various redistributional strategies on the per capita income levels of low income groups. But while there are good reasons for believing that in many countries demographic factors have played an important part in determining both the level and distribution of income, the Ahluwalia–Chenery model is almost entirely without a demographic component. In their simulations the population *Research Fellow, Centre for Population Studies, London School of Hygiene and Tropical Medicine, University of London; Staff Economist, Development Research Center, World Bank, Washington; and Senior Economist, Brandt Commission Secretariat, Geneva, respectively. The views expressed in this article are those of the individual authors and not necessarily those of their respective institutions.

FERTILITY, MORTALITY AND INCOME

41

growth rates of the income groups are exogenously determined, and are simply assumed constant throughout, at 2 per cent, 2·5 per cent and 3 per cent for the 'rich', 'middle' and 'poor' income groups, respectively. These assumptions are clearly unrealistic if the time frame of the analysis extends beyond two or three decades. 3 In our view, an analysis of the very long run must treat population growth endogenously if one is not to omit a very important potential feedback mechanism whereby a redistribution of income may be expected to raise levels of per capita income: namely that, ceteris paribus, an income redistribution may slow the rate of population growth and hence, in many circumstances, lead to higher per capita incomes. The model presented below is thus of a segmented economy, in which fertility and mortality are endogenously related to the level and distribution of income. Although the model is simple — and in any event the analysis presented can only constitute a first step — it nevertheless allows us to explore the possible demographic feedback of a redistributional strategy. In doing so, we are marrying the economists' new conventional wisdom about income distribution (as exemplified in Redistribution with Growth) with the demographer's belief that rising living standards will assist in the reduction of fertility. (In our model, fertility is affected by income directly, and indirectly by the impact of income on mortality. We are well aware that income is a poor proxy for those features of development which affect fertility and mortality—education, health, female status, modernisation etc. But in poor countries, at least, higher incomes may be a necessary, if not a sufficient, condition for enjoyment of such advantages by low income groups.) The paper is divided into three main parts. Firstly, we outline the essential features of the simulation model, in particular, the concept of a 'segmented economy', the economic and demographic differentiation of the baseline (i.e. starting) population, and the main relations determining the level and distribution of income in the economy. We then deal with the central equations of the model: those linking fertility and mortality to the level of income, which are estimated on the basis of both cross-sectional and time-series observations. Secondly, we present the results of simulations based on this system of equations. While these results have important implications, we show that a more plausible representation of demographic changes consequent on increasing income will be obtained if the estimated relationships between income, fertility and mortality are modified to incorporate shifts over time. The demographic implications of an appreciable redistribution of (rising) national income are also explored. Finally, we draw together the main conclusions that emerge from the simulations and briefly discuss some of their implications for policy.

II

THE SIMULATION MODEL

The essential structure of the model is that of a 'segmented', closed economy as specified by Ahluwalia and Chenery [1974]. In order to capture the relationship between income distribution and other variables, the population is divided into three groups ranked in terms of income per head:

42

POPULATION A N D DEVELOPMENT

the top 20 per cent, the middle 40 per cent and the bottom 40 per cent. For ease of reference, these groups will be called the 'Rich', 'Middle' and 'Poor', respectively. Within each group, individuals (or rather households) have identical levels of income, fertility and mortality. This simplification is somewhat crude; but it serves our purpose well enough, and none of the qualitative propositions advanced below depends on it in a crucial way. The three components of the model are as follows: the first is a standard framework for projecting population and age structure using fertility and mortality parameters. The second determines the level of GNP and its distribution among the three groups. The third component, which can lay claim to be the most important, is the link connecting fertility, mortality and income, income being a proxy for the myriad socio-economic influences on fertility and mortality, which are usually correlated with G N P per head — albeit imperfectly so. A The Population Structure The population of each income group, Ni, is differentiated by age in conventional demographic five year age-groups. A summary of the baseline age distributions of the three income groups and the total population is given in Table 1. These age structures provide the initial conditions for all the projections presented below. The age distribution of the 'Poor' is a stable population model with demographic parameters that are broadly consistent with the econometrically estimated values that are assumed to characterise this income group at time zero. The age distributions of the 'Middle' and 'Rich' income groups were generated from the initial stable age distribution of the 'Poor' in such a way that they are also broadly consistent with their respective starting levels of fertility and mortality. The age structure presented in Table 1 reflects our assumption that the 'Rich' practice a measure of fertility control: their age pyramid exhibits a narrowing at the base, and comparative ageing over the distribution as a whole. However, the age structure of the total population — with over 42 per cent aged 0–14 — can be seen as generally typical of the age pyramids of many contemporary developing countries. The total population at time zero was assumed to be 100,000. Obviously, the total population is the sum of the populations in each income group, and the initial sizes of the groups were twenty, forty and forty thousand, respectively. The age structure of each income group was projected by five year intervals using the familiar component method of population projection. 4 Because the groups grew at different rates, at the end of each projection period people were redistributed by age between groups so that the population shares of the Rich, Middle and Poor remained 0·2, 0·4, 0·4. B The Level and Distribution of Income In our view, the determinants of both the growth of national income and its distribution over the very long run are so imperfectly understood that it is best, for these purposes, to set both the growth rate and distribution of income exogenously. Thus, we part company with Casetti [1977], who allows for the effects of demographic changes on labour supply, and thence

FERTILITY, MORTALITY A N D INCOME

43

aggregate output through the use of a production function. However, his output trajectories owe far more to the (exogenously determined) rates of saving and technical progress than to (endogenous) labour supply, so few nuances are lost by making the arithmetic of growth completely direct. Also, as we have remarked already, he assumes a homogeneous population, and so makes no allowance for either changes in income distribution or possible aggregation bias in estimating variables which depend on individuals' incomes, a bias which can enter even if relative inequality does not change. All projections were based on the assumption of an initial GNP per capita (Y o ) of US$100 in 1970 market prices, so as to conform with the income unit of account of the data used in the econometric estimation procedure. The initial income shares (σ1 ,σ 2 , σ3) for the Rich, Middle and Poor were 0·64,0·28, and 0·08 respectively. These were chosen to be roughly representative of the low income, 'high inequality' group of countries as defined by Ahluwalia [1974], i.e., countries in which the income share of the 'Poor' is less than 12 per cent. Table 2 gives the groups' starting levels of personal disposable income that are implied by these assumptions. 5 The rate of growth of GNP, g, was assumed to be constant throughout each simulation run. In each period, the level of average income per head, Y t , is given by (i) its level in the previous period, (ii) the rate of growth in the economy and (iii) the overall rate of population growth, r: Y

=

1 + g 1 + r Yt_1

(1)

Note that r is endogenous and should decline over time, so the constancy of g implies that the growth rate of income per head will accelerate after a sufficient elapse of time. The level of income per head in each income group at time t, Yit, in turn depends upon (i) the level of income per head in the population as a whole, (ii) the current distribution of income between the Rich, Middle and Poor, and (iii) the population share, pi, of each group: Yit = —σit Y,

(2)

The simulation runs presented and discussed below are of two basic types. In the first, 'constant shares', the income shares are kept at their initial, highly unequal distribution (64, 28, 8) over time. In the second, 'changing shares', the income shares are shifted from this initial pattern to a considerably more egalitarian distribution. The chosen 'target distribution' allocates 33 per cent of national income to the Rich, 40 per cent to the Middle, and 27 per cent to the Poor. (An example of a country with such an equal distribution is Bulgaria in 1962 with a GNP per head of US$530.) Figure 1 illustrates this 'changing shares' strategy in which income shares are changed linearly (with respect to income) from the initial to the 'target' distribution as GNP per head rises from US$100 to US$300, but stay constant thereafter, the income of Rich individuals then being almost three times that of Poor individuals.

% Share of Total Income

10

20

30

40

50

60

70

100

GNP per capita US $

200

300

Poor

Middle

Rich

The 'Changing Shares' Strategy : Income Redistribution with Increasing Income

Figure 1

44 POPULATION A N D DEVELOPMENT

45

FERTILITY, MORTALITY A N D INCOME

C The Link between Fertility, Mortality and Income As noted above, we have assumed that the fertility and mortality of the Rich, Middle and Poor are determined by their respective income levels. In turn, the resulting rate of 'national' population growth is one of the principal factors determining the future growth of national income per head, and hence that of each group (see equations (1), (2)). The simulation model therefore requires equations relating income per head to both fertility and mortality parameters. The fertility parameter used here is the Gross Reproduction Rate GRRi which is approximately equal to half the total fertility rate (TF R i ); the mortality parameter used is the life expectation at birth (Ei). One approach to this problem, adopted by numerous authors, 6 is to estimate these relationships using the cross-sectional evidence provided by the aggregate data for each of a group of countries. Indeed, faced with an almost complete dearth of information on fertility and mortality change by income group within a country, this is an understandable strategy, and it is the initial course adopted in the present work. To supplement the crosssectional estimates based on a group of 146 countries, we have also derived a set of estimates based on time series data for Taiwan over the period 1950–75, a period which saw very sharp declines in both mortality and fertility in that country. The data-base for the estimation is presented in the Appendix. Here we examine the functional forms chosen and present the estimates of their parameters. In examining the relationship between income per head and life expectation at birth, a priori considerations suggest that: (i) as Yi becomes large, Ei approaches asymptotically an upper bound (currently somewhere around 70 years); (ii) when Yi is small, Ei takes a low value; (iii) the relationship may have a convex, as well as a concave section. A plausible candidate is the 4-parameter logistic function:

A E = 1 + c.exp [ —b (Y -

(3)

Y*)]

Here, lim E = A c , and there is a point of inflection at Y = Y*. y∞

If Y 100, then E(100) 40 implies that c must be close to unity. The estimates obtained are as follows (t-values in parenthesis):7

Ao cross section

71·16 (63-16)

time series

68-55 (257-58)

b 0-00369 (8-23) 0-01988 (13-12)

c

Y*

N

R2

1-00 (11-44)

-39-07

146

0-748

4-76 (4-06)

-39-07

17

0-977

(to)

Life Expectation at Birth

40

45

50

55

60

65

70

E

50

100 150 200 250

600 650 US $

Equation 3

700 750

Equation 5

Equation 4

Equation 3

800 850 900 950 1000

Cross Section

Tim by Income : Time-Series and Cross-Sectional Estimates of Equation

GNP per capita:

Time Series

by Income

300 350 400 450 500 550

Fertility (GRR) and Mortality (R)

Figure 2

1.0

1.5

2.0

2.5

3.0

GRR

46 P O P U L A T I O N A N D DEVELOPMENT

FERTILITY, MORTALITY A N D INCOME

47

The two functions thus estimated are depicted in Figure 2, from which it is apparent that, within the observed range of income per head, Taiwan has a much lower mortality level (i.e. higher life expectation at birth) than the global 'norm' at the same income level — a reflection, perhaps, of its relatively equal income distribution and certainly of its standards of public health. Turning to the relationship between income and fertility, one might expect that when per capita income is low, total fertility is relatively high (possibly about seven live births); but that as income increases, so fertility will eventually decline to a 'replacement level', i.e., the level at which, given mortality conditions, the population will eventually just reproduce itself. In terms of contemporary 'favourable' mortality levels of the industrialised countries, this corresponds to a TFR ofjust over two live births per woman (i.e. a GRR of a little over one live female birth per woman). The crosssection data were applied to the form GRR = ko + k1 exp [ — α(Y -

Y') 2 ]

(4)

which attains a maximum at Y' and is also symmetric about that value. For the time series data, however, a far better fit was obtained using the 3parameter logistic: GRR = ko + !

+

e x p [α(Y

_

(5)

100)]

In both cases, the asymptotic value of GRR is ko as Y becomes sufficiently large.8 The estimates are as follows: cross section

k0 1-26 (13-55)

k, 1 76 (16-38)

α Y' 3-38 × 10~ 6 134-14 (3-02) (2-17)

time series

1-32 (1011)

7-63 (14-50)

7-87 × 10~ 3 (8-14)

N 145

R2 0-680

16

0-984

The corresponding functions are depicted in Figure 2. Again, it is noteworthy that the onset of rapid fertility decline occurs at much lower levels of income per capita in Taiwan than the international 'norm'. These results suggest that in attempting to 'predict' fertility, it also seems desirable to take the level of mortality into consideration. First, it can be argued that mortality conditions influence the fertility decisions of prospective parents, who may want to be fairly certain that a particular number of their children survive to adulthood. Secondly, to the extent that there is sufficient orthogonality between Ei and Yi, the introduction of a mortality parameter as a separate influence on fertility should correct for some of the obvious shortcomings of income per head as a determinant of, rather than a proxy for, such things as education, public health, nutrition and income distribution. This consideration suggests that a suitable functional form is:

48

POPULATION A N D DEVELOPMENT

GRR = k 0

+

ko 1 + γexp β(Y

-

(6)

o

Y'

There were insufficient degrees of freedom to estimate (6) using the time series data; the cross-section estimates are as follows: 9 k„

k,

105 0-77 (609) (10-36)

A0

β

71-82 (459-77)

0-013 (0-50)

7

e

Y'

0-022 0-30 - 1 0 0 (2-11) (39-91) (00)

N

R2

145

0-772

The overall goodness of fit is very good and shows a marked improvement over that for equation (4). Moreover, with the exception of β, the individual t-values range upwards from the acceptable (at the five per cent level) to the very highly significant. In other respects also, the estimates accord very well with a priori desiderata. At affluent levels of living, E approaches three score years and ten. And in those circumstances, fertility also declines to bare replacement levels. However, the low t-value for β indicates that there is fairly strong collinearity between Y and E. Nevertheless, the functional forms and the estimated parameters of equations (3) and (6) seem plausible as an initial basis for setting the values of some parameters to be used in the simulation model. Plainly, the next step is to substitute the estimated value of E(Y) from equation (3) into equation (6) to give a reduced form of GRR as a function of income alone. This is, in fact, the trajectory of fertility with respect to income per head when mortality moves in a 'normal' manner. Of course, if a regime were to stress (or neglect) public health provisions relative to the 'norm', then neither mortality nor fertility would be 'normal' for that level of G N P per head. We will return to such departures from the 'norm' shortly. On the basis of the levels of per capita income assigned to the groups in our initial population, the estimates of equation (3) and the reduced form of (6) were used to provide starting levels of fertility and mortality for each group. As can be seen from Table 2, the Rich, with appreciably higher incomes, have significantly higher life expectation (54 years) and lower fertility ( G R R = 2 · 5 ) than both the Middle and Poor groups. These groupspecific demographic parameters are those used in the generation of the age-structure of the Rich, Middle and Poor presented in Table 1. The weighted 'national' levels of fertility and mortality implied by the figures for income groups in Table 2 are a GRR of 31 live births, and a life expectation at birth of just over 43 years. As noted with reference to the income-group age structures, these starting levels of fertility and mortality are entirely plausible as representations of income-group demography, and of course they are consistent with one another in terms of the assumptions on which the model is based. However, in the light of the current debate 10 concerning the limitations of large-scale simulation models, it is appropriate to stress that our initial parameters are purely hypothetical constructs. In spite of the fact that they are few in number, and derive from what might be thought of as

FERTILITY, MORTALITY AND INCOME

49

relatively 'hard' data, there exists no direct way of corroborating their validity as ideal-typical constructs. And as we will show below, even more rigid strictures are required vis-à-vis the main equations relating income to fertility and mortality in the model. The weaknesses in our estimation procedures need no further emphasis here. III THE SIMULATIONRUNS RUNS III THE SIMULATION Using the assumptions about initial conditions outlined in Tables 1 and 2 we present four sets of simulations (Runs 1 – 4), each of which is based on different combinations of the cross-sectional and time-series estimates of equations (3) – (6). Simulation Run 1 is based on the simple cross-sectional estimates of equations (3) and (4). Run 2 is also based on the cross-sectional estimate of E from equation (3), but the behaviour of fertility is set by the parameters of equation (5) estimated from the time series (i.e. Taiwan) data. Therefore in comparing Runs 1 and 2 we can examine the changes introduced by different rates of fertility decline. Run 3 uses the crosssection mortality estimate of equation (3) and substitutes it into equation (6): it is therefore based on the cross-sectional trajectory of fertility with respect to income per head when mortality moves in what we termed above a 'normal' (i.e. cross-sectional) manner. Run 4 also incorporates equation (6), but the mortality path is that dictated by the time-series estimates of the parameters of equation (3). Therefore, Runs 3 and 4 enable us to examine the differences that are introduced if mortality declines of varying speeds are assumed, under conditions in which the level of mortality has a direct influence on fertility. Simulations 1 – 4 were all based on an assumed rate of growth of GNP of five per cent per annum. Thus at any time, while per capita incomes may vary, the size of total income (i.e. the product of the total population and GNP per capita) will be the same in all simulations. Each simulation was run separately under the assumptions of both 'constant' and 'changing' income shares described in Section II. We have labelled these the A and B series, respectively. For reasons of space we give the results in differing degrees of detail and by ten or twenty year intervals for time periods up to 150 years. 11 The results of the simulations are presented in Tables 3 – 1 3 . We now examine some of the implications of Runs 1–4 each in turn, before making some more general comments, on which we base a fifth series of simulations.

A Runs 1A and 1B Runs 1A and 1B (see Tables 3, 4) are based on the simple cross-sectional estimates of equations (3) and (4). Both runs are characterised by very slow declines in fertility (as measured by the gross reproduction rate) and mortality. Only in Run 1B do the level of fertility and mortality at the 'national' level attain their asymptotic limits. 12 In both 1A and 1B the rate of population growth remains at a very high rate throughout, and after 150 years the total population size in each has multiplied thirty-fold. We view such huge increases in total numbers as improbable. But the most interesting feature of these simulations is that they illustrate some circumstances under which – in the relatively short run, and at very low

50

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income levels – a redistributional strategy might lead to higher rates of population growth, and therefore lower levels of per capita income. Comparing the two simulations it will be noted that it is only after 100 years that the rate of population growth under 'changing shares' (1B) falls below that under 'constant shares' (1A), while it takes about 150 years for per capita incomes in the former to become greater than those in the latter. In this simulation then, redistribution only produced higher per capita incomes over the very long run. Several reasons account for the initially higher rates of population growth and correspondingly lower per capita income levels in the simulation incorporating the redistribution of income. First, in contrast to the Rich, the fertility response of the poorer income groups is inelastic to increases in income over the relevant per capita income range. Under 'changing shares' the fertility decline of the Rich is, delayed by approximately thirty years. This is because redistribution curtails their per capita incomes below the level at which there is a significant reduction in fertility. But at the same time, the correspondingly increased incomes of the Middle and Poor groups do not provoke a fall in their fertility 13 because at these income levels fertility is inelastic to changes in income. Clearly, these effects of income redistribution may be especially large if there is a range of incomes for which fertility actually rises with income. This is indeed the case for equation (4), which has a 'hump' at an estimated US$134. Such an outcome cannot be ruled out, for better nourished and healthier mothers may ovulate more regularly and may suffer fewer spontaneous abortions and stillbirths. If such 'biological' influences are at work, then a once and for all redistribution of income from the Rich to the Poor may initially increase fertility if the Rich are on one side of the 'hump' and the Poor are on the other. However, the fertility levels of both the Middle and Poor groups peak earlier in 1B than in 1A because the steady redistribution of rising incomes is powerful enough to haul them over the fertility hump while the growth in average income per head is not much affected. Thus, fertility in 1B exceeds that in 1A for the first 75 years because the fertility of the Rich stays at a high level in 1B, relative to 1A. This brings us to the second side of the story of the faster rates of population growth in the early decades under 'changing shares': comparatively fast mortality decline. After fifty years, aggregate life expectation in Run 1B is 52·4 years compared to only 50·0 years in 1 A; this is so despite a slightly reduced aggregate per capita income level in the 'changing shares' run. Of course, the higher life expectancies in Run 1B are a direct consequence of redistribution. Thus, in contrast to fertility under equation (4), mortality under equation (3) is responsive to the increased incomes of the Middle and Poor groups in the relevant income range. B Runs 2A and 2B Simulations 2A and 2B incorporate the much faster Taiwanese fertility decline estimated for equation (5). Not surprisingly, at the end of 150 years, per capita income levels in these runs are much higher than their counterparts in the first two simulations, and the total population sizes attained are much reduced, the trajectories of mortality being fairly similar.

FERTILITY, MORTALITY A N D INCOME

51

The form of equation (5) does not allow a fertility 'hump', so what we have termed the 'biological' fertility effect of increasing the incomes of the poor is not at work in 2A and 2B. 1 4 But the same general comments concerning the relative elasticity of both fertility and mortality to increases in income are again applicable. As a result, per capita incomes under 'changing shares' are lower than under 'constant shares' over the first 100 years. However, the faster fertility response at lower income levels of equation (5) (see Figure 2) means that, in these simulations, 'changing shares' brings about lower fertility than 'constant shares' at an earlier time period than was the case in simulations 1A and 1B. Thus fertility in Run 2B is lower than that in 2A after sixty years, while the aggregate rate of population growth is lower after eighty years. As in the first simulations, mortality under 'changing shares' is appreciably lower than that under 'constant shares' after sixty years or so. Improved life expectancy for the Poor is especially marked. Per capita income levels in 2B rise above those in 2A after little over a century, compared with nearly 150 years in the first simulations. Also, the total population in 2B is about 20 per cent less than that in 2A after 150 years; the corresponding difference between 1A and 1B is negligible. C Runs 3A and 3B The third set of simulations is based on the cross-section estimates of equations (3) and (6). These simulations incorporate the trajectory of fertility with respect to income when mortality moves according to the cross-sectionally estimated parameters of equation (3). The fertility declines in both 3A and 3B are faster than those of the previous simulations, and as a result the growth in total numbers is lower after a few decades. With a faster income-fertility response, there is little difference in aggregate fertility between 'constant' and 'changing' shares over the first forty years. But after that time the fertility decline in the latter is markedly faster, and total population in 3B lies below that in 3A after 75 years. After 150 years the population under 'changing shares' reaches zero population growth, its total population having increased almost nine-fold, and being about 40 per cent lower than that in the 'constant shares' run. For the first seventy years there are small differences in per capita incomes between the two runs, but after that time per capita income levels under 3B are markedly higher as a direct result of lower rates of population growth (see Figure 3). As in the previous simulations life expectation is higher under 'changing shares' for all time periods irrespective of whether average per capita income is above or below that in constant shares. However, as we have noted, at later time periods per capita incomes in 3B are well above those in 3A, and this affects the level of mortality in addition to the affects stemming from the more equal distribution of income. Through the mechanism of increased levels of per capita income, reduced fertility generates higher aggregate life expectation at birth. D Runs 4A and 4B The fourth set of simulations (4A and 4B) are also based on equation (6), but they incorporate the much faster mortality decline implied by the Taiwanese parameters estimated for equation (3) (see Figure 2). In these

GNP Per Capita US $

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10, 000

10

20

30

40

50

60

Runs 4A and 4B

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70

80

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Growth of Per Capita Income in Runs 3A, 3B, 4A and 4B

Figure 3

100

110

120

130

140

150

4A 3A

4A 3B

Time (Years)

52 P O P U L A T I O N A N D DEVELOPMENT

FERTILITY, MORTALITY A N D INCOME

53

runs redistribution has a very similar effect to that in 3A and 3B, i.e. after an initial period, 'changing shares' induces a fertility decline which, in turn, leads to reduced rates of population growth and higher per capita incomes. Because of the introduction of the faster mortality decline, levels of life expectation are now higher than was the case in the third set of simulations. 15 The effects of redistribution in terms of increases in aggregate life expectation are also much more pronounced: for example, after sixty years redistribution brings about an increase in life expectation of just under four years in the third set of simulations, compared with an increase of a little under ten years in these fourth runs. 16 By itself we would expect the faster mortality decline to increase the rate of population growth and reduce per capita incomes. Indeed, over the first 100 years, per capita income levels are a little lower in 4A and 4B than in 3A and 3B. However, as Figure 3 shows, over the long run the differences between the two sets of simulations are minimal. 17 In both 4A and 4B the faster mortality declines also induce faster fertility decline, and after 150 years both have lower populations than their counterparts in Run 3. Recently some writers appear to have questioned whether mortality decline will bring about any fertility decline at all. 18 Others seem to have questioned the wisdom of health programmes which reduce mortality but also bring about a more rapid population increase. However, while recent work may have indicated that the link between mortality and fertility declines may not be as simple or as strong as was previously thought, we remain sceptical of such extreme views. The fact remains that few populations have reduced their fertility to replacement levels without also experiencing either prior or concurrent mortality declines of some sort. And as one of the present authors 19 has argued, while fertility decline may not fully 'compensate' for mortality decline in the short run, the situation regarding the long term is far less clear cut. E Runs 1–4: Some Further Comments In discussing Runs 1–4 a number of issues were raised to which we will return. But collectively, these simulation results also raise new questions, which, in turn, enable us to evaluate some of the issues posed earlier in a better light. Taken together, the results of Runs 1–4 provide tentative support for a number of statements. First, it is clear that however useful they may be in exploring issues of an analytic nature, simple estimation procedures for the kinds of equations used above have some important limitations in representing dynamic situations. In all runs we stipulated that total national income should grow at the fairly rapid rate of five per cent per annum. But in spite of this, both the fertility and mortality declines produced in Runs 1–4 are exceptionally slow. For example, in no simulation does fertility decline to its lower asymptote in less than a century; even in Run 2B, which incorporates the Taiwanese fertility decline and 'changing shares', the gross reproduction rate is as high as 1·8 after eighty years. The mortality declines are generally just as unrealistic; for example, in Run 3B the upper limit of life expectation is finally attained after 125 years. One of the results of these slow transitions is an improbable growth in total numbers: in no simulation does the

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population increase by less than eight–fold, while in some total numbers increase thirty-fold. To our knowledge, few if any population projections for the future envisage such massive increases in total numbers. Likewise no projections are based on assumptions involving such protracted fertility and mortality declines. The implication is that cross-section or time-series parameter estimates of the kind used above are poor predictors of future fertility and mortality behaviour. A similar conclusion based on a study of regression coefficients of income on fertility was reached by Janowitz [1971]. However, cross-section estimates continue to be used as indicators of future trends. A second point to arise is that if the demographic transition is to occur within a reasonable period of time, it must be postulated that in the future, fertility will fall to relatively low levels at low levels of income per head. One of the reasons why parameters estimated on a cross-sectional basis are not applicable to the likely future experience of many LDCs stems from the fact that they stem from a data set including a large number of low fertility and low mortality countries (e.g. those of Europe and North America) which also happen to be very rich. Yet many LDCs cannot realistically anticipate comparable material living standards for many decades - if not longer. In many countries incomes will increase much more slowly than those in Runs 1–4, but fertility declines are still anticipated. So in the future, fertility in many countries will fall to relatively low levels at low per capita incomes. Of course, these statements are consistent with the proposition that any single regression line (whether or not cross-sectionally estimated) simply cannot describe the kind of experience of all countries past and future. However, one might expect countries at broadly similar levels of development to experience roughly comparable marginal declines in fertility and mortality for a given increase in income. Viewed in this light, the better regression fits obtained on a regional basis [Kirk, 1971; Janowitz, 1971], are not surprising. Thirdly, a single relationship between income and both fertility and mortality is inappropriate as a characterisation of fertility and mortality declines by income group. This point is analagous to that already made visà-vis the experience of different countries, only in this case the argument applies within the nation state. We can illustrate the proposition with reference to Run 3A, although the argument applies generally to all the other simulations. As Table 7 shows, in 3A, after sixty years the 'Rich' have reached the lowest possible levels of fertility and mortality. However for the 'Middle' and, particularly, the 'Poor' income groups at this time, the transition has scarcely begun. Indeed, the GRR of the 'Poor' only falls below 3·0 after ninety years, and the lowest levels of fertility and mortality are attained by this group only after 150 years. Now while it is likely that the 'Poor' would exhibit higher fertility (and mortality) throughout (and possibly subsequent to) the demographic transition, there is no reason to suppose that their transition would be effectively delayed by the ninety years or so indicated in Run 3A. What little evidence there is points to fertility and mortality levels falling more or less contemporaneously in all income groups, although there may admittedly be some relatively minor lags. But for fertility and mortality to fall in this manner in Run 3A, one

FERTILITY, MORTALITY A N D INCOME

55

must necessarily stipulate that each income group experiences different levels of fertility and mortality at the same level of income. For example, it might be reasonable to suggest that after 100 years all income groups have reached the lowest level of fertility (GRR = 1·04). But as the groupwise levels of income per head presented in Table 7 show, for these conditions to hold, the 'Poor' at year 100 must have near-replacement fertility at income levels below those of the 'Rich' at time zero; yet the Rich at time zero have fertility levels well in excess of GRR = 1·04. To conclude, the 'Poor' must attain low levels of fertility (and mortality) at much lower income levels than will the 'Rich'. One way of incorporating these observations into our model would be to specify, for each individual income group, separate equations linking income per head to both fertility and mortality. But a more straightforward way of proceeding is to represent the experience of different income groups in terms of a shifting relationship between income and fertility/mortality over time. This second alternative seems preferable (a) because it involves making relatively simple changes to our original equations rather than independently specifying six separate relationships, and (b) because it seems inherently plausible that the declines in fertility and mortality in response to the income changes of the Rich, Middle and Poor are related. In this context it is worth noting that one analysis of national crosssectional data on income and mortality has detected a limited shift over time. Preston [1975] writes that 'the relationship between life expectancy and national per capita income has shifted upwards during the twentieth century', and that 'Factors exogenous to a nation's level of income per head have had a major effect on mortality trends in more developed as well as less developed countries.' He suggests that improvements in health technologies are an important factor underlying shifts in the relationship. 20 F Runs 5A, 5B and 5C For these reasons, we now present a final set of simulations which incorporate time-shifts in the relationships linking income per head with fertility and mortality. The time-shifts were introduced into the crosssectional estimates of equations (3) and (6). This fifth set of simulations can therefore be compared with the standard reference runs 3A and 3B, in order to evaluate the effect of shifting the relationships over time. Figure 4 compares the original regression lines with those chosen as 'targets' and illustrates the nature of the changes. The relevant parameters of equations (3) and (6) were changed by regular increments over a period of fifty years in order to bring about the shifts. 21 While we contend that these alterations give a better representation of the dynamics of the situation, it must be emphasised that the changes are assumed not estimated; there exist no data for a direct assessment of their plausibility. There can be little doubt that Runs 5A and 5B are more consistent with common expectations of demographic change than runs 3A and 3B. Total population growth is now much lower, especially after 40-50 years; more importantly, aggregate fertility and mortality decline much faster than was the case in 3A and 3B. Again, the fertility and mortality declines by income groups are concentrated into a shorter time period, and as a result, the

Life Expectation of Birth

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45

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iiJ

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0

Figure 4

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:n> 400

B = 0.0264

500 600 GNP per capita US

B = 0.0132

5

Baseline and Target Fertility (GRR) and Mortality (E ) Curves

700

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56 P O P U L A T I O N A N D DEVELOPMENT

GNP Per Capita US $

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20

:1)

40 50

tJ)

70

80

Growth of Per Capita Income in Runs 5A, 58 and 5C

Figure 5

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120

1:1)

140

150 Time(Years)

5C 5A

5B

FERTILITY, MORTALITY A N D INCOME 57

58

POPULATION A N D DEVELOPMENT

timing of their relative declines is more plausible: the ninety year lag before the onset of the fertility decline of the 'Poor' is approximately halved in Run 5A. In both cases, the population has stabilised by year 150; 5A's level is then just over 30 per cent higher than 5B's. For the first thirty years, per capita income under 'constant' and 'changing shares' is almost the same. But subsequently, as Table 12 shows, income levels in 5B are greater than those in 5A. For all time periods after thirty years the rate of population growth under 'changing shares' is lower than under 'constant shares'. After 100 years the difference in rates of population growth amounts to 0·6 per cent per annum, and total per capita incomes are 40 per cent higher in the simulation incorporating the redistributional strategy. Clearly, both the fertility and mortality declines of both the 'Middle' and 'Poor' income groups are considerably hastened under 'changing shares'. The pronounced differences in the rate of population growth between Runs 5A and 5B leads us to pose a further question. It will be recalled that in the model, the level of future per capita income is determined both by the growth rate in the economy, and the rate of population growth (equation 1). Hitherto, in all simulations we have assumed a growth rate in the economy of five per cent per annum. But, after a lag, per capita incomes are always higher under 'changing shares' than under 'constant shares', even though the size of total national income is the same because the average population growth rate over the long run is higher under 'constant shares'. However, the implied reduction in the long run supply of labour under 'changing shares' prompts the idea that total output might therefore slow up a little. Also, we might take the (pessimistic) position that capital accumulation would be less rapid if incomes were redistributed from 'Rich' to 'Poor' households – at least, until the 'Poor' attain the initial income levels of the 'Rich'. In order to explore this possibility, simulation 5C incorporates 'changing shares', but the economy is assumed to grow at only 4·75 per cent per annum. The growth paths of per capita national income for Simulations 5A, 5B and 5C are illustrated in Figure 5. As can be seen, for the first eighty years, per capita income levels are lower in 5C than in 5A. However, for later time periods per capita incomes are markedly higher in 5C, even though the rate of growth of total national income is 0·25 per cent per annum lower than that in 5A. Thanks to income redistribution, mortality declines faster in the simulation with the slower rate of economic growth. Figure 6 compares the fertility declines in 5A and 5C, both by income group, and for the total population. It will be observed that in the case of the 'Rich', redistribution actually leads to a slightly slower fertility decline in Run 5C. However, for the 'Middle' income group, and particularly in the case of the 'Poor', fertility declines faster under 5C than under 5A. In these simulations the redistributive strategy brings about replacement levels of fertility some thirty years earlier. This is so despite the significantly slower rate of economic growth in 5C.

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Figure 6

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FERTILITY, MORTALITY A N D INCOME 59

60 IV

POPULATION AND DEVELOPMENT DISCUSSION AND CONCLUSIONS

Any conclusions arising from a modelling exercise of this kind must be viewed with a large measure of caution. The issues that form the subject of the paper need to be examined both in and outside the formal framework of models. Results are necessarily predetermined by the specification of the model, and any attempt at drawing policy implications must be tempered by the knowledge that strong simplifications are involved. This said, the simulations enable us to evaluate the effects of differing assumptions in a dynamic context; moreover, we contend that they raise a number of important issues of both a substantive and methodological nature. Here, we briefly draw out some of these implications and refer to other work. In all the simulations, after varying periods of time, fertility under 'changing shares' is substantially lower than under 'constant shares'. The lowered fertility consequent on redistribution, results directly from the non-linear form of the estimated functions linking income per head to fertility. If the form of these estimated equations does indeed characterise dynamic situations, then a redistributive strategy will always raise the fertility of the rich by less than it lowers the fertility of the poor provided fertility is convex in incomes over the relevant range. In each of the estimated equations (4), (5) and (6), fertility is a decreasing function of per capita incomes, but it is not subject to diminishing returns everywhere in all cases. 22 The data, particularly on distribution, are often poor; but several regression and other analyses of country-data indicate that a more equal distribution of income is associated with lower levels of fertility. While the relationship is certainly complex, and the direction of causation may not be entirely one-way, the authors of these studies all suggest that a redistributive strategy will lead to reduced fertility [Bhattacharyya, 1975; Repetto, 1974; Rich, 1973; Kocher, 1973]. In the model and its underlying reasoning fertility reduction is not the outcome of a more equal income distribution in itself; rather it results from the consequent absolute rises in income experienced by the poorer income groups. 23 Therefore, if the fertility response to changes in income is inelastic over a given income range, we would expect no fertility change to result from a change in income distribution. This possibility was noted in the early simulation runs: at first, redistribution actually led to higher aggregate fertility, because the fertility response of the 'Poor' and 'Middle' income groups did not respond immediately to the higher incomes brought about by 'changing shares'. In addition, through 'biological' influences the fertility of the poorer income groups might actually be temporarily increased. But the simulations also illustrated that over the long run, aggregate fertility will always be lower under 'changing shares' precisely because the 'Poor' will attain sooner those levels of income at which the fertility response is elastic. Moreover, for reasons stated earlier, we consider the more responsive income-fertility declines of the fifth set of simulations to be a far more plausible representation of the dynamics of fertility decline; and in such circumstances, the initial delay in fertility decline (if any) consequent on redistribution is much shorter. Therefore, while both fertility enhancing processes may result from a redistributional strategy, and thus lead to higher aggregate fertility, in practice these effects are likely to be small and relatively short-lived.

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These arguments apply to the circumstances of a poor country on the verge of the demographic transition. But at very high income levels both fertility and mortality are also inelastic to increases in income. Under these circumstances we would expect a redistribution of incomes to have a negligible demographic effect. As a corollary, income-independent indices such as the Gini coefficient are inappropriate measures for relating income distribution to the values of demographic parameters, because they define the problem of distribution solely in relative terms. 24 The simulations also illustrate some of the problems involved in economic-demographic modelling. We noted that there is no direct way of corroborating our initial assumptions regarding the representativeness of income-group demography. But a more important issue is that of determining the demographic changes it is plausible to associate with a given change in income, and the nature of this relationship. Simulations 1–4 demonstrated that the estimates of equations such as those in Section II, whether derived on a cross-sectional or time-series basis, must be poor predictors of future trends. And indeed it is a valid result in itself to show that such procedures do not provide a sound basis for extrapolating into the future. Introducing time-shifts into the relationships undoubtedly gives a better representation of what must occur in a dynamic context, but the changes so introduced necessarily remain rather arbitrary. Similar problems must characterise many modelling exercises, and presumably become more acute as the models themselves become more complex. The fact that single regression lines prove problematic in a dynamic context, and the introduction of time-shifts into the relationships linking income and fertility, indicate that factors other than increasing income and life expectancy levels also help bring about a decline in fertility. That future fertility declines with increasing income – whether on an international or income-group basis – will probably occur at a faster rate than in the past is almost certainly supported by Kirk's [1974] observation that fertility declines are accelerating over time. In many countries future fertility transitions will occur while levels of income per head remain low. One implication is that policy-makers should emphasise policies which might be expected to accelerate the provision of those correlates of fertility decline which may be enjoyed at relatively low incomes. In turn, this implies that in discussing policy options, the term 'redistribution' cannot be taken to refer narrowly to the redirection of monetary income alone. And of course, if one actually examines the list of such policy options open to planners it is clear that in addition to income transfers, redistributive measures likely to affect fertility include: (i) the redistribution of medical facilities; (ii) the redistribution of (increased) employment opportunities; (iii) land reform, and (iv) the increasing provision of education (particularly for females). Again, such strategies would only affect fertility levels after a period of time. Moreover, in the short term some of these policies might provoke higher fertility: for example, land reform could conceivably increase the demand for labour in the household economy. But in the medium term we would expect these redistributive policies to reduce levels of fertility; the provision of more widespread educational opportunities may be particularly potent. 25

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The simulations suggest that mortality reduction is another tangible benefit of such redistributive strategies. In all runs, 'changing shares' leads to significantly higher aggregate life expectation than 'constant shares' within a few decades: this is true even when average per capita income levels are temporarily reduced. Again, this stems directly from the non-linear form of the estimated equations linking per capita income with life expectation. Several studies have concluded that differences in income distribution cause variation in mortality levels between countries [Preston, 1975; Rodgers, 1977]. Of course, ceteris paribus, higher life expectation increases the rate of population growth. But, as we argued earlier (and as Runs 4A and 4B suggest) we expect that over the long run there is at least a partial compensation in terms of lowered fertility. Moreover the simulations suggested that, through its effect on income per head, fertility decline may itself contribute to higher life expectations at later stages of the transition. But despite (and in some measure, because of) mortality decline, in all simulations per capita incomes are eventually higher under 'changing shares' than under 'constant shares', because reduced fertility consequent on redistribution produced slower population growth and thus higher levels of income per head. Because of the youthfulness of age-structures typical of many developing countries, it takes a little time for reductions in personal fertility to be translated into reduced rates of population growth. But small differences in the timing of the beginning of fertility decline, can result in very large differences in the growth and eventual size of the stationary population [Frejka, 1973]. It is for these reasons that in the simulations presented here per capita incomes are eventually much higher under 'changing shares'. As is illustrated in Figures 5 and 6, redistribution may hasten both the timing and the speed of fertility decline, thus leading to higher per capita incomes. It is sometimes suggested that investments in the kinds of redistributive strategies mentioned above – for example, making access to education universal, or land reform – are less productive in terms of the growth of total national income than investments in more conventional sectors such as manufacturing industry. We do not accept that there is such a long-run 'growth cost' attached to these types of redistribution. But as the fifth series of simulations indicate, even should this be so, under quite plausible assumptions an economy might still pursue redistribution costing up to 0·25 per cent off the annual rate of growth of G N P without per capita income levels suffering. In that example, reduced rates of population growth are enough to compensate for a quite sizeable 'growth cost' amounting to 0·25 per cent per annum. In conclusion, the simulations provide tentative support for the 'new orthodoxy' so far as low and middle income countries are concerned. Particularly in low income countries – which often display high income inequalities – redistribution should have a significant impact on fertility, mortality and levels of living. The analysis suggests that by 'redistribution' we cannot simply mean changes in income shares. Rather we are talking of altering such things as employment structures, basic health coverage, educational opportunities, patterns of landholding, and so on. Policy-

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makers must accelerate the provision of correlates of fertility decline which may be enjoyed at relatively low income levels, because in the future, if fertility declines are going to occur at the rates described here, in many developing countries they will, of necessity, do so at low levels of per capita income. To hold that economic growth per se – the benefits of which may in any case accrue in relatively few hands – will bring about fertility declines at the speeds envisaged here is clearly untenable. The simulations suggest that the short–term response to such redistributive strategies may be slightly higher aggregate fertility levels. And there may well be a short time lag before such policies lead to lower fertility and reduced population growth. But over the medium to long term such redistributive strategies are likely to result in reduced rates of population growth and higher levels of per capita income. Our initial studies suggest that this may be true even should such policies have an associated 'growth cost' – which in any event we doubt. Medium- to long-term benefits do not always accord with political time horizons. And redistributional strategies must be evaluated on the basis of other considerations than those mentioned here. Nevertheless, if the demographic response occurs as postulated the contents of this paper constitute a modest challenge to the view that poor countries cannot afford redistributive policies. The authors would feel their work justified if this paper only succeeds in raising questions about what that response may be.

APPENDIX THE DATA GNP Per Countries

GRR

LEB

AFRICA Burundi Comoro Islands Ethiopia Kenya Malawi Mauritius Mozambique Reunion Rwanda Somalia S. Rhodesia Uganda United Rep. of Tanzania Zambia Angola Central African Republic Chad Congo Equatorial Guinea Gabon United Republic of Cameroon Zaire Algeria

310 300 3-30 3-30 300 1-59 2-80 2-13 3-40 300 3-25 300 3-30 3-40 3-20 2-70 2-60 2-85 2-50 200 2-70 2-90 3-50

390 42-5 380 500 410 65-5 43-5 630 410 410 51-5 500 44-5 44-5 38-5 410 38-5 43-5 43-5 410 410 43-5 53-2

Capita

(US$ '70 Market

60 140 80 150 80 240 240 800 60 70 280 130 100 400 300 140 80 300 210 630 180 90 300

Prices)

64

POPULATION AND DEVELOPMENT GNP Per

Countries

GRR

LEB

2-53 3-34 3-44 3-40 304 2-90 2-50 2-75 3-20 200 3-30 2-80 3-30 305 2-60 305 2-80 3-30 2-90 3-50 3-30 310 2-90 3-30 3-20

52-4 52-9 52-9 48-6 54-1 43-5 460 51-5 43-5 500 410 400 43-5 41-0 38-5 43-5 43-5 38-0 38-5 38-5 410 400 43-5 410 38-0

210 1770 230 120 250 110 90 760 180 160 90 120 310 120 250 310 240 70 140 90 120 230 190 140 60

CARIBBEAN Barbados Cuba Dominican Republic Guadeloupe Haiti Jamaica Martinique Puerto Rico Trinidad and Tobago

1-50 1-97 3-38 2-25 2-42 2-65 2-25 1-37 1-65

69-1 69-8 57-8 69-4 500 69-5 69-4 72-1 69-5

570 530 350 760 110 670 910 1650 860

MIDDLE AMERICA Costa Rica El Salvador Guatemala Honduras Mexico Nicaragua Panama

2-27 302 2-96 3-55 3-15 3-38 2-47

68-2 57-8 52-9 53-5 63-2 52-9 66-5

560 300 360 280 670 430 730

SOUTH AMERICA Argentina Chile Uruguay Bolivia Colombia Ecuador Guyana Paraguay Peru

1 45 1-80 1-43 300 2-87 3-07 2-22 3-03 2-83

68-2 62-6 69-8 46-8 60-9 59-6 67-9 61-9 55-7

1160 720 820 180 340 290 370 260 450

Egypt Libyan Arab Republic Morocco Sudan Tunisia Botswana Lesotho South Africa Swaziland Cape Verde Islands Dahomey Gambia Ghana Guinea Guinea-Bissau Ivory Coast Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo Upper Volta

(US$'70

Capita

Market

Prices)

FERTILITY, MORTALITY AND INCOME

65

GRR

LEB

GNP Per Capita (US$'70 Market Prices)

3-20 2-58 2-51

65-5 64-7 61-4

530 980 420

NORTH AMERICA Canada United States

116 107

72-4 71-3

3700 4760

ASIA China, Peoples Rep. of Japan Hong Kong Korea, Dem. Rep. of Korea, Rep. of Mongolia Burma Indonesia Khmer Republic Laos Malaysia Philippines Portuguese Timor Singapore Thailand Viet Nam, Dem. Rep. of Viet Nam, Rep. of Afghanistan Bangladesh Bhutan India Iran Nepal Pakistan Sri Lanka Cyprus Iraq Israel Jordan Kuwait Lebanon Saudi Arabia Syria Arab Republic Turkey Yemen, Peo. Dem. Rep. of

1-84 105 1-47 2-53 1 94 2-73 2-70 2-70 3-25 300 2-78 310 300 1 35 310 3-00 300 3-35 3-52 3-00 2-80 3-35 3-00 3-50 205 1-36 3-45 1-78 3-45 3-50 305 3-50 3-45 2-84 3-50

61 6 73-3 700 60-6 60-6 60-7 50-0 47-5 45-4 40-4 59-4 58-4 400 69-5 58-0 48-0 40-5 40-3 35-8 43-6 49-5 51-0 43-6 49-8 67-8 71-4 52-7 71-0 53-2 67-2 63-2 45-3 54-0 56-9 44-8

160 1920 970 330 250 920 80 80 130 120 380 210 110 920 200 100 200 80 100 70 110 380 80 100 110 950 320 1960 250 3760 590 440 290 310 120

EUROPE Bulgaria Czechoslovakia German Dem. Rep. Hungary Poland Romania Denmark Finland Iceland Ireland Norway

105 105 1-02 0-97 102 1-27 0-93 0-82 1-27 181 119

71-8 69-3 72-6 69-5 70-1 67-2 73-9 70-4 73-9 71-8 74-5

760 2230 2490 1600 1400 930 3190 2390 2170 1360 2860

Countries Surinam Venezuela Brazil

66

POPULATION A N D DEVELOPMENT GNP Per Capita (US$'70 Market Prices,

GRR

LEE

0-97 117 2-37 MO Ml 100 1-40 114 108 108 1-20 0-89 0-98 Ml 0-96

73-3 72-3 68-6 71-8 72-0 70-8 680 72-1 616 71-2 72-9 72-6 70-6 70-8 73-8 72-4

1020 650 2010 2720 3100 2930 2890 2430 3320

OCEANIA Australia N e w Zealand Papua N e w Guinea Polynesia Fiji

1-39 1-48 2-93 216 1-55

72-4 72-0 47-7 63-2 70-0

2820 2700 300 1890 430

USSR

118

70-4

1790

Taiwan Years 1950 1955 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973

2-94 3-17 2-91 2-78 2-70 2-65 2-59 2-47 2-34 2-34 204 209 1-99 1-94 1*80 1-63 1-55

54-6 62-5 64-3 64-4 650 65-3 65-8 66-5 67-2 66-9 66-9 66-8 67-9 68-6 68-9 69-4 690

129 180 202

Countries Sweden United Kingdom Albania Greece Italy Malta Portugal Spain Yugoslavia Austria Belgium France Germany, Fed. Rep. of Luxembourg Netherlands Switzerland

1-26

Sources: U N [1975] and World Bank [1972].

4040 2270

600 1090 1760 810

660

206 214 223 236 257 278 293 315 337 352 377 411 448 495

FERTILITY, MORTALITY AND INCOME

67

TABLE 1 SUMMARY BASELINE PERCENTAGE AGE DISTRIBUTIONS OF THE RICH, MIDDLE, POOR A N D TOTAL POPULATIONS

Age Group 0- 4 5-14 15-49 50 +

Rich

(%)

15-57 25-70 47-56 11-17

Population Middle (%)

Poor

(%)*

17-24 25-76 46-77 10-23

17-25 25-39 46-74 10-62

Total

Population 16-91 25-60 46-92 10-57

T h i s age structure is a stable population model, with a rate of population growth of 2·3% per annum, a life expectation at birth of 40·2 years, and total fertility rate of 6·4 live births per woman. Source: N. Carrier and J. Hobcraft, 'Demographic Estimation for Developing Countries Population Investigation Committee, London, 1971. pp. 192-193.

TABLE 2 INITIAL CONFIGURATION OF INCOMES, FERTILITY A N D MORTALITY BY INCOME GROUP

Income group

Rich Middle Poor