Report No. EC-157

FILE COpy

This report may not be published nor may it be quoted 'JS rep,'esenting the view of the Bank and its affiliated organizations. They do not accept responsibility for its accuracy or completeness.

INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT

INTERNATIONAL DEVELOPMENT ASSOCIATION

A COST-BENEFIT APPROACH TO EDUCATIONAL PLANNING

Economics Department Prepared by: Mark Blaug

IN DEVELOPING COUNTRIES

December 20, 1967

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TABLE OF CONTENTS

Page

PREFACE ...................................................... " .. i

I. INTRODUC'l'ION •••••••••••••••••••• e - • • • • • • • • • • • • • • • • • • • • • • • • • 1

II. THE MANPOWER-FORECASTING APPROACH •••••••••••••••••••••••••• 3

1. International Compariso~s •••••••••••••••••••••••••••••

2. The Mediterranean Regional Project ot OECD •••• o •••• v ••

3. Some Standard Objections to the M.R.P •••••••••••••••••

4.

5~

The Links Between Occupation and Education

Testing the Accuracy of Manpower Forecasts

... ' ........ .

............

3

7

9

10

12

6. Long-Term or Medium-Term Forecasts •••••••••••••••••••• 15

III. THE RATE-OF-RETURN APPROACH •••••••••••••••••••••••••••••••• 19

1. Some Standard Objections to Rate-ot-Return Analysis ••• 19

2. lmperfections in the Labor Market ••••••••••••••••••••• 21

3. Shadow Rates ot Return •••••••••••••••••••••••••••••••• 22

4. The &:nphasis on Employment Opportunities •• p, iI......... 25

5. Predicting Rates of Return •••••••••••••••••••••••••••• 28

IV. TWO VIEWS OF THE STATE OF THE WORLD •••••••••••••••• ~ ••••••• 30

1. Variable Versus Fixed Coetticients •••••••••••••••••••• 30

2. The Doctrine ot Educational Flexibility ••••••••••••••• 33

3. The Integration ot Various Approaches ••••••••••••••••• 35

V. THE APPROACH IN PRACTICE . ................................. . 37

No.

PREFACE

This study originated from a grQwing awareness that the methods currently used in planning expenditure for education in developingcoun­tries fail to take into account important links between the educait:i.onal system and the economy. For one thing, current methods take the ~~conomic value of more education largely for granted ; they do not attempt t'9mea­sure the benefits of the various types of education in monetary te:t:~,~)~ thus precluding systematic economic analysis of the benefits as well/ as the costs of providing additional education by type and level. As a corol­lary, little, if any, attention is being paid to the role of earnings in the demand for and supply of educated people in a country; both are af­fected by earnings and not independent of each other.

The present paper takes up both these issues. It was prepared for us by Dr. Mark :Blaug of the University of· London Institute of Education and the London School of Economics. The study first reviews the methods of educational planning currently in use. It then shows that they are complementary rather than conflicting, and integrates them into one ~p­proach. The essenc,e of this integrated ,approach is that it systematically compares the cost of various types of education with the employment and earnings prospects of the people to be so educated, due account being taken of the changes in the demand for and supply of educated people likely to occur over the relevant time period. In other words, what is suggested here is forecasts of demand and supply schedules as functions of earnings, the latter reflecting the marginal productivities of the different types of education concerned. No mean task, the feasibility of which remains to be tested.

The author gratetully acknowledges the stimulating comments on an earlier draft from Bank staff members, particularly in the Education Division of the Projects Department and the Investment Planning Division of the Economics Department. Discussions with Cornelis van Dijk, Jochen Scbmedtje, Hans Thias and Herman van der Tak. were especially helpful. How­ever, the views expressed in this paper are those of the author, and he alone is responsible for them.

This study is part of continuing work fin' the Economics Department on problems of sector and project analysis. Earlier studies given circula­tion outside the Bank include The Evaluation of Agricultural pro~ects: A study ot Some Economic and Financial Aspects (EC-128, M~ 7,196 }, Economic As ects of Wa.ter Utilization in Irri ation Pro ects (EC-132, January 22, 19 5 , On Estimating the Economic Cost ot Capital (with special reference to developing countries) (EC-138, October 21, 1965), An Economic Reappraisal of a Road Pro ect: The First Iranian Road Loan of 1959 (IRN-221) (EC-147, Se;ptember 2 ,19 ,The Economic Choice between droelectric and Thermal Po~er Developments (World Bank Staft Occasional Papers Number One, 19 ",­and Quantification of Road User Savings (World Bank Staff Occasional Papers Number Two, 1966).

Andrew M. Kamarck Director

Economics Department

I. INTRODUCTION

1. This paper attempts to show how cost-benefi t'analysis can be applied to educational projects in low-income countries. It is specifi­cally addressed to the operational problems of certain in'ternational agen­cies, such as thec,International Bank tor Reconstruction and Development, that must assess applications for loans to finance particular, educational projects without time to conduct elaborate survers or to carry out primary research into the condi tiona prevail,ingin particular countries. However, I will contine most of my discussion to "programs": by an educational program is meant an entire field ot activity that is either justified in terms ot the economic development . plan ot the country and/or in terms or certain widely accepted social gous - say, expansion ,ot thesecon-da'i'y school system - while a "project'~ iJ.3& separable activity fitting into a larger program - say, the construction of teacher traini.'1g colleges whose graduates are designed to take up teaching in secondary schools.

2. I take it tor granted that proJect aid cannot be evaluated with-out evaluating the larger program of which it is a part. Hence, project evaluation begins by!lookilng at programs, or, better still, by appraising the entire educational plan of the country. In other words, the question: "How much should a country spend on education?" can be approached at three levels ot generality. Firstly, what proportion or tile national income should be devoted to education? Secidndly, how should the total be divided among the dirteren~ levels or tiers of the educational system? Thirdly, at any particular level, how should the total be divided among academic and vocational. education, among expansion of numbers and improvements ot quality~) among formal education and inrormal training, and so ont This paper is largely directed towards the second type of question, but indica­tions will be given or how the third can be approached.

3. 1"urthermore, I shall s~ very little about the budgetary implica-tions of educational projects or about the effects of particular projects on the structural balance of the educational system. Once again, I take it for granted that project evaluation will take account ot the future current expenditures entailed by the project, as well as the role of such built-in constraints as a lack of qualified teachers that m., impede the realiza­tion ot a project. It I B8¥ nothing about these things, it is not because they are unimportant - tar trom it - but because I have nothing new to sq about them: They are pta't ot the tool-kit of every educational planner.

4. Having said what the paper is not about, it is time to suggest its substantive content. The bulk of it is devoted to a review ot the methods that are now being used both in developing and advanced ccuntries to assess the economic benefits ot educational programs and projectsf As will become evident in the course of the argument, I contend that non~ ot the current methods alone is satisfactory and that, indeed, the entire field of edl1~a.tional planning stands in need of a broader approach. In

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previous contributions, I have argued this case for the developed coun .... trIes.!.1 Hut. results for the developed world cannot be eusily trntm­ferred t.o tht'" leas developeu countries. It remains to be seen, therefore, whether tht.! thenis will stand up in the context of low-income countries. Hence, this paper.

5. Before we begin our discussion of methods of a5sesoing the economic benefits of educational programs, there is one difficult.y that ~e m~t raise at the outseto An increasing number ot low-income cowltries todaY gear educational expansion to specific economic objectives, such as maximizinp; the rate ot growth ot incrnne, or perhaps more accurately, they gear educational plans to specitic economic objectives provided certain 80cial and political constraints are satisfied, such as securing greater equality of' educational opportWli ty, or meeting some of the pressures for Wliversal primary education. However, cOWltries differ in the degree to which they all()w economic'obJectives to override other considerations and, cle&rly, it is impossible to evaluate educational programs until it is

.. discovered just what objectives ·the country in question is aiming to sat­is:r;y. III the la:nguage of economists, no decisions about planning can be taken until the planner has made up his mind about hiB own preference tunction.. For the purpose of clarifying the discussion,we will assume throughout most ot what follows that educational planning in low-income countries gives priority tp. economic considerations, without however stipu­lating in advance what thes" considerations are. In other words, ~e are goi~g to assume that the typical developing country is like Tanzania, which in 1963 decided first to concentrate on secondary and higher education in order to meet specific manpower targets and then to devote any remaining remources to expanding primary and intormal adult education for purposes ot accomplishing not only economic but also social and political objectives. In the tinal section or the paper, however, we will return to the soci a1 and political goals of education in order to ask whether they can somehow be added to the economic goals.

6. Having isolated the economic objectives of educational. planning, one might th~nk that one could proceed straightaw~ to consider the appli­cation ot cost'-benefit analysis to investment decisions in education. How­ever, thia 11 not the case. The issue is contelted by two oppol1ng Ichools ot tho~ht, with numerous sub-varietie. thereof:· the manpower-torecastlnF. approach and the rate-ot-return approach. The manpower-forecasting approach tells the educational planner to tailor the expansion ot the educational

-' .. _--'.-'---. ---\r'--Y M • .Blaug, '\(The Rate ot Return on Investment in ~ducation in Great

Britain", ~he Manchester School, September, 1965; "An Economic Interpretation or the Pri vateDemand tor Education*'. Economl.ca, May, 1966; "Approaches to Educational Planning". Econom"tc·'JO'Urnal, June, 1967. The present paper is closely relate~-particular~ to the latter study. All three. papers are reprinted by the Unit tor Economic and Statistical. Studies on Higher Education at the London School ot Economics.

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system to quantitative forecasts of the demand for highly qualified manpower. The rate-of-return approach advises the planner to calculate the internal rate of return on investment in education and to supply just enough schooling to equalize the yield of investment in human capital with the yield of investment in physical capital. It is not obvious that these two approaches will give the same answer and, indeed. they rarely do. The advocates of manpower-forecasting reject the notion that monetary earnings in low-income countries reflect people's contribution to productive capacity, an assumption that underlies the rate-of-return approach. The proponents of rate-of-return analysis, on the other hand, are equally critical of the idea that :tuture manpower requirements can be predicted with sufficient accuracy to constitute a firm basis for long-term planning of education. The manpower-forecasting approach is clearly in the ascendancy in low-income countries, but, as the costs of education do not enter into the manpower-forecasting approach as an independent variable, this development is one that must make most economists feel uncomfortable. To be sure, the rate-or-return approach is cast into the mould ofeost-benetit analysis but as almost all developing countries lack d&ta on the earnings of educated people, this approach, even if one waived all the obJectiona to it, seems to be non-operational at the present time.

1. Hew then are we to evaluate educational projects in developing count~ies? Must we make do with the manpower-forecasting approach as "quasi-cost-benefit-analysis", expressing costs in monetary and benefits in physical terms? Or should we wait upon the further development ot rate­of-return analysis? Is either or both of these approaches wrong or can they somehow be combined? The contention of this paper is that the two approaches are basically complementary rather than competitive, but not as these approaches are now conceived in the literature or as they are actually applied in (~ifferent countries. Thus, without turther ado, we must begin by showing why either approach considered by itself is unsatisfactory.

II. THE MANPOWER-FORECASTING APPROACH

1. International Comparisons

8. One ot the tirst educational plans for a developing country that was based on the manpower-forecasting approach, namely, the Ashby Report on Nigeria, 2/ employed an empirical rule-of-thumb provided by F.H. Harbison: if' GNP was to grolT by X percent per year, the stock ot third-level higher educated manpower should grow by 2X and the stock of second-level manpower by 3X percent per year. What the Harbison-rule implies is that the ratio ot the stock of educated manpower to national income does not matter; all that counts is rates of growth. No rationale and certainly no international comparati ve evidence was ever published, either by Harbison or by anyone

y lnvestment in Education, !he Report of the Commission on Post-School Certificate and Hisher Education in Nigeria (Nigeria: Federal Ministry of Education, 1960).

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else, to justi~y the famous 2:1 and 3:1 growth ratios. 3/ However, Tinbergen's Netherlands Economic Institute has tested the following re­gression equation linking second and third-level manpower to GNP and GNP per head with cross-section data derived from 23 developed and developing countries. 4/

(1) N3 = 5.20 (y x 10-6 )10202 (y)-0.164 P

(2) N2 c l63.67-(Y x 10-6)1.314 (1)-0.655 P

where N3 = third-level manpower stocks N2 ~ second-level manpower stocks Y • National income in 1957 u.s. dollars y P

= National income per head in 1957 u.s. dollars

Equation (1) can be rewritten as:

N3 = 5.20 (y x 10-6 )1.202-0.164 (p ~ 10-6)°.164

= 5.20 (y x 10-6 )1.038 (p x 10-6 )°.164

and, similarly, tor equation (2):

N2 • 163.67 (y x 10-6 )°.659 (p x 10-6 )0.655

l~ other words, a one percent increase in national income tends to be associated with a 1.038 percent increase in third-level and a 0.659 per­cent increase in se~;ond-level manpower stocks, while the corresponding

11

'J/ They are mentioned but not Justified in F.H. Harbison, "The Strategy of Human Resource Development in Modernizing Economics", Policy Conference on Economic Growth and Investment in Education, III (Paris: OECD, 1962), p. 15; F.H. Harbison and C.A. Myers, Education, Manpower and Economic Growth (New York: McGraw-Hill, 1964), pp. 200, 207-Apart from Nigeria, the Harbison-rule has been used uncritically ·'for purposes of forecasting in East Africa and. in South-East Asia: see G. Hunter, Ed.ucation for A Developin, Region. A Study in East Africa (London: George Allen & Unwin, 1963 , p. 59, and G. Hunter, Higher EDcuation and Development in South-East Asia, Volume III, Part I, High Level Manpower (Paris: U.N.E.S.C.O. - I.A.U., 1967), p. 23. For same theoretical arguments to throw doubt on the Harbison rule-of­thumb, see E.R. Rado and A.R. Jolly, "The Demand for Manpower. An tast African Case Study", Journal of Development Studies, April, 1965, pp. 231-33.

1jj Netherlands Economic Institute ,,"Financial Aspects of Educational Expansion in Developing Regions: Some Quantitative Estimates", Financing of Education for Economic Growth (Paris: OECD, 1966).

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population elasticities are 0.164 and 0.655, respectively. Thus, the sug­gestion is t.hat the number of college graduates and lother professional men whqse work JreqUires a college degree or equivalent training should grow somewhat fe.ster than national income, the exact rat:ii:o depending on the rate of "growth elf population .•

9. Wi.thout quibbling over the size of the sBll1ple from which these estimates are derived - only 23 countries out of about 150 around the world - the fact of the matter is that equation (1), represents the outcome of the intersection of a series of demand and slr,f>pl.y schedules and not the demand for graduates in the economist's sense of the term "demand", or even of graduates required to produce a given level or ]~ate of growth of national income. The very fact that the sample includes onle or more Asj.an countries with he:avy graduate unemployment is enough to suggest that equntion (1) is tar from specit,ying the optimum level of graduates tor given levels of national. income. Educati,onal planning based on sllch regression equati·ons runs ~che danger of merely reproducing past misallt.)cations of manpower in more 'advanced countries. 21

10. This point is se rarely understood that it m$Y be useful to spend .ano~.;her moment o;p. it. The need for manpow'er for'ecasting deri v~!s trom the fac.t that in ~bst all countries surpluses and/or deficits of educated pe(Jple exist; in other words, ordinary carket f(.)rces cannot be trusted tel balance the relevant supplies and demands. By implication, manpower forecasts must assume that the market has ever,y~here tailed to allocate manpower resources optims.lly. For that reason, attempts to estimate the educational structure of the labor force as a 'direct function of the rate of growth of national income or output per head with the aid of cross­section data for different countries falls short of solving the problem of forecasting the manpower requirements of econcGic growth.

11. Even if we waive this point, howe"er, it is not clear how one should interpret Tinbergen-type regression$. For example, educational planners in Uganda have been using Tinbergen regressions since 1962 as a basis for expanding the educational system'. Gi ven the recent growth rates

of GNP and population in Uganda, 13 shouliJ. be growing at roughly 0.82 times and ~ at roughly 0.67 times the rs.te of growth of income. Never­theless, when we apply the absolute valu'!S for Y and P in Uganda in 1966

2/ In view of this tact, it is s~rising that India, a country with hea'\7 graduate Ullemployment, should now be basing its educational planning on the Tinbergen equation: Report ot the Indian Education Commission (1964-66) •. Education and National DeVelO~ment (Delhi: Ministry of Education, Governmebt of India Press, 19 6), pp. 94, 99, and the more detailed version ot the torecast by T. Burgess, P.R.G. Layard, and P. P~t, Manpower and Educati onal Devel01ment in India (1961-86) (London: Oliver & Boyd, forthcoming, 1968 •

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to equations (1) and (2), actual. N3 was 99 percent of the formula value,

whereas N2 was only 42 percent of th~ formula value. 6/ In other words, the crucial shortage in Uganda appears to be in middle-level, not in high­level manpower, and yet high-level manpower is supposed. to grow faster than middle-level manpower. The real difficulty is that it is not clear what we

should be aiming at: is it to get N3/Y and N2

/Y up to a certain level or

is it merely to mainta.in certain growth rates of N3 and N2 in relation to Y? To put the same point differently.: should we worry if a certain country is above or below the regression line, or should we p~ attention only to the slopes of the regression? No doubt, clever arguments can be tound on both sides of this question, but. the fact remains that the planning impli­cations of Tinbergen regressions are tar from selt-evident.

12. The main weakness of Tinbergen-type regressions is, ot course, their tailure to s~ anything about the costs of different levels and types of education. As we shall see, this is a typical feature of all variants of the manpower-forecasting approach. Furthermore, Harbison rules-ot-thumb and Tinbergen regression manage, surprisingly enough, to go directly from education to income, thus bypassing all the problems ot occUJ)ational classifications I, Recently, we have been furnished with cross­section data on the economically active population ot 30 countries in 1960, . cross-classified by four occupations, eight economic sectors, and two mea­sures of educational attainment. 11 The conclusions of this study' are difficult to summarize except to sSf that the moment one begins ~o look at individual economic sectors rather than at the whole economy, or at individual occupational categories rather than at the entire employed population, the beautiful simplicity of equations (1) aDid (2) evaporates; what we are left with is an amazingly heterogeneous and diverse picture whose implications for planning are even more difficult to assess than those ot the Tinbergen regressions. 81 Moreover, the results are qlri: te different when educational attainment is measured as the mean yelirs of schooling of workers in each occupatitJn than when it is measured as the cumulative

.§J

'1J

See J.A. Smyth and B.L. Bennett, "Rates of Return on Investment in Education: A Tool tor Short Term Educational Planning, Illustrated with Ugandan Data", '!be World Year Book ot Education 1967. Educational Planning, eds. G.E. Bered~, J .A. Lauwerys, M. Blaug. (IDndon: Evans BrOSe, 1967), pp. 307-8.

P.R.G. Layard and J .C. Saigal, "Educational and Occupational Chuac­teristics of Manpower: An International Comparison" • British Journal of Industrial Relations, July, 1966, reprinted by the Unit tor Eco­nomic and Statistical Studies on Higher Education, London School of Economics, 1966. See also M.A. Horowitz, M. ~elman, I.L. Herrn­stadt, Manpower Requirements for Planning. An International Compari­son Approach (Boston: Northeastern University, 1966), Vol. I.

The results are conveniently summarized in scatter-diagrams at the close of the Layard-Saigal article.

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frp.qll-:!ncy of workers with' X years of schooling or more in ea.ch oc{.'uPR.t, i nrl.

Suffice it. to say, that Tinber~en regressions for the whole economy or for indi.vidual sl=?ctors and occupa.tions may serve as a check. on the "feA.sjblp" (;in t,he R~nSp. of:" a.ttained by other cOWltries) rates of e;rowth of stock~ ()f

educa.ted manpower for p:i ven ra.tes of growth of GNP or output per hea.d. but however far they are refined they ca.n never specify optimwn levels of grQ:wth: they CRn tell the educa.tiona.l planner what is or ha.o bcen but. they cannot by themselves tell him wha.t shC?.uld be.

13." T'nis is so because, first of all, they totally neglect the q.uestion of the optimum level of formal education tor particular occupa­tions, implying that the occupational structure of a labor force simply does not matter from the point of view of economic growth; a.nd, secondly, beca.use they fail to dea.l wit'h the "identi.fication problem", that is, how to sepa.rate the forcet:; of supply from the forces of demand. The task of the manpower forecaster is, presumably; to find a method of estimating the optimum amount and. type of education for each job-cluster that is not a mere reflection of existing occupation~education relationships. After all, in most countries educated people, however irrationa.lly produced, ha.ve somehow been absorbed into employment by upgrading and downgrading of' jobs; what we observe tod8¥ may simply represent the misa.llocations of the past .• Tinbergen regressions are not of much help in solving this basic di f.ficulty.

2. The Mediterranean Regional Project of OECD

14. This brings us to a manpower-forecasting. method in which occupa-tions figure prominently, the OECD Mediterranean Regional Project, to date one of the most sophis·ticated examples ot the manpower-forecasting approach to educational planning. The basic method applied in the M.R.P. is to proceed stepwise from a projection of a desirable GNP in some future year to the supply of educated manpower "required" to realize tha.t GNP in the target year. 9/ The steps are as follows: (1) the desirablp GNP in, say, 1975 is broken down by major sectors, such as agricQlture, manufacturing, transport, distribution .. , and the like; (2) the correspond­ing average labor-output coefficients, the reciprocals of the sectoral productivities ot labor, are applied to the sectoral GNP targets, yielding a torecast of the sectors' labor requirements; (3) the labor torce in each sector is distributed among a number ot mutually exclusive occupatibnal categories; and (4) the occupational structure ot the labor force is con­verted into an educational structure by applying a standard measure of the level of formal education :tha.t is :teq.uired to perform adequately in each occupation. I,n other words, we multiply the desira.ble GNP by four coeffi­cients:

------,----2/ The method is explained and defended by R.S. Parnes, Forecast~

Educational Needs for Economic and Social Development (Paris: OECD, 1962). The M.R.P. Country Reports for Spain, Italy, Greece, Yugoslavia., and Turkey give concrete evidence of how the basic method was applied in particular countries.

G.N.P. L (G.N.P.). s. s

G.N.P. G.N.P. s

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L..J.. • L e L -- = workers of educations e in occupa-

s Lj tions ~ in sectors s

where G.I.P. = G.I.P. originating in each sector • L = the labor force in each sector s Lj = the labor force in each occupation

L • the labor force.with each level ot ~ducation. :/e /l L-

Finally, an allowan<:e is made for deaths, retirements and emigration, that is, tor replacements as well as for additions to the stock of educated manpower. The final result is a condi tiona! forecast of the demand for educated people in 1975, cionditional, that is(t on the aChievement of the GNP target.

:(

15. The difficulties in this method center largely on steps (2) and (4), although step (3) also raises controversial questions. The standard procedure for f6recasting labor-output coefticients, step (2), is to extra­polate past trends , either as a functi.on of output or as a function of time. When time series on the productivity ot labor are not available, the standard alternative device is that of adopting the coetticient ob .... served in more advanced countries, on the notion that there ,are definIte "manpower growth paths tI that all economies follow in the course of de!ivel­opment; a variant of this is to take the labor-output coefficient ruling in the most advanced.sector ot the econolDJ on the grounds that the bE!lst­practice technique ot, that sector will eventually become the average·,· practice technique ot all sectors. Lastly, t'here i. the tec~niQ.ue· ot ask­ing employers to estimate their own tuture labor requirements, given a certain rate of expansion, in the market for their products.

16. The problem ot forecasting the productivity ot labor is, of course, an old problem, in applied economics and one that is perfectly familiar to most economists. This is not so with the ditficulti.es en­countered in step (4) of M. R.P., namely, the translation oflabolr require­menta by occupation into labor requirements bY' educational qualifications: there is little guidance here in the economic jl.i terature. The simI/lest method 01' converting occupation into education' is to apply the ml1!8Jtl number of years ot schJ'~~ling currently observed in eac:h occupation or jl)b·-cluster, &1'though the m£de would perhaps be a better me.lsure than the me8J:l," How­ever, the concept of mnimlDll educat fonal at tail!lltllent for ! at is fac'\~lory per­formance in a job is not adequatel1' expressed 'by a scalar such al~ the mean or modal years ot schooling. In any case, thi:s is not what the ~~ducational planner wants to know: his decisions have to bt~ made in terms C;):~ ditferent types of education. The problem, therefore, ;Ls that of sp,ecif'y'iJ:1g & vector that depicts the combination ot varying amoun'tsand types of frJrl/Dal educa­tion required in difterent occupations. So t'ar , despite m.any at:tempts to develop such educational vect9rs , it cannot l')e =:said that this technical difticulty has been s~tisfactorily resolved.

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3. Some Standard Objections to th~ M.R.P.

17 • So much then for the method. What can we s a.y about its validi ty? First of" all, the advance of both total-tactor productivity (output per unit ot both labor and capital) and labor productivity (output per unit of labor) among sectors in an economy like that ot the United States - the only country for which there is historical data to answer the question - is quite irregular over time and seems to exhibit no simple patterns or trends that could be seized upon by a manpower forecaster. 10/ Furthermore, it is doubt­ful, simply on theoretical grounds, that all countries move along the same "me.npower growth paths", that is, arrive at similar occupational distribu­tions of the labor force for identical levels of income per head. 11/ At any rate, as we just saw, the comparative evidence is ambiguous, a.ndnot such as to allow us to conclude that manpower forecas.ts can be based merely on imitation of richer countries. Similarly, we know too little about the rate ot diffusion of b~st-practice techniques within and between indus­tries to introduce simple assumptions as to the process of "catching up" with the mlost advanced industry or sector of an economy. And, tinally, the tachnique of asking business firms to forecast their labor require­ments at i:ncome growth rates 'that they may have never experienced before assumes thlat they can predict their market shares independently of the activities of rival firms. Thus, step (2) , involving a prediction of the productivity of labor, lacks firm theoretical or empirical foundations. Unfortunately, the forecasts of the M.R.P. have been found to be highly sensi ti ve to small changes in the labor-output coefficient. 12/

18. Even if we could somehow predict productivity changes, we still have to cross the hurdle of occupational classifications and of converting these into educational equivalents, steps (3) and (4) of the M.R.P. exercise. Here the first problem is that there appears to be no unique relationship in the labor force bet"Aeen education.al background and occupational affilia­tion, except for professions such as medicine and teaching where custom and tradition impose a minimum entrance qualification. For example, there is considerable variance about the mean in the number of years of),~chooling observed in different occupations in the 1960 u.S. Census of Population. Furthermore, although there is a moderate association between education af!d occupation in the American labor force, both in 1940 'and in 1960, the significance of this association has been declining over time and most of the change that occurred in those twenty years was attributable, not to a shift from jobs requiring little to thOSe requiring more education, but

J.W. Kendrick, Productivity Trends in the United States (Princeton: Princeton University Press, 1961), chap. 6 j pp. 133-89.

The case fc:)r the existence of manpower growth paths is thoroughly canvassed ,dth skepti.cal conclusion by R.G. Hollister, "The Economics of Manpower Forecasting", International Labour Review, March, 1961~.

12/ R.G. Holli~l~ter, A Technical Evaluation of the First Stage of the Medi1;erra.nE!~an Regional Project (Paris: OECD, 1966), pp. 60-62.

!J

- 10 -

rather to a rise in educational attainments 'W'i thin occupations, that is, to "upgrading". 13/ No d,Qubt, these findings about the United States are not necessarily applicable to the developing countries but all the time­series evidence that we do have - &11 of which pertains to developed or semi-developed countries - points in the same direction. 14/

4. The Links Between Occupation and Education

19. The difficulty in step (4) is deeper, however, than the lack of a one-to-one relationship between 'occupation and education. Manpower forecasters themselves are the first to admit that the association between occupation and education is a loose one but they do insist that it is con­strained within definite limits; they see their task as forecasting the minimum. educational requirement of a job. However, satisfactory performance in an occupation is a complicated function of nati ve ability, psycho-motor skills, work experience, on-the-job training, formal educational prepara­tion, etc., and we are far from understanding just how much formal educa­tion contributes to this mix. The notion that it is possible to stipulate the minimum educational preparation for an occupation is in fact based upon an arbitrary judgment that there is a unique relationship between occupation and education.

20. On purely theoretical grounds, one could think of two extreme ways in which education and occupation are linked: (i) there is a certain well defined educational qualification for each occupation, such that with less education it is impossible to carry out the assigned task, but that more education adds nothing to the individual's productivity; and (ii) the pro­d~ctivity of a person increases proportionately to the amount of education received. These two cases are illustrated by lines A and B, respectively, in Figure 1: Figure 1 refers to a specific occupation and measures educa­tional qualification as a scalar on the horizontal axis and productivity (or performance rating in the occupation) on the vertical axis. In reality, the two cases will take the form of relationships that are characterized by S-shaped curves such as AI or B' in Fig. 1: initially, the productivity of a worker increases at an increasing rate as he is given more education, but beyond a certain point it grows at a decreasing rate and eventually levels off. Whether the S-shaped curves are typically steep, so that they approach

See J.K. Folger, C.B. Nam, "Trends in Education in Relation to Occupatipnal Structure", Sociology of Education, Fa.ll, 1964, and the author's Education of the American Population (a 1960 Census f..1onograph) (Washington, D. C.: U.S. Government Printing Office, 1967), pp. 165-73.

See, fo.r example, Parnes, op. ci t., pp. 112-113; C .A. Anderson, "Pat­terns and Variability in Distribution and Diffusion of Schooling", Education and Economic Develo ent, eds. C.A. Anderson, M.J. Bowman

Chicago: Aldine Publishing Company, 1965), pp. 321-324; and Hollister" A Technic;al Evaluation, op It ci t., pp. 60-62.

". d

Productivity

- 11 -

/, /

/" I /' I

./ / ~~ ----'

Fig. 1

A ~ - - - ..-- -:::..--

/1(1 /~

I ./ I,. BI/ , .. /

~

Education

the for.m of AI, or rather flatter, so that the,y resemble B', is one of the main questions in the debate between advocates of the manpower requirements and rate-of-return approaches.

21. If the real world is correctly depicted by AI, the relationship between occupatiollL and education is a purely tecl?~ical one and can be determined by "Job-analysis". that is, analysis of the skill content of a Job" But suppole that B' is representative ot the real world. In that case, there is no such thins as a minimum educational requirement tor the job; there is an optimum amount of education tor the job but it cannot be determined without introducing the e~r.niDgs of workers, a variable that so tar has been steadfastly ignored by manpower torecasters. In other words, if B' is the actual relationship between job-performance and edUcational attainment of a job incumbent, the task of translating forecasts of num­bers in different occupations into numbers with certain amounts of edu­cation, concei ve4 asa purely physical relationship, is doomed at the out­set: worker A with 12 years ot schooling ml\V' be twice a. product1Te in job X as worker B with 8 years ot schooling, but it A costs three times as much as B, the optimum amount at education for job X is nevertheless 8 years. 15/

22. Even it we set this objection aside, we are not justified in performing step (4) merely by reading ott the existing fit at occupation and education in the economy, or even 't/./ extrapolating past trends in the association between occupation and education. It cannot be emphasized. enough that what we are trying to do is to forecast the demang tor educated

and A. Ziderman , A Prelimin

- 12 -

manpower. However, the educational "profiles" currently or recently asso­ciated with each occupation are rather the outcome of the supply of edu­cated people in the past than an expression of the intrinsic educational requirements tor these occupations.

5. Testing the Accuracy ot Manpower Forecasts

23. The kind of theoretical objections to the manpower-forecasting approach that we have been summarizing in the last tew pages could be con­tinued almost indefinitely. At sane point, however', we must ask: what is the empirical evidence that the demand tor educated manpower can be accurately predicted! The moment we pose the question, we become aware of the methodo­logical paradox ot testing the accuracy of manpower forecasts ot the M.R.P.­type. Suppose, for example, that we have planned that GNP is to grow at a certain rate in future years and have made a forecast of the manpower needed to sustain that rate of growth. Suppose also that at the terminal year ot the plan ve have actually produeed the manpower that we predicted would be demanded, but that the rate of growth of GNP fell short of its tar­get value. Can we now conclude that the manpower forecast was accurate' and that the GNP target was not achieved for reasons other than manpower shortages, or can we 18\Y' the blame on the manpower forecast! Suppose we had achieved the GNP target but failed to produce the required manpower, what could we then conclude about the accuracy of the manpower forecast!

24. Questions such a8 these reveal one of the most surprising aspects of all m.anpower forecasting: the total failure to submit the torecast to any scientific rule tor assessing its accuracy. There is an enormous li t­erature on manpower torecasting and by now there must be Bome thirty or torty countries that have based educational plans in one wrq or the other on manpower forecasts. Most of these are long-term 15-year torecasts and it is too early yet to tell whether they will prove accurate or not. How­ever, without a single exception allot these forecasts are single-valued conditional forecasts, conditional that is on the achievement of a GNP growth target, and single-valued conditional forecasts can rarely be re­Jected on the grounds ot a simple comparison of forecast with outcome. The point is, it is ditticult to discover where the fault lies. There are tour possibilities:

The Manpower-Forecasting Hypothesis

~ Hit Miss power Target Target

Hit Not Rejected Rejected

Miss Rejected Not Rejected

- 13 -

25. Insotar as we can learn anything trom post mortems ot short and medium-term torecasting in developing countries, the evidence about the Soviet Union and Iran - the only countries in which such post mortems have been conducted - suggests that even such forecasts can go wide of the mark; 16/ how much truer this must be tor 15-year torecasts. Indeed, all the evidence shows that we do not yet know how to forecast beyond three or tour years with anything remotely resembling the margins 0 f error that are regarded as tolerable in general economic forecasting. What is worse: if we go on making single-valued torecasts, it is clear that we cannot get better at it because we vill rarely obtain a clear retutation of our tore­casting methods. This is why repeated tailures to torecast reliably in cases where the GNP target was actually achieved have taught us little, and despite twenty years ot experience we are not much wiser tod~ about the nature ot the demand tor educated manpower.

26. We haTe spoken so tar exclusively of conditional manpower tore-casts, that is, predictions of manpower requirements that are subject to the achievement ot certain growth targets • Although this paper deals with developing countries and with conditional manpower forecasta rather -than with straighttorward projections ot manpower demands in unplanned developed econOJllies , it is worth inquiring whether anything can be learned trom the projection techniques that have been used in advanced countries. The Bri tiah record ot manpower proj ections, baed on employers' estimates ot needs, is widely agreed to be one ot almost total tailure. 17/ ~e American record is more ditticult to judge as the methodology ot AlDerican manpower

On the Soviet Union, see N. DeWitt, Educational and Protessional Emplgyment in the U.S.S.R. (Washington, D. C.: National Science Foundation, 1961), pp. 511-517. On Iran, see G.B. Baldwin, "Iran's Experience with Manpower Planning: Concept, Techniques, and Lessons", Man ower and Education. Count Studies in Econ,omic Devalo ent, eds., F.B. Harbison, C.A. Myers New York: McGraw-Hill, 19 5 •

W See C.A. Moser and P.R.G. !.qard, "Planning the Scale of Higher Education in Great Britain: Some Statistical Problems". Journal ot the R~al Statistical Societl. series A. vol. 27. pt. 4.1964. pp. 4~-489, reprinted by the Unit tor Economic and Statistical Studies on Higher Education, London School ot Economics, 1965.

- 14 -

proj~ctors is highly eclectic 18/ and errors in prediction have been as common on the side of enrollments as on the side of employment. 19/ Nevertheless, little comfort is derived from the various American efforts at predicting the demand for scientists, engineers and technicians, not to mention teachers, doctors and dentists. 20/

27. The point we are making is not that manpower forecasting is as yet an imperfect art but that, as it is presently conceived, it cannot get better. Manpower forecasters never declare at, the outset by what stand­ards their forecasts are to be judged and thus, when the forecasts prove to be inaccurate, there is no way of improving them so as to insure that they will become more accurate in the future. The first prerequisite of a scientific forecast is that it furnishes a range of estimates of the magnitude of the critical variables and coefficients, so that any inac­curacies in the forecast can be used to improve the effort at production.

18/

19/

A leading American proj ection simply extra.polated the ratio of employ­ment of scientists and engineers in a given industry to total employ­ment in that industry on the basis of evidence of a linear trend between 1954 and 1959 The Lon -Ran e Demand for Scientific and En ineerin Personnel (Washington, D. c.: National Science Foundation, 19 1 • But in two cases, the chemical industry and the electrical equipment industry, further detailed investigation threw doubt on the assumption of' a stable employment fraction for scientific manpower (ibid., pp. 16-17,21-24). See also Scientists, En ineers and Technicians in the 1960's: Re uirements and Su 1 Washington, D. C.: National Science Foundation, 19 3 , and H. Goldstein and S. Swerdloff, Methods of Lon -Term Pro ection Re uirements for and Supply of Qualified Manpower U.N.E.S.C.O. Statistical Reports and Studies. Paris: U.N.E.S.C.O., 1967).

For a thorough review, see J. K. Folger" "Scientific Manpower Planning in the United states", The World Yearbook of Education 1967. Educa­tional Planning, OPe cit.

20/ An interesting test-case is the demand for teachers. Here there is no problem about forecasting labor productivity, as pupil/teacher ratios are invariably an administrative decision, nor any difficulty about specifYing the minimum educational qualification for the Job, as there is usually a legal requirement for entry into the profes­sion. Nevertheless, the record of teacher forecasts is as poor as all other manpower forecasts: see W. Lee Hansen, "Human Capital Requirements for Educational Expansion: Teacher Shortages and Teacher Supply", Education and Economic Development, OPe cit.; M.J. Bowman, "Educational Shortage and Excess", Cana.dian Journal of Economics and Political Science, November, 1963, pp. 446-461; and A.M. Cartter, 11'A New Look at the Supply of College Teachers'" The Educational Record, Summer, 1965, pp. 267-277. Another depress­ing example is the case of doctors: see W. Lee Hal113en, '" Shortages' and Investment in Health Manpower", The Economics of Health and Medical Care (Ann Arbor, Mich.: The University of Michigan, 1965), pp. 79-92.

- 15 -

In the words of the Technical Evaluation of the Mediterranean Regional ProJ­ect: "educational strategy should be tormulated with the uncertainties engendered by technological change clearly in mind. For this reason, objectives of labor force flexibility might, for example, receive more stress in the planning of educational structure and content..... Man-power requirement estimates which conceal these uncertainties, by present­ing single value estimates of requirements rather than ranges of alterna-ti ves, may do great disservice to formulators of educational policy." 21/

6. Long-Term or Medium-Term Forecasts?

28. The whole question of testing the accuracy of manpower forecasts has been complicated in the past by the insistence on long-term forecast­ing fi:rteen or even twenty years ahead. It is the length of time required to produce skilled protessional people that is alwqs cited by manpower forecasters as the rationale for long-term torecasting. The actual pro­duction-period involved in training a scientist or an engineer is about fifteen years, but the effective production-period may be still longer owing to the fact that the educational system is a hierarchical input-output structure: it is usually necessary first to feed back an intermediate output of teachers if we want to get a higher final output of scientists and engineers. In consequence it is likely that labor markets for highly qualified manpower are subject to cobweb effects, without a tendency to converge towards equilibrium. When excess demand for ~ specialized skill raises its relative earnings, the increase in the supply ot that skill, supposing students are made aware of' and respond to the rise in prospective earnings, takes f'i ve or ten years to materialize. Because ot this lag in the adjustment of supply, there is every chance that market torces will overshoot the equilibrium, so that what was a shortage turns suddenly into a glut. As earnings fall, the reverse effect takes place. This dyn8lt'ic adjustment process m~ never produce market-clearance in anyone period but rather continuous fluctuations in earnings associated with successive phases of labor shortages in one tield and labor surpluses in another. Given the strong probability that there will be structural disequilibria in the distribution ot educated manpower among occupations, and the high cost ot such disequilibria when they occur, it is imperative that we try to forecast the demand tor scientitic or technical manpower at least ten or fitteen years ahead, in the same wa;y that we forecast the demand tor

:: electric power before we commit ourselves to building a hydro-electric , dam that takes almCist a decade to complete. Needl~88 to sq, it we are looking at· underdeveloped countries where equilibrium in the labor market is itself a very dubious concept, the entire argument on1.y gains torce: we must look at least tii'teen years ahead; the results 80 tar may not

giJ Hollister, OPe cit., p. 62.

- 16 -

have been very accurate but at any rate there is no alternative but gradually to improve the quality of forecasts. This, in brief, is the main thesis of the proponents of manpower forecasting. 22/

-)

29. The cOlUlter-argument is not that manpower forecasts have so far proved extremely inaccurate but rather that they are not conducted as if accuracy mattered. Furthermore, it is far from true, as we shall see, that the long production-period of scientific manpower needs to be taken as a datum tor purposes ot forecasting. The fact that it takes fifteen years to educate an engineer does not imply that we must predict the demand for engineers in 1982, not unless there is one and only one occupation that an engineer can fill or, at any rate, a set ot tasks that only' a formally' qualitied engineer can perform. It may be a great mistake to encourage developing countries to lean so heavily on schools for the production of engineers; more, not less, reliance on on-the-Job training would seem to be desirable. Even it the emphasis is to be put on education rather than training, it is far trom obvious that the curriculum of engineers in de­veloping countries should be as time-consuming as in developed countries. That is to S8¥, precisely what is the appropriate educational qualification for the tasks that have to be carried out in most developing cOlUltries? These are the planning problems that should be asked and, in view of the in­accuracy of long-term manpower torecasts, as well as for other reasons, there is a premium on devices tor shortening the length of the production-period of highly qualified manpower.

30 • Ultimately, the case for manpower torecasting is one ot accuracy. The leading manpower forecasters insist that long-term torecasts, even ot the crudest kind, distinguishing merely between occupations requiring general academic education and those requiring scientitic and technical preparation, are useful in guiding the e~location ot educational expenditures among levels and branches of the educational system. That would be true it one could rely on them. Even the forecasters themselves, however, warn against educational expansion closely tied to torecasts of manpower requirements because they

For a powerful detence ot the manpower-torec~sting approach along these lines, see G. Bombach, "Long-term Requirements tor Qualif'ied Manpower in Relation to Economic Growth", Economic Aspects of' Higher Education, ed. S.E. Harris (Paris: OECD, 1964), pp. 201-223, as revised in "Manpower Forecasting and Educational Policy", Sociology of' Education, Fall, 1965, pp. 343-374.

- 17 -

have little confidence in their reliability. g]/ The question is not whether to forecast or not to forecast - let me make this clear - but rather whether to forecast inaccurately' as much as ten or fifteen years ahead or to fore­cast three or four years ahead with a much better chance of being accurate.

31. It is hardly surprising that l5-year forecasts go wrong. What is surprising is the unwillingness of most forecasters to experiment with medium-term forecasts, gradually lengthening the period of the forecast as the art of forecasting improves. But the art cannot improve unless the forecaster commits himself before'the forecast is made as to the degree of' inaccuracy that he will tolerate. Let us grant that sane knowledge of the future ten or fifteen years hence, however hazy, is better than nothing. Let us even grant that a forecast as long as fifteen"years ahead is indis­pensable. In that case, it is a minimum methodological requirement that the admitted haziness of the long-term. forecast should be built directly into '~he forecast itself. Ideally, the forecaster should hedge his bets by stipulating the probability distribution of all possible outcomes, or; at &ny' rate, the variance ot possible outcomes around the mean. But much less stringent devices would suftice to meet the point. For instance, one plausible hypothesis is that the variance around the estimated mean of the forecast increases with the square of the length ot time over which we are forecasting, producing an error-band that steadily widens as we look tur­ther into the tuture. Thus, the margin of error in predicting the demand

for manpower might be (1 ! 0.02) the 1967 figures by 1968, (1 ! 0.02)2 the + 1967 figures by 1969, and so forth, amounting to an error of -22 percent

W The principal line ot detense is simplicity itselt: "The aceptics call attention to the large margins ot error that are likely at 'Yirtually every stage ot the· torecasting process: the estimate of GNP fitteen years in advance; the distribution thereot among the :Y&rious sectors and branches of the econom;y; the estimation of future manpower structure within each ot the branches; and the equation of occupations with required educational qualification. H But "so long as one granta that manpower conaiderationa are one ot the elements that ouaht to intluence educational deciaiona, then all such decisions, it they purport to be rational, invol ... e manpower torecut, whether or not they are explicitly made", Plannins Education tor Social and Economic Development, ed. H.S. Parnes (Paris: OECD, 1963), pp. 74-75. This misses the point. If lons-te~ forecasts are really as subject to error as the aut~,or himself admits (op.cit., pp. 13, 30), it is difficult to see how they can bejustitiedj the tact that all educa­tional decisions have manpower implications makes errors more serious, not less.

- 18 -

+ in 10 years and -35 percent in 15 years; the same argument, possibly with & ditterent margin ot error, applies to the supply' ot manpower. The growth paths tor a particular type ot educ&ted manpover might then look as tollows: 24/

Quantity ot Manpower Type X

Fig. 2

min. demand \ \ \ ")max.

.... -\ -------/ \

I~~~~J-~~~~' ~. ~ ___ .- --- I

-.---..---

o

----'---T-'-

min. supply

max. supply

--1

, ___ I

I 1

. Time n

surplus

deticit.

32. The diagram is, ot course, purely illustrat1 ve. No one knows whether the tuture supply ot educated manpower is more uncertain than the :fUture demand or whether the posi ti ve and ~egat1 ve margins ot error in torecasting demand and supply are in tact equal, as suggested above, nor whether the compounding error-term should be 1, 2, or 3 percent. However, until some such conception of discounting the uncertain tuture enters explicitly into manpower torecalting, the case for long-ter.a manpower tore­cuting, particularly ot the single-valued type t lackl intellectual tounda-tion. '§}

~ The presentation used here is semi-logarithmic.

25/ Recent extensions ot M.R.p.-type forecasting to Latin America have included alternatiye tQreQasts baaed on different assumptions, but still no account il taken ot the essentially probabilistic nature of lODg-term predictioDs: Education, Human Resources and Development in Argentina (Paris: OECD, 1967).

- 19 -

III It THE RATE-OF-RETURN APPROACH

1. Some Standard Objections to Rate-ot-Return Analysis

33. We' turn now to the rate-ot-return approach. Here one starts with a cross-tabulation of the labor torce by age, education and annual earn­ings. From these, one constructs age-earnings protiles by years of school­ing, that is, cross-section data tor a particUlar year is used to project lifetime earnings associated with additional education~ It is convenient to treat the costs of education as merely negative earnings, with the result that one can proceed immediately to calculate the present value of the net earnings differentials associated with extra $ducation at dif­ferent discount rates. The internal rate of return on investment in educa­tion is simply that particular discount rate that sums the present value of the net lifetime earnings differentials to zero, or, alternatively expressed~ that sets the discounted value ot the costs of a certain amount of education equal to the discounted value of the addi tiona! future earnings anticipated from. it. 261 When the costs in question are the total resource costs to the economy and earnings are taken betore tax, one speaks of the "social" (ecpnomic) rate of return; when the costs are merely the direct out-of­pock,et costs ot students plus the earnings they forego while studying, and earn\~tngs are taken a:rter tax, one speaks of the "pri vate" (financial) rate of return. In either case, an allowance must be made for the fact that the el~ings associated with additional education cannot be entirely at­tributed to education alone: indi vidual earnings are partly determined by native ability, tamily background, social class origin, work experi-ence, &)d so torth.

34. On the b~sis ot sanewhat less than adequate empirical evidence, sane authprs have agreed that about two-thirds ot the observable earnings differentials associated with years of schooling in the United States are statistically attributable to differ~nces in educational attainment. 27/ There is vlrtually no evidence on this question for low-income countries: insotar as higher educated individuals are scarcer in all developing COUD­tries than ltnthe United States, probably more than two-thirds of earnings differentials in developing countries are due to educational differences; on the other 'hand , if higher education in low-income countries is more selective in terms pt social class than in the United States, as appears

26/ That is, n

~ Et - Ct = a

t= (1 + r)t

where r is the internal rate of return, E is the additional earnings associated with a certain amount of education over t years, C is the costs of that education over t years, t=O is the year that edu­cation begins and t=n is the year ot retirement from the work fo:rce.

For a discussion ot the evidence, see M. Blaug, "The Rate of Return on Investment in Education in Great Britain", Manchester School:., op.cit.

- 20 -

to be the case in many Latin American countries, but not however in the countries of' Tropical Africa, the figure of two-thirds could be an over­estimate of the influence of education on earnings. The real difficulty is differences in family background rather than differences in native abili ty. Among the many functions of an educational system is that of selecting brains and talent for turther develoment. Unless we can con­ceive of a cheaper way of performing the selection function, the fact that education only rend~rs the clever students more productive, rather than every student regardless of his individual ability, is neither here nor there: if a country employs a growing proportion of highly productive people and if these people only are more productive because of their edu­cation, it does not matter whether we call their productivity a return to education or a return to ability. However, if education is largely re­ceived by people from privileged social classes, the returns to education merely reflect the lack of equality of educational opportunities. We ~ not be able to alter the distribution of genetic endowments in a population, but we can certainly alter the pattern of educational opportunities. In point of fact, measured abf.lity as distinct from genetic endowment at birth is highly intercorrelated with social class background, so that the problems of ability differences and differences in social class origin are virtually one and the same. Until we have more evidence on the parti­cipation by social class in education in developing countries, we can only provisionally assume that the bulk of the earnings differentials that are associated with educational attainments in less developed countries are attributable to the process of schooling to the same extent as in advanced countries.

35. Thus, taking account only of two-thirds ot the earnings difter­entials associated with additional education, the CODDD.onsense interpreta­tion of rates of return on investment in education, calculated in the manner suggested above, is that they represent best estimates of' the average yield of additional expenditures on education. In one sense, they are merely a SUDmlary statistic expressing the prevailing relationship between the costs of more schooling and the additional earnings that ~ be more or less contidently expected to result trom it.

36. Rate-of-return analysis has been subject to a good deal of criticism as a general approach to educational planning. Some of the ob­jections that have been advanced are worth re-emphasizing in the present context of low-income countries. Firstly, the rate-ot-return approach assumes that existing earnings differentials in favor of educated people reflect their superior productivity; obviously, if' there is no relation­ship whatever between relative earnings and relative contributions to out­put, rate-of-return calculations are meaningless. Secondly, if' the demand f'or and supply of educated people increase at different rates in the future th$n in the past, actual rates of return on investment in education will differ from those that have been calculated. Thirdly, rate-ot-return analysis ignores the consunption benet! ts ot education, and, more seriously,

- 21 -

it ignores all the monetary benefits other than those that accrue directly to the educated individual. Of these three, the first two are the most serious: the manpower-forecasting approach also ignores the consumption as well as the spillover benefits of education and is thus subject to the third criticism as well.

31. The nub of the case against the rate-or-return approach is that it assumes a perfectly competitive labor market in which earnings are brought into line with the relative scarcities of people with different skill attributes. But labor markets in developing countries are riddled with socialii conventions and traditional hiring practices. In addition, most of the highly educated in low-income countries are employed in the public sector and the value ot their product may never be submitted to a market test. Thus, rate-ot-return calculati ons tor college graduates in low-income countries, if they could be. carried out at all, would measure not so much their productivity as the strength of their control over their own earnings. Lastly, developing countries are interested in structural not in marginal changes ot their educational sY$tems; rate-ot-return anal­ysis at best measures the current pay~off trom education, and what we want to know in ~ountries with rapidly expanding educational systems is the rate ot return on investment in education in 1970 or 1915, not in 1967.

38. We will take these two central objections in turn.

? • Impertections in the Labor Market

39. The argument about imperfections in labor markets must be granted, at least as a general description of the situation in developing coun­tries. 28/ At the same time, it is worth noting that we know very little abou.t the operations of labor markets in low-income countries. Data on . wages and salaries in relation to the education of the labor force are only available tor Venezuela, Mexico, Colombia, India, Nigeria, Uganda and Israel, 29/ but, with the exception of India and Isarel, 'the data reter to

29/

For a brilliant catalogue of labor market imperfections in under­developed cO'Wltries - such as rigid technologies, custOD1!&ry' hiring practices, ignorance ot skill-subs't;i tution potentials, the high cost of spreading informe.tion both amon~; the buyers and the sellers of skills, skill labelling by paper qllalifications in response to im­perfect knowledge, etc. - see H. Lejrbenstein, "Shortages and Surpluses in Education in Underdeveloped Cour.Ltries: A Theoretical For~", Education and Economic Development #1 Ope cit.

See Shoup, et.al., The Fiscal Systf~m of Venezuela. A Report (Balti­more: John Hopkins Press)! Chap_ 15, pp~ 406-24; M. Carnoy, "Rates of Return to Schooling in Latin Amferica", Journal of Human Resources, Summer, 1967, pp. 359-14; A.M. Nal:la, Gounden, "Investment in Education in India", ibid, pp. 341-58; 6.S. lBowles, "A Planning Model tor the Etficient Allocation ot Resources :Ln Education". Quarterly Journal of Economics, M"", 1961; Smyth and Bennett, op. ci t •. i' and R. Klinov-Malul, The Profitability of Inves~ment in Education in Israel (Jerusalem: Maurice Falk Institute for Economi(! Research in lsrael, 1966).

- 22 -

small selective samples rather than a r&ldom sampl~ or a census of the popu­lation. We know that skill differentials are generally wider in developing than in developed countries and that these differentials have been narro~ing over time. 30/ This argues that economic forces broadly conceived are at work in labor markets in developing countries. On the other hand, the nar­rowing of skill differentials is to' some extent explainable by the tendency, particularly in many African countries, to raise the legal 'minimum wages of unskilled workers irrespective of demand and supply conditions. Fur­thermore, wage differentials between larger and smaller firms are much wider in developing than in developed countries, and wide variations in p~ for identical skills are not ~~common even within a single town or district. In addition, wage and salary earners are· better off relative to average income per head in developing than in developed countries, sup­porting the idea that developing countries are characterized by economic "dualism" • There is also the well-attested fact of gra.duate unemployment in India and unemployment of primary school leavers in West Africa. 31/ All these go to show that economic forces work at best slowly and with ma~ fits and starts in the labor markets of low-income countries.

40. What tollows? Do we throw aw~ earnings and with it the rate-ot-return approach to educational planning, or do we meet the problem head on by attempting to reform the salary structure as an integral teature of educational planning' When the price system is irrational, in the sense t:I tailing to retlect relati ve scarcities, textbooks of planning tell us to impute "shadOW" or "accounting prices" to resources. In the present context, this implies that we should impute specitic scarcity-prices to people with ditterent educational qualifications. Once it is granted that what matters 1s the relatiye product1vities of more or less educated people, and that these can only be compared it they are expressed in terms ot some common denominator like money j the imp'l1tation ot shadow wages cannot be avoided.

41. Easier said than done. Just how are W~ to go about estimating shadow wages T Surely, this is the task of the Ministry of Labor, not ot the Ministry of Education? Curiously enough, howev'er, educational planning is precisely the tramework in which we can be$~n to tackle the question ot salary retorm.

3. Shadow Rates of Return

42. A possible starting point is to calculate the current rate of return on investment in various levels of education by using publi~ salary scales in the government sector - scales whose entry points are frequently

31/

For an excellent review ot the scattered evidence, see K. Taira, "Wage Differentials in Developing Countries: A Survey of the Findings", International Labour Review, March, 1966. See M. Blaug, P.R.G. Layard, and M. Woodhall, The Causes of Educated Unemployment in India (London: Oliver & Boyd, torthcoming, 1968), and A. Call$way, "Unemployment Among African School Leavers", Journal of Modern African Studies, Vol. I, x, 1963, pp. 351-71.

- 23 -

~efined in terms of formal educational. qualifications ot applicants and which allow explicitly for age and/or seniority - on the 8ss1.UIlption that public,:, pa.y scales reflect earnings-by-education in the private sector. A sample survey of business enterprises may show that this is an invalid assumption, in which case an average of public and private salary scales can be struck. 32/ Suppose we noy rank the three or four educational levels in terms of the rate-of-return, calculated in the manner described ~bove. We can then alter salary scales up or down by imputing new accounting prices to educated labor, recalculate the rate-or-return, and compare the new ranking of educational levels with the old. As soon as we reach an imputed salary scale that does in fact alter the ranking, we stop and start afl;"esh with a new set of accounting prices. We will probably never find. the true shadow price of labor in this way, but we m8¥ be able to "bracket" it.

43. The difficulty is, ot course, that rate-ot-return on investment in eciucatioll depends as much on the costs ot education as on the earnings diffE~rentials of educated people. Since the costs of education consist largElly of the costs of hiring teacilers, who are themselves the product of the educational system, an irrat'ional salary structure trequently implies that the costs of educationiare also irrational. Thus, as we aSSUDlle new accounting prices of educated labor, we must teed these prices back into the accounting cost ofe- teachers before recalculating the rate­of-return.

44. The object of the entire exercise is to find the critical range of salary differentials by education that produces a particular ranking of educational levels; this critical range "brackets" the true shadow prices of educated labor. It may then be possible to reach agreement by discus-sion as to where the shadow prices actually fall within the range. Job analysis in government departments and in business enterprises would help to settle the question. I am assuming, however, that this kind ot research cannot be carried out in time to assist in reforming salaries. However, there is reason to think that possible disagreements between different planners in the same country about shadow prices tor labor will be minimi'zed by the kind of sensi ti vi ty-analysis of earnings differentials that we have been outlining. In most developing countries, teachers' salaries are set well below other salaries for equivalently 'educated people, so that the teed-back problem in recalculating the rate-ot-return is not as serious as might be assumed at first glance: so great are the differences between the costs ot di'fferent levels of education that the ranking-order ot levels is in fact insensitive to a wide array of salary scales. In consequence, invest­ment criteria in education are frequently unaffected by the view that salaries for certain grades of labor are 30 to 40 percent more or less than 'what they should be. This sort of information is well worth having.

-_._--------------32/ Both Carnoy in Mexico and Bowles in Nigeria were able to collect

representative earnings-by-education data in a rew months, using only a few local assistants; see H. Carnoy, op.cit., and S.S. Bowles, op.cit.

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If' prices are irrational, planning based on such prices must lea.d to wrong answers. And if we want to create a system of rational pricing, it cer­tainly helps to know what magnitudes we are arguing a.bout.

45. An illustrative example may help to clarify the argument. One study in Uganda found the following social (economic) rates of return to four levels of education by using 1965 government salary scales grossed up by the estimated excess of salaries in the private over the public sector: 33/

(1) HSC (2) Primary (3) esc (4) Uni v.

78 percent 66 percent 22 percent 12 percent

In terms of total resource costs to the economy, the ranking from high to low is: (1) Univ.; (2) CSC; (3) HSC; and (4) primary. In terms of salary differentials, the ranking from high to low is: (1) Univ.; (2) HSC; (3) CSC; and (4) primary. The rate-ot-return ranking is, as we see: (1) HSC; (2) pri­mary; (3) CSC; and (4) Univ. The lifetime earnings ot university graduates is two or three times that of secondary school leavers and twenty times that of primary school leavers, but the total resource cost of a tmi versity education, even ignoring the cost of secondary education leading up to it, is almost sixty times that of a prima.ry education. In consequence, the rate-of-return on uni versi ty education is less than a fifth of the yield of primary education. This leads to conclusions for planning diametrically opposed to those of the six manpower forecasts that have been made in East Africa in recent years. 34/ No doubt, government salary scales in Uganda are artificially inflated. However, the interesting point is that a back-of-the­envelope calculation shows tnat if' we cut in half the difference between the starting salary of uni versi ty graduates and the bottom of the p~ scale, and recalculate the rate of return, primary education still wins by a long shot in terms of yield compared to university education.

46. Even manpower forecasters concede that some account must be taken of the costs of different amounts and types of education. The difficulty of introducing cost'considerations in the manpower forecasting approach is

Smyth and Bennett, Ope ci t. Uni v. = uni vers i ty graduates; HSe = Higher School Certificate holders, a level roughly equivalent to the first year of an American college; CSC = Cambridge School Certificate, a level roughly equivalent to a junior high school diploma; and primary - completed primary schooling. An average pupil entering primary school at the age of 5 would receive a leaving certificate at the age of 12, a Cambridge School Certificate at 16, a Higher School Certificate at 18, and a university degree at 21.

For a review of East African manpower forecasts, see E. Rado, "Man­power Planning in East Atrica", World Year Book of Education, op.cit. and G. Skorov, Integration of the Educational and Economic Plannins in Tanzania. African Research Monograph 6 (Paris: U.N.E.S.C.O. - In­ternational Institute for Educational Planning, 1966).

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simply the tendency to operate with fixed coefficients linking output with occupation and occupation with education. It is only when it is admitted that differentially educated personnel might be equally effective from the standpoint of output that it is possible to raise the question of minimizing educational costs. In fact, few cases will ever be found where two men with different amoUflts or types of education are equally effective In the same occupation; usually, everything will be different, including their contributions to output. In all such cases the only w~ to compare them is via a common measure of their output and this ;is, after all, what is meant by shadow prices for educated labor.

47. The problem of educational planning is ultimately one of deter-m1n1ng the potential economic value of different amounts and types of edu­cation. Therefore, why not confront the problem directly ,by imputing different economic values and calculating the effect of these imputations on decisions about educational expenditures? In so doing, one is at the same time contributing to the development of a more rational salary struc­ture and, possibly, to more rational patterns of educational finance. For example, three years of university education in Uganda is a highly profit­able investment to private individuals who have gained a Higher School Certificate because Ugandan university education is wholly tax-financed, so that the social costs of education are much greater than the private costs. Although the· government has some control over the number of HSC leavers via the number of secondary school places it makes available, it cannot entirely control the numbers that opt for university education. Thus, in 1965/66 there were same 900 Ugandans studying at the University of East Africa but same 2,300 Ugandans studying overseas, a third of Which were financing themselves. The Ugandan Government m~ welcome this devel­opment in the belief that there is a prevailing shortage of highly qual~fied

·manpower. However, not all the overseas students return to Uganda and, in the meantime, the high cost of uni versi ty education in Uganda is partly due to the existence of considerable excess capacity at Makerere, the University of East Africa. In some other developing countries, like Zambia and the U.A.R., the private financial benefits of cost~free university education

, have produced the tear of an actual or imminent surplus of highly quali-.. fied manpower, a problem which appears to be impossible to solve without

a reform either ot the salary structure or of the finance of higher educa­tion. At any rate, salary reform is on the agenda in a great many develop-: ing countries and the present proposal to calculate shadow rates of return on educational investment would make some contribution to this long neg­lected question.

4. The :&Dphasis on Employment OpportWlities

48. The simple point is that rate-of-return analysis does not mean Slavish adherence to actual earnings and actual costs in total disregard of what earnings and costs mean in a particular country. Indeed, educa­tional planning in low-income countries could be considerably improved mer~ly by p~ing attention to the economic benefits of education to the individual, even when rates of return are not actually calculated. We have been speaking so far exclusively of amounts of education; here is an

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example, however, of an investment decision ab.out types of ed.ucation which looks quite different when approached in a framework that emphasizes em­ployment opportunities. It has been asserted again and again that urban unemployment of school leavers and the flight-trom-farming in East and West Africa is the result of the academic nature of African education: the curriculum, adapted from more advanced countries, is said to generate unrealistic employment expectations of clerical work, to encourage a disdain for manual occupations, and in this W8¥ to inhibit rather than to promote rural progress. Since most Africans are dependent on agricul­ture, so the argument runs, the only W8:3 in which schooling can make a significant contribution to the economic development of Africa is by impart­ing an agricultural bias to the curriculum at all levels of the educational system, capped by the expansion of junior agricultural colleges at the upper secondary level.

49. This thesis has been examined with respect to Ghana in a remark-able recent book by P.J. Foster and discarded as the "vocational school fallacy in development plauning."35/ Foster shows that proposals to increase the provision of agricultural and technical education in Ghana were empha­sized in every major document related to education in the Gold Coast from 1847 to the grant of independence in 1957. Nevertheless, none of these proposals were ever implemented; whenever it was attempted to implement them in some fashion or other, the experiments had to be abandoned eventu­ally. The question that immediately arises is : despite a century of effort by successive colonial governors and e~ucational missions to establish agricultural schools and to devise special agricultural curricula, why do African schools continue to this day to favor an academic type of instruc­tion along western lines?

50. The answer that Foster provides is as simple as it is penetrating. Throughout the period of colonial rule, the SUbsistence sector of agricul­ture held out few prospects tor school leavers and the prima~ function of formal education was to provide the more able African youngsters with entry into the European-dominated exchange sector. Within the exchange economy, however, there was relatively greater demand for clerical and commercial skills than for technical skills. In view of the slow rate of enlargement of the e~lchange economy, the financial rewards and employment opportunities for technically trained individuals, ~hether in farming or in manufacturing, were never commensurate with those in the clerical field. Thus, the graduates of the academic schools were alw~s at an advantage compared to the graduates of vocational or vocationally oriented institutions. In the circumstances, the pressure tor academic-type education was nothing else than the reflex of the demand and supply situation in the labor market.

35/ P.J •. Foster, Education and Social Change in Africa (London: Routledge & Kegan Paul, 1965). The book is summarized by the author in an article entitled "The Vocational School Fallacy in Develop­ment Planning", Education and Economic Development, op.cit.

. .,

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African students, far from being irrational in insisting on academic edu­cation, correctly appraised the actual job opportunities that were avail­able. An academic education was thus pre-eminently a vocational education allowing entry to the most prestigeful and highly paid-occupations.

51. This picture has changed very little since independence. At present, only about 15 percent of the total employed labor force of 2.5 million are full-time employees in the modern exchange sector of the Ghanaian economy; wage employment opportunities are growing at about 4 percent per annum, creating 25,000 new jobs every year; but the annual output of middle schools alone has now risen to about 30,000 stUdents. The only reason this has not led to unemployment of middle school leavers is the fact that employment in the public sector has been growing faster than employment in. the exchange economy. Government jobs, however, are largely of a clerical and administrative nature. The progressive enlarge­ment of the public sector has, therefore, led to an even greater demand for academic secondary school education than in the d~s before independ­ence. It is not the schools that are the villains of the piece but the exchange economy which has not grown rapidly enough to create new jobs of a technical nature. On the basis of a number of sBmple-surveys of secondary school pupils in Ghana, Foster demonstrates the remarkably realistic job expectations of African schoolchildren. His results have been confirmed in a follow-up study by Clignet and Foster of secondary education in the Ivory Coast, 36/ which surveyed the employment records of secondary school graduates and discovered that unemployment was largely confined to the graduates of technical and agricultural schools, rather than the graduates of the academic-type schools. If such findings are representative of the less developed countries in general, the popular notion that curriculum is a major determinant of vocational aspirations will have to be abandoned, and with it the idea that one can generate economic development by according high priority to agricultural and tech­nical education.

52. Manpower forecasts in West and East Africa tend to suggest that there are serious shortages of middle level manpower in technical fields. A cursory glance at job opportunities and salary prospects in these econo­mies shows that this cannot be the whole story. Even if it is thought that the existing salary structure does not adequately reflect relative shortages in various fields, the tact remains that reeommendations that run against the grain of private rate-of-z'eturn calculations will always fail. Thus , rate-of-return analysis, whether of the numerical or of the casual type, can throw light on decisions about types as well as about different amounts ot education. It forms, as we are beginning to see, an indispensable ad­junct to manpower torecasts.

R. Clignet, P. Foster, The Fortunate Few. A Study of Secondary Schools and Students in the I~ory Coast., "( Chicago, Ill.: North­western University Press, 196b),pp. 185-189_

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5. Predicting Rates ot Return

53. We turn now to the second major objection against rate-ot-return analysis: its inability to predict the future benefits ot education. At this point we must mention what has sometimes been described as an alt ern at i ve approach to educational planning: the demand-tar-places ap­proach. The term is self-explanatory: education should be regarded as an individual rather than a collective aim; planners should project the private

,demand for education, and provide educational facilities accordingly. It is doubtful whether this approach has independent status as a method of educational planning for the simple reason that tees in all underdeveloped countries are considerably below the costs of educating a student. A reduction in fees for higher education would probably do little to increase the demand for higher education - the real bottleneck, at least in Africa, is the number of secondary school places made available - but a rise in fees would certainly depress the demand, at least in the short run. Hence, the demand-for-places approach takes for granted the existing subsidies to secondary and higher education and these in turn can only be defended in terms of manpower forecasts or the measured returns from education. Never­theless, given the level of fees an.d grants, and even given the level of provision of secondary education, projections of the demand for particular subjects and courses do have same meaning because they are largely volun­tary choices and hence subject to shifts over time that may be predictable.

54. Both the manpower-forecasting and demand-for-places approach have elements in common that distinguish them from rate-of-return analysis. The manpower-forecasting approach tells the educational planner how many scientists, engineers, technicians, and so forth" he should supply by, say, 1975, but as we recall, without regard either to their prospective earn­ings or to the relative costs of producing them. In short, it provides the planner with a forecast of one point on the 1975 demand schedule for a particular skill. If, for any reason, the supply target sti:B.t:ulated in the manpower forecast is not met, so that relative earnings change, the edu­cational planner has no w~ of knowing in 1975 whether the error was due to an inaccurate forecast of the shift in the demand curve between 1966 and

41975 or simply to the mistaken assumption that stUdents choose to study

•

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particular subjects without regard to earnings prospects. 37/ On the other hand, a prOjection of the private demand for education tells the educa­tional planner how many stUdents with different types of professional preparation may be expected to be forthcoming by 1975. He has no way of' knowing whether these students can be absorbed in the labor market without a change in the pattern of relative earnings. If relative earnings alter, it is very likely that this will affect the structure of the private de­mand for education by fields of specialization.

55. Be that as it may, both'of these approaches do provide the educational planners with exact magnitudes to aim at. Rate-or-return calculations, on the other hand, seem merely to provide a signal of direc­tion: invest more or invest less. But how much more or less? To this the approach seems to give no other answer than: a little more or less, after which the yields will have to be recalculated. Rate-ot-return analysis does not even make the attempt to forecast either the demand or the supply of educated manpower. At best, it can indicate how the two are matched at present. So runs the criticism.

56. This criticism is damaging in the extreme and yet it is as much a criticism of the manpower-forecasting and demand-for-places approaches as ot rate-of-return $Jlalysis. What we should be trying to predict is the rate at which a set of demand and supply curves are, as it were, drifting to the right with the passage of time. Manpower forecasts are concerned with the demand side ot the picture; demand-for-places prOjections are

Take the case of engineers: ~ engineers are employed in 1967 at lalaries I; a manpower-forecast states that the demand for engineers in 1975 will be q'; as this torecast ignores the earnings ot engineers, apparently the notion is that their supply is entirely a matter of the facilities made available for the study of engineering (henc:'e, the supply curve is perfectly inelastic). In 1975, however, instead ot q' engineers at salaries s, q" engineers are employed at salaries s', that is f\1 we observe intersection B instead ot c. Are we on the same demand curve, D', the el'ror being due to the tailure to meet our educational supply target, or are we on a difterent demand curve D",

Earnings ot Engineers . '"

, " B s ___ . _______ ---~, I 1.)"

s r---.----~~-- .. -~i,

Fig. 3

q q'" ql Quantity of Engineers

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concerned with the supply side; the intersections of the two curves deter­mine the pattern of earnings that enter into the rate-of-return calcula­tions. If both demand and supply are responsive to earnings, there is a feedback effect that complicates estimates of the slopes of these func­tions. But the slopes are only one problem. The w~ in which demand and supply shirt over time is determined on the demand side by the pace of technical change, and on the supply side by the incomes of parents and students and by administrative decisions about educational facilities. 38/ In short, the shifts of the demand and supply curves, as well as the slopes, are also influenced by the pattern of earnings of educated people and by the costs of educational provisions. All this is to s~ that if the demand and supply functions in the labor market for a particular category of man­power are correctly predicted for the years ahead, the consequence is neces­sarily an implicit prediction of marginal rates of return on educational investment. We are, therefore, driven to the conclusion that either all three methods of educational planning are valid when used in conjunction with one another, or there is something wrong with manpower torecasting, or demand-tor-places projections, or both. To deny this conclusion, one must assert that both the relative earnings of highly qualified manpower and the costs of different amounts and types of education vary little over time and hence can be left out of account. But this is precisely what manpower torecasters argue. We have come at last to what really lies be­hind the quarrel between manpower forecasters and rate-of-return advocates: it is nothing less than a totally different view of how economic systems work.

IV. TWO VIEWS OF THE STATE OF THE WORLD

1. Variable Versus Fixed Coefficients

57. Suppose we had an educational system that did not per.mit students to specialize until their second or third year of higher education, that provided a perfectly general education for everyone until the ages of nine-· teen or twenty, that made full use of team teaching and new edUcational media in the interest of keeping pupil:teacher ratios as flexible as pos­sible and capable of ranging trom 10:1 to 300:1. Suppose also that voca­tional counselling was so efficient that students were extremely well in­formed of career opportunities. Suppose further that employers' 'demand for different skills was highly elastic, that capital was an almost perfect substitute for labor and that, in addition, workers with different skill characteristics were good substitutes for one another. In short, there were always many people who could perform a given job and anyway the Job eould al~taY's be displaced by a machine. Lastly, suppose that most special­ized skills were acquired on-the-job, not learned in schools, and that

The list of factors is not meant to be exhaustive. details, see M. Blaug, Economica, 1966, op.cit.

For further

•

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technical change demanding new and hitherto untamiliar skills proceeded smoothly without tits and starts • In that case, would it really matter that highly qualitied manpower takes a longer time to produce than most physical assets? To forecast manpower requirements under these circum­stances would be almost meaningless for the simple reason that, in this sort of world, educated manpower could never be a bottleneck to economic growth. Projections of the private demand for education and calculations ot the rate of return, however, would be perfectly meaningful in such a world, and, as a matter of fact, the only guides available in decision­making in education.

58. We go to the other extreme and imagine a world created in the image of the manpower forecaster: students and parents would be poorly informed of career prospects and more interested in acquiring education for consumption than tor investment reasons; specialization by subjects would start very early; pupil:teacher ratios would be fixed and unalterable and all school buildings and school equipment would be indivisible and highly specific in each use; the demand schedules for separate skills would be highly inelastic and the elasticity of substitution between labor and capital as well as the elasticity of substitution between men with different skills would be well below unity; industr,y would provide virtually no train­ing and the pace of technical change would be so rapid that the demand tor people with ditferent skill attributes would shift through time un­evenly and irregularly. Obviously, in this sort ot world, the private de­mand for ed,ucation would be so unstable as to make it impossible to extra­polate existing trends and all rate-of-return calculations would be irrelevant: labor-output coefficients would be technically determined and earnings associated with education and even the costs of supplying various skills could be ignored.

59. To fix the distinction firmly in our minds, the followi~g table reiterates the contrast we have been drawing: items (1) to (4) cover what might be called the "education market", whereas items (5) to (7) deal with the labor market in which educated people are hired.

"~I

1.

2.

3.

4.

5.

6.

7.

Extreme Version of the Manpower-Planning

Approach

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Students acquire more education tor non-economic reasons only.

Students choose major subjects in ignorance of, or with no regard to career prospects.

All education is specialized and specialization starts at age of entry.

All input coefficients in schools are fixed: complete indivisi­bility and specificity of teachers, plant and equipment.

The demand curves for separate skills shift discretely.

Extreme Version of the Anti~anpower Planning

Approach

1. Students acquire more education for economic reasons only.

2. Students are well informed and attentive to career prospects.

3. All education is general and there is no specialization at any age.

4. All input coefficients in schools are variable: complete divisi­bility and nonspeciticity of teachers, plant and equipment.

5 • The demand curves for separate skills shift continuously.

6. Infinite elasticities of sub-Zero elasticities ot substitu­tion between skilled men. ~. stitution between skilled men.

Zero elasticities ot demand tor all separate skills.

7. Infinite elasticities of demand for all separate skills.

\

60. Enough has been said to suggest that the quarrel really is about the real world. What we have is a picture of a ,"tontinuum: to the right is the sort of neo-classical universe that we meet :~,,~ textbooks on economic theory, characterized by 8ubstitutabilities in both the educational system and the productive process; to the lett is a Leontief~type universe of fixed-input coefficients, characterized by extreme camplementarities in both the education market and the labor market. Needless to s~, the real world lies somewhere in' between. Tb resolve the conflict that we are ex­amining, we need to decide whether less developed countries are typicallY to be- found nearer to the lett end or to the right end. of the continuum, and whether they are so tar to the lett that educational planning can safely ignore the costs of and earnings associated with education. But even this knowledge ot the present situation would not bring us very much turther: skill differentials do not remain constant over t~e; nor do the costs of education. So we seem to be right back to the problem with

-which we started. Is there any way out of this dilemma?

•

.~

- 33 -

2. The Doctrine of Educational Flexibility

61. Planning has been defined as the process of preparing a set of decisions tor action to be taken in the future. 39/ Since it is oriented to the future, planning partakes of all the difficulties that characterize decision-making under uncertainty. Further.more, even the present relation­ship between the supply of qualified students and the demand for educated people fro. industry and government is little understood in most low­income countries. In the circumstances it is alw~s better to build into the system the kind of flexibility that allows it to adjust automatically to bottlenecks and surpluses. In short, educational planning in develop­ing countries should in large part consist of action designed to move the educational system and the labor market closer to the right end of the con­tinuum, characterized by a multiplicity of alternatives in producing and utilizing educated manpower; wherever a country starts from, such action must contribute to a smoother adjustment of education to the economy ~d vice versa. It is easy to see that the rigidities of educational systems and labor market practices in developing countries strengthen the argument tor manpower forecasting. But the price of rigidity is that errors are more disastrous. This is the great paradox of the manpower-forecasting approach: the very lack ot synchronization between education and the economy that justifies manpower forecasting also makes for irremediable waste of resources when forecasts go wrong. In contrast, if the economy is sufficiently flexible to adjust to erroneous forecasts, even crude esti­mates of manpower requirements can serve as useful guides, but, at the same time, there is less reason to forecast manpower requirements.

62. Just how can developing countries move towards the right-hand side of the continuum? This is not the place for a full-scale exposition of the doctrine of educational flexibility, but a brief sketch of same of the concrete measures that might be considered will convey the gist of the argument. First of all, the length of specialized courses should be re-examined to see it they really need to be as long as in advanced coun­tries. For example, Tanzania recently shortened the length of medical courses and now produces doctors in five instead of in seven years; this makes Tanzanian doctors less qualified than British doctors but that is all to the good. The aim of a poor country is not to maximize the numbers of highly skilled people but to m.inimize the cost of producing th.e flow of essential skills,' skills that can be produced in a variety of ways. Secondly, all specialized professional education must be scrutinized anew to see if it really is necessary to produce these skills in formal insti­tutions: in any rapidly changing economy there is a premium on people with general rather than narrow vocational education because the for.mer can be

See C.A. Anderson, M.J. Bowman, "Theoretical Considerations of Educational Planning", Educational Planning, ed. D. Adams (Syracuse, N.Y.: Syracuse University Press, 1964), pp. 4-8.

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retrained to suit new requirements. On the whole, there is much less re­liance on training in industry in developing than in developed countries and as yet very few developing countries have entertained the idea of subsidizing private industry in order to encourage the development of on-the-Job and. off-the-job training. English-speaking Africa, for example, has imitated almost every feature of British education except the prac,tice of producing technicians by "sandwich courses" and "day releas.e", that is, by part-time courses for people already employed in industry.'" Along similar lines, there is a case for adult literacy campaigns, particularly, if they are work-oriented, if necessary at the expense of school educa-tion. 40/ Thirdly, coming back to schools, there is the provision of career information both in schools and in labor exchanges which can be a very potent instrument for influencing students' choices in what are thought to be socially desirable directions. Fourthly, there is the reform of the salary structure that we have. already mentioned. Broadening the argument, any policy action that increases the flexibility with which resources are combined within the educational system must improve the capacity of schools to adjust to shortages and surpluses of various types of manpower. That is, any action that encourages educational innovation in school, such as constructing school buildings easily adaptable. to various class sizes, train­ing teachers to use new educational media, such as closed-circuit television and programmed instruction, must ease manpower problems. Of course, the case for new educational media is not one to be decided solely or even largely on manpower grounds, but the fact remains that the more teachers are replaced by mechanical aids, the easier it is to expand enrollment or to adjust student:teacher ratios.

63. To sum up: much educational planning should. consist of reforms of the educational system, as well as an active interventionist attitude

40/ It is amazing to realize that the 1961 Addis Ababa Conference on the Development of Education in Africa recommended"that 5 percent of educational expenditures should be devoted to literacy and adult education, and this figure was then regarded as a definite improve­ment on the existing situation - this in a continent with 95 percent illiteracy rates~ Even so, very few African countries now spend as much as 5 percent of their educational budget on adult literacy. To be sure, we know next to nothing about the economic returns of liter­acy campaigns and the UNESCO Experimental World Literacy Programme, wholly devoted to functional literacy teaching, is not likely to give us results before 1970. Nevertheless, since primary school enroll­ments in Tropical Africa amount to no more than 35 percent of the age group, to write off all illiterate adults as economically value­less by concentrating 95 percent of the educational budget on children and adolescents is at best a dubious policy. Some effort must be made to see whether 19:1 is really the appropriate ratio be-tween schooling and adult education. All one can safely say at the present state of knowledge is that educational authorities should devote more resources to experiments with literacy campaigns in particular areas. For further discussion on the economics of literacy campaigning, see M. Blaug, ilLiteracy and Economic Develop­ment", The School Review, Winter, 1966.

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to a~~ forms of producing educated manpower, whether by education or by training. In general, educational planning in developJnv, countries should concern itself more directly with the reciprocal impact of t.be educational system and the labor market. Rather than accepting exiutinp, educational patterns and prevailing hiring policies as data in theplnnninp. process, much of the effort of educational planners should be directed at altering these so as to give full scope to the process by which government and industry a.dapt its demand to the supply of educated manpower, and the supply of students adjusts itself automatically to the changing demands from government and industry.

3. The Integration of Various Approaches

64. Enough has been said to show that neither of the two concurrent approaches to educa.tional planning, nor any of the three concurrent approaches if we add demand-for-places prOjections to the other two, has any logical priority over the others. Faced with an uncertain future, educational planning must diversifY its portfolio of metpods and techniques. Clearly there are upper limits to the elasticity of SUbstitution for cer­tain critical skills, that is, skills involving long formal preparation and training. And no matter how much we postpone specialization, the effective lead-time of scientific and technical manpower is sufficiently' long to create problems. It takes years to put up a complex of school buildings and, obviously, tores-ight is indispensable to the decision to begin building. In addition, students base their career decisions on to­d~'s market forces and only a forecast can reveal the situation that they will confront when they eventually enter the labor market. There can be no question, therefore, about the necessity of trying to take a forward look at manpower demands and, in principle, we should look forward as far as possible. However, the period over which we can useflllly forecast the demand for manpower in the present state of knowledge is much more limited than is usually admitted. The fact that manpower forecasting hes so far ignored. the costs of education and training, neglected the lower levels of skill, assUmed a rigid educational speci fi.cation for each Job, and committed itself to single-valued forecasts is no accident, but an inherent feature of the static input-output framework in which it is conceived. A great deal of experience has been accumulated in the postwar years with static input-output analys.18 as an instrument of economic forecasting. Most economists agree that input-output analysis can improve a short-term fore­cast that looks two or three years ahead. Some visionaries in the field of economic planning have begun to experiment with dynamic input-output analysis so as to extend the period of useful forecasting. But no one pretends that we can yet forecast even large aggregates like GNP with any degree of accuracy for periods longer than five years. Yet, in the field of manpower planning, practitioners talk blitbely about ten to fifteen years' forecasts with the aid of static input-output matrices.

65. There would seem\(,~o be little point in continuing to waste resources on long-term single-valued forecasts whose results are suspected even by the forecasters themselves. These resources could be. much more profitably invested in improving our knowledge of the current stock of qualified manpower and disseminating this knowledge to students and employers.

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It is no accident that after two decades of considerable activity in manpower forecasting, no developing countr,y has anything lik~ adequate da.ta on the distribution of the labor force by sectors, occupations, earn­ings, and years of schooling. Such data is not even expensive to collect since it can be gathered by sample surveys. The fact is that the mystique of long-term forecasting has discouraged investigations ot the current stock of manpower , while convictions about the imperfections of labor markets in developing countries have inhibited research on the earnings of educated people to determine the precise degree of imperfection.

66. Faced with the difficulties of manpower forecasting, difficulties that seem to increase at a progressive rate the longer the time-period over which we are flJrecasting, the remedy is to begin modestly with short­term forecasts, extrapolated with a compounding margin of error as suggested above. As we accumulate more experience, we can begin to adjust the margin of error, gradually producing more and more reliable medium-term and eventually long-term forecasts. As a check on such forecasts of demand, we ought to make continuous rolling projections of the future supply of educated people. The forecasts of demand ought to be of the type that provides a range of alternative values for different estimates of the pro­jected supply. If the demand for educated people depends in any way upon earnings, and this will necessarily be so if there is any substitutability between educated people, changes in supply are just as capable of altering wages and salaries as changes in demand, i.e., the quantity demanded of educated people is not independent of its supplY. It follows that manpower forecasts must always be combined with demand-for-places projc~ctions. As we combint~ forecasts of demand with proj ections of supply, we start think­ing quite .naturally of earnings associated with education as possible indicators of impending shortages and surpluses. And since the costs of training vt1.rious types of specialized manpower differ considerably, ,re will be led. to consider variations in earnings in relation to variations in the costs of education. This is rate-of-return analysis, whether we call it that or not. It earnings are inflexible and fail to indicate developing slhortages and surpluses of manpower, the remedy lies in imputing "accounting prices" to labor of different skills and calculating the critical rates of retl~n that lead to definite investment priorities in education. By making such calculations on a year-to-year basis, we keep a continual check on labor markets for highly qualified manpower and gradually develop insights into the ways in which education interacts with economic growth.

67. Rates of return in our context, then, are stmply checks on the validity of our forecasts ot demand and supply. If we get different answers from r8~e-of-return calculations than from manpower forecasts, it may be that (l) earn;ngs are unrelated to the marginal productivity of labor; (2) the co.sts"ot education are artificially intlated; (3) future rates of return diverge considerably trom present rates; or (4) the man­power forecasts are all wrong. Which ot these four factors or which com­bination of them is responsible for the difference in answers cannot be

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settled on !:. priori grounds. What we have been trying to do is to con­struct a framework in which such factors as (1) to (4) can be systema­tica.lly consideredD Educational planning at the mODlent is more art than science, given our crude knowledge of the inter.actions between education and the economy. The only thing that can be said for the approach recom­mended here is that it cannot fail to improve the art and may even in time convert it to science •

v. THE APPROACH IN PRACTICE

68. The discussion so far has been fairly abstract and perhaps' far removed from the practical problems of evaluating specific educational projects in a limited period of time in a particular developing country. However, some general lessons have emerged. Educational missions arriving in a typical developing country will. find themselves conl~onted at best with a five to fitteen-Y'ear forecast of t;he demand for third-level man­power, a more or less thought-out long-te.rm development plan for primary education, a few casual statistics about the distribution of the labor force by sectors and broad occupational gr.'oupings, some census data about the proportion of literates in the population by age-groups and geogra­phical location, and masses of fairly incoherent figures about the edu­cational system: percentages of age-groups enrolled, drop out rates, number of repeaters, student:teacher ratios, unit costs by various cate­gories, and the like. The first task of the mission should be to look at the shape of the "educational pyramid", and, in particular, at the flow­~~amics of the educational system between the three levels and between various types of schools within each level. The second task of the mis­sion should be to evaluate the manpower for!!cast: any decisions about the top of the educational pyramid are bound to have implications all the w~ down to the base. If the forecast is of the familiar type - single-valued, confined to university graduates and those with equivalent qualifications," tixed occupation-education coefficients, la1'Jf:>r-output coefficients adapted from more advanced countries, and so on - it can be virtually dismissed out-ot-hand. One simple check on reliability is sensitivity-analysis: slightly ditferent coefficients should be applied at every stage of the exercise to see which coefficients are responsible tor the final forecast. Another check is to construct age-education-earnings profiles from govern­ment salary scales, where education is defined as a certain paper quali­fication.. Figures on the costs of different levels of education are usually readily available in most countries and with these and age-education­earningsprotiles t rates of return are quickly calculated. If these give resul~s different tram the manpower forecast, a sample. survey of large pri vate firms can show whether go,,"ernment P8\Y scales are out of line vi th relative earnings in the private sector. The latter can be treated as accounting prices and used to recalculate the rate ot ~eturn for renewed comparison with forecasts of manpower requirements. Checks such as these could be demanded before the mission arrives in the country: indeed, this would stimulate educational planners in developing countries to begin to think in cost-benefit terms.

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69. Obviously, earnings data are not sufficient to give an impres-sion of the functioning of labor markets in a developing CO'Wltry. Devel­oping countries typically lack data on unemployment by age and education, or, for that matter, evidence on how youngsters actually find employment and support themselves while looking for work. Nevertheless, interviews with personnel managers in large industrial enterprises, directors of labor exchanges, agricultural extension officers,and vocational coun­sellors in schools can provide rough orders of magnitude of the unemploy­ment problem. At the same time, these interviews can be used to gather information on minimal hiring standards, promotion practices, and the extent of training on-the-job, all of which are aspects of labor-market adj ustments •

70. A few weeks spent this way can quickly give a mission a "feel" for the interactions or lack of interactions between education and the economy, so as to better assess the significance of the manpower forecast. In a good many cases, the mission will find itself dismissing the decep­tive numerical accuracy of the manpower forecasts as untenable and will then have to start allover again to examine priorities de ~.

71. It is perfectly true that if the project in question is, say, the cons~ruction of a certain number of teacher training schools, these will not open their doors until three to four years after the visit of the mission, and their graduates will not take up employment until five to six years after the date of the project evaluation. Thus, medium-term forecasts of the demand tor secondary school graduates and projections of the likely supply of secondary school leavers are unavoidable. However, everything we know about the art of forecasting at the present time sug­gests that five-year forecasts are inadequate by themselves to justif'y' a project. The new element that ought to be introduced into the evalu­ation is labor-market analysis. What will happen to employment opportuni­ties if secondary education is expanded? Will earnings change or is this the sort of labor market where upgrading or labor hoarding always absorbs an increase in the supply of educated people? Does the public sector always take up the slack in the private sector? What becomes of youngsters who cannot find employment tor their skills? Do they eventually find paid em­ployment in which they do not make use of their skills, or do they go back to subsistence farming? These are the sorts of questions that must be asked b.Y a mission. To label this rate~of-return analysis is to suggest that there is another method ot educational planning just as precise as manpower forecasting or demand-for-places prorJections. No doubt, a rate­of-return calculation looks very precise but, of course, as such it is al­most as meaningless as a single-valued long-term manpower forecast. For one thing, it loses too much information on the actual structure of educa­tional 60sts by fields of specialization, not to mention the structure of age-earnings profiles by levels of educ~tion. But more to the point, its

, significance is wholly depen'dent on the competi ti ve functioning ot labor markets. No simple rules can be laid down about the workings of such mar­kets in developing countries. Certainly, we can be ~ure that they depart

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very seriously from the model of perfect competition. On the other hand, even if employers ignore salaries in hiring labor, students do not ignore prospective earnings in choosing education. For this reason alone, salaries must come into the picture. Salaries, however, are only part of the story: hiring practices, whatever they are, and the character of occupational choices are necessary ingredients of the evaluation of an educational plan.

72. It may be useful to set down a check-list of the ideal data requirements for cost-benefit analysis of educational projects, simply as a summary of what has gone before:

(1) Cross-section data on earnings before and after tax by age, sex, education, occupation, and sector of activity.

(2) Private and social costs of education per stUdent per level, per school, and per subject.

(3) Public sector pay scales for purposes of analyzing the structure of public employment by age, job title, and educational qualification.

(4) Unemployment by age, sex, and educational attainment.

(5) Vacancy statistics for key skills.

(6) Starting salaries and minimal hiring standards for well­defined occupations in the private sector.

(7) Distribution of fringe benefits in both public and private sector by age, sex, occupation, and education.

(8) Job-analysis of selected occupations in both the public and private sector and evaluation of the content of the educational qualifications of typical incumbents of these occupations.

(9) Extent of on-the-job and off-the-job training in both public and private sector.

73. Project evaluation must never lose sight of the educational program to which the project belongs. A particular point to watch for is whether the educational program includes measures to increase the flexibility of the educational system and to improve its responsiveness to economic needs. The great danger of rapid educational expansion is that it may merely impose existing rigidities on larger numbers, with the re3ult that flexibility is even more difficult to achieve at the end of the plan than at the beginning. .All educational plans should provide some resources for experiments in new educational media, new curricula, and new terminal examinations. Perhaps as much as two to four percent of all educational expenditures should be devoted to vocational counselling and

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career guidance. Indeed, one of the first questions that a mission must seek. to answer is how much of the Ministry of Education's efforts are de­voted to fact-finding and to the dissemination of intormation about educa­tional choices and career prospects, both to students and emplqyers. Missions should look with particular favor at efforts to create shorter courses, multiple channels of further education, additional part-time and evenin~ education, and more diverse paper qualifications, all of which strengthens the capacity of the educational system to adjust automatically to changing economic requirements. Educational plans that leave all this out may do more damage than a policy of laissez-faire.

74. Throughout this paper, we have made things easy for ourselves by pretending that all countries are united in pursuing purely economic ob-j ecti yes for education. Of course, this is not the case. A fair wtq of summarizing the general situation is that some countries give top prior­ity to the production of highly qualified manpower and the next order of priority to universal. primary education, 'While other countries neatly re­verse this order. Such ditrerences are explained by differences in the strength of the demand for education and the political power of governments to resist that demand, combined with various degrees of ideological com­mitment to the principles of central economic planning. Wben governments favor primary education at the expense of secondary and higher education, the work of missions is made much more difficult and indeed at times im­possible, because no economic rationale will be supplied for the particula.r pattern of expansion that has been adopted. This raises a general question, however, about multiple objectives that deserves a new paper to do it Justice. Even when governments submit educational expenditures to economic criteria, other social and political objectives will account for at least part of the decisions that are taken. All we can sq here is that the best way to deal with a multiplicity ot objectives is to treat them as if they were additive: first construct a scale ot educational priorities as if nothing mattered except economic growth t an,d then consider whether'the­priorities need to be altered to satist,y such non-economic objectives as

. equalizing educational opportunities, providing a broadly literate elec­torate, forging a national culture, and so forth.

75. Bringing all the strands of the arguments together, what we have been proposing is simply that the scarce resources available for education in each country should be husbanded according to a scale ot priorities which reflect, however crudely, the estimated costs and expected benefits of educational projects. B,y benefits we mean the productivity of educated people and there is no reason to interpret these as measured exactly by the actual earnings of educated people. Nor is there any reason to contine these to pecuniary rewards, provided some quanti tati ve estimate 1s placed upon the non-pecuniary rewards t whatever they are. To ask for cardinal values of non-pecuniary returns is a counsel of perfection that we could not satisfY even in advanced countries. All that is required is an ordinal

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ranking of such non-monetary beneti ts relative to costs.. 41/ In most countries, however, the missions themselves will have to undertake this work. In a short period of time, even casual impressions of priorities can lead to superior formulations of educational plans than are now current in many developing countries. There is no reason to be apologetic about the failure to match the tangible, quickly comprehended, numerical precision of manpower torecasts. In a continent like Africa, for example, where there is no record of long-sustained development from which trends might be discerned, the most that experts can safely recommend is movement in a particular direction tor a limited period of time. We do not possess a panacea for educational planning; we cannot accurately toresee the future ten or fifteen years ahead; and we do not know exactly how much education to provide in order to accomplish specified targets of economic growth. Still, it is better to be hazily right than exactly wrong.

See the similar argument of W.C.Cash, teA Critique of Manpower Plan­ning and Educational Change in Africa", Economic Development and Cultural Change, October, 1965.