DISCUSSION PAPER

Report No.: ARTJ 42

Education, Experience and ImperfectProcessing of information in the Adoption

of Innovations

by

Alastair jT Fischer

Research UnitAgriculture and Rural Development Department

Operational ?olicy StaffWorld Bank

June 1985

111a Vicr.-F pn4ice;z,ed h&ar' are ?6j11 wassa of the authov(s) D znd tghey oTlhd niot!-T I-aas 1rece ae raffec g thooe off th l World Bank,

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The author is a member of the Department of Economics, University

of Adelaide and a consultant to the World Bank. The World Bank does notaccept responsibility for the views expressed herein which are those of theauthor and should not be attributed to the World Bank or to its affiliatedorganizations. The findings, interpretations and conclusions are in partthe results of research supported by the Bank (RPO 672-29); they do notnecessarily represent official policy of the Bank. The designations employedand the presentation of material in this document are solely for the convenienceof the reader and do not imply the expression of any opinion whatsoever on thepart of the World Bank or its affiliates concerning the legal status of anycountry, territory, city, area or of its authorities or concerning the

delimitation of its boundaries or national affiliation.

EDJCATICN, EXPERIENCE AND IMPERFECJ PI 3ESSING O INEOMAI(N

IN E ADOPrICN OF INNOVATIONS

A.J. Fischer

I. Introduction

Before a decision-maker decides whether or not to adopt a new

production technique, he must necessarily learn of its existence and

have some idea ot its properties. In particular, a risk-neutral

decision-maker will be unlikely to adopt the technique unless he

believes that its profitability exceeds that ot the existing technique.1

It is likely that his ideas will change as he learns more about the

technique. If he adopts eventually, at same stage his ideas will change

tran "do not adopt at this point" to "adopt". If the decision-maker

were a profit maximiser and a perfect processor of information, we could

characterise the decision-making process in Bayesian terms. The

decision-maker could be assumed to have uncertain prior beliefs

regarding innovation profitability, and would collect information which

would alter his prior view, weighting his prior beliefs and new

information optimally to obtain revised (posterior) beliefs.2

Department of Econanics, University of Adelaide. This paper wasdrafted while the author was on leave at the World Bank. Mheauthor gratetully acknowledges the support of the World Bank inproviding a cata set and its analysis: and aiso wishes toacknowledye the helpful ccmments of Gershon Feder, Melissa Gibbs,Hans binswanger and bob Lindner.

1 He may, however, trial-use a technique, of whose profitability heis uncertain, in order to generate intormation which is of value initself.

2 This set of assumptions follows the same lines as the approachtaken by U'Mara (1971), Lindner et al. (1979), Stoneman (1981), and

However, psychologists have tou.C that in the laboratory, people

are not particularly good at processing information. Most people

display a range of biases and inconsistencies and adopt a number of

simplifying heuristics for judging between alternatives.3 These

simplifications scmetimes result in nonoptimal decisions being

reached. While this implies that people do not act in conformity with

Bayes Theorem, it is argued in this paper that a quasi-Bayesian

optimizing framework may still be an appropriate characterisation of

behaviour.

In the context of the assimilation of new information into prior

beliets, psychologists have made two significant discoveries. b'irstly,

people are otten tound to be conservative in their incorporation ot new

intormation, "anch'oring" at the value of their prior beliets (' cwards,

1968). (That is, people put too yreat a weight on their prior beliefs

and too little on new intormation to be efficient in terms of bayes

Theorem).

Secondly, when the quality of the information that people receov:

is low or the information not perfectly relevant to their own

circunstances, they fail to recognise the data imperfections to a large

enough degree, and therefore incorporate imperfect information into

their beliefs to too great an extent, compared with their incorporation

of error-free infonmation (Gettys et al., 1973; Trope, 1978). In

behavioural decision theory, this is known as a problem in "ocascaded" or

eeder and u'Mara (1982),

-3 Kahnenan et al. (1982) yive a yeneral introduction to the area inpsychology devoted to work in the field, known as behaviouraldecision theory. Uther reviews of, and relevant work in, thisfield are Pitz and Sachs (1984), 1inhorn and Hogarth (1981), Siovicet al. (1977), Scholz (ed.), (1983) and Wallsten (ed.), (198U).

3.

"rmuitistaye" interence (Petersen, 1973; Schum, lU8U). Thus in the real

world, since data will not usually be pertectly relevant to the

decision-maker's own circumstances, this effect operates to sane degree

in a direction opposite to that of the conservatism noted by Edwards.

These things are incorporated into a model of the adoption of an

innovation later in this paper.

In the literature on the adoption of innovatic.is, it is known that

both education and experience are important determinants of adoption

speed (Rogers, 1983). If the timing of adoption depends on beliefs

about innovation profitability, the ways in which education and

experience can affect the timing of the adoption decision are via (a)

the rate of intormation collection, (b) the manents of the prior belief

distribution, and (c) the efficiency ot processing information. There

will also be a lag between the decision to adopt and the act of

adoption, which couid also depend on education and experience, but this

last aspect is not considerred further in the model to be developed.

We argue in this paper that, among decision-makers with the same

prior beliefs and faced with the same additional information, those who

have more education and/or experience should be more efficient

information processors because they are prone to make less-serious

mistakes in processing the information. For this reason, they usually

become earlier adopters of those innovations which turn out to be

profitable. We assume that the kinds of processing errors made are the

two which have been identified above.

This paper develops a simple model of the mean beliefs about the

profitability of an innovation, o,i the assumption that the decision-

maker is risk-neutral and acts as it hle were a bayesian processor of

4.

information. we then show how this framework must be modified to

incorporate impertect processing. In particular, we show that

conservatism in incorporating new intormation is a rational response of

a decision-maker who knows tran experience that he is not a perfect

processor of intormation. We also argue that uncertainty of data

quality, along with people's response to this uncertainty, is

understandable and not necessarily inconsistent with optimising

behaviour. Hence a Bayesian-like optimising framework can be justified.

Within this framework, a number of predictions are obtained. These

predictions are measured alongside empirical work and also against a

broad range of studies conducted mainly by sociologists (Rogers,

1983). Rogers groups the tindings from some hundreds of studies into anumber of "Generalisations". The predictions of the imodel are in

agreement with the majority tindings tor all relevant Generalisations.

Betore proceeding to the model proper, we discuss the results from

psychological research and the appropriateness of usiny an optimising

tramework.

II. Putting the Psychological Findings into an Optimising Eramewrk

Given that people do not act as efficient (Bayesian) processors of

information, it may appear unclear as to how such a situation may bemodelled. If people know from experience that they are imperfect

information-processors, it is plausible that they learn fram thisknowledge, and make adjustments to their behaviour accordingly. Thus,people who adjust their behaviour in the knowledge that they are poorprocessors of information will not usually act in accordance with bayesT'heorem. In tact, scme departure fran bayes Theorem, in this second-

5.

best situation, will be optimal.

Let us take a simple example to show that it will be optimal tor

imper-ect intormation-processors to be conservative in their use ot new

information. The numbers in the example may be generalised to symbols

it it is thought that this would add to its applicability.

Suppose that a risk-neutral4 decision-maker attemnpting to maximise

profit knows from past experience that only 40 per cent of innovations

in his area of operation are suitable tc his own circumstances. If a

particular innovation is in fact suitable and he adopts it, it results

in 10 units in extra profit compared with existing practice; if

unsuitable and he adopts, it is worth (-10). In advance, without any

extra information, he does not known whether a particular innovation

will be worth (+10) or (-10).

His payoff rnatrix is given by

State of Nature

Strategy Successtul Unsuccesstul

Adopt 10 -10

Do not adopt 0 0

If he adopts all innovations, his payoff equals

10(0.4) + (-10)(0.6) = -2

4 Risk-neutrality is assumed so that only the mean value ofprofitability needs to be considered, and not its variance oruncertainty with respect to the mean. Maximising profitability isa polar assumption, and given that the decision-maker is assumed(a) to maximise and (b) not to be concerned with maximising or evenconsidering anything but profit in his utility function, then theassumption of risk neutrality is merely another simplifying device.

.- ....

6.

If he adopts none, his payoff equals 0. If he adopts half of all

innovations (necessarily at random with respect to their profitability)

his payoff is (-1). Thus, without further information (other than the

knowledye of the existence ot certain innovations), he will not adopt

any such innovations. T'he tact that the payott is negative for

innovatiny without any intormation about the innovations should not be

surprising; it is one explanation ot the otten substantial lag between a

decision-maker's time of awareness of the innovation.and his tirst use

of it.5

Let us suppose in the next period the decision-maker is in receipt

of all available new information, which now allows some, but not total,

discrimination between innovations if it is used efficiently. Suppose

that the raximum effective Luse of inforrLation allows the decision-maker

to correctly categorise 70 per cent of what would be successful

innovations as successful. He adopts these innovations-. He- wrongly

cateyorises 30 per cent ot what would be successful innovations as

unsuccesstul, anO (wronyly) does not adopt these. Conversely, of the

innovations which would be unsuccessful, he wrongly categorises 30 per

cent as successtul and adopts them. (This tiyure ot 30 per cent does

not need to coincide with the previous tigure ot 3U per cent; it is done

5 Moreover, the fact that many innovations, probably the vastmajority of them, are never widely adopted, is also consistent witha negative value of prior beliefs about innovation profitabilitygenerally. Rogers (1983, pp. 92-3), referring to the pro-innovation bias of the literature, writes "Undoubtedly, hybrid cornwas profitable for each of the Iowa farmers in the Ryan and Gross(1943) study, but most other innovations that have been studied donot have this extremely high degree of relative advantage. Manyindividuals, for their own good, should not adopt them" (italics inthe original) and (on page 74) adds: "Only 1 idea in 540 resultsin a successful new product (Marting, 1964, p. 9). Only 8 per centof the approximately 6,000 new consumer items introduced each yearhave a life expectancy of even one year (Conner, 1964)",

7.

for simplicity only). The other 70 per cent he correctly recognises as

unsuccessful and does not adopt.

The payoff from the strategy of adopting 70 per cent of successful

and 30 per cent of unsuccesstul innovations is given by

10(U.7)(U.4) + (-10)(U.3)(U.6) = +1

which is preferable to doing nothiny.

However, if on beiny given this additional information, he is

unable to process it with maximum efficiency, he may well not make a

profit by adopting. If he cannot discriminate better than 60-40 in

favour of successful innovations, and better than 40-60 against

unsuccessful ones, his payoff from adoption will be negative.

The above analysis is equivalent to the use of Bayes Theorem with

prior probability of discovering a succesc.ul innovation of 0.4,

posterior probability of G.28 - = 0.61 with optimal use of theprobaility 0.28 + 0.18

information, and posterior probability of 1/2 with only 60-40 ability to

discriminate. rhe poor information processor in this situation (less

than 60-40 discrimination) will theretore require evern more information

betore he beyins to adopt any innovation at all. It will appear to an

observer who is unaware of the processor's imperfect processiny ability

that he has been overly conservative in his use of new information.

However, the processor has been rational in the knowledge of his

shortcomings, and not simply irrational. The difference is not merely.

semantic, in that rationality in the face of shortcomings can be

modelled in an optimising framework using a modified form of Bayes

Theorem. That is, we shall assume, when modelling the process more

generally, that the poor information processor acts like an efficient

8-

information processor who has less information.

The actions of the poor information processor in the example (lessthan 60)-40 discrimination, negative payoff to adoption) may becharacterised in other ways. We could (rather perversely) assume thatthis processor could process the information efficiently (even when wekncw he really can't) and that in order to generate a negative payoff,he lowers the mean ot his prior beliets (in the example, below (-2)).This alternative, however, appears. to be less plausible and less.consistent, as there is no reason to suppose that decision-makers shouldbe consistently downwardly biased regarding the proportion of successfulinnovations in the past. However, it is of course possible (perhaps

likely) that the poor information-processor may also be a poor utiliser:of innovations, and it is this which would lead him to a lower mean ofprior beliefs about innovation profitability. It is also likely that

the variance of prior beliefs is higher for poor information

processors: scmeone who cannot utilise information well, may also havea vaguer idea of the past than an efficient user of information. Thesehypotheses also form part of the model in the tollowing section.

This picture ot the poor intormation processor will be combinedwith that of the quality ot intormation in the model. It is aryued thatinformation is otten not ot pertect relevance to the aecision-maker's

own circumstances. Learning by looking at what someone else does willnot yenerate the same-information as learning by doing.

In the case of agriculture, the information given by an extensionofficer may relate to the performance of a plant variety in soils andconditions which pertain sane distance away, and which are not the sameas those which pertain to the local farmer. In the case of a f inn

9.

deciding whether to purchase a particular piece of new equipment, theinformation given by salesmen miyht accurately describe the equipment'sperformance in a samewhat different working environment.

Such information which is available to the decision-maker forceshim to make a two-stage inference: first, he must translate theinformation, generated scmewhere else, to his cwn circumstances,

generally with an error; second, he must then use the translatedinformation to decide whether the innovation will in the long term beworthwhile. Until now, most modelling has considered only the second ofthese problems, acting as if the first does not exist. Yet in the realworld, both torms ot uncertainty will generally be present. Consistentwith the results of the behavioural decision experiments mentionedearlier, we shall assume that pertect infoni-ation processors recogniseimperfect data and allow for it in terms of the correct amount ofincreased uncertainty, but that poorer processors tend not to recognisethe imperfections, and to underestimate the increased uncertainty thatthe imperfections add. The following discussion aims to 3haC that it isunderstandable for an inexperienced person to confuse the poor qualityof the data with his own imperfect processing abilities.

Let us suppose for a moment that a decision-maker is in possessionof scme information which is of perfect relevance to him: for example,it could relate to his trial use of a new piece of equi>xnent.Nonetheless, the information will very often be fragmentary: it mayrelate only to a short time-period and only limited environmentalconditions. Unlike theoretical exercises in which the fonm of thedistribution of the likelihood function is assumed to be known, as also(sametimes) is the variance of the distribution, generally in the realworld these things do not pertain. In particular, the frequency of

occurrence of outliers is unknown in the real work with much

uncertainty, in a variety of circumstances. Thus, when there is onij a

small amount of information available, the decision-maker's lack of

knowledge of the form of the distribution of the likelihood function is

likely to substantially increase the variance of his subjective

beliefs. On the other hand, if data is available in abundance, both

memory and people's ability to do calculations are limited. Again, Lhis

adds to the uncertainty of scmeone trying to make a decision.

On top or this, very otten people have to rely on information which

is not perfectly relevant to their own circumstances. It is otten very

ditricult to know, particularly in regard to something new, the extent

to which intormation trcm a tunctionally or geographically ditterent

area can be translated to a decision-maker's own circumstances. When a

decision-maker reviews the past, therefore, he often will not know

whether an error of judgment which he has made has been due to his poor

processing of perfectly relevant information, or the poor quality of the

infonmation itself. Those with experience of translating information

for related innovations may know better what allowances to make for this

first stage -¢ inference, but those without such experience, who do not

know how to distinguish between the error of sampling perfectly-relevant

information fram that of translatin,g imperfect infonmation, are more

likely to lump such errors together. However, while the error of

samlpling perfectly-relevant inrormation decLines with increasing sample

size, that ot translating it trcm another source often will not decline

with increasing sample size. As sample size increases, theretore, an

inexperienced decision-maker who has lumped the two errors together will

tend to decrease the error of imperfect information too much. He will

be misled by continuing (but essentially irrelevant) reports of the

protitability ot the innovation in circumstances clitterent tran his own.

To canplete the discussion regarding the link between education and

experience, and the speed of adoption, it is noted that we would expect

that each piece of information is worth more to an eff icient processor

of information, still in the process of evaluation of alternatives, than

is the corresponding piece of information to an inefficient processor.

Thus, since the information is likely to have higher marginal value to

an efficient user of it, such users will seek information sooner, and so

adopt protitable innovations sooner. This will not be universally true,

of course: tor example, once an erficient user ot information has made

a detinite decision to adopt, or alternatively, not to adopt (rather

than still be in the evaluative process), information gained beyond this

point ot time may be ot very little value. Inetficient users yet to

make a decision will yive this intormation more value. It will also be

untrue of intormation of little or no relevance: the etticient utiliser

will (by definition) recognise this and will not place much value on it.

There may also be other personal characteristics of decision-makers

which determine their speed of information-gathering and processing

ability. These may be incorporated into the framework proposed in the

model in the next section if so desired, but to do so in present

circumstances would add little to understanding for the price of rather

more lengthy algebra.

III. The Model

We assume that a decision-maker's prior knowledge ot the perceived

mean profitability of the application of the innovation (per unit of a

tixed resource) is given by6 N(yo, i ). we also assume that new

intornmation about innovation protitability is received in discrete

units, so that it can be sampled as the independently-distributed randcm

variable X - N(p, a where normality is again assumed for

convenience. p is assumed to be unknown to the decision-maker, but

(since the innovation is assumed to be profitable) p > a, where a is the

known mean of the existing technique. As for the example, a > yo. We

assume that y , 52 and a2 are known, and that the decision-maker is

risk-neutral.

In this formulation, X is assumed to be of perfect relevance to the

decision-maker, and to be generated as X. = + sit where co D N(0, a

h(£ i£3) = 0. It the decision-maker is also a pertect intormation

processor, the mean value ot the decision-maker's beliets regarding 1.,

given a sample ot m pieces ot intormation, will be yiven by bayes

Theorem as

6 The distribution N(y0, 62) may be seen as a sort of generalisationof the discrete prior distribution in the example in Section II,with normality imposed. The example had values (-10, 10) withprobabilities (0.6, 0.4) respectively; its mean is -2 and itsvariance is 96, given that the values and probabilities are knownwith certainty. These values (-2, 96) characterise the first twomcmnerts of the prior distribution of the mean of a sample of oneinnovation. More yenerally, when the prior may take many possiblevalues, and not just two of them, and when their orobabilities areUncertain, the distribution of the prior perceived mean ofprofitability can be given by N(y , 62), where normality is assumedfor convenience. A significant difference between the model inthis section and the numerical example in the last section is thatthe model in this section deals only with an innovation which weknow is to be successful. In order to yet the result in theexample it was necessary to postulate innovations of which aportion are unsuccesstul. The proposition which is illustrated bythe example is then used in the model ot this section as anassumption: decision-makers act as if they were using a moditieaBayes Theorem in an attempt to optimise returns.

13.

YO XMyo~m

60 a,/m -m

Y where X = Xim 1

6c a/rn0

=y + (1 ) Xm, say. (1)

If X is generated from a single source of imperfect relevance to the

decision-maker, such that

Xi + B + ei, where si - N(O, a )2 E(eiej) = 0

and 7 where B - N((, a2 2 E(eiB) = °

in this case, Y will be given by

y

o 2 26 a /m + a

m 162 +2 ;260 a/m+al+a13

This again assumes perfect information processing.

2Note that the magnitude of a will depend on how relevant the

decision-maker perceives the information fran an imperfect source to

be. If the source is close geographically in the case of agriculture,

2or close functionally in the case of an industrial firm, a will be

smal2L otherwise it may be large. More information fran the single

source will reduce the effect of a in equation (2) but not that of

7 B has been given a mean of U without loss of generality. Should anonzero mean for b be chosen, we may define a new j' and B', whereB' has zero mean, and the mean of B has been absorbed into .', withsimilar properties to p.

14.

aB e The eftect of ab will decline in (2) it a new source of

information is tapped, which is either "close" to the decision-maker

(geographically or tunctionally) or it the new source is not completely

correlated with the original source.

Finally, we introduce the ettect ot ditterent i.nformation-

processing skills (into-skills tor short). Let k = into-skill, where k

depends on experience and education (and probably other personal

variables as well - but these latter are ignored).

Fran the discussion in Section II, we assume the following

regarding the components of equation (2).

2 2(a) m = a

wlhere m(k) has the tollowing properties: m(0) 0, m(X) = m, m'(k)

> U, m'(k) < U.

FIGURES l(a) and 1 (b)

(,-gh) B.

m(U) 0 implies that those with zero info-skill cannot

assimilate any information at all: they act only by

innate prior beLiefs or instinct.

15.

m(X) - m: those witih much education and experience can process

all new information efficiently. (This formulation also

captures the assumption that those with more into-skill

actually gather more information at a given time, as well

as being capable of using it more efficiently).

See Figure l(a) for the relationship between m(k) and k.

(b) a2 = C2(k)

2where a(aB)/ak -aB > 0; aEB < 0.

This implies that those with greater info-skill recognise

imperfections in information more efficiently. The relationshipbetween a2(k) and k is shown in Figure l(b).

() 52 2(c) 60 =do(k),

a 62(k)where 0k - 6' < U, and d" > U.ak

Peowle with more info-skill have samewhat less vague priors

based on experience.

(d) y = y (k)

a yo(k)where ak y > O.

Those with more info-skill have also had better experiencewith innovations (on average) in the past than those without.

16.

The model now beccnes

'Yo (k )i

do(k) a2 /m(k)4+ a:(k)Ym(8,k) =- - ( 3)

2 2-d0(k) a /m(k) + a (k)

since the decision-maker (being risk-neutral) will adouL the innovation

as soon as ym(8,k) exceeds , we see that the speed ot adoption depends

on how soon y (ii,k) (called simply y hereafter) reaches a. In turn,

adoption speed depends on how tast ym is growing. we wish to find out

how y responds to changes in k, the level of education ard experience.

2Putting - + a2 + 62 = A, we obtain, replacing Xm by )i for

simplicity,

ay O (m + ) aym ( y) 622a2 2 an2 20A M A

aym ( 2 2 2 = aand a m a /m___Ii 0

and soay (t3,k) - y iam ay aa2 ay a62 ayay-

M M am + M aM a=k = am 2 aak Caa3 ad 0

0a 2

22 + ( ( 2)6] +2 ½ (k) + - a+ ((+) 0+ --)- (+) -

(4)

17.

In (4), ignoring for the mcment the last term in square brackets, we see

that ay is conposed of two terms of opposite signs within the brackets

and a positive term, probably small, outside of them. When information2is perfectly relevant (i.e., when a2 = 0) the middle term in square

brackets disappears and - > 0. Thus, when information is perfectly

relevant, those who are info-skilled will be the tirst to adcpt the

innovation. The only exception likely here is where the third term in

square brackets is important: this would be where more inf&-skilled

people were relatively-more dogmatic in their beliefs that i nnovations

were unprofitable, presumably from some bad past experience. In this

case it may require an unskilled information processor, mindless of his

peers, to begin the adoption process.

On the other hand, when both m and aB are large (that is, there is

a lot of information, but all of it fran scme distant geographic or

functional area), the first term in square brackets becomesay

insignificant and if the final term is small, -m- < 0. That is, when

there is a lot of intormation, all of dubious quality, the most info-

skilled will not be the first to adopt, as they have correctly noted

that a is large, and have properly discounted the low quality

information. However, those not so skilled are (in the nodel) inclined

to treat information of whatever quality in much the s&-ne way, so that

should anyone adopt the innovation, it will be saneone trom among those

who are not the best information processors. once the performance of

ahhis innovation has been observed by others belonging to the same

functional or geographic area as the maverick earliest adopter, however,

tFli value for a2 will fall, and if the innovation performs well in the

nore relevant circumstances, it will be adopted more generally, the

initial adoption being followed by those who are most skilled in

information gathering and processing.

If the final term is large, however, it is possible that this term

may dcaninate. This would say that decision-makers with less education

and experience, having learned that innovations have not been as

profitable tor them as for others with more education and experience,

are so "pessiXnistic" as a result, that they will not be the tirst to

innovate, despite the encouraging signals (tram near and/or tar) which

try to entice them with samething else that is new.

IV. E&irical Results

Can the time lag between the appearance of the innovation on the

market and its adoption by decision-makers be explained by their

different infonmation processing abilities, and in particular, by their

education and experience? We note that this lag should be inversely

related (as a linear transformation) to ym(B,k), the perceived

profitability of the innovation compared with that of the existing

technique.

The time to adoption (TIMALXPT) will therefore be a function both

of prior beliefs of protitability (at time of awareness) together with

the content ot the information obtained subsequently, modified by

differing processing abilities. We shall call the information modified

by processing ability, the "perceived-intonmation" ot the processor.

Fran the model of Section III, the mean of prior beliefs (y0), the

variance of prior beliefs (3 ) and the variance of perceived-information2 0+ a2) are determined by education and experience, as well as by a

hfamber of other variables.

Thus,

y (k) PRIuMEAN = f1 (EDUJCAT, EXPER, OTHER) (5)

19.

62(k) 2 PRIORVAR = f2 (EDUCAT, EXPER, OTHER) (6)

2 INFOMEAN = f3 (OTHER) (7)

+ a(k) - INFOVAR - f (EDUCAT, EXPER, OTHER) (8)

k'ran this,

TIMALXJP =( () = y (PRIUME:AN, PRIORVAR, INVOMEAN, INtUVAR)

= y (EDUCAT, EXPER, OrHER) (9)

The effect of education and experience on the mean and variance of

prior beliefs may be tested fran equations (5) and (6), if prior mean

and variance can be measured. We have attempted to do this for a sample

of South Australian farmers. These farmers were interviewed about some

potential innovations, including elicitation of the mean and variance of

their beliefs about innovation profitability soon after their awareness

of them. There are two problems surrounding the elicitation of prior

beliefs. one is that the elicitation process itself may be faulty:

what farmers say their beliefs are, may not be what they really

believe. Different elicitation methods in such circumstances may lead

to different statements of befiets. Second, additional information

usually acccmpanies the announcement of a new process or technique.

Farmers will not usually receive exactly the same additional information

at this time, but even if they do, their own differing circumstances

will be such that they will interpret the information differently so

that the prior beliefs at time of awareness will inevitably containvarying (nonzero) amounts of specific information about the innovation.

20.

Unfortunately, the elicitation of prior beliefs has provedexceedingly difficult. At this stage, no worthwhile relationships

between prior beliefs and processing abilities have been found, but itis clear that whether an underlying relationship existed or not, theelicitation process has given answers for individual farmers which have

been internally inconsistent and so overconfident (in terms of lowvariance of Prior beliefs) as to render the particular data meaningless

for the task of finding a relationship. Since the links betweeneducation and experience on the one hand, and prior mean and priorvariance on the other, are likely to be tenuous, for this paper it isassumed that no such relationship exists, and that in equation (9),education and experience effect TIMAD though their effects on processing

skills (i.e., through INEOVAR in the unobservable equation (8)), and notindirectly via prior beliefs.

That is, in our model in the previous section, we assume that thelast terni in square brackets in (4) and the term outside of them, aresmall or insignficant. Since the system of equations is recursive, it

is appropriate to use O.LL.S to estimate (9) (see Pindyck and Rubinfeld,

Chapter 11). Some indirect evidence of the additional value ofeducation and experience between the tilm of awareness and the time ofadoption has been obtained tram a data set of wheat farmers in Haryana

State, India. F'or the most part, the results of this data set confirmthe model's predictions.

Idi 1983, a. sample of 323 farmers tran Karnal district and a sample

of 569 farmers fran Jind district were asked (i) whether they had heardabut 10 different techniques, and (ii) whether they had adopted them,within the last three years, or more than three years ago. For three ofthe techniques in both districts, knowledge and adoption were almost

ccmplete, so no meaningful analysis to distinguish adopters frannonadopters was possible. Of the remaining seven techniques, a logit

regression was run separately for Karnal and Jind farmers, giving 14

equations in all for both time of awareness and time of adop.tion. Since

one of these techniques (zinc sulphate) had not diffused in Jind to a

great -nough extent, the logit regression did not have enough

observations to be meaningful, and only 13 equations converged and were

estimable. The two sets of loyit regressions had dependent variables,

respectively, of TIMDWUPT taking the value of 1 if the farmer adopted

more than three years ayo, U otherwise; and TIMAWAMIs, takiny the value

ot 1 if the farmer was aware of the technique more than three years ago,

U otherwise. F'or all regressions, there were nine independent

variables, four related to education and experience and five related to

other factors.

The four variables related to education and experience were:

EDUAGR: Binary variable equal to 1 if the farmer had ever

participated in any short-term training in agriculture.

EIDUHEAD: The number of years of schooling of the head of the

household.

LNAGE: Loyarithm of age of the tarmer.

M1M5GUVT: binary variable equal to 1 if the farmer hol-ds a

position in village government, cooperative society or

block committee. (This variable is likely to be related

to status and respect and not necessarily reflect either

education or experience, though it has been used as a

proxy for the latter two variables in this instance).

The signs on these variables could be expected to be positive.Alternative regressions in which EDU(HEAD was replaced by a variablerelating to the number of years of schooling of the most educated memberof the household gave similar results, and are not included. The othervariables included in the regressions were:

CASTE: Equal to 1 if the farmer is a Jat.

CREDIT: Equals 1 if the farmer is likely to have a credit problem.IRRIG: Equals 1 if the farmer uses irriyation.

LNOPLAND: Logarithn of land operated.

TENANT: Equals 1 if tne tarmer rents 10% or more of operated land.

*T'heir expected signs were: CASTE - no preconceived ideas, CREDITnegative, IRRIG - positive., LNOPLAND - positive, ThNANT - probablynegative.

The results of the logit regressions are sunmarised in thefollowing table, which shows the number of regressions in which thecoefficients of each variable are positive and the number which arenegative, tie number of times that the t value is greater than 2 inabsolute value, the number of times between 1.5 and 2, ancl thearithmetic average t value over the 13 equatins.

At this stage, scinething should be said about the use of theaverage value of t. There was a high degree of concurrence betweEn theanswers yiven by any one farmer to the questions about the seventechniques: it a farmer said he was aware of one technique more than 3years previously., it was likely that he would be aware of each of theother techniques more than 3 years previously, and similarly foradoption. Conversely, if a farmer said he was unaware of one technique,

23.

Aipticn AwarensSs

Variable S. d! Ties Significance NO. of Ti1es S4ificance

b3 (-) jtj>2 1.5<ltl<2 Ave t (+) (-) Itt>2 1.5Cltl<2 Ame t

CASTE 5 8 l-1l* 1,-2 -0.3 2 11 1,-4 1r-l -1.1

CREDIT 4 9 1 1 0.1 5 8 1,-1 1 -0.1

ECXAPR 5 8 -1 -2 -0.6 2 11 -2 -1 -0.9

EtJHEAD 12 1 3 2 1.0 10 3 1 1 0.6

IRRIG 11 2 5 2 2.3 10 3 5 1 2.0

LNXE 11 2 0 2 0.8 11. 2 1 1 0.7

LNZIPLAND 4 9 1,-i 1'-i -0.4 4 9 2,-i 0 -0.1

MihPE83 13 ( 5 2 1.7 13 0 5 1 1.7

T.NANr 6 7 1 2 0.3 7 6 1 2 0.3

* Ihis denotes that t > 2 on one occasion and t < -2 on one occasion outot the 13 regressions.

it was relatively irore likely he would give the same answer for each of

the other techniques. Thus for Karnal, the 7 equations for adoption

mostly had similar magnitudes for the t values for each variable, as did

those for awareness. Ditto for Jind, but with 6 equations. Thus, for

Karnal, there are not as many as 7 independent t-values for each

variable, but the equivalent of more than one. For the thirteen

equations taken t?ogether, those from Jind are independent of those for

Karnal, so that there are in excess of two independent t values. The

average value of t is used as a summary measure, because apart from

IRRIG, the t-values tor Jind and Karnal did not differ a great deal from

the average, either within areas or between.

In detail, we aeal firstly with the time of adoption. Results for

Karnal and Jind were similar overall, except for IRRIG, because in

24.

Karnal almost all farms were irrigated, and there were not enough

nenirrigated observations to give a signif icant results t values for

this variable were small and erratic for Karnal, but were high and

positive for Jind, where a large minority of farms did not use

irrigation. If caste had an effect, the effect differed in sign for

different practices in both Karnal and Jind; overall, therefore, caste

had no pervasive effect in a single direction. Credit constraints,

amount --f land operated and renting land were not significant overall,

leaving the four variables most closely related to education or

experience. Of these, MEMBGOVr was the most important explanator of the

time of adoption, all thirteen regressions shcwing the expected sign,

and an average t-value of 1.7, of similar magnitude in both Jind and

Karnal. Since tarmers who have adopted one technique are very likely to

have adopted others, it cannot be argued that the MEMI3GOVT coefficient

in either Jind or Karnal, over the 6 or 7 equations taken together, is

necessarily significant. However, since the set of seven Karnal

equations is independent of the six Jind equations, high t-values for

both areas, taken together, makes MEMBGOVT a significant variable

overall (the probability that two independent observations of t are both

greater than 1.7 is .002). EDUHEAD is probably also significant overall

(the probability that two independent observations of t are both greater

than unity is .025), although the effect is not as strong as that of

MEMBGOVr. LNAGE may also be significant, though the effect is weaker

again (the probability that two independent observations of t are both

greater L,.an 0.8 is .049). EDlUAGR, on the other hand, is in the wrong

direction, and may be significantly so, its etFect being a little less

than that of LNAGE in tenns of absolute t-values (the probability thattwo independent observations of t are both less than -U.6 is U075). If

this result is significant, it is possible either. that short

25.

agricultural courses are a waste of time and money, or that courses were

designed primarily for the least-torward looking tarmers, or tor tarmers

frau the most backward villages.

Therefore, tor explaining the time of adoption, after allowing for

variables as diverse as farm size, credit constraints, tenure, caste and

irrigation (of which onl.y irrigation was important) it is found that,

taken together, education and experience variables play a significant

role in the predicted direction.

We now turn to the time of awareness. Basically the same results

still apply for the variables not concerned with education and

experience. The results suggest, though not very strongly, that

education and experience are not as important in determining time of

awareness as they ara for time of adoption, for the following reasons.

In the first place, EDUHEAD has the expected sign less often, is

significant less often, and has a lower average t-value for awareness

than it had tor adoption. Next, LNA(.E has a marginally lower average t-

value for awareness than for adoption; next, EDUAGR has the expected

sign in only 2 out of 13 equations tor awareness ccmpared with 5 out of

13 for adoption, and an average t-value of -0.9 rather than -0.6.

Finally, the average explanatory power of the regretsions is not as

great for awareness as for adoption. The 13 logit regressions for

adoption correctly categorize on average 67 per cent of cases, ranging

fram 57 per cent for sowing depth in Karnal to 85 per cent for

application of potash in Jind, whilp the same regressions for awareness

as dependent variable explain 65 per cent of cases overall, ranging frcm

5S per cent for potash in Karnal to 82 per cent for potash in Jind.

None of these pieces of evidence is individually very strong, .. in

total they suyyest that education and experience, inasmuch as they are

26.

less of a detenminant of the time of awareness than the time of

adoption, help farmers to process information in reaching a decision to

adopt. This explanation, however, does not rule out the possibility

that education and experience influence farmers' prior beliefs and their

rate of information collection along with the influence on processing-

ability. The implication from the regression which suggests that

TIMMARE is related to education and experience (as postulated) is that

in the early stages of information collection, the more educated and

experienced farmers collect information at a faster rate.

The model's predictions are also consistent with the general tenor

of the sociological literature on innovation diffusion, as given by the

following "Generalisations" due to Rogers (1983). The number of studies

(supporting, not supporting) of the Generalisation are included in

parentheses in each case.

(1) Earlier knowers of an innovation:

(a) have more education than later knowers (17-7).

(b) have more exposure to all forms of communication: mass media

(18-11), interpersonal channels (16-2), change agent contact

(13-3), social participation (11-2) and cosmopoliteness (5-O))

(2) JMass media channels are relatively more imaportant at the awareness

stage, and interpersonal channels are relatively more important at

the persuasion stage (18-2).

(3) Mass media channels are relatively more important than

interpersonal channels for earlier adopters than for later adopters

(8-2) .

27.

(4) Cosmpolite channels are relatively more important than localite

channels for earlier adopters than wor later adopters (9-0).

(Note: Generalisations 2, 3 and 4 are broadly consistent with the

theory of Section II on the quality of information, "interpersonal"

and "localite" channels being regarded as "higher quality").

(5) The rate of awareness is more rapid than the rate of adoption

(2-0); earlier adopters have a shorter awareness to adoption lag

than later adopters (5-1).

(Note: This is consistent with the mnore "innovative" tarmers

those with more education and experience, ceteris paribus - seeking

information faster and evaluating it faster.)

(6) The relative advantage of an innovation, as perceived by members of

a social system, is positively related to its rate of adoption

(29-14).

(Note: In our admittedly narrow context, (. - a)r the relative

profitability of the innovation, is our measure of "relative

advantage".)

(7) Earlier adopters have:

(a) more years of education (203-72)

(b) greater literacy (24-14)

(c) a yreater ability to deal with abstractions (5-3)

(d) yreater rationality (use of the most effective means to reach a

given end) (11-3)

(e) greater intelligence (5-0)

(f) more favourable attitude to change (43-14)

(g) more exposure to all forms of mass ccmmunication: mass media

(80-21); interpersonal channels (46-14); change agent contact

(135-21); social participation (109-40); cosmpoliteness (132-42)

28.

(h) greater khowledge (61-19)

(W) seek more information (12-2).

(Note: Generalisation (7) (f) is as close as Rcgers' Generalisa-

tions go towards discussion of what we have labelled "prior

beliefs").

All 25 yeneralisations above show a majority ot stuaies consistent withthe predictions ot the model.

V. Discussion and 11EEar

This paper integrates several major findings by behavioural

decision psychologists into a business decision model, the development

of which has been mainly within the economics discipline. It shows thatcertain aspects of the behaviour of people who make decisions underuncertainty can, in qualitative terms, be captured by a model whichallows them to be poor processors of information. The model stayswithin the mainstream of econcnics by assuning that decision-makers areattempting to optimize returns, subject to the constraint of being

imperfect intormation processors. The conclusion reached by means of anexample is that poor intormation processors who are aware of theirlimitations will act as if they were using Bayes Ttheorem, but utilizing

a smaller quantity of intormation than is actually available to them.This conclusion has also been reached empirically in experiments done by

behavioural decision psychologists. A defense is therefore provided ofthe use of an optimizing framework and of a modified form of BayesTheorem to describe the actions of decision-makers when they adapt tonew circumstances in their environment.

29.

One criticism of the paper is that the analysis, particularly inSection II, is not rigorous. However, given that it is not really knownhow it is that scme people make better decisions than others when giventhe same information, a more rigorous approach is probably not

justified.

Another criticism that could be leveled at the approach is that thereasoning could be circular. It could be sugyested that if we observesomeone departing tram bayes Theorem even more than usual, we simplycall him an even poorer information-processor who nevertheless is stilloptimizing, rather than admit that neither an optimizing tramework norbayes Theorem are appropriate descriptions of his behaviour. Tb some

degree this criticism may have validity, to the extent that the

distinction between "departures from Bayes Theorem" and "poorinformation-processor" has not been drawn out. However, the framework

oi the paper is useful in that it does predict conservative behaviour inthe incorporation of new infonmation, which is also the experimentallyobserved result; this cannot be the result of circularity in reasoning,as the prediction could have been in the other direction. Thecircularity arises from not being able to predict independently theextent ot the conservatism. beyond this, the optimizing model has givenrise to other predictions which also are in the same direction asbeiLaviour observed in the real world. The weakness ot the model is that

inlik,- the unmodified 8ayes Theorem, which gives exact predictions, wecan nOw do 1no wore than state the direction of changes.

Cn ths second strand xdken fran behavioural decision theory, thecascading of inferences (or in the language of this paper, the qualityof information) the results of behaviour observed in the laboratory havebeen taken as given, with the suggestion that it is likely that people

30.

confuse the quality of information and the quality of information-

processing. To paraphrase the earlier discussion about this, using the

symbols of the model used in Section III, when m pieces of imperfect

information are available frcm the single source, the actual variance of2 2

estimated mean profitability, R., is given by m- + a2. It would e easya + anfor an inexperienced decision-maker to act as if this were

mexcept that this will understate variance, especially if a2 is large

Bccmpared with a2. To avoid this, someone with general experience, but

no, expexience specif'ic to the innovation, may mocify the variance toaF + a

*- m* , where m* < m, to compensate for the underestimation. The

formulation in the model is a yeneralised tomn ot the last expression.

The formal model used in Section III goes one step further back,

and suggests that decision-makers' personal characteristics such as

experience and education should affect the rate of information-

gathering, prior beliefs about innovation profitability, and

information-processing skills. Given the kinds of deviation from

perfect information-processing described above, it is suggested that

better information gatherers and processors (those with more education

and experience) will deviate less from perfect information-processing

(i.e., fran Bayes Theorem) than will poor processors.

The prediction from the- model is that, tor the most part, these

factors make better-educated and experienced decis-mn-makers earlier

adopters ot innovations which turn out to be profitable. The main

exceptions are where the only information available (of which there may

be a lot) is fram a distant geographic or functional area believed to be

of marginal relevance to decision-makers, or in those places where most

decision-makers are dogmatic in their beliefs that innovations are never

profitable. In the former case; it will pay the best-informed to wait

until scieone close to them (and with less experience) who has mistaken

the poor-quality information for that of higher-quality decides to

adopt. The best-informed will follow suit only when it is clearer that

the innovation will in fact be profitable. In the latter case, the

"best-informed18 , who are dogmatic about all innovations being

unprofitable, are in fact wrong.

A criticisn of this part of the model is that it is assumed that

the "mistakes" which info-skilled decision-makers are better at avoiding

are those that the behavioural decision psychologists have identified

and are included in this model (or at least, which operate in the same

direction as those identified torms ot error). It is possible that

skill in intormation processing is determined in the main by other

factors, and that the identified shortcomings modelled here are not very

important. This aspect cannot be answered in this paper, as it depends

on behavioural decision experiments, few of which are likely to have

been done.

That a number of predictions of this model are shown to be in

qualitative agreement with a range of studies in the adoption of

innovations indicates utility in integrating psychological and economic

approaches. It is likely that further work in this area, as well as in

the wider areas of beliefs, anticipations, adjustments and forecasts,

will also benefit fram a closer relationship of these disciplines. More

general incorporation into economic models at findings about the way

people actually behave rather than how they "ought" to behave should

lead to better models and predictions.

,.

32.

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Rogers, E.M., (1983), Dittusion of Innovations, (3rd edition), New York:I`ree Press/MacMillan.

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DtzcSCUSSN ?A?E?.SAGR/Research Uni t

Reoort 'No. ARU IAgricultural Mechanization: A Comparative Historical Perspective

by Hans P. Binswanger, October 30, 1982.

Report No.: ARU 2The Acquisition of Information and the Adoption of New Technology

by Gershoni Fader and Roger Slade, September 1982.

Report No.: ARU 3Selectelkng Contact Farmers for Agricultural Extension: The Training and,

Visit System in Haryana, Indiaby Gershon Feder and Roger Slade, August 1982.

Report No. : ARU 4The Impact of Attitudes Toward Risk on Agricultural Decisions in Rural

India.by Hans P. Binswanger, Dayanatha Jha, T. Balaramaiah and Donald A. SillersMay 1982.

Report No.: ARU 5Behavioral and Material Determinants of Production Relations in Agricultureby Hans P. Binswanger and Mark R. Rosenzweig, June 1982, Revised 10/5/83.

Recort No.: ARRU 6The Demand for Food and Foodgrain Quality in India

by Hans P. Binswanger, Jaime B. Quizon and Gurushri Swamy, November 1982.

Recort No.: ARU 7Policy Implications of Research on Energy Intake and Activity Levels with

Reference to the Debate of che Energy Adequacy of Existing Diets inDevelopment Countriesby Shlomo Reutlinger, May 1983.

Reoort No.: ARU 8"fore Effective Aid to the World's Poor and Hungry: A Fresh Look at

Uniced States Public Law 480, Title II Food Aidby Shlamo Reutlinger, June 1983.

Renort No.: ARL 9Factor Gains and Losses in the Indian Semi-Arid Tropics:

A Didactic Approach to Modeling the Agricultural Sectorby Jaime B. Quizon and Hans °. Binswanger, Sepcem.ber L983, Revised May 1984.

teoart No.: ARU 10The Distribucion of lacome in India's Northern Wheat Region

by Jaime B. Quizon, Hans P. Binswanger and Devendra Gupta, Auxusc 1993.Revised June 1984.

Report No.: ARU 11Population Density, Farming Intensity, Patterns of Labor-Use and Mechanization

by Prabhu L. Pingali and Hans P. Binswanger, September 1983.

Reoort No.: ARU 12The Nutritional Impact of Food Aidi Criteria for the Selection of

Cost-Effective Foodsby Shlomo Reuclinger and Judit Katona-Apte, September 1983.

Oiscussion Papers (Cont'd.)

Report Yo.: ARU 13Project Food Aid and Equitable Growth: rtncom*e-T:ansfer Efficiency First!by Shlomo Reutlirger, August 1983.

er No. .RU 14lutritional Lmpact of Agricultural Projects: A Conceptual, Framework forModifying the Design and Implementation of Proj ects

by Shlomo ReuClinger, August 2, 1983.

Report Mo,: ARU 15Pacterns of Agricultural Protection by H{ans P. Sinswanger and Pasquale L.Scandizzo, .ovember 1l, 1983.

Report No.: ARU 16Factor Costs, racome and Supply Shares in tndian Agriculture

by Uanjan Pal and Jaime Quizon,, December 1983.

Reoort No.: ARU 17Behaviaral and Material Decerminants of Production Relations in Land Abundant

t.r3oical Agricultureby Hans P. Binswanger and John Mcrnaire, January 1984.

Report NJo.: ARU 18The Relatioa 3etween Far= Size and Farm Productivicy: The Role of Family

Labor, Supervision and Credit Conscraints*by Gershon Feder, December 1.983.

Revort No. : A.RU 19.A Comparacive Analysis oe Some Aspects of the Trai.aing and Visic System ofAgricultural Extension Lai India

by Gershon Feder and Roger Slade, F.ebruary 1984.

Reort No.: ART 2 0Distributional Consequences of Alternative Food Policies in Indiaby Hans P. Binswanger and Jaime B. Quizon, August 31, 1984.

Report .o.: ARU 21Income Distribution in India: The Impact of Policies and Growth in the AgricuutaalSector, by Jaime B: Quizon and Hans P. Binswanger, November 1984.

Report NTo.: 4RL ?22Population Density and Agricultural Intensification: A Study of the Evolution oQTechnologies in Tropical Agriculture, by Prabhu L. Pingali and Hans P. BinswangeK,October 17, 1984.

Report No.: ARU 23The EvoLution of Farmining Systems and Agricultural Technology in Sub-Saharan Africa,by Hans P. Binswanger and Prabhu L. Pingali, October 1984.

N .: ARU 24Poplaton ensty nd armng ystMs - The Clzanaing Locus of Innovations andTechnical Change, by Prabhu L. Pingali and Hans', Binswanger, October 1984.

,epor No.: G eH25TheTrinig nd Visit Extension System: An Analysis of Operations andffects, bY G. Feder, R.H. Slade and A.K. Sundaram, November 1984.

Reprt o.:ARU 26Tne Role of Public Policy in the Diffusion of New Agricultural Technology,

by Gershon Feder and Roger Slade, October 1984.

Report : ARU 2Fertilizer Subsidies: A Review of Policy Issues with Special Emphasison Western Africa, by Haim Shalit and Hans P. Binawanger, November 1984.

Repo,rt No.: ARU 28From Land-Abundance to Land-Scarcity: The Effects of Population Growthon Production Relations in Agrarian Economies, by Mark R. Rosenzweig,Hans P. Binswanger, and John Mecncire, November 1984.

Report No.: ARU 29The Impact of Rural Electrification and Infraseructure on Agricultural

Changes in India, 1966-1980, by Douglas F. Barnes and Hans P. Binswanger,December 1984.

Report No.: ARU 30Public Tractor Hire and Equipment Hire Schemes in Developing Countries

(with Special Emphasis on Africa). A 3tudy prepared by the OverseasDivision, National Institute of Agricultural Engineering (OD/NIAE), byP.J. Seager and R.S. Fieldson, November 1984.

Report No.: ARU 31Evaluating Research System Performance and Targeting Research in LandAbundant Areas of Sub-Saharan Africa, by Hans P. Binswanger, January 1985.

Report No.: ARU 32On the Provision of Extension Services in Third World Agriculture, byAlastair J. Fischer (Consultant), January 1985.

Report No.: ARU 33An Economic Appraisal of Withdrawing Fertilizer Subsidies in India, by

Jaime B. Quizon, April 1985.

Report No.: AR7 34The Impact of Agricultural Extension: A Case Study of the Training and VisitMethod (T&V) in Haryana, India, Gershon Feder, Lawrence J. Lau andE;rger H. Slade, March 1985.

Report No.: ARU 35Managing Water Managers: Deterring Expropriation, or, Equity as a Control

Mechanism, by Robert Wade, April 1985.

Report No.: ARU 36Common Property Resource Management in South Indian Villages, by RobertWade, April 1985.

Report No.: ARU 37On the Sociology of Irrigation: How de we Know the Truth about Canal Performance?

by Robert Wade, May 1985.

Report No.:, ARU 38Some Organizations concerned with Animal Traction Research and Development

in Sub-Saharan Africa, by Paul Starkey, April 1985.

Raeort No.: ARU 39The Economic Consequences of an Open Trade Policy for Rice in India,

by Jaim Quizon and Jaes Barbieri, June 1985e

Report No.: ARUY 49,Agricultural Mechanization and the Evolution of Farming Systems in

Sub-Saharan Africa, by Prabhu 'L Pingali, Yves Bigot and Hans PGBinswanger, May 1985.

eReort No.: ARU? 41Eastasian Financial Systems as a Challenge to Economics: The Advantages

of 'Rigidity', with particular reference to Taiwan, by Robert Wade,June 1985.