some reflections on the importance of forecasting to policy-making

Canadian Public Policy

Some Reflections on the Importance of Forecasting to Policy-MakingAuthor(s): Mervin DaubSource: Canadian Public Policy / Analyse de Politiques, Vol. 10, No. 4 (Dec., 1984), pp. 377-383Published by: University of Toronto Press on behalf of Canadian Public PolicyStable URL: http://www.jstor.org/stable/3551227 .

Accessed: 16/06/2014 00:54

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

.

University of Toronto Press and Canadian Public Policy are collaborating with JSTOR to digitize, preserveand extend access to Canadian Public Policy / Analyse de Politiques.

http://www.jstor.org

This content downloaded from 194.29.185.230 on Mon, 16 Jun 2014 00:54:48 AMAll use subject to JSTOR Terms and Conditions

http://www.jstor.org/action/showPublisher?publisherCode=utp

http://www.jstor.org/action/showPublisher?publisherCode=cpp

http://www.jstor.org/stable/3551227?origin=JSTOR-pdf

http://www.jstor.org/page/info/about/policies/terms.jsp


MERVIN DAUB* School of Business, Queen's University

SOME REFLECTIONS ON TEE

UIMPORTANCE OF FORECASTING

TO POLKICY-MAING

I semble qu'on conteste de plus en plus

I'importance de la pr6vision 6conomique dans la d6termination ou I'application des

politiques. L'auteur de cet article soutient que cette attitude ne vient pas de I'inexactitude supos6e des pr6visions. Elle r6sulte plut6t de

changements importants dans les facteurs qui affectent la demande et l'offre de telles pr6visions. L'auteur met en garde contre le

rejet d'un r6le pour les previsions; une telle conclusion lui parait irresponsable puisque les previsions ont beaucoup a offrir aux responsables des politiques.

he importance of economic forecasting to the policy process is increasingly being

questioned whether by editorialists, executives or finance ministers. Gone are the not so distant times when forecasting was king, when one could assert with the confidence of Lasswell in his foreword to Ascher (1978:xi) that 'fore-

casting is a fundamental component of public and private policy-making'.

Some (Hartle, 1979) would argue that it was

always less important to the policy process than other activities such as lobbying. No doubt they are not, therefore, surprised by the present turn of events. Others, who find it difficult to con- ceive of policy-making without an important role for forecasting, wonder why it has lost

pride of place. Why has this skepticism about

forecasting developed? The simple answer usually offered by many

he importance of economic forecasting to the policy process is increasingly being

questioned. The paper argues that such a

development has little to do with supposed inaccuracy. Rather it is principally the result of

important changes in the factors which affect the demand and supply of such forecasts. It cautions that the rejection of a role for

forecasting would be foolish for it has much to offer the policy-maker.

policy-makers is that they've lost faith in forecasts because forecasts have become less accurate. A careful consideration of the record indicates this to be untrue. Inaccuracy cannot, therefore, be the reason. Other factors must be at work, factors which masquerade as a concern with accuracy but are really the ones responsible for this opinion about forecasting. The purpose of what follows is to substantiate this claim. If the record is not well-known, or the effects which changes in these other factors are having not properly understood, there is a very real

danger that policy-makers may reject economic forecasts when they have much to offer.

I Which Forecasts, How Inaccurate?

Consider this issue of the supposed inaccuracy of forecasts. Which 'forecasts,' how 'inaccurate'?

Canadian Public Policy - Analyse de Politiques, X:4:377-383 1984 Printed in Canada/lmprime au Canada



There are literally tens of thousands of 'forecasts' which influence economic and political activity each day. Short-term, medium-term, long-term forecasts of economic, technological, political, social variables all play their part. This much is obvious. What is less obvious is that the term

'forecasting' has somehow come to include all of these. This is certainly the implication of the

seeming judgement of many that the failure of one forecast is the failure of all such forecasts; that the supposed failure to anticipate the economic recession of 1982, for example, is reason

enough to call into question the credibility of all forecasts at all times.1

The record aside, it is important that policy- makers be clear about which forecasts they have in mind when reaching such a conclusion. One

suspects that most do not base their own expectations formation, and hence judge forecasters, on so broad an interpretation. Instead they focus on a selected few variables. It is further

likely that for many the chosen set includes real

growth, prices, the exchange rate, interest rates and unemployment. Thus in what follows 'fore-

casting' will be taken to refer to predictions of this small subset of key aggregate economic variables. This is clearly not the most general definition one might give the term. But it is the reference interpretation to which most policy- makers subscribe when questioned on the sub-

ject.2 Equally necessary is a consideration of the

term 'accuracy,' and ultimately the record with

respect to these key variables. Exactly how inac-

curate,' for example, is a 'failure to fathom'?3 What is the record?

There are two senses to the word 'accurate,' one absolute, the other relative. Exactly predicting the actual is absolute accuracy, predicting it to 'within some neighbourhood' of the actual is relative accuracy. Measurement error makes absolute accuracy only theoretically obtainable. Relativity is thus the norm. But 'relative to what, and within what region of relativity?'. 'Relative to other less costly alternative forecasting mechanisms; or to other forecasters; or to what it costs to be wrong' are all possible answers. 'Relative to what it costs to be wrong' is attractive. For the private sector policy-maker it may be reasonably easy to

measure lost profits, or the extra cost of re-

financing. But what of the public policy-maker's concern for 'social welfare'? Where is the sum-

mary measure of his/her 'social welfare' loss? Several attempts have been made to consider

explicitly this latter issue without much success, the latest using control theory (Saiyed and

Preston, 1982). It remains a difficult question. Accordingly, researchers into the accuracy of forecasts customarily look at forecasting records 'relative' to other forecasters (both domestic and foreign, in the present period as

opposed to earlier ones, etc.), to alternative 'mechanistic' forecasting techniques, and in the extreme to the error one might expect on the basis of chance alone (this being the real implication of choosing to test, for example, whether an average error is different from zero at some level of significance).4 They almost always treat one variable at a time simply because they cannot know what variables would be included in the social welfare function and what importance policy-makers would attach to each.5

Thus, a very exact reply to the question of 'how accurate are Canadian forecasters' is 'if

you tell me to which variable you are referring and what you mean by accuracy, I will give you the answer'. If one means, 'are they as good, or better (hopefully) than the mechanistic alterna-

tives,' the answer is 'it depends on the variable and the period in question but generally yes'.6 If one means, 'is one forecaster better than another' or 'are Canadian forecasters better or worse than those in other countries,' the answer

again depends on the variable and the period in

question but generally speaking no.7 If one means 'but what then is the average size of the error in forecasting Canada's real GNP during the seventies and is it significantly different from zero error at the .05 significance level,' the answer would be that 'within the statistical limitations of measurement and testing, the

average error in forecasting the percentage change in real GNP was .10. This average error was not significantly different from zero at the .05 level.'8

Such a discussion establishes at once both a definition and a record. Moreover, it makes it quite clear that the claim of supposed 'inaccuracy' is not justified on the basis of the record.

378 Mervin Daub



Why then does it seem that forecasts are more inaccurate and thus to be rejected?

As noted above, the position taken here is that it is not really the accuracy of the forecasts which is being called into question. Rather, criticizing the accuracy of the forecasts is merely a convenient way of summarizing a generalized feeling of dissatisfaction with forecasts, the ex-

planation for which really lies elsewhere. To understand this it is necessary to begin by con-

sidering why policy-makers demand these aggregate economic forecasts in the first place.

II The Changing Nature of Demand

In light of what has been alleged about inaccu-

racy one might suppose that the main reason

policy-makers demand forecasts is a 'seeking after the truth about the future'. Indeed, the

very nature of the indictment seems to be that

they are disappointed with forecasters for not

having better delivered in this regard. Once this

implication is pointed out, policy-makers ack-

nowledge that forecasts are used (demanded) in a number of other ways which bear little resemblance to a desire for revealed truth.9

Forecasting is, for example, but one of a number of ways policy-makers might choose to deal 'economically' with an uncertain future. There are others. Lobbying, hedging or the

stocking of inventory are among the alternatives.

Hedging against future interest rate fluctuations is similar to spending money to forecast those future interest rates. The cost of lobbying to

'arrange' the future may be less expensive in certain instances than paying to forecast the

'unarranged' future. To the extent that the relative prices of these alternatives change, so too will the demand for traditional forecasting of

aggregate variables.

Forecasting is also used as an organizational tool. It might serve, for example, to help insure that the organization is being consistent in its

thinking about the future. The role of forecast-

ing in most budgeting exercises is a case in point. As well, it may aid in 'rallying the troops to

greater efforts'. One thinks in this regard of the annual forecasts of automobile sales or certain

provincial budgets (both of which are usually predicated on buoyant aggregate predictions).

Yet another of these 'sociological' reasons for

demanding aggregate forecasts, indeed one of the more important, is to demonstrate (if only implicitly) that 'reasonable care' has been taken in the way taxpayers' (shareholders', donors') funds have been committed.10 Not nearly so often acknowledged is the fact that failure is made easier if it can be shared with (or trans- ferred entirely to) the forecasting community.

A more subtle purpose for demanding these

forecasts, one which is perhaps more global in nature and which can be viewed either from a

sociological or economic perspective, is that forecasts help economic agents 'to herd together'. They are the vehicles whereby we declare, and hear declared, our individual thoughts about the future. Through such an exchange we come to better understand and deal with our mutual future. Economists might put it that forecasts have an informational value which in a market

economy aids the Invisible Hand in its working out of collectively acceptable solutions.

What is to be noted is that the factors affect-

ing some of these reasons for demanding forecasts have changed recently in important ways. The relative prices of some of traditional aggregate economic forecasting's substitutes have fallen. Futures markets in interest and currency rates, for example, have become highly effective alternatives to forecasting. As the cost of hedging falls, and an understanding of its use grows, the demand for traditional interest and exchange rate forecasts falls. Further, many more aggregate forecasts are now available in the public domain partly because of increased regulatory activity. Whereas in the past when such forecasts were much more privately produced and guard- ed, today, because of the public good character- istics of forecasting information, many potential demanders prefer to 'piggy back' on published forecasts rather than pay the direct cost of ac-

quiring the information. While this could be seen as merely a shift in demand from internal to external sources, the writer would argue that there has been a reduction in the total amount of fore-

casting services demanded because of the pre- sence of these informational external economies. As well, policy-makers are more sophisticated about how forecasts can be used to organize, manipulate and otherwise direct activity. This,

Forecasting and Policy-Making 379



as well as the abuses which sometimes accom-

pany such activity (Wach, 1982) have also altered the demand for forecasts.

These changes in the demand for forecasts have very little to do with supposed inaccuracy. Rather they are motivated principally by relative

price changes and changes in 'tastes' (primarily education). Equally important have been changes in the nature of the supply of these forecasts.

III A Revolution in Supply

There have been fundamental changes both in the technology and in the nature of inputs used in the forecasting industry. Particularly since the war, the theory behind, and operation of, the business have evolved considerably. It is worthwhile citing several examples to make the

point. In the modern period forecasting as a guide

to policy-making dates unquestionably from the thirties. Roosevelt's request of Mitchell and Burns to be provided with some means of predicting the late thirties, Keynes' General Theory (1936), and the increased attention to aggregate economic statistics (growing out of Kuznet's work) are perhaps most important in this regard.

But the major changes came after the war.

Surveys of investment, employment and consu- mer purchasing expectations were developed; work was undertaken to shape aggregate economic statistics into a full system of national accounts; the estimation of simple econometric models was begun; the work on 'indicators' was

adapted to Canada. All were technological inno- vations which completely altered the nature and quality of economic forecasts available to

policy-makers relative to earlier periods in

history. After a brief period in the sixties, the com-

puter, communications and education revolu- tions brought another technological 'leap forward'. Able now to learn of, and process, large amounts of data at dramatically lower costs, and with many more trained economists, systems people and MBAs in the labour force, the forecasting business became by the late seventies as different from the fifties and sixties as that period had been from earlier times.

Until one talks with those 'pioneers' of the

forties, it is difficult to appreciate how quickly and radically this relatively young industry has evolved. It was perhaps natural that everyone would be carried away with its potential only to be disappointed later with the quality of its

product. It is a pattern which has been repeated elsewhere on many occasions and explains in

large part the current scepticism about the role of traditional forecasting in the policy process.

There are several issues in particular which come to mind in this regard. The first relates to what may be termed a mismatch of supply to demand. To understand the nature of this concern it is first necessary to understand that Canadian forecasters are part of a larger, world- wide intellectual community. As such, they have been (and are) influenced by work elsewhere

(particularly in the United States).11 This is to be expected. Its relevance is principally that it has contributed to the development of a kind of prism through which the Canadian economy is viewed, one formed principally by American ideas about what are the important issues, how (and what) data are to be gathered on these issues and how (and in what way) they are to be studied (modelled).

This statement is not intended as yet another thinly disguised plea for Canadian nationalism. Nor is it meant to belittle the unique contribu- tions of Canadian scholars and statisticians, or to comment on the appropriateness of monetary and fiscal theory, econometric modelling and like considerations over the last half century. No doubt it benefits substantially from hind-

sight. Rather, it is intended in this context to

suggest that Canadian efforts may have unduly focussed on concerns which, while also appropriate, were relatively less so here than elsewhere.

Consider, for example, the issue of disaggregation. Canada's economic structure is regionally and jurisdictionally disparate. Yet given Ontario and Quebec's preponderant influence, any aggregate models of the Canadian economy will relate principally only to them. These will be of little use to the Alberta or BC policy-maker whose economies bear little resemblance to those of Ontario or Quebec or to those in the federal government who may wish to tailor policies to specific regions. Inasmuch as they are more important to Canada than elsewhere, one would

380 Mervin Daub



have thought that much early effort would have

gone into provincial (or regional) models and data. Only recently has work gone forward on

developing such regional data and models. The same might be said for industrial sector

disaggregation. Certainly the CANDIDE genera- tion of models did have structural detail. But the relative absence of good energy models, for

example, to predict the impacts on the oil and

gas industry of the oil shocks or proposed NEP

legislation is only one example of the lack of

specific sectoral models which could be cited.

Again work has gone forward recently. This absence at a crucial time of regional and

sectoral models is suggested by some policy- makers as one of the reasons why they have become disillusioned with aggregate economic forecasts.1 2

While no doubt important, this is not the only supply-related reason for a sense of disillusion with forecasts. Another is the coming into a new era in which the emphasis in theory and

policy has shifted from short-term intervention to longer-term structural considerations. As

such, there is a perception that monetary and fiscal fine-tuning issues are relatively less important than they once were. The models, which were particularly designed to address them, seem somehow pass6 or irrelevant to the discus- sions of the 'Japanese challenge' or 'industrial

policy'. Further, there is a good deal of debate in the

economics profession at large growing out of the research and experience of the seventies as to the appropriate theoretical characterization of the economy. Policy-makers have become

increasingly aware of issues such as 'rational

expectations,' 'causality' and others which continue to cause disquiet in economics (McCloskey, 1983). As noted above, they have also become

increasingly technically sophisticated. They are thus more informed about the inherent limitations (e.g., the difficulty of exogenous variable

prediction, structural-shift tracking problems, data, estimation, etc.) of the major methodolo-

gies used to make predictions. Yet another supply-related change has been

the increased speed which the market place now demands of forecasters.13 However, in certain circumstances the reestimation of a large

model system may be necessary, something which cannot readily be done that well, that

quickly. To be sure, estimation times are drop- ping dramatically due to simplification of

models, better on-line data access and improved software. But one ought not to expect miracles.

Accuracy is still a sacrifice one needs to make to speed. If such speed is absolutely required, subsequent faults are as much due to the unre- alistic expectations of the user as they are to the inadequacies of the forecaster.

There is no reason why the forecaster is immune from all this turmoil. Unfortunately, predictions are required. One cannot gracefully withdraw until a later time when all problems will have been solved and/or the world is more stable. Those forecasters thus provide a focus for

many of the concerns discussed here. It is likely, therefore, that the attack on the 'inaccuracy' of these forecasters is really at heart a reflection of the dissatisfaction with the supposed uncertainties and inadequacies of economics and statistics more generally.

IV Summary and Conclusion

This brings the discussion full circle. It may be summarized as follows. The role of aggregate economic forecasting in the public policy process is under a cloud principally because of

alleged inaccuracy. An examination of which

types of forecasts might most likely be consid-

ered, the definition of accuracy, and finally the record indicate that this indictment is not well- founded. It is argued that such a charge is merely a convenient way to summarize the real causes of dissatisfaction which lie elsewhere, in particular in the changing nature of the demand for, and supply of, forecasts.

Several such causes were identified. The increased public availability of such forecasts, the development of futures markets, the growing sophistication of users, and other factors bearing on demand, have all altered the way people view and use forecasts, and what they expect of them. Supply-related considerations such as a certain mismatch of existing forecasting 'tech-

nology' to recent and expected policy concerns, perceived theoretical and empirical uncertainties (about appropriate models, the adequacy of




existing ones, etc.), and an excessive concern with speed which puts at risk the quality of

output, have added to the calling into question of the contribution which forecasting can be

expected to make to the policy process. Also stressed was the fact that aggregate

economic forecasting, at least in its present form, is a relatively young industry by modern standards (as compared to steel). The early promise has not necessarily been met. Disap- pointment of this sort is not, however, new to industrial venture. Moreover, the problems of the industry are well-known. Perhaps it will even be the case that when the data are finally in, errors in recent times will have been large by historical standards. But it is also likely that research will show that forecasters in other countries, or those using alternative 'mechanistic'

methodologies, will have done no better.14 All effort is not, therefore, in vain. There is simply much to be done.

It is worth noting by way of conclusion that all of this may seem like an apologia for economic forecasting. It is not so intended. What is intended is to argue that for a considerable

period of time during the seventies and early eighties many policy initiatives appeared tied in all their most important respects to forecasts. Indeed it was often the case that whether such an initiative lived or died depended on whether it possessed the appropriate 'seal of good fore-

casting'. There is now a real danger that policy- makers and those who judge them are erring in the opposite direction. They now may well consider a forecast of little use whatsoever. In this respect they appear to have swung from an almost 'rational modelism' to an equally strong sense of forecasting atheism.

What is needed is some appreciation of the middle ground. A certain sense of sophistication and maturity on the part of the user, one of

humility and integrity on the part of the sup- plier should be the order of the day. Both must

recognize that aggregate forecasts are nothing more nor less than one element in the policy- maker's tool kit. Despite present concerns, it is likely they will continue to be a uniquely appropriate vehicle for satisfying many of the traditional needs which policy-makers have when developing and selling initiatives, i.e., to show

evidence of having used reasonable care, to serve as a means for the spreading of responsibility, to provide a tool for organizational motivation, and to act as a signal of one's intentions to the

larger world. Furthermore, carefully developed and appropriately documented, such forecasts will continue to represent the best and most

internally consistent guess about the future one can expect given current knowledge. For all these reasons aggregate economic forecasting still has much to offer the policy-maker.

Notes

* The author wishes to thank an anonymous referee for extensive comments on an earlier draft.

1 See, for example, Nelles (1983:28). A discussion of why this fallacy of composition exists is not germane to the present paper. That it seems to be so has been noted by researchers working on expectations formation and is one of the unfortunate facts of life for most professional forecasters.

2 Such a conclusion grows out of an extensive series of interviews with users and suppliers of forecasts inside and outside government done within the context of a study by the author of the Canadian forecasting industry (a report on which is forth- coming).

3 Nelles (1983:28), 'This same company failed to fathom the disastrous plunge in corporate profits,...'.

4 Forecasting records are usually measured by summary statistics such as average errors, standard deviations and the like. These being absolute measures of 'accuracy,' they are themselves 'inaccurate' for the measurement reasons noted earlier in the text. Fora more extensive discussion of the implication for stated accuracy of the choice of a significance level see Daub (1973).

5 Given such a weighting, the task is relatively straightforward. Indeed this is the point of the control theory literature referred to above.

6 See Daub (1973, 1981), McNees (1976) and Zarnowitz (1979).

7 See Daub (1977) or McNees (1976). 8 Daub (1981:504). In light of recent history one

might also well have asked what the record of forecasters is with respect to predicting major 'turning points'. Modern time series research and the extensive empirical work on capital markets has made most researchers leary of the term 'turning point'. One person's 'turning point' may well be another's 'trend' (itself an equally pre- carious term). Assuming there is a general agree- ment on the definition of such a turning point (e.g., the latest fall in real GNP in Canada) it nonetheless is true that no systematic investigation of turning point errors in the Canadian context has been undertaken (although Daub (1973:77) does note the issue in connection with less important but more volatile series such as investment). The principal reason is the relative absence

382 Mervin Daub



of such actual turning points in the major variables during the period since the war. It is clear that in the recent episode there were those who missed the downturn. However, Daub and Sankaran (1984) report that a preliminary investigation of the real GNPforecasts for the period suggests that the record is not as bad as is popularly supposed. But until such time as one can study the record thoroughly, it is unwise to conclude anything about the turning point errors.

9 See footnote 2 in this regard. 10 Indeed in the case of the regulatory hearings such

a use of forecasts is extremely explicit. The phone, hydro and gas companies (among others) must clearly demonstrate that all rate and capital re- quests are based on 'reasonable person' forecasts of the economy (region, industry, etc.).

11 These ties are both direct (e.g., Data Resources and Chase Econometrics are Canadian subsidiaries of American corporations) and indirect.

12 Waslander (1982:2-5) suggests that forecasters are not necessarily completely to blame. The structural detail, for example, has, after all, been main- tained (the same cannot be said for regional data in particular which continue to be unconscionably weak). The models have, however, been used mostly for macro-economic analysis and policy guidance. Waslander cites several reasons for this relative inattention to available sectoral forecasts. These include the feeling that politics (e.g., lobbying for quotas) and macro policies are likely to be more important to the outcomes of sectors than forecastable sector-specific factors. If this is so, it certainly warrants attention to the prediction of macro variables. It also suggests that policy-makers ignore sectoral forecasts for other reasons.

13 As noted, one of the reasons for this is the explo- sion in the number of MBAs, economists and others who are much more sophisticated in the use of analytical systems. Many are also familiar with modern computer technology. Finally they recognize that with modern integrated capital markets any chance for differential gain from information must materialize quickly or be lost. For all these reasons, they have come to think in terms of, to need, and to expect, speed.

14 Moreover, as Baille et al. (1983) suggest, alternatives such as future markets have their own costs and inadequacies.

References

Ascher, W. (1978) Forecasting: An Appraisal for Policy-Makers and Planning (Baltimore: Johns Hopkins Press).

Baille, R., R. Lippens and P. McMahon (1983) 'Testing Rational Expectations and Effi-

ciency in the Foreign Exchange Market,'

Econometrica, 51:3:553-564. Daub, M. (1973) 'On the Accuracy of Canadian

Short-Term Aggregate Economic Forecasts,' Canadian Journal of Economics, 6:1:90- 107.

(1977) 'On the Nature of Errors in Aggregate Economic Forecasts,' Review of Economics and Statistics, 2:230-233.

(1981) 'The Accuracy of Canadian Short-term Economic Forecasts Revisited,' Canadian Journal of Economics, August, 14: 3:499-507.

and S. Sankaran (1984) 'The Turning Point Record of Real GNP Forecasts,' unpublished paper, School of Business, Queen's University.

Hartle, D. (1979) Public Policy Decision Making and Regulation (Montreal: Institute of Re- search on Public Policy).

McCloskey, D. (1983) 'The Rhetoric of Eco- nom ics,' Journal of Economic Literature, 21: 2:481-517.

McNees, S. (1976) 'An Evaluation of Economic Forecasts: Extensions and Update,' New England Economic Review, 3:20-44.

Nelles, V. (1983) 'The Numbers Game,' Satur- day Night, July, 28-37.

Saiyed, H. and R. Preston (1982) 'Optimal Control: An Application Using Candide Model 2.0,' discussion paper 215, Economic Council of Canada.

Wachs, M. (1982) 'Ethical Dilemmas in Fore-

casting for Public Policy,'Public Management Forum, November/December, 562-568.

Waslander, B. (1982) 'Forecasting and Policy Planning - Some Recent Canadian Experi- ence,' unpublished paper, Ministry of State for Economic Regional Development, Gov- ernment of Canada.

Zarnowitz, V. (1967) 'An Appraisal of Short- Term Economic Forecasts,' occasional paper 104, National Bureau of Economic Research.

(1979) 'An Analysis of Annual and Multiperiod Quarterly Forecasts of Aggregate Income, Output and the Price Level,' Journal of Business, 52:1-33.




some reflections on the importance of forecasting to policy-making

Documents