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TIIE HANDBOOK OF ECONOMIC METHODOLOGY

The Handbook of Economic Methodology

Edited by

Jolm B. Davis Deparh11ent of Economics, Marquette University, USA

D. Wade Hands Department of Economics, University oj Puget Sound, USA

U skali Miiki Deparrmelll of Philosophy. Erasmus University, Rotterdam, The Netherlands

Edward Elgar Cheltenham, UK· Northampton, MA. USA

@ John B. Davis . D. Wade Hanus and Uskali Mtiki, 1998 All rights reserved . No pan of this publication muy be rcproou!':cd. stored in a retrieval system or transmitted in any form or by any rncall~, elec troni c. mec hanical or phoulCopying, record ing. or otherwise wilhoullhc prior permission o f the publisher. r'ublished by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road C heltenham Glos GL50 21A

UK Edward Elgar Pu blishing, Inc. William Prau 1·louse 9 Dewey Court Northampton

Massachusetts 0 1060 USA

This hook has been printed on demand to kt,:cp Ihc litle in print.

A catalogue rc(;oro for thi s book is available from the Brit ish Library Libra ry or Congress Cataloguing in Pulllicaliun Datu The hand book of economic mClhudolugy/cditcd by John B. Davis, D. Wade Han ds ami Uskali Mak i. Includc~ bibliographical refere nces (p. ). I. Economics-Melhodology- Handbooks. manuaIS.CIC. I . Davis , Jo hn Il ryan . lI . Hand s. D. Wade, m , Miiki. Uskali , HBI 31.H354 1998 3JO'.ol -ary to assume that the distribution of preferences over individual agents is extremely unequal. If the d istribution of preferences is restricted to a certai n class, as Grandmont assumes, some further restrictions on the aggregate excess demand functions can Ix: derived. Hildenbrand (1983) analyses the question why the 'Law of Demand' holds true for many estimated demand curves. He proposes to restrict the distribution of income (endowmenL.. ) Lo a certain class. In particular, he shows thaL the average income e ffect is negative if the income distribution is independent of the price system and has a decreasing density fu nction. This, together with the negativity of the substilution effect, implies thaI aggregate demand c urves are decreasing in their own price. Thus, by imposing a restriction on the income distribution, Hildenbrand is able to derive an empirically testable restriction that aggregate demand curves must satisfy. Along the same lines, Hildenbrand ( 1994) analyses properties of market demand that are created when aggregating individual demand relationships. Methodological lessons There are two main points made in the literature on aggregation that are of interest from a methodological point of view, The early literature reviewed in the first two sections above shows that in macroeconomics one cannot accept both of the fo llowing statements: (a) the structure of aggregate relalionships is analogous to the structure of their microeconomic counterpart, and (b) aggregate variables are simply the sum o f the indiv idual variables, T he aggregation literature has shown that macroeconomists have to remove one ofthe two statements from their vocabulary. Klein suggests abandoning the first, whi le May and Pu propose abandoning the second. The other important point i:-; that there i:-; no a priori !,'l'ound to believe that aggregate excess demand functions satisfy properties that guarantee the stability and unity of the equilibri um poinl in general equilibri um theory. In this sense the Debrew'MantellSonnenschein result is q ui te damaging for the potential usefulness o f thc general equilibrium research program. More recent literature is interesting in the sense that different authors show that by imposing some restrictio ns on the aggregation procedure some properties o f aggregate demand are 'created' that do not have their individ ual level counterpart, MAARTEN C.W, J ANSSEN

Referen ces Debreu, G. (t974). 'Excess Demand Functions', Journal 0/ Marhemarical Economics, I, 15-21 . Gorman. W. (t953), 'Community Preference Field~', Ecmrnmt!rrictl, 2t , 63-80. Grondrrn)l1t, 1.-M. (t987), 'Distribution of Preferellces und the Luw of Demand', Economurica, 55, t55--61. Hicb. J. ( 1939). Vulue lind Capirol. Ollfonl: Clarendon Press. Hildenbrand, W. (1983), 'On tile "Law of Demand''', Economerrica, 51. 997- tOI9. Hildenbrand, W. (1994), Market D~nullld, Princelon, NJ: PrincetOll Ulliversity Press. Kinnull. A. (1989), 'lk Intrinsic LimitsofModcm Economic Thcofy: The Emperor lias noOothes'. Economic J(Jurnnl, 99, (supplement). t26-39. Klein, L (1946:1), 'Mucl'Oe«Inomics IlI1d the Theory of Rational Behuvior' . Ec;onomerrica, 14.93-108. Klein. L. (l946b), 'RclTUU'ks on the Theory of Aggregation', Econometr;ca, 14. 303- 12. May, K. (1946). 'The Aggreg:uion Problem fOf a One Industry Model'. F.corwnrerr;ca. 14, 285- 98. May , K. (1947), ' Technological Change IUIII Aggn:gotioll', £Conomerrica, 15. 5 t-63. Notof. A. ( 1948). 'Sur 10 Possibilitt de Construction de CcnaillS Macromodtlcs', Econnmetrica, 16, 232-44.

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The handbook of economic methodology

Nelson. A. (1984). ·Son.::: lssues Surroundlnlll'le Reductioo from Macroeconomics to Microeconomics', Philosophy o/Sdtnct, 51. 573-94. PII, S. ( 1946), 'A NOIe on MlICroccollOlI1ics', Economttrico, 14. 299-302. Sonnenschein, H. (1971), 'Market EltteU Demand Furn::rions', Econometrica. 40. 549-63.

Althusser, Louis Undoubtedly !.he most important Marxist philosopher of the second half of !.he twentieth century, A1thusser (1913-90) was moved by an early appreciation of 'the crisis of Marxism' to attempt no less than a complete theoretical and philosophical reconstruction of that systcm . This reconstruction was quite singular and suggestive, givcn the philosopher's commitment to remain within Marxism whilc reshaping its contours in line with the concerns and the imagination of post-World War II French intellectual culture. Of note in this context are his intellectual (in some cases, also personal) relationships wilh S8rtrC, Uvi-Stntuss, Barthes, Lacan, Foucault and Derrida. Altbusser's work influenced (segments within) the Marxist community to accept and even to make significant contributions to a number of trends (suucl\ualism, poststructuralism and deconstruction) that, more recently, seem to have condensed into a general postmodemist revolution. It is thus that Althusser comes to have an important place in the mid1990s reconfiguration of the methodological environment for economists. What, in A1thusser's work of the 1960s, is most suggestive of more recent intellectual trends is a ccrtai n openness and fluidity of tho oeuvre. His texts arc punctuated by bold suggestions (of, for example, an 'epistemologicaJ break' between the young and the mature Marx) such as only a worthy philosophcr would pose, by limiting and yet heroic concepts (for exa.mplc, a 'last instance dctennination by the economy') and by gencrous, yet studied, acknowledgements of errors and exaggerations (lheorelicism). Certain ly this flu idity was required by tho vicissitudes of post-World War II. Marxism, yet the biographical materilll that followed Althusser's death suggests that it can also be explained by the transitional epics that punctuatcd Ihc philosopher's life. A prisoner in Gennany during World War II, Althusser first negotiated a personal transfonnation from a prewar right-wing to a postwar left-wing Catholic. Pushed thcn by Papal intolerance of left-wing Catholicism, he negotiated a new identity as a member of the French Communist Party, only to face the problem of how to remain institutionally linked with 'the workers' movement' while rescuing it from institutionalized (Stalinist) rigidity. Finally, Althusser had to negotiate his life through the phases of a chronic manicdepression that set in after the war and besieged him, req uiring frequen t hospitalization, until his death. Given these circumstances, it is reasonable to think that Althusser's re negotiation of Marlt ism through the prism of psychoanalytic concepts (Freud's 'overdctermination', Lacan's ego-ego mirror) was partially illuminated by hi s intimate understanding of the exploratory manoeuvres, the exccsses and the recoveries of any process of 'charac ter transfonnation' . In the I 960s, Althusscr tried to develop a rigorous map for Marxist theory. He produced a 'symptomatic reading' through which he tried to remove from Marx's texts the elements of economism and of humanism left by an incomplete break with Hegelian essentialism. Many readers saw in this work (primarily For Marx in 1965, and Reading Capital in 1968) an attempted theoretical 'purity', and denounced it as a quasi-Slalinist attempt to provide Marxism wilh an official doctri ne. (A number of such criticisms has been compiled in Elliott, 1994, which also contains an extensive bibliogruphy of Althusscr's published texts.) But, although he

The handbook of economic methodology 7 rumself acknowledged a certain theoreticism, Althusserexplaincd this work as. rather, an attempt to dcStalinizc Marxism: the strong anti-essenlialism of the work was the premise to an equally strong critique of the teleology (hidden in the form of 'the dialectic') through wruch official Marx ism had justified its statist and totalitarian practices. In this vein, Alth usser progressively moved towards rej ecting the view that Marxism itself was 'a system' and to replacing it with a view of it as 'an intervention in philosophy' (or history, or economics). This transformation moved Marxism even beyond the modernization of it that Althusser had imagined. Apart from the facl that the recognition of Marxism, and of other theoretical positions, as 'discourse' was an anticipation of the procedures of deconstruction, the idea of Marxism as un intervention - in philosophy, or economics, or historiography - carries the implication that these discursive formations have thei r (relative) autonomy and are nol reducible, as Marxists bad once had it, to mere bourgeois manifestations of an underlying reality. If nothing else docs, this recogn ition of the autonomy of discourse(s) by itself represents a significant tum in the Marxist traditio n. Also recognizable in Alth usser's attack on essentialism is a postm odemist critique of modernist concepts of science (see Amariglio, 1987, for an early appreciation of this thread). The specific application of this poslmodernisl anti-essenlialism to Marxism produced a reevaluation of its economics, its concepts of social processes and agents, and its vision of the dynamics of capitalist processes and socialist construction. Central to this re-evaluation was the elaboration, most fully developed in Resnick and Wolff (1987), of Althusser's adaptation of the Freudian concept of 'overdctermination' to social processes. The implication of this concept, that no social process can be in principle abstraCted from the theorist' s gaze, has given to Marxism a new theoretical impulse to construct concrete analyses of all social process - qui te different from the erstwhile theoretical impulse, which only the best Marxist had been able to resist to eschew the irreducible concreteness of social practices in order to proclaim presumed class totalities and historical trajectories. After more than a decade of relative silence by and on Althusser, a number of texts have more recently given a new voice to Althusser and the A1thusserian enterprise. Together with the publication of new material from the Althusserarchives, two anthologies (Kaplan and Sprinker, 1993 ; Callari and Ruccio, 1996) point to a further evolution of the thought of A1thusser towards a redefinition of tile fundamental category of Marxist philosophy, the very category of materialism. For Althusser, materialism eame to stand less as a reference to the traditional 'material ' conditions of production and more as a reference to the pri macy of the contingent, of the 'aleatory' and of the '(XIl itical' in history and society. This last major development in the thought of Althusser parallels well some of the more recent methodological emphases on uncertainty and on the role o f identities (race and gender, and others, as well as class) in the constitution of economic processes and of different fonns of economic communities. ANTONIO C AlJ..AR I

Bibliography Ahhu.w:r. Louis ( 196.5). For Marx. ttan5. Ben Brewster. London: Allen Lnne. Atlhuuer, Loui ~ (1976). Essays in ~If-Critjcism. tnIni . Grahamc: Lock. London: New Left Books. A1thusser, Louis (1993). 1M Furun UuU Fortv~r. A Menwir, cd. OlivierCorpet and Yann Moul ier Boutang, tran~. Richard Veasey, New Yon:: The New Press. Atlhusser, Louis and Etienne 8a1ibar (I'Ma). Uft I~ capital. Paris: Frnn~ois MllSpero. Amoriglio, Jack (1987). 'Marxism agai nst Economic Scie nce. Althusscr'S Leiacr', RtUIlff: h in Pnlitical Ecanamy.

10, t59-94.

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Th e handbook of economic methodology

Callari, Anlonio llnd David RUl-ciu (eds) (1996), l'oslmod~m Maltrialism and till! Future of Murxist Theory. £Ssays in th~ Althusstri(1n TroJiilian, HallOverlLondOI1: Wes[eywl Universily Press. I!lliou, G~gOl)' (ed.) (1994). Allhusser. A Critical Readtr. OltfordiCambridse. USA: Blackwell. Kaplan. Ann om! MichPl:I Sprinkcr (cds) (1993). The AIIJW.f.ftrian ugflC)'. Lofldoa/New York: Vern>. Resnie!:. Slepilcn lind Richard Wolff (1981), Knuwledse and ClosJ: A MarxiOll Critique of Political &onoffl},. Chicago: University of Chicago~.

Analogy Common language makes use of many loose metaphors. where well-recognized attri butes of a given enti ty H.r"C carried over to another entity, in order to suggest fOf" the latter an original and often figurative meaning. Scientific language makes use of more precise analogies, where duly established properties of a given system are transferred to another system, in order to acquire a deeper undeJ"1;landing of its structure and behaviour. Intflld isciplinary analogies, developed by comparative sciences. look for a correspondence between characteristics of objects in nearby subfields. and eventually give rise to unified models by ide ntification or reduction of these subfields . Interdisciplinary analogies, devclopt'":H ",o)L I'( HolD) =k I,I'(H,)I'(DI H,)

(1)

1=0

where H o' ... , Hi' .. " Hi( are mutually exclusive and form an exhaustive set. Expressing any two hypotheses, for example, Ho and Hi' by equation ( I) and taking the ratio of the two lead to Bayes' theorem : (2)

Both equatio ns induce philosophically appealing interpretations which have attracted many empirical researchers. Equation (I) shows a systematic and consistent way of revising our confide nce in an ex isting hypothesis flo with available data 0; equation (2) provides us with a simple criterion for choosing between two mutually exclusive hypotheses by the magrutudes of their probabilities conditioned upon the same data. In the case of statistical inference where the research objective concerns the unknown values of certain parameters, say 9, of a sel of observed variables. say X, equation ( I) is transformed into: p(SI X) = KP(X 1 S)P(S)

(3)

where" is known as the normalizing sealar, peS) the prior density function, P(Xl9) the sampling density function generally approximated by the likelihood function L(X. 9) and p(9/X) the posterior density funct ion. Correspondingly, equation (2) becomes known as the posterior odds test of two differently speci fied priors. such as P(9O> and P(9j ), againstlhe same data sample:

1'(801X) L(X.80)1'(80) P(8j IX) = L(X.8j )P(8j )

(4)

The philosophical interpretation of equation ( I) and the statistical applicability of equation (3) have fonned the main impetus for the developmcnl of Bayesian econometrics. Applications of Bayesian statistical methods appeared in econometrics in the early 1960s, when the paradigm of the structural model approach was being standardi7..ed in econometrics.

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Th~

handbook of ~collomic methodology

Within the struc lUral model paradigm. econometricians focused on devising estimation techniques for given structu ral (economic) models, for example:

y",j3Z+£

(5)

where I Y, ZI '" X, and E denotes random disturbance. However. it was quite common in practice to find a disparity hctwcen the sample estimates and the a priori expected value rangc.~ of the structural parameter 13. Given the strong belief in struc tural models, econometricians naturally thOUght of resolving the problem by restricting the parameter estimates within the expected ranges. Since Bayesian inference allows for explicit probability fonnulation of the expected values into priors, the Bayesian approach was wkcn as the general solution to the problem of accommodating sample infonnation with a prieri information within a consistent probability framework. Under the Bayesiun appruach, the esti mated structural parameter, fo r example, ~ of equation (5), is assumed (0 take the expected value (called the Bayesian estimator) o f the posterior density function P(fYX). As the expected value remains invariant to the nonnaJizing scalar Ie for most o f the distribution types encountered in econometrics, it is generally sufficient to have o nly the joint density P(X f"'I 13) for the purpose of obtaining the point estimates Hence Bayesian econometrics is mainly concerned with fonnulating the prior p(p).and solving various kjnds of mathematical problems in deriving its joint density with L{X, Il) in working out the expectation of the posterior. Mathematically, the imposed prior appears to be the single source of difference in comparison with the classical statistical methods as far as thc parameter esl.imates are concerned. Philosophically, the imposed prior ClI " be regarded as apriorism or even subjectivism. Therefore the use o f pardilleter prior density func tions symbolizes Bayesian econometrics, and someti mes even more broadly the Bayesian approach in statistics. Bayesian econometrics remained within the orthodox slrUcturul modd parddigm for quite a lo ng time. It was not until the development of extreme bounds by Edward Leamer in the early 1980s that Bayesian econometrics struck and revealed the limits of the structural model pantdib'TTl. Extreme bounds analysis basically studies the effects of changing priors on the p.>5terior Bayesian estimate for a given model fonn, for example the effects of imposing different P j (13), Pj (13) o n Pj (JYX) and Pj (JYX) for model (5), following the logic of equation (4). The essential purpose of Leamer's proced ure is to make explicit and fonnalize, under a single principle of Bayesian statistical inference, various ad hoc data-mining activities used to modify given Slruc tural models for the results expected by applied moddlcrs. From the viewpoint of econometric modelling procedure, the Leamer school can be regarded as performing model misspecification analysis by Bayesian methods. However. it is in general misleading to consider Baycsian econometrics as II separate methodological school in econometrics. Bayesian methods have been applied by all the major methodological schools of econo metrics as an alternative, and often mathematically more convenient, statistical route. For example, Bayesian priors have becn used by the astructuralist, V AR (vector-autoregression) modelling school as a me."UlS of reducing the number of parllme!ers o f VAR models; Bayesian methods have been utilized in cointegration analysis of the dynamic modelling school to circumvent certain technical difficulties met by the classical statistics approach in handling non-stationary features of economic variables. An important reason for the wide applicabi lity of Bayesian methods is the identity of the Bayesian estimator with the classical parameter estimator under many situations. The identity is transparent in the case of

p.

The handbook oj ecollomic methodology 35 (he specific prior of no a priori infonnation. When there is some a priori information, the nonBayesian econometricians would try to specify such informalion into additional parameter restrictions on the given structural model (or the likelihood function), whereas Bayesian econometricians would simply write it into an informative prior. In thc case of updating parameter estimates as sample data increase, for instance, the classical recursive estimation method produces parameter estimates which are identical to the Bayesian estimates, which utilize the estimated parameter density of the original sample as the prior. When the two approaches generate mathematically identical estimators, it seems difficult to accommodate the identity with the difference in intcrpretation which the Bayesian econometrics auaches to the two types: the classical estimator represents the sample outcome conditioned upon the given hypothesis, P(Plx), whereas the Bayesian estimator comes from the inverse conditional state, P(XlP). This logical difficulty is, unfonunately. rarely observable owing 10 the proctical inability of the c1as....ical approach to specify, into explicit restrictions, those kinds of a priori information defined only ill vague terms, a situation wh ich has defi nitely laid the advantage with the Bayesian approach. Resistance to Bayesian econometrics is therefore mostly based on two arguments: the fragility of the Bayesian parameter estimates due to the arbitnuily specified priors. and the limited role of Buyesian methods in making model choices because of the dependence of the prior specification on given models. These correspond to the broad philosophical criticisms of the genero1 Bayesian approach both for its subjectivity and for thc contrasts between the smoothness of the Bayesian learning process and the observed irregularity of the actual process of scientific discovery. These arguments ignore the fact thaI Bayesian methods can produce results identical to those produced by classical methods. The key problem with Bayesian econometrics lies in the discrepancy between the mathe matical results obtained by means of Bayesian methods and the methodological interpretation of these results. The problem can be traced (0 a fai lure to satisfy one or more of the three basic requiremenL~ underlying Bayes' formula ( I ). namely. that the HI form a closed set, that the H i are mutually exclusive and that D and HI are probablistically dependent events. The first requirement is frequently violaLCd whcn thc Bayesian approach is applied to tackle the issue of model choice, or more broadly that of scientific discoveries. since any new non-lrivial discoveries tend to open up the originally defined closed set {H j } in such a way as to alter the probability measure assigned on I H j }, which has sustained the Bayesian methods. This poi nt is often lost in philosophical discussions of the scientific significance of the Bayesian approach. When Bayesian methods are appl ied in studies of different priors. the second requirement is frequelllly forgotten. It is quite comlllon to find cases where Bayesian experiments are carried out with different types ·of prior de nsity functions forthe same set of structural parameters. If we represent these cases by Ihe Bayesian posterior oc\ds test, we get:

p,{PI X) L{X,P)p'{P) p,{P) Pj{PI X) • L(X,P)P;{P) • Pj{P)

for i1:j.

This suggests thaI all the effons of combining the priors with the likelihood becomes redundant, and that the priors thus defi ned are in connict with the third requirement, since the likelihood remains unaffected however the prior is specified. Undcr such circumstances, the Bayesian pri nciple collapscs to a simp le multiplication principle. that is. a simple multiplication of the

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likelihood with the prior. Most of the economclric cases where the Bayesian approach has been applied fall into that category. Unfortunalely, the lack of independence has been deeply concealed by the conditional nature of structural models, for example, Y is conditioned upon Z in equation (5), and by the confusion over the nature of the relationshi p between X and the sample estimalor of 13: that is, whether it is of a function/mapping induced by theconditioning of Yupon Z. or of a condilionaltype in ils own right. Because the basic three requirements arc seldom met in practical si tuations where the Bayesian approach has been applied. most Bayesian methods developed in econometrics so far are devoid of Bayes' formula or Bayes' theorem. The tenn ' Bayesian ' is thus a m.isnomer for 'the prior'. Any philosophical interpretations which have been elaborated from the idea of conditional probabilities of what actually results from the application of the multiplication principle to join two independent probabilities. the likelihood and the prior, can therefore be cxtremely misleading. The gap between the appealing intcrpretations evolved from equations ( I) and (2) and most of the Bayesian applications in econometrics is yet to be fill ed. D UO Q IN

Bibliogr-aphy DowI. T., R. UUenlllLll WId C. Sims (1984). 'Fo!wuting and Conditional PYojoction Using Rcalistic Prior Disuibutions'. Economttric ReI-·lews. 3. 1-100. F\nrens.l.·P" M. Moucho.rt nnd 1.-M. Kolin (1990), Eluntllls of Ba}'I!Sifln Sialislia, New York: Mllf'Cellkkker. Howson. C. Ulld P. Utbach (1989). Scitaliflc Rea.(()IIiJlg: n,e B P(B) implies that p(A B) > peA); that is, if A causes B, then B causes A. Consider an example: suppose that in a mal for every 100 patients (50 given aspirin, 50 given a placebo) the following average results occur:

I

I

Aspirin

No

Yes

Does not end

40

20

Ends

\0

30

=

=

Headache

In this case the P (headache ending I taking aspirin) 30/50 315 > 40/ 100:= 215 := P (headache ending). The probabilistic account would therefore conclude that taking a~pirin causes headaches to end. But the same data show that P (taking aspirin I headache ending) := 30/40 "" 314 > 1/2 "" 501100 = P (taking aspirin). According to Ihe definitions, the headache ending causes patients to take aspirin: but even the advocales of the probabilistic account naturally resist this implication.

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The handbook of economic methodology

The preceding example illustrates an important problem in econometric analysis, known as observationo.l equivalence. In probabilistic accoun ts the problem of observational equivalence is typically solved by following Hume and imposing the condition that causes must precede effects. Thus P(Br+1 IA,) > P(B,+J) does not imply that p (A,+118,) > P(A'+J)' where the subscripts are time indices. We rule out the conclusion that the headache ending causes the patient to receive the aspirin, because in no case does the ending of the headache precede the receiving of the aspirin. in the probabil.istic account, the concept of cause is eonflated with method of infcrring cause, and issues of ontology are ignored altogether. In economics, the most prominent definition of the causal relation, due to Clive Granger, is based upon such a probabilistic account of causal.ity. A variable A Granger~callses a variable B if the probability of B conditional on its own past history and the past history of A docs not equal the probability o f B conditional on its own past history alone. This definition directly supp:>rts an inferential procedure since standard statistical tools, such as n::gression. provide empirical measures of cond itional probabilities. Granger-causalilY is subjecllo several paradoxes or puzzles, some of which have analogues in philosophical accounts of probabilistic causality. For example, suppose that there is a mechanism that allows money to contro l nominal income, and the central bank uses this mechanism optimally to reduce the variance of nominal income as much as possible. To reduce me variation in nominal income, me bank must create appropriately o ffsetting variations in money. Suppose that, in an extreme case, the central bank is perfectly successful and is able to eliminate all of the variation in nominal income. Then , because nominal income is unchanging, there is a zero correlation between past changes in the money supply and nominal income. In reality, nominal income will not be constant, because of unpredictable current shocks from many sources, but an optimally chosen money suppl y would eliminate that part of the variation in nominal income that was predictable fro m past information. The variations in the money supply that achieve mis control will not be correlated with nominal income because mey are chosen precisely 10 eliminate the predictable variation and they are definition ally uncorrelated with the unpredictablc variation. A Granger-causality test, therefore, will faU to indicate causality between money and nominal income, despite the fact that ex hypothe...·j moncy causes income. To take another example, economic relations are supposed in many cases to depend critically o n people's expectations of the fu ture values o f economic variables. In formin g these expectations, people often may be better informed about the future course of the variables conditional on current infonnation than an econometrician ever can be. Suppose that the general price level is detemtined by the interaction of the supply o f and demand for money and mat the demand for money is inversely related to expected inflation. If people anticipate a future increase in the supply of mo ney, they will expect inflation and thus reduce meir current demand for money. which in turn raises the curre nt general price level in order to equilibrate the emergent excess suppl y of money. Current prices will thus be correlated with future money, so mat a Granger-causality test will indicate that prices cause money when ex hypothesi money causes prices. This might be regarded simply as a problem of an o mitted third cause, so that incorporating the expectations into the relationship wou ld eliminate the problem. The problem arises, however, because people's information is better than me econometrician's information; and, in a complex economy, this is an irreducible fact. To eliminate me information differential completely would require contemporaneous observation of the subjective expectations

The handbook 0/ economic methodology 5 1 of every economic agent in the economy - a thing not only impracticable. bm impossible in principle. A nother reason to doubt the general usefulness of a probabilistic account of causality in economics is the way in which it uses te mporal order to solve the problem of observational equivalence. Properly speaki ng, a variable measured at time t cannot Granger-cause another variable measured at lime t. There are at least two rca'«) ns why any conccpt of causality that depe nds fundamentally on tem poral order will not be generally useful for economics. First, economic data (such as, GNP, consumption or the consumcr price index) arc collectcd at fa irly wide intervals - years. quaners, months. but rarely weeks, days or hours - but economic acti vity goes on more or less conti nuously. Consumption this quarter, for example, is the n likely to be rela ted to GNP this quarter: that is, simultaneously. A stock answer to thi s is that the simultaneous relation is only apparent, and would disappear were data collected at fine enough intervals. However, the conceptual problems of defining, for example, GNP at an interval o f a day or an hour arc overwhelming, and it would be beller to admit simultaneous causation than to be caught in that conceptual morass. Second, much economic theory is about steady-states. the configu rations of economic variables that arise when time is allowed to run notionally 10 infi nity. The relationship between economic variables in long-run steady states may be qui te different from what it is in the shortrun proccs. PCB) is regarded as a cause o f B. It is recog nized that A and B might have common causes, or that thi rd causes m ight intervene between them, or that probabi lities might be calculated with respect to no n-homogeneous reference classes, and so forth. In the earlier examplc, despite the fac t that the probabilities indicated that the endi ng of the headache prima/acie caused the taking of the aspi rin, the advocate of the probabilistic approach rejeets the concl usion. Somehow the probabilities got it wrong. But what does that mean'? To ask how the approach could go wro ng is to have a strong idea of what it is to be right. Certai nly, this is partly a matter of causal intuitions. Beyond that there appears to be an implicit notion of causal structure involved in selling the agenda for probabilistic causality. Structural accou nts reject the view that probabili ty relations are useful in defining the concept of cause, although they may grant them a pan in the epistemology of inferring causal structure fro m observations. Structural accllunll; mllst deline the conecpt of causc d ifferently. Causes have sometimes been regarded as necessary condi tions for their effects. But this is not acceptable in its unadorned form since the same effect might be ac hieved from d ifferent causes - even simultaneously occurring different causes (the case known as causal overdetermination). Causes have sometimes been regarded as sufficient for their effects. But this will not do in its unadorned form either: for example. the match is the cause of the explosion, but only in conj unction w ith other n ecc.~sary factors - the explosive, air, the righ t hum idity cond itions and so forth. Many accOunts of causation have tried to start either with necessity

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The handbook of economic methodology

or suffic iency and add qualilicalions Ihal would eliminate these obvious. as well as more sophislicated, objections. J.L. Mackie provides a wetl·known alternative elucidation of the concept of cause. A cause, says Mackie. is an INUS condition, an insufficient, non·redu ndant member of a set of 1m necessary but sufric icnt conditions for the effect. The set {match. explosive, air, low humidity) is sufficient for the explosion: there would be no explosion with the match alone. so it is insufficient; there would be no explosion from that particularly complex if the match were missing. so it is no n-redundant but there are other sets of sufficient conditiuns (exp losive, e lectric ignitor), for example, so the whole complex is unnecessary: the match is, therefore, a cause of the explosion. Whether causation is elucidated principally through lNUS conditions or through some variation on a sufficiency or necessity criterion, an appeal is made to the logic of conditional statements (' if p, then q'). Such conditionals are sometimes known as 'counterfactuals' because 'if p, then q', may be true even whenp is false. To assert a counterfactual is to assert lhe ex istence of a disposition: the natu re o f the situation is such that q is disposed to occur when p does. Such dispositions need not he detenninistic; q may occur only with some probabi lity. Nevertheless, invariance is implicit in counterfactuals and related dispositional claims. A counterfacrual cannot be rightly asserted if. whcn its antecedents are fulfilled. it no longer entai ls (probabilistically at least) the same consequence. Invariance of this sort underwrites an intuitively appealing notion: causes are efficacious in bringing about the ir e ffects. A causal structure can then be seen as a network o f counterfactual relations that maps out the underly ing mechanisms through which one thing is used to conlfOt or manipulate another. In effect, the structural account makes the empirical claim that there is something morc to causality 'in the objectS,' than Humess purely associative notion of causality allows. What more there is needs to be fleshed out in two directions. On the one hand. the appeal to counterfactuals raises ontological issues about the ex istence of modalities such as necessity or probability. On the other hand, economically satisfactory dc...cri ptions of causal mechanisms are needed. Although econom ists generally neglect the metaphysical issues, structunal accounts of causatio n have a long history in econometrics. The classic account is due to Herbert Simon. For Si mo n, the following equations illustrate a case in which XI CIIUSCS~:

( I) (2)

where the aif are lixed coefficients and thex,.s are variables. In this case the value of XI is independent of the value of Xi, but the value of -'1: changes depending upo n the value of Xl. An immediate difficulty with Simon's account ofstJUcture. as he himselfrecognil.cd, is that it is subject to another version of tile problem of observational equivalence. There is an infinite number of linear combi nations of equations ( 1) and (2) lbat have difFerent coefficients, with every possible apparent causal onier, cach yielding the same values for thex,s. For S imon, the un iquely correct causal structure is the one that is invariant to interventions. On this view, equations (I) nnd (2) rcprcscnta true causal structure on ly if the may be treated as parameters lhllt can be selected independenlly of each other. The invananec o f equlltions (I) and (2) permits them to be treated countcrfactually; they represent what would happen to A1.,

at

The handbook of economic: melhudulugy

53

fo r example. if Xl were different, say, because a lO were different. Through the notion of invariance, Simon's account of causal structure links the nolion - frequen tly supponcd by economists and econometricians - that causality is related to (hypothetical controllability) to the conditional analysis of causation well known to philosophers. Causal inference The probabilistic account of causal ity runs the conceptual problem of defining what a cause is together with Ihe epistemological problem of inferring causes from evidence in order to avoid making any ontological commitment to what causes are 'in the objects'. Thus, for example, in Granger's analysis causes are defined hy the procedure (that is, conditional probabilities) through which they are inferred. Structural accounts afe appealing in economics partly because they keep the issues of definition, ontology and epistemology distinct. Not defining cause by an inferential procedure, the structural account accommodates a variety of evidence. including, in the right circumstancCo.'i, the evidence of conditional probabilities. Two inferential questions must be kept distinct. The [irst is, givcn A W1d B. what is thc direction of causal innuence, if W1y, between thcm?The second is, given that A causes B. what is the suength of the causal influence of A o n B? Probabilistic accounts arc very appealing in fields in which causal direction is implicitly known. One reason for even the advocates of probabilistic accounts resisting the implication that the ending of headaches causes the receiving of aspirin is thaI it is a controlled experiment in which the assignment of aspirin or placebo to patients is made independently (temporally , but more importantly) logically prior to thedetermination of whether thcir headaches ended. The causal direction is from aspirin to the end of headaches if there is any carlSal connectioll at all. This situation, for example, is frequent in medical research: with fairly straightforward controls, probabilistic methods can be used to assess the causal efficacy of a pathogen or a treatment (implicit structural understanding, of course, informs the design of those controls). Prior eOllunilment to structure in economics is the basis for the usc of probabilistic methods to me(lJure causes in identified econometric systems of equations that is the legacy of Trygve Haavelmo and the Cowles Commission to economctrics. Unfonunately, economics does not support implicit structural comm itments as well as some other fields , such a.~ medicine, do. It is often thc case that more than nne causal ordering is consistent with the theoretical and inslitutional constraints of economics and that alternative causal orderings arc obscrvationally equ ivalent in the sense that they generate the same probability distribution for the data. This is the upshot of the debate over 'explicit causal chain' models or 'process W1alysis' that raged for 15 years from the late I940s. One side of this debate (Hennann Wold, Robert Strotz and others) argued that, since causality is both. logically and temporally asymmetric, economctrieally estimated systems should renect Ihis asymmetry through the use of recurs ive systems of equations. The other side (Herbert Simon, Robert Basmann and others) pointed out that, because of observational equivalence. the purely syntactic distinction between recursive and simultaneous systems was nOt enough to capture the causal asymmetry. Other fields are, of course, not immune from the problem of observational equivalence. Whether the HlV viruses cause AIDS is a question of causal direction no different in pri nciple from whether money causes prices or taxes cause spending. (The heterodox view of the virologist Peter Duesburg is that mv is simply the most common opportunistic infcction of AIDS patients and not the cause of their disease.)

54

The handbook of economjc methodology

The estimation of identified systems of equations assumes an answer to the question of causal direction in order to p ursue the question o f measurement. Granger-causality assumes a temporal structure in order to address the question of causal direction using purely probabilistic methods. A third approach is to recognize lhatthe very nOlion of structure implies that a causal structure must remain invariant in some dimensions to interventions and, indeed, transmit interventions in one part o f the structure to other parts of the structure. In a broad sense, it is this nOlion that underlies Arnold Zellner's criterion that a causal relation is one that supports 'predictability according to law'. Zellner's criticism o f Granger-causality is that il relies on correlations that may not be lawlike, thaI may not be invariant once used to guide causal production (for example, monetary policy). Unfortunately, Zellner' s account is not very helpful because it merely transfonns the problem from 'what is a cause and how might one infer it?' to 'what is a law and how might one infer it?' It sets up invariance (Iawlikeness) itself as defining the causal relation, where in a richer structural account invariance is a property with a complex relation to causality. Kevin Hoover's struetuml approach to causal inference considers competing causal orderings. The causal asymmetries of these orderings is reflected in the asymmetries of alternative conditional probability distributions. The joint probability distribution of A and B may be ractored into marginal nnd conditional distrihutions in IWO ways: D(A, B) = D(A 1 8)D{8) D(81 A)D(A). If A causes 8, the second of these factorizalions is the more stable in the sense that D{A) will remain invariant in the face of structural interventions in the process detenllining B and (with certain qualifications) D(B 1 A) will remain invariant in the face of interventions in the process determining A. Regressions provide empirically applicable econometric analogues to the conditional probability distributions, and invariance can be evniunted using slandrud cconomctric tests for structural stability. The mcthod relics heavily on the identification and assignment of intcrvcntions to the A or the 8 processes typically based on a combi nation of historical, institutional and statistical knowledge. In Hoovcr's anniysis, in variance is used instrumentally to reveal underlying causal structure. Recently, other researchers. panicularly Clark Glymour and his coworkers and Judea Pearl and his coworkers. have developed other methods of cnusnl inference lhnt are grounded in a structuralist approach . Applications of these methods [0 economics are only just beginning, so that it is difficult to assess their ultimate importance. KEVIN D. HOOVER

=

Bibliography 8asmann. R.t.. (1988), 'CausaiityTests and Obsetv:u:ionally Equivalent Repl~l1ariofls ofEcMometric Models·.Am",,,I of EcOllf)melricJ. Annals. 39, 69-tOI . C'Il1wrighl. Nancy (1989). NulJlu:'s Cupm:ilies find The;r MellSuremenl. Oxford: Clan::ndon Press. GTtUlgcr. C.W.J. (1980). 'Testing for Causality: A Personal Viewpoint'. Jouf7IiJI ofEmnonric D)'Mmics and Con/roI. 2 (4), November, 329--52. Hoover. Kev in D. ( 1990). 'The Logic ufCuusoJ Iliferem:e: EconOIfICtrics and the Conditional Analysis of Causation'. t:cO"OllliCI IlJId Philosophy. 6 (2). 207-34. Hume. David ( 1738). A Trt!ariu of tlunrllJl Nmurt!. Pearl, Judea (1995), 'Causal Diagl1lms for Empirical Research'. BiC/me/ritC/. 81. (4). Dettrnm. 669-88. SoJrlloo. Westey C. (1984). Scientific Explanation ond Iht Cousal S,ruClure of Ihe lVorld, P1ince(On: Princeton University Press. Simon. Herbert A. (1953), 'Causal Orderiog and Identifiability'; reprinted 1957 in Mudeb of Mon. New York: Wiley. Spines. Peter. Clark Glymour and Richard Seheines ( 1993). ·Causation. Prediction and Sean:h', New York/Berlin; Springer-Verlag. Suppes. Patrick (1970). 'A Probabilistic Tht:ory ofCausoJity·. Aero Phi/osophico f'tnnica. Fasc. XXtV.

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Wold. Hennann (19S4). 'Causality and Economctri~' , F.ctlntlmt!lriC(l . ll, 162- 71 . Zellner, Arnold (1979), 'Causality and Econom:trics'. in Karl Brunner and AIM H. Meltzcr (cds), Thru IupW5 tlf Policy Making.' Kno~'It!dgt!, Do/a and fllllj/II/i olls, Carnegie-Rochester Conference Serie.~ on Public Policy. vol. 10. A nl~te"'lUIl: North-Holland.

Ceteris paribus Ceteris paribus is a Latin expression made up of the flexible words cetems (the other, thu.t which exislS besides, the reminder) and par (similar, equal in effect). Various trnns.lations include 'other things being equal ' and 'olher things being constanl' . Although ubiquitous in economic science since its inception (piimics, 1997). tl1ccxpression has received detailed methodological allenlion in narrow conlcxL.; only. Equivalenls of ceteris paribus are used by AristoUe in Posterior Allalytics and by Marcus Tullius Cicero (106-43 Be) in his Irealise On DUlie.f. Thc abbrcviatcd expression ceteris pariblls was subsequently lldopted by scholastic authors like Thomas Aquinas ( 1224/5- 74), whose philosophy was strongly innuenced by Aristotle. The great mediaeval economic thinkcr Peter John Olivi (Petrus lohannis 0., doctor speculatil'us, 1247/8-98) employs ceteris pariblls (CP) e1auses in distinctly ccunumic contexts with regard both to value detenninants and to intertcmporal cho ice in his set of three lreatises on buying and selling, usury and restitution. Olivi, once a suspected heretic, remai ned for a long lime an important bullargely uncredited source. whose lexlS wen; extensively copied down the centuries by, among omers, SI Bernardino of Siena (1380-- 1444) and SI Antonio of Plorence (1389- 1459). In the late scholastic era, the economic writings of the School of Salamanca integrate the CP clause into monetary doctrines anticipating, for example.lhe quantily theory of money: ceteris pnribrlS, the price level varies inversely with the quantily of money (Martin de Azpilcucla Navarro, 1556; cl'. Grice-Hutchinson, 1993). In Ule English economic lilcmtun:. CP clauses date back alleast to 1662 and the Jesuit-educated Wiiliam Petty ( 1623-87: see Persky, 1990) who adds a (labour) cost e lement to the supply of sil ver from the Americas in the framework Oflhc quantity theory of money. The CP clause becomes an object of method%gicnl inq uiry with John SlUart Mill's ( 1806-73) definition of political economy as the study of ' Ihe course of action into which mankind, living in a state of soc iety, would be impelled' if acting solely ' from the desire of , weahh ' and its two pcnnane nt countermotivcs of 'aversion to labour' and positive time \ preference (Mill, 1836). Mill's d istinctions give rise to two kinds of assumptions that could be called ceteris absentibus c lauses: first. pure economic theory studies economic action in Ihe absence of non-economic moti ves and, second, it omits minor economic canses that operate in particular circumstances only. The latter, however, can be brought 'within lhe place of Ihe abstract science' by successive theories of more limited scope. Omitted causes are labelled 'disturbing causes' if they modify the attainment o f a state of affairs that would be brought about by major economic causes only. In The Principles of Political Economy (Mill. 187 1). explicit CP clauses ore rare and fall into several groups. many ofwruch involve (economic) time. The fi rs t group covers the constancy - rather than absence - of non-economic causes. Thailand rcnL.; arc constantly lower but vary inversely with lhe money ratc of interests, ceteris paribus. presupposes the constancy over time o f tile non-economic desire for 'power and dignity' associated with landed property which drives land prices up relative to agricultural revenue to keep land rents below the money rent (ibid:

56

The handbook oj economic methodology

649). A second group involves an ass umption o f normali ty. W arti me conditions that, exceptionally, keep both llind rents and interest rates high are excluded by a CP clause. A third group ofCP clauses implicitly defi nes the appropriate time frame for asse rtions ahuut supply and de mand reactions. [millcdi atc and temporary prices vary with s uppl y and demand shocks. The (IUantily theory of money is a special case, where the price level temporarily increases with an increase in the quan tity of money, ceteris paribus, be fore the supply of mo ney adjusts. Mi ll offers no detailed explanation of the relation of the CP clause to dis turbing causes. nor is this done by his follower. both in methodology and theory, John Elliot Cairnes ( 1923-75). The latter. nevenheless, consciellliously points out when and how political economy involves what he wldcrstands to be CP clauses. In one of his numerous examples, Cairnes asks how quickly the supply of fin ished manufacturing, of raw vegetable and of animal proeJUCl, 1934) is fu ll of discussions of the theories of the major economists fro m Quesnay onwards, but what Commons was doi ng in these discussions was to look for what was useful for his own purposes. T he theories of other economislS were 'raw material to be blended with his own experiential knowledge and shaped into tools for understanding and solving present-day prohlems' (Biddle,

199 1,87-8). MALCOLM R UTHERFORD

Bibliography Biddle. Jeff E. (199Oa), ' Purpose and Evolution in Commons's Insti tutionalism', Hislory af Po/ilic«/ Economy. 22 . (Spring). 19-47. Bidd le, JeffE. ( 199Ob), 'The Role ofNegOliationai PsyelKllogy in J.R. Common. U(c') - U(c") trivially follows; because the last inequality does not depend on the particular representation U (sec the uniqueness part of the VNM theorem). it would seem naturdl to interpret it as implying that the individual' s intensity of preference of cover c· is stronger than the intensity o r his prercrence or c' over c". He nce the conclusion that VNM had gone beyond the purely ordinalist stand at which the Paretian school had stopped. By and large. the ' measurability controversy' resulted in the rejection or this optimistic interpretation. It was shown to rely on a superficial understanding of the uniqueness part of lhe VNM theorem. To make sense of a cardinal index in the desired sense. o ne should first or all impose a special axiom on the pre ference relation on outcomes, to the effect that preference differences or intensities are meaningful. In the presence of su itably strengthened versions of the preordcring and continuity axioms. this added axiom will have the efrect of determining another utility function Won outcomes. which is itself 'unique up to a linear transformation'. But unless this is explicitly required by adding still another axiom,

The Illmdbook of economic methodology

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there is no reason why Wshould be a linear transformation oflhe VNM index. U. In olherwords, the uniqueness parI of Ihe theorem does provide a fonnal method for comparing utility differences but the numbers derived in this way might be un related 10 the measurement of preference differences on the outcome set. The refutation sketched here is in accord with Fi! £, remaining latent or bankrupt. The subsequent contractionary shock E:2 leads firms with b ~ ~ inlo bankruptcy, firms wilh b < E.:z remaining in production. And

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E,

'2

Figure 2

3

5

6 T'Imc

t

Aggregate demand shocks

~i milarl y for the subsequent expansionary shock Ej and contractionnry shock £4' Figure 3 uses

the Mayergoyz (199 1) half-pJaneditlgrom to show how these shocks affect the balance between finns in (P) and oul (UB) of production. Each point on the half-plane a