Critical dilemmas in the methodology of economics facing ... · Critical dilemmas in the methodology of economics facing the crisis Alessio Moneta Institute of Economics Scuola Superiore

Introduction Causality

Critical dilemmas in the methodology ofeconomics facing the crisis

Alessio Moneta

Institute of EconomicsScuola Superiore Sant’Anna, Pisa

[email protected]

28 November 2014

Seminar SeriesCritical economics in the times of crisis

Scuola Superiore Sant’Anna

Dilemmas in methodology of economics 1/22


Introduction

B The crisis of the economy and the crisis of economics

B The point of view of methodology

B Need of a critical assessment



Outline

B Some dilemmas in the methodology of economics

• What is a dilemma?

B The dilemma of causality: data vs. theory driven

B Other “dilemmas”:

• Explanation through models: abstraction vs. idealization

• Confirmation: falsifiability vs. calibration

• Economics as a moral science vs. natural science.



Causality

B Importance and variety of causality issues in economics.

• understanding the causes of recession / current economic crisis

• predicting the effect of a policy intervention

• what is the effect of monetary/fiscal policy?

• retrospective and prospective questions

• singular vs. general causes

• measurement: can we estimate the fiscal multiplier?

B Notwithstanding the variety of problems all these issues share adilemma



The dilemma of causal inference

B We face a dilemma in our attempts to uncover causalrelationships:

• We let theory guide us

We build theoretical models. But our theoretical/backgroundknowledge is uncertain. At least there is no consensus on theassumptions upon which theoretical models are built. It is easy tobuild models with conflicting policy implications by (sometimesslightly) modifying the initial conditions.

• We do not let theory guide us

We start from the data. We estimate empirical/econometric modelsand we try to find evidence for causality from them. (Cfr. VARmodels, Instrumental Variables, natural experiments, etc.). We cannotguarantee that our conclusions are true of the sample (problem ofinduction). But suppose we get good evidence for that. Without anunderstanding of the underlying mechanisms, it is difficult togeneralize to other settings / applications (problem of externalvalidity).



Deductivist vs. inductivist

B Disputes in the history of economic thought that partially reflectthis dilemma:

• J.S. Mill (1837) and his critics.

• Methodenstreit (C. Menger vs. G. Schmoller), beginning XX cent.

• “Measurement without Theory” controversy (A. Burns, W.C.Mitchell, D.R. Vining vs. T. Koopmans), 1940s - 1950s.

• Cowles Commission approach vs. Granger causality VAR (Cooleyvs. C. Sims), 1980s.

• “Mostly Harmless Econometrics”debate (J.D. Angrist. J.S. Pischke,G. Imbens vs. J. Heckman, A. Deaton), current.



Solutions?

• Integration of both sources of knowledge.

• Searching for robust empirical and theoretical knowledgemaking explicit the sensitivity problem.



The dilemma of philosophers

B What is causation? The philosophy-of-science point of view:

• regularity/probabilistic account

• counterfactual accounts

• interventionist accounts

• mechanistic accounts

B Each of these accounts presents problems/counterexamples

• pluralistic view

• inferentialist /epistemic account (cfr. Reiss 2012, Williamson 2006):causality has several indicators



The probabilistic indicator

B Scholars who choose the second horn of the dilemma emphasizethe importance of the probabilistic indicator.

• “let us the data speak for themselves” (cfr. Sims 1972, 1980)

• Granger-causality



The probabilistic indicator

B Sloppy issues: implicit causality and hidden background theory

• Cfr. the (in)famous paper by Reinhart and Rogoff (AER 2010):

“Our approach is decidedly empirical...Our main finding is that across bothadvanced countries and emerging markets, high debts/GDP levels (90 percent andabove) are associated with notably lower growth outcomes.”

• Critique by Herndon, Ash and Pollin (CJE 2013):

“selective exclusion of available data, coding errors and inappropriate weighting ofsummary statistics lead to serious miscalculations that inaccurately represent therelationship between public debt and GDP growth among 20 advanced economies.”



Probabilistic/statistical dependence

B One important indicator of causation is probabilisticdependence.

But correlation is not causation.

More in general: statistical (i.e. probabilistic) dependence is notcausation

What is statistical dependence?

intuitively, two random variables X and Y are statistical associated(i.e. dependent) if the the realization of X gives useful informationabout the likely realization of Y

statistical dependence is a property of the distribution function

fXY(x, y) 6= fX(x)fY(y)



Statistical dependence and causation

B There are different measures of statistical dependence, e.g.:

• correlation (Pearson correlation coefficient):

ρXY = corr(X, Y) =cov(X, Y)

σXσY=

E[(X− µX)(Y− µY)]

σXσY

• linear regression coefficient β = cov (X,Y)σ2

X= ρXY

σYσX

• Granger causality

f (Xt+1|Ωt) 6= f (Xt+1|Ωt\Yt),

• etc.

B one difference between statistical dependence and causality:

s.d. is symmetrical (but measures of s.d. can be asymmetrical)

causality is asymmetrical



Statistical dependence and causation

B In which sense is s.d. an indicator of causality?

B Principle of the common cause (cfr. Reichenbach 1956):

if X and Y are statistical dependent either (i) X causes Y, (ii) Ycauses X, (iii) or there is a common cause Z causing X and Y.

B But there is also the following possibility:

X and Y are prima facie statistical dependent but they result in thisway because of a not adequately specified statistical model.

B Thus, it is crucial to correctly specify a statistical model. How todo that?

• statistical testing

• integrating theoretical and background knowledge

B The particularity of economics as social science: statisticaldependencies generated by social structures / interactions.



Other indicators

B counterfactual dependence

B interventions

B mechanism



Intervention and causation

B The idea here is that X causes Y if the manipulation of X willresult in the manipulation of Y.

B J.S. Mill (1837) and the impossibility of running controlledexperiments in economics



Ideal experiments in economics

B The impossibility of running controlled experiments is not seenas insurmountable

B Haavelmo (1944)

• nature can run experiments for us

• variety of independent sources of variation

• conformity to well-defined distributions

• similarity to randomized controlled trial



Experiments as benchmark?

B In Mostly Harmless Econometrics (2009) J.D. Angrist and J.S. Pischkeclaim that the exploitation of natural experiments (random assignmentof treatment independent of potential outcome) has induced a “credibilityrevolution” in empirical economics

Much of the research we do ... attempts to exploit ... readily available sources ofvariation. We hope to find natural or quasi-experiments that mimic a randomizedtrial by changing the variable of interest while other factors are kept balanced. Canwe always find a convincing natural experiment? Of course not. Nevertheless, wetake the position that a notional randomized trial is our benchmark (Angrist andPischke 2009: 21).

B But without an understanding of the underlying mechanism thisreduces to knowledge of (conditional) statistical independence.



Mechanisms

B Mechanistic account of causation: if X causes Y, we expect to be amechanism from X to Y.

A mechanism is something which can be decomposed into partswhich transmit a “causal message”, such that the transition fromone part to another is governed by some understandableprinciples.

B There is this (pernicious!) idea in mainstream economics thatunderstandable means that it has to be reduced to rationality ofthe agents, and that rationality means optimal allocation.



Failure of macroeconomic models in the crisis

B This idea is at the heart of DSGE models, seen as responsible ofthe “failure of academic economics” in the face of the crisis byColander et al. (2008, 2009), Buiters (2009).

B Importance of the other horn of the dilemma. Here econometricshas been based on calibration, not on estimation, let alone ontesting.



The connection to the other dilemmas

• The riddle of explanation through economic models: abstractionvs. idealization.

• Empirical validation: falsifiability vs. calibration.

• Economics as a moral science / natural science / practicalscience.



Conclusions

Three pleas to reorient economics in the face of the crisis:

B Need of a critical view in two (quite different) meaning:

• “Popperian” sense: severe empirical testing (notwithstanding thedifficulties of falsificationism).

• “Marxian” sense: debunking implicit assumptions, recoveringimportant variables or structures that are left out in the abstractionof economic modelling.

B More pluralism, but refusal of the anything-goes view.

B Rethinking of the facts/values distinction. The failures ofeconomics as objective science is also a failure of an idea ofobjectivity.

Thus, what kind of science is economics?



“...the master-economist must possess a rare combination of gifts. He mustbe mathematician, historian, statesman, philosopher—in some degree. Hemust understand symbols and speak in words. He must contemplate theparticular in terms of the general, and touch abstract and concrete in thesame flight of thought. He must study the present in the light of the past forthe purposes of the future. No part of man’s nature or his institutions mustlie entirely outside his regard. He must be purposeful and disinterested in asimultaneous mood; as aloof and incorruptible as an artist, yet sometimes asnear the earth as a politician .”

J. M. Keynes, “Alfred Marshall, 1842-1924”, The Economic Journal, (Sept.1924).


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Critical dilemmas in the methodology of economics facing ... · Critical dilemmas in the methodology of economics facing the crisis Alessio Moneta Institute of Economics Scuola Superiore