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What is the Nobel committee thinking?

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Presentation on theme: "What is the Nobel committee thinking?"— Presentation transcript:

1 Doing (and Teaching) Economics for the Real World Dani Rodrik IEA, June 2017

2 What is the Nobel committee thinking?
What’s the big idea? Can they both possibly be right?

3 In a nutshell… “Economics is a science of thinking in terms of models joined to the art of choosing models which are relevant…” Keynes (1938) “There’s no easy line in summarizing my contribution,” he protested. “It is industry-specific. The way you regulate payment cards has nothing to do with the way that you regulate intellectual property or railroads. There are lots of idiosyncratic factors. That’s what makes it all so interesting. It’s very rich It’s not a one-line thing.”

4 In the land of the Econ …where “modls” rule
Axel Leijonhufvud, “Life Among the Econ,” Western Economic Journal, September 1973.

5 … and determine attitudes towards other social sciences

6 … though even the natives cannot agree which “modl” to use

7 But why models: Borges on scientific method

8 Argument Models are key to the “scientific” nature of economics
understand complex social reality by laying bare a very large variety of causal relationships, one at a time Economists have the wrong take on their discipline succession of ever-better models, till we get the right model? Economics advances not by settling on “the model,” but by generating useful collection of models an inventory of partial explanations non-universality and context-specificity This view of economics counters typical critiques of economics as well as economists’ own description of their practice Economists are good at making models, but poor at navigating among them

9 Models at work: what does economics have to say on the effects of
minimum wages on employment? competition on innovation? expansionary fiscal policy on economic activity? capital inflows on economic growth? trade liberalization on economic performance? …. Different models, different results (and empirical analysis rarely ever conclusive, often pointing to different outcomes for different places and times)

10 Example: minimum wages
What are the employment consequences of a minimum wage imposed by the government?

11 Effects of minimum wage under two different kinds of market structure
L A competitive market

12 Effects of minimum wage under two different kinds of market structure
L A competitive market

13 Effects of minimum wage under two different kinds of market structure
MC w w S S D D L L A competitive market A monopsonistic market

14 Effects of minimum wage under two different kinds of market structure
MC w S S D D L L A competitive market A monopsonistic market

15 Can empirics do away with models?
“Let facts on the ground determine policy” “We don’t need theory if we have good evidence” RCTs, especially Big Data no need for theorizing any more; simply uncover the patterns in the data Counterarguments: every “fact” requires an interpretive frame models provide the interpretive frame example: toin cosses regularities versus causation without causal stories we don’t know when a regularity stops being one RCTs require causal stories/extrapolation to be applicable, and we need models for that

16 Economic models as … fables
are simple dfgdfgdfgdfgdfgdfgd dfgdfgdfgdfgdfgdfgd are not real dfgdfgdfgdfgdfgdfgd have clear storyline dfgdfgdfgdfgdfgdfgd have characters that can be animals or objects dfgdfgdfgdfgdfgdfgd typically have a moral provide interpretive short-cuts are multiple, one for every situation Economic models simplicity: ceteris paribus assumption reality: stylized abstractions, untrue assumptions storyline: clear cause-and-effect, if-then relationships characters: random shocks, exogenous structural parameters, “nature”… moral: policy implication interpretation: analytic shortcut multiplicity: context-specificity (cf. Rubinstein 2006; Cartwright 2008)

17 Economic models as … experiments
isolate effects of specific cause/intervention can be replicated by anyone experiments produce dissimilar results in diverse settings lab experiments do not pretend to represent “real world” field experiments need to be extrapolated to other settings in both cases, external validity not assured and has to be argued and supplied from outside Economic models clarify causal links by simplifying gdfgdfgdfg can be reproduced by anyone different models for different contexts do not claim to be representations of real world relevance of a model depends on “extrapolation” additional techniques needed to sort out the usefulness/relevance of available models (see below)

18 What makes models “scientific”?
Clarifying nature of hypotheses: explicit causal chains simplification both a necessity and a virtue: isolation => what precisely does an explanation depend on? “a model is an experiment, and vice versa” (Mäki; Gilboa et al.) Model selection after the fact in real time A method for sorting out disagreements “arguments that can be shown to be wrong” vs. those that are “not even wrong” (W. Pauli) we can agree on what we disagree on, even when empirical evidence is too weak to discriminate among models Accumulation of knowledge how economics advances (slide) Nature of “authority” rests on “quality” of models (judged by principles widely shared by practitioners), not on reputation/status/network

19 What makes models “scientific”?
Clarifying nature of hypotheses: explicit causal chains simplification both a necessity and a virtue: isolation => what precisely does an explanation depend on? “a model is an experiment, and vice versa” (Mäki; Gilboa et al.) Model selection after the fact statistical tools in real time applied policy analysis (e.g., growth diagnostics) Nature of “authority” rests on “quality” of models (judged by principles widely shared by practitioners), not on reputation/status/network

20 What makes models “scientific”?
Clarifying nature of hypotheses: explicit causal chains simplification both a necessity and a virtue: isolation => what precisely does an explanation depend on? “a model is an experiment, and vice versa” (Mäki; Gilboa et al.) Model selection after the fact statistical tools in real time applied policy analysis (e.g., growth diagnostics) A method for sorting out disagreements “arguments that can be shown to be wrong” vs. those that are “not even wrong” (W. Pauli) we can agree on what we disagree on, even when empirical evidence is too weak to discriminate among models Nature of “authority” rests on “quality” of models (judged by principles widely shared by practitioners), not on reputation/status/network

21 What makes models “scientific”?
Clarifying nature of hypotheses: explicit causal chains simplification both a necessity and a virtue: isolation => what precisely does an explanation depend on? “a model is an experiment, and vice versa” (Mäki; Gilboa et al.) Model selection after the fact statistical tools in real time applied policy analysis (e.g., growth diagnostics) A method for sorting out disagreements “arguments that can be shown to be wrong” vs. those that are “not even wrong” (W. Pauli) we can agree on what we disagree on, even when empirical evidence is too weak to discriminate among models Accumulation of knowledge not by one model replacing another, but by richer set of models and better understanding of where they apply Nature of “authority” rests on “quality” of models (judged by principles widely shared by practitioners), not on reputation/status/network

22 Math and economic models
Models do not require math, in principle any causal statement contains an implicit model In practice, math often useful to clarify (and make explicit) the nature of assumptions, relationships, conclusions ensure conclusions follow logically from assumptions “economists use math not because they are smart, but because they recognize they are not smart enough”

23 Maintained assumptions in economics
Are some assumptions (modeling conventions) so important that they always have to be preferred? individual rationality less and less a benchmark “micro-foundations” methodological individualism a strength of economics, but can come at cost of introducing other, misleading simplifications e.g., representative agent, infinite horizon, rational expectations, …

24 How to figure out the relevant model – the craft of economics
Verify direct implications Verify incidental implications (comparative statics) Verify critical assumptions (cf. M. Friedman) Verify mechanisms Note parallels with external validity in randomized controlled trials Theoretical models face an “external validity” problem just as lab experiments or RCTs do (which, by the way, are viewed as the gold standard in evidence).

25 Example: growth diagnostics as model selection (I)
Why is growth low: Neoclassical model: physical and human capital “Endogenous” growth model: R&D and competition Trade & growth model: trade policy, exports Dual economy model: structural change/industrialization “Chicago” model: too much government Structuralist model: too little government Institutionalist model: property right & contract enforcement

26 Example: growth diagnostics as model selection (II)
Explicit search for “diagnostic signals”: “If story A is correct, signals x, y, z must be present…” Direct evidence “shadow” prices: returns to education, real interest rates, cost of transport,… benchmarking potentially helpful Indirect evidence if a constraint binds, effects must show up in differential outcomes for activities that differ in their intensiveness in that constraint informality, internalization of finance, self-enforcement of contracts elimination of other plausible constraints A constant cannot explain a change high growth episodes of the past cause us to ask what has changed

27 What does all this imply for teaching economics
Cherish diversity of models Emphasize plurality of critical assumptions, behavioral relations, rather than benchmark, competitive model teach multiple models side by side teach (or at least talk about) model selection in practice Emphasize logics beyond competitive market eq’m second-best thinking coordination failures small-numbers interactions: game theory models Emphasize economics as a way of thinking, rather than a defense of/argument for free markets and efficiency

28 Re-evaluating critiques of economics
Simplistic/reductionist theories: a feature, not a bug Inappropriate universalistic claims: a problem Reification of markets and material incentives: perhaps “Conservative bias”: not clear Failure to predict: no; conditional predictions at best Methodological biases, that crowd out new ideas: perhaps, but a feature of all scientific disciplines Loss of ambition from a program to transform society to merely understanding how a particular form of market society works economists as dentists or plumbers

29 Real failings originate from behavioral and sociological aspects of profession
Mistaking a model for the model expecting the same model works all the time overlooking alternative models with different implications over-confidence, hubris Categorical preference for certain axioms assumption of rational, forward-looking individuals operating in perfectly competitive markets Preference for questions that are amenable to available tools of analysis substantive implications of common tractability assumptions neglect of issues involving scale economies until analytical tools were developed Implicit political-economy theorizing in policy discussions economists’ training endows them with no way to evaluate alternative social states other than through lens of allocative efficiency

30 Final word Recognition of economics as portfolio of models:
forces economists to be more humble about how much they really know enables greater understanding of the variety of social phenomena where such understanding is possible closes some of the gap with other traditions in social sciences (critical, cultural, humanist, constructivist, interpretive) an economist’s answer to “what about x which you left out of your model…?” is/should be “OK, let’s write down a model of it…”

31 Want to read more?


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