This is very good. I have some follow-up comments at the end:
Why Can't We All Just Get Along? The Great Multiplier Debate, by Menzie Chinn:
I've been thinking about why the numbers that are typically bandied about in
policy circles (at least that I'm familiar with) have so little impact on the
overall general and blogosphere debate (see some examples
here
and
here). I think it's part ideological, and part methodological. I can't do
much about the first (e.g., tax cuts good, spending on goods and services bad --
unless on defense; or alternatively "let the market adjust no matter how long it
takes"). But at least I can lay out why reasons why there is disagreement on the
size of the multipliers. ...
The starting point in the analysis is to realize that there are three key
ways in which to obtain "multipliers".
- Estimation of structural macroeconometric models, with identification a la
the Cowles Commission
approach.
- Calibration of microfounded models (including real business cycle models,
and New Keynesian dynamic stochastic general equilibrium models).
- Estimation of vector autoregressions (VARs) and associated impulse-response
functions, with identification achieved by a variety of means.
Traditional macroeconometric models. Most of the estimates I
have cited
[1]
[2]
are based upon the first approach. One estimates a model with many equations,
including the components of aggregate demand (C, I, G, X, M), supply side (price
setting, wage setting), and potential GDP. The framework most popular in policy
circles is one that might be characterized as "the neoclassical synthesis",
wherein wherein prices are sticky in the short run, and perfectly flexible in
the long run. ... Now even within this category, there is a wide diversity of
specifications...
A key reason for the academic disenchantment with these types of models
included the view that the identification schemes used were untenable (e.g., why
is income in the consumption function but not in the investment?). Another
source is the combined impact of the inflationary 1960's and 1970's, and the
Lucas Critique. On the latter point, I'd point out that unless policy changes
are really massive, the Lucas Critique (a.k.a. Econometric Policy Evaluation
Critique) isn't really relevant (see
[1]).
Models with micro-foundations in general equilibrium
Micro-founded models are often associated with real business cycle models.
However, the association is not one-for-one. It's true the early real business
cycle models worked off of utility functions and production functions. But the
modern generation of dynamic stochastic general equilibrium (DSGE) models in the
new Keynesian mode incorporate microfoundations as well (utility functions,
production functions, investment functions, etc.) but also incorporate
rigidities such as price stickiness. Purists will say everything has to be
microfounded. Well, that's a matter of taste, but the fact of the matter is that
it's very hard to calibrate simple real business cycle models without rigidities
to match the moments of actual real world data, even after the data's been
HP-filtered (I'm sure this blanket statement will get me in trouble, but I think
that that's a fair assessment). So DSGEs do better at mimicking real data,
especially after numerous rigidities are incorporated. ... For a survey of how
DSGEs have been incorporated into policy analysis, see
the survey by UW PhD Camilo Tovar.
It's useful at this point to ask how are these models calibrated? For the
deep parameters (intertemporal rate of substitution, for instance), one can rely
upon some estimates -- then pick the one that you like (and is in the range of
estimates). Oftentime, the combination of parameter values is selected to mimic
the time series properties of actual (filtered) data. So say one believes one
should not appeal to ad hoc Keynesian models. It's not clear that RBCs or DSGEs
get you away from the problem that one has to appeal to the data to get
multipliers since the models are calibrated to mimic real world data. In other
words, while the theoretical bases of the models may differ,... the differences
in terms of multipliers might not be as big as one might think.
VARs Vector autoregressions are regressions of multiple
variables on lags of themselves. The underlying shocks can be identified by
putting them in a recursive ordering (called a Cholesky decomposition), or using
restrictions based on theory (say, money has no contemporaneous impact on
prices; or money has no impact on output in the long run). VARs were initially
proposed as a way of getting around "incredible identifying assumptions", in the
Cowles Commission
approach to econometrics embodied in the old style macroeconometric models. But
of course, people can disagree about which restrictions make the most economic
sense. (For instance money is neutral in the long run seems natural, but not all
theoretical models have that implication.)
The much cited
Romer and Romer model of fiscal policy impacts is a particular sort of VAR,
in which only one equation is focused on, and extra-model information is used to
identify exogenous tax changes (remember, they don't analyze government spending
changes). ... A good summary of where these types of fiscal multipliers come
from was in Box 2.1 in
Chaper 2 of the
April 2008 World Economic Outlook.
My bottom line There are indeed a wide variety of estimates
regarding the size of multipliers. Different models -- and assumptions within
those model categories -- lead to different estimates. It's important to
understand the underpinnings of those estimates (and this is where many of the
people who cited the Romer and Romer study went wrong). Hence, one has to have
an understanding of the very complicated models before taking strong stands in
favor of one estime over another.
In my experience, as far as policy organizations such as central banks,
government agencies and multilateral agencies go, reference is made to a
number of models. Their assessments of multiplier magnitudes will then
reflect some weighting of the various model predictions. That is why I will put
more wieght upon assessments by organizations (that have to make
decisions upon these judgments) than a single academic study, regardless of how
well I respect the academics involved (and sometimes, these academics are
working outside their area of research expertise...)
As an aside,
here are the impacts of various fiscal experiments in response to a negative
shock in a DSGE developed by the IMF... Notice that the public investment shows
the biggest impact, while under the base assumptions the impact of transfers and
tax cuts are about the same.
Models are built to answer specific questions. If you are interested in
traveling to an unknown area of the city, then a street map - a model of the
world that answers the how do I get there question - is useful. It is even more
useful if it is built specifically for your question. If I am traveling by car,
I want to know where the roads are, a map showing all of the underground conduits in the city and nothing else is useless. But suppose I try to use a map designed for
a car to find the best route for traveling by bicycle. I can probably find a way there
using this map, but it may not give the best possible answer. Some roads may be
hard to travel on, it may not show smaller roads that a bicycle rider might want
to take, and if it only shows, say, freeways where bikes are prohibited, then it isn't
much use at all. But more importantly, it may omit bike routes. I don't know how
it is where you live, but here there is a fairly extensive set of bike paths
that are separate from the roads used by cars. And these routes can save you
lots of time - if you know about them, i.e. if your map shows them. So the best
map would show bike routes and roads suitable for bikes, and omit everything
else, and it could also show things like elevation changes if it's an areas with
lots of hills (which isn't usually part of a road map).
My point is this. Since the Great Moderation, and since Lucas convinced
everyone that growth is where all the action is, the questions we have been
asking have been mainly about growth and, where stabilization is concerned, about the use of optimal monetary policy rules to offset economic fluctuations (and whether monetary policy was responsible for the Great Moderation). Thus, the models were built to look at
these particular questions. Nobody, or hardly anybody, was asking questions
about the use of fiscal policy to stabilize the economy. Hence very few models were built to look at this issue.
Because of that, because we are essentially using a map designed to guide cars
to ask questions about the best way to travel by bicycle, we may not get the
best answer to the question (that is, the answer to the question of what's the
fastest way to get there - akin to asking about the fastest way for the economy
to recover - may be wrong).
Why was fiscal policy ignored? Two reasons come to mind. First, we thought the economy was much more
flexible than ever before. If a shock hit, even a big one, it might cause a bit
of a pause, but the economy would quickly recover and keep on growing. It was
the Terminator economy, and there was no shock big enough to keep it from
quickly reassembling itself and picking up where it left off. And the two
recessions during that time period, along with the experience of 9-11 and
Katrina, all led people to believe that the economy had, in fact, undergone a
shift and was now more robust than ever. The exact source of this shift was the subject of great debate, was it monetary policy, financial innovation, just good
luck, better technology such as computers, the list was very long, but whatever
the cause, the shift itself was taken to be permanent. Thus, the need for stabilization policy, fiscal policy in particular, was believed to be greatly diminished. The fact that fiscal policy might be needed in a deep recession because monetary policy would be rendered ineffective was discounted and ignored because the the belief was that a deep recession couldn't occur, the economy was too robust and flexible for that.
The second factor was the
belief that if stabilization policy is needed, monetary policy was superior in every way to fiscal policy. Monetary policy could
be implemented faster, with less distortions, it could be reversed quickly, it was in
the hands of independent, public minded shepherds, there wasn't any dimension,
or so it was thought, upon which fiscal policy would be better than monetary policy, and vast amounts
of research were devoted to getting the monetary policy component of
stabilization policy correct. In the process, fiscal policy was dismissed as
irrelevant, at least as a stabilization tool, and largely ignored by researchers
(fiscal policy was still used to try to promote growth - that's the whole
supply-side argument about cutting taxes, but not as a stabilization tool). That's not to say government spending
and taxes weren't included in models and analyzed theoretically, or even
empirically, but to the extent that happened, the questions were not focused on
how fiscal policy could be used as a stabilization tool -- the models were not
constructed to answer this question.
So it shouldn't surprise us that most of the estimates on multipliers come
from the old style, many equation, structural macroeconometric models, the
traditional approach described above. These models were built at a time when
questions about fiscal policy were in the forefront, so answers to fiscal policy questions
come out of this framework easily. For this reason, because the models were
built with this question in mind, there is an abundance of evidence about
fiscal policy multipliers, particularly government spending multipliers, from research conducted during this time period. The next
approaches to macro modeling and estimation, the micro-founded models and the VAR models, came into being as the
fiscal policy question was falling by the wayside (most VAR models do not even include government spending and taxes). Thus, as you may have
noticed, there isn't much in the way of evidence from these models that we can
reply upon (and that's not even considering the fact that we have very little
data from recessionary episodes to inform us on these issues). The models will
be built - I guarantee you they are being built presently - but for now we have
what we have.