I was at a meeting in London recently, organised by
research agenda in macroeconomics for developing countries. This made
me think about how to make progress.
The US as the shared dataset for mainstream macroeconomics
All existing knowledge on macroeconomics is rooted in data about
the US economy. The US is seen as a canonical developed
country. Economists all over the world have treated it as a common
object of study, when building macroeconomics. It is a shared
dataset. Researchers and Ph.D. students routinely pull out a paper
from the literature, and replicate the results, as a first stage of
offering innovations: all this is rendered convenient by using the US
as a shared dataset. New work is generally obliged to demonstrate
value-add in the context of the US dataset.
The US works as a shared dataset because it has high quality
data. Good quality data starts right after 1945, because there was no
destruction within the country, hence the early post-war years are not
distorted by unusual reconstruction. There was a steady shift away
from dirigisme from 1945 onwards, but for the rest there has
been no regime change: events like the breakdown of communism or the
rise of the European Union or the Euro have not taken place.
In the US, a high quality statistical system has produced good
aggregative data. Organisations like NBER have processed this data
nicely to create datasets about the business cycle. High quality
datasets are available about households, firms and financial
markets. Household- and firm-level data has been nicely utilised to
obtain numerical values for parameters in macroeconomic models: why
estimate something using macro data when you know it using
gigantic and well trusted micro datasets? Finally, the major question
for macro today is the fusion with finance, and the US has nice data
for the financial system.
As a consequence, facts about the US are the shared dataset used in
all mainstream macro research across the world.
The insights developed in this literature, which has examined the
US economy, have been transported with fair success, into other
developed countries. Thus, this emphasis on the US as a common dataset
has delivered good results. As an example, the revolution in monetary
policy which was thought through by Friedman, Lucas, etc. was created
using US data. It has usefully reshaped central banks worldwide. US
data was essential for inventing inflation targeting, but inflation
targeting has worked well outside the US.
The major obstacle on building a macroeconomics for developing
countries
The major obstacle that interferes with doing macroeconomics in
developing countries is data.
India is a good example of what goes wrong. The standard GDP data
is in bad shape. The annual GDP data is deplorable, and the quarterly
GDP data that is so essential for doing macroeconomics is worse. The
IIP is untrustworthy. Put these together, and we don't have an output
series, really.
The BOP data is measured fairly well. Some
inflation data is now starting to come together. The statistical
system run by the government does not produce seasonally adjusted
data [succor]. Given the
absence of the Bond-Currency-Derivatives Nexus, the bulk of data
about interest rates that is required is missing; policy makers are
blind. The standard household survey (NSSO) is in bad shape: it
does not produce panel data, surveys are only conducted once in a few
years, and there are incentive issues about the front-line staff who
interact with households.
The large firms are observed using the CMIE database; the small
firms are not observed using the ASI dataset. The CMIE household
survey is starting to generate knowledge about households, but this
only got started a few years ago. While the CMIE datasets (on firms
and households) can be aggregated up to create many interesting macro
series, so far this process has only begun in a small way.
Faced with these problems, it is not surprising that little is
known, at present, about macroeconomics in India. We know numerous
important questions, and we know that we don't know the answers. The
roadmap to progress is often, though not always, blockaded by data
constraints.
Many such problems bedevil the statistical system in other
developing countries also.
Economists have complained about bad data in developing countries
for decades, and that hasn't changed things. And there is a uniquely
perverse problem. Incremental progress with a gradually improving
statistical system does not get the job done for us: By the
time a country gets to good institutions and thus a good statistical
system (e.g. Taiwan, South Korea, Israel, Chile), the country is not a
developing country anymore and is thus not a useful dataset for
studying the macroeconomics of developing countries. Chile has world
class databases on households and firms, but you can't extract
microeconomic facts using these datasets and use them in
calibration if your object of inquiry is the canonical developing
country.
A proposal
How can we make progress? I feel the first idea that we need to
agree on is that we do not need many developing countries to build a
great literature. We need a shared dataset, a lingua franca, a
replication platform, using which we will build a literature. We need
a country that will play the role, for the macroeconomics of
developing countries, that has been played by the United States in
conventional macroeconomics.
The second idea is that we should be a little more ambitious. We
should not merely sit around hand-wringing, complaining about a
problem that isn't going to solve itself. When scientists in other
disciplines identify questions that call for evidence, they write
funding proposals (sometimes running to billions of dollars) and
organise themselves to create those datasets. Could we do
similarly?
Specifically, imagine that we pick one canonical developing
country. It's got to be a typical developing country in most
respects. And, it should not be a conflict zone, it should have the
basics of law and order and physical safety so that operations can be
mounted in it. Christopher Adam of Oxford suggests that Tanzania is a
good choice.
Imagine that, the system of interest (a developing country) keeps
running, but it gets instrumented up to world class. In essence, we
try to place first world instrumentation into a third world
country. (To the extent that this data improves decision making in the
country, we would suffer from `Heisenberg' effects).
This will call for financial resources and, more importantly,
organisational capability. The physicists know how to organise
themselves to build the Large Hadron Collider. Most of the time,
economists do not organise themselves as laboratories or teams doing
complex projects. This will be a bridge that we will have to
cross.
As with the Large Hadron Collider, this is not a short-term
project. It is a project that needs to run for 25 years, in order to
generate a strong dataset.
At first, the project will generate useful facts for calibration,
drawing on household survey and firm databases. Gradually, as the span
of the time-series builds up, the full picture will start becoming
clear.
If this works, it can ignite a literature where researchers from
all across the world do replicable work off a common dataset. Perhaps
Tanzania could then play a role, for the macroeconomics of developing
countries, that is comparable with the role played by the United
States in mainstream macroeconomics.
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