I was at a meeting in London recently, organised by the IGC, on the subject of the 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 plausible 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 flying 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.
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.