I’m going to do a couple of case studies in growth diagnostics. The first country is Russia for reasons I’ll explain in the next post. The second country is likely to be China, but you’re still free to send your suggestions.

I’m using a constraint analysis framework by Hausmann–Rodrik–Velasco (HRV). HRV developed a comprehensive, yet structured, framework with a 10-year record of practical applications. It includes a formal model and handy heuristics. It’s also compatible with the literature on growth factors, such as physical and human capital.

# The Formal Model

The formal model comes from HRV (2004) — an early draft that still contains all the math of an augmented neoclassical model of economic growth. The equation of interest:

where is the return on capital defined as

The first equation describes accumulation of capital and consumption under distortions. The distortions are denoted with the Greeks and fall into five categories:

A very formal approach would require picking values for these parameters and simulating the model to compare it with actual values of consumption and capital. A well-calibrated model would predict responses to the changes in the parameters, which would immediately reveal the constraint. I won’t follow this approach because some parameters have no direct or estimable counterparts in the data.

Instead, I’m using this formal model for discipline and test candidate constraints with heuristics. The summary so far:

# Heuristics

The shortcut to growth constraints is a useful table from HRV (2008):

Compared to the formal model, this table includes human capital and specific tests for each constraint mentioned in the header.

Estimating the responses to constraints may be challenging. For example, if you have an indicator for expropriation, you can’t readily say by how much an increase in “expropriation” would reduce economic growth. There’s no universal solution to this problem. For this, I’ll focus on constraints we can estimate with reasonable confidence.

# The Helpers

A candidate for the binding variable is often a compromise among different priorities. The interest rate has to balance inflation and unemployment. Taxes raise some costs via taxation and reduce other costs via public goods. Macro stability after government spending cuts may be followed by political instability.

In this case, growth diagnostics would send contradictory signals. You must increase and decrease the same variable simultaneously! This seems possible in politics, but not in mathematics. To clarify such ambiguities, constraint testing requires a few more models.

Though the list of models is open, most of the job is done by a few conventional macro tools.

# The Next Post

In the text post, I’ll briefly review the Russian economy and challenges it poses to growth diagnostics.

The entire case study will be accompanied with the replication files, which I try to make suitable for an immediate replication for any other major economy.

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