Russia Growth Diagnostics (4): Human Capital

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I find it difficult to diagnose human capital in Russia because the literature on topic is scarce and the market differs from what economists know well. Like with the rest of economics, economists know much about human capital in the United States and almost nothing about human capital in other countries.

Still, a couple of points.

First, being careful with interpreting the data. The relations between education and output became an instant classic after Mankiw, Romer, and Weil (1992):


While Russia is clearly under the regression line and underperforms for its stock of human capital, such cross-country comparisons have the following limitation. Acemoglu, Gallego, and Robinson (2014) remind us that cross-country regressions overestimate the contribution of human capital, at least, its traditional proxies. If you measure the impact of education on wages in mico, the reported effect is like five times smaller than cross-country analysis implies.

For Russia, the wage premium for each year of formal education varies from 5 to 9%. Denisova and Kartseva (2007) show occupational premia for being an engineer, lawyer, and (drum roll!) economist.

But these premia don’t mean that the Russian economy needs more of these types. Depending on the model chosen by authors, these evidences leave questions. How much does positive selection contribute to the premia? What are the costs of switching to another profession? Is physical relocation of workers expensive? We have to answer these questions to make robust policy recommendations.

The second point to mention, education in Russia is a state-owned enterprise, much more centralized than banks mentioned in the previous post. Heavy centralization creates distortions. When government sets policies incorrectly, this lever just moves the entire education system in a wrong direction. Such a system either needs more discretion at low levels or evidence-based public policies. Better to have both, but government doesn’t tolerate the discretion. Then you need evidences for policies.

Few are available even for changes in aggregate variables. Like, how would GDP respond to one more year of education for each citizen? In terms of the HRV framework, GDP may be insensitive to changes in raw human capital. Also accounting is tricky when you compare the gains from more education against (1) full costs of the respective programs, including foregone earnings, (2) benefits of investing the same amount in physical capital or technology.

Careful policymaking could equalize returns to investments, which are now distorted. Government discourages investments in human capital by taxing labor more than financial capital. A person prefers investing in stocks and real estate to education because his wage-related tax rates are about four times higher than taxes on income from financial assets. This is sort of a good example, because we can understand the magnitude of the distortion. Some other distortions have no clear estimates.

Where to move from here? I’d say, randomized evaluations for effective policymaking, plus James Heckman and Stefanie Stantcheva for rigorous thinking about human capital. For any mid-income country, not just Russia.

Next Post

It was supposed to be about infrastructure, but infrastructure shares many conceptual problems with human capital. I’m not even sure that with all cost overruns infrastructure investments are more predictable than money put in human capital. Perhaps, here benevolent stakeholders should do very local analysis, when specific infrastructure projects are compared against each other.

Attention to opportunity costs. Governments like to spend on construction because it creates opportunities for corruption. First, costs have no direct market pricing. Second, stealing inputs in a capital-intensive industry is easier, since in labor-intensive industries workers monitor their managers. That makes wages an unlikely source for corrupted officials.

So instead, there will be a post on government failures.