Russia Growth Diagnostics: Conclusions

After a week of writing about the Russian economy, I put together the pieces of its growth diagnostics. The conclusion follows.

Contents

  1. Getting Started
  2. Introduction to Russia
  3. Finance
  4. Infrastructure and Human Capital
  5. Uncertainty
  6. Taxes and Laws
  7. Market Structure and Competition

The Hausmann–Rodrik–Velasco framework (HRV) that I use throughout the series is explained in the first post. The replication files are available on GitHub.

Conclusion

I picked Russia’s industrial policy as the top issue. Economists rightfully reach for their revolvers when they hear this. Local barons use “industrial policy” to justify protection for their industries. Usually it ends badly, even backed by good historical examples. The United States, Germany, and South Korea protected their “infant industries” at some point of time. But the isolated impact of protectionism is unclear in these cases. It may have any sign. Besides, protection for the 19th-century manufacturing sectors says a little about what a country should promote now.

So what should a country promote with industrial policies? In Russia, less protection for losers and more benefits for winners. This rule is hard to follow when the government appoints losers and winners. The market just supports those who get government protection. It must be the other way around! Government may support companies that do well in competitive, often international, markets. Usually, it’s not about handpicking specific firms, but about simple rules for entire sectors. A good industrial policy for Russia would imply less discretion in industrial policy making.

Since this series concerns only diagnostics, I’m not commenting on actions that can make sense in this case. However, I hope this series shows well that for upper-middle income countries, like Russia, the HRV framework must go much deeper because constraints become less obvious as economic complexity increases.

Appendix: What deserves attention

Industrial policy is important, but overall I took four issues from the previous posts in the series. The table below ranks each of them on a scale from 1 to 5 according to:

Impact

  • How large is the possible impact of changes in this constraint on economic growth?
  • How far is the current situation from the optimal one?

Confidence

  • How many evidences are available (or potentially available) for managing changes in this direction?
  • Confidence in normative recommendations that economics can offer here
  • Clarity of recommendations and their quantitative substance
  • The “distortion potential” for stakeholders in cherry picking or changing recommendations in their favor

Feasibility

  • Are there stakeholders who lose from changes in this direction?
  • Do these stakeholders have policy making power?
  • Are there stakeholders who win and can support changes in policies?

This is my arbitrary scale, and you may have your own. Now back to the list. The four constraints belong to “Taxes and Laws” (formal taxes, formal regulations) and “Market Structure and Competition” (industrial organization, industrial policy):

screenshot

(More here means more potential for growth. The scale is for ranking only and doesn’t reflect the size of the gap between constraints.)

Formal taxes. As mentioned in the post, Russia combines its own idiosyncratic taxes with the same taxes that disincentivize accumulation of human capital in developed countries. Changing this situation could be technically easy because taxes are specific and quantitatively transparent mechanisms. However, taxes are vulnerable to politics (partly because of their transparency) and changes here are likely to face much opposition.

Formal regulations. Regulations constrain growth through two channels. First, they are direct and often not justifiable costs that always end in prices. So the citizen pays for them. Second, regulations grant power to corrupt officials. Corruption discredits both good and bad regulations, with more negative consequences for law enforcement and state capacity. That’s a quite conventional simplification, and the key nontrivial question is how large the distortions are (see rough estimates in this post). As for confidence, the burden of proof for regulations must be on the regulator and lawmakers. This is an idealistic picture for any country, but the point here is that many proofs don’t fit well what economics already knows.

Industrial organization. I put de-facto competition above de-jure regulations because competitive firms actually find ways to optimize regulatory costs, minimize corruption, and somehow do this without breaking the law. I would exemplify this with multinationals, which are more productive than domestic firms even in bad environments. For the entire range of issues that arise here, see the previous post.

Industrial policy. Like regulations, the policy of keeping weak firms afloat is paid by citizens, including workers who suffer poor management and lower wages. This part of low-performing firms can’t live without subsidies of some sort, but these subsidies are a bad form of job security. The key policy problem is how to relocate capital and labor to the best firms without unemployment and loss of physical capital. It’s technically difficult and economic knowledge here is limited. Why did I mark it as the most feasible then? Certain cases could have many winners, including powerful decision makers, and this is more important than administrative complexities.

This doesn’t say how much exactly the Russian economy could get from solving problems in these areas. The impact depends on the changes in question. But the exercise in growth diagnostics just organizes many issues in a single framework, which should ideally direct attention to more specific issues.

Russia Growth Diagnostics (7): Market Structure and Competition

< Part 6: Taxation and Laws

Thirty years ago the entire Russian economy was managed by the state. Some estimates of the current state presence reach 50% of GDP, and this makes the current market structure a likely source of inefficiencies. Let’s see if it is.

Industrial Organization

Over the last 15 years, Russia transformed several state-owned industries according to a simple rule: take an industry and separate it into a natural monopoly and firms that will compete with each other. After the separation, the state retains control over the natural monopoly and privatizes the rest. This was done for electricity, railroads, utilities, and oil. Maybe not so competitive in details, newly created firms are separate business entities and have to think about profit.

The state retained control over certain industries. The Russian government is a monopolist in banking and gas production, although this doesn’t imply the textbook behavior with prices above and supply below competitive equilibrium. I already discussed banking in the post on finance and just restate here that the industry is open for new competitors, including foreign banks.

The third interesting part is the industries where the state consolidated assets, instead of privatizing them. The newly created “state corporations” manage state assets in the defense industry, nuclear energy, and hi-tech. They don’t have many competitors in these markets.

Regional economies may lack local competition. This includes company towns with a single major employer, former state enterprises that have been privatized by geographical clusters, and other peculiarities of an economy in transition.

Government procurement is the last suspect for distorted competition. Technically, it should be the most efficient market, where each pen is bought from an auction. In practice, this system involves collusions and incentives to spend more. Still, it’s difficult to separate trivial corruption from attempts to overcome formalism. Though the price isn’t the only criterion in procurement auctions, ex ante requirements to the bidders are incomplete and leave space for price dumping by incompetent firms. Is this procurement system a constraint in general? Yes, in the sense that private firms would be better at picking suppliers.

I haven’t discussed privately owned sectors, like retail and cellular, but they would benefit from the same things the state sector would. Namely, an independent and powerful agency that protects competition on the ongoing basis, not just at the moment when the state privatizes assets. Secondly, government price setting, including price ceilings for natural monopolies, is based on some arbitrary factors, rather than economic principles. Of course, government uses some ad-hoc models, but until these models remain secret, government pricing casts doubts.

Industrial Policy

By “industrial policy” I mean incentives for reallocating resources to productive industries. This smells anti-market sentiments, but it isn’t. Many markets have already been distorted (not just by government), and these distortions are costly. Hsieh and Klenow (2011) estimate a 50% productivity gain for China and India if these countries moved resources into more productive industries.

An overview of Russian productivity growth from Kaitila (2015):

screenshot

The total factor productivity (TFP) depends on the economy-wide factors and allocation of inputs across firms. The economy-wide factors are all those things I discussed in the previous posts. Here I take on the distribution of inputs, mostly physical capital and labor.

McKinsey (2009) review productivity in Russia from a business perspective. Academic sources are listed in the table above, and I also recommend Bessonova (2007, in Russian). Most sources reach a similar conclusion: high dispersion of productivity within industries, which means that unproductive firms survive. In a hypothetical market economy, unproductive firms don’t live long before they lose capital and employees. Something keeps them afloat in Russia, and it retards TFP growth.

Variety, Complexity, and Innovations

This section is due to HRV’s discussion of product diversification in economic development. Hausmann and Hidalgo (2011) and Klinger and Lederman (2006) use trade data for tests. Trade data underestimates the diversification of the Russian economy because Russia is a big domestic market and its export mostly consists of commodities. Anyway, here’re the numbers:

Source
Source

The primary industries dominate export, but implications are unclear. First, there’re currency appreciation and other distortions caused by high commodity prices. Second, productivity of the entire economy may be insufficient to compete under current exchange rates. It means that developing new industries wouldn’t diversify export.

Does the economy need some domestic diversification? If the economy desperately needed some intermediate inputs, it would exhaust the trade surplus. But the trade balance is positive, and import consists mostly of consumer goods, not of important or innovative capital goods.

Overall, creating new productive industries is a good idea, but it’s called venture investing. And few investors are good at this trade.

Summary

Industrial policy and competition restrain growth. But they are the free lunch: an opportunity to get more output using the same inputs. Meanwhile, the inputs, capital and labor, won’t come to Russia in the nearest future. So this lunch is more desirable than policies aimed at investments and labor participation.

Russia Growth Diagnostics (6): Taxation and Laws

< Part 5: Uncertainty

Taxation and law enforcement are a bunch of different factors, whose impact on growth could be described best as “it depends”. So my goal here will be modest. For each issue, I briefly outline the references on topic, discuss the magnitude of a possible impact on growth, and check if this issue is a candidate for a growth constraint.

Inflation

Despite inflation being an important topic in macro, economic growth theory barely touches inflation at all. For rare examples, see Barro (1995), Bruno and Easterly (1998), IMF (2014). Scarce evidences suggest that problems with growth may start after inflation shoots above 4–15%. But this connection is not traced back to monetary policy. A typical confounding issue: high inflation may be a consequence of other factors (incompetent state, currency crises), so GDPPC won’t grow if monetary authorities simply target low inflation. It’s a desirable, but not sufficient condition.

The CPI in Russia remains within the 4–15% interval for the last ten years. It’s above the world average, but the values converge. More interestingly, the price indices of GDP components:

infln_gdc

Government had been increasing wages in the public sector, so prices in government consumption, which includes services, grew faster. The second possible explanation of this acceleration is more speculative: government procurement overheats some markets.

Taxes

The traditional metrics of tax burden (government revenue to GDP ratio) isn’t informative in our case. Tax burden positively correlates with GDPPC, but its components aren’t born equal. The desirable approach is twofold. First, investigating welfare implications of the taxes as it’s done in public economics and, in particular, optimal taxation theory. Second, measuring the market value of government spending. Since the Russian government is a price setter in many markets, the deviation of the price from marginal social value may be large. So not all rubles the Russian government spends are equally useful.

The Russian tax system has its own peculiarities: heavy fossil fuel subsidies, flat income tax rate at 13%, dependence on commodity prices, and revenues concentrated in the central government. These practices are at odds with what developed countries do. There’re common problems as well. One worth mentioning is the tax incentives related to accumulation of human versus physical capital (see this post).

Regulations and Costs of Doing Business

The Russian government improves important regulations (tax administration) and worsens others (oversight of the mass media). It also retains excessive control in areas where control makes little sense (import–export operations and internal migration, even before 2014).

Business does complain about these regulations, but it’s supposed to in any country. Less so in Sweden, more in the United States and Russia. How can we understand it it’s real? Two popular tools — surveys and composite indices — don’t suit well. Surveys for their usual problems. Composite indices, like Doing Business, for the limited range of issues and excessive formalism (see Pritchett, 2010).

Perhaps the best approach is to identify the most harmful regulations, rather than trying to find an aggregate variable which says that Russia would gain y% of GDP if it reduced regulations by x%.

Corruption

Olken and Pande (2012, Table 1) summarize things we know about corruption. Dreher and Herzfeld (2005) explain why it’s important. Rothstein and Holmberg (2011) show correlates of corruption.

Russia demonstrates high indicators of corruption (Transparency International, World Bank). Russian business considers corruption a real obstacle to operations, even when these concerns are compared among the sample of Eastern European and the former Soviet Union countries (BEEPS 2013).

How to measure the magnitude of the problem? Gorodnichenko and Sabirianova Peter (2007) measure the market of bribes in Ukraine at 1% of GDP, using plausible assumptions and additional controls. Their estimate is a lower bound of corruption because it’s calculated as the difference in incomes between public and private employees, excluding likely risk premia for corrupt officials.

Russia must have a smaller, but somewhat comparable market. A market that large reflects two things. One is an informal tax on citizens. Another is misallocation of resources when public officials rank projects by their corruption potential, not economic value. Misallocation of resources hurts economic development more than the tax does. But in the Russian case, both channels are important.

Law Enforcement

Xu (2011, p. 458) makes a comprehensive literature survey. Four papers in this survey study the direct relationships between law and GDP: three by Ross Levine (1998, 2000, 2002) and one by Daron Acemoglu and Simon Johnson (2005). A key summary of the latter (watch Panel A and C):

Source
Source

So-called contractual institutions lose significance when instrumented (Panel A, model 3). Private property institutions don’t, and their coefficients imply a big impact (Panel C). It’s a very rough indicator of what we should pay attention to.

Russia is near the world median in terms of Panel C (the important one): private property rights are a potentially big and significant negative factor. But unlike tax rates, the policy implications of these indices are very obscure.

Summary

In terms of these five domains (inflation, taxation, regulations, corruption, and legal enforcement), Russia performs worse than developed countries. And this constraint has two interesting details.

First, corruption and poor legal enforcement ease the problems caused by excessive regulations and taxation. It means that fighting corruption before easing regulations may lead to worse outcomes. However, this idea is often manipulated to justify corruption as a consequence of regulations. But corruption also stimulates regulations because the public official without regulations would have no power to sell. The connection is, of course, twofold, and that’s how one should treat it.

Secondly, weak performance in these domains pushes businesses into the informal sector. In the short term, businesses save on costs and become more competitive. Over time, however, everyone loses because the informal sector involves more risk, less legal protection, and less access to credit.

Russia Growth Diagnostics (5): Uncertainty

< Part 4: Infrastructure and Human Capital

While checking for government failures, I closely follow HRV (2008), who break the failures into three groups: ex ante risks, taxation, and law enforcement. Let’s start from the first group.

Uncertainty and Investments

Ex ante risks refer to the volatility of expected returns to economic activity. Low expectations reduce investments and growth slows down. Here I discuss only the risks associated with government actions (or inactions).

We should distinguish these risks from the risks arising in particular economic activities. Acemoglu and Zilibotti (1997) developed a model in which agents are unable to invest profitably because of scarce diversification opportunities. Instead, agents choose low-risk activities, which don’t create stable growth. While this model is intended to explain the surprising economic uprising after 1800, it can also describe some modern economies. Some, but not Russia’s.

Russia has enough economic opportunities for diversification and a financial system capable of implementing it. Industry-related risks can be mitigated. Then the candidate problem is a sort of uncertainty that affects the entire economy.

How does this uncertainty affect growth? The results in the literature are sensitive to model specifications. I exemplify it with a simple regression of annual growth on volatility of growth (where I take only the period starting at the dissolution of the Soviet Union):

growth_f_volay

A strong connection appears only in the OECD sample. For control, alternative models, and more coherent coverage of the topic, I recommend Jones and Manuelli (2005).

An informative, but limited, perspective comes from investments in fixed capital: buildings, machinery, and other things that last. In an uncertain environment, we expect investments in fixed capital to fall.

gdi_gdn

The stagnation of investments continues in BRICS and G7 since 2008. For Russia, I included the private sector to indicate that not government alone supports this level (though national accounts treat state-owned enterprises as private). You can look at FDI, which fell sharply in the recent years, but hot money and parent companies in offshores make this indicator even less informative than domestic investments.

The overall stagnation is not bad (in the meantime, Greece halved investments in fixed capital to take an example of bad expectations). Yes, government spending and procurement are responsible for maintaining this level, but while speaking about uncertainty, the efficiency of spending is a separate question.

Testing Constraints

Political and social risks. Business worried about these risks right after the 2008 crash (see BEEPS 2009), but calmed down by 2013 (see that year’s BEEPS). Can worries significantly constrain growth? Declining investments would be the major channel in this case, but we saw that this channel was okay. This may change after 2014, so we’ll see.

Tax policy risk. Expectations of higher taxes are justified because one half of government revenues comes from taxes on natural resources. Commodity prices fall and government has to compensate the loss of revenue by increasing taxes on consumption and income.

Labor market risks. This is relevant mostly for households that may expect unemployment and reduce consumption in advance. Unlike the United States, where adjustments happen mostly through unemployment, the Russian economy more actively enables two other channels: working hours and real wages. These are milder forms of shocks, though at the expense of overall productivity. People pay less attention to the risk of losing the entire income and the impact on the current consumption (and growth) is small.

History of expropriation. There’re expropriation cases similar to what happens in some Latin American countries. In Russia, expropriation is limited and selective, and so is its impact on growth.

Macro uncertainty. Oil prices affect exchange rates, which, in turn, change costs in import-dependent industries, such as manufacturing. Commodity prices directly affect the revenues of exporters. The large trade flows aggravate these effects. The composition of trade implies volatility as the export side consists of commodities, half of which is oil.

Each of these five tests shows a positive result, so uncertainty hinders growth in Russia. This idea is reinforced with the difficulties of securing long-term financing (see the other post). Even if businesses have high returns or sound business plans, investors adjust these returns for the economy-wide uncertainty and reject requests for funding.

Russia Growth Diagnostics (4): Human Capital

< Part 3: Finance

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):

gdc_gdp_humca

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.

Russia Growth Diagnostics (3): Finance

< Part 2: Introduction to Russia

I test for financial constraints in Russia in two steps. First, measuring the amount of resources potentially available to Russian companies. Second, checking whether the financial sector mediates these resources efficiently.

HRV do these test to separate two groups of symptoms: those caused by financial constraints and the symptoms of low social returns to investments. In other words, an economy may have big growth opportunities that get no funding because of some problems in the financial industry.

Savings and Capital Markets

In some narratives, banks create money out of nothing and lend them to firms. In others, the central bank purposefully keeps the rates high and, thus, deprives business of credit. Such monetary issues aside, the economy can invest only what it hasn’t consumed yet. International borrowing may smooth this choice between consumption and investment, though only temporarily. Let’s see the internal resources first.

Savings are defined as GDP minus consumption: S = Y - ( C + G ) in the GDP definition Y = C + G + I + X - M). Savings already include investments I. The remaining term in S is net export X - M. It’s basically the part of output that has not been invested or consumed, and therefore, is potentially available for business investment:

gdc_si_ts

The gap between these two lines is Russia’s trade balance. It’s positive, while the average savings rate at 30% stands above the world median:

hist_savg

So Russia doesn’t seem constrained in domestic resources.

How about the access to international capital markets? The government has (at least, had before 2014) a very favorable macro and could borrow abroad. Large companies also had access to external credit. They even accumulated some debt. This debt isn’t entirely “external” since capital moves mostly between Russia and a few offshores, where Russian business owns proxy parent companies. When a Cyprus proxy lends to its Russian subsidiary, this can’t be called an “access to international debt markets” (until each word is taken in separate quotes).

For this and the recent sanctions, Russia has problems with reaching international capital markets. But given the trade surplus, financial resources don’t bind on average.

The Financial Sector

Maybe the resources are abundant and the financial sector mediates them poorly? Consider key players.

The Central Bank

Does the Central Bank of Russia (CBR) set rates too high? Adjusted for inflation, it does not seem so. In fact, if you take the GDP deflator, instead of the CPI, the rates turn negative. The CBR key rate (sort of a Federal Funds Rate for Russia) remained below the deflator for years.

Russian business complains about high lending rates, but reducing the key rate won’t help much under full employment. Worth recalling: the market rates depend on the key rate, macro risks, and firm-specific risks. Even taken together, they still don’t beat the deflator, meaning that the real lending rate is already low:

gdc_rir_ts

This plot is based on a somewhat arbitrary average market lending rate, so we’ll look at this market closer.

Banks

A monopolistic banking sector may induce credit rationing and high market rates. Is it monopolistic in Russia?

Russian financial assets are managed by commercial banks and large industrial holdings. A non-banking asset management industry (mutual funds, private equity, hedge funds) is virtually absent. Banks manage household savings and industrial groups manage their own corporate investments.

In banking, assets are concentrated in three banks, with Sberbank alone having as much assets as the other nine banks in the top ten. The government owns major stakes at these largest banks. State-owned banks are managed independently from each other, so they compete to some extend.

This structure does not generate excessive profits for banks. The net interest spread declines:

gdc_nis_ts

Many discussions in Russia concern long-term funding. A popular suggestion is to get the funds from people. A recent innovation is the tax-deductible retirement account (similar to the US IRA) at brokers and asset managers.

Though undoubtedly useful for citizens, such supply-side solutions do not add much to long-term investments, because matching maturities isn’t the only way to get things done. Short-term debt also can fund long-term investments. Alternatively, banks can smooth fluctuations in short-term deposits and supply long-term debt to businesses.

Why neither happens? Because long-term investments themselves must be sufficiently attractive.

Why not Finance?

Russia has freely available financial resources and a competitive financial industry. Maybe not as much and as competitive as somewhere else, but it’s not the main problem. The lending rates appears to be high to businesses because business returns nearly equal these rates. It’s expected after finding no signs of wedges in the financial industry.

Next

Since the funds don’t bind, it’s time to find out what contains returns to investments. The factor of human capital follows.

References

Data sources: PWT7 and World Bank.

Notes: More on variable definitions and computations will be available in the Stata file later. For more detailed checks of the financial industry, I recommend the IMF data.

Growth Diagnostics in Russia (2): Introduction to Russia

Russia’s per capita output equals about 45% of the US output. Russia also had a successful decade of high growth in the 2000s, but right now has a recession. This growth diagnostics series starts with understanding what was behind the recent decades and what should be the baseline expectation for GDP growth. I focus on two benchmarks.

The Long-Term Trend

The long-term trend reflects fundamental factors. But big changes rarely happen, so in many countries output just gravitates around this line. For this benchmark, I use 1.9% per year in 1885–2011, calculated from Andrei Markevich’s data.

rus_gdppc_1885

The Reference Group of Countries

The Russian economy can also be compared with the countries resting at the same stage of development. I take the mean of annual real GDP per capita (GDPPC) growth in countries that share a GDPPC decile with Russia (that is, the composition of Russia’s decile changes by year). Calculated from PWT 8.1.

gdc_decile_ts

The History of Growth

Ok, the last chart. Why did Russia outperform the peers before 2008?

(1) The economic recovery after the post-1991 structural changes. The dissolution of the Soviet Union was followed by a Great Depression in all former Soviet republics:

gdc_fsu_ts

So one part of the story is accelerated comeback to the point where the country had already been in 1990. You can see how the economy re-employs the capital put aside in the 1990:

gdc_rkna2emp_rkna

(2) Oil prices in the 2000s. Kudrin and Gurvich (2014) estimate the contribution of high oil prices to Russia’s GDP at 9.4% annually.

Undoubtedly, there’re more factors, but these two sources of growth have faded in the recent years and this should be reflected in our expectations. Anyway, since the last 25 years looked like a roller coaster, the suitable baseline for growth diagnostics is 2% annual growth of the long-term trend.

The Next Posts

The ongoing recession in Russia doesn’t affect a lot of what I’m going to write in the next posts. Smoothing macro fluctuations is important, but developing countries must also think about reforms that change their long-term growth trends. A loss of 4.5% of GDP in a recession costs more that $150 billion to the Russian citizens. But having only 1/2 of the US per capita output implies an opportunity cost of the entire Russian GDP, that is, $3,700 billion each year. This motivates thinking about fundamentals.

References

  • GDPPC (modern): PWT 8.1, World Bank (2014)
  • GDPPC (historical): Maddison (2013)

Growth Diagnostics in Russia: Getting Started

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:

screenshot

where r is the return on capital defined as

screenshot

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

screenshot

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:

Hausmann et al. - 2004 - Growth Diagnostics
Hausmann, Rodrik, and Velasco, “Growth Diagnostics.”

Heuristics

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

Hausmann et al. - Doing Growth Diagnostics in Practice A Mindbook
Hausmann et al. – Doing Growth Diagnostics in Practice A Mindbook

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.

Please, subscribe to updates here.

Public Policies and Persistence of Institutions

A fabulous series of works on institutional persistence emerged in the 2000s. To name a few:

This series has several things in common. First, the common narrative says that very old decisions influence current economic performance. Melissa Dell finds a 25% decline in current consumption due to forced labor in the 16-century mines. Nathan Nunn quantifies the impact of slavery on current output per capita in Africa. Abhijit Banerjee and Lakshmi Iyer report a 15% increase in current crop yields in territories owned by cultivators in the 19th century, compared to the territories of then-landlords:

Banerjee and Iyer, “History, Institutions, and Economic Performance.”
Banerjee and Iyer, “History, Institutions, and Economic Performance.”

A passerby may say that, of course, slavery and ownership last — that’s our history. This position is so general that it’s always true. The authors did much more than that. They showed by how much history matters — and the numbers are serious.

One unintended consequence, though. These results reassure the pessimists in developing countries. The problem is, of course, that developing countries aren’t developing much. The countries have some economic growth — often induced by commodity prices and imported technologies — and few successful fundamental reforms. People out there rarely see changes and don’t ask for them, so the past matters because it actually equals the present.

Let’s see how economists can encourage these people.

In a sense, any persistent connection between the past and the present is a public policy failure. If a 18th century earthquake destroyed a bridge and the local community didn’t rebuild it since then, the earthquake naturally worsens the current village performance. This happens due to the earthquake and the (absent) mitigation policies.

The earthquake stands for any lasting factor of development. A lasting factor (earthquake) determines policies (endure the loss of the bridge) and these policies affect current outcomes (village output). This is the standard line in the literature. The lasting factor can be anything. Institutions just happened to be the group large enough to be statistically significant in small samples, which always constrain research in economic growth. This group is a big bunch of laws and rules, which wouldn’t manifest itself in regressions if taken separately.

What do we know about such lasting factors?

Well, sometimes they last. Of course, there’s a publication bias favoring historical persistence. Imagine, instead, the flow of publications enumerating historical factors that do not affect the present. A lot of boring reading, for sure. Instead, we have a few dozens of highly cited papers with robust positive results. These citations signal two things. First, authors brilliantly did their job. Second, few results of this sort exist. Perhaps, citations would be diluted if results could be tested in different settings. Just look at randomized trials, which are very important but authors basically no longer seek publishing there as they test same policies across countries.

Secondly, historical factors leave many questions. Are they resistant to public policies? Are they resistant to informed public policies? To exogenous policies, like international aid programs? To a different set of institutions?

These questions hint at the idea that some societies respond to challenges and, therefore, flourish. But for the other societies, the data always shows persistence. Especially to large exogenous shocks, which dominate the literature because they are nearly the only way to measure the impact.

I mean, all the papers I mentioned exploit a very strong “treatment”: A well-armed army of foreigners attack an indigenous population and establish particular rules. Why is it common in publications? Because such a situation is accompanied by a suitable identification strategy: invaders grab whatever comes first (that’s randomization) and divide previously homogeneous regions (that’s discontinuity). Still, these invasions are only one part of history.

So we need to know more about the economic significance of this sort of persistence. We may discover that persistence vanishes. For example, the conflict between relatively equal France and Germany in the Napoleonic Wars (1803−1815):

The Consequences of Radical Reform: The French Revolution
The Consequences of Radical Reform: The French Revolution

The treatment group includes the German territories occupied by the French. The French induced growth-enhancing reforms, so the treatment group grows faster:

The Consequences of Radical Reform: The French Revolution
The Consequences of Radical Reform: The French Revolution

The difference is becoming unimportant as both groups develop. The control group reaches the same level of reforms with a 10-year lag, but it does reach the same level. The gap in urbanization is also closing relative to the level of urbanization.

History matters until you find solutions to very specific problems. Returning to Melissa Dell’s 25% consumption gap in Peru, what would happen to this gap if Peru got richer? What would happen with persistence in growing India and Africa, featured in the other articles? The good question is, therefore, what can we do about this persistence?

Economic Convergence of US States in 1987-2014

Over the last quarter century, less productive states in America had been catching up with the rich states. I’m talking about unconditional beta convergence:

usa_convere_1987_2014

GSP PC is Gross State Product per capita. The green line excludes territories colored with red (see “Technical details” for the reasons and OLS estimates). Otherwise, less developed states grew faster than the rest.

However, most of this convergence occurred before 2000:

gsp_convere_periods

Slopes after 1992 are not statistically significant. Including the post-2008 recovery:

usa_convere_2009_2014

And after the 1990s, the dispersion of GSP (another perspective on the catch-up process) no longer declines in the US:

sd_usa_gsp_ts

Unconditional convergence is not an empirical fact, but something we expect in the workhorse models of economic growth. It exists in the data (especially for developed countries) and in some intra-country studies. Its disappearance from the American economy is not a good sign, however. Unconditional growth acceleration leaves a possibility that regions can catch up with the best neighbors simply by waiting long enough. Now, it’s not the case: states must do something.

A return to the pre-2000 conditions (whatever it means) would not necessarily help. One confirmed source of unconditional convergence is manufacturing (see Dani Rodrik’s 2013). But until the costs of getting manufacturing jobs back to the US remain unclear, that’s not a plan.

I’m going to write more on this problem later, but you can explore the GSP dataset yourself:

Data and code

Technical details

The GSP data is a straightforward extension of SIC-NAICS files from the BEA. See “usa_gdp_state_convere_data.do” for details.

Key charts exclude three US regions. District of Columbia is out because it’s small and not a state. North Dakota and Alaska have been heavily influenced by oil production and prices. Their inclusion would be distortive. I kept Texas because its oil sector is relatively small compared to the entire state’s economy.

Anyway, convergence is still disappearing in the 2000s, even if we keep all 50 states:

reg_usa_gsp_convere_period

“Log of real GSP” refers to the level of GSP at the beginning of the period (e.g. for 1987, it’s 1987-1992). On the left-hand side of the regression is cumulative 5-year growth.

With 48 states:

reg_usa_gsp_convere_noout_period

So the overall convergence looks deceptive:

reg_usa_gsp_convere_1987_2014

For completeness, the data on oil production by state from the EIA: chart.