## 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:

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:

So Russia doesn’t seem constrained in domestic resources.

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:

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:

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.

## Free Cheese, not in the Mousetrap

OECD has a nice cost-benefit analysis of returns to education. First, what a high school gives to students:

Okay, huge net benefits for a degree. Even more interesting is the “unemployment effect.” Here lies a monetary value of higher employment security. The degree holder spends less time unemployed due to job security during crises and later retirement. This component is especially high in Slovak and Czech Republic. These countries have one of the lowest wealth inequality in the world, but it looks like the effect of relatively low incomes in the top quantiles. Their labor markets need more highly educated employees, as the market quickly absorbs high skilled candidates and low-skilled workers remain jobless (unemployment rates of 14 and 7% for Slovakia and Czech Republic, respectively). You can compare it to Korea, which has a more balanced labor market: a degree holder earns more but not because she gets jobs faster.

Net lifetime gains from having Bachelor’s, Master’s, or PhD:

Eastern Europe could do a lot better given its middle-income status. Slovenia and Czech Republic would greatly benefit from more educated workers. A Hungarian with a tertiary degree creates more benefits for others than for herself, which makes a case for government support. It’s not necessarily support for education spending in this particular case. Free labor migration within the EU creates difficulties for public spending on education. Political support for these subsidies is low because students who get free education may migrate to high-income Germany and United Kingdom. High returns to tertiary education in Eastern Europe discourage this move, but they cannot fully offset the income gap between the West and the East.

So, it’s a case of the European Union without unity. Countries still have independent budgets (with exception of “stability and growth” rules), collect and spend their public revenues, but have to distort policies in response to other members stealing employment, demand, capital, or workforce. So, hypothetically, net beneficiaries from the brain drain should compensate losers for free public education.

But the point is, countries with fewer emigrants keep the returns to education and should invest in it more. Both by increasing public spending and by facilitating student loans. It’s not rocket science, just more care about people’s future.

## Die Each Day As If It Was Your Last

“Live Each Day As If It Was Your Last” quote wanders around for a while, so here’s a quick thought on this.

First, those living their days as their last don’t live well. The poor in developing countries face many life-threatening risks. These risks make their ordinary days more like their last days. Technically, it means lower discount rates:

An annual discount rate stands at 8–15% in developing countries and at 3–7% in developed ones. Since the rate discounts exponentially, poor nations basically ignore the future.

Short-sighted decisions, which high discount rates are about, hurt the present. Families save less, banks have fewer deposits, businesses borrow and hire reluctantly, so the economy grows slowly and incomes stagnate. People avoid long-term commitments because they don’t expect to live that long. In street terms, the last-day thinking leads to more crimes, less education, and post-apocalyptic surroundings. How about wages equaling 1/10th of those in Europe and US?

Europe had them in the Middle Ages. The Church tried to raise the discount rate artificially by promising eternal existence. (“And they will go away into eternal punishment, but the righteous will go into eternal life.”)  The attempt didn’t impressed the congregation, which sinned like crazy. After all, the problem was solved differently.

For this, it’s even more surprising to hear the last-day advice after all these years of hurting experience.

## Getting things done: startup edition

Publicly available startup data includes firms that exist just as online profiles. So, maybe these firms will do their product some other time or they will disappear. It’s better to exclude such startups from stats and look at who survives.

Funding is a good filter here. Getting seed funding means a startup at least has a team and idea. But over the years, the fraction of series A deals decreases:

If a smaller fraction of startups gets next-stage funding, it means that fewer startups survive after getting seed money. This survival rate indicates how well startups get prepared for doing business. The fewer firms lost on the way, the lower risks investors bear.

The major startup nations from CrunchBase:

China and Israel do well here. The US makes other countries look like dwarfs on charts, so it has a separate graph:

About 80% of startups live their first to fifth funding stage. Having more stages isn’t that common. By the later stages, a startup either becomes a company with more conventional funding (revenue, bank loans, bonds, public equity), or gets acquired by another company, or disappears.

Replication files: https://github.com/antontarasenko/blog-replication-files/tree/master/2014-09/08_cb_funding_stage

## Startups across countries

A few plots in addition to yesterday’s post on startups.

### Startups and economic development

Sources: CruchBase.com dataset and Penn World Table 7.0.

That’s not a bad fit for relations between startups and GDP. The number of startups in the dataset seems to be a good indicator of entrepreneurial activity in general.

### Startup nation

Here’s an illustration for Dan Senor and Saul Singer’s thesis about Startup Nation:
Israel has relatively more startups than the US. Tel Aviv and Silicon Valley drive the numbers for their countries, so it’s not exactly a nation-wide phenomenon. You call the book Startup City, though the result is no less impressive.

### Web data and language barriers

Like other sources based on voluntary reporting, CruchBase may have data biased on one or another way. For example, it may underrepresent countries, in which English is not a major language. And we expect a bias in favor of bigger firms. And here’s the case:

China and Russia indeed either have bigger startups on average or just underreport to CrunchBase. The latter is the case because these are exactly two major countries that stand behind a language firewall. They have their own Facebooks, Twitters, and Amazons. So, we expect them to be less active on CruchBase. More so:

The surprising break after the 90th percentile separate countries into two groups. What are the groups? Look here:

(US and UK are excluded to make the graph readable. 100+ startup countries included.)

Group 1 are countries with < 0.02 startups per 1,000 inhabitants and Group 2 are the rest. And in result Group 2 contains countries with an explicitly high role of English language. So, the break indeed looks like a language thing.

Nevertheless, language per se is not a big factor in development, so it doesn’t bias the data on GDP in a systematic way. (You can also control the very first plot for the percentage of English-speaking population.)

## Investing and failures in startups

The efficient market hypothesis got a bad press after 2008. Not surprisingly. It’s a half-truth. For instance, what Robert Shiller identified as genuine mispricing Robert Lucas called a minor deviation. Also, the hypothesis has many interpretations, and here’s one of them.

On the left we have the mean of money that startups received over their lifetime. On the right is a rude measure of risk: the ratio of acquisitions to closed companies in the respective market. So, enterprise software has three successful acquisitions per one failure. I dropped “operating” startups because it’s difficult to interpret their success.

The graph is interesting because clean tech gets much funding but has one acquisition per two failures. Analytics gets small funds (not so sexiest as it was called?), but gives very stable outcomes. These two are exceptions because in general funding match the risk measure. And so in other markets: it’s enough for one product (like housing) to have abnormal pricing for the entire market to be under risk.

That is an attempt to make complex things embarrassingly simple, of course. For example, some may insist that average funding is a measure of capital intensity, not of competition among investors. Or what we should honestly calculate returns, as was done here. But it all seems to be half-truths, including this piece. We have to keep watching.