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.

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Growth Diagnostics: A Crash Course

In the mid-2000s, Ricardo Hausmann and Dani Rodrik developed a growth diagnostics framework for dealing with persistent economic growth failures:

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

With this decision tree:

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

The symptoms in the table indicate binding constraints. According to Hausmann and Rodrik, easing binding constraints would accelerate economic growth. Therefore, government should address its country’s constraints first. But before it must find these constraints.

Such evidence-based prioritizing could balance politics and fashion as the major determinants of public policies. It’s worth remembering that the major cost of government is not public spending, but the time wasted on implementing wrong reforms. This cost grows exponentially when measured against the scenario in which evidence-based policies indeed change things for better.

To be sure, governmental decisions are always accompanied by some sort of research. This research, however, often suffers from biases and politics. An economist needs a framework that keeps him disciplined. Hausmann-Rodrik growth diagnostics is such a framework.

A crash course in this framework would look like this:

  1. Hausmann, Klinger, and Wagner, “Doing Growth Diagnostics in Practice.”
  2. Hausmann, Rodrik, and Velasco, “Growth Diagnostics,” in Serra and Stiglitz, The Washington Consensus Reconsidered.
  3. World Bank, Country case studies.

I’d add two things to these materials.

First, a formal growth model. Doing diagnostics without modeling may seem easier, but after a while you’ll lose the big picture. As you lose the big picture, you can no longer rank priorities, even with microeconomic estimates of returns to policies. The Solow model and its modifications would suffice.

Also, unlike the authors, I’m more cautious about focusing on a single constraint. First, economic evidences are often inconclusive (but better than non-economic non-evidences). Second, governments fail to implement at least some of the policies that they’ve planned. In the end, you may advocate a wrong policy that isn’t going to be implemented anyway! As a remedy, I recommend stylized diversification akin to the Kelly criterion, when government allocates efforts proportionally to the expected payoffs from each constraint-policy pair.

In the next posts, I’ll review a couple of major economies in the spirit of Hausmann and Rodrik. Stay tuned!

Government Resistance to Human Capital

James Heckman has a great paper called “Policies to foster human capital“. Apart from excellent integration of economics into government policies, this paper relentlessly reminds the reader about the lifetime aspects of investments in human capital:

Heckman and Carneiro - 2003 - Human Capital Policy
Heckman and Carneiro – 2003 – Human Capital Policy

Heckman refers to the evidences that late investments do not pay off. Late learning costs money, including foregone earnings, but it only modestly increases wage rates and generates less human capital, since investments are made closer to retirement.

Heckman also criticizes the excessive attention to formal education, which is all about a few narrow cognitive abilities and in any case just one of many ways to acquire skills.

This paper was published in 2000. To count how many of its insights made it into government policies, I looked into the 2013 Economic Report of the President. This report includes a chapter on human capital.

The chapter discusses three things. First, labor inputs of women and immigrants. Second, rising debt and enrollment bias in college education. Third, educational opportunities for adults. So, formal education and adult learning?

Why does this influential publication promote the exact policies that Heckman criticized fifteen years ago? Alan Krueger — who edited the 2013 report — is a brilliant labor economist himself. The entire Council of Economic Advisors is well staffed. So it’s not about competence.

The problem seems to be in the complexity of the policies that Heckman proposes. When economists remind the Congress about student debt, they have better chances of being heard than economists who suggest targeted programs in preschool education. Student debt is something that even US presidents had. Preschool education? TL;DR.

But rephrasing Neil deGrasse Tyson, science is true whether you read it or not. The complex stuff still explains well why policies fail when consensus is reached.

For the United States, this complex stuff is about fine-tuning the system that works well. For the middle-income countries, on the other hand, ignorance is costlier, not least because these countries tend to aggravate mistakes with more government intervention. Policy makers in these countries often frame human capital accumulation as another industrialization. Like, if we could raise savings-investments to 50% of GDP, we can accumulate human capital in the same way. Obviously, it doesn’t work this way; and for BRICS, it’s an important challenge.

As for the least developed countries, the major innovators out there are international organizations, which recently got an open letter from Chris Blattman asking to “stop hurting” poor people with skill training programs. For the same reason: the programs don’t work as intended; even if being accumulated, this human capital solves no important problem.

“Stop hurting” is a good suggestion because it encourages policy makers to do less, which is the idea they like. The second part is more difficult: stop hurting and accept better policies. Some of the better policies look unconventional but remain as simple as training programs. Their main disadvantage is that they are not invented here, that is, in government. And before anything happens, someone has to market these policies as if they were government’s genuine invention.

Thinking Like Lee Kuan Yew

This is Lee Kuan Yew’s miracle everybody’s talking about today:

Data from Maddison
Data: Maddison

The rising green line includes better healthcare, education, security, housing, and other benefits of economic growth. A distinctive feature of Singapore—compared to virtually all developed countries—it hasn’t closed its borders after becoming rich:

Data: WDI
Data: WDI

Which shows how good institutions adapt immigrants and the country continues to grow in per-capita terms. “They’re stealing our jobs” and other forms of intellectual racism never look for the examples like this.

How much did Lee contribute to this success? Scarce evidences on personal contributions to economic growth (like Jones and Olken, “Do Leaders Matter?” [ungated working paper]) leave some space to leaders to affect history. But in specific cases, impact evaluation is informal. In this case, endorsements are also overwhelmingly positive—for the last thirty years or so. Then how did he do it?

Lee shares his executive experience in his well-known book From Third World to First. Perhaps, it’s a bad guide to development because readers may screen it for confirmatory evidences that reenforce their own opinions about economic policies. But the book has two valuable qualities that rarely coincide. First, it’s written by a top politician. Second, it’s written by someone who thinks hard.

In the book, Lee explains his decisions and their reasonable foundations. Why is his reasoning important for others? Because economic development is all about context. When a policy maker copies a decision without reasoning, for a start, he doesn’t understand the decision. Then he applies it to a wrong situation. Industrial policies in developing countries are full of this misunderstanding.

A politician rarely explains himself, and when he does, he is torn between embarrassment and empty words. In contrast, Lee has the point and refutable defense. His colleagues also recall that he’s okay to change his opinion. It seems trivial with all sophisticated economic research on topic; but when leaders lack these qualities, it’s irrelevant how much we know about development (or anything else, for that matter).

So, unlike most commentators of the day, I’d pay tribute not to what Lee Kuan Yew has done but how he thought about these things. The book is a good source to learn it.

Economics of Poor Governance

Ideas about “structural reforms” get copy-pasted from one development report to another, and for a good reason. These recommendations—basically about improving the economy’s fundamentals—indeed matters. But guess who’s supposed to implement them? Governments! And the quality of reforms depend on the quality of governments that implement them. What’s happening to government?

QoG Dataset
QoG Dataset (which aggregates other sources)

So, why is that?

The rule of law, which government is supposed to provide, is crucial for economic development:

QoG Dataset
QoG Dataset

But the rule of law implies incorruptible public officials:

QoG Dataset
QoG Dataset

Which is not the case when a country lacks specific institutions:

QoG Dataset
QoG Dataset

However, these institutions undermine narrow political power and, therefore, unlikely to emerge:

QoG Dataset
QoG Dataset

In brief, we ask corrupted officials to stop being corrupted and limit their power. Actually, it does make sense because development economists also address honest officials, who are more numerous and try to change things. It may work. Ruling parties in non-democracies attempt to improve governance without giving much power away to the press or opposition. They initiate genuine openness reforms, let citizens request information, complain, sue the government—unless it becomes political.

Still, the quality of government stagnates around the world. Partly, it happens because dark forces hiding in ruling parties defend their interests. Hey, that’s the problem we’ve started from! Not surprisingly. The literature says about the benefits of good governance, but its recommendations follow from the relationships found in developed countries, which already have uncorrupted governments to enforce the rule of law and the rest. Demanding Switzerland-style governance from corrupted governments looks like a hopeless idea. Not only the dark force resists, we also have few reliable solutions in mind—solutions that would be feasible given all peculiarities of political institutions in developing countries.

What sort of knowledge is to look for? Perhaps, of two types:

  1. How to improve governance when the dark force resists? Honest judiciary, transparent elections, and able police create conditions for economic development. Well done. Now we want to know more about paths to these conditions. Much work has been done before in law and political studies. But as an ignorant economist, I see much space for improvements. Economists got their invisible hands on these subjects just recently and noticed the scarcity of (a) formal models, (b) suitable data. These are nice things to have. Theories escaping these two pieces leave us in the Middle Ages, which is not nice.
  2. How to run an economy with dark forces? Given its history, economic theory paid more attention to well-governed nations, not to those with problems. For one example, corruption creates information asymmetries of the type that economists haven’t paid much attention to. Take a firm that has political connections and can generate profits above the market average. Can it gain access to capital? No. For this, it must credibly disclose its superiority to banks, which is impossible because the advantage comes from informal political connections. One way or another, capital finds these opportunities, but imperfections remain, so the equilibrium level of investments is lower than the economy and technology allow. So, the problem has implications that have not been studied as deeply as the economics of good governance.

These issues arise in development economics; it’s impossible to ignore them. But the field is small compared to the rest of econ-worlds:

Screen Shot 2014-11-25 at 8.55.44 PM

Economics of poor governance awaits intellectual reinforcement from other fields. Wellbeing of 6 bn people is at stake.

Private sector and economic development

Failures of development programs for Africa led to the opinion that tens of billions of dollars are just wasted. This belief is very comfortable to the small-government types. The evidences seemingly prove something about ineffective governments. The favorite ideological conclusion predicts that the private sector would eradicate poverty if foreign governments reduced their involvement in Africa.

But the private sector was there for the last 10,000 years. It didn’t succeed at all. And there’s no reason to expect anything else. The history of economic development is the history of governments building suitable conditions for the private sector to work. In fact, we have stories of economic growth without private sector (the Soviet Union’s output more than quadrupled in 1917–1991) and no stories of growth under small government.

The private sector is necessary but not sufficient for improving Africa’s performance. Free markets degenerate into monopolies once some market participant gets ready to capture political power. Monopolists select the government. This clientelist government serves the interest of the few, and under some mutations appear as single-party dictatorships or oligarchies. Latin America’s past is full of these examples.

The worst enemy of the private enterprise is businesspeople who say that they’d be better off without the government. Maybe some of them will, but at the expense of private businesses in general. Kinda class enemy within.

Economic development consists of building constructive state capacity that ensures sustainable competition and certain public goods, such as education. And here billions spent on Africa by international donors make sense. International programs improved over the last fifty years. They also helped understand development at large. Very few now agree that buying lots of machinery is enough to create sustainable growth.

This expertise is valuable, but societies need more of it. This is why foreign aid to developing regions should be larger, not smaller. Many efforts are more effective there, than in Western Europe or North America. Providing right medications in Africa can save a human life at the cost of a movie ticket in California. In these cases, the best idea is not to reduce development efforts, but to make them more responsive to new evidences.

Real Limits to Growth

 

The Limits to Growth predicted the demise of economic growth back in 1972. Though the book received much criticism since then, Graham Turner recently confirmed that current development follows the patterns predicted by the book.

But there’s one problem, which is well-known to economists in growth economics. The resource ceiling ignores technology as the capacity to switch between limited resources. The confirmatory evidences Turner found belong to the upcoming trend. Indeed, the world economy consumes more oil and food. It’s okay. Bad things are supposed to happen when the economy per capita will be unable to consume more goods and services.

And the model hasn’t yet confirmed these bad expectations. It’s unlikely to. When some resource becomes scarcer, its price increases, and humans demand more efficient technologies, like energy-saving appliances and fuel-efficient cars. The world had an oil price shock already in the 1970s. It made better air conditioning and small cars popular even in the US.

The limit of growth comes not from too little oil, but from too much oil. Everyone invests in oil technologies for more than a century because no one sees a cheaper and more abundant resource. These investments made fossil fuels very efficient and attractive. Alternative energy can hardly compete with them.

Fossil fuels impose indirect costs, affect the environment, and crowd out investments into alternative energy. They are difficult to deal with. And their prices go down, thanks to fracking and other extraction methods.

Technologies may save the world from running out of oil, but they’re themselves powerful enough to slow down development. Nuclear weapon is making troubles around for more than 60 years. Hitler nearly obtained the atomic bomb. And Germany would get it not by surprise, like a terrorist organization, but because it was one of the most developed societies in the world before the 30s. Technologies aren’t safe in the hands of most advanced and democratic countries.

So, the limits to growth are trickier than the finiteness of certain resources. And these limits are less predictable.

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.)