Columbia Economics Review: Fall 2011

Columbia Economics Review Vol. II, No. 1

Fall 2011

(Risk) Sharing is Caring

Consumption Risk Sharing in the Eurozone Governor Daniel K. Tarullo Takes Our Questions FOMC Dissension, Bank Resolution, and Industrial Organization Targeting Nominal GDP An Interview with Scott Sumner Going Gray: Who Pays?

Aging Economies: Demographics and Growth in Japan

COLUMBIA ECONOMICS REVIEW P U B L I C AT I O N I N F O R M AT I O N The Columbia Economics Review (CER) aims to promote discourse and research at the intersection of economics, business, politics, and society by publishing a rigorous selection of student essays, opinions, and research papers. CER also holds the Columbia Economics Forum, a speaker series established to promote dialogue and encourage deeper insights on economic issues.

2010-2011 EDITORIAL BOARD Editor-in-Chief Skanda Amarnath Managing Editor Hadi Elzayn Benjamin Eckersley Senior Editors Rui Yu Andrea Folds Matthew Yeaton Justin Kahn Trevor Cohen Dinorah Villalobos

Associate Editors Kevin Zhang Rakhi Agrawal Varun Parmar Akshay Menon

Business Managers Alex Millet Bora Kim Rich Medina Publisher James Ma

Art Editor Cindy Pan

Web Developer Stephen Pratt

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R E L AT E D N O T E S Opinions expressed herein do not necessarily reflect the views of Columbia University or the Columbia Economics Review, its staff, sponsors, or affiliates.

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TA B L E O F C O N T E N T S

Interviews & Events 4 | Governor Daniel K. Tarullo Takes Our Questions FOMC Dissension, Bank Resolution, and Industrial Organization 6 | Targeting Nominal GDP An Interview with Scott Sumner

Business & Finance: Eurozone Risks 10 | Pricing Risk? It’s All Greek To Me Pricing of CDS on Greek Public Debt During the Eurozone Crisis 13 | (Risk) Sharing is Caring Consumption Risk Sharing in the Eurozone

Features: Europe Then & Now 17 | Vouching for Privatization The Effect of Privatization Method on Inequality in Eastern European Economies 23 | Saving Europe The Determinants of the Household Savings Rate in the European Union

Theory & Policy 27 | Going Gray: Who Pays?

Aging Economies: Demographics and Growth in Japan from 1975-1999

Fall 2011

4

F edspeak

Governor Tarullo Takes Our Questions FOMC Dissension, Bank Resolution, and Industrial Organization On October 20, 2011, Federal Reserve Governor Daniel K. Tarullo delivered a public address at Columbia. He met beforehand with a group of CER board members.

Q: What do you think of the current dissension within the FOMC? Is it conducive to making monetary policy effective?

Do you think there are factions developing within the Federal Reserve? (James Ma, CC ’14) “I would certainly not say that it’s factional at all. This is certainly not the first time that there have been dissents over monetary policy over the years. You probably have read about the famous period in the ‘80s when several members of the Board of Governors were dissenting from something that the Chairman wanted to do, and there have, fairly regularly, been dissents, particularly from some of the Reserve Bank members of the FOMC. So that phenomenon is not unknown by a long shot.

I think in the last decade or so of the Greenspan period, you really didn’t see much dissent. And so there’s a bit of a tendency to think of the recent period as being more different in one sense of history than it probably should be. The experience you’re having now is more the norm.

And you probably know that in some countries—England is a good example—Bank of England not only regularly has dissents, but Mervyn King, the Governor of the Bank of England, sometimes is on the losing side. So the notion of disagreement within a central bank or within the members of a monetary policy committee in a central bank should really not be a troubling one. Indeed, some say that it would actually be a virtue, because it means you don’t have group-think; you’ve got more debate and the like.

“...the notion of disagreement within a

central bank or within the members of a monetary policy committee in a central bank should really not be a troubling one.”

I think the issue arises in some people’s minds—and this is true for long-time Fed-watchers, sort of our version of Kremlinologists, people who watch every word that all of us say—is whether the public manifestations of the disagreement make it harder for the committee majority to communicate effectively with markets and the public. That gets to the efficacy of the monetary policy steps that you choose to take, once you’ve had your debate. It’s hard for me to judge from the inside, but talking to some of these long-time Fed-watchers, I think they have the sense that at times over the last year-and-a-half, they did have more difficulty than they previously had in figuring out, ‘Exactly what is the center-of-gravity on the committee saying?

What are they telling us about their future intentions? Can we figure out their reaction function?’

There have been some changes to try to make sure that we don’t have those problems. We’ve instituted having the Chairman hold press conferences four times a year, which give another opportunity, in addition to his monetary policy testimony, for mediating the center-of-gravity. What Chairman Bernanke will try to do is reflect what those voting for a particular FOMC action agree upon. Now even though at the last meeting there were 7 of us voting one way and 3 the other, there are certainly going to be nuances or differences in perspective among at least some of those voting in the majority. Those nuances may be reflected in our own public statements. If you parse what I say tonight, because I am going to talk about monetary policy tonight, and then you compare to what Bernanke or Yellen or others on the committee would have said, you may say, ‘Well yes, so I see some similarities, I see some differences.’ But I think that we’ve all got an incentive to try to communicate where we agree and why we agreed on a particular action.

I don’t think anybody should be afraid of negative votes, certainly not afraid of disagreement within the committee. Every environment I’ve been in up to this point in my life—which is to say inter-agency meetings and the like, the United States Senate, law faculties—the disagreement is kind of rampant. The question is do you channel it constructively once there is a decision-making process that has gone on? The last thing I’d say is wouldn’t you be surprised if in a period after a financial crisis, with very low growth and zero-bound interest rates, there was no disagreement? In hard times, tough policy calls are going to engender more disagreement than you would probably have in normal times.

I think back fondly now to the 90s. Once we had the Asian crisis contained and we were growing great guns—yes, there were disagreements among parties and between parties, the administration and parts of congress. But it all felt as though the stakes were modest because, basically, things were going ok.

When things are not going ok, the stakes are higher for everybody. I was your age when there was another period in which things were going quite badly; things were a lot worse in a lot of ways in the 70s and particularly the early 70s. [These situations] just generate more disagreement and sometimes more of an edge on the disagreement.”

Q: How effective do you see the resolution authority, as constructed in Dodd-Frank, given its ability to wind down crossborder banks? (Skanda Amarnath, CC ’13) “That’s a good question, and an important part of the unfinished agenda. If you were asking what are the big unfinished agenda items on the international regulatory front, one would be resolution and resolution planning.

As to the cross-border issue, I think just about everybody acknowledges that this is something that needs work because

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5 it literally, by definition, cannot be solved by one jurisdiction alone. If one jurisdiction places a financial institution into some sort of insolvency or conservatorship regime, that may well have automatic legal consequences in other jurisdictions. It can be either in a de facto sense, or a de jure default on the obligations of the firm in another jurisdiction. Then, of course, you’ve got the issue of ring-fencing of assets. Does every country try to grab hold of the assets in their jurisdiction? There are definitely going to be challenges in trying to manage any insolvency, or even non-insolvency resolution regime cross-border without some more formal or informal agreements.

I can remember when I was Articles Editor of the Law Review, and we got a submission proposing a comprehensive international bankruptcy regime for banks. This is 30 years ago; so the idea has been around for a long time. That leads you to one of two conclusions: one, it was an idea that was around for a long time and now its time has come; two, the reason the idea has been around for a long time is its time is never going to come.

In order to [have an international bankruptcy regime for banks] you’d have to have a harmonization of [various] kinds of law in all major jurisdictions. You’d have to have debtor/creditor law

harmonized. You’d have to have the basic bankruptcy law harmonized. You’d probably have to have some market regulatory things harmonized. You’d have to have financial regulation law harmonized. You’d have to get everyone to agree that they were essentially going to tell their domestic courts, ‘you don’t get to decide, but the court in London, for example, gets to decide.’

Just to state those problems is to indicate the difficulties you’d have in achieving such a treaty.

So the other route, and the route that the international community has headed down, is an effort to combine some discrete formal changes in law with a series of less law-bound devices for trying to deal with the insolvency or the stress of the large institutions. One of the major tools is resolution planning.

Dodd-Frank requires the Federal Reserve and the FDIC to get resolution plans from all of our big institutions, but we are working with our counterparts in other major financial centers to try to have a coherent resolution plan for big multi-nationally active financial institutions that takes into account their operations everywhere. If you do some of that, you can achieve two things: one for certain, one possibly.

The one certain accomplishment is that people will say, ‘Wait

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6 a second, this thing is going to be impossible to resolve if it gets into trouble. When Lehman failed, it had more than 3,000 separately incorporated legal entities within it. It wasn’t based on business lines or liquidity lines; it was based on tax, accounting, and other idiosyncratic advantages. So it was really hard to pull it all apart. You do a resolution plan; you see this sort of RubeGoldberg-like structure and you say, ‘We’ve got to get changes in now so that if things go badly later, this is going to be more manageable.’ For the banks, by the way, this is another area where they themselves learned some lessons from this crisis.

Some of them have done some simplification, but, the lure of tax advantages is really high. It’s pretty clear to me that supervisors are going to have to cast a critical eye on the way in which an organization is structured. So that’s one thing that can be done.

A second thing is going to be more controversial. That would be potentially requiring financial firms, to have separate capital and/or liquidity requirements on a national basis. If Jagdish [Bhagwati] were here, he would begin to talk about what the advantages and disadvantages of restricting capital flows are going to be. If you tell a bank ‘You’ve got to have capital and liquidity basically trapped in each country,’ it won’t be able to deploy the capital across boundaries on an opportunistic basis —saying, for example, ‘There’s a lot of growth in India right now. Let’s do a lot more lending in India against our global capital base.’ If instead [you] have to have a certain amount of capital in your British operation, and your French operation, and your Mexican operation, and so on, then you won’t be able to do that. And that means there will be certain inefficiencies in capital allocation. On the other hand, it means that if the system comes under stress, there will not be the kind of urges to ringfence or rush for exits that you might never want to see. So that possible approach has costs and benefits and is being debated in a lot of countries right now.

Having said all of that, I would note that the whole resolution issue is not a binary one. It’s not a question of, ‘Were you not resolvable in 2008 and are you today?’ I think it’s a process, and this is a process that we’re going through, the FDIC is going through, our counterparts around the world are going through.

I think it’s going to take time to get to the point where people have a higher degree of confidence that they can effectuate a resolution without significant disruptions in financial markets.

I’m not saying people can’t do it—it’s important to be able to do it—but it’s also important for people to believe you can do it.

Because remember, when a financial crisis begins, the number one question is, ‘How do counterparties react? How do investors around the world react?’ Is there a form of contagion in which everybody says, ‘Good heavens, I don’t want to be anywhere near x if there are any problems at all.’ So they just pull back every line they have. If that happens, in a curious fashion, it doesn’t much matter if, in fact, you could have done a pretty good job of resolving the institution.

Q: A lot of what you were saying about these structures is that they are really complicated and really expansive in terms of organization and their flows of capital. Is that related to what you talked about with the economies and diseconomies of scale? Could you talk about what seems to be the direction of this body of thought? (Hadi Elzayn, CC ’13) “I gave that talk at the Fed’s research conference in September – it was basically a plea to academics to take their research in a particular direction. I have been meeting with people from economics departments, and finance groups at business schools.

When I said, ‘I think you guys should be looking at X,’ they

looked at me and they say, ‘Can we get data on Y?’ It’s not always a meeting of the minds on what needs to be done, but I’ve been saying for some time that I don’t think we really know what the economies of scope and scale are in financial services.

What I did in that speech was to elaborate a bit on why we don’t know as much as we’d like, and why it’s so important that we do know more.

I think why we don’t know—it’s really odd but I think I’m right about this—is because finance or financial intermediation really is different in some fundamental ways from other industries. That’s why we have a whole sub-discipline of economics called finance. For that reason, industrial organization economics has shied away from looking at the structure of the financial services industry, with the exception of very traditional commercial banking. And that exception exists because for years antitrust law was very strict with respect to bank mergers. So you had a lot of demand for analysis of deposit and lending markets, but, except for that, you have seen very little work on the economies of scope and scale in certain kinds of capital market activities, and in particular, trading activities and the like. Now, I think normally you would say ‘That’s important if you’re doing an antitrust analysis,’ which it is. But as to why we should care unless we’re confronted with an antitrust issue, I think the reason is the following: We are now all painfully aware that there are at least potential societal costs associated with very large very complex financial institutions—very interconnected financial institutions that either are regarded as too big to fail or if they do come under severe stress, could cause very big negative externalities for the economy. So we know that there are at least potential costs associated with these institutions.

“we are working with

our counterparts in other major financial centers to try to have a coherent

resolution plan for big multi-nationally active financial institutions...”

But that’s almost like saying that we have a sense of what’s on the cost side of the ledger. But what’s on the benefits side? Well if you believe that it took a two-trillion-dollar financial institution in order to be able to effectuate certain societally valuable capital market activities, then you’d say, ‘Ok, there are situations in which there’s a risk associated with this kind of institution, but if we want these benefits, we really need to live with those risks.’ Suppose though, that a really good line of research were to produce the answer that it’s really hard to find any such benefits—that firms have to be big, but not as big as some firms are. Well at that point, your cost-benefit analysis has shifted substantially.

What I was saying at that talk was that I thought we needed to have teams of people with both IO and finance backgrounds working together to study this issue. The IO people could be made to feel more comfortable with the peculiarities of the financial services industry, and the finance people would be introduced to different ways of thinking about the structure of industries. I have to say, though, that as I looked out at the audience I saw the finance people on one side of the room, and a little island of IO people sitting on the other side of the room. So I haven’t had enormous success forging that connection just yet.”

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I N T E R V I E W

Targeting Nominal GDP An Interview With Scott Sumner

Prominent economists on both the left and the right have been calling for the Federal Reserve to target nominal gross domestic product, instead of inflation, to increase accommodation. The chief exponent of nominal GDP level targeting since the beginning has been none other than Scott Sumner, Professor of Economics at Bentley University. Professor Sumner has called for a level targeting regime for nominal GDP, in which the central bank specifies the future path for total nominal spending on goods and services. He offers his daily insights at his blog The Money Illusion.

Q: Can you explain to the readers why you favor nominal GDP targeting over inflation targeting?

I think that there’s a couple ways to look at that. First of all, nominal GDP targeting still allows the Fed to keep inflation relatively low on average. It would allow for more variation than a strict inflation target. However, it’s actually desirable not to have inflation completely constant. That’s because if you have an oil shock and the Fed tries to hold inflation constant, when oil prices rise sharply, they [the Fed] would have to have a tight money policy that depressed the prices of other goods, and that could push you into a recession. Indeed, it’s sort of what happened in 2008. Supply shocks are better accommodated by nominal GDP targeting.

Now if you target nominal GDP instead at 5%, then, when there is an adverse supply shock, inflation goes up a little bit automatically and growth goes down a little bit. It cushions the blow. Instead of trying to put all of the adjustment process and pressure on output, and not on inflation at all, you would get the same kind of low average inflation over the long-term with nominal GDP targeting, but you would get less fluctuation in real GDP and have a less severe business cycle along the way.

Another problem is I don’t really trust the inflation numbers to be actually measuring what we want them to measure. We think of this concept of inflation in the abstract, but when you get down to the details, it’s actually hard to know exactly what we’re trying to measure. The government data shows housing costs going up 7.5% during the last five years, but the Case-Shiller Index shows housing prices down 32%. Yet the Case-Shiller index is not part of the CPI (consumer price index). Instead, the CPI reflects a 7.5% rise in housing prices, which is based more on rental equivalent.

There’s a real ambiguity over which number better reflects what’s going on in the economy in a macroeconomic sense. I would argue that the Case-Shiller number showed that there’s a lot of deflation occurring in housing, and that’s why there’s so much unemployment in the housing construction industry. The official CPI numbers don’t necessarily measure the things we would really like it to measure. Now it may be measuring rental equivalent, but if you think about all that the Fed is trying to do, it’s not really trying to necessarily stabilize the cost of living for a typical consumer.

It’s trying to provide broader macroeconomic stability, stability to the whole economy. I think they sometimes become too focused on this narrow measure of inflation as constructed by the government, and they’re missing some of the broader movements in asset prices and the like.

Q: The Chairman at least suggested yesterday that using metrics on both sides of the mandate would be better than a single metric, such as nominal GDP, that combines the two. Why a single metric?

That’s a tough one. There are several ways of looking at that.

First of all, let me just say that in the technical sense, I agree with Bernanke. There are metrics that treat the two separately, such as the Taylor Rule, which, in principal, should be able to do about as well. Yet I think what we found in this crisis was that in the political sense, it really doesn’t work that way because the Fed has one fairly explicit target, which is the inflation rate that they seem to want to be around 2%. Everything else is very vague. What happens is that it tends to become de facto inflation targeting.

Now I’m not saying the Fed is completely ignoring unemployment. To give you an example of the problem with the current approach: in 2010, Ben Bernanke pushed for quantitative easing two, and that was because inflation had fallen below the Fed’s 2% target. Yet when this was announced, it became hugely unpopular because the news media was reporting that the Fed was trying to raise Americans’ cost of living and trying to push up inflation. The average person has no idea why inflation could be too low. Generally, people don’t understand the relationship between inflation and unemployment in the business cycle. They saw no reason for the Fed to be trying to push up their cost of living in the middle of this recession. It therefore became very controversial and the Fed fairly quickly abandoned the policy. The economy then slowed again when the Fed announced they were not going to be continuing it in spring.

I would argue if the Fed targeted nominal GDP and have that be the number people focus on, the newspaper reports would call it nominal income, probably a better term for the average voter. It’s much easier to explain to the public that you’re trying to boost national income, or nominal income in America, because incomes have been too low during the recession, than it is to try to explain you’re trying to boost inflation because inflation is too low.

The point is that the average person is bewildered that the Fed is trying to boost inflation. They could understand why the Fed is trying to boost incomes because incomes are low during this recession. People understand why that hurts during the downturn. Now in principle, Bernanke has said we can look at inflation and unemployment; we can have this dual mandate. But they end up focusing on inflation, and when they try to adjust when it’s too low, it becomes very controversial. They did recognize last year that unemployment was a problem. They needed somewhat higher inflation, and the minute they did that [a second round of quantitative easing], they ran into a buzzsaw. I think that the politics of nominal GDP targeting are much superior to what’s called the flexible inflation target.

Q: Would you welcome a move to a price-level targeting regime from the Federal Reserve?

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8 I think that could be a good move. The advantage there is when you fall short of your target, it [price-level targeting] allows you to do an easier money policy to catch up to the old trend line without frightening people that you’re abandoning your inflation targeting. For instance, recently, people like Paul Krugman and Ken Rogoff have argued for a 4% inflation target, and I can see why they want to stimulate the economy. That’s very controversial because it would look like the Fed is abandoning its implicit promises for 2% inflation. On the other hand, we’ve only had a little over 1% inflation per year on average for the last 3 years, despite the recent uptick. So if you go back to the beginning of the liquidity trap and allow the Fed to promise to catch up to the old trend line, then that would allow them to do more than 2% inflation without ever actually changing the number, which would otherwise be a loss of credibility. I think that’s what level targeting allows you to do: a little bit of extra inflation when you need it without explicitly changing your inflation target because you’re just trying to catch up from a previous inflation shortfall.

Q: Given the costs of greater inflexibility and risks to credibility, what do you see as the benefits to an explicit target, whether nominal GDP or inflation? You have mentioned how you would like to see the Federal Reserve ‘target the forecast.’

I guess the metaphor I like for ‘targeting the forecast’ is if you’re driving down the road; imagine trying to stay on the road by looking in the rearview mirror. You’re going to go off course and end up in the ditch. You’d have a lot of trouble driving that way, and that’s essentially what the Fed is doing. They’re looking at past data to see whether they made mistakes, and then they’re trying to make corrections. I’m trying to have them look down the road at market indicators for what’s likely to happen and steer towards those. Essentially, adjust policy until the market expects you to succeed in whatever your target is, whether the price level or nominal GDP.

“adjust policy until the market expects you to succeed in whatever your target is.”

In 2008, right after Lehman Brothers failed, they [the FOMC] had a meeting and decided to not to cut interest rates because they saw an equal risk of recession and inflation. That was a period when inflation had previously been high over the past 12 months because of the oil shock, but the actual forecast of financial markets were only for about 1.2% inflation over the next 5 years. The financial market forecast that, not only is there no risk of inflation, there’s also the risk that inflation would be too low, not too high. In fact, both indicators were suggesting that the Fed should ease policy right away.

It hesitated and things got rapidly worse when they didn’t move forcefully at that time. That was just two days after Lehman Brothers failed and would be an example of where the forward-looking approach would have given the Fed the right answer and their backward-looking approach may have been too cautious.

Q: Have other central banks tried some form of nominal income targeting? If so, what have been the results?

Unfortunately, nobody has explicitly done it as far as I know. A lot of well-known economists, especially in the 1980s and into the 90s, talked about the idea. So it’s been kicked around by people who are much more well-known than I am.

I would say this: it’s widely believed that the Bank of England is informally looking at nominal GDP. Right now in England, inflation

has been relatively high, about 4-5%. That’s partly due to special factors like a big increase in their value-added tax and also higher oil prices, which we have here too. They’ve had this temporary burst in inflation. If they simply went with inflation targeting, they probably should be tightening monetary policy when inflation is that high. But they’ve noticed that their nominal GDP growth is still relatively low and so, not only are they not tightening monetary policy, but they are also moving in the direction of—I believe they just announced—another round of quantitative easing, although I don’t know the details. The actual performance of the Bank of England seems to be confirming rumors in the press that the Bank of England is implicitly targeting nominal GDP, not inflation. This is based on off-the-record interviews with people at the Bank of England. I would point to the Bank of England as a place where there’s a lot of interest in the concept, but it hasn’t been formally tried because they still have a mandate from the government to target inflation. That would be the closest example I could give you.

Q: What direct actions does the Federal Reserve need to take, and on what scale, to meet a higher rate of nominal GDP growth?

I’m going to throw a curveball in here. If they actually do an explicit target, I don’t really have a strong opinion on whether they would need to do a lot of buying of assets because simply the change in expectations produced by the target would tend to lower the demand for, what we call, base money or bank reserves. As you know, right now the system is bloated with an extraordinary amount of money that has been pumped in by the Fed, an amount that would normally cause a lot of inflation except for the fact that interest rates are near zero. Banks don’t have many good alternatives, and so they’re just sitting on all this money. Now if there was faster expected nominal GDP growth, the banks might be more aggressive about moving that money out into the economy, and maybe the Fed wouldn’t have to add any at all.

Nevertheless, I certainly recognize that it’s also possible they would have to add money. In that case, I would tell the Fed to commit to being aggressive in doing QE (quantitative easing) with no limit on the time horizon, saying, ‘We’ll continue doing it and we’re going to watch market indicators of inflation expectations and other indicators of real growth.’ I’ve recommended they create a nominal GDP futures market, which would be very useful. In the absence of that, look at as many indicators that relate to what the market thinks nominal growth will be over the next couple of years, and try to calibrate your policy to a point where it seems to have boosted market expectations up close to the target.

Q: Would you still want the Federal Reserve to stick to purchasing US Treasuries, or would you hope for the purchases to be made in a riskier class of assets?

I actually would stick to the Treasury securities because I have a different take on the transmission mechanism than most people.

I don’t think a lot of this fiddling around with mortgage-backed securities does a whole lot of good. It might do a little, but I think the real key problem in the economy is the real lack of confidence in where nominal growth is going in the future. I still think that the main tool the Fed has is shaping expectations. This is actually a fairly widespread view among New Keynesian economists and some Rational Expectations economists, that expectations are really important. The real question is will the Fed promise and be credible. There’s this Peter Pan problem: just because you wish it does that make it so? So you wish for faster nominal GDP growth, but how does it actually happen? That’s where I think you have to have the implied promise that you will do whatever it takes.

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9 You will keep buying securities as long as necessary. In the extreme case, where you ran out of Treasury securities—that would never happen in my opinion—but if it did, then you could buy things like mortgage-backed securities. But I would actually prefer to stick with relatively safe assets and not have the Fed trying to pick winners and losers and micromanage different sectors of the economy, but just try to boost overall spending in a very neutral way. Just provide as much money as the public wants to hold.

The real irony is when you have extremely tight money it starts to look like extremely easy money for two reasons. Extremely tight money will drive inflation and growth much lower, and that will push interest rates close to zero. When that happens, people will hold on to a lot of cash and banks will hold onto a lot of reserves.

It would look to the average person like money is very easy with low interest rates and all of this money sloshing around, but it’s really a sign of lack of confidence in the economy. If you can change the expectations, and make them much more positive in terms of growth and nominal spending, people won’t want to hold on to as much cash. What will tend to happen, actually, is that the Fed will not have to do nearly as much as you might think.

A similar example occurred in Switzerland recently, where there was tremendous speculation in favor of the Swiss Franc, pushing its value up and creating deflation there. The Swiss government, finally, just took a firm stance and said ‘we’re going to just cap the exchange rate at 1.2 per Euro.’ It turned out that when that happened, people stopped expecting the Swiss Franc to keep going up. The Swiss Central Bank did not have to intervene all that much to make it work because the speculators lost interest once they saw that they were not going to be able to profit from buying a Swiss Franc that kept going up in value. Before [the intervention] occurred, a lot of people were saying the Swiss National Bank were going to have to buy so many assets to stop this speculative attack on its currency. Yet then it actually didn’t have to do that much at all once credibility was established. That would be my analogy from the foreign exchange market Q: If the economy does turn around, is there a risk, as some do worry, of the Fed posting its first loss from selling off assets in a rising-rate environment? And if so, what would the implications be for Fed independence?

I guess I could answer that several ways. First of all, I personally don’t think the losses would necessarily be that large. The Fed has a lot of wiggle room because much of their ‘liabilities’ is just currency in circulation and that’s always going to be out there, about a trillion dollars of it. It’s called a liability, but realistically it will never have to be redeemed. Second of all, the Fed normally earns a lot of profits and last year, the profits were more than twice normal.

If they do take a big loss during a single year, I don’t think the Treasury would necessarily have to step in, but if it did, what I would point out is that the government would not be taking a loss.

Only one branch of the government, the Fed, and any losses to the Fed would be more than offset by the gains to the Treasury. The Fed is only buying a certain percentage of the Treasury bonds; it’s always less than a 100%. The Treasury on the other hand owes a 100%; 100% of Treasury bonds are a liability to the US Treasury. If the price of Treasury bonds were to fall and the Fed took a loss, that would be a more than equal gain to the Treasury, for which those bonds are a liability.

Essentially, the common argument you get against stimulus is that you’re bailing out the fiscal authorities by printing money.

When people talk about the potential losses, they’re forgetting that side of the coin, that stimulus does somewhat bail out the fiscal authorities to some extent. I’m not proposing high inflation or

anything like that, but even a more normal rate of nominal GDP growth would dramatically reduce our budget problems and our debt problems going forward. Those gains to the Treasury would be much greater than the losses for the Fed. So I don’t think that should be a determining factor in their decision.

We’ve already done so many unconventional things beginning with the Fed’s bailing out of Bear Stearns, and all of these unconventional asset categories that were set up, the TARP, and the TALF. To not do the right thing to get out of a recession because it might require some sort of unconventional transfer of money from the Treasury to the Fed seems to be very shortsighted, given all the things that have been done that have been unproductive but very revolutionary compared to what the Fed’s initial role was. I would add that I’m still not convinced that the Fed is likely to lose that much money on this. Efficient Markets Hypothesis says on average there should be neither gains nor losses because they’re going to buy the assets at current market prices, which will reflect expectations of what the Fed will be doing in the future. That’s why I don’t think it should be a determining factor there.

“The real irony is when you have extremely tight money it starts to look like extremely easy money.”

We can’t say we’re not going to grow to prosperity because prosperity would raise interest rates and the Fed would then lose money on its portfolio. It’s like the people who don’t want prosperity because that means gas prices will go up, and we’ll have to pay more to commute for work if we don’t have 10 million unemployed people. It’s just not a sensible argument to make.

Probably from within the Fed, it does seem a little frightening when you get letters from Republican leaders. They’re the people who have to confirm Fed nominees. It is a little bit intimidating when they get all of these attacks from inflation hawks. I think that if the Republicans were in office, they wouldn’t be making these attacks because they would want growth. I have a fairly cynical view of all of that.

Q: On a more light-hearted note, how does a self-proclaimed libertarian conservative react to those on the left, such as Krugman and Delong, embracing your ideas? Discomforting at all?

No, not at all. Let me describe my views: I’m a right-wing liberal, which I describe as a person who has liberal values and has a more conservative worldview on how important incentives are. In other words, I differ from more dogmatic conservatives by having different values from them. I’m somewhat utilitarian in my approach. When I read people like Yglesias, Delong, and Krugman, they seem to have the same kind of utilitarian-type values, rather than ‘tradition’ and ‘authority’ and all of these sorts of things that conservatives value. A dogmatic libertarian would say taxation is theft since it’s your property. I would say rich people only get rich because they work in a collaborative society with billions of other people. Taxes should be set in such a way that it produces the greatest benefits for the greatest good. I don’t quite end up as far left as progressives because I think taxes have more of a disincentive effect, a distorting effect, than do many progressive economists. So I get where those guys are coming from, and I feel like if I make good arguments, I should be able to convince them occasionally on an issue here or there to look at in a different way. I’m not trying to overcome a completely different values system.

Columbia Economics Review

10

Pricing Risk? It’s Greek To Me

Pricing of CDS on Greek Public Debt During the Eurozone Crisis Stefano Giulietti (Yale University)

The Greek sovereign debt crisis is a growing threat to the solvency of the European banking system. Markets have certainly been paying close attention to the situation, as investors seek to hedge and speculate on Greece’s sovereign risk. The most commonly used tool for hedging this credit risk is a credit default swap (CDS), a type of derivative that protects the lender in case of default. In 2010, Greece found itself facing an unsustainable 144 percent debt-to-GDP ratio. Previous years of steady growth and falling bond yields had allowed the country to run large deficits.

Yet when Prime Minister Papandreou took power in 2009 and revealed that the previous government had deliberately misreported debt data, investors began to lose faith in the country’s solvency, and yields on Greek bonds increased dramatically.

A CDS can be thought of as an insurance policy against credit risk: when a lender purchases a CDS, the risk that the borrower will default is shifted from that lender to the CDS seller. Moreover, buying a CDS without holding the underlying bond (a “naked CDS”) is equivalent to short-selling that bond. The buyer of the CDS agrees to make periodic payments to the seller and in return receives a payoff if the underlying security experiences a credit event. In other words, the buyer of a CDS on Greek debt will be en-

titled to the par value of the bond in the case of a Greek default.

Given the high liquidity in derivatives markets and the fact that CDS allow investors to reduce exposure to risk for a lower price, CDS are more frequently traded than the underlying bonds themselves for the purposes of hedging and speculation.

CDS have thus become a bellwether of a country’s credit risk; when investors fear that a country’s solvency is at higher risk, CDS prices rise.

This paper looks at key macroeconomic variables to examine their influence on Greek CDS prices by exploring the perception of Greek credit risk. The conclusions have major implications for both investors and policy-makers.

The regression in Equation 1 uses monthly Bloomberg data from March 2009 to February 2011, aiming to include a period of relative stability in Greece between March and November 2009. The dependent variable is the price of the CDS on senior 5-year Greek bonds, denominated in U.S. dollars. Ten factors are then used as independent variables that might affect CDS prices: German CDS prices, volatility index (VIX), yield on Greek bonds, German-Greek bond yield spread, eurozone consumer confidence, Greek inflation, unemployment, debt-to-GDP ratio, changes in real GDP and changes in ECB Columbia Economics Review

interest rates. The regression provides us with a strongly significant outcome, giving a p-value of 0.0000. The values for Rsquared and adjusted R-squared—0.9918 and 0.9865 respectively—demonstrate that our model should reliably predict CDS prices. The first regression tells us how Greek CDS prices respond to a one-unit deviation in the respective independent variables. In the second regression the coefficients tell us how CDS prices react in response to a onestandard deviation change in one of the independent variables.

The results of our analysis allow us to build a model of CDS prices on 5-year Greek senior sovereign debt during the crisis. The model is given in Equation 1.

In order to verify the functional form of the model specified above, we run a Ramsey’s RESET test, which determines whether or not a linear model is appropriate. The non-significant F-value obtained after the RESET test reveals that our model is adequately specified. Additionally, the White test and the Breusch-Pagan test confirm that the model is homoskedastic and inferences from data analysis will be unbiased. A Durbin-Watson statistic of 1.83341 reveals that autoregression is not a concern for the efficiency of the model.

A Cochrane-Orcutt estimation and a PraisWinsten estimation produce coefficients

11 very close to those of the OLS regression, further proving that the efficiency of the model is not at risk because of autocorrelation. This analysis will highlight the coefficients of this model, their statistical significance and their practical importance for CDS pricing.

German CDS can be thought of as a benchmark for the prices of other CDS within the eurozone. Because German CDS tend to reflect any general changes in eurozone economic conditions, it is likely that deterioration in German credit risk would imply a concurrent increase in general European credit risk and thus directly influence the price of Greek CDS. The coefficient is positive (0.4) but not statistically significant. This makes sense intuitively because German CDS are influenced by different factors than those that influence Greek CDS: the shocks affecting one market are not necessarily transmitted to the other.

The Volatility Index is strongly correlated with Greek CDS prices, since it is statistically significant and has a high standardized coefficient, indicating that volatility has a particularly heavy weight on the price of CDS. When market risk and investor fear rise, CDS prices rise. As Greece is in a precarious fiscal situation and its debt is subject to intense speculation, swings in CDS prices have outpaced movements in the VIX. For a change of one standard deviation in the VIX, CDS prices vary by 1.57 standard deviations in the same direction. Higher volatility means that the security’s value can be spread out over a larger range of values and can therefore change dramatically in either direction; it is natural that investors will require a premium during times of high volatility.

The Greek government bond yield and the yield spread between Greek and German bonds are collinear. The yield spread is

omitted in the first regression, while Greek government bond yield is omitted in the regression with standardized coefficients in order to mitigate the effects of their collinearity.

The yield of 5-year senior Greek government bonds is significantly correlated with CDS prices. Because bond yield and CDS prices are both measures of credit risk, they tend to move together. Investors will require a higher yield on Greek bonds when they become riskier; similarly, CDS will become more expensive if the likelihood of default increases.

“Why, then, were CDS

prices not behaving consist-

ently with risk patterns and bond spreads?”

Yield Spread refers to the difference in yield between Greek 5-year government bonds and the corresponding European benchmark represented by German 5-year government bonds. It is important to look at the spread between the two yields in order to understand the relative changes in perceived credit risk. For example, if yield on Greek bonds falls but yield on German bonds falls by a proportionately greater amount, risk in Greece has become higher relative to Germany. We would expect the coefficient for Yield Spread to be positive, since the variable is a measure of Greek credit risk. The result of the regression is unexpected and somewhat surprising: the coefficient is large and negative (-1.84). Intuitively, the two variables should exhibit a positive, roughly one-to-one correlation because if two markets price credit risk equally, they should be co-integrated.

Indeed, Blanco et al. found that the CDS-

(1) CDS Price = 0.02GRM + 1.57VIX - 1.84SPR + 0.09CON + 0.17INF + 0.017UNE + 0.019DEB - 0.039GDP - 0.095ECB GRM: Price of CDS on senior 5-Yr German government bonds VIX: Volatility Index SPR: Difference (spread) between the German 5-Yr bond yield and Greek 5-Yr bond yield CON: Consumer confidence index INF: Inflation rate in Greece UNE: Unemployment rate in Greece DEB: Greek public debt as a percentage of GDP GDP: Real Greek gross domestic product ECB: Interest rate set by the European Central Bank.

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bond basis—i.e., the difference between CDS premium and bond spread—is usually positive and very narrow. Deviations from this equilibrium are short-term phenomena because the CDS market, which is relatively thicker and more liquid, usually leads the bond market in the price discovery process.

Therefore, other factors must be influencing the size and direction of movements in CDS prices relative to bond spreads.

The coefficient’s magnitude is clearly explainable. The negative sign could perhaps be due to the cheapest-to-deliver (CTD) option embedded in CDS contracts. Derivatives and futures provide contract holders the right to deliver different grades of underlying bonds. Sellers of protection will always want to deliver the cheapest available underlying, thus the CDS price will factor in the CTD option and will be discounted accordingly. Among all eligible government bonds, the holder of the short position will select the bond that maximizes his or her profit at the time of settlement. When the CTD option is embedded in a CDS contract, CDS will be priced at a greater discount than the corresponding bonds. If credit risk on Greek bonds—as measured by the GreekGerman bond yield spread—increases, CDS prices will increase by a proportionately larger amount. In practice, a CDS seller sells a CTD option on default: in the case of a credit event, he or she will receive the worst (cheapest) bond among those in the delivery basket. Bonds in the delivery basket must have the same seniority, but their prices may differ because of accrued interest. Since the CDS buyers have the right to choose the cheapest underlying bond for delivery, their profit could be higher than if they were simply short-selling one single type of bond. In other words, in the case of borrower default, a CDS can generate a higher profit than short-selling the bond. This is why movements in relative spread correspond to larger movements in CDS prices: when credit risk increases, potential return on CDS increases by a greater amount.

The cheapest-to-deliver option still does not explain the negative sign of the coefficient. In fact, since the relative spread and the prices of CDS both measure credit risk, they should be positively correlated. The lack of positive correlation shows that CDS markets and bond markets were highly segmented during the Greek crisis, probably because of the differences in liquidity. Since CDS allow investors to transfer credit risk more easily than the cash market, “the demand for credit risk transfer is highest when and where there is highest risk, i.e., on more volatile and less certain markets, making the [CDS] market more liquid” (Criado et al., 2010). This is certainly the case for the European sovereign

12 debt crisis, as shown by patterns in the bidask spread of CDS and government bonds.

Nevertheless, the negative coefficient cannot be exclusively due to segmentation. An analysis of risk patterns reveals that when CDS spreads started widening in November 2009, net risk on Greece was highly volatile but did not increase (Boone at al., 2010). This divergence may be due to the fact that investors who bought protection were unwinding their positions back into the market, taking profits and selling protection to new investors who had not previously held it. In practice, “the holders of protection shifted at new prices in the market, but there was in general not any ‘new’ (or more) risk being taken” (Boone et al., 2010).

“One noteworthy finding

is that differences in bond spreads between the and

Greek

German benchmark are

negatively correlated with

CDS prices.”

Why, then, were CDS prices not behaving consistently with risk patterns and bond spreads? Three factors could possibly explain this anomaly, although it is not possible to test them numerically because of difficulties in isolating or quantifying the variables.

First of all, differences in liquidity between the CDS and bond markets may have a negative effect on the latter (Fontana, 2010). CDS trading could divert capital away from the cash bond market and onto the derivatives market, making debt more expensive due to a liquidity premium. In a climate of high economic uncertainty and volatility, liquidity and other frictions have a stronger influence on fixed-income securities than on CDS, meaning that investors will prefer trading derivatives and disregard the bond market.

However, bonds and CDS are effectively separate markets, and it would be impossible to assess whether the latter is effectively diverting capital from the former (Criado et al., 2010). It is incorrect to say that increased demand for CDS corresponds to decreased demand for bonds, because CDS investors do not effectively take positions in the underlying fixed income market.

Secondly, the negative sign on the coefficient might be significantly influenced by counterparty risk. The credit risk of a bond depends exclusively on the issuer. The value of the protection provided by the CDS, on the other hand, depends on the seller’s

creditworthiness because it is the seller who is responsible for settling the contract (Fontana, 2010). Most protection sellers are financial institutions, such as AIG, whose creditworthiness was damaged during the recession and eurozone debt crisis. When market conditions deteriorate, the sellers’ solvency is at risk. This risk is factored into CDS prices, which decrease as a result of lower perceived creditworthiness. In practice, as market risk increases, a corresponding deterioration in counterparty risk will drive down CDS prices. A CDS buyer is therefore exposed to the credit risk of the bond issuer plus the solvency risk of the CDS seller. Thirdly, the CDS-bond basis may have deviated from parity because of changes in financing costs (Fontana, 2010).

During a crisis, financing costs increase because credit is less easily available and thus more expensive. In actuality, higher financing costs can lead to a decrease in the CDS bid price, which in turn lowers the mid-price.

Coefficients on most other macroeconomic indicators show little statistical significance.

Consumer confidence in the eurozone (EUCCEMU Index) has remained strongly and persistently negative before and during the crisis, ranging from -9.4 to -34.2. Consumer confidence is positively correlated with CDS prices, although the coefficient is small and not statistically significant.

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Unemployment in Greece has also remained at high levels, peaking at 15.9 percent in February 2011. It is positively related to CDS prices, but the lack of statistical significance could possibly be explained by the fact that international financial market participants react to deficit reduction plans, which almost necessarily involve (temporary) increases in unemployment.

The standardized coefficient for Debt/ GDP is positive: deterioration in public finances necessarily corresponds to a higher likelihood of default. However the coefficient, albeit positive, is not statistically significant (p-value=0.8). This indicates that a high debt-to-GDP ratio can trigger the crisis, but there must be a wide range of other factors involved in the escalation of the crisis itself.

The standardized coefficient for Greek GDP is negative, but not statistically significant. As was the case with unemployment, it may well be that investors do not act on decreases in GDP if they result from austerity measures.

On April 13, 2011, the European Central Bank raised its key interest rate by 25 basis points to 1.25 percent, thereby marking the beginning of a tighter monetary policy. Our regression finds that the coefficient for ECB rates is not only very low, but also statistically insignificant. In other words, Greek CDS prices are not influenced by the key central bank interest rates, because other factors play a much more important role.

Finally, the inflation rate in Greece (ECCPGRYY Index) is positively related to CDS prices, with a p-value close to statistical significance (0.078). Inflation erodes the purchasing power of money, thereby discouraging future investments and savings.

The model developed in this paper provides some insight on the behavior of Greek CDS prices during the sovereign debt crisis. One noteworthy finding is that differences in bond spreads between the Greek and German benchmark are negatively correlated with CDS prices. This result sheds light on perceptions of liquidity premiums and counterparty risk during times of financial distress, as well as the effects of higher financing costs. It is important to note that this analysis refers to the period of the ongoing eurozone debt crisis, and not to “normal” times in financial markets. Besides providing a framework for understanding Greek CDS prices, the model demonstrates that the CDS market, at least with regards to the distressed Greek sovereign, is highly responsive to volatility.

As the economy gradually returns to prerecession levels of activity, risk premiums on CDS should better reflect country-specific credit risk, rather than perceptions of global market conditions.

13

(Risk) Sharing is Caring

Consumption Risk Sharing in the Eurozone Felipe M. Goncalves

On January 1, 1999, eleven countries in Europe joined the newly formed European Monetary Union (EMU), accepting the Euro as their main currency. Two years later, Greece joined as well, relinquishing, as had the countries before them, control of their own monetary policy. In return, the nations expected several economic benefits: currency and price stability, reduced transaction costs, and the disappearance of exchange-rate risk in intra-EMU financial transactions. In the succeeding 12 years, enough data have been collected to make some reliable claims about monetary integration in these twelve nations.

Particularly interesting is the effect monetary integration has had on income and consumption risk sharing. In a macroeconomic context, risk sharing refers to the ability of a country or region to ‘separate’ its income and consumption growth rate from shocks to its production growth rate, helping to insure each country against an individual downturn in its own production. In this case, once we control for growth in production across the eurozone, the national growth rate should have no explanatory power for income and con-

sumption growth. Put more generally, a nation’s income/consumption is only susceptible to aggregate shocks in production.

When looking at national data, we define income to be Gross National Income (GNI), consumption to be Final Consumption and production to be Gross Domestic Product (GDP). GNI and GDP have the relationship given in Equation 1, with Net Factor Income (NFI) being the total profit (or loss) on foreign assets held by domestic individuals. The idea of GNI being ‘insured’ against the risk of a downturn in GDP means that profits from foreign assets are negatively correlated with GDP. Final Consumption is then defined in Equation 2.

GNI = GDP + Net Factor Income

(1)

There are many reasons to presume that risk sharing has increased with the introduction of the Euro; the universal currency reduces previous transaction costs that arose from the need to exchange currencies when purchasing foreign assets.

Final Consumption = GNI − Gross National Savings Columbia Economics Review

(2)

If thought of as insurance, risk sharing should increase because Mossin (1968) shows that lower transaction costs should raise the optimal level of insurance to purchase. The Euro also eliminates the risk associated with variable exchange rates because the value of an investor’s foreign assets no longer depends on fluctuations in the exchange rate.

On the other hand, the effect of monetary integration on risk sharing is not immediately obvious. The elimination of exchange-rate risk can be seen in some circumstances as the loss of a risk sharing opportunity. As Demyanyk, et al. (2008) explains, investors in France and Germany can no longer exchange French and German government bonds as a form of risk sharing because there is no fluctuation between the two assets due to exchange rates. They also argue that increased financial integration from monetary unification may make financial assets within the EMU more highly correlated with each other.

After assessing whether there is efficient risk sharing following the introduction of the Euro, we estimate the preferences of each country in the EMU using a Generalized Method of Moments (GMM) procedure introduced by Chiappori, et al.

(2011). This procedure rests on the belief that risk is being shared efficiently because

14

∆lnGNIit-∆lnGNIt=μi+ β (∆lnGDPit-∆lnGDPt )+Eit it is under this condition that each country reveals its attitude towards risk. This study also tested and strongly rejected the hypothesis that risk aversion is homogeneous across individuals when studying the case of rural Thai villages. This question of homogeneity of risk preferences across eurozone countries is important from a policy perspective. In an environment of aggregate uncertainty, the most risk tolerant members of a group gain from this uncertainty because they are paid to bear a large portion of the risk. While, intuitively, reducing aggregate uncertainty seems desirable, it could potentially reduce the welfare of more risk tolerant members within the group.

For the methods employed, we assume that each nation has Constant Relative Risk Aversion preferences of the form U(x)=x^(1-γ)/(1-γ), which has been shown

by Chiappori and Paiella (2011) to approximate well the preferences of individuals.

We also assume that income and consumption risk sharing take the same functional form and perform the same tests on both. Therefore, whenever income is used in an equation, it is interchangeable with consumption. We do this with the awareness that authors such as Demyanyk, et al.

(2008) believe the processes to be different.

β=β0+Eit β1

(3)

Euro, and zero otherwise. β1, therefore, measures the change in risk sharing after the Euro.

Our other regression will be based on Chiappori, et al. (2011), takes the form in Equation 5, where cit is a seasonal dummy variable which we use only for quarterly data, and dit is a dummy variable for time period. This regression is a simplified version of the true functional form derived by Wilson (1968). Chiappori et al (2011) show that this simplified form is biased towards rejecting risk sharing, which implies that if our standard errors are such that our β is insignificant, then we can be fairly certain that risk sharing is taking place.

(4)

We run two types of regressions to estimate the change in income and consumption risk sharing before and after the Euro.

The first was introduced by Demyanyk, et al. (2008) in Equation 3 where GNIit and GDPit measure income and production for each nation or region, and GNIt and GDPt measure income and production at the level of the EMU. The same regression can be run with Final Consumption in place of GNI as a test of consumption risk sharing. β measures the effect of ∆lnGDPit on ∆lnGNIit that cannot be explained by a change in aggregate production. Under efficient risk sharing, β should be statistically insignificant; that is, when a change in GNIit is different from a change in GDPit, this change is idiosyncratic and cannot be explained by a difference in the change in GDPit from the change in GDPt . For the sake of testing the effect of the Euro, we impose a specific structure on β in Equation 4, where Eit is a dummy variable that takes the value of one for a country once it introduces the

The motivation for using the tests from both Demyanyk, et al. (2008) and from Chiappori,et al. (2011) is that their applicability depends on the data being used. For example, Europe-wide data only exist at the yearly level, while the Demyanyk test requires quarterly data.

After testing for risk sharing, and under the hypothesis that it is taking place, we further estimate each nation’s risk aversion. To do so, we use a GMM procedure developed by Chiappori, et al. (2011). This estimation is based on the assumption that under risk sharing the more risk tolerant members of the group will accept more volatility in their income than the more risk

ln Iit= ai+bit+ξ(iq(t))+dt+βlnGDPit+Eit (5) averse members. Thus, we can estimate how strongly each country’s consumption is correlated with aggregate shocks in production. It is important to note that if there Columbia Economics Review

is no consumption risk sharing, the ordering of risk preferences across regions may simply reflect an ordering of how much consumption fluctuates in each nation.

The data are taken from Eurostat. Quarterly and yearly statistics are available at the EMU level, while only yearly figures are available for the national level. The national yearly statistics span from 1995 to 2009 and include data on GNI, GDP and final consumption at both the national and EMU level (allowing us to perform the Demyanyk regressions). The national quarterly statistics are from 1995 to 2010 and consist of GNI, GDP, and consumption.

The regional data run from 1995 to 2007 and include GDP and primary income of households, which is simply referred to as income. For all of the data sets, we ignore the countries that joined the EMU after 2001 because there is not enough data available to make any significant conclusions.

When testing for income risk sharing, the results seem to strongly suggest that no income risk sharing is taking place. Before 1999, the β in the Demyanyk regression is .927 and very statistically significant. The coefficient on a change in β from before and after the Euro is very insignificant and hardly changes the β. These regressions therefore seem to suggest that income risk sharing is actually worsening over time.

Furthermore, Demyanyk, et al. (2008) argue that these types of regressions should give a β bounded between 0 and 1, however, our estimates of β go above 1 beginning in 2002, suggesting that either the statistics have some measurement errors or ∆lnGDP is not a good measure of aggregate shocks.

The data on consumption risk sharing at the national yearly level suggest a different conclusion. The β from the Demyanyk regression is .622 and statistically significant. The coefficient on a change in β is .171, which is larger than in the income regression and slightly less insignificant.

We perform the same series of regressions as before for several years. By 2000, β is insignificant at the 10% significance level, and stays insignificant through 2009, suggesting that consumption risk sharing is taking place soon after the introduction of the Euro.

To show the change in association between consumption and production before and after the Euro, we ran the two regressions in Equations 6 and 7. This gives us an idea of how drastically consumption risk sharing changes between these two time periods.

We complement the Demyanyk regressions with regressions based on Chiappori, et al. (2011) of the form of Equation 5. The

15 results are very similar to those in the Demyanyk regressions but, unlike in the Demyanyk regression, the second regression shows the change in β from before to after the Euro to be statistically significant and negative, with a t-statistic of -3.55. The case for consumption risk sharing continues to improve in the years after 1999, most notably through 2002, though this may in part be caused by a reduction in data points used. Thus both of our tests suggest consumption risk sharing at the national level.

lnIit =ai +bit+ξiq(t) +dt +βlnGDPit +εit (6) lnIit =ai +bit+ξiq(t) +dt +βlnGDPit +εit (7)

Country Germany Ireland Belgium Spain Greece Portugal Italy Finland France Netherlands Austria Luxembourg

Gamma 8.718396 19.92032 30.12048 38.02281 60.24096 73.52941 81.30081 96.15385 120.4819 135.1351 256.4103 277.7778

values of Eit and ηit. Any dependence between these residuals represents dependence between income and production after controlling for an aggregate trend. Figure 1 shows the residuals for all years before 1999, and Figure 2 shows the residuals for all years after 2001. It is very clear from just these two graphs that risk sharing has increased. The relationship between the residuals is much weaker and ambiguous after 2001 than it is before 1999.

∆lnCit −∆lnCt =μi +εit

(8)

∆lnGDPit − ∆lnGDPt = αi + ηit

(9)

lnIit =αi +βit+δt +εit (10) lnGDPit =ai +bit+dt +ηit (11) It is important to note that the results from the quarterly national statistics and the yearly regional statistics are drastically different. At first thought they should be similar in that they both should capture more fluctuation in income and GDP

0

yres1999

-.1

-.05

0.0 -0.5

yres1999

0.5

.05

.1

1.0

Perhaps the main reason that the Chiappori regression delivers a stronger case for consumption risk sharing is that the group-wide time shock dt is a more flexible representation of aggregate effects than EMU wide GDP. In theory it allows the group that is sharing risk to be wider than the EMU, possibly all of Europe, in which case EMU GDP is an imperfect representative of group-wide production.

Because the quarterly national data do not have corresponding statistics at the aggregate EMU level, the Demyanyk regressions cannot be performed. Therefore, we use the regression from Chiappori, et al. (2011) (based on Equation 5). Like in the yearly statistics, the quarterly statistics strongly reject income risk sharing before the Euro, with a t-statistic of 38.17 for β, and strongly suggest that risk sharing does not increase following entry into the EMU. The quarterly data on consumption challenge the results from the national yearly

statistics. Similar to before, β is statistically significant before entering the EMU, but introducing the Euro does not seem to have a strong effect. The t-statistic on the change in β is only .09.

It is unclear why the quarterly and the national data suggest differing conclusions regarding consumption risk sharing. One explanation is that the standard errors are not taking into account that a single quarterly figure holds less information than a single yearly figure. The fact that there are four times as many data points does not necessarily mean there is four times as much information given that the time span of the data sets remains the same. The quarterly data are missing for France and Greece, which may also explain some of the discrepancy.

The only yearly regional data available are for primary income of households and GDP; we use Primary Income of Households as our measure of income. The results from these data are drastically different from both national data sets. β is still statistically significant before the EMU, although now the change due to the Euro is statistically significant as well. The coefficient of the change is very minor, only -.0057. Unlike with the national data, β reaches below statistical significance by 2002. However the t-statistic for 2003 is 4.27, which arouses some suspicion about the data. We know that because β in the aggregate time shock is ignored, the test is biased towards rejecting risk sharing.

Therefore, the data give good reason to believe there is income risk sharing in the EMU beginning in 2001.

Figures 1 and 2 capture well the change in relationship between income and production before and after the Euro. To achieve these figures, we ran the regressions in Equations 8 and 9 and then plotted the

-0.04

-0.02

0.0

0.02

0.04

0.06

xres1999 Figure 1: plot of residuals for income and production until 1999 (after re- gressing on individual-specific intercept, time trend and an aggregate time dummy)

-.1

-.05

0

.05

.1

xres1999 Figure 2: correlation of residuals for income and production starting in 2001 (after regressing on individual-specific intercept, time trend, and ag- gregate time dummy)

Columbia Economics Review

16 Germany is often thought to be highly risk averse given its high savings rate, Germany ranks as the least risk averse in the list. Of course, Germany is also described as a ‘lender of last resort’ to other European countries during crises. This second observation is consistent with the idea that the most risk tolerant member of a risk-sharing group is willing to take on more risk than any other member.

“...though financial inte-

gration increases as part of monetary unification, the

benefit of this may be offset by the fact that financial

EMU may now be more highly correlated with each other.”

assets within the

than the national yearly statistics, and they both should have more confident results about whether risk sharing does or does not occur. Yet the type of fluctuation in quarterly data is very different from the fluctuation in the regional data.

A possible explanation for this is that the majority of production volatility is occurring at the sub-national level. If this volatility is smoothed out at the national level, then we will not observe much risk sharing. As Demyanyk, et al. (2008) argue, it is also possible that there hasn’t been much risk to share.

All of the regressions that assume the structure of Equation 4 systematically underestimate the effect of the introduction of the Euro. β1 was often statistically insignificant, and failed to signal the drastic change that often took place in β by 2002.

This suggests the hypothesis that markets move slowly in response to monetary integration and that risk sharing takes some time to establish. At the same time, the fact that the estimations of β changed so rap-

idly from year to year suggests the need to be cautious in proceeding to inference.

“In a macroeconomic context, risk sharing refers to the ability of a country or region to

“separate” its growth rate... from shocks in the growth rate of its production.”

Table 1 shows the estimation of risk aversion for each country in the EMU. We used yearly national data on consumption. The validity of these estimations requires many assumptions. We proceed under the hypothesis that the coefficient for GDP is zero and assume that Equation 6 is the true functional form for consumption. The countries are listed in order of least to most risk averse. Although Columbia Economics Review

It is noteworthy how spread out the estimations of γ are from each other. No two countries are very close in risk aversion, suggesting that the previous policy remark is worth considering. With such a dispersion of risk preferences, a reduction of aggregate risk may reduce the welfare of countries such as Germany, Ireland, and Belgium, and should be taken into account when EMU-wide policy is being conducted.

The preceding analysis suggests a variety of conclusions. The regional data suggest a large amount of income risk sharing after the Euro, while the national data, both yearly and quarterly, strongly reject that risk sharing is occurring. This may partially be due to the fact that the exact indicators used vary; we use GNI for national data and primary income of households for our regional data. The national data may also not be sensitive to within-country risk sharing. If the aggregate shocks at the national level are very similar to the shocks at the EMU level, it is possible that this is the source of the discrepancy. The Eit indicator variable does not measure the effect of the Euro very well, due to the short timespan being studied, and this should be corrected in future research. All of this being said, it appears that the EMU is a good approximation for a region that is sharing risk. There is certainly more that can be done to analyze risk sharing in the EMU; this paper is only a small contribution to that analysis.

17

Vouching for Privatization

The Effect of Privatization Method on Inequality in Eastern European Transition Economies Alissa Bonneau (Stanford University)

The trade-off between inequality and growth is a persistent point of contention in development economics. In transition economies, low economic growth has been blamed on high levels of inequality.

A large, economically homogenous middle class may in fact be crucial to a successful transition. Nevertheless, economic development may require some degree of income inequality. Wealth concentrated in the hands of a few individuals allows them to have the necessary resources to build industries that generate growth. Additionally, redistributive policies enacted to reduce inequality may hinder economic growth by perverting incentives for productive activity. For emerging economies such as China and Vietnam, understanding the potential trade-offs between economic growth and income inequality is pivotal to formulating economic policy.

The transition from a planned to a market economy in Eastern Europe, following the fall of the Soviet Union in 1991, allowed a small segment of society to rise above the rest. The high commodity prices and foreign investment of the 1990s led to an explosive growth in wealth levels for the handful of industrial economies in this region. Moscow, once an unglamorous area, is now emerging as one of the most prominent luxury markets in the world.

Yet not all citizens of former communist countries have benefited equally from the transition to a more open economy. A stark reduction in equality across transition economies is viewed as a natural part of transition along with economic growth.

Nonetheless, inequality did not increase uniformly in the region. This paper looks at the effects of privatization methods on both income and consumption inequality in all transition economies of Eastern Europe for which sufficient data can be obtained for the decade following their transitions. Except where noted, all countries in Table 1 are included in the analysis.

Transition economies in Eastern Europe used three methods of privatization as defined in the European Bank of Reconstruction and Development’s (EBRD) 1999 Transition Report—voucher privatization, sale privatization, and management-employee buyout (MEBO). Voucher privatization, also known as mass-privatization, refers to privatization in which citizens are either given or can inexpensively purchase vouchers representing shares in a stateowned company. Sale privatization refers to privatization in which shares are sold to the public and to foreigners. Managementemployee buyout (MEBO) refers to privatization in which only members of the firm can buy the shares.

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After initial analysis, an empirical method will also be applied to different groupings of the countries to see if results are stronger for certain categories. I will identify the countries by two factors: whether or not the country was a member of the Soviet Union (New Independent States of the Soviet Union (NIS), also known as “Former Soviet Republics,” or “Post-Soviet States”) and whether or not the country eventually became a member of the European Union.

The purpose of the NIS/non-NIS classification is to help distinguish groupings by varying initial conditions. While all transition economies were offered roughly the same liberal policy package, there was a broad range of initial conditions. These initial conditions, combined with differences in the implementation of the policy package, contributed to differences in levels of income and consumption inequality for these countries. The new-EU/non-EU classification groups countries based on final conditions. I hypothesize that final political conditions will be a weaker differentiating factor than initial conditions with regard to inequality.

There are many ways to measure inequality, but the Gini coefficient is one of the most effective because it captures the whole distribution of inequality in a country. It is a useful measurement

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Figure 1A. Income Inequality Trends by Privatization Method

in this context because it assigns a single numerical value to a country at any time, allowing for both cross-sectional and time-series comparisons. The Gini coefficient can describe both income inequality and consumption inequality. When analyzing a Gini coefficient data set, it is important to consider the measurement–income, consumption, expenditure, or earnings–as well as the unit of analysis–household, family, or individual person–to ensure comparable data. There is no single source of Gini coefficient data that spans the years and countries in this study. Thus, I use data from four major sources: the World Bank Measuring Income Inequality Database, the TransMONEE Database, the World Bank World Development Indicators Database and the World Income Inequality Database (WIID).

Consumption inequality figures exhibit more equality among countries than income inequality figures because at the lowest levels of income, consumption is usually higher than income, due to social programs or credit. Furthermore, consumption is usually smaller than income at higher income levels because of higher savings rates. Therefore, income inequality demonstrates a larger spread, while consumption inequality usually has a smaller range of inequality among countries.

1997 is a crucial year in transition because at that point most of the countries had begun their transitions to a market economy. This was also before the Russian financial crisis of 1998, which affected all of the countries in the area. Armenia, Azerbaijan, Georgia and Romania had the highest levels of income inequality in 1997, with Romania having the maximum at 61.

Belarus had the minimum income inequal-

ity at 25 (even though Belarus’ transition began in 1994, it is considered to be one of the least transformative). Two-thirds of the countries had greater income inequality than the U.S. at this time.

Figures 1A and 1B present average income and consumption Gini coefficient trends, grouped by privatization method from 1992 to 2002. Income inequality rose on average for all countries studied from 1992 to 1995 and then settled into a lower range between 37 and 43. After 1994, income inequality in voucher countries was always higher than that in sale countries.

In turn, sale countries had persistently higher inequality than MEBO countries

on average. For consumption inequality, however, voucher countries became significantly more unequal than sale and MEBO countries, even though all three categories of countries eventually converged towards a Gini coefficient between 30 and 35.

Equation 1 will guide my methodology for determining the effect of privatization method on changes in inequality.

The equation employs three time-specific dummy variables, SALE, VOUCHER and MEBO, each taking the value of zero (0) in the years prior to privatization–the value of unity (1) in the year of privatization and subsequent years–in countries that adopted sale, voucher and MEBO privatization methods, respectively.

These variables are analyzed in terms of their effects on the dependent variable, %∆GINIit, a measurement of the change in income or consumption inequality on country i at time t over the previous year.

The error term, εit, captures all other factors that influence the dependent variable other than the independent variables.

There are a variety of other factors that contribute to a country’s level of inequality and must be controlled for to determine the effect of privatization methods.

These factors must be considered carefully because, according to Kaasa (2003), “The direction of these influences [of factors affecting inequality], however, is often unclear: whether a higher value of a certain factor causes higher or lower inequality depends on the characteristics of the economic system and the overall lev-

Table 1. Change in Income and Consumption Inequality of Eastern European Countries

Country Belarus Kyrgyz Republic Georgia Macedonia, FYR Slovak Republic Czech Republic Albania Ukraine Moldova Lithuania Slovenia Estonia Russian Federation Kazakhstan Bulgaria Latvia Hungary Poland Armenia Croatia Romania Tajikistan

Privatization Method MEBO Voucher Voucher MEBO Sale Voucher MEBO Voucher Voucher Voucher MEBO Sale Voucher Sale Sale Sale Sale Sale Voucher MEBO MEBO Sale

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Percent Change in Income Gini Coefficient, 10 Years After Privatization -24.1 -19.7 -17.2 -16.2 -14.5 -14.2 -13.1 -13.1 -8.3 -7.8 -7.6 1.8 10.3 13.1 13.3 15.6 22.0 24.6 35.3 38.1 113.2

Percent Change in Consumption Gini Coefficient, 10 Years After Privatization 13.3 -46.2 6.9 43.2 8.4 12.0 -31.4 0.9 -12.9 9.1 -9.4 -24.9 -1.3 6.1 43.7 0.1 16.4 -34.7 10.8 23.6 8.5

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Table 2. Multiple Regression Results: Variables Regressed Against Income Gini Coefficient β (1) -0.41 (1.04)

(2) -0.53 (1.86)

(3) -0.31 (1.98)

(4) -1.49 (2.22)

Sale

1.06 (1.11)

-0.16 (2.41)

0.28 (2.71)

-1.03 (2.94)

MEBO

1.41 (1.20)

0.79 (1.64)

0.77 (1.79)

-0.04 (1.92)

∆ Agriculture Value-Add

0.02 (0.13)

0.02 (0.13)

0.01 (0.13)

∆ Manufacturing Value-Add

0.06 (0.11)

0.06 (0.11)

-0.02 (0.13)

∆ Services Value-Add

0.00 (0.14)

-0.01 (0.15)

0.01 (0.15)

Stock of Foreign Reserves

-0.06 (0.16)

-0.03 (0.17)

Oil Rents as a Share of GDP

0.15 (0.94)

-0.02 (0.95)

-0.06

0.33 (0.28) -0.06

Voucher

Growth in GDP

Adjusted R2

0.00

-0.04

Standard errors are presented in parentheses No figures are significant at the 10 percent level

el of development of the country.” Kaasa systematized factors affecting income inequality into five groups: (1) economic growth and overall development level of a country, (2) macroeconomic factors, (3) demographic factors, (4) political factors and (5) historical, cultural and natural factors. While it is impossible to control for all of these factors, I will attempt to control for the most significant ones, using Economist Intelligence Unit (EIU) Country Data and World Bank World Development Indicators data.

First, to address economic growth and overall development of a country, percentage change in real GDP per capita, GDPPCit, over the previous year is added to the regression. Changes in the structure of the economy must be addressed as well. As a country’s workers move up to a higher sector (e.g., from agriculture to manufacturing to services, due to technological change), income inequality tends to increase. Thus, percentage change in real agriculture, manufactur-

ing and services, each in terms of valueadded to the economy over the previous year, are added.

Additionally, I address macroeconomic

factors. Inflation is included in the form of percentage change in Consumer Price Index over the previous year. Official recorded unemployment as a percentage of labor force is included for unemployment. Change in government consumption as a percentage of GDP over the previous year addresses the size of government. Finally, total external debt as a percentage of GDP, as well as stock of foreign reserves, are included to address debt issues.

To address demographic figures, percentage change in mid-year population over previous year is included. According to Kaasa, these factors include the age-structure of the population, population growth and density, urbanization and the level of education and health of the population. Due to the high degree of correlation among these variables, I only include population.

According to Kaasa, the main political factors affecting income inequality include the share of private versus public sector, already addressed above with share of government consumption and the method of privatization used. Thus, no additional political factors are included.

Historical and cultural factors cannot be consistently accounted for as they include immeasurable things like people’s attitudes toward income inequality and the extent of the shadow economy. Yet natural factors, such as geography and natural resources, can be accounted for.

Thus, a country’s annual oil rents, as a share of GDP, are included to control for the disparity in natural resources.

Thus, the final regression is Equation 2.

Many of these factors are likely to be correlated with one another, raising the issue of collinearity in the analysis. Prin-

Figure 1B. Consumption Inequality Trends by Privatization Method

Columbia Economics Review

20

%ΔGINIit = β0 + β1SALEit + β2VOUCHERit + β3MEBOit + εit cipal component analysis (PCA) is used to reduce the data set and draw a large number of variables together to form only a few factors. The set of initial variables is organized so that groups of closely related indicators, “components,” are determined. Then, factor analysis is used to determine which factors compose the majority of the main components and thereby explain most of the variance in the data set. The effects of privatization method can then be studied while controlling for these primary factors only.

After these analyses, I find values for the coefficients β1, β2 and β3, representing the effects of sale, voucher and MEBO methods of privatization on the Gini coefficients of the transition countries.

The basic data is examined in the form of all available Gini coefficient data from the years 1990-2004. Based on initial descriptive statistics, the voucher method is associated with the highest levels of income inequality. With Bennet, Estrin and Urga (2006) finding that the voucher method was associated with the largest increase in GDP, this implies an association between income inequality and GDP growth.

While the voucher method is associated with the highest levels of inequality looking at all data, there is a different trend when looking at percentage change in consumption inequality through the decade following privatization. When looking at the change in the Gini coefficient between the year immediately following privatization and ten years following privatization, the voucher method was associated with a decrease in the Gini coefficient (i.e., more equality) for all voucher countries, except for Moldova, Georgia and Czech Republic. Table 1 lists the countries’ percent change in income and consumption Gini coefficient over this time period. Countries excluded in these tables, due to data unavailability, are Azerbaijan, Bosnia & Herzegovina, Turkmenistan and Uzbekistan. Additionally, the Slovak Republic is excluded from consumption inequality data, and Tajikistan is excluded from income inequality data.

Income inequality in Table 1 demonstrates a more mixed pattern of changes in inequality than those of consumption inequality. While eleven countries saw decreasing income inequality and ten saw increasing income inequality, the decreases in income inequality were much

(1)

smaller in magnitude than decreases in consumption inequality. Romania, a country that used the MEBO method, stands out as having the largest increase in income inequality. However, Belarus saw the largest decrease in income inequality and also used the MEBO method. In general, voucher countries were more likely to see a decrease in income inequality and sale countries were more likely to see an increase.

Why was the voucher method associated with the highest initial levels of consumption inequality? The consumption Gini coefficients for this set of countries were shown in Figures 1A and 1B. The voucher method was applied to countries with the highest levels of consumption and income inequality at the start of privatization and the lowest levels of expenditure per capita. The countries with lower levels of inequality at the onset of transition used the MEBO and sale methods. Thus, the methods were not randomly assigned and cannot be considered treatments—there is simultaneity in the choice of method and the level of inequality. All transition economies

experienced extreme changes in Gini coefficient levels during the first five years of the transition and then exhibited little change in the latter half of the decade, as shown in Figures 1A and 1B. The effects of privatization methods on the annual percentage changes in income and consumption inequality, as determined by the multiple regression analysis, are shown in Tables 2 and 3, respectively.

(As in Table 1, countries excluded from the regression include: Azerbaijan, Bosnia & Herzegovina, Turkmenistan and Uzbekistan; Slovak Republic from consumption inequality and Tajikistan from income inequality are excluded as well.)

To determine which of the aforementioned control variables most affected inequality, I first conducted principal component analysis to determine which factors explained most of the variance in the data. These factors are: changes in each of manufacturing, agriculture and services value-added, stock of foreign reserves and oil rents and growth in GDP. Four forms of the regression equation were conducted. In form (1), only the three privatization methods were regressed against the Gini coefficient.

In form (2), changes in each of manufacturing, agriculture and services value-

%ΔGINIit = β0 + β1SALEit + β2VOUCHERit + β3MEBOit + β4%ΔGDPPCit + β5%ΔAGRICULTUREit + β6%Δ MANUFACTURINGit + β7%ΔSERVICESit + β8%ΔCPIit + β9UNEMPLOYMENTit + β10%ΔGOVTCONSit + β11DEBTit + β12FOREIGNRESERVESit + β13%ΔPOPULATIONit + β14OILRENTSit + εit (2) SALEit: Dummy variable for sale method of privatization VOUCHERit: Dummy variable for voucher method of privatization MEBOit: Dummy variable for MEBO method of privatization GDPPCit: GDP per capita AGRICULTUREit: Agriculture sector value-add MANUFACTURINGit:: Manufacturing sector value-add SERVICESit: Services sector value-add CPIit: Consumer Price Index UNEMPLOYMENTit: Unemployment as a percent of labor force GOVTCONSit: Government consumption as a percentage of GDP DEBTit: Total external debt as a percentage of GDP FOREIGNRESERVESit: Stock of foreign reserves POPULATIONit: Mid-year population OILRENTSit: Oil rents as a share of GDP εit: Error term Columbia Economics Review

21 added are added as control variables. In form (3), stock of foreign reserves and oil rents are added. And finally, in form (4), growth in GDP is also controlled for.

In all four forms of the equation, none of the privatization methods or controlled factors had significant effects on changes in income inequality. The voucher method was associated with an average 0.41 percent decrease in Gini coefficient, while the sale and MEBO methods were associated with increases of 1.06 percent and 1.41 percent respectively.

The privatization methods had direc-

“However, inequality did not increase uniformly in

Eastern European transition economies� tionally similar, but far more significant, effects on changes in consumption inequality. In the first-form equation, with no control variables, the voucher method was associated with a 1.80 percent average decrease in consumption inequality, significant at the 5 percent level. The sale method was associated with a 1.02 percent annual increase and the MEBO method with a 1.85 percent annual increase, with only the MEBO method having a significant increase. As more controls are added to the regression, the magnitude of the effect of the voucher method on reducing consumption inequality increases as well as its significance. The effect of the sale method on increasing consumption inequality is significant at the 10 percent level only in the second-form equation, while the MEBO method’s effect on increasing consumption inequality remained significant in all forms of the equation. Additionally, the change in the value-added of the manufacturing sector had a significant effect on consumption inequality in all three forms of the equation for which it was included. A 1 percent increase in the manufacturing value-added was associated with a 0.14 percent decrease in consumption inequality. Thus, manufacturing is associated with more consumption equality, perhaps because the sector is indicative of a strong middle class.

As discussed above, the voucher method was applied to the countries with the highest levels of initial consumption inequality. The next question is whether the reduction in inequality was caused by implementation of the voucher method Columbia Economics Review

22 or simply the convergence of inequality levels in Eastern Europe. Eight of the ten transition economies that used the voucher privatization method are former members of the Soviet Union (i.e., NIS countries). Additionally, as shown in Figures 1A and 1B, voucher countries on average had higher levels of income and consumption inequality in 1994 during transition than other countries. Whether NIS countries were prescribed the voucher method due to former-Soviet status or initially high levels of inequality is difficult to determine.

In the multiple regression equation, the effects of the privatization methods on income inequality remained insignificant when looking at all NIS, or all non-NIS, countries grouped together. The effects of privatization method on consumption inequality in NIS countries, in the fourthform equation, were similar but larger in magnitude to the results for all transition economies. The voucher method was associated with a 3.97 percent decrease, significant at the 1 percent level, and the MEBO method was associated with a 6.21 percent increase, significant at the 10 percent level. The effect of the sale method was insignificant but associated with a 1.4 percent increase. Among non-NIS countries, the voucher method had an insignificant effect, reducing consumption inequality by 1.74 percent on average. The effect of the sale method was again insignificant, though the MEBO was associated with a 2.73 percent increase, significant at the 10 percent level. Thus, there were only changes in magnitudes and significance of the effects between NIS and non-NIS country groupings, but the direction of the influences remained the same.

Similar to the NIS/non-NIS country groupings, privatization methods had insignificant effects on income inequality for countries in EU/non-EU groupings (in all forms of the equation). The one exception to this was a 4.64 percent increase in the income Gini coefficient, associated with the MEBO effect in the first-form equation and significant at the 5 percent level, although this was with no control variables).

In the multiple regression on the consumption Gini coefficient, the effects of privatization method on consumption inequality in EU countries, in the fourthform equation, lost all significance. The effects on non-EU countries remained

significant; the voucher method was associated with a 3.49 percent decrease in consumption inequality, significant at the 10 percent level and the MEBO method was associated with a 3.73 percent

increase in consumption inequality, significant at the 5 percent level. Thus, the results are less significant for countries that eventually joined the EU than for the other transitioning countries.

My analysis has shown that the voucher method of privatization was associated with a reduction in both consumption and income inequality, with significant results for reducing consumption inequality. Countries where the sale method was used experienced an increase in consumption inequality, but this may be explained by the correlation of share distribution to initial income levels. Countries that chose the management-employee buyout method also saw an increase in consumption inequality. In MEBO privatization only employees of the firm could purchase shares from the state, which usually concentrated shares in the hands of fewer people than other privatization methods.

The voucher method was applied to

countries with the highest initial levels of inequality as well as to countries with lower standards of living in the 1990s. It is possible that this method enabled greater distribution of capital, since shares of state enterprises were made available to all citizens. These results show that a transition economy is more likely to avoid massive increases in consumption inequality if it uses a voucher method of privatization, in addition to successfully implementing credit markets and welfare programs.

Analysis of these results in conjunction with Bennett, Estrin and Urga’s (2007) finding that the voucher method was the only method to significantly increase GDP growth, lead to the conclusion that the voucher method was the most successful method of privatization in Eastern European transition economies. This conclusion can bear weight on the decisions for governments of countries currently undergoing transition from closed to open economies.

Table 3. Multiple Regression Results: Variable Regressed Against Consumption Gini Coefficient β

(1)

(2)

(3)

(4)

-1.80** (0.77)

-2.66*** (0.92)

-2.92*** (0.97)

-3.28*** (1.10)

Sale

1.02 (0.83)

1.96* (1.04)

1.67 (1.12)

1.22 (1.28)

MEBO

1.85** (0.89)

2.14*** (0.81)

2.07** (0.88)

1.83* (0.95)

∆ Agriculture Value-Add

-0.05 (0.06)

-0.04 (0.06)

-0.04 (0.06)

∆ Manufacturing ValueAdd

-0.14***

-0.14***

-0.17***

(0.05)

(0.05)

(0.06)

0.11 (0.07)

0.11 (0.07)

0.11 (0.07)

Stock of Foreign Reserves

0.05 (0.08)

0.06 (0.08)

Oil Rents as a Share of GDP

-0.04 (0.45)

-0.10 (0.46)

0.13

0.10 (0.14) 0.13

Voucher

∆ Services Value-Add

Growth in Real GDP 2

Adjusted R

0.04

0.14

Standard errors are presented in parentheses *Denotes statistical significance at the 10 percent level **Denotes statistical significance at the 5 percent level ***Denotes statistical significance at the 1 percent level

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23

Saving Europe

The Determinants of the Household Saving Rate in the European Union Wang Guan

In recent years, household savings rates of countries and regions have conspicuously diverged. Over the last two decades, savings rates increased significantly in East Asia, fluctuated in Latin America and fell in subSaharan Africa. At the same time, household savings rates in many developed countries declined dramatically and have remained near record lows, particularly for the U.S., the U.K. and Canada.

This disparity in savings rate performance raises a number of intriguing questions. Why are savings rates so different across countries and over time? What are the key drivers?

Which policies have the greatest impact on savings? From a policy perspective, answering these questions is not only relevant but also crucial to numerous macroeconomic issues. To begin with, such answers will help us evaluate the effectiveness of fiscal policy in elevating national savings. Second, it sheds light on the question of whether financial liberalization, which generally encourages lending for consumers and lending, hinders or promotes private savings. Finally, comparing economic determinants, relating to macroeconomic indicators, with strategic determinants, relating to government policies, will help reveal the most effective approach for boosting savings.

This paper addresses these issues empirically by analyzing a cross-country, macroe-

conomic data set on household savings rates and related variables of countries in the European Union (EU). The EU is chosen over other regions for several reasons. First, its commitment to regional integration makes the EU one of the most important economic unions. If considered as a single economy, the EU generated a nominal gross domestic product of $16.45 trillion in 2009, amounting to 28.4 percent of the world’s total economic output (IMF World Economic Outlook Database, 2010). Despite the vast literature on household saving behavior, very few of the previous panel studies specifically addressed EU countries. Second, household savings is an indispensable part of national savings. In the context of the current financial crisis, the need to study the drivers of household savings becomes more urgent, as this study is likely to yield significant policy implications to combat debt and deficits that have beset the continent. Third, despite the cultural similarity and economic interdependence of European nations, the household savings rates vary significantly across Europe. In 2009, the household net savings rates ranged from -0.45 percent in Denmark to 12.86 percent in Sweden, according to the data provided by AMECO (see Figure 1). It is interesting to explore what accounts for this large variation. Fourth, even though statistics in Europe vary from country to country, Columbia Economics Review

the EU has attempted to impose universal measures of statistics in various aspects. This will help in dealing with the problem of data incomparability that has plagued most of the cross-country studies on household savings.

The existing literature suggests a variety of determinants of household savings that establish a substantial theoretical framework.

The following provides a selective survey of potential determinants.

The life-cycle hypothesis seems to be the most basic and fundamental determinant. According to this hypothesis, the age structure of the population can influence household saving behavior. A population with a large proportion of working-age people should boast a high household savings rate because individuals save the most when they earn the most. As these workers reach retirement age, they wind down savings to maintain consumption, thereby decreasing the savings rate. This theory thus predicts a negative relationship between the household savings rate and the oldage dependency ratio—the ratio of retired people to the working population (Sarantis and Stewart, 2001). With a few exceptions, this predicted correlation is generally supported by empirical evidence.

The life-cycle hypothesis also implies a positive impact of income growth on the savings rate. Because working individuals, in

24

relation to retired people, obtain most of the fruits of income growth, and since those who work are the major contributors to household savings, a higher growth rate generally lifts up the saving rate. This is often referred to as the income effect. But one can just as easily argue that the rise of expected future income should have a negative effect on the savings rate as workers, with lighter future monetary burdens, have the incentive to consume more today. Thus, with expectations of higher future income, they spend what they would otherwise save today, driving the savings rate down. This is the substitution effect. Due to these opposing factors, the impact of income growth on savings is theoretically ambiguous. Empirically, however, income growth is often found to have a positive impact on savings, revealing that the income effect overrides the substitution effect in these cases.

Other indicators of a country’s macro environment such as inflation, real interest rates and terms of trade are also expected to affect household saving behavior. It is widely accepted that an improvement in a country’s terms of trade—a rise in the price ratio of a country’s exports to its imports—will increase its savings from the increase in real income, a phenomenon known as the Harberger-Lauren-Metzler effect.

This can be explained by the fact that the real income level of a country can be measured as “the purchasing power of its exports in world markets” (Ostry and Reinhart, 1991). This is especially true for open economies. The effect of terms of trade on savings

depends on its influence on real income. This effect is also theoretically ambiguous, for the same reason that income growth has an uncertain impact on savings (Ferruci and Mirralles, 2007). Not surprisingly, most empirical evidence seems to attest to the validity of this effect, just as it supports the positive impact of income growth.

Furthermore, as pointed out by Ferruci and Mirralles (2007) as well as Masson et al.

(1998), the impact of inflation is also somewhat uncertain. As high inflation erodes the value of savings, the current savings rate should decline accordingly. On the other hand, this effect can be offset automatically because inflation also leads to higher nominal interest rates, yielding higher returns on savings (Ferruci and Mirralles, 2007). Moreover, since unexpected high inflation indicates high macroeconomic volatility, it may raise precautionary savings (Hondroyiannis,

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2006). These opposing effects explain the lack of a significant coefficient for inflation in empirical studies. Finally, the theoretical impact of real interest rates is also rather ambiguous due to several effects that work in differing directions. It is not surprising that a great deal of uncertainty can be observed from a variety of empirical literature.

Fiscal policy can also influence the savings pattern of the private sector. The Ricardian equivalence hypothesis suggests that private sector savings adjusts in response to public sector deficits or surpluses (Ferruci and Mirralles 2007). As public savings decreases, the government will finance its spending through taxes or by issuing bonds. Since the government will eventually repay the bonds by raising taxes, taxpayers will have to pay higher taxes in the future. They therefore put aside savings now in anticipation of future tax increases. In other words, a decline in public savings is offset by a rise in private savings (Hondroyiannis 2006). If this equivalence holds, the private savings rate should be negatively correlated with public savings.

Empirical results seem to support these suspicions: the estimated coefficients of public savings and government budget surplus are all significantly negative across studies.

Following the same logic of the Ricardian equivalence hypothesis, a decrease in foreign savings will also lead to a rise in private savings. This is particularly true in open economies, where agents can use foreign borrowing to smooth consumption over time. In other words, foreign savings is generally expected to act as a substitute for domestic savings. It is very likely that foreign savings can crowd out private savings. Previous studies have represented foreign savings with the current account balance, defined as negative foreign savings, which is expected to have a positive effect on household savings rate.

Edwards (1995) and Masson et al. (1998) confirm this hypothesis.

Tax policies, social security and welfare systems may also influence household saving. The structure of a country’s tax system

25 may affect savings in a number of ways. Direct taxes, which consist of taxes on incomes, profits and capital gains of households and corporations, may be particularly detrimental to household savings. As Callen and Thimann (1997) point out, an increase in income taxes, which constitute a major part of direct taxes, can adversely affect the household savings rate for two reasons. The first is that high-income households, which generally save the most, are affected more than others due to the progressive tax structure. The other is that the working-age population, who make up most of the savers in society, also pay most of the income taxes. On the other hand, indirect taxes are more evenly distributed. The empirical results of Callen and Thimann support the existence of negative correlation between direct taxes and household savings.

The social security and welfare systems may also play a role in determining household savings at a macroeconomic level. Governments typically provide a variety of welfare programs to guarantee a minimal level of well-being and cushion individuals from unemployment, health expenditures and lost wages during retirement. These welfare programs can thereby substitute for precautionary private savings; hence, an increase in these benefits may have a negative impact on the level of household savings. Using different proxies, many previous studies empirically confirm this negative relation.

Demographic factors, such as urbanization and the unemployment rate, are also possible determinants of the savings rate. According to Loayza et al. (2000), rural residents tend to save more because they lack the means to diversify away from their mostly agricultural income, which entails high uncertainty.

Due to this precautionary savings motive, an increase in urban residents relative to total population should have a negative effect on the household savings rate, which is supported by previous empirical studies like Loayza et al. (2000) and Edwards (1995). The effect of the unemployment rate on household savings, however, is not as definite. The unemployed tend to have lower savings, similar to the retired, which adds downward pressures on the household savings rate. On the other hand, a higher unemployment rate generally indicates higher uncertainty in society, inducing households to save more. The empirical results of Edwards (1995) seem to favor the former explanation.

The panel data set used in the analysis contains 22 countries in the European Union over the period 1995-2008, as these data are readily available in the annual macro-economic database (AMECO) of the European Commission’s Directorate General for Economic and Financial Affairs. The depend-

ent variable in the regression model is the household net savings rate (HSR). The set of explanatory variables are listed along with the regression in Equation 1.

Data is from AMECO, World Development Indicators, International Finance Statistics, Government Finance Statistics and World Economic Outlook Database.

Masson et al. (1998) consider the industrialand developing-country panels separately to account for the general behavioral differences between the two groups. Similarly, this paper also divides the sample into higherand lower-income countries, expecting differences between the two sets of estimates.

There is significant variance for annual per capita income within individual EU states.

In 2009, the nominal GDP per capita ranged from 11,140 USD for Lithuania to 55,992 USD for Denmark (World Development Indicator). This paper defines poor countries as those with real GDP per capita below 10,000 USD (the base year being 2000) every year during the period 1995-2008.

Table 1 reports the results of the crosssectional, time-series estimation. The first and second columns are restricted to rich and poor EU-member states respectively, while the third column refers to the complete sample. While some results turn out as expected, others differ considerably from previous empirical studies.

The growth rate of real GDP per capita does not have a positive effect on the household savings rate, as suggested by most empirical evidence. The signs of this variable are insignificant for the complete sample as well as for the subgroup of poorer countries, whereas the sign is significant and negative for richer countries. As mentioned in earlier, income growth is expected to be subject to

opposing effects on savings, namely, the income effect and the substitution effect: a higher growth rate might catalyze savings since working individuals, the major savers in society, enjoy more benefits of income growth than those who do not work; on the flip side, a higher growth rate also boosts expectations of future income, inducing individuals to consume rather than save today.

The finding suggests that the substitution effect offsets the income effect in the European Union, and that the former overrides the latter in richer EU countries. This matches the results of Masson et al. (1998), who discovered that “GDP growth is weakly associated with savings for industrial countries, but much more strongly and significantly for developing countries,” even though the estimated coefficients of GDP growth for the two subgroups are both positive. This indicates that the substitution effect relative to the income effect is stronger in richer nations than that in less prosperous ones, just as the estimates imply here.

Likewise, the coefficients of terms of trades are found to be insignificant in every regression. This lends no support to the Harberger-Lauren-Metzler effect, since the effect of terms of trade on savings largely depends on its influence on real income level.

Real short-term interest rates are possibly the most controversial and unresolved issue in the study of the determinants of the savings rate: they appear to encourage household savings for every regression, even though the association is relatively weak for richer countries. The inflation rate is also found to have a positive impact on the household savings rate, although the relationship is less evident for richer countries.

This is not that surprising given the mixed

HSRit = β0 + β1Yit + β2TOTit + β3RSIRit + β4lNFit + β5OADit + β6URBit + β7UNEMit + β8GFit + β9CAit + β10DTit + β11STit (1) Y: growth rate of real GDP per capita TOT: terms of trade – the price ratio of a country’s export to its import RSIR: real short-term interest rates INF: inflation rate OAD: ratio of people aged 65 and over to the working-age population URB: proportion of total population that lives in urban areas UNEM: unemployment rate GF: ratio of government fiscal surplus/deficit to GDP CA: ratio of current account balance to GDP DT: share of direct taxes in general government tax revenue ST: share of social transfers in kind in general government expenditure Columbia Economics Review

26 theoretical effects of inflation on savings.

The results concerning the demographical factors are also somewhat intriguing.

None of the coefficients of the old-age dependency ratio in the three regressions is found to have a significant correlation with the household savings rate. This may cast doubt on the prevalent life-cycle hypothesis but is actually consistent with the previous findings of Hondroyiannis (2006). Hondroyiannis conducted a panel co-integration study on the determinants of private savings in 13 European countries and found that an upward shock in the old-age dependency ratio actually increases private savings, contrary to what the life-cycle hypothesis predicts. The fact that the retirees do not necessarily save less than the working-age population must be one of the unique features of countries in the European Union. One possible explanation can be that the pension system is so generous in Europe that senior citizens do not need to wind down savings as much as working-age individuals do. Moreover, this effect is often automatically compounded by the possibility that workingage individuals, with their financial future better protected by the pension benefits, are under less pressure to save. Alternatively, it can be explained with respect to the precautionary savings motive: In Europe, population aging is on an upward trend during this period at the same time the provision of public pensions might not be able to keep up with this trend due to budget constraint; as a result, individuals have the tendency to save more. Both explanations make theoretical sense. Unfortunately, this study lacks sufficient data to engage in any further analysis.

The coefficients of the urbanization ratio are found to be negative across the three regressions. Generally speaking, this finding supports the hypothesis that rural residents tend to save more than urban citizens due to the precautionary nature of savings. The unemployment rate is found to have a distinct detrimental impact on savings for poorer countries, a mildly adverse impact for middle-income countries and no significant impact for richer countries.

Of the four remaining variables, the government fiscal balance and current account balance unambiguously affect the household savings rate in the expected direction.

The coefficients of government fiscal surplus and deficit are statistically significant and negative, hovering around -0.5 in all three samples. This supports the existence of Ricardian equivalence in Europe. Moreover, the current account balance also correlates positively with the household savings rate as expected. These findings confirm the propo-

sition that both public and foreign savings crowd out domestic private savings in the European Union. The absolute values of the coefficients of both variables are significantly below unity in every regression, indicating that the degree of offset for both scenarios is not one-to-one.

Social transfers in kind, on the other hand, do not exhibit a significant impact on household savings. The indifference of the household savings rate to the change of this variable is quite curious: it is universally acknowledged that European nations have the most generous social security nets provided by the government, yet a change in social transfers in kind does not necessarily reduce the savings rate. It is possible that the social security systems in Europe are so developed that they barely affect household savings anymore. The operating principle in this case is diminishing marginal returns: as the coverage and generosity of the social security system expand, its marginal effect on household savings dwindles to nonexistence.

The results of direct taxes are another puzzling finding in this study. The coefficients of this variable are surprisingly positive and significant for the complete sample and richer countries and interestingly insignificant for poorer countries. This finding contradicts the results of Callen & Thimann (1997) as well as their hypothesis. To investigate this, recall that the theoretical negative ef-

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fect of direct taxes on savings relies on the assumption that income taxes tend to affect two groups the most, namely the high-income households and the working population, who are presupposed to be the major savers in society. However, based on these results, the working-age individuals do not necessarily save more than the retirees do in the European Union—the coefficients of the old-age dependency ratio are insignificant in every regression.

Admittedly, the regression model itself is not without defects. The issue of household savings is essentially the convergence of factors such as the ones investigated here; a simple linear function might fail to capture its subtleties. It is also evident that several variables are missing due to the data availability constraint. It is difficult—or impossible—to obtain reasonable statistical proxies for many of these variables, thus posing additional challenges to further studies on household savings.

Based on the empirical results, several avenues exist to raise household savings, but policymakers should only act when raising household savings aligns with improved social welfare as well. From a policy perspective, the results suggest that the government should not reduce the provision of social welfare just to induce savings, as there is no clear correlation between social benefits and the savings rate.

27

Going Gray: Who Pays?

Aging Economies: Demographics and Growth in Japan’s Prefectures from 1975-1999 Anoushka Vaswani

What are the implications of an aging population on economic growth? As one of the most rapidly aging countries in the world, Japan faces this critical economic question. In the January issue of Foreign Affairs, Former U.S. Secretary of Commerce Peter Peterson calls the aging of the world’s population a “global hazard” more potent than “the proliferation of nuclear, biological and chemical weapons, other types of high-tech terrorism, deadly super-viruses, extreme climate change, the financial, economic, and political aftershocks of globalization and the violent ethnic explosions waiting to be detonated in today’s unsteady new democracies.” The Economist less sensationally warns that Japan “is heading into a demographic vortex” and “needs a grand plan for an aging population.”

Demanding more medical support than younger generations, the elderly often place strong demands on familial resources, personal savings and government, thereby constraining government finances. Negative outlooks abound: Milton Ezrati suggests that the standard of living could drop by 18 percent in Japan, and in an analysis using the IMF’s macroeconomic model (MULTIMOD), Mühleisen and Faruqee (2003) predict that Japanese demographic trends will

yield a 20 percent reduction in real GDP compared to a a stationary population.

Other economists, however, have presented alternatives to these apocalyptic prophecies on the economics of aging.

Bloom, Canning and Fink argue that aging does not necessarily undermine economic growth. Changes in the age structure of the population prompt behavioral responses that can mitigate the negative effects of aging. As fertility rates decline, countries may experience an increase in female labor force participation. Individuals with increased life expectancies and sets of earnings opportunities may have a higher incentive to save and invest in their children’s education and their own.

While population aging is unavoidable, the implications on economic growth are uncertain.

We examine this question using regionalized data on Japan’s 47 prefectures.

Holding other factors constant, economies with a larger share of older people relative to working-age adults should experience slower economic growth, given that the elderly are generally less productive than the working-age. Furthermore, they will also have a higher proportion of elderly dependent on a smaller group of working individuals, placing a greater burden on the economy. When comparColumbia Economics Review

ing economic growth across Japan’s prefectures from 1975 to 1999, however, this analysis finds that aging has an ambiguous effect on economic growth.

Japan’s fertility rate has been declining, and average life expectancy has been climbing. Compared to other major Asian economies and the U.S., Japan has had the most dramatic drop in population growth. By 1980 the average annual rate of increase fell under 1 percent, and more recently this growth rate has actually been negative.

These trends have had significant consequences for Japan’s demographic structure. In 2009, the number of elderly individuals in Japan, representing 22.7 percent of the total population, peaked at 29.01 million. Japan, experiencing one of the fastest rates of aging worldwide, currently holds the highest share of individuals aged 65 and over in the world.

The basic growth accounting equation, derived from the Solow-Swan growth model, stipulates that GDP growth is driven by the contribution of new technology, new capital investment or increases in the labor force. The basis of this paper rests on the fact that an individual’s demands and contributions vary with his or her stage in life. The young tend to consume more than they produce until they reach their prime

28

Figure 1. Growth in Gross Prefectural Domestic Product per Capita 1975-1999 vs Percentage above Age 65 in 1975 (Prefectures)

working years, whereas the old generally tend to work and save less. Thus, age structure influences labor supply, earnings, savings and productivity—all components of the growth accounting equation. This relationship implies that a prefecture’s age composition plays a significant role in its economic growth.

As the share of older people increases relative to that of younger individuals, holding life-cycle behavior constant, labor supply and capital should decline, thereby reducing the growth rate. If the percentage of elderly does not necessarily correspond to a reduction in economic growth, then we must search for alternative effects that occur as the population ages to explain this trend.

Holding other factors constant, a prefecture largely composed of older citizens should grow slower than a prefecture made up of adults in their prime working years. Commentators such as Peterson and Ezrati, who were alarmed about the consequences of an aging population, share this view.

Examining the following analyses, we can see that labor force participation has been falling as Japan’s population has been aging. From 1975 to 2000, the percentage of the population aged 65 and over grew in every prefecture, with the prefecture Shiga experiencing the smallest increase of 73.12 percent. On the other hand, the average decline in labor force participation across the prefectures during this period was 3.8 percent, while the standard deviation among prefectures in 2000 was 2.4 percent.

Although labor force participation generally declined over this period, the drop in labor force participation may be more modest than expected given the increases in aging.

Looking at real gross prefectural domestic product growth (GPDP) versus the per-

centage of elderly in the initial year across prefectures, some prefectures display a negative relationship between the share of elderly and economic growth, while others show the opposite. This suggests that the share of elderly alone does not explain much of the variation in growth.

To assess the implications for the average Japanese citizen, changes in real GDP per capita may serve as a more useful measure for economic growth and changes in the standard of living. Accounting for these two problems, Figures 1, 2 and 3 examine real GPDP per capita growth versus the percentage of elderly in the initial year.

Figure 1 and 2 demonstrate almost no relationship, while Figure 3 again seems to indicate a slight positive correspondence.

This is the surprising part: from the perspective of GPDP and particularly GPDP per capita, aging seems to exert an ambiguous effect on growth.

Since Figures 1 to 3 do not show an aging population reducing labor supply, savings and economic growth, further examination is needed. It is important

to recognize that falling fertility and increasing longevity, the two drivers of aging, have different consequences for economic growth and that changes in the age composition of a population also produce changes in age-specific behavior.

The ratios in Equations 1, 2, and 3 were calculated for Japan as well as each of its prefectures. Figure 4 illustrates changes in these over time for Japan. These ratios are useful because they serve as indicators of the number of people a workingage individual will need to support.

From 1975-1990, the child dependency ratio seemed to be falling faster than the aged dependency ratio was rising, resulting in a declining total dependency ratio. Starting in 1990, this trend reverses as the aged dependency ratio increases relatively faster than the child dependency ratio falls, producing a rising total dependency ratio.

What are the implications of an aging population on economic growth?

As one

of the most rapidly aging countries in the world,

Japan faces this critical economic question.

Thus, the aging of a population has two effects: decreased youth dependency from falling fertility rates and increased age dependency from longer life expectancies. The rise in the proportion of working-individuals, generally produc-

Figure 2. Growth in Gross Prefectural Domestic Product per Capita 1975-1990 vs Percentage above Age 65 in 1975 (Prefectures)

Columbia Economics Review

29 ing more than they consume relative to dependents, creates a “demographic dividend” to economic growth. While economists have several different explanations for Japan’s rapid post-war growth and subsequent lost decade, Japan’s high-growth period overlaps with a falling dependency ratio, while Japan’s lost decade corresponds to a rising dependency ratio due to an increasing elderly population (see Figure 4). The competing phenomena of decreasing youth dependency and increasing aged dependency may explain some of the ambiguity observed in Figures 1 to 3.

Furthermore, Bloom, Canning, Fink and Finlay suggest a connection between a drop in fertility rates and an increase in female labor force participation. As the dependency ratio fell from 1975-1990, female labor force participation rose. Female labor force participation has been falling since 1990, interestingly in conjunction with the movement of the dependency ratio. Greater female labor force participation due to declining youth dependency could potentially be offsetting some of the negative effects on labor supply due to aging from 1975-1990.

As Figure 4 indicates, the dependency ratio is set to rise in Japan, which is potentially worrisome for Japanese growth.

But while the dependency ratio equates age dependency with youth dependency, this assumption may not hold in reality. There have been several studies on the notion of a “compression in morbidity,” the shortening in length of life spent in chronic ill-health accompanying increased life expectancies. An elderly population that places fewer demands on healthcare and productively contributes to the economy for longer is less burdensome to economic growth. Because of Japanese firms’ mandatory retirement systems, rather than examining actual elderly employment rates, it is useful to consider the ratio of people aged 60 and above wishing to work over time for Japan. With the exception of 1992, this ratio has been steadily rising, almost reaching 50 percent in 1997, indicating that the notion of a dependent elderly may not be entirely accurate.

To further explore the “compression

in morbidity,” for each prefecture available in 2000, this paper looks at the average number of hours worked by people over 60 per prefecture. Given that many Japanese firms historically released their elderly workers, the negative correlation between this ratio and the percentage of elderly in the prefectures is expected.

Similarly, we divided the hours worked by people aged 15 to 34 by the number of people aged 15 to 34 in each prefecture. The interesting result is that there

Dependency Ratio=(population of age 0-14 & 65+)/(population of age 15-64)*100 Child Dependency Ratio=(population of age 0-14)/(population of age 15-64)*100 Aged Dependency Ratio=(population of age 65+)/(population of age 15-64)*100 Columbia Economics Review

is a strong positive relationship between the hours per worker aged 15 to 34 and the share of elderly. Even if the elderly do not formally work, a healthier elderly cohort can more productively contribute to their family’s income and well-being.

Having the support of an elderly parent may allow an adult to work longer hours.

Other studies, which may offer an alternative explanation for this finding, have found that improvements in life expectancies, linked to both aging and healthier populations, have positive effects on economic growth such as increasing worker productivity. The compression of morbidity and a more productive elderly population may mitigate the effects of aging. These factors could partially explain the ambiguous relationships illustrated in Figures 1 and 2 as well as the puzzling relationship in Figures 3, which

30

Figure 3. Growth in Gross Prefectural Domestic Product per Capita 1990-1999 vs Percentage above Age 65 in 1990 (Prefectures)

cover the most recent period when major medical advances for the elderly population would have been made.

Another surprise comes from looking

Labor force participation has been falling as Japan’s population has been aging. at savings per household (savings/number of households) for Japan over time from 1975 to 1999. As the proportion of prime savers drops relative to the elderly, we would expect this ratio to fall, but instead we see a distinct rise. Increased life expectancy has been linked to higher savings rates. While an ideal response to increasing longevity may be working longer, individuals can also save more to finance consumption later. In Mühleisen and Faruqee’s simulations of the Japanese economy with the IMF’s macroeconomic model, they found that a decline in the number of young adults led to a rise in private savings. This behavioral shift may also help explain the results in Figures 1 through 3.

Even though some factors previously discussed help elucidate Figure 3, the slope of the trend lines remains puzzling. Migration from regions with a labor surplus to regions with a labor deficit due to the rising share of elderly could mitigate differences in economic growth due to aging. Looking at net migration rates versus share of elderly in different years, however, did not reveal immigration to older prefectures. During Japan’s lost decade, an interesting result emerges when analyzing unemployment. While the unemployment rate has generally been higher for individuals aged 60 and

above, the rate for 20 to 29 year olds still rose. Until 1998, the court’s interpretation of the Labor Standards Law made it difficult to fire workers under the retirement age. Firms opted for policies such as hiring freezes rather than firing workers. Thus, prefectures with a higher proportion of elderly may have actually experienced lower unemployment rates.

Furthermore, Naohiro Ogawa observes that, during the lost decade, younger age groups received positive familial transfers from the elderly age group. Younger prefectures may have not only experienced more unemployment, but their youth may have not received as much familial assistance during the harsh economic period, making the elderly a desirable crowd to draw. This could therefore help explain the relationships evident in Figure 3.

Analyzing the relationship between economic growth and aging populations at the prefectural level, this paper finds that aging has an ambiguous effect on growth, not conforming to theories of clear-cut harm or benefit that other econ-

omists have suggested. The potential decline in labor supply can be offset by positive changes that accompany falling fertility rates and increasing longevity; these include reduced youth dependency and a healthier, more productive elderly population, which is less of a burden on economic growth and increases incentives to save. Other variables outside this paper may also mitigate the consequences of aging. For example, longer life expectancy has also been found to heighten incentives to invest in education, extending the time over which the investment can be regained.

These findings determine that rather

Japan’s lost decade corre-

sponds to a rising dependen-

cy ratio due to an increasing elderly population than being a cause for panic, Japan’s aging population is uncertain even in the face of the indubitable transformation that the population’s composition is experiencing. A critical concern that this paper does not explore is the effects of aging on Japan’s pension system. From a ratio of 12 workers supporting each pensioner, falling birthrates are expected to lower this number to two in 2025. Financing pension payments with current taxation presents a challenge as the share of the working-age population drops. The growing elderly population will exert a significant impact on Japan’s budget deficit. Future economic growth in Japan and its prefectures will depend on the country’s success in adapting to the changing demographic environment.

Figure 4. Dependency Ratios (Japan), 1975-2000

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