Mankiw and Conventional Wisdom on Europe

Greg Mankiw wrote a week ago in the Sunday New York Times, ably explaining the  conventional view that the Euro is a bad idea, and that even countries as small as Greece (11 million people) need national currencies. Excerpt:

Monetary union works well in the United States. No economist suggests that New York, New Jersey and Connecticut should each have its own currency, and indeed it would be highly inconvenient if they did. Why can’t Europeans enjoy the conveniences of a common currency?

Two reasons. First, unlike Europe, the United States has a fiscal union in which prosperous regions of the country subsidize less prosperous ones. Second, the United States has fewer barriers to labor mobility than Europe. In the United States, when an economic downturn affects one region, residents can pack up and find jobs elsewhere. In Europe, differences in language and culture make that response less likely.

As a result, Mr. Friedman and Mr. Feldstein contended that the nations of Europe needed a policy tool to deal with national recessions. That tool was a national monetary policy coupled with flexible exchange rates. Rather than heed their counsel, however, Europe adopted a common currency for much of the Continent and threw national monetary policy into the trash bin of history.

Making matters worse, however, was the common currency. In an earlier era, Greece could have devalued the drachma, making its exports more competitive on world markets. Easy monetary policy would have offset some of the pain from tight fiscal policy. Mr. Friedman and Mr. Feldstein were right: The euro has turned into an economic liability that has exacerbated political tensions. For this, the European elites who pushed for the currency union bear some responsibility.

I am a big euro fan. This seems a good moment to explain why I don't accept this conventional view, despite its authority from Milton Friedman to Marty Feldstein and Greg Mankiw and even to Paul Krugman.

Short: I am also a big meter fan. I don't think each country needs its own measure of length, or to shorten it when local clothiers are having trouble and would like to raise cloth prices.

Longer: This conventional view is deeply old-Keynesian. In this view, each region, including ones as small as Greece (11 million) or Ireland (4.6 million), less than the Los Angeles metro area (13 million), suffers "demand" shocks, which governments must actively offset with fiscal stimulus or monetary policy.

This strikes me as one of those many stories that people repeat all the time until they believe it, but whose foundations are seldom examined.  (There is a "thesis topic" label here for such examination. Comparisons of US states to European countries on these dimensions seems fruitful.)

What are these local demand shocks for small open economies in the eurozone? "Aggregate demand" is, well, aggregate, not regional.  Changing fortunes of local industries is more what we call "supply," not "demand." For small open economies (LA) much "demand" comes from other cities and states, not local.

What is this "fiscal union," apparently providing countercyclical Keynesian stimulus at the right moment?  In the US, we have Federal contributions to social programs such as unemployment insurance. Europe has the common agricultural policy and many other subsidies. We do not have systematic, reliably countercyclical, timely, targeted, and temporary local fiscal stimulus programs. Just how big is the local cyclical variation in state or local level government spending or transfers? (And why does fiscal union matter so much anyway? If you're a Keynesian, then local borrow and spend fiscal stimulus should be plenty. The union matters only when countries near sovereign default and can't borrow.)

The local and cyclical qualifiers matter. Yes, both US and Europe have some pretty large cross-subsidies. But most of these are permanent. The rest of the nation subsidizes corn ethanol to Iowa year in and year out. Social security payments come year in and year out, and transfer money from states with workers to those with retirees. Monetary policy has at best short-run effects, so the argument for currency union has to be about local cyclical, recession-related variation in economic fortunes, not permanent transfers.

And Federal fiscal transfers only started in the 1930s. We had a currency union in 1790, and no substantial Federal fiscal transfers at all until the 1930s. How did we get along all this time?

A sense in which this is a centrally old-Keynesian argument is that Greg is not making a second, common, and also wrong (in my view) case for national currencies: the view that currency union demands central bailouts of sovereign debt.  No, Greg (and the conventional wisdom he echoes) has in mind only the necessity of Keynesian countercyclical policy. Aphorisms such as "currency union demands fiscal union" are dangerous, as they have many meanings.

So, this conventional view presumes that there really are big regional "demand" shocks; that there is a big, important Keynesian fiscal multiplier, even away from the zero bound, and that our government really does a lot of recession-related fiscal transfers, larger than Europe's (agricultural subsidies, etc.) and that the US pre WWII was a disastrous too-large currency area. I'm not convinced on any of these points.

(To be sure, I will admit a multiplier of about one for state to state transfers. If the federal government takes money from the citizens of New York, and sends the money to people in Florida,  businesses will move from New York to Florida to follow the money and GDP will rise in Florida. And decline in New York.)

Consider Greece, "In an earlier era, Greece could have devalued the drachma, making its exports more competitive on world markets. Easy monetary policy would have offset some of the pain from tight fiscal policy." So, Greece's GDP is falling because of "tight fiscal policy?" Calamitous regulation, corruption, closed markets, and now closed banks, frozen payments are not relevant? Tight fiscal policy? Greece is still running primary deficits. After blowing through one and a half GDP's worth of what are now transfers from the rest of the EU, they've run through another half a GDPs' worth, and GDP collapses more. Really, Greece's economic problems are.... a lack of adequate borrowing and spending? And all Greece needs is one more devaluation, and suddenly will be shipping Porsches to Stuttgart in return for worthless pieces of paper rather than the other way around?

Greg passes on the labor mobility story. Here too I'm dubious and curious to see numbers. The story is also told that there is less and less labor mobility in the US, especially of people leaving dying regions. And there are lots of Polish-plumber stories from Europe, that open borders leads to lots of migration.  Here again, cyclical migration, on the scale for which  monetary policy can substitute, seems unlikely. How big are business-cycle frequency migration flows across states in the US vs. Europe?

Again, the US  until 1933 poses an interesting challenge. Your school stories of westward migration were not a business cycle frequency response to demand shocks. And when people traveled by horse or foot, the vast majority of Americans never moved more than 20 miles from where they were born. The costs of labor mobility in Europe today are vastly smaller than the costs of labor mobility in the US 19th century.

Conversely, and perhaps more centrally, I  less trusting of the stabilizing influence of central banks. Dispassionate omniscient central banks can, in theory, wisely spot demand shocks and cleverly devalue currencies to offset them, while not responding to supply shocks, political demands, and so forth. The same technocrats could quietly redefine the meter as needed to let tailors respond to shocks without changing prices.

But the history of small-country central banks is not so reassuring. Grece and Italy's repeated devaluations and inflations did not bring great prosperity.

Joining a common currency is a pre-commitment against bad monetary policy as well as foreswearing of hypothetical good monetary policy. Political forces seldom think there's enough stimulus.  When Greece and Italy they joined the euro, they basically said, defaulting and inflating now will be extremely costly. They were rewarded for the precommitment with very low interest rates. They blew the money, and are now facing the high costs they signed up for. But that just shows how real the precommitment was.

Micro, macro and politics interconnect. The case for separate currencies is to protect the economy from sticky wages, sticky prices, and sticky people. But none of these stickinesses are written in stone. A plausible answer to my question about pre-new deal US is that prices and wages were not sticky (whatever that means) before the era of regulation. Well, that is a loss, and only very imperfectly addressed by artful devaluation of the currency.  Not every block can have its own currency, so local and industry variation within a country remains hobbled by sticky prices, wages, and people. If sticky wages,  prices and people are the central economic problem, we ought to have a lot of policies to unstick them. We do the opposite, and Europe even more so. The very social programs that Greg implicitly praises for fiscal stimulus tie people to location and undermine labor market flexibility.

The strongest case for a separate currency might come from a small economy like Chile, which sells one product (copper), subject to big price fluctuations, and otherwise is pretty closed, and has institutions with sticky nominal wages that it doesn't want to fix. When the price of copper declines, price times marginal product of labor declines, so real wages should decline, and the value of haircuts provided to copper miners should decline as well. Chile may prefer to keep nominal wages steady and let the exchange rate rather than wage rate discourage imports.

But even Chile exports a lot more than copper these days. Texas is still booming despite a large decline in oil prices. The same argument does not hold for company towns within the US, which do not use their own currency. Stanford  has extremely sticky wages (tenure), and suffers "demand" shocks, (positive lately), without offsetting fiscal stimulus and tremendous labor immobility. It takes a year to hire faculty. But nobody thinks Stanford should have its own currency, and periodically devalue that currency. Why not? Because we are open.

So I think a lot of the conventional view seems to think implicitly of fairly closed economies, operating in parallel. But Europe's economies are open. Moreover, the whole point of the eurozone is to open them further. Small open economies are much worse candidates for their own currency.

Surely each block should not have its own currency, nor each city. We'd probably all agree that very small countries should not -- Luxemburg, say. So the question is really whether the Greece that Greece wants to be -- more open than today -- is effectively of the same size.

So, to sum up, Greg's article very nicely summarizes the conventional view. Recognize that this conventional view is deeply old-school Keynesian, both in its view of fluctuations, the need for constant "demand" management, and the success of "demand" managers to do their job. There is room for disagreement on that theory, and more productively on the underlying facts Greg passes on.

Behavioral Public Choice

In a number of blog posts, (here ) I've complained about the lack of behavioral public choice theory, and highlighted some efforts in that direction.

Much behavioral economics documents that people do stupid things, and then jumps to the conclusion that parternalistic government can do things for us better. But wait, those government functionaries are also human, also behavioral, and placed in group and social settings that psychology as well as economics warns us are particularly prone to bad outcomes.

Marginal revolution highlights an interesting new paper that breaks in to this field, Behavioral public choice: The behavioral paradox of government policy by Ted Gayer and W. Kip Viscusi. A quote:

In this article we examine a wide range of behavioral failures, such as those linked to misperception of risks, unwarranted aversion to risk ambiguity, inordinate aversion to losses, and inconsistencies in the tradeoffs reflected in individual decisions. Although such shortcomings have been documented in the behavioral literature, they are also reflected in government policies, both because policymakers are also human and because public pressures incorporate these biases. The result is that government policies often institutionalize rather than overcome behavioral anomalies.

I haven't read it, but it seems interesting, and the field seems wide open. The defense of freedom never was that freedom is perfect, merely that government control is worse.

I am interested that behavioral economics seems so focused on mistakes of individual decision making, as nicely summarized in the quote. In fact the most obvious thing about humans is that we are social animals, not that we are poor individual decision-makers. I would think that behavioralists would be bringing social psychology more than individual decision making to economics. But maybe this just reveals how little I know about either.

China crash?

Meanwhile, on the other side of the world, China is doing everything in the textbook to ignite a "bubble."

I dislike that usually undefined term, which carries a lot of normative baggage. But there are a set of steps that governments often take unwittingly and are later criticized for. China's doing them on purpose. And these steps quite often precede large market declines.

Short sales ban: Financial Times: "opened a probe into market manipulation"  ... "The investigation is likely to focus on short selling."  The usual witch hunt, with Chinese characteristics. Owen Lamont has a splendid paper on what often follows short-sales bans. The weekend before TARP and Lehman, the US instituted a short-sales ban on bank stocks, just in case there was someone out there who did not know banks were in trouble and they should sell now. Europe instituted a CDS selling ban in the first PIGS crisis...

Lending to encourage highly leveraged speculation: Wall Street Journal: "Under the planned move, China’s central bank will indirectly help investors borrow to buy shares in a market that had already seen a rapid buildup in debt from so-called margin financing." Procyclical credit supply is named by just about every account of a "bubble" followed by a crash.

Prices depend on supply and demand. As well as increasing demand, limit supply: "A halt to new stock listings."

And more. Quartz offers "A complete list of the Chinese government’s stock-market stimulus (that we know about)" including  "People’s Bank of China will “provide liquidity assistance” to China Securities Finance Corp., a company owned by the stock regulator. The company will use the money to lend to brokerages, which could then make loans to investors to buy stocks."

This scenario often ends badly.

The only thing I can think of that can actually stop a crash is for the central bank to directly print money to buy stocks. And not just a little bit. A pre-announced and limited quantity won't work. The US QE took billions to alter bond prices a few basis points at most. One has to commit to a price floor and a "do what it takes" amount of money, no matter how large or inflationary. I don't know of it ever being tried. It will be interesting to see if China goes that far. They could hide the fact with extensive bailouts of people "borrowing" to buy stocks, or otherwise cover losses or promise to cover losses.

Of course, the right strategy is to leave it alone. The whole point of stocks is that they go down on occasion, without runs, without defaults, and without financial distress. Unless the people and institutions holding them are highly leveraged. Didn't we just learn this lesson?

Asset Pricing Summer School

I’m going to offer my online course “Asset Pricing” over the summer. The intent is a “summer school” for PhD students, either incoming or between the first year of foundation courses and the second year of specialized finance courses.

At least one university is going to use this more formally: Require completion of the class for their PhD students (either incoming or between first and second year,) and organize a TA and group meetings around the class. We have found that this sort of social organization helps a lot for students to get through online classes.

The course offers a free “certificate” for achieving a certain grade level in the class, which gives an incentive to actually do the problems. Faculty can tie achievement of the “certificate’ to whatever carrots and sticks they want to offer. For example, one instructor is going to treat achievement of the “certificate” as an assignment for his fall PhD class, and include it in the grade.

Since the class covers most of the basics, this structure may free a faculty member teaching next year to focus the PhD classes on more advanced material. It’s also useful as a “flipped classroom,” allowing the faculty member to spend less time on algebra and derivations, and more on intuition, extensions, and current research.

This session won’t have TAs on my part, though I will monitor the forums and attend to glitches as they crop up.

The class is free. To sign up or see the classes, follow these links

Part 1: https://www.coursera.org/course/assetpricing
Part 2: https://www.coursera.org/course/assetpricing2

The class experience consists of watching short lecture videos, doing the assigned reading, answering quzzes and fairly extensive problem sets, and taking an exam. The course has discussion forums which are quite useful.

The class starts next Monday, June 8. It is open for registration now, and will be open for students to see materials and start work by the end of the week. Part 1 (7 weeks) ends July 27, and Part 2 (7 weeks) ends Sept 14. The two parts may be taken independently. Students not wishing a grade may use these materials freely and just do whatever parts seem interesting. I've also set up the grading pretty flexibly to allow people to adjust their schedules rather than follow the week by week rigid schedule.

This is a bit late notice, but I hope blog readers will pass on notice to PhD students or prospective ones, and to faculty members who are teaching PhDs in the fall and might find this resource useful.

The syllabus:

Part I
Week 1 Stochastic Calculus Introduction and Review. dz, dt and all that.
Week 2 Introduction and Overview. Challenging Facts and Basic Consumption-Based Model
Week 3 Classic issues in Finance. Equilibrium, Contingent Claims, Risk-Neutral Probabilities.
Week 4 State-Space Representation, Risk Sharing, Aggregation, Existence of a Discount Factor.
Week 5 Mean-Variance Frontier, Beta Representations, Conditioning Information.
Week 6 Factor Pricing Models -- CAPM, ICAPM and APT.
Week 7 Econometrics of Asset Pricing and GMM.  Final Exam

Part II
Week 1 a) The Fama and French model b) Fund and performance evaluation.
Week 2 Econometrics of classic linear models.
Week 3 Time series predictability, volatility and bubbles.
Week 4 Equity premium, macroeconomics and asset pricing.
Week 5 Option Pricing.
Week 6 Term structure models and facts.
Week 7  Portfolio Theory and Final Exam

On writing well

The WSJ notable and quotable picked a lovely snippet from “On Writing Well” (1976) by William Zinsser, who died May 12 at age 92. 

Clutter is the disease of American writing. We are a society strangling in unnecessary words, circular constructions, pompous frills and meaningless jargon. 

Who can understand the clotted language of everyday American commerce: the memo, the corporate report, the business letter, the notice from the bank explaining its latest “simplified” statement? What member of an insurance plan can decipher the brochure explaining the costs and benefits? What father or mother can put together a child’s toy from the instructions on the box? Our national tendency is to inflate and thereby sound important. The airline pilot who announces that he is presently anticipating experiencing considerable precipitation wouldn’t think of saying it may rain. The sentence is too simple—there must be something wrong with it. 

But the secret of good writing is to strip every sentence to its cleanest components. Every word that serves no function, every long word that could be a short word, every adverb that carries the same meaning that’s already in the verb, every passive construction that leaves the reader unsure who is doing what—these are the thousand and one adulterants that weaken the strength of a sentence. And they usually occur in proportion to education and rank.

Though each sentence is spare,  Zinsser includes some long and concrete lists. Notice how effective that combination is.

From the New York Times Obituary

His advice was straightforward: Write clearly. Guard the message with your life. Avoid jargon and big words. Use active verbs. Make the reader think you enjoyed writing the piece. 

He conveyed that himself with lively turns of phrase: 

“There’s not much to be said about the period except that most writers don’t reach it soon enough,” ... 

“Abraham Lincoln and Winston Churchill rode to glory on the back of the strong declarative sentence,” ..

Zinsser's book was an inspiration to me.  I highly recommend it to economists and PhD students. (My reading list for a PhD writing workshop.)

Measure your time. You may think you're a social scientist, but in fact you're a writer.

Small shoes and headroom

I talked with Kathleen Hays and Michael McKee on Bloomberg Radio last week, and they asked (twice!) a question that comes up often in thinking about Fed policy: shouldn't the Fed raise rates now, so it has some "headroom" to lower them again if another recession should strike?

I could only answer with my standard joke: That's like the theory that you should wear shoes two sizes too small because it feels so good to take them off at the end of the day.

But the question comes up so often, it's worth thinking about a little more seriously. Under what views about the economy does this common idea make any sense?

One way to think about the question: is the effect of interest rates on the economy path-dependent, so that a given level of short-term interest rates has more "stimulative" effect if it comes from a previously high value than if short-term interest rates were zero all along?

The usual answer is no. The model is usually a linear system, in which lowering the rate from a high value has the same effect as raising to the same rate coming from a low value.  In fact, the usual model goes the other way:  If, say, a new recession hits in June 2017 and you want more stimulus then,  having had rates at zero all along is more "stimulative" than having raised them to 3% between now and then, and lowering rates all of a sudden.  In equations, if \(y_t = \sum \theta_j i_{t-j} + \varepsilon_{t} \) with \(\theta_j \ge0 \) then the partial derivative of any \(y_t\) with respect to any \(i_{t-j}\) is the same no matter what the path of interest rates before time \( t-j\), and raising \( i_t \) today lowers future \( y_{t+j} \) given any set of shocks \(\{\varepsilon_t\}\)  You need some sort of nonlinear system where a higher interest rate today \(i_t\)  makes \( y_{t+j}\) more sensitive to some future rate  \(i_{t+k} \).

Another way to think about this question is to think about what sort of state variables the interest rate affects. If the Fed raises rates now, the economy will be in a different state in June 2017. So in what view of things does raising rates now put the economy in state such that the economy can better weather a shock, or, more to the point, a state in which lowering rates back to zero will be more "stimulative" than if rates were zero all along? People usually think that raising rates between now and May 2017 would lower inflation, output and employment over what they would have been otherwise. Then, once rates go to zero again in June 2017, inflation, output, and employment will be lower than if interest rates had been zero all along.

If the economy were to boom on its own, with inflation, output and employment rising, and the Fed were to follow that good news by raising rates, then yes the Fed would have more "headroom." But that's not an argument that the Fed can get the "headroom" by acting now.

In fact, the opposite  story has been told by those who advocate forward guidance and raising the inflation target. They argue that the Fed should keep rates lower and for longer, in order to raise inflation (the "state variable"). Higher inflation then indeed gives the Fed "headroom" to lower real rates by lowering nominal rates in the next recession.

What does it take to turn this around, and to justify the idea that raising rates gives "headroom" to lower them in the future? The main answer I can think of is to turn the conventional stories around. Suppose that raising interest rates raises inflation, as I have speculated before (here). The desired "headroom" is the desire to raise inflation, so that when June 2017 comes around the same nominal rate (0) corresponds to a lower real rate. I doubt many people articulating the policy view want to travel to Fisher-land and reverse the effect of interest rates on inflation.

You still need a second belief: that despite the wrong sign on inflation the conventional theory has the right sign on output: That lowering rates in June 2017 will fight that recession, even as it will lower inflation again. My little model didn't deliver that. Maybe other models do.

Loud disclaimer: I'm not advocating any position here. I'm just thinking out loud about what kind of views, if any, lie behind this common idea that raising rates now gives the Fed some sort of "headroom" to stimulate the economy in the event of a future recession.

This is a good case for real economic models. There is a lot of cause and effect chat surrounding monetary policy and financial policy that is way ahead of (if you're being polite) or outside of (if you're being accurate) any well-understood or even well-articulated economic model. By tying ideas together, perhaps a policy belief ("headroom") can open one's mind to an interesting causal channel (Fisher equation), or perhaps seeing that channel needed can reverse a policy belief.

Unit roots in English and Pictures

After my unit roots redux post, a few people have asked for a nontechnical explanation of what this is all about.

Suppose there is an unexpected movement in any of the data we look at -- inflation, unemployment, GDP, prices, etc.  Now, how does this "shock" affect our best estimate of where this variable will be in the future? The graph shows three possibilities.


First, green or "stationary."  There may be some short lived dynamics, the little hump shape I drew here. Then, given enough time, the variable will return to where we thought it was going all along. For unemployment, suppose your best guess of unemployment in 2050 was 5%. Then you see an upward unexpected 1% spike in today's unemployment. Ouch, that means that we're going back to a recession. But perhaps this news does not change your view of 2050 unemployment at all.

Second, blue or "pure random walk." That's more plausible (though no longer thought to be true) of stock prices. If the price goes up unexpectedly, your expectation of where the (log) price will be in the future goes up one-for-one, for all time.

Third, black, "unit root." This option recognizes the possibility that a shock may give rise to transitory dynamics, and may come back towards, but not all the way towards your previous estimate. As you can see the "unit root" is the same as a combination of a stationary component and a bit of a random walk. Perhaps seeing unemployment rise 1%, you think most of it will work itself out, but that even in the long run labor markets will be sticky and we'll never quite get back.

The "unit root" is most plausible and verified in the data for log GDP. Recessions and expansions have a lot of transitory component that will come back. But there are permanent movements too. Unemployment, being a ratio, strikes me as one that eventually must come back. But it can take a longer time than we usually think, which is interesting.

This is very simplified. A few of the issues:

For GDP the question is whether it will come back to a linear trend extrapolated from past data, not back to a level as I have shown.

Most of the issue is how standard statistical procedures work in these circumstances.

As you can see from the graph, the pure question whether the series will come back in an infinite time period is not really knowable. It could be that the series will come back eventually, but take a very long time. It could be stationary plus a second very slow moving stationary component. This is a statistical problem but not really an economic problem. The appearance of unit roots are economically interesting as they show a lot of "low frequency" movement, series that are coming back slowly -- even if they do come back eventually. The economics of "slumps" and (we hope, someday) "booms" is hot on the agenda, and this is one indication of the fact.

This is all much more interesting if you look at multiple series together. For the canonical example, if you just look at stock prices, they are very very close to a random walk. A price rise or decline are permanent. However, if you see stock prices rise relative to dividends, that's almost entirely stationary. GDP and consumption have a similar relationship. As in the latest recession, if GDP declines with a big consumption decline, that looks pretty darn permanent. GDP declining and people still consuming is much more likely to go away.

I hope this helps.

Consumption-based model and value premium

The consumption based model is not as bad as you think. (This is a problem set for my online PhD class, and I thought the result would be interesting to blog readers.)

I use 4th quarter to 4th quarter nondurable + services consumption, and corresponding annual returns on 10 portfolios sorted on book to market and the three Fama-French factors. (Ken French's website)

The graph is average excess returns plotted against the covariance of excess returns with consumption growth. (The graph is a distillation of Jagannathan and Wang's paper, who get any credit for this observation.  The lines are OLS cross-sectional regressions with and without a free intercept.)


By comparison, the CAPM is the usual disaster. If we plot average returns against the covariance of returns with the market (rmrf) or against market betas, there is very little pattern. In particular, the hml portfolio, which by itself captures almost all the pricing information in the ten b/m portfolios (that's the point of the Fama-French model) has a 5% average return and a slightly negative market beta. The fact that the hml portfolio is right on the line in the previous graph is the main point of that graph.

There is an essentially correct story in the consumption-based model: value stocks and small stocks have higher average returns. And they have correspondingly higher covariance with consumption growth. Value and small stocks tend to do poorly in years of bad consumption growth, though they have little systematic correlation with the market.

Is this perfect? No. The model is \(E(R^e) = cov(R^e, \Delta c)) \times \gamma\) where \(R^e\) = excess return, \(\Delta c\) is consumption growth and \( \gamma\) is the risk aversion coefficient. The mean returns are so large -- and the volatility of consumption growth so small -- that the slope coefficient = risk aversion coefficient is 80, a bit hard for most people to swallow.

Also, this is the linearized model. The true nonlinear model is \(E(R^e) = -cov(R^e_{t+1}, (c_{t+1}/c_t)^{-\gamma})\), and raising things to the 80th power is a lot different than multiplying by 80. On the other hand, perhaps this is the key to good performance. If you think the underlying correct model works in continuous time,  which is linear, \( E_t(dR^e) = -E_t(dR^e, dc)  \gamma \), then perhaps the linearized model is a better approximation to annual time-averaged data than is the discrete-time model that pretends all consumption happens in one big lump every December 31. Furthermore, if you raise consumption growth to the 80th power, all the covariance of returns with marginal utility comes in one or two big spikes. The model becomes a model of rare disasters in marginal utility, not one of repeated events. Perhaps, but life would be so much easier if markets were about repeated risks not once per century disaster covariances.

The larger point: Very few researchers have really given the consumption model a good go to see just how full the glass might be. Hansen and Singleton famously rejected the model, but they used monthly seasonally adjusted consumption data, a bunch of low-power instruments, and no treatment of time aggregation (consumption is sum for the month, returns are 30th to 30th), or the durability of most "nondurable" goods. (Shirts are "nondurable." I get all mine at Christmas, hence 4th quarter to 4th quarter works pretty well for me!) Their point was mostly an illustrative example of GMM methodology not a serious Fama-French style empirical investigation of just how far a model can go. (The Fama-French model is also rejected!) It took 25 years before Jagannathan and Wang produced this simple graph. Can we do even better?

Sure, the consumption-based model won't work at a 5 minute interval. But is there some essence of truth in it, that stocks which fall more in business cycles, as measured by consumption, must pay a higher rate of return.  Just how far does that truth go? I think one could do far better by thinking hard about time aggregation,  data construction, durability, seasonal adjustment, and the appropriate frequency to evaluate such a model. And by trying to see just how far the model can go, rather than statistically rejecting its perfection.

In the end  "why are people afraid of value stocks and leave attractive returns on the table?" must come down to 1) they're morons, they haven't figured it out 2) the value premium isn't really there or 3) value stocks do badly in bad times, so make a portfolio riskier. That consumption is also low in these bad times seems pretty natural.

Update

From "Cross-Sectional Consumption-Based Asset Pricing: A Reappraisal" by Tom Engsted and Stig Vinther Møller at University of Aarhus. Thanks to Stig for the link. BOP and EOP are beginning of period and end of period consumption. In a discrete time model, do you treat the sum of consumption over the year as happening at the beginning of the year, or the end of the year? Treating it at the beginning produces the dramatic graph on the left.

This is a small instance of the many explorations one can do to see if there is some power to the consumption-based model, rather than just take it literally and reject it.

A bigger point. Means are pretty insensitive to timing. But covariances and correlations of white noise series are exquisitely sensitive to timing, measurement error, and so forth. \(cov(a_t,b_t)\) may be large, and \( cov(a_{t-1} b_t)=0\). Another approach is to create time averaged returns. I did this a long time ago here. Average january-january, feburary-february, march-march, etc. returns and compare them to the growth of annual macro data. The right thing to do is to explicitly model time aggregation -- the fact that consumption is reported as an annual average -- along with seasonal adjustment.

Banking at the IRS

A while ago in two blog posts here and here I suggested many ways other than currency to get a zero interest rate if the government tries to lower rates below zero. Buy gift cards, subway cards, stamps;  prepay bills, rent, mortgage and especially taxes -- the IRS will happily take your money now and you can credit it against future tax payments; have your bank make out a big certified check in your name, and sit on it, don't cash incoming checks. Start a company that takes money and invests in all these things (as well as currency).

Chris and Miles Kimball have an interesting essay exploring these ideas "However low interest rates might go, the IRS will never act like a bank." Their central point: sure that's how things work now. But with substantial negative interest rates, all of these contracts can change. It's technically possible in each case for people and businesses to charge pre-payment penalties amounting to a negative nominal rate.

Reply: Sure, in principle. Nominal claims can all be dated, and positive or negative interest charged between all dates.

But this did not happen in the US and does not happen in other countries for positive inflation and high nominal rates,  despite symmetric incentives, and at rates much higher than the contemplated 3-5% or so negative rates.  Yes,  with large nominal rates there is pressure to pay faster,  inventory cash-management to reduce people's holdings of depreciating nominal claims, but this pervasive indexation of nominal payments did not break out. The IRS did not offer interest for early payment.

More deeply, what they're describing is a tiny step away from perfect price indexing. If all nominal payments are perfectly indexed to the nominal interest rate, accrued daily, then it's a tiny change to index all prices themselves to the CPI, accrued daily. If "how much you owe me," say to rent a house, is legally, contractually, and mechanically determined as a value times e^rt, and changes day by day, then e^(pi t) is just as easy.

So, price stickiness itself would (should!) disappear under this scenario.

Price stickiness has always been a bit of a puzzle for economists. As the Kimballs speculate how easy it is to index payments to negative interest rates, so economists speculate how easy it is to index payments to inflation. Yet it seems not to happen.

So this point of view strikes me as a bit of a catch-22 for its advocates, who generally are of the frame of mind that prices and nominal contracts are sticky and that’s why negative nominal rates are a good idea to "stimulate demand" in the first place.  If we can have negative nominal rates and change all these legal and contractual zero-rate promises to allow it, then prices won't be sticky any more!   Conversely, I should be cheering, as it amounts to a broad push to unstick prices. That has long seemed to me the natural policy response to the view that sticky prices are the root of all our troubles. It would allow negative rates, but eliminate their need as well.

Alas, the world seems remarkably resistant to time-indexing all payments.

Blanchard on Countours of Policy

Olivier Blanchard, (IMF research director) has a thoughtful blog post, Contours of Macroeconomic Policy in the Future. In part it's background for the IMF's upcoming conference with the charming title Rethinking Macro Policy III: Progress or Confusion?” (You can guess my choice.)

Olivier cleanly poses some questions which in his view are likely to be the focus of policy-world debate for the next few years.  Looking for policy-oriented thesis topics? It's a one-stop shop.

Whether these should be the questions is another matter. (Mostly no, in my view.)

As a blogger, I can't resist a few pithy answers. But please note, I'm mostly having fun, and the questions and essay are much more serious.

Financial regulation

... Where do we stand? Are some dimensions of systemic risk easier to measure (e.g., leverage in the banking sector vs. interconnectedness of banks and non-banks or risks outside the banking sector)? How should we assess the experience with stress-tests?  And have we made enough progress in reducing systemic risk since the crisis, e.g., with Dodd-Frank, the Vickers commission, the Financial Stability Board, etc?

Answer: "Systemic risk" is barely defined. The idea that regulators will, this time, really really, understand risks taken by the big banks, see trouble ahead, and stop the banks from failing, is a triumph of hope over repeated experience.
The only progress -- and it's big -- is the slow realization that banks can and should issue lots more equity.

Macro Prudential Policies

... Do we have or can we develop tools to deal with the different types of risk, from high housing prices, to insufficient capital in some financial institutions, to sudden drops in liquidity in some financial markets?

Using these tools ...raises political economy issues.  In a housing boom, increasing the loan to value ratio may be politically difficult.  Questions:  Given these issues, when should we use macro prudential tools, or should we use tougher, non contingent financial regulation? To be concrete, should we aim for variable capital ratios and decide when to adjust them, or just give up on the variable part, and aim for high but constant capital ratios?

Answer: The hubris that the Davos set will be able to figure out just the right amount of capital, and then fine-tune that month-to-month and bank-to-bank is astounding. "Political economy concerns" is putting it mildly. The IMF's "bubble" or "imbalance" is the local Congressman's boom, and he or she will be hopping mad if the Fed restricts credit to his district or pet industry in favor of another. 

The fact that our regulators are still talking about liquidity betrays a fundamental confusion of individual vs. systemic risks. Liquidity is the plan, "if we lose money we'll sell assets." To who? Regulators demanding liquidity to plan for a financial crisis is like the FAA making sure everyone on the plane has enough money to buy a parachute in case of engine failure.  

Finally, it is clear that both financial regulation and macro prudential tools are likely to lead financial actors to adjust and explore ways of getting around them. Questions:  In this game of cat and mouse, can the macro prudential regulators hope to win?  Or will regulation and tools become increasingly complex and possibly counterproductive?

That's easy. No and Yes. Actually I'm being too pessimistic. Regulatory capture works both ways. An easy forecast: Stress-testers at the Fed will be getting lucrative salary offers to move to the private sector and help pass stress tests. Which they will increasingly do. 

Monetary Policy 

...  Questions:  Under the highly realistic assumption that financial regulation and macroprudential tools do not fully take care of financial stability, [Highly realistic indeed! You just answered the first set of questions as I did!] should monetary policy take financial stability into account?  And if so, how?  Can the interest rate or other monetary policy tools reduce financial risk?   How should macro prudential tools and monetary policy be coordinated?  Should they both be under the responsibility of the central bank?

 Let's remember that the crash of 1929 was, at least in the standard history, sparked by the Fed trying to restrain what they saw as the bubble in the stock market.

If this is the case, and central banks have tools which can have effects on very specific sectors of the economy, can they retain full independence?

No. In a democracy, independence comes with limited authority. The financial central planner cannot and will not long stay independent.   

The zero ... lower bound on the  interest rate set by central banks was thought to be a theoretical curiosum, unlikely to happen, and, in any case, easy to combat if reached.   If reached, central banks could, through announcements of future monetary policy, increase expected inflation and achieve large negative interest rates.  We have learned that this was simply wishful thinking.  The zero lower bound could be reached, inflation expectations are not easy to manipulate, and it may take a very long time to exit.

Three cheers. Wow, Olivier, who wrote one of the most influential calls for announcements of higher inflation targets, looks at the data and calls it "wishful thinking." Bravo. 

.. Quantitative Easing,... Questions:  ...should central banks eventually return to the traditional mode of intervening at the short end of the market, or should they continue to buy and sell longer maturity sovereign or corporate bonds?   Should the balance sheets of central banks return to their pre-crisis size, or remain permanently larger?  If the central bank intervenes along the yield curve, how should monetary policy and debt management by the Treasury be combined?

Large balance sheet, interest-paying reserves, open to everyone. Some crisis interventions reveal very desirable permanent states of affairs. Stop fooling around with direct intervention in long-term debt, mortgage-backed security markets, and don't follow other central banks to buying and selling stocks, foreign exchange, etc.

Fiscal Policy

... Questions: What is a dangerous level of debt? That which markets doubt you can repay. Seriously, if you're growing fast with a good long run plan for containing expenditures and raising revenue without ruinous taxation, a lot. If not, a lot less. ... What do we know about confidence effects?  You mean statements by officials that "engender confidence?" Go back to the Romans, burn incense at the Temple of Jupiter. More seriously, we've learned that speaking loudly with no stick doesn't work. ...Should the old idea of the fiscal golden rule, the separation of a current and of a capital account, be resurrected? Separating two sides of an accounting identity sounds like an interesting golden rule. I think it would be golden to separate the current account and capital account I run down at the apple store -- they give me stuff, I don't have to give them money. Olivier surely has something more sophisticated in mind, and I'm revealing I'm a rube at this policy-speak coded language. 

Most observers agree that the fiscal stimulus early in the crisis was instrumental in limiting the decrease in output.   I'm glad he said "most" not "all"....

Capital inflows, exchange rate management and capital controls

The crisis has reinforced the notion that international capital flows can be very volatile, with emerging markets being particularly vulnerable.  Back to previous comment. Capital can try to flow, but unless goods flow in the other direction, all it does is to lower prices. Unless you can pass a rule to get rid of accounting identities. See above.  Policy makers have responded with a panoply of tools, from capital controls A polite word for expropriation to macro prudential measures aimed at shaping flows, What a lovely little policy-ese phrase  and FX intervention. .... And what does the experience since the crisis say about the optimal opening of the capital account, even in the long run? Translated to English, back to the de-globalized protectionist world. If capital can't flow, neither can goods. 

The International Monetary and Financial System

.... Questions: ... Should we reexamine the rules of the game for exchange rates?   How can we improve on the process of sovereign debt restructuring?

As Olivier's essay moves on, and gradually reverts to the  obfuscatory Orwellian prose of the international policy world, I get more and more animated. I mean just who is this "we?" Who is going to tell you you're not allowed to buy euros for your vacation this summer ("capital controls"), tell your bank not to give you a loan ("macro-produential policy"), decide how many billions to siphon from your pocket to the owners of large banks ("recapitalization" "process of sovereign debt restructuring"), not allowed to expand your business in a new country ("macro prudential measures aimed at shaping flows") and so forth? When there even is a "we," unlike most sentences with no subjects, like "the optimal opening of the capital account."

What should be the role of international forums such as the G20?

Aha, now I get it. 

Arezki, Ramey, and Sheng on news shocks

I attended the NBER EFG (economic fluctuations and growth) meeting a few weeks ago, and saw a very nice paper by Rabah Arezki, Valerie Ramey, and Liugang Sheng, "News Shocks in Open Economies: Evidence from Giant Oil Discoveries" (There were a lot of nice papers, but this one is more bloggable.)

They look at what happens to economies that discover they have a lot of oil.

An oil discovery is a well identified "news shock."

Standard productivity shocks are a bit nebulous, and alter two things at once: they give greater productivity and hence incentive to work today and also news about more income in the future.

An oil discovery is well publicized. It incentivizes a small investment in oil drilling, but mostly is pure news of an income flow in the future. It does not affect overall labor productivity or other changes to preferences or technology.

Rabah,Valerie, and Liugang then construct a straightforward macro model of such an event.

Utility comes from consumption and work. The production function has an oil sector and non-oil sector. There are adjustment costs to investment and to reallocation of capital between oil and non-oil sectors. The consumption good is tradeable, and the economy sells oil internationally to get it as well as to produce it.

They compute impulse-response functions to big oil discoveries, and compare the model dynamics to the response functions. It's a nice fit and an intuitive story. After the shock hits, during the period of investment, the current account declines -- borrow money, buy oil investment goods and also borrow to finance higher consumption now. GDP is basically flat, as oil investment is a small fraction of the economy. Savings also declines. Consumption goes up right away, and then stays up in permanent income fashion. (You have to look closely at the green line, because the vertical scale is too small.) Investment rises, to build those oil wells.

Employment declines, as there is a wealth effect encouraging leisure but no higher productivity of labor to encourage work. This is why productivity shocks, emphasizing a temporarily higher marginal product of labor, are important in real business cycle models.

Once oil comes on line, the current account changes sign, as the economy exports oil and pays back debt. GDP, including the oil, rises. Consumption stays were it was, by permanent income logic. And investment returns to zero.

Valerie, presenting the paper, was a bit discouraged. This "news shock" doesn't generate a pattern that looks like standard recessions, because GDP and employment go in the opposite direction.

I am much more encouraged. Here are macroeconomies behaving exactly as they should, in response to a shock where for once we really know what the shock is. And in response to a shock with a nice dynamic pattern, which we also really understand.

My comment was something to the effect of "this paper is much more important than you think. You match the dynamic response of economies to this large and very well identified shock with a standard, transparent and intuitive neoclassical model. Here's a list of some of the ingredients you didn't need: Sticky prices, sticky wages, money, monetary policy, (i.e. interest rates that respond via a policy rule to output and inflation or zero bounds that stop them from doing so), home bias, segmented financial markets, credit constraints, liquidity constraints, hand-to-mouth consumers, financial intermediation, liquidity spirals, fire sales, leverage, sudden stops, hot money, collateral constraints, incomplete markets, idiosyncratic risks, strange preferences including habits, nonexpected utility, ambiguity aversion, and so forth, behavioral biases, nonexpected utility, or rare disasters. If those ingredients are really there, they ought to matter for explaining the response to your shocks too. After all, there is only one economic structure, which is hit by many shocks. So your paper calls into question just how many of those ingredients are really there at all."

Thomas Philippon, whose previous paper had a pretty masterful collection of a lot of those ingredients, quickly pointed out my overstatement. One needs not need every ingredient to understand every shock. Constraint variables are inequalities. A positive news shock may not cause credit constraints etc. to bind, while a negative shock may reveal them.

Good point. And really, the proof is in the pudding. If those ingredients are not necessary, then I should produce a model without them that produces events like 2008. But we've been debating the ingredients and shock necessary to explain 1932 for 82 years, so that approach, though correct, might take a while.

In the meantime, we can still cheer successful simple models and well identified shocks on the few occasions that they appear and fit data so nicely. Note to graduate students, this paper is a really nice example to follow for its integration of clear theory and excellent empirical work.

Bachmann, Berg and Sims on inflation as stimulus

Rüdiger Bachmann, Tim Berg, and Eric Sims have an interesting article, "Inflation Expectations and Readiness to Spend: Cross-Sectional Evidence" in the American Economic Journal: Economic Policy.

Many macroeconomists have advocated deliberate, expected inflation to "stimulate" the economy while interest rates are stuck at the lower bound. The idea is that higher expected inflation amounts to a lower real interest rate. This lower rate encourages people to spend today rather than to save, which, the story goes, will raise today's level of output and employment.

As usual in macroeconomics, measuring this effect is hard. There are few zero-bound observations, fewer still with substantial variation in expected inflation.  And as always in macro it's hard to tell causation from correlation, supply from demand, because from despite of any small inflation-output correlation we see.

This paper is an interesting part of the movement that uses microeconomic observations to illuminate such macroeconomic questions, and also a very interesting use of survey data. Bachman, Berg, and Sims look at survey data from the University of Michigan. This survey asks about spending plans and inflation expectations. Thus, looking across people at a given moment in time, Bachman, Berg, and Sims ask whether people who think there is going to be a lot more inflation are also people who are planning to spend a lot more. (Whether more "spending" causes more GDP is separate question.)

The answer is... No. Not at all. There is just no correlation between people's expectations of inflation and their plans to spend money.

In a sense that's not too surprising. The intertemporal substitution relation -- expected consumption growth = elasticity times expected real interest rate -- has been very unreliable in macro and micro data for decades. That hasn't stopped it from being the center of much macroeconomics and the article of faith in policy prescriptions for stimulus. But fresh reminders of its instability are welcome.

At first blush, this just seems great. Finally, micro data are illuminating macro questions.


It's cleaner than the  Hagedorn, Manovskii and Mitman paper I blogged last week, because many of the aggregation issues are absent. There, I complained that employment in one state might be  gained by business moving from another, which would not be an available channel for the whole economy. Here, if we know that people who expect more inflation spend more, it's an easier jump that if we all expect more inflation we all want to spend more. This aggregation problem is usually one of the biggest stumbling blocks for the project to measure macro effects from micro data.

Now, for a little whining. This isn't really criticism as I don't know how to do any better. But it does make for a very well-done example in which to ponder the limitations of the micro evidence on macro questions methodology.

Here are Table 1 and 2, the "baseline specification."

It's a probit regression. The left hand variable is whether a person answered yes or no to the question,

Q1: “About the big things people buy for their homes—such as furniture, a refrigerator, stove, television, and things like that. Generally speaking, do you think now is a good or a bad time for people to buy major household items?” 

The main right hand variable, ("Inflation expectations (1Y)") is the answer to the question,

Q2: “By about what percent do you expect future prices to go (up/down) on the average, during the next 12 months?”

The main fact is that the top row of numbers are all essentially zero, decently well measured, and nonetheless statistically insignificant. Where it is significant, in the zero-bound years, it's negative -- higher inflation expectations are associated with plans to spend less, not more!

So far, so good. But what are all those other numbers in the table? Well, these are "controls," extra right hand variables in the regression.

What in the world are they doing there? The fact is not "people with higher inflation expectations don't plan to spend any less." The fact is that "people with higher inflation expectations, holding constant their expected financial situation and income, their expected change in nominal interest rate and aggregate business conditions, ..., a long vector of aggregate variables, and then the whole Table 2 of demographic variables, don't plan to spend any less." Hmm.

The long list of "controls" brings back memories of all the regression horror stories I was taught in graduate school (thank you Tom Rothenberg).

Left shoe sales = a + b price + c right shoe sales + error. 

Wage = a + b education + c industry + error. 

(In case the latter isn't obvious: including industry helps a lot to "explain" wages and raise R2. But the point of education is to let you change industries from fast food to computers, so you absolutely do not want to "control" for industry!)

What are all the controls doing here? Could we not at least start with OLS, a clean digestible fact, or a graph so that poor bloggers have something to brighten up posts?

I asked the correspondent who sent me the paper (thanks) who opined that the referees probably made the authors do it, and out of a reasonable concern. Maybe the correlation between inflation expectations and spending plans across people does not measure the causal effect, what if we change inflation and leave other things constant?  It could well be that the correlation of expectations across people is zero, reflecting other forces at work, but if we raise everyone's inflation expectations, then we would raise everyone's spending.

Most simply, just because we put inflation expectations on the right hand side of a regression and spending on the left, does not mean that changes in inflation expectations across people cause their spending plans to change.

Demographic controls seem reasonable. Suppose the fact was that women all expected higher inflation and planned to spend a lot, while men expected low inflation and did not plan to spend a lot. One would not want to use that correlation to measure how increasing expected inflation for all of us would affect our spending. Such a demographic correlation is much more likely a result of other causes affecting both variables (inflation expectations and spending). This really remains the deep issue of micro to macro implications: Does a correlation across people tell us what happens if something affects all of us?

But if demographic controls changed the result a lot over OLS, one would be very suspicious. A correlation that survives controls is a lot more persuasive than a correlation that only emerges with controls. It's much nicer to say there is a raw correlation, and verify that it is not the result of differences between demographic groups, than to say the correlation is only measured after demographic controls. Because no set of controls is perfect. (The implicit assumption "my controls perfectly capture all the reverse causation or all third variable influences" pervades regression analysis.)

Many of the controls are macro variables. There are almost as many controls here as time data points. Year dummies would have removed all the time-series variation and left us the pure cross section a lot more simply.

The first set of controls for other expectations strikes me as the most fishy. Why would we measure the effect of a change in expected inflation holding constant expected unemployment? The whole point of the macro experiment is to raise both expected inflation and to lower expected unemployment.

This is the hard nut of all regression analysis: why does the right hand variable vary? People spend a lot of effort on the left hand variable, but that's actually less important. What caused the variation in your data? We don't have randomized experiments. Why is it that households have such widely (insanely!) varying expectations of inflation? Until we know that, it's really going to be hard to tell whether their similarly widely varying spending plans are because of higher inflation expectations, or because inflation and spending plans are both results of some third cause.

The paper isn't much help on this issue. At least I wish they (or much of any regression work) at least asked the question. They don't even really discuss the "controls" in this way; why expected inflation varies, and then control for determinants of expected inflation that are correlated with determinants of spending.

The discussion of the control variables sounds a lot like the habit of assuming everything on the right is a "cause," and fishing for R2, like left shoes in the right shoe equation, and industry in the wage equations.

With respect to the coefficients on the economic control variables, we obtain for the most part plausible and significant estimates,... the expected financial situation of the household and its real income, the expected business conditions (idiosyncratic and aggregate), the current financial situation, and the current real household income all have significantly positive effects on the reported spending readiness. In addition, a positive judgement of US economic policy also affects spending dispositions positively. Moreover, an expected increase in future nominal interest rates makes people want to spend more today,  while higher economic uncertainty in the form of stock market volatility, inflation volatility and higher unemployment rates (both current and expected) decrease the probability that people find buying conditions favorable ...

But enough whining. My point is that micro, regression-based analysis has its limitations too. This seemed like a good example on which to remind graduate student readers of common regression pitfalls: Always ask what caused the variation in the right hand variable. Use minimal controls, not the kitchen sink. Make sure the partial effects of your regression (move x holding z constant) make sense. And so on.

But I don't think I could have done better, as making sense of why people's expectations are as widely dispersed as they are seems a big challenge.

It's still a powerful observation, and I trust it's there in the OLS with minimal controls. People who expect more inflation do not plan to spend more. If you think raising all our expected inflation will make us all spend more, you have some creative explaining to do.

Update: Eric responds:

On your point about all the control variables . . . we did (more or less) what you suggest in the blog post. If you look at Table 3, we drop all of the idiosyncratic control variables in one specification and get essentially the same results; also in Table 3 we do the version with time fixed effects instead of aggregate controls. If you go to the online appendix, in Table 8 we show raw correlations between expected inflation and buying attitudes. We also split the raw correlation by a large number of different demographics. In Figure 7 we show plots of time-varying raw correlations between expected inflation and spending attitudes -- it is the analog of Figure 6 in the main paper which plots a time-varying marginal effect based on the probit estimation. Basically this all shows exactly what you ask for in the blog post -- the correlation/coefficient between expected inflation and buying attitudes does not depend on the controls.

I admit not reading all the way through or the online appendix. They also confirm that the early drafts started with raw correlations. There is an interesting writing (and editing and refereeing) conundrum, should a paper start with the "main" result, or should one start with suggestive robust facts and correlations and then address objections with a more sophisticated model. It's not an easy question -- Most papers drag you through 10 tables of motivation and summary statistics and suggestive correlations before getting to the point, and I really admire that this paper had the main result on Table 1.  OTOH, by going the other way around busy bloggers miss the interesting correlations in online appendix Table 8!

Unemployment insurance and unemployment

"The Impact of Unemployment Benefit Extensions on Employment: The 2014 Employment Miracle" by Marcus Hagedorn, Iourii Manovskii and Kurt Mitman is making waves. NBER working paper here. Kurt Mitman's webpage has an ungated version of the paper, and a summary of some of the controversy. It's part of a pair, with "Unemployment Benefits and Unemployment in the Great
Recession: The Role of Macro Effects" also including Fatih Karahan.

A critical review by Mike Konczal at the Roosevelt Institute blog, and a more positive review by Patrick Brennan at National Review Online are both interesting. Both are thoughtful reviews that get at facts and methods. Maybe the tone of the economics blogoshpere is improving too. Bob Hall's comments and response on the earlier paper are also worth reading. This is a bit deja-vu from the observation that North Carolina experienced a large drop in unemployment when it cut benefits. My post here, WSJ coverage, and I think there are some papers which google isn't finding fast enough at the moment.

The basic issue: I think it's widely accepted, if sometimes grudgingly, that unemployment insurance increases unemployment. If you pay for anything, you get more of it. People with unemployment insurance can hold out for better jobs, put off moving or other painful adjustments, and so on. The earlier paper points out that there are important general equilibrium effects as well. We should talk about how UI affects labor markets, not just job search.

Quick disclaimer. Let's not jump to "good" and "bad."  Searching too hard and taking awful jobs in the middle of a depression might not be optimal. Pareto-optimal risk sharing with moral hazard looks a lot like unemployment insurance.  Perhaps that disclaimer can settle down the tone of the debate.

But the question remains. How much?  How much does unemployment insurance increase unemployment? And the related macro question, just why did unemployment in the US suddenly drop coincident with sequester and the end of 99 week unemployment benefits?

Method is important. Too much media coverage starts and stops with "study finds unemployment insurance raises unemployment." And then the next day "study finds unemployment insurance crucial to stopping people from dying in gutters." If we focused on the facts, we'd all get along better.

In macro, we always are faced with the problem that interpreting time series, we never know what else changed. Sure, congress lowered unemployment benefits and the economy took off. But lots of other stuff happened. Maybe it's "despite" not "because."

This paper is part of a new breed trying to get around this problem by looking at cross-sectional evidence. Roughly, the evidence in this paper builds on the fact that Congress' action had different effects in different states.

Bob Hall described the strategy compactly:

They compare labor markets with arguably similar conditions apart from the UI benefits regime. In their work, the markets are defined as counties and the similarity arises because they focus on pairs of adjacent counties. The difference in the UI regimes arises because the two counties are in different states and UI benefits are set at the state level and often differ across state boundaries. The research uses a regression-discontinuity design, where the discontinuity is the state boundary and the window is the area of the two adjacent counties....

Table 3 contains the basic number, which the authors digest as

We find that a 1% drop in benefit duration leads to a statistically significant increase of employment by 0.0161 log points.  In levels, 1.8 million additional jobs were created in 2014 due to the benefit cut.

(Small complaint: economists should not write that jobs "were created," especially economists writing in the search, match and labor-supply tradition, to say nothing of passive voice and strong causal inferences.) I tried to digest the fact a bit more, but stopped here:

Column (1) of Table 3 contains the results of the estimation of the effect of unemployment benefit duration on employment using the baseline specification in Equation (6).

If commenters can vocalize the actual fact in words, fixed effects, controls and all, I'd be grateful.

Bob Hall echoes standard but important complaints.

The issues that arise in evaluating the paper are those for any regression-discontinuity research design: (1) Are there any other sources of discontinuous changes at the designated discontinuity points that might be correlated with the one of interest? (2) Is the window small enough to avoid contamination from differences that do not occur at the discontinuity point but rather elsewhere in the window?

In words, is there something else about state policies that changed at the same time in the "generous" vs. "stingy" states? And are counties really small enough to capture only the border effects?

The deeper issue in evaluating this paper, I think, comes from blowing the county results up to the aggregate, as Bob but it

The authors conclude that, absent the increase in UI benefits, unemployment in 2010 would have been about 3 percentage points lower.

The jump back from micro to macro isn't so easy either.  For example, suppose the expansion came from selling more goods from expanding states to contracting states. Then you'd see a micro effect but no macro effect. I don't think that's the case, but I have been skeptical about other papers jumps from micro to macro. For example, if the Federal government spends a trillion dollars in the desert, and a bunch of businesses move to sell donuts to the construction workers, you get a nice stimulus. That doesn't mean stimulus works for the economy as a whole.

This is a small nitpick. The basic fact is interesting, and I think a lot harder to dismiss.

It's interesting that so much of the pushback, both from Bob and from Mike Konczal's critical review comes down to theory, not the fact.

Update: Wednesday's Wall Street Journal covers the paper. The WSJ spends more time on the macro question, the claim that unemployment insurance actually boosts the economy via stimulus. 

Loggerheads

Government Debt Management at the Zero Lower Bound is a very nice and interesting paper by Robin Greenwood, Sam Hanson, Josh Rudolph, and Larry Summers.

Figure 1. Comparing Quantitative Easing and Treasury Maturity Extension, 2007–2014 ...the cumulative change in 10-year equivalents (scaled as a percentage of GDP) associated with the respective balance sheet policies undertaken by the Federal Reserve and the Treasury. Positive values increase the interest rate risk placed in public hands (Treasury policies), while negative values decrease it (typically Fed QE, but also Treasury maturity shortening in 2008–2009).

First point, what the Fed taketh away, the Treasury giveth. (Hence the title of this post). The Fed bought lots of long-term debt, with the idea that this would raise the price, lower the interest rate on the long-term debt, and thus stimulate the economy.

At the same time, however, the Treasury was selling lots of long-term debt. Interest rates are very low, and debts are high, so it's a great time to lock in low-rate financing. Homeowners and businesses are doing the same thing.


Alas, the Treasury and Fed are part of one budget constraint, so you can't have it both ways. As it turns out, the Treasury sold even more than the Fed bought, so by their calculations, during the period of QE, the private sector absorbed more long-term government debt!

...despite successive rounds of QE, the stock of government debt with a maturity over 5 years that is held by the public (excluding the Fed’s holdings) has risen from 8 percent of GDP at the end of 2007 to 15 percent at the middle of 2014. Said differently, the volume of 10-year duration equivalent debt has doubled from 13 percent of GDP to 26 percent of GDP over the same interval. Pressure to absorb long-term government debt has actually increased rather than decreased over the last six years!

The closing section, and main point, I think, is a proposal for how  Fed and Treasury should divide responsibility to avoid such loggerheads.

QE

Obviously, if the public has been holding more, not less, long term debt overall, that calls in to question if or how QE "worked.''

If  QE works, does it work down some price-pressure, portfolio-balance, segmented-market, demand-for-maturity curves? Or does it work by signaling Fed intentions about interest rates, and not at all per se? The fact that Treasury also changes maturity structure in private hands can let us sort out the two stories

But if the direct supply effects of QE have been offset by the massive expansion in outstanding government debt and the Treasury’s decision to extend the debt maturity, then what explains the large market impact of QE announcements documented in so many studies, as well as the fact that estimates of term premia on long-term bonds have been steadily driven into negative territory and remain miniscule today, as shown in Figure 3? The most natural explanation is that the Fed’s announcements about its intended asset purchases also conveyed information about its future policies, including both the likely path of future short-term rates and the Fed’s willingness to undertake further asset purchases in response to evolving economic conditions...

... there are reasons to think that announcements of Fed asset purchases may have a greater impact on term premia than comparably sized Treasury supply announcements. Consistent with this, Rudolph (2014) provides event-study evidence suggesting that Fed announcements have about twice the impact as Treasury announcements of a similar size...

I might add that event studies around announcements of future purchases tell us what the market thinks the effect of those purchases will be. Rational expectations is not a solid guide to events outside of all historical experience.

I've long wondered, if the Fed wanted to move long term rates, why did it not just do it -- buy and sell as required to nail the long term bond rate to 2%, say? It turns out this has a precedent:

A few months after the U.S. entered World War II, and in the midst of a rapid increase in government spending, the Fed and the Treasury agreed to fix the entire yield curve of Treasury securities. Three-month bill yields were limited to 0.375% and bond yields were held at 2.5%. The Fed stood ready to buy or sell any amount of treasury securities necessary to maintain this positively sloped yield curve

Optimal maturity structure: Liquidity premium vs. insurance against rate rises 

They argue for a much shorter maturity structure overall, trading the liquidity and low interest rate benefits of issuing short term debt against the insurance against interest rate rises benefits of issuing longer debt.

the main messages we take from these counterfactual exercises are (1) that the additional budgetary volatility incurred by shifting the government debt into short-term securities is less than is commonly supposed, and (2) that doing this would have allowed the government to capture liquidity premia on an ongoing basis

Here's the simulation. They compare surplus/deficit and total debt under existing debt and what would have happened if the Treasury issued only 3 month bills.

Figure 8 Debt and Deficits under Counterfactual Debt Management Plans. The counterfactual exercise measures the path of deficits and debt supposing that the U.S. Treasury had financed itself using rolling 3-month Treasury bills starting in 1952. ....

You can see with 3 month bills that interest-induced changes in deficits come sooner. But there isn't a big difference. From this they conclude that the interest-rate-insurance that comes from long term debt isn't a big deal.

I've been worrying about the interest-costs that a rise in interest rates would imply for some time now,  and advocating longer maturity structures on that basis. (For example in an earlier blog post  here and a longer paper here and most recently in "monetary policy with interest on reserves" ). So what's the difference? I'd make a two little complaints

1. The  US maturity structure is quite short. Last time I put together the numbers, the US rolls over half our debt every two years. And historically, it's been much shorter. So shortening down to three months doesn't change things a lot. How would lengthening to perpetuities work here?
2. The danger is a large debt to GDP ratio and the risk of a rate rise. Now we have $18 trillion of debt, so interest rates rising to 4% means $760 billion more deficits.  The graphs show two important data points really. At the end of WWII we had big debt/DGP. And interest rates stayed low until the 1970s. At the end of the 1980s, we had a big rise in real rates. And a low debt/GDP ratio.  So, Russian roulette, the gun clicked twice, doesn't mean we're safe. This isn't about averages, it's about risk management. 

Still, it's a challenging calculation, and to answer it properly requires a simulation of possible interest rate paths and debt stocks. 

They opine on real vs. nominal debt, too, arguing for more nominal debt, and much else. The whole thing is a good read.