How Much Federal Debt Does the Fed Own?

The fine folks at FRED, the economic data service of the St. Louis Fed, recently added seven new data series showing how various measures of federal debt compare to the economy as a whole, as measured by GDP.

I particularly enjoyed this one, showing the federal debt owned by the Federal Reserve banks.

Quantitative easing gets all the press these days and understandably so given the recent spike in Fed ownership of Treasuries, now equivalent to almost 11 percent of annual GDP. But the chart also reminds us of that brief period early in the financial crisis when the Fed sold lots of Treasuries so it could make loans and buy other assets.

P.S. Anyone know how to get the FRED graph’s vertical axis to start at 0?

Niall Ferguson’s Mistake Makes the Case for Metadata

Harvard historian Niall Ferguson goofed on Bloomberg TV yesterday. Arguing that the 2009 stimulus had little effect, he said:

The point I made in the piece [his controversial cover story in Newsweek] was that the stimulus had a very short-term effect, which is very clear if you look, for example, at the federal employment numbers. There’s a huge spike in early 2010, and then it falls back down.  (This is slightly edited from the transcription by Invictus at The Big Picture.)

That spike did happen. But as every economic data jockey knows, it doesn’t reflect the stimulus; it’s temporary hiring of Census workers.

Ferguson ought to know that. He’s trying to position himself as an important economic commentator and that should require basic familiarity with key data.

But Ferguson is just the tip of the iceberg. For every prominent pundit, there are thousands of other people—students, business analysts, congressional staffers, and interested citizens—who use these data and sometimes make the same mistakes. I’m sure I do as well—it’s hard to know every relevant anomaly in the data. As I said in one of my first blog posts back in 2009:

Data rarely speak for themselves. There’s almost always some folklore, known to initiates, about how data should and should not be used. As the web transforms the availability and use of data, it’s essential that the folklore be democratized as much as the raw data themselves.

How would that democratization work? One approach would be to create metadata for key economic data series. Just as your camera attachs time, date, GPS coordinates, and who knows what else to each digital photograph you take, so could each economic data point be accompanied by a field identifying any special issues and providing a link for users who want more information.

When Niall Ferguson calls up a chart of federal employment statistics at his favorite data provider, such metadata would allow them to display something like this:

 

Clicking on or hovering over the “2” would then reveal text: “Federal employment boosted by temporary Census hiring; for more information see link.” And the stimulus mistake would be avoided.

I am, of course, skimming over a host of practical challenges. How do you decide which anomalies should be included in the metadata? When should charts show a single flag for metadata issues, even when the underlying data have it for each affected datapoint?

And, perhaps most important, who should do this? It would be great if the statistical agencies could do it, so the information could filter out through the entire data-using community. But their budgets are already tight. Failing that, perhaps the fine folks at FRED could do it; they’ve certainly revolutionized access to the raw data. Or even Google, which already does something similar to highlight news stories on its stock price charts, but would need to create the underlying database of metadata.

Here’s hoping that someone will do it. Democratizing data folklore would reduce needless confusion about economic facts so we can focus on real economic challenges. And it just might remind me what happened to federal employment in early 2009.

Economic Growth Slows to 1.5%

The economy grew at a tepid 1.5% annual rate in the second quarter, according to the latest BEA estimates. That’s far below the pace we need to reduce unemployment.

Weak growth was driven by a slowdown in consumer spending and continued cuts in government spending (mostly at the state and local level), which overshadowed rapid growth in investment spending on housing–yes, housing–and equipment and software:

Housing investment expanded at almost a 10% rate in the second quarter, its fifth straight quarter of growth. Government spending declined at a 1.4% rate, its eighth straight quarter of decline.

2,000 Years of Economic History in One Chart … and Another

Michael Cembalest, head of investment strategy at JP Morgan, is famous for his beautiful, insightful charts. His latest (courtesy of Paul Kedrosky) illustrates two millennia of world economic history:

According to the chart, India (orange) and China (red) together comprised more than two-thirds of the globe’s economic activity back in year 1 (well, not so much the globe, but the chosen countries). By 1950, their share had fallen to only one-eighth, thanks to the growth of the United States (green), Western Europe (shades of blue), Russia (gray), and Japan (yellow). Since then, China has been gaining share.

Not surprisingly, the chart has already attracted attention in the blogosphere. Over at the Atlantic, Derek Thompson slices and dices the data to see how much of the pattern reflects  the ebbs and flows of population vs. productivity.

At the Economist, meanwhile, K.N.C. channels Edward Tufte, expressing appropriate alarm about the compressed x-axis. The first millennium gets as much real estate as the 1990s. K.N.C. then offers another approach:

Given data limitations, this chart also compresses the x-axis, but using bars and variable-width gaps make it much clearer that there are jumps between years. The focus on a limited number of countries also makes it clear that the chart omits countries that account for 30-40% of world GDP. In Cembalest’s chart, in contrast, one wonders what happened to South America, the Nordic countries, Canada, Africa ex Egypt, etc. His listed countries appear to sum to 100% of world GDP, but large swathes of the world are unaccounted for.

Are Two Economic Clocks Better Than One?

A man with one clock always knows the time. A man with two clocks is never sure.

This week brings the two heavyweights of economic statistics. On Thursday morning we got the latest read on economic growth, and on Friday we learn how the job market fared in May.

Government statisticians and outside commenters usually emphasize a particular headline number in these reports. For the economy as a whole, it’s the annual growth rate of gross domestic product (GDP), which logged in at a mediocre 1.9 percent in the first quarter. For jobs, it’s the number of nonfarm payroll jobs created in the past month (115,000 in April, but that will be revised on Friday morning).

In each case, the government also reports a second measure of essentially the same thing. Jobs day aficionados are familiar with this. The payroll figure comes from a survey of employers, but the Bureau of Labor Statistics also reports results from a survey of people. That provides the other famous job metric, the unemployment rate, and a second count of how many people have a job. The concept isn’t exactly the same as the payroll measure–it includes a broader array of jobs, for example, but doesn’t reflect people holding multiple jobs–but it’s sufficiently similar that it can be an interesting check on the more-quoted payroll figure.

The downside of this extra information, however, is that it can foster confusion. In April, for example, payrolls increased by 115,000, but the household measure of employment fell by 169,000. Did jobs grow or decline in April?

Another, less well-known example happens with the GDP data. The Bureau of Economic Analysis calculates this figure two different ways: by adding up production to get GDP and by adding up incomes to get gross domestic income (GDI). In principle, these should be identical. In practice, they differ because of measurement challenges. As Brad Plummer notes in a piece channeling Wharton economist Justin Wolfers, the two measures tell somewhat different stories about recent economic growth. In Q1, for example, GDI expanded at a respectable 2.7 percent, much faster than the 1.9 percent recorded for GDP. Is the economy doing ok or barely plodding along?

Such confusion is the curse of having two clocks. We can’t be sure which measure to believe. Experts offer good reasons to prefer the payroll figure (e.g., it’s based on a much larger survey) and GDP (e.g., income measurement is difficult for various technical reasons, including capital gains). But there are counterviews as well; for example, at least one paper finds that GDI does a better job of capturing swings in the business cycle.

Despite this confusion, two clocks are better than one. They remind us of the fundamental uncertainty in economic measurement. That uncertainty is often overlooked in the rush to analyze the latest economic data, but it is real. There are limits to what we know about the state of the economy.

In addition, a weighted average of two readings may well provide a better reading than either one alone. If one clock says 11:40 and another says 11:50, for example, you’d probably do well to guess that it’s 11:45. Unless, of course, you have reason to believe that one clock is better than the other.

The same may well be true for GDP and GDI – the truth is likely in the middle. (This is less true with the jobs data; because of the larger sample, I weight the payroll measure much more heavily than the household measure, at least for monthly changes.)

P.S. For more on GDP vs. GDI, see Dean Baker and Binyamin Appelbaum.

Jobs Report – The Soft Side of Mediocre

As expected, today’s jobs data showed a slowing labor market. Payrolls expanded by 115,000 in April, less than hoped or expected. Upward revisions to February and March added another 53,000 jobs, however, so the overall payroll picture is better than the headline. The unemployment rate ticked down to 8.1%, the labor force participation rate slipped to 63.6%, weekly hours were unchanged at 34.5, and hourly earnings increased a measly penny from $23.37 to $23.38.

Put it all together, and this report is on the soft side of mediocre.

Unemployment and underemployment both remain very high, but they’ve been moving in the right direction. After peaking at 10% in October 2009, the unemployment rate has declined by about 2 percentage points. The U-6 measure of underemployment, meanwhile, peaked at 17.2% and now stands at 14.5%:

(The U-6 measures includes the officially unemployed, marginally attached workers, and those who are working part-time but want full-time work.)

The U.S. Economy is Weak, Uncertain, and Fragile

At least according to the latest Kauffman survey of economics bloggers by Tim Kane. Here’s the word cloud of responses when the bloggers (including me) were asked for up to five adjectives to describe the U.S. economy in Q4 2011:

Comparing this to the last survey in July, the good news is that “vulnerable” has gotten smaller. The bad news is that “recovering” has disappeared (at least I couldn’t find it):

Is Our Luck Running Out on Oil Supplies?

In an excellent new paper, Jim Hamilton asks whether the “phenomenal increase in global crude oil production over the last century and a half” reflects technological progress or good fortune in finding new reserves. The two aren’t completely distinct, of course. Better technology helps find more resources. But the heart of the question remains: have we been lucky or good?

Based on a careful reading of production patterns in the United States and around the world, Jim concludes that we’ve been both and worries that the luck part may be coming to an end:

My reading of the historical evidence is as follows. (1) For much of the history of the industry, oil has been priced essentially as if it were an inexhaustible resource. (2) Although technological progress and enhanced recovery techniques can temporarily boost production flows from mature fields, it is not reasonable to view these factors as the primary determinants of annual production rates from a given field. (3) The historical source of increasing global oil production is exploitation of new geographical areas, a process whose promise at the global level is obviously limited.

Most economists view the economic growth of the last century and a half as being fueled by ongoing technological progress. Without question, that progress has been most impressive. But there may also have been an important component of luck in terms of finding and exploiting a resource that was extremely valuable and useful but ultimately finite and exhaustible. It is not clear how easy it will be to adapt to the end of that era of good fortune.

These arguments should be familiar to anyone who’s followed the peak oil debate, but Jim brings a welcome rigor to the discussion.

He also includes some charts illustrating how various states and regions have passed their production peaks. Here, for example, are the United States, North Sea, and Mexico:

And he discusses how oil prices affect the economy. All in all, a great survey.

P.S. If you are interested in the details, Jim’s post over at Econbrowser sparked some thoughtful comments.

Why Did Sargent and Sims Win a Nobel Prize?

Because they developed methods to help distinguish between cause and effect in the macroeconomy.

The Royal Swedish Academy of Science released a very readable account of their contributions here. Here’s the introduction:

The economy is constantly affected by unanticipated events. The price of oil rises unexpectedly, the central bank sets an interest rate unforeseen by borrowers and lenders, or household consumption suddenly declines. Such unexpected occurrences are usually called shocks. The economy is also affected by more long- run changes, such as a shift in monetary policy towards stricter disinflationary measures or fiscal policy with more stringent budget rules. One of the main tasks of macroeconomic research is to comprehend how both shocks and systematic policy shifts affect macroeconomic variables in the short and long run. Sargent’s and Sims’s awarded research contributions have been indispensable to this work. Sargent has primarily helped us understand the effects of systematic policy shifts, while Sims has focused on how shocks spread throughout the economy.

One difficulty in attempting to understand how the economy works is that the relationships are often reciprocal. Is it policy that influences economic development or is there a reverse causal relationship? One reason for this ambiguity is that both private and public agents actively look ahead. The expectations of the private sector regarding future policy affect today’s decisions about wages, prices and investments, while economic-policy decisions are guided by expectations about developments in the private sector.

A clear-cut example of a two-way relationship is the economic development in the early 1980s, when many countries shifted their policy in order to combat inflation. This change was primarily a reaction to economic events during the 1970s, when the inflation rate increased due to higher oil prices and lower produc- tivity growth. Consequently, it is difficult to determine whether the subsequent changes in the economy depended on the policy shift or on underlying factors beyond the control of monetary and fiscal policy which, in turn, gave rise to a different policy. One way of studying the effects of economic policy would be to carry out controlled experiments. In practice, however, varying policies cannot be randomly assigned to different countries. Macroeconomic research is therefore obliged to use historical data. The laureates’ foremost contribution has been to show that causal macroeconomic relationships can indeed be analyzed using historical data, even in cases with two-way relationships.

There are good reasons to believe that unexpected shifts in economic policy may have other effects than anticipated changes. It is not trivial, however, to distinguish between the outcomes of expected and unexpected policy. A change in the interest rate or tax rate is not the same as a shock, in the sense that at least part of the change might be expected. This is a longstanding insight in the context of the stock market. A firm which reports improved earnings and higher forecasted profits might still encounter a drop in its share price, simply because the market expected an even stronger report. Moreover, the effects of an unanticipated policy shift might depend on whether it was implemented independently of other shocks in the economy or was a reaction to them.

Sargent’s awarded research concerns methods that utilize historical data to understand how systematic changes in economic policy affect the economy over time. Sims’s awarded research instead focuses on distinguishing between unexpected changes in variables, such as the price of oil or the interest rate, and expected changes, in order to trace their effects on important macroeconomic variables. The questions which the laureates have dealt with are obviously interrelated. Although Sargent and Sims have carried out their research independently, their contributions are complementary in many ways.

Solow on Keynes and Uncertainty

Over at the New Republic, Bob Solow offers a thoughful review of Sylvia Nasar’s new book, Grand Pursuit: The Story of Genius. Along the way, Solow provides a characteristically clear explanation of what he views as John Maynard Keynes most important contribution:

Back then [in the 1930s], serious thinking about the general state of the economy was dominated by the notion that prices moved, market by market, to make supply equal to demand. Every act of production, anywhere, generates income and potential demand somewhere, and the price system would sort it all out so that supply and demand for every good would balance. Make no mistake: this is a very deep and valuable idea. Many excellent minds have worked to refine it. Much of the time it gives a good account of economic life. But Keynes saw that there would be occasions, in a complicated industrial capitalist economy, when this account of how things work would break down.

The breakdown might come merely because prices in some important markets are too inflexible to do their job adequately; that thought had already occurred to others. It seemed a little implausible that the Great Depression of the 1930s should be explicable along those lines. Or the reason might be more fundamental, and apparently less fixable. To take the most important example: we all know that families (and other institutions) set aside part of their incomes as saving. They do not buy any currently produced goods or services with that part. Something, then, has to replace that missing demand. There is in fact a natural counterpart: saving today presumably implies some intention to spend in the future, so the “missing” demand should come from real capital investment, the building of new productive capacity to satisfy that future spending. But Keynes pointed out that there is no market or other mechanism to express when that future spending will come or what form it will take. Perhaps God has not yet even decided. The prospect of uncertain demand at some unknown time may not be an adequately powerful incentive for businesses to make risky investments today. It is asking too much of the skittery capital market. Keynes was quite aware that occasionally a wave of unbridled optimism might actually be too powerful an incentive, but anyone in 1936 would take the opposite case to be more likely.

So a modern economy can find itself in a situation in which it is held back from full employment and prosperity not by its limited capacity to produce, but by a lack of willing buyers for what it could in fact produce. The result is unemployment and idle factories. Falling prices may not help, because falling prices mean falling incomes and still weaker demand, which is not an atmosphere likely to revive private investment. There are some forces tending to push the economy back to full utilization, but they may sometimes be too weak to do the job in a tolerable interval of time. But if the shortfall of aggregate private demand persists, the government can replace it through direct public spending, or can try to stimulate additional private spending through tax reduction or lower interest rates. (The recipe can be reversed if private demand is excessive, as in wartime.) This was Keynes’s case for conscious corrective fiscal and monetary policy. Its relevance for today should be obvious. It is a vulgar error to characterize Keynes as an advocate of “big government” and a chronic budget deficit. His goal was to stabilize the private economy at a generally prosperous level of activity.

A second characteristically Keynesian theme meshes very well with the first. In a complex economy, many business decisions have to be made in a fog of uncertainty. This is especially true of investment decisions, as already discussed: a lot of money has to be placed at risk today in an enterprise whose future success can only be guessed. (Much the same can be said of consumer purchases of expensive durable goods.) The standard practice is to focus on the uncertainty and think about it in terms of probabilities, which at least allow for an orderly analysis and orderly decision-making. Keynes preferred to focus on the fog. He thought that some of the important uncertainties were essentially incalculable. They would end up being dealt with in practice by a mixture of apprehensiveness, rules of thumb, herd behavior, and what he called “animal spirits.” The point of this distinction is not merely philosophical: it suggests that long-term investment behavior will sometimes be irregular, unstable, and given to doldrums and stampedes. Expectations can be volatile, and transmit their volatility widely. Passive or perverse policy can be dangerous to the economy’s health.

Solow thus credits Keynes with pioneering the “uncertainty” meme, although in a different sense than many commentators invoke it today.

His whole review is well worth a read if you are interested in the history of economic thought, including Fisher, Hayek, and Schumpeter.

P.S. Solow’s comments on Hayek are less enthusiastic than for Keynes, but he does note that “the Mises-Hayek critique of central planning was convincing (and clearly confirmed by subsequent facts).”