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Posts Tagged ‘Macroeconomics’

The U.S. economy has recovered slowly since the official end of the Great Recession in 2009. Mark Lasky and Charles Whalen of the Congressional Budget Office just released a study asking why. Their answer: two-thirds of the slowness (relative to past recoveries) reflects weak growth in the economy’s potential. The potential labor force, capital stock, and productivity are all growing less rapidly than they did following past recessions. The other third reflects cyclical weakness, particularly in government, housing, and consumer spending.

CBO’s Maureen Costantino and Jonathan Schwabish turned those results into a nifty infographic (click to make larger):

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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?

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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.

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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.

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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.

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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.

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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.)

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