Tracking the Stimulus

In her recent speech about the impact of the stimulus effort, Christina Romer, Chair of the President’s Council of Economic Advisers, noted that “as of the end of June, more than $100 billion had been spent.”

If you visit the government web site tracking the stimulus (Recovery.gov), however, it will tell you that the government had paid out only about $60 billion by July 3. (You can find this figure in the chart at the lower right hand corner of the home page.)

Why does Christi report a figure so much larger than the one reported on the official website? Because Recovery.gov isn’t tracking all of the budget effects of the stimulus.

Christi’s figure includes the $60 billion of spending reported on Recovery.gov plus an internal estimate, prepared by Treasury, of the tax reductions resulting from the stimulus effort through June 24. Those tax reductions are obviously a big deal, totaling $40 billion or slightly more through the end of June.

Based on conversations with friends and journalists, I get the sense that some users of Recovery.gov do not realize that its figures cover only the spending side of the stimulus story, not the tax side.

As a result, I think Recovery.gov is (unintentionally) confusing people into thinking that the stimulus effort to date is smaller than it has actually been.

I have two suggestions for how to fix this:

Step 1: Reduce Confusion: Recovery.gov should slap a warning label on the home page chart (and everywhere else it reports aggregate figures) that says something like: “These figures reflect only the new Federal spending that has resulted from the recovery act. The act also included significant tax reductions that aren’t reflected here.” 

Step 2: Provide the Information: Of course, it would be even better if Treasury would release official estimates of the week-by-week or month-by-month tax reductions flowing from the recovery act. These figures would obviously be estimates — and thus not able to be audited to the same degree as the spending programs — but would be invaluable to analysts trying to track the impact of the stimulus effort.

P.S. As I noted last week, the Congressional Budget Office recently estimated that the total budget impact of the stimulus effort reached about $125 billion through the end of July.

Google Is Still Wrong About Unemployment

Everyone who follows the U.S. economy closely knows that the unemployment rate was 9.4% in July, down 0.1% from June.

Everyone, that is, except Google.

If you ask Google (by searching for “unemployment rate United States“), it will tell you the unemployment rate in July was 9.7%.

What’s going on? Well, it turns out that Google is directing users to the wrong data series. As I discussed last month, almost everyone who talks about unemployment is using (whether they know it or not) data that have been adjusted to remove known seasonal patterns in hiring and layoffs (e.g., many school teachers become unemployed in June and reemployed in August or September). Adjusting for such seasonal patterns is standard protocol because it makes it easier for data users to extract signals from the noisy movements in data over time.

For unknown reasons, Google has chosen not to direct users to these data. Instead, Google reports data that haven’t been seasonally adjusted and thus do not match what most of the world is using.

This is troubling, since I have high hopes for Google’s vision of bringing the power of search to data sets. The ability of users to find and access data lags far behind their ability to find and access text. I am hopeful that Google will solve part of this problem.

But data search is not about mindlessly pointing users to data series. You need to make sure that users get directed to the right data series. So far, Google is failing on that front, at least with unemployment data.

 P.S. As I discussed in a follow-up post last month, Wofram Alpha has an even more ambitious vision for making data — and computation — available through search. I like many of the things Alpha is trying to do, but they are lagging behind Google in several ways. For example, as I write this, they haven’t updated the unemployment data yet to reflect the new July data. (Click here for Alpha results.)

Bing isn’t trying yet.

We’re #1 (Unfortunately)

Yesterday’s GDP report confirmed what many had already suspected: the current economic downturn is the worst since World War II.

According to the advance estimate, GDP fell at a 1.0% annualized pace in the second quarter, somewhat better than consensus estimates (which were looking for a decline in the 1.5% range). Revisions to last year, however, revealed than earlier parts of the recession were more severe than originally estimated.

Putting it all together, GDP has declined by an estimated 3.9% over the past four quarters. That edges out the recession of 1957-58, when GDP fell by 3.7% in just two quarters, as the deepest contraction in GDP since World War II.

To put this in context, the following chart shows the magnitude of all GDP declines since 1947:

Worst Downturn Since WWII (August 1)

There have been 25 such declines, ranging in length from one to four quarters. The current downturn beats all the others.

There wasn’t room to include the dates of the downturns in that chart, so here’s one that shows just the top five declines:

Continue reading “We’re #1 (Unfortunately)”

Broad Weakness in Q2 GDP

The economy contracted at a 1.0% pace in the second quarter, according to the advance estimate from the Bureau of Economic Analysis. That’s bad, of course, but much better than the 5.4% and 6.4% pace of declines in the two previous quarters.

Whenever the GDP data come out, the first thing I look at is Table 2, which shows how much different sectors of the economy contributed to the growth (or, in this case, the decline). The most striking thing about Q2 is how broad the weakness was:

Broad Weakness in Q2 2009

As the chart shows, Q2 witnessed declines in every major category of private demand: consumer spending, residential investment, business investment in equipment and software (E&S), business investment in structures, and exports. Wow.  To find the last time that happened, you have to go all the way back to … the fourth quarter of last year, when it was even more severe. But before that, you have to go back five decades to the sharp downturn of the late 1950s.

Not surprisingly, government spending helped offset the declines in private spending. Most of the boost came from defense spending, but state and local investment also helped (perhaps some glimmers of stimulus?).

A sharp decline in imports, finally, was the biggest contributor to growth in Q2, at least in an accounting sense. It’s important to choose your words carefully here, since declining imports are clearly not the path to prosperity. In a GDP accounting sense, however, import declines do boost measured growth. Why? Well consider the fall in consumer spending. That decline affected both domestic production and imports. GDP measures domestic production, so we need a way to net out the decline in consumer spending that was attributable to imports. That’s one of the factors being captured in the imports figure.

Note: If the idea of contributions to GDP growth is new to you, here’s a quick primer on how to understand these figures. Consumer spending makes up about 70% of the economy. Consumer spending fell at a 1.2% pace in the second quarter. Putting those figures together, we say that consumer spending contributed about -0.9 percentage points (70% x -1.2%, allowing for some rounding) to second quarter growth.

As I mentioned a few weeks ago, today’s GDP release is particularly important because the fine people at the BEA have gone back and made revisions to the entire history of GDP statistics. I will post again once I have a chance to review how history has changed.

Wolfram Alpha, Unemployment, and the Future of Data

I’ve received a number of helpful responses to my post about the strengths and weaknesses of Google’s efforts to transform data on the web. Reader DD, for example, reminded me that I ought to run the same test on Wolfram Alpha, which I briefly mentioned in my post on Google’s antitrust troubles.

Wolfram Alpha is devoting enormous resources to the problem of data and computation on the web. As described in a fascinating article in Technology Review, Wolfram’s vision is to curate all the world’s data. Not just find and link to it, but have a human think about how best to report it and how to connect it to relevant calculation and visualization techniques. In short:

[Wolfram] Alpha was meant to compute answers rather than list web pages. It would consist of three elements, honed by hand …: a constantly expanding collection of data sets, an elaborate calculator, and a natural-language interface for queries.

That is certainly a grand vision. Let’s see how it does if I run the same test “unemployment rate United States” I used for Google:

Continue reading “Wolfram Alpha, Unemployment, and the Future of Data”

Google, Unemployment, and the Future of Data

Google may eventually solve the problem of finding data on the web. Too bad its first effort reports the wrong numbers for unemployment.

Since leaving public service, I have occasionally pondered whether to start a company / organization to transform the way that data are made available on the web. The data are out there, but they remain a nuisance to find, a nuisance to manipulate, and a nuisance to display. I cringe every time I have to download CSV files, import to Excel, manipulate the data (in a good sense), make a chart, and fix the dumb formatting choices that Excel makes. All those steps should be much, much easier.

There are good solutions to many of these problems if you have a research assistant or are ready to spend $20,000 on an annual subscription. With ongoing technology advances, however, there ought to be a much cheaper (perhaps even free) way of doing this on the net.  With some good programming, some servers, and careful design (both graphic and human factors), it should be possible to dis-intermediate research assistants and democratize the ability to access and analyze data. At least, that’s my vision.

Many organizations have attacked various pieces of this problem, and a few have even made some headway (FRED deserves special mention in economics). But when you think about it, this is really a problem that Google ought to solve. It has the servers, software expertise, and business model to make this work at large scale. And with its launch of a search service for public data it has already signaled its interest in this problem.

As a major data consumer, I wish Google every success in this effort. However, I’d also like to use their initial effort, now almost three months old, as a case study in what not to do.

Google’s first offering of economics data is the unemployment rate for the United States (also available for the individual states and various localities). Search for “unemployment rate united states” and Google will give you the following graph:

Google UE

Your first reaction should be that this is great. With absolutely no muss and no fuss, you have an excellent (albeit sobering) chart of the unemployment rate since 1990. I would add myriad extensions to this – e.g., make it easier to look at shorter time periods, allow users to look at the change in the unemployment rate, rather than the level, etc. – but the basic concept is outstanding.

Unfortunately, there is one major problem:  That’s the wrong unemployment rate.

Click over to the Bureau of Labor Statistics, open a newspaper (remember them?), or stay right here on my blog – all of them will tell you that the unemployment rate in June was 9.5% not 9.7%.

Continue reading “Google, Unemployment, and the Future of Data”

Better GDP Data

Every five years, the fine people at the Bureau of Economic Analysis update the way that they measure the U.S. economy.  Yesterday, the BEA released a helpful document that outlines some of the upcoming improvements.  Among the things that caught my eye:

  • BEA will employ plain English, rather than bureaucratese, to describe the three vintages of GDP estimates, which are reported one, two, and three months after the end of each quarter. Those vintages are currently known as the Advance estimate, the Preliminary Estimate, and Final estimate. The latter two names always struck me as nonsensical: “Preliminary” sounds like it should come before “Advance,” and “Final” estimates aren’t really final. Hence the new names: the Advance Estimate, the Second Estimate, and the Third Estimate.  A definite improvement.

Continue reading “Better GDP Data”