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Posts Tagged ‘Measurement’

In high school, I learned that absolute zero (about -460° Fahrenheit or -273° Celsius) is as cold as you can get. At that point, all motion ceases, and you can’t get any colder.

So it was a bit of a head-scratcher to learn that physicists recently created a gas whose temperature is below absolute zero. Seems impossible, right?

Well, no. Turns out that the high-school definition of absolute zero doesn’t capture the modern notion of temperature. As Empirical Zeal explains, temperature isn’t only about motion, it’s about an object’s willingness to give up energy. And physicists have been creating negative-temperature objects for more than 60 years.

Measurement is a recurring theme on this blog, so I found this intriguing. All those years, and I didn’t actually know how physicists really measure temperature. But what really caught my eye is that Empirical Zeal uses some ideas from economics to explain what negative temperatures are all about.

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Using the ideas of exchange, marginal utility, and utility maximization, he illustrates how negative temperatures are like a world in which the Dalai Lama should give all his money to Warren Buffett.

I can’t do justice in an excerpt, so please click on over if you are interested.

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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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In a new paper, my Tax Policy Center colleague Eric Toder and I argue that the federal government is larger than conventional budget measures suggest. Why? Because many tax preferences are effectively spending programs. Adding these “spending-like tax preferences” back to federal spending and revenues gives a better picture, we think, of the federal government’s true size.

In 2007, for example, federal spending was officially recorded as 19.6 percent of GDP. If you add in the tax preferences that Eric and I believe are effectively spending (the SLTPs), that figure rises to 23.7 percent. In round terms, the government was one-fifth larger than traditional budget figures indicate:

And that’s not all. We also consider the many user fees and premiums that the government charges for various services, ranging from regulatory activity (e.g., patent fees) to Medicare premiums. Such payments are treated as negative spending in official budget calculations. This is sometimes done as a pure budget gimmick to make the government look smaller. More often, however, it’s done for a good reason: to focus on government activities that are funded collectively. That’s an important thing to measure when budgeting. But it’s not the only one. If you want to know how much economic activity is occurring through government agencies, you should consider the gross size of those activities, not just the net. The third column thus adds back user fees and premiums to get the full size of the federal government: 25.4 percent of GDP in 2007.

Eric and I would be the first to argue that the size of government, by itself, tells you little. Small governments can be dysfunctional, and large ones can be well-run. But government size plays a central role in many political discussions. Given that attention, we think it’s worthwhile to consider whether existing measures fairly capture its true size.

And, as a crucial corollary, whether they fairly capture the implications of potential policy changes. As I will discuss in a subsequent post, our measure of government size has several important implications. For example, some “tax increases” (e.g., closing loopholes and reducing many other tax preferences) actually make the government smaller.

P.S. The figures in our paper are based on information from the administration’s 2012 budget. Some historical figures have changed slightly since then, due to revisions in GDP and budget figures.

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Over at the Tax Policy Center’s blog, TaxVox, my colleague Roberton Williams examines the pitfalls that afflict some efforts to measure a person’s tax rate:

Investment manager James Ross last week told New York Times columnist James Stewart that his combined federal, state, and local tax rate was 102 percent.  No doubt, Ross did pay a lot of tax to the feds and the two New Yorks, city and state. But did he really pay more than all of his income in tax?

No, he did not.

As Stewart made clear past the wildly misleading headline (“At 102%, His Tax Rate Takes the Cake”), Ross’s tax bills totaled 102 percent of his taxable income, a measure that omits all exclusions, exemptions, and deductions. Using that reduced measure of income inflates Ross’s effective tax rate far above the share of his total income he paid in taxes.

Deeper into his column, Stewart explains that Ross’s tax bill was just 20 percent of his adjusted gross income (AGI), a more inclusive measure that does not subtract out exemptions and deductions. Because he took advantage of many preferences, Ross’s taxable income was only a fifth of his AGI, resulting in that inflated 102 percent tax rate. But even AGI doesn’t include all income. Among other things, it leaves out tax-exempt interest on municipal bonds, contributions to retirement accounts, and the earnings of those accounts. Ross almost surely paid less than 20 percent of his total income in taxes

Stewart’s article demonstrates the common confusion about effective tax rates, or ETRs. There are many ETRs, depending on which taxes you count and against what income you measure them. Including more taxes drives up ETRs. Using a broader measure of income drives them down. And interpreting what a specific ETR means requires a clear understanding of both the tax and income measures used.

In short, you need to be careful with both the numerator and the denominator when measuring someone’s tax rate. And you need to be doubly careful when comparing tax rates across individuals or groups.

TPC released a short report today that illustrates that point for taxpayers of different income levels. Rachel Johnson, Joe Rosenberg, and Bob Williams show how including different taxes and using different income measures (AGI versus a broader measure of cash income) can have big effects on ETRs.

As Bob concludes his blog post:

The bottom line is you can use these numbers to tell many different stories, some more valid than others, depending on the taxes you include and the income measure you use. The broadest measure of income provides the most meaningful gauge of the relative impact of taxes on households. Narrower measures can yield absurd results—James Ross didn’t pay 102 percent of his income in taxes—and ignore important differences in households’ ability to pay.

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You can’t manage what you don’t measure.

That’s good advice, as far as it goes. But it has a dark underside: managing the measurement rather than actual outcomes.

Over at the New York Times, Al Baker and Joseph Goldstein recount a troubling example. To keep reported crime rates low, New York’s Finest may be under reporting the crimes that actually occur:

Crime victims in New York sometimes struggle to persuade the police to write down what happened on an official report. The reasons are varied. Police officers are often busy, and few relish paperwork. But in interviews, more than half a dozen police officers, detectives and commanders also cited departmental pressure to keep crime statistics low.

While it is difficult to say how often crime complaints are not officially recorded, the Police Department is conscious of the potential problem, trying to ferret out unreported crimes through audits of emergency calls and of any resulting paperwork.

As concerns grew about the integrity of the data, the police commissioner, Raymond W. Kelly, appointed a panel of former federal prosecutors in January to study the crime-reporting system. The move was unusual for Mr. Kelly, who is normally reluctant to invite outside scrutiny.

The panel, which has not yet released its findings, was expected to focus on the downgrading of crimes, in which officers improperly classify felonies as misdemeanors.

But of nearly as much concern to people in law enforcement are crimes that officers simply failed to record, which one high-ranking police commander in Manhattan suggested was “the newest evolution in this numbers game.”

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