Cafe Hayek

On America's Middle Class

In two of his Forbes columns, NYU's Thomas Cooley challenges - with data - the widespread misunderstanding that America's middle-class is disappearing.  Here, and then here.

(HT Greg Mankiw)  The Terry Fitzgerald work that Cooley mentions was mentioned here at the Cafe a while back.

I raise, though, one objection to Cooley's otherwise excellent columns.  In the first one linked to above, he credits technological change for the explosive economic growth of the 1980s and 1990s.  I don't accept technology as an explanation.  A rush of technology has causes; it must be explained.  Technology is not a variable exogenous to economic growth.

Baseball graphics

If you like baseball, graphic design, and the presentation of information, you will like this (HT: mark Lanoue).

Absolute mobility, quantified

This post looked at the proportion of children who have done better than their parents. Here's a measure of the amounts.

Half empty or half full? Both no doubt. Just don't tell me that people in the bottom 40% haven't received any of the economic gains of the last 30 years:

Fancy empirical work

In the last part of this post, I issued a challenge:

I continue to ask the question: name an empirical study that uses sophisticated statistical techniques that was so well done, it ended a controversy and created a consensus—a consensus where former opponents of one viewpoint had to concede they were wrong because of the quality of the empirical work.

I then discussed why The Monetary History of the United States while an extraordinary piece of empirical work, didn't count. It wasn't fancy regression analysis, but rather a careful marshalling of facts. Not the same thing. I concluded:

I am open to other suggestions. I'd like one example, please. One example, from either micro or macro where people had to give up their prior beliefs about how the world works because of some regression analysis, ideally usually instrumental variables as that is the technique most used to clarify causation.

One example. There should be dozens. Or hundreds. But I'll take one.

Both Tyler Cowen and Bryan Caplan responded to the challenge. And there were a lot of interesting comments. Here is Tyler's list:

1. The interest-elasticity of investment is lower than people once thought.

2. We have a decent sense of the J Curve and why a devaluation or depreciation doesn't improve the trade balance for some while.

3. Dynamic revenue scoring tells us over what time horizon a tax cut is partially (or fully) self-financing.

4. Most resale price maintenance is not for goods and services involving significant ancillary services.

5. More policing can significantly lower the crime rate (that one does use instrumental variables).

6. The term structure of interest rates is whacky.

Bryan's answer is the entire field of behavioral economics.

Lauren Landsburg, in a very thoughtful email, suggests Time on the Cross, Fogel and Engerman's study of slavery.

Most or all of these observations miss the point, or at least the point I was trying to make.

Empirical work is very important.

Facts matter.

A careful study of the facts can have tremendous influence.

Sophisticated regression analysis can narrow our guesses as to magnitudes. But I don't think we need fancy regression to conclude that people aren't always rational. Or that police can reduce crime. Or to look at the nature of resale price maintenance. On the stuff where people have priors and bias—such as the dynamic impact of taxes on revenue—I don't think the empirical evidence is very convincing of the skeptic.

I understand that science moves slowly and that people at the margin are who eventually count.

But I really don't think the empirical record of sophisticated empirical work is very impressive. In fact, I think I could make a case that sophisticated empirical work is most productive for publishing papers and less productive at establishing truth or useful findings that are reliable.

Wrong

Over at Freakonomics (HT: Planet Money), Justin Wolfers cites this graph as proof that the rich have gotten most of the income gains in the last 35 years:

 

This alas, is a meaningless chart. It tells you nothing about who got the gains of the last 35 years. Why? Because they're not the same people in the quintiles. Starting in 1973, and it's not a coincidence, the divorce rate in the United States began to rise. The number of families increased dramatically simply because of divorce. There was also an increase in the number of families headed by single women with children. The quintile breaks-points changed, not because the economy was growing or shrinking but simply because of changes in the types of families.

The chart is highly misleading. It implies that poor people have done poorly while rich people have thrived. Rich people have thrived. But so have poor people. If you look at longitudinal studies of the same people (the Michigan PSID for example) you get a totally different picture than this one. And that's because this one is designed to fulfill a political agenda. It's a beautiful example of how facts by themselves are not meaningful. There is nothing dishonest about the chart, just its interpretation.

I invite my students from last semester to remember what else we came up with when talking about a graph just like this one.

In the comments to Wolfers post, nosybear writes:

Shows what we know - some time in the last three decades we as a nation decided the rich should get richer, the poor, well, not so much. Education alone doesn’t account for it. Taxation does. We’ve apparently decided generational wealth is a good thing and oligarchies should run the country.

But this isn't true. There is no "we." This isn't explained by taxation. How would that work? The left is angry about measured inequality but they can't point to the mechanism that leads to all the poor people being held down. Especially when we know that educational attainment is growing in America and education does lead to higher income.

I'm suggesting that there is no explanation because there is nothing to be explained. the phenomenon that the rich "have gotten almost all of the gains" is a statistical artifact.

There have been changes in the distribution of income, the amount of inequality, and the returns to education since 1973. This chart distorts what really happened.

Sociology

My colleague Bryan Caplan at EconLog on how economics is evolving:

I've studied economics for over twenty years.  The more I think about it, though, the more I realize that I don't know what "economics" means anymore. 

Textbooks may say that economics is about "incentives" or "trade-offs."  But you can publish papers in econ journals about the effect of birth weight on educational attainment.  I don't see any incentives or trade-offs there.  Or take Emily Oster's early research arguing that hepatitis, not infanticide or selective abortion, explained a lot of Asia's gender imbalance.  Some economists asked, "How is this economics?"  But if some economists argue that the gender imbalance is driven by incentives, how can you object if other economists say that the real explanation is medical?  Or consider happiness research.  Economists like Justin Wolfers are in the vanguard; but the connection to incentives or trade-offs is unclear.

You could deplore all this as a loss of focus.  But I see massive progress.  Economics has grown hard to define because we now focus primarily on real-world problems, not "literatures."  If we want to understand income determination, we don't waste time with topological proofs.  We still think about supply and demand, but we also think about policy, psychology, behavioral genetics, and much more.  As a result, we come to understand the world, instead of solving unusually difficult homework problems.

He goes on to say that what economists do, might best be called "sociology" the word we already have to describe the study of the social world, which is what Bryan is arguing economists increasingly study.

I am reminded of what George Stigler said: "There is only one social science and we are its practitioners."

Having noted that, I have to disagree with Bryan on a few things. I think too much of modern empirical economics is the economics-free application of sophisticated statistical techniques that does little to actually advance our understanding of the social world. It's not just that it isn't about trade-offs or incentives, the role of trade-offs and incentives are ignored. I also don't think we've made "massive progress" in understanding the social world. We've made massive progress in publishing papers on the social world. But understanding? Not so much. We treat the natural world as if the sophisticated tools of statistics can turn reality into a natural experiment. But the world is usually (always?) too complex for the results to be reliable.

I continue to ask the question: name an empirical study that uses sophisticated statistical techniques that was so well done, it ended a controversy and created a consensus—a consensus where former opponents of one viewpoint had to concede they were wrong because of the quality of the empirical work.

When I asked Ian Ayres that question, someone who advocates increased use of statistical techniques in various aspects of life, his answer was the Levitt and Donahue study of abortion and crime. A strange answer as it is highly controversial and widely dismissed by skeptics.

My example used to be the Monetary History of the United States by Friedman and Schwartz that created a consensus that the Fed and the money supply play a crucial role in inflation and business cycles. But The Monetary History is a collection of facts rather than the use of the fancy techniques so in vogue today. I'm not sure there's anything sophisticated in the empirical work. Is there even a single regression in it? I don't know. But the point is that I'm not saying that facts are irrelevant to our understanding of the world. I'm saying that attempts to use statistical technique to tease out causation in a complex world is incredibly un-credible.

I am open to other suggestions. I'd like one example, please. One example, from either micro or macro where people had to give up their prior beliefs about how the world works because of some regression analysis, ideally usually instrumental variables as that is the technique most used to clarify causation.

One example. There should be dozens. Or hundreds. But I'll take one.

Go, Megan, go

Megan McArdle points out the flaws in a recent study of bankruptcy and people get really angry at her. She's right. It's a lousy study. Not every sentence of it. Not every number. But it's not a quest for truth and this kind of critique is always worth making.

Feynman on social science

He's a skeptic. (HT: G.H. Dericks) He's right.

Bad Prediction, Bad Graphics

A number of people have sent me versions of this graph that Greg Mankiw has now noted. I delayed posting on it because the graph, which criticizes the Obama administrations predictive abilities, is so bad:

The graph is supposed to show that unemployment with the stimulus package is the same or maybe worse than what the Obama Adminsitration economists said would happen if the stimulus package didn't pass.

The basic point is right. The unemployment rate has continued to rise rather than peak and decline.

But the triangles that are supposed to represent the current levels of unemployment are incredibly large. Why aren't they points? Or smallish circles. Which part of the triangle represents the measure of unemployment? The midpoint? The top? The right vertex?

Worse, the underlying graph is very hard to read. It is taken from the Romer-Bernstein forecast of the effects of the stimulus plan. But the horizontal axis is bizarre. For 2009, for example, there are two tick marks, Q1 and Q3, first quarter and third quarter. Where are Q2 and Q4 measured for 2009 or any year. Presumably, Q2 is halfway between Q1 and Q3 and Q4 is in the no man's land halfway between 2009's Q3 and 2010's Q1.

But now look where the triangles are located. Because they are so big, they overlap multiple quarters. The base of the April 2009 triangle ranges from Q2 all the way over to Q3. But either way, they're not in the right place. They span Q1 and Q3. But we don't even have all the data for Q2 of 2009. So what is the April triangle doing over Q2 and Q3? Why is a month of unemployment put into a chart that has quarterly measures.

But the basic point is likely to be correct. The underlying Romer-Bernstein chart says that unemployment will be 8% for the second quarter of 2009 if there's a stimulus plan and about 8.5% if it doesn't pass. I don't think we're going to average 8% for April, May and June.

Finally, I don't even know what quarterly unemployment is. I assume it's the average during the quarter.

Something is missing

In this article (HT: Drudge) citing fears of a prominent neuroscientist that our kids are ruining their brains through their use of social websites, something is missing. Can you figure out what it is?

A quiz

Find the error in the following picture (HT: Andrew Sullivan). It is a very frightening picture but it is also frightfully misleading. Do you see the error?

Sullivan sees the error, too, but don't click through till you've taken a shot at it.

None of this (or this) is to suggest that everything is hunky-dory. It's just that accuracy and the facts  matter.

On Monday, around mid-day, I'll post what I think is the right measure of how bad things are. It's not as bad as the above, but it is not a pretty picture.

Where do the American people stand on the stimulus?

In this post, I speculated that many (most) Americans were skeptical of the stimulus package. In the comments, Minus Ten cites a WSJ article and poll. The headline:

Obama, Stimulus Proposals Enjoy Broad Backing in Poll

It's always good to check out the wording of the questions used to draw such a strong conclusion. So I went to the actual poll results. At first glance, it appears pretty open and shut. Here is question 29:

29. When it comes to the economic stimulus plan proposed by the Obama administration, which of these two statements comes closer to your point of view?  
 
Statement A: The economic stimulus plan is a good idea because it will help make the recession shorter, get people back to work, and provide money for transportation, education, and Medicaid programs.
 
Statement B: The economic stimulus plan is a bad idea because it will do little to shorten the recession, the jobs are temporary, and it will significantly increase the deficit.
 
Statement A/Economic stimulus is a good idea..................... 57
Statement B/Economic stimulus plan is a bad idea............... 36 
Not sure................................................................................... 7

You can quibble with the wording. But it's not bad. And 57 to 36 does look like pretty broad support.Of course there is also question 27:

27. Which of the following concerns you more? 
 
That the federal government will spend too MUCH money to try to boost the economy and as a result will drive up the budget deficit, OR
 
That the federal government will spend too LITTLE money to try to boost the economy and as a result the recession will be longer?
 

That the federal government will spend too MUCH money..... 60
That the federal government will spend too LITTLE money.... 33
Not sure..................................................................................... 7

Again, I would have worded it a little differently. But again, it's close enough. So what does it tell you about support for the stimulus package? Maybe the headline should have been:

Americans Confused About Fiscal Policy, Deficit

Or maybe:

Americans Hope for Free Lunch--Support Stimulus Package When They Don't Think Too Much About the Cost

Ordinary Americans Growing Wealthier Over the Long-Run

In the September 2008 issue of The Region, Terry Fitzgerald, Senior Economist at the Minneapolis Fed, has another revealing article.  In this one he explains that

after adjusting the Census Bureau data for three key factors — inflation-adjusted median household income for most household types increased by roughly 44 percent to 62 percent from 1976 to 2006. The only household types with substantially lower growth were “working-age male householder without spouse present” and “male householder with children but without spouse,” but these types constitute just 10 percent of all households. Household income inequality increased notably over this period; nonetheless, middle American households had substantial income gains.

Fitzgerald's analysis is careful and data-rich; it can be found by clicking here, and then clicking on "Where Has All the Income Gone?"  (An earlier, related article by Fitzgerald is discussed here.)

Rising Incomes and Falling Income Statistics

Here's a letter that I sent recently to the Washington Post, in response to the same Robert Samuelson column that Russ blogged on:

Robert Samuelson helpfully explains why the data routinely cited to show the alleged economic stagnation of middle-class Americans are misleading ("The Real Economic Scorecard," September 3).  In particular, he's correct that average or median income can stagnate or fall even if everyone's income rises.  Here's how I explain this possibility to my students:

Imagine what the average or median income would be in a room occupied only by Bill Gates, Warren Buffet, and Bono.  Now imagine that I enter the room and accept their offer to become their full-time shoe-shiner at an annual salary of $500,000.  Because this income is higher than I earned before entering the room, I'm richer. And because my entering the room does not lower their annual incomes, none of them is poorer.  But my presence in the room (with my new income still far below that of each of these men) dramatically lowers the room's average income, and pretty significantly lowers its median income, even if the income of each of these men rises during the current year.

Everyone is richer, yet average and median incomes are lower.  As Mr. Samuelson points out, this possibility is not merely academic.

Sincerely,
Donald J. Boudreaux

Blinded by partisanship

When I first took economics, I learned from my textbook (Samuelson) the fallacy of post hoc, ergo propter hoc. After this, therefore because of this. Alan Blinder commits this fallacy in this New York Times article (HT: James Gambrell).

After noting that Democrats and Republicans have different economic policies, Blinder argues, drawing on work of Larry Bartels in Unequal Democracy, that Democrats run the economy better than Republicans:

Data for the whole period from 1948 to 2007, during which Republicans occupied the White House for 34 years and Democrats for 26, show average annual growth of real gross national product  of 1.64 percent per capita under Republican presidents versus 2.78 percent under Democrats.

That 1.14-point difference, if maintained for eight years, would yield 9.33 percent more income per person, which is a lot more than almost anyone can expect from a tax cut.

Blinder is aware of the fact that the President doesn't run the economy. He adds:

Such a large historical gap in economic performance between the two parties is rather surprising, because presidents have limited leverage over the nation’s economy. Most economists will tell you that Federal Reserve policy and oil prices, to name just two influences, are far more powerful than fiscal policy.

Most economists will also tell you that Presidents don't even control fiscal policy. Here in the United States we have three branches of government. The Presidency is one of them. Then there is what is called the Congress. They have a say, too. Then there are external events besides fiscal policy and monetary policy and oil prices that affect the economy and that are beyond the President's control. Demographics. Cultural trends. Technology. None of these are controlled by the President. There's also war. You can argue the President controls whether we go to war, but I'd argue you'd want to factor in war separately if you want to assess the quality of a President's economic policies.

Blinder does admit that the future may not be like the past:

Furthermore, as those mutual fund prospectuses constantly warn us, past results are no guarantee of future performance.

But then he reassures the reader with this jaw-dropping sentence:

But statistical regularities, like facts, are stubborn things. You bet against them at your peril.

What? Statistical regularities are stubborn things? No they're not. They are dominated by randomness almost by definition. That's why they're called regularities. They reveal a pattern in the data. Nothing more. Nothing less. Without a theory (and saying Democratic presidents are better at running the economy than Republican presidents is not a theory) it's just a regularity.

And what does that last sentence mean? Bet against the statistical regularities of the past at your peril? So he's saying that if McCain is elected, you can be pretty sure that he'll do a worse job than Obama would have. You can bet on it. It's a result you can rely on.

That is going to be a difficult bet to enforce. I know it's an idiomatic express to mean it's practically a sure thing. But thinking about the impossibility of determining the winner of such a bet makes it clear that the whole argument is meaningless.

Measuring inequality (with contest results)

In this earlier post, I challenged readers to challenge this chart:

I asked you to:

Write ONE sentence explaining ONE thing that is wrong with concluding that these numbers are evidence that the US economy has become more tilted toward the rich at the expense of the poor.

There were 206 responses before I cut off the comments.

It's a pretty stark and dramatic picture. On the surface, it seems to indicate that the only people who got ahead in America over the last 30 years are the richest of the rich, the top 1%. The bottom 90% saw essentially no improvement. And this was mostly  during a time when the economy was  booming. The goal, I believe, of the people who draw such charts, is to destroy the argument that a rising tide lifts all boats. And the chart seems to show that.

So what is wrong with concluding from the chart that the economy is broken or at least hopelessly tilted toward the rich?

The most important argument made was that there are compositional changes that might account for the depressingly small improvement for the bulk of the population:

IRONMAN said it very simply: The composition of U.S. households has changed from 1946 through 1976 and through 2006.

A number of you speculated on what the changes might have been. ROSS wrote: There is inconsistency in the data because these numbers are for households and not individuals and will be affected by changes in the marriage and divorce rate.

ESOX LUCIOUS wrote: Immigration heavily skews the data for the poor to become poorer because quite a few of the immigrants arrive in a state of poverty.

MESA ECONOGUY was definitely on the right track: The comparison of household income is fake, because the number of households has increased at a much faster rate than the number of people (due mostly to divorce), creating artificially plummeting incomes per household, but nearly constant income per person.

These points are correct. Of course, they don't refute the implicit claim of the chart. But they do make you wonder.

In support of the importance of the argument, between 1976 and 2006, the population in the US rose from 218 million to 300 million, an increase of 38%. But in the Piketty and Saez data, the data used to create the chart, the number of taxpaying units, which is actually their estimate of the number of households, rose 63%. So there has been an enormous increase in smaller households. The main factor, presumably, is an increase in the divorce rate over the period.

Even if single women earned the same amount as their husbands (and my guess is that they did not), an increase in the divorce rate would depress any growth in average household income.

Here's a simple illustration of this distorting effect, taken from page 20 of my visual essay, Half Full,:

Every individual's income doubles, but at the same time, half of the households divorce. Even though everyone's income has doubled, the growth in household income is only 33%, rather than 100%. Median household income is unchanged, even though the median individual has seen 100% growth in income.

In creating this example, I was interested in the distortions caused by  looking at quintiles rather than the top 1% or bottom 90%. But let's look at what has happened to the bottom 80% in this example. The average income of the bottom 80% of households in the first situation is 100%. After divorce and a doubling of everyone's income, the average income of the bottom 80%  has increased by only 17%. (There are 12 households in the bottom 80% and their total income is $1400. So average is $117.)

How important is this effect? I don't know. But as I point out in Half Full, there is evidence that most Americans are doing much better than they were in the 1970s.

There were many other good points made in the comments that are relevant. I liked PATRICK's observation: The chart shows that the incomes of the top 1% have increased at a higher rate than those of the bottom 90% of households, any argument as to the "cause" of this discrepancy can not be inferred from the numbers alone and must be considered speculation or guess-work.

Sensible points about the fishiness of the choice of starting date and breakpoint of 1976 were made by UNIT, LOWCOUNTRYJOE, BRET, and KIRZNER FERVOR.

Thanks to everyone who participated in the contest.

Inequality Chart Contest

Recently, Don welcomed Muirgeo back to the comments section of the blog and I noticed a mere 242 comments were made in response. I started reading and stopped when I got to the posting of a chart by Muirgeo. I recognized the chart immediately and stopped reading any further into the comments. So forgive me if some people have responded already.

Muirgeo's comment on the chart is: "Anyone care to take a shot at explaining this data and how it's consistent with libertarianism?" I'm not quite sure what to make of this comment. But I've seen the chart before and I've thought it would make an excellent basis not for a blog post, but rather for a course on how to think about numbers. Certainly the authors of the chart and the authors of the study that the chart is based on, see something wrong with an era where economic growth goes disproportionately to the richest 1%. The implication of these authors (I am not indicting Muirgeo here so let's stay away from that) is that something has gone terribly wrong with the American economy. Perhaps they are right. And of course there are many things that have been done since 1976 that were wise and many things that have been unwise.

Numerous folks believe, however, that the economy has become too unregulated, too unfettered, too free market since 1976 and that that is what explains the pattern of the data in the chart.

So here's the contest:

Write ONE sentence explaining ONE thing that is wrong with concluding that these numbers are evidence that the US economy has become more tilted toward the rich at the expense of the poor.

You can submit more than one sentence. But only one at a time. So each comment can have only one sentence. Any comments containing references to Muirgeo or others will be deleted.

I will take the best sentences and post them (with the author's names) along with the chart along with comments of my own if there is anything else to be said.

At that point, Muirgeo (and others) will have the opportunity to defend the chart or critique the critiques.

Particularly diligent contestants will want to consult this source for further information on how the numbers were generated.

UPDATE: There are now more than 200 comments so I am closing the contest. Tomorrow, August 21, I hope to post the best sentences with some commentary.

Sickness is bad for your health

Evidently, smoking, obesity and diabetes are bad for your health. So is sickness. Death is bad for your life expectancy. So discovers the New York Times:

THROUGHOUT the 20th century, it was an American birthright that each generation would live longer than the last. Year after year, almost without exception, the anticipated life span of the average American rose inexorably, to 78 years in 2005 from 61 years in 1933, when comprehensive data first became available.

But new research shows that those reassuring nationwide gains mask a darker and more complex reality. A pair of reports out this month affirm that the rising tide of American health is not lifting all boats,...

I'm going to stop the quote there for a moment.

Is the reality really "darker and more complex?" Does anyone really think that "the rising tide of American health" lifts all boats?

Does anyone think that rising life expectancy is really a birthright?

If you go sky-diving every week without a parachute or even with one, you don't live as long as the average. If you smoke a lot and eat too much, you might not live as long as the average:

The most startling evidence came last week in a government-sponsored study by Harvard researchers who found that life expectancy actually declined in a substantial number of counties from 1983 to 1999, particularly for women. Most of the counties with declines are in the Deep South, along the Mississippi River, and in Appalachia, as well as in the southern Plains and Texas.

The study, published in the journal PLoS Medicine, concluded that the progress made in reducing deaths from cardiovascular disease, thanks to new drugs, procedures and prevention, began to level off in those years. Those gains, as they shrank, were outpaced by rising mortality from lung cancer, chronic obstructive pulmonary disease and diabetes. Smoking, which peaked for women later than for men, is thought to be a major contributor, along with obesity and hypertension.

Read the rest of the story if you really want to know why this unsurprising absolutely unstartling finding confirms the world view of John Edwards.

Mark Perry on Krugman and the Jobless Rate

Carpe Diem's Mark Perry carefully investigates and clearly explains -- much better than I do here -- the likely source of Paul Krugman's mistaken claim, in yesterday's New York Times, that "the percentage of prime-working-age Americans without jobs . . . is historically high."

Mark reached the same conclusion that I reached: Krugman mistook "men" for "all workers."

Using what is likely the same data source used (but carelessly so) by Krugman -- and looking at the jobless rates of both men and women -- Mark concludes

Krugman says "the percentage of prime-working-age Americans without jobs is historically high," which is clearly not accurate. It would be more accurate to say that it is close to being historically low.

Krugman's Peculiar Sense of History

Paul Krugman makes a claim in his column (appearing in today's New York Times) that is both wrong and misleading.

The column features his familiar theme that ordinary Americans are suffering terribly as a result of the decrepit shape of America's current, GOP-ransacked economy.  Krugman's offending statement is the one in bold:

Our [that is, ordinary Americans'] bleakness partly reflects the fact that most Americans are doing considerably worse than the usual economic measures let on. The official unemployment rate may be relatively low — but the percentage of prime-working-age Americans without jobs, which isn’t the same thing, is historically high. [emphasis added]

Sounds ominous.  But what does this figure mean?  The closest statistic that I can identify to "percentage of prime-working-age Americans without jobs" is to start with (one minus) the labor-force-participation rate of Americans aged 25-54.  Then adjust that figure for the unemployment rate of Americans in that age group.

For example, if 90 percent of Americans between the ages of 25-54 are participating in the labor force (that is, either have or are seeking employment), then ten percent of Americans in this age-group are without jobs by choice (some, for example, are stay-at-home parents).  These ten percent of Americans without jobs are not "unemployed," for they are not in the labor force.

Then, to determine the actual number of prime-working-age Americans who are "without jobs," we must add to this 10 percent of Americans, in this age group, who are without jobs because they aren't in the labor force whatever percentage of the remaining 90 percent of Americans aged 25-54 are unemployed.  BUT because there's no reason to suppose that unemployment is hitting workers in this age group today significantly any harder or less hard (relative to workers in other age groups) than in the past -- and because, by Krugman's own admission, today's unemployment rate of 5.1 percent isn't especially high -- we can ignore this unemployment rate for my purposes.

So what am I getting at?  This: Krugman's statement that "the percentage of prime-working-age Americans without jobs, which isn’t the same thing, is historically high" is, as I said above, both wrong and misleading.

Look at Figure 1B on this page from the San Francisco Fed.  (HT Russ.)  It does not show the labor-force-participation rate for all Americans aged 25-54; rather, it breaks down the participation rate for Americans in this age group by sex.

Look at the labor-force-participation rate of men (aged 25-54).  That rate is indeed lower than in the past.  For the full time period reported in this Figure, that rate is indeed at an all-time low.  It peaked in 1953, and has been declining ever since.

Now look at the labor-force-participation rate of women (aged 24-54).  Not surprisingly, it has surged in recent decades, although leveling off a bit since around 1990 and even declining a tiny bit since around 2000.

It's highly unlikely that, given that today prime-working-age women participate in the labor force at rates vastly higher than was true even in, say, 1975, that the percentage of all Americans aged 25-54 who are "without jobs" is today lower than it was thirty or even twenty years ago.

Whatever is the percentage of prime-working-age Americans who are today "without jobs," it surely isn't -- contrary to Krugman's hysterical claim -- "historically high."

One possibility is that Krugman saw these, or similar, data and confused "men aged 25-54" for "all workers aged 25-54."  If so, then he's correct that the percentage of men in this age group "without jobs" is "historically high" (where "history" here is confined by the data that go back no farther than the late 1940s).  But today's "historically high" figure is part of a trend that began during the first year of the presidency of Dwight Eisenhower.  At that time, George W. Bush was seven years old.

(Note also that, although the labor-force participation rate of prime-working-age men has been declining for a half century, the trend is slight.  Even today's "historically" low figure is pretty darn high.)

So, to summarize.  Krugman is simply wrong to assert that the percentage of Americans of prime-working-age without jobs is "historically high" -- and misleading to suggest that, whatever this percentage might be today, that it is evidence of some major economy malady.  A rise in the percentage of prime-working-age persons "without jobs" might just as well reflect greater middle-class prosperity -- resulting in more years of schooling or early retirement -- as it reflects economic hardship (such as workers so discouraged by their futile job searches that they just call such searches to a halt).

BTW, this post from Megan McArdle on labor-force-participation is worth reading.

Striking out

Sometimes I get depressed about the quality of statistical work in economics. Then I read something from another social science. Here is a recent study where psychologists find that having the initial "K" increases your chance of striking out when playing professional baseball. Why? Well, it's obvious isn't it? The letter "K" is used when keeping score in baseball to represent striking out. So it's obvious now isn't it? Still don't get it? Neither do I. But hey, it's in the data. Between 1913 and 2006, players with first or last initial "K" struck out 18.8% of the time compared to 17.2% for the fortunate players unhandicapped by their initials. Here is the "explanation" of the authors:

Despite a universal desire to avoid striking out, K-initialed players strike out more often.  For those players, we argue that the explicitly negative performance outcome may feel implicitly  positive. Even Karl “Koley” Kolseth would find a strikeout aversive, but on the whole, he might  find it a little less aversive than players who do not share his initials, and avoid it less  enthusiastically.

But why? Why would having the initial "K" make striking out more pleasant? I just don't get it. The authors go on to "test" their theory by looking at grades of a sample of MBA students:

The MBA students in our sample are well aware of a direct connection between academic  performance and successful job placement. Nevertheless, despite the pervasive desire to achieve  high grades, students with an unconsciously-driven fondness for C’s and D’s were slightly less  successful at achieving their conscious goal.

That is, Charles Darwin received poorer grades than Alan Alda. But it turns out that Alan Alda didn't do better than the non-ABCD initialed:

Interestingly, A- or B-initialed students did not perform better than students whose  initials were grade-irrelevant. There are two possible explanations for this. First, students with  grade-irrelevant initials may already be maximally motivated to succeed. Second, because  performance is determined by motivation and ability, any increased motivation to succeed that  arises from having initials that match positive performance outcomes may not necessarily  translate into increased performance.

There is, of course, a third explanation: there is no real relationship and the authors have been fooled by randomness. Yes, their results are statistically significant. But how many relationships did they explore before finding the ones that were statistically significant. And ho many relationships are there to explore? To really test the theory, you'd have to look at baseball players with the initial "E" and see if they commit more errors than others. You'd have to look at guards in the NBA to see if those with initials "A" have more assists. Centers whose initials include an "R" should be better rebounders. You'd have to look and see whether students with the initials IC were more likely to take an "incomplete" in a class.

I guess Rabbi Jonathan Sacks, the Chief Rabbi of England should have been a football player. Or maybe he just gets fired more often than the average Briton because it doesn't bother him as much as someone with a different last name.

Did Kafka know baseball scoring? Does this explain why he found success in life so difficult? Is this why he named a character "K"?

Do players whose initials are a backwards "K" strike out looking more than the average?

Are workers falling behind because of rising health care costs?

As is often the case, the glass is half-empty over at the Washington Post (HT: Steve Chapman). The headline on today's front page:

Rising Health Costs Cut Into Wages

Higher Fees Squeeze Employers, Workers

The article begins:

Recent history has not been kind to working-class Americans, who were down on the economy long before the word recession was uttered.

The main reason: spiraling health-care costs have been whacking away at their wages. Even though workers are producing more, inflation-adjusted median family income has dipped 2.6 percent -- or nearly $1,000 annually since 2000.

Employees and employers are getting squeezed by the price of health care. The struggle to control health costs is viewed as crucial to improving wages and living standards for working Americans. Employers are paying more for health care and other benefits, leaving less money for pay increases. Benefits now devour 30.2 percent of employers' compensation costs, with the remaining money going to wages, the Labor Department reported this month. That is up from 27.4 percent in 2000.

So the point is that because of rising health-care costs, workers are worse off. Pretty depressing. And there's a graphic to help make the point:

Unfortunately, the text accompanying the graphic is a non-sequitur--"The annual increase in the cost of benefits for workers has far outpaced wage increases in recent years." That sounds bad. That sounds like the costs of health care is growing faster than wages and higher health care costs would seem to imply a lower standard of living for workers. But it doesn't imply that at all. It simply says that the benefits component of compensation has been growing faster than the wage component.

And what does it mean that the "cost of benefits" is rising? What does the word "costs" mean in that phrase? It's a cost to employers. But to workers, benefits are, well, benefits. Higher compensation. What the chart shows is that benefits have been rising rapidly. That's actually a good thing. Yes, part of that increase is that employers are covering health care costs that have been rising. But it also includes other types of benefits, forms of compensation that are usually tax-free to employees.

To find out whether workers are doing better or worse than they once did, you want to look at whether total compensation is growing faster or slower than inflation.  Look at the graphic--it shows that other than a couple of months at the end of 2005, total compensation has been growing between 3% and 4% a year since 2000. But between 2000 and 2007, the CPI increased about 20% or less than 3% per year. So real compensation has been rising.

It's not clear from the graphic whether this is compensation per worker or per hour or whether it's total compensation across all employees. So the conclusion could be distorted by increases in the labor force since 2000. But what is clear is that the graphic has nothing to do with the pessimism of the article and seems to refute it.

As for the 2.6% fall in median income since 2000, the one seemingly hard piece of evidence in the  article, if the number is from the Census (which I assume it is), the Census income figures don't include health care benefits or employer contributions to retirement. So those income figures tell us nothing about the role of rising health care costs' effect on the standard of living of the median worker.

The rest of the article is a series of anecdotes about how expensive health care is. There is no hard evidence for the central claim that rising health care costs have reduced the standard of living of the average American. In fact, the graphic implies the exact opposite.

 

How do we know what we know about economics?

I was asked the other day by a reporter if NAFTA had been a good thing or a bad thing for America. I said that both the proponents and opponents of NAFTA had no legitimate, unassailable or even suggestive, statistical evidence on their side. I said it was absurd to think that in a $14 trillion economy, you could tease out the impact of increased trade with Mexico and Canada and disentangle it from the thousands of other changes going on.

I suggested that anyone who provided an empirical case for or against the agreement was essentially being dishonest--using statistics selectively to make the case for a pre-existing world-view.

The reporter found this viewpoint unacceptable. Surely, he said, economics can help us answer the question of whether NAFTA has been good for America or bad. Or at least good or bad, for say Ohio.

I said no, there was no empirical evidence that would be decisive. It isn't just that it's hard to measure the net impact precisely. I argued that it can't be measured.

He refused to accept this answer. Surely I could answer the question of NAFTA's worth. I was just ducking the question.

I said, no, I wasn't ducking the question. I was answering the question. He just didn't like my answer.

But he couldn't accept the answer as being "I don't know," or more accurately, we can't know if by "know," you mean some kind of defensible statistical evidence.

The most well-known think tank that views NAFTA negatively, the Economic Policy Institute, argues that between 1993 and 2004, Ohio lost 49,886 jobs because of NAFTA.

Not 50,000, but 49,886.

For the U.S. as a whole, EPI estimates that 1,015,290 jobs have been lost. Not a million, but 1,015,290.

But it's not just the precision of the estimate that makes the calculation ridiculous. It's the methodology that measures jobs displaced (the verb the EPI study usually uses) by assuming that imports destroy jobs and exports create jobs. With this methodology, a trade deficit reduces the number of jobs.

By this logic, America would have fewer jobs if foreigners suddenly decided to give us cars rather than selling them to us. Free foreign cars would destroy jobs in the American car industry. Inexpensive foreign cars do the same thing. So do cars made by Americans that are produced with robots instead of human welders. If you believe that imports destroy American jobs, so does better technology that makes workers more productive.

If the EPI was as anti-technology as they are anti-trade deficit, they could go out and measure the number of buggy manufacturing jobs destroyed by the auto industry and the number of eyeglass manufacturing jobs destroyed by lasik surgery or the number of jobs in the medical profession destroyed by pharmaceutical innovation. Surely those numbers could be estimated with some rough accuracy.

And surely they would tell us nothing about whether the world is a better place because of those innovations. And they would tell us nothing about the impact of those innovations on total employment.

But most of us understand that higher productivity doesn't mean fewer jobs in the United States overall. There are two ways to see it. One is to see the increase in the total number of jobs over time as we have replaced people with machines in every corner of the economy where it's possible. So this suggests that technology doesn't destroy jobs.

The same is true of the apparent effect of trade deficits. Manufacturing employment was shrinking as a share of the total employment between 1950 and 1975 when the trade deficit was essentially zero. We run a trade surplus in agriculture. Yet agricultural employment shrinks rather than rises.

There appears to be no relationship between the number of jobs in America (or in Ohio for that matter) and the trade deficit or increased productivity.

But the opponents of technology or trade could argue that the failure to observe a negative correlation between jobs and technology or jobs and trade deficits is spurious. True, the number of jobs in America has grown while technology has gotten better and better. But if we hadn't had technology we'd have even more jobs. So we have to control for the other factors that might have made jobs increase rather than decrease.

That's very hard to do. I'd settle for hearing a list of the factors that might plausibly be offsetting the alleged effects of trade and technology. In the absence of that list, I'm pretty comfortable arguing that trade deficits and technology have little effect on the total number of jobs (rather than the number of jobs in particular industries). And the reason I feel that way is partly because of the total number of jobs rising but mainly, I suspect, because I have a belief about how the world works.

I use the word "belief" which might lead some to dismiss my views as simply a matter of ideology. But my faith in my view of how the world works has empirical support for its tenets. It's not just a fantasy or a dogma or convenient. I just don't have holistic empirical evidence on trade having no impact on the number of jobs.

My view of the world is that when we widen the ability of our fellow citizens and ourselves to trade with people other than just our own kind or nationality or religion or color, that that in turn allows trade to take place more productively. That in turn creates wealth, a higher standard of living. And that in turn creates new opportunities for employment that come along and replace the old.

This view of the world is supported by logic and lots of empirical evidence. But again, it's not the empirical evidence of an overarching kind that let's me evaluate one corner of increased specialization, say the overall impact of NAFTA.

When I told the reporter that the net job loss numbers of the EPI are meaningless because they miss many of the jobs that are created, he said, so tell me where to look for those new jobs. And again, I had to disappoint him. I said that I couldn't tell him where to look.

Why?

Because when we trade more with each other and use our limited resources more effectively, two kinds of new opportunities get created. We take the wealth and resources that have been freed up by more efficient trade (or the same effect when productivity increases) and we use those extra resources to produce more of what we like and to get some new stuff we couldn't have had before. And yes, we also get an expansion of export-related jobs.

But the export-related jobs can't capture all the job gains from having a more effective use of our skills and time and energy that comes from trading more widely. Especially if you run a trade deficit. That means that a lot of the new jobs that are going to be created aren't going to have anything to do with trade. Just like finding ways to make steel with fewer people and more machines leads to new jobs in the steel-machinery making business but in lots of other industries that you can't identify, that come from people being able to spend less on steel and having more resources to spend on other things.

So I can't tell the reporter where to look. He has to look everywhere. It doesn't make for a very good feature story.

So I'm sorry I can't be more helpful. But I do think economics has a lot to say in helping us understand the effects of increased trade. It just doesn't come from the bottom line of an empirical study.

P.S. I am ignoring any bureaucratic complications that occur in a real-world trade agreement. I wish we would just eliminate our tariffs and quotas rather than do all the bureaucratic and legal compliance wrangling involved in NAFTA.

P.P.S. For a pictorial version of these ideas with the data alluded to in the discussion, go here.

More on debt

Martin Brock, in response to this post, writes:

Still, the median debt is up 151% since 1989, adjusted for inflation, and if the "wealth effect" theory of the personal saving rate is correct, much of this increase is consumer debt (the cars and such). I don't see how the clarification makes this increase any less gloomy. Maybe it's not very gloomy at all, but if it's gloomy before the clarification, it's still gloomy after.

It's funny how numbers can be used to scare and mislead. One hundred and fifty one percent seems like a big increase. But the size of that number tells you nothing. It does sound scary. But it tells you nothing. Nothing. All it tells you is that in 1989, the median family with debt had debt of 22,000. In 2004, the median family with debt had debt of 55,000. Good or bad news?  You can't tell. It tells you nothing by itself. It could mean people are living more and more beyond their means. It could mean that people have more wealth and income and are buying bigger houses and nicer cars. There is nothing inherently gloomy or cheerful until you know more information.

The fact that net worth is up from 68,000 to 93,000 makes it more likely that it's cheery. But let's look a little deeper. Here's another chart from the Federal Reserve Board's Survey of Consumer Finances, the source of all of these data:

Look at the line called Goods and Services. Flat. That's trying to measure people living beyond their means, using debt to buy furniture, food and so on. That's the line the Washington Post should have used to look at whether debt was being used for what the article called "day-to-day expenses." What this chart shows is that the increase in debt is due to people buying bigger houses and investing in a little more education.

Worse than I thought

As I mentioned in this post, there were some strange things in the Washington Post article on the middle class, particularly in the graphic:

So let's take a closer look. As Randy pointed out in the comments, $55,000 seems like a lot of debt for the median family in 2004.  Look again. According to the chart, debt is $55,000 but mortgage debt by itself is $95,000.  A reader might think that the $55,000 is on top of mortgage debt. The description around the graphic says: "household debt more than doubled as more families used debt to finance day-to-day expenses." Is that what that $55,000 is about? Is that on top of mortgage debt? Whoa.

Well, no, it turns out.

The chart is incredibly misleading. Only the top line (income) and the bottom line (net worth) are true medians measured across all families.

The debt figure of $55,000 in 2004 (which supposedly is 151% higher than in 1989 to pay for day-to-day expenses) is actually ALL forms of debt INCLUDING mortgage debt. So how can that be? How can the median family have only $55,000 of all kinds of debt when there's $95,000 of mortgage debt all by itself?

That's because each line of the chart (other than the top line and the bottom line) is a subset of all families and a different subset.

So among families that have mortgage debt (maybe 40-50% of all families) the median mortgage debt among those families is $95,000.

But among families that have any kind of debt, (about 3/4 of all families) the median indebtednes including all kinds of debt is $55,000. That includes mortgages debt. So that $55,000 number has nothing to do with "day-to-day" expenses. And the scary 151% increase in "Debt" from 1989 to 2004 includes people choosing larger mortgages and nicer cars and going to college and everything people finance via borrowing.

So you can't add up any of the lines of the chart or even compare them to each other. They're each for a different subset of the population, the population who have that kind of debt or asset.

(This explanation of how those numbers must have been calculated comes from a Federal Reserve Board economist who compared the numbers in the Post graphic to Fed data from the Survey of Consumer Finances.)

 

The bottom line is the bottom line—net worth is up. That's a good place to start. The rest of the chart is basically meaningless for drawing the kind of conlcusions implied in the text.

Different Data

Here's a letter that I sent today to the New York Times:

Paul Krugman continues his drumbeat message that ordinary Americans are stagnating economically ("Winter of Our Discontent," November 26).  But data that he frequently cites to support his claim (especially from economists Thomas Piketty and Emmanuel Saez) are not of real flesh-and-blood persons through time; they are of statistical categories such as deciles or quintiles of income earners. Changing demographics and movements of persons from quintile to quintile mask potentially huge changes in the underlying reality.

Sure enough, recent data from the IRS that are of real-life persons reveal that ordinary Americans are prospering.  Economist Thomas Sowell summarizes some germane revelations of these data: "People in the bottom fifth of income-tax filers in 1996 saw their incomes rise 91 percent by 2005. The top 1 percent ... saw their incomes decline a whopping 26 percent.  Meanwhile, the average taxpayers' real income rose 24 percent between 1996 and 2005."

Sincerely,
Donald J. Boudreaux

A caveat: I briefly looked for these IRS data on line and couldn't find them (a fact, I'm certain, due to my being pressed today for time).  Sowell doesn't say if these data are adjusted for inflation or not.  My guess is that they are so adjusted.  But even if the reported percentage changes are in nominal dollars, then (1) the growth by 2005 in the real incomes of 1996's bottom fifth of income-tax filers would still be an impressively large 73 percent, and (2) the fall by 2005 in the incomes of 1996's top one percent of income-tax filers would be even larger than what the nominal figures (if nominal they be) suggest.

UPDATE: Tom Armstrong sent to me the pdf containing the IRS data.

Caveat emptor

Buyer beware, even when the item is only truth and the price is zero. The Washington Post reports (ht: Drudge):

The United Nations' top AIDS scientists plan to acknowledge this week that they have long overestimated both the size and the course of the epidemic, which they now believe has been slowing for nearly a decade, according to U.N. documents prepared for the announcement.

AIDS remains a devastating public health crisis in the most heavily affected areas of sub-Saharan Africa. But the far-reaching revisions amount to at least a partial acknowledgment of criticisms long leveled by outside researchers who disputed the U.N. portrayal of an ever-expanding global epidemic.

The latest estimates, due to be released publicly Tuesday, put the number of annual new HIV infections at 2.5 million, a cut of more than 40 percent from last year's estimate, documents show. The worldwide total of people infected with HIV -- estimated a year ago at nearly 40 million and rising -- now will be reported as 33 million.

Self-interest appears to have played a role in the estimates, as we cynical economists often point out:

Having millions fewer people with a lethal contagious disease is good news. Some researchers, however, contend that persistent overestimates in the widely quoted U.N. reports have long skewed funding decisions and obscured potential lessons about how to slow the spread of HIV. Critics have also said that U.N. officials overstated the extent of the epidemic to help gather political and financial support for combating AIDS.

"There was a tendency toward alarmism, and that fit perhaps a certain fundraising agenda," said Helen Epstein, author of "The Invisible Cure: Africa, the West, and the Fight Against AIDS." "I hope these new numbers will help refocus the response in a more pragmatic way."

We're Richer Today. Period.

Tyler, Arnold, and other bloggers have mentioned this nice study by Terry Fitzgerald that appears in the Minneapolis Fed's September 2007 issue of The Region.  (It's the first of three articles; the remaining two articles by Fitzgerald on this topic aren't yet published.)  In this opening number, Fitzgerald offers

a glimpse of the key findings from this article on wages. Microeconomic statistics showing stagnation, and macroeconomic statistics exhibiting growth, measure “wages” quite differently. When the data are adjusted so that they more closely measure the same conceptual object, the disparity between the microeconomic and macroeconomic statistics largely evaporates, and I find that labor income per hour for middle America has not stagnated. Rather, the economic compensation for work for middle Americans has risen significantly over the past 30 years.

This conclusion (and Fitzgerald's analysis from which it follows) makes sense to me.  While being aware of the dangers lurking in the practice of drawing conclusions about complex reality from one's own personal experiences, I nevertheless am certain that ordinary Americans today are immensely wealthier than they were 30 years ago.  I well remember the mid-1970s.  All telephones in middle America were attached to walls or desks; the television repairman was still necessary; going on-line was something New Yorkers did whenever they queued -- which, back then, they did often in order to buy gasoline for their automobiles that broke down much more frequently than do today's cars.  This was a time before cable t.v. was widespread, and before anyone but the super-rich could watch movies on demand in their own homes.  Prepared foods in supermarkets were tasteless and probably toxic.  And no one outside of New York City and a handful of other major metropolitan areas had any hope of browsing in a bookstore with a respectable selection of titles.

For further evidence that the 1970s was no golden age of abundance, come shop with me from a 1975 Sears catalog - and then let's see how much these goodies will cost us.

A Note on Income Data

This letter appearing in today's edition of the Wall Street Journal makes a good point:

Mr. [Alan] Reynolds [in an October 25 WSJ article] uses the term "individual" income several times when explaining the top 1% of income earners, and therein lies his problem. If only the IRS taxed me and my working wife based on our "individual" income we would save several thousands of dollars per year. The problem is that both taxes and the statistics are based on "household" income, which is really a euphemism for "marriage" income. If the statistics were based purely on "individual" income, I'm sure the wage disparity would not be that much worse today as when Ward and June Cleaver were raising the Beaver.

What's happened is that women entered the workforce and in the past few decades educated women have had their incomes match or even exceed that of men. Educated and upper income people have a tendency to marry one another. And because lower-income people are less likely to be married than upper-income individuals, the statistics are skewed even more. To have an honest discussion on this subject you first have to start out with honest statistics.

Steve Walde
Easton, Conn.

My response to Robin's latest

Robin Hanson continues to give me a hard time. First he quotes me:

When scholars can run hundreds of multivariate regressions at very low cost, it easy to convince yourself that the results that confirm your prior beliefs are the "right" results.  The Pragmatists ... believed that the rationalism of Descartes had a dangerous element of hubris. ... Your grandmother is right. She believes in certain things. When you ask her to justify her beliefs she shrugs and says she can't. ... Norms of behavior that survive, survive because they're effective even when no one understands why.

 

The Cartesian belief that you should examine every one of your beliefs ... the pragmatists argued that that was akin to examining the planks of your boat while you were at sea. ... All of which is to say that we shouldn't pretend to be scientific when we're only doing something that has the veneer of science. That's much more dangerous than saying, I don't know or we can't answer that question. ... I also did not mean to imply that data and evidence are irrelevant in how we form our beliefs about what is true. Or that our biases never get overturned.

Then Robin responds, combining my observations on pragmatism with my argument that we are unlikely to be able to use econometric analysis to settle the question of whether concealed handguns reduce crime:

I'm still confused.  Does Russ think his life will "sink" if he further considers his reasons for his believing in concealed handguns, even though it was he who raised this issue?  Does he think only scientists should want reasons for their beliefs?  Does he believe concealed handguns deter crime because his grandma had hidden guns and lived a long life?  Or is it because grandma's society had them and was prosperous?

Yes, if one prosperous society had concealed handguns that is at least weak evidence for the reasonableness of that behavior.  But since other prosperous societies forbid concealed handguns, I don't see how you can have more than a weak belief on this basis, unless you rely on one of those complex data analyzes Russ distrusts.  Russ doesn't seem to think there is simple clear data on concealed handguns, and he hasn't taken up my suggestion to claim that he is persuaded by theory, either complex and subtle or simple and clear.   And yet Russ still seems to retain a strong belief.  This seems literally unreasonable to me.

Robin is taking my observation about pragmatism and applying it to handguns. I didn't mean to. I brought up pragmatism in order to highlight the general dangers of excessive faith in reason. Assuming that econometric analysis always trumps an anecdote is an example of the potential dangers of econometric analysis. Yes, relying on anecdotes is lousy science. But lousy econometrics is lousy science, too. What my podcast with Ayres made me realize is that lousy econometrics may be the norm rather than the exception.

Such cynicism can come cheap. It also seems to leave us with anecdotes. Well, there's also common sense, intuition and general lessons gleaned from experience and empirical work that is less prone to manipulation.

So again, my question to my better-read colleagues in the profession--give me an example of a statistical analysis that relies on instrumental variables, for example, that is done well enough, that is so iron-clad, that it can reverse the prior beliefs of a skeptic.

Consider this excerpt from the summary of a paper by Dube, Eidlin and Lester that finds that Wal-Mart lowers wages when it comes to town:

A potential problem with studying store openings to estimate the impact on wages is that  Wal-Mart does not choose randomly where to expand.  If Wal-Mart’s expansion into  local markets were random throughout the United States across the ten-year period of study, then we could simply look at what happened to wages in counties after Wal-Mart's entry as compared to before.  But Wal-Mart may have taken into account several factors  for expanding into certain markets and not others, including the cost of labor in those  areas at that time.  Economists call this problem “selection bias.” In other words, Wal-Mart’s own criteria for expansion into certain markets may interfere with our ability to  test for a causal relationship between Wal-Mart entry and a change in local wages.   

In this paper, we devise a novel way to resolve this problem.  We take the fact that WalMart store openings spread out over time starting from Arkansas and moved outward to  the coasts, much like a ripple from a drop of water.  In other words, the farther a county was from Arkansas—ground zero for Wal-Mart—the later it experienced the Wal-Mart  growth spurt.  This was an actual pattern of expansion, one that made sense for the  company as it focused on utilizing its distribution networks most effectively and lowering  overall costs of expansion.  By following this ripple of store openings across the country  and over time, we are able to test whether retail wages follow a similar ripple pattern.   Looking at store openings based on both how far the county is from Wal-Mart’s “ground  zero” and the year in question, our estimates are not subject to the selection bias that is often a problem for similar studies. 

Results and Implications 

We find strong evidence that in urban and suburban counties (counties that are part of a  Metropolitan Statistical Area), a Wal-Mart store opening led to a 0.5% to 0.8% reduction  in average earnings per workers in the general merchandising sector.  This finding is consistent with Wal-Mart jobs paying about 10% lower wages than the jobs they  displaced.  A Wal-Mart store also reduced average earnings per grocery worker in that  county by 0.8% to 0.9%.  Taking both wage and possible employment effects into  account, we found that a single Wal-Mart store reduced the total earnings of general  merchandise and grocery workers in that county by about 1.3%. In rural counties, the pattern was different.  A Wal-Mart store opening there was associated with an increase in the average earnings per general merchandise worker and a  decrease in the average earnings per grocery worker.  However, combining wage and  employment effects, the impact on the total take-home pay of the affected retail  workforce was a wash.

Are you convinced by this evidence? I'm not. Does it lead you to conclude that large cities and suburbs should keep out Wal-Mart in order to protect workers?

At a conference, I asked one of the authors (Dube) why wages in urban areas fell more than in rural areas. Wouldn't you expect an urban area with lots of jobs to be less affected by the entry of Wal-Mart than in a rural area? Even if you believe that Wal-Mart has some monopoly power, wouldn't it be smaller in larger cities than in rural areas? His response: only if you have a neoclassical view of the world. We got interrupted before I could ask him what his view of the world was. But I suspect his view of the world is agnostic--he lets the data speak. The truth is there. I say no. The illusion of truth is there. Using distance from Bentonville is clever but does it really work? How would you know? Does it convince you that Los Angeles should keep out Wal-Mart? (I don't know what the authors actually believe on this latter question. I do know that some people have interpreted their results as evidence for keeping Wal-Mart out of an area.)

Here's how my biased neoclassical view works. One, I'm not convinced that Wal-Mart reduces wages because I'm not convinced that distance from Bentonville is a good instrument. What other instruments did you try first? (Here is Emek Basker's paper that critiques a paper that uses a similar methodology.) But even if you could convince me that it is a good instrument, my next argument is that Wal-Mart might lower wages, but that's because it draws low-wage workers into the labor force and the lower overall wages you observe are a composition effect. Those lower wage are a blessing not a curse. You won't convince me that increasing the demand for labor is going to lower the wages of workers who already are employed. Their wages can still go up when Wal-Mart arrives. And even if wages are lower in places where Wal-Mart arrives, I'm still going to argue that causation runs the other way, that Wal-Mart's entry is a response to lower wages, that you didn't hold everything else constant, your instrument is flawed. You're going to find it very hard to convince me that an increase in demand holding everything else constant lowers wages. I'm going to presume you really didn't/couldn't hold everything else constant.

So whose view is more scientific? I'm not so sure. I'm open to attack. Give me a hard time. I'm not interested in debating the merits of Dube, Eidlin, and Lester—if you think their paper is not a good example of instrumental variables, give me a better one. Give me one that's so well done it will cause me to doubt my neoclassical world view or other views I hold on policy or human behavior. Is it possible? Is there an empirical study on the other side that would convince Dube, Eidlin and Lester that Wal-Mart is good for workers? Show me an example where sophisticated statistical analysis has won over the skeptics or trumped common sense because it was so well done.

For what it's worth, I am less of a hard-core neoclassical economist than when I left Chicago. Why? I suspect that various kinds of evidence have persuaded me to take a richer viewpoint. So again, evidence and facts matter.   

Finally, a commenter at Overcoming Bias, Constant, does a much better job than I did explaining the differences between the Pragmatist and the Cartesian:

I will try to approximate the pragmatist position as it contrasts with the Cartesian position. The Cartesian approach is to discard every idea unless it can be verified. The point is to believe only things which are certainly true, and to avoid believing falsehoods. It is a maximally skeptical approach. In contrast, the pragmatist approach is to keep every idea until it is falsified (or found wanting in some other particular way). So the Cartesian gets rid of all the planks of his boat that he can't demonstrate to his satisfaction are necessary for the boat's operation, while the pragmatist keeps all the planks of his boat unless he can demonstrate to his satisfaction that a particular plank is unnecessary.  

Applying this to the current case, the pragmatist approach says that if you have a belief about guns and the evidence does not contradict the belief, then keep the belief. The Cartesian approach says that if you have a belief about guns and the evidence does not demonstrate this belief, then discard the belief.

A few more empirical thoughts

Colleague Robin Hanson gives me a hard time over at Overcoming Bias (reacting to this earlier post):

If Russ relies little on data to draw his conclusions, then on what does he rely?  Perhaps he relies on theoretical arguments.  But can't we say the same thing about theory, that we mainly just search for theory arguments to support preconceived conclusions?  If so, what is left, if we rely on neither data nor theory? 

Try saying this out loud: "Neither the data nor theory I've come across much explain why I believe this conclusion, relative to my random whim, inherited personality, and early culture and indoctrination, and I have no good reasons to think these are much correlated with truth."  That does not seem a conclusion worth retaining.  If this is really your situation, you should move to a nearly intermediate position of uncertainty.   Either you should believe that truth-correlated data or theory has substantially influenced your belief, or you should retain only a very weak belief.

In this week's EconTalk podcast I added this postscript to try and clarify what I was saying:

In last week’s podcast I spoke with Ian Ayres about the power and limitations of data analysis. Ayres emphasized the power and I kept mentioning the limitations, especially in the postcript I added after the interview. I want to clarify a few issues. My basic point was that when it comes to high-powered sophisticated statistical techniques, our biases as researchers and as consumers of that research often triumph over truth. The truth is elusive in complex systems with many things changing at once. It’s hard to isolate the independent effect of one particular variable. When scholars can run hundreds of multivariate regressions at very low cost, it easy to convince yourself that the results that confirm your prior beliefs are the “right “ results. The ones that failed must be the “bad ones.”

When I was in college at U of NC, I took a wonderful course from Richard Smyth where I learned about the American philosopher Charles Peirce and the philosophy of pragmatism. Peirce and the Pragmatists, which include William James and others, believed that the rationalism of Descartes had a dangerous element of hubris. The worship of rationality could lead to deluding oneself to the reliability of one’s thoughts. Prof. Smyth but it this way—your grandmother is right. She believes in certain things. When you ask her to justify her beliefs she shrugs and says she can’t. Some things you do because that’s just the way they’ve always been done. You feel superior to your grandmother because you only do things that are rational. If you can’t justify something via reason, you simply reject it. But your grandmother (and Hayek), were on to something. Norms of behavior that survive, survive because they’re effective even when no one understands why.

Prof. Smyth discussed the Cartesian belief that you should examine every one of your beliefs. If it passes the test of reason, keep it. Otherwise, throw it out. Seems reasonable. But the pragmatists argued that that was akin to examining the planks of your boat while you were at sea. Tearing them out because they look imperfect is the road to ruin. It’s particularly true when you’re less than objective in deciding whether to reject or accept a belief.

Smyth quoted Benjamin Franklin: When fortresses and virgins get to talking, the end is in sight. That is—when you’re besieged, once you start negotiating, it’s easy to talk yourself into giving in and finding a way to justify it as the right thing to do.

All of which is to say that we shouldn’t pretend to be scientific when we’re only doing something that has the veneer of science. That’s much more dangerous than saying, I don’t know or we can’t answer that question.

I certainly didn’t mean to imply that Ian Ayres or John Lott (whose work came up in the conversation with Ayres) are biased researchers. I also did not mean to imply that data and evidence are irrelevant in how we form our beliefs about what is true. Or that our biases never get overturned. The example of Friedman and Schwartz’s Monetary History of the United States is the gold standard. Facts can be decisive. Statistical analysis can persuade. But I am struck by how few controversial viewpoints in the field of economics have been accepted based on sophisticated statistical analysis. By sophisticated analysis, I mean for example, the use of instrumental variables with a data set that has limited information about the myriad of factors that affect the variable we care about.

Where does that leave us? Economists should do empirical work, empirical work that is insulated as much as possible from confirmation bias, empirical work that isn’t subject to the malfeasance of running thousands of regressions until the data screams Uncle. And empirical work where it’s reasonable to assume that all the relevant variables have been controlled for. And let’s not pretend we’re doing science when we’re not.

Fooling ourselves

At the University of Chicago, where I went to graduate school, a quote from Lord Kelvin, was carved in stone at the Social Sciences Research Building: When you cannot measure...your knowledge is meager and unsatisfactory. We graduate students took in this credo like mother’s milk. I came out of Chicago with a tremendous confidence in the power of economics and the ability to quantify that power.

But over the years, I have become increasingly skeptical of the power of statistical techniques to measure causation in complex systems. Edward Leamer’s indictment of modern econometrics, “Let’s take the ‘con’ out of econometrics” is the best known critique of our habits as empirical economists but it has not been taken to heart by the profession.

My thoughts on this issue came to a head with my recent podcast with Ian Ayres on his new book SuperCrunchers. The book is about the power of statistics to improve decision-making. And of course, facts and numbers are crucial for making wise decisions. And there are many examples where statistical analysis helps us in our private and political lives to overcome irrational prejudice or bad ideas. 

But in the course of preparing for the interview, I realized in a way I hadn’t before, that how we feel about the reliability of statistical results lines up incredibly neatly with our political and ideological biases. One example I use in the podcast is the debate over whether allowing citizens to carry concealed handguns deters crime. John Lott and others say yes and trot out the analysis that proves they're right. Their opponents trot out a different analysis and prove the advocates for concealed carry are wrong.

Now I happen to believe that concealed handguns do deter crime and allowing concealed handguns is a good thing. And you can claim that the evidence that shows I'm right is "good" statistical analysis. The other side disagrees. They claim it's "bad" statistical analysis. Who's right? I have no idea. But what's clear to me is that my belief in the virtues of allowing concealed hand guns has little to do with the empirical evidence. And I would argue that the opponents are really in the same boat. They just don't like guns and they've dressed up their prejudices in fancy statistical analysis.

I came to this realization because Ayres thinks LoJack deters car theft. LoJack is a hidden device you put in your car that lets the police trace your car.  Ayres and Steve Levitt found that LoJack has an incredible deterrent effect on car theft. But they think John Lott's work on concealed hand guns is irrevocably flawed. But LoJack is the same thing as a concealed handgun. Ayres and Levitt should like Lott's work and Lott should like Ayres and Levitt. But Lott doesn't like Ayres and Levitt and the feeling is mutual.

It's obvious why neither respects the other. It's not the quality of the empirical evidence. It's just bias. Ayres and Levitt don't like guns and Lott doesn't like the idea that insurance companies (and criminals) could ignore the impact of LoJack if it's really so big. Ayres and Levitt see LoJack as an example of market failure and Lott thinks market failure in this case requires too much ignorance.

The nature of the analysis is such that neither side can convince the other that "their" analysis is reliable. That's not always true. As I suggest in the podcast, Milton Friedman was able to convince the skeptics that inflation is everywhere and always a monetary phenomenon. Friedman won the debate. But how many other studies can you think of where someone staked out a controversial position and convinced the skeptics based on empirical analysis? I think it can be done, but it's rare. And in today's world, most of the interesting empirical claims are being made in cases where  the data are too incomplete and the issue is so complex that we can't move to a consensus. The empirical work doesn't improve our understanding of what's going on. It masks what's going on. It gives a patina of science when in effect the numbers aren't really informing the debate.

In the case of crime, to  isolate the effect of LoJack or concealed hand guns, you have to control for any other causes of crime and control for the simultaneity problem that causation could be running the other way. I'm not sure that can be done. My other favorite example of this is WalMart. There are economists out there who claim that WalMart lowers wages when it comes to a town, even a big town, such as Los Angeles. I don't find this argument believable. But the proponents of such arguments claim to just be using the numbers to tell them what's going on rather than relying on their prejudices as I am. But the numbers can't be crunched sufficiently well to come to a conclusion on WalMart. To do that, you have to control for a bunch of factors that can't be controlled for in real-world data situations.

And that's why there are studies on the other side showing how great WalMart is for a town. But are we moving toward a consensus about the impact of WalMart? Do the analyses improve our understanding? I don't think so. And that's because of the way modern econometrics is done. Regression is cheap so we buy a lot of it. Leamer's point is that this is "faith-based" empirical work. You just keep running the regressions including or excluding this or that, trying this or that specification until you find the result that confirms your worldview before you started the work.

The pragmatists (Peirce and James) and Hayek understood the dangers of rationality and what is essentially fake science. I'll write more on this another time and maybe do a podcast just on this issue.

I've closed comments here. If you want to comment, please listen to the Ayres podcast at EconTalk and let's talk over there in the comments section to the Ayres podcast.

Poverty and immigration

Robert Samuelson points out wisely that the measured poverty rate is a misleading measure of economic progress when there is immigration (a common theme here at the Cafe):

The standard story is that poverty is stuck; superficially, the statistics support that. The poverty rate measures the share of Americans below the official poverty line, which in 2006 was $20,614 for a four-person household. Last year, the poverty rate was 12.3 percent, down slightly from 12.6 percent in 2005 but higher than the recent low, 11.3 percent in 2000. It was also higher than the 11.8 percent average for the 1970s. So the conventional wisdom seems amply corroborated.

It isn't. Look again at the numbers. In 2006, there were 36.5 million people in poverty. That's the figure that translates into the 12.3 percent poverty rate. In 1990, the population was smaller, and there were 33.6 million people in poverty, a rate of 13.5 percent. The increase from 1990 to 2006 was 2.9 million people (36.5 million minus 33.6 million). Hispanics accounted for all of the gain.

Consider: From 1990 to 2006, the number of poor Hispanics increased 3.2 million, from 6 million to 9.2 million. Meanwhile, the number of non-Hispanic whites in poverty fell from 16.6 million (poverty rate: 8.8 percent) in 1990 to 16 million (8.2 percent) in 2006. Among blacks, there was a decline from 9.8 million in 1990 (poverty rate: 31.9 percent) to 9 million (24.3 percent) in 2006. White and black poverty has risen somewhat since 2000 but is down over longer periods.

Monkeys and Typewriters

In Fooled by Randomness, Nassim Nicholas Taleb gives the example of a person who wants to hire someone to type a copy of Homer's Odyssey. Suppose an infinite number of monkeys are typing on an infinite number of typewriters. One of them comes up with a perfect copy of the Illiad. This enterprising monkey shows up at your door, applying for the job of Odyssey typist. But you're a monkey, you reply skeptically. He takes out the perfect copy of the Illiad. You're stunned. And you apologize and hire him on the spot, foolishly interpreting the perfect manuscript of the Illiad as an indication of the monkey's skills.

Taleb's point is that before you decide that monkeys are good at typing, you want to know more about the denominator—the number of monkeys trying to type the Illiad. When people claim that this stock-picker or that fund manager is a genius because he's beaten the S&P 500 for 15 straight years, you should ask how many people are out there picking stocks and what proportion of those would be expected to beat the S&P for purely random reasons. It's possible there are fund managers who are genuinely skilled but the fact that they do well over some limited time period is not proof.

A more mundane application is asking for references from a contractor. I always ask for two or three references. But a contractor who has been in business for any length of time will be able to find a few good ones—even a few raves—that may not be representative of the typical experience of the firm's clients. The better strategy is to ask for references from the last three clients or clients from the last six months.

Here is a wonderful page on the infinite monkey metaphor. It will give you a healthy sense of what low-probability events really mean:

One computer program run by Dan Oliver of Scottsdale, Arizona, according to an article in The New Yorker, came up with a result on August 4, 2004: After the group had worked for 42,162,500,000 billion billion years, one of the "monkeys" typed, “VALENTINE. Cease toIdor:eFLP0FRjWK78aXzVOwm)-‘;8.t . . ." The first 19 letters of this sequence can be found in "The Two Gentlemen of Verona". Other teams have reproduced 18 characters from "Timon of Athens", 17 from "Troilus and Cressida", and 16 from "Richard II".^[20]

A website entitled The Monkey Shakespeare Simulator, launched on July 1, 2003, contained a Java applet that simulates a large population of monkeys typing randomly, with the stated intention of seeing how long it takes the virtual monkeys to produce a complete Shakespearean play from beginning to end. For example, it produced this partial line from Henry IV, Part 2, reporting that it took "2,737,850 million billion billion billion monkey-years" to reach 24 matching characters:

RUMOUR. Open your ears; 9r"5j5&?OWTY Z0d…

Even more entertaining is this study of actual monkeys:

In 2003, lecturers and students from the University of Plymouth MediaLab Arts course used a £2,000 grant from the Arts Council to study the literary output of real monkeys. They left a computer keyboard in the enclosure of six Sulawesi Crested Macaques in Paignton Zoo in Devon in England for a month, with a radio link to broadcast the results on a website. One researcher, Mike Phillips, defended the expenditure as being cheaper than reality TV and still "very stimulating and fascinating viewing".^[24]

Not only did the monkeys produce nothing but five pages^[25] consisting largely of the letter S, the lead male began by "bashing the hell out of" the keyboard with a stone, and the monkeys continued by urinating and defecating on it. The zoo's scientific officer remarked that the experiment had "little scientific value, except to show that the 'infinite monkey' theory is flawed". Phillips said that the artist-funded project was primarily performance art, and they had learned "an awful lot" from it. He concluded that monkeys "are not random generators. They're more complex than that. … They were quite interested in the screen, and they saw that when they typed a letter, something happened. There was a level of intention there."^[24][26]

Clutch hitting

It is very hard for sports fans, sports writers and even athletes do accurately assess which of their  players perform well under pressure and which do not.

This brilliant dissection (Rated R for language) of a recent sports column on the topic of "hitting in the clutch" shows how powerful numbers can be for clarifying fuzzy thinking.

More on Interpreting Statistics

Here's more, now from The Economist.com, on the need to be careful when interpreting statistics -- such as that infant-mortality rates in the United States are higher than in many countries much poorer than America.  A snippet:

Comparing infant mortality rates between countries is fraught with uncertainty—after all, it's hard to argue that every country's figures are reliable. But it's still worth asking what more we can do to stop babies from dying. Defined as death before one year of age, infant mortality frequently gets framed in the United States as a problem of insufficient health-care funding. In December, for example, a New York Times column blamed it on the lack of a single-payer health insurer. However, a closer look reveals the counterintuitive possibility that high infant mortality in the United States might be the unintended side effect of increased spending on medical care.

Read the whole Economist.com post.

(HT: Liberty Alone)

Using and Misusing Statistics

Statistically speaking....

Who Shoulders the Tax Burden?

The IRS just released some data.  One of the most interesting reported facts is this:

For tax year 2004.... Taxpayers with an AGI [adjusted gross income] of at least $328,049, the top 1 percent of taxpayers, accounted for 19 percent of total AGI, representing an increase in income share of 2.2 percentage points from the previous year. These taxpayers accounted for 36.9 percent of the total income tax reported, an increase from 34.3 percent in 2003 [emphasis added].

(HT: Andy Roth at the Club for Growth)

Income and Health

Alert, alert. Do not miss this. Best thing I've seen in a long time. Three people told me about in the last two days. And I'm pretty sure someone else told me about it before that and I just missed it. Don't miss it.

So go here. It is a spectacular presentation on how international measures of life expectancy and other measures of health are getting better over time and the relationship to income. It's also has some spectacular examples of how averages can be misleading. But equally compelling is the way the data are presented. This is so cool.

When you're done, more info here.

HT: Avi Hoffman, Ville (in the comments at EconTalk on the Easterbrook podcast) and Ben Parizek.

Autism and TV

Here's a very interesting article in the WSJ (HT: Steve Saletta) on the use of what is called  instrumental variables in econometrics to argue that television watching causes autism. It seems like a goofy idea and it may be. The more interesting issue for me is the use of sophisticated statistical techniques to try and observe cause and effect. Too much of the time, for my taste, instrumental variables are used to awe and amaze rather than to ferret out the truth. And too many economists have become enamored of technique for technique's sake.

The paper discussed in the Journal is a perfect example and the reporter, Mark Whitehouse does a superb job explaining the econometrics for a non-expert:

Prof. Waldman, a recognized expert in the field of applied microeconomics, doesn't pretend to be an authority on autism. He became engrossed in the subject in the fall of 2003, when his 2-year-old son, David, was identified as having an autism-spectrum disorder. Hoping to eliminate any potential triggers, Prof. Waldman supplemented the recommended therapy with a sharp reduction in television watching. His son had started watching more TV in the summer before the diagnosis, after a baby sister was born.

Prof. Waldman says his son improved within six months and today has fully recovered -- a surprising result, given that autism is typically a lifetime affliction. "When I saw the rapid progress, which was certainly not what anyone had been predicting, I became very curious as to whether television watching might have played a role in the onset of the disorder," he says. He tried to get medical researchers interested in the idea, to no avail.

In late 2004, he decided to look into the subject himself, ultimately putting together a research team with Cornell health economist Sean Nicholson and Nodir Adilov, a professor of economics at Indiana University-Purdue University in Fort Wayne.

In principle, the best way to figure out whether television triggers autism would be to do what medical researchers do: randomly select a group of susceptible babies at birth to refrain from television, then compare their autism rate to a similar control group that watched normal amounts of TV. If the abstaining group proved less likely to develop autism, that would point to TV as a culprit.

Economists usually have neither the money nor the access to children needed to perform that kind of experiment. More broadly, randomized trials seldom lend themselves to studying economic questions, particularly the more traditional ones. It would be unfair to randomly subject some people to a higher tax rate just to see how it affects their spending.

Instead, economists look for instruments -- natural forces or government policies that do the random selection for them. First developed in the 1920s, the technique helps them separate cause and effect. Establishing whether A causes B can be difficult, because often it could go either way. If television watching were shown to be unusually prevalent among autistic children, it could mean either that television makes them autistic or that something about being autistic makes them more interested in TV.

The ideal instrument is a variable that is correlated with A but has no direct effect of its own on B. It should also have no connection to other factors that might cause B. If data in a study nonetheless show that the instrumental variable is linked to B, it suggests that A must be contributing to B.

After giving a few examples of previous studies that used instrumental variables, the article gets back to Waldman and autism:

By putting together weather data and government time-use studies, they found that children tended to spend more time in front of the television when it rained or snowed. Precipitation became the group's instrumental variable, because it randomly selected some children to watch more TV than others.

The researchers looked at detailed precipitation and autism data from Washington, Oregon and California -- states where rain and snowfall tend to vary a lot. They found that children who grew up during periods of unusually high precipitation proved more likely to be diagnosed with autism. A second instrument for TV-watching, the percentage of households that subscribe to cable, produced a similar result. Prof. Waldman's group concluded that TV-watching could be a cause of autism.

Criticism quickly arose, illustrating some of the perils of the economists' approach. For one, instruments are often too blunt. As Prof. Waldman concedes, precipitation could be linked to a lot of factors other than TV-watching -- such as household mold -- that could be imagined to trigger autism. At best, his data reflect the effect of television on those children who changed their habits because of rain or snow, not on those who did it for other reasons such as a desire to watch educational shows.

On the surface, the whole thing sounds vaguely plausible. Watching TV is a brain activity. It's imaginable that watching TV affects your brain and could, at a crucial developmental stage, cause the brain to do various things, both good and bad.

But then you start to think about it a little more. They didn't show a relationship between TV and autism. They showed a relationship between rainfall and autism. Then they made the leap that more rainfall means more TV. But more rainfall means a thousand other things, too. The "theory" that encouraged the study was based on ONE observation, that Waldman's kid watched less TV and got better. But surely there were thousands of things that were different in the Waldman household over those six months.

Then there is the problem of defining autism. There are different degrees of autism. Were they all lumped together? How precise was the relationship between rainfall and autism? What was the confidence interval? Did the definition of autism change over the period? The diagnosis of autism is much more common today than in the past, either because of environmental factors or more plausibly, a higher awareness of the phenomenon by both doctors and patients and a widening of the definition of what is considered autistic. Maybe an increase in rainfall just happened to coincide with an expansion of the definition. Surely the increase in cable TV subscriptions is correlated with that expansion as well. And is there really so much variation in snowfall and rainfall in Oregon, California and Washington states? Why were those states chosen? If anything, people in Portland and Seattle are used to lots of rain and snowfall and probably don't respond by watching TV a lot when it's raining. Did the authors control for these possible effects?

When I hear these kind of speculative findings (and they are very common in the profession these days) I always want to ask the researchers how confident they really are in their findings and have they really discovered anything we didn't already know.

In the case of the autism study, I'd ask the following: does the econometric firepower brought to bear on the problem add anything more to our knowledge than the anecdotal story that Michael Waldman's son got better after watching less TV? I'm not convinced. But here's what I really want to say to the authors of the study.

You found a correlation between rainfall and autism that might indicate a correlation between TV and autism. Let's take a thousand kids with autism who currently watch a lot of TV. Let's do a real experiment where we remove their access to television. How much are you willing to bet that there will be a statistically significant reduction in autism? A thousand dollars? Ten thousand dollars? Ten dollars? Put your money where your econometric mouth is. You found something. Do you really believe it?

The future might not be like the past

Public discourse on global warming reached a new low recently with this assessment by Ellen Goodman:

By every measure, the U N 's Intergovernmental Panel on Climate Change raises the level of alarm. The fact of global warming is "unequivocal." The certainty of the human role is now somewhere over 90 percent. Which is about as certain as scientists ever get.

I would like to say we're at a point where global warming is impossible to deny. Let's just say that global warming deniers are now on a par with Holocaust deniers, though one denies the past and the other denies the present and future.

Where to begin in analyzing that last sentence? The moral vulgarity of it? Or just the scientific inaccuracy? True, Holocaust deniers deny the past. Global warming deniers are denying the reliability of statistical models of the past being used to forecast the future. My understanding of the science is that the models of the past aren't quite theory-free but they're not exactly theory-robust, either. That is, we don't fully understand the causal relationship between human activity and global temperature, so much of what is being predicted about the past is an extrapolation.

(Mark Steyn, on the other hand, knows where to begin. He is brilliant. HT: CHCH, Don Boudreaux)

Gregg Easterbrook at ESPN (and the author of The Paradox of Progress) has a very nice summary of bad predictions made in the past year, reminding us that in many areas of life, the future is not like the past. Most of them are sports predictions, but he throws in some nice gems from outside the sports world:

Bad Hurricane Predictions: The year of Katrina and Rita, 2005, obviously was awful for Atlantic cyclones, with a record 15 hurricanes. Both the National Oceanographic and Atmospheric Administration, and media-favorite hurricane forecaster William Gray of Colorado State University, predicted 2006 would be bad, too. In December 2005, Gray predicted for 2006 nine hurricanes, five of them intense; there was an 81 percent chance a major hurricane would strike land in the United States in 2006, Gray and CSU said with ridiculous pseudo-precision. In May 2006, NOAA forecast eight to 10 hurricanes, six of them intense. The 2006 hurricane season would be "hyperactive," NOAA declared: "The main uncertainty is not whether the season will be above normal but how much above normal it will be." Actual: In 2006 there were five Atlantic hurricanes, two of them intense -- pretty much smack on the 20th century average for the Atlantic basin. None made landfall in the United States.

Easterbrook also gives this beautiful example of a really bad prediction from 1986:

In June 1986, Newsweek ran its infamous "Marriage Crunch" cover story, which pronounced that a college-educated career woman of age 30 had only a one-in-five chance of winning a husband, while an educated professional woman of age 40 had essentially no chance. At a time when this must have sounded funny to someone at Newsweek, the magazine declared that a single 40-year-old career woman was "more likely to be killed by a terrorist" than to find a man who would say "I do." Twenty years later in June 2006, Jeffrey Zaslow of the Wall Street Journal checked to see how these predictions stood the test of time. Women aged 30 to 40 in 1986 when Newsweek declared them unmarriageable are aged 50 to 60 now, Zaslow reasoned. Crunching Census Bureau stats, he found that 90 percent of college-educated American women between the ages of 50 to 60 have married at least once. Zaslow tracked down the 10 career-shark single women who were named in the 1986 Newsweek cover story as certain spinsters: eight later married.

Animation Contest Update with caveats

I should have mentioned that the numbers in the minimum wage scenario are just for illustration and should not be taken as reliable. I've updated the post to say that. But here is where I got the numbers that I used in the illustration.

According to the BLS, there were just under 2 million workers who earned the minimum wage or less in 2005. But the number of workers who earn less than $7.25 was just a rough guess. According to this December 2005 report from the Center for Economic and Policy Research,  a group that supports an increase in the minimum wage, about 7 million workers would get a raise if the minimum wage were increased to $7.25 an hour.  So I used that figure, assuming it would be an upper bound. But someone has just emailed me suggesting that according to the BLS, that number should be 11 million. I'll resolve this apparent discrepancy. Finally, most studies suggest that a 10% increase in the minimum wage causes a 1-3% decrease in employment. So a 40% increase in the minimum wage from $5.15 to $7.25 would cause a 4-12% decrease in employment (using the crude assumption that the effects are linear) or a loss of between 280,000 and 840,000 jobs. I used one million just so we wouldn't need a fraction of a stick figure if each stick figure represented a million jobs. The bottom line is that you shouldn't quote these figures as "facts."

Comic genius

Sometimes students have trouble with multivariate analysis. Finally a site has been created that will help them see the light and make them laugh at the same time. Even you who have mastered relational analysis will enjoy it. It's brilliant. What would you call it? Venn-triloquism? Tufte meets the Simpsons? Don't miss it. Thank you, Jessica Hagy.

And then go here for more delight.

Engerman on Slavery

Stan Engerman talks about slavery in the latest episode of EconTalk. Stan is an amazing man. He reads as much as anyone I know, but one of the things that makes him exceptional is that he actually remembers most (all?) of what he reads. So this podcast includes discussions of Brazilian slavery, African slavery in the 20th century, the American Civil War, Xenophon, Hume, Adam Smith, how slavery evolved to maintain incentives, slavery in the Bible, Time on the Cross and the reaction to it, the heights of slaves, the frequency of compensation paid to ex-slaves vs. compensation of slave owners, the Lincoln-Douglas debates and so on.

Lauren Landsburg, who is the editor of the Library of Economics and Liberty (the web site that hosts EconTalk), wrote me recently about how Time on the Cross changed her life:

Fogel and Engerman's Time on the Cross was the book that opened up the world of economics to me.

As a grad student at Yale in 1976, I was writing my master's thesis in Chinese history under Arthur Wright.  I kept noticing how classical Chinese court documents routinely recorded some strange data--something called the money supply.  Intrigued, I turned to friends who'd taken economics courses; and, rebelling against Wright's pointed skepticism that historical data could be taken seriously because it would be biased, I opened the pages of a recent book that friends assured me would help sate my belief that surely _something_ could be gleaned from at least the fact that the data were of interest to the court.  The book, my friends told me, talked about something called "cliometrics"--statistical methods for extracting whatever might be learned even from biased historical data.  Best of all, it was written clearly enough that I wouldn't have to wade through any of the economics courses I'd so long held in contempt.

The book was Time on the Cross, and I never turned back once I turned its pages.  I promptly left Yale and applied to the U. of Chicago in economics, where I found my intellectual home--a way of thinking that believes in challenging theory with evidence, and that has the courage to confront controversy.

P.S. Wright and I debated bitterly about the use of data in studying history.  He finally threw up his hands and gave me a gentleman's C--an F for a grad student at Yale.  He died on the golf course a few weeks later.  I went to Chicago.

It's hard for people today to remember the viciousness of the initial reaction to the book. It was a work of courage and has become a classic.

Let's Go Googling

In the comments to this recent post, there's a thoughtful debate among Cafe Hayek readers about the state of our standard of living. One reader, Joan, makes the following argument:

The fact that we are having a discussion about whether or not we are better off is proof the the economy has not been performing well. In 1980 no one questioned we were better off than in 1950, that 1950 was better than 1920 or 1920 was better than 1890.

There have been some interesting responses to Joan's claim, but I want to make a different point.

It is very difficult to know from casual observation how the overall economy is performing or what is happening to inequality or to the standard of living of the average American over time. When unemployment was 25%, as it was in 1933, I suspect most people had an idea of how bad things were. But when unemploment is 4.5% and the economy is growing, your casual observations are inevitably tainted by your circle of friends, your neighborhood, your city, your region. You have no idea how people are doing overall. You may not even have an honest assessment of your own situation relative to your past or your parents.

The level of inequality in the national economy is not a palpable, perceivable, noticeable phenomenon. Changes in that level are particularly difficult to notice or perceive.
The only reason people think inequality is growing is because of articles in newspapers reporting on government data and research using that data. Those data are flawed but more importantly, how those numbers are interpreted are flawed.

For example, the claims being made about the amount of money going to the top 1% or top 10% often implicitly assume that the people in the top 1% are the same people a decade later. So if the share going to the top 1% climbs, the implication is that the top 1% are corralling more of the money for themselves using the Fed or tax policy or brainwaves or crushing unions or some other nefarious strategy.

But they are not the same people. If the economy is growing dramatically and if there are opportunities for entrepreneurs, a few people are going to be much much better off than they were before. They will be catapulted from the bottom or the middle of the distribution into the top 1% or the top .1%. When the founders of Google created Google they became extremely wealthy. But they were only able to do that by pleasing others, by getting others to use their service. Before Google was successful, they weren't in the top 1%. They were students, first, then struggling entrepreneurs. They were catapulted into the top 1% out of nowhere.

Because of their success and the success of others like them, they displaced other in the top 1%. But by doing so, they didn't make any one poorer. The resulting "inequality" has no meaning, really, at least not in the sense in which the worriers about inequality mean the term. It's not like they suddenly got more money and people at the bottom got less. Another way to say it is that people at the bottom got a smaller share of a bigger pie. So they can easily be better off. Isn't that what we really care about? Improving people's lives?

Why would we ever be alarmed or concerned about this kind of growth in so-called inequality?

And we would never be aware of it in the absence of misinterpreted government data. And that would be good. We wouldn't be missing anything.

Instead of being focused on outcomes, outcomes that are prone to misinterpretation and manipulation by self-interested parties, we should focus on the the fairness of the rules. If you go to college and study something serious, do you do well in America? Do the rich put up barriers to success that prevent hard-working, intelligent non-rich people from succeeding? Instead of showing me government statistics, show me rules or institutions that prevent people from escaping poverty.

One alleged such institutional change is the decline in unions. But the decline in unions wasn't the result of a conspiracy. It's an endogenous change caused by the transformation in the kind of jobs we do. Unionization has been declining steadily during times of growing measured inequality and times of a more stable distribution of income. In most American occupations, the lack of unions means nothing. Workers don't need unions to keep their wages high. So show me something else that rich Americans are doing to keep the poor, poor. I don't see it.

Those who complain about inequality fail to understand that the level of inequality in America is an emergent phenomenon. It is not intended or designed by anyone. It is the result of millions of decisions made by millions of Americans. "Fixing" it is not a matter of tweaking this policy or that. And if I am right about the importance of entrepreneurs and others who benefit from a growing economy, growing "inequality" is the result of a growing economy rather than a social problem.

The Real Story of Job Growth

The numbers are broken.

The September job numbers are out. The Washington Post reports:

The U.S. economy created far fewer jobs in September than anticipated, but revised job figures for previous months showed a surprisingly substantial uptick over what was previously reported.

The national economy created 51,000 jobs in September -- analysts had been predicting 120,000 -- and the unemployment rate dipped slightly from 4.7 percent to 4.6 percent, according to figures released today by the Labor Department.

At the very end of the article, in the very last paragraph, in the very last sentence, comes the truly newsworthy part of the article. Here's the last paragraph:

The good news was that job gains for both July and August turned out to bigger than previously estimated, taking some of the bite out of September's figures. The report also showed that job growth during the 12 months ending in March may have been 45 percent higher than previously reported. The Labor Department said payrolls for the 12 months that ended in March 2006 will be revised upwards by a whopping 810,000 jobs, the biggest revision since 1991.

I like that word "whopping." The Post reporter, Daniela Deane, realizes that an average monthly error over 12 months of 67,000 jobs is an incredible error.

These revisions are because the Bureau of Labor Statistics establishment survey is a sample of existing businesses that has trouble capturing some sources of new job growth—small businesses and the self-employed. The BLS household survey which used to move roughly in parallel as a measure of job creation has been diverging wildly from the establishment survey. The pessimists quotes the establishment survey. The optimists quote the household survey. Revisions of this size suggest the optimists had a point.

(HT: Brian Wesbury)

More Data on Middle-Class Americans

Greg Mankiw links to this interesting essay by Stephen Rose; it's entitled "What's (Not) the Matter With the Middle Class?"  I believe it to be a superb essay.  (More on that in a moment.  But first....)

At the very least, the continuing debate over the economic fortunes of ordinary Americans shows the truth of the claim, attributed to Ronald Coase, that "if you torture the data long enough, they'll confess."

Data -- expecially data from the social sciences -- very seldom speak clearly.  They are always the result of highly complex, often conflicting, forces, most of which are not directly observable.  Every statistic, being the result of choices investigators make about how to slice up and label and explore empirical reality, will have some fault, some weakness, something that can be criticized.

Every statistic reported in Rose's essay can be challenged as missing this or over-emphasizing that; each statistic here can be interpreted differently from how Rose inteprets it.  But, alas, if we're to understand reality as much as we possibly can, we have no choice but to rely upon statistics and, in every case, to use our knowledge and good judgment to assess their lessons.

So, what do Rose's data show?  In short, most Americans are thriving.  Here's a key selection:

It's true that the middle class is shrinking -- but that's because more families are better off. The share of prime-age adults in households with real incomes above $100,000 rose by 13.1 percentage points from 1979 to 2004. The share of households making less than $75,000 dropped by 14 percent. Fully 41 percent of prime-age American adults are in households with incomes above $75,000. 

Among married-couple households the picture is even brighter. In 2004, the median income for these households was $70,000, and $78,000 for couples with two earners.

I focus on prime-age households (age 25-59), which are 68 percent of the population, because including the very young and the very old distorts the picture of what's really happening with the middle class. Many young workers get paid very little, but few will keep their low salaries as they move up in their careers. Older Americans distort the wage and income picture because they're no longer working. Their incomes may shrink, but their standard of living may not diminish. Indeed, Americans age 55-64 have greater net wealth than any other group

Interpreting Data

Cornell University psychologist Tom Gilovich makes one of the most useful observations I've encountered in some time.  Here's economist Robert Frank's summary of Gilovich's observation:

As the psychologist Tom Gilovich has suggested, someone who wants to believe a proposition tends to ask, “Can I believe it?”  In contrast, someone who wants to deny its truth tends to ask “Must I believe it?"

As much as I'd like to deny that Gilovich's observation applies to me, I can't.  It does apply.  But now being aware, I at least try to take a more critical stance toward propositions that correspond closely to my priors, and to take a more forgiving stance toward propositions that contradict my priors.

I thought of Gilovich's observation when I saw this graph in today's Econoblog discussion between Brian Wesbury and Barry Ritholtz.

When I first saw this graph, I thought exactly what Brian Wesbury wants me to think: "Yep, the tax cuts that kicked in May 2003 spurred investment spending in the U.S."  And perhaps they did.  The data, as they say, will bear that interpretation.

But as I look more closely and critically I see that the trend of business investment from the depth of the downturn through today might not have had anything to do with the tax cut.  Although still negative until the first quarter of 2003, business investment began to improve in the first quarter of 2002.  Furthermore, note that business investment was higher in late 1999 and early 2000 than it has been at anytime since the May 2003 cut in taxes.

None of the above is to disagree with Brian Wesbury.  I agree with him that these tax cuts likely explain much of the observed change in business investment over the past few years.  But it's interesting how ambiguous even seemingly unambiguous pictures and charts and graphs and historical anecdotes become if you look at them critically, trying your best to see in them not what you want to see but what someone with a view very different from your own wants to see.

I'll not here allow myself to get sucked into the black hole of methodology, save to express agreement with Deirdre McCloskey (and here I paraphrase) that no one was ever convinced by raw data of the truth of a proposition that he or she did not already hold to be true.  Data are important, but the theoretical filters in our minds are no less so.

The Numbers are Broken

NOTE: I'm no longer convinced about the claim I make at the bottom of this piece about the CPI incorrectly accounting for increases in housing costs.  When I get the logic straight, I'll post again.

Since 1997, real GDP has increased by over 20%.  Yet the average person seems to be standing still.  From today's Wall Street Journal (rr):

When viewed over a longer time period, 1997 to 2004, wages beat inflation, rising 19.9%, compared with a gain of 17.7% for the CPI.

That's pitiful.  In that eight year period, the average worker gained 2% while the economy was going nuts.  Where's the rest of the 20% growth in GDP going?  According to some, the richest eleven people in America, Bill Gates and ten of his friends just keep all the gains for themselves.  They have a system figured out that's so secretive no one else knows about it.

But average people, even poor people, seem to be doing much better than they once did.  What's going on?

Here's an alternative theory: the numbers are broken.  Not in the usual way.  The usual way is to talk about fringe benefits or composition effects that I've written about before.  But there's a bigger problem, a more basic problem.  And here I have to give a hat tip to my Dad who has been pushing this theme for a long time.

The CPI does not measure the cost of living.

Everyone in economics knows the standard problems with the CPI.  It tries to measure the increase in the cost of buying a particular bundle of goods.  It's hard to deal with quality improvements and inevitably, the CPI understates inflation because it ignores people's ability to substitute away from goods that have gotten more expensive.  The BLS tries to fix both of these problems periodically.  They try and adjust for quality and every once in a while, they change the weights on the goods in the bundle to take account of people making different choices than they did in the past.

But the biggest problem and one that I suspect is responsible for making real wages appear stagnant when people's living standards are rising, is how to handle housing.

When housing prices are rising, are you better off or worse off, all things held equal?

Answer: it depends who you are.  As a first approximation, prospective home buyers are worse off.  Home owners, about 70% of the population, are better off.  But the CPI treats the gain to home owners as a cost.  Economics teaches that even if you own your house, there is an "opportunity cost" to living in it.  This idea argues correctly that you can always sell your house, so as your house gets more valuable, the cost of living in it rather than selling it rises.  This is true.  But there is a paradox.  The cost of living in it has gone up, but unlike most price rises, this one makes you better off, not worse off. 

Before I moved to Washington, DC, the salary that would induce me to move here was affected by the high cost of housing in DC.  But if housing prices double over the next five years (as they have in the previous five), will my standard of living fall if my salary fails to keep pace?  No.

About 22% of the CPI uses an implicit rent for homeowners.  How the BLS does it is extremely complex and is by its nature, something of a wild guess.  What they try and do is estimate how much your house would cost you to rent it.  So when rental rates of similar homes of equal quality go up, they use that percentage increase as a measure of housing inflation for homeowners.  But that's wrong.  An increase in rental rates (usually reflecting an increase in housing prices) actually indicates a higher standard of living for the homeowner.

Some people (here and here for example) actually argue the exact opposite.  They argue that the CPI understates the cost of living because instead of using housing prices, it uses the cost of renting, and rental costs haven't kept up with housing prices:

But it would be absolutely absurd to use changes in housing prices as representative of changes in the cost of living because 70% of Americans own their own house.  For those folks, rising housing prices improves your standard of living in the form of higher wealth.  But even using rental rates is wrong because a 3% rise in the rental rate of houses is actually a benefit to the homeowner who doesn't pay that rent out of pocket.  It merely represents a higher opportunity cost of owning vs. renting.

I know, there are lots of complications to this argument, lots of caveats and lots of footnotes.   But the bottom line is that to correctly account for the impact of housing prices on my well-being you would have to take account of depreciation and taxes and maintenance and capital gains and expected capital gains.  Too complicated.  You can't leave housing out of the index.  That would make the index meaningless.  But the index as currently estimated is a poor measure for deflating my salary and particularly poor when housing prices and rental rates are rising steadily.