Relevant and even prescient commentary on news, politics and the economy.

Participants dropping out at astounding rate

Mish at Global Economic Analysis says it loud and clear as well as Mike Kimel and Ken Houghton here at Angry Bear:

I will stick with what I have said on many occasions “People are dropping out of the labor force at an astounding, almost unbelievable rate, holding the unemployment rate artificially low.”

The reason is not a recount based on the 2010 census, nor is it purely demographics, nor is it Obamanomics. The reason is severe and sustained fundamental economic weakness, coupled with existing purposely-distorted definitions of what constitutes “unemployment”.

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Tim Duy on employment/population ratio

Via Mark Thoma Tim Duy looks at reporting that misses the point even though information can be teased out of the piece. The article can be found here.

I think the article would have felt better if it began not with the impression that baby boomers are the driving force behind recent declines in the participation rate, but could be more of an influence in over time. This, I sense, is what Maki really wants to say:

The effect of the baby-boomer exit from the labor force will become more evident in the coming decade, Maki said. The policy implications may be more pressing, as Fed officials keep interest rates near record low levels for longer than may be required given the likely drop in the jobless rate. That may fuel price pressures in the economy, he said.

Then again, maybe not:

“It means there is less slack in the economy than is commonly perceived, and the slack will diminish more quickly than people think,” Maki said. As a result, “there are more inflationary risks with the very accommodative monetary policy we have now than one might believe.”

I think the near-term reality is a little less dire. And the article eventually gets there:

To be sure, the outlook for jobs may brighten as the economic expansion develops, drawing more people back into the workforce and limiting declines in unemployment. In addition, some economists argue that retiring baby boomers may not be the best explanation for the decrease already in train in the participation rate.

“Demographic trends are pushing down, over time, the normal labor force participation rate,” said Michael Feroli, chief U.S. economist at JPMorgan Chase & Co. in New York. Nonetheless, he said, “the speed of the decline seen this year is in excess of what one would expect just given the demographic trend.”

A much more measured analysis, one I think consistent with the data. Too bad it wasn’t the central point of the story. Yes, demographic shifts are likely to put downward pressure on labor force participation rates. But the tendency for those 65 and older to work longer than expected pushes in the other direction. Moreover, an improving economy would also increase labor force participation, especially among younger workers. Simply put, the aging of the baby boomers is just one of many factors currently influencing labor force participation rates and, by extension, the amount of slack in the labor markets. (bolding is mine)

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Notes toward A Blog Post on Chrystia Freeland’s Interview of GE CEO Jeff Immelt

UPDATE NOTE: The following isn’t complete. Many of my notes from the latter part of today’s interview can be found on Twitter, hashtagged #Immelt. At the moment, I both (1) don’t have easy access to them and (2) have other things that need to be done. Feel free to look there, and/or mention anything you want discussed here.)

The fake “news” of the day will be Immelt’s disparaging of America and Americans.

The semi-real news of the day will be that Immelt threw President Obama under the bus four or five times before finally saying that he “respects the President and respects the Presidency.” While this is progress from Jack Welch thinking that Buying George W. Bush the office meant that his firm would be exempted from cleaning up the PCBs GE dropped into the Hudson River (it did result in a nine-year delay and the likelihood that taxpayers, not GE, will foot the large majority of the bill), it’s not exactly a ringing endorsement of the man who gave us the Unforced Error of Simpson-Bowles.

Jeff Immelt, unlike Henry Aaron, believes that Simpson-Bowles is what we need for “growth.”

Jeff Immelt admits that, while the Board of Directors has some input, CEO pay is all about “getting what you believe you deserve.”

Jeff Immelt declares that if unemployment gets back down to 6%, no one will care about his being paid $21.5 million last year (about 40% of which appears to be an increase in his pension benefits; other GE pension contributors haven’t been so fortunate) to continue running GE into the ground to a standstill.

Jeff Immelt says that the US is 25th in math and 26th in science. (He’s wrong on the latter; we’re 17th.) He then spewed some horseshit about the “crisis” of Germans believing that it’s easier to find skilled workers in Mexico than it is in the United States.

Why do I call this horseshit? Well, let’s look at the two countries compared by Immeltian standards (link is PDF):

There are two three possibly-reasonable explanations. Either (1) there are a lot of Stupid Germans or (2) the places where Germans trying to hire are Significant Laggard or “Business Friendly, School Crappy” States.

Oops, or (3) the Germans pwnd Jeff Immelt, who then didn’t check the data.

And that’s without noting that, if you adjust for demographic issues such as poverty or consider racial inequalities, the U.S. is right at the top, no matter what Jeff Immelt says.

Otherwise, mostly, Jeff Immelt lies through his teeth, and Chrystia Freeland—who was tougher on George Soros last year—lets him get away with saying it.

It is left as an exercise whether this is because her boss openly declaring this was going to be a powder-puff interview (“I’m a big fan” of a man who has lost 60% of shareholder value for his investors over the past ten years) or because she decided to let Immelt hang himself. (I know which way I’m betting.)

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Updates to emlpoyment/population ratios

Bill McBride offers a look at the employment/population ratios by age at Calculated Risk, also offered at Mark Thoma’s Economist View:

There will probably be some “bounce back” for both men and women (some of the recent decline is probably cyclical), but the long term trend for men is down.

And several updates in graph form, a general look below:

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The Historical Relationship Between the Economy and the S&P 500, Part 3: The S&P 500 and the Employment to Population Ratio

by Mike Kimel

The Historical Relationship Between the Economy and the S&P 500, Part 3: The S&P 500 and the Employment to Population Ratio

A few weeks ago, I had two posts looking at the relationship between the S&P 500 and real GDP (Part 1 and Part 2).

The first post noted that using data from 1950 to the present, the adjusted close of the S&P 500 appears to lead real GDP by about 10 quarters… and real GDP appears to lead the S&P 500 by 16 quarters. That is, each factor appears to influence the other with a lead time of a few years. In the second post, I noted that while the S&P 500’s influence over real GDP appears to have remained relatively stable over the decades, real GDP’s influence over the stock market seems to have gone by the wayside some time in the 1970s.

Today’s post is similar to Post #1, but instead of looking at how the S&P 500 influences real GDP and vice versa, it looks at the relationship between the S&P 500 and the civilian employment to population rate.

The adjusted close for the monthly S&P 500 comes from Yahoo. The employment to population ratio is generated by the Bureau of Labor Statistics, but I pulled it off the Federal Reserve Economic Database (FRED) maintained by the Federal Reserve’s St. Louis Branch. Monthly data is available for both series – data for the S&P 500 was available going back to 1950, and the employment to population ratio was first computed in 1948.

Following the same practice as in Post #1, I computed the correlation between the S&P 500 and the employment to population ratio, the correlation between the S&P 500 and the employment to population ratio lagged one month, the correlation between the S&P 500 and the employment to population ratio lagged two months, and so on, all the way through 15 years worth of lags. I also computed the correlation between the employment to population ratio and lags of the S&P 500. Graphically, it all looks like this:

Figure 1.

The graph shows that the S&P 500 doesn’t seem to lead the employment to population ratio. The correlation between the S&P 500 and the employment to population ratio in the same period exceeds the correlation between the S&P 500 and any lagged employment to population ratio.

However, the employment to population ratio does appear to lead the S&P 500… and the correlation is highest at about 123 months… or about ten years. In other words, the share of the population that is employed seems to lead the stock market, with the strongest effect generally being observed ten years out.

I’ll be looking at how stable that result is in the next post in the series, but for now, let’s just take it on faith that the result doesn’t just go away when we change the dates in our sample. So… assuming the results are stable, they suggest the following story: more employed people mean more people putting money in the stock market, more people buying stuff, and more companies making stuff. Nevertheless, all these good things happening take a while to have an effect on stock prices. In fact, a ten year lag time seems to indicate that the benefits of more employment don’t get felt until the next business cycle comes around.

Now the really bad news… here’s what the employment to population ratio looks like:

Figure 2.

I note a few sorry portents….
1. The employment to population ratio peaked at 64.7% in the year 2000.
2. Its since dropped quite a bit, and is now at a level last since in the 1980s.

Anyway, in closing, a few more notes
a. I am not an investment adviser. I’m merely looking at some historical correlations, and this is only part of the story. I would strongly advise against trading on this information.
b. If you want my spreadsheet, drop me a line via e-mail with the name of this post. My e-mail address is my first name (mike), my last name (kimel – with one m only), and I’m at gmail.com.

The Historical Relationship Between the Economy and the S&P 500, Part 3: The S&P 500 and the Employment to Population Ratio

A few weeks ago, I had two posts looking at the relationship between the S&P 500 and real GDP (Part 1 and Part 2).

The first post noted that using data from 1950 to the present, the adjusted close of the S&P 500 appears to lead real GDP by about 10 quarters… and real GDP appears to lead the S&P 500 by 16 quarters. That is, each factor appears to influence the other with a lead time of a few years. In the second post, I noted that while the S&P 500’s influence over real GDP appears to have remained relatively stable over the decades, real GDP’s influence over the stock market seems to have gone by the wayside some time in the 1970s.

Today’s post is similar to Post #1, but instead of looking at how the S&P 500 influences real GDP and vice versa, it looks at the relationship between the S&P 500 and the civilian employment to population rate. The adjusted close for the monthly S&P 500 comes from Yahoo. The employment to population ratio is generated by the Bureau of Labor Statistics, but I pulled it off the Federal Reserve Economic Database (FRED) maintained by the Federal Reserve’s St. Louis Branch. Monthly data is available for both series – data for the S&P 500 was available going back to 1950, and the employment to population ratio was first computed in 1948.

Following the same practice as in Post #1, I computed the correlation between the S&P 500 and the employment to population ratio, the correlation between the S&P 500 and the employment to population ratio lagged one month, the correlation between the S&P 500 and the employment to population ratio lagged two months, and so on, all the way through 15 years worth of lags. I also computed the correlation between the employment to population ratio and lags of the S&P 500. Graphically, it all looks like this:

Figure 1.

The graph shows that the S&P 500 doesn’t seem to lead the employment to population ratio. The correlation between the S&P 500 and the employment to population ratio in the same period exceeds the correlation between the S&P 500 and any lagged employment to population ratio.

However, the employment to population ratio does appear to lead the S&P 500… and the correlation is highest at about 123 months… or about ten years. In other words, the share of the population that is employed seems to lead the stock market, with the strongest effect generally being observed ten years out.

I’ll be looking at how stable that result is in the next post in the series, but for now, let’s just take it on faith that the result doesn’t just go away when we change the dates in our sample. So… assuming the results are stable, they suggest the following story: more employed people mean more people putting money in the stock market, more people buying stuff, and more companies making stuff. Nevertheless, all these good things happening take a while to have an effect on stock prices. In fact, a ten year lag time seems to indicate that the benefits of more employment don’t get felt until the next business cycle comes around.

Now the really bad news… here’s what the employment to population ratio looks like:

Figure 2.

I note a few sorry portents….
1. The employment to population ratio peaked at 64.7% in the year 2000.
2. Its since dropped quite a bit, and is now at a level not seen since the 1980s.

Anyway, in closing, a few more notes
a. I am not an investment adviser. I’m merely looking at some historical correlations, and this is only part of the story. I would strongly advise against trading on this information.
b. If you want my spreadsheet, drop me a line via e-mail with the name of this post. My e-mail address is my first name (mike), my last name (kimel – with one m only), and I’m at gmail.com.

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Employment to population numbers

Calculated Risk revisits the employment to poulation ratios:

…What happens to the participation rate is an important question. If the Civilian noninstitutional population (over 16 years old) grows by about 2 million per year – and the participation rate stays flat – the economy will need to add about 100 thousand jobs per month to keep the unemployment rate steady at 8.9%.

If the population grows faster (say 2.5 million per year), and/or the participation rate rises, it could take significantly more jobs per month to hold the unemployment rate steady. As an example, if the working age population grows 2.5 million per year and the participation rate rises to 65% (from 64.2%) over the next two years, the economy will need to add 200 thousand jobs per month to hold the unemployment rate steady.

That is why forecasting the participation rate is important – and why reports of the number of jobs needed to hold the unemployment rate steady are all over the place (and can be very confusing – and I’m guilty of using different numbers).

Here is a look at some the long term trends (updating graphs through February 2011)…

Worth a visit this weekend. It also points to questions regarding other types of policy questions for planners.

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EMPLOYMENT REPORT

The employment report was bad, as private payroll employment only rose 83,000. Moreover, the household survey showed a drop in employment of -350,000.


The unemployment rate fell. But that is because the – 350,000 fall in household survey was offset by a -652,000 drop in the labor force. A labor force contraction is really bad news as it implies public confidence in the availability of jobs deteriorated.


Moreover, the average workweek on private nonfarm payrolls fell 0.1 to 34.1 hours.
As a consequence of modest employment gains and a drop in the workweek the index of aggregate hours worked only rose 0.1%. However, the three month growth rate of hours worked did expand t0 3.3% (SAAR), almost exactly the same as last month.

In addition, average hourly earnings also fell and the year over year growth in average hourly earnings fell to 1.7% . The drop in workweek also caused the gain in average weekly earnings to tick down as well. Average hourly earnings growth is now where it bottomed in the 1986 and 2004 wage cycles– the lowest gains in this series since it was first recorded in 1967.

Regardless of how it is calculated, the current gap between employment and the long term trend of employment is at post WW II record levels.

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Those Low Rates

Via (what else?) Alea’s Twitter feed, John Taylor defends himself against Ben Bernanke:

“The evidence is overwhelming that those low interest rates were not only unusually low but they logically were a factor in the housing boom and therefore ultimately the bust,” Taylor, a Stanford University economist, said in an interview today in Atlanta.

It’s not actually that they’re not saying the same thing. Bernanke argued (and I agreed) that low rates did not cause the housing bubble. We have had low rates without producing housing bubbles before. (Other asset bubbles are another question.) Indeed, the last lasting housing bubble peaked just as the Federal Funds rate did:

More accurately (and also via ATF), Caroline Baum takes Bernanke to task for sleight-of-hand:

For example, Bernanke takes great pains to rebut criticism that the funds rate was well below where the Taylor Rule…suggested it should be following the 2001 recession. The Taylor Rule uses actual inflation versus target inflation and actual gross domestic product versus potential GDP to determine the appropriate level of the funds rate.

Substitute forecast inflation for actual inflation, and the personal consumption expenditures price index for the consumer price index, and — voila! — monetary policy looks far less accommodating, Bernanke said.

It’s always easier to start with a desired conclusion and retrofit a model or equation to prove it.

Ouch. Is it a great day when the journalist is making more sense about the economist’s work than another economist is?

But more to the point, the argument that rates were kept unnaturally low from ca. 2002 through ca. 2005 depends very much on the idea that the Fed does not have two jobs. (Once again, h/t to Dean Baker.)

The other half below the break

As Baker notes at the link above, “the dual mandate [of the Fed] is full employment (defined as 4.0 percent unemployment) and price stability.”

Let’s be generous. I’ve plotted the Civilian Employment/Population Ratio and the Official Unemployment Rate below. The blue line at 4.5 applies only to the Unemployment Rate (red line). (I didn’t plot it at 4.0 because that would be cruel.)

So what we have is a situation where (1) the Employment/Population Ratio by the end of 2006 is barely back near the level it was at the end of the recession of 2001 and (2) it is only near the end of 2006 that the Official Unemployment Rates approaches the official target rate (which it hadn’t seen since before the 2001 recession).

It seems apparent that Taylor’s “Rule” (which considers inflation and GDP, but not employment per se) is not compatible with official Fed mandates. In such a context, Caroline Baum’s “gotcha” is more a case of her using inappropriate variables—and Bernanke substituting a more appropriate model, given the Fed’s mandates—than it is a case of Bernanke “retrofitting.”

No wonder John Taylor says we should worry about inflation; in his world, we never have to worry about unemployment, so long as there are enough bubbles to inflate GDP.

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employment report

By Spencer,

The employment report was very encouraging.

Most importantly, aggregate hours worked were unchanged at 91.1 as compared to 104.1, 101.7 and 99.7 over the last three quarters. An unchanged reading is a massive improvement from the 8% to 9% rate of decline over the past three quarters. With positive productivity this impies that thrird quarter real GDP growth could easily be positive..

Moreover, the manufacturing work week rose from 39.5 to 39.8 hours and overtime hours were 2.9 hours versus 2.8 in the second quarter. Much of this was auto and confirms the other reports that at least auto output is rebounding. The hours worked together with productivity strongly impies that manufacturing output rose in July — to be reported about mid-month. Moreover, the average workweek and overtime hours are traditional leading indicators.


Wage growth improved, but not enough to reverse the sharp slowing in average hourly earnings growth.

With hours worked stable and hourly earnings rising average private weekly earnings rose from $611.49 to 614.34.
The improvement in weekly earnings is a welcome sign for what I consider the greatest risk to the recovery, the unprecedented decline in nominal, repeat nominal, income so far this year. For a sustained recovery nominal and real income growth has to improve at some point. Normally, real income growth is a lagging indicator at bottoms but it also kicks in soon after the bottom. Tax cuts are offsetting some of this weakness but a sustained recovery requires growth in real income.

The consensus forecast is for a very weak recovery. But the consensus forecast is always for a weak recovery. The actual historic record is for recoveries to be proportional to the recession. That is, severe recessions have strong recoveries and mild recessions have weak recoveries.
I’m not making a forecast or taking a position that the consensus is wrong, or that those who expect no recovery are wrong either. But at every bottom economists always have a long list of reasons why this recovery will be weak. And they are usually wrong. In 1981, I won the National Association of Business Economists annual forecasting contest by forecasting an average or normal recovery from the 1980 recession. It was the strongest forecast in the competition.

Footnote. Despite the increase in the minimum wage the teenage unemployment rate actually fell.

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