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A hurricane workaround for industrial production

A hurricane workaround for industrial production

Last week I mentioned that the regional Fed surveys plus the Chicago PMI can be used as a workaround to account for the effects of hurricanes on Industrial Production. It isn’t pretty and by no means is it perfect, but for the (hopefully only) two or three months that we need it, we can use the workaround to give us the underlying trend in production, particularly for manufacturing.This is a two-step correlation.

The first correlation is between the regional Fed indexes and the ISM manufacturing index.  This is something Bill McBride, a/k/a Calculated Risk, has been keeping track of for years.  Here’s his graph going back all the way to 2000:

While the correlation isn’t perfect, most notably in the years 2010 and 2011, when the regional Fed average was high, and in 2015 and 2016, when it was too low, in general it holds, with the two rising or falling between positive and negative in tandem, even if we just use the Empire State and Philly indexes.

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A thought for Sunday: the most important issue in the 2016 election was…

A thought for Sunday: the most important issue in the 2016 election was . . .

This is a post I’ve been meaning to write for several months. For a while after the election last year, there was a debate about whether the “economic anxiety” in the (white) working class was the most important factor vs. was it simply a matter of racism. The consensus has nearly settled on the narrative that racism was decisive, to the point where “economic anxiety” has become a taunt, and some who embrace identity politics actively disparage progressive economic issues.

I’m here to show you data that – in part – disputes that consensus. What was the most important issue in the 2016 presidential election?  The below data on that issue all comes from the Voter Study Group, from its survey published several months ago: “Insights from the 2016 Voter survey.”

In the below graphs, the potency of various issues are examined in terms of how well they lined up on a liberal/conservative or favorable/unfavorable axis, but for simplicity’s sake it is pretty clear that they correlate with a vote for Clinton (left) or Trump (right).  The more vertical the line, the more decisive the factor, whereas a horizontal line means that the factor made essentially no difference in whether a vote was for one candidate or the other.  the 2016 results are in red, vs. the 2012 results in gray. What I’ve done is to delete the names of the nine factors they tested, so you won’t be swayed by any pre-existing opinion you might have had about the factor.  Here they are:
I’ll give away one finding right away.  The most decisive factor, shown at the right of the lowermost column, is party affiliation. D’s voted for Clinton. R’s voted for Trump.
But after that, it’s pretty clear that the close runner-up for most decisive factor in how people voted is the issue at the left of the middle column, which was …
the economy!
That’s right. The single most decisive factor in the 2016 vote was how people felt about the economy.

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The asterisk in real median household income

The asterisk in real median household income

This is a follow-up to the post I wrote last week about the latest data on real median household income.
One of the things I notes is that “households” includes the millions that are composed of retirees, a burgeoning demographic due both to healthier longevities and the demographics of the Boomer generation.
This morning Jared Bernstein helpfully includes a graph of real median household income excluding those over age 65:

Households headed by working age adults did finally surpass their 2007 income, but were still 3.4% below the all-time highs of incomes of 2000.

But mainly I wanted to follow up on that break in the graph in 2013.  It was caused by a change in methodology by the Census Bureau.

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Hurricane workarounds for industrial production and housing

Hurricane workarounds for industrial production and housing

Hurricane Harvey has already affected some of the August data releases.  Irma has already started to affect some weekly releases, and will undoubtedly affect the September monthly releases.
I have already begun to adjust for the hurricanes in the case of initial jobless claims.  But what of the monthly data?
While there is nothing so timely and precise as backing out affected states from the initial jobless claims report, there are workarounds that can at least tell us if there has been any significant change in trend for both the industrial production and housing reports.
I will put up separate posts, but to cut to the chase, we can use the Regional Fed reports (minus Dallas, and adding the Chicago PMI) to give us a reasonable estimate of industrial production in the non-hurricane affected areas. Similarly, we can make use the regional breakdowns in the housing report by subtracting the South and determining the trend in the remaining 60% of the country outside of that census region.  I have already looked at this morning’s housing report, and it turns out the effect is not what you would think!  I’ll have that post up by tomorrow.
Unfortunately there is no regional or state-by-state breakdown of retail sales or regional consumption expenditures on any sort of timely basis, so we’re kind of stuck there.

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2.5 cheers for 2016’s new high in real median income!

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How to kill Social Security in 2 easy steps

How to kill Social Security in 2 easy steps
Here’s Kevin Drum advocating for step 1:

 the best way to address retirement security is to continue reforming 401(k) plans and to expand Social Security—but only for low-income workers. Middle-class workers are generally doing reasonably well, and certainly as well as they did in the past. We don’t need a massive and expensive expansion of Social Security for everyone, but we do need to make Social Security more generous for the bottom quarter or so of the population that’s doing poorly in both relative and absolute terms. This is something that every liberal ought to support, and hopefully this is the bandwagon that President Obama in now on.

Step 2:
Now that 3/4 of the population will be paying into a system to transfer their income to the bottom 1/4, you have instantly created a majority constituency that will benefit from killing the now-welfare program.
Why does Kevin Drum want to kill Social Security?

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Hurricane adjusted initial claims for week of Sept. 2: 239,000

Hurricane adjusted initial claims for week of Sept. 2: 239,000

Last week I promised I would repeat an exercise I first undertook in 2012 when Superstorm Sandy disrupted the initial claims data: estimating what the initial jobless claims would have been, but for the hurricane.

In 2012 I created that adjustment by backing out the affected states (NY and NJ) from the non-seasonally adjusted data.  That gave me the number of initial claims filed in the other 48 states.  I compared that with the same metric one year earlier, and multiplied by the seasonal adjustment.

What that does is give me the number if the affected states had the same relative number of claims during the given week, as all of the unaffected states.  In 2012, it showed that Sandy was not masking any underlying weakness in the economy.

The state by state data is released with a one week delay.  So what follows is the analysis for the week of September 2, the number for which was reported one week ago. This week I only had to back out Texas.  Next week I will undoubtedly have to back out Florida as well.

Here is the table for the Week of September 3 in 2016 vs. September 2 this year:

Metric                              2016                   2017

Seasonally adjusted:       257,000              298,000

Adjustment for total:       1.18%                1.19%

Not seasonally adjusted: 217,715              250,621

Texas claims:                     15,707                63,788

NSA claims ex-TX           202,008              186,833

TX as % of total:              7.2%                   n/a

2017 w/ TX adjustment:  n/a                      201,405

If we use the 2016 weekly seasonal adjustment of 1.18% for the adjusted 201,405 total, this gives us ~238,000.

If we use the 2017 weekly seasonal adjustment of 1.19% for the adjusted 201,405 total, this gives us ~240,000.

Thus the hurricane-adjusted initial jobless claims number for the week of September 2, 2017 is 239,000.

The underlying national trend in initial jobless claims remains very positive.

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A note on Hurricane Harvey and unemployment claims

A note on Hurricane Harvey and unemployment claims

Initial jobless claims for last week were reported at 298,000 this morning, a jump of over 50,000 from recent levels.

As most people probably already know, this huge jump had everything to do with Hurricane Harvey shutting down southeastern Texas, including the entire 7 million Houston metro area. Undoubtedly, the effect will last for weeks.

Fortunately, if we want to know what jobless claims would be ex-Harvey, there is a way to figure that out.  Although I haven’t felt the need to dwell on weekly claims for several years now, I’ll start to calculate this again next week.

I did this before, in 2012, after Superstorm Sandy.  Here’s how I described the process then:

I wanted to try to find out how much of this morning’s initial claims number was still due to Sandy. To do so, I checked the BLS breakdown of initial claims by states, which gives the unadjusted state-by-state initial claims numbers. I deducted NY and NJ, the two states most hit by Sandy, and compared the number as deducted with the unadjusted number minus NY and NJ this week one year ago. Since the seasonal adjustment should be almost identical, that should give me the “real” ex-Sandy initial claims number, assuming NY and NJ would, ex-Sandy, have layoffs at a similar rate to all the other states.

To do the same thing for Harvey, I’ll simply calculate the number for all states except Texas.  Because the state by state data is reported with a one week delay, that won’t be until next week.

Of course, I might have to account for Irma and maybe even Jose in the next few weeks as well.  But, one bridge at a time . . . .

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The August jobs report smacked of late cycle deceleration

The August jobs report smacked of late cycle deceleration
As promised, here is my abbreviated and late take on this morning’s employment report.

While the additions to temporary positions (a leading indicator for jobs overall), and construction, and manufacturing jobs were welcome, this report sure looked like late cycle deceleration.

The YoY% growth in jobs – a very un-noisy metric – declined again slightly:


The number of people not in the labor force who want a job shot back up:

Those who are involuntarily part-time went sideways:

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Trickle-down, with the emphasis on “trickle”

Trickle-down, with the emphasis on “trickle”

Since the turn of the Millennium, a torrent of corporate tax cuts has resulted in a trickle of investment growth.

This morning Dean Baker objects to:

the argument … that reducing corporate taxes will lead to more investment and thereby greater wage growth in the future. The data from the last seventy years show there is no relationship between aggregate profits and investment.

As can be seen, there is no evidence that higher corporate profits are associated with an increase in investment. In fact, the peak investment share of GDP was reached in the early 1980s when the after-tax profit share was near its post war low. Investment hit a second peak in 2000, even as the profit share was falling through the second half of the decade. The profit share rose sharply in the 2000s, even as the investment share stagnated. In short, you need a pretty good imagination to look at this data and think that increasing after-tax profits will somehow cause firms to invest more

I was a little puzzled why Dean didn’t differently scale the two series so it would be easier to see any leading/lagging relationship.  Further, since corporate profits are a long leading indicator, and nonresidential fixed investment is more of a coincident indicator, I was pretty sure that there would be a correlation.

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