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

Labor Hours decreased in 3rd Quarter… It is noteworthy

From what I read, nobody has pointed out the drop in Nonfarm Business Sector: Hours of All Persons in the 3rd quarter. (seasonally adjusted) The index of hours decreased from 110.66 to 110.53. This is actually noteworthy…

Nonfarm Business Sector: Hours of All Persons since 1967. (link to graph)

hours down

You can see that hours increase through a business cycle. Looking closely at the numbers, hours really do continually increase through the business cycle. There are very few exceptions when hours decrease during an expansion of a business cycle.

But when hours do tick downward, normally a recession starts within 3 quarters. That has been the pattern since 1967… There has only been a few exceptions where hours ticked downward with no recession.

That is why the decrease in hours during the 3rd quarter is interesting. Are hours peaking?

If hours decrease again in the 4th quarter, history says to watch for a slump.


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Forecasting the 2016 election economy, first forecast: the long leading indicators

– by New Deal democrat      

Forecasting the 2016 election economy, first forecast: the long leading indicators

Last week I showed that, going back 160 years, roughly 3/4 of all US Presidential election results correlated positively with whether or not at the time of the election campaign, the US was in a recession or not. More than 2/3 of the time, it accurately predicted the Electoral College winner, and 80% of the time, it accurately showed the winner of the popular vote.  In fact, if we simply go by the metric of whether or not the US was in recession during the 3rd Quarter of the election year, then 84% of the time the winner of the popular vote was from the incumbent party if the economy was expanding, and from the opposition party if the economy was in recession.

We now have enough information to make a good forecast as to whether or not the US economy will be in recession in Q3 2016.  That means we can make a reasonable forecast as to which party’s candidate will win the popular vote.

Prof. Geoffrey Moore, who for decades published the Index of Leading Indicators, and founded the Economic Cycle Research Institute (ECRI) in 1993, wrote  Leading Economic Indicators: New Approaches and Forecasting Records describing and explaining what he called “long leading indicators,” that is, economic metrics that reliably turn a year or more before the onset of a recession.  He identified 4:

– corporate bond yields

– housing permits and starts

– real money supply

– corporate profits

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Forecasting the Presidential election: simply knowing whether the economy is expanding or in recession gives you the correct answer more than 2/3 of the time (Part 1)

by New Deal democrat

Forecasting the Presidential election: simply knowing whether the economy is expanding or in recession gives you the correct answer more than 2/3 of the time

If you want a quick and dirty guide to whether an incumbent political party will retain control of the White House in a Presidential election, simply knowing whether the economy will be in expansion or recession in the 3rd or 4th quarter of the election year gives you the correct answer more than 2/3’s of the time.

The NBER maintains the official list of US recessions going back over 160 years to 1854.  During that time, there have been 33 recessions, and 40 Presidential elections.  Eleven of those Presidential elections have taken place during a recession (measured by the 3rd or 4th Quarter of the election year).

In only 3 cases has the incumbent party been successful maintaining control of the White House (and in one of those cases, the incumbent party’s candidate lost the popular vote, but won in the Electoral College).  In the other 8 cases, the incumbent party lost, including at least 3 of the biggest political turning points in US history (1860, 1932, 1980).

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For Profit College, Student Loan Default, and the Economic Impact of Student Loans

For Profit Goes on Probation

The University of Phoenix has been placed on probation by the Department of Defense preventing the university from recruiting on military bases. The probation comes after the Federal Trade Commission and the California Attorney General’s investigation into the University of Phoenix recruiting methods, its high costs, and the resulting poor student performance.

This is not the first time Phoenix-U has been in trouble. In 2013 the University of Phoenix was threatened with probation by the accreditation board for a lack of “‘autonomy’ from its corporate parent -– a development that prevented the university from achieving its ‘mission and successful operation.'” In other words, the for-profit university #1 priority by its owners was to turn a profit at the expense of teaching, retaining, and graduating its students. This is precisely what I had alluded to previously on higher rates of defaults.

Student Loan Defaults

An interesting analysis by the NY Fed suggests students with lower amounts of student loan debt are more likely to default than those students with higher amounts. This is a new take on student loan debt and associated default as it was always thought the higher the debt the greater risk of default. Student Loan Debt has increased as more attend college, costs to attain an undergraduate degree have increased, even higher costs are sustained for Masters and Doctorate degrees, and students have been staying in school longer. Coming out of college the study finds amongst students loan debt is distributed rather evenly over time with one third being held by those in the 20s, one third held by those in the thirties, and one third held by those forty years of age and older. A large percentage of those borrowers or ~39% of them have loans of less than $10,000 and it is the holders of debt who have been defaulting at a higher percentage. The study goes on to break it down as to why they might be defaulting more frequently than tose with higher amounts of debt.

Using Equifax credit data, the NY Fed broke down the data into loan origination cohorts of student loan borrowers and using the same Equifax data, developed default rates for each cohort. Taking the origination date information for each academic year, the Fed was able to assign borrowers to loan-origination-completion-cohorts. The analysis did not reveal dropout or graduation information; however by using loan origination data, the methodology used does approximate whether students left school finished their education or just left school.

invisible hand

As shown on the graph and nine years out for the 2005 and the 2007 cohorts 24% of the students and greater had defaulted on their student loans by the 4th quarter of 2014. While the data for the 2009 cohort is incomplete and depicts five years as opposed to nine years, the data depicts a worsening default rate at 5 years then what can be found with the 2005 and 2007 cohorts at 9 years. Typically what we read and hear about is a 3-year window as reported by the Department of Education and is discussed by the news media. The 3-year window default rate is much less for each of the cohorts with the 2005 cohort being ~1/2 or 13% of what it is in 2014 as shown by the Fed study.

As I mentioned above, a large percentage of those who defaulted had student loan debt of less than $10,000. 34% of those borrowers in that group who defaulted on their student loans had balances of less than $5,000. 21% of the 2009 cohort were in this category of < $5,000 in student loans five years out which depicts a worsening trend when compared. A closer examination of the 34% also reveals this group to be made up of students who attended community college, did not finish, perhaps discovered this is not what they wanted to do, or the curriculum did not fit their needs. What the NY Fed concludes is the default rate worsens when a much longer period of time is taken into consideration as opposed to the 3 year window the Department of Education looks at and which the public hears about in the news. The longer the period, the higher the default and it continues through years 4 through nine for the first two cohorts. As shown the default rate for the 2009 cohort is already higher. Those who had lower amounts of student debt in the end may have defaulted due to a worsening economy or potentially did not get the payback expected from a two year degree at a community college or for-profit school. The study also revealed those who are current today with their student loans did experience stress in making payments and 63% of those student loan borrowers appear to have avoided delinquency and default over the last decade. On the other side of the coin, student loan borrowers with $100,000 of debt had a default rate of 18% which has been attributed to their being higher earners after graduation.

Economic Impact of Student Loan Debt

invisible hand

One aspect of the fall-out resulting from increased student loan debt as suggested by the Fed study is decreased home ownership. From 2008 onward the study depicts a steady decrease in the numbers of graduates burdened by student loans investing in homes. Dropping from a high of ~34% in 2008, the percentage of homeowners and having student loan debt has declined to ~23% in 2014. What has occurred, those 27-30 year old having no student loan debt have surpassed those with student loan debt in home ownership. While both groups experienced a decrease in home ownership during bad economic times, the decrease for those having student loans was far more severe. The decrease in home ownership still continues for both groups with those having less debt owning homes at a higher level.

invisible hand

A similar situation holds true for new auto ownership. The numbers of 25-year old college graduates purchasing automobiles and with student loan debt retreated from the market place at a faster pace than those without student loan debt. It is only recently have increased numbers of both groups returned to the market place to buy automobiles. While the purchase of automobiles has increased for all 25 year old people, the numbers of college grads with student loan debt no longer surpass those without student loan debt and at best are at the same level as those without student loan debt. Student loan debt is a burden and more of a burden in harsher economic times.

Much of the retreat from the market place is due to large loan and higher interest rates on undergraduate student loans, even higher interest rates and balances on graduate and doctorate student loans, higher balances due to the increased costs of colleges across the board, and longevity in paying back student loans. There are no controls on colleges and universities to rein in costs and it is the only cost to increase at a faster rate than healthcare. The higher costs play out in student loan debt as states do not subsidize colleges to the same ratio as they did 20 years ago, Pell Grants have not kept up with the costs of colleges, and parents can not afford the increased cost out of pocket either.

For the purchases of homes, cars, appliances; the bank assesses your ability to repay the loan as these loans can be discharged in bankruptcy unlike student loans which can not be discharged. Many college graduate households today consist of two married adults both of which are burdened with having student loans to pay off making the situation even more precarious. The result is increased risk.

invisible handUsing the same data, the NY Fed reviewed the risk rates of 25 and 30 year olds with and without student loan debt. As can be expected, those households with student loan debt were deemed a higher risk due to student loan balances and higher interest rates and a decrease of potential income over time. Those students would be less likely to obtain a loan or a loan with lower interest rates. A higher interest rate adds to an already high financial burden.

There are probably many other reasons why young households may have retreated from the market place; cultural changes in how younger households view home ownership, automobiles, and other purchases; higher costs of financing; lowered expectations of future earnings; unwillingness to take on more debt, etc. The fact of the matter is, not only does the market place view them as a higher risk; but, these college-educated young buyers are not buying homes, autos, etc. or making large investments at the same level that once existed and it does not bode well for the economy.

It also never ceases to amaze me the number of anti-educational opinions which flare up when the discussion of student loan default arises. There are always those who will prophesize there is no need to attain a higher level of education as anyone could be something else and be successful and not require a higher level of education. Or they come forth with the explanation on how young 18 year-olds and those already struggling should be able to ascertain the risk of higher debt when the cards are already stacked against them legally. In any case during a poor economy, those with more education appear to be employed at a higher rate than those with less education. The issue for those pursuing an education is the ever increasing burden and danger of student loans and associated interest rates which prevent younger people from moving into the economy successfully after graduation, the failure of the government to support higher education and protect students from for-profit fraud, the increased risk of default and becoming indentured to the government, and the increased cost of an education which has surpassed healthcare in rising costs.

There does not appear to be much movement on the part of Congress to reconcile the issues in favor of students as opposed to the non-profit and for profit institutes.

The Race Between Education and Technology

Just Released: Young Student Loan Borrowers Remained on the Sidelines of the Housing Market in 2013

University of Phoenix Accreditation Hits Snag As Panel Recommends Probation

For-profit college banned from recruiting military students

The Student Loan Landscape

Looking at Student Loan Defaults through a Larger Window

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Will the Fed be optimistic, pessimistic or surprised?

The Fed rate… What is going on with the Fed rate? The Fed say they would like to raise it in December. Others say they should wait.

Using an Effective Demand Version of the IS-LM curve

In a normal IS-LM model, the interest rate is on the y-axis and output on the x-axis. The following model will keep the interest rate on the y-axis, but put a measure for the utilization of labor and capital for output on the x-axis. The measure is TFUR which multiplies the capacity utilization rate by (1 – unemployment rate). This version of the IS-LM model makes it easier to compare business cycles.

path analysis 1

The model shows that the Fed rate would normally rise as the business cycle expands and more labor and capital are utilized. Eventually the Fed rate will normalize at an interest rate equal to the inflation target + the natural real rate. According to this model, the normalization of the Fed rate will take place at the effective demand limit.

In the above model, the vertical dashed green line is the effective demand limit based upon an effective labor share of 80%. The effective demand limit is the projected labor share at full employment. (Labor share is calculated by labor share index: non-farm business * 0.762) The effective demand limit is the limit that the TFUR utilization of labor and capital will reach at full employment.

The Model before the Crisis

This graph adds actual data from 1stQ 2002 to the present.

path analysis 2

As the expansion ensued after the 2001 recession, labor share was around 80%. The curving red arrow shows how the Fed rate was moving along a path associated with an 80% effective labor share, 2% inflation target and an estimated natural real rate of 2.3%.

The Fed rate was heading toward normalization at 4.3%. But then something unusual happened during the expansion. Labor share fell quickly to 77.3%. Effective labor share had never fallen to that level before. The up-sloping path shifted left.

path analysis 3

All of a sudden, the Fed rate was too low according to the prescribed path. Was the Fed surprised by the changes it began to see in the economy? I think so… The Fed rate appeared to rise faster toward the new green dot of normalization but really utilization of labor capital were slowing down. Imbalances in core inflation and bubbles were developing. Ultimately, the Fed rate rose above the normalization rate of 4.3% to control inflation pressures and bubble imbalances.

The economy went into recession for many reasons, but one reason is that the Fed rate probably went a bit too high. The Fed rate should have stayed near 4% during 2007 in my opinion.

After the Crisis

The economy is confusing after the crisis. There are large differences in estimating potential output. There are large differences in estimating the natural real rate. So this graph shows two views of the economy.

path analysis 5

The blue line shows a more optimistic view than the yellow line. The blue line is based on a higher utilization of labor and capital at full employment (82%), yellow (80%). The blue line is also based on a higher natural real rate (1.5%), yellow (-1.0%).

The blue line sees normalization eventually at 3.5%, yellow 1.0%. These points are shown by the green dots.

The red dot along the zero lower bound shows us where the economy was in the 3rd quarter. We can see that the Fed rate is lower at the same TFUR than before the crisis. It has actually never been this low at a TFUR of 74% since at least the 1960s.

If one agrees more with the blue line, they see the Fed rate getting ready to liftoff. If one, like Larry Summers and others, agrees more with the yellow line, then they would say that the Fed rate is far from liftoff.

Note: According to the model, as the effective labor share rises, the vertical dashed green line slides right leading to a higher Fed rate at normalization. The current effective labor share is lower now than before the crisis at 75%. So far the utilization of labor and capital is meeting up with resistance at 75% on the x-axi. If the effective demand limit at 75% holds, the Fed rate will have no space to rise and will stay at the ZLB. The Fed would end up being surprised by the unseen limit.

Which line would you agree with? How might you change the line to your own views? Would you change the slope of the line?

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Are You a Liberal or a Conservative? Are You Sure?

(Dan here….perhaps food for thought on a Sunday)

Scott Baker is a professor at the Henry George School, the State Coordinator of the NY Chapter of The Public Banking Institute,  and the author is America Is Not Broke!

Are You a Liberal or a Conservative? Are You Sure?

Quick, without looking at the answers – or at what your favorite pundit is saying – how would you answer the following questions? Is it Liberal or Conservative?

1. Being opposed to rescuing the big financial institutions

2. Wanting America to become (more) energy self- sufficient

3. Being in favor of expanding the Space Program

4. Supporting Family Farms

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Wait: Maybe Europeans are as Rich as Americans

I’ve pointed out multiple times that despite Europe’s big, supposedly growth-strangling governments, Europe and the U.S. have grown at the same rate over the last 45 years. Here’s the latest data from the OECD, through 2014 (click for larger):

Screen shot 2015-11-06 at 5.19.42 PM

And here’s the spreadsheet. Have your way with it. More discussion and explanation in a previous post.

You can cherry-pick brief periods along the bottom diagonal to support any argument you like. But between 1970 and 2014, U.S. real GDP per capita grew 117%. The EU15 grew 115%. (Rounding explains the 1% difference shown above.) Statistically, we call that “the same.”

Which brought me back to a question that’s been nagging me for years: why hasn’t Europe caught up? Basic growth theory tells us it should (convergence, Solow, all that). And it did, very impressively, in the thirty years after World War II (interestingly, this during a period when the world lay in tatters, and the U.S. utterly dominated global manufacturing, trade, and commerce).

But then in the mid 70s Europe stopped catching up. U.S. GDP per capita today (2014) is $50,620. For Europe it’s $38,870 — only 77% of the U.S. figure, roughly what it’s been since the 70s. What’s with that?

Small-government advocates will suggest that the big European governments built after World War II are the culprit; they finally started to bite in the 70s. But then, again: why has Europe grown just as fast as the U.S. since the 70s? It’s a conundrum.

I’m thinking the small-government types might be right: it’s about government. But they’ve got the wrong explanation.

Think about how GDP is measured. Private-sector output is estimated by spending on final goods and services in the market. But that doesn’t work for government goods, because they aren’t sold in the market. So they’re estimated based on the cost of producing and delivering them.

Small-government advocates frequently make this point about the measurement of government production. But they then jump immediately to a foregone conclusion: that the value of government goods are services are being overestimated by this method. (You can see Tyler Cowen doing it here.)

That makes no sense to me. What would private output look like if it was measured at the cost of production? Way lower. Is government really so inefficient that its production costs are higher than its output? It’s hard to say, but that seems wildly improbable, strikes me as a pure leap of faith, completely contrary to reasonable Bayesian priors about input versus output in production.

Imagine, rather, that the cost-of-production estimation method is underestimating the value of government goods — just as it would (wildly) underestimate private goods if they were measured that way. Now do the math: EU built out governments encompassing about 40% of GDP. The U.S. is about 25%. Think: America’s insanely expensive health care and higher education, much or most of it measured at market prices for GDP purposes, not cost of production as in Europe. Add in our extraordinary spending on financial services — spending which is far lower in Europe, with its more-comprehensive government pension and retirement programs. Feel free to add to the list.

All those European government services are measured at cost of production, while equivalent U.S. services are measured at (much higher) market cost. Is it any wonder that U.S. GDP looks higher?

I’d be delighted to hear from readers about any measures or studies that have managed to quantify this difficult conundrum. What’s the value or “utility” of government services, designated in dollars (or whatever)?

Update: I can’t believe I failed to mention what’s probably the primary cause of the US/EU differential: Europeans work less. A lot less. Like four or six weeks a year less. They’ve chosen free time with their families, time to do things they love with people they love, over square footage and cubic inches.

Got family values?

I can’t believe I forgot to mention it, because I’ve written about it at least half a dozen times.

If Europeans worked as many hours as Americans, their GDP figures would still be roughly 14% below the U.S. But mis-measurement of government output, plus several other GDP-measurement discrepancies across countries, could easily explain that.

Cross-posted at Asymptosis.


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Full text of the Trans-Pacific Partnership agreement