Comments & Context

Telling it like it is in the Great Plains

We thought you all might like this comment on gas prices and overall spending from one of our tax contacts in the Great Plains:

I think of savings on gas as pre-spent money. Spending on gas is inelastic, and the average consumer saves about $1,500 a year [for every dollar drop in price]. For the working-class person that $1,500 may have already been spent. Now, instead of being $150 in the hole each month, they are only in for $25. That would explain why the $500 payment back in 2001 didn’t provide substantial gains, and why we didn’t see an increase in spending when gas prices halved.

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’flation

The late Ed Hart of the late Financial News Network used to refer to the set of topics around broad price changes as “’flation,” which neatly covers inflation, deflation, and disinflation in a single word.

Some analysts in the U.S. are getting worried about the “in-“ kind of ’flation. With core inflation hitting 2.2% for the year ending in January—though the headline figure, dragged down by the collapse in oil prices, was just 1.3%—hawks are fretting that the Fed has fallen behind the curve.

Maybe, but maybe this is better news than it seems. Graphed below are headline and core inflation for the U.S., the eurozone, and Japan. The latter two are in or near deflation, a sign of profound and extended economic weakness. The U.S., for all its troubles, is not suffering from those maladies. That 2.2% core reading is just slightly above the average since 2000; the 1.4% headline is 0.8 point below that average.
It might be a good idea to relax and give thanks that the U.S. is not caught in what our beloved if irascible John Liscio used to call “the tractor beam of deflation.” There’s plenty of time to get on top of this one if the rise persists.

 

Consumer price index all and core

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Don’t make promises you can’t keep

Taking a page from Charles Kindleberger’s research on the increase in credit’s effect on asset prices, David O. Lucca, Taylor Nadauld, and Karen Shen, researchers at the New York Federal Reserve Bank, have posted a paper putting a price on what increased student loan availability has done to college tuition levels. (Link at foot of article.)

This is a careful paper: the authors lay out concerns about their methodology, stress areas where they see weakness, and balance the whole thing against the benefits to our workers and to our nation of an increase in the share of workers with college degrees. Nonetheless, it is a sobering piece of work that should force a number of deep questions, and, one hopes, policy changes.

In opening the discussion the authors note that much study has been devoted in recent years to the relationship between cheap credit and the 2002-6 housing bubble, and point out that although student loans fund a capital investment, whether delineated broadly as an educated work force, or on an individual level, while mortgages fund an asset, credit is crucial to both, and both enjoy the support of government-sponsored programs.

Federal student aid programs are governed by the 1965 Higher Education Act (HEA), which outlined 6 mandates, the fourth, Title IV, authorizing financial aid to support access to post-secondary education through the Federal Pell Grants program and the Federal Direct Loan Program. The researchers looked at three amendments to HEA: the increase in subsidized loan caps in 2007-8 school year; in unsubsidized caps in the 2008-9 school year, and the gradual increase in Pell Grants between 2207-8 and 2010-11 to link changes in tuition following increases access to loan and grant funding.

Although changes in caps theoretically apply to all institutions, the percentage of students who meet the requirement to gain access to those funds is not evenly distributed, and the students at program caps, say those with funding needs larger than the maximum loans available before cap increases, are the ones most likely to take the loans. As of 2012, about 60% of post-secondary students attended four-year public universities, 29% not-for-private private liberal art colleges and research universities, and 11%, Title IV “less-than-two-year” for-profit outfits, largely vocation and specializing in technology, business, photography, and fashion, including cosmetology and hair styling. That last 11% receives more than 77% of their funding through Title IV, with the largest share of near-the-cap students.

Between 2001 and 2012 yearly student-loan initiations increased from $53 billion to $120 billion, and in recent years 90% of those initiations came through federal student aid programs. Over the same stretch, the average tuition “sticker” price in constant 2012 dollars rose from $6,950 to $10,200, or 46%. (The authors focus on sticker rather than net tuition because it is more readily available, and because of the effects of institutional grants and the like.)

The authors found, not surprisingly, that the institutions with most aid exposure before the cap increases underwent “disproportionate” increases in tuition around those changes. For subsidized loans the authors found that 70 cents of each newly available dollar was captured as a tuition increase, and that approximately 55 cents of each Pell Grant dollar was so captured. Highest pass-throughs took place in the more expensive four-year private institutions with “relatively high-income students but average selectivity.”

Regarding this they conclude:

From a welfare perspective, these estimates suggest that, while one would expect a student aid expansion to benefit its recipients, the subsidized loan expansion could have been to their detriment, on net, because of the sizable and offsetting tuition effect.

But it was good for the stock market. On the for-profit front Lucca et al. found evidence of “large abnormal stock market responses for a portfolio of all publicly traded for-profit institutions,” following the passage of the aid increases that summed up to 10% across the segments. They also found that the average increase in the sticker price in these outfits averaged $180, but averaged $56 in the not-for-profit colleges and universities.

Here’s some food for thought via a severe example of tax dollars gone astray. Surely some of the for-profit outfits meet their stated goals, but Corinthian Colleges Inc. (CCI), the largest such for-profit network, was not among them. In April 2015, under Federal criminal investigation, Corinthian closed down its operations, and filed for bankruptcy in May. Charges include misleading students on their career options and Corinthian’s ability to place them following graduation, the graduation rates themselves, and loan issues. California Attorney General alleges CCI targeted single parents close to the poverty level via aggressive marketing. So that’s one place our tax dollars earmarked to increase access to post-secondary education have been. Note to for-profit educators: Don’t make promises you can’t keep.

But we can end on an encouraging note. As is often the case, the action is at the margin. In working to ascertain whether student debt expansion increased access to post-secondary education over the short-haul, the NY Fed researchers found that only the availability of Pell grants increases access. These grants go directly to low-income students, who are most likely to be on the enrollment margin. And although they also push up tuition, they do so at a lower rate than the loan-based increases.

If we’re serious about increasing access to college for those who want to attend, we’d be wise to funnel funds into grant programs that advance that goal, and to do so at the expense of outfits like CCI.

Credit Supply and the Rise in College Tuition: Evidence from the Expansion in Federal Student Aid Programs

by Philippa Dunne· · 0 comments · Comments & Context

What gas dividend?

Oil (and gasoline) prices have crept off their lows; they’re up about 25% this year, though they’re still well below year-ago prices. But many analysts have been scratching their heads wondering why the decline in energy prices hasn’t done more to goose up retail spending. It may be that's not really much of a conundrum. 

PCE

Here are several ways of making that point. First, there’s the graph above, showing the annual growth rate in nominal expenditures on energy and all other forms of consumption from the national income accounts. If energy prices could add to or subtract from other forms of consumption, you’d expect the two lines to look more like mirror images. In fact, they move more together than apart. The correlation coefficient between the two series is 0.43. Lag the non-energy spending a year, on the supposition that there might be a delayed reaction time, and the correlation coefficient actually falls to 0.28.

Moves in energy consumption are overwhelmingly dominated by price. The correlation coefficient between the annual change in the gasoline price and energy expenditures is 0.97. (That’s made even more remarkable by the fact that more than a third of energy spending is on services like electricity, which are not priced like gas.) While people may drive more or less, or raise or lower the thermostat, based on price movements, it appears they don’t change their behavior all that much. They just take the price—and it doesn’t seem to have much effect on other spending.

One gets similar results looking at the BLS’s Consumer Expenditure Survey (often abbreviated CEX, so as not to confuse it with Current Employment Statistics, the official name of the payroll survey). The CEX can be volatile, and dependent on the unreliable memories of the surveyed in describing their spending habits, but it’s still the best household-based measure of expenditures we have—and it allows for fairly detailed demographic analysis.

Ene-Nonene-CEX

As the first graph above shows, we don’t find the mirror image between energy and non-energy spending growth that you might expect in the aggregate. The correlation between energy and non-energy spending growth is 0.28—positive, not negative. And the story is similar when you look at poor (bottom quintile), middle-income (third quintile), and affluent (top quintile) households. The correlation weakens the higher you go up the income ladder (0.52 for the first, 0.42 for the third, and 0.14 for the fifth), but they remain positive for all five. 

Not surprisingly, there’s a tight relation between income growth and spending. A regression of the annual change in non-energy spending in the CEX on the annual change in household income yields an impressively high r2 of 0.53. So the “mystery” of why lower energy prices are not translating into higher retail spending may not be a mystery at all. The best explanation of tepid retail growth is tepid income growth.

Advanced industries

February 11, 2015

 The Brookings Institution just published a paper outlining the importance of our advanced industries (AI), and a call to do more to support its growth. To be classified as advanced  industries must spend more than $450 per worker on R&D, putting them in the 80th percentile, and the share of workers whose jobs demand a high degree of STEM (Science, Technology, Engineering and Math) knowledge must exceed the national average of 21%. Overall, industries so classed constitute our tech sector at its broadest: most manufacturing operations including aerospace, automotive, navigational instruments, medical equipment and supplies; energy including electric power, mining and extraction; and services, including architecture and engineering, data processing, satellite communication and scientific research. 

Between 1980 and 2013 such industries grew 30% faster than the rest of the economy, or 5.4% annually, but employment levels held basically steady, largely because of productivity advances. Since the Great Recession both activity and employment have been on the rise, the sector adding one million direct jobs between 2010 and 2013, with employment and output rates 1.9 and 2.3 times faster than the nation as a whole. And AI employment has a high multiplier: those 1 million direct jobs likely generated another 2.2 million outside the industry itself, which compares to about 6 million total jobs created nationally over the same stretch. Advanced services were responsible for 65% of the total AI jobs, with computer design by itself up 250,000. Industries building transportation equipment have been adding jobs, finally, after decades of losses. A lot of attention has been focused on contributions to national payroll gains made by AI subsector gas and oil extraction since the recession. 

A boost? Certainly, but dwarfed by the mothership.

 Each worker in the sector generates about $210,000 in value added, in comparison to the $101,000 average overall, which helps offset the sharp increase in wages. In 2013 the average AI worker earned $90,000 annually, close to twice the overall average. Between 1975 and 2013 inflation-adjusted AI wages were up 63%, compared with 17% outside the sector. Total AI employment was about 12.3 million in 2013, about 9% of total overall employment, with another 27 million jobs riding AI's coattails. Together with the directly employed, that’s about one-fourth of US employment. Surprisingly, about half of the workers in the sector hold less than a bachelor’s degree, and Brookings ranks it as an accessible field. And one where more workers are needed.

Computer systems design owns the largest share of total AI employment, 13.8%, followed by architecture and engineering, 11.0% and management and technological consulting, 9.6%. Between 2010 and 2013 fastest rates of employment growth took place in information services, 10%; railroad rolling stock, 8.4%; automotive, 8.1%, and gas and oil extraction 7.6%.

Advanced industries tend to thrive in urban areas: San Jose is our most advanced hub, with 30% of the workforce laboring in the sector, followed by Seattle, 16%, Wichita, 15.5%, Detroit, 14.8%, and San Francisco, 14%. San Jose, Detroit and Seattle have the broadest array of advanced industry. (We’ll be presenting more data on this in coming months.)

 We are losing international share, and there are a host of serious problems we like to nag about, but even in its somewhat weakened state our AI sector a powerful thing. And if we’re worried about productivity, as we should be, it’s a great place to start.