Employment & Productivity

Counterfactual Failure Alert!

5d0320d4-f194-468f-927a-0ea2798f35e3We’ve been focusing on productivity in recent issues and had some questions concerning possible measurement of high-tech productivity from some readers in the last weeks. Wouldn’t that be nice, but, alas, two new studies dim such hopes.

The two papers make many of the same points. All agree that there is a measurement problem, but that it was likely larger during the high growth period of 1995 to 2004 than it is today. In “Does the United States have a productivity slowdown or a measurement problem?” authors David M. Byrne, John G Fernald, and Marshall B. Reinsdorf suggest some adjustments to IT-related sectors, which make recent growth in investment and GDP look a bit better, and adjustments to durable goods manufacturing, which make labor productivity look even worse.

In his “challenge to mismeasurement explanations,” University of Chicago professor Chad Syverson argues that the highest estimate of the surplus generated by internet-linked technologies is only one-third of the “purportedly mismeasured GDP,” even using his “very expansive assumptions about the value of leisure time.” Equally bad, to account for even a modest piece of the $2.7 trillion hole left by the productivity slow-down, output and productivity in information and communications technologies (ICT) would have to be multiples of what is currently reported. For example, combined employment in computer/electronic manufacturing, information, and computer systems design rose 3.6%, from 5.58 million to 5.78 million, between 2004 and 2015. If those workers actually did produce the output lost to the productivity slowdown, real value added per worker would have risen an “astounding” 363% over those years, far outpacing the 83% growth in durable goods manufacturing between 1995 and 2004.

Syverson throws cold water on the argument that Gross Domestic Income being slightly higher than Gross Domestic Product since 2004 suggests that people are being paid to make things that are sold at heavy discounts: that disparity is based on “unusually high” capital income [read profits], not labor income. He points out that if the annualized 1.5% drop in labor productivity were to persist over the next 25 years it would compound to a 45% decrease in per capita income.

Byrne et al. argue that since domestic production of IT hardware has decreased, the effect of mismeasurement was likely larger before the as-measured slowdown after 2004. They also make the point that consumer benefits from Google searches and fun on Facebook are non-market (akin to playing soccer with one’s children), and anyway are too small to compensate for the loss in “overall wellbeing” driven by slower market-sector productivity growth. They see real improvement in matching products and services with consumers, but nest these in a continuum of changes that once included the shift in responsibility for doing the laundry from a domestic washer-person, to a family-member, and finally to a washing machine.

They point the finger at the decline in economic dynamism including the dearth of start-ups and slower reallocation of resources following productivity shocks, and note the possibility that the fast-growth period between 1995 and 2004 might be the anomaly. And they admit they don’t know what gains will come from cloud computing, and that more IT-driven increases may be in the offing.

The productivity showdown is widespread, and Syverson matches OECD labor productivity data with ICT intensity, which he measure as access to broadband, for close to 30 countries. He regresses labor productivity with broadband penetration for a -0.001 co-efficient, a point estimate that implies that one standard deviation (SD) in broadband penetration comes with one-fiftieth of a SD in the magnitude of the slowdown. This coincides with work showing that differences in the slowdown in total factor productivity in U.S. states are not correlated with each state’s ICT intensity.

Syverson writes that the “intuitive and plausible empirical case for the mismeasurement hypothesis faces a higher bar in the data,” and that the slowdown is real. In closing he notes that whether the slowdown will end “anytime soon is an open question.” We’re not seeing it yet, so that’s a question we have to address.

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Federal Reserve’s LMCI Weakens Further

Recently we’ve been writing about the three-consecutive-month decline in the Fed’s Labor Market Conditions Index (LMCI), which is updated on the Monday after the employment report. The sluggish April report caused the LMCI to decline further, making it a four-month streak. The decline may not look like much on the graph on p. 7, but four-month running declines are rare outside recessions or the months immediately preceding them. There were such streaks during the “mid-cycle slowdowns” of 1985–1986 and 1995–1996, but those followed periods of Federal Reserve tightening. Otherwise, none.


We suppose you could call the end of QE and the upward nudge in fed funds a tightening, but the central bank remains extraordinarily indulgent with little immediate prospect of a hostile turn. So count us slightly more troubled than we were the time time we wrote about this.

Nothing Mysterious about this “Mystery”

People sometimes describe significant changes in productivity growth as a “mystery.” For sure, it’s a complicated question, but for us the greatest mystery is why so few people care that trend U.S. productivity growth is approaching 0, the worst performance since modern statistics began. And why do one has the will to lead a charge in hopes of reversing that trend.

But the “why” is, of course, something worth investigating. As we’ve written in the past (most recently in our February 11 issue), a low level of investment in equipment and software is part of the explanation—a contrast with the late 1990s, when we saw a burst in such investment and a consequent sharp, though short-lived, acceleration in productivity growth. Both ended with the investment bust of the early 2000s. Productivity growth then entered a long downtrend that shows no sign of stabilizing yet. Investment (as a share of GDP) fell sharply from 2000 to 2004, picked up some into 2006, and collapsed with the Great Recession. It’s recovered from the depths of 2009–2010, but remains below its 1950–2007 average. As we point out regularly when we review the Fed’s financial accounts, it’s not because Corporate America is short of funds. Managers seem to prefer stock buybacks and M&A to capital expenditures.

But that’s not all. Two recent papers offer some interesting other theories of the productivity slowdown: the malignant effects of a credit bubble on the real economy (and not just after, but during), and a low rate of business startups.

The first paper, by Claudio Borio, Enisse Kharroubi, Christian Upper and Fabrizio Zampolli of the Bank for International Settlements, investigates the effects of credit booms, labor reallocations across major sectors, and financial crises on productivity growth. Their data comes from the experience of 21 advanced economies since 1969. Borio & Co. model changes in overall productivity growth as the joint product of “common,” economy-wide features and specific movements of labor across sectors with different rates of productivity growth. They find that credit booms often lead to the withdrawal of labor from high-productivity-growth sectors, especially manufacturing, and its shift to lower-growth ones, especially construction. (This was certainly a feature of the housing bubble in the U.S.: from 2004 through 2006, annual employment growth in construction ran between 3–7%, while manufacturing growth bobbed about between 0% and -1%.)

But the subsequent bust also does serious productivity damage. As we know all too well, post-financial-crisis recessions are deeper, longer, and more persistent than the garden-variety kind. Unlike the bubble phase, where the damage is mostly the result of the bad reallocation of labor, the post-crisis phase damages both the “common” component and the allocation component. The combined effects of the bubble and bust amount to a cumulative negative shock to productivity growth of 4 percentage points that can last nearly a decade.

The second paper, written by François Gourio, Todd Messer, and Michael Siemer and published by the Chicago Fed, looks at state level data in the U.S. to determine the effects of firm entry on macro variables like the growth in GDP, productivity, and population. They find significant and persistent effects. For example, a 1% increase in the number of startups leads to an 0.1 point increase in GDP growth on impact, rising to 0.2 point after three years, and persisting (at a declining rate) for 12 years. The effects on productivity is slower, barely above 0 at first, but rising to 0.1 point and persisting (with little decay) for nearly a decade. Employment effects are more modest, on the order of 0.05 point, but persisting for up to twelve years. All in all, “a one standard deviation shock to the number of startups leads to an increase of GDP around 1.2%.” That’s not trivial.

As the graph below shows, the annual growth in employing business establishments has picked up nicely from its 2009 lows, but even so, it’s just around its 1976–2007 average. (This series comes from the Quarterly Census of Employment and Wages, which is more timely than the series that Gourio et al. used, from the Business Employment Dynamics.) That pickup is encouraging, however, and might mean good things for future employment and productivity growth—if it’s sustained. But, as the next section shows, it may not be.



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How Disruptive is That?

Strong growth in transportation and warehousing, led by limo services (which is where Uber and the like would show up if they are getting picked up), has added fuel to the argument that Uber is a disruptive technology. That’s going to be hard to prove: the author of the theory of disruptive innovation, Harvard Business Schools’s Clayton Christensen, argues that it isn’t.

It’s worth taking a look at the article. Christensen at al. suggest that his theory may is “in danger of becoming a victim of its own success,” and that many of the people who throw it around have not “read a serious book or article on the subject.” In his recap he notes that his use of “disruption” relates to the process in which established businesses are challenged by small companies with fewer resources. He notes that incumbent companies tend to focus on the demands of their most demanding (read lucrative) clients, which causes them to go too far for some segments, and not far enough for others. The newcomer targets the neglected segments, and offers services, generally at lower prices, to them. The established businesses remain focused on their higher paying clients, allowing the newcomers to move up the ladder. Disruption occurs when mainstream customers flock to the newcomers’ offerings. Christensen suggests that Uber is not disruptive because it doesn’t cater to the low end of the market, and because the product it offers is not perceived as lower quality than current taxi services.

So remember, if you want to be a disruptive technology, you have to start out cheap and bad, and then get better while remaining cheap.

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Establishment Surge!

The most vibrant piece of the most recent QCEW report for Q2 2015 was the 2.8% over-the-year increase in the number of employing establishments. These are not necessarily startups, as they can be new locations of existing corporations, but it is nevertheless encouraging to see some action in this vital component of the job machine. As we often point out, young businesses generate a mighty share of gross job gains, even if they fail at an alarming rate as well. The failure rate, though, has a bright side in that it reallocates resources, which contributes to productivity. (As does job churn—when workers move between jobs they often carry innovative ideas with them.)

And new employing establishments are the ones who would really benefit from low rates on their loans, which they can still ink.This is, of course, only one quarter, which doth not a trend make. It would be great news if demand remains steady enough to give would-be entrepreneurs the confidence to build their businesses.

Here’s the super-sectoral breakdown:


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