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Opening Gambits

There seems to be a consensus that the high level of job openings—September’s reading of 6.5% of employment is off from March’s all-time high of 7.3%, but it’s still at the 95th percentile of all months since December 2000—has contributed to inflationary pressures. We’ve long been skeptical about what the openings component of the JOLTS program measures, wondering whether it’s boosted by employers who say, “sure would like an app programmer for $10 an hour but I can’t find anyone!” And a lot of us wouldn’t mind being 35 again either.

We thought a look at recent state openings data might be clarifying. (We used three-month averages to smooth some of the volatility, which is greater at the state level than nationally.) It was clarifying all right: openings bear no relationship to wages.

That point is made by the maps below. The states are shaded so that high, medium, and low openings and high, medium, and low wage growth are represented by the same colors. If openings and wage growth were tightly correlated, the maps would look a lot more similar than they do.

Another way of making the same argument is with the scattergram below. Here too, there’s no relationship at all. The eye doesn’t want to add a trendline because there is none.

The lack-of-trendline point can also be made with a simple regression of average hourly earnings growth on openings. That yields an r2 of 0.02, meaning that the openings explain all of 2% of the variation in earnings growth. Worse, there’s a 15% chance that the relationship is entirely random. And the relationship is flattered by the two outliers (DC and Alaska, labeled in the scatter). Take them out and the r2 falls to 0.00 and the chance of randomness rises to 87%.

Aside from the fact that this has never looked like a wage-driven inflation, these opening stats explain nothing.

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Frontier Knowledge & Start-Up Quality

In their 2015 research paper “Where is Silicon Valley?,” Jorge Guzman and Scott Stern set out a new method of ranking entrepreneurial ventures focusing on quality, not quantity as had prior reports, in part to provide policy-makers with information on how to promote entrepreneurship for economic and social progress.

They assemnbled five metrics, firm-name characteristics (named after the founder, long, short?), is it local or part of a regional trading cluster or high-tech industry cluster; is it a corporation, LLC, or incorporated in Delaware, and does it gain control of formal intellectual property rights within one year? They do not include location in order to step around the pitfall of assuming that businesses in a given location have a given level of quality. The quality metric is the probability of an initial public offering or an acquisition within 6 years of founding.

Their results are not surprising, but some of the magnitudes may be. Of course in California Silicon Valley stands out, with a quality ranking 20 times the average, and 90 times the lowest ranked cities. Quality is tied to the proximity of research universities and national labs. Finally, the high stakes are apparent in the difficulty of reaching the growth metric: Even those firms ranked in the top 1% have just a 5% chance hitting it.

Fast forward to 2021 and More than an Ivory Tower, the Impact of Research Institutions on the Quality and Quantity of Entrepreneurship, by Valentina Tartari and Scott Stern, who take on the possibly circular logic of the relationship of research institutes and start-up quality. (The former are often located in innovative environments and can themselves be sources of demand.) Three steps gets them there: assess annual business registration records using the analytics outlined above by zip code; link to presence or absence of research university or labs; and consider changes in Federal funding of those institutions, and whether it is directed to research or other activities.

They found that changes in Federal research commitments to universities are “uniquely linked” to positive changes in the quality-adjusted quantity of entrepreneurship, but that increases in non-research funding to universities as well as research funding to national laboratories has either neutral or no impact. In their conclusion they underscore that their research supports MIT’s Jonathan Gruber and Simon Johnson’s argument laid out in Jump-Starting America, for establishing a set of regional innovation hubs to support local “entrepreneurial ecosystems,” outside the established “superstar” hubs.

They suggest that although universities and national labs both conduct significant research, universities distinguish themselves both by also producing students, who often launch start-ups in the area—they suspect students with frontier knowledge play an important and “often underappreciated” role in disseminating knowledge generated at universities to activities in the private sector— and by promoting “policies and rules that encourage openness” and enhance “fluidity between research and industry.”

One of the reasons researchers so dislike non-competition employment constraints.
There’s much more, including interactive maps, from the team at Start-up Cartography Project.

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Inflation? ISM Indexes

Since disinflation, flirting occasionally with deflation, took over the economic scene in the early 1980s, there have been a few “inflation is back!” scares. How do current concerns stack up?
Price measures in the ISM reports confirm the verbal alarm expressed by respondents recently. The manufacturing price index was 89.6 in April; services, 76.8. Manufacturing is at the 98th percentile in the series’ history (which begins in 1948); services, at the 99th (a much shorter series—it begins in 1997). But, as the graph on the top of p. 4 shows, we’ve been here many times before. We’ve graphed only manufacturing, below, because of its much longer history. The services index traces a path very similar to manufacturing since it began in 1997; the correlation coefficient is 0.82.

Oddly, the low-inflation era since the early 1980s looks little different from the rising inflation era before it. That cautions against drawing any trend conclusions from the high current readings. But, over the last twenty years, the two price indexes do track moves in the CPI pretty well. Here’s a graph of the actual yearly change in the CPI against one predicted by the ISM services price subindex.

It’s suggesting that the CPI “should” be rising at a 3.5%, almost a full point above where it was in March, and considerably above the Fed’s 2.0% target (though that target looks to be in abeyance, at least for now).

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Buybacks: A Turn toward Prudence, for Now

According to preliminary numbers from Standard & Poor’s, buybacks among S&P 500 components rose rose 15% in the third quarter, after a 55% decline in the second, leaving them 42% below a year earlier. Measured as a percent of GDP, buybacks are now in the same neighborhood they were last seen in in 2010—though that’s still well above where they were any time between 1998 and 2004.

Measured as a percentage of operating earnings, as in the second graph, buybacks fell to their lowest levels since 2010, 32%. Despite the increase in dollar volume, buybacks declined as a percentage of earnings because earnings were up 42% in the quarter. In the 40 quarters between 2010 and 2019, buybacks fell below 40% of earnings in just two of them; the average over that period was 53%.

And our third measure, buybacks relative to dividends, saw an uptick in the quarter, but remain at their lowest level since 2009, 88%. The second and third quarters were the only two since 2010 where traditional dividends exceeded buybacks; that was true of only half the quarters between 1998 and 2009. Between 2010 and 2020Q3, S&P firms bought back $5.7 trillion in stock and paid $3.9 trillion in dividends, a combined total of $9.6 trillion. Over the same period, earnings totaled $10.6 trillion. So, they paid out 90% of earnings to shareholders. After all these years of heavy transfers to shareholders—which, it must be said, have been very friendly to stock prices—you have to wonder why corporations don’t have better things to do with their profits. It’s as if Corporate America has become an old rentier, more interested in collecting cash than investing and innovating. Maybe this pandemic-induced reduction in buybacks marks a change of heart, but we suspect things will go back to the old ways in a few quarters.

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Dwindling Labor Share

Here we revisit a familiar topic: the decline in the labor share of national income. We were prompted to revisit by a recent post to the St. Louis Fed’s website with the provocative title “Capital’s gain is lately labour’s loss” (Anglo spelling in original). It draws on the work of Loukas Karabarbounis and Brent Neiman (KN), which we’ve also discussed, though mostly in passing. KN’s data runs only through 2012; the St. Louis Fed post draws on their in-house FRED database to update it through 2017 for five major economies.

The declining labor share is interesting for several reasons. For decades, most economists assumed the share to be constant, which makes it far easier to develop economic models of production functions and economic dynamics. But the labor share is not constant. In their examination of 59 countries with at least 15 years of data between 1975 and 2012, BN found 42 with a declining labor share, measured against corporate value-added. A major reason is the declining cost of investment goods; the St Louis researchers present a series showing a near-relentless decline in the price of capital goods vs. consumer goods since 1948, with an acceleration in the 1980s. (The average decline from 1948 through 1979 was 1.6%; from 1980 to 2016, 2.6%.) That makes it easy to substitute capital for labor, to the detriment of labor’s share.

The extension of the data for the five years doesn’t change the fundamental story. The labor share in the five economies shown in the graph on the top of p. 7 was little changed between 2012 and 2017, despite sharp declines in the unemployment rates in most. You might think that a tightening labor market might boost labor’s share, but that hasn’t happened.

As the graphs above show, the declines happened to different degrees and over different intervals for the five countries shown. The declines range from 3 percentage points in the US to 8 in Canada; expressed as percentage (not point) declines, they range from 5% in the US to 11% in Canada (with the other four countries not far behind). Remember, it had been a well-established axiom in economics that this was not supposed to happen.

BN find that most of the decline in the labor share has happened within industries, so it can’t be explained by compositional changes such as the shift from manufacturing to services, which, among other things suggests things other than globalization are at work (given the varying exposure of different industries to international competition). And they also find a decline in the labor share within China, which makes it an unlikely culprit for the decline in the labor share in the richer countries.

They conclude that the decline in the price of investment goods accounts for about half the decline in labor’s share. An interesting question is what accounts for the other half. They don’t examine the effect of labor market deregulation and the declining power of unions, but those seem like worthy avenues of investigation, as they have been in other research pieces.