Macro Roundup

Phil Gramm and Mike Solon, "Biden Turns Back the Progressive Clock,"Wall Street Journal, July 14, 2021, https://www.wsj.com/articles/biden-turns-back-the-progressive-clock-11626286594

Biden Turns Back the Progressive Clock

In a sweeping executive order aimed at reimposing Progressive Era regulatory policy across the U.S. economy, President Biden recounted the foundational myths of modern progressivism. The first canon of progressivism holds that breaking up the consolidating industries or trusts in the late 19th and early 20th centuries, and regulating those industries heavily until the late 1970s, benefited the economy and gave “the little guy” a fighting chance. Consumers, workers and the economy took a hit, according to the progressive myth, when the regulatory structure was overturned 40 years ago.

Progressive interpretation claims that the industrial concentration of the late 19th and early 20th centuries was evidence of the rise of monopolies, but if the consolidating industries were exhibiting anticompetitive behavior, output would have fallen and prices would have risen. The opposite happened. As economist Thomas DiLorenzo showed in a classic study, output in industries accused of being monopolistic during the debate on the Sherman Act in 1890 increased by 175% from 1880 to 1890—seven times the growth rate of the economy as a whole. The average price of products sold by these same industries fell three times as fast as the Consumer Price Index.

Hard economic data consistently shows that the industrial concentration at the turn of the 19th century was driven by vigorous competition arising from transformative technology and improved industrial organization. Efficient producers mastered the economies of scale to provide consumers with lower prices and improved product quality. Competition and new technology destroyed the pricing power of emerging trusts, and most failed to survive. Even the oil and sugar trusts, which existed because of protective tariffs, faced relentless price competition.

Many trusts welcomed progressive regulation. A comprehensive study by economist George Hilton and historian Gabriel Kolko showed convincingly that railroads championed regulation. They were more than happy to have the feds set freight rates. Market forces had driven down revenues per ton-mile by some 18% between 1870 and 1890. The first major action of the Interstate Commerce Commission was to ban price competition by outlawing price rebating, which the owners of an overbuilt railway system welcomed. When interstate trucking became an effective competitor of the railroads, the ICC muted that competition by regulating the new industry.

The 1970s brought two recessions, double-digit inflation and an end to America’s postwar economic dominance, setting off an intense policy debate. As economists, regulators, politicians, business leaders and policy advocates debated why the U.S. economy seemed to be losing its exceptionalism, a consensus formed around the belief that regulations based on the Progressive Era principles that Mr. Biden now seeks to revive were hurting consumers, workers and the economy.

Experts charged with protecting consumers, like then-Senate Judiciary Committee staffer (and Harvard antitrust law professor) Stephen Breyer and Civil Aeronautics Board Chairman Alfred Kahn, provided the hard evidence that convinced their bosses, Sen. Ted Kennedy and President Jimmy Carter, that America needed to reform agency regulation and end regulation of vast sectors of the economy. Any doubt that the consensus that Progressive Era regulation had failed ended when Ralph Nader testified before Kennedy’s subcommittee, denouncing regulations that kept prices high and choices low, and protected regulated industries.

When airline deregulation passed the Senate in 1978, Kennedy declared: “The success of this legislation is the result of a clear consensus... that rigid federal economic regulations of the airlines tends to increase prices, foster inefficiency in operations and perpetuate Government control, bureaucracy and red tape.... This consensus has been built brick by brick in recent years by serious and careful nonpartisan examinations of... the nature of government and competition in the 1970s.”

The Senate passed airline deregulation 82-4 on Oct. 14, 1978, and Sen. Joe Biden voted for it. But Democratic deregulation was only beginning. As Kennedy noted: “The restrictive price and entry provisions of the Federal Aviation Act... were copied directly from the Interstate Commerce Act of the 1880s governing railroads.... We need to now look beyond the airlines and the corrective measures we are voting upon today and recognize the fact that... heavy regulation, as illustrated by the airline industry, is not the answer.”

On April 1, 1980, the Senate voted on the Staggers Rail Act to deregulate railroads. Again, Mr. Biden voted for it. In signing the Motor Carrier Act of 1980, which Mr. Biden also voted for, President Carter sounded the death knell on Progressive Era regulation: “This historic legislation... will remove 45 years of excessive and inflationary Government restrictions and red tape.... No longer will trucks travel empty because of rules absurdly limiting the kinds of goods a truck may carry. No longer will trucks be forced to travel hundreds of miles out of their way for no reason or prohibited senselessly from stopping to pick up and deliver goods at points along their routes. The Motor Carrier Act of 1980 will bring the trucking industry into the free enterprise system, where it belongs.”

Far from hurting consumers, as progressive myth alleges, deregulation of the U.S. transportation system unleashed a wave of invention and innovation that reduced logistical transportation cost—the cost of moving goods as a percentage of gross domestic product—by an astonishing 50% over 40 years. Airline fares were cut in half on a per mile basis, while air cargo surged from 5.4% of all shipments to 14.5% by 2012, making air transit for people and packages a routine part of American life. “Our economy would be much smaller and per capita income significantly lower without these far-sighted changes,” according to FedEx CEO Fred Smith.

In this Carter-Kennedy led reform, the duty of government was to protect the consumer from harm, not to protect the producer from competition. Without the productive energy released by deregulating airlines, trucking, railroads, energy and communications, the U.S. might not have found its competitive legs as its postwar dominance in manufacturing ended in the late 1970s. The benefits of deregulation to this day continue to make possible powerful innovations that remake the world. Amazon, FedEx and Facebook are but a tiny fraction of a long list of progeny produced by lifting the heavy hand of Progressive Era regulation. We reimpose that regulation at our own peril.

Good finding from Leamer that permanent separations from manufacturing have been closely correlated with the length and depth of recent American recessions both on the national and state level, "... Using the recession recovery point equal to the month when private payrolls first exceeded their previous peak level, this paper argues that it was the negative secular trend in manufacturing jobs that was the most important determinant of the length and depth of the last three recessions/ recoveries. This negative secular trend changed the layoff/recall pattern of jobs in manufacturing into permanent displacements, a malady that lengthened the recovery periods and that is not the explicit target of either traditional monetary policy or traditional fiscal policy. Using the ideas gathered from an examination of the US two-digit sectoral data for the US overall, attention turns to the recession/recoveries of the 50 US states in the last three national recession periods. Regressions that explain the lengths and depths of the recessions in 50 US states reveal the importance of construction jobs, but the most important predictor was manufacturing jobs: the greater the share of manufacturing jobs prior to the recession, the worse was the recession/ recovery...."

His takeaway, "...The role of declining manufacturing jobs in making our recessions deeper and longer lasting has not to my knowledge had the attention of housing.... The long-term decline in manufacturing jobs from over 30% of jobs in 1950 to about 8% today has eliminated many of the best jobs for high school graduates, and devastated many communities around the United States. This decline in jobs was caused by a combination of technology, globalization and low savings. Process improvements in manufacturing have continuously increased worker productivity which means fewer manufacturing workers unless that force is offset by a combination of population growth and product innovations. Globalization which integrates high-wage countries with low-wage countries shifts the labor-intensive manufacturing work to the low-wage countries, leaving the high-wage countries with fewer manufacturing jobs.In addition, a country with a low savings rate needs a real exchange rate that is high enough to create an external deficit large enough to close the gap between savings and investment. This can shift the workforce out of the tradables sector into the nontradable service sector work: fewer manufacturing jobs and more restaurant work. The biggest public policy contributor to this outcome is probably the large deficit run by the Federal Government. Policies to increase national saving like tax breaks to encourage more savings for retirement would help out, but the fundamental technological and globalization forces cannot be reversed....In conclusion,there is substantial evidence that the behavior of job shares in construction and manufacturing predicted the length, depth and severity of the last two recessions. Theregressions that are used to support this conclusion have rather low R-squares, which means that there is a lot more than just manufacturing and construction that matter, and it remains to be seen if the results in all these regressions would be upended if other variables are included. All this is looking backward but the negative secular trend in manufacturing may be at an end, in which case what we learned about the last three recessions may not tell us much about future ones...."

The evidence, "... The unemployment data illustrated in Figure 4 reveal that the unemployment rate was always above its previous peak level when payrolls returned to their value at the previous peak. Both GDP and the unemployment rate are therefore suggesting that the recovery was not completed when payrolls returned to their previous peak level....”

“…Table 7 is a similar set of results for the Great Recession 2008/09 period with explanatory variables equal to construction, manufacturing and information employment shares in 2006, 2000 and 1990. The results on the left have a mixture of signs of coefficients and the results on the right with a reduced parameterization is again designed to make the results more understandable. All variables in the results on the right have unit standard errors and the coefficients are “beta-values.” The largest t-values and the largest beta-values are highlighted. Again manufacturing jobs contribute most noticeably to length, depth and severity of the recession, but here the manufacturing variable is not the level but the increase in employment share from 1990 to 2000 or to 2006. The change in share variables have negative coefficients which might seem hard to understand. The explanation for the negative coefficient lies in the scatter diagram at the right which compares the change in the manufacturing employment share from 2000 to 2006 with the change in the previous period from 1990 to 2000. What this scatter indicates is that a large decline in manufacturing employment in one period predicts a large decline in the subsequent period. What this suggests is that troubles in manufacturing before Again manufacturing jobs contribute most noticeably to length, depth and severity of the recession, but here the manufacturing variable is not the level but the increase in employment share from 1990 to 2000 or to 2006. The change in share variables have negative coefficients which might seem hard to understand. The explanation for the negative coefficient lies in the scatter diagram at the right which compares the change in the manufacturing employment share from 2000 to 2006 with the change in the previous period from 1990 to 2000. What this scatter indicates is that a large decline in manufacturing employment in one period predicts a large decline in the subsequent period. What this suggests is that troubles in manufacturing before…"

"...In preparation for the study of the variability of recession outcomes across US states,Figure 19 illustrates the national employment shares of manufacturing, construction and information since 1987 with the official recessions in red and the periods during which total payrolls were below their previous peak in yellow except that payrolls in the last month of each recovery exceeded the previous peak.These three sectors are thought to be “foundation” jobs on which the other jobs are constructed. The foundation sectors sell most of their output outside the state, to other states or to other nations. These are the sectors in which a state earns revenues that can be used to purchase goods and services produced elsewhere. If these foundation jobs are established in a state, then they attract support jobs in restaurants, health care, education, government and so on. The apparent exception to this statement is the construction sector which sells it’s output locally, but the finance needed to support the purchase of homes or other structures comes from the national or global bond market, bringing revenue that can be spent locally in restaurants and hospitals and schools. The declining share of manufacturing until 2010 probably contributed to both the depth and the length of the recessions, since this created large numbers of permanently displaced workers who had to find jobs in other sectors, something that probably took significantly longer than the recalls that occurred after earlier recessions. The construction sector probably contributed to the length and depth of the 1990 recession and the 2008/09 recession when the share of construction jobs fell substantially but not much in the 2001 recession when the share of construction jobs was rather constant. The Information jobs share bubbled up in the late 1990s but fell from then on. Those jobs are likely to have had adverse consequences especially for the 2001 recession but also played a role in the 2008/09 downturn....”

Explaining the Depth and Lengths of Three Recessions in 50 States Via “Foundational Jobs” Separations

“…The difference between “Layoffs and Recalls” versus “Permanent Separations” in manufacturing is made abundantly clear in Figure 9 which illustrates manufacturing jobs from cycle peak to cycle recovery point. Here we see the V-shaped patterns with a strong bounce back of manufacturing jobs except for the last three recessions: 1990, 2001, 2007. (2020 was a shutdown not a recession.) That confirms with the logic of Figure 8...."

“… The reason why the underlying long-term direction of job growth separates the labor market performance into these two categories is explained with the two illustrations in Figure 8.Each illustration begins with a cost-cutting phase with a downward stroke representing the job loss and a shorter upward stroke representing the amount of hiring that is necessary to get back to previous peak output, allowing for the improvements in productivity put in place during the recession, as was discussed above. To that cost-cutting partial V is added a secular time trend to reflect the long-term trend in employment, contrasting up from down. The first illustration shows that when positive growth in demand is added to the recession cost-cutting, the result is a happy V: layoffs followed by recalls. The lower image adds to the cost-cutting partial V a secular decline, and that is what produces the lazy L, with permanent layoffs and no recalls. The message of these images is that secular increases in jobs support a layoff and recall pattern but a secular decline creates permanently separated workers who may need to move to new locations and/or acquire new skills and/or greatly reduce their aspirations before they can find another job. That takes a lot more time than a recall….”

“…One important reason for a slow recovery is the prevalence of permanent job separations... One of the most important reasons for the slowing recoveries is illustrated in the manufacturing employment data in Figure 7 with log scales that allow straight lines to represent constant rates of growth This figure has three straight arrows, the first arrow identifying the period of generally rising employment in manufacturing and the other two identifying period of declining employment, the first labeled suggestively “Layoffs and Recalls” and the other two “Permanent Separations.”..”

“…To make clear that the last three recoveries have been unusually long,Figure 5 is a bar chart with one bar for each of the eleven expansions, with a blue segment representing the number of months from peak to trough and an orange segment representing the additional months until payrolls return to the previous peak. Until recently the recessions lasted a bit under 12 months and the recoveries occurred in another 12 months, under 24 months (2 years) in total. The previous peak was recovered in under two years in the first five recessions and the sixth was the first to exceed two-years, but by only one month. The seventh (1980) was very short-lived and then commenced a sequence of four expansions with variable length recessions but much longer recoveries....”

Nancy Rose, "Concerns About Concentration," Aspen Institute, 2019, https://www.economicstrategygroup.org/wp-content/uploads/2019/12/Maintaining-the-Strength-of-American-Capialism-Concerns-About-Concentration.pdf

Her takeaway from the current evidence, “….government has likely retreated too far from the role it assumed almost 130 years ago with the passage of the Sherman Antitrust Act to ensure open, fair, and competitive markets. Rebalancing competition law to invigorate enforcement will require a combination of agency action and legislative intervention. But some competition problems may not be actionable through antitrust enforcement. In these cases, recognizing that both markets and regulation are imperfect is essential to determining whether intervention is likely to improve outcomes, and to designing effective policy in those cases…”

Her four primary takeaways from the current concentration literature:

1) “…Studies of broad industry categories at the national levelsuggest increased concentration of revenue among the largest firms over the past 20 to 40 years….”
2) “…Rising national concentration is not mirrored by increased concentration at the more local level, which recent work suggests has declined on average. A plausible explanation for this divergence is growth in the national revenue share of the largest firms in most industry categories accompanied by expansion of those firms into geographies..”
3)“…Aggregate estimates of average markups or profit rates appear to have increasedover time….”

4)“…There is vigorous debate over the implications of these patterns in aggregateconcentration and markups for the state of competition….”

Industries should be defined narrowly"...economy-wide concentration studies typically use the North American Industrial Classification system (NAICS) or Standard Industrial Classification (SIC) codes to define industries, with levels of aggregation that range from very broad one or two digit sectors ("Manufacturing") to more narrow four - (SIC) or six-digit (NAICS) industry-specific codes ("Breakfast Cereal Manufacturing."Aggregations less specific than the four digit SIC industry code are almost surely too expansive to provide insight into anything beyond the question of whether large firms in broad sectors are getting larger. As an example, the NAICS three digit "Food Manufacturing" industry comprises manufacturers of breakfast cereal, chocolate and confectionary, dog and cat food, and animal slaughterhouses, among many, many others. It is difficult to think of what one could learn from changes in firm revenue shares, let alone concentration, across this mix of activities. The specificity of four digit SIC or six digit NAICS codes generally produces more interpretable industry definitions, though even those are rarely well-defined markets from a competitive standpoint…”
Measures of revenue shares should be built up from establishment data, not from assignment of top-line, firm level sales, "...Some studies in this literature rely on firm level databases, such as Compustat that report a primary industry code for a firm, typically at a four-digit SIC level. The assignment of all of a firm's revenue to one code in most cases systemically biases measures of industry concentration upward. It is much more accurate to measure industry revenues in the United States using the establishment level data produced by the Economic Census..."

Concentration measures should be based on the universe of firms, not only large or publicly traded firms, "...Economic activity in many small and privately held firms may be missing in databases such as Orbis or Compustat, which rely on publicly reported financial data, such as 10-Ks, that privately held firms may not disclose.If the total commerce in these firms is significant, individually or in the aggregate, statistics excluding their activities may be misleading and will distort changes when the companies sampled change overtime. This is especially problematic for studies of European concentration based on Orbis data, which expanded coverage of small and midsize European companies over time.
Concentration measures should reflect the size distribution of firms, "...Industrial organization economists and antitrust practitioners prefer the Hirschman Herfindahl Index, or HHI, which is the sum of squared market shares of all firms. Higher HHI's reflect more concentrated revenue, with an upper limit of 1 (or 10,000, if shares are measured as 0-100%) for monopoly. This provides more information about revenue distribution than concentration ratios, which are the revenue share accounted for by the largest N firms (commonly four or eight, denoted as CR4 or CR8). For example, a CR8 or 80% could reflect one firm with a 75% share, or eight firms with 10% shares, with very different implications for market structure. The HHI would distinguish between these situations….”

Concentration measures should reflect the appropriate geographic scope of a given product market, "This is aspirational and is virtually never satisfied in aggregate studies of concentration. Almost all studies apply a single geographic aggregation, typically national, to all industries. This is too narrow for markets with globally traded goods, such as aircraft, cement, or petroleum, and much too broad for markets with locally delivered goods and services, such as scheduled airline service between cities, concrete, or retail gasoline. Furthermore, to the extent that imports or exports are important in a given market, measures built up from sales only by U.S. entities could have severe mismeasurement. This is also problematic for firm-level data sources like Compustat, for which U.S. sales may be a fraction of firms' recorded global revenue..."

MIT's Nancy Rose on what best practices for measuring concentration should be:

Nancy Rose argues that many of the most commonly used aggregated measures of concentration across industries should not be used to study concentration dynamics given methodological challenge and we should look at industry level studies, which that this point while showing an increase in concentration it's still well under what is commonly though of as anti-competitive

But as Ben pointed out to me there is no published series such as Barkai’s. It requires a little work to back out what the economic profits actually are.”

Ben and I are thinking about this and Ben drew a meaningful distinction btw economic profits and accounting profits. So the series you liked to on FRED can be hard to interpret because companies try to limit their own tax burdens. To the extent that they’re measuring economic profits, it seems like they’re only measuring the profits that firms were unable to shift into something else. One way of approaching it is how Barkai attempted to define profits in his Declining Labor and Capital Shares (which was cited in chapter) this is pre-tax"....I draw a distinction between capital costs and pure profits and show that this distinction is critical for understanding the decline in the labor share. Capital costs are the annual costs of using all capital inputs in production. In a world in which firms lease all of their capital inputs, constructing capital costs would be simple: we would sum all annual leasing expenses. Pure profits are what a firm earns in excess of all production costs (material inputs, labor costs, and capital costs).Firms that use a lot of expensive equipment have high capital costs. Firms that charge consumers high prices relative to the cost of production have high pure profits. An increase in the capital share, equal to the ratio of capital costs to gross value added, at the expense of the labor share is indicative of a substitution from labor to capital inputs into production. By contrast, an increase in the pure profit share, equal to the ratio of pure profits to gross value added, is indicative of an increase in market power and a decline in competition...Consistent with earlier research,I find that pure profits were very small in the early 1980s. However, pure profits have increased dramatically since the early 1980s. In the main specification, the pure profit share (equal to the ratio of pure profits to gross value added) increases by 13.5 percentage points. To offer a sense of the magnitude, the value of this increase in pure profits amounts to over $1.2 trillion in 2014, or $14.6 thousand for each of the approximately 81 million employees of the non-financial corporate sector….”

Bill Dupor on the impact of halting the extended Federal unemployment insurance, "...This policy difference across states generates an interesting question. What is happening to RSP participation in “halting” and “non-halting” states?...As shown in the figure below, the blue and orange lines report the continuing claims indexes for non-halters and early halters, respectively... After May 15, and particularly for the last three weeks of available data, there is a substantial separation in the two series. Relative to May 15, non-halting states’ claims have fallen by 7% whereas early halters’ claims have fallen by 12%.This difference has been persistent over the past three weeks for which the data are available...."

2. I excluded states that will end participation after June 19. Though the RSP recipients in these states continue to receive the emergency benefits, these participants know that they’ll soon lose the federal benefits. Excluding these states provides for a more direct intergroup comparison.

Bill Dupor, "What Has Been the Early Impact of States Halting Federal Jobless Benefits?," Federal Reserve Bank Of St. Louis, July 13, 2021, https://www.stlouisfed.org/on-the-economy/2021/july/early-impact-states-halting-jobless-benefits

What Has Been the Early Impact of States Halting Federal Jobless Benefits?

Starting on June 12, several states have stopped accepting the federal government’s emergency unemployment insurance (UI) benefits, which were put in place in response to the pandemic. These benefits included a $300 weekly add-on for UI recipients, extended eligibility to unemployed people who otherwise would not be covered by their states’ programs, and a duration extension beyond those provided by regular state programs (RSPs).

Other states will be joining this group in the upcoming weeks, and this early termination will be in place in more than half of U.S. states by mid-July.1 The remaining states plan to maintain these emergency benefits until the federally mandated end of the program in September.

Importantly, all U.S. states will continue to operate their RSPs regardless of whether they halt the emergency benefits. While RSP details vary somewhat across states, a typical RSP offers benefits for up to 26 weeks and, up to some cut-off, a replacement rate of 40% to 50% of recent past wage income.

Halting and Non-Halting States

This policy difference across states generates an interesting question. What is happening to RSP participation in “halting” and “non-halting” states?

One possibility is that claimants on RSPs in halting states, recognizing that the $300 weekly add-on and duration extension have ended or will soon end, might drop out of the RSP for which they still qualify in light of the less-generous benefits. Those dropping out of RSPs in halting states might in turn be taking jobs and thus boosting employment levels in their respective states.

To explore this question, I considered two groups of states: (1) states that are continuing participation in emergency pandemic UI programs and thus continuing the generosity of those benefits, and (2) states that ended participation on or before June 19.2 I call the former group the non-halters and the latter group the early halters.

Next, for each group, I summed the total continuing claims in RSPs by week. I took each resulting time series and transformed it into an index equal to 100 for (the week ending on) May 15. I chose May 15 because this is roughly the midpoint of the dates that the early halters announced their intention to terminate emergency benefits.

Continuing Claims in Early Halting States Have Declined More

As shown in the figure below, the blue and orange lines report the continuing claims indexes for non-halters and early halters, respectively.

For both groups, the fall in continuing claims likely reflects the improved labor market, together with some claimants losing regular state benefits because they reached the maximum number of weeks afforded by their state programs.

After May 15, and particularly for the last three weeks of available data, there is a substantial separation in the two series. Relative to May 15, non-halting states’ claims have fallen by 7% whereas early halters’ claims have fallen by 12%. This difference has been persistent over the past three weeks for which the data are available.

Time will tell whether this difference persists, as new weekly data arrive and additional states implement plans to cease providing emergency benefits. Upcoming employment reports will tell whether the difference in RSP continuing claims is also associated with a difference in employment growth across the two groups.

Notes and References

1. A few of these states are no longer providing the $300 add-on but are continuing with other emergency benefits.

Nicolas Crouzet and Janice Eberly, "Rents And Intangible Capital: A Q+ Framework," National Bureau Of Economic Research, July 2021, https://www.nber.org/papers/w28988

Comparison to existing literature“…These findings are qualitatively consistent with the recent literature arguing that pure profits as fraction of value added have been growing over the last three decades (Guti´errez and Philippon, 2017; Barkai, 2020; Karabarbounis and Neiman, 2019). However, they differ quantitatively. For instance, Barkai (2020) finds that the pure profit share rose from -5.6% in 1984 to 7.9% in 2014, an increase of 13.5 p.p. over the period. Karabarbounis and Neiman (2019), in their “case⇧,” find that the pure profit share must have risen by about 13 p.p. over the same period. We find an increase in rents of half that magnitude…”

On the paper’s limits, “…However, it has three limitations. First, it does not allow for non-convex adjustment costs. Second, it abstracts from financial constraints. The next subsection discusses extensions in this direction. Third, it assumes that rents, µ, are exogenous. In particular, they do not depend on past investment, in contrast, for instance, with models of customer capital.9 In this sense, our results are restricted to “neoclassical” models of the firm, and provide a benchmark against which the effects of other frictions on the investment gap can be compared….”

“… From the standpoint of the model, these changes are driven by three underlying forces,reported in Figure 2: a greater importance of intangibles in the production function; higher rents; and a decline in user costs, more pronounced for physical than for intangible capital.The top left panel of Figure 2 shows that even using the relatively narrow definition of intangibles in the NFCB data, the share of intangible capital in production,⌘, increased substantiallyafter 1985, from 0.17 to 0.29 in 2015.25 The behavior of the intangible share approximately mimics the behavior of the measured ratio of intangible to physical capital at replacement cost, which increases rapidly after 1985. The effects of the intangible share on the overall investment gap are magnified by the rise in rents after 1985. The top right panel of Figure 2 reports estimates of the rents implicit in Equation (13). In order to facilitate comparison with existing estimates, we express them as the flow value of rents relative to value added, which is related to the parameter controlling rents in the model...Rents, as a fraction of value added, increase from 1.5% in 1985, to 7.7% in 2015 — a cumulative 6.2 percentage point (p.p.) change over three decades. Expressed as markups over value added, this is an increase from 1.015 in 1985, to 1.083 in 2015. Finally, we note two other features of our time-series for the investment gap. First, the gap is elevated during the 1960s; the decomposition attributes this to a combination of low user costs (driven by the low interest rates of the period), and high rents.27 Second, the gap is particularly small during the 1975-1985 period. The model primarily attributes this reversal to the large increase in discount rate and the decrease in growth rates around the early 1980s, which, by reducing the present value of future rents, pushes the average value of installed capital closer to its marginal value….”

Specifically"...from a theoretical perspective, we show that the gap between average Q and marginal q for physical capital, which we call the “investment gap”, can be decomposed into three distinct terms: a term capturing rents to physical capital, a term capturing the value of installed intangible capital, and a term capturing rents to intangible capital. The last element of this decomposition, an interaction term that is new to our analysis, is particularly important: it clarifies the fact that rising rents and rising intangibles cannot be meaningfully analyzed in isolation, as their interaction contributes to the gap between investment and returns. Moreover, this decomposition is very general, as our framework nests a number of existing investment model..."

Second:"..we show that this interaction term is empirically important to the recent rise in the investment gap. Importantly, we show how each term in our decomposition can be quantified using data on profits, investment, valuations, and estimates of the intangible capital stock within the structure of the model. In aggregate data, the interaction term accounts for between one-quarter and one-half of the investment gap, depending on how broad the definition of intangibles is. In addition, our approach leads to lower estimates of the increase in total rents than existingwork. As we show, this is equivalent to a smaller estimate of the decrease in total user costs of capital. This occurs because while including intangibles raises valuations, it also boosts the user cost of capital due to higher depreciation rates (hence reducing rents)...."

Evidence, “…Figure 1 reports the investment gap decomposition... The decomposition emphasizes three main finding. First, the investment gap is large during two distinct periods: 1960-1970, and after 1985. The wedge between average Q and marginal q is therefore not strictly a hallmark of the post1980s period. Second, rents attributable to physical capital — the first term in Equation (13) — play a sizable (though somewhat declining) role in explaining the investment gap: they account 61% of it in 2015, compared to 67% in 1965.24 Third, rents attributable to intangibles — the third term in Equation (13) — have become markedly more important in recent years. In 2015, 25% of the investment gap reflects the combined effects of high rents and a large stock of intangibles, compared to 10% in 1965, using the BEA measure of R&D capital only, the narrow measure of intangibles available in these data….”

Core takeaway, "...This research provides a general decomposition of the gap between average Q — which is observable — and marginal q — the shadow value that drives investment. This decomposition captures the effects of unmeasured capital, such as intangibles, and also the effect of rents. We use measurement of the gap to shed light on the growing divergence between physical investment and valuations, which our approach interprets as being driven by the combined effects of growing rents and growing intangible capital. With a relatively narrow measure of intangibles (R&D capital), one-third of the investment gap reflects a combination of growth in the intangible capital stock and rents generated by intangible capital. Expanding the definition of intangibles beyond R&D increases this contribution to about two thirds. In addition to these aggregate effects, sectoral results show that rents on intangibles are largest in some of the fastest growing sectors in the economy, such as Tech and Health, and that within these sectors, rents are highest in subsectors with rapid growth in intangibles, as well…”

New NBER argues that the capital investment shortfall in the US has been driven by the rise of intangible capital which allow firms with market power to extract more rents. They estimate that 1/3 of the gap in capital investment is driven by intangibles particularly in fast-growing sectors, "... the divergence between returns and investment can be cast as a rising gap between the average value of business capital, or Tobin’s average Q, and its marginal value, or Tobin’s marginal q. We directly observe rising average Q in the data, via market values, while marginal q is a shadow value measured implicitly by lackluster investment. A gap between the average value of capital and its marginal value can arise and grow for a number of reasons..."

Research from the Boston Fed ~ directionally mirroring Natasha Sarin’s work finds that including Social Security and retirement wealth in the wealth concept results in significantly lower top shares and less growth in the concentration of wealth,

Their core takeaway, “…Focusing on two pre-retirement age groups from 1989 to 2019,we find that using an expanded measure of wealth that includes DB pension and Social Security resources to estimate wealth concentration in the United States results in a level that is lower than estimates based on more commonly used measures of market net worth. We present this finding through several wealth concentration measures, including shares of wealth held by the top of the distribution, ratios of wealth held at different points in the distribution, and the Gini coefficient. We also find that while wealth concentration has risen over the past three decades, it has done so more slowly for this expanded wealth measure….”

The data, “….To present an alternative and, we think, useful addition to the measurement of wealth concentration, we use the Survey of Consumer Finances (SCF) to develop an expanded measure of wealth that incorporates both estimates of defined benefit (DB) wealth and the expected present value of Social Security among the 40- to 59-year-old population. We directly incorporate the work of Sabelhaus and Volz (2019), who impute the value of DB wealth of current workers in the SCF using labor market and pension plan characteristics in the survey along with high-quality external data on DB plan assets. In this section, we discuss the SCF and our methods for (1) estimating earnings histories of survey respondents, (2) calculating future Social Security benefits, and (3) estimating age-forward SCF net worth, as well as DB pensions, to the point of retirement….”

Findings, “…Our estimates show that the value of DB pensions and Social Security are significant relative to other forms of wealth—throughout the wealth distribution but especially at the lower half of the wealth distribution. Indeed, we find that, even for the median household, the present value of DB pensions and Social Security benefits accounts for more than half of all wealth. With respect to their effects on the distribution of wealth, we find that (1)including DB pension and Social Security wealth results in markedly lower measures of wealth concentration, and (2) trends toward higher wealth inequality over time, while moderated, are still present. In particular, the “90/50 ratio”—the ratio of wealth held by those at the 90th percentile of wealth to those at the 50th percentile—is reduced by nearly half for the 50- 59 age group (from 13.4 to 6.8 in 2019) and for the 40-49 age group (10.7 to 6.4) when we include the estimated value of Social Security. The “50/10 ratio” declines even more with the inclusion of Social Security; for 2019, the ratio falls from 13.1 to 4.3 among those aged 40 to 49 and from 21.3 to 4.2 for the 50-59 age group. The share of wealth held by the “top 5 percent” drops from about 72 percent down to 51 percent when defined contribution (DC) plan and DB pension wealth are added to non-retirement wealth; it falls even further, to 45 percent, when Social Security benefits for those aged 40 to 59 are included. The inclusion of each measure, however, has a somewhat different effect: Social Security decreases wealth concentration “at the top,” whether we look at the top 5 percent’s share of wealth or the 90/50 ratio; DB decreases the top 5 percent’s share, but in more recent years, it actually increases the 90/50 ratio. The top 5 percent’s share of our expanded wealth measure rises 8 fewer percentage points compared with the top 5 percent’s share of non-retirement wealth over the 1989-2019 period….”

Impact on Wealth Inequality, “….We find that wealth inequality rose over the 1989-2019 period and that the inclusion of Social Security and retirement plan wealth has an impact on both the level of inequality and its trend. In this section, we focus on ratios of wealth held at the 90th percentile of the wealth distribution to wealth held at the 50th percentile (P90/P50), as well as ratios of wealth held at the 50th percentile to wealth held at the 10th percentile (P50/P10), as measures of the skewness of the wealth distribution. In Figure 3, we see that among the 40-49 age group, the P90/P50 of non-retirement wealth rose from 4.9 in 1992 to 6.0 in 2019; among 50- to 59-year-olds, it climbed from 5.2 to 8.0. The P90/P50 of expanded wealth for the younger age group rose from 3.9 in 1992 to 6.4 in 2019. For 50- to 59-year-olds, the combined wealth P90/P50 rose from 3.4 in 1989 to 6.4 in 2019. In contrast, the P50/P10 ratio of expanded wealth declined slightly for 40- to 49- year-olds, from 4.4 to 4.3 from 1992 to 2019. It also decreased for 50- to 59-year-olds, falling from 4.9 to 4.2 over the same time period….. In Table 2, we show the top 5 percent’s and top 10 percent’s shares of different wealth concepts for another perspective on wealth concentration. Here, we see that a large share of all types of wealth are held at the top of the distribution, but especially so for non-retirement wealth. For instance, those in the top 10 percent of the non-retirement wealth distribution among 40- to 49-year-olds held 60 percent of non retirement wealth in 1989 and 75 percent in 2019. Including Social Security and retirement wealth in the wealth concept results in significantly lower top shares and less growth in the concentration of wealth. For all households in our sample, we estimate that in 2019 the top 5 percent of the distribution held 72 percent of non-retirement wealth but 51 percent of wealth including DB and DC pensions and only 45 percent of expanded wealth, which includes net Social Security wealth.12 Social Security is very broadly held and has an equalizing effect; the top 5 percent held only 8 percent of this component in 2019. From 1989 to 2019, the top 5 percent’s share of non-retirement wealth rose 18 percentage points, while its share of expanded wealth rose only 10 percentage points. Similar trends are seen when comparing the share of wealth held at the top 5 percent and top 10 percent of the distribution for both age groups over time: Concentration decreases substantially when DB and Social Security wealth are considered, with increases in wealth concentration remaining over time for all wealth concepts…”

Lindsay Jacobs, Elizabeth Llanes, Kevin Moore, Jeffrey P. Thompson, and Alice Henriques Volz, “Wealth Concentration in the United States Using an Expanded Measure of Net Worth,” Federal Reserve Bank Of Boston, June 2021, https://www.bostonfed.org/publications/research-department-working-paper/2021/wealth-concentration-in-the-united-states-using-an-expanded-measure-of-net-worth.aspx