Macro Roundup

McKinsey data on intangibles, core findings, "...Even through economic disruptions, intangibles investments have increased. Over the past 25 years, the United States and ten European economies (Austria, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, Sweden, and the United Kingdom) achieved 63 percent growth in gross value added (GVA), a measure of economic growth. During this period, the investment share of intangibles increased by 29 percent. Rising investment in intangibles has been associated with increasing total factor productivity of entire economies. Growth in investment in intangibles slowed after the global financial crisis, and productivity growth decelerated, too, suggesting a link. Investing in intangibles correlates with productivity and sector growth. In the past quarter century, intangibles investment has risen in all sectors, and data from INTAN-Invest indicates that there is an observable link between investment in intangibles and GVA growth. It also indicates a strong association with total factor productivity. Sectors that have invested the most in intangibles— more than 12 percent of their GVA— achieved 28 percent higher growth than other sectors in GVA, at more than 2.7 percent per year between 1995 and 2019. The relationship is strongest in knowledge-intensive services such as financial services and in innovation-driven services such as telecommunications, media, and technology. This apparent correlation reflects the synergistic nature of different types of intangibles. Companies with top-notch digital analytics attract the best talent, and that talent improves the quality and scope of the analytics. Reflecting such synergies, companies and sectors that invest across intangible categories post higher GVA than their peers. Regardless of the sector, companies that invest more in intangibles grow more. The new survey indicates that top growers, defined as companies in the top quartile of GVA growth by sector in 2018-19 (whose median growth was 20 percent) are investing 2.6 times more in intangibles than low growers, defined as the bottom 50 percent of companies for GVA growth in 2018-19 (whose median growth was 3 percent).The gap increases to between five and seven times in sectors such as financial services where competitive advantage is anchored in knowledge..."

“…Most other sectors invested less than 12 percent of their GVA in intangible capital and have achieved below-average rates of GVA growth, at 2.1 percent per year. In general, a relationship can be observed between higher intangibles investment and higher growth, although there are some exceptions. One exception is manufacturing, which has invested 15.3 percent of its GVA in intangibles but has experienced broadly flat growth. This appears to reflect the fact that manufacturing has invested particularly heavily in innovation capital, specifically R&D, which entails large sunk costs as companies seek to develop the right new products. Another example is healthcare (Exhibit 6)….”

“…Three economies stand out for having achieved both high investment in intangibles and robust growth in GVA: Sweden, the United Kingdom, and the United States. Sweden achieved annual 2.4 percent GVA between 1995 and 2020, the United States 2.3 percent, and the United Kingdom 2.2 percent. All three economies have larger shares of knowledge and innovation sectors, in which intangibles investment is prominent. Conversely, economies that posted lower rates of growth in GVA over this period were more exposed to resource- or labor-intensive sectors that tend to invest less in intangibles. Many other factors determine growth, of course, and they should be examined in detail, but there does appear to be a link between investment in intangibles and GVA growth at the whole-economy level. An intangibles-rich economic model is not the only way for an economy to promote productivity and growth. Nevertheless, economies that are experiencing growth in intangibles investment are also posting growth in total factor productivity (Exhibit 4…”

Edward Glaeser, and James Poterba take the lay of the land on America's infrastructure, their bottom line, "...Our assessment of the role of economic analysis in infrastructure investment suggests several broad conclusions. First, it is difficult to place high confidence in widely discussed measures of infrastructure “need.” The most reliable way to develop such estimates would be by applying cost-benefit analysis on a project-by-project basis and aggregating the results. But that approach is expensive, given the vast array of potential infrastructure projects, and it is subject to gaming by overstating future benefits and low-balling costs. Estimates of the returns to maintaining existing infrastructure are often higher than estimates of the returns to undertaking new projects, which suggests the importance of guarding against “ribbon-cutting bias” toward new initiatives on the part of both elected leaders and the heads of government agencies. Any major infrastructure initiative should emphasize careful ex ante analysis of project costs and benefits, with oversight where feasible of padding by advocates of the assumptions regarding future costs and benefits. Second, infrastructure projects in the United States are expensive relative to those in other nations. The precise reasons are difficult to identify, but they include project designs that incorporate many features that remediate adverse project effects, such as highway noise and the inconvenience of disruption while building, required wages for workers that may exceed area norms, project delay through regulatory processes, and weak procurement and project management by the relevant government agencies.Third, user fees warrant greater consideration as a source of infrastructure project financing. Such fees, along with congestion charges, can improve the efficiency of infrastructure use. While there are concerns about the distributional effects of user fees and burdens on low-income groups in particular, the pattern of infrastructure use across income groups suggest that some user fees are progressive—higher income households use airports, for example, more than their lower income counterparts. Public transit, particularly buses, is a notable exception. Rather than carry out income redistribution by exempting infrastructure use from charges, policymakers could consider targeted redistribution programs, such as transit vouchers for low-income households or infrastructure-use rebates mediated through the tax system. Some states currently provide income tax relief for renters or for commuters who can document their travel costs. Finally, public-private partnerships can provide a means to increase operational efficiency, but arguments that they allow project sponsors to access low-cost capital should be viewed with caution. In some cases, the cost of capital for private entities may exceed that for public sector borrowers and relying on private finance rather than public funding may ultimately increase the 42 cost of the project. Some state and local governments may be attracted to these partnerships because they relieve current cash flow constraints, but they may come at a price in terms of the long-term cost of infrastructure services...."

Edward Glaeser, and James Poterba, "Economic Perspectives on Infrastructure Investment," Aspen Economic Strategy Group, July 14, 2021, https://www.economicstrategygroup.org/publication/poterba-glaeser-infrastructure-investment/

Evidence:“…Flyvbjerg, Bruzelius, and van Wee (2008) compare the capital costs for urban rail projects around the world. The costs for the six systems in the United States that were included in the analysis range from $88 million per kilometer (Atlanta) to $147.5 million per kilometer (Baltimore). Thirteen out of 17 of the European systems in the study, and five out of six in Asia or Latin America, had costs below $88 million per kilometer. Levy (2011) argues that these comparisons understate the cost disadvantage of U.S. projects, noting that “the American projects examined are quite old, from the 1980s, and many have large above-ground parts.” He further identifies three New York City projects with costs of $1.3 billion, $1.7 billion, and $4 billion per km, as well as San Francisco’s Central Subway, which cost $500 million per kilometer even though, as a light rail tunnel, it was a less demanding project. Levy’s updated Transit Cost database reports actual or projected cost-per-kilometer data, converted to $US using purchasing power parity exchange rates, on 540 different projects, including 256 that were completed by 2020. We inflation-adjust these cost estimates using the CPI and assume that the median dollar was spent in the year that was half-way between the start and end date of the project. We treat projects with average years beyond 2021 as having an average year of 2021. 7 Table 4 presents our findings.For the 19 projects in the database that are in the United States, the average cost was $1,601 million per mile, compared with a non-U.S. global average of $478 million. The median U.S. project was $965 million per mile, compared with a non-U.S. median of $299 million. The database also contains information on the share of the rail system that is underground. When we restrict our analysis to the 255 projects that are 100% in tunnels, the median cost of the 11 U.S. projects is $1,379 million per mile, compared with a non-U.S. global median of $341 million. While precise comparisons are difficult, many projects in densely populated foreign cities have substantially lower per-mile costs than their U.S. counterpartsThe cost breakdown highlights a number of important patterns. First, real estate costs are a tiny share of the project’s total cost, despite New York City’s sky-high property prices. Second, neither administrative and regulatory costs, nor the even smaller category of environmental mitigation (contained within construction and only $2.14 million) were significant causes of the high costs. Third, the two largest elements in construction costs were tunneling ($3.1 billion) and stations and intermodal facilities ($2.3 billion). The very expensive station construction is one reason why East Side Access was the most expensive project in the database,but the tunneling on its own is extraordinarily costly by global standards. While direct environmental mitigation itself was a small component of the accounting costs, environmental factors play a much larger role in the overall cost of the project by changing the nature of construction itself. For example, BVP (2018) explain that “the Environmental Impact Statement (EIS) required construction activities in Manhattan to take place in the subterranean realm, with almost all equipment and spoils transitioning through the 63rd Street tunnels to staging sites in Sunnyside Yards,” which typically meant “laborers filling burlap bags with spoils that were then loaded onto trains to Queens (or in some cases, the Bronx) and then unloaded and sorted by laborers.” The Metropolitan Transit Authority (MTA) estimated that it could have saved $75 million in schedule-related costs alone by deploying a simpler system similar to that used in other projects…Arguably, the conditions for tunneling in Manhattan are as difficult as anywhere in the world, although cost estimates for projects in London, which are all completely underground, are only one-third as high as those in New York City. Labor costs are higher in the United States than elsewhere, and especially so in New York City, but this reflects institutions as well as generally high labor costs. The Bureau of Labor Statistics (BLS) reports that in May 2020, the median hourly wage for electricians in the New York City metropolitan area was $36.13, and the mean was $40.48.9 BVP (2018) report a minimum hourly wage for electricians of $65 on the East Side Access project and an added $62 dollars per hour in benefits, making the per-unit labor cost for the project a multiple of the prevailing wage….”

Why does it cost so much to build infrastructure in the U.S.?“…Beyond accounting, there are three deeper explanations for why infrastructure costs are so high in the United States. First, it is possible that conditions are more demanding and that raw materials and labor are more expensive than elsewhere. This explanation suggests that higher costs are unavoidable but should be considered in discussions of optimal infrastructure policy, since higher costs are a good reason to build less infrastructure. Second, it is possible that the agencies charged with building infrastructure are poorly designed to manage costs. In this case, there may be changes to infrastructure building practices that could lower costs and stretch infrastructure budgets. Third, it is possible that external factors, especially the threat of litigation or political backlash, lead to expensive forms of mitigation, which change the nature and cost of building projects(Altshuler and Luberoff 2005)…”

Evidence from the GI Bill passed after the 2001 attacks finds that the money did expanded both college enrollment and BA completion, but reduced average annual earnings particularly for individuals with lower test scores and fewer occupational skills who the opportunity costs of enrolling in college as opposed to working is high. Results suggest talent is the limiting constraint, "...For the full set of Army veterans separating during this period,we find positive impacts on educational attainment but negative impacts to earnings. This result suggests that the housing subsidies are so generous as to bedrawing veterans into education and out of the labor market - even for veterans with low marginal returns to education.We explore whether this negative impact is driven by changes in college type, heterogeneity in the returns to college, or reductions in labor force experience. Our results suggest that some combination of lost experience and enrollment choices of veterans from military occupations that have lower predicted returns explains some of the effect. Veterans who were already enrolled when the new policy arrives see very modest degree attainment gains and suggestive evidence of a negative effect on earnings seven years after initial enrollment....Overall, our results suggest caution in providing education subsidies of this generosity to non-traditional students. While educational attainment increased modestly as a result of the benefit expansion, subsequent earnings did not. Furthermore, our analyses suggest that it is unlikely that earnings will ever grow enough to recover the earnings lost during the first seven years following separation….”

Note the negative earnings could be driven by the low value add of many of the schools (the for profit education sector)

Bottomline:“…We find that the PGIB raises the educational attainment of veterans at both the B.A. and A.A. levels. Considering the “overall” impacts of PGIB, the policy raised B.A. attainment by 2 percentage points and associate’s degree attainment by 0.4 percentage points. The impacts for already enrolled veterans are smaller in magnitude and may be zero. These impacts are not large given the generosity of the program and the responses found in studies of earlier GI Bills. Angrist (1993) found that veteran’s benefits on average raised schooling by 1.4 years. We are finding average increases of 2 months!Angrist and Chen (2011) find that veteran status (appropriately instrumented) raises average years of schooling by 0.24 to 0.27 years, raises B.A. attainment by 5 percentage points and associate’s attainment by 6 to 9 percentage points. Despite the large benefit amounts, PGIB delivers smaller educational gains than earlier GI Bills. Given the increase in bachelors attainment of 2.75 percentage points and an average increase in benefits of $8600, this implies a cost per additional bachelor’s degree of around $313,000. This calculation uses the differences in differences estimates. If we instead use the geographic variation to estimate impacts of total benefits used and increased bachelors attainment from a $1000 increase in annual benefits offered, we obtain $1460/.0046 = $317,000 per additional bachelor’s degree. These estimates are on the higher end of the range of estimates summarized by Dynarski (2003) and Dynarksi Hyman Schazenbach (2013). Our most remarkable findings are the negative impacts on labor income for the sample of veterans exposed to the program. Seven years after their separation from the Army, the PGIB lowered wages by about 1.8 percentage points. There are numerous possible explanations for this finding. One possibility is that this estimate constitutes a medium-term effect caused by the reduced labor market experience reflective of the schooling gains that we find. This is not our preferred explanation because the negative impacts survive all specification checks in which we control for years of experience, and the additional months of schooling are small relative to the average reduction in earnings. Our hypothesis is that the negative earnings returns stem from (a) the low value-added of many of the schools chosen by veterans under the PGIB combined with (b) the generosity of the BAH, which may be inducing veterans to forego valuable labor market human capital accumulation in favor of very marginal school enrollments.By pursuing schooling opportunities of marginal value, veterans may be missing out on opportunities to build occupation- or firm-specific human capital or to immediately put their Army-taught skills to work in the labor force. Admittedly, this is a more complex version of the experience story, rather than a rejection of the experience hypothesis. Our findings motivate ongoing work such as Barr et al, et al. (2019), who are providing additional information to separating veterans about the earnings returns to various frequently attended colleges and chosen majors. The contrast between PGIB impacts and that of earlier GI Bills may be driven by the changing composition of the Army. The Korea and Vietnam era GI Bills studied by Angrist and Chen (2011) involved conscription, which likely included large groups of young men who were well suited to college-going and further education. The PGIB was implemented in an all-volunteer Army, and so is offered to a group of young women and men who have already chosen the military over college after leaving high school…”

Dataset: "...We use detailed administrative data for a large set of individuals to examine the effect of increased benefits for higher education. We merge the full set of U.S. Army veterans separating between 1994 and 2016 with National Student Clearinghouse (NSC) data and population-based administrative tax return data. The NSC provides measures of college-going and degree attainment. Information from the 1098-T tax form provides supplementary information on college-going. The 1098-Ts are particularly useful because they have complete coverage of forprofit institutions while the NSC does not. Tax return data also contain information on the utilization of federal tax benefits for higher education, labor income, total income, and demographic characteristics such as marital status and family size. We supplement these with third-party reported information on labor income from Forms W-2 and 1099-MISC. These information returns provide individual-level measures of earnings that are available even if an individual does not file a tax return. Because these administrative tax records are available through 2017, we are able to track income and demographic outcomes for eight or more years after separation from the Army and initial enrollment in college..."
Evidence Related To Enrolment:"...Figure 6 uses “ever enrolled” as the outcome, as measured using NSC data, and plots the coefficients on the interactions between eligibility status and separation year. The estimate for the 2009 cohort is that PGIB raised enrollment by 5 percentage points relative to the 2002 cohort. In Figure 7, we perform a similar exercise using data from Form 1098-T to measure enrollment across various windows post-separation. Using the largest window (within 6 years of separation), we again find enrollment impacts of about 5 percentage points for the 2009 cohort. In addition to confirming the estimates from the NSC data, this figure provides further support for treating the 2007 and prior cohorts as being in the pre-period. The vertical line in each panel indicate the first cohort whose enrollment could have been affected by the higher benefit levels. For example, in panel (a), enrollment within a year of separation could have been affected for those separating in 2008 (as the benefits were available in fall 2009), but would not have been affected for those separating in 2007. The figure illustrates that even though the 2007 and prior cohorts were eligible for the PGIB benefits when they became available in 2009, there was no effect on their decision to enroll. This is consistent with the general pattern of post-separation enrollment and benefit use which tends to occur quickly after separation or not at all…”

“…Figure 19 presents our standard event study for labor income measured seven years after separation, illustrating a reduction in labor income of about $1,000 for cohorts separating in 2008 and after. In Table 7, we show PGIB’s impacts on wages, log wages, labor income, log labor income, and indicator variables for positive earnings and labor income, each measured seven years after separation from the Army. The PGIB has consistently negative impacts on wages and labor income for veterans. In Panel A, the PBIG reduces annual earnings seven years after separation by about $900. In the log wages specification (which exclude zeroes), the negative impact is roughly 2 percentage points. The impacts of the PGIB on whether a veteran “has W-2 wages” is small, negative and not statistically significant…”

Andrew Barr, Laura Kawano, Bruce Sacercote, WIlliam Skimmyhorn and Michael Stevens, "You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings" National Bureau Of Economic Research, July 2021, https://www.nber.org/papers/w29024

Tracy Alloway, "One Simple (Political) Reason to Explain Bond Yields," Bloomberg, July 21, 2021, https://www.bloomberg.com/news/articles/2021-07-21/one-simple-political-reason-to-explain-bond-yields

One Simple (Political) Reason to Explain Bond Yields

Joe and I have pointed out before that on a longer-term chart the move lower in U.S. Treasury yields doesn’t look so odd.

The Bear Traps report points out that higher yields as politicians coalesce around an agenda isn’t necessarily a new dynamic. In fact they say the yield curve has a history of steepening as control of the U.S. government shifts from two parties to one party. That’s exactly what happened in late 2016, when President Donald Trump won the presidency with Republicans in control of both the Senate and the House of Representatives.

You can see the dynamic from the (rough) chart below, which shows bond yields moving up when there’s a big spike in Republican or Democrat confidence, according to the Michigan survey of consumers (which I’ve used as a proxy for the parties securing greater control). Of course, the opposite move can also happen when a party loses control — with bond yields heading lower after the GOP lost control of Congress in late 2018.

“As you move from two-party to one-party control, the curve steepens (higher long-end yields) as global investors digest the political risk of having one party with their hands on the purse strings,” Bear Traps wrote in its report published on Tuesday. “In the summer of 2017, tax cuts were deemed ‘dead’— bond yields crashed, by November/December of that year, the GOP finally delivered of the fiscal juice, and the curve re-steepened.”

"...The world’s unicorn herd is multiplying at a clip that is more rabbit-like. The number of such firms has grown from a dozen eight years ago to more than 750, worth a combined $2.4trn. In the first six months of 2021 technology startups raised nearly $300bn globally, almost as much as in the whole of 2020. That money helped add 136 new unicorns between April and June alone, a quarterly record, according to cb Insights, a data provider. Compared with the same period last year the number of funding rounds above $100m tripled, to 390. A lot of this helped fatten older members of the herd: all but four of the 34 that now boast valuations of $10bn or more have received new investments since the start of 2020....The torrent of cash can be explained by two factors. The first is a divestment spree by the startups’ early venture-capital (vc) backers. These stakes command top dollar from investors desperate for exposure to the pandemic-era digitisation wave. Exits, via public listings and acquisitions, more than doubled globally year on year, to nearly 3,000. The proceeds are flowing back into new vc funds, which have so far this year raised $74bn in America alone, nearing the record $81bn in 2020 in half the time. The venture capitalists cannot spend the dough fast enough. In the three months to June Tiger Global, a particularly aggressive New York investment firm, made 1.3 deals on average every business day. The second reason for soaring valuations is more competition among investors. Relative newcomers to tech-investing, such as pension funds, sovereign-wealth funds and family offices, are encroaching on the private markets that used to be dominated by vc firms from Sand Hill Road in Palo Alto. In the past quarter “non-traditional” investors in America took part in nearly 1,800 deals that together raised $57bn. Many may have been encouraged by the success of earlier forays by dabblers from outside the vc world. Their annual returns from exited investments in a first round of financing have averaged 30% in the past decade, reckons PitchBook, another data firm. That is more than double the 10-15% for veteran vcs...."

Economist Staff, "Technology unicorns are growing at a record clip,"The Economist, July 22, 2021, https://www.economist.com/business/2021/07/19/technology-unicorns-are-growing-at-a-record-clip

Technology unicorns are growing at a record clip

Aileen lee, a venture capitalist who founded an investment firm called Cowboy Ventures, coined the term “unicorn” in 2013 to refer to what was then a rare, mythical species: privately held startups valued at $1bn or more. Any magical attributes aside, today they are commonplace—and becoming ever more so. Consumers, who stand to benefit from an array of novel, often cheap products and services, can expect to enjoy the ride. Investors betting on the unicorn derby should tread more carefully.

The world’s unicorn herd is multiplying at a clip that is more rabbit-like. The number of such firms has grown from a dozen eight years ago to more than 750, worth a combined $2.4trn. In the first six months of 2021 technology startups raised nearly $300bn globally, almost as much as in the whole of 2020. That money helped add 136 new unicorns between April and June alone, a quarterly record, according to cb Insights, a data provider. Compared with the same period last year the number of funding rounds above $100m tripled, to 390. A lot of this helped fatten older members of the herd: all but four of the 34 that now boast valuations of $10bn or more have received new investments since the start of 2020.

The latest tech darlings are no longer primarily Uber-esque marketplaces for matching services with consumers. Instead, they offer, or are developing, sophisticated products, often in more niche markets. Some 25% of the funding in the second quarter went to financial-technology firms, with lots also flowing into artificial intelligence, digital health and cyber-security (see chart).

The torrent of cash can be explained by two factors. The first is a divestment spree by the startups’ early venture-capital (vc) backers. These stakes command top dollar from investors desperate for exposure to the pandemic-era digitisation wave. Exits, via public listings and acquisitions, more than doubled globally year on year, to nearly 3,000. The proceeds are flowing back into new vc funds, which have so far this year raised $74bn in America alone, nearing the record $81bn in 2020 in half the time. The venture capitalists cannot spend the dough fast enough. In the three months to June Tiger Global, a particularly aggressive New York investment firm, made 1.3 deals on average every business day.

The second reason for soaring valuations is more competition among investors. Relative newcomers to tech-investing, such as pension funds, sovereign-wealth funds and family offices, are encroaching on the private markets that used to be dominated by vc firms from Sand Hill Road in Palo Alto. In the past quarter “non-traditional” investors in America took part in nearly 1,800 deals that together raised $57bn. Many may have been encouraged by the success of earlier forays by dabblers from outside the vc world. Their annual returns from exited investments in a first round of financing have averaged 30% in the past decade, reckons PitchBook, another data firm. That is more than double the 10-15% for veteran vcs.

This winning streak may yet end in tears. That is what happened two years ago, when richly valued firms with shaky business models either fizzled after their initial public offerings (like Uber and Lyft, two ride-hailing rivals) or never got that far (WeWork, an office-rental firm whose flotation was shelved after investors got cold feet). Many recently listed unicorns continue to bleed cash. According to The Economist’s calculations, those that went public in 2021 made a combined loss of $25bn in their latest financial year.

Assessing whether the remaining ones are worth their lofty valuations looks harder than ever. Like their predecessors, they do not disclose financial results. At the same time, extrapolating from the earlier unicorns, which tended to pursue growth at all costs in winner-takes-all markets, offers little help because today’s lot often aim to capture good margins by selling genuinely unique technology. This could be a more sustainable strategy—if the technology works. But it is harder for non-experts to evaluate, especially based on what is often little more than a prototype. Nikola and Lordstown, two electric-vehicle companies that listed in 2020 via reverse mergers with special-purpose acquisition companies (spacs), are under investigation by American authorities over allegedly exaggerating the viability of their technology.

Another risk comes from politics. Authorities around the world are growing warier of letting tech firms get too big or entering regulated markets such as finance or health care. As part of a broader crackdown against big tech firms China’s government recently sabotaged the operations of Didi, by banning its app from Chinese app stores days after the firm’s $68bn initial public offering in New York, ostensibly over misuse of users’ data. Such moves have chilled investors’ appetite for Chinese startups, funding for which has actually declined in the past two quarters. In America the Securities and Exchange Commission is beginning to scrutinise the use of cryptocurrencies. Many crypto-exchanges set investors’ pulses racing in last year’s bitcoin rush. Now the market capitalisation of Coinbase, one of the biggest, has shrunk by half, or $56bn, since peaking after its listing in April.

Investors, then, had better beware. For everyone else, the unicorn stampede is a boon. Because venture investments involve mostly equity and little debt, even flops such as WeWork or cautionary tales like Didi pose little risk to the financial system. So long as venture capital is bankrolling lossmaking startups while they offer subsidised services or develop clever new products, consumers have no reason to look the gift horned horse in the mouth.