Macro Roundup

Economists are mainly focusing on the surge of new firms in America. But the trends are wider. Using data for a range of rich countries we estimate that in the fourth quarter of 2021 the number of “enterprise entries”—ie, newly formed companies—was 15% higher than the average before the pandemic (see chart). An extra 1m or so firms have sprung to life across the oecd group of mostly rich countries since the first lockdowns, compared with the pace of business creation before 2020...In America during the 2010s the share of people who worked for large companies (ie, those with more than 1,000 employees) was rising. In 2021 it fell, with the proportion of people working for small firms moving up.Britain is experiencing similar trends. In Germany new business creation is slightly higher than it was in 2019. And in France the number of startups is about 70% higher than was usual before the pandemic....Three explanations for the startup boom stand out. The first relates to family finances...The second factor relates to economic reallocation...The third explanation is hard to measure, but could have the longest-lasting effects. The pandemic, by reminding people that life is short, may have encouraged them to take more risks. It would not be the first time. In America from 1918, after the first world war had ended and the Spanish flu epidemic had faded, an even bigger startup boom began, as more people plucked up the courage to set out alone.

Economist Staff, "All over the rich world, new businesses are springing to life,"The Economist, April 23, 2022, https://www.economist.com/finance-and-economics/2022/04/23/all-over-the-rich-world-new-businesses-are-springing-to-life

All over the rich world, new businesses are springing to life

The lasting effects of the covid-19 pandemic on the economy are starting to become clear. Surveys suggest that Americans who can work from home are likely to do so for two or three days a week in the post-covid world, compared with hardly at all in 2019. Companies have regained their appetite for capital spending. And the pandemic appears to be provoking a shift towards higher levels of entrepreneurship around the rich world.

Economists are mainly focusing on the surge of new firms in America. But the trends are wider. Using data for a range of rich countries we estimate that in the fourth quarter of 2021 the number of “enterprise entries”—ie, newly formed companies—was 15% higher than the average before the pandemic (see chart). An extra 1m or so firms have sprung to life across the oecd group of mostly rich countries since the first lockdowns, compared with the pace of business creation before 2020.

Some of these new firms are in glamorous industries. Caroline Girvan incorporated her fitness business in Northern Ireland in October 2020. (Her at-home videos, which your correspondent has discovered are impossibly difficult to keep up with, have racked up more than 250m views.) With global venture capital booming, startups from Triple Whale (e-commerce) in Columbus, Ohio, to Payrails (fintech) in Berlin are receiving lots of investment. Yet most of the companies set up during the pandemic have nothing to do with Silicon Valley or its pretenders. They are construction firms, consultancies and the like.

More entrepreneurship is likely to be good for the economy. New businesses try out fresh ideas and ways of doing things, while drawing capital and people away from firms that are stuck in their ways. Many economists draw links between the low rate of entrepreneurship after the financial crisis of 2007-09 and the weak productivity growth of the 2010s. In addition, a recovery with lots of startups tends to create more jobs, since young firms typically seek to expand and thus hire new staff.

Three explanations for the startup boom stand out. The first relates to family finances. From about 2017 onwards labour markets in the rich world noticeably strengthened, putting money in workers’ pockets. With a financial cushion in place, people may have felt comfortable trying something new—which might explain why business creation picked up shortly before the pandemic. Then governments plumped the cushion considerably, as they handed out vast amounts of cash via stimulus cheques or furlough schemes in 2020 and 2021. At the same time, people cut back on spending. The result was a huge rise in saving, and an acceleration in startups.

The second factor relates to economic reallocation. The pandemic has prompted profound changes in consumption habits, meaning that demand has shifted across both geographies and industries. City centres are less busy than suburbs, while industries favoured by social distancing—online retail, for instance—remain more popular than activities that require in-person attendance. Entrepreneurs are responding. In France the number of hospitality startups is 22% below its pre-pandemic level, but those in the information and communication sector are up by 26%.

The third explanation is hard to measure, but could have the longest-lasting effects. The pandemic, by reminding people that life is short, may have encouraged them to take more risks. It would not be the first time. In America from 1918, after the first world war had ended and the Spanish flu epidemic had faded, an even bigger startup boom began, as more people plucked up the courage to set out alone.

The empirical evidence presented in this article suggests that VC financing is positively associated with firm innovation and growth in the United States... Figure 6 displays the shares of patents and patents adjusted by the quality (proxied by citations) of VC-backed companies. It confirms the increasing importance of VC-backed companies for innovation...Regression analysis is now conducted to evaluate the performance of VC-backed and non-VCbacked firms along four dimensions for the years following an initial public offering (IPO) of stock: the R&D-to-sales ratio, growth rate of employment, growth rate of sales, and firm market value (in natural logarithm). The results are presented in Table 1. The regressions are based on U.S. public companies between 1970 and 2014. To compare VC-backed companies with their non-VC-backed counterparts, a VC dummy is entered as an independent variable that takes the value of 1 if the company is funded by VC before its IPO. In all regressions, industry dummies, year dummies, and a year dummy for the IPO are included. In addition, a cross term is added between the VC dummy and the number of years since the firm’s IPO. As shown in the first row of Table 1, VC-backed companies are more R&D intensive and grow faster than their non-VC-backed counterparts. On average the R&D-to-sales ratio of a public VCbacked company is higher than its non-VC-backed counterpart by 5.2 percentage points, and it grows faster—by 4.9 percentage points in terms of employment and 7.0 percentage points in terms of sales. These superior performances translate into higher market values: VC-backed companies are valued 37.3 percent higher than their non-VC-backed counterparts. The difference in performance, however, gradually dwindles over time, as shown by the negative estimates of the regression coefficients in the second row. As a consequence, the performances of VC- and non-VC-backed public companies tend to converge in the long run, though the speed of convergence is fairly low, as revealed by the magnitudes of the estimates in the second row....As demonstrated in Table 3, increasing VC funding in an industry in a given year is associated with a higher growth rate of employment and sales in the subsequent three years. According to IV regressions (2) and (4), a one-standard-deviation increase in logged industry-level VC funding is associated with increases of 1.3 percentage points and 1.9 percentage points in annual employment and sales growth, respectively, following funding.

Jeremy Greenwood, Pengfei Han, and Juan Sánchez, "Venture Capital: A Catalyst for Innovation and Growth,"Federal Reserve Bank Of St. Louis, April 2022, https://files.stlouisfed.org/files/htdocs/publications/review/2022/04/06/venture-capital-a-catalyst-for-innovation-and-growth.pdf

Figure 1 displays the trends in the share of children living in with married parents by parental income quintiles. The results from 1979 to 2018 are similar to those of Reeves and Pulliam over the same years. Living with married parents become less common among children in all five income categories, but this change was especially pronounced in the bottom two-fifths...Within the second-poorest fifth, 88 percent of children lived with married parents in 1967. The share reached a low of 54 percent in 2012 but was still less than 56 percent in 2020. The share of bottom-fifth children living with married parents was especially low in 1967 -just 63 percent. That fell to 36 percent in 2020; barely a third of the poorest children today live with two married parents...the low point was actually 1992. Since that low of 31 percent, the share of the poorest children living with married parents has risen 5 percentage points. In fact, it rose 6.8 points between 1997 and 2000 before falling before 2002 and 2003...in Figure 4, I provide the same trends by parental educational groups to complete the analyses.While living with married parents did not become more common after 1992 among children with the lowest-educated parents, the share of living with two parents did rise. After falling from 78 percent in 1976 to 60 percent in 1992, it rose to 64 percent in 2020. That was higher than in every year between 1983 and 1999. The increase was concentrated between 1992 and 2001.…. A Hypothesis: Welfare Reform?...Trends in the share of disadvantage children living with married parents or two parents generally both shifted after 1992

Scott Winship, "A Half Century Decline In Marriage...That Ended 30 Years Ago For Disadvantage Kids,"American Enterprise Institute, April 2022, https://www.aei.org/wp-content/uploads/2022/04/Half-Century-Decline-in-Marriage-.-.-.-That-Ended-30-Years-Ago-for-Disadvantaged-Kids.pdf

Women...now make up 44% of college-educated workers in science, technology, engineering, and mathematics (STEM) occupations. However, a closer look at the underlying data reveals divergent patterns of gender (in)equality within STEM. Men continue to make up the vast majority of degree recipients in STEM fields that are spatially and mathematically intensive — geoscience, engineering, economics, mathematics, computer science, and physical science — while women are overrepresented in life sciences, psychology, and social sciences....In 2017, 49% of all bachelor’s degrees in science and engineering were awarded to women. This number masks substantial gender disparities across fields, however. Women earned two-thirds of bachelor’s degrees in life sciences, psychology and social science fields — 62% in biology, 78% in psychology, and 61% in social science. In contrast,women were only slightly over one quarter of degree recipients in the combined math-intensive fields of geoscience (39%), engineering (22%), economics (32%), mathematics (42%), computer science (19%), and the physical sciences (40%)....Gender disparities within science, technology, engineering, and mathematics bachelor’s degrees have become more pronounced since 2000...Gender differences carry over from education to the STEM workforce.

A leaky pipeline reduces the share of women rising up the ranks of career hierarchy in academia. Women were 45% of junior full-time faculty but only 33% of senior full-time faculty in 2019. However, the pipeline leaks more in fields where women are overrepresented in the lower ranks. In psychology, for instance, the share of women was 68% among junior full-time faculty and 53% among senior faculty, a 15 percentage-point difference. With a 6 percentage-point difference, pipeline leakage is smallest in computer science: the share of women was 25% among junior full-time computer science faculty and 19% among senior faculty. What explains the differences in pipeline leakage by field? It is possible that the smaller number of women who choose to enter the male-dominated fields may be highly positively selected — especially motivated — compared to those who enter the more female-friendly fields. This is consistent with research showing that a large number of female entrants into STEM fields has a negative impact on gender equality in that field in terms of career outcomes. In psychology and the social sciences, it may be that the higher degree of female representation in the lower ranks indicates a shift in the preferences of men: the number of junior male faculty has declined since 1981. This may be due to occupational “tipping”, whereby men start to exit an occupation when the fraction of women in that occupation rises above a certain threshold.

What this Means:

As a smaller proportion of women enter geoscience, engineering, economics, mathematics, computer science, and physical science at the undergraduate level, the career pipeline in these fields will become even smaller. Less gender diversity could be detrimental to the quality and relevance of knowledge generated in these fields, by narrowing the range of ideas generated and reducing success in problem solving. We should then expect the same outcome in life sciences, psychology and social sciences if women continue to saturate them while men exit. A widening of the uneven gender distribution between STEM fields helps perpetuate the gender pay gap. This, in turn, can have negative consequences for gender equality outside the labor market if, for example, lower female-male wage ratios erode women’s decision-making power in the family and reduce women’s well-being.

The share of women holding academic positions has increased substantially but also varies greatly by field. In 2019, women comprised 39% of all academic positions held by science, engineering and health doctorate holders at 2- and 4-year colleges or universities. This reflects a substantial increase from just 9% in 1973 and 22% in 1993. Gender gaps vary noticeably by fields. Women held only 19% of academic positions in engineering, 26% in physical and geosciences, 26% in mathematics and statistics, and 23% in computer science. This is in contrast to 60% of positions in psychology, 47% in life sciences, and 42% in social sciences including economics. Among academic position types, women are more likely than men to hold “other” full-time positions (instructor, adjunct, lecturer, research associate, administrator) as well as post-doc and part-time positions.

The STEM fields in which women are underrepresented typically pay higher salaries than fields with high shares of women. For example, the median salary in 2019 for full-time workers holding the highest degree in their field was $105,000 for computer scientists and $100,000 for engineers, both male-dominated occupations. Among female-heavy occupations, median salary was $74,000 for psychologists and $64,000 for biological, agricultural and environmental scientists.

Gender differences carry over from education to the STEM workforce. While the college-educated STEM workforce had almost reached gender parity in 2019, with a female share of 44%, large gender gaps remain between types of STEM jobs. Although only 29% of the science and engineering (S&E) workforce in 2019 was female, women were 57% of S&E-related workers (e.g. nurse, dental hygienist). The representation of women coincides with the patterns of gender balance in STEM degrees. Women scientists and engineers comprise 48% of biological, agricultural and environmental life scientists, 81% of psychologists, and 64% of social scientists; and much smaller shares of geoscientists (30%), engineers (16%), economists (24%), mathematical scientists (42%), computer scientists (24%), and physical scientists (35%). Even within the S&E-related workforce, women comprise 70% of health-related occupations — such as nurses, pharmacists, and physician assistants — but only 21% of computer and information systems managers.

Female attrition along the STEM educational hierarchy is driven by the very disciplines that, paradoxically, seem to attract large numbers of women at the entry level. In general, women earned a smaller share of doctoral degrees than bachelor’s degrees in 2017, reflecting the well-known leaky pipeline in women’s progress up the career hierarchy. In STEM fields, the pipeline is leakiest in life science, psychology and social science fields, which are female-dominated at the undergraduate level — the female share of degree recipients in these fields was 58% at the doctoral level compared with 66% at the bachelor’s level in 2017. In contrast, the four fields with the lowest female shares among bachelor’s degrees recipients — geoscience, engineering, economics, and computer science — have higher female representation among PhD recipients (see here).

Gender disparities within science, technology, engineering, and mathematics bachelor’s degrees have become more pronounced since 2000. The number of women earning bachelor’s degrees in life sciences, psychology and social sciences grew faster than the number of men since 2000. These fields, which started out with high female shares at the turn of the century, became even more disproportionately represented by women at the undergraduate level. Similarly, even as the numbers of women in many of the STEM fields that started out with a disproportionate representation of men have been increasing, the number of men earning bachelor’s degrees in several of these fields grew faster than the number of women — this trend was most pronounced in computer science and mathematics, while engineering was an exception (see chart).

While women earn close to half of all STEM bachelor’s degrees, there is wide variation in women’s representation across fields. In 2017, 49% of all bachelor’s degrees in science and engineering were awarded to women. This number masks substantial gender disparities across fields, however. Women earned two-thirds of bachelor’s degrees in life sciences, psychology and social science fields — 62% in biology, 78% in psychology, and 61% in social science. In contrast, women were only slightly over one quarter of degree recipients in the combined math-intensive fields of geoscience (39%), engineering (22%), economics (32%), mathematics (42%), computer science (19%), and the physical sciences (40%).

Prathi Seneviratne, "Are Women Reaching Parity with Men in STEM?"Econofact, April 15, 2022, https://econofact.org/are-women-reaching-parity-with-men-in-stem

Are Women Reaching Parity with Men in STEM?

Women have made tremendous gains in education and now make up 44% of college-educated workers in science, technology, engineering, and mathematics (STEM) occupations. However, a closer look at the underlying data reveals divergent patterns of gender (in)equality within STEM. Men continue to make up the vast majority of degree recipients in STEM fields that are spatially and mathematically intensive — geoscience, engineering, economics, mathematics, computer science, and physical science — while women are overrepresented in life sciences, psychology, and social sciences. If anything, these gender disparities appear to be widening slightly over time at the undergraduate level, with potential implications for gender differences in employment, pay, academia, and beyond.

The Facts:

Women have made dramatic inroads in education since the 1990s. From making up less than a third of the college-educated workforce in the early 1990s, women have come to surpass men — making up 52% of the workforce with at least a college degree in 2019. The representation of college-educated women in STEM occupations has also been trending upwards since the early 1990s.

Credit spreads produced non-overlapping confidence intervals in predicting future returns for two-thirds of the asset classes we tested—a remarkable degree of statistical power....Below we show the distribution of returns to the Fama-French value factor relative to changes in credit spreads. The value factor works well when spreads are falling and poorly when spreads are rising. The cheap cyclical stocks captured by the value factor are heavily impacted by changes in credit conditions. Notably, this is the opposite pattern shown by 10-year Treasurys, which is why the two asset classes serve as good complements in a business-cycle based strategy. We can see another interesting relationship with the Fama-French size factor. The size factor delivers higher one-month forward real returns when credit spreads are above their 10-year median. The size premium appears to work better when the price of liquidity is more costly, when spreads are above median, and when liquidity is widely available, the returns to investing in small-cap stocks are lower.These confidence interval tests are confirming basic economic intuition.

Johann Colloredo-Mansfeld, "Confidence,"Verdad, April 2022, https://mailchi.mp/verdadcap/confidence

Confidence

Every day investors are greeted with a stream of headlines proclaiming what is special about this moment in market history. Some narratives from the past 12 months have included Evergrande, supply-side inflation, demand-side inflation, the Federal Reserve raising rates, and most recently Russia’s invasion of Ukraine.

These events, of course, are important and newsworthy, but how can they inform an investment strategy and generate actionable signals for investors? How can we separate the signal from the noise and contextualize each current news event within a framework that has stood the test of time?

When we’re looking at stocks, we make sense of the options by using ranking models based on cross-sectional analysis. We look back at what factors (size, value, etc.) would have ranked stocks effectively in the past and then look at which stocks best fit those criteria today.

But cross-sectional analysis is not very well suited to parsing narratives and understanding how asset classes are priced relative to economic conditions. To understand whether a signal is robust, we look not only for whether the signal produces high or low returns in a given asset class but also for the range of potential outcomes. We want to make sure that the signal produces non-overlapping confidence intervals, which is a more difficult test of the signal’s predictive strength.

The best way to understand this test is visually, with examples. Compare, for example, how changes in credit spreads predict the future returns of 10-year Treasurys (left-hand side of Figure 1) to how changes in the Federal Reserve rate predict the future returns of 10-year Treasurys (right-hand side of Figure 1).

We have found that the level and direction of credit spreads are a powerful macroeconomic indicator. Below, we show a table that looks at stocks (divided by factor), bonds (corporate and government, divided by duration and credit rating), and commodities from 1975 to 2022 and whether credit spreads produced non-overlapping confidence intervals in predicting one-month future returns. The green highlights indicate assets for which a given indicator has been reliably predictive at the one-month horizon.

We wanted to highlight a few of these relationships so you can understand why we spend so much time talking about credit spreads. Below we show the distribution of returns to the Fama-French value factor relative to changes in credit spreads.

We can see another interesting relationship with the Fama-French size factor. The size factor delivers higher one-month forward real returns when credit spreads are above their 10-year median.

We find running these types of analyses to be essential to separating real signals from noise. On Google trends, “interest rates” has ~90x more search interest than “credit spreads.” For all the mindshare that interest rate hikes are currently taking up, we don’t see much predictive signal in this noise. So, just as Warren Buffett cuts wheat from chaff by reading annual reports in the bathtub, we give closest attention to credit spreads to gauge the relative attractiveness of asset classes.

Figure 1:Corporate profits pre-tax, w/o IVA and CCAdj, in bn.$ SAAR (blue line, left log scale), and as share of GDP (bold red line, right scale).NBER defined recession dates peak-to-trough shaded gray. Source: BEA via FRED, NBER, and author’s calculations.

Menzie Chinn, "Corporate Profits, pre-tax, w/o IVA and CCAdj,"Econbrowser, April 16, 2022, http://econbrowser.com/archives/2022/04/corporate-profits-pre-tax-w-o-iva-and-ccadj

Corporate Profits, pre-tax, w/o IVA and CCAdj

10 seconds on FRED allows one to see a time series. 20 seconds in a stats package (or even Excel) allows one to see ahead of time if one is going to write something stupid.