“Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University

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Entrepreneurial Spillovers Across Coworkers

Melanie Wallskog Stanford University

Date Posted:: November 15, 2021
Is Database:: Database

Individuals Who Work With 1 Standard Deviation Higher Share Of Entrepreneurial Coworkers are 2.5pp More Likely To Become Entrepreneurs, An 8% Increase Relative To Mean.

Individuals exposed to a higher share of entrepreneurial coworkers are significantly more likely to become entrepreneurs themselves. Specifically, working with a one standard deviation (10 percentage points) higher share of entrepreneurial coworkers increases the likelihood of becoming an entrepreneur by 2.5 percentage points, representing an 8% increase relative to the average likelihood. This effect is amplified when coworkers are from successful entrepreneurial backgrounds, suggesting that the quality of entrepreneurial experience matters. The spillovers are most pronounced in sectors like accommodation and food services, indicating that these effects are not limited to high-tech industries. Overall, these spillovers contribute to a 3% increase in aggregate entrepreneurship, highlighting their potential impact on economic growth and innovation.

Melanie Wallskog, “Entrepreneurial Spillovers Across Coworkers,” Stanford University, November 11, 2021,
http://wallskog.su.domains/files/wallskog_jmp.pdf

Firm stats, “…Beyond firm survival, I explore other measures of firm success, including size, in terms of employment, payroll, and revenue, and revenue productivity. As shown in Table9, the patterns are generally similar to those for firm survival: individuals who work with more entrepreneurs tend to start “worse" firms, unless their entrepreneurial coworkers themselves were successful. For example, in column 1 of Panel A, I find that a one standard deviation (14.7 percentage point) increase in the share of coworkers who were recently entrepreneurs predicts 5.5% lower entry year employment. Yet, as column 2 shows, this pattern is offset if the entrepreneurial coworkers ran large firms | i.e., if their entrepreneurial firms were in the top 10% of entry year log employment, amongst firms that entered in the same year and industry: conditional on general exposure to entrepreneurial coworkers, a future entrepreneur with a one standard deviation (3.8 percentage point) higher share of coworkers who were entrepreneurs to particularly large firms has 4.3% higher entry year employment.As Panel B shows, these patterns persist with the inclusion of entrepreneurial firm industry fixed effects: individuals' future entrepreneurial success is predicted by their coworkers' success, even within the industry in which they start their firm. Table 9 shows similar patterns for the likelihood of a future entrepreneur's firm being in the top 10% of entry year employment, payroll, and revenue, relative to firms that enter in the same year and industry (where I measure entrepreneurial coworkers' successes by analogous measures). The one exception is in terms of revenue productivity, where on its own, general exposure to entrepreneurs predicts a marginally higher probability of starting a particularly productive firm (column 9), but this appears to be driven by the particularly productive coworkers (column 10). Conditional on exposure to entrepreneurial coworkers, a future entrepreneur who works with a one standard deviation (4.5 percentage point) higher share of coworkers who were entrepreneurs at particular high-productivity firms is on average 0.6 percentage points more likely to run a particularly productive firm, a 9.9% increase relative to the mean outcome.57These patterns suggest that what entrepreneurial coworkers teach individuals depends on the experiences of the coworkers, with successful entrepreneurs having a greater capacity to improve future entrepreneurs' prospects. In the remainder of this section, I consider additional outcomes that complement these standard measures and provide additional insight into the mechanisms of these spillovers….”

By Sector, “…Sector of individual's establishment Because workplaces and entrepreneurship patterns vary by industry, it is plausible that spillovers may vary dramatically across sectors.45 In fact, most sectors have similar coefficients to the aggregate coefficient, with some exceptions. Figure5shows the extensive margin spillovers by the sector of the individual's current establishment, estimated in a single regression by interacting the share of coworkers who were recently entrepreneurs with indicators for each sector, while continuing to include industry fixed effects that control for baseline differences in future entrepreneurship rates. There appears to be few spillovers for workers in the agriculture, utilities, and health sectors (which likely have high entry costs due to regulation) but substantial spillovers in the management and accommodation and food services sectors.46The fact that spillovers exist in most sectors but are strongest in the accommodation and food service sector suggests two conclusions. First, these spillovers exist across the economy | these spillovers are commonplace, and are not driven by the culture or structure of a single sector. Second, because the spillovers are largest in the relatively low-technology accommodation and food services sectors, these spillovers are unlikely to be predominantly transmitting knowledge of complex technologies or promoting innovation..”

“…Table 3 presents the point estimates from model (4) as controls are gradually added. As the table shows, individuals who work with proportionally entrepreneurial coworkers are more likely to become entrepreneurs in the future, regardless of the inclusion of controls. As more controls are added, this relationship decreases marginally but remains relatively stable. In the full speciation (column 8), the coefficient on the share of coworkers with entrepreneurial experience is 0.025: this predicts that an individual whose entire set of coworkers have entrepreneurial experience is 2.5 percentage points more likely to become an entrepreneur themself, compared to an individual who works with no entrepreneurial coworkers.27Recall that only 3.1% of the sample become entrepreneurs subsequently, such that 2.5 percentage points is very large relative to 3.1%, suggesting an 80% increase relative to the mean. However, this interpretation may be misleading, since very few individuals work with all former entrepreneurs. Instead, consider an increase in one standard deviation: the estimated model predicts that individuals who work a one standard deviation (9.5 percentage points) higher share of entrepreneurial coworkers are 0.236 percentage points more likely to become entrepreneurs in the next five years. This gap is still large: a 0.236 percentage point increase in the predicted future entrepreneurship maps into a 7.6% increase relative to the mean;28 this increase is comparable to the prior findings in the literature. To provide a simple evaluation of the size of this estimate, I conduct a back-of-the-envelope calculation to approximate how much the spillovers boost aggregate entrepreneurship. I predict the number of “additional" future entrepreneurs that start firms in the presence of spillovers by multiplying the coefficient on the share with the mean share of coworkers with entrepreneurship experience (0.03356) and the number of individuals (46.68 million). This calculation yields a predicted additional 39,000 future entrepreneurs, which amounts to a 2.75% increase. While this back-of-the-envelope calculation is inherently simple, ignoring any general equilibrium forces or dynamics of how spillovers aggregate over time, it does suggest a meaningful role for spillovers: spillovers boost aggregate entrepreneurship by 3%...”

Key finding“…I find evidence of positive extensive margin spillovers: individuals who work with one standard deviation (10 percentage points) higher share of entrepreneurial coworkers are 2.5 percentage points more likely to become entrepreneurs themselves within the next five years, an 8% increase relative to the average likelihood…”

The evidence

“…Entrepreneurial coworkers It is additionally important to note that coworkers with recent entrepreneurial experience were not necessarily the most productive entrepreneurs: indeed, many of their firms no longer exist, and they are now primarily employed at a different firm. As discussed below,this likely shapes the lessons that these coworkers can teach. Table2documents summary statistics on the firm outcomes for coworkers and individuals who were entrepreneurs within the previous five years (1999-2003).The table shows the outcomes both in terms of the average coworkers (columns 1 and 2, i.e., average outcomes for coworkers who were previous entrepreneurs) and at the individual level (columns 3 and 4, i.e., average outcomes for individuals who were previous entrepreneurs). For this paper, the past outcomes of the average set of entrepreneurial coworkers (columns 1 and 2) describe the average “treatment" that individuals face in the workforce. The average past outcomes of previous entrepreneurs in general (columns 3 and 4, many of whom started their current firm) provide benchmarks for the success of the average set of entrepreneurial coworkers. Table 2 highlights that entrepreneurial coworkers generally started relatively unsuccessful (i.e., shortersurviving, smaller, and less productive) firms, although some were relatively successful. Less than half of individuals' entrepreneurial coworkers started _rms that survive to age 5, on average, while over 60% of all recent entrepreneurs' firm ssurvive that long. Alongside these higher exit rates, many entrepreneurial coworkers have since returned to being a standard worker: while around 52% of the recent entrepreneurs in general started their current firms, less than 9% of an individual's entrepreneurial coworkers are entrepreneurs of the current firms, on average. This means that many of these entrepreneurial coworkers have left the firm they started and now work at someone else's firm. Yet, some entrepreneurial coworkers started relatively successful firms; for instance, on average 15% of entrepreneurial coworkers started firms that were in the top 10% of entry year log employment, amongst firms that started in the same year and industry as them. Taken together, these summary statistics suggest that exposure to entrepreneurial coworkers is likely quite different from, e.g., mentorship through start-up accelerator programs: the average entrepreneurial coworker did not start a hyper-productive firms.However, the fact that there is heterogeneity in these entrepreneurial coworkers' outcomes | some individuals get “lucky" and work with successful former entrepreneurs | allows me to explore the roles of both exposure to more entrepreneurs and to more successful entrepreneurs in SectionsIIIandIVbelow….”

“..Despite the fact that future entrepreneurs are different from workers in general, they work and become entrepreneurs across the economy. Figure2shows the sectoral composition of all individuals and future entrepreneurs, plotting the shares of all individuals compared to the shares of future entrepreneurs employed in each 2004 primary firm sector (based on NAICS codes), along with the share of future entrepreneurs' firms' entry year sectors. As the figure shows, future entrepreneurs work in all industries in 2004 and start firms in all industries, though they disproportionately work and start firms in construction, professional/ scientific/technical services (e.g., R&D and law and accounting services), and accommodation and food services, and less often appear in manufacturing and health, compared to the general workforce. Nearly half of future entrepreneurs start firms in the same sector as their 2004 establishment….”

Workers vs. entrepreneurs, “…Entrepreneurs are quite different from the general labor force. Yet, entrepreneurs work and start firms in all sectors of the economy, making the potential scope of entrepreneurial spillovers large. For the 2004 sample, in Table1 I present entrepreneurial, demographic, job, and establishment characteristics of all individuals in 2004 and of those who become entrepreneurs between 2005 and 2009. Relative to the general population, future entrepreneurs tend to be young, male, educated, White and Asian, born outside the U.S., higher earning, and working at smaller, younger firms.They also tend to work with more entrepreneurial coworkers, which I explore more systematically in the remainder of the paper. While entrepreneurs tend to be younger than workers, there is a distinct inverse-U relationship between age and entrepreneurship, as shown in Figure 1. Entrepreneurship rates by age peak around age 35, with the share of individuals aged 35 who are entrepreneurs being nearly double that of individuals aged 20 or 70….”

Summery statistics, useful factoids

Core findings, “…In my extensive margin analysis, I estimate regression models of whether individuals who work with more former entrepreneurs in 2004 are more likely to become entrepreneurs themselves subsequently, between 2005 and 2009. I find evidence of positive spillovers: individuals who work with one standard deviation (about 10 percentage points) higher share of coworkers who were entrepreneurs in the past five years are 8% more likely to become entrepreneurs themselves in the next five years, relative to the average likelihood. Through a back of- the-envelope calculation, this pattern suggests that spillovers generate an additional 3% of entrepreneurs in the aggregate. While the average extensive margin spillover is large (8%, relative of the mean),there is substantial heterogeneity. The spillovers tend to be amplified by exposure to relatively more successful entrepreneurial coworkers (i.e., entrepreneurs whose firms were relatively large or productive). While spillovers exist in most sectors, they are largest in the accommodation and food service sector, suggesting that the aggregate evidence of spillovers are not driven by the “high tech" sector that previous literature has studied….Additionally, these spillovers are stronger for younger individuals and do not exist for near-retirement individuals, with individuals learning" the most from their relatively older coworkers. The spillovers are strongest across coworkers who may be more likely to regularly interact in the workplace or to form mentorship relationships, namely coworkers who earn similar wages or who are of the same sex or immigration status. Finally, spillovers generate new entrepreneurs: individuals who themselves have recent entrepreneurial experience are not more likely to become entrepreneurs after working with more entrepreneurial coworkers, consistent with these individuals having had their own experiences and thus little to learn from entrepreneurial coworkers….”

Her basic story, “…I study the entrepreneurial outcomes of the individuals who become entrepreneurs between 2005 and 2009 and find evidence of both spillovers of institutional knowledge and entrepreneurial skill. I estimate regressions models of whether an individual's future entrepreneurial firm's characteristics vary if they were exposed to more entrepreneurial coworkers in 2004. I find that individuals who work with more entrepreneurial coworkers tend to start firms that are smaller in both employment and sales and are less likely to survive, consistent with a net pattern of individuals on average simply being inspired or learning the institutional knowledge needed to start a firm, as this leads to less productive individuals choosing to become entrepreneurs. However, if the individuals' entrepreneurial coworkers ran larger or longer-surviving firms, the individuals are more likely to start firms that are larger and more likely to survive. These results suggest scope for some true productivity gains via entrepreneurial skill spillovers, if the spillovers are from particularly successful entrepreneurs…”

Controls that address your concern, “… Coworkers are not randomly assigned to individuals, creating identification concerns, despite the rich set of controls included in the baseline specifications. For instance, entrepreneurship-prone individuals may cluster at firms or establishments for a variety or reasons not captured by the observable controls, such that the spillovers I measure may not be evidence of learning from coworkers. Instead, these spillovers may reflect firm or establishment effects, consistent with the literature of entrepreneurs “spawning" from their employers. Or, these spillovers may reflect pure spurious correlation, if these entrepreneurship-prone individuals work at firms for reasons entirely unrelated to entrepreneurship, for instance if they are also more educated. Additionally, individuals living and working in particular locations or industries may experience common business shocks that make entrepreneurship more or less attractive. I address these identification concerns through several robustness analyses. First, I show that these spillovers do not reflect entrepreneurial individuals simply clustering at certain firms or establishments. Instead, individuals disproportionately appear to learn from their true coworkers other workers at their establishment as opposed to other workers at the same firm but at other establishments or workers who work at the same establishment either before they joined or after they leave.Second, I show that these spillovers cannot be fully accounted for by selection into having specific coworkers; the results are not driven by individuals seeking out entrepreneurial coworkers. Instead, spillovers are also apparent from coworkers who joined an individual's establishment after they joined, whom the individual should not have been able to select on when choosing to join the firm. Finally, spillovers are not driven by local or industry common shocks, as the patterns persist with the inclusion of additional location and industry fixed effects. To further bolster a causal interpretation of the spillovers, I present direct survey evidence of entrepreneurial spillovers across coworkers. By linking individuals who become entrepreneurs to firm owners in a Census survey, I analyze reported motivations for these individuals' entrepreneurship. I find that the individuals who previously worked with more former entrepreneurs are more likely to report that they had an entrepreneurial role model who led them to start a firm. In other words, the individuals who I predict become entrepreneurs because of their exposure to entrepreneurial coworkers are more likely to report having an entrepreneurial role model, consistent with these spillovers actually existing…”

She doesn’t explicitly address that question but she does implicitly address it (in her words her goal)“…I find that if an individual interacts more with former entrepreneurs, then they are more likely to become an entrepreneur subsequently. I compare the future entrepreneurship of individuals who work with more coworkers who were recently entrepreneurs to those who work with proportionally fewer entrepreneurial coworkers. Effectively, I want to compare individuals who are very similar, both in terms of their own demographics and entrepreneurship experience and their current firms, but who have different exposure to entrepreneurial experience….”

Ed Comment: Doesn’t seem very convincing. Aren’t engineers/programmers who work for google more likely to become entrepreneurs? Many went there to learn/steal ideas from technological frontier.

Ed Comment:“how do they remove selection bias -- entrepreneurial people choosing to work together or idea rich environments logically producing entrepreneurs? That's different than learning from nearby entrepreneurs.”

The Startup Surge Continues: Business Applications on Track for Second-Largest Annual Total on Record

Daniel Newman Economic Innovation Group

Date Posted:: August 4, 2023
Is Database:: Database

Based on first-half new business formation, 2023 will be just shy of 2021’s record number of new firms likely to hire employees. @InnovateEconomy

Early-stage business activity across the United States remains robust through the first half of 2023, as the pace of new business formation strengthened over last year. Individuals filed nearly 2.7 million applications to start a business between January and June of this year, a 5% increase over 2022 and a staggering 52% increase over the same period in 2019. One-third of those filings were for new businesses likely to hire employees—a key subset of applications from the Census Bureau’s Business Formation Statistics demonstrating a “high propensity” to hire staff, if and when the business becomes operational. The volume of likely employer applications also remained well above prepandemic levels, surpassing the total from the first six months of 2019 by 36%.

How The Titans of Tech Investing Are Staying Warm Over The VC Winter

Economist Staff The Economist

Date Posted:: February 27, 2023
Is Database:: Database

Money flowing into startups globally fell by a third in 2022 as valuations declined; however, there is still $300B of VC dry powder in the US alone. @TheEconomist

The tech-heavy Nasdaq index fell by a third in 2022, making it one of the worst years on record and drawing comparisons with the dotcom bust of 2000-01. According to the Silicon Valley Bank, a tech-focused lender, between the fourth quarters of 2021 and 2022, the average value of recently listed tech stocks in America dropped by 63%. And the plunging public valuations dragged down private ones. The value of older, larger private firms (“late-stage” in the lingo) fell by 56% after funds marked down their assets or the firms raised new capital at lower valuations. What new VC funding there is increasingly flows into mega-funds. Data from PitchBook, a research firm, show that in America in 2022 funds worth more than $1bn accounted for 57% of all capital, up from 20% in 2018.

Money flowing into startups globally fell by a third in 2022 as valuations declined; however, there is still $300B of VC dry powder in the US alone.

“…The tech-heavy Nasdaq index fell by a third in 2022, making it one of the worst years on record and drawing comparisons with the dotcom bust of 2000-01. According to the Silicon Valley Bank, a tech-focused lender, between the fourth quarters of 2021 and 2022, the average value of recently listed tech stocks in America dropped by 63%. And the plunging public valuations dragged down private ones. The value of older, larger private firms (“late-stage” in the lingo) fell by 56% after funds marked down their assets or the firms raised new capital at lower valuations. What new VC funding there is increasingly flows into mega-funds. Data from PitchBook, a research firm, show that in America in 2022 funds worth more than $1bn accounted for 57% of all capital, up from 20% in 2018….”

Economist Staff, “How the titans of tech investing are staying warm over the VC winter,” The Economist, February 26, 2023, https://www.economist.com/business/2023/02/26/how-the-titans-of-tech-investing-are-staying-warm-over-the-vc-winter

How the titans of tech investing are staying warm over the VC winter

Venture capitalists are not known for their humility. But the world’s biggest investors in innovation have been striking a more humble tone of late. In a recent letter to investors Tiger Global, a hedge fund turned venture-capital (vc) investor, reportedly admitted that it had “underestimated” inflation and “overestimated” the boost the pandemic would give to the tech startups in its portfolio. In November Sequoia, a Silicon Valley vc blue blood, apologised to investors in its funds after the spectacular blow-up of ftx, a now defunct crypto-trading platform that it had backed. Speaking in January, Jeffrey Pichet Jaensubhakij, the chief investment officer of gic, one of Singapore’s sovereign-wealth funds, said that he was “thinking much more soberly” about startup investing.

The vc giants’ newfound contrition comes on the back of a gigantic tech crash. The tech-heavy nasdaq index fell by a third in 2022, making it one of the worst years on record and drawing comparisons with the dotcom bust of 2000-01. According to the Silicon Valley Bank, a tech-focused lender, between the fourth quarters of 2021 and 2022, the average value of recently listed tech stocks in America dropped by 63%. And the plunging public valuations dragged down private ones (see chart 1). The value of older, larger private firms (“late-stage” in the lingo) fell by 56% after funds marked down their assets or the firms raised new capital at lower valuations.

This has, predictably, had a chilling effect on the business of investing in startups. Soaring inflation and rising interest rates made companies whose promised profits lie primarily in the distant future look less attractive today. Scandals like ftx did not help. After a decade-long bull run, the amount of money flowing into startups globally declined by a third in 2022, calculates cb Insights, a data provider (see chart 2). In the final three months of 2022 it fell to $66bn, two-thirds lower than a year earlier; the number of mega-rounds, in which startups raise more than $100m, fell by 71%. Unicorns, the supposedly uncommon private firms valued at more than $1bn, became rarer again: the number of new ones contracted by 86%.

This turmoil is forcing the biggest venture investors—call them the vc whales—to shift their strategies. For Silicon Valley, it signals a reversion to a forgotten style of venture capitalism, with fewer deep-pocketed tourists splashing the cash and more bets on young companies by Silicon Valley stalwarts.

Misadventure capital

To understand the scale of vc’s reversal of fortune, consider its earlier bonanza. Between 2012 and 2021 annual global investments grew roughly ten-fold, to $638bn. Conventional vc firms faced competition from a new breed of investor from beyond Silicon Valley. These included hedge funds, the venture arms of multinational companies, from Shell to Samsung, and the world’s sovereign-wealth funds, some of which began investing in startups directly. Dealmaking turned frenetic. In 2021 Tiger Global inked almost one new deal a day. Across vc-dom activity “was a bit unhinged”, says Roelof Botha, boss of Sequoia Capital, “but rational”, given that low interest rates meant that money was virtually free. And “if you weren’t doing it, your competitor was.”

What passed for rationality in the boom times now looks somewhat insane. The downturn has spooked the vc funds’ main sources of capital—their limited partners (lps). This group, which includes everyone from family offices and university endowments to industrial firms and pension funds, is growing more nervous. And stingier: lower returns from their current investments leave lps with less capital to redeploy, and collapsing stockmarkets have left many of them overallocated to private firms, whose valuations take longer to adjust and whose share of some lps’ portfolios thus suddenly exceeds their quotas. Preqin, a data firm, finds that in the last quarter of 2022 new money flowing into vc funds fell to $21bn, its lowest level since 2015.

What new vc funding there is increasingly flows into mega-funds. Data from PitchBook, a research firm, show that in America in 2022 funds worth more than $1bn accounted for 57% of all capital, up from 20% in 2018. How the vc whales behind these outsize pools of capital adapt to the vc winter will determine the shape of the industry in the years to come.

The venture cetaceans can be divided into three big subspecies, each typified by big-name investors. The startups they finance range from the newly founded, in need of “seed” funding, to the somewhat older, later stage firms that are looking to rapidly grow. First there is the conventional Silicon Valley royalty, such as Sequoia and Andreessen Horowitz. The second group comprises the private tourists, such as Tiger and its New York hedge-fund rival, Coatue, as well as SoftBank, a gung-ho Japanese investment house. Then there are the sovereign-wealth funds, such as Singapore’s gic and Temasek, Saudi Arabia’s Public Investment Fund (pif) and Mubadala of the United Arab Emirates. As well as investing directly, these entities are lps in other vc funds; pif, for example, is a large backer of SoftBank’s Vision Fund.

Together these nine institutions ploughed more than $200bn into startups in 2021 alone, or roughly a third of the global total (not counting the state funds’ indirect investments as lps). All nine have been badly damaged by last year’s crash. Sequoia’s crossover fund, which invested in both public and private firms, reportedly lost two-fifths of its value in 2022. Temasek’s listed holdings on American exchanges shrank by about the same. SoftBank’s mammoth Vision Funds, which together raised around $150bn, lost more than $60bn, wiping out their previous gains. In a sign that things were terrible, its typically garrulous boss, Son Masayoshi, sat out its latest earnings call on February 7th. Tiger reportedly lost over half of the value of its flagship hedge fund and marked down its private investments by about a quarter, torching $42bn in value and leading one vc grandee to speculate that the hedge fund might turn itself into a family office.

All three groups have reined in their investments. But each has responded to the downturn in distinct ways. That is in part because it has affected them to different degrees.

The private outsiders have been hardest hit. The combined number of startup investments by the three firms in our sample fell by 76% between the second half of 2021 and the same period in 2022. Tiger has lowered the target for its latest fund from $6bn to $5bn; its previous one raised $13bn. In October Phillipe Laffont, Coatue’s boss, said that the hedge fund was holding 70-80% of its assets in cash. The firm has also raised $2bn for its “tactical solutions fund”, designed to give mature startups access to debt and other resources, as an alternative to raising equity at diminished valuations during a market downturn. SoftBank has all but stopped investing in new startups. Instead, in the second half of 2022 most of its capital went to well-performing portfolio firms, says Lydia Jett, a partner at the Vision Fund.

The other two groups are also retrenching, but not as drastically. According to data from PitchBook, in the second half of 2022 the number of deals struck by Sequoia and Andreessen Horowitz fell by a combined 47%. Direct investments by the four sovereign funds in our sample slowed by a more modest 31% in the same period, no doubt thanks to their governments’ deep pockets and longer time horizons.

Taken together, venture capitalists’ slowing pace of investment has left them with a record amount of capital that lps had already pledged to stump up but that has yet to be put to use. Last year the amount of this “dry powder” was just shy of $300bn in America alone (see chart 3). According to data from PitchBook, our five private whales are sitting on a combined $50bn or so; the sovereign investors hold their numbers close to their chest but are likely to be of a similar order of magnitude, all told. Some of it may wait a long while to be deployed, if it ever is. But some will find grateful recipients. Who those recipients are also depends on which group of whales you look at.

Conventional vcs and the hedge funds are focusing on younger “early-stage” firms. in part because volatility in the public markets makes it harder to value more mature companies that hope to list in the near future. Mr Botha says that Sequoia has doubled the number of “seed” deals with the youngest companies in 2022, compared with 2021. In January the firm launched its fifth seed fund, worth $195m. Last April Andreessen Horowitz launched an “accelerator” programme to nurture startups. About half the startups Tiger backed in 2022 were worth $50m or less, compared with just a fifth in 2021, according to PitchBook.

Early-stage firms are unlikely to be the only recipient of vc cash. David DiPietro, head of private equity at T. Rowe Price, a fund-management group, thinks that startups selling “must-have” products, such as cyber-security services, or cost-cutting tools, such as budgeting software, should fare well. Money will also keep flowing to well-managed businesses with strong balance-sheets., expects Kelly Rodriques, chief executive of Forge, a marketplace for private securities. Firms with buzzy new technologies, such as artificial-intelligence chatbots and other forms of whizzy “generative ai”, are also likely to attract investments—especially if those technologies already work in practice and underpin a viable business model.

Another category of startups likely to gain favour comprises those involved in industries that governments deem strategic. In America, that means climate-friendly technology and advanced manufacturing, on which Uncle Sam is showering subsidies and government contracts. Some 8% of the deals all our whales made in the second half of 2022 involved firms working on technologies to combat climate change, for example, up from 2% in the same period of 2021. Last year Andreessen Horowitz launched an “American Dynamism” fund, which partly invests in firms that rely on government procurement, such as Anduril, a defence-tech startup.

Sovereign-wealth funds are likely to be looking elsewhere. Seed deals are simply too small for them: whereas the typical early-stage American company is worth about $50m, in 2021 the median value of startups backed by the sovereign funds was a whopping $650m. And to them, what counts as “must-have” startups is somewhat different, determined less by the market or other states’ strategic imperatives, and more by their own governments’ nation-building plans.

On February 16th pif said it would take a stake in vspo, a Chinese platform for video-game tournaments. This is part of a plan dreamed up by Muhammad bin Salman, the Saudi crown prince, to invest $38bn in “e-sports” by 2030 and make Saudi Arabia a gamer’s mecca. Temasek invests heavily in firms that develop technology to boost food production. In the past year it backed Upside Foods, a startup selling lab-grown meat, and InnovaFeed, a maker of insect-based protein. This is motivated by Singapore’s goal of locally producing 30% of the city-state’s nutritional needs by 2030, up from about 10% in 2020. Rohit Sipahimalani, chief investment officer of Temasek, thinks that over the next few years his focus will shift towards “breakthrough innovation rather than incremental innovation”, on the back of government support of strategic tech.

One group of firms is likely to see less investment from our whales, however: those in China. The Communist Party’s harsh two-year crackdown on consumer technology may be easing but the vc titans remain wary of what was until recently one of the world’s hottest startup scenes. An executive at a big venture fund says that in the past, foreign investors in China knew that the government would be respectful of their capital. Now, he sighs, it feels like the government “has pulled the rug out from underneath us”.

Tiger has said that there is a “high bar” for new investments in China. gic has reportedly scaled back its investments in China-focused private funds. Mr Sipahimalani of Temasek says diplomatically that he is trying to avoid investing in “areas caught in the cross-hairs of us-China tension”. Sequoia is reportedly asking external experts to screen new investments made by its Chinese arm into quantum computing and semiconductors, two such contentious areas. All told, the number of our whales’ deals with Chinese startups fell from 22% of the total in 2021 to 16% in 2022.

After the dotcom crunch vc investments needed nearly two decades to return to their previous peak. Today’s tech industry is more mature, startups’ balance-sheets are stronger and, according to the Silicon Valley Bank, their peak valuations relative to sales are lower than in 2000-01. This time the whales of vc are unlikely to need 20 years to nurse their wounds. But the experience will have lasting effects on the sort of businesses they back.

Google and Meta’s Advertising Dominance Fades as TikTok, Streamers Emerge

Patience Haggin Wall Street Journal

Date Posted:: January 4, 2023
Is Database:: Database

According to data from Insider Intelligence, Google and Facebook’s share of digital advertising was 48.4% in 2022, and is expected to decline to 44.9% in 2023, as Amazon, TikTok, and digital streamers gain share. @WSJ

For the first time in nearly a decade, the two largest players in online advertising are no longer raking in the majority of U.S. digital-ad dollars, a decline that industry insiders expect to continue in years to come. Alphabet Inc.’s; Google and Facebook parent Meta Platforms Inc. accounted for a combined 48.4% of U.S. digital-ad spending in 2022, according to estimates from research firm Insider Intelligence Inc. Their combined U.S. market share hadn’t been under 50% since 2014, said Insider Intelligence, which expects that number to drop to 44.9% this year.

According to data from Insider Intelligence, Google and Facebook’s share of digital advertising was 48.4% in 2022, and is expected to decline to 44.9% in 2023, as Amazon, TikTok, and digital streamers gain share.

“…For the first time in nearly a decade, the two largest players in online advertising are no longer raking in the majority of U.S. digital-ad dollars, a decline that industry insiders expect to continue in years to come. Alphabet Inc.’s; Google and Facebook parent Meta Platforms Inc. accounted for a combined 48.4% of U.S. digital-ad spending in 2022, according to estimates from research firm Insider Intelligence Inc. Their combined U.S. market share hadn’t been under 50% since 2014, said Insider Intelligence, which expects that number to drop to 44.9% this year….”

Patience Haggin, “Google and Meta’s Advertising Dominance Fades as TikTok, Streamers Emerge,” Wall Street Journal, January 4, 2023, https://www.wsj.com/articles/google-and-metas-advertising-dominance-fades-as-tiktok-netflix-emerge-11672711107

Google and Meta’s Advertising Dominance Fades as TikTok, Streamers Emerge

Alphabet Inc.’s Google and Facebook parent Meta META Platforms Inc. accounted for a combined 48.4% of U.S. digital-ad spending in 2022, according to estimates from research firm Insider Intelligence Inc. Their combined U.S. market share hadn’t been under 50% since 2014, said Insider Intelligence, which expects that number to drop to 44.9% this year.

The ad businesses of Google and Meta are still growing, but Insider’s data suggest the pace is slower than the rest of the U.S. digital-advertising market. Insider forecasting analyst Zachary Goldner said the erosion of their combined market share was the result of brands having access to more advertising formats.

“All marketers want more options,” Mr. Goldner said.

Google and Meta each faced headwinds in 2022, as people spent less time online than in the early days of the pandemic; marketers concerned about a possible economic downturn reined in ad spending; Amazon and ByteDance Ltd.’s TikTok continued their emergence as a force in digital advertising; and more streaming services started to embrace advertising.

Meta and other social-media companies including Snap Inc. also suffered from Apple Inc.’s 2021 decision to require apps on its devices to ask users if they wanted to be tracked. The majority of iPhone users opted not to be, hitting the heart of Meta’s business: its ability to target ads at users with precision and prove to marketers that the ads generate sales.

Google wasn’t as affected by Apple’s move, because its flagship search-ad business relies on customer intent—users’ search terms immediately reveal what they are interested in—rather than data collected from app and web tracking. Its U.S. digital-advertising market share slightly rose to 28.8% in 2022, Insider Intelligence said, but is expected to fall to 26.5% this year.

Google didn’t respond to a request for comment. On the company’s most recent earnings call in October, executives talked about how search-based advertising tends to do well during challenging economic times.

Meta had no comment. In its most recent earnings call, Meta said the tracking restriction continued to affect its ad business but noted the effect had diminished.

Apple’s tracking restriction had an impact for emerging e-commerce companies, which are important to Meta’s ad business. Supergut Chief Executive Marc Washington said the maker of gut-health products used to spend about 80% of its ad budget on Meta’s Facebook and Instagram platforms, with the rest going to Google. In early 2022, he noticed that the cost of bringing in new customers through advertising on Meta’s platforms was twice as high as it was before Apple’s privacy changes. Supergut shifted about half of what it spent on Meta to TikTok, a short-form video platform popular with younger audiences.

Mr. Washington said Meta remained Supergut’s most efficient advertising platform, but said there are early indications of “TikTok ads performing as well and in some cases better than some of our Meta campaigns.”

TikTok’s command of the U.S. digital-ad market more than doubled in 2022, Insider Intelligence said, thanks to its nearly 100 million U.S. monthly active users, the virality of the platform and its hold over Gen Z, millennials and influencers. Still, its overall share remains small, accounting for 2% of U.S. digital-ad spending, according to Insider Intelligence, which expects that number to grow to 2.5% this year.

The gains came as TikTok has received increased scrutiny from Washington, D.C., over national-security concerns U.S. officials say the Chinese-owned app poses—accusations that TikTok has disputed. The company also faces competition from Google’s YouTube and Meta’s Instagram, which have launched offerings that mimic its short-form video-recommendation format.

One already large digital-advertising player that gained market share in the U.S. last year was Amazon, whose ad business is powered by its ability to target users by their purchase and browsing history. The e-commerce giant accounted for 11.7% of U.S. digital-ad spending last year and is poised to grow to 12.4% in 2023, Insider said.

“Our advertising is at the point where consumers are ready to spend,” Amazon Chief Financial Officer Brian Olsavsky said on the company’s October conference call.

Sam Bloom, chief executive of digital-ad agency Camelot Strategic Marketing and Media, said some of its clients that had withheld selling on Amazon started doing so last year, using Amazon ads to promote their products on the platform.

Other retailers have followed in Amazon’s footsteps by building digital-ad businesses based on their consumer data, known as retail media networks. Combined, Walmart Inc., eBay Inc., Etsy Inc. and Instacart took in about 1.4% of digital-ad dollars spent in the U.S. last year, according to Insider.

Edwin Fu, chief executive of Placements.io Inc., a tech company that works with digital- publishers and online retailers on billing systems for their ad inventory, described the rise of retail media networks as a “massive race to do what Amazon did.”

Streaming services also are commanding a larger slice of the digital-advertising pie. Mr. Bloom said many of its clients are shifting their spending away from traditional television and toward video platforms in an effort to reach younger viewers.

Insider said Roku Inc., Walt Disney Co.’s Hulu, Paramount Global’s Pluto TV and Paramount+, Fox Corp.’s Tubi and Comcast Corp.’s Peacock accounted for about 3.6% of U.S. digital-ad spending last year. The trend is expected to accelerate now that the streaming industry’s two-largest players, Netflix Inc. and Disney+, have launched ad-supported versions.

Vincent Létang, executive vice president of global market research at Magna, a media- investment firm that is part of Interpublic Group of Cos.‘ Mediabrands, called Netflix and Disney’s entry into the market “a game-changing moment” for ad-supported streaming. “They bring a potentially huge number of viewers,” he said, and a wealth of premium video content.

Given the emergence of new digital-advertising alternatives, Google and Meta’s declining market share is hardly surprising, said digital ad consultant Ratko Vidakovic. The surprise was actually their 2021 performance, an aberration that he said was caused by the pandemic.

U.S. digital-ad spending grew by a whopping 41% in 2021, according to media and data giant GroupM, part of WPP PLC. That growth number dipped to 10.7% last year and is expected to slip to 9.2% this year. GroupM expects overall ad spending in the U.S.—excluding political advertising—to grow by 5.5% this year.

Despite the expectations of slowing growth, digital-advertising platforms will take in an ever-larger share of ad dollars this year, GroupM said: Nearly two-thirds of total U.S. ad spending is expected to go to digital advertising in 2023, compared with less than half in 2019, the last year before the pandemic.

China and the West are in a race to foster innovation

Economist Staff The Economist

Date Posted:: October 13, 2022
Is Database:: Database

The US leads with an $800bn investment in corporate R&D, venture capital, and state funding, 3.8% of GDP, surpassing China’s $660bn 2.7% of GDP.

The US leads with an $800bn investment in corporate R&D, venture capital, and state funding, representing 3.8% of GDP, surpassing China's $660bn or 2.7% of GDP. This investment strategy underscores the West's commitment to maintaining a technological edge over China, which has rapidly increased its R&D spending to 85% of America's by 2020. Despite China's coordinated efforts through state-owned enterprises and industrial subsidies, the US benefits from a more distributed and bottom-up approach involving private businesses, venture capital, and philanthropic funders. The global competition for technological dominance is intensifying, with both regions focusing on strategic industries like AI, semiconductors, and biotech. However, China's system faces challenges in fostering future breakthroughs due to its top-down approach, while the US continues to leverage its open market and diverse talent pool to drive innovation.

American’s $800B investment in corporate R&D, venture-capital investment, and government funding/subsidies represents 3.8% of GDP, maintaining an edge over China’s $660B (2.7% of GDP)

“…The Economist has totted up corporate spending on R&D, venture-capital investment, direct government funding and, for advanced technologies, implicit funding through subsidies, and subtracted the overlap among these categories. This calculation confirms that America maintains a slight edge, spending about $800bn or 3.8% of GDP in 2020. That compares to about $660bn in China after adjusting for differences in the cost of living, or 2.7% of GDP….”

Economist Staff, "China and the West are in a race to foster innovation,"The Economist, October 13, 2022, https://www.economist.com/briefing/2022/10/13/china-and-the-west-are-in-a-race-to-foster-innovation

China and the West are in a race to foster innovation

China’s government is planning on winning the ai race, winning future wars and winning the future,” warned Todd Young, an American senator, in July. “At stake” in the West’s technological competition with China, echoed a report from American officials and businessmen in September, “is the future of free societies, open markets, democratic government, and a world order rooted in freedom not coercion.” This week the head of a British intelligence agency joined the chorus, urging “deep investments” in new technology to counter China’s growing prowess.

The anxiety is easy to understand. In 2008 China spent a third as much as America did on research and development (r&d) and about half as much as Europe, after adjusting for differences in the cost of living. By 2014 it had surpassed Europe. By 2020 its spending was 85% of America’s.

The fruits of this investment are becoming apparent: in August a Japanese research institute calculated that China now produces more of the world’s most highly cited academic research than America does. Since 2015 more patents have been issued in China than in America. China’s output of a basket of sophisticated goods including information technology, pharmaceuticals and electronics is expected to surpass America’s this year, according to a report published by the Information Technology and Innovation Foundation, an American think-tank. “China has become a serious competitor in the foundational technologies of the 21st century,” concluded another report last year from the Belfer Centre at Harvard University.

Small wonder, then, that Western countries are embarking on a frantic effort to retain or regain their technological edge. On October 7th America issued fierce new restrictions on exports to China of advanced semiconductors and related equipment. The new rules could be as crippling to the Chinese chip industry as previous American sanctions were to Huawei, a Chinese telecoms firm, says Greg Allen, who used to head the artificial-intelligence (ai) unit at America’s Department of Defence. “It’s a total clamp down, trying to cut off every head of the hydra of China’s chip industry.”

As well as trying to disrupt the flow of technology abroad, America’s government is investing more in innovation. In August Congress approved $370bn of spending on green energy, including lots of money for research. The month before it passed the Chips and Science Act, which provides $52bn over five years for the semiconductor industry, some of which will incentivise private r&d.

The act also revamps the National Science Foundation (nsf) to put more emphasis on applied science and technology and potentially doubles its funding. Germany, Japan and South Korea are making multi-billion-dollar investments in computer chips. Last year Britain announced the $1bn Advanced Research and Invention Agency (aria) to supercharge high-risk, high-reward science.

The result of all this is a global boom in investment in innovation. In 2020 the world’s spending on r&d exceeded $2.1trn, over 2.5% of global gdp, a record. The binge has three notable features. The first is the heavy involvement of governments, which are unwilling to leave investment to capital markets and are instead both funding r&d and subsidising production of certain high-tech goods. Both China and the West explicitly link such spending to geopolitical competition. “Technological innovation has become the main battlefield of the international strategic game,” said China’s president, Xi Jinping, in a speech last year to Chinese scientists. “We’re in a multi-generation era-defining competition against the ccp,” rhymes Mr Young, one of the sponsors of the Chips Act.

The second feature of the new era is experimentation with different types of funding that couple industrial policy with efforts to promote risk-taking or private-sector rigour. America and Britain, for instance, are reviving research missions akin to America’s cold-war quest to put a man on the Moon. China, meanwhile, is using “guidance funds”, in which the state takes a stake alongside private investors, to steer money to startups in ai and chips, among other advanced technologies.

Third, governments are trying to ensure their country captures more of the benefits of innovation. That can mean both preventing exports of some goods and using industrial policy to promote domestic production.

But there remain big differences in approach between China and the West—most notably the far more muscular role the state still plays in directing innovation in China to favoured industries. The West, in contrast, relies on a more diffuse network of universities, non-profits and private businesses that have more freedom to set their own priorities. There is little doubt that China’s system has helped it catch up with the West in some existing technologies, but analysts question whether it will be as good at generating future breakthroughs. The answer will determine the outcome of the global battle for technological dominion.

America’s government invested lavishly in innovation during the cold war, through such organisations as the nsf and the Defence Advanced Research Projects Agency (darpa). Its spending peaked at 1.86% of gdp in 1964. But after the fall of the Berlin Wall federal spending on r&d fell well below 1% of gdp. Private investment, meanwhile, doubled from 1% of gdp in 1979 to 2% in 2017. Giant tech firms such as Google, Facebook (now Meta), Amazon and Apple sprouted in America. China spawned similar titans, such as Alibaba, Baidu, JD.com and Tencent.

But on both sides of the Pacific the age of free-flowing private capital left many disappointed. The Communist Party has called the spread of big consumer-tech firms a “disorderly expansion of capital”. It has obliged China’s internet giants to follow its priorities, blocking share sales and issuing abrupt regulations to cow wayward firms. It seems to want less video-gaming and online commerce and more ai, chips and green tech.

Many Americans have similar misgivings. Peter Thiel, a fabled investor, has argued that there has been too much investment in “bits” (software and analytics) and not enough in “atoms” (hardware and manufacturing). “With chips we were caught behind the eight ball,” says Eddie Bernice Johnson, a Democrat from Texas who chairs the committee that drafted the Chips Act, “It was a national security imperative.” Mr Young of Indiana agrees: “The totally free-market theories of Friedman, Hayek—they don’t make sense when you’re facing an existential threat that plays with market forces.”

Buying breakthroughs

To allow a proper comparison of the sums devoted to innovation on the two sides of the Pacific, The Economist has totted up corporate spending on r&d, venture-capital investment, direct government funding and, for advanced technologies, implicit funding through subsidies, and subtracted the overlap among these categories. This calculation confirms that America maintains a slight edge (see chart 1), spending about $800bn or 3.8% of gdp in 2020. That compares to about $660bn in China after adjusting for differences in the cost of living, or 2.7% of gdp.

China and the West are in a race to foster innovation: Extended Excerpt Image 1

But China’s spending is growing far quicker than the West’s. China’s investments are also more co-ordinated. Although its government and America’s both directly dispense only about 15-20% of their country’s expenditure on innovation, state-owned enterprises and industrial subsidies massively increase the influence of the state in China (see chart 2). Different arms of government have also set up nearly 2,000 “guidance funds” in which the state invests alongside private capital. The Chinese government began investing in semiconductors in this way as early as 2014, with a $20bn “Big Fund”. The second iteration of the fund has raised nearly $30bn. The state is now China’s biggest investor in venture-capital and private equity, contributing over 30% of the total.

China and the West are in a race to foster innovation: Extended Excerpt Image 2

All this allows the government to steer money towards its goals, in what is called juguo tizhi or the “whole-of-the-nation system”. Whereas in America the share of vc devoted to strategic industries as defined by the Belfer Centre report (ai, semiconductors, biotech, energy and quantum computing) has gradually grown from 10% to 20% over the past decade, in China it soared from 15% in 2019 to 35% in 2020 in line with government directives (see chart 3).

China and the West are in a race to foster innovation: Extended Excerpt Image 3

Yutao Sun and Cong Cao, two Chinese academics, argued in Nature last year that juguo tizhi had helped develop “a few state-led sectors with clear goals, such as high-speed rail and large passenger aircraft”. It was less effective, however, in “areas where there is no leader to follow”. Only 6% of China’s r&d spending is on basic research, compared with 17% of America’s.

What is more, juguo tizhi can also lead to misallocation of funds. A paper published in the journal Econometrica in July suggests that Chinese spending on r&d spurs less growth in productivity than that of neighbouring Taiwan. That is in part because the state often supports soes, even if they are less productive. Several studies suggest that corporate r&d in China is about half as productive as in America (although they do not focus exclusively on advanced technology).

America’s expenditure, meanwhile, is much more diffuse. Private businesses account for about 60%, venture capital for nearly 20% and foundations, charities and universities more than 5%. In a recent presentation, Pierre Azoulay, a professor at mit, notes that the “Cambrian explosion of philanthropic funders” is a “silver lining” compared with the perceived sclerosis in government funding. From 2010 to 2019, research funding from non-profits nearly doubled, from $12bn to $22bn. “Our system is unique because its more distributed and bottom-up; not top-down,” says Maria Cantwell, another senator.

America may also be more daring in its investments. The Institute for Progress (ifp), an American think-tank, is helping government agencies distribute grants more effectively, says Caleb Watney, a co-founder. Erwin Gianchandani of the nsf cites “golden tickets” as an example. Rather than the standard consensus-based process to allocate funding, a single reviewer can champion a project.

Michael Lauer, head of extramural research at America’s National Institutes of Health (nih), lists a handful of new programs where labs receive funding with far fewer strings attached than normal. The Other Transactions Authorities, a recent nih program that quickly funds unconventional projects, disbursed over $2bn in 2020 and 2021.

Hunting for lightbulbs

America is also creating more “moonshot” programs in an effort to replicate the success of darpa. Last year it launched the $1bn Advanced Research Projects Agency for Health (arpa-h) to focus on ambitious biomedical research. The total amount of funding in this category increased from about $4bn in 2021 to nearly $6bn in 2022. Tom Kalil of Schmidt Futures, another organisation that aims to shape policy on innovation, says this heralds a shift towards more risk-taking. China lacks any equivalent funding agencies, notes Mr Cao of Nottingham University China.

It is possible to exaggerate the strengths of both the American and Chinese systems. For all the talk of moonshots, notes a former White House official, nih was still slow to fund research on covid-19 at the beginning of the pandemic. And researchers are still often buried in paperwork. The new funding does little to address the administrative burden in the current system—researchers lose about 40% of their time to that, notes Tony Mills of the American Enterprise Institute, another think-tank. By the same token, although thousands of new firms have sprung up in China in favoured industries, such as ai and semiconductors, most of them do not seem to have achieved much.

Neither system has a monopoly on results. According to a report published in September by the Special Competitive Studies Project, a research group organised by Eric Schmidt, a former ceo of Google (and a former member of the board of The Economist), China is dominant in some industries, such as 5g telecoms. It makes some 80% of the world’s lithium batteries. But the West is ahead on biotech, cloud computing and ai. It has been the source of most fundamental advances in these fields, such as crispr (a gene-editing technology) and the transformer architecture that underpins many big ai models.

Although few advanced computer chips are made in America, American firms tend to design them. asml, a Dutch manufacturer of chipmaking equipment, has a monopoly in the advanced lithography needed to make the fanciest ones. And the Chips Act is prompting Intel and tsmc, two of the biggest chipmaking firms, to build new semiconductor fabs in America. Intel is also set to spend nearly $20bn on new chip factories in Germany.

China has built first-rate ai models by throwing money at researchers and firms. Wu Dao, its version of gpt-3, an American ai model that can write like a human, uses ten times more parameters to train itself. Yet the chips used for much of this sort of machine-learning, gpus, were originally developed to generate graphics for video games—one of the industries on which the Chinese government has cracked down most ferociously. By the same token, it was not until September that a Chinese firm developed a vaccine against covid-19 that is as effective as Western ones—a recent high-stakes test of its capacity to innovate. “China does best in products where manufacturing is complex but the science is mature, for example in batteries,” says Dan Wang, an analyst at Gavekal Dragonomics, a research group.

But China is trying to mend some of the failings of its system. It boosted funding for basic research by 16% last year, in an attempt to foster more breakthrough discoveries. It is also trying to reduce centralisation. In July the party announced new rules to increase scientists’ autonomy. “There is evidence that China has recognised the limits of using a blunt metric to evaluate scientists,” adds Mr Wang. “Thus universities are starting to move towards the peer-review system of the West.”

There are some mistakes, however, from which China shows little sign of retreating. Mr Xi’s decision to rein in the tech industry contributed to an 11% contraction in venture-capital investment from the first three quarters of 2019 to the same period in 2022. In America vc investment grew by 70% in that time. China’s stubborn zero-covid policy, meanwhile, is driving foreign capital and talent out of the country. A survey by the German Chamber of Commerce in May found that nearly a third of foreign workers plan to leave.

Such talent is crucial to competitiveness. In the past China benefited from both foreign investment and large cohorts of “sea turtles”, students and researchers who work or study abroad and later return. The share of Chinese students studying abroad who eventually returned home rose from 25% in 2004 to 65% in 2019. And from 2015 to 2019, the number of academic papers published involving co-operation between American and Chinese researchers grew by over 10% a year, according to Nature.

Yet in 2020 this growth in academic collaboration between the two countries abruptly stalled. Less than half as many Chinese received visas to study abroad in the first half of 2022 as in the first half of 2019. This pulling apart is bad for the world, but China may suffer more. It does not have as diverse a pool of researchers as the West. According to data from MacroPolo, a think-tank, although 60% of the world’s best ai researchers work in America, over two-thirds of them are foreign (and over a quarter of them Chinese). In contrast, China draws overwhelmingly on domestic talent: almost all its best ai researchers are Chinese, and 70% of them have studied only in China.

America is not only open to foreign expertise, it also benefits from a big network of alliances with other technologically advanced countries. Collectively, America, Britain, France, Germany, Japan and South Korea spend over twice as much on r&d as China. China, by contrast, has few allies, and none that are powerhouses of research and innovation.

Bulbs blown

American politicians do not seem to understand the advantage conferred by their country’s openness, however. The original draft of the Chips bill included a provision to boost skilled immigration. Although some politicians, including Mr Young, gave it cautious support, it had to be removed to ensure the support of more Republicans, in particular. (America’s allies, happily, are doing better. Britain has devised a scheme to provide visas to graduates of top universities. Australia and Canada, already home to lots of immigrants, are increasing immigration further.)

Whatever the limits to America’s openness, however, China’s growing isolation—both self-imposed and enforced by restrictions like America’s new rules on tech exports—is far more severe. After several years when its technological rise seemed unstoppable, the outlook suddenly seems much less clear. In the coming days Mr Xi will preside over China’s 20th Party Congress. Across the Pacific, America’s Congress will be debating how much money to devote to the Research initiatives at agencies like the nsf. You can be sure each will be on the other’s mind.

Co-Working in Close Proximity: Knowledge Spillovers and Social Interactions

P. Roche National Bureau of Economic Research

Date Posted:: June 27, 2022
Is Database:: Database

Startups within 20 meters of each other in a US tech co-working hub experienced significant knowledge spillovers, with proximity effect diminishing rapidly beyond 20 meters.

In a study of a major US tech co-working hub, startups within 20 meters of each other experienced significant knowledge spillovers, as evidenced by a higher likelihood of adopting peer technologies. This proximity effect diminishes rapidly beyond 20 meters, where startups behave as if on different floors. The study used random office assignments to control for location bias, revealing that doubling the distance between startups reduces peer technology adoption by 3.5% and the likelihood of adoption by 1.7%, both significant at the 1% level. Social interactions in shared spaces like kitchens extend the influence range, highlighting the role of spatial features in promoting knowledge exchange. Startups benefit most from environments with balanced diversity, provided they engage socially.

We examine theinfluence of physical proximity on between-startup knowledge spillovers at one of the largest technology co-working hubs in the United States. Relying on the random assignment of office space to the hub's 251 startups, we find that proximity positively influences knowledge spillovers as proxied by the likelihood of adopting an upstream web technology already used by a peer startup. This effect is largest for startups within close proximity of each other and quickly decays: startups more than 20 meters apart on the same floor are indistinguishable from startups on different floors. The main driver of the effect appears to be social interactions. While startups in close proximity are most likely to participate in social co-working space events together,knowledge spillovers are greatest between startups that socialize but are dissimilar.Ultimately, startups that are embedded in environments that have neither too much nor too little diversity perform better, but only if they socialize.

Maria P. Roche, Alexander Oettl and Christian Catalini, "(Co-)Working in Close Proximity: Knowledge Spillovers and Social Interactions,"National Bureau Of Economic Research, June 2022, https://www.nber.org/papers/w30120

Core Findings

“…The setting for our study is one of the largest technology co-working spaces in the United States. The building consists of five floors, covering 9,300 m2 (100,000 sq.ft.). One challenge in examining the relationship between location and knowledge spillovers is that rms and individuals may choose to locate in areas where knowledge exchange is already likely to be high. To deal with this potential endogenous location choice, we rely on the random assignment of office space to the hub's 251 startups. We measure knowledge spillovers as the instance of adopting a component of a peer rm's technology stack. Using floor plans to measure geographic distance, we find that close physical proximity greatly influences the likelihood of these knowledge spillovers. This effect, however, quickly decays with distance where startup rms that are more than 20 meters (66 feet) away are no longer influenced by each other. Strikingly, being located more than 20 meters apart, but on the same floor does not appear to differ from being located on a different floor altogether. In addition, we find that when firms overlap with common areas at the hub (e.g., kitchens), the distance of influence increases, revealing the important role that these spatial features play in extending geographic reach and in promoting knowledge spillovers..”

The Evidence

“. .Table 3 presents the results from assessing the effect of distance on the amount of peer technology adoption (ln(AdoptCountij + 1)) using a standard OLS model and using a linear probability model to estimate the likelihood of adopting a technology from a peer rm 1(AdoptTechij ). In the full model (Columns 2 and 4), using rm-x-room fixed effects and controlling for industry, business model, gender, age and pre-period technology overlap, we find that the doubling of distance between two dyads reduces both the amount of peer technology adoption by 3.5% and the likelihood of any peer technology adoption by 1.7%, with both point estimates significant at the 1% level. As seen, the magnitude and statistical significance of the effect remains largely unchanged with the inclusion of additional controls….”

“…We next loosen the (log)linearity assumption of distance on technology adoption by breaking our distance measure into quartiles and estimate equation (1) using these indicators rather than the continuous measure of distance. Figure 2 displays these regression results graphically. We construct our omitted category as firms that are on different floors allowing us to estimate the full set of (same-floor) distance quartiles. The results obtained from this approach suggest that startup firms located within 20 meters of each other are those most influenced by each other. Being more distant, however, greatly reduces the influence of peers. Put differently, for technology adoption influence, firm pairs that are not within 20 meters of each other on the same floor behave as if they were on different floors altogether…”

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

Economist Staff The Economist

Date Posted:: April 27, 2022
Is Database:: Database

Newly formed companies across the OECD rose 15% in Q4 2021 compared to pre-pandemic levels, a significant surge in entrepreneurship.

In Q4 2021, the number of newly formed companies across the OECD was 15% higher than the pre-pandemic average, marking a significant surge in entrepreneurship. This trend is evident in various countries, with France seeing a 70% increase in startups compared to pre-pandemic levels, while Germany also reports higher business creation than in 2019. The shift is partly due to economic reallocation and changes in consumption habits, as well as increased financial security from government stimulus measures. Additionally, the pandemic has spurred a cultural shift towards risk-taking, reminiscent of the post-1918 startup boom in America. This rise in entrepreneurship is expected to boost economic dynamism by fostering innovation and job creation, as new firms typically expand and hire more staff.

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.

All over the rich world, new businesses are springing to life: Extended Excerpt Image 1

Not everywhere is booming. In the 2000s Italians founded about 400,000 firms a year. They probably formed half that number in 2021. But most places are more vibrant. 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. Who said the French didn’t have a word for “entrepreneur”?

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.

Individuals Who Work With 1 Standard Deviation Higher Share Of Entrepreneurial Coworkers are 2.5pp More Likely To Become Entrepreneurs, An 8% Increase Relative To Mean.

Based on first-half new business formation, 2023 will be just shy of 2021’s record number of new firms likely to hire employees. @InnovateEconomy

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