Macro Roundup

“…We investigate the importance of Veblen effects on work hours, namely the manner in which a desire to emulate the consumption standards of the rich influences individuals’ allocation of time between labor and leisure. Our model of the choice of work hours captures Veblen effects by taking account of the influence of the consumption of the well-to-do on the marginal utility of consumption by the less well-off. The main result is that work hours are increasing in the degree of income inequality.We use data on work hours of manufacturing employees in ten countries over the period 1963-1998, along with three different measures of income inequality to explore this hypothesis. Using both OLS and country-fixed-effects estimates,we find that greater inequality predicts longer work hours. Its effects are large, and estimates are robust across a variety of specifications.Additional evidence suggests that while greater inequality may induce longer hours for conventional incentive reasons, this mechanism does not account for our results. We show that in the presence of Veblen effects, a social welfare optimum cannot be implemented by a flat tax on consumption but may be accomplished by more complicated (progressive) consumption taxes or by subsidizing the leisure of the rich….”

Samuel Bowles and Yongjin Park, “Emulation, Inequality, and Work Hours: Was Thorsten Veblen Right?”The Economic Journal, November 2005, http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0297.2005.01042.x/abstract

“…We present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child's probability of attending college and her parents' income rank. We also calculate transition probabilities, such as a child's chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the "birth lottery" - the parents to whom a child is born - are larger today than in the past….”

Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner, “Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility.” National Bureau Of Economic Research, January 2014, http://www.nber.org/papers/w19844

“…We construct a model of household decision-making in which agents consume a private and a public good, interpreted as children's welfare. Children's utility depends on their human capital, which depends on the time their parents spend with them and on the parents' human capital. We first show that as returns to human capital increase, couples at the top of the income distribution should spend more time with their children.This in turn should reinforce assortative matching, in a sense that we precisely define. We then embed the model into a transferable utility matching framework with random preferences, a la Choo and Siow (2006), which we estimate using US marriage data for individuals born between 1943 and 1972. We find that the preference for partners of the same education has significantly increased for white individuals, particularly for the highly educated. We find no evidence of such an increase for black individuals. Moreover, in line with theoretical predictions, we find that the "marital college-plus premium" has increased for women but not for men….”

Pierre-André Chiappori, Bernard Salanié, and Yoram Weiss. “Partner Choice, Investment in Children, and the Marital College Premium,” American Economic Review, August 2017, https://www.aeaweb.org/articles

Research presented at Brooking's finds Chinese growth overstated by an average of 1.7% y/y btw 2008-2016. If true their GDP is 12% smaller then official figures suggests. two thoughts a) implications for their debt to GDP ratio ~30bps higher then we thought (see chart) b) their gross security spending as a % of GDP is prob really high

"....China’s national accounts are based on data collected by local governments. However, since local governments are rewarded for meeting growth and investment targets, they have an incentive to skew local statistics. China’s National Bureau of Statistics (NBS) adjusts the data provided by local governments to calculate GDP at the national level. The adjustments made by the NBS average 5% of GDP since the mid-2000s. On the production side, the discrepancy between local and aggregate GDP is entirely driven by the gap between local and national estimates of industrial output. On the expenditure side, the gap is in investment. Local statistics increasingly misrepresent the true numbers after 2008, but there was no corresponding change in the adjustment made by the NBS. Using publicly available data, we provide revised estimates of local and national GDP by re-estimating output of industrial, construction, wholesale and retail firms using data on value-added taxes. We also use several local economic indicators that are less likely to be manipulated by local governments to estimate local and aggregate GDP. The estimates also suggest that the adjustments by the NBS were insufficient after 2008. Relative to the official numbers, we estimate that GDP growth from 2008-2016 is 1.7 percentage points lower and the investment and savings rate in 2016 is 7 percentage points lower..."

Wei Chen, Xilu Chen, Chang-Tai Hsieh, and Zheng (Michael) Song, "A forensic examination of China’s national accounts," Brookings Paper's on Economic Activity, March 7, 2019, https://www.brookings.edu/bpea-articles/a-forensic-examination-of-chinas-national-accounts/

Michael Beckley, "China's Economy Is Not Overtaking America's,"Journal Of Applied Corporate Finance, May 18, 2020, https://www.aei.org/research-products/journal-publication/chinas-economy-is-not-overtaking-americas/

"...China’s economic growth over the past three decades has been spectacular, even miraculous. Yet the veneer of double-digit growth rates has masked gaping liabilities that limit China’s ability to close the wealth gap with the United States. China has achieved high growth at high costs, and now the costs are rising while growth is slowing. As I explain in a recent book, data that accounts for these costs reveal thatthe United States is several times wealthier than China, and the gap appears to be growing by trillions of dollars every year.1 This conclusion may surprise many people, given that China has a bigger GDP, a higher investment rate, larger trade flows, and a higher economic growth rate than the United States. How can China outproduce, outinvest, and outtrade the United States—and own nearly $1.2 trillion in U.S. debt—yet still have substantially less wealth?....The reason is that China’s economy is big but inefficient. It produces vast output but at enormous expense. Chinese businesses suffer from chronically high production costs, and China’s 1.4 billion people impose substantial welfare and security burdens. The United States, by contrast, is big and efficient. American businesses are among the most productive in the world; and with four times fewer people than China, the United States has much lower welfare and security costs. GDP and other standard measures of economic heft ignore these costs and create the false impression that China is overtaking the United States economically. In reality, China’s economy is barely keeping pace as the burden of propping up loss-making companies and feeding, policing, protecting, and cleaning up after one-fifth of humanity erodes China’s stocks of wealth.....China’s productivity growth has not only been unspectacular; it has been virtually nonexistent.5 By contrast, productivity improvements have accounted for roughly 20% of U.S. economic growth over the past decade, as it has for most of the past 100 years.6...China’s private sector is relatively efficient, but it is shackled to a bloated state sector that destroys nearly as much value as it creates.11... All told, more than one-third of China’s industrial capacity goes to waste and nearly two-thirds of China’s infrastructure projects cost more to build than they will ever generate in economic returns.12 Total losses from this waste are difficult to calculate, but the Chinese government estimates that it blew nearly $7 trillion on “ineffective investment” between 2009 and 2014.13...As just one example, China’s unused capacity in steelmaking exceeds the total combined steel production capacity of Japan, the United States, and Germany.10....The unsurprising result of all these burdens, plus the wasted investment highlighted above, has been a dramatic rise in China’s debt, from 100% of GDP in the 1990s to greater than 300% in 2019.19.... With a per capita income six times greater than China’s, the United States not only has more surplus wealth to pay down its debts it also has much lower interest rates....China’s household and corporate borrowers have been hit with rising interest rates that now consume 20% of China’s GDP.21 Roughly a quarter of China’s thousand biggest firms owe more money in interest than they earn in gross profits; and 45% of all new loans in China are being used to pay interest on old loans, a practice that analysts are calling “Ponzi finance.” Writing off these bad loans will cost China somewhere between $1.5 trillion and $10 trillion, with the latter figure nearly equal to China’s GDP.22 To put that number in context, consider that the United States spent 8% of its GDP writing off bad loans after the 2008 financial crisis.23.....Ultimately, the only way for China to solve its debt problem without gutting social spending is to increase its productivity, which in turn will require innovation. The Chinese government understands this well. Since 2007, it has tripled R&D spending, employed more scientists and engineers than any other country, and mounted the most extensive corporate espionage campaign in history......These moves, however, have yet to turn China into an innovation powerhouse. China produces only half the high-technology output and highly-cited scientific studies as the United States, holds five times fewer international patents, and pays more royalties for technology than it takes in.24...China is a major player in high-technology supply chains, but Chinese firms mainly focus on low-tech activities...For those reasons, Deloitte and Boston Consulting Group both argue that the United States increasingly rivals China as the world’s most cost-competitive manufacturing nation.33....China now leads the world in retractions of scientific studies due to fraud, one-third of Chinese scientists have admitted to plagiarizing or falsifying results (versus 2% of U.S. scientists), and nearly two-thirds of China’s R&D spending has been lost to corruption.35/////This culture of fraud extends throughout China’s economy.....According to the World Bank and the UN, human capital—the knowledge, skills, and labor embodied in a nation’s population—constitutes more than half of the wealth of most countries. Both organizations estimate that the U.S. stock of human capital is several times greater than China’s.38 China has four times the population of the United States, but the average American worker generates seven times the output of the average Chinese worker.39....China also loses 400,000 of its most highly educated workers every year to foreign countries in net terms, including thousands of scientists, engineers, and “inventors” (people that have registered at least one patent).52The United States, by contrast, nets one million workers annually from all foreign countries, including roughly 20,000 inventors and 15,000 scientists and engineers, 5,000 of whom come from China.....The U.S. workforce is not only better educated but also healthier than China’s. China loses 40% more years of productive life per capita on average from major ailments.53....China is aging more rapidly than any society in history. The number of Chinese aged 65 and older will more than triple by midcentury, from 130 million in 2015 to 400 million by 2050.63 Meanwhile China’s workforce will shrink by 212 million—about one-third of the current total. At that point, senior citizens will account for more than 30% of China’s population versus only 20% of the U.S. population.....The United States can feed its population with only 1% of its workforce in agriculture whereas China devotes 30% of its workforce to farming—and still depends on food imports to feed its population.64....Roughly one-third of China’s provinces and two-thirds of its major cities suffer from extreme water scarcity.67....The United States generates more than three times as much wealth from each gallon of water as China.68....The United States generates roughly 40% more wealth per unit of energy than China.76....American farmers produce 30% more food per hectare than Chinese farmers.....China must recognize that its economic engine is not strong enough to support grand ambitions for territorial conquest and regional hegemony. Its best option, therefore, is to become a responsible stakeholder in the existing international order. The United States, on the other hand, must recognize that China is nowhere close to dominating East Asia, let alone challenging the United States for global primacy. And so Instead of preparing for preventive war, the United States should reinforce the existing East Asian balance of power...."

Conference Board. 2013. “Departing CEO Age and Tenure.” The Conference Board, CEO Succession Practices.https://www.conference-board.org/retrievefile.cfm

"....This paper has set out methods, presented data and analyzed investment and sources of economic growth including intangibles for the European Union and the United States. Our data for investment cover the EU27 member countries and the US in 1995-2005, and for the EU15 economies, the US plus Czech Republic and Slovenia we add the years 2006-2009. Our growth accounting covered 14 EU countries plus the US. Despite the emerging parity in the contribution of intangible capital to European growth, for some countries in Europe, this stands against a backdrop of smaller rates of growth of labor productivity than typically experienced in the UK and US. And in most cases the shortfall can be attributed to lower multifactor productivity growth. This could result from structural factors orthogonal to intangible capital per se, such as a greater ability to 37 obtain financing for innovation in certain countries. It also could be associated with returns to scale or intangible capital deepening itself, as suggested by Figure 9, which shows a positive association between MFP growth and the contribution of intangible capital deepening (and one that is much stronger than that for tangible capital, figure 10).Figure 9 - Intangible Capital and Spillover Effects

We are, of course, conditioned to think in terms of spillovers from R&D because the topic is well researched, but spillovers occur whenever an investment in innovation by a firm or an industry creates a platform that spurs follow on innovations and new or expanded markets (think “apps” for mobile phones, or the “clusters” that form around a successful business). To better understand the spillovers that are suggested by Figure 5 suggests deeper study of the returns to specific intangible assets and channels through which the returns accrue. With the advent of the IT revolution and Internet technology, the spillover channels may be quite different than the “distance” models formulated and studied by Zvi Griliches some time ago (see Griliches 1992 for a review).

A deeper dive into such topics is the subject of a forthcoming companion paper (Corrado, Haskel, JonaLasinio 2012) 38 Figure 10 - Tangible Capital and Spillover Effects The harmonized estimates of intangible capital for Europe introduced in this paper are preliminary, but key advances were made that substantially enhance their usefulness in policy discussions. The new growth accounting results are the broadest available results for Europe to date. Although Europe invests less in intangibles than the US, intangibles account still account for between 1/5 and 1/4 of the average growth of market sector labor productivity in Europe. In addition to improved measurement, advanced countries need to build a deeper understanding of the returns to intangible assets and of the role intangibles play in the national and international innovation diffusion process..."

Corrado, Carol, Jonathan Haskel, Cecilia Jona-Lasinio, and Massimiliano Iommi. July 2012. “Intangible Capital and Growth in Advanced Economies: Measurement Methods and Comparative Results.” Institute for the Study of Labor (IZA).https://www.conference-board.org/pdf_free/workingpapers/EPWP%201203.pdf

“…We estimate the monetary return to attending a highly selective college using the College and Beyond (C&B) Survey linked to Detailed Earnings Records from the Social Security Administration (SSA). This paper extends earlier work by Dale and Krueger (2002) that examined the relationship between the college that students attended in 1976 and the earnings they self-reported reported in 1995 on the C&B follow-up survey. In this analysis, we use administrative earnings data to estimate the return to various measures of college selectivity for a more recent cohort of students: those who entered college in 1989. We also estimate the return to college selectivity for the 1976 cohort of students, but over a longer time horizon (from 1983 through 2007) using administrative data. We find that the return to college selectivity is sizeable for both cohorts in regression models that control for variables commonly observed by researchers, such as student high school GPA and SAT scores. However, when we adjust for unobserved student ability by controlling for the average SAT score of the colleges that students applied to, our estimates of the return to college selectivity fall substantially and are generally indistinguishable from zero. There were notable exceptions for certain subgroups. For black and Hispanic students and for students who come from less-educated families (in terms of their parents’ education), the estimates of the return to college selectivity remain large, even in models that adjust for unobserved student characteristics….”

Stacy Dale and Alan Krueger, “Estimating the Return to College Selectivity Over the Career Using Administrative Earnings Data.” National Bureau Of Economic Research, June 2011, http://www.nber.org/papers/w17159

Why Workers Without College Degrees Are Fleeing Big Cities

Last month the Census Bureau confirmed a confounding dynamic taking hold across the American landscape: Superstar cities, the nation’s economic powerhouses, hotbeds of opportunity at the cutting edge of technological progress, are losing people to other parts of the country.

Santa Clara County, Calif., home to most of Silicon Valley, lost 24,645 people to domestic migration, its ninth consecutive annual loss.

The trend is becoming widespread. Eight of the 10 largest metropolitan areas in the country, including those around New York, San Francisco, Los Angeles and Miami, lost people to other places in 2018. That was up from seven in 2016, five in 2013 and four in 2010. Migration out of the New York area has gotten so intense that its total population shrank in 2018 for the second year in a row.

As the chart above shows, 30 of the 44 largest counties, with populations above one million, recorded more domestic outflows than inflows of people in 2018.

This growing flow of people out of the hotbeds of innovation and economic activity underscores how lopsided the distribution of opportunity has become.

Places like Cupertino and Mountain View in Santa Clara County may still offer the best, most highly paid opportunities for the highly educated — lawyers and programmers seeking jobs at Apple or Google. The median family in that county makes $122,700 a year. In King County it is $105,512, way above the national median of $76,000.

The problem is that workers without a four-year college degree don’t earn anywhere near that much.

Consider janitors. In 1960, a janitor in the Deep South could more than double his income by moving to Santa Clara County, even after accounting for higher housing costs. Since then, janitors’ earnings have grown much more slowly in Silicon Valley and King County than in the Deep South. And this is upending the distribution of prosperity across the country.

The divergence of economic opportunity for workers on either side of college is not a new phenomenon. But we are only beginning to understand how it has reconfigured the rationale for migration: Moving to opportunity is not what it once was.

Forty or 50 years ago, someone with no more than a high school diploma had much better job opportunities in a big city than in a small town. Not only did those at the bottom of the wage scale — janitors, cashiers at 7-Eleven — make more money in dense urban centers, but these places also offered data entry, bookkeeping and other jobs that paid middle-class wages while requiring little or no college experience.

But as my colleagues Emily Badger and Quoctrung Bui noted recently in an article citing the work of the M.I.T. economist David Autor, big cities have lost their luster for workers without four-year college degrees. They will make no more in New York or San Francisco than they would in, say, small-town Alabama.

The clerical jobs that cities used to offer are largely gone, replaced by computer software or outsourced to other parts of the world. Moreover, the wage bump that big cities used to offer janitors and cashiers has mostly disappeared. Those who used to hold middle-income positions have dropped down the wage scale, competing for jobs at the bottom.

And even as big-city wages flatlined for workers without the requisite college degree, big-city housing prices soared ahead.

Rising rents affect everybody, of course. But housing sucks up a bigger share of the income of the poor. In 1960, housing absorbed 17 percent of a janitor’s household earnings in King County, compared with 9 percent of a lawyer’s, according to data from Mr. Ganong. By 2017, lawyers’ households had to pay almost 15 percent of their incomes. Janitors’ households had to pay 38 percent.

In the middle of the 20th century, rich urban clusters drew people from across the educational spectrum. Moving to the big city in search of a good job was as sound a decision for a worker who hadn’t completed high school as for someone with a college degree.

That attraction has faded even for the most-educated workers. Changes in the way the Census Bureau records domestic moves — in 1940 it asked people where they were five years ago; in 2016 it asked where they were the year before — reduces reported migration rates. Still, migration has subsided significantly over the last few decades. In 2016, more people with a bachelor’s degree or more left the nation’s richest metropolitan areas than arrived seeking opportunities there. Rising housing costs seem to deter them, too. For workers who haven’t cleared the college threshold, this is doubly true. For them, New York or Los Angeles or Seattle or San Francisco no longer makes sense.

It is not obvious how to turn big cities back into magnets of opportunity for the working class. Many of the jobs that offered a leg up to those without a college background no longer exist and won’t come back. But there are policies that could make a difference. Relaxing zoning regulations to make it easier to build could slow rising rents, ensuring that the housing supply keeps up with demand.

And yet the prospects do not look good. California lawmakers last week shelved Senate Bill 50, which would have forced cities to allow denser housing near public transit and removed density limits in wealthier areas close to job centers and good schools. The bill faced stiff opposition from many municipal governments and residents enamored with low-density living — not to mention rising property values.

The single-family-home ethos may eventually lose its grip on the politics of America’s superstar cities. Until then, it will make sense for those without a degree to look for opportunities elsewhere, in places like Kansas City or Des Moines or Las Vegas.

Eduardo Porter and Gilbert Gates, “Why Workers Without College Degrees Are Fleeing Big Cities,”New York Times, May 21, 2019, https://www.nytimes.com/interactive/2019/05/21/business/economy/migration-big-cities.html

Venture Funding of U.S. Startups Last Year Was Most Since 2000

The money spigots for U.S. startups opened last year to their widest since the peak of the dot-com boom in

Venture capitalists pumped $48.3 billion into U.S. startups in 2014, according to data today from the National Venture Capital Association and PricewaterhouseCoopers, the most since investors piled $105 billion into closely held companies in 2000. The 2014 total was up 61 percent from $30 billion in 2013 and was more than double the $20.4 billion invested in 2009.

The venture number caps what was a year of megadeal funding rounds and rising valuations for startups, especially in the technology industry. Mobile car-booking company Uber Technologies Inc. reeled in two financings that each exceeded $1 billion last year, ending with a valuation of $40 billion. Venture fundings of more than $500 million hit a six-year high last year, according to researcher CB Insights.

“I think we’re in an environment where there are a lot of things to celebrate, but we’re getting to the point of excessive in the amount of capital being raised and valuations,” said Joe Horowitz, managing general partner at Jafco Ventures.

The flood of money has renewed debate about whether Silicon Valley is in a bubble. Venture capitalists including Andreessen Horowitz’s Marc Andreessen, Benchmark’s Bill Gurley and Breyer Capital’s Jim Breyer have all cautioned in recent months that the good times may not last for startup fundraising

“When the market turns, and it will turn, we will find out who has been swimming without trunks on,” Andreessen said in a Twitter post in September. Bloomberg LP, the parent of Bloomberg News, is an investor in Andreessen Horowitz.

Other Investors

The NVCA and PricewaterhouseCoopers data doesn’t fully capture the amount of money being poured into U.S. startups because the survey only tracks investments from venture capitalists. Other types of investors have also increasingly started putting money into startups, including hedge funds, sovereign wealth funds and international companies. Last month, for example, Chinese Web company Baidu Inc. agreed to invest in San Francisco-based Uber.

Apart from Uber, other U.S. startups that benefited from the easy-money environment last year were virtual-reality startup Magic Leap Inc. and mobile messaging company Snapchat Inc., as well as media startup Vice Media, according to NVCA and PricewaterhouseCoopers data.

Bigger Numbers

Many of the startups taking in money are receiving larger funding amounts. The number of investing deals last year rose to 4,356 last year, up 3.9 percent from 2013, illustrating how more money flooded into a relatively constant number of companies, according to the NVCA and PricewaterhouseCoopers report.

“The inflow of capital and public market funds is very real and sizeable,” said Steve Harrick, general partner at Institutional Venture Partners. “The investment climate is very good in terms of opportunities.”

Venture capitalists last year paid particular attention to software startups, which accounted for 41 percent of total investments. It was the largest share the category has held since at least 1995, according to the data.

Among the most active venture firms in 2014 were New Enterprise Associates, Andreessen Horowitz, Kleiner Perkins Caufield & Byers, Google Ventures and Khosla Ventures, CB Insights said.

Even with the warnings from some venture capitalists, others said the rush of money is set to continue. Charles Kane, a senior lecturer at the Massachusetts Institute of Technology’s Sloan School of Management, said venture investors still need to deploy the huge amounts of funding they’ve raised.

“Next year will be bigger than the past year,” Kane said. “The venture capital firms that have gone out to raise money haven’t had trouble raising money.”

Eric Newcomer, “Venture Funding of US Startups Last Year Was Most Since 2000.” Bloomberg Businessweek, January 16, 2015, http://www.bloomberg.com/news/articles/2015-01-16/it-s-official-startup-funding-last-year-was-biggest-since-2000

“…Concentration has increased over the last 30 years or so in a variety of industries. This development has raised concern about weakened competition and resulting harm to consumers. Calls for tougher antitrust enforcement have become louder. There is also concern that rising concentration may be at least symptomatically related to declining business dynamism and lower productivity growth. There is, however, only sporadic evidence on these matters. This paper provides more systematic evidence on the interplay between concentration, prices and productivity across several hundred US manufacturing industries over two 15 year periods from 1982-2012. The consistent pattern is that high and rising concentration has been on average associated with better productivity growth.Rising concentration has also been associated with widening margins of price over input costs. On balance, the net price effects are trivial. Accordingly some skepticism about tougher merger policy may be warranted, since this would risk harm to productivity without benefiting consumers….”

Sam Peltzman, “Productivity and Prices in Manufacturing During an Era of Rising Concentration,” Social Science Research Network, May 10, 2018, https://papers.ssrn.com/sol3/papers.cfm

Research from FRBR, while market concentration has risen nationally, concentration of smaller markets as gone down. "...We have shown...that the increase in market concentration observed at the national level over the last 25 years is being shaped by enterprises expanding into new local markets. This expansion into local markets is accompanied by a fall in local concentration as firms open establishments in new locations. These observations are suggestive of more, rather than less, competitive markets...."Counterintuitively (figure 15) if you net out the largest firm, concentration is unchanged suggesting that either small business stay in business or when a big firm enters a market other big firms do as well.

"....Using U.S. NETS data, we present evidence that the positive trend observed in national product market concentration between 1990 and 2014 becomes a negative trend when we focus on measures of local concentration. We document diverging trends for several geographic definitions of local markets. SIC 8 industries with diverging trends are pervasive across sectors. In these industries, top firms have contributed to the amplification of both trends. When a top firm opens a plant, local concentration declines and remains lower for at least 7 years. Our findings, therefore, reconcile the increasing national role of large firms with falling local concentration, and a likely more competitive local environment....In this paper, we use the National Establishment Time Series (NETS) dataset to document four main facts regarding national and local product-market concentration in the U.S. economy between 1990 and 2014. Our first fact is that the observed positive trend in market concentration at the national level has been accompanied by a corresponding negative trend in average local market concentration.We measure concentration using the Herndahl-Hirschman index (HHI), but our findings hold for a variety of statistics..... The second fact shows that local concentration is falling across SIC 8 industries that together account for 78% of employment and 72% of sales.Furthermore, conditioning on industries where national concentration is rising, industries where local concentration has declined account for the majority of employment overall (72% of employment and 66% of sales) across all major sectors. The presence of these diverging trends is always large but more pronounced in services, retail trade, and FIRE relative to wholesale trade and manufacturing. This ordering is natural given that transport costs are less relevant in the latter two sectors. Together, these first two facts underscore an unmistakable decline in local concentration on average that is pervasive across all sectors..... Our third fact shows that among SIC 8 industries that exhibit this pattern, top firms have accelerated these trends. That is, excluding the top
firm in each industry (in terms of national sales in their SIC 8 industry in 2014), the national increase in concentration becomes naturally less pronounced.Perhaps more surprisingly, the decline in local concentration also becomes less pronounced.Put another way, large firms have materially contributed to the observed decline in local concentration. Among industries with diverging trends, large firms have become bigger but the associated geographic expansion of these firms, through the opening of more plants in new local markets, has lowered local concentration thus suggesting increased local competition. In the considerably smaller set of industries where we observe increases in both national and local concentration, top firms have also been responsible for both forms of concentration......Our fourth fact establishes that among industries with falling local concentration, the opening of a plant by a top firm is associated with a decline in local concentration at the time of the opening, and that this lower level of concentration persists for at least the next 7 years.This observation provides further evidence that in those industries, large enterprises do not enter and dominate the local market but instead lower its concentration, either by competing with the previous local monopolist or by simply adding one more establishment that grabs a proportional market share from other local establishments. In any case, the notion that entry by large firms eliminates local producers to the point of increasing concentration is certainly not supported in the vast majority of industries where most of U.S. employment resides...."

Rossi-Hansberg, Esteban, Pierre-Daniel Sarte, and Nicholas Trachter. April 17, 2019. “Diverging Trends in National and Local Concentration.” Federal Reserve Bank of Richmond.https://www.princeton.edu/~erossi/DTNLC.pdf

“…This paper calculates the effects of immigration on the wages of native US workers of various skill levels in two steps. In the first step we use labor demand functions to estimate the elasticity of substitution across different groups of workers. Second, we use the underlying production structure and the estimated elasticities to calculate the total wage effects of immigration in the long run. We emphasize that a production function framework is needed to combine own-group effects with cross-group effects in order to obtain the total wage effects for each native group. In order to obtain a parsimonious representation of elasticities that can be estimated with available data, we adopt alternative nested-CES models and let the data select the preferred specification. New to this paper is the estimate of the substitutability between natives and immigrants of similar education and experience levels. In the data-preferred model, there is a small but significant degree of imperfect substitutability between natives and immigrants which, when combined with the other estimated elasticities,implies that in the period from 1990 to 2006 immigration had a small effect on the wages of native workers with no high school degree (between 0.6% and +1.7%). It also had a small positive effect on average native wages (+0.6%) and a substantial negative effect (−6.7%) on wages of previous immigrants in the long run….”Ottaviano, Gianmarco, and Giovanni Peri. February 1, 2012. “Rethinking the Effect of Immigration on Wages.” Journal of the European Economic Association.https://academic.oup.com/jeea/article-abstract/10/1/152/2182016

“Does Deutschland Do Digital?” November 21, 2017. The Economist.https://www.economist.com/news/business/21678774-europes-biggest-economy-rightly-worried-digitisation-threat-its-industrial

Yet it would be unfair not to mention the “Bosch Start-up Platform” which the firm has recently set up in Ludwigsburg, a short drive away. It has already incubated a handful of firms. One has developed a wireless sensor to improve asparagus yields, another builds agricultural robots that kill weeds. Germany’s manufacturers are clearly changing. The question is whether they can do so fast enough.

Compare Silicon Valley and Germany’s equivalent for industrial firms, the region around Stuttgart, and the cultural differences are clear. Google’s campus in Mountain View offers about 30 different canteens with free food and generous opening hours. Visitors can see some self-driving cars, but the scenery is dominated by bicycles in primary colours which employees use to pedal between the many buildings. At Bosch in Gerlingen, employees all stream into a huge, centralised corporate cafeteria at lunchtime. And the most striking vehicles are green self-driving lawnmowers that keep the grass around the firm’s office block in perfect trim.

The engineering background of many managers also explains why they focus more on incremental improvements than on making drastic gains in their products’ functionality and ease of use. “Germans are really good at reducing theSpaltmass, the clearance between the parts of a car’s body,” says Christoph Keese, who was one of the Springer executives who spent a few months in Silicon Valley and has written an influential book about the experience. But the user interfaces for German products are a different matter. They are often so complex, says Mr Keese, that the word that appears most often on their displays isAnwenderfehler(“user error”).

Many employees, too, are not ready for the digital world. At German universities computer scientists are trained much like engineers, meaning they are focused on precision, explains Clemens Westerkamp of the University of Applied Sciences in Osnabrück. This mindset, he says, is a big advantage when building highly reliable systems, which are required in many industries. But it is a drawback in the world of software and data, where quick thinking and risk-taking are more important. “The battle for industrial platforms will be a fight between German precision and American speed,” says Mr Westerkamp.

A fourth and final hurdle relates to German corporate culture. Although German firms are no longer as hierarchical as they once were, they are still managed from the top down; risk-taking is discouraged. A key to developing successful platforms and services is to put them in the hands of autonomous teams—rather like internal startups—and to be prepared to shut projects down if they fail to take off. Many German giants now have “accelerators” to incubate new business ideas, but lots are just public-relations exercises. Only a few have gone as far as Klöckner, which has set up an incubator for internal startups in Berlin, far away from its headquarters in Duisburg, with the aim of digitising the firm’s entire supply chain.

Third, platforms are about sharing the data they gather. The best new services are often the result of combining information from different sources. But in Germany this can be difficult. One barrier is privacy: in addition to the country’s strict data-protection laws, there is a widespread suspicion of anything that smacks of monetising data. Even among younger Germans, nearly two-thirds worry that data generated by their cars will be sold on, according to a recent study by Deloitte, a consulting firm. Many Germans are wary of what has come to be calledPlattform-Kapitalismus, meaning the dominance of new markets by mighty foreign providers such as Google and Facebook (see chart 2). This is seen as somehow unfair and an example of American “cowboy capitalism”, which adds to the reluctance of German firms to create and promote their own platforms. Mittelstand firms too are reluctant to share data: they fear they could lose control of their intellectual property and thus their competitive edge.

Shy about sharingUser-unfriendly

Second, successful platforms tend to be “open”, meaning chiefly that their owners encourage other firms, including competitors, to build applications that run on them. Bosch, Trumpf, Siemens and others claim to be open, but it is not clear how welcoming they will be in practice. German bosses tend to think that they can offer all the necessary services themselves and do not like the idea of inviting others onto their platform. German carmakers could probably build the dominant operating system for vehicles if they overcame their rivalries. But it is unclear whether BMW, Daimler and VW’s Audi subsidiary will turn Here, the digital-maps business they are buying from Nokia, into a bona fide platform. Such proprietary thinking also makes German industrial firms reluctant to produce open-source software, or to give away other forms of intellectual property, in the way that Tesla, an American maker of electric cars, has done with its patents in an effort to spread its technology.

If that offers some comfort to German manufacturers, it only goes so far. First, platforms thrive when other firms, developers and customers make use of them. Most big IT companies have grown up managing such “ecosystems”, but this will not come easy to manufacturers. As Sangeet Paul Choudary, a technology analyst, puts it, they are steeped in “pipe thinking”: firms order supplies, fashion them into products and then send them towards customers. Now they must think more like a gardener, and maintain a park which attracts and retains other firms and customers, in order to flourish.

Yet the economics of the markets for industrial equipment, and the other business-to-business products many German firms make, are different, explains a report this month by Stiftung Neue Verantwortung, a think-tank, and other organisations. These markets move more slowly and are more complex. The expertise of incumbents is hard to replicate and, more important, in many cases they control access to the data their products generate. As a result, network effects are not as strong in these sorts of business.

Fortunately for many German industrial firms—and, for that matter, their counterparts in other countries—they have more time to adapt than consumer firms. In consumer markets, digital newcomers can quickly become dominant by exploiting the “network effects” in some technology platforms: the more people use them, the more apps and other offerings they generate, and the stronger they get versus any rivals. This is why German makers of cars and heating equipment look with trepidation at Google’s work on self-driving cars and intelligent thermostats.

The first aim of this bureaucracy is to produce templates and test beds for trying out innovative digital services, and to publicise interesting examples. Its second aim is to convince two groups of the need for change: the smaller among Germany’s Mittelstand firms, and the industrial trade unions, which, under the country’s “co-determination” model of labour relations, have a say in many management decisions. “Creating acceptance is key,” says Matthias Machnig, a senior official who leads the Industrie 4.0 effort at the ministry of economic affairs.

Does Deutschland do digital?

SINCE it was founded in 1923 Trumpf, a family-owned company based near Stuttgart, has had one main mission: making things that make things. It started out with motorised hand shears and other tools to work sheet metal. It then invented fabrication machines with a numerical-control system and later was among the first to use lasers to cut metal. A prime example of a firm from Germany’s industrial Mittelstand that has outgrown the label (which literally means “mid-sized trades”), the firm today has annual sales of €2.7 billion ($3.2 billion) and more than 10,000 employees worldwide.

Trumpf’s roots in metalworking and other hardware stand in stark contrast to what it is trying to achieve next: building a new business purely based on software and data. Unveiled last month, its online offering, called Axoom, connects machines built by Trumpf and others, and uses the data it collects from them to help customers organise their production—for instance, to warn them when they are running out of material or to order it directly from the supplier. Much like smartphones, Axoom will be able to run “apps” from other providers, such as software to schedule workloads, or to predict when machines will need a spare part.

The company’s attempt to redefine itself is emblematic of the leap that manufacturers, in Germany and everywhere else, now have to make. The much-discussed “internet of things” (IoT) is becoming a reality on factory floors: industrial machines and the products they make are increasingly packed with sensors and connected to the internet (see Schumpeter).

As a result, the rules in many industries, from construction equipment to cars, are changing: making things matters less and knowing things more. In many cases the successful companies will no longer be the ones that make the best products, but the ones that gather the best data and combine them to offer the best digital services. And the biggest winners of all may be those that control a “platform”, a layer of software that combines different kinds of devices, data and services, on top of which other firms can build their own offerings—just as Trumpf is trying to do with Axoom.

Mastering this sort of transformation ought to be on the agenda of any country with a big manufacturing base (see chart 1). But nowhere is the sense of urgency more developed than in Germany, where the fear that digitisation threatens its position as a leading industrial nation has been given added piquancy by Volkswagen’s recent emissions scandals. The first half of the battle to master the digital world was lost, according to Timotheus Höttges, the boss of Deutsche Telekom. “The question now is: how do we win the second half?”

Smart services do not have to be closely related to the principal use of the product. A high-end car, for instance, has the digital horsepower of 20 personal computers and generates 25 gigabytes of data per hour of driving, estimates Gabriel Seiberth of Accenture, a provider of IT and consulting services. Rather than just building vehicles, he argues, carmakers should be thinking about how to provide, and profit from, the entertainment and e-commerce services that could be offered on the screens inside a vehicle. “The car will become a central part of a person’s digital life,” says Mr Seiberth.

That strikes a particular chord in Germany. Some fear that its carmakers, which directly or indirectly employ one in seven workers nationwide, could be demoted to low-margin metal-bashers, while American tech giants make most of the money by providing the software and the in-car entertainment—and perhaps, in time, designing the cars themselves.

Apple and Google are pressing carmakers to install the operating systems they have designed for cars’ entertainment systems, which in practice will suck up all sorts of other data about the car and its occupants. Carmakers are realising that to give up this territory would risk their “sovereignty over the data” generated by their vehicles, in the words of Wilko Stark, Daimler’s strategy chief. They could end up like Samsung, whose profits from smartphones are limited by the fact that it depends on Android, Google’s mobile operating system.

It is not just German carmakers which worry about this sort of future, however. The American tech giants are also trying to establish platforms for the “smart home”, which collect data from appliances, heating systems and the like. “Whoever controls the platforms will rule the future,” declares Henning Kagermann, the head of Acatech, who coined the term “Industrie 4.0” for German industry’s collective efforts to make the transition to a digital, internet-connected future.

Despite such concerns, in some ways Germany’s industry seems well prepared for the rise of services and platforms. Its bosses are alive to the threat. Axel Springer, a publishing giant, started a trend among German firms by having some of its top people live in Silicon Valley for several months. The experience led the firm to invest in a consulting firm, gratingly called Hy!, which introduces old-economy executives to startups and helps them plan their digital transformation.

Some firms have already acted on the insight that digital platforms are crucial for their futures. One is Trumpf. Another is Klöckner, a metals trader, which has created a platform to connect steelmakers with construction firms and other customers. Germany’s engineering and manufacturing giants, too, have started to take platforms seriously: Bosch now offers an “IoT Suite” to help other companies create new services around connected devices. Deutsche Telekom has teamed up with other firms to establish Qivicon, a smart-home platform to rival Apple’s and Google’s.

The government is trying to help. It has taken the lead in Industrie 4.0, creating around it an unwieldy structure that ropes in everyone from trade associations and ministries to unions and academics. Only in a country with Germany’s corporatist tradition would an effort to make businesses more agile and responsive to change be so stuffed with steering committees, working groups and advisory boards.

“…We use a massive, matched employer-employee database for the United States to analyze the contribution of firms to the rise in earnings inequality from 1978 to 2013. We find that one-third of the rise in the variance of (log) earnings occurred within firms, whereas two-thirds of the rise occurred due to a rise in the dispersion of average earnings between firms. However, this rising between-firm variance is not accounted for by the firms themselves but by a widening gap between firms in the composition of their workers. This compositional change can be split into two roughly equal parts: high-wage workers became increasingly likely to work in highwage firms (i.e., sorting increased), and high-wage workers became increasingly likely to work with each other (i.e., segregation rose). In contrast, we do not find a rise in the variance of firm-specific pay once we control for the worker composition in firms. Finally, we find that two-thirds of the rise in the within-firm variance of earnings occurred within mega (10,000+ employee) firms, which saw a particularly large increase in the variance of earnings compared with smaller firms….”

Song, Jae, David Price, Fatih Guvenen, Nicholas Bloom, and Till von Wachter, “Firming Up Inequality,” The Quarterly Journal of Economics, October 25, 2018, https://academic.oup.com/qje/article-abstract/134/1/1/5144785

Atila Abdulkadiroglu, Joshua Angrist and Parag Pathak, “The Elite Illusion: Achievement Effects At Boston And New York Exam Schools,” Econometrica, January 2014, https://economics.mit.edu/files/9518

“…The results reported here suggest that an exam school education produces only scattered gains for applicants, even among students with baseline scores close to or above the mean in the target school. Because the exam school experience is associated with sharp increases in peer achievement, these results weigh against the importance of peer effects in the education production function. Our results also fail to uncover systematic evidence of racial composition effects. The outcome most strengthened by exam school attendance appears to be the 10th grade English score, a result driven partly by gains for minorities.Given the history of racial preferences (and their more recent elimination) in Boston’s exam schools, this finding seems worth further exploration. Overall, however,while the exam school students in our samples typically have good outcomes, most of these students would likely have done well without the benefit of an exam school education. Of course, test scores and peer effects are only part of the exam school story. It may be that preparation for exam school entrance is itself worthwhile.The RD design captures the impact of peer composition and possibly other changes at admissions cutoffs, while ignoring effects common to applicants on both sides. Likewise, unique features of an exam school education may boost achievement in specific subject areas. Students who attend Boston Latin School almost certainly learn more Latin than they would have otherwise. The many clubs and activities found at some exam schools may expose students to ideas and concepts not easily captured by achievement tests or our post-secondary outcomes. It is also possible that exam school graduates earn higher wages…Still, the estimates reported here suggest that any labor market gains are likely to come through channels other than peer composition and increased cognitive achievement. Can these results be reconciled with those of other studies reporting a strong association between group averages and individual outcomes? Every context is different, and the absence of peer effects in one setting does not prove that such effects are unimportant elsewhere.At the same time, it is worth emphasizing the high risk of specification error in peer analysis….the relationship between an individual-level variable and any group average of this variable is essentially mechanical. Likewise, any regression of one outcome variable on the group average of another outcome variable is biased toward a finding of peer effects due to the presence of common shocks. Random assignment to groups fails to solve either of these problems. Finally, any regression of an individual outcome on a combination of observationally varying individual- and group-level treatments is almost sure to produce something that looks like a peer effect for reasons related to the information content of the average (see, e.g., Acemoglu and Angrist (2000) and Moffitt (2001)). It is perhaps unsurprising, therefore, that experimental or quasi-experimental manipulation of predetermined peercharacteristics is less likely to uncover a peer effect than are other study designs. Estimates from the Moving to Opportunity demonstration, for example, which manipulated peer composition by randomly assigning housing vouchers, are consistent with the results reported here in offering little evidence of causal peer effects…”

Not surprising to me that school doesn’t change cognitive outcomes (remember all these kids were motivated enough to take the entrance exam in the first place) but hard to believe it doesn’t have an impact.