Macro Roundup

"... The three main takeaways from a broad analysis of cross-country firm-level patterns are that (1)market power has increased moderately across advanced economies, as indicated by firms’ price markups over marginal costs rising by close to 8 percent since 2000, but not in emerging market economies; (2) the increase has been fairly widespread across advanced economies and industries, but within them, it has been concentrated among a small fraction of dynamic—more productive and innovative—firms.... As regards performance, the top 10 percent of firms in the markup distribution are, on average, about 50 percent more profitable, more than 30 percent more productive, and more than 30 percent more intensive in their use of intangible assets than the other 90 percent (Figure 2.5, panel 3).....and (3) although the overall macroeconomic implications have been modest so far, further increases in the market power of these already-powerful firms could weaken investment, deter innovation, reduce labor income shares, and make it more difficult for monetary policy to stabilize output. Even as rising corporate market power seems, so far, more reflective of “winner-takes-most” by more productive and innovative firms than of weaker pro-competition policies, its challenging macroeconomic implications call for reforms that keep future market competition strong...."

Wenjie Chen, Federico Díez, Romain Duval, Callum Jones, and Carolina Villegas-Sánche, "World Economic Outlook, The Rise of Corporate Market Power and It's Macroeconomic Effects," International Monetary Fund, April 2019, https://www.imf.org/en/Publications/WEO/Issues/2019/03/28/world-economic-outlook-april-2019

“…economic profit is distributed unequally along a power curve, with the top 10 percent of companies capturing 80 percent of positive economic profit….After adjusting for inflation, today’s superstar companies have 1.6 times more economic profit, on average, than the superstar companies of 20 years ago….. The middle 60 percent of companies record near-zero economic profit on average, showing how hard it can be to defy market forces. The bottom 10 percent destroy as much value as superstars create.We label companies in the top 10 percent as superstar companies…..As economic profits grow larger, so do economic losses at the other end of the distribution. The bottom 10 percent of companies destroy as much value as the top 10 percent create, and today’s bottom-decile companies have 1.5 times more economic loss, on average, than their counterparts of 20 years ago (Exhibit 1). That means for every company that creates economic value, there is another company that destroys economic value.Yet these value-destroying companies continue to survive, holding on to their resources for increasingly longer durations and continuing to attract capital. A growing number are turning into “zombie” companies, unable to generate enough cash flow even to sustain interest payments on their debts. The impact of these economic losses goes beyond these companies’ investors, managers, and workers: it drives down the returns for healthy companies that compete for the same resources or profits….For the vast majority of the world’s largest companies, those making up the middle 60 percent of the power curve, economic profit is hard to retain. They record near-zero economic profit, on average…..Superstar companies stand out for their size. Compared with median companies with annual revenues greater than $1 billion, superstar companies are seven times larger by revenue and capital investment (Exhibit 2). They are also productive, with 20 percent higher capital and labor productivity on average compared with median companies……Global economic data indicate that over the past 20 years, 70 percent of growth in GDP and gross surplus (an economic measure of profit) occurred in just five worldwide superstar activities: financial services, such as banking, credit, insurance, and asset management; internet-, media-, and softwareproduct development; pharmaceutical and medicalproduct development; professional services, such as R&D, management, and other high-skilled services; and real estate, rental, and leasing. Just two sectors of activity, financial services and real estate, account for about 45 percent of all the gains in gross surplus (Exhibit 3)….”

Sree Ramaswamy, Michael Birshan, James Manyika, Jacques Bughin, and Jonathan Woetzel, “What every CEO needs to know about ‘superstar’ companies,” McKinsey Global Institute, April 2019, https://www.mckinsey.com/featured-insights/innovation-and-growth/what-every-ceo-needs-to-know-about-superstar-companies

Gordon paper

"... Taking real GDP per hour worked as the broadest measure of labor productivity, its growth rate in the U.S. has declined from 2.7 percent per year during 1950-72, to 1.9 percent during 1972- 2005, to 1.1 percent during 2005-17. In the 15 Western European members of the European Union prior to its 2004 enlargement (the “EU-15”), the same growth measure declined even more sharply from 4.7 percent per year during 1950-72, to 2.0 percent during 1972-2005, to a mere 0.8 percent during 2005-17....There are two important differences in the timing and pace of the productivity growth slowdown in the U.S. and EU-10. The first, already identified above, is that the post-1972 slowdown in the EU was much sharper than in the U.S. The second is that the U.S. enjoyed a temporary productivity growth revival between 1995 and 2005 that is missing in the EU experience. Dividing up the postwar into periods delineated by 1972, 1995, and 2005, the EU experienced a continuous deceleration from 1950-72 to 1972-95 to 1995-2005 to 2005-2015, whereas the U.S. experienced a slowdown from 1950-72 to 1972-95, then a marked but temporary growth revival in the 1995-2005 interval, followed by another slowdown after 2005.....Despite these differences, this paper provides a new interpretation that points to similarities between the U.S. and EU-10 experienceboth in the aggregate and in the identity of those industries that made the largest contributions to changes in productivity growth over the intervals divided at 1972, 1995, and 2005. In ourinterpretation the EU was playing catchup to the U.S. over the entire period from 1950 to 1995. Starting in 1950 at a productivity level only 50 percent of the U.S., the EU caught up to 81 percent by 1972, growing much more rapidly than the U.S. as it rebuilt after the war and implemented the large backlog of inventions and innovations that had propelled U.S. growth in the first half of the 20th century. After 1972 it continued to catch up but at a slower pace, and we show that EU productivity growth in 1972- 95 was a mirror image of U.S. growth in the prior 1950-72 interval, not only in the sense that the growth rates of productivity were identical, but also that there is a high correlation across industries between the EU 1972-95 and the U.S. 1950-72 in the industry-by-industry productivity growth rates.By linking the U.S. experience in 1950-72 to the EU-10 growth pattern of 1972-95, we are able to point to another striking similarity. The productivity growth slowdown from these early periods to the most recent 2005-15 interval was an almost identical -1.68 percentage points for the EU-10 and -1.67 points for the U.S. Further the industry-by-industry composition of the slowdown was very similar, with a high correlation across industries in the extent of the early to- late slowdown....We show that both the pace of aggregate productivity growth during 1972-95 for the EU-10 as well as its industrial composition matched very closely the growth record of the U.S. in the previous 1950-72 time interval. A striking finding is that for the total economy the “early-to-late” productivity growth slowdown from 1972-95 to 2005-15 in the EU-10 (-1.68 percentage points) was almost identical to the U.S. slowdown from 1950-72 to 2005-15 (-1.67 percentage points). There is a very high EU-U.S. correlation in the magnitude of the early-to-late slowdown across industries. This supports our overall theme that the productivity growth slowdown from the early postwar years to the most recent decade was due to a retardation in technical change that affected the same industries by roughly the same magnitudes on both sides of the Atlantic....The set of conclusions for the EU-10 are straightforward. First, the EU-10 clearly lagged behind in ICT-investment, as demonstrated by the uniformly lower values for ICT indicators in Table 7. Second, ICT-intensity did not spur a productivity revival in EU-10 SP industries, and at best mitigated the slowdown in EU-10 CP industries. Finally, while the Electrical Machinery industry helped drive growth in the U.S. commodities industry, the same industry experienced lower growth in the EU-10 and did not respond to the ICT revolution.....Our point of departure is that, following a catch-up spurt of rapid growth in the first 25 years after World War II, when Europe was chasing the technological frontier that the U.S. had established before and during the war, in the next time interval 1972-95 the characteristics of EU-10 productivity growth were surprisingly similar both in overall pace and also in industry composition to that achieved by the U.S. in the prior 1950-72 time period. These results indicate that, if in 1972 you had known the rate of productivity growth by industry achieved in 1950-72 for the U.S., you would have been able to do a quite a good job of predicting productivity growth by industry in the EU-10 in the subsequent 1972-95 period (a relationship exhibited in Figure 9a)..... one of the most striking results of this paper is that the slowdown in EU-10 productivity growth from the 1972- 95 average growth rate to 2005-15 was exactly the same (-1.68 percentage points) as the slowdown in U.S. productivity growth from its 1950-72 average growth rate to 2005-15 (-1.67 percentage points). The start-interval and end-interval growth rates were very close, from 2.54 to 0.87 percent for the U.S. and from 2.31 to 0.63 percent for the EU-10. The slowdown over the same time intervals in the subset of industries that produce commodities was also almost identical (-2.15 percentage points for Europe vs. -2.13 points for the U.S.) Even more striking about what we call this “early-to-late change” is that there is a correlation coefficient of 0.81 across industries between the U.S. and EU-10 in the magnitude of their growth slowdowns (a pattern shown in Figure 10). The correlation coefficient rises to 0.85 for industry contributions to the total slowdown, that is, when industry productivity growth rates are weighted by the value added share of each industry.16 Thus the productivity growth slowdown is a transatlantic disease, not only in its overall magnitude but in the composition of industries making the biggest contributions. This supports our overall theme that the productivity growth slowdown from the early postwar years to the most recent decade was due to a retardation in technical change that affected the same industries by roughly the same magnitudes in the U.S. and in the EU-10...."

Robert J. Gordon and Hassan Sayed, "The Industry Anatomy of the Transatlantic Productivity Growth Slowdown," National Bureau of Economic Research, March 2019, https://www.nber.org/papers/w25703

"...Comparing these synthetic countries to their European counterparts reveals a virtually identical gradient in inequality and intergenerational mobility...."

Thor Berger and Per Engzell, "Brave Old World: Immigration, inequality and intergenerational mobility in the US," Center for Economic and Policy Research, March 28, 2019, https://voxeu.org/article/immigration-inequality-and-intergenerational-mobility-us

"...We use the regional concentration of ancestral groups to create ‘synthetic’ European countries (aggregates of places within the US with a heavy overrepresentation of a given group). Comparing these synthetic countries to their European counterparts reveals a virtually identical gradient in inequality and intergenerational mobility. Hence, the Great Gatsby curve also emerges when one compares levels of income inequality and intergenerational mobility across these synthetic European countries (see Figure 1). Is it about people or places? Can these differences be taken as evidence for differences in income mobility between communities within the US rather than, for example, Scandinavian Americans being more upwardly mobile than their fellow countrymen? The answer is arguably yes, because the black population shows similar patterns of income mobility in these areas. Further support for this interpretation is found when we study outcomes for children that received different exposure to, for example, places with British and Scandinavian ancestry during their childhood, due to family moves. In other words, it seems that European immigrant groups have made their imprint on communities, which shape opportunities for the local population more broadly...."

"...We first needed to determine the political leanings of just a sample of economists, which we did by linking about 2,000 economists to their campaign contributions and political petition signatures. These economists represent faculty from all types of institutions including many from the most prestigious universities in the country. We determined that there was a 60-40 liberal-conservative split among the economists in our data....."

Zubin Jelveh, Bruce Kogut and Suresh Naidu, "Economists Aren’t As Nonpartisan As We Think, FiveThirtyEight, December 8, 2014, https://fivethirtyeight.com/features/economists-arent-as-nonpartisan-as-we-think/

We must point out that the relationships we describe are correlations — we can’t claim that ideology is causing different research results. Although people’s political beliefs don’t change drastically after their mid-20s, it could be that economists’ views are influenced by their findings throughout their careers. In our paper we describe different ways we investigated this possibility and found no impact on our results.

We also stress that our results don’t mean economists are deliberately altering findings. It’s more likely that these correlations are driven by research areas and the methodologies employed by economists of differing political stripes.10 Economics involves both methodological and normative judgments, and it is difficult to imagine that any social science could completely erase correlations between these two.

So what do our results mean for public policy and the science of economics?

Policymakers may need to “re-center” economists’ findings by adjusting for ideology. Take the area of tax rates for high earners. The average optimal tax rate reported by economists in our data is 41 percent. Using our model, we can also estimate that these economists as a group are slightly left of center. We can then figure out what optimal top tax rate a hypothetical centrist economist would report: 33 percent.11

Political disagreements inside economics are probably a healthy reflection of political debates in society overall. While some economists will disagree with this (even the claim that economics is politicized is probably itself political), it’s likely that all stakeholders in economic research understand that the science of economics contains the larger American political splits within it.

CLARIFICATION (Dec. 8, 10:58 a.m.): The article has been updated to make clear that the table only shows the most ideological phrases when economists write about macroeconomics.

CORRECTION (Dec. 14, 11:53 a.m.): A chart in an earlier version of this article was missing about 40 percent of its data points and made the relationship it showed look weaker than it actually is. We’ve updated the article with the correct version of the chart.

Using these associations, we could then predict the ideology of any economist not in our sample by feeding his or her body of work into our algorithm. The results weren’t perfect, but the algorithm showed promising ability in distinguishing between liberal and conservative economists. To understand how well it performed, imagine that you randomly pick two economists and predict their ideologies. Random guessing would get it right 50 percent of the time and a perfect model would be able to produce the right affiliation 100 percent of the time. Our algorithm got it right 74.1 percent of the time,6 and was consistently able to extract a signal from inherently noisy text. If peer review and other professional norms are working as intended, then we shouldn’t be able to find any connection between ideology and text.

We then used these predicted ideologies to get a better understanding of the role political beliefs play in economics.

We first found that an economist’s research area is correlated with his or her political leanings. For example, macroeconomists and financial economists are more right-leaning on average while labor economists tend to be left-leaning. Economists at business schools, no matter their specialty, lean conservative. Apparently, there is “political sorting” in the academic labor market.

If there is job-market sorting, then maybe editors of journals are responsible. After all, academics make their careers off publishing. So we looked at whether journal editors influenced the ideology of the papers appearing in their journals.

We have good news for economics here: There’s no evidence that publication decisions are determined by editor ideology.

Then what explains our ability to detect ideology from text? Since ideology differs by field, another possibility is that a journal’s peer reviewers are already ideologically aligned. Liberal economists may choose to study labor markets because they know others in the field have similar beliefs. This is not only more ideologically comforting — it also makes it easier for those who share an ideology with reviewers to publish.

At this point, you may object to the idea that the text of a paper completely captures the findings it reports. What if a conservative and a liberal economist used ideologically tinged language in their papers, but both came up with the same numerical result about, say, the fiscal spending multiplier? The size of these multipliers, which quantify how much an increase in government spending changes economic growth, were intenselydebated during the financial crisis, with conservatives faulting fiscal expansion for increasing the deficit with no economic gain and liberals advocating for much greater deficit spending.

To test if ideology influences policy, e.g. taxes or deficits, we collected numerical research results from fields that have direct impacts on public policy,7 including government spending, minimum wages, various tax rates, and income mobility. The results from these fields map quite nicely onto the liberal-conservative ideological spectrum. For example, liberals think the fiscal multiplier is high, meaning the government can improve economic growth by increasing spending, while conservatives believe the multiplier is close to zero or negative. A similar logic applies to numerical results in the other fields.

As the following chart shows, we found a (significant) correlation8 when we compared the ideologies of authors with the numerical results in their papers.9 That means that a left-leaning economist is more likely to report numerical results aligned with liberal ideology (and the same is true for right-leaning economists and conservative ideology).

Economists Aren’t As Nonpartisan As We Think

According to purists, the field of economics is supposed to be free of political ideology. Economics views itself as a science1 and the prevailing consensus, best articulated by Nobel-winner and Chicago-school doyen George Stigler, is that “the dominant influence” in economics “is the set of internal values and pressures of the discipline” which help keep it nonpartisan.

And yet most well-known economists achieve their star status in the political sphere. The econowonks reading this could name the politics of economists Paul Krugman and Greg Mankiw without much hesitation. And a little Googling would uncover the ideological leanings of Martin Feldstein, John Cochrane, Christina Romer or Jonathan Gruber. Even Thomas Malthus and David Ricardo had disagreements over free trade that were mirrored in the House of Commons.

Still, economists’ beliefs are separate from what makes them successful academics: their research prowess. We can easily discount the political opinions economists express in op-eds. But they’re also publishing research in peer-reviewed journals. Are findings on tax rates, minimum wages or government spending also influenced by ideology?

This is not just navel-gazing, as economics research informs public policy.2A recent Congressional Budget Office analysis of the minimum wage describes how results from academia influenced the CBO’s projections. If those findings were at least partially influenced by political beliefs, then the CBO’s assessments may be unwittingly biased.

So we set out to test the idea of nonpartisan economics on a large scale. In a recent paper, we researched whether economists’ political leanings were associated with their professional work.3 The answer: yes.

Our main proxy for research was the text that appeared in academic papers. To identify political leanings, we had to figure out a way to measure the ideology of any economist — not just people like Krugman and Mankiw. Luckily, there’s a growing body of research on how to detect people’s ideology (or mood or personality type) from the way they write.

We first needed to determine the political leanings of just a sample of economists, which we did by linking about 2,000 economists to their campaign contributions and political petition signatures.4 These economists represent faculty from all types of institutions including many from the most prestigious universities in the country. We determined that there was a 60-40 liberal-conservative split among the economists in our data.5

With this knowledge about actual ideology in hand, we built an algorithm that discovered the relationships between political leanings and word choice in about 18,000 academic papers written by our sample of economists. For example, when writing about business cycles, we discovered conservatives were more likely to write “laissez faire” or “monetary economics” while liberals more often discussed “Keynesian economics” and “labor markets.” (The table below shows the most ideological phrases when economists write about macroeconomics.)

"....The recent slowdown in U.S. population growth thus understates the problem in many places. Even as the national growth rate has decelerated, the share of all counties falling below it has increased steadily from 64% from 1990 to 2000, to 73% from 2000 to 2010, to 86% from 2010 to 2017. About half of U.S. counties are now losing population outright each year—an historically high proportion. Over 50 million people, or 15% of the U.S. population, live in counties that have shrunk over the past decade, and another 11 million live in counties with largely flat populations.... The story for prime working age population is even more dramatic, with 80% of counties losing population ages 25 to 54 over the last decade. Together these counties were home to 149 million Americans in 2017. The proportion of counties losing prime working age population annually has been well above historic highs since 2000, where Moody’s Analytics forecasts it to remain into the next decade. These projections mean that, by 2037, 67% of U.S. counties will contain fewer prime working age adults than they did in 1997, even though the United States as a whole will add 24.1 million prime working age adults and 98.8 million people in total over that same period...."

Adam Ozimek, Kenan Fikri and John Lettieri, "From Managing Decline to Building The Future Could A Heartland Visa Help Struggling Regions?" Economic Innovation Group, April 2019, https://eig.org/wp-content/uploads/2019/04/Heartland-Visas-Report.pdf

T. Robert Fetter, Andrew L. Steck, Christopher Timmins and Douglas Wrenn, "Learning by Viewing? Social Learning, Regulatory Disclosure, and Firm Productivity in Shale Gas," National Bureau of Economic Research, December 2018, https://www.nber.org/papers/w25401

"...Firms in the oil and gas industry, as in many industries, rely on secrecy as a mechanism to capture value from their investments in innovation and maintain their competitive advantage....In this paper, we shed light on whether innovations in fracturing fluids do in fact constitute a competitive advantage. In particular, we compare detailed, well-level information on inputs to test whether the chemicals used become more similar with public disclosure....Our paper makes three primary contributions. Our first contribution is to the literature on the relationship between secrecy and innovation: We provide insight into the importance of the social tradeoffs policy makers face when considering disclosure laws. There may be a compelling social benefit from disclosing specific information, such as when production processes involve toxic chemicals in residential areas, but this social benefit must be weighed against the ability of firms to realize economic returns from their innovations. We provide evidence that innovations in hydraulic fracturing chemicals are valuable, and furthermore that innovation decreases following the disclosure rule suggesting that this tradeoff may be important at least in this context.Second, this paper expands the literature on the empirical effects of transparency or disclosure regulations in a new direction. Our analysis is the first, to our knowledge, to combine a study of the effects of information disclosure regulations with social learning. We find strong evidence that disclosure regulations about an emerging technology can enable social learning or spillovers that would not have occurred otherwise.Prior papers in the industrial organization literature have examined the effect of price or quality disclosure on market structure and competition....while other work has documented the effects of disclosure laws on environmental and health outcomes.....as well as investor behavior....Separately, other authors have studied the phenomenon of social learning....without considering the role of disclosure regulations specifically. Our research bridges these streams. Third, we study the role that chemical additives have played in the development of hydraulic fracturing technology. Several recent papers, beginning with Covert (2015), and continuing with Steck (2018) and Agerton (2018), have documented the rise of hydraulic fracturing and the role that learning plays on different firm decisions. We are the first to seriously consider the role of chemicals in firm learning, and thereby contribute to the literature on hydraulic fracturing specifically and social learning about emerging technologies more generally.... We find evidence for convergence in chemical inputs following the mandatory public disclosure law, and a link between that convergence and well productivity. In sum, the evidence suggests that disclosure laws affecting oil and gas production firms in Pennsylvania opened a new channel for social learning, and that operators who exploited this channel were able to increase their wells' productivity. This finding supports the argument that public disclosure laws can erode innovative firms' competitive advantages. Our last finding, that the disclosure law may be associated with a reduction in innovative activity, further corroborates this concern, and suggests that policymakers ought to consider the potential long-run costs of reduced innovation. Nonetheless, the net effect of such disclosure laws on public welfare over the longer term remains an open question: any welfare loss from reduced innovation needs to be weighed against the gains from transparency and dissemination...."

they don't estimate a net effect on sector productivity, ie the increase in productivity versus decreased innovation