Month: May 2017

Life and work is more than reading and writing and arithmetic. Being able to function well with others in a wide range of situations is extraordinarily important–for many jobs, at least as important–as explicitly cognitive skills. The Future of Children has devoted is Spring 2017 issue to nine articles about \”Social and Emotional Learning.\” After reading through the articles, my sense is that the subject is of potentially enormous importance, and that the state of current knowledge and practice is  fragmented and incomplete, with difficulties in deciding what traits to study, at what ages, and how to measure them.  Here are a few snippets.

From the the introductory essay by Stephanie M. Jones and Emily J. Doolittle, called \”Social and Emotional Learning: Introducing the Issue:\”

\”Research increasingly suggests that social and emotional learning (SEL) matters a great deal for important life outcomes like success in school, college entry and completion, and later earnings. This research also tells us that SEL can be taught and nurtured in schools so that students increase their ability to integrate thinking, emotions, and behavior in ways that lead to positive school and life outcomes. Although the term social and emotional learning has been around for 20 years, we’ve recently seen a rapid surge in interest in SEL among parents, educators, and policymakers. … All 50 states have SEL standards in place at the preschool level, and four (Illinois, Kansas, West Virginia, and Pennsylvania) have SEL standards for kindergarten through 12th grade. …  We also know that teachers believe SEL skills can be taught, although they may not always know the best way to do so in their classrooms. … SEL goes by many other names. Common terms for this set of skills include character education, personality, 21st-century skills, soft skills, and noncognitive skills, just to name a few. …

\”But what are we talking about when it comes to SEL? Researchers, educators, and policymakers alike have trouble pinning down exactly what’s included in this broad domain—and what isn’t. The popular press has highlighted a wide array of skills, such as grit, empathy, growth mindset, social skills, and more. At its core, SEL involves children’s ability to learn about and manage their own emotions and interactions in ways that benefit themselves and others, and that help children and youth succeed in schooling, the workplace, relationships, and citizenship. To effectively manage emotions and social interactions requires a complex interplay of cognitive skills, such as attention and the ability to solve problems; beliefs about the self, such as perceptions of competence and autonomy; and social awareness, including empathy for others and the ability to resolve conflicts. …

\”Decades’ worth of research suggests that something other than academic skills and content knowledge strongly influences success in school and beyond. Indeed, SEL skills may be just as important as academic or purely cognitive skills for understanding how people succeed in school, college, and careers. In addition, preliminary evidence suggests that SEL skills could be central to understanding and remediating stubbornly persistent gaps in achievement defined by income and racial/ethnic differences. …\”

From the abstract of the article called \”SEL Interventions in Early Childhood,\” by Megan M. McClelland, Shauna L. Tominey, Sara A. Schmitt, and Robert Duncan:

\”Three strategies appear to make interventions more successful, the authors write. First, many effective SEL interventions include training or professional development for early childhood teachers; some also emphasize building teachers’ own SEL skills. Second, effective interventions embed direct instruction and practice of targeted skills into daily activities, giving children repeated opportunities to practice SEL skills in different contexts; it’s best if these activities grow more complex over time. Third, effective interventions engage children’s families, so that kids have a chance to work on their SEL skills both at school and at home. Family components may include teaching adults how to help children build SEL skills or teaching adults themselves how to practice and model such skills. 

\”Are early childhood SEL interventions cost-effective? The short answer is that it’s too soon to be sure. We won’t know how the costs and benefits stack up without further research that follows participants into later childhood and adulthood. In this context, we particularly need to understand how the long-term benefits of shorter, less intensive, and less costly programs compare to the benefits of more intensive and costlier ones.\”

From the abstract for \”Promoting Social and Emotional Competencies in Elementary School,\” by  Stephanie M. Jones, Sophie P. Barnes, Rebecca Bailey, and Emily J. Doolittle:

\”But evidence from the most rigorous studies of elementary-school SEL programs is ambiguous. Some studies find few or no effects, while others find important and meaningful effects. Or studies find effects for some groups of students but not for others. What causes such variation isn’t clear, making it hard to interpret and act on the evidence. What are the sources of variation in the impacts of SEL programs designed for the elementary years? To find out, Stephanie Jones, Sophie Barnes, Rebecca Bailey, and Emily Doolittle examine how the theories of change behind 11 widely used school-based SEL interventions align with the way those interventions measure outcomes. Their central conclusion is that what appears to be variation in impacts may instead stem from imprecise program targets misaligned with too-general measures of outcomes. That is to say, program evaluations often fail to measure whether students have mastered the precise skills the programs seek to impart.\”

From the abstract for \” Social and Emotional Learning Programs for Adolescents,\” by David S. Yeager:

\”On the positive side, Yeager finds that effective universal SEL can transform adolescents’ lives for the better. Less encouragingly, typical SEL programs—which directly teach skills and invite participants to rehearse those skills over the course of many classroom lessons—have a poor track record with middle adolescents (roughly age 14 to 17), even though they work well with children. But some programs stand out for their effectiveness with adolescents. Rather than teaching them skills, Yeager finds, effective programs for adolescents focus on mindsets and climate. Harnessing adolescents’ developmental motivations, such programs aim to make them feel respected by adults and peers and offer them the chance to gain status and admiration in the eyes of people whose opinions they value.\”

Those interested in this subject might also look back at my post on \”The Economics of Noncognitive Skills\” (October 14, 2016).

Erik Brynjolfsson and Tom M. Mitchell serves as co-chairs of a \”Committee on Information Technology, Automation, and the U.S. Workforce,\” which produced the report \”Information Technology and the U.S. Workforce: Where Are We and Where Do We Go from Here?\”  (March 2017). The report offers a nice overview of the extremely widespread adoption and application of information technology across the economy (which I\’ll skip over here) as well as discussion of the effects of information technology on productivity, employment, and inequality. Here, I\’ll just pass along a few comments on these themes. The emphasis of the report is on summarizing the various views and providing links to research, rather than on trying to draw hard bottom lines.

Technology isn\’t Destiny

\”[E]conomic and societal changes occasioned by technological developments are shaped, not just by the availability of new technologies and their features, but also by ideologies, power structures, and human aspirations and agendas. Technologies are not exogenous forces that roll over societies like tsunamis with predetermined results. Rather, our skills, organizations, institutions, and values shape how we develop technologies and how we deploy them once created, along with their final impact.\”

Productivity Measures Output/Inputs, not Technological Progress or Welfare

\”A related, but more fundamental, issue is that productivity is neither a measure of technological progress nor welfare. Productivity is based on gross domestic product (GDP), which is in turn a measure of production or output. However, technological progress can increase welfare without increasing output. For instance, if Wikipedia replaces a paper encyclopedia or a free GPS mapping app replaces a stand-alone GPS device, then consumers can be better off even if output is stagnant or declining.\”

[In passing, I\’ll note that the Spring 2017 issue of the Journal of Economic Perspectives, where I toil in the fields as Managing Editor, has a three-paper symposium bearing on the issue of how or how much the measures of economic growth and productivity are biased by the arrival of new digital technologies. The three papers are \”Underestimating the Real Growth of GDP, Personal Income, and Productivity,\” by Martin Feldstein; \”Challenges to Mismeasurement Explanations for the US Productivity Slowdown,\” by Chad Syverson; and \”How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches,\” by Erica L. Groshen, Brian C. Moyer, Ana M. Aizcorbe, Ralph Bradley and David M. Friedman.]

Information Technology is Shifting Employment with Occupational Categories

\”There have also been substantial shifts in employment in various occupational categories. For instance, the employment rate in clerical and sales jobs has fallen sharply, while employment in professional jobs has grown, as shown in Figure 3.1.\”

Technology Certainly Makes Some Jobs Obsolete, But Despite Centuries of Dire Predictions, It Has Not Led to Collapsing Demand for Labor

\”Predictions that new technologies will make workers largely or almost entirely redundant are as old as technological change itself. Although the story might be apocryphal, the famous Roman historian Pliny the Elder recounts how the Roman Emperor Tiberius killed an inventor who had supposedly invented unbreakable glass for fear of what this would do to the glassmaking trade. Queen Elizabeth I similarly refused to grant William Lee a patent for his mechanical knitting machine, arguing, “Consider thou what the invention could do to my poor subjects.” … 

\”However, predictions of widespread, technologically induced unemployment have not come to pass, at least so far. Technological changes over the last 200 years (and presumably many of those that came before) stimulated demand, created new markets, and fueled wage growth with few adverse consequences for aggregate employment. To be sure, technologies did and will continue to decimate particular occupations. As the Luddites feared, artisans lost their jobs in spinning and then weaving
as new technologies automated tasks they had previously performed. Similarly, the replacement of horses by automobiles eliminated the need for blacksmiths. But as these jobs disappeared, new ones sprang up to operate, manage, and service the new technologies. For instance, in the late 1800s, the replacement of the stagecoach by the railroad went hand in hand with the creation of new work for managers, engineers, machinists, repairmen, and conductors. Simultaneously, there was a boom in a range of new service occupations, from teaching to entertainment to sales. Nonetheless, simultaneous automation of a broader range of tasks could create unemployment or perhaps reduce aggregate levels of employment for an extended period of time.\”

Technology Contributes to Wage Inequality, but How Much is Uncertain

\”There is a debate in the research literature, and indeed, among committee members, about how much of the increase in inequality should be attributed to technology. There are three prominent narratives implicating technological change as a force toward greater inequality over the last several decades. First, many new technologies have replaced labor-intensive, routine, and physical tasks and expanded demand for labor in jobs that require social skills, numeracy, abstract thinking, and flexibility. This shift is often said to be responsible for higher returns to a college education and the widening income gap between skilled and less skilled workers. Second, as labor-intensive tasks are automated, the share of income going to capital relative to labor can increase, which may also help to explain the falling share of labor in overall GDP … Third, improvements in communication technologies have contributed to what has been termed the “superstar phenomenon” whereby the most successful performers in any occupation can now command a larger share of the global market. This in part reflects their improved ability to sell to not only customers in local markets, but also with greater ease to those in regional, national, and even global markets as improved communications technologies reduce the costs of reaching a broader audience. … 

\”It bears repeating that this discussion of technology and inequality should not be read to imply that all aspects of inequality and its recent increase in much of the Western world can simply be attributed to technological change: one must also consider the role played by the demise of unions, concessions on wages and job structures, the offshoring of work to places with lower wage rates, tax policy, and the increasing contribution played by financial investments in the accumulation of wealth. Indeed, increasing inequality in the United States has a political dimension, a phenomenon that is, of course, not new.\”

Technology is Altering the Nature of Work and Career Relationships

\”IT is enabling new work relationships, including a new form of on-demand employment. Although current digital platforms for on-demand work directly involve less than 1 percent of the workforce, they display significant growth potential. Many employers are increasingly viewing their relationship with employees as a short-term commitment rather than a lifelong investment. As Manpower Group CEO Jonas Prising recently put it, `Employers have gone from being builders of talent to consumers of work.\’\”

\”Those branches of politics, or of the laws of social life, in which there exists a collection of facts or thoughts sufficiently sifted and methodized to form the beginning of a science, should be taught ex professo [that is, by an expert]. Among the chief of these is Political Economy; the sources and conditions of wealth and material prosperity for aggregate bodies of human beings. This study approaches nearer to the rank of a science, in the sense in which we apply that name to the physical sciences, than anything else connected with politics yet does. I need not enlarge on the important lessons which it affords for the guidance of life, and for the estimation of laws and institutions, or on the necessity of knowing all that it can teach in order to have true views of the course of human affairs, or form plans for their improvement which will stand actual trial. 

\”The same persons who cry down Logic will generally warn you against Political Economy. It is unfeeling, they will tell you. It recognises unpleasant facts. For my part, the most unfeeling thing I know of is the law of gravitation: it breaks the neck of the best and most amiable person without scruple, if he forgets for a single moment to give heed to it. The winds and waves too are very unfeeling. Would you advise those who go to sea to deny the winds and waves—or to make use of them, and find the means of guarding against their dangers? My advice to you is to study the great writers on Political Economy, and hold firmly by whatever in them you find true; and depend upon it that if you are not selfish or hard-hearted already, Political Economy will not make you so.\”

An airline pilot who pays attention to the wind isn\’t \”unfeeling,\” and neither is a boat captain who pays attention to the water.As economists are quick to note, recognizing the existence of scarcity, budget constraints, and incentives, and thinking about either how best to work within the available constraints or how best to relax those constraints, is not a matter of hard-heartedness.  It\’s a matter of caring enough about the actual problem at hand to face it honestly.

Would high school students learn more if school start-times were moved back? For example, the American Association of Pediatrics recommends that adolescents should have schedules that allow them to sleep until 8AM. Jennifer Heissel and Samuel Norris offer some actual evidence on the point in “Rise and Shine: The Effect of School Start Times on Academic Performance from Childhood through Puberty,” Journal of Human Resources, published online before print, April 19, 2017 (the abstract is here, but many readers will have access to the article through an institutional library subscription).

David Figlio has a nice overview at \”Start high school later for better academic outcomes,\” a report posted at the Brookings Institution website (May 25, 2017). Rather than provide an inferior copy of his discussion (with includes mentions of some other evidence and a more in-depth discussion), I\’ll just offer some excerpts of his discussion of the Heissel and Norris paper.  Figlio writes:

\”The authors focus their attention on the relationship between sunlight and sleep, and take advantage of the fact that the state of Florida, where they conduct their research, is divided into two time zones. The sun comes up an hour later, on the clock, in the Eastern Time Zone than a few miles west in the Central Time Zone, but schools only partially account for this difference when setting their start times, so, on average, students in the Central Time Zone in Florida have more than half an hour more sunlight before school starts than do their counterparts in the Eastern Time Zone, and some have an hour or more additional sunlight, depending on when school starts.

\”One might be concerned that people living in different parts of Florida are somehow different in other ways as well, and Heissel and Norris are able to deal with this concern by concentrating on students who moved between time zones, while remaining in the northern part of Florida (typically called the Panhandle). Some students moved between the Eastern Time Zone and the Central Time Zone, thereby gaining extra sunlight in the morning before school, while others moved from the Central Time Zone to the Eastern Time Zone, thereby losing some sunlight before school starts. Their strategy, therefore, is to compare the same students’ test performance before versus after their cross-time zone moves. The authors found that people making these eastward and westward moves in the Florida Panhandle were similar across a large range of characteristics, and tended to follow similar over-time test score trends prior to their moves.

\”What happens when children get an extra hour of sunlight before starting school? (The authors estimate the effect of each additional minute of pre-school sunlight, and I’m presenting the effects of a 60 minute difference for ease of explication.) If they are young, math scores are barely affected—the estimated score improvement is just one percent of a standard deviation—but reading scores increase by six percent of a standard deviation. But once they reach puberty (approximately at age 11 for girls and age 13 for boys) math scores improve by eight percent of a standard deviation and reading score improvements remain at six percent of a standard deviation. …

\”Heissel and Norris carried out a thought exercise in which, for every Florida panhandle school district, they assigned the school district’s earliest start times to elementary students, the middle start times to middle school students, and the latest start times to high school students. This calculation would move elementary school start times 22 minutes earlier, middle school start times 13 minutes earlier, and high school start times 44 minutes later, on average.

\”Heissel and Norris estimate that making these scheduling switches would raise average math performance by six percent of a standard deviation and average reading performance by four percent of a standard deviation. While not earth-shattering performance changes, they are extremely impressive for a policy change that would cost school districts little to implement – and are approximately one-fourth the difference between an excellent-performing school and an average-performing school.\”

When I\’ve heard discussions of later start times for high school, several issues usually come up. First, there is an aversion to having the youngest children start earlier. Frankly, this has always seemed a little odd to me. I\’ve got no evidence on the point, but a lot of young children seem to wake up pretty early, and parents who are depending on school for child care have good reason to wish that their younger children were out the door a little sooner. Second, there\’s an argument that if the high school day starts later, it also needs to end later, which in turn means less time for high school sports and activities, or for after-school jobs. Third, there\’s an argument that lots of high school students are staying up until the middle of the night as it is, and if they can get up later, they will just stay up later.

\”The American Academy of Pediatrics strongly supports the efforts of school districts to optimize sleep in students and urges high schools and middle schools to aim for start times that allow students the opportunity to achieve optimal levels of sleep (8.5–9.5 hours) …\”  But of course, getting a sufficient quantity of sleep is not just about school start times. It\’s about the outside-school load of homework, school activities, jobs, family life, social life–and about developing a habit where the regular bedtime is a hour or more before midnight, not after.

An enormous number of pricing decisions in the modern economy, not only in financial markets but in many on-line markets in general, are facilitated by computer algorithms. Indeed, a market may include the interaction of several different computer algorithms from buyers, sellers, and market-makers. Maureen K. Ohlhausen, who is Acting Chairman of the U.S. Federal Trade Commission, asks \”Should We Fear The Things That Go Beep In the Night?Some Initial Thoughts on the Intersectionof Antitrust Law and Algorithmic Pricing,\” in a speech given at the Concurrences Antitrust in the Financial Sector Conference on May 23, 2017. As as setting for her discussion of issue raised by algorithmic pricing, she offers a hypothetical example of small-town gas stations, which goes like this:

\”I want you to imagine a small, rural town somewhere in the desert southwest. This little town is modest and utterly unremarkable in every way. But in the center of town, clustered around the only stoplight, are three gas stations. These three stations have the only gas for sale within 150 miles in every direction. The date is 1970, so this is a time with no internet, no personal computers, no cell phones, and certainly no algorithmic pricing. All three gas stations currently charge exactly the same price for a gallon of gas. Prices may go up and down as the wholesale price of gasoline moves, but all three stations generally charge identical prices, and have been charging essentially identical prices for years. 

\”All of that is about to change. At 6:00AM one bright clear Monday morning, the owner of the first gas station gets out his ladder and leans it against the big price sign out front. He then climbs up the ladder and changes the price, making it five cents a gallon more expensive. Then he takes his ladder down, walks over to a lawn chair in the shade and sits down to have a cup of coffee. At 10:00AM, he gets the ladder back out and lowers his price back down five cents so his price is now the same as everybody else’s price. He repeats that same pattern of behavior every Monday morning. He never directly talks to his competitors about the prices he is charging or why he is doing what he did. 

\”Has he violated the antitrust laws just by changing his price for four hours? If that is all he has done, the answer is no. Generally, firms are free to set whatever prices they choose, as long as they act independently. Nor would it be unlawful for one of the other gas stations to decide, on his own, to follow the lead of our analog-era friend with the ladder and start raising his own prices on Monday mornings. Even if all the stations in the town ultimately decide to follow the lead of the first station and raise prices five cents, then keep those high prices in place, the antitrust laws do not condemn this behavior. 

\”So why don’t we enforcers take action in this situation to prevent conscious parallelism? The simple reason is that there is no sensible remedy here. In a free market, individual actors are free to set their prices on the basis of all the information legally available to them. It is axiomatic that we cannot tell firms to ignore the public behavior of their rivals when they set prices without deleting the “free” in free market. Enjoining this kind of behavior would inevitably lead to price regulation, which is completely inimical to the underlying purposes of the antitrust laws. Because we cannot police this sort of behavior directly, instead we try to make sure, primarily through our merger enforcement program, that the conditions that allow this kind of behavior to take place generally don’t arise in the first place. We also prohibit explicit agreements to set prices collusively and exchanges of competitively sensitive, non-public information between competitors. 

\”Fortunately for all of us, there are many, many reasons why this kind of informal pricing interdependency frequently fails or breaks down in the real world. For example, when the products are highly differentiated, or the market participants have different cost structures, or transactions are relatively infrequent, it is very difficult to maintain stable, interdependent pricing just by watching the behavior of your rivals. So the specific facts of my gas station example are very important. 

\”Everybody is selling the exact same commodity product. Transactions happen frequently, with each individual transaction relatively small and unimportant, so there is little risk of major losses associated with price leadership. Also, there are few participants in the market, making it easier to get to a point of tacit consensus. Finally, and most critically, there is complete price transparency because everybody can see the prices everyone else charges just by looking at those big signs. If we take away any one of those facts, the whole thing will generally fall apart on its own. For example, if firms could somehow secretly discount and steal market share from their rivals, they have a significant incentive to do that and so on. …

\”So while our friend with the ladder may eventually, informally lead everyone’s prices higher, things look a lot different from a legal perspective if he walks over to one of his competitors and starts talking to him about prices. Suddenly we now have conduct that has nothing to do with independently setting prices and reacting to market conditions. The policy considerations that tolerate unilateral but interdependent pricing no longer apply. Once competitors reach an agreement setting price or output, they are engaged in behavior with no social utility and an enforcement response by the government is warranted. So there is a critical legal difference between concerted behavior among competitors aimed at influencing prices and unilateral decision-making in light of observed market conditions. Setting prices together is illegal, while observing the market and making independent decisions is not.\” 

Ohlhausen uses the small-town gas station example to argue that the issues raised by algorithmic pricing are reasonably familiar, that similar issues have come up in well-known cases in the past, and so the antitrust authorities will have power to act if needed.

For example, one concern about algorithmic pricing is that the players in the market may use it as a way of colluding with each other behind the scenes, in a way that isn\’t easily visible in market prices. Ohlhausen points out that more than 20 years ago, back in 1993, airlines were using the information on-line reservation systems as a way of limiting competition, but the antitrust authorities and the courts had no trouble understanding the problem and addressing it. She writes: \”This is because the type of technology used to communicate with competitors is wholly irrelevant to the legal analysis. Whether it is phone calls, text messages, algorithms or Morse code, the underlying legal rule is the same – agreements to set prices among competitors are always unlawful.\”

Ohlhausen proposes the \”guy named Bob\” rule: \” \”Everywhere the word `algorithm\’ appears, please just insert the words `a guy named Bob\’. … If it isn’t ok for a guy named Bob to do it, then it probably isn’t ok for an algorithm to do it either.\”

My sense is that Ohlhausen is surely correct about the underlying law here: that is, doing something with an algorithm does not offer any protection or immunity against antitrust rules. I also like the small-town gas station metaphor and the \”guy named Bob\” rule, as nice concrete ways of illustrating these issues. But I do worry that while algorithmic pricing doesn\’t alter the rules of antitrust, it may make collusion easier.

As one example, a few years back there was a scandal about the LIBOR benchmark interest rate (that is, London Interbank Offered Rate). The rate was set by having a number of big banks send in the interest rates they were being charged for borrowing. But it turned out that the individual actually submitting the rates were not sending actual numbers, but instead shading them up or down. As a result, the LIBOR would be a little higher or lower than it should have been. The effect was only a small amount for a short time, but LIBOR is linked to something like $300 trillion of financial instruments around the world, so a trader who knew that the rate was artificially high or low could find a way to cash in. This manipulation went on for years, and led to prosecutions at 15 major global financial institutions.

Yes, this particular case was eventually detected and prosecuted. But having algorithms ready to pounce on small and short-term movements in LIBOR clearly helped to facilitate the entire scheme. One wonders about the possibility of other cases, perhaps using market benchmarks or interest rates that are less prominent than LIBOR and involving far fewer participants, which are not detected. The problem here is not specifically algorithms, but rather that the internet offers lots of possibilities for those who wish to collude, and algorithms can make it easier to cash in on such collusion.

There are other cases where the algorithms themselves may become problematic. Algorithms are starting to offer the possibility of \”personalized pricing,\” which refers to presenting online shoppers with a carefully designed series of options and prices and \”if-you-buy-now\” sales that–in combination with their past buying patterns and well-known behavioral biases–push that person toward a certain choice.

In other cases, as we move deeper into the world of big data and artificial intelligence, algorithms for buying and selling and setting prices are increasingly moving beyond basic rules, like buying only when a price falls below a certain level. Algorithms are now analyzing past patterns of players in the market, including how those player have reacted in the past, and then planning and implementing a potentially multi-step strategy. My guess is that some point there will be an antitrust case featuring the \”algorithm defense,\” which basically says: \”Hey, I just set up the smart learning algorithm and let it run. How could I know that it would interact with other smart learning algorithms in a way that led to collusion?\” And the antitrust authorities (or other law enforcement) will need to argue that when a guy named Bob sets up and signs off on an algorithm, Bob needs to be personally responsible for what that algorithm does.

Before the Great Recession, cross-border financial assets were on the rise. Since then, the overall rise in cross-border assets has leveled off and the pattern of cross-border assets has shifted somewhat from international debt  to foreign direct investment–and also by the rise of global \”financial centers.\” Philip R. Lane and Gian Maria Milesi-Ferretti lay out the patterns in \”International Financial Integration in the Aftermath of the Global Financial Crisis,\” which appears as IMF working paper WP/17/115 (May 10, 2017). They summarize:

\”In particular, we have shown how the very fast growth in cross-border positions in relation to global GDP has come to a halt since the financial crisis, reflecting both a retrenchment of cross-border banking activity and the increased weight of less-financially-integrated emerging and developing economies in global GDP. Across country groups, we have documented the disproportionate role played by financial centers—both small offshore centers and a few larger advanced economies—in total holdings, as well as the growing but still relatively modest role played by emerging and developing economies. Across financial instruments we have shown how the retrenchment in cross-border banking activity and the much more modest increase in portfolio positions relative to pre-crisis trends has been offset by rapidly increasing FDI [foreign direct investment] positions. These have reflected to an important extent claims on and from financial centers, where pass-through financial vehicles as well as the shifting domiciles of multinationals have played a crucial role.\”

Here\’s an illustrative figure of global financial external assets and liabilities. The height of the bars shows the rise up to 2007, and the leveling out since then. The colors of the bars show three categories, the fairly familiar categories of advances and emerging/developing countries, and the perhaps less familiar category of \”financial centers,\” which in this paper includes \”advanced economies with sizable financial center activity (Belgium; Hong Kong S.A.R.; Ireland; Luxembourg; Netherlands; Singapore; Switzerland; and the United Kingdom), emerging economies with similar characteristics (Mauritius and Panama) as well as small financial centers (such as Bermuda and the
Cayman Islands).\” The authors note of these financial centers that \”as of 2007, these accounted for close to 10 percent of world GDP but over 43 percent of global financial assets. By 2015, their share in world GDP had declined to 8 percent, but their global share of external assets remained around 43 percent.\”

We also document how cross-border FDI positions have continued to expand, unlike positions in portfolio instruments and other investment. This increase is primarily explained by FDI positions vis-à-vis financial centers, which include an important role for so-called special purpose vehicles. This suggests that the increased complexity of the corporate structure of large multinational corporations is playing an important role in this respect. …

While most of the larger financial centers have important multinational corporations with
extensive cross-border activities, other factors play an important role in explaining both the size and composition of FDI claims and liabilities as well as their dynamics.

The first is the growing importance of Special Purpose Entities. These are legal entities with “little or no employment; or operations, or physical presence in the jurisdiction in which they are created by their parent enterprises which are typically located in other jurisdictions (economies)” (OECD, 2008). Such vehicles are used to raise capital or hold assets/liabilities and generally perform no production activities. Statistics on the relative importance of SPEs in total FDI are only available for a limited set of countries. Helpfully, those include the Netherlands and Luxembourg, which are the countries with the largest stocks of FDI claims and liabilities after the United States. The vast majority of their FDI claims and liabilities (over ¾ for the Netherlands and over 90 percent for Luxembourg) are indeed SPEs. Total FDI claims by SPEs for just these two countries have grown by over $3.5 trillion between 2007 and 2014—over ¼ of the increase in the stock of global FDI claims during the same period.

The second factor is the increased tendency of multinational companies to move their
domicile to a financial center. To the extent that the company is moving from a country
where it has larger production facilities than in the financial center this will generally
increase the stock of global FDI (think for instance of a U.S. pharmaceutical company with important local production facilities moving its headquarters to Ireland). In that case, global FDI would increase by the value of the U.S. production facilities minus the value of any facility previously located in Ireland. Indeed, the stock of FDI claims overseas by Ireland has increased by $600 billion between 2007 and 2014, and by that date it is over 5 times Irish GDP. The counterpart to an increase in FDI assets in the countries hosting re-domiciled firms is a matching increase in foreign portfolio equity liabilities, given that the underlying shareholders of the entities remain the same.

My own interpretation of these patterns is that the kind of foreign direct investment which involves a company taking real management interest in running a firm elsewhere, and which often involves transfers of skill and technology, may well be in decline. However, the kind of foreign direct investment that involves setting up foreign headquarters and foreign financial vehicles as a way of pushing back against tax and regulatory issues is on the rise. 

Meat, and especially beef, has a heavy environment footprint by the time you take into account the farm products and land needed to produce it. In particular, if one looks out at the emerging and not-yet-emerging markets across the global economy, and start calculating what kinds of farm output would be needed for a large share of the world to have the meat consumption of high-income countries, it\’s hard to see how it would work. However, there are ongoing efforts to think about how one might increase world production of protein with a smaller environmental footprint.

For example, I\’ve written before about \”First Burger Grown from Stem Cells Served in London\” (January 21, 2014). In \”Tradeoffs of Cultured Meat Production\” (May 16, 2016), I pointed to some studies suggesting that \”carneries\”–that is, factories that produce meat–could have a much smaller effect on land use and associated environmental costs than traditional meat production.

In a forthcoming issue of Global Food Security, Peter Alexander, Calum Brown, Almut Arneth, Clare Dias, John Finnigan, Dominic Moran, Mark D.A. Rounsevell ask the question: \”Could consumption of insects, cultured meat or imitation meat reduce global agricultural land use?\” A \”corrected proofs\” version article has been freely available online since April 22, 2017, but at some point when the article is published in the journal it will only be available through libraries and subscriptions. (The doi address will remain https://doi.org/10.1016/j.gfs.2017.04.001.)

Here\’s an illustrative figure from the paper. The top panel shows calories per unit of agricultural land; the bottom panel shows protein per unit of agricultural land. On both measured, beef is the lowest producer on the far right-hand side. The most efficient producer in both cases is soybean curd, followed by mealworm larvae and adult crickets. Cultured meat is in the middle of the pack, not too different from eggs, poultry, and pork.

Given the tumultuous changes in the US economy in recent years, I would have guessed that average \”job tenure\”–that is, the average time that someone with a job has held that job–was declining. My guess would have been wrong. Henry R. Hyatt and James R. Spletzer present the evidence that job tenure has been mostly on the increase since about 2000 in \”Shifting Job Tenure Distribution\” (U.S. Census Bureau, Center for Economic Studies, May 2016, CES 16-12R). For example, they write:

\”According to published statistics from the Current Population Survey (CPS), the proportion of workers with five or more years of tenure on their main job has increased from 44% to 51% from 1998 to 2014, and the proportion of workers with one year or less of tenure on their main job has decreased from 28% to 21%. We document a similar shift in the job tenure distribution for the years 1998 to 2013 using LEHD [Longitudinal Employer-Household Dynamics] microdata, which is nearly identical to the CPS tenure distribution once differences between these source data are accounted for.\” 

Here are a couple of illustrative figures. The top panel shows median job tenure in the Current Population Survey data, going back to 1951. The bottom panel shows the share of jobs that fall into certain job tenure categories: for example, the top line shows the share of employed people who have held a job for at least five years.

Here\’s a figure based on the Longitudinal Employer-Household Dynamics data, which is a database in which state governments send administrative records about who works where to the US Census Bureau, which combines them in a way that preserves anonymity for workers and employers. The database covers 95% of private sector jobs. Again, you can see the rise in the share of jobs held for more than five years.

What\’s going on behind these numbers? Hyatt and Spletzer forthrightly answer: \”While there is ample evidence that the labor market is shifting toward more stable jobs, the causes and consequences of declining dynamics and increasing stability remain unknown.\” But they do massage the data in way that offers some clues. For example, they note that older workers tend to have stayed at their jobs longer, and the US workforce is getting older. So they ask: To what extent does the rise in job tenure just reflect an older workforce? Or they note that the US economy has been experiencing a slowdown in the start-up rate of new companies for about 20 years now, so more workers are with older firms. Again, to what extent does the rise in job tenure reflect the the fact that US firms are older?

The answers to these questions vary with whether they are using Census or LEHD data, but in rough terms, the older workforce can explain about one-third of the rise in job tenure and the growing age of firms can explain about one-sixth of the change, but about half of the rise in job tenure is not explained by the factors they are able to consider.

There is a knee-jerk temptation to feel as if longer job tenure is likely to be a good thing–more stability for workers. But there are tradeoffs.

1) To the extent that longer job tenure is being driven by fewer start-ups and a smaller share of the US workforce being employed at new firms, it is clearly a mixed blessing.

2)The US labor market \”churns,\” as economists sometimes say. There are always millions of people separating from jobs and being hired for new jobs. In good times, the new jobs outnumber the separations; in bad times, it\’s the reverse. But along with the lower rate of start-up companies, the US labor market has been less showing less churn in the last 15-20 years. Both rates of hiring and of separation rates dropped off for the first decade of the 2000s, although they have rebounded a bit since then. Of course, some of the reason that job separation rates fell around 2007 is that, in a weak economy, workers have a greater tendency to hold on to the job they\’ve got. In addition, jobs where someone has just been hired are on average shorter-term, and so the drop in the hiring rate seems linked to longer job tenure. But of course, a less flexible job market with lower hiring rates is a mixed blessing, too.

3) The job tenure calculations are based on workers with jobs. Thus, a worker who is unemployed for  a time and then returns to the workforce in a new job will tend to reduce job tenure. But there has also been a decline in the US labor force participation rate–that is, a greater number of adults who don\’t have jobs and aren\’t looking for a job. I suspect that if some of of these people had stayed in the labor force, they might have been more likely to be moving in and out of various jobs, which would have pulled down the average job tenure.

4) One benefit of longer job tenure is that workers develop experience and skills which make them a good \”match\” for their employers, and thus enable them to get wage increases. But at least in the last 10 years or so, \”The increase in average real earnings since 2007 is less than what would be predicted by the shift toward longer-tenure jobs because of declines in tenure-held-constant real earnings. Regression estimates of the returns to job tenure provide no evidence that the shift in the job tenure distribution is being driven by better matches between workers and employers.\” Of course, earnings after about 2007 are sharply affected by the Great Recession.

But overall, the picture that emerges is that job tenure rates aren\’t up because of workers who are more productively  matched to stable jobs. Instead, job tenure rates are up from a combination of a less dynamic economy and a less fluid labor market, combined with a Great Recession that caused more workers to cling to the job they had.

As they explain, \”Of the 26 million businesses in 2014, 95 percent were pass-throughs, while only 5 percent were C-corporations.\” But most businesses are very small: for example, 41% of those 26 million businesses were sole proprietorships. Moreover, 99% of all companies and 95% of C-corporations had receipts of less than $10 million.

The share of total business income in C-corporations is falling, and share in partnerships and S-corporations is rising, to the point where pass-through corporations now receive more than half of all business income.

The tax rules are different in a variety of ways across these corporate forms. For example, if you receive a payment from a company for your labor, you owe Social Security and Medicare payroll taxes on that amount, but if you receive a share of profits, then then you don\’t owe those payroll taxes. The decision about what is a payment for labor and what is a share of profits contains a large element of discretion.  Thus, substantial time and energy goes into finagling the legal structure of the corporation, in part to take advantage of tax differences.

For example, the shares of C-corporations are \”closely held\” by a small number of investors, which can make them similar in some ways to S-corporations. Krupkin and Looney write:

\”While they are not “pass-throughs,” many closely-held C corporations in which the owners are also managers share certain similarities with pass-throughs and, in practice, the income of their owners is often taxed much like that of sole proprietors. Owner/managers of closely-held C corporations often pay themselves wages, which are deductible from corporate-level tax, in lieu of dividends, which are not. This way, they maintain the limited liability and legal benefits of incorporation, but avoid the two levels of corporate tax by receiving their income as wages. As a result, the taxes they face are more similar to general partners or sole proprietors than to, say, publicly-traded C corporations.\”

Conversely, it is increasingly common for large firms to organize themselves as partnerships and S-corporations: indeed, there are now even hybrid forms like partnerships which issue stock that is publicly traded.

\”Large businesses are responsible for nearly all of the sales and profits of C-Corporations, and a substantial majority of sales and profits of partnerships and S-corporations. Among sole proprietorships, in contrast, only 9 percent of sales and less than 1 percent of profits came from large businesses. Most hedge funds, private equity funds, law, consulting, and accounting firms are partnerships; these businesses can be large, global enterprises. Indeed, in 2014, about a quarter of partnership business income was earned in finance, real estate, and holding companies sectors, and about 13 percent by law firms. With the advent of publicly-traded partnerships, a few pass-throughs are now owned by thousands of shareholders and trade on stock exchanges like public C-corporations. Similarly, large S-corporations compete directly with large C-corporations in industries like engineering and construction, trade, and professional services.\”

In thinking about corporate tax reforms, it\’s important to remember that the shareholder-owned C-corporation is actually less than half of all business income. Any sensible reform has to take into account the entire universe of US corporations. Krupkin and Looney cite one prominent estimate: \”According to one U.S. Treasury study, if the relative shares of pass-through and C-corporate activity were held at 1980 levels, the average tax rate on business income in 2011 would have been 28 percent instead of 24 percent. This translates to more than $100 billion in lost revenue in 2011 alone.\”

In thinking about \”corporations\” more broadly, it\’s also important to remember there are huge differences between mega-giant C-corporations like Walmart, Apple, and ExxonMobil and the overwhelming number of other corporations out there–not only on the obvious dimension of size, but also in their legal form.

For some previous takes on this subject, see:

•  Out of the US Corporate Income Tax\” (December 22, 2014)
• \”Follow-Up: Opting Out of the US Corporate Income Tax (February 10, 2015)