Administrators and Part-Timers: Changes in U.S. Higher Education Workforce

U.S. higher education has been seeing three main changes in staffing patterns in the last decade or so: 1) part-time faculty are way up; 2) administrators are way up; 3) and staff are down. In the middle of all this, the number and pay of full-time tenure track faculty hasn't changed much. Donna M. Desrochers and Rita Kirshstein describe the patters in  "Labor Intensive or  Labor Expensive? Changing Staffing and Compensation Patterns in Higher Education," as a Februrary 2014 "Issue Brief" for the Delta Cost Project at the American Institutes of Research. The analysis is based on a dataset maintained by the National Center for Education Statistics. Here is some of the evidence that caught my eye.

The total number of employees U.S. higher education rose 25% from 2000 to 2012--but the nubmer fo students in higher education increased, too. Thus, when looking at employees in higher education, it is most useful to adjust for the number of students. This figure shows that among public institutions, the number of employees per 1,000 students has been flat or declining since 2000. For private institutions, which tend to have greater financial resources, it has been rising. Research institutions have more employees, because running the administrative apparatus requires them.

But within these overall employee numbers, a shift is occurring. This figure shows changes in different categories of employees over time just for research universities, again expressed per 1,000 full-time equivalent students.

Overall, full time faculty are about 20-25% of the employees in higher education, with the number being a little lower in research-oriented institutions and higher in teaching-oriented institution. The number of faculty relative to the number of students (the blue line) has barely budged in the public research universities, and has risen in the private ones.  But even among full-time faculty, the share with a time-limited contract instead of a conventional tenure arrangement is declining: "Among full-time faculty only, the share of non-tenure-track professors increased about 3 percentage points between 2004 and 2012. By 2012, these non-tenure track positions represented more than one third of assistant professors, 18 percent of associate professors, and 12 percent of full professors ..."

Part-time faculty, shown by the purple line in the earlier figure, have risen. Again, looking at faculty per 1,000 students, the number of full-time faculty has risen only at private research universities. In other types of institutions, the number of full-time faculty has either stayed constant with the number of students or dropped--while the share of part-timers per student has risen among all institutions.

The number of professional staff per student has risen substantially, as shown by the orange line above. The study offers this definition: "Professional (support and service): Positions that provide student services, academic, or professional support and generally require a bachelor’s degree. Examples include business/financial analysts, human resources staff, computer administrators, counselors, lawyers, librarians, athletic staff, and health workers."

In both types of research universities, the number of "nonprofessional" staff--technical, clerical, service/maintenance--has fallen relative to the number of students. This group is about one-quarter of the employees at higher education institutions. What has actually happened here is the total number of these employees hasn't changed much, but they are serving a larger number of students over time--probably due in part to the information technology revolution.

The picture that emerges from all this is fairly clear. When it comes to employment, colleges and universities have tried to hold down faculty costs in dealing with the expanding numbers of students by the use of time-contract faculty and part-timers. The nonprofessional staff are dealing with the increased number of students by using improved information technology and other capital investments, without a need for  a higher total number of staff. But the number of professional staff is rising, both in absolute terms and relative to the number of students. Desrochers and Kirshstein report these patterns in a neutral tone: "Growing numbers of administrative positions (executive and professional) and
changes in faculty composition represent long-standing trends. The shifting balance among these positions has played out steadily over time in favor of administrators, and it is unclear when a tipping point may be near. Whether this administrative growth constitutes unnecessary “bloat” or is justified as part of the complexities involved in running a modern-day university remains up for debate."

I'll only add that institutions are defined by their people. As the full-time and tenured faculty become a smaller share of the employees of the institution and the professional administrators become a larger share, the nature and character of the institution inevitably changes. In this case, colleges and universities have become less about faculty, teaching, and research, and more about the provision of professional services to students and faculty. As far as I know, this shift was not planned or chosen, and the costs and benefits of such a shift were not analyzed in advance. It just happened.

Winter 2014 Journal of Economic Perspectives is Live!

The Winter 2014 issue of the Journal of Economic Perspectives is now freely available on-line, courtesy of the publisher, the American Economic Association. Indeed, not only this issue but all previous issues back to 1987 are available. (Full disclosure: I've been the Managing Editor since the journal started, so this issue is #107 for me.) I'll probably blog about some of these articles in the next week or two. But for now, I'll first list the table of contents, and then below will provide abstracts of articles and weblinks.

Symposium: Manufacturing
"US Manufacturing: Understanding Its Past and Its Potential Future," by Martin Neil Baily and Barry P. Bosworth
"Competing in Advanced Manufacturing: The Need for Improved Growth Models and Policies," by Gregory Tassey
"Management Practices, Relational Contracts, and the Decline of General Motors," by Susan Helper and Rebecca Henderson

Symposium: Agriculture
"Global Biofuels: Key to the Puzzle of Grain Market Behavior," by Brian Wright
"Agricultural Biotechnology: The Promise and Prospects of Genetically Modified Crops," by Geoffrey Barrows, Steven Sexton and David Zilberman
"Agriculture in the Global Economy," by Julian M. Alston and Philip G. Pardey
"American Farms Keep Growing: Size, Productivity, and Policy," by Daniel A. Sumner

Articles
"From Sick Man of Europe to Economic Superstar: Germany's Resurgent Economy," by Christian Dustmann, Bernd Fitzenberger, Uta Schönberg and Alexandra Spitz-Oener
"When Ideas Trump Interests: Preferences, Worldviews, and Policy Innovations," by Dani Rodrik
"An Economist's Guide to Visualizing Data," by Jonathan A. Schwabish

Features
"Recommendations for Further Reading," by Timothy Taylor
"Correspondence: The One Percent,"  Robert Solow, N. Gregory Mankiw, Richard V. Burkhauser, and Jeff Larrimore

_________________________________________

And here are the abstracts and links:

Symposium: Manufacturing

US Manufacturing: Understanding Its Past and Its Potential Future
Martin Neil Baily and Barry P. Bosworth
The development of the US manufacturing sector over the last half-century displays two striking and somewhat contradictory features: 1) the growth of real output in the US manufacturing sector, measured by real value added, has equaled or exceeded that of total GDP, keeping the manufacturing share of the economy constant in price-adjusted terms; and 2) there is a long-standing decline in the share of total employment attributable to manufacturing. The persistence of these trends seems inconsistent with stories of a recent or sudden crisis in the US manufacturing sector. After all, as recently as 2010, the United States had the world's largest manufacturing sector measured by its valued-added, and while it has now been surpassed by China, the United States remains a very large manufacturer. On the other hand, there are some potential causes for concern. First, though manufacturing's output share of GDP has remained stable over 50 years, and manufacturing retains a reputation as a sector of rapid productivity improvements, this is largely due to the spectacular performance of one subsector of manufacturing: computers and electronics. Second, recently there has been a large drop in the absolute level of manufacturing employment that many find alarming. Third, the US manufacturing sector runs an enormous trade deficit, equaling $460 billion in 2012, which is also very concentrated in trade with Asia. Finally, we consider the future evolution of the manufacturing sector and its importance for the US economy. Many of the largest US corporations continue to shift their production facilities overseas. It is important to understand why the United States is not perceived to be an attractive base for their production.
Full-Text Access | Supplementary Materials

Competing in Advanced Manufacturing: The Need for Improved Growth Models and Policies
Gregory Tassey
The United States has underinvested for several decades in a set of productivity-enhancing assets necessary for the long-term health of its manufacturing sector. Conventional characterizations of the process of bringing new advanced manufacturing products to market usually leave out two important elements: One is "proof-of-concept research" to establish broad "technology platforms" that can then be used as a basis for developing actual products. The second is a technical infrastructure of "infratechnologies" that include the analytical tools and standards needed for measuring and classifying the components of the new technology; metrics and methods for determining the adequacy of the multiple performance attributes of the technology; and the interfaces among hardware and software components that must work together for a complex product to perform as specified. If the public–private dynamics are not properly aligned to encourage proof-of-concept research and needed infratechnologies, then promising advances in basic science can easily fall into a "valley of death" and fail to evolve into modern advanced manufacturing technologies that are ready for the marketplace. Each major technology has a degree of uniqueness that demands government support sufficiently sophisticated to allow efficient adaptation to the needs of its particular industry, whether semiconductors, pharmaceuticals, computers, communications equipment, medical equipment, or some other technology-based industry.
Full-Text Access | Supplementary Materials

Management Practices, Relational Contracts, and the Decline of General Motors
Susan Helper and Rebecca Henderson
General Motors was once regarded as the best-managed and most successful firm in the world. However, between 1980 and 2009, GM's US market share fell from 46 to 20 percent, and in 2009 the firm went bankrupt. We argue that the conventional explanation for this decline—namely high legacy labor and healthcare costs—is seriously incomplete, and that GM's share collapsed for many of the same reasons that many highly successful American firms of the 1960s were forced from the market, including a failure to understand the nature of the competition they faced and an inability to respond effectively once they did. We focus particularly on the problems GM encountered in developing the relational contracts essential to modern design and manufacturing, and we discuss a number of possible causes for these difficulties. We suggest that GM's experience may have important implications for our understanding of the role of management in the modern, knowledge-based firm and for the potential revival of manufacturing in the United States.
Full-Text Access | Supplementary Materials

Symposium: Agriculture

Global Biofuels: Key to the Puzzle of Grain Market Behavior
Brian Wright
In the last half-decade, sharp jumps in the prices of wheat, rice, and corn, which furnish about two-thirds of the calorie requirements of mankind, have attracted worldwide attention. These price jumps in grains have also revealed the chaotic state of economic analysis of agricultural commodity markets. Economists and scientists have engaged in a blame game, apportioning percentages of responsibility for the price spikes to bewildering lists of factors, which include a surge in meat consumption, idiosyncratic regional droughts and fires, speculative bubbles, a new "financialization" of grain markets, the slowdown of global agricultural research spending, jumps in costs of energy, and more. Several observers have claimed to identify a "perfect storm" in the grain markets in 2007/2008, a confluence of some of the factors listed above. In fact, the price jumps since 2005 are best explained by the new policies causing a sustained surge in demand for biofuels. The rises in food prices since 2004 have generated huge wealth transfers to global landholders, agricultural input suppliers, and biofuels producers. The losers have been net consumers of food, including large numbers of the world's poorest peoples. The cause of this large global redistribution was no perfect storm. Far from being a natural catastrophe, it was the result of new policies to allow and require increased use of grain and oilseed for production of biofuels. Leading this trend were the wealthy countries, initially misinformed about the true global environmental and distributional implications.
Full-Text Access | Supplementary Materials

Agricultural Biotechnology: The Promise and Prospects of Genetically Modified Crops
Geoffrey Barrows, Steven Sexton and David Zilberman
For millennia, humans have modified plant genes in order to develop crops best suited for food, fiber, feed, and energy production. Conventional plant breeding remains inherently random and slow, constrained by the availability of desirable traits in closely related plant species. In contrast, agricultural biotechnology employs the modern tools of genetic engineering to reduce uncertainty and breeding time and to transfer traits from more distantly related plants. Critics express concerns that the technology imposes negative environmental effects and jeopardizes the health of those who consume the "frankenfoods." Supporters emphasize potential gains from boosting output and lowering food prices for consumers. They argue that such gains are achieved contemporaneous with the adoption of farming practices that lower agrochemical use and lessen soil. The extensive experience with agricultural biotechnology since 1996 provides ample evidence with which to test the claims of supporters and opponents and to evaluate the prospects of genetic crop engineering. In this paper, we begin with an overview of the adoption of the first generation of agricultural biotechnology crops. We then look at the evidence on the effects of these crops: on output and prices, on the environment, and on consumer health. Finally, we consider intellectual property issues surrounding this new technology.
Full-Text Access | Supplementary Materials

Agriculture in the Global Economy
Julian M. Alston and Philip G. Pardey
The past 50-100 years have witnessed dramatic changes in agricultural production and productivity, driven to a great extent by public and private investments in agricultural research, with profound implications especially for the world's poor. In this article, we first discuss how the high-income countries like the United States represent a declining share of global agricultural output while middle-income countries like China, India, Brazil, and Indonesia represent a rising share. We then look at the differing patterns of agricultural inputs across countries and the divergent productivity paths taken by their agricultural sectors. Next we examine productivity more closely and the evidence that the global rate of agricultural productivity growth is declining—with potentially serious prospects for the price and availability of food for the poorest people in the world. Finally we consider patterns of agricultural research and development efforts.
Full-Text Access | Supplementary Materials

American Farms Keep Growing: Size, Productivity, and Policy
Daniel A. Sumner
Commercial agriculture in the United States is comprised of several hundred thousand farms, and these farms continue to become larger and fewer. The size of commercial farms is sometimes best-measured by sales, in other cases by acreage, and in still other cases by quantity produced of specific commodities, but for many commodities, size has doubled and doubled again in a generation. This article summarizes the economics of commercial agriculture in the United States, focusing on growth in farm size and other changes in size distribution in recent decades. I also consider the relationships between farm size distributions and farm productivity growth and farm subsidy policy.
Full-Text Access | Supplementary Materials

Articles

From Sick Man of Europe to Economic Superstar: Germany's Resurgent Economy
Christian Dustmann, Bernd Fitzenberger, Uta Schönberg and Alexandra Spitz-Oener
In the late 1990s and into the early 2000s, Germany was often called "the sick man of Europe." Indeed, Germany's economic growth averaged only about 1.2 percent per year from 1998 to 2005, including a recession in 2003, and unemployment rates rose from 9.2 percent in 1998 to 11.1 percent in 2005. Today, after the Great Recession, Germany is described as an "economic superstar." In contrast to most of its European neighbors and the United States, Germany experienced almost no increase in unemployment during the Great Recession, despite a sharp decline in GDP in 2008 and 2009. Germany's exports reached an all-time record of $1.738 trillion in 2011, which is roughly equal to half of Germany's GDP, or 7.7 percent of world exports. Even the euro crisis seems not to have been able to stop Germany's strengthening economy and employment. How did Germany, with the fourth-largest GDP in the world transform itself from "the sick man of Europe" to an "economic superstar" in less than a decade? We present evidence that the specific governance structure of the German labor market institutions allowed them to react flexibly in a time of extraordinary economic circumstances, and that this distinctive characteristic of its labor market institutions has been the main reason for Germany's economic success over the last decade.
Full-Text Access | Supplementary Materials

When Ideas Trump Interests: Preferences, Worldviews, and Policy Innovations
Dani Rodrik
Ideas are strangely absent from modern models of political economy. In most prevailing theories of policy choice, the dominant role is instead played by "vested interests"—elites, lobbies, and rent-seeking groups which get their way at the expense of the general public. Any model of political economy in which organized interests do not figure prominently is likely to remain vacuous and incomplete. But it does not follow from this that interests are the ultimate determinant of political outcomes. Here I will challenge the notion that there is a well-defined mapping from "interests" to outcomes. This mapping depends on many unstated assumptions about the ideas that political agents have about: 1) what they are maximizing, 2) how the world works, and 3) the set of tools they have at their disposal to further their interests. Importantly, these ideas are subject to both manipulation and innovation, making them part of the political game. There is, in fact, a direct parallel, as I will show, between inventive activity in technology, which economists now routinely make endogenous in their models, and investment in persuasion and policy innovation in the political arena. I focus specifically on models professing to explain economic inefficiency and argue that outcomes in such models are determined as much by the ideas that elites are presumed to have on feasible strategies as by vested interests themselves. A corollary is that new ideas about policy—or policy entrepreneurship—can exert an independent effect on equilibrium outcomes even in the absence of changes in the configuration of political power. I conclude by discussing the sources of new ideas.
Full-Text Access | Supplementary Materials

An Economist's Guide to Visualizing Data
Jonathan A. Schwabish
Once upon a time, a picture was worth a thousand words. But with online news, blogs, and social media, a good picture can now be worth so much more. Economists who want to disseminate their research, both inside and outside the seminar room, should invest some time in thinking about how to construct compelling and effective graphics.
Full-Text Access | Supplementary Materials

Features

Recommendations for Further Reading
Timothy Taylor
Full-Text Access | Supplementary Materials

Correspondence: The One Percent
Robert Solow, N. Gregory Mankiw, Richard V. Burkhauser, and Jeff Larrimore
Full-Text Access | Supplementary Materials

Halfway to Full Economic Recovery

Since the Great Recession officially ended about 4 1/2 years ago back in June 2009, the natural question has been: When does the U.S. economy get that jolt of bounceback growth to make up for what was lost? The Congressional Budget Office gives its answer in its just-published report "The Budget and Economic Outlook: 2014 to 2024:" "CBO projects that real GDP will grow notably faster over
the next few years than it has over the past few years. On a fourth-quarter-to-fourth-quarter basis, real GDP is projected to increase by 3.1 percent this year, by 3.4 percent per year in 2015 and 2016, and by 2.7 percent in 2017 ... By the second half of 2017, CBO projects, real GDP will return to its average historical relationship with potential (or maximum sustainable) GDP ..."

In short, although the prediction is that the U.S. economy is roughly halfway from the end of the recession to a full economic recovery, this is a case where the glass is actually half-full, rather than half-empty, because the heartier period of economic growth is coming. Here are a few of the details.

Here's a figure showing how the Great Recession reduced economic output below its potential, and the CBO projection for bounceback in the next few years.

Household wealth relative to income, which took an enormous hit during the Great Recession from the double-whammy of falling housing prices and a falling stock market, has now moved back to higher levels.

However, business investment hasn't only just started its bounceback, and the CBO projections suggest that it will be leading the way in the next few years.

What about the unemployment rate and the labor market? The CBO has also just published "The Slow Recovery of the Labor Market" to tackle that subject. The grim fact here is that after the end of the average U.S. recession, the number of jobs takes a couple of quarters to start growing again. But after the  end of the Great Recession, the number of jobs kept falling, and has been slower to recover (as shown by the flatter slope of the lower line in the figure).

Yes, the unemployment rate has fallen from 10% in October 2009 to 6.7% in December 2013, which is painfully slow but still better than a sharp stick in the eye. How much of the remaining unemployment is because of a lack of demand in the economy, and how much is because of "skill mismatches"? Here's the CBO:

Of the roughly 2 percentage-point net increase in the rate of unemployment between the end of 2007 and the end of 2013, about 1 percentage point was the result of cyclical weakness in the demand for goods and services, and about 1 percentage point arose from structural factors; those factors are chiefly the stigma workers face and the erosion of skills that can stem from long-term unemployment (together worth about one-half of a percentage point of increase in the unemployment rate) and a decrease in the efficiency with which employers are filling vacancies (probably at least in part as a result of mismatches in skills and locations, and also worth about one-half of a percentage point of the increase in the unemployment rate).

But even with the lower overall unemployment rate, the long-term rate of unemployment--that is, the unemployment rate where joblessness has lasted more than 26 weeks--remains historically high.

Also, the share of U.S. workers participating in labor force has declined, which raises the possibility that at least some of them would have preferred to keep working, but became discouraged about their job prospects and gave up. Notice, however, that the decline in labor force participation actually started back around 2000. It was fairly well-known among economists that as the period in which women were pouring into the (paid) labor force came to and end, and as the Baby Boom generation aged, and as a greater share of young people started to attend college, labor force participation rates would tend to drop off.

So the difficult question is how much of the decline in labor force participation is a result of these longer-term trends, and how much is a result of discouraged workers leaving the workforce because of the Great Recession? Here's how the CBO answers that question:

Of the roughly 3 percentage-point net decline in the labor force participation rate between the end of 2007 and the end of 2013, about 1½ percentage points was the result of long-term trends (primarily the aging of the population), about 1 percentage point was the result of temporary weakness in employment prospects and wages, and about one-half of a percentage point was attributable to unusual aspects of the slow recovery that led workers to become discouraged and permanently drop out of the labor force.

Economics of Human Sacrifice

Ben Richmond has an interview with Peter Leeson at the online magazine Motherboard, titled "There's a Rational Explanation for Human Sacrifice." In the introduction, Richmond points out that Leeson has "published papers on the medieval European practice of putting rats and vermin up on trial, an African society that poisons chickens to tell the future, and he has an upcoming paper on the practice of auctioning off wives. So ritualized sacrifice fit right in his wheelhouse." The academic paper, "Human Sacrifice," was published in the inaugural issue of a new journal, the Review of Behavioral Economics. Here, I'll draw on the interview and underlying article. 

Leeson's example of human sacrifice is the Kond people of India who lived in the Eastern Ghats mountain range of India in the first half of the 19th century, "the most significant and well-known society of ritual immolators in the modern era" When the British encountered this group around 1835, they discovered that human sacrifice was widespread. The Kond population as a whole was several hundred thousand people. There was no central government, but many tribes that included several villages each. The villages often raided each other, stealing cattle, food, and tools.

At least once each year, and often several times, tribes would purchase victims--typically non-Konds. Some tribes would purchase only one victim; others might buy 20 or more. After a wild three-day festival, the victim would be killed in some ceremonial and brutal way that always ended with the victim being torn into pieces. Sometimes the crowd tore apart the victims. In other cases, the victims were drowned in pig's blood or beaten to death before being torn into pieces. Then a representative of each village would take a strip of flesh from the victims and take it back to the village, where it was cut into smaller pieces so that everyone had a piece to bury in their field.

According to Kond belief, a victim or meriah had to be purchased. They did not view criminals or prisoners of war as suitable for sacrifice. Also, the price was high. Leeson explains (citations omitted):  "Konds’ unit of account was an article of such property they called a “life” (or gonti). A life consisted of property such as “a bullock, a buffalo, goat, a pig or fowl, a bag of grain, or a set of brass pots . . . . A hundred lives, on average . . . consist[ing] of ten bullocks, ten buffaloes, ten sacks of corn, ten sets of brass pots, twenty sheep, ten pigs, and thirty fowls.”  Meriah prices were rendered in these units. And their prices were considerable. ... [A] single meriah cost a purchasing community “from ten to sixty” lives. This constituted a “very great expense attendant upon procuring the victims” for sacrifice."

This human sacrifice practices of the Konds raise many questions (!), but from an economic perspective, Leeson focuses on two: Is there a way it might make economic sense to reduce your own wealth? And if so, is there a reason it might make sense to do so by spending the wealth on human sacrifices, rather than just, say, burning up crops and livestock, giving away land, or destroying tools?

For the first question, Leeson argues that when the risk of conflict is very high, and there is no good way to protect property from attackers, then those who have property may have an incentive to
make themselves worse off. He writes: "In agricultural societies nature produces variation in land’s output. This variation creates disparities between communities’ wealth. Absent government, wealth disparities induce conflict between communities, as those occupying land that received a relatively unfavorable natural shock seek to plunder those whose expected wealth is higher. If conflict’s cost is sufficiently high, it is cheaper for communities to protect their property rights by destroying part of their wealth. Wealth destruction depresses the expected payoff of plunder and in doing so protects rights in wealth that remains." As a close-to-home example, Leeson points out that people who live in high-crime neighborhoods may choose to drive beat-up cars or avoid showing any wealth as a way of making themselves less of a target. Leeson writes: "The poverty displayed by some well-known groups — from Gypsies to ascetics — may reflect their members’ rational decisions to have more secure property rights in less wealth instead of less secure property rights in more wealth."

But if one wishes to destroy wealth, why do so by the method of high-priced purchase of victims for human sacrifice? Leeson suggests that unlike burning crops or some other method, the purchase meant that the sellers of the victims would carry the news of the purchase price far and wide. Thus, it was not possible to fake by destroying only a small amount of a crop. The festival around the sacrifice meant that the destruction was widely seen and acknowledged, and the news would travel broadly, along with being communicated via pieces of the victim to those who had not attended.

I have a hard time seeing Leeson's explanation as the only reason behind the Kond practice of human sacrifice. For example, it seems plausible that human sacrifice might also serve as a way of making ferocity acceptable and binding together the group, in societies that were often either attacking others or defending themselves. But an economic-based explanation for human sacrifice need not be the exclusive truth in order to be a productive part of a fuller understanding.

In my mind, perhaps the strongest point in Leeson's argument is that when the British were trying to stamp out the Kond practice of human sacrifice, for years they tried violent punishment and they tried reason, without success. However, what did work was when the British offered to provide the tribes with a guarantee of security and a dispute-resolution mechanism--in effect, with a centralized government authority--the Kond tribes were immediately willing to give up their practice of human sacrifice. This pattern certainly suggests that the tribes, at least, viewed the sacrifice as a way of keeping civic order.

Income Mobility

The Census data most often used for studying the distribution of income is a snapshot of income each year. With this data, you can see how many people are in the top and bottom income groups, and all the groups in between, but you can't tell whether the same people are in the top or the bottom groups. In other words, the usual data on inequality doesn't allow you to look at the mobility of people across the income distribution. The degree of income mobility might matter a great deal to how one thinks about income inequality. For example, if those in the top or bottom groups are often there for a relatively short period of time, or if those from one generation of a family have a better chance over time of ending up in a different part of the income distribution than the previous generation of that family, one might feel differently about the rise in income inequality over the previous few decades.

There has been some evidence on mobility across the income distribution over the last few decades, often using the Panel Study of Income Dynamics,  a dataset that started tracking a nationally representative sample of 5,000 families back in 1968, and has been looking at them year-by-year, along with their descendants and the families of their descendants, since then. Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner take a whack at this this issue focusing on intergenerational income mobility in "Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility?" The January 2014 paper, along with data and supporting materials, is available here.

Their bottom line is that the amount of intergenerational income mobility isn't changing over time. They look at intergenerational income mobility with a variety of calculations, but here's one illustrative figure: "This figure plots the difference in average income percentiles for children born to low vs. high-income parents in each year from 1971-1993. On average, children from the poorest families grow up to be 30 percentiles lower in the income distribution than children from the richest families, a gap that has been stable over time. For children born after 1986, estimates are predictions based on college attendance rates."




Summarizing work in this area, they write: "Putting together our results with evidence from Hertz (2007) and Lee and Solon (2009) that intergenerational mobility did not change significantly between the 1950 and 1970 birth cohorts, we conclude that rank-based measures of social mobility have remained stable over the second half of the twentieth century in the United States. However, intergenerational mobility is significantly lower in the U.S. than in most other developed countries, especially in some parts of the country such as the Southeast and cities in the Rust Belt."

To be clear, saying that intergenerational mobility of incomes hasn't changed is quite different from arguing that the distribution of income hasn't changed.  As they point out, a lot of the change in the distribution of income--a greater share of income going to the extreme upper end--doesn't seem to have much effect on the rate of intergenerational mobility across different parts of the income distribution: "However, much of the increase in inequality has come from the extreme upper tail (e.g., the top 1%) in recent decades, and top 1% income shares are not strongly associated with mobility across countries or across metro areas within the U.S."

Indeed, given that the distribution of income has been widening out, it is actually a little surprising that intergenerational mobility has been fairly stable: after all, if the levels of income distribution are farther apart, moving between those levels would seem to be  harder. The authors use a ladder diagram to illustrate this theme:



But to put this same point another way, winning the "birth lottery" now has a bigger effect than it did a few decades ago, because stable intergenerational mobility is not offsetting the rising inequality of the income distribution. Of course,  the "American Dream," that term coined by Pulitzer Prize-winning historian named James Truslow Adams back in 1931, defines the powerful value in America people should have equal opportunity to follow their dreams--in both material income-earning and nonmaterial terms--rather than living in a society calcified by income and class distinctions.

Two points about this research finding are worth emphasizing. First, these authors are a very high-profile and high-powered group, and some of them (Saez in particular) have been strongly associated with arguments about the rising share of income at the top of the income distribution and the desirability of higher marginal tax rates. In other words, this is not a finding that can be dismissed as propaganda from low-level right-wing economists. Second, calculating intergenerational income mobility is a data hard problem. This group has a fairly remarkable dataset, and has made a fairly remarkable effort, but there are sure to be future studies that offer additional subtleties or even contrary findings.

In the details, their result is actually based on four interlocking sets of calculations. As they write: "For children born during or after 1980, we construct a linked parent-child sample using population tax records spanning 1996-2012. This population-based sample consists of all individuals born between 1980-1993 who are U.S. citizens as of 2013 and are claimed as a dependent on a tax return filed in or after 1996. We link approximately 95% of children in each birth cohort to parents based on dependent claiming, obtaining a sample with 3.7 million children per cohort."

But the first graph shows data back to 1971. How were they included? Again, they used tax data, but in this case they needed to used samples of that data. "We first identify all children between the ages of 12 and 16 claimed as dependents in the 1987-98 SOI [Statistics of Income, the official tax data] cross-sections. We then pool all the SOI cross-sections that give us information for a given birth cohort. For example, the 1971 cohort is comprised of children claimed at age 16 in 1987, while the 1982 cohort is comprised of children claimed at ages 12-16 in 1994-98. The SOI sample grows from 4,331 children in 1971 to 9,936 children in 1982.

But there's another problem: their standard measure of mobility across the income distribution is to look at children's income at age 30, and then compare it to their parent's income. But as they write: "We cannot measure children’s income at age 30 beyond the 1982 birth cohort because our data end in 2012." You'll notice that in the figure above, the line includes those born into the early 1990s. For those born from 1983-1986, they look at earnings as of age 26, which they argue are pretty close correlated with earnings at age 30. Then for those born from 1987-1993, they project future income based on rates of college attendance.

In other words, the seemingly simple graph above, along with their other calculations, is based on samples of thousands of children from tax data for 1971-1980. They have tax-based data on 3.7 million children from 1980 to 1993, but they only have income up to age 30 for the first couple of years, then income up to age 26 for another few years, and then projections based on college attendance after that.

Thus, while this study and a several previous studies suggests that intergenerational mobility of incomes hasn't shifted much over time, the issue is certain to be revisited as new evidence emerges over time.

Eating Out

One of the subtle, substantial shifts in the American way of life is that people are spending more of their food budget eating away from home. And when they do so, they tend to eat less healthy food. The Economic Research Service of the U.S. Department of Agriculture offers this graph to illustrate the shift in spending on food prepared away from home.



USDA reports: "Between 1977-78 and 2005-08, U.S. consumption of food prepared away from home increased from 18 to 32 percent of total calories. Meals and snacks based on food prepared away from home contained more calories per eating occasion than those based on at-home food. Away-from-home food was also higher in nutrients that Americans overconsume (such as fat and saturated fat) and lower in nutrients that Americans underconsume (calcium, fiber, and iron)." They cite a December 2012 report, "Nutritional Quality of Food Prepared at Home and Away From Home, 1977-2008," by Biing-Hwan Lin and Joanne Guthrie. That study finds: "In the past three decades, FAH [food at home] has changed more in response to dietary guidance, becoming significantly lower in fat content and richer in calcium, whereas FAFH [food away from home] did not."

Sure, it's possible to overeat dramatically at  home, too. Sometimes people do sit down in front of the television with a family-sized bag of chips or a quart of ice cream. But most people wouldn't grill a burger or deep-fry chicken for lunch, not to mention the ubiquitous (and irresistable) french fries and a sugared soda. Most people don't go to a restaurant and buy an apple and a bowl of lentil soup, either. The causes of obesity are many and mixed, but it seems plausible that paying others to tempt us with food, rather than spending time ourselves to make food, is part of the pattern.

How Pedestrian Countdown Signals Cause Auto Accidents

Pedestrian countdown signals at crosswalks show how much time is left before the light turns yellow, thus letting pedestrians know if they should rush to cross the street--or perhaps wait for the next light. But when these signals were introduced in Toronto, the rate of rear-end auto accidents was higher at the intersections with pedestrian signals compared to neighboring intersections. Sacha Kapoor and Arvind Magesan tell the story in "Paging Inspector Sands: The Costs of Public Information," which appears in the most recent issue of the American Economic Journal: Economic Policy (6:1, pp. 92–113).  (The title was obscure to me: I'll introduce Inspector Sands at the end.)

The story starts a few years back when the city of Toronto decided to change over its existing streetlights to a more energy-efficient variety. Then the city decided that while doing the change-over, it would also install pedestrian countdown signals at the same time. It would start in the places where it was cheapest to retrofit, and then work across the city. This history matter for the economic analysis, because the pedestrian countdown signals were installed for reasons and in an order that had nothing to do with whether an intersection was known to be unsafe or whether previous accidents had occurred. Thus, one can reasonably compare intersections with signals to nearby intersections without, and do so before and after the signals are installed.

"Our empirical analysis reveals that countdown signals resulted in about a 5 percent increase in collisions per month at the average intersection. The effect corresponds to approximately 21.5 more collisions citywide per month. The data also reveals starkly different effects for collisions involving pedestrians and those involving automobiles only. Specifically, although they reduce the number of pedestrians struck by automobiles, countdowns increase the number of collisions between automobiles. That the total number of collisions increased while collisions involving pedestrians decreased suggests that pedestrian countdown signals had a very significant effect on driver behavior. In fact, we find that collisions rose largely because of an increase in tailgating among drivers, a finding that implies drivers who know exactly when traffic lights will change behave more aggressively."

In short, the pedestrian countdown signals were good for pedestrians. But some of the drivers were watching the signals, trying to squeeze through before the light changed, and rear-ending other cars.

There's are some narrow lessons here about pedestrian countdown signals and a broader lesson about how information works. Here are two narrow lessons, which come out of a  more detailed analysis of the data: "The first is cities might benefit from installing countdowns at historically highly dangerous intersections and from not installing them at historically safe intersections. The second conclusion
is that while countdowns can improve safety in historically dangerous cities, they may be detrimental to safety in historically safe ones." Also, instead of having a pedestrian countdown signal that is visible to cars, it might make more sense to have a verbal countdown that could only be heard by pedestrians.

The broader lesson is that it's common to assume, without a lot of thought, that more information shared more broadly will make everyone better off. But the case of Toronto's countdown signals is an example of where making information available only to some (pedestrians) and not to others (drivers) is socially beneficial.

Another example involves the story of Inspector Sands in the title of the article. Kapoor and Magesan write: "Few know who Inspector Sands is, and no one has ever met him. This is for good reason. Theater companies in the United Kingdom are believed to use the code name “Inspector Sands” in order to alert ushers to pending emergencies, such as fires and bomb threats, without inciting panic among their patrons. When theater  staff learn of a fire, for example, they page Inspector Sands to the fire’s location. When ushers arrive they can put out the fire or help to evacuate the premises in a discrete and orderly manner. By ensuring the threat remains hidden from the public eye, the code name allows ushers to complete the tasks without having to deal with panicked crowds." Thus, Inspector Sands is a case, like pedestrian countdown signals, where information is revealed in a limited way to some, because revealing it to all would risk causing harm.

Full disclosure: The AEJ:EP is published by the American Economic Association, which also publishes the Journal of Economic Perspectives, where I work as Managing Editor.