Time Watching Television

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I recently ran across this historical data from the Neilson company for the time American households spend watching television, per day.


I rarely watch 8 hours of television per week, much less per day. I had a conversation the other day in which someone was incredulous that I have never seen an episode of Seinfeld, or Friends, or actually any sitcom in the last decade or so. I told them that I used to watch M*A*S*H now and then, and they looked at me with pity.

Economists sometimes quote the old proverb: "De gustibus non est disputandum." There's no arguing over taste. We tend to accept consumer tastes and preferences as given, and proceed from there. I suppose that those of us who blog, and then hope for readers, can't really complain about those who spend time looking at a screen. I certainly have my own personal time-wasters, like reading an inordinate number of mysteries. I assume that for many people the television is on in the background of other activities. But at some deep level, I just don't understand averaging 8 hours of television per day. I always remember the long-ago jibe from the old radio comedian Fred Allen: "Television is a medium because anything well done is rare."



Note: Thanks to Danlu Hu for downloading the data and creating this figure.

How Retirement Age Tracks Social Security's Rules

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Back in 1983, as one of the steps taken to bolster the long-run finances of the Social Security System, was to phase in a rise in the "normal" or "full" retirement age. The normal retirement age for receiving full Social Security benefits had been 65, with "early retirement" with lower benefits possible at age 62. Under the new rules, the normal retirement age remained 65 for those born in 1937 or earlier--and thus turning 65 before 2002. It then phased up by 2 months per year, so that for those born six years later in 1943 or after, the normal retirement age is now 66. Written into law is a follow-up increase where a rise in the normal retirement age from 66 to 67 will be phased in, again at a rate of two months per year,  for those born from 1955 to 1960. 

How has this change altered actual retirement patterns? What are the reasons, either for retirees or for the finances of Social Security, to encourage still-later retirement?


Economists have long recognized that what a government designates as the "normal" retirement age has a big effect on when people actually choose to retire. Luc Behaghel and David M. Blau present some of the recent evidence in "Framing Social Security Reform: Behavioral Responses
to Changes in the Full Retirement Age," which appears in the November 2012 American Economic Journal: Economic Policy (4(4): 41–67). (The journal isn't freely available on-line, but many in academia will have access through a library subscription.)

Consider the following graphs from Behaghel and Blau. Each one is for those born in a different year, from 1937 up through 1942, as the normal retirement age phased up. These people are the ones hitting the normal retirement age of 65 in the early and mid-2000s. The solid line shows the probability of retirement at each age. The early retirement age of 62 is marked with a vertical red line; the previous normal retirement age of 65 is marked with a vertical red line; and the actual retirement age for that year as it phases up two months per year is marked with a vertical red line. The dashed line, which is the same in all the figures, shows for comparison the retirement pattern for those born over the 1931-1936 period.

The main striking pattern is that the probability of retiring at a certain age almost exactly tracks the changes in the normal retirement age: that is, the solid line spikes at the red vertical line showing the normal retirement age. There is also a spike at the early retirement age of 62. Here are the patterns.






The evidence here seems clear: People are making their retirement choices in synch with the government-set normal retirement age. This pattern isn't new, as the authors point out, a spike in retirement age at 65 became visible in the data back in the early 1940s, about five years after Social Security became law. Still, the obvious question (for an economist) is why people would make this choice. If you retire later than the normal retirement age, your monthly benefits are scaled up, so from the viewpoint of overall expected lifetime payments, you don't gain from retiring earlier. A number of possible explanations have been proposed: 1) people don't have other sources of income and need to take the retirement benefits as soon as possible for current income; 2) people are myopic, or don't recognize that their monthly benefits would be higher if they delayed retirement; 3) many people are waiting until age 65 to retire so that they can move from their employer health insurance to Medicare; 4) some company retirement plans encourage retiring at age 65.

However, none of these explanations give an obvious reason for why the retirement age would exactly track the changes in Social Security normal retirement age, so it seems as if a final "behavioral" explanation is that the "normal" retirement age announced by the government, whatever it is, is then treated by many people as a recommendation that should be taken. Choosing a retirement date in this way is probably suboptimal both for individuals and for the finances of the Social Security system.

From the standpoint of individuals, there's a widespread sense among economists that many retirees would benefit from having more of their wealth in annuities--that is, an amount that would pay out no matter how long they live. In the Fall 2011 issue of my own Journal of Economic Perspectives, 
Shlomo Benartzi, Alessandro Previtero, and Richard H. Thaler have an article on "Annuitization Puzzles," which makes the point that when you delay receiving Social Security, you are in effect buying an annuity: that is, you are taking less in the present--which is similar to "paying" for the annuity-- in exchange for a larger long-term payment in the future. They write: "[T]he easiest way to increase the amount of annuity income that families have is to delay the age at which people start claiming Social Security benefits. Participants are first eligible to start claiming benefits at age 62, but by waiting to begin, the monthly payments increase in an actuarially fair manner until age 70. "

They further argue that a good starting point to encouraging such behavior would be to re-frame the way in which the Social Security Administration, and all the rest of us, talk about Social Security benefits. Imagine that, with no change at all in the current law, we all started talking about a "standard retirement age" of 70. We pointed out that you can retire earlier, but if you do, monthly benefits will be lower. If the choice of when to retire was framed in this way, my strong suspicion is that many more people would react differently than when we announce that the "normal retirement age" is 66, and if you wait then your monthly benefits will be higher. Again, people seem to react to what the government designates as the target for retirement age.

However, this labeling change might encourage people to work longer, but it would not affect the solvency of the Social Security system, because those who wait longer to retire are, in effect, paying for their own higher monthly benefits by delaying the receipt of those benefits. However, the Social Security actuaries offer a number of illustrative calculations on their website about possible steps to bolster the financing of the system. One proposal about phasing back the normal age of retirement looks like this:  "After the normal retirement age (NRA) reaches 67 for those age 62 in 2022, increase the NRA 2 months per year until it reaches 69 for individuals attaining age 62 in 2034. Thereafter, increase the NRA 1 month every 2 years."

Thus, this proposal would represent no change in the rules for Social Security benefits for anyone born before 1960--and thus in their early 50s at present. Under this proposal, those born after 1960 would face the gradual phase-in--but of course, they would also benefit from having a program that is much closer to fully funded. would face the same phase-in as currently exists. The actuaries estimate that this step by itself would address about 44% of the gap over the next 75 years between what Social Security has promised and the funding that is expected during that time. Given the predicted shortfalls of the Social Security system in the future, and the gains in life expectancy both in the last few decades and expected in the next few decades, and the parlous condition of large budget deficits reaching into the future, I would be open to proposals to phase in a more rapid and more sustained rise in the normal retirement age for Social Security benefits.

Driverless Cars

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Automobile travel transformed how people relate to distance: it decentralized how people live and work, and gave them a new array of choices for everything from the Friday night date to the long-distance road trip. I occasionally marvel that we can take our family of five, with all our gear, door-to-door for a getaway to a YMCA family camp 250 miles away in northern Minnesota--all for the marginal cost of less than a tank of gas. Driverless cars may turn out to be one of those rare inventions that transform transportation even further. KPMG and the nonprofit Center for Automotive Research published a report on "Self-driving cars: The next revolution." last August. It's available from the KPMG website here, and from the CAR website here. I missed the report when it first came out, but then saw this story about it in a recent issue of the Economist magazine.

Many people have heard about the self-driving cars run by Google that have already driven over 200,000 miles on public roads. The report makes clear that automakers are taking this technology very seriously as well, and developing the range of sensor-based and connected-vehicle technologies that would be needed to make this work. Examples of the technology include the Light Detection and Ranging (LIDAR) equipment that does 360-degree sensing around a car. The LIDAR systems that Google retrofitted into cars costs about $70,000 per car. Dedicated Short-Range Communication (DSRC) has certain standards and a designated frequency for short-range communication, and can thus be focuses on vehicle-to-vehicle and vehicle-to-infrastructure communication. Ultimately, these would be tied together so that self-driving cars could travel closely together in "platoons" and minimize traffic congestion. And it's not just Google and the car companies. Intel Capital, for example, recently launched at $100 million Connected Car Fund.   

 Of course, it is always possible that driverless cars will run up against insurmountable barriers. But skeptics should remember that the original idea of the automobile looked pretty dicey as well. Millions of adults will be personal driving motorized vehicles at potentially high speeds? They will drive on a network of publicly provided roads that will reach all over the country? They will fill these vehicles with flammable fuel that will be dispensed by tens of thousands of small stores all over the country? If the social gains seem large enough, technologies often have a way of emerging. With driverless cars, what are some of the gains? Quotations are from the report: as usual, footnotes are omitted for readability.


Costs of Traffic Accidents
Self-driving cars have the potential to save tens of thousands of lives, and prevent hundreds of thousands of injuries, every year.  "In 2010, there were approximately six million vehicle crashes leading to 32,788 traffic deaths, or approximately 15 deaths per 100,000 people. Vehicle crashes are the leading cause of death for Americans aged 4–34. And of the 6 million crashes, 93 percent are attributable to human error. ... More than 2.3 million adult drivers and passengers were treated in U.S. emergency rooms in 2009. According to research from the American Automobile Association (AAA), traffic crashes cost Americans $299.5 billion annually." Moreover, an enormous reduction in crash risk would allow a redesign of cars to be much lighter.

Costs of Infrastructure

Driverless cars will allow many more cars to use a highway simultaneously. "An essential implication for an autonomous vehicle infrastructure is that, because efficiency will improve so dramatically, traffic capacity will increase exponentially without building additional lanes or roadways. Research indicates that platooning of vehicles could increase highway lane capacity by up to 500 percent. It
may even be possible to convert existing vehicle infrastructure to bicycle or pedestrian uses. Autonomous transportation infrastructure could bring an end to the congested streets and extra-wide highways of large urban areas."

They could reduce the cost of design of highways. "[T]oday’s roadways and supporting infrastructure must accommodate for the imprecise and often-unpredictable movement patterns of human-driven vehicles with extra-wide lanes, guardrails, stop signs, wide shoulders, rumble strips and other features
not required for self-driving, crashless vehicles. Without those accommodations, the United States could significantly reduce the more than $75 billion it spends annually on roads, highways, bridges, and other infrastructure."

Driverless cars will alter the need for parking.  Imagine that your car will drop you at your office door, head off to park itself, and come back when you call it.  "In his book ReThinking a Lot (2012), Eran Ben-Joseph notes, “In some U.S. cities, parking lots cover more than a third of the land area, becoming the single most salient landscape feature of our built environment.”"

Costs of Time
Driverless cars might be faster, but in addition, they open up the possibility of using travel time for work or relaxation. Your car could become a rolling office, or a place for watching movies, or a place for a nap. "An automated transportation system could not only eliminate most urban congestion, but it would also allow travelers to make productive use of travel time. In 2010, an estimated 86.3 percent of all workers 16 years of age and older commuted to work in a car, truck, or van, and 88.8 percent of those drove alone ... The average commute time in the United States is about 25 minutes.
Thus, on average, approximately 80 percent of the U.S. workforce loses 50 minutes of potential productivity every workday.  With convergence, all or part of this time is recoverable. Self-driving vehicles may be customized to serve the needs of the traveler, for example as mobile offices, sleep pods, or entertainment centers." I find myself imagining the overnight road-trip, where instead of driving all day, you sleep in the car and awake at your destination. 
 

Costs of Energy
The combination of much lighter cars, being driven much more efficiently, could dramatically reduce energy use. Lighter cars use less fuel: "Vehicles could also be significantly lighter and more energy
efficient than their human-operated counterparts as they no longer need all the heavy safety features, such as reinforced steel bodies, crumple zones, and airbags. (A 20 percent reduction in weight corresponds to a 20 percent increase in efficiency.)"  "Platooning alone, which would reduce the effective drag coefficient on following vehicles, could reduce highway fuel use by up to 20 percent..." "According to a report published by the MIT Media Lab, “In congested urban areas, about 40 percent of total gasoline use is in cars looking for parking."

 Costs of Car Ownership
Most cars are unused for 22 hours out of every day. I already know people in cities like New York who own a car, but keep it in storage for out-of-city trips. I know people who use companies like ZipCar, a membership-based service that lets you have a car for a few hours when you need it. Driverless cars may offer a replacement for car ownership. Need a car? A few taps on your smart-phone and one will come to meet you, and take you where you want to be. The price will of course be lower if you don't mind being picked up in an automated carpool. 


Mobility for the Young and the Old
Imagine being an elderly person who has become uncomfortable with driving, at least at certain times or under certain conditions. Driverless cars would offer continues mobility. Imagine being able to put your teenager in a car and have them safely delivered to their destination. Imagine always having a safe ride home after a night on the town.


How Fast?
The fully self-driving car isn't right around the corner. Clearly, costs need to come down substantially and a number of complementary technologies need to be created. However, we do already have cars in the commercial market with cruise control and anti-lock brakes, as well as cars that sense potential crash hazards and can parallel park themselves. Changes like these happen slowly, and then in a rush. As the report notes, "The adoption of most new technologies proceeds along an S-curve, and we believe the path to self-driving vehicles will follow a similar trajectory." Maybe 10-15 years? Faster?

72 is the New 30?!!

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Everyone knows that human life expectancies have been improving. But just how extraordinary and incomparable that improvement has been is not widely understood. Demographers Oskar Burgera, Annette Baudischa, and James W. Vaupel offer two remarkable sets of comparisons in "Human mortality improvement in evolutionary context," which appears in a recent issue of the Proceedings of the Natural Academy of Sciences (October 30, 2012, vol. 109, no. 44, 18210-18214). From their abstract:

"The health and economic implications of mortality reduction have been given substantial attention, but the observed malleability of human mortality has not been placed in a broad evolutionary context. We quantify the rate and amount of mortality reduction by comparing a variety of human populations to the evolved human mortality profile, here estimated as the average mortality pattern for ethnographically observed hunter-gatherers. We show that human mortality has decreased so substantially that the difference between hunter-gatherers and today’s lowest mortality populations is greater than the difference between hunter-gatherers and wild chimpanzees. The bulk of this mortality reduction has occurred since 1900 and has been experienced by only about 4 of the roughly 8,000 human generations that have ever lived. Moreover, mortality improvement in humans is on par with or greater than the reductions in mortality in other species achieved by laboratory selection experiments and endocrine pathway mutations."

 Their first main set of comparisons is to look at human mortality declines in a very long-run evolutionary context: from hunter-gatherers to modern humans. They focus to some extent on modern Sweden and Japan as examples of the highest life expectancies for modern humans (in what follows "y" is the writers' abbreviation for "years, and footnotes and references to figures are omitted).

"That is, Swedes in 1900 had mortality profiles closer to hunter-gatherers than to the Swedes of today. This relative difference between Swedes recently and those 100 y ago has emerged in a rapid revolutionary leap, as this distance is far greater than that between hunter-gatherers and chimps. The recent jumps in mortality reduction are remarkable in the context of mammal diversity because age-specific death rates for hunter-gatherers are already exceptionally low, probably among the lowest of any nonhuman primate or terrestrial mammal (especially if body size is controlled for), and lower than even captive chimpanzees at all ages. The human mortality profile, however, is so plastic that over the past century the populations doing best managed to achieve very large reductions in death rates that were already low compared with those of other species. ...

"For example, hunter-gatherers at age 30 have the same probability of death as present-day Japanese at the age of 72: hence the age of a person in Japan that is equivalent to a 30-y-old hunter-gatherer is 72. In other words, compared with the evolutionary pattern, 72 is the new 30. ...

"In gross comparative terms, this means that during evolution from a chimp-like ancestor to anatomically modern humans, mortality levels once typical of prime-of-life individuals were pushed back to later ages at the rate of a decade every 1.3millions years, but the mortality levels typical of a 15-y-old in 1900 became typical of individuals a decade older about every 30 y since 1900."

Their second main set of comparisons is to look at the gain in human mortality compared with the gains in mortality achieved in laboratory situations, by manipulating the genetics and the environment of fruit flies, nematode worms, mice, and the like.

"Fruit fly selection experiments achieve significant extensions in life span by rearing successive generations from eggs laid by old individuals. In one classic example, mean life span increased by about 30% in 15 generations , for a rate of change of almost 2% per generation, and in another by about 100% in 13 generations, or just over 5% per generation. For human hunter-gatherers, mean life span at birth is about 31 ...  For Swedes, it was about 32 in 1800, 52 in 1900, and is 82 today. So life expectancy increased by about 165% from hunter-gatherers to modern Swedes and at a rate of about 12% per generation since 1800.

"Some of the most promising directions for understanding the physiological mechanisms of aging come from experiments with mutations that affect the endocrine pathway. These impressive experiments have extended mean life span in nematode worms by >100%, fruit flies by ∼85% , and laboratory mice by ∼50%. Dietary restriction, which involves suppressing caloric intake of an organism, has extended life span in nematodes by 100–200%, fruit flies by ∼100%, and mice by ∼50%. Hence recent human mortality improvement is often greater than that achieved by manipulated strains of model organisms relative to the wild type, especially when single mutations or
physiological pathways are manipulated. However, experiments that simultaneously manipulate multiple pathways in organisms such as yeast and nematode worms can achieve much greater life span extensions. The majority of laboratory studies where mammals are the model organism have been done on mice and yield percentage life span increases less than those gained by humans."

It's unclear just what the recent changes in human life expectancy mean for the long run, because they are so without parallel either in the evolutionary record or in the lab. It seems unlikely that the huge gains in human life expectancy since 1900 or so can be related to large changes in genetics or physiological processes: not enough generations have passed. As the authors ask: "Why does the human genome give humans a license to drastically reduce mortality by nongenetic change?" The answer is not yet clear, but what is clear is that there is a "biologically unique" plasticity in the human mortality decline that has already occurred.

Economics and Natural Disasters

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In the aftermath of Hurricane Sandy, many teachers and students of economics will find themselves searching for background materials that provide some background in the economics of natural disasters. Here are a few examples from the last few years.

David Stromberg laid out the economic arguments about natural disasters in "Natural Disasters, Economic Development, and Humanitarian Aid," appearing in the Summer 2007 issue of my own Journal of Economic Perspectives. (This article, like all JEP articles back to the start of the journal 1987, is freely available to all courtesy of the American Economic Association.) Stromberg makes the fundamental point that the economic analysis of natural disasters is built on three factors: the incidence of the natural disasters themselves, the number of people exposed to the disaster, and the vulnerability of the population to that disaster. In fact, Stromberg traces this distinction back to letters between Voltaire and Rousseau in the aftermath of the great Lisbon earthquake of 1755. Voltaire had written a poem on how terrible the earthquake was; Rousseau had responded by pointing out that it was not the quake, but the interaction between human society and the quake, which was at issue. Here's Stromberg (footnotes and citations omitted):

"[I]n 1755 an earthquake devastated Lisbon, which was then Europe’s fourth-largest city. At the first quake, fissures five meters wide appeared in the city center. The waves of the subsequent tsunami engulfed the harbor and downtown. Fires raged for days in areas unaffected by the tsunami. An estimated 60,000 people were killed, out of a Lisbon population of 275,000. In a letter to Voltaire dated August 18, 1756, Jean-Jacques Rousseau notes that while the earthquake was an act of nature, previous acts of men, like housing construction and urban residence patterns, set the stage for the high death toll. Rousseau wrote: “Without departing from your subject of Lisbon, admit, for example, that nature did not construct twenty thousand houses of six to seven stories there, and that if the inhabitants of this great city had been more equally spread out and more lightly lodged, the damage would have been much less and perhaps of no account.”

"Following Rousseau’s line of thought, disaster risk analysts distinguish three factors contributing to a disaster: the triggering natural hazard event (such as the earthquake striking in the Atlantic Ocean outside Portugal); the population exposed to the event (such as the 275,000 citizens of Lisbon); and the vulnerability of that population (higher for the people in seven-story buildings)."

Of course, this insight implies that what events are classified as a "natural disaster" is not just about size of the natural event, but about how many people are affected. Thus, the WHO Collaborating Centre for Research on the Epidemiology of Disasters (CRED) maintains an Emergency Events DatabaseCenter that colleges data on natural disasters, where a disaster is defined as 10 or more people reported killed, 100 or more people reported affected, a declaration of a state of emergency, or a call for international assistance. Here are some of their figures showing global trends in natural disasters from 1975 through 2011. The first graph shows the number of such disasters over time: the total was rising into the early 2000s, but has leveled off since then.

The second and third graph show the number of people killed, and the number of people affected by such disasters. The trendline for number of people killed has been dropping over time, with occasional spikes: in 2010,  the earthquake in Haiti, or in 2007, the cyclone that hit Myanmar and the major earthquake in China. However, the number of people affected by natural disasters is rising over time, which one would expect as a result of growing population levels, if nothing else.


Finally, the fourth graph shows monetary losses from natural disasters. Of course, this graph is driven by whether the disasters hit high-income or middle-income countries, where the measured economic costs of damage are higher than in low-income countries.

The best way of dealing with natural disasters is often before they occur: early warning systems, advance planning, encouraging natural protections like minimizing deforestation or protecting wetlands, building codes, flood control, and more. For a nice overview of such efforts around the world, I recommend the 2010 report on "Natural Hazards, UnNatural Disasters: The Economics of Effective Prevention," from the World Bank. The report begins: "The adjective “UnNatural” in the title of this report conveys its key message: earthquakes, droughts, floods, and storms are natural hazards, but the unnatural disasters are deaths and damages that result from human acts of omission and commission. Every disaster is unique, but each exposes actions—by individuals and governments at different levels—that, had they been different, would have resulted in fewer deaths and less damage. Prevention is possible, and this report examines what it takes to do this cost-effectively."

The World Bank report is focused more on low-income countries, but similar lessons about prevention apply to high-income countries, as well. In the New York Times on Tuesday, David W. Chen and Mireya Navarro discuss "For Years, Warnings That It Could Happen Here." They talk about proposals that have been floating around the New York metro area for years now about levy systems, storm surge barriers, floodgates in subways,  moving people and economic activity away from low-lying areas, and in general having plans in place. Not many storms will pack the wallop of Hurricane Sandy, but New York City is a huge agglomeration of people living on a coastline who will inevitably be susceptible to storm and flood damage.

Two other quick references: First, the National Flood Insurance Program will almost certainly not have the money to pay for the damage from Hurricane Sandy. For background on that program and how it works, and why its inability to fund these damages was completely predictable, Erwann O.  Michel-Kerjan, lays it out in "Catastrophe Economics: The National Flood Insurance Program," in the Fall 2010 issue of my own Journal of Economic Perspectives. 

Second, the aftermath of natural disasters is often a motivation for teachers of economics to discuss the extent to which, even if price restrictions don't make economic sense most of the time, they might be justifiable in the aftermath of a natural disaster to prevent "price-gouging." Michael Giberson of Texas Tech University a nice readable essay on "The Problem with Price Gouging Laws" in the Spring 2011 issue of Regulation magazine. I blogged about the article here. He points out that 31 states have such laws, and that the completely predictable problems with such laws are that they
discourage bringing supplies into disaster areas, they discourage conserving on key resources, they concentrate economic losses on local merchants, and they worsen the economic losses in the disaster area.

Are CEO's Overpaid?

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Are CEO's Overpaid? I confess that my knee-jerk answer to this question is "YES"! But Steve Kaplan makes a strong case for a more nuanced answer in Executive Compensation and Corporate Governance in the U.S.: Perceptions, Facts and Challenges," published in July as Chicago Booth working paper 12-42. Here's a sketch of some of  his arguments and graphs.

 First, just as a matter of getting the facts straight, CEO pay relative to household income did spike back in the go-go days of the dot-com boom in the late 1990s, but since then, it is relatively lower. Kaplan argues that there are two valid ways to measure executive pay. One measure looks at actual pay received, which he argues is useful for seeing whether top executives are paid for performance. The other measure looks at "estimated" pay, which is the amount that financial pay packages would have been expected to be worth at the time were granted. This calculation requires putting a value on stock options, restricted stock grants, and the like, and estimating what these were worth at the time the pay package was given. Kaplan argues that this measure is the appropriate one for looking at what corporate boards meant to do when they agreed on a compensation package.

Here's one figure showing actual average and median pay totals for S&P 500 CEOs from 1993 to 2010. Average pay is above median pay, which tells you that there are some very high-paid execs at the top pulling up the average.  Also, average CEO pay spikes when the stock market is high, as in 2000 and around 2006 an  2007. Median realized pay seems to have crept up over time.  
Here's a figure showing estimated pay--that is, the value of the pay packages when they were granted. But this time, instead of showing dollar amounts, this graph shows average and median CEO pay as a multiple of median household income. Average pay again spikes at the time of the dot-com boom. Kaplan emphasizes that estimated CEO pay is on average lower than in 2000 and that the median hasn't risen much. My eye is drawn to the fact that median pay for CEOs goes from something like 60 times median household income back in 1993 to about 170 times median household income by 2010.

An obvious question is whether these pay levels are distinctive for CEOs, or whether they are just one manifestation of widening income inequality across a range of highly-paid occupations. Kaplan makes a solid case that it is the latter. For example, here's a graph showing the average pay of the top 0.1% of the income distribution compared with the average pay of a large company CEO.Again, the story is that CEO pay really did spike in the 1990s, but by this measure, CEO pay relative to the top 0.1% is now back to the levels common in the the 1950s.

  

Kaplan also points out that the pay of those at the top of other highly-paid occupations has grown dramatically as well, like lawyers, athletes, and hedge fund managers. Here's a figure showing the pay of top hedge fund managers relative to that of CEOs in the last decade. Kaplan writes: "The top 25 hedge fund managers as a group regularly earn more than all 500 CEOs in the S&P 500. In other words, while public company CEOs are highly paid, other groups with similar backgrounds and talents have done at least equally well over the last fifteen years to twenty years. If one uses evidence of higher CEO pay as evidence of managerial power or capture, one must also explain why the other professional groups have had a similar or even higher growth in pay. A more natural interpretation is that the market for talent has driven a meaningful portion of the increase in pay at the top."    

Kaplan also compiles evidence that CEOs of companies with better stock market performance tend to be paid more than those with poor stock market performance, and that CEOs have shorter job tenures. He writes: Turnover levels since 1998 have been higher than in work that has studied previous periods. In any given year, one out of 6 Fortune 500 CEOs lose their jobs. This compares to one out of 10 in the 1970s. CEOs can expect to be CEOs for less time than in the past. If these declines in expected CEO tenures since 1998 are factored in, the effective decline in CEO pay since then is larger than reported above." And the CEO turnover is related to poor firm stock performance ..."

To me, Kaplan makes a couple of especially persuasive points: the run-up in CEO salaries was especially extreme during the 1990s, and less  so since then (depending on how you measure it); and the run-up in CEO salaries reflects the rise in inequality across a wider swath of professions. While I believe the arguments that job tenure can be shorter for the modern CEO, especially if a company isn't performing well, it seems to me that most former CEO's don't plummet too many percentiles down the income distribution in their next job, so my sympathy for them is rather limited on that point.

In this paper, Kaplan doesn't seek to address the deeper question of why the pay for those at the very top, CEOs included, has risen so dramatically.  While the demand for skills at the very top of the income distribution is surely part of the answer, I find it hard to believe that these rewards for skill increased so sharply in the 1990s--just coincidentally during a stock market boom. It seems likely to me that
  cozy institutional arrangements for many of those at the very top of the income distribution--CEOs, hedge fund managers, lawyers, and athletes and entertainers--also plays an important role.  

Labor's Smaller Share

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Margaret Jacobson and Filippo Occhino have been investigating the fact that labor has been receiving a declining share of total economic output over the last few decades. I posted on their work last February in "Labor's Declining Share of Total Income."  Now they have written "Labor’s Declining Share of Income and Rising Inequality," which is "Economic Commentary" 2012-13 published by the Federal Reserve Bank of Cleveland.


The starting point is to look at labor income relative to the size of the economy. The top line in the figure shows labor income as a share of GDP, as measured in the national income and product accounts from the U.S. Bureau of Economic Analysis. The lower line in the figure shows the ratio of compensation to output for the nonfarm business sector, as measured by the U.S. Bureau of Labor Statistics. The measures are not identical, nor would one expect them to be, but they show the same trend: that is, with some ups and downs as the economy has fluctuated, the labor share of income has been falling for decades, and is now at an historically low figure.

 This fact lies behind much of the rise in inequality of incomes over this time. The income that is not being earned by labor is being earned by capital--and capital income is much more concentrated than is labor income. Jacobsen and Occhino offer an intriguing figure that measures the inequality of labor income and the inequality of capital income. The measure used here is a Gini coefficient, which "ranges between 0 and 1, with 0 indicating an equal distribution of income and 1 indicating unequal income." (Here's an earlier post with an explanation of Gini coefficients.)
The figure has two main takeaways. First, labor income has become more unequally distributed over time, but since the early 1990s, the big shift in income inequality is because capital income is more unequally distributed. Second, capital income tends to rise during booms and to fall in recessions. Thus, it seems plausible that the inequality of capital income has dropped in the last few years of the Great Recession and its aftermath, but will rise again as economic growth recovers.

What has caused the long-run decline of the labor share of income? Jacobson and Occhino explain this way: "[W]e begin by looking at what determines the labor share in the long run. The main factor is the technology available to produce goods and services. In competitive markets, labor and
capital are compensated in proportion to their marginal contribution to production, so the most important factor behind the labor and capital shares is the marginal productivities of labor and capital, which are determined by technology. In fact, one important cause of the post-1980 long-run decline in the labor share was a technological change, connected with advances in information and
communication technologies, which made capital more productive relative to labor, and raised the return to capital relative to labor compensation. Other factors that have played a role in the long-run decline in the labor share are increased globalization and trade openness, as well as changes in
labor market institutions and policies."

There is no particular reason to believe that these trends will continue--or that they won't. But the declining share of income going to labor suggests the importance of finding ways to increase the marginal product of labor, especially for workers of low and medium skills, perhaps by focusing on the kind of training and networking that might help them make greater use of the advances in information and communication technology to improve their own productivity.