Presidential Predictions From Economic Data

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The performance of the economy clearly has an influence on presidential elections. What are such models saying about the like outcome of an Obama-Romney election? Here are three examples: from Ray Fair, from Douglas Hibbs, and from the team of Michael Berry and Kenneth Bickers.

For some years now, Ray Fair has been experimenting with different relationships between economic variables and the outcome of national elections. For example, my own Journal of Economic Perspectives published an article he authored on this subject back in the Summer 1996 issue.  Just after the results of the November 2010 elections were known, he published "Presidential and Congressional Vote-Share Equations: November 2010 Update." Based on the fit of the past  historical data, he offers this equation:


VP = 48.39 + .672*G - .654*P + 0.990*Z

VP is the Democratic share of the two-party presidential vote in 2012. G is the growth rate of real per capita GDP in the first 3 quarters of 2012. P is the growth rate of the GDP deflator in the first 15 quarters of the Obama administration. And Z is the number of quarters in the first 15 quarters of theObama administration in which the growth rate of real per capita GDP is greater than 3.2 percent at an annual rate.

Obviously, the third quarter GDP of 2012 GDP numbers will not be officially announced until after the election--although one can argue that they will be perceived by people in the economy as they happen. Just for the sake of argument, if one plugs in a 1.5% growth rate for real per capita GDP in the first three quarters of 2012, a 1.5% growth rate for the GDP deflator (a measure of inflation) and 2 quarter of real per capita GDP growth above 3.2% (that is, fourth quarter 2009 and fourth quarter 2011), then Fair's equation says that Obama will get 50.4% of the two-party vote--and thus (assuming not too much weirdness in the Electoral College) would narrowly win re-election.


An obvious question about Fair's equation is that it doesn't t include a separate variable for the unemployment rate. Why not? The answer is that researchers like Fair have experimenting with a wide variety of different kinds of economics data. For Fair, the equation given above is the one that predicts best over the elections from 1916 up through 2008. It's worth remembering that presidents have often been re-elected with a fairly high unemployment rate, like Franklin Roosevelt during the Great Depression, or Ronald Reagan when the unemployment rate was 7.4% in October 2004.

However, my guess is that in the context of 2012, Fair's equation will tend to overstate Obama's changes. I think the sustained high rates of unemployment will be more salient in the 2012 election than they may have been in some past elections. Also, Fair's equation is that it gives Obama credit for the low rate of inflation during his term of office--but it's not clear that in 2012, the low rate of overall inflation is getting Obama a lot of political credit. Fair's equation gives Obama extra credit for the two good quarters of economic growth during his presidency--and I'm not sure that in the broader context of a slow recovery, Obama will get much political credit for those two quarters.

Douglas Hibbs takes a related but different approach to economic variables and presidential election outcomes in his "Bread and Peace" model. He starts off by using the average of per capita real economic growth over the incumbent president's term, and uses that to predict the incumbent's share of the two-party vote. The result, as shown in the best-fit line in the figure below, is that some of the years where the incumbent did much worse than expected were years with a large number of military fatalities, like 1952 and 1968.



Thus, Hibbs added a second explanatory variable, military fatalities, and with the addition of that extra factor most of the points showing actual voting shares fall closer to best-fit line.  As the figure shows, based on economic data through the end of 2012, Obama would receive 45.5% of the two-party vote. The Hibbs equation doesn't give Obama credit for a low inflation rate.

 

Like Fair, Hibbs doesn't include an unemployment variable. But of course, these sorts of disputes over what should be included in drawing connections from economic data to election results just illustrates a broader point: every election has unique characteristics, starting with the actual candidates. In the 1996 election, Bill Clinton did better than would have been expected based on Hibbs formula. In the 2000, Al Gore (who was from the "incumbent" party), did considerably worse than would have been expected. Public perceptions of the candidates, non-economic policies, and the actual events during the campaign clearly matter as well.

Michael Berry and Kenneth Bickers take a different approach in "Forecasting the 2012 Presidential Election With State-Level Economic Indicators."  Instead of trying to predict the overall national popular vote, they instead estimate an equation for each of 50 states and the District of Columbia. They also use a more complex set of variables. They emphasize economic variables like the change in each state in real per capita income, the national unemployment rate, the state-level unemployment rate. But they also include variables for the vote received by that party in the previous election, a variable for whether the incumbent is a Democrat or a Republican, and for how many terms in a row the presidency would be held by a particular party. These terms then interact with the economic variables. A variable for the home state of each presidential candidate is also included.

The strength of the Berry/Bickers approach is that with this extra detail, their equation correctly predicts all of presidential elections from 1980 to 2008. The weakness is that if you add enough variables and complexity, you can always create an equation that will match the past data, but your results can be quite sensitive to small tweaks in the variables that you use. Thus, it's hard to have confidence that a complex equation is will be an accurate predictor.  For what it's worth, their equation predicts that Romney will win the 2012 election rather comfortably, with more than 52% of the popular vote. As they point out: "What is striking about our state-level economic indicator forecast is the expectation that Obama will lose almost all of the states currently considered as swing states, including North Carolina, Virginia, New Hampshire, Colorado, Wisconsin, Minnesota, Pennsylvania, Ohio, and Florida."


In terms of narrowly economic influences on the presidential race, there are just two more monthly unemployment reports that will come out before the election in November. There won't be too much new economic data, either. Thus, my suspicion is that the ways in which economic outcomes affect presidential preferences are in large part already being taken into account in the presidential polls, which currently show a tight race.

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.

What's Up With the Dodd-Frank Legislation?

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Back in July 2010, President Obama signed into law the Dodd-Frank Wall Street Reform and Consumer Protection Act. The difficulty with the law has always been that while it was fairly clear on its goals, it did not specify how to reach those goals--instead turning over that task to current and newly-created regulatory agencies.  If you're looking for an update on how the law is proceeding, a good starting point is the Third Quarter 2012 issue of Economic Perspectives, published by the Federal Reserve Bank of Chicago, which has six articles on the Dodd-Frank legislation.

Douglas D. Evanoff and William F. Moeller offer an overview of the goals and approach of the law in their opening piece (footnotes and citations omitted):

"The stated goals of the act were to provide for financial regulatory reform, to protect consumers
and investors, to put an end to too-big-to-fail, to regulate the over-the-counter (OTC) derivatives markets, to prevent another financial crisis, and for other purposes. ... Implementation of Dodd–Frank requires the development of some 250 new regulatory rules and various mandated studies. There is also the need to introduce and staff a number of new entities (bureaus, offices, and councils) with responsibility to study, evaluate, and  promote consumer protection and financial stability. Additionally, there is a mandate for regulators to identify and increase regulatory scrutiny of systemically important institutions.  ... Two years into the implementation of the act, much has been done, but much remains to be done."

How are those rules coming along? The law firm of Davis Polk & Wardwell publishes a regular Dodd-Frank report. The September 2012 edition summarizes:

• "As of September 4, 2012, a total of 237 Dodd-Frank rulemaking requirement deadlines have
  passed. This is 59.5% of the 398 total rulemaking requirements, and 84.6% of the 280
  rulemaking requirements with specified deadlines.
• "Of these 237 passed deadlines, 145 (61.2%) have been missed and 92 (38.8%) have been
  met with finalized rules. Regulators have not yet released proposals for 31 of the 145 missed
  rules.
• "Of the 398 total rulemaking requirements, 131 (32.9%) have been met with finalized rules and
  rules have been proposed that would meet 135 (33.9%) more. Rules have not yet been
  proposed to meet 132 (33.2%) rulemaking requirements.

The July 2010 Davis Polk update--the two-year anniversary of the legislation--offers some additional detail: "The two years since Dodd-Frank’s passage have seen 848 pages of statutory text expand to 8,843 pages of regulations. Already at almost a 1:10 page ratio, this staggering number represents
only 30% of required rulemaking contained within Dodd-Frank, affecting every area of the financial markets and involving over a dozen Federal agencies."

It's important to  recognize that writing a new regulation isn't as simple as, well, just writing it. Instead, there is often first an in-house study, followed by a draft regulation, which then is open to public comments, and then can revised, and eventually at some point a new regulation is created. It's not unusual for a regulation to get dozens or hundreds of detailed public comments.

This blizzard of evolving rules has to create considerable uncertainty in the financial sector. Matthew Richardson discusses the complexities of one particular issue in his contribution to the Chicago Fed publication. He picks one example: the problem that many banks made very low-quality subprime mortgage loans. What does the Dodd-Frank legislation do about this basic issue? As he describes, the act: 1) Sets up a Consumer Finance Protection Bureau in title X to deal with misleading products; 2)
Imposes particular underwriting standards for residential mortgages; 3) Requires firms performing securitization to retain at least 5 percent of the credit risk; and 4) Iincreases regulation of credit rating agencies. Each of these tasks requires detailed rulemaking. And as Richardson points out, "with all of these new provisions, the act does not even address what we at NYU Stern consider to be a primary fault for the poor quality of loans—namely, the mispriced government guarantees in the system that led to price distortions and an excessive buildup of leverage and risky credit."

I'm skeptical of anyone who has strong opinions about the Dodd-Frank legislation, because here we are more than two years later, less than halfway toward figuring out what rules the legislation will actually put in place. Wayne A. Abernethy of the American Bankers Association is one of the authors in the Chicago Fed symposium. Yes, he is speaking for the bankers' point of view. But his judgement about the overall process seems fair to me:

"At least in the financial regulatory history of the United States, there has never been anything like it. I have seen no definitive count of the number of regulations that the Dodd–Frank Act calls forth. The numbers seem to range between 250 and 400—numbers so large that they are numbing. It all defies hyperbole. The Fair and Accurate Credit Transactions Act, adopted in 2003, astonished the financial industry with more than a dozen significant new regulations to be written. ...

"One of the most common criticisms of Dodd–Frank implementation has been a lack of order and coordination in the regulatory process. Instead, the Dodd–Frank Act has succeeded in replacing the financial crisis with a regulatory crisis.  ... As agencies are grappling with impossible rulemaking tasks, most of them are also engaged in major structural reorganizations and shifts in the areas of responsibility. ... Nothing like this has ever been tried before in the history of the United States. Writing 400 financial regulations of the highest significance and the greatest complexity in a couple of years has clearly been too much to expect. ... Getting on with the work to end our self-inflicted regulatory crisis should be among the highest priorities."

 I'm someone who believes that financial regulation needed shaking up. Many of the broad goals of the Dodd-Frank legislation make sense to me: rethinking bank and regulation to deal with macroeconomic risk, not just the risk of an individual institution going broke; figuring out better ways to shut down even large financial institutions when needed; and better regulation of certain financial instruments like credit default swaps and repo agreements; a closer look at technologies that allow ultra-high-speed financial trading; and others.

The Dodd-Frank legislation is almost not a law in the conventional meaning of the term, because it mostly isn't about actual specific activities that are prohibited. Instead, it's about handing over the difficult problems to regulators and telling them to fix it. I'm not sure there was an easy alternative to this regulatory approach: the idea of Congress trying to debate, say, appropriate regulation of the over-the-counter swaps market is not an encouraging thought. But stating a goal is not the same as solving a problem. The passage of Dodd-Frank, in and of  itself, didn't solve any problems.

IMF on Public Debt Overhang

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The IMF discusses "The Good, the Bad and the Ugly: 100 Years of Dealing with Public Debt Overhangs," in Chapter 3 of its most recent World Economic Outlook. The IMF Fiscal Affairs Department has been compiling data on gross government debt-to-GDP ratios going back to 1875. This chapter focuses on the experience of the 22 advanced economies where there is good data over most of this period. What lessons can be drawn?

For starters: "Public debt levels above 100 percent of GDP are not uncommon. Of the 22 advanced economies for which there is good data coverage, more than half experienced at least one high-debt episode between 1875 and 1997. Furthermore, several countries had multiple episodes: three for Belgium and Italy and two for Canada, France, Greece, the Netherlands, and New Zealand."

Here's a figure showing the 26 episodes for these 22 countries where government debt exceeded the 100% level. One way to eyeball this chart is to draw a mental line horizontally at the 100% debt/GDP ratio. It becomes clear that the experiences here are very diverse: some of countries are on their way to taking on much more debt at this level, while others are about to reduce their debt dramatically. Another lesson that comes out of the figure is that the episodes of high government debt/GDP are essentially in four time periods: the last quarter of the 19th century, the years after World War I, the years after World War II, and the last few decades. But there are almost no examples of high government debt/GDP ratios in this group of advanced economies from the 1950s up through the early 1980s.

Many of the episodes in which debt/GDP ratios decreased dramatically were in the aftermath of war--sometimes in accompaniment with hyperinflation after the war that ate away the value of the debt. Only three of these 26 debt overhang episodes ended in actual default: Germany (defaulted in 1932), and Greece (defaulted in 1894 and 1932).

How does taking on a lot of debt affect economic growth? The IMF authors are suitably reticent here about any claims of causality. As they point out, high government debt might cause slower economic growth, but it's also plausible that slower economic growth makes a debt/GDP problem worse, by raising the desire of government to borrow in hard times while holding down the denominator of the debt/GDP ratio. (For an argument and some supporting evidence that the causality does run from higher government debt to slower growth, Carmen Reinhart, Vincent R. Reinhart, and Kenneth S. Rogoff make that case as  part of their article in the Summer. 2012 issue of my own Journal of Economic Perspectives.) Thus, the IMF authors stick to looking at correlations, without making an argument about causality. 

One of those correlations struck me as especially interesting. When the separate cases where government debt was above 100% of GDP and rising from cases where debt is above 100% of GDP and falling, they find that growth performance is notably better in those cases where debt/GDP ratios are falling. In this sense, it's not just the high level of debt that's the problem, but whether the government is showing a clear ability to address the debt/GDP ratio. 

They consider several episodes of public debt overhang, and seek to distill some lessons. Here are some lessons, intermixed with some of my own reactions: 

"The first key lesson is that a supportive monetary environment is a necessary condition for successful fiscal consolidation." In my own mind, one of the strongest arguments for the Federal Reserve promising to keep interest rates low for some  years into to the future doesn't have anything to do with how it might influence current borrowing and demand; instead, it's because low interest rates help to reassure investors that the the large and growing U.S. government debt is affordable. If U.S. interest rates were to spike upward, paying the interest on the U.S. government debt could become much harder in a hurry. Of course, this insight also emphasizes that the Federal Reserve feels a need to facilitate the huge U.S. budget deficits--not because it favors the deficits, but because the alternative of not facilitating them could be worse. 

"Second, debt reduction is larger when fiscal measures are permanent or structural and buttressed
by a fiscal framework that supports the measures implemented." What the authors mean here is that when governments try to address their debt problems with nonspecific policies like across-the-board spending cuts or spending caps, they often are failing to make any actual real choices about what will change. The countries that have had some success in recent decades in reducing debt/GDP ratios--Belgium in the 1980s, Italy around 1992, Canada around 1995--succeeded because they made specific choices about reforming pensions, entitlements and other spending programs.

Their third lesson is that strong external demand is a huge help. Looking at the world economy today, the potential source of strong external demand is the rapidly growing "emerging market" economies like China, India, Brazil, and others. 

Finally, their fourth lesson is that it takes years to address a massive debt overhang. I sometimes think of the trajectories of U.S. government spending and taxes heading off into the future, both rising, but under current projections slowly drawing further apart. If the government can take real steps to make the rise in the spending line a little flatter and the rise in the tax line a little steeper, then the problem is gradually solved. The IMF authors look at annual deficit/GDP ratios, and find that "sustained improvements of more than 1 percentage point a year are rare." 

Where does the U.S. economy stand? The Congressional Budget Office is my go-to source for deficit and debt projections. Its August 2012  report has projections from 2012 to 2022. Using those numbers, the U.S. gross government debt passed the 100% debt/GDP ratio this year--and thus would be counted in future episodes of public debt overhang. Of course, the CBO and others often emphasize not "gross" government debt, which includes debt that the government owes to itself (like the Treasury bonds held in the Social Security trust fund), and instead look at "debt held by the public. On that measure, using the CBO "alternative fiscal scenario," which is a more realistic view, 
debt held by the public is at a 73% debt/GDP ratio in 2012 and is headed for a 90 percent debt/GDP ratio by 2022. 

To me, all of this says that the U.S. government debt burden is still at a level that remains bearable by the enormous U.S. economy. But it's high time to start making those specific medium-term changes that will keep the the U.S. 2012 episode of public debt overhang one that is short, and over the next few years turns a rising debt trajectory into a declining one. 

The Drop in Personal Interest Income

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Low interest rates are good for borrowers, but lousy for savers. Here's a graph showing personal interest income, which dropped by about $400 billion per year--a fall of more than one-fourth--as interest rates have plummeted.  




One of my local newspapers, the (Minneapolis) Star Tribune, offered a nice illustrative set of anecdotes in a story last Sunday about this consequences of this change for those who were depending on interest-bearing assets--often those who are near-retirement or in-retirement, and who want to hold safe assets, but who are receiving a much lower return than they might have expected.
 Moreover, as the article points out, it's not just individual savers who are affected. Pension funds, life insurance companies, long-term care insurance companies, and others who keep a substantial proportion of their investments in safe interest-bearing assets are receiving much less in interest than they would have expected, too.


I'm someone who supported pretty much everything the Federal Reserve did through the depths of the recession and financial crisis that started in late 2007: cutting the federal funds rate down to near-zero percent; setting up a number of agencies to lend money to make short-term liquidity loans to number of firms in financial markets; and the "quantitative easing" policies that involved printing money to purchase federal debt and mortgage-backed securities. But the recession officially ended back in June 2009, more than three years ago. It's time to start recognizing that ultra-low interest rates pose some painful trade-offs, too. Higher-ups at the Fed were reportedly saying back in 2009 that when the financial crisis was over, they would unwind these steps--but with the sluggishness of the recovery, they haven't done so.

 A year ago, for example, I posted on Can Bernanke Unwind the Fed's Policies? I posted last month on "BIS on Dangers of Continually Expansionary Monetary Policy," in which the Bank of International Settlements states: "Failing to appreciate the limits of monetary policy can lead to central banks being overburdened, with potentially serious adverse consequences. Prolonged and aggressive monetary accommodation has side effects that may delay the return to a self-sustaining recovery and may create risks for financial and price stability globally."

I lack the confidence to say just when or how the Fed should start backing away from its extremely accommodating monetary policies, but after jamming the monetary policy pedal quite hard for the last five years, it seems time to acknowledge that monetary policy in certain settings like the aftermath of a financial crisis and an overleveraged economy is a more limited tool than many of us would have believed back in 2006. Moreover, the U.S. economy has a very recent example from the early 2000s in which the Federal Reserve kept interest rates too low for too long in the early 2000s, and it helped to trigger the boom in lending and borrowing, much of it housing-related in one way or another, that led to the financial crisis and the Great Recession. The dangers of ultra-low interest rates and quantitative easing may not yet outweigh their benefits, but the potential tradeoffs and dangers shouldn't be minimized or ignored.

Are Groups More Rational than Individuals?

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"A decision maker in an economics textbook is usually modeled as an individual whose decisions are not influenced by any other people, but of course, human decision-making in the real world is typically embedded in a social environment. Households and firms, common decision-making agents in economic theory, are typically not individuals either, but groups of people—in the case of firms, often interacting and overlapping groups. Similarly, important political or military decisions as well as resolutions on monetary and economic policy are often made by configurations of groups and committees rather than by individuals."

Thus starts an article called "Groups Make Better Self-Interested Decisions," by Gary Charness and Matthias Sutter, which appears in the Summer 2012 issue of my own Journal of Economic Perspectives. (Like all articles appearing in JEP back to 1994, it is freely available on-line courtesy of the American Economic Association.) They explore ways in which individual decision-making is different from group decision making, with almost all of their evidence coming from behavior in economic lab experiments. To me, there were two especially intriguing results: 1) Groups are often more rational and self-interested than individuals; and 2) This behavior doesn't always benefit the participants in groups, because the group can be less good than individuals at setting aside self-interest when cooperation is more appropriate. Let me explore these themes a bit--and for some readers, offer a quick introduction to some economic games that they might not be familiar with.

There has been an ongoing critique of the assumption that individuals act in a rational and self-interested manner, based on the observations that people are often limited in the information that they have, muddled in their ability to process information, myopic in their time horizons, affected by how questions are framed, and many other "behavioral economics" issues. It turns out that in many contexts, groups are often better at avoiding these issues and acting according to pure rationality than are individuals.

As one example, consider the "beauty contest" game. As Charness and Sutter point out: "The name of the beauty-contest game comes from the Keynes (1936) analogy between beauty contests and financial investing in the General Theory: “It is not a case of choosing those which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.” Similarly, in a beauty-contest game, the choice requires anticipating what average opinion will be."

The game works like this. A group of players is told that they should choose a number between 0 and 100, and the winner of the game will be the person who chooses a number that is (say) 1/2 of the average of the other choices. In this game, the rational player will reason as follows: "OK, let's say that the other players choose randomly, so the average will be 50, and I should choose 25 to win. But if other players have this first level of insight, they will all choose 25 to win, and I should instead choose 12. But if other players have this second level of insight, then they will choose 12, and I should choose 6. Hmmm. If the other players are rational and self-interested, the equilibrium choice will end up being zero."

The players in a beauty contest game can be either individuals or groups. It turns out that groups choose lower numbers: that is, as a result of interacting in the group, they tend to be one step ahead.

Here's another example, called the "Linda paradox," in which players get the following question (quoting from Charness and Sutter):

"Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable:
(a) Linda is a bank teller.
(b) Linda is a bank teller and is active in the feminist movement."

Notice that Linda is a bank teller in both choices, but only active in the feminist movement in the second choice: that is, the second choice is a subset of the first choice. For that reason, it is impossible for choice b to be more likely than choice a. However, early research on this question found that 85% of individuals answered b. But when the game is played with groups of 2, and with groups of 3, the error rate drops.

Charness and Sutter offer a number of other examples, but the underlying themes are clear. In many settings, a group of people is likely to be better than an individual at processing a question, processing information, and acting in a rational answer. However, there are a number of settings in which pure self-interest can be self-defeating, and a more cooperative approach is useful. It turns out that individuals are often better than groups at setting aside pure self-interest and perceiving such opportunities.

Here's an example called the "trust game." In this game, the first player starts with a certain sum of money. Player 1 divides the money and passes part of it to Player 2. The experimenter triples the amount passed to Player 2. Player 2 then divides what is received, and passes part of the money back to Player 1.  In this kind of game, clearly what's best for both players is if Player 1 gives all of the money to Player 2, thus tripling the entire total, and trusts that Player 2 will return enough to make such this worthwhile. However, a strictly self-interested Player 2 may see no reason in this game to send anything at all back to Player 1, and Player 1, perceiving this, will then see no reason to send anything to Player 2. If both players act in a self-interested manner, both can end up worse off.

It turns out that when groups play this game, when they are acting they send less of the pot from Player 1 to Player 2 than do individuals, and they return less of the pot from Player 2 to Player 1 than do individuals. Thus, groups pursue self-interest in a way that reduces the potential returns from cooperation and trust, as compared with individuals.

Much remains to be done in comparing actions of groups and individuals in a wider variety of contexts. But these results intrigue, because they seem to point toward an economic theory of when group decision-making might be preferable to that of individuals, and vice versa. For example, when looking at a potentially complex problem, where the appropriate decision isn't clear, getting a group of people with diverse backgrounds and information can be quite helpful in leading to a more rational decision. But groups can also become large enough that they don't work well in gathering input from individuals, and become unable to move ahead with decisions.


The results also suggest that economists and social scientists should be cautious in making quick-and-dirty statements about how economic actors either do engage or don't engage in rational self-interested behavior. For example, it's possible to have a bunch of individuals who can't manage to lose weight or save money when acting on their own, but who find a way to do so when acting acting as a group and reinforcing each other. A person may act irrationally in some aspects of their personal life, but still be a useful member of a highly rational group in their work environment.  On the other side, in situations calling for gains from cooperation, pitting one group against another may be dysfunctional.  For example, many negotiations in business and politics follow the model of designating a lead negotiator, and descriptions of such negotiations often suggest that good negotiators form a bond with those on the other side that helps a compromise to emerge.