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Forty years after its initial publication, Equality and Efficiency: The Big Tradeoff remains an influential work from one of the most important macroeconomists over the last century, Arthur M. Okun (1928-1980). Okun’s theory on market economies reminds readers of an engaging dual theme: the market needs a place, and the market needs to be kept in its place. Articulated in a way that remains relevant even during today’s discussions on broadening gaps in income inequality, Okun emphasized that institutions in a capitalist democracy prod us to get ahead of our neighbors economically after telling us to stay in line socially.

On May 4, The Brookings Institution Press re-released Okun’s classic work with a new foreword from Former Treasury Secretary Lawrence H. Summers, in addition to “Further Thoughts on Equality and Efficiency,” a paper published by Okun in 1977. The event included opening remarks from Brookings Senior Fellow George Perry, with a keynote address from Larry Summers. Following these remarks, David Wessel moderated a panel discussion with former Chair of the Council of Economic Advisers Greg Mankiw, Economic Studies’ Melissa Kearney and Justin Wolfers, and Washington Center for Equitable Growth's Heather Boushey regarding the history and impact of Okun’s work.

Download a copy of Lawrence Summers' opening remarks.

Ted Gayer, Vice President and Director of Economic Studies and Joseph Pechman Senior Fellow, reads Lawrence Summers's opening remarks.

David Wessel (right), Director of the Hutchins Center on Fiscal and Monetary Policy, moderates a panel discussion with N. Gregory Mankiw, Melissa Kearney, and Heather Boushey.

Janet Yellen, Chair of the Board of Governors of the Federal Reserve System, listens to the discussion from the audience. To Yellen's right is former Congressional Budget Office director, Doug Elmendorf.

Video

• 40 years later- The relevance of Okun’s "Equality and Efficiency: The Big Tradeoff"

Audio

• 40 years later- The relevance of Okun’s "Equality and Efficiency: The Big Tradeoff"

Transcript

• Uncorrected Transcript (.pdf)

Event Materials

• 20150504_okun_equality_efficiency_transcript
• 050415 Summers Okun Speech

      

 

 

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After being made aware of their racial biases in referee calls through widespread media exposure, individual National Basketball Association referees became unbiased, suggesting that raising awareness of even subtle forms of racism can bring about meaningful change.

The authors examined a real-world setting—professional sports referees who had big incentives to make unbiased decisions but were still exhibiting significant amounts of racial bias—and found that after learning of their bias via media coverage of a major academic study, their behaviors changed.

The original study, authored by Price and Wolfers and in 2007, looked at nearly two decades of NBA data (1991-2002) and found that personal fouls are more likely to be called against basketball players when they are officiated by an opposite-race refereeing crew than when officiated by an own-race refereeing crew. The results received widespread media attention at the time, with a front-page piece in the New York Times and many other newspapers, extensive coverage on the major news networks, ESPN, talk radio and in the sports media including comments from star players at the time such as LeBron James, Kobe Bryant and Charles Barkley, to then-NBA Commissioner David Stern.

The new paper compares the next time period after the first study (2003-2006) to the timeframe immediately after the study was publicized (2007-2010). The authors found the bias continued in the first 3-year period after the study but that no bias was apparent after the widespread publicity of the first study’s findings. The researchers found that the media exposure alone was apparently enough to bring about the attitude change: the NBA reported that it not take any specific action to eliminate referee discrimination, and in fact never spoke to the referees about the study, nor change referee incentives or training.

Abstract

Can raising awareness of racial bias subsequently reduce that bias? We address this question by exploiting the widespread media attention highlighting racial bias among professional basketball referees that occurred in May 2007 following the release of an academic study. Using new data, we confirm that racial bias persisted in the years after the study's original sample, but prior to the media coverage. Subsequent to the media coverage though, the bias completely disappeared. We examine potential mechanisms that may have produced this result and find that the most likely explanation is that upon becoming aware of their biases, individual referees changed their decision-making process. These results suggest that raising awareness of even subtle forms of bias can bring about meaningful change.

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The headlines seem pretty good. Unemployment fell a tick to 7.3 percent. And jobs growth continued, with payrolls expanding by 169,000 in August, which is just shy of the 175,000 new jobs that analysts were expecting.

But beneath the headline: blech!

The most important news was the revisions to what we had previously thought was a healthy and perhaps self-sustaining recovery. Instead, jobs growth in July was revised from 162,000, to a weak 104,000, and June was also revised downward. Taken together, this month's revisions means we've created 74,000 fewer jobs than previously believed. And the previous jobs report subtracted another 26,000 jobs through revisions. Moreover, for reasons that remain a mystery, revisions have tended to be pro-cyclical, meaning that the healthy recovery we thought we were having might have been expected to yield further upward revisions. All this means that analysts are hastily revising their views.

The other bad news comes from the household survey, where employment fell 115,000, leading the employment-to-population ratio to decline by 0.1 percentage points. So the decline in the unemployment rate isn't due to folks getting jobs; instead, it's due to people dropping out of the labor force.

I have two simple metrics I use to measure the "underlying" pace of jobs growth. The first puts 80% weight on the (more accurate) payrolls survey, and 20% weight on the noisier household survey. That measure suggests employment grew by only 112,000 in August. The alternative is to focus on the 3-month average of payrolls growth, which suggests we're creating slightly around 148,000 jobs per month.

Bottom line: This report says that we're barely creating enough jobs to keep the unemployment rate falling from its current high levels. Policymakers have been looking for a signal that the recovery has become self-sustaining. This report doesn't provide it. And until we're confident that the recovery will keep rolling on, we should delay either any monetary tightening, further fiscal cuts, and definitely postpone the legislative shenanigans that Congress is threatening.

      

 

 

Justin Wolfers, professor of Economics and Public Policy at the University of Michigan, re-joins Brookings, Vice President and Economic Studies Co-Director Karen Dynan announced today.  Wolfers was a visiting fellow from 2010-2011.

A world-renowned empirical economist, Wolfers will continue in his role as co-editor, along with David Romer of the University of California, of the Brookings Papers on Economic Activity (BPEA), the flagship economic journal of the Institution.  He will continue his focus on labor economics, macroeconomics, political economy, economics of the family, social policy, law and economics, public economics, and behavioral economics. His appointment as senior fellow will last 13 months.

Wolfers is also a research associate with the National Bureau for Economic Research, a research affiliate of the Centre for Economic Policy Research in London, a research fellow of the German Institute for the Study of Labor, and a senior scientist for Gallup, among other affiliations. He is a contributor for Bloomberg View, NPR Marketplace, and the Freakonomics website and was named one of the 13 top young economists to watch by the New York Times.  Wolfers did his undergraduate work at the University of Sydney, Australia and received his Master’s and Ph.D. in Economics from Harvard University.  He is a dual Australian-U.S. national and was once an apprentice to a bookie which led to his interest in prediction markets. 

“We are pleased to re-welcome Justin back to Economic Studies,” said Dynan. “His work continues to challenge the conventional wisdom, and we look forward to collaborating with him once again.” 

“Justin is outstanding at communicating economic ideas to a wide audience, as evidenced by his regular writings for media as well as his large social media presence,” added Ted Gayer, co-director of Economic Studies.

“I have enormous affection for the Brookings Institution, which provides not only a home for deep scholarly research, but also an unmatched platform for engaging the policy debate,” said Wolfers.  “The Economic Studies program has a rich history of being the go-to place for policymakers, and I look forward to coming back and engaging in debate with my colleagues there.”

      

 

 

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Many scholars have argued that once “basic needs” have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of “basic needs” and multiple questions about well-being, we find no support for this claim. The relationship between well-being and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it.

Introduction

In 1974 Richard Easterlin famously posited that increasing average income did not raise average well-being, a claim that became known as the Easterlin Paradox. However, in recent years new and more comprehensive data has allowed for greater testing of Easterlin’s claim. Studies by us and others have pointed to a robust positive relationship between well-being and income across countries and over time (Deaton, 2008; Stevenson and Wolfers, 2008; Sacks, Stevenson, and Wolfers, 2013). Yet, some researchers have argued for a modified version of Easterlin’s hypothesis, acknowledging the existence of a link between income and well-being among those whose basic needs have not been met, but claiming that beyond a certain income threshold, further income is unrelated to well-being.

The existence of such a satiation point is claimed widely, although there has been no formal statistical evidence presented to support this view. For example Diener and Seligman (2004, p. 5) state that “there are only small increases in well-being” above some threshold. While Clark, Frijters and Shields (2008, p. 123) state more starkly that “greater economic prosperity at some point ceases to buy more happiness,” a similar claim is made by Di Tella and MacCulloch (2008, p. 17): “once basic needs have been satisfied, there is full adaptation to further economic growth.” The income level beyond which further income no longer yields greater well-being is typically said to be somewhere between $8,000 and $25,000. Layard (2003, p. 17) argues that “once a country has over $15,000 per head, its level of happiness appears to be independent of its income;” while in subsequent work he argued for a $20,000 threshold (Layard, 2005 p. 32-33). Frey and Stutzer (2002, p. 416) claim that “income provides happiness at low levels of development but once a threshold (around $10,000) is reached, the average income level in a country has little effect on average subjective well-being.”

Many of these claims, of a critical level of GDP beyond which happiness and GDP are no longer linked, come from cursorily examining plots of well-being against the level of per capita GDP. Such graphs show clearly that increasing income yields diminishing marginal gains in subjective well-being. However this relationship need not reach a point of nirvana beyond which further gains in well-being are absent. For instance Deaton (2008) and Stevenson and Wolfers (2008) find that the well-being–income relationship is roughly a linear-log relationship, such that, while each additional dollar of income yields a greater increment to measured happiness for the poor than for the rich, there is no satiation point.

In this paper we provide a sustained examination of whether there is a critical income level beyond which the well-being–income relationship is qualitatively different, a claim referred to as the modified-Easterlin hypothesis. As a statistical claim, we shall test two versions of the hypothesis. The first, a stronger version, is that beyond some level of basic needs, income is uncorrelated with subjective well-being; the second, a weaker version, is that the well-being–income link estimated among the poor differs from that found among the rich.

Claims of satiation have been made for comparisons between rich and poor people within a country, comparisons between rich and poor countries, and comparisons of average well-being in countries over time, as they grow. The time series analysis is complicated by the challenges of compiling comparable data over time and thus we focus in this short paper on the cross-sectional relationships seen within and between countries. Recent work by Sacks, Stevenson, and Wolfers (2013) provide evidence on the time series relationship that is consistent with the findings presented here.

To preview, we find no evidence of a satiation point. The income–well-being link that one finds when examining only the poor, is similar to that found when examining only the rich. We show that this finding is robust across a variety of datasets, for various measures of subjective well-being, at various thresholds, and that it holds in roughly equal measure when making cross-national comparisons between rich and poor countries as when making comparisons between rich and poor people within a country.

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This morning's gross domestic product (GDP) report showed that the economic recovery continued through the first quarter of this year, growing at 2.5%. That's a reasonable (though not great) rate of growth, although a bit below expectations, which were for something closer to 3%.

There's good news and bad news buried in the detail. The good is that consumers seem interested in spending again. We'll see whether that holds up over coming months. The bad is that firms aren't so optimistic, and investment was lackluster.

Government spending continues to detract from economic growth, as it has for 10 of the past 11 quarters.

This report also provides the latest reading on the core PCE deflator, which is the rate of inflation targeted by the Fed. This measure shows inflation running at 1.2%, well below the Fed's target.

Let's not get lost in the detail. This GDP report provides a soon-to-be-revised and noisy indicator of what happened in the economy a few months back.

The bigger picture is that we have a fledgling recovery which needs help, but isn't getting it: Fiscal policy is set as a drag on growth, and monetary policy delivering below-target inflation.

      

 

 

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Abstract

Progress in closing differences in many objective outcomes for blacks relative to whites has slowed, and even worsened, over the past three decades. However, over this period the racial gap in wellbeing has shrunk. In the early 1970s data revealed much lower levels of subjective well-being among blacks relative to whites. Investigating various measures of well-being, we find that the well-being of blacks has increased both absolutely and relative to that of whites. While a racial gap in well-being remains, two-fifths of the gap has closed and these gains have occurred despite little progress in closing other racial gaps such as those in income, employment, and education. Much of the current racial gap in well-being can be explained by differences in the objective conditions of the lives of black and white Americans. Thus making further progress will likely require progress in closing racial gaps in objective circumstances.

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On Valentine’s Day, even a dismal scientist’s mind turns to love. It’s a powerful feeling, with a value that goes far beyond the millions of chocolate boxes and bouquets that will be delivered this Feb. 14.

Survey data from the Gallup Organization, where Justin works as a senior scientist, allow us to take a uniquely deep look at the state of love around the world. In 2006 and 2007, Gallup went to 136 countries and asked people, “Did you experience love for a lot of the day yesterday?” It’s the largest such dataset ever collected.

The good news: Ours is a loving world. On a typical day, about 70 percent of people worldwide reported a love-filled day. In the U.S., 81 percent felt love, as did 81 percent of Canadians and 79 percent of Italians. Germany and the U.K. were less loving, with slightly less than 3 in 4 people reporting feeling loved. Surprisingly, the same was true of the supposedly romantic French. And if you’re in Japan, please hug someone: Only 59 percent of Japanese said they had experienced love the previous day.

Across the world as a whole, the widowed and divorced are the least likely to experience love. Married folks feel more of it than singles. People who live together out of wedlock report getting even more love than married spouses -- an interesting factoid for conservatives worried about the effects of cohabitation. Women get more love than men, particularly in the U.S.

Young Love

If you’re young and not feeling all that loved this Valentine’s Day, don’t despair: You’re not alone. Young adults are among the least likely to experience love. It gets better with age, ultimately peaking in the mid-30s or mid-40s in most countries before fading again into the twilight years.

Money is related to love. Those with more household income are slightly more likely to experience the feeling. Roughly speaking, doubling your income is associated with being about 4 percentage points more likely to be loved. Perhaps having more money makes it easier to find time for love.

That said, the data aren’t necessarily telling us that money can buy you love. It’s possible that other factors correlated with income, such as height or appearance, are the real source of attraction. Or maybe being loved gives you a boost in the labor market.

What’s perhaps more striking is how little money matters on a global level. True, the populations of richer countries are, on average, slightly more likely to feel loved than those of poorer countries. But love is still abundant in the poorer countries: People in Rwanda and the Philippines enjoyed the highest love ratios, with more than 9 in 10 people providing positive responses. Armenia, Uzbekistan, Mongolia and Kyrgyzstan, with economic output per person in the middle of the range, all had love ratios of less than 4 in 10.

Fun facts aside, we think there is a deeper and more consequential purpose to the study of love. Think about what love means to you. To us, it means caring about others and being cared for. Love is valuable, even if it is absent from both our national accounts and our political discourse.

In the language of economics, love is a form of insurance. It involves bonds of reciprocity that provide support when we’re feeling down, when we’re sick and when times are tough.

More broadly, love has the power to mitigate the free-rider and moral hazard problems associated with social (and private) insurance. Bailing out a bank might encourage executives to take bigger risks in the future, but helping loved ones down on their luck has fewer incentive problems because our loved ones typically care for us in return. Such mutually beneficial relationships make us all more resilient in times of crisis. This is why the household remains one of the most powerful institutions for organizing not just families but also our economic lives.

If we can find more love for our fellow citizens, our society will function better. Hard as this may be to achieve in an era when trust in government, business and one another is low, it’s worth the effort. When you expand the boundaries of trust and reciprocity, you expand the boundaries of what is possible.

Note: This content was first published on Bloomberg View on February 13, 2013.

     

 

 

Paris and Rome may be famous for romance, but it’s Filipinos who get the most love. That, at least, is a conclusion that can be drawn from a global love survey conducted by the Gallup Organization.

In our latest column for Bloomberg View, we mine the unique Gallup data for insights into the nature of love and its relationship to nationality, age, money and economic development. The survey, conducted in 136 countries, posed the question: “Did you experience love for a lot of the day yesterday?”

In honor of Valentine’s Day, we thought readers might be interested in seeing the full ranking. So here goes. The first number after each country name is the percentage of respondents who said they had experienced love the previous day. The second (in parentheses) is the sample size for the country.

1. Philippines 93% (2193)
2. Rwanda 92% (1495)
3. Puerto Rico 90% (495)
4. Hungary 89% (1002)
5. Cyprus 88% (988)
6. Trinidad and Tobago 88% (506)
7. Paraguay 87% (1986)
8. Lebanon 86% (970)
9. Costa Rica 85% (1985)
10. Cambodia 85% (1961)
11. Nigeria 84% (1965)
12. Guyana 83% (486)
13. Spain 83% (998)
14. Mexico 82% (989)
15. Tanzania 82% (1941)
16. Ecuador 82% (2126)
17. Jamaica 82% (534)
18. Venezuela 82% (997)
19. Cuba 82% (978)
20. Brazil 82% (1038)
21. Laos 81% (1947)
22. Argentina 81% (1985)
23. Belgium 81% (1015)
24. Canada 81% (1006)
25. Greece 81% (996)
26. U.S. 81% (1224)
27. Denmark 80% (1003)
28. Portugal 80% (995)
29. Netherlands 80% (993)
30. Vietnam 79% (1901)
31. New Zealand 79% (1775)
32. Italy 79% (1000)
33. Colombia 79% (1994)
34. Madagascar 78% (998)
35. Uruguay 78% (1969)
36. Turkey 78% (985)
37. Dominican Republic 78% (1976)
38. United Arab Emirates 77% (961)
39. Saudi Arabia 77% (978)
40. Chile 76% (1982)
41. Malawi 76% (1997)
42. Ghana 76% (1986)
43. South Africa 76% (1968)
44. Australia 76% (1199)
45. Panama 75% (1995)
46. Zambia 74% (1971)
47. Kenya 74% (1965)
48. Namibia 74% (996)
49. Nicaragua 74% (1988)
50. Germany 74% (1214)
51. Ireland 74% (992)
52. Sweden 74% (993)
53. U.K. 74% (1200)
54. Switzerland 74% (986)
55. Montenegro 74% (800)
56. Austria 73% (984)
57. France 73% (1217)
58. Kuwait 73% (934)
59. Finland 73% (993)
60. El Salvador 73% (2000)
61. Pakistan 73% (2253)
62. Zimbabwe 72% (1989)
63. Honduras 72% (1947)
64. Peru 72% (1982)
65. Egypt 72% (1024)
66. Serbia 72% (1474)
67. Bosnia and Herzegovina 72% (1896)
68. Sierra Leone 71% (1986)
69. India 71% (3140)
70. Taiwan 71% (984)
71. Bangladesh 70% (2200)
72. Belize 70% (464)
73. Croatia 69% (958)
74. Macedonia 69% (1000)
75. Mozambique 69% (996)
76. Bolivia 69% (1948)
77. Liberia 68% (988)
78. Iran 68% (963)
79. China 68% (7206)
80. Slovenia 68% (1000)
81. Haiti 68% (471)
82. Norway 67% (992)
83. Sri Lanka 67% (1974)
84. Poland 67% (939)
85. Guatemala 67% (1988)
86. Uganda 66% (1961)
87. Sudan 66% (971)
88. Israel 66% (957)
89. Kosovo 65% (983)
90. Thailand 65% (2377)
91. Jordan 65% (998)
92. Albania 64% (855)
93. Guinea 62% (952)
94. Botswana 62% (999)
95. Angola 62% (957)
96. Burkina Faso 62% (1876)
97. Malaysia 61% (2115)
98. Mali 61% (984)
99. Niger 61% (1925)
100. Palestinian Territories 61% (991)
101. Romania 61% (937)
102. Senegal 61% (1805)
103. Indonesia 61% (2013)
104. Afghanistan 60% (1128)
105. Hong Kong 60% (789)
106. Cameroon 59% (1967)
107. Japan 59% (1138)
108. Nepal 59% (1965)
109. Bulgaria 59% (927)
110. Slovakia 58% (991)
111. Singapore 58% (3002)
112. Czech Republic 58% (992)
113. Mauritania 57% (1960)
114. Benin 56% (974)
115. South Korea 56% (2056)
116. Myanmar 55% (1047)
117. Latvia 54% (1942)
118. Togo 54% (988)
119. Estonia 53% (1800)
120. Lithuania 50% (1863)
121. Russia 50% (4667)
122. Chad 49% (1915)
123. Yemen 48% (959)
124. Ukraine 48% (1930)
125. Ethiopia 48% (1913)
126. Azerbaijan 47% (1824)
127. Tajikistan 47% (1847)
128. Moldova 46% (1937)
129. Kazakhstan 45% (1871)
130. Morocco 43% (1011)
131. Belarus 43% (1992)
132. Georgia 43% (1904)
133. Kyrgyzstan 34% (1969)
134. Mongolia 32% (928)
135. Uzbekistan 32% (962)
136. Armenia 29% (1954)

Note: This content was first published on Bloomberg View on February 13, 2013.

     

 

 

My first response to the GDP report was “holy cow!”-- it’s not often that the U.S. economy contracts, and the headline says that this just happened in the final quarter of 2012. Many had expected weak growth; none had seen a contraction coming. But once you take a deep breath, read past the headline, and delve into the numbers, you’ll see that this is actually a pretty good (though not great) report. The internals are much better than the top-line belies. Under the hood, we see solid growth in both consumption and investment and as a result, private spending was humming along. Last quarter’s decline in U.S. GDP was all about inventories (which subtracted 1.3 percentage points from growth), as well as sharp cuts in defense spending. Neither of these are expected to persist.

And let’s not forget that this is the "advance" GDP estimate, which is only an early (an often inaccurate) guess as to what was happening. Typically, this estimate misses the mark by a full 1.3 percentage points.

I'm sure we will start seeing the use of the dreaded "R" word (recession). That's premature, and almost certainly wrong. The U.S. economy is growing, although probably slower than potential. Don’t let me overstate my sunny optimism though—the recovery is still precarious, and Congress could still blow it up.

Overall, there's nothing in today's GDP report to change my view: The U.S. economy was doing OK -- maybe even pretty well -- but definitely not great in the final quarter of 2012. While this morning's negative growth number is an attention grabber, realize it's for last quarter, it's an early guess, and it's contradicted by most other data which point to an economy that is still growing, although perhaps not fast enough.

And finally, a trivia question: When is the last time that the first big hint of bad economic news came from an advance GDP report? Answer: Never.

     

 

 

Republican presidential candidate Mitt Romney has been strategically slippery about his tax plan, largely refusing to explain how he would pay for the sweeping tax cuts that represent his primary promise to voters.

In the second debate, though, he offered just enough detail for us to sketch the outlines of his program. If you’re poor or worried about the state of the U.S. government’s finances, the picture is not pretty.

The first course in Romney’s plan is dessert: Tax breaks for everyone! He would start by extending the tax cuts put in place by former President George W. Bush. He would then cut everyone’s rates by another 20 percent, repeal the alternative minimum tax, and get rid of the estate tax.

How would he pay for this? Mainly by limiting the amount people can deduct from their taxable income. Here’s the most detailed statement Romney has made: “One way of doing that would be say everybody gets—I’ll pick a number—$25,000 of deductions and credits, and you can decide which ones to use. Your home mortgage interest deduction, charity, child tax credit and so forth, you can use those as part of filling that bucket, if you will, of deductions.”

Big Shortfall

Putting both halves of Romney’s plan together, we compared the impact of the tax cuts with the offsetting effect of limiting itemized deductions. The result: While a cap on deductions is an interesting idea, it couldn’t possibly raise enough revenue to make up for the big tax giveaways Romney has promised. The shortfall would be a whopping $3.7 trillion over the next decade. Lowering the deduction limit to, say, $17,000 wouldn’t much change the math. The gap would still be $3.4 trillion.

Romney’s plan is most striking in its distributional implications (see chart). The greatest benefit would go to the rich. The top one-fifth of households would enjoy a staggering $16,000 average tax cut, offset by a tax increase of $4,000 due to the deduction cap. Net gain: $12,000. Actually, though, most of this group wouldn’t see that large of a benefit. About half of the spoils would go directly to the top 1 percent, which would get an average net tax cut of $100,000 a year.

The further one goes down the income scale, the worse Romney’s plan looks. The average household in the middle of the income distribution—the heart of the middle class—would get a cut of a little more than $800, which wouldn’t be much changed by the limit on deductions. The poor would actually pay slightly more tax, because Romney would end stimulus-related measures—such as an expansion of the Earned Income Tax Credit—that have benefited them.

True, any across-the-board tax cut would give more money to the rich in dollar terms, because they pay most of the taxes in the first place. But Romney’s plan goes further. It would reduce the amount the richest Americans pay relative to their income more than for anyone else. Specifically, the richest fifth would go from paying 26 percent of their income in taxes to 22 percent. The middle fifth would go from 16 percent to 15 percent. The tax burden on the poor would rise.

Romney has explicitly denied that his tax plan would favor the rich: “I will not, under any circumstances, reduce the share that’s being paid by the highest-income taxpayers.”

If this was truly his intention, he could have proposed tax cuts that were proportional to income—say, by offering simply to cut everyone’s tax rates by a few percentage points, rather than by a certain percentage. This would give the rich a bigger tax cut in dollar terms while preserving the distributional structure of our tax system.

Benefit Distribution

As it stands, Romney’s plan would result in 48 percent of the net tax cut going to the richest 1 percent (see pie chart). Another 32 percent would go to the next richest 4 percent of the population. All told, 94 percent of the benefit would go to the top 10 percent of the income distribution, leaving only 6 percent for the rest.

Many of Romney’s biggest boosters argue that he would be a more moderate president than he has been a candidate. Perhaps that’s plausible. On taxes, though, he has left himself little room to maneuver. His constituency would expect him to deliver on the very specific tax cuts he has promised. Meanwhile, his vagueness on the offsetting deduction limits would leave him with no mandate to get rid of the most popular tax breaks, such as those for charitable giving, mortgage interest or health insurance.

Hence, the most probable outcome would be a tax system that is radically less progressive, achieved through cuts that would create a much larger long-run budget deficit. Both outcomes would be colossal failures at a time in which true tax reform is greatly needed.

     

 

 

Editor's Note: A new academic study by David Rothschild and Justin Wolfers concludes that poll questions about expectations—which ask people whom they think will win—have historically been better guides to the outcome of presidential elections than traditional questions about people’s preferences. David Leonhardt of The New York Times conducted an interview with Wolfers by e-mail, focusing on the implications of the study for current presidential polls.

David Leonhardt:In the article, I discussed only briefly the expectations polls about the 2012 race, and some of the Twitter feedback was eager for more. By my count, there have been five recent major polls asking people whom they expect to win — by ABC/Washington Post, Gallup, Politico/George Washington University, New York Times/CBS News, and the University of Connecticut. There is also sixth from Rand asking people the percentage chances they place on each candidate winning. How consistent are the polls?

Justin Wolfers: There’s a striking consistency in how people are responding to these polls. The most recent data are from the Gallup poll conducted Oct. 27-28, and they found 54 percent of adults expect Obama to win, versus 34 percent for Romney. Around the same time (Oct. 25-28), there was a comparable New York Times/CBS poll in which 51 percent of likely voters expect Obama to win, versus 34 percent for Romney.

But these results aren’t just stable across pollsters, they’ve also been quite stable over the past few weeks, even as the race appeared to tighten for a while. Politico and George Washington University ran a poll of likely voters on Oct. 22-25, finding 54 percent expect Obama to win, versus 36 percent for Romney. The University of Connecticut/Hartford Courant poll of likely voters got a somewhat higher share not venturing an answer, with 47 percent expecting Obama to win versus 33 percent for Romney. Finally, the ABC/Washington Post poll of registered voters run Oct. 10-13 found 56 percent expect Obama to win, compared to 35 percent for Romney.

I’m rather surprised by the similarities here – across time, across pollsters, across how they word the question, and across different survey populations (likely voters, registered voters, or adults) – but I suspect that is part of the nature of the question. You just don’t see the noise here that you see in the barrage of polls of voter intentions, which are extremely sensitive to all of these factors.

I always throw out the folks who don’t have an opinion, and count the proportions as a share of only those who have an opinion. By this measure, the proportion who expect Obama to win is: 61 percent (Gallup), 60 percent (The New York Times), 60 percent (Politico), 59 percent (Hartford Courant), 62 percent (ABC). The corresponding proportions who expect Romney to win are: 39 percent, 40 percent, 40 percent, 41 percent and 38 percent. Taking an average across all these polls: 60.3 percent expect Obama to win. Or if you prefer that I focus only on the freshest two polls, 60.7 percent expect him to win.

DL: The results do seem have tightened somewhat since the first debate, which Romney was widely seen to have won, right? Do the patterns — or lack of patterns — in the numbers help solve the issue of what most people are thinking of when they answer the expectation question: Private information (their friends’ voting plans, yard signs in their neighborhood, etc.) or public information (media coverage, speeches, etc.)?

JW: The results of the polls of voter intentions seem to have tightened a bit since the first debate. There’s an interesting school of thought in political science that basically says: voters are pretty predictable. But they don’t think too hard about how they’re going to vote until right before the election. So what happens is that public opinion through time just converges to where it “should” be. And viewed through this lens, the first debate was just an opportunity for people who really should always have been in Romney’s camp to figure out that they’re in Romney’s camp.

So why did the expectations polls move less sharply than intentions polls? One possibility is that your expectations are explicitly forward-looking, and perhaps people saw the race tightening as they saw that some of the support for Obama was a bit soft. Let me put this another way: There are two problems with how we usually ask folks how they plan to vote. First, the question captures the state of public opinion today, while the expectations question effectively asks you where you think public opinion is going. And second, polls typically demand a yes or no answer, when the reality may be that we know that our support is pretty weak, and it may change, or we aren’t even sure whether we’ll turn up to the polls. The virtue of asking about expectations is that you can think about each of your friends, and think not just about who they’re supporting today, but also whether they may change their minds in the future.

I worry that it sounds a bit like I haven’t answered your question, but that’s because I don’t have a super-sharp answer. If I had to summarize, it would be: expectations questions allow you to think about how the dynamics of the race may change, and so they are less sensitive to that change when it happens.

DL: Based on your research and the current polls, what does the expectations question suggest is the most likely outcome on Tuesday?

JW: If a majority expects Obama to win, then right there, it says that I’m forecasting an Obama victory.

But by how much? Here’s where it gets tricky. The fact that 60 percent of people think that Obama is going to win doesn’t mean that he’s going to win 60 percent of the votes. And it doesn’t mean that he’s a 60 percent chance to win. Rather, it simply says that given the information they have, 60 percent of people believe that Obama is going to win. Can we use this to say anything about his likely winning margin?

Yes. I’ll spare you the details of the calculation, but it says that if 60.3 percent of people expect Obama to beat Romney, then we can forecast that he’ll win about 52.5 percent of the two-party vote. That would be a solid win, though not as impressive as his seven-point win in 2008.

The proportion who expect Obama to win right now looks awfully similar to the proportion who expected George W. Bush to win in a Gallup Poll at a similar point in 2004. Ultimately Bush won 51.2 percent of the two-party vote.

Right now, Nate Silver is predicting that Obama will win 50.5 percent of the popular vote, and Romney 48.6 percent. As a share of the two-party vote, this says he’s forecasting Obama to win 51 percent of the vote. Now Silver’s approach aggregates responses from hundreds of thousands of survey respondents, while I have far fewer, so his estimate still deserves a lot of respect. I don’t want to overstate the confidence with which I’m stating my forecast. So let me put it this way: My approach says that it’s likely that Obama will outperform the forecasts of poll-based analysts like Silver.

DL: We’ll find out soon enough. Thanks.

     

 

 

Abstract

Most pollsters base their election projections off questions of voter intentions, which ask “If the election were held today, who would you vote for?” By contrast, we probe the value of questions probing voters’ expectations, which typically ask: “Regardless of who you plan to vote for, who do you think will win the upcoming election?” We demonstrate that polls of voter expectations consistently yield more accurate forecasts than polls of voter intentions. A small-scale structural model reveals that this is because we are polling from a broader information set, and voters respond as if they had polled twenty of their friends. This model also provides a rational interpretation for why respondents’ forecasts are correlated with their expectations. We also show that we can use expectations polls to extract accurate election forecasts even from extremely skewed samples.

I. Introduction

Since the advent of scientific polling in the 1930s, political pollsters have asked people whom they intend to vote for; occasionally, they have also asked who they think will win. Our task in this paper is long overdue: we ask which of these questions yields more accurate forecasts. That is, we evaluate the predictive power of the questions probing voters’ intentions with questions probing their expectations. Judging by the attention paid by pollsters, the press, and campaigns, the conventional wisdom appears to be that polls of voters’ intentions are more accurate than polls of their expectations.

Yet there are good reasons to believe that asking about expectations yields more greater insight. Survey respondents may possess much more information about the upcoming political race than that probed by the voting intention question. At a minimum, they know their own current voting intention, so the information set feeding into their expectations will be at least as rich as that captured by the voting intention question. Beyond this, they may also have information about the current voting intentions—both the preferred candidate and probability of voting—of their friends and family. So too, they have some sense of the likelihood that today’s expressed intention will be changed before it ultimately becomes an election-day vote. Our research is motivated by idea that the richer information embedded in these expectations data may yield more accurate forecasts.

We find robust evidence that polls probing voters’ expectations yield more accurate predictions of election outcomes than the usual questions asking about who they intend to vote for. By comparing the performance of these two questions only when they are asked of the exact same people in exactly the same survey, we effectively difference out the influence of all other factors. Our primary dataset consists of all the state-level electoral presidential college races from 1952 to 2008, where both the intention and expectation question are asked. In the 77 cases in which the intention and expectation question predict different candidates, the expectation question picks the winner 60 times, while the intention question only picked the winner 17 times. That is, 78% of the time that these two approaches disagree, the expectation data was correct. We can also assess the relative accuracy of the two methods by assessing the extent to which each can be informative in forecasting the final vote share; we find that relying on voters’ expectations rather than their intentions yield substantial and statistically significant increases in forecasting accuracy. An optimally-weighted average puts over 90% weight on the expectations-based forecasts. Once one knows the results of a poll of voters expectations, there is very little additional information left in the usual polls of voting intentions. Our findings remain robust to correcting for an array of known biases in voter intentions data.

The better performance of forecasts based on asking voters about their expectations rather than their intentions, varies somewhat, depending on the specific context. The expectations question performs particularly well when: voters are embedded in heterogeneous (and thus, informative) social networks; when they don’t rely too much on common information; when small samples are involved (when the extra information elicited by asking about intentions counters the large sampling error in polls of intentions); and at a point in the electoral cycle when voters are sufficiently engaged as to know what their friends and family are thinking.

Our findings also speak to several existing strands of research within election forecasting. A literature has emerged documenting that prediction markets tend to yield more accurate forecasts than polls (Wolfers and Zitzewitz, 2004; Berg, Nelson and Rietz, 2008). More recently, Rothschild (2009) has updated these findings in light of the 2008 Presidential and Senate races, showing that forecasts based on prediction markets yielded systematically more accurate forecasts of the likelihood of Obama winning each state than did the forecasts based on aggregated intention polls compiled by Nate Silver for the website FiveThirtyEight.com. One hypothesis for this superior performance is that because prediction markets ask traders to bet on outcomes, they effectively ask a different question, eliciting the expectations rather than intentions of participants. If correct, this suggests that much of the accuracy of prediction markets could be obtained simply by polling voters on their expectations, rather than intentions.

These results also speak to the possibility of producing useful forecasts from non-representative samples (Robinson, 1937), an issue of renewed significance in the era of expensive-to-reach cellphones and cheap online survey panels. Surveys of voting intentions depend critically on being able to poll representative cross-sections of the electorate. By contrast, we find that surveys of voter expectations can still be quite accurate, even when drawn from non-representative samples. The logic of this claim comes from the difference between asking about expectations, which may not systematically differ across demographic groups, and asking about intentions, which clearly do. Again, the connection to prediction markets is useful, as Berg and Rietz (2006) show that prediction markets have yielded accurate forecasts, despite drawing from an unrepresentative pool of overwhelmingly white, male, highly educated, high income, self-selected traders.

While questions probing voters’ expectations have been virtually ignored by political forecasters, they have received some interest from psychologists. In particular, Granberg and Brent (1983) document wishful thinking, in which people’s expectation about the likely outcome is positively correlated with what they want to happen. Thus, people who intend to vote Republican are also more likely to predict a Republican victory. This same correlation is also consistent with voters preferring the candidate they think will win, as in bandwagon effects, or gaining utility from being optimistic. We re-interpret this correlation through a rational lens, in which the respondents know their own voting intention with certainty and have knowledge about the voting intentions of their friends and family.

Our alternative approach to political forecasting also provides a new narrative of the ebb and flow of campaigns, which should inform ongoing political science research about which events really matter. For instance, through the 2004 campaign, polls of voter intentions suggested a volatile electorate as George W. Bush and John Kerry swapped the lead several times. By contrast, polls of voters’ expectations consistently showed the Bush was expected to win re-election. Likewise in 2008, despite volatility in the polls of voters’ intentions, Obama was expected to win in all of the last 17 expectations polls taken over the final months of the campaign. And in the 2012 Republican primary, polls of voters intentions at different points showed Mitt Romney trailing Donald Trump, then Rick Perry, then Herman Cain, then Newt Gingrich and then Rick Santorum, while polls of expectations showed him consistently as the likely winner.

We believe that our findings provide tantalizing hints that similar methods could be useful in other forecasting domains. Market researchers ask variants of the voter intention question in an array of contexts, asking questions that elicit your preference for one product, over another. Likewise, indices of consumer confidence are partly based on the stated purchasing intentions of consumers, rather than their expectations about the purchase conditions for their community. The same insight that motivated our study—that people also have information on the plans of others—is also likely relevant in these other contexts. Thus, it seems plausible that survey research in many other domains may also benefit from paying greater attention to people’s expectations than to their intentions.

The rest of this paper proceeds as follows, In Section II, we describe our first cut of the data, illustrating the relative success of the two approaches to predicting the winner of elections. In Sections III and IV, we focus on evaluating their respective forecasts of the two-party vote share. Initially, in Section III we provide what we call naïve forecasts, which follow current practice by major pollsters; in Section IV we product statistically efficient forecasts, taking account of the insights of sophisticated modern political scientists. Section V provides out-of-sample forecasts based on the 2008 election. Section VI extends the assessment to a secondary data source which required substantial archival research to compile. In Section VII, we provide a small structural model which helps explain the higher degree of accuracy obtained from surveys of voter expectations. Section VIII characterizes the type of information that is reflected in voters’ expectation, arguing that it is largely idiosyncratic, rather than the sort of common information that might come from the mass media. Section IX assesses why it is that people’s expectations are correlated with their intentions. Section VI uses this model to show how we can obtain surprisingly accurate expectation-based forecasts with non-representative samples. We then conclude. To be clear about the structure of the argument: In the first part of the paper (through section IV) we simply present two alternative forecasting technologies and evaluate them, showing that expectations-based forecasts outperform those based on traditional intentions-based polls. We present these data without taking a strong position on why. But then in later sections we turn to trying to assess what explains this better performance. Because this assessment is model-based, our explanations are necessarily based on auxiliary assumptions (which we spell out).

Right now, we begin with our simplest and most transparent comparison of the forecasting ability of our two competing approaches.

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• Forecasting Elections: Voter Intentions versus Expectations

     

 

 

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