NBER WORKING PAPER SERIES

VIEWS AMONG ECONOMISTS:PROFESSIONAL CONSENSUS OR POINT-COUNTERPOINT?

Roger GordonGordon B. Dahl

Working Paper 18728http://www.nber.org/papers/w18728

NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

Cambridge, MA 02138January 2013

We would like to thank Anil Kashyap for inspiring us to write this paper, and both Anil Kashyap andBrian Barry for comments on an earlier draft. We also thank Paul Krugman, Monika Piazzesi, andJustin Wolfers for their insights as discussants at the 2013 ASSA meetings, and seminar participantsat UC San Diego. The views expressed herein are those of the authors and do not necessarily reflectthe views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies officialNBER publications.

© 2013 by Roger Gordon and Gordon B. Dahl. All rights reserved. Short sections of text, not to exceedtwo paragraphs, may be quoted without explicit permission provided that full credit, including © notice,is given to the source.

Views among Economists: Professional Consensus or Point-Counterpoint?Roger Gordon and Gordon B. DahlNBER Working Paper No. 18728January 2013JEL No. A11,H0,J0,Z18

ABSTRACT

To what degree do economists disagree about key economic questions? To provide evidence, we makeuse of the responses to a series of questions posed to a distinguished panel of economists put togetherby the Chicago School of Business. Based on our analysis, we find a broad consensus on these manydifferent economic issues, particularly when the past economic literature on the question is large. Anydifferences are unrelated to observable characteristics of the Panel members, other than men beingslightly more likely to express an opinion. These differences are idiosyncratic, with no support forliberal vs. conservative camps.

Roger GordonDepartment of Economics 0508University of California, San Diego9500 Gilman Drive, Dept. 0508La Jolla, CA 92093and NBERrogordon@ucsd.edu

Gordon B. DahlDepartment of EconomicsUniversity of California, San Diego9500 Gilman Drive #0508La Jolla, CA 92093-0508and NBERgdahl@ucsd.edu

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Views among Economists: Professional Consensus or Point-Counterpoint?*

Roger Gordon and Gordon B. Dahl

(UCSD)

To what degree do economists disagree about key economic questions? Our self-image

as a profession would be that our views on economic questions are based on the accumulated

academic evidence, both theoretical and empirical. As a literature first develops, the evidence

will still be ambiguous, with both alternative theories and contradictory empirical estimates,

easily leading to disagreements. But as evidence accumulates, a professional consensus should

emerge, perhaps more quickly among those working in the relevant field.

An alternative perception, reflecting the traditional fresh-water/salt-water divide in

macroeconomics, is that economists coalesce into different camps, to a degree reflecting a

liberal/conservative divide, with one group focusing on evidence that government intervention is

almost always too costly ex post to be justified and another that market failures are all too

frequent and can be alleviated by well-designed policy interventions. Compounding this divide

is differences in the degree of importance given to distributional vs. efficiency implications of

alternative policies.

These camps have traditionally been associated with particular universities, with

Chicago, Rochester, UCLA, and Stanford arguably reflecting the conservative camp, and MIT,

Harvard, and Berkeley reflecting the more liberal camp. Such a division could also show up

among Ph.D.’s from different schools, who each received training from a different set of faculty.

* Contact information: Roger Gordon (contact author), Department of Economics, UCSD, 9500 Gilman Dr. #0508, La Jolla, CA 92093-0508; rogordon@ucsd.edu. Gordon B. Dahl, Department of Economics, UCSD, 9500 Gilman Dr. #0508, La Jolla, CA 92093-0508; gdahl@ucsd.edu. We would like to thank Anil Kashyap for inspiring us to write this paper, and both Anil Kashyap and Brian Barry for comments on an earlier draft. We also thank Paul Krugman, Monika Piazzesi, and Justin Wolfers for their insights as discussants at the 2013 ASSA meetings, and seminar participants at UC San Diego.

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There may also be generational divides among economists, with a changing consensus

reflecting not so much each of us changing our views based on the accumulating evidence, but

instead those with out-dated views gradually retiring and being replaced by younger economists

whose views rely on more recent evidence.

This perception of different camps among economists is reinforced by the use of

economic spokespeople for Presidential candidates and sitting Presidents. The job of such a

spokesperson is to defend the politician’s positions in public, regardless of the economic advice

the spokesperson is giving internally, assuring a perception of politicized views. This

perception is reinforced by the media’s use of economists with polarized views in their point-

counterpoint debates.

These spokespeople are also under pressure to be “one-armed” economists, avoiding the

many qualifications coming out of the academic literature. Even non-spokespeople can be

subject to an “expert bias”, a bias towards more certainty than is justified, with this bias perhaps

stronger for men than women.

A hybrid view would be that economic priors (conservative vs. liberal) matter when the

empirical evidence is weak, but not when the empirical evidence is strong. The degree of

polarization would then depend (negatively) on the depth of the associated economic literature.

To provide evidence on the degree to which there is a consensus vs. polarized camps of

economists, we make use of the responses to a series of questions posed to a distinguished panel

of economists put together by the Chicago Booth School of Business, using responses up through

October 30, 2012. By design, the top seven Departments are equally represented on the Panel.

The Panel includes economists from different cohorts, women as well as men, and members of

different political persuasions. Each week since late September, 2011 this Panel has been asked

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to respond to a statement expressing a particular view on an economic question of current

interest.1 Panel members could “strongly agree”, “agree”, be “uncertain”, “disagree”, or

“strongly disagree” with the statement, or alternatively decline to express an opinion or simply

not answer. Members also indicated their confidence in their response, on a scale from one to

ten.

The aim of this paper is to examine these responses, to see to what degree opinions differ

within the Panel.2 To what degree is there more consensus among those from a particular school,

or those from a particular cohort or a particular gender? When opinions differ, does this division

broadly correspond to a liberal/conservative divide? Is there less disagreement on topics with a

large academic literature?

Our null hypothesis is that economists have homogeneous views, informed by whatever

academic evidence exists. The evidence may be ambiguous, but economists would then agree on

this ambiguity. Under our alternative hypothesis, we expect to see diverse views, perhaps linked

to a liberal/conservative divide, and perhaps with less diverse views within Departments or

cohorts and when the academic literature is large.

Based on our analysis, we conclude that there is close to full consensus among these

Panel members when the past economic literature on the question is large. When past evidence

is less extensive, differences in opinions do show up. But there is no tendency for those with the

same gender, from the same cohort, from the same Department, or with Ph.D.’s from the same

school, to have similar views. There are certainly some idiosyncratic views expressed, but we

1 Both panelists and the general public are welcome to suggest questions. Draft versions of the question are usually sent to panelists in advance, who have the opportunity to point out problems with wording or suggest improvements. This process minimizes the chance that a question will be vague or hard to interpret. The questions and each respondent’s answers can be found at http://www.igmchicago.org/igm-economic-experts-panel. 2 Prior research on economists’ opinions includes Kearl et al (1979), Alston, Kearl, and Vaughan (1992) and Fuchs, Krueger, and Poterba (1998).

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found no evidence of different camps. We did find, though, that younger economists are less

willing to express an opinion, while men and those with experience working in Washington have

fewer such inhibitions. On net, the main finding is of a broad consensus on these many different

economic issues.

The description of the data and of our empirical methods is described in the next section,

along with the resulting estimates. Section II then provides a brief summary.

I. Empirical Evidence

The first question we face in the analysis is how to define differences in views among our

panel members. Consider for example the responses to the question asking whether the Fed’s

new policies in 2011 will increase GDP growth by at least 1% in 2012. The responses were 41%

“uncertain”, 37% “disagree”, and 17% “strongly disagree”. Initially, we assume that these

responses imply consensus, since no respondents “agree” even though respondents differed in

their extent of disagreement with the statement.

By this definition, there is an extraordinarily high level of consensus among our Panel

members.3 Only 6% of the responses in our data violate this definition of consensus, responding

“disagree” when the consensus is “agree”, or conversely.4 Similarly, for 32 out of the 80

questions, there are no such disagreements.

Disagreements are not confined to questions where one might expect polarized views.

The greatest disagreement, for example, arose with the following statement from May 23, 2012,

where opinions were almost equally divided between “agree”, “disagree”, and “uncertain” (or 3 Throughout our analysis, we group together “agree” with “strongly agree”, and “disagree” with “strongly disagree”. We also group together “uncertain” with “no opinion”, finding these two responses have very similar correlations with other observables. In contrast, “did not answer”, while correlated with gender and age, had no significant correlation with question characteristics, suggesting nonresponses occur simply when respondents are too busy rather than when they are unsure of the best response. 4 If we instead define consensus to be identical answers among the three categories, we still find only minor disagreements: only 30% of the responses deviate from the modal response for a question.

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“no opinion”): “New technology for fracking natural gas, by lowering energy costs in the United

States, will make US industrial firms more cost competitive and thus significantly stimulate the

growth of US merchandise exports.” Yet there is no obvious partisan division on this issue.

What we’ll argue later is that such disagreements are much more common when the academic

literature on an issue is small, as is the case for “fracking”.

In spite of these observed differences in responses, though, views among our Panel might

still be homogeneous. This could occur if each individual responds randomly, reflecting

ambiguities in the evidence and disagreement with some elements of a statement but agreement

with other parts. To allow for such random responses, we now assume under the null hypothesis

that the probabilities of any of the three possible answers to a given question are the same for all

respondents.

To test whether all individuals have the same probabilities, we first tabulated the number

of times each individual agreed with the consensus, was uncertain, or disagreed with the

consensus.5 We modeled these counts as coming from a multinomial distribution, with

probabilities differing by question, and conducted a chi-square test of homogeneity to judge

whether the distribution of responses was consistent with individuals having a common set of

response probabilities.6

The resulting test strongly rejects homogeneous responses, with a p-value less than 0.001.

Deviations, though, take many forms: some individuals are much more likely to disagree with

the consensus, some are more likely to agree, and others are more likely to be uncertain.

To what degree are these deviations linked to observable characteristics of the Panel

5 These tabulations are available in the Online Appendix. 6 In particular, we simulated the distribution of the chi-squared statistic under the null hypothesis, bootstrapping the estimated probability of each response for each question, and then measured the probability of seeing a chi-squared statistic as extreme as that implied by the actual responses.

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members? (Under the null hypothesis, individual responses to each question would be entirely

random, given the overall probabilities in the Panel as a whole.) To provide some evidence, we

estimated a multinomial logit model forecasting the probability of each response as a function of

the characteristics of each respondent. The characteristics we included were:7 a dummy for

female, vintage of Ph.D.,8 a dummy indicating experience working in Washington, and a

dummy indicating expertise about the specific topic.9

Results are reported in Table 1. To aid in interpretation, the table provides marginal

effects evaluated at the means of the other covariates. In the first specification, we find that men

are 8 percentage points less likely to be uncertain compared to women. This is a sizeable effect

given that one-fourth of individuals answer “uncertain” on average. None of the other

coefficients are statistically significant. Using a likelihood ratio test for the joint significance of

all the personal characteristics other than effects of gender on being “uncertain”, we find a p-

value of 0.73, showing no systematic differences in views other than men being more likely to

express an opinion.

This first specification has not controlled for question characteristics, which could be

important since some questions evoke more consensus than others. We therefore added the

following additional controls to capture systematic variation by question in the fraction giving

each response: a dummy indicating that the response follows immediately from undergraduate

price theory, a dummy indicating a large academic literature on the topic, a dummy indicating at

7 In an additional specification, we included dummy variables indicating the university where the respondent got his or her Ph.D. as well as the university where they are currently employed. The resulting coefficients were not individually or jointly significant, and are omitted from the reported specification. 8 Cohorts are defined to equal: zero to fifteen years since Ph.D., sixteen to thirty years since Ph.D., and greater than thirty years since Ph.D. 9 Here, we assigned each question to a specific field or fields. An individual is an expert if there is any overlap among the fields covered by the question and the individual’s primary field of membership at NBER. The specific assignments are available in the Online Appendix. For the few panel members who were not affiliated with NBER, we “assigned” them to the obvious programs.

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least a few academic publications on the topic, an indicator for whether responses could reflect

distributional concerns and an indicator for whether responses could differ depending on views

about the efficiency of the relevant markets.10

Table 1. Multinomial Logit Estimates of the Probability of “Uncertain”

and “Reject Consensus” Relative to “Agree with Consensus”.

Notes: 2,834 observations in each specification. The omitted category is a young male, without experience working in Washington, without any expertise on the topic, facing a question with no past academic literature. Marginal effects are evaluated at the means of the other variables.

We find that the probabilities that respondents are uncertain or reject the consensus view

plummet when the academic literature is large. Compared to having no past literature, when

10 The data assignments were simply a judgment call, and are listed in the Online Appendix.

Variable

Marginal Effect

t-test

Marginal Effect

t-test

Marginal Effect

t-test

Uncertain (average rate = 0.245) Male -0.083 -3.89 -0.084 -3.93 -0.072 -3.81 Washington 0.013 0.65 0.016 0.81 0.019 1.04 Vintage: older -0.016 -0.77 -0.013 -0.63 -0.009 -0.45 mid-career -0.010 -0.51 -0.010 -0.52 -0.003 -0.16 Expert -0.022 -1.21 -0.004 -0.20 -0.034 -2.04 Lit: int. micro -0.247 -11.38 large -0.234 -10.84 medium -0.048 -2.05 Perfect markets? -0.030 -1.44 Redistribution? -0.045 -1.89 Avg. confidence -0.017 -2.38 Reject consensus (average rate = 0.060) Male 0.001 0.06 -0.001 -0.07 -0.002 -0.23 Washington 0.005 0.42 0.006 0.58 0.004 0.39 Vintage: older 0.017 1.39 0.015 1.33 0.015 1.29 mid-career 0.005 0.45 0.004 0.38 -0.000 -0.01 Expert 0.010 0.98 0.012 1.20 0.007 0.72 Lit: int. micro -0.083 -6.82 large -0.066 -4.95 medium -0.034 -2.43 Perfect markets? 0.023 1.79 Redistribution? 0.008 0.62 Avg. confidence 0.010 2.34 Question dummies? No No Yes

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there is a large literature the fraction uncertain is 25 percentage points lower (compared to a

mean of 25 percent), while the fraction rejecting the consensus is 8 percentage points lower

(compared to a mean of 6 percent). Also, when responses could vary depending on the degree of

confidence in efficient markets, the chance of rejecting the consensus is a little over 2 percentage

points higher. There is also less uncertainty when the statement implicitly raises efficiency or

distributional issues.

Note that the coefficients on the individual characteristics are robust to the inclusion of

the additional controls in the second specification. In principle, individual non-response patterns

could have been correlated with characteristics of the questions. By finding no change in these

individual coefficients, we infer that non-responses are largely unrelated to characteristics of

each question.

As a more complete control for differences across questions, the third column includes

separate dummy variables for each question. Most of the coefficients on the individual

characteristics remain qualitatively unaffected. The exception is that experts are now found to be

significantly less likely to be uncertain.11 Of course, experts know the existing literature much

better, so that this effect is entirely consistent with our null hypothesis that opinions simply

reflect the state of the existing academic literature.

The only statistically significant deviation from homogeneous views, therefore, is less

caution among men in expressing an opinion, perhaps due to a greater "expert bias”. Personality

differences rather than different readings of the existing evidence would then explain these

gender effects.

We certainly see differences among respondents in the confidence they assign to their

responses. To document these differences, we regress an individual’s reported confidence on 11 This final specification also includes an additional control variable, “average confidence”, which we discuss later.

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each question against a set of individual characteristics. We include the same individual controls

used above, along with dummy variables for the university where the individual is currently

employed and also dummy variables for the university where they received their Ph.D. In

addition, we added the indicators for questions dealing with market efficiency or distributional

issues, and controls for the size of the literature on the question, expecting respondents to be

more confident when the literature is large.

Table 2. OLS Estimates of the Determinants of Confidence. Variable Coefficient t-test Male 0.33 2.47 Washington 0.49 3.67 Vintage: older -0.35 -2.04 mid-career 0.84 6.87 Expert 0.68 6.59 From: Chicago 1.51 2.90 Harvard 0.81 1.73 MIT 1.31 2.74 Princeton -0.21 -0.40 Stanford 0.28 0.57 Yale -0.26 -0.51 At: Chicago -0.26 -1.37 Harvard -0.28 -1.42 MIT 0.55 2.68 Princeton 0.74 3.42 Stanford 0.49 2.47 Yale 1.04 5.02 Literature: int. micro 1.16 8.40 large 1.14 8.41 medium 0.24 1.92 Perfect markets? -0.07 -0.53 Redistribution? -0.01 -0.08 R-squared 0.114

Notes: 2,612 observations. Confidence ranges from 1 to 10, with a mean of 6.1 and a standard deviation of 1.0. The omitted category is a young male, without experience working in Washington, without any expertise on the topic, with a Ph.D. from Berkeley, now teaching at Berkeley, facing a question with no past literature.

Results appear in Table 2. Respondents are dramatically more confident when the

academic literature on the topic is large. Not surprisingly, experts on a subject are much more

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confident about their answers. The middle-aged cohort (the one closest to the current literature)

is the most confident, while the oldest (and wisest) cohort is the least confident. Men and those

who have worked in Washington do show some tendency to be more confident. Respondents

who got their degrees at Chicago are far more confident than the other respondents, with almost

as strong an effect for respondents with Ph.D.’s from MIT and to a lesser extent from Harvard.

Respondents now employed at Yale and to a lesser degree Princeton, MIT, and Stanford seem to

be more confident.

Given such clear differences in the confidence expressed by members of the Panel, we

therefore included a confidence measure in the last column in Table 1, defined to equal the

individual’s average confidence expressed on all questions other than the current question, to

avoid possible reverse causation. The intent here is to control for personality differences, so that

the remaining variation is more likely to be due to different interpretations of the academic

literature. As expected, individuals with a higher mean confidence are less likely to answer that

they are uncertain. More confident individuals are also more willing to disagree with the

consensus. However, the inclusion of this variable does little to change the coefficients on

gender, leaving open the possibility that men tend to interpret the existing literature differently.

The above results estimate the degree to which individuals differ in their likelihood of

joining the consensus view on a topic. They provide no evidence, though, on whether

individuals group into different schools of thought, agreeing on when to disagree with the

consensus. As a first test for the existence of different schools of thought, we examine whether

people with similar characteristics provide correlated responses, controlling for the expected

pattern of responses coming out of Table 1.

In implementing this test, we created a one-dimensional measure of responses with “1”

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indicating the consensus response, “-1” indicating the opposite response, and “0” indicating a

response of “uncertain”. The expected value of each individual’s response is based on the above

multinomial logit model, including just the question dummies.

We then regress the constructed correlation between the responses for each pair of

individuals against a vector of dummies capturing shared characteristics: shared experience

working in Washington for the same political party, shared experience working in Washington

but for different political parties, being faculty in the same Department, having Ph.D.’s from the

same school, being members of the same cohort, having the same gender, and sharing expertise.

Table 3. OLS Estimates of the Determinants of Correlation in Responses. Variable Coefficient t-test Both Washington / same party 0.068 1.84 Both Washington / diff. party 0.020 0.52 Same expertise 0.038 2.30 Both older -0.057 -1.87 Both mid-career 0.019 0.94 Both younger -0.028 -1.69 Same university 0.008 0.45 Same Ph.D. program 0.008 0.57 Same gender -0.018 -1.37 R-squared 0.021

Notes: 820 observations. The average correlation over all respondent pairs is 0.083. The omitted category is pairs of individuals lacking any of these common traits, e.g. from different cohorts, with different genders and different fields of expertise.

Results are reported in Table 3. Here we find that pairs of individuals sharing experience

working in Washington for the same political party tend to agree with each other. However, if

two individuals both worked in Washington but for different political parties, they are no more

likely to disagree than are any two members of the sample, undermining support for polarized

views even among those who have worked in Washington. The agreement among those working

for the same party could simply be shared knowledge about particular policy issues.

Among the other findings, those sharing expertise are much more likely to agree with

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each other. More striking, though, is that two younger individuals are more likely to disagree

than are two individuals from different cohorts; the same is true for two older individuals.

Plausibly, some of the less risk-averse young respond to the professional payoff to successfully

challenging existing views. When one is old, there may simply no longer be any professional

cost from disagreeing with the consensus. Non-results are also intriguing: there is no tendency

for those from the same Ph.D. program or those currently teaching at the same school to agree

with each other, contrary to common presumptions.

The results so far do nothing to identify panel members with conservative versus liberal

views. To make progress here, we tried two different approaches. In the first, we looked for

clusters in the data, using a variety of distance-based clustering methods and several

specifications for each method.12 The data show no systematic evidence of clustering into two or

even a few roughly equal-sized camps, yielding only weak evidence that three or four

idiosyncratic individuals differ from the rest of the sample, and from each other.

As a second approach, we focused on the subset of questions that we flagged previously

as likely to generate disagreements due to distributional concerns or due to debates on the

possible importance of market failures. Here, we went a step further and classified one answer

as consistent with a “Chicago price theory” perspective, and the other answer as likely reflecting

distributional concerns or concerns about market failures or behavioral anomalies.13 Our null

hypothesis was that individuals are homogeneous and therefore act based on the response rates

for the Panel as a whole, sometimes giving the “Chicago price theory” response and sometimes

disagreeing with this response or expressing uncertainty. If there were liberal and conservative

12 We explored both hierarchical clustering and a procedure based on random-partition distributions. We thank David B. Dahl for advice and help in the use of clustering techniques. In her role as a discussant of our paper, Monika Piazzesi explored k-means clustering and also found no systematic evidence of clustering. 13 These classifications can be found in the Online Appendix.

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camps, in contrast, some would consistently give the “Chicago price theory” response and others

would consistently give the opposite response. Here, the data were entirely consistent with the

null hypothesis: implementing a chi-squared test of homogeneity (see footnote 4) yields a p-

value of 0.70. There is no support for different camps in our data, only idiosyncratic views

among some Panel members.

II. Summary

The immediate finding when examining the responses of this Panel of economists is the

remarkably high degree of consensus. The few disagreements that exist seem to arise largely

when the academic literature on a question is small or non-existent, allowing differing prior

beliefs to remain an important determinant of posterior views. Men and those with experience

working in Washington have fewer such inhibitions. Surprisingly, there are no detectable

systematic differences in views across Departments, or across school of Ph.D. In addition, there

is no evidence to support a conservative versus liberal divide among these Panel members, at

least on the types of questions included so far in the surveys.

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References

Alston, Richard M., J. R. Kearl, and Michael B. Vaughan. 1992. “Is There a Consensus Among

Economists in the 1990’s?” American Economic Review Papers and Proceedings, 82

(2): 203-209.

Fuchs, Victor R., Alan B. Krueger, and James M. Poterba. 1998. “Economists’ Views about

Parameters, Values, and Policies: Survey Results in Labor and Public Economics.”

Journal of Economic Literature, 36 (3): 1387-1425.

Kearl, J.R., Clayne L Pope, Gordon C. Whiting, and Larry T. Wimmer. 1979. “A Confusion of

Economists?” American Economic Review Papers and Proceedings, 69(2): 28-37.

Online Appendix Table 1. Characteristics of Panel Respondents. PhD PhD Current

Name Year From Univ Field Female Wash

Acemoglu, Daron 92 HAR MIT LAB 0 0

Alesina, Alberto 86 HAR HAR MAC 0 0

Altonji, Joseph 81 PRI YAL LAB 0 0

Auerbach, Alan 78 HAR BER PF 0 1

Autor, David 99 HAR MIT LAB 0 0

Baicker, Katherine 98 HAR HAR PF 1 1

Bertrand, Marianne 98 HAR CHI LAB 1 0

Chetty, Raj 03 HAR HAR PF 0 0

Chevalier, Judith 93 MIT YAL IO 1 0

Currie, Janet 88 PRI PRI LAB 1 0

Cutler, David 91 MIT HAR PF 0 1

Deaton, Angus 74 HAR PRI LAB 0 0

Duffie, Darrell 84 STA STA FIN 0 0

Edlin, Aaron 93 STA BER IO 0 1

Eichengreen, Barry 79 YAL BER MAC 0 0

Fair, Ray 68 MIT YAL MAC 0 0

Goldberg, Pinelopi 92 STA YAL INT 1 0

Goldin, Claudia 72 CHI HAR LAB 1 0

Goolsbee, Austan 95 MIT CHI PF 0 1

Greenstone, Michael 98 PRI MIT PF 0 1

Hall, Robert 68 MIT STA MAC 0 0

Holmström, Bengt 78 STA MIT FIN 0 0

Hoxby, Caroline 94 MIT STA LAB 1 0

Judd, Kenneth 80 HAR STA PF 0 0

Kashyap, Anil 89 MIT CHI MAC 0 0

Klenow, Pete 91 STA STA MAC 0 0

Lazear, Edward 74 HAR STA LAB 0 1

Levin, Jonathan 99 MIT STA IO 0 0

Maskin, Eric 76 HAR HAR FIN 0 0

Nordhaus, William 67 MIT YAL MAC 0 1

Obstfeld, Maurice 79 MIT BER INT 0 0

Rouse, Cecilia 92 HAR PRI LAB 1 1

Saez, Emmanuel 99 MIT BER PF 0 0

Scheinkman, José 74 CHI PRI FIN 0 0

Schmalensee, Richard 70 MIT MIT IO 0 1

Shin, Hyun Song 88 MIT PRI FIN 0 0

Stock, James 83 BER HAR MAC 0 0

Stokey, Nancy 78 HAR CHI MAC 1 0

Thaler, Richard 74 CHI CHI FIN 0 0

Udry, Christopher 91 YAL YAL LAB 0 0

Zingales, Luigi 92 MIT CHI FIN 0 0

Notes: PhD From and Current University categories are BER=Berkeley; CHI=Chicago, Rochester; HAR=Harvard, Cambridge, LSE, Wisconsin; MIT=MIT, Oxford; PRI=Princeton; STA=Stanford; YAL=Yale. Field categories are defined by primary NBER affiliation: MAC=macro (EFG, ME, POL); INT=international (IFM, ITI); FIN=finance (AP, CF); LAB=labor (LS, ED, AG, DAE, DEV); PF=public finance (PF, EEE); IO=industrial organization (IO, LE). Three panel members are not in the NBER; Ray Fair and James Stock are assigned to MAC, Eric Maskin is assigned to FIN. Female is an indicator equal to 1 for women. Wash is an indicator for experience serving in Washington.

Online Appendix Table 2. Characteristics of Survey Questions. Size of Question Type

Date Topic Literature Fields D M C

9/29/11 Monetary Policy Medium MAC

10/6/11 Taxes 1 Large LAB,PF

10/6/11 Taxes 2 Int Micro LAB,PF

10/13/11 Education Medium LAB,PF 1 1

10/20/11 Exchange Rates Small INT

10/27/11 Stock Prices 1 Large FIN

10/27/11 Stock Prices 2 Medium FIN

11/3/11 Tax Reform 1 Large PF 1 1

11/3/11 Tax Reform 2 Large FIN,PF

11/10/11 Buy American Large INT,LAB

11/17/11 Healthcare Int Micro LAB,PF 1 -1

12/1/11 Italy's Debt 1 Small INT

12/1/11 Italy's Debt 2 Small INT

12/8/11 Drug Use Policies 1 Int Micro LAB,PF

12/8/11 Drug Use Policies 2 Medium PF 1 1

12/15/11 Carbon Tax Int Micro PF 1 1

1/5/12 Congestion Pricing Int Micro PF 1 1 1

1/12/12 Gold Standard 1 Large INT

1/12/12 Gold Standard 2 Medium INT

1/19/12 Inequality & Skills Large LAB

1/27/12 Executive Pay 1 Medium FIN,LAB 1 -1

1/27/12 Executive Pay 2 Small FIN,LAB 1 -1

2/2/12 Rent Control Int Micro PF 1 1 1

2/9/12 Economic Stimulus 1 Large MAC

2/9/12 Economic Stimulus 2 Medium MAC

2/16/12 Short Selling Medium FIN 1 1

2/23/12 Healthcare Licensing 1 Medium LAB,PF,IO 1 1

3/1/12 Bank Bailouts Int Micro MAC,FIN

3/8/12 Free Trade 1 Int Micro INT

3/8/12 Free Trade 2 Int Micro INT 1 1 1

3/15/12 Gasoline Prices Small IO

3/22/12 Too Big to Fail 1 Small FIN,IO

3/22/12 Too Big to Fail 2 Medium FIN,IO

3/29/12 School Vouchers 1 Medium LAB,PF

3/29/12 School Vouchers 2 Medium LAB,PF 1 1 -1

4/5/12 Fannie & Freddie Small FIN,PF

4/12/12 Ticket Resale Small 1 1

4/19/12 Security Screening Small

4/26/12 Price Gouging Int Micro PF,IO 1 1 -1

5/3/12 French Labor Policies 1 Small MAC,PF

5/3/12 French Labor Policies 2 Small LAB

5/11/12 Cuba's Economy Small MAC,INT

5/15/12 Fracking Small INT,FIN,IO

5/31/12 Fiscal Cliff Large MAC,PF

6/7/12 College Tuition Medium LAB 1 1

6/14/12 China-US Trade 1 Large INT

6/14/12 China-US Trade 2 Large INT,LAB 1 -1

6/21/12 Laffer Curve 1 Large MAC,LAB,PF

6/21/12 Laffer Curve 2 Large MAC,LAB,PF

6/29/12 Europe 1 Small MAC,INT,LAB 1 -1

6/29/12 Europe 2 Small MAC,INT

6/29/12 Europe 3 Small MAC,INT

Online Appendix Table 2, continued. Characteristics of Survey Questions. Size of Question Type

Date Topic Literature Fields D M C

7/12/12 Healthcare & Taxes Medium MAC,PF

7/19/12 Cable-Sat TV Fees Medium IO

7/26/12 Online Sales Taxes Medium PF

8/2/12 Obesity & Soft Drinks Small LAB

8/9/12 Money Market Funds 1 Small FIN

8/9/12 Money Market Funds 2 Small FIN

8/9/12 Money Market Funds 3 Small FIN

8/16/12 Student Loans 1 Medium FIN,LAB

8/16/12 Student Loans 2 Int Micro FIN,LAB

8/23/12 Trade Barriers Sugar Int Micro INT

9/6/12 European Debt 1 Small INT

9/6/12 European Debt 2 Small INT

9/6/12 European Debt 3 Small INT

9/19/12 Ethanol 1 Medium INT,PF

9/19/12 Ethanol 2 Large PF

9/25/12 QE3 1 Medium MAC

9/25/12 QE3 2 Medium MAC

9/25/12 QE3 3 Large MAC

10/1/12 US State Budgets 1 Int Micro PF

10/1/12 US State Budgets 2 Medium PF

10/9/12 Tax Capital & Labor 1 Int Micro FIN,PF 1 -1

10/9/12 Tax Capital & Labor 2 Medium FIN,PF

10/9/12 Tax Capital & Labor 3 Int Micro FIN,PF

10/11/12 Presidents and Jobs Medium MAC,LAB

10/18/12 Medicare 1 Medium PF,IO 1 1

10/18/12 Medicare 2 Medium PF,IO 1 1 -1

10/25/12 Manufacturing 1 Small IO 1 -1

10/25/12 Manufacturing 2 Int Micro IO

Notes: Size of Literature indicates a) whether the answer follows immediately from intermediate price theory, b) whether there is a large academic literature on the topic, c) at least a few papers on the topic, or d) virtually no academic research on the topic. For the Question Type columns, “D” takes the value of 1 if the topic evokes distributional concerns and “M” takes the value of 1 if the topic raises concerns about market efficiency. A 1 in the “C” column indicates that agreement with the statement seems consistent with a “Chicago price theory” perspective, while a -1 indicates distributional or market failure concerns. These data assignments reflect judgment calls by the authors.

Online Appendix Table 3. Tabulation of the Number of Times Each Individual Agreed with the Consensus, Was Uncertain, or Disagreed with the Consensus. | Responses

Name | Disagree Uncertain Agree| Total

---------------------+---------------------------------+----------

Acemoglu, Daron | 6 19 49 | 74

Alesina, Alberto | 3 6 32 | 41

Altonji, Joseph | 2 17 52 | 71

Auerbach, Alan | 5 22 53 | 80

Autor, David | 7 25 48 | 80

Baicker, Katherine | 1 34 40 | 75

Bertrand, Marianne | 2 31 44 | 77

Chetty, Raj | 0 20 40 | 60

Chevalier, Judith | 3 14 58 | 75

Currie, Janet | 8 20 51 | 79

Cutler, David | 6 21 47 | 74

Deaton, Angus | 11 14 55 | 80

Duffie, Darrell | 3 19 56 | 78

Edlin, Aaron | 6 10 40 | 56

Eichengreen, Barry | 3 21 56 | 80

Fair, Ray | 1 23 51 | 75

Goldberg, Pinelopi | 6 18 52 | 76

Goldin, Claudia | 4 24 52 | 80

Goolsbee, Austan | 4 12 51 | 67

Greenstone, Michael | 1 17 50 | 68

Hall, Robert | 5 18 44 | 67

Holmström, Bengt | 4 13 50 | 67

Hoxby, Caroline | 5 15 30 | 50

Judd, Kenneth | 6 14 47 | 67

Kashyap, Anil | 3 9 68 | 80

Klenow, Pete | 3 8 69 | 80

Lazear, Edward | 9 8 38 | 55

Levin, Jonathan | 1 16 28 | 45

Maskin, Eric | 4 13 58 | 75

Nordhaus, William | 5 19 51 | 75

Obstfeld, Maurice | 2 23 55 | 80

Rouse, Cecilia | 1 11 15 | 27

Saez, Emmanuel | 6 14 55 | 75

Scheinkman, José | 3 9 48 | 60

Schmalensee, Richard | 4 9 64 | 77

Shin, Hyun Song | 8 23 46 | 77

Stock, James | 1 6 22 | 29

Stokey, Nancy | 5 25 48 | 78

Thaler, Richard | 4 24 48 | 76

Udry, Christopher | 3 18 53 | 74

Zingales, Luigi | 5 11 58 | 74

---------------------+---------------------------------+----------

Total | 169 693 1,972 | 2,834

Notes: The value of the chi-square test statistic is 149.97, which has a p-value less than .001. To calculate the appropriate p-value, we simulated the distribution for this test statistic using 10,000 iterations, allowing for different response probabilities for each question, and bootstrapping the response probabilities. See online Stata code for simulation details.

Online Appendix Table 4. Top Five Questions with the Most Disagreement. Percent

Topic Disagree Question

Fracking 34.4 New technology for fracking natural gas, by lowering energy costs in the United States, will make US industrial firms more cost competitive and thus significantly stimulate the growth of US merchandise exports. Obesity & 29.0 Taxes or bans on large bottles of soft drinks containing sugar are not Soft Drinks likely to have a significant effect on obesity rates because people will substitute towards consuming excessive calories in other ways. European 21.2 A substantial sovereign-debt default by some combination of Greece, Debt 2 Ireland, Italy, Portugal and Spain is a necessary condition for the euro area as a whole to grow at its pre-crisis trend rate over the next three years. Education 21.1 Public school students would receive a higher quality education if they all had the option of taking the government money (local, state, federal) currently being spent on their own education and turning that money into vouchers that they could use towards covering the costs of any private school or public school of their choice (e.g. charter schools). College 20.6 An important reason why private college and university tuition has Tuition risen faster than the CPI during the past few decades is because competition for faculty members — whose potential earnings in other sectors have steadily improved — has driven up their pay faster than their productivity. Note: “Percent Disagree” is the percent of respondents who disagree with the consensus view.