Fifty shades of QE : comparing findings of central bankers and academics.
Date: 2021-08-19
Institutions: European Central Bank
Subjects: central bank, comparative analysis, economics profession, macroeconomics, research

Brian Fabo, Martina Jančoková,  
Elisabeth Kempf, Ľuboš Pástor 

Working Paper Series 

Fifty shades of QE:  
comparing findings of  
central bankers and academics 

No 2584 / August 2021 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Abstract

We compare the ﬁndings of central bank researchers and academic economists regarding

the macroeconomic eﬀects of quantitative easing (QE). We ﬁnd that central bank papers

ﬁnd QE to be more eﬀective than academic papers do. Central bank papers report larger

eﬀects of QE on output and inﬂation. They also report QE eﬀects on output that are more

signiﬁcant, both statistically and economically, and they use more positive language in the

abstract. Central bank researchers who report larger QE eﬀects on output experience more

favorable career outcomes. A survey of central banks reveals substantial involvement of bank

management in research production.

Keywords: economic research, quantitative easing, QE, central bank, career concerns

JEL: A11, E52, E58, G28

ECB Working Paper Series No 2584 / August 202111. Introduction

Since the 2008 ﬁnancial crisis, central banks around the world have deployed unconven-

tional monetary policy tools such as quantitative easing, forward guidance, and long-term

reﬁnancing operations. The popularity of these tools has grown since the outbreak of the

COVID-19 pandemic. For example, the Federal Reserve, the European Central Bank, and

the Bank of England all announced new large-scale asset purchases in March 2020.

The eﬀectiveness of unconventional monetary policy (“QE”) has been a subject of intense

debate in both academic and policy circles. A signiﬁcant part of the research on QE originates

in central banks (Martin and Milas (2012)). This research, which is widely cited in the media,

often ﬁnds QE to be eﬀective.1 However, it has an aspect of self-assessment: when central

banks evaluate QE, they are judging their own policy. Whether this aspect has any bearing

on research output is an empirical question that we address in this paper.

We compare the ﬁndings of central bank researchers (“central bankers”) and academic

economists (“academics”) regarding the eﬀectiveness of QE. We construct a dataset com-

prising 54 studies that analyze the eﬀects of QE on output or inﬂation in the U.S., UK, and

the euro area. For each study, we record its baseline estimates of the eﬀects of QE on the

level of GDP and the price level, along with their signiﬁcance. We also collect a variety of

other study-speciﬁc information, such as publication status and methodology used, as well as

detailed biographical information of the 116 diﬀerent authors. We then compare the ﬁndings

of studies written by central bankers with those written by academics.

We ﬁnd that central bank papers report larger eﬀects of QE on both output and inﬂation.

Central bank papers are also more likely to report QE eﬀects on output that are signiﬁcant,

both statistically and economically. For example, while all of the central bank papers report

a statistically signiﬁcant QE eﬀect on output, only half of the academic papers do.

In

addition, central bank papers use more favorable language—more positive adjectives and, to

a lesser extent, fewer negative adjectives—in their abstracts. Overall, central bank papers

ﬁnd QE to be more eﬀective than academic papers do.

1For example: “The good news is that, by most accounts, QE appears to have succeeded at boosting
growth and lifting inﬂation. Martin Weale, a member of the BoE’s interest-rate setting Monetary Policy
Committee, found asset purchases worth 1% of national income boosted UK gross domestic product by about
0.18% and inﬂation by 0.3%. A study by John Williams, president of the San Francisco Federal Reserve,
concluded that asset purchases had reduced the US unemployment rate by 1.5 percentage points by late
2012 and helped the economy avoid deﬂation.” The Financial Times (2015).

ECB Working Paper Series No 2584 / August 20212We also uncover diﬀerences in methodological choices. For example, central bank papers

are more likely to use dynamic stochastic general equilibrium (DSGE) models rather than

vector autoregression (VAR) models. Yet our main result—that central bankers are more

optimistic than academics in their assessments of QE—continues to hold even when we

control for model choice. Diﬀerences in research quality are also unlikely to explain our

results because the gap between central bankers and academics is very similar when we

condition on published papers only, as well as when we weight each paper by its citations.

Our results are robust to the inclusion of various controls, and they are not driven by central

bankers from any single country, nor by QE programs in any single country.

To explore one possible mechanism, we relate central bankers’ research ﬁndings to their

subsequent career outcomes. We collect employment histories for all central bank authors

and convert their job titles to numerical ranks on a six-point scale. For each author-paper

pair, we measure the author’s subsequent career outcome by the ﬁrst change in the author’s

rank following the paper’s ﬁrst public release. We ﬁnd that authors whose papers report

larger eﬀects of QE on output experience more favorable career outcomes. A one standard

deviation increase in the estimated eﬀect is associated with a career improvement of about

half a rank, such as moving halfway from Economist to Senior Economist. This evidence

suggests a potential role for career concerns in explaining our results.

These concerns appear to be stronger for senior central bankers because for them, we

ﬁnd a stronger relation between the estimated QE eﬀects and subsequent career outcomes.

Motivated by this ﬁnding, we look whether the gap between the ﬁndings of central bankers

and academics is larger for papers whose authors are more senior. We ﬁnd that it is, though

only marginally so. Our results are consistent with the idea that senior central bankers

report larger eﬀects of QE because they have a stronger incentive to do so.

Not all central bankers face the same incentives. Top management of the German Bun-

desbank has taken a critical view of QE, especially in the context of the European Central

Bank (ECB). Former Bundesbank oﬃcials Axel Weber and J¨urgen Stark reportedly quit

their ECB positions in protest over QE, and the current Bundesbank president, Jens Weid-

mann, has also publicly opposed it. Mindful of their bosses’ views, Bundesbank researchers

could potentially face career concerns very diﬀerent from those of their colleagues at other

central banks. Indeed, we show that studies co-authored by Bundesbank employees ﬁnd QE

to be less eﬀective at raising output compared to academic studies. While this evidence is

weak statistically, it is suggestive of managerial inﬂuence on research outcomes.

ECB Working Paper Series No 2584 / August 20213To shed more light on this inﬂuence, we survey heads of research at the world’s leading

central banks. We have received responses from 24 central banks employing over 750 research

economists in total. These responses reveal substantial involvement of bank management in

research production. In most banks, management participates in the selection of research

topics, typically by negotiating with the researcher. Direct topic assignments occur “some-

times” (“often”) in 50% (21%) of the responding banks. In most banks, research papers are

reviewed by management prior to public distribution; such reviews happen “always” (“of-

ten”) in 38% (21%) of the responding banks. Management also approves papers for public

distribution: typically by the head of research (“always” in 67% of the banks), but sometimes

also by the bank board (at least “sometimes” in 33% of the banks). Unlike central bankers,

academics face little if any managerial interference in their research on QE.

As we note earlier, one possible mechanism behind our results is that central bankers face

career concerns. A central bank economist may worry that the nature of her ﬁndings could

threaten her employment status or rank. Such a concern could aﬀect research outcomes even

if it is completely unfounded as long as the economist perceives a nonzero probability of such

a threat. This channel could operate at multiple levels because not only researchers but also

their superiors want to get promoted. For example, a head of research may be reluctant to

defend a subordinate’s inconvenient ﬁndings in front of the bank’s board.

Besides career concerns, a central bank economist may worry that bank management

could block the release of studies that ﬁnd the bank’s policy to be ineﬀective, or to have

undesirable side eﬀects. A recent example, from a diﬀerent public institution, is the con-

troversial release of Andersen et al. (2020). That study ﬁnds that World Bank payouts of

foreign aid are followed by jumps in the recipient countries’ deposits in ﬁnancial havens,

suggesting leaks to the pockets of the countries’ elites. According to The Economist (2020),

after the study passed an internal peer review at the World Bank, it was “blocked by higher

oﬃcials.” After a substantial delay, the study was eventually released in February 2020.2

A central bank economist may also care about the bank’s reputation, favoring conclusions

that validate the bank’s actions. The economist may even care about her own reputation

2In their evaluation of World Bank research, Banerjee et al. (2006) argue: “Internal research that was
favorable to Bank positions was given great prominence, and unfavorable research ignored... there was a
serious failure of the checks and balances that should separate advocacy and research.” Similarly, in their
independent review of the Bank of International Settlements’ research, Allen et al. (2016) note “a tendency
for analysis and research to be slanted to support the ‘house view’, especially in regard to monetary policy.”

ECB Working Paper Series No 2584 / August 20214if she is senior enough to have participated in the formation of the bank’s policy. For

example, Bernanke (2020) oﬀers a strong endorsement of QE. Given his unique experience,

Ben Bernanke is exceptionally qualiﬁed to assess the eﬀectiveness of QE. At the same time,

QE is an important part of his legacy as it was adopted while he was Fed Chair.

A more benign explanation for our results, one that does not involve incentives, relies on

diﬀerences in prior beliefs combined with selection. Researchers who believe in the power of

policy interventions could self-select into policy institutions such as central banks, whereas

policy skeptics could end up in academia. Researchers could then favor evidence supporting

their priors. Moreover, their priors could be reinforced during the research process, either

through the conﬁrmation bias (Nickerson (1998)) or through the feedback received from

colleagues, whose priors may be similar. We have no direct evidence on the validity of this

mechanism, or the mechanisms described in the previous two paragraphs.

A by-product of our work is a meta-analysis of the macroeconomic eﬀects of QE. Aver-

aging across all 54 studies and standardizing QE program size to 1% of the country’s GDP,

QE increases the output level (price level) by 0.24% (0.19%) at the peak. The average cu-

mulative eﬀect on output (prices) is 58% (63%) of the peak eﬀect. About 88% (84%) of

studies estimate statistically signiﬁcant eﬀects on output (prices). Across the three regions

studied, QE is the most eﬀective in the U.S., in terms of raising both output and prices.

Our study is related to the literature inspecting the credibility of scientiﬁc research (e.g.,

Ioannidis (2005); Fanelli (2009)). It is well known that industry-sponsored scientists may act

in the interests of their sponsors. This problem has been extensively documented in biomed-

ical research. Many studies ﬁnd that research sponsored by the pharmaceutical industry

tends to draw pro-industry conclusions (e.g., Bekelman et al. (2003)). A similar bias could

potentially aﬀect central bankers; in fact, while academic medical researchers are merely

sponsored by industry, central bank economists are directly employed by central banks. Cen-

tral banks evaluating their own policies is not unlike pharmaceutical ﬁrms evaluating their

own drugs. Both have skin in the game. The problem is particularly acute for central banks

that view their research as part of their own policy, because research supportive of a pol-

icy could potentially enhance the policy’s eﬀectiveness. On the other hand, alleviating this

problem is the strong desire of central banks to protect their reputation.

Academic economists who judge central bank policies may not have skin in the game,

but they have their own incentive to ﬁnd strong results because they face the pressure to

publish. Academics’ career concerns are commonly summarized as “publish or perish.” These

ECB Working Paper Series No 2584 / August 20215concerns seem weaker for central bankers, who can often substitute policy work for journal

publications. The need to publish creates a pressure for academics to ﬁnd signiﬁcant results

because of the well-known publication bias: journals are more likely to publish positive results

than negative ones (e.g., Fanelli (2010a)). This bias is particularly strong in economics (e.g.,

De Long and Lang (1992); Fanelli (2010b)). Some authors do not submit null ﬁndings

(Franco, Malhotra and Simonovits (2014)); others inﬂate the values of just-rejected tests by

choosing “signiﬁcant” speciﬁcations (Brodeur et al. (2016)). Ioannidis et al. (2017) argue

that many results in the economic literature are exaggerated.

The publication bias is not the only thorn in the side of academic research in economics.

Academics do not always disclose their private ﬁnancial aﬃliations (Carrick-Hagenbarth

and Epstein (2012)). Zingales (2013) discusses how academic research could be corrupted by

economists’ outside employment opportunities or their desire to gain access to proprietary

data. Reported estimates of policy-relevant parameters, such as ﬁscal multipliers, reﬂect the

authors’ national backgrounds and political orientation (Asatryan et al. (2020); Jelveh et al.

(2018)). Other problems include scientiﬁc misconduct (List et al. (2001)) and the inability

to replicate economic ﬁndings (Christensen and Miguel (2018)).

Our study is also related to the literature on career concerns. This large literature ﬁnds

evidence of such concerns not only for private-sector workers such as analysts and executives,

but also for public-sector workers such as banking regulators (Lucca et al. (2014)) and federal

government employees (Blanes i Vidal et al. (2012)). In contrast, there is little work on the

incentives of central bankers. That work focuses mostly on the voting members of a central

bank’s monetary policy committee (e.g., Sibert (2003); Hansen et al. (2018)). We are not

aware of any prior work on the incentives or biases of central bank research economists.

2. Data

We construct a dataset comprising studies of the eﬀects of unconventional monetary

policy on output and inﬂation. We aim to cover all studies, published and unpublished, that

analyze the policy eﬀects for at least one of three economies: the United States (US), the

United Kingdom (UK), and the euro area (EA). For ease of exposition, we refer to these

economies, including EA, as “countries.” We include papers studying the EA as a whole,

but not papers studying individual European countries. We focus on papers containing a

quantitative analysis, either model-based or empirical, of the eﬀects on output, inﬂation,

ECB Working Paper Series No 2584 / August 20216or both. We restrict our attention to output and inﬂation because these macroeconomic

variables are of primary interest to central banks. We do not consider studies of the eﬀects

of policy on asset prices unless they also analyze the eﬀects on output or inﬂation.

To identify the papers, we began by manually searching for 40 keywords in the Google

Scholar and RePEc IDEAS databases. The keywords are listed in the Appendix, which is

available on the authors’ websites. These keywords cover not only quantitative easing but

also other unconventional monetary policy tools that operate through central bank balance

sheets, such as long-term reﬁnancing operations. Since about 80% of the papers in our

sample study quantitative easing, we refer to all papers as “QE” studies, for brevity.

We conducted the search in July and August of 2019. To allow for a lag between a paper’s

public release and its indexing in the databases, we included only papers released prior to

July 2018. For each of the 40 keywords, we selected all papers on the ﬁrst 10 pages of search

results in both portals, with each page containing about 10 papers. All of these papers were

independently evaluated for inclusion by two economists. Both of them read each paper’s

title, abstract, and introduction to assess whether the paper contains the kind of analysis

described earlier. This analysis produced the ﬁrst tier of papers in our sample.

To construct the second tier, we proceeded in three steps. First, we selected all papers

cited in the references of all ﬁrst-tier papers. Second, we selected all papers that cite any

of the ﬁrst-tier papers, using the Google Scholar functionality to query for all documents

citing a speciﬁed paper. Third, for each paper selected in the ﬁrst two steps, we read its

title, abstract, and introduction to determine its eligibility. Those papers deemed eligible

constitute the second tier of papers in our sample. We repeated this procedure one more

time, creating a third tier. While it is possible for our procedure to have missed a relevant

paper, such a paper would have to escape our deep, 40-keyword, two-portal search, not cite

any of our sample papers, and not be cited by any of our sample papers.

We include only papers that contain original quantitative estimates, thus excluding review

papers. We exclude papers not written in English, as well as master’s and bachelor’s theses.

We include all other types of papers: journal publications, working papers, book chapters,

and policy papers. Our ﬁnal sample consists of 54 papers written by 137 authors, 116 of

whom are unique. All 54 papers are listed in the Appendix.

For each paper, we collect information on the year of ﬁrst public distribution, year of

journal publication (if any), publication outlet, authors’ names, and the methodology used

(e.g., a DSGE or VAR model). We obtain impact factors and article inﬂuence scores from

ECB Working Paper Series No 2584 / August 20217Clarivate Analytics Web of Science for the year of the paper’s publication.

We record the eﬀects of QE on the level of GDP and the price level as implied by the

authors’ baseline model. The baseline model is the speciﬁcation that is typically discussed in

the abstract, introduction, or conclusion as the main result, or is used for comparison with

other studies. If multiple models are presented without any prioritization, we average across

these models. We distinguish four estimated eﬀects: (i) the peak eﬀect of the QE program

studied (Total Peak Eﬀect); (ii) the cumulative eﬀect of QE, deﬁned as the eﬀect at the end

of the time period studied by the authors (Total Cumulative Eﬀect); (iii) the peak eﬀect

after standardizing QE program size to 1% of GDP (Standardized Peak Eﬀect); and (iv)

the cumulative eﬀect after the same standardization (Standardized Cumulative Eﬀect). The

construction of all four variables is described in detail in Section 2.2. We code all variables

based on the published version of the paper if such a version is available at the time of our

search.

If the paper is unpublished at the time of our search, then we code all variables

based on the paper’s most recent version that we can ﬁnd online.

We also record the authors’ assessments of the statistical and economic signiﬁcance of

their estimated eﬀects of QE. Whenever available, we use the authors’ own verbal assessment

of statistical signiﬁcance.

If unavailable, we infer statistical signiﬁcance from conﬁdence

intervals reported in the corresponding ﬁgure or table, using the peak eﬀect. We also gather

the conﬁdence level used by authors to assess signiﬁcance (e.g., 95%, 90%, or 68%). When

statistical signiﬁcance is not discussed and no standard errors or p-values are reported, we

set the variable to missing. For economic signiﬁcance, we always use the authors’ own verbal

assessment. For example, if a study states the eﬀect of QE is “negligible”, we code economic

signiﬁcance as zero; if the eﬀect is “sizable”, we code it as one. For ambiguous cases, we

code economic signiﬁcance as 0.5. Examples include studies stating that the eﬀect of QE

is positive upon impact but disappears quickly, or that it is positive but sensitive to model

speciﬁcation. When economic signiﬁcance is not discussed, we set it to missing.

Finally, we manually collect information on the employment history, job titles, and ed-

ucational background for the 116 authors by using online searches and information from

public LinkedIn pages. To determine author aﬃliation, we use the author’s main employer

at the time of the paper’s ﬁrst public distribution, as determined by our search in the sum-

mer of 2019. We categorize all authors whose primary aﬃliation is a central bank as central

bankers. We classify authors from the Bank of International Settlements (BIS) as 0.5 central

ECB Working Paper Series No 2584 / August 20218bankers due to the close ties between the BIS and the central banking community.3 We refer

to all other authors as academics.

To maximize the quality of our dataset, two teams of two economists independently went

through all 54 studies and constructed the key quantities of interest, including the estimated

eﬀects of QE and their signiﬁcance. The teams then compared notes and discussed all

controversial cases before reaching convergence. Furthermore, to facilitate replication by

other researchers, we disclose our full paper-level dataset in the Appendix. The Appendix

also discusses the external feedback we have received on our dataset.

2.1. Summary Statistics

In this section, we brieﬂy summarize selected features of our data. In the Appendix, we

provide a more detailed data description in the form of tables and bar charts.

The papers in our sample appear in each year between 2010 and 2018, with three to

ten papers per year. They do not have to contain estimates for both output and inﬂation,

but about 90% of them do. More than 57% of the papers are published in peer-reviewed

journals. The average impact factor of those journals at the time of the paper’s publication

is 1.42. 35% of the papers use DSGE models. The average paper is written by 2.54 authors

and it studies the eﬀects of QE in 1.26 countries. The euro area receives the most attention,

but each of the three countries is studied by at least 13 papers.

As for the authors, 60% of them are primarily aﬃliated with a central bank. Central

banks employing the most authors are the Bank of England (21), EA national central banks

(20), the ECB (17), and the Federal Reserve (16). Academics are employed mostly at

universities in Europe (18), UK (10), and the U.S. (9). 17% of the authors are female and

89% hold a PhD degree. Most authors have earned their PhD’s at prestigious universities in

the U.S. and UK such as Princeton (8) and Cambridge (6). The average author experience

(i.e., the number of years since earning the highest educational degree) is 11 years.

2.2. The Eﬀects of QE on Output and Inﬂation

For each paper and country studied, we record the estimated eﬀects of QE on output

(i.e., real GDP or industrial production) and inﬂation (i.e., CPI) based on the authors’

3In the Appendix, we show that our main results are robust to classifying BIS authors as full central
bankers. They are also robust to classifying researchers at the International Monetary Fund and the World
Bank as 0.5 central bankers, although such an alternative classiﬁcation seems harder to justify.

ECB Working Paper Series No 2584 / August 20219baseline speciﬁcation. As a rule, we record the eﬀects on the level —the level of output and

the price level. Letting Y denote the actual level of the outcome variable (i.e., with QE)
and ˆY denote its counterfactual level (i.e., without QE), we are interested in the percentage
diﬀerence, (Y − ˆY )/ ˆY . If the paper reports the eﬀect of QE on the level of output or prices,

we record the peak and cumulative eﬀects as displayed in Figure 1. If the paper reports only

the eﬀects on the growth rate, we sum up the individual growth estimates to determine the

impact on the level. We describe the details of this conversion, and list the estimated eﬀects

for each paper-country pair in our sample, in the Appendix.

We focus on the eﬀect most prominently advertised in the paper, ignoring estimates from

robustness checks, alternative speciﬁcations, and extensions. We standardize the eﬀects to

a QE program size equal to 1% of the respective country’s GDP at the time QE was ﬁrst

introduced. For the U.S. and UK, we use 1% of the annualized 2009 Q1 GDP, consistent

with Weale and Wieladek (2016). For the EA, whose asset purchase programs started in

2015 Q1, we use 1% of the annualized 2015 Q1 GDP. We obtain GDP estimates from the

FRED database. Performing the standardization also requires the size of aggregate asset

purchases for each QE program. We report our estimates of these sizes in the Appendix.

Following Weale and Wieladek (2016), we include Treasury purchases for the U.S. programs,

and all securities purchased under the Asset Purchasing Facility for the UK programs. For

the EA, program size includes all securities purchased under the Asset Purchase Program,

because asset-backed securities are a small fraction of the overall program size.

We show the means, medians, and standard deviations of the estimated QE eﬀects in the

Appendix. The average (median) paper in our sample estimates that QE increases output

by 1.57% (1.25%) at the peak. Standardized to a QE program size equivalent to 1% of

GDP, the average (median) peak eﬀect on output is 0.24% (0.16%). As for inﬂation, the

average (median) study ﬁnds that QE raises the price level by 1.42% (0.93%) at the peak.

Standardizing to 1% of GDP, the average (median) eﬀect on the price level is 0.19% (0.11%).

For both output and inﬂation, the average standardized cumulative eﬀect is equal to about

60% of the average standardized peak eﬀect, indicating that about 40% of the peak eﬀect

vanishes by the end of the period studied.

There is substantial heterogeneity in the eﬀectiveness of QE across the three countries.

Focusing on standardized eﬀects, which are easier to compare across countries due to diﬀer-

ences in QE program sizes, QE is most eﬀective at raising output in the U.S., followed by

the EA and UK. For inﬂation, QE is again the most eﬀective in the U.S.

ECB Working Paper Series No 2584 / August 202110Most studies conclude that the estimated eﬀects of QE on output and prices are positive

and statistically signiﬁcant, consistent with prior reviews of this literature (e.g., Dell’Ariccia

et al. (2018)). However, there is also substantial heterogeneity in point estimates. Under-

standing whether some of this heterogeneity is related to the institutional environment in

which authors operate is the goal of the following sections.

3. Research Outcomes and Central Bank Aﬃliation

This section conducts a systematic comparison of the research ﬁndings of central bankers

and academics regarding the eﬀectiveness of QE. These ﬁndings include the estimated eﬀects

of QE on output and inﬂation, the statistical and economic signiﬁcance of these eﬀects, and

the tone of the language used to summarize the paper’s results.

3.1. The Eﬀect of QE on Output

Figure 2 reports histograms for the estimated eﬀects of QE on the output level, separately

for studies with at least one central bank author (“CB”) and those with no such authors

(“Not CB”). The four panels correspond to the four measures introduced previously. For all

of them, the distributions of central bank papers are shifted visibly to the right, indicating

that such papers ﬁnd systematically larger eﬀects of QE on output.

The same result follows from Panel A of Table 1, which compares the means and medians

of the estimated eﬀects of QE on output across papers with and without at least one central

bank author. Both types of papers ﬁnd QE to be successful at raising output, on average,

but central bank papers ﬁnd substantially larger eﬀects. This is true based on both means

and medians, indicating that the gap is not driven by outliers. Moreover, the outliers in

Figure 2 do not seem to be low-quality papers, judging by their publication success. Among

the ﬁve papers ﬁnding the largest eﬀects on output, the publication rate is 100%.

Table 2 conﬁrms the result based on regression evidence. We regress the estimated output

eﬀect on the share of central bank authors, CB Aﬃliation, deﬁned as the share of authors

who are aﬃliated with a central bank at the time of the paper’s ﬁrst public distribution.

In the strictest speciﬁcations, shown in columns (3) and (6), we also include country ﬁxed

eﬀects and controls for the number of authors and average author experience:

yij = αj + β [CB Aﬃliation]i + γ(cid:48)Xi + (cid:15)ij ,

(1)

ECB Working Paper Series No 2584 / August 202111where yij is the eﬀect of QE on output estimated by study i for country j’s QE, αj is a

ﬁxed eﬀect for the country in which QE takes place, and Xi are the two controls. The latter

control is log(3+average author experience). We add three to ensure the logarithm is always

well deﬁned, given that the minimum value of author experience in our sample is −2, for an

author who wrote their paper two years prior to earning a Ph.D.

Columns (3) and (6) show that changing the share of central bank authors from zero to

100% is associated with a 0.723 percentage points larger peak eﬀect and a 0.512 percentage

points larger cumulative eﬀect on output (Panel A). These are sizable magnitudes relative to

the unconditional means of 1.57% and 0.87%, respectively. The results based on standardized

eﬀects, reported in Panel B, are also economically large. Going from zero to 100% central

bank authors corresponds to a 0.152 percentage points larger standardized peak eﬀect: an

increase by two thirds of the unconditional mean. For the standardized cumulative eﬀect, the

diﬀerence is 0.122 percentage points, equivalent to 87% of the unconditional mean. These

results show that the diﬀerences in research ﬁndings observed in Panel A are not due to

central bankers studying larger QE programs.

To assess statistical signiﬁcance, we report t-statistics based on standard errors clustered

at the paper level. By clustering in this way, we allow the residuals (cid:15)ij in equation (1)

to be correlated within papers that analyze multiple QE programs. Such papers make

various choices, methodological and expositional, that can aﬀect their assessments of all QE

programs. Therefore, we model (cid:15)ij as having a group structure: (cid:15)ij = υi + ηij, where υi is a

random component speciﬁc to paper i and ηij are mean-zero and uncorrelated.

A potential concern about inference based on cluster-robust standard errors is that the

variance estimator converges to the true value as the number of clusters goes to inﬁnity,

whereas we have at most 54 clusters. To address this concern, we implement a wild clus-

ter bootstrap procedure (Cameron et al. (2008)). To compute the p-values, we use the

post-estimation command boottest developed by Roodman et al. (2019), assuming the null

hypothesis and using Webb weights and 10,000 replications. We report these p-values in

square brackets. We generally use these p-values, which tend to be more conservative than

t-statistics, to assess statistical signiﬁcance. In Panel A of Table 2, the coeﬃcient on CB

Aﬃliation is signiﬁcant at either the 5% level or the 10% level for the peak eﬀect, but it

is insigniﬁcant for the cumulative eﬀect. In Panel B, the coeﬃcient is signiﬁcant at the 5%

level in all columns except for column (3), where it is signiﬁcant at the 10% level.

ECB Working Paper Series No 2584 / August 2021123.2. The Eﬀect of QE on Inﬂation

Figure 3 reports histograms for the estimated eﬀects of QE on the price level, analogous to

the histograms for output plotted in Figure 2. Just like for output, the distributions of central

bank papers are shifted to the right relative to academic papers, indicating that central bank

papers tend to ﬁnd QE to be more eﬀective at raising prices. The same conclusion follows

from Panel B of Table 1, which compares the means and medians of the estimated eﬀects of

QE on inﬂation across papers with and without central bank authors.

In Table 3 we repeat the regression analysis from Table 2, but with a diﬀerent dependent

variable: yij in equation (1) is now the estimated eﬀect on inﬂation rather than output.

According to Panel A, columns (3) and (6), changing the share of central bank authors

from zero to 100% corresponds to a 1.279 percentage points larger peak eﬀect and a 1.394

percentage points larger cumulative eﬀect on prices. These eﬀects are large relative to the

unconditional means of 1.42% and 0.89%, respectively. In Panel B, the coeﬃcients on CB

Aﬃliation are even larger than the corresponding unconditional means: 0.201 percentage

points for the peak eﬀect and 0.190 percentage points for the cumulative eﬀect. All of the

coeﬃcients in Table 3 are statistically signiﬁcant at the 5% level.

3.3. Signiﬁcance

Next, we examine the statistical and economic signiﬁcance of the eﬀects of QE on output

and inﬂation. Our main interest is in whether studies by central bankers and academics diﬀer

in their assessments of this signiﬁcance. One advantage of looking at signiﬁcance is that it

is directly comparable across studies with no need for any standardization or conversion in

the construction of the peak and cumulative eﬀects of QE.

We ﬁrst compute the shares of studies that ﬁnd a statistically signiﬁcant eﬀect of QE

on output, separately for central bankers and academics. The diﬀerence is striking: while

half of the academic papers ﬁnd a signiﬁcant eﬀect, all of the central bank papers do. The

diﬀerence in the assessments of economic signiﬁcance is almost equally large. For inﬂation,

the diﬀerences are smaller but still notable; for example, while 75% of the academic papers

ﬁnd a statistically signiﬁcant eﬀect, about 90% of the central bank papers do.

Table 4 shows the extent to which these diﬀerences are statistically signiﬁcant and robust

to the inclusion of control variables. We estimate the regression speciﬁcation in equation

(1), with yij redeﬁned to denote either statistical or economic signiﬁcance, for the eﬀects of

QE on either output or inﬂation. For output (Panel A), the coeﬃcient on CB Aﬃliation is

ECB Working Paper Series No 2584 / August 202113always positive and signiﬁcant at the 5% level, whether the dependent variable is statistical

or economic signiﬁcance. The magnitude of the eﬀect is also large: the estimate in Panel

A, column (3), implies that increasing the share of central bank authors from zero to 100%

corresponds to a 36.6 percentage points higher likelihood of the study ﬁnding a statistically

signiﬁcant eﬀect of QE on output. The magnitude is even larger, 39.9 percentage points,

for economic signiﬁcance of the QE eﬀect on output. For inﬂation (Panel B), we also ﬁnd

economically large eﬀects, but they are not statistically signiﬁcant.

3.4. Alternative Speciﬁcations

We consider various modiﬁcations of our baseline regression (1), as analyzed in Sections

3.1 through 3.3. We summarize the results here and show the tables in the Appendix.

Recall that the main independent variable in regression (1), CB Aﬃliation, is the fraction

of the paper’s authors who are aﬃliated with a central bank. Our results in Tables 2 through

4 hold also when we replace this granular measure by an indicator we call Discrete, which is

equal to one if at least one of the authors is aﬃliated with a central bank or the BIS, and zero

otherwise. In addition, we replace CB Aﬃliation by two zero/one indicators: Mixed, which

is equal to one if the share of central-bank-aﬃliated authors is strictly between zero and one,

and Pure CB, which equals one if all of the authors are central bankers. We ﬁnd positive

point estimates of the coeﬃcients on both indicators in all 36 speciﬁcations considered in

Tables 2 through 4. Moreover, the estimated slope on Pure CB exceeds that on Mixed in

33 of the 36 speciﬁcations, suggesting that central bankers tend to ﬁnd larger eﬀects of QE

when they have no academic coauthors.

Diﬀerent central banks may have diﬀerent research-vetting policies. Motivated by this

possibility, we separate central bank authors by the country of the bank they work for. We

replace CB Aﬃliation in equation (1) by four zero/one indicators: EA CB is equal to one if

at least one of the authors is aﬃliated with the ECB or a national central bank in the euro

area, UK CB equals one if at least one author is aﬃliated with the Bank of England, US

CB equals one if at least one author is at the Federal Reserve, and Other CB equals one if

at least one author is at another central bank or the BIS. The omitted group is academics.

We ﬁnd positive point estimates of the slopes on all four indicators, suggesting that our

results in Tables 2 to 4 are not driven by authors from any single country. Fed researchers

tend to ﬁnd the largest eﬀects of QE on output, whereas Bank of England researchers tend

to ﬁnd the largest eﬀects on inﬂation. Euro area central bankers ﬁnd relatively weak QE

ECB Working Paper Series No 2584 / August 202114eﬀects, largely due to the weaker eﬀects reported by Bundesbank researchers (Section 4.3).

However, the diﬀerences across central banks are not statistically signiﬁcant.

Taking a diﬀerent country-by-country perspective, we focus on the country in which QE

takes place. We observe that the point estimates of β in equation (1) are generally positive

for all three countries, though their statistical signiﬁcance is mixed. Looking at the eﬀects of

QE on output, the β estimate is the largest, and signiﬁcant, for QE conducted in the U.S. In

other words, the gap between the output eﬀects reported by central bankers and academics

is largest when they analyze U.S. QE. For the eﬀects of QE on inﬂation, the β estimates are

large, and typically signiﬁcant, in both the U.S. and UK.

Motivated by the strong results we ﬁnd for the U.S., we dig deeper into them by con-

sidering the three main QE programs in the U.S. separately. The point estimates of β are

positive and large for all three programs, but they are often insigniﬁcant because of small

sample sizes: we have 12 studies of the output eﬀects of QE1, 12 studies for QE2, and only

4 studies for QE3. The estimates are similar across the three programs, indicating that our

results are not driven by any individual U.S. program.

We do not control for the speciﬁc QE program in our baseline regressions because the

choice of which program to study is made by the authors. For example, if QE1 is perceived

to have been more eﬀective than QE2 or QE3, an author aiming to report stronger QE eﬀects

can choose to analyze the ﬁrst round of QE rather than its later rounds. Nevertheless, we also

report results when controlling for QE program dummies, thus comparing central bankers

and academics analyzing the same QE program. For papers studying more than one program,

more than one dummy is switched on at the same time. Adding QE program dummies tends

to reduce the statistical signiﬁcance of the results: the estimate of β is signiﬁcant in 17

(22) speciﬁcations at the 5% (10%) level, out of all 36 speciﬁcations considered in Tables 2

through 4, whereas in those tables, β is signiﬁcant in 25 (28) speciﬁcations at the 5% (10%)

level. The decline in statistical signiﬁcance is unsurprising because we can only consider QE

programs studied by at least two papers. Nevertheless, even with QE program dummies,

the point estimates have the same signs as their counterparts in Tables 2 through 4 in all

speciﬁcations, and their magnitudes are economically signiﬁcant.

We further explore whether central bankers are more optimistic when they study QE

launched by their own central bank. We add to equation (1) a variable capturing the share of

authors who are aﬃliated with the central bank of the QE program studied. We also include

paper ﬁxed eﬀects, thus comparing the eﬀects of QE in diﬀerent countries as estimated by

ECB Working Paper Series No 2584 / August 202115the same paper. We do not ﬁnd support for the hypothesis. Naturally, with paper ﬁxed

eﬀects, statistical power is limited because few papers study multiple QE programs. Recall

that the average (median) paper in our sample studies QE in only 1.26 (1) countries.

As noted earlier, our sample contains papers studying not only quantitative easing but

also other unconventional monetary policy programs, such as long-term reﬁnancing opera-

tions. When we exclude those other programs from the analysis, keeping only QE narrowly

deﬁned, we ﬁnd results similar to those in Tables 2 through 4. We also ﬁnd similar eﬀects

when we control for the time gap between the QE program studied and the year of the pa-

per’s ﬁrst release. This alleviates the concern that the reported diﬀerences could be driven

by diﬀerences in the timing of studies by academics and central bankers.

3.5. Tone

We now compare the tone of the language that central bankers and academics use when

they assess QE. We focus on the paper’s abstract, which summarizes the paper’s main

ﬁndings in a non-technical manner. As we are unaware of any lexical sentiment model

trained on the economic research literature, we create our own lexicon, which we show in the

Appendix. We consider adjectives such as “signiﬁcant,” “sizable,” and “large” as positive,

conveying the message that QE is eﬀective, and adjectives such as “small,” “negligible,” and

“weak” as negative. We compute the shares of positive and negative adjectives out of all

adjectives in the abstract. The abstract’s “sentiment score” is the share of positive adjectives

minus the share of negative adjectives. We estimate the model

yi = δU S

i + δU K

i + δEA

i + β [CB Aﬃliation]i + γ(cid:48)Xi + (cid:15)i ,

(2)

where yi is the sentiment score for the abstract of study i; δU S

i

, δU K
i

, and δEA

i

are indicators

equal to one if the study analyzes QE in the U.S., UK, or EA, respectively, and zero otherwise;

and Xi are the same controls as in equation (1). If a paper studies QE in multiple countries,

then multiple indicators are switched on.

Panel A of Table 5 shows that central bankers use more positive language than academics

when describing their results. Column (3) shows that a 100 percentage point increase in

the share of central bank authors is associated with an increase in the sentiment score of

0.056, which is equal to 85% of one standard deviation of the sentiment score. The result

is signiﬁcant at the 5% level. In Panels B and C, we decompose the sentiment score into

ECB Working Paper Series No 2584 / August 202116the shares of positive and negative adjectives, and we run the analysis separately for both

shares. We ﬁnd that central bank studies use both more positive adjectives and fewer negative

adjectives. The ﬁnding based on positive adjectives is economically larger and, unlike the

one based on negative adjectives, it is statistically signiﬁcant.

That central bankers use more favorable language is not surprising given our results in

Sections 3.1 through 3.3. Nonetheless, we ﬁnd it reassuring that our main result is robust to

using a diﬀerent type of measurement, one based on text rather than numbers. Moreover,

the estimates of β in equation (2) remain similar when we add controls for the magnitudes

of the reported eﬀects on output and inﬂation. Thus, central bankers use more favorable

language than academics even when describing eﬀects that are equally large. However, this

result is weaker compared to Table 5. Recall that the β estimate in column (3) of Panel A

of Table 5 is easily signiﬁcant at the 5% level. The estimate remains signiﬁcant at the 5%

level when we add controls for output eﬀects, standardized or not, but the p-value rises to

about 0.1 when we control for inﬂation eﬀects. See the Appendix for details.

We also analyze the text of the papers’ conclusions. The results are similar to those based

on the abstract—the point estimates of β are positive when the left-hand side variable is

either the sentiment score or the fraction of positive adjectives, and they are negative for the

fraction of negative adjectives—but the results are not statistically signiﬁcant, as we show in

the Appendix. Compared to the abstract, the conclusions usually contain more discussion

unrelated to the paper’s core contribution, such as directions for future work.

The Appendix also shows results from the analysis that computes the abstract’s senti-

ment score based on two alternative dictionaries: the Harvard IV4 semantic dictionary and

the Loughran and McDonald (2011) ﬁnancial dictionary. We do not ﬁnd signiﬁcant diﬀer-

ences between central bankers and academics based on those dictionaries. However, we ﬁnd

the results based on our simple dictionary far more credible because we designed it speciﬁ-

cally for economic research. In contrast, the Harvard IV4 dictionary is designed for use in

a variety of contexts outside economics, and the Loughran-McDonald dictionary is designed

for the analysis of 10-K reports of publicly-traded companies. The positive and negative

labels assigned to words in these dictionaries do not reﬂect the meaning of these words in

the economics literature. As a result, these dictionaries do not contain key adjectives that

clearly indicate positive language in our context, such as “signiﬁcant,” “large,” and “con-

siderable.” Moreover, they do contain many words that are irrelevant and potentially even

misleading in our context. For example, the words classiﬁed as negative in the Loughran

ECB Working Paper Series No 2584 / August 202117and McDonald (2011) lexicon under “A” include “abnormal,” “absence,” “against,” “after-

math,” “antitrust,” “anomaly,” and even “argue” and “argument,” thereby casting the whole

economic literature in a rather negative light (incorrectly, we believe).

3.6. Methodological Choices

Researchers analyzing the same phenomenon by using diﬀerent methodologies can arrive

at diﬀerent conclusions. We examine two methodological choices that authors make: which

model to use and how to report statistical signiﬁcance. We ﬁnd diﬀerences between central

bankers and academics in both dimensions. We summarize the results here and show the

tables in the Appendix.

First, we explore diﬀerences in model choice. The most commonly used models in our

sample, by far, are DSGE and VAR models. We redeﬁne yi to be an indicator equal to one

if the study uses a DSGE model and zero if it uses a VAR model. We then regress yi on the

share of central bank authors, again using a linear probability model following equation (2).

We ﬁnd that central bankers are more likely to use DSGE models. With country dummies

and controls, papers with 100% central bank authors are 31.9 percentage points more likely

to use a DSGE model than papers with no central bank authors. This result is highly

economically signiﬁcant, though it is statistically signiﬁcant only at the 10% level.

In our baseline regression (1), we do not control for the model chosen by the authors

because model choice could be strategic. Both DSGE and VAR models give their users some

ﬂexibility: DSGE models require a variety of structural choices, whereas VAR models rely

on a speciﬁc econometric speciﬁcation. Either way, a user aiming for a particular outcome

can pull on multiple levers to get closer to that outcome. Nonetheless, we rerun our baseline

regressions after adding controls for model ﬁxed eﬀects (DSGE, VAR, or other). We ﬁnd that

the estimated β coeﬃcients remain positive in all 36 speciﬁcations considered in Tables 2

through 4, and they are statistically signiﬁcant at the 5% (10%) level in 23 (29) speciﬁcations.

The magnitudes of these coeﬃcients become larger in 27 speciﬁcations and smaller only in

9 speciﬁcations. Our main results are thus robust to controlling for model choice.

Second, we test whether central bankers and academics are equally likely to disclose the

conﬁdence level (or, alternatively, the standard error) used to assess statistical signiﬁcance.

When this level is not disclosed, it is more diﬃcult to corroborate the author’s verbal as-

sessment of statistical signiﬁcance. We estimate a linear probability model that regresses an

indicator equal to one if the paper does not disclose the conﬁdence level, and zero if it does,

ECB Working Paper Series No 2584 / August 202118on the share of central-bank-aﬃliated authors. This matches the regression speciﬁcation in

equation (2), with yi denoting an indicator for nondisclosure. We restrict the sample to

studies that comment on the statistical signiﬁcance of either output or inﬂation.

We ﬁnd that central bankers are somewhat less likely to disclose the conﬁdence level.

With country dummies and controls, papers with 100% central bank authors are 15.8 per-

centage points less likely to report the width of the conﬁdence interval than papers with no

central bank authors. However, this relation is not statistically signiﬁcant.

Finally, we ask which studies assess signiﬁcance by using more conservative conﬁdence

intervals—ones constructed at the 95% conﬁdence level, rather than lower levels, such as

90% or 68%. A study using a 95% conﬁdence interval is less likely to ﬁnd signiﬁcance.

We redeﬁne yi in equation (2) to be an indicator equal to one if the study uses a 95%

conﬁdence interval, and zero otherwise. With country dummies and controls, papers with

100% central bank authors are 20.2 percentage points less likely to use a 95% conﬁdence

interval than papers with no central bank authors. This result is economically sizable, but

it is statistically signiﬁcant only at the 10% level. The magnitudes are similar if we add

a control for a dummy variable indicating whether the study uses Bayesian or frequentist

inference.

4. Potential Mechanisms

This section explores potential reasons why central bankers are more optimistic than

academics in their assessments of QE. One possible mechanism is career concerns. In prin-

ciple, bank management could make promotion decisions in a way that encourages bank

employees to assess the bank’s policies favorably. We test this hypothesis in Section 4.1.

Bank management can also directly inﬂuence research outcomes at various stages of the re-

search production process, from topic assignment, through internal review, to the approval

for public distribution, as we show in Section 4.2. If bank management is skeptical of QE,

the bank’s research tends to be skeptical as well, as we show in the context of the German

Bundesbank in Section 4.3. The evidence in Sections 4.1 through 4.3 is consistent with

managerial inﬂuence on central bank research outcomes. In Section 4.4, we discuss other

potential mechanisms, such as diﬀerences in prior beliefs and diﬀerences in research quality.

ECB Working Paper Series No 2584 / August 2021194.1. Career Concerns

To examine the potential for career concerns inﬂuencing central bank research, we relate

the research ﬁndings of central bankers to the bankers’ subsequent career outcomes. After

manually collecting employment histories for the central bank authors in our sample, we

convert their job titles to numerical ranks. We create these ranks on a six-point scale for

both central bankers and academics, as described in the Appendix. We restrict the analysis

to authors who remain aﬃliated with a central bank and experience at least one career update

within ﬁve years following the paper’s ﬁrst public distribution. We impose the ﬁrst ﬁlter

because it is unclear whether transitions to academia or the private sector should be treated

as promotions or demotions. The purpose of the second ﬁlter is to reduce the noise induced

by stale CV information, since authors may not regularly update their job titles.4 These

ﬁlters result in a sample of 33 central bankers (27 of whom are unique) and 23 papers. We

then compute a new variable, career outcome, deﬁned as the diﬀerence between the author’s

rank after her ﬁrst career update following the paper’s ﬁrst distribution, and her rank at

the time of that ﬁrst distribution. Out of these 33 authors, 19 experience a promotion, 4

experience a demotion (3 of which are associated with a move to a diﬀerent central bank),

and 10 experience no change in rank.

Are central bank researchers more likely to get promoted if they ﬁnd QE to be more

eﬀective? To address this question, we regress the authors’ career outcomes on the reported

eﬀects on output, country ﬁxed eﬀects, and controls:

yaij = αj + β Eﬀectij + γ(cid:48)Xai + (cid:15)aij ,

(3)

where yaij is the diﬀerence between author a’s rank after her ﬁrst career update following

the ﬁrst release of study i examining QE in country j, and her rank at the time of the

ﬁrst release. Note that yaij does not vary across j for given values of a and i. In addition,

Eﬀectij is the eﬀect of QE on output estimated by study i for country j’s QE, αj is a
ﬁxed eﬀect for the country in which QE takes place, and Xai are controls. These controls

4In the Appendix, we report results obtained when we include authors with no career updates, and when
we treat departures to academia and the private sector as demotions. Treating departures as demotions leads
to similar conclusions. Including authors with no career update within ﬁve years of the paper’s distribution
leads to insigniﬁcant results. This is expected, because the absence of a career update may be due to either
stale CV information or ﬁxed review periods at central banks and, as a result, the signal-to-noise ratio for
these types of career outcomes is likely to be low.

ECB Working Paper Series No 2584 / August 202120include author experience and the number of authors, as before. In addition, we control for

the number of years since the author’s most recent career update and for dummy variables

indicating the author’s rank at the time of the paper’s ﬁrst release, because these variables

are important determinants of subsequent career outcomes. The dummy variables are six

indicators δr

ai, where δr

and δr

ai = 1 if author a has rank r at the time of paper i’s ﬁrst release,
ai = 0 otherwise, for r ∈ {1, 2, . . . , 6}. Compared to including just one control for
the author’s rank, including these six controls allows for non-linearities in the relationship

between author rank and promotion outcomes.

In Panel A of Table 6, we show that reporting larger eﬀects on output, peak or cumulative,

is associated with more favorable career outcomes. The point estimate in column (3) implies

that a one standard deviation increase in the peak eﬀect is associated with a subsequent

career improvement by 0.59 ranks (= 0.485 × 1.21). In column (6), a one standard deviation

increase in the cumulative eﬀect on output corresponds to a subsequent career improvement

by 0.57 ranks (= 0.460×1.23). Both estimates are signiﬁcant at the 5% level. For comparison,

a one-unit change in rank is equivalent to moving, for example, from Economist to Senior

Economist, or from Deputy Director to Director.

Panel B of Table 6 shows that the positive relation between career outcomes and estimated

output eﬀects holds also for standardized eﬀects, with statistical signiﬁcance at the 5%

level in column (6) and the 10% level in column (3). A one standard deviation rise in the

standardized peak eﬀect corresponds to a 0.75 rank improvement (= 2.661 × 0.28).

Of course, a positive association does not establish a causal link. Career outcomes and

research output could be correlated for other reasons. For example, papers reporting larger

eﬀects could be easier to publish. Publications, in turn, could lead to promotions. To address

this concern, we control for an indicator equal to one if the paper came out in a peer-reviewed

journal and zero otherwise. The results are similar to those in Table 6 (see the Appendix).

For another example, employees who care so much about their employer that they are willing

to distort their research ﬁndings could show their aﬀection also in other ways, such as by

working hard, and they could earn a promotion that way. This channel seems harder to

control for. Whether central bank research is biased by career concerns is an important but

messy question for which clean identiﬁcation seems diﬃcult to come by.

Among the outcome variables analyzed in Section 3, career outcomes are most closely

related to estimated eﬀects on output. For economic signiﬁcance of output, we ﬁnd an

economically strong relation once all control variables are included, but the relation is not

ECB Working Paper Series No 2584 / August 202121statistically signiﬁcant. We do not relate career outcomes to statistical signiﬁcance of esti-

mated eﬀects because there is no variation in the subset of central bank papers (recall that

they all ﬁnd statistical signiﬁcance). There is no signiﬁcant relation between career outcomes

and estimated eﬀects on inﬂation, as we show in the Appendix.

4.1.1. Seniority

Are career concerns stronger for senior or junior central bankers? It is not obvious for

whom we should expect to see a stronger relation between research ﬁndings and career

outcomes. On the one hand, research output may be a more important criterion in the

promotions of junior researchers, for whom research represents the bulk of their work. On

the other hand, support from top management may matter more for the career advancement

of senior researchers. There may also be more discretion in promotions at the senior level.

To address this question, we repeat the analysis from Table 6, but we interact Eﬀectij
from equation (3) with the author’s career rank, or Seniority. We ﬁnd that the interaction

between the eﬀect on output and Seniority is positive and signiﬁcant. A one standard

deviation increase in Seniority raises the sensitivity of career outcomes to the estimated

eﬀect on output by about 50%. The interaction between the eﬀect on inﬂation and Seniority

is also positive, but it is signiﬁcant only at the 10% level, and only for standardized eﬀects. In

contrast, for output, the interaction is positive for both standardized and non-standardized

eﬀects, as well as for both peak and cumulative eﬀects, and it is signiﬁcant at the 5% level

in three of the four cases. The table is in the Appendix.

The above results are consistent with career concerns being stronger for senior central

bank researchers. If that is the case, and if seniority does not play a similar role for academics

(it is not clear why it should), we should expect to see larger diﬀerences in research ﬁndings

between central bankers and academics when the authors are more senior.

To test this prediction, we repeat the analysis from Tables 2 through 4, except that we

interact CB Aﬃliation with the rank of the most senior author on the team, Max Seniority.

For each of the 12 outcome variables (three tables with four variables each), the estimated

coeﬃcient on the interaction between CB Aﬃliation and Max Seniority is positive. Thus, the

ﬁndings of central bankers and academics are further apart if there is a more senior person

on the team. The interaction coeﬃcient is not always statistically signiﬁcant (it is signiﬁcant

at the 5% level in one speciﬁcation and at the 10% level in six additional speciﬁcations), but

its magnitudes are large. For example, if the rank of the most senior author increases by

ECB Working Paper Series No 2584 / August 202122one standard deviation, the diﬀerence in the estimated peak eﬀects between a study with

zero central bankers and 100% central bankers increases by more than 0.8 percentage points.

This is true for both output and inﬂation. See the Appendix for details. Our results are

consistent with the hypothesis that central bankers who are more senior report larger QE

eﬀects, relative to academics, because they face stronger career concerns.

4.2. Survey of Central Banks

Independent of the potential promotion channel, bank management can inﬂuence research

outcomes in a number of ways. For example, management can assign a topic to a researcher,

signaling the topic’s importance to the bank. Superiors can suggest methodologies, data

sources, and literature. If they are not convinced by the paper’s results, superiors can return

the paper with suggestions for improvement. They can provide helpful guidance and valuable

resources. However, besides anecdotal evidence, the economics profession knows little about

the extent of management involvement in central bank research.

To ﬁll this gap, we conduct a survey of research practices at the world’s leading central

banks. We organized the survey in cooperation with the National Bank of Slovakia.5 We

reached out to 54 heads of research, covering the central banks in all OECD countries and all

EU member states, including the ECB, the Federal Reserve Board, and 12 regional Feds. In

return for participating, we promised to share aggregated results with the respondents. We

assured them that no individual responses would be published, and that only anonymized

responses could be pooled and used for research purposes. We sent out the initial invitation

on July 3, 2020; a reminder went out ten days later.

The survey contains four main questions, each containing three to six multiple-choice

subquestions, for a total of 18 questions. We also asked for the number of research-active

economists in full-time equivalents employed by the bank, the bank’s name, and an email

address to which we can send summary results. We have received 24 responses, representing

a response rate of 44.4%. The 24 central banks employ over 750 researchers in total.6

Figure 4 presents the aggregated responses to the four main questions. In response to

the ﬁrst question, “How are research topics selected in your central bank?”, 20 (15) cen-

5We thank Martin ˇSuster, the bank’s head of research, for his generous help throughout the process.
6We originally received 25 responses but one respondent asked to withdraw from the survey after the ﬁrst
public circulation of our paper. The main conclusion from the survey—that management is substantially
involved in research production—is unaﬀected by the exclusion of this respondent.

ECB Working Paper Series No 2584 / August 202123tral banks indicate that research topics are at least sometimes (often) mutually agreed by

researchers and management, and 17 banks respond that topics are at least sometimes as-

signed by management. Responding to the second question, “How are draft research papers

reviewed/commented on in your institution, prior to their public distribution?”, 21 (14) cen-

tral banks indicate that papers are reviewed at least sometimes (often) by management. In

9 banks, this review happens for all papers. The third question is, “How are your institu-

tion’s draft research papers approved for public distribution?”. Bank management and, most

commonly, the head of research, is frequently involved in approving papers for publication:

20 (18) banks respond that the head of research approves papers at least sometimes (often).

The bank board also gets involved in the approval process, at least sometimes, in eight banks.

Finally, when asked “What criteria can lead to the paper being rejected (i.e., not approved

for public distribution)?”, most central banks list “substandard methodology, unreliable data,

deﬁcient modeling approach”, followed by “results not robust or not signiﬁcant”. The latter

criteria are used by 18 (10) banks at least sometimes (often).

The survey evidence reveals substantial involvement by management in the research pro-

cess at most central banks. This involvement creates an opportunity for bank management

to inﬂuence research outcomes, oﬀering a potential explanation for our ﬁndings in Section

3. However, such an interpretation is subject to numerous caveats.

First and foremost, the fact that management involvement exists does not imply that it

aﬀects research outcomes as measured in our study. Management involvement is necessary

but not suﬃcient for research outcomes to be inﬂuenced by management; the survey evidence

thus supports only the necessary condition. The involvement can take diﬀerent forms, many

of which help improve the quality of research output without introducing any bias. For

example, many research directors view their role largely as helping their staﬀ write better

papers. The ﬁrst two survey questions pool research directors and senior managers into a

single “management” category, masking the diﬀerent roles of these two types of managers,

as well as their potentially diﬀerent sensitivities to “undesirable” policy messages.

Second, given the survey’s brevity, the responses cannot reveal the full range of practices

across banks. For example, economists in many banks split their time between policy work

and their own research. Management is likely to be more involved in the selection of topics

for policy work than for individual research, yet the ﬁrst survey question does not distinguish

between the two types of work. In addition, whether a study of the eﬀectiveness of QE counts

as research or policy work may diﬀer across banks.

ECB Working Paper Series No 2584 / August 202124Finally, the set of central banks in our survey sample diﬀers from the set of banks whose

economists are in our pool of authors who have analyzed QE. While the two sets overlap,

the overlap is modest. We do not know how similar the research processes in the two sets

of banks are. If they are substantially diﬀerent, then our survey sheds little light on the

involvement of bank management in the production of studies on QE.

4.3. The Bundesbank

If central bankers’ ﬁndings regarding QE are colored by the views of bank management,

we should see weaker QE eﬀects reported by researchers at central banks whose management

has taken a critical stance towards QE. A prominent example is the German Bundesbank,

whose top management has publicly criticized the ECB for its bond-buying program. Ac-

cording to media outlets, former Bundesbank president Axel Weber and vice president J¨urgen

Stark resigned from their ECB positions, allegedly in protest over QE, and the current Bun-

desbank president, Jens Weidmann, has also publicly opposed QE. Could these skeptical

views of the bank’s top brass be reﬂected in the writings of the bank’s researchers?

We test this hypothesis by repeating the analysis in Tables 2 through 4, replacing CB

Aﬃliation in regression (1) with three indicators: German CB is equal to one if at least one

of the authors is employed by the Bundesbank, Other EA CB is equal to one if at least one

author works at the ECB or a euro area national central bank other than the Bundesbank,

and Non-EA CB is equal to one if at least one author is from a central bank outside the

euro area or the BIS. The omitted group are academics. If a paper has authors from both

the Bundesbank and another central bank, multiple indicators are switched on.

We ﬁnd that Bundesbank authors ﬁnd strikingly diﬀerent results regarding the eﬀective-

ness of QE in raising output. Bundesbank papers report smaller eﬀects of QE on output

compared to academics, on average, whereas other central banks, both inside and outside

the euro area, ﬁnd larger eﬀects. This pattern holds for all four measures of output, as we

show in the Appendix. For example, the average estimated peak eﬀect on output for Bun-

desbank papers is 0.88 percentage points smaller than the average peak eﬀect for academics.

In contrast, other central banks in the euro area ﬁnd eﬀects that are 0.44 percentage points

larger, and banks outside the euro area ﬁnd eﬀects that are 0.69 percentage points larger,

on average, compared to academics. The diﬀerence in the point estimates for German CB

and Other EA CB is mostly statistically signiﬁcant based on cluster-robust standard errors

but not based on bootstrapped p-values, in part because there are only four Bundesbank

ECB Working Paper Series No 2584 / August 202125papers in our sample, and only two of them study the eﬀect of QE on output. Nevertheless,

the diﬀerent signs and large magnitudes of the coeﬃcients support the managerial inﬂuence

interpretation of our main results.

As for the QE eﬀect on inﬂation, the diﬀerences between Bundesbank authors and other

central bankers are much smaller. Moreover, the Bundesbank estimates exceed those of

academics. These results are not surprising. German opposition to ECB’s QE has been

based largely on concerns about redistribution within the euro area, not about QE being

ineﬀective at raising inﬂation. On the contrary, a popular view in Germany is that QE could

be too eﬀective in that regard. The view that printing money causes inﬂation is traditionally

strong in Germany, whose collective memory is still scarred by the hyperinﬂation that took

place in the Weimar Republic in the early 1920s.

4.4. Alternative Mechanisms

While our evidence in Sections 4.1 through 4.3 is consistent with managerial inﬂuence on

central bank research outcomes, the evidence is not causal, and it is therefore inconclusive. In

this section, we discuss other mechanisms that could potentially contribute to the observed

diﬀerences between the ﬁndings of central bankers and academics. In principle, some of these

mechanisms could be more important than career concerns in explaining our results.

One such mechanism is reputation concerns. These could involve concerns about the

bank’s reputation and, for very senior researchers, concerns about their own reputation. Like

career concerns, reputation concerns reﬂect researchers’ incentives because in both cases, a

researcher derives a private beneﬁt from reaching a particular research outcome. We have

no evidence on the potential contribution of reputation concerns to our results.

Another potential mechanism is diﬀerences in prior beliefs combined with selection. Re-

searchers with diﬀerent priors about the eﬀectiveness of policy interventions may self-select

into diﬀerent institutions. For example, if researchers optimistic about QE select into central

banks, or the pessimists select into academia, this selection could explain the diﬀerences in

research outcomes between central bankers and academics. Furthermore, any diﬀerences in

priors can be reinforced during the research process, both by the researcher herself (conﬁr-

mation bias) and by the feedback she receives from like-minded colleagues. This mechanism

does not involve any distorted incentives. We have no evidence in favor of, or against,

diﬀerences in priors, but we do have evidence on three other potential explanations.

One of them is that papers on QE written by central bankers and academics are of

ECB Working Paper Series No 2584 / August 202126diﬀerent quality. For example, if central bank papers were of higher quality and the eﬀects of

QE were truly strong, then we would expect central bank papers to ﬁnd stronger QE eﬀects.

Given the management involvement documented earlier, it is indeed possible that central

banks have a more rigorous vetting process for new working papers compared to universities,

allowing central banks to discard lower-quality papers. Moreover, central bankers may simply

know more about QE than academics, given the nature of the subject.

However, higher research quality at central banks seems unlikely to explain our results,

for ﬁve reasons. First, papers written by central bankers and academics are of comparable

quality, based on three measures of quality: publication status, journal impact factor, and the

article inﬂuence score. We show this, as well as the following two results, in the Appendix.

Second, the gaps in research ﬁndings between central bankers and academics remain largely

unchanged once we condition on published papers only. Third, we ﬁnd very similar results

when we replace OLS regressions with weighted least squares, weighting each paper by its

Google Scholar citations as of September 2019.7 Fourth, central bank papers are somewhat

less likely to report standard errors around their estimates (see Section 3.6). Finally, to

explain the opposite results regarding output for Bundesbank authors, a story based on

diﬀerential research quality would have to assume that Bundesbank research is of diﬀerent

quality compared to other central banks.

Another possible explanation for our results is diﬀerences in methodology. Recall from

Section 3.6 that central bankers use DSGE models more often than academics do. This choice

need not be strategic; it may be guided by the popularity of DSGE models in central bank

policy work. If DSGE models generate stronger eﬀects of QE compared to VAR models, this

diﬀerence could potentially explain our results. However, when we add controls for model

choice to regression (1), our results continue to hold (see Section 3.6).

Finally, as conveyed to us by a central banker, it is possible that academics seek sensa-

tional results—such as that vast amounts of spending by central banks were ineﬀective in

improving macroeconomic outcomes—to boost their reputations. Two facts cast doubt on

this explanation. First, academic reputation is generally judged by the publication record.

Finding a null result makes a paper harder, not easier, to publish. Consistent with this view,

ﬁnding larger eﬀects of QE on output increases the odds of publication in a peer-reviewed

7Speciﬁcally, we weight each paper by the logarithm of one plus the number of citations for the paper,
divided by the logarithm of one plus the average number of citations across all papers released in the same
year. The scaling addresses the fact that older papers tend to have more citations.

ECB Working Paper Series No 2584 / August 202127journal (see the Appendix). Second, if the results were driven by academics’ career concerns,

then we should see stronger results among junior authors, who strive to earn tenure. In con-

trast, recall from Section 4.1.1 that diﬀerences between the ﬁndings of central bankers and

academics are more pronounced among senior authors.

5. Conclusion

Central bank economists are more optimistic than academics in their assessments of the

macroeconomic eﬀects of QE. Based on a sample of 54 studies, studies written by central

bankers report stronger eﬀects of QE on both output and inﬂation. Central bank studies

are also more likely to report signiﬁcant QE eﬀects on output, and their abstracts use more

favorable language, compared to those written by academics.

Whose ﬁndings are closer to the truth remains unclear. Our evidence does not imply that

central bank research is biased; perhaps academic research is biased toward insigniﬁcance,

despite the publication bias in academic journals. While the gap between the ﬁndings of

central bankers and academics could have various sources, our evidence suggests some role

for career concerns at central banks. We ﬁnd that central bankers whose papers report larger

eﬀects of QE on output have better career outcomes. The somewhat weaker eﬀects found

by Bundesbank researchers are also consistent with career concerns. Finally, our survey

reveals that in most central banks, management inﬂuences research topics, reviews papers,

and approves them for public distribution. The involvement of bank management in the

production of bank research extends far beyond that of university management in academic

research. Some involvement of bank management seems necessary, given the broader mission

of a central bank. The extent to which this involvement aﬀects research outcomes remains

unclear, creating opportunities for future research.

Importantly, we do not argue that central bank research should be discounted. In many

ways, central banks are in an excellent position to provide accurate assessments of their own

policies. Like pharmaceutical ﬁrms studying their own drugs, central banks have superior

information about their own products, exceptionally strong expertise in the subject matter,

and an intense concern for their reputation. In addition, central banks are public institutions

of the highest integrity. They understand that the eﬀectiveness of their policy is predicated

on their own credibility. We are not questioning that credibility. We simply oﬀer novel

evidence on a previously unexplored aspect of central bank research.

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ECB Working Paper Series No 2584 / August 202130Figure 1: Visualization of the Peak and Cumulative Eﬀects. The ﬁgure illustrates how we compute
the peak and cumulative eﬀects of QE on the level of the outcome variable for the most common case, in
which the authors plot the eﬀect of QE on the level of the outcome variable.

ECB Working Paper Series No 2584 / August 202131(a) Total Peak Eﬀect

(b) Total Cumulative Eﬀect

(c) Standardized Peak Eﬀect

(d) Standardized Cumulative Eﬀect

Figure 2: Eﬀects of QE on Output by Central Bank Aﬃliation. The ﬁgure plots histograms for the
estimated eﬀects on output, separately for papers with and without CB-aﬃliated authors. Studies with 0.5
central bankers, but no “full” central banker, are excluded. Panels A and B show the total estimated peak
and cumulative eﬀects of the QE program studied on the level of output. Panels C and D show the estimated
peak and cumulative eﬀects of the QE program studied on the level of output, after standardizing the QE
program size to 1% of GDP.

0.2.4-1.000.001.002.003.004.005.006.00CB0.2.4-1.000.001.002.003.004.005.006.00Not CB0.25.5.75-2.00-1.000.001.002.003.004.005.006.00CB0.25.5.75-2.00-1.000.001.002.003.004.005.006.00Not CB0.2.4.6-.250.25.5.7511.251.51.75CB0.2.4.6-.250.25.5.7511.251.51.75Not CB0.25.5.75-.250.25.5.7511.251.51.75CB0.25.5.75-.250.25.5.7511.251.51.75Not CBECB Working Paper Series No 2584 / August 202132(a) Total Peak Eﬀect

(b) Total Cumulative Eﬀect

(c) Standardized Peak Eﬀect

(d) Standardized Cumulative Eﬀect

Figure 3: Eﬀects of QE on Inﬂation by Central Bank Aﬃliation. The ﬁgure plots histograms for the
estimated eﬀects on inﬂation, separately for papers with and without CB-aﬃliated authors. Studies with 0.5
central bankers, but no “full” central banker, are excluded. Panels A and B show the total estimated peak
and cumulative eﬀects of the QE program studied on the price level. Panels C and D show the estimated peak
and cumulative eﬀects of the QE program studied on the price level, after standardizing the QE program
size to 1% of GDP.

0.25.5.75-1.000.001.002.003.004.005.006.007.008.00CB0.25.5.75-1.000.001.002.003.004.005.006.007.008.00Not CB0.2.4.6-9.00-7.00-5.00-3.00-1.001.003.005.007.00CB0.2.4.6-9.00-7.00-5.00-3.00-1.001.003.005.007.00Not CB0.3.6.9-.250.25.5.7511.251.51.75CB0.3.6.9-.250.25.5.7511.251.51.75Not CB0.25.5.75-.75-.5-.250.25.5.7511.251.51.75CB0.25.5.75-.75-.5-.250.25.5.7511.251.51.75Not CBECB Working Paper Series No 2584 / August 202133(A) How are research topics selected in your central bank?

(B) How are draft research papers reviewed / commented
on in your institution, prior to their public distribution?

(C) How are your institution’s draft research papers ap-
proved for public distribution?

(D) What criteria can lead to the paper being rejected (i.e.,
not approved for public distribution)?

Figure 4: Survey of Central Banks. The ﬁgure reports survey responses of 24 central banks.

51243411531212712Assigned by managementMutually agreed on by researchers and managementSelected by individual researcher(s)AlwaysOftenSometimesRarelyNever420510243127231112109573Not reviewedBy outside reviewersBy individual peers within the institutionBy dedicated comitteeBy managementAlwaysOftenSometimesRarelyNever2121931119222051315162222215610Automatically approvedBy outside reviewersBy comittee of external and internal membersBy comittee of internal membersBy management: Head of researchBy management: Bank boardAlwaysOftenSometimesRarelyNever738421055413441223613Results not robust or not significantSubstandard methodology, unreliable data, deficient modeling approachTopic not interesting from the perspective of your central bankLack of policy relevanceAlwaysOftenSometimesRarelyNeverECB Working Paper Series No 2584 / August 202134Table 1: Eﬀects of QE on Output and Inﬂation by Central Bank Aﬃliation

This table reports the means and medians (in parentheses) for the estimated eﬀects of QE on
output and inﬂation, as well as for indicators of statistical signiﬁcance, separately for papers with
and without CB-aﬃliated authors. Studies with 0.5 central bankers, but no “full” central banker,
are excluded. We always report the eﬀect on the output level or price level, in percent. Standardized
eﬀects refer to the eﬀect of a QE program size equivalent to 1% of GDP. The unit of observation
is the paper-country.

All

CB

Not CB

Panel A: Eﬀect on Output
Peak eﬀect on output

Standardized peak eﬀect on output

Cumulative eﬀect on output

Standardized cumulative eﬀect on output

Panel B: Eﬀect on Inﬂation
Peak eﬀect on inﬂation

Standardized peak eﬀect on inﬂation

Cumulative eﬀect on inﬂation

Standardized cumulative eﬀect on inﬂation

Panel C: Signiﬁcance
Statistical signiﬁcance: output

Statistical signiﬁcance: inﬂation

1.57
(1.25)
0.24
(0.16)
0.87
(0.40)
0.14
(0.04)

1.42
(0.93)
0.19
(0.11)
0.89
(0.75)
0.12
(0.08)

0.88
(1.00)
0.84
(1.00)

1.75
(1.53)
0.28
(0.18)
1.06
(0.42)
0.18
(0.06)

1.79
(1.17)
0.24
(0.15)
1.35
(0.82)
0.18
(0.11)

1.00
(1.00)
0.89
(1.00)

1.00
(1.00)
0.11
(0.10)
0.48
(0.05)
0.04
(0.01)

0.54
(0.40)
0.05
(0.04)
-0.21
(0.14)
-0.01
(0.01)

0.50
(0.50)
0.75
(1.00)

ECB Working Paper Series No 2584 / August 202135Table 2: Eﬀects of QE on Output

This table regresses the estimated eﬀects of QE on output on the share of authors with central
bank aﬃliation. In Panel A, we use the total estimated eﬀect of the QE program studied on the
level of output. Panel B uses the estimated eﬀect on the level of output, after standardizing the
QE program size to 1% of GDP. Controls include the number of authors and the logarithm of three
plus the average author experience. t-statistics, reported in parentheses, are based on standard
errors clustered at the paper level. p-values obtained using the wild cluster bootstrap procedure
(10,000 repetitions) are reported in square brackets. The unit of observation is the paper-country.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.789
(2.16)
[0.041]

58
0.072

(1)

0.164
(2.38)
[0.021]

58
0.060

0.770
(2.17)
[0.038]

X

58
0.103

Peak Eﬀect
(2)

0.163
(2.48)
[0.018]

X

58
0.170

0.723
(1.83)
[0.085]

X
X
58
0.112

(3)

0.152
(2.11)
[0.052]

X
X
58
0.206

0.620
(1.60)
[0.115]

57
0.043

0.526
(1.51)
[0.134]

X

57
0.091

0.512
(1.36)
[0.167]

X
X
57
0.096

Cumulative Eﬀect
(5)

(6)

(4)

0.140
(2.17)
[0.021]

57
0.048

0.127
(2.17)
[0.020]

X

57
0.078

0.122
(1.90)
[0.049]

X
X
57
0.106

ECB Working Paper Series No 2584 / August 202136Table 3: Eﬀects of QE on Inﬂation

This table regresses the estimated eﬀects of QE on inﬂation on the share of authors with central
bank aﬃliation.
In Panel A, we use the total estimated eﬀect of the QE program studied on
the price level. Panel B uses the estimated eﬀect on the price level, after standardizing the QE
program size to 1% of GDP. Controls include the number of authors and the logarithm of three plus
the average author experience. t-statistics, reported in parentheses, are based on standard errors
clustered at the paper level. p-values obtained using the wild cluster bootstrap procedure (10,000
repetitions) are reported in square brackets. The unit of observation is at the paper-country level.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.409
(3.42)
[0.002]

53
0.142

1.493
(3.33)
[0.002]

X

53
0.239

1.279
(2.79)
[0.011]

X
X
53
0.298

1.700
(2.24)
[0.011]

53
0.126

1.687
(2.20)
[0.013]

X

53
0.126

1.394
(2.04)
[0.044]

X
X
53
0.195

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.197
(2.61)
[0.007]

53
0.110

0.227
(2.73)
[0.005]

X

53
0.248

0.201
(2.73)
[0.004]

X
X
53
0.296

0.205
(2.31)
[0.004]

53
0.106

0.220
(2.31)
[0.004]

X

53
0.186

0.190
(2.41)
[0.007]

X
X
53
0.226

ECB Working Paper Series No 2584 / August 202137Table 4: Signiﬁcance

This table regresses the statistical and economic signiﬁcance of the estimated eﬀects of QE on output
and inﬂation on the share of central bank aﬃliated authors. In Panel A (B), the dependent variable
is the reported statistical and economic signiﬁcance of the eﬀect on output (inﬂation). Controls
include the number of authors and the logarithm of three plus the average author experience.
t-statistics, reported in parentheses, are based on standard errors clustered at the paper level.
p-values obtained using the wild cluster bootstrap procedure (10,000 repetitions) are reported in
square brackets. The unit of observation is the paper-country.

Panel A: Eﬀect on Output

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.412
(2.42)
[0.041]

41
0.233

0.388
(2.41)
[0.035]

X

41
0.280

0.366
(2.20)
[0.043]

X
X
41
0.298

0.335
(2.78)
[0.019]

66
0.139

0.344
(2.78)
[0.019]

X

66
0.145

0.399
(3.42)
[0.005]

X
X
66
0.250

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.202
(1.18)
[0.339]

38
0.044

0.202
(1.25)
[0.283]

X

38
0.118

0.164
(1.11)
[0.372]

X
X
38
0.208

0.196
(1.29)
[0.222]

60
0.041

0.207
(1.36)
[0.197]

X

60
0.043

0.248
(1.86)
[0.093]

X
X
60
0.137

ECB Working Paper Series No 2584 / August 202138Table 5: Tone of the Abstract

This table regresses measures of the tone of the paper’s abstract on the share of central bank
aﬃliated authors.
In Panel A, the dependent variable is the sentiment score, computed as the
diﬀerence in the percentage of positive and negative adjectives in the abstract. In Panel B (C),
the dependent variable is the percentage of positive (negative) adjectives in the abstract. Controls
include the number of authors and the logarithm of three plus the average author experience. t-
statistics, reported in parentheses, are based on robust standard errors. p-values obtained using
the wild cluster bootstrap procedure (10,000 repetitions) are reported in square brackets. The unit
of observation is the paper.

Panel A: Sentiment Score

CB Aﬃliation

Country Dummies
Controls
Observations
R2

Panel B: Percentage of Positive Adjectives

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

0.046
(2.05)
[0.049]

54
0.081

(1)

0.033
(1.61)
[0.125]

54
0.052

Panel C: Percentage of Negative Adjectives

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

-0.013
(-1.35)
[0.197]

54
0.040

(2)

0.053
(2.59)
[0.014]

X

54
0.129

(2)

0.040
(2.15)
[0.043]

X

54
0.128

(2)

-0.013
(-1.22)
[0.252]

X

54
0.048

(3)

0.056
(2.60)
[0.013]

X
X
54
0.133

(3)

0.043
(2.22)
[0.030]

X
X
54
0.136

(3)

-0.013
(-1.16)
[0.272]

X
X
54
0.052

ECB Working Paper Series No 2584 / August 202139Table 6: Career Outcomes and Eﬀects of QE on Output

This table regresses career outcomes on the author’s estimated eﬀects of QE on output. The
dependent variable is the diﬀerence between the author’s rank after her ﬁrst career update following
the paper’s ﬁrst circulation, and her rank at the time of ﬁrst circulation. In Panel A, we use the
total estimated eﬀect of the QE program studied on the level of output.
In Panel B, the QE
program size is standardized to 1% of GDP. Controls include the number of authors, the logarithm
of three plus the researcher’s experience, the number of years since the author’s last career update,
as well as dummy variables indicating the author’s rank at the time of the paper’s ﬁrst circulation.
We restrict the sample to authors who remain aﬃliated with a central bank and experience at
least one career update after the paper’s ﬁrst circulation. t-statistics, reported in parentheses, are
based on standard errors clustered at the author level. p-values obtained using the wild cluster
bootstrap procedure (10,000 repetitions) are reported in square brackets. The unit of observation
is the author-paper-country.

Panel A: Total Program Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.264
(2.32)
[0.027]

34
0.030

0.219
(1.85)
[0.037]

X

34
0.066

0.485
(2.65)
[0.018]

X
X
31
0.553

0.204
(1.78)
[0.079]

32
0.027

0.204
(1.25)
[0.234]

X

32
0.076

0.460
(2.12)
[0.019]

X
X
30
0.550

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.407
(1.41)
[0.231]

34
0.044

1.009
(1.15)
[0.356]

X

34
0.062

2.661
(1.86)
[0.082]

X
X
31
0.553

2.311
(2.00)
[0.041]

32
0.051

1.838
(1.45)
[0.134]

X

32
0.081

4.095
(2.15)
[0.022]

X
X
30
0.569

ECB Working Paper Series No 2584 / August 202140Appendix to

“Fifty Shades of QE:

Comparing Findings of Central Bankers and Academics”

This appendix presents additional results to accompany the paper “Fifty Shades of QE:
Comparing Findings of Central Bankers and Academics.” The contents are as follows:

Appendix A describes the construction of our main dataset and variables. It also contains
the complete dataset used for our main analyses.

Appendix B presents descriptive statistics.

Appendix C presents alternative speciﬁcations to accompany our main results on diﬀer-
ences in research ﬁndings between central bankers and academics.

• Appendix C.1 reports alternative speciﬁcations, including replacing our continuous
measure of CB Aﬃliation with a discrete measure, treating BIS authors as central
bankers, restricting the sample to QE papers only, and controlling for the time gap
between the launch of the QE program studied and the year of the paper’s ﬁrst release.

• Appendix C.2 replaces our continuous measure of CB Aﬃliation by two zero/one

indicators.

• Appendix C.3 shows our main results using non-clustered standard errors.

• Appendix C.4 restricts the sample to papers published in peer-reviewed journals.

• Appendix C.5 adds controls for the QE program studied.

• Appendix C.6 adds controls for the model chosen (DSGE vs. VAR).

Appendix D adds the share of authors who are aﬃliated with the central bank of the
speciﬁc QE program studied.

ECB Working Paper Series No 2584 / August 202141Appendix E presents our main results by type of central bank.

Appendix F presents our main results separately for each country studied.

Appendix G presents our main results separately for each of the three U.S. QE programs
studied.

Appendix H presents additional analyses to accompany our results on the diﬀerences in
tone between central bankers and academics.

Appendix I presents additional analyses to accompany our results on methodological
choice.

Appendix J presents additional analyses to accompany our results on career outcomes.

• Appendix J.1 reports the relationship between career outcomes and other research

outcomes (inﬂation and economic signiﬁcance).

• Appendix J.2 adds a control for paper publication.

• Appendix J.3 includes authors with no career updates in the ﬁve years following the

paper’s ﬁrst release.

• Appendix J.4 treats departures to academia and the private sector as demotions.

Appendix K presents results after adding interactions with author seniority.

Appendix L regresses publication quality on research outcomes.

Appendix M explains our treatment of external feedback we have received on our dataset.

ECB Working Paper Series No 2584 / August 202142Appendix A. Dataset and Variable Construction

This section provides additional information on our sample selection procedure and vari-

able deﬁnitions.

Appendix A.1. Dataset

Table A.1 contains the 40 keywords that we used to manually search the Google Scholar
and RePEc IDEAS databases. The complete reference to all 54 papers included in our ﬁnal
sample is listed in Table A.3.

We manually collect information on the employment history, job titles, and educa-
tional background for the 116 authors by using online searches and information from public
LinkedIn pages. To determine author aﬃliation, we use the author’s main employer at the
time of the paper’s ﬁrst public distribution, as determined by our search in the summer of
2019. We categorize all authors whose primary aﬃliation is a central bank as central bankers.
We classify authors from the Bank of International Settlements (BIS) as 0.5 central bankers
due to the close ties between the BIS and the central banking community. We refer to all
other authors as academics.i We convert job titles into numerical ranks using the dictionary
presented in Table A.2.

Table A.3 reports key variables at the paper level for our sample. The only data we do

not share are author-level data, which represent personal information.

Appendix A.2. The Eﬀect of QE on Output and Inﬂation

For each paper and country studied, we obtain the eﬀect of QE on the level of GDP and
on the level of prices as implied by the authors’ baseline model. We distinguish four eﬀects:
the estimated peak eﬀect of the QE program studied (Total Peak Eﬀect); the estimated
cumulative eﬀect of the QE program studied, deﬁned as the eﬀect at the end of the time
period studied by the authors (Total Cumulative Eﬀect); the estimated peak eﬀect of the
QE program studied, after standardizing QE program size to 1% of GDP (Standardized Peak
Eﬀect); the estimated cumulative eﬀect of the QE program studied, deﬁned as the eﬀect at
the end of the time period studied by the authors and after standardizing QE program
size to 1% of GDP (Standardized Cumulative Eﬀect). We always look for the main eﬀects
advertised in the paper for each country covered, typically presented in the abstract and in
the main graphs or tables of the paper. We discard all the robustness checks, alternative
modiﬁcations or extensions and only look at the estimate most prominently communicated
by the authors.

iThe only exception is Bhattarai et al. (2015), which we treat as having 50% central bankers despite all

authors being academics, because the paper came out as a Dallas Fed working paper.

ECB Working Paper Series No 2584 / August 202143As a rule, we always record the eﬀect of QE on the level of output (i.e., real GDP or
industrial production) or on the price level (i.e., CPI). Let Y denote the actual level of the
outcome variable (with QE) and ˆY denote its counterfactual (without QE). We are interested
in the percentage diﬀerence in the level of the outcome variable with and without QE, i.e.,
we want to compute (Y − ˆY )/ ˆY . Below we explain in more detail how we obtain these
estimates.

Case 1: Levels on y-axis

In the most straightforward case, when the paper reports the eﬀect of QE on the output
and price level, we record the peak and the cumulative eﬀect as displayed in Figure A.1 below.

Figure A.1: Visualization of case 1 (levels on y-axis)

Case 2: Growth rates on y-axis

If a paper reports only growth estimates, we sum up the individual eﬀects on the growth
rate, denoted as yt, to determine the impact on the level of the outcome variable (see Figure
A.2 below). The peak eﬀect is computed as the sum of the yt’s that are consecutively positive,
i.e., y1 + y2 + y3 + y4 = (cid:80)T
t=1 yt · 1 {y1, .., yt > 0}. The cumulative eﬀect equals the sum of all
the yt’s that are plotted, i.e., y1 + y2 + · · · + y7 + y8 = (cid:80)T
t=1 yt. If the ﬁgure plots annualized
growth rates on the y-axis but uses a higher frequency (e.g., week, month, or quarter) on the
x-axis, then we ﬁrst de-annualize the growth rates assuming that the authors of the paper
annualized growth rates using the formula yt,annual = (1 + yt)n − 1. We then compute the
peak eﬀect and cumulative eﬀect as above.

ECB Working Paper Series No 2584 / August 202144Figure A.2: Visualization of case 2 (growth rates on y-axis)

Case 3: Log-levels on y-axis

If the paper reports the eﬀect of QE on the log level of the outcome variable, we convert
the log-level eﬀect to a level eﬀect as follows using the formula (Y − ˆY )/ ˆY = elog Y −log ˆY − 1.
Figure A.3 below illustrates an example. For the peak eﬀect, the ﬁgure implies log Y −log ˆY =
20%. Hence, (Y − ˆY )/ ˆY = 22.14%. For the cumulative eﬀect, log Y − log ˆY = 10%, which
means (Y − ˆY )/ ˆY = 10.52%.

Figure A.3: Visualization of case 3 (log-level on y-axis)

Special cases

In the following, we discuss how we treat special cases.

• Standardized eﬀects. If a paper reports the eﬀect of a particular QE program after
adjusting QE program size to x% of GDP, we multiply the reported eﬀects by the
size of the respective QE program to get the total program eﬀect. For example, if the
authors report the eﬀect for a QE shock equivalent to 1% of the country’s GDP prior
to QE, and the size of the actual QE program studied was 7% of pre-QE GDP, then

ECB Working Paper Series No 2584 / August 202145we multiply the reported eﬀects by a factor of seven. See below for more information
on how we determine QE program size.

• Multiple QE programs. If a paper studies and reports results separately for multiple
QE programs in the same country, then we use the estimates for the QE program most
prominently discussed by the authors in the abstract, introduction and conclusion. If
the authors do not emphasize one program in particular, then we choose the program
that shows the largest eﬀect on output. If a paper studies multiple QE programs in
the same country but does not report the results separately, and the paper adjusts the
QE shock size to be x% of GDP prior to QE, then we assume that (i) the QE eﬀect
reported in the paper is the average eﬀect across all QE programs studied, and (ii) the
QE program with the largest size has the largest eﬀect on output.

• Multiple sample periods. If a paper shows both full-sample and sub-sample results,

we always use the full-sample results.

• Multiple models. If a paper reports results from multiple models, then we use the
estimates for the model most prominently discussed by the authors in the abstract,
introduction and conclusion. If there is ambiguity, we compute the average across all
models shown.

• Multiple policy experiments. If a paper reports the eﬀect of QE and also the eﬀect

of QE combined with other policies, we use the former eﬀect.

• Data frequency. We assume that authors show quarter-on-quarter growth rates,

unless the authors explicitly state that they use annualized rates.

• Unknown shock size. For papers where the shock size used to report the main
results does not allow for a proper mapping to the size of the QE program studied,
we set the eﬀects on output and inﬂation to missing and only collect information on
economic and statistical signiﬁcance as well as tone. Examples include studies that
model the QE shock as a one-standard deviation increase in the propensity to QE or
as a hypothetical decrease in the sovereign yield of a certain number of basis points.

Table A.4 lists the estimated eﬀects for each paper-country pair in our sample.

Standardization

In order to standardize the eﬀects to a common shock size, we proceed as follows. We
use 1% of the respective country’s GDP around the time QE was ﬁrst introduced as the
standardized shock size. For the U.S. and the UK, we therefore use 1% of the annualized

ECB Working Paper Series No 2584 / August 2021462009 Q1 GDP, consistent with Weale and Wieladek (2016). For the euro area, which started
asset purchase programs in 2015, we use 1% of the annualized 2015 Q1 GDP. Performing the
standardization requires an estimate of the size of the aggregate asset purchases for each QE
program, as well as GDP. GDP estimates are obtained from the FRED database. We report
our estimate of the size of the asset purchases for each QE program in Table A.5. Following
Weale and Wieladek (2016), we include Treasury purchases for the U.S. programs, and all
securities purchased under the Asset Purchasing Facility for the UK programs. For the euro
area, program size includes all securities purchased under the Asset Purchasing Program,
because asset-backed securities are a small fraction of the overall program size.ii The variable
shock size in Table A.4 contains the factor by which we divide the total program eﬀect to
obtain the standardized eﬀect.

Appendix A.3. Textual Analysis

To analyze the tone of the paper, we ﬁrst separate the abstract and conclusion from the
rest of the paper, and identify adjectives using the Natural Language Toolkit in Python. We
then use the world list presented in Table A.6 to classify adjectives as indicating a stronger
or weaker relationship in our context. If a word from Table A.6 is immediately preceded by
either “no” or “not”, we assign the opposite sentiment to it (e.g., “not large” is classiﬁed as
negative). The sentiment score, deﬁned as the share of positive adjectives minus the share of
negative adjectives, for each paper abstract is reported in Table A.3. For robustness, we have
also constructed a version of the sentiment score that uses a three-word distance rather than a
one-word distance for negation (following Loughran and McDonald (2011)). The correlation
between our baseline measure and this alternative measure is 96%, and the results reported
in Table 5 in the main paper are virtually unchanged if we use this alternative measure.

Appendix A.4. Variable Deﬁnitions

Table A.7 provides deﬁnitions for all variables used in the analysis.

iiSee https://www.ecb.europa.eu/mopo/implement/omt/html/index.en.html.

ECB Working Paper Series No 2584 / August 202147Table A.1: List of Search Words in Google Scholar and RePEc IDEAS

1. Quantitative easing
2. Quantitative easing UK
3. Quantitative easing US
4. Quantitative easing EA
5. Quantitative easing Inﬂation
6. Quantitative easing CPI
7. Quantitative easing Output
8. Quantitative easing GDP
9. QE
10. QE UK
11. QE US
12. QE EA
13. QE Inﬂation
14. QE CPI
15. QE Output
16. QE GDP
17. Quantitative easing macroeconomic impact
18. QE macroeconomic impact
19. Quantitative easing macroeconomic eﬀects

20. QE macroeconomic eﬀects

21. LSAP
22. Large-scale asset purchases
23. APP
24. Asset purchase programme
25. SMP
26. Securities markets programme
27. LTRO
28. Long-term reﬁnancing operation
29. Operation twist (# since 2009)
30. APF
31. Asset purchase facility
32. Funding for lending
33. Balance sheet policies GDP
34. Balance sheet policies Inﬂation
35. Balance sheet policies macroeconomic impact
36. Balance sheet policies macroeconomic eﬀects
37. Unconventional monetary policy GDP
38. Unconventional monetary policy Inﬂation
39. Unconventional monetary policy macroeco-
nomic impact
40. Unconventional monetary policy macroeco-
nomic eﬀects

ECB Working Paper Series No 2584 / August 202148Table A.2: Dictionary of Job Titles to Numerical Ranks

The table presents the dictionary used to convert job titles into numerical ranks.

Rank

Job title

Central Bankers
1
2
3
4
5
6

Researcher, economist, PhD economist, research associate/economist, expert
Senior researcher, analyst, research economist or economist
Principal/lead researcher or economist
(Senior) Adviser
Deputy Director/Head of Section/Team head/Manager
(Senior) Director, Head, Chief economist, (Vice) President, Senior manager, Deputy Governor

Academics
1
2
3
4
5
6

PhD student, pre-PhD
Post-doc, lecturer
Assistant professor
Associate professor
Full professor
Senior full professor, distinguished professor, chair, or anyone in a managerial position

ECB Working Paper Series No 2584 / August 202149E
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ECB Working Paper Series No 2584 / August 202153d
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ECB Working Paper Series No 2584 / August 202154Table A.5: Unconventional Monetary Policy Programs: Size Estimates

Program

Size (in bn)

Panel A: QE Programs
UK QE 1iv
UK QE 2
US LSAP 1v

£200
£175
$300

Share of
GDPiii

13.06%
11.43%
2.08%

US LSAP 2

US LSAP 3

$600

$790

4.17%

5.49%

Description

Purchases of government bonds announced in Nov-2009.
Purchases of government bonds announced in Jul-2012.
Purchases of long-term Treasury securities announced in
Mar-2009. Following Weale and Wieladek (2016), we only
consider purchases of Treasury securities and ignore
purchases of mortgage-based securities and agency debt.
Purchases of long-term Treasury securities announced in
Nov-2010.
Purchases of long-term Treasury securities announced in
Sep-2012.

EA APPvi

e1,080

10.38% We consider purchases as per the original announcement in
Jan-2015, i.e., e60 bn per month for a period of 18 months.
We exclude subsequent program extensions and
enlargements.

Panel B: Non-QE Programs
US Operation
Twist 1vii
EA LTROs < 14
monthsviii

e787

$400

2.78%

7.57%

EA 3 year LTROsix e1,019

9.80%

EA TLTROsx

e448

4.31%

We consider the initial amount of $400 bn announced in
Sep-2011.
Refers to 6-month LTROs dated Mar-2008 (e66 bn),
12-month LTROs dated May 2009 (e614 bn), 12-month
LTROs dated Aug-2011 (e50 bn), and 13-month LTROs
dated Oct-2011 (e57 bn).
Includes the ﬁrst operation sized e489 bn, which took place
in Dec-2011, and the second operation sized e530 bn,
which took place in Feb-2012.
Approximate gross borrowing from the ﬁrst TLTROs
program announced in Jun-2014.

iiiFollowing Weale and Wieladek (2016), we use the annualized GDP in the ﬁrst quarter of the year in
which the country initiated its QE program. For the UK and U.S., the reference year is 2009; for the Euro
area, it is 2015. GDP data is retrieved from FRED, tables UKNGDP, GDP, and EUNNGDP.

iv

Source
quantitative-easing.

for

UK

programs:

https://www.bankofengland.co.uk/monetary-policy/

v

Source

for U.S.

programs:

https://www.newyorkfed.org/markets/programs-archive/

large-scale-asset-purchases.

viSource for EA program: https://www.ecb.europa.eu/pub/pdf/other/eb201501_focus01.en.pdf.
viiSource: https://www.federalreserve.gov/newsevents/pressreleases/monetary20110921a.htm
viii Source: Table 1 in Bluwstein and Canova (2016).
ixSource: https://www.ecb.europa.eu/pub/pdf/other/mb201203_focus03.en.pdf.
xSource: https://www.ecb.europa.eu/pub/pdf/other/ebbox201703_05.en.pdf, Chart A.

ECB Working Paper Series No 2584 / August 202155Table A.6: Dictionary for Tone Analysis

The table presents our dictionary of positive and negative adjectives used to classify the tone of
the paper’s abstract and conclusion. The positive adjectives are ordered by the number of times
they occur in the abstracts and conclusions of the papers in the sample. The negative adjectives
are paired up with their positive counterparts whenever possible.

Positive

signiﬁcant
large
eﬀective
important
considerable
major
strong
robust
useful
powerful
substantial
desirable
certain
successful
meaningful
sizable
desired
extraordinary
big
huge
non-negligible
high
beneﬁcial

Negative

insigniﬁcant
small
ineﬀective
unimportant
limited
minor
weak
modest
useless
powerless
marginal
undesirable
uncertain
unsuccessful
meaningless
little

tiny
negligible
low

ECB Working Paper Series No 2584 / August 202156Table A.7: Variable Descriptions

Variable

Description

Dependent variables
Peak eﬀect on output

Cumulative eﬀect on out-
put

Standardized peak eﬀect
on output

Standardized cumulative
eﬀect on output

Peak eﬀect on inﬂation

Cumulative eﬀect on inﬂa-
tion
Standardized peak eﬀect
on inﬂation

Standardized cumulative
eﬀect on inﬂation

Statistical signiﬁcance of
output

Economic signiﬁcance of
output

Statistical signiﬁcance of
inﬂation

Economic signiﬁcance of
inﬂation

The maximum impact of the QE program shock on the level of output (i.e., real
GDP or industrial production). The variable is expressed in percent.
The impact of the QE program shock on the level of output (i.e., real GDP or
industrial production) at the end of the time period studied. The variable is
expressed in percent.
The maximum impact of the QE program on the level of output (i.e., real GDP or
industrial production), using a QE program size equivalent to 1% of the country’s
GDP prior to QE. The variable is expressed in percent.
The impact of the QE program on the level of output (i.e., real GDP or indus-
trial production) at the end of the time period studied, using a QE program size
equivalent to 1% of the country’s GDP prior to QE. The variable is expressed in
percent.
The maximum impact of the QE program on the level of prices (i.e., CPI). The
variable is expressed in percent.
The impact of the QE program on the level of prices (i.e., CPI) at the end of the
time period studied. The variable is expressed in percent.
The maximum impact of the QE program on the level of prices (i.e., CPI), using a
QE program size equivalent to 1% of the country’s GDP prior to QE. The variable
is expressed in percent.
The impact of the QE program on the level of prices (i.e., CPI) at the end of the
time period studied, using a QE program size equivalent to 1% of the country’s
GDP prior to QE. The variable is expressed in percent.
Indicator equal to one if the authors state that the peak eﬀect of the QE program
on either the level of output or on output growth is positive and statistically
If the authors do not make explicit statements
signiﬁcant, and zero otherwise.
about statistical signiﬁcance, we use the conﬁdence intervals reported in the paper.
Variable equal to one if the authors state that the estimated eﬀect of the QE pro-
gram on either the level of output or on output growth is economically signiﬁcant;
zero if the eﬀect is stated to be economically insigniﬁcant or small; and 0.5 if the
eﬀect is stated to be somewhat economically signiﬁcant.
Indicator equal to one if the authors state that the peak eﬀect of the QE program
on either the level of prices or on the inﬂation rate is positive and statistically
signiﬁcant, and zero otherwise.
If the authors do not make explicit statements
about statistical signiﬁcance, we use the conﬁdence intervals reported in the paper.
Variable equal to one if the authors state that the estimated eﬀect of the QE pro-
gram on either the level of prices or on the inﬂation rate is economically signiﬁcant;
zero if the eﬀect is stated to be economically insigniﬁcant or small; and 0.5 if the
eﬀect is stated to be somewhat economically signiﬁcant.

Continued on next page

ECB Working Paper Series No 2584 / August 202157Table A.7 – continued

Variable

Description

Share of positive adjec-
tives in abstract
Share of negative adjec-
tives in abstract
Sentiment
stract

score in ab-

Unreported CI

95% CI

Model

Share of positive adjectives out of all adjectives in the paper’s abstract, using the
dictionary of positive adjectives from Table A.6.
Share of negative adjectives out of all adjectives in the paper’s abstract, using the
dictionary of negative adjectives from Table A.6.
Share of positive adjectives out of all adjectives in the paper’s abstract minus the
share of negative adjectives out of all adjectives in the paper’s abstract. We use
the dictionary of positive and negative adjectives from Table A.6.
Indicator equal to one if the paper does not report a conﬁdence interval or does not
specify the width of the conﬁdence interval used to assess statistical signiﬁcance,
and zero otherwise.
Indicator equal to one if the paper uses a 95% conﬁdence interval, and zero oth-
erwise.
Indicator equal to one if the paper uses a DSGE model, and zero if it uses a VAR
model.

Main independent variable
CB aﬃliation

Max seniority

German CB

Other EA CB

Non EA CB

Control variables
Author experience

Number of authors

The share of authors who are aﬃliated with a central bank at the time of the
paper’s ﬁrst public circulation, as determined by our search in the summer of
2019. Authors who are aﬃliated with the BIS are treated as 0.5 central bankers.
The numerical rank of the most senior author. We convert job titles into numerical
ranks using the dictionary from Table A.2. Seniority is undeﬁned for researchers
who are neither central bankers nor academics, such as researchers working at the
BIS, IMF, World Bank, or the private sector.
Indicator equal to one if at least one of the authors is employed at the Bundesbank,
and zero otherwise.
Indicator equal to one if at least one of the authors is employed at the ECB or at
a euro area national central bank that is not the Bundesbank, and zero otherwise.
Indicator equal to one if at least one of the authors is employed at a central bank
outside of the euro area or at the BIS, and zero otherwise.

The number of years since the author’s highest obtained educational degree, aver-
aged across all authors of the paper.
The number of authors of the paper.

ECB Working Paper Series No 2584 / August 202158Appendix B. Descriptive Statistics

This section provides descriptive statistics of our sample. Figure B.4 plots selected sum-
mary statistics at the paper level, by year and country studied. Figure B.5 focuses on the
authors. Panel A shows the number of authors by employer type and Panel B shows the
number of authors by PhD institution, after restricting to employer types and PhD institu-
tions with at least three aﬃliated authors in our sample. Figure B.6 reports the number of
papers by QE program studied, separately for papers with and without at least one central
bank author.

Table B.8 provides additional descriptive statistics at both the paper level (Panel A)
and the author-paper level (Panel B). Table B.9 reports the means, medians, and standard
deviations of the estimated eﬀects of QE on the levels of output and prices, for the full
sample as well as by country.

ECB Working Paper Series No 2584 / August 202159(a) Papers by Year

(b) Papers by Country Studied

(c) CB Aﬃliation by Year

(d) CB Aﬃliation by Country Studied

(e) Outcome Variables by Year

(f) Outcome Variables by Country Studied

Figure B.4: Summary Statistics. The ﬁgure plots summary statistics at the paper level. Panel A reports
the number of research papers by calendar year of ﬁrst circulation. Panel B reports the number of research
papers by country studied. Panels C and D show the average share of central bank (CB)-aﬃliated authors
by calendar year of ﬁrst circulation and by country studied, respectively. Panels E and F show the average
share of papers that study the eﬀects of QE on output and prices by calendar year of ﬁrst circulation and
by country studied, respectively.

0246810Number of research papers2010201120122013201420152016201720180510152025Number of research papersUKUSEA0.2.4.6.81Share of CB-affiliated authors2010201120122013201420152016201720180.2.4.6.81Share of CB-affiliated authorsUKUSEA0.2.4.6.81Share of research papers201020112012201320142015201620172018OutputPrices0.2.4.6.81Share of research papersUKUSEAOutputPricesECB Working Paper Series No 2584 / August 202160(a) Author Count by Employer

(b) Author Count by PhD Institution

Figure B.5: Number of Authors by Employer and PhD Institution. The ﬁgure plots the number of
authors by employer type (Panel A) and by PhD institution (Panel B). We restrict the graphs to employer
types and PhD institutions with at least three aﬃliated authors. The unit of observation is at the author-
paper level in Panel A, and the author level in Panel B.

05101520Number of authorsOther CBPrivate SectorUniversity (US)University (UK)BIS/IMF/WBFederal ReserveEuropean Central BankUniversity (Europe)National CB (EA)Bank of England02468Number of authorsHarvard UniversityNew York UniversityStanford UniversityUniversity of ChicagoUniversity of OxfordYale UniversityEuropean University InstituteGhent UniversityUniversity of CambridgePrinceton UniversityECB Working Paper Series No 2584 / August 202161Figure B.6: Number of Studies by QE Program. The ﬁgure plots the number of studies by QE program
studied, separately for papers with and without CB-aﬃliated authors. Here we pool central bankers and
BIS authors.

051015Number of studiesLTROs + SMP + CBPP (EA)LSAP 1 - 2 (US)Operation Twist 1 (US)QE 1 - 2 (UK)QE 2 (UK)TLTROs (EA)3-year LTROs (EA)LSAP 1 - 3 (US)LTROs < 14 months (EA)LSAP 2 (US)LSAP 1 (US)QE 1 (UK)APP (EA)CBNot CBECB Working Paper Series No 2584 / August 202162Table B.8: Summary Statistics

This table reports summary statistics for our sample of 54 research papers (Panel A) and 137
authors (Panel B).

Variable

Panel A: Paper-Level Variables
Number of authors
Number of countries studied
DSGE
Sentiment score in abstract
Published
Impact factor
Years to publication

Panel B: Author-Level Variables
CB aﬃliation
Female
Author experience
PhD degree
Seniority
Years since last career update

N

54
54
54
54
54
29
31

137
137
127
137
114
125

Mean

Median

2.537
1.259
0.352
0.044
0.574
1.423
1.806

0.602
0.168
10.992
0.891
3.474
3.936

2.000
1.000
0.000
0.047
1.000
1.268
2.000

1.000
0.000
9.000
1.000
4.000
3.000

SD

0.985
0.589
0.482
0.066
0.499
0.873
1.558

0.478
0.375
9.861
0.313
1.815
4.083

ECB Working Paper Series No 2584 / August 202163Table B.9: Eﬀects of QE on Output and Inﬂation

This table reports the mean, median (in square brackets), and standard deviation (in parentheses)
for the estimated eﬀects of QE on output and inﬂation, as well as for indicators of statistical
signiﬁcance. We always report the eﬀect on the output level or price level, in percent. Standardized
eﬀects refer to the eﬀect of a QE program size equivalent to 1% of GDP. The statistics are reported
for the full sample (All ) and for studies of QE in the U.S., UK, and EA. The unit of observation
is the paper-country.

Panel A: Eﬀect on Output
Peak eﬀect on output

Standardized peak eﬀect on output

Cumulative eﬀect on output

Standardized cumulative eﬀect on output

Panel B: Eﬀect on Inﬂation
Peak eﬀect on inﬂation

Standardized peak eﬀect on inﬂation

Cumulative eﬀect on inﬂation

Standardized cumulative eﬀect on inﬂation

Panel C: Signiﬁcance
Statistical signiﬁcance: output

Statistical signiﬁcance: inﬂation

All

US

UK

EA

1.57
[1.25]
(1.21)
0.24
[0.16]
(0.28)
0.87
[0.40]
(1.23)
0.14
[0.04]
(0.26)

1.42
[0.93]
(1.52)
0.19
[0.11]
(0.24)
0.89
[0.75]
(1.94)
0.12
[0.08]
(0.26)

0.88
[1.00]
(0.33)
0.84
[1.00]
(0.37)

1.25
[1.01]
(0.92)
0.36
[0.23]
(0.35)
0.59
[0.35]
(0.93)
0.17
[0.06]
(0.33)

1.07
[0.77]
(0.99)
0.30
[0.18]
(0.36)
0.78
[0.58]
(1.03)
0.21
[0.13]
(0.37)

0.92
[1.00]
(0.28)
0.92
[1.00]
(0.28)

1.67
[1.30]
(1.22)
0.12
[0.10]
(0.09)
0.68
[0.49]
(0.77)
0.05
[0.03]
(0.06)

2.14
[1.03]
(2.36)
0.15
[0.08]
(0.14)
0.77
[0.74]
(3.44)
0.04
[0.06]
(0.23)

0.73
[1.00]
(0.47)
0.67
[1.00]
(0.50)

1.77
[1.41]
(1.41)
0.22
[0.15]
(0.27)
1.24
[0.37]
(1.62)
0.16
[0.04]
(0.29)

1.25
[0.96]
(1.02)
0.12
[0.10]
(0.10)
1.07
[0.85]
(1.08)
0.11
[0.08]
(0.11)

0.94
[1.00]
(0.24)
0.88
[1.00]
(0.34)

ECB Working Paper Series No 2584 / August 202164Appendix C. Additional Analyses for Main Results

In this section, we consider various modiﬁcations of our baseline regression (1), as ana-

lyzed in Sections 3.1 through 3.3 in the main paper.

In Appendix C.1, we provide additional robustness tests for our main results. For
example, we show that our main results are robust to classifying BIS authors as full central
bankers. They are also robust to classifying researchers at the International Monetary Fund
and the World Bank as 0.5 central bankers, although such an alternative classiﬁcation seems
harder to justify. Recall that the main independent variable in regression (1), CB Aﬃliation,
is the fraction of the paper’s authors who are aﬃliated with a central bank. In Appendix
C.1, we show that our main results hold also when we replace this granular measure by an
indicator we call Discrete, which is equal to one if at least one of the authors is aﬃliated
with a central bank or the BIS, and zero otherwise.

As noted in the main paper, our sample contains papers studying not only QE but also
other unconventional monetary policy programs, such as long-term reﬁnancing operations.
As we show in Appendix C.1, when we exclude those other programs from the analysis,
keeping only QE narrowly deﬁned, we ﬁnd results similar to those in Tables 2 through 4 in
the main paper. We also ﬁnd similar eﬀects when we control for the time gap between the
QE program studied and the year of the paper’s ﬁrst release.

In Appendix C.2, we replace CB Aﬃliation by two zero/one indicators: Mixed, which is
equal to one if the share of central-bank-aﬃliated authors is strictly between zero and one,
and Pure CB, which equals one if all of the authors are central bankers. In Appendix C.3,
we further show that our results are robust to using regular OLS standard errors instead of
clustered standard errors. In Appendix C.4, we document that the diﬀerence in research
ﬁndings between central bankers and academics remains largely unchanged once we condition
on published papers only.

We report results after controlling for QE program dummies in Appendix C.5, thereby
comparing central bankers and academics analyzing the same QE program. For papers
studying more than one QE program, more than one dummy is switched on at the same
time. Finally, in Appendix C.6 we show that our main results are robust to adding controls
for model ﬁxed eﬀects (DSGE, VAR, or other).

ECB Working Paper Series No 2584 / August 202165Appendix C.1. Main Results: Additional Speciﬁcations

Table C.10: Alternative Speciﬁcations for Eﬀects on Output

This table presents robustness tests for our results on the eﬀects of QE on output using alternative
deﬁnitions of central bank aﬃliation. All regressions use the same speciﬁcation as in Table 2,
column (3) and (6) in the main paper. For brevity, we only report coeﬃcients of interest and
suppress control variables. In Panel A (B), the dependent variable is the program’s estimated peak
(cumulative) eﬀect on the output level, respectively. In Panel C (D), the dependent variable is the
program’s estimated peak (cumulative) eﬀect on the output level, respectively, after standardizing
QE program size to 1% of GDP. Treat BIS as central bank treats researchers who are aﬃliated with
the Bank of International Settlements as aﬃliated with a central bank. Treat BIS/IMF/WB as 0.5
central banks treats researchers who are aﬃliated with the Bank of International Settlements, the
International Monetary Fund, or the World Bank as 0.5 central bankers. Discrete measure uses
an indicator equal to one if at least one of the authors is associated with a central bank, 0.5 if at
least one of the authors is associated with the BIS, and zero otherwise. QE papers only restricts
the sample to studies of large-scale asset purchases. Control for time gap includes a control for the
time gap (in years) between the launch of the QE program studied and the year of the paper’s ﬁrst
release. t-statistics are based on standard errors that are clustered at the paper level. p-values are
obtained using the wild cluster bootstrap procedure (10,000 repetitions).

Panel A: Peak Eﬀect on Output

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

Panel B: Cumulative Eﬀect on Output

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

β

0.723
0.812
0.700
0.670
0.710
0.929

β

0.512
0.474
0.473
0.504
0.345
0.695

t-stat

p-value

1.83
2.02
1.77
1.82
1.74
2.36

0.067
0.044
0.076
0.069
0.082
0.018

t-stat

p-value

1.36
1.27
1.26
1.67
0.99
1.73

0.173
0.203
0.207
0.095
0.321
0.084

ECB Working Paper Series No 2584 / August 202166Panel C: Standardized Peak Eﬀect on Output

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

Panel D: Standardized Cumulative Eﬀect on Output

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

β

0.152
0.166
0.141
0.158
0.142
0.158

β

0.122
0.116
0.112
0.126
0.088
0.139

t-stat

p-value

2.11
2.28
1.96
2.43
2.12
2.05

0.035
0.023
0.049
0.015
0.034
0.041

t-stat

p-value

1.90
1.81
1.79
2.12
1.87
2.01

0.057
0.070
0.073
0.034
0.062
0.044

ECB Working Paper Series No 2584 / August 202167Table C.11: Alternative Speciﬁcations for Eﬀects on Inﬂation

This table presents robustness tests for our results on the eﬀects of QE on inﬂation using alternative
deﬁnitions of central bank aﬃliation. All regressions use the same speciﬁcation as in Table 3,
column (3) and (6) in the main paper. For brevity, we only report coeﬃcients of interest and
suppress control variables. In Panel A (B), the dependent variable is the program’s estimated peak
(cumulative) eﬀect on the price level, respectively. In Panel C (D), the dependent variable is the
program’s estimated peak (cumulative) eﬀect on the price level, respectively, after standardizing
the QE program size to 1% of GDP. Treat BIS as central bank treats researchers who are aﬃliated
with the Bank of International Settlements as aﬃliated with a central bank. Treat BIS/IMF/WB
as 0.5 central banks treats researchers who are aﬃliated with the Bank of International Settlements,
the International Monetary Fund, or the World Bank as 0.5 central bankers. Discrete measure uses
an indicator equal to one if at least one of the authors is associated with a central bank, 0.5 if at
least one of the authors is associated with the BIS, and zero otherwise. QE papers only restricts
the sample to studies of large-scale asset purchases. Control for time gap includes a control for the
time gap (in years) between the launch of the QE program studied and the year of the paper’s ﬁrst
release. t-statistics are based on standard errors that are clustered at the paper level. p-values are
obtained using the wild cluster bootstrap procedure (10,000 repetitions).

Panel A: Peak Eﬀect on Price

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

Panel B: Cumulative Eﬀect on Price

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

β

1.279
1.223
1.275
1.078
1.491
1.539

β

1.394
1.356
1.416
1.174
1.739
1.272

t-stat

p-value

2.79
2.60
2.70
2.34
2.73
2.97

0.005
0.009
0.007
0.020
0.006
0.003

t-stat

p-value

2.04
1.86
1.95
1.69
1.99
1.85

0.041
0.063
0.051
0.091
0.047
0.065

ECB Working Paper Series No 2584 / August 202168Panel C: Standardized Peak Eﬀect on Price

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

Panel D: Standardized Cumulative Eﬀect on Price

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

β

0.201
0.205
0.191
0.167
0.242
0.195

β

0.190
0.188
0.183
0.155
0.237
0.143

t-stat

p-value

2.73
2.81
2.59
2.99
2.53
2.83

0.006
0.005
0.010
0.003
0.011
0.005

t-stat

p-value

2.41
2.36
2.28
2.45
2.30
2.35

0.016
0.018
0.023
0.014
0.021
0.019

ECB Working Paper Series No 2584 / August 202169Table C.12: Alternative Speciﬁcations for Signiﬁcance

This table presents robustness tests for our results on the statistical and economic signiﬁcance of
the eﬀect of QE using alternative deﬁnitions of central bank aﬃliation. All regressions use the
same speciﬁcation as in Table 4, column (3) and (6) in the main paper. For brevity, we only report
coeﬃcients of interest and suppress control variables. In Panel A (B), the dependent variable is the
statistical (economic) signiﬁcance of the eﬀect of QE on output. In Panel C (D), the dependent
variable is the statistical (economic) signiﬁcance of the eﬀect of QE on inﬂation. Treat BIS as
central bank treats researchers who are aﬃliated with the Bank of International Settlements as
aﬃliated with a central bank. Treat BIS/IMF/WB as 0.5 central banks treats researchers who
are aﬃliated with the Bank of International Settlements, the International Monetary Fund, or the
World Bank as 0.5 central bankers. Discrete measure uses an indicator equal to one if at least one
of the authors is associated with a central bank, 0.5 if at least one of the authors is associated with
the BIS, and zero otherwise. QE papers only restricts the sample to studies of large-scale asset
purchases. Control for time gap includes a control for the time gap (in years) between the launch
of the QE program studied and the year of the paper’s ﬁrst release. t-statistics are based on robust
standard errors that are clustered at the paper level. p-values are obtained using the wild cluster
bootstrap procedure (10,000 repetitions).

Panel A: Statistical Signiﬁcance for Output

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

Panel B: Economic Signiﬁcance for Output

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

β

0.366
0.402
0.374
0.458
0.419
0.353

β

0.399
0.368
0.389
0.438
0.334
0.414

t-stat

p-value

2.20
2.49
2.23
2.61
1.79
1.91

0.028
0.013
0.026
0.009
0.074
0.056

t-stat

p-value

3.42
3.09
3.24
3.71
2.64
3.17

0.001
0.002
0.001
0.000
0.008
0.002

ECB Working Paper Series No 2584 / August 202170Panel C: Statistical Signiﬁcance for Price

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

Panel D: Economic Signiﬁcance for Price

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
QE papers only
Control for time gap

β

0.164
0.090
0.164
0.158
0.322
0.327

β

0.248
0.218
0.177
0.240
0.327
0.234

t-stat

p-value

1.11
0.54
1.14
0.88
1.49
1.78

0.267
0.589
0.255
0.381
0.136
0.076

t-stat

p-value

1.86
1.59
1.33
1.71
1.97
1.63

0.062
0.113
0.184
0.087
0.049
0.102

ECB Working Paper Series No 2584 / August 202171Appendix C.2. Indicator Variables for CB Aﬃliation

Table C.13: Eﬀects of QE on Output: Indicator Variables for CB Aﬃliation

This table repeats Table 2 in the main paper, after replacing the share of central bank authors
with two indicator variables: Mixed CB/Academics is an indicator equal to one if the share of
CB-aﬃliated authors is greater than zero but smaller than one, and zero otherwise; Pure CB is an
indicator equal to one if the share of CB-aﬃliated authors is equal to one, and zero otherwise. The
omitted group is academics. Controls include the logarithm of the average author experience and
the number of authors.

Panel A: Total Program Eﬀect

Mixed CB/Academics

Pure CB

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Mixed CB/Academics

Pure CB

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.678
(2.23)
[0.042]
0.872
(2.25)
[0.032]

58
0.084

(1)

0.170
(2.56)
[0.011]
0.178
(2.49)
[0.014]

58
0.077

0.698
(2.26)
[0.033]
0.860
(2.28)
[0.031]

X

58
0.117

Peak Eﬀect
(2)

0.157
(2.43)
[0.018]
0.174
(2.58)
[0.012]

X

58
0.181

0.633
(1.55)
[0.225]
0.831
(2.02)
[0.080]

X
X
58
0.123

(3)

0.158
(1.95)
[0.106]
0.170
(2.38)
[0.037]

X
X
58
0.216

0.330
(1.13)
[0.272]
0.731
(1.78)
[0.080]

57
0.055

0.292
(1.02)
[0.313]
0.624
(1.70)
[0.092]

X

57
0.099

0.230
(0.64)
[0.532]
0.599
(1.60)
[0.113]

X
X
57
0.105

Cumulative Eﬀect
(5)

(6)

(4)

0.102
(1.48)
[0.112]
0.146
(2.16)
[0.017]

57
0.048

0.090
(1.35)
[0.171]
0.129
(2.17)
[0.022]

X

57
0.076

0.089
(1.10)
[0.293]
0.125
(1.99)
[0.047]

X
X
57
0.105

ECB Working Paper Series No 2584 / August 202172Table C.14: Eﬀects of QE on Inﬂation: Indicator Variables for CB Aﬃliation

This table repeats Table 3 in the main paper, after replacing the share of central bank authors
with two indicator variables: Mixed CB/Academics is an indicator equal to one if the share of
CB-aﬃliated authors is greater than zero but smaller than one, and zero otherwise; Pure CB is
an indicator equal to one if the share of CB-aﬃliated authors is equal to one, and zero otherwise.
The omitted group is academics. Controls include the logarithm of three plus the average author
experience and the number of authors.

Panel A: Total Program Eﬀect

Mixed CB/Academics

Pure CB

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Mixed CB/Academics

Pure CB

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.962
(1.95)
[0.072]
1.389
(3.43)
[0.003]

53
0.126

0.986
(1.96)
[0.069]
1.496
(3.33)
[0.003]

X

53
0.225

0.354
(0.56)
[0.786]
1.263
(2.47)
[0.030]

X
X
53
0.305

1.163
(1.35)
[0.204]
1.796
(2.13)
[0.012]

53
0.127

1.161
(1.33)
[0.205]
1.780
(2.12)
[0.012]

X

53
0.127

0.327
(0.43)
[0.682]
1.467
(1.87)
[0.089]

X
X
53
0.216

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.139
(3.05)
[0.005]
0.221
(2.75)
[0.004]

53
0.125

0.141
(2.63)
[0.012]
0.252
(2.82)
[0.002]

X

53
0.263

0.053
(0.57)
[0.618]
0.220
(2.81)
[0.006]

X
X
53
0.333

0.118
(1.69)
[0.075]
0.237
(2.42)
[0.003]

53
0.130

0.118
(1.68)
[0.095]
0.251
(2.42)
[0.003]

X

53
0.209

0.015
(0.15)
[0.885]
0.213
(2.51)
[0.005]

X
X
53
0.283

ECB Working Paper Series No 2584 / August 202173Table C.15: Signiﬁcance of the Eﬀects of QE: Indicator Variables for CB Aﬃliation

This table repeats Table 4 in the main paper, after replacing the share of central bank authors
with two indicator variables: Mixed CB/Academics is an indicator equal to one if the share of
CB-aﬃliated authors is greater than zero but smaller than one, and zero otherwise; Pure CB is an
indicator equal to one if the share of CB-aﬃliated authors is equal to one, and zero otherwise. The
omitted group is academics. Controls include the logarithm of the average author experience and
the number of authors.

Panel A: Eﬀect on Output

Mixed CB/Academics

Pure CB

Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

Mixed CB/Academics

Pure CB

Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.500
(2.77)
[0.039]
0.500
(2.77)
[0.030]

41
0.431

0.481
(2.69)
[0.033]
0.473
(2.64)
[0.031]

X

41
0.460

0.621
(4.12)
[0.029]
0.437
(2.91)
[0.020]

X
X
41
0.592

0.193
(1.33)
[0.230]
0.382
(2.94)
[0.018]

66
0.162

0.197
(1.32)
[0.244]
0.396
(2.93)
[0.017]

X

66
0.170

0.374
(2.59)
[0.073]
0.463
(3.92)
[0.003]

X
X
66
0.296

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.039
(0.18)
[0.950]
0.250
(1.18)
[0.447]

38
0.078

0.042
(0.19)
[0.943]
0.246
(1.22)
[0.387]

X

38
0.148

0.151
(0.59)
[0.876]
0.202
(1.33)
[0.371]

X
X
38
0.218

0.093
(0.54)
[0.608]
0.222
(1.32)
[0.222]

60
0.048

0.098
(0.56)
[0.594]
0.236
(1.42)
[0.187]

X

60
0.051

0.210
(1.17)
[0.372]
0.278
(1.85)
[0.103]

X
X
60
0.147

ECB Working Paper Series No 2584 / August 202174Appendix C.3. Regular OLS Standard Errors

Table C.16: Eﬀect on Output with Regular OLS Standard Errors

This table repeats Table 2 in the main paper without clustering standard errors. Controls include
the logarithm of three plus the average author experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.789
(2.08)
[0.042]

58
0.072

0.770
(2.01)
[0.040]

X

58
0.103

0.723
(1.82)
[0.075]

X
X
58
0.112

0.620
(1.58)
[0.129]

57
0.043

0.526
(1.33)
[0.152]

X

57
0.091

0.512
(1.25)
[0.190]

X
X
57
0.096

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.164
(1.89)
[0.021]

58
0.060

0.163
(1.94)
[0.020]

X

58
0.170

0.152
(1.78)
[0.038]

X
X
58
0.206

0.140
(1.66)
[0.030]

57
0.048

0.127
(1.48)
[0.034]

X

57
0.078

0.122
(1.39)
[0.063]

X
X
57
0.106

ECB Working Paper Series No 2584 / August 202175Table C.17: Eﬀect on Inﬂation with Regular OLS Standard Errors

This table repeats Table 3 in the main paper without clustering standard errors. Controls include
the logarithm of three plus the average author experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.409
(2.90)
[0.001]

53
0.142

1.493
(3.16)
[0.001]

X

53
0.239

1.279
(2.69)
[0.005]

X
X
53
0.298

1.700
(2.71)
[0.004]

53
0.126

1.687
(2.60)
[0.004]

X

53
0.126

1.394
(2.13)
[0.013]

X
X
53
0.195

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.197
(2.51)
[0.003]

53
0.110

0.227
(3.04)
[0.001]

X

53
0.248

0.201
(2.65)
[0.004]

X
X
53
0.296

0.205
(2.46)
[0.005]

53
0.106

0.220
(2.67)
[0.004]

X

53
0.186

0.190
(2.26)
[0.006]

X
X
53
0.226

ECB Working Paper Series No 2584 / August 202176Table C.18: Signiﬁcance with Regular OLS Standard Errors

This table repeats Table 4 in the main paper without clustering standard errors. Controls include
the logarithm of three plus the average author experience and the number of authors.

Panel A: Eﬀect on Output

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.412
(3.45)
[0.015]

41
0.233

0.388
(3.22)
[0.014]

X

41
0.280

0.366
(2.91)
[0.014]

X
X
41
0.298

0.335
(3.21)
[0.004]

66
0.139

0.344
(3.21)
[0.005]

X

66
0.145

0.399
(3.84)
[0.001]

X
X
66
0.250

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.202
(1.29)
[0.171]

38
0.044

0.202
(1.29)
[0.148]

X

38
0.118

0.164
(1.03)
[0.224]

X
X
38
0.208

0.196
(1.57)
[0.147]

60
0.041

0.207
(1.59)
[0.115]

X

60
0.043

0.248
(1.93)
[0.044]

X
X
60
0.137

ECB Working Paper Series No 2584 / August 202177Appendix C.4. Published Papers Only

Table C.19: Eﬀects of QE on Output: Published Papers Only

This table repeats Table 2 in the main paper, after restricting the sample of papers to published
papers only. Controls include the logarithm of three plus the average author experience and the
number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.058
(2.07)
[0.055]

35
0.095

(1)

0.226
(2.21)
[0.032]

35
0.072

1.105
(2.19)
[0.042]

X

35
0.131

Peak Eﬀect
(2)

0.223
(2.43)
[0.021]

X

35
0.204

1.023
(1.89)
[0.074]

X
X
35
0.150

(3)

0.175
(1.82)
[0.086]

X
X
35
0.310

1.241
(2.20)
[0.045]

35
0.117

1.006
(1.99)
[0.056]

X

35
0.160

0.951
(1.75)
[0.081]

X
X
35
0.171

Cumulative Eﬀect
(5)

(6)

(4)

0.222
(2.30)
[0.024]

35
0.071

0.190
(2.25)
[0.024]

X

35
0.110

0.157
(1.71)
[0.087]

X
X
35
0.163

ECB Working Paper Series No 2584 / August 202178Table C.20: Eﬀects of QE on Inﬂation: Published Papers Only

This table repeats Table 3 in the main paper, after restricting the sample of papers to published
papers only. Controls include the logarithm of the average author experience and the number of
authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.043
(2.08)
[0.084]

31
0.060

1.367
(2.26)
[0.047]

X

31
0.275

1.198
(2.04)
[0.066]

X
X
31
0.312

1.085
(2.53)
[0.030]

31
0.070

1.267
(2.14)
[0.044]

X

31
0.158

1.184
(2.20)
[0.014]

X
X
31
0.242

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.193
(1.53)
[0.151]

31
0.062

0.267
(1.83)
[0.076]

X

31
0.253

0.204
(1.58)
[0.102]

X
X
31
0.306

0.193
(1.59)
[0.096]

31
0.062

0.243
(1.62)
[0.068]

X

31
0.163

0.204
(1.51)
[0.084]

X
X
31
0.203

ECB Working Paper Series No 2584 / August 202179Table C.21: Signiﬁcance of the Eﬀects of QE: Published Papers Only

This table repeats Table 4 in the main paper, after restricting the sample of papers to published
papers only. Controls include the logarithm of the average author experience and the number of
authors.

Panel A: Eﬀect on Output

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.463
(2.24)
[0.094]

23
0.322

0.437
(2.29)
[0.062]

X

23
0.405

0.527
(2.26)
[0.094]

X
X
23
0.583

0.270
(1.85)
[0.088]

37
0.122

0.240
(1.46)
[0.211]

X

37
0.158

0.267
(1.66)
[0.147]

X
X
37
0.198

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.118
(1.06)
[0.437]

19
0.041

0.107
(0.96)
[0.494]

X

19
0.241

0.031
(0.36)
[0.743]

X
X
19
0.289

0.045
(0.26)
[0.812]

31
0.003

0.029
(0.16)
[0.870]

X

31
0.007

0.044
(0.29)
[0.776]

X
X
31
0.216

ECB Working Paper Series No 2584 / August 202180Appendix C.5. Controlling for QE Program Studied

Table C.22: Eﬀects of QE on Output: Controls for QE Program Studied

This table repeats Table 2 in the main paper, after replacing country ﬁxed eﬀects with indicator
variables for the QE program studied. Controls include the logarithm of three plus the average
author experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

QE Program Dummies
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

QE Program Dummies
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.789
(2.16)
[0.041]

58
0.072

(1)

0.164
(2.38)
[0.019]

58
0.060

0.701
(1.86)
[0.065]

X

58
0.360

Peak Eﬀect
(2)

0.088
(1.89)
[0.054]

X

58
0.667

0.646
(1.56)
[0.127]

X
X
58
0.367

(3)

0.076
(1.54)
[0.125]

X
X
58
0.674

0.620
(1.60)
[0.116]

57
0.043

0.351
(1.04)
[0.279]

X

57
0.354

0.323
(0.90)
[0.341]

X
X
57
0.356

Cumulative Eﬀect
(5)

(6)

(4)

0.140
(2.17)
[0.019]

57
0.048

0.061
(1.36)
[0.143]

X

57
0.495

0.054
(1.11)
[0.226]

X
X
57
0.500

ECB Working Paper Series No 2584 / August 202181Table C.23: Eﬀects of QE on Inﬂation: Controls for QE Program Studied

This table repeats Table 3 in the main paper, after replacing country ﬁxed eﬀects with indicator
variables for the QE program studied. Controls include the logarithm of three plus the average
author experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

QE Program Dummies
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

QE Program Dummies
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.409
(3.42)
[0.002]

53
0.142

1.376
(2.49)
[0.016]

X

53
0.415

1.237
(2.23)
[0.029]

X
X
53
0.448

1.700
(2.24)
[0.010]

53
0.126

1.511
(1.64)
[0.050]

X

53
0.296

1.336
(1.56)
[0.091]

X
X
53
0.320

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.197
(2.61)
[0.008]

53
0.110

0.176
(2.23)
[0.018]

X

53
0.354

0.148
(2.08)
[0.029]

X
X
53
0.398

0.205
(2.31)
[0.005]

53
0.106

0.169
(1.81)
[0.034]

X

53
0.244

0.141
(1.66)
[0.077]

X
X
53
0.281

ECB Working Paper Series No 2584 / August 202182Table C.24: Signiﬁcance of the Eﬀects of QE: Controls for QE Program Studied

This table repeats Table 4 in the main paper, after replacing country ﬁxed eﬀects with indicator
variables for the QE program studied. Controls include the logarithm of three plus the average
author experience and the number of authors.

Panel A: Eﬀect on Output

CB Aﬃliation

QE Program Dummies
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

QE Program Dummies
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.465
(2.55)
[0.040]

36
0.284

0.324
(1.96)
[0.049]

X

36
0.617

0.305
(1.46)
[0.183]

X
X
36
0.627

0.343
(2.69)
[0.027]

61
0.154

0.312
(2.19)
[0.073]

X

61
0.277

0.370
(2.63)
[0.029]

X
X
61
0.351

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.237
(1.21)
[0.357]

33
0.080

0.183
(1.06)
[0.279]

X

33
0.374

0.236
(1.24)
[0.211]

X
X
33
0.537

0.198
(1.22)
[0.254]

55
0.047

0.189
(1.03)
[0.329]

X

55
0.116

0.234
(1.37)
[0.207]

X
X
55
0.182

ECB Working Paper Series No 2584 / August 202183Appendix C.6. Controlling for Model Choice

Table C.25: Eﬀects of QE on Output: Controls for Model Choice

This table repeats Table 2 in the main paper, after adding model ﬁxed eﬀects. The model can
be either DSGE, VAR, or other. Controls include the logarithm of three plus the average author
experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Model FE
Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Model FE
Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.876
(2.42)
[0.020]

X

0.839
(2.39)
[0.019]

X
X

58
0.092

58
0.117

(1)

0.167
(2.26)
[0.024]

X

Peak Eﬀect
(2)

0.174
(2.46)
[0.010]

X
X

58
0.071

58
0.178

0.792
(1.99)
[0.062]

X
X
X
58
0.123

(3)

0.161
(1.98)
[0.065]

X
X
X
58
0.210

0.703
(1.82)
[0.073]

X

0.607
(1.77)
[0.071]

X
X

57
0.064

57
0.117

0.596
(1.54)
[0.120]

X
X
X
57
0.120

Cumulative Eﬀect
(5)

(6)

(4)

0.146
(2.07)
[0.020]

X

0.137
(2.16)
[0.014]

X
X

57
0.051

57
0.085

0.130
(1.74)
[0.071]

X
X
X
57
0.109

ECB Working Paper Series No 2584 / August 202184Table C.26: Eﬀects of QE on Inﬂation: Controls for Model Choice

This table repeats Table 3 in the main paper, after adding model ﬁxed eﬀects. The model can
be either DSGE, VAR, or other. Controls include the logarithm of three plus the average author
experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Model FE
Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Model FE
Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.590
(3.22)
[0.007]

X

1.600
(3.15)
[0.010]

X
X

53
0.249

53
0.300

1.318
(2.41)
[0.035]

X
X
X
53
0.356

1.759
(2.19)
[0.043]

X

1.747
(2.12)
[0.047]

X
X

53
0.132

53
0.132

1.381
(1.81)
[0.098]

X
X
X
53
0.197

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.236
(2.44)
[0.008]

X

0.265
(2.60)
[0.007]

X
X

53
0.151

53
0.292

0.234
(2.43)
[0.009]

X
X
X
53
0.318

0.224
(2.11)
[0.020]

X

0.243
(2.13)
[0.013]

X
X

53
0.143

53
0.213

0.208
(2.05)
[0.021]

X
X
X
53
0.242

ECB Working Paper Series No 2584 / August 202185Table C.27: Signiﬁcance of the Eﬀects of QE: Controls for Model Choice

This table repeats Table 4 in the main paper, after adding model ﬁxed eﬀects. The model can
be either DSGE, VAR, or other. Controls include the logarithm of three plus the average author
experience and the number of authors.

Panel A: Eﬀect on Output

CB Aﬃliation

Model FE
Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

Model FE
Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.440
(2.47)
[0.038]

X

0.421
(2.51)
[0.028]

X
X

41
0.295

41
0.332

0.397
(2.33)
[0.023]

X
X
X
41
0.365

0.311
(2.61)
[0.023]

X

0.322
(2.67)
[0.022]

X
X

66
0.158

66
0.164

0.393
(3.44)
[0.005]

X
X
X
66
0.268

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.160
(0.85)
[0.544]

X

0.179
(0.96)
[0.445]

X
X

38
0.065

38
0.147

0.165
(1.02)
[0.450]

X
X
X
38
0.222

0.200
(1.34)
[0.211]

X

0.205
(1.40)
[0.187]

X
X

60
0.070

60
0.075

0.244
(1.85)
[0.108]

X
X
X
60
0.177

ECB Working Paper Series No 2584 / August 202186Appendix D. Aﬃliation with Central Bank of QE Program Studied

This section explores whether central bankers are more optimistic when they study QE
launched by their own central bank. We add to equation (1) in the main paper the variable
Same-Country CB Aﬃliation, which captures the share of authors who are aﬃliated with the
central bank of the QE program studied. We also include paper ﬁxed eﬀects, thus comparing
the eﬀects of QE in diﬀerent countries as estimated by the same paper.

ECB Working Paper Series No 2584 / August 202187Table D.28: Aﬃliation with Central Bank of QE Program Studied

This table repeats the analysis in Tables 2 to 4 in the main paper, after adding the share of
authors who are aﬃliated with the central bank of the QE program studied (Same-Country CB
Aﬃliation). Controls include the logarithm of three plus the average author experience and the
number of authors. t-statistics, reported in parentheses, are based on standard errors clustered at
the paper level. p-values obtained using the wild cluster bootstrap procedure (10,000 repetitions)
are reported in square brackets.

Panel A: Eﬀect on Output

Same-Country CB Aﬃliation

CB Aﬃliation

Country FE
Paper FE
Observations
R2

Panel B: Eﬀect on Inﬂation

Same-Country CB Aﬃliation

CB Aﬃliation

Country FE
Paper FE
Observations
R2

Total Program Eﬀect

Standardized Eﬀect

Peak

(1)

(2)

Cumulative
(4)
(3)

Peak

(5)

(6)

Cumulative
(8)
(7)

-0.697
(-1.19)
[0.25]
1.112
(1.89)
[0.06]

X

58
0.144

0.956
(0.83)
[0.38]

X
X
19
0.691

-0.821
(-1.40)
[0.18]
0.958
(1.63)
[0.12]

X

57
0.139

-0.291
(-0.27)
[0.46]

X
X
19
0.461

-0.144
(-1.21)
[0.25]
0.233
(2.07)
[0.03]

X

58
0.233

-0.155
(-0.80)
[0.10]

X
X
19
0.692

-0.110
(-0.90)
[0.42]
0.182
(1.65)
[0.12]

X

57
0.123

-0.054
(-0.32)
[0.53]

X
X
19
0.496

Total Program Eﬀect

Standardized Eﬀect

Peak

(1)

(2)

Cumulative
(4)
(3)

Peak

(5)

(6)

Cumulative
(8)
(7)

0.836
(1.58)
[0.14]
0.854
(1.96)
[0.06]

X

53
0.328

2.892
(4.33)
[0.03]

X
X
19
0.927

0.943
(1.71)
[0.09]
0.915
(1.49)
[0.16]

X

53
0.218

0.296
(0.53)
[0.48]

X
X
19
0.735

0.029
(0.26)
[0.87]
0.186
(2.96)
[0.00]

X

53
0.297

-0.090
(-0.64)
[0.30]

X
X
19
0.831

0.091
(0.77)
[0.56]
0.144
(2.45)
[0.02]

X

53
0.238

-0.013
(-0.14)
[0.82]

X
X
19
0.674

ECB Working Paper Series No 2584 / August 202188Panel C: Signiﬁcance

Same-Country CB Aﬃliation

CB Aﬃliation

Country FE
Paper FE
Observations
R2

Eﬀect on Output

Eﬀect on Inﬂation

Statistical
(1)

Economic
(2)

Statistical
(3)

Economic
(4)

0.052
(0.59)
[0.48]
0.348
(2.09)
[0.05]

X

41
0.300

0.040
(0.36)
[0.73]
0.366
(2.63)
[0.03]

X

60
0.225

0.319
(2.09)
[0.02]
0.050
(0.34)
[0.80]

X

38
0.270

0.154
(1.06)
[0.30]
0.181
(1.07)
[0.33]

X

53
0.138

In Panel C, we cannot include paper ﬁxed eﬀects, unlike in Panels A and B, due to zero variation in statistical signiﬁcance

within the set of central bank papers that study more than one country.

ECB Working Paper Series No 2584 / August 202189Appendix E. Results by Central Bank

In this section we repeat our main tests from Tables 2 through 4 in the main paper, after
separating central bank authors by the country of the central bank they work for. Table
E.29 reports our results after replacing CB Aﬃliation in equation (1) in the main paper by
four zero/one indicators: EA CB is equal to one if at least one of the authors is aﬃliated
with the ECB or a national central bank in the euro area, UK CB equals one if at least
one author is aﬃliated with the Bank of England, US CB equals one if at least one author
is aﬃliated with the Federal Reserve Board or a regional Fed, and Other CB equals one if
at least one author is aﬃliated with another central bank or the BIS. The omitted group is
academics.

In Table E.30, we repeat the same analysis after replacing CB Aﬃliation with three
indicators: German CB is equal to one if at least one of the authors is employed by the
Bundesbank, Other EA CB is equal to one if at least one author works at the ECB or a
euro area national central bank other than the Bundesbank, and Non-EA CB is equal to
one if at least one author is from a central bank outside the euro area or from the BIS. The
omitted group is again academics. If a paper has authors from both the Bundesbank and
the ECB or another euro area national central bank, then both the German CB indicator
and the Other EA CB indicator are equal to one.

ECB Working Paper Series No 2584 / August 202190Table E.29: The Eﬀects of QE by Type of Central Bank

This table repeats the analysis in Tables 2 to 4, after replacing the share of central bank authors
by four indicators: EA CB is an indicator equal to one if at least one of the authors is aﬃliated
with the ECB or a national central bank in the euro area; UK CB is an indicator equal to one if at
least one of the authors is aﬃliated with the Bank of England; US CB is an indicator equal to one
if at least one of the authors is aﬃliated with the Federal Reserve Board or a regional Fed; Other
CB is an indicator equal to one if at least one of the authors is aﬃliated with another central bank
or with the BIS. The omitted group is academics.

Panel A: Eﬀect on Output

Total Program Eﬀect

Standardized Eﬀect

EA CB

UK CB

US CB

Other CB

Country FE + Controls
Observations
R2

Peak
(1)

0.324
(0.72)
[0.475]
0.533
(1.14)
[0.322]
0.873
(1.47)
[0.198]
0.686
(1.74)
[0.119]

X
58
0.104

Cumulative
(2)

0.520
(1.20)
[0.220]
-0.094
(-0.38)
[0.686]
0.588
(1.17)
[0.264]
0.495
(0.99)
[0.357]

X
57
0.123

Peak
(3)

0.135
(2.05)
[0.026]
0.123
(1.83)
[0.142]
0.258
(1.14)
[0.320]
0.115
(2.23)
[0.059]

X
58
0.227

Cumulative
(4)

0.134
(1.92)
[0.082]
0.031
(0.90)
[0.317]
0.258
(1.18)
[0.266]
0.094
(1.70)
[0.125]

X
57
0.154

ECB Working Paper Series No 2584 / August 202191Panel B: Eﬀect on Inﬂation

Total Program Eﬀect

Standardized Eﬀect

EA CB

UK CB

US CB

Other CB

Country FE + Controls
Observations
R2

Panel C: Signiﬁcance

EA CB

UK CB

US CB

Other CB

Country FE + Controls
Observations
R2

Peak
(1)

0.331
(0.97)
[0.334]
1.764
(2.63)
[0.016]
0.715
(0.83)
[0.464]
0.281
(0.57)
[0.626]

X
53
0.322

Cumulative
(2)

0.745
(1.40)
[0.183]
1.454
(1.17)
[0.274]
0.911
(0.91)
[0.397]
0.512
(0.75)
[0.482]

X
53
0.175

Peak
(3)

0.102
(2.15)
[0.031]
0.226
(3.12)
[0.028]
0.178
(0.67)
[0.651]
0.067
(1.37)
[0.241]

X
53
0.282

Cumulative
(4)

0.140
(2.22)
[0.064]
0.128
(1.36)
[0.205]
0.206
(0.73)
[0.584]
0.061
(1.06)
[0.319]

X
53
0.206

Eﬀect on Output

Eﬀect on Inﬂation

Statistical
(1)

Economic
(2)

Statistical
(3)

Economic
(4)

0.229
(1.45)
[0.120]
0.564
(3.01)
[0.063]
0.328
(1.72)
[0.471]
0.404
(2.73)
[0.054]

X
41
0.481

0.449
(2.87)
[0.004]
0.268
(1.93)
[0.124]
0.228
(0.79)
[0.491]
0.175
(1.15)
[0.321]

X
66
0.274

0.011
(0.08)
[0.932]
0.337
(1.38)
[0.432]
0.054
(0.32)
[0.827]
0.154
(0.98)
[0.629]

X
38
0.252

0.181
(1.43)
[0.164]
0.213
(1.25)
[0.321]
0.173
(0.56)
[0.616]
0.164
(1.09)
[0.339]

X
60
0.129

ECB Working Paper Series No 2584 / August 202192Table E.30: The Bundesbank

This table repeats the analysis in Tables 2 to 4, after replacing the share of central bank authors by
three indicators: German CB is an indicator equal to one if at least one of the authors is employed
at the Bundesbank; Other EA CB is equal to one if at least one of the authors is employed at the
ECB or at a euro area national central bank that is not the Bundesbank; Non-EA CB is equal to
one if at least one of the authors is from a central bank outside of the euro area or from the BIS.
The omitted group is academics. t-statistics, reported in parentheses, are based on standard errors
clustered at the paper level. p-values obtained using the wild cluster bootstrap procedure (10,000
repetitions) are reported in square brackets. The unit of observation is the paper-country.

Panel A: Eﬀect on Output

German CB

Other EA CB

Non-EA CB

Country FE + Controls
Observations
R2

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

-0.884
(-2.17)
[0.166]
0.444
(0.95)
[0.349]
0.688
(1.90)
[0.140]

X
58
0.127

Cumulative
(2)

-1.171
(-2.61)
[0.139]
0.648
(1.59)
[0.096]
0.313
(1.11)
[0.259]

X
57
0.140

Peak
(3)

-0.082
(-1.05)
[0.368]
0.142
(1.98)
[0.021]
0.143
(2.38)
[0.048]

X
58
0.222

Cumulative
(4)

-0.116
(-1.52)
[0.222]
0.143
(1.97)
[0.084]
0.097
(1.90)
[0.052]

X
57
0.129

ECB Working Paper Series No 2584 / August 202193Panel B: Eﬀect on Inﬂation

German CB

Other EA CB

Non-EA CB

Country FE + Controls
Observations
R2

Panel C: Signiﬁcance

German CB

Other EA CB

Non-EA CB

Country FE + Controls
Observations
R2

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

0.461
(0.96)
[0.508]
0.279
(0.62)
[0.603]
0.908
(1.64)
[0.224]

X
53
0.244

Cumulative
(2)

0.640
(1.34)
[0.235]
0.722
(1.44)
[0.144]
0.926
(1.18)
[0.311]

X
53
0.161

Peak
(3)

0.094
(1.82)
[0.117]
0.088
(1.60)
[0.108]
0.141
(2.10)
[0.088]

X
53
0.250

Cumulative
(4)

0.101
(1.88)
[0.117]
0.133
(2.28)
[0.037]
0.104
(1.51)
[0.178]

X
53
0.195

Eﬀect on Output

Eﬀect on Inﬂation

Statistical
(1)

Economic
(2)

Statistical
(3)

Economic
(4)

0.187
(1.04)
[0.369]
0.228
(1.39)
[0.151]
0.440
(2.81)
[0.039]

X
41
0.457

0.240
(1.53)
[0.214]
0.419
(2.80)
[0.002]
0.206
(1.46)
[0.247]

X
66
0.256

-0.339
(-1.07)
[0.464]
0.085
(0.81)
[0.391]
0.167
(0.97)
[0.736]

X
38
0.298

-0.074
(-0.32)
[0.810]
0.254
(2.16)
[0.038]
0.188
(1.26)
[0.312]

X
60
0.167

ECB Working Paper Series No 2584 / August 202194Appendix F. Results by Country Studied

In this section we repeat our main tests after partitioning the sample by the country in

which QE takes place.

ECB Working Paper Series No 2584 / August 202195Table F.31: Eﬀects of QE on Output: By Country Results

This table repeats the analysis in Table 2 in the main paper, separately for each of the three
countries studied. Controls include the logarithm of three plus the average author experience and
the number of authors.

Panel A: Peak Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.419
(0.64)
[0.53]

17
0.023

(2)

0.397
(0.52)
[0.62]

X
17
0.026

(3)

1.244
(3.29)
[0.01]

19
0.309

(4)

1.115
(2.56)
[0.05]

X
19
0.372

(5)

0.668
(0.89)
[0.40]

22
0.035

Panel B: Cumulative Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

(2)

-0.242
(-0.52)
[0.62]

16
0.019

-0.250
(-0.51)
[0.62]

X
16
0.026

(3)

1.072
(2.56)
[0.01]

19
0.222

(4)

1.102
(2.63)
[0.06]

X
19
0.279

(5)

0.691
(0.85)
[0.42]

22
0.029

Panel C: Standardized Peak Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.025
(0.51)
[0.62]

17
0.015

(2)

0.026
(0.45)
[0.66]

X
17
0.016

(3)

0.352
(2.92)
[0.01]

19
0.174

(4)

0.290
(2.10)
[0.10]

X
19
0.208

(5)

0.120
(0.85)
[0.50]

22
0.030

(6)

1.088
(1.23)
[0.28]

X
22
0.151

(6)

1.211
(1.26)
[0.25]

X
22
0.164

(6)

0.220
(1.08)
[0.41]

X
22
0.203

ECB Working Paper Series No 2584 / August 202196Panel D: Standardized Cumulative Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

(2)

-0.021
(-0.58)
[0.58]

16
0.024

-0.021
(-0.55)
[0.59]

X
16
0.035

(3)

0.254
(3.13)
[0.00]

19
0.100

(4)

0.224
(2.66)
[0.02]

X
19
0.110

(5)

0.138
(0.96)
[0.41]

22
0.036

(6)

0.248
(1.21)
[0.31]

X
22
0.225

ECB Working Paper Series No 2584 / August 202197Table F.32: Eﬀects of QE on Inﬂation: By Country Results

This table repeats the analysis in Table 3 in the main paper, separately for each of the three
countries studied. Controls include the logarithm of three plus the average author experience and
the number of authors.

Panel A: Peak Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

2.659
(2.48)
[0.03]

14
0.231

(2)

2.581
(2.11)
[0.05]

X
14
0.280

(3)

1.363
(2.71)
[0.02]

18
0.311

(4)

1.097
(2.27)
[0.04]

X
18
0.395

(5)

0.731
(1.46)
[0.18]

21
0.081

Panel B: Cumulative Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

3.450
(1.54)
[0.13]

14
0.183

(2)

3.518
(1.52)
[0.17]

X
14
0.372

(3)

1.320
(2.24)
[0.05]

18
0.273

(4)

1.126
(2.01)
[0.08]

X
18
0.335

(5)

0.687
(1.31)
[0.22]

21
0.063

Panel C: Standardized Peak Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.180
(2.50)
[0.04]

14
0.298

(2)

0.172
(2.11)
[0.07]

X
14
0.314

(3)

0.450
(2.17)
[0.02]

18
0.252

(4)

0.383
(2.26)
[0.04]

X
18
0.290

(5)

0.064
(1.31)
[0.22]

21
0.068

(6)

0.453
(0.92)
[0.38]

X
21
0.253

(6)

0.370
(0.67)
[0.52]

X
21
0.135

(6)

0.036
(0.76)
[0.46]

X
21
0.244

ECB Working Paper Series No 2584 / August 202198Panel D: Standardized Cumulative Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.241
(1.46)
[0.16]

14
0.201

(2)

0.244
(1.40)
[0.21]

X
14
0.353

(3)

0.379
(1.74)
[0.04]

18
0.178

(4)

0.325
(1.99)
[0.02]

X
18
0.200

(5)

0.061
(1.21)
[0.25]

21
0.053

(6)

0.030
(0.56)
[0.58]

X
21
0.123

ECB Working Paper Series No 2584 / August 202199Table F.33: Signiﬁcance: By Country Results

This table repeats the analysis in Table 4 in the main paper, separately for each of the three
countries studied. Controls include the logarithm of three plus the average author experience and
the number of authors.

Panel A: Statistical Signiﬁcance for Output

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.734
(2.63)
[0.06]

11
0.413

(2)

0.712
(2.03)
[0.17]

X
11
0.444

(3)

0.266
(1.06)
[0.47]

13
0.137

(4)

0.247
(1.09)
[0.42]

X
13
0.295

(5)

0.239
(1.09)
[0.47]

17
0.146

Panel B: Economic Signiﬁcance for Output

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.290
(1.58)
[0.15]

19
0.131

(2)

0.367
(2.13)
[0.06]

X
19
0.330

(3)

0.272
(1.37)
[0.24]

22
0.086

(4)

0.382
(1.64)
[0.21]

X
22
0.171

(5)

0.461
(2.22)
[0.07]

25
0.216

(6)

0.240
(1.01)
[0.46]

X
17
0.173

(6)

0.442
(2.06)
[0.08]

X
25
0.283

Panel C: Statistical Signiﬁcance for Inﬂation

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.527
(1.40)
[0.24]

9
0.176

(2)

0.463
(1.05)
[0.38]

X
9
0.361

(3)

0.266
(1.06)
[0.46]

13
0.137

(4)

0.247
(1.09)
[0.42]

X
13
0.295

(5)

(6)

-0.027
(-0.24)
[0.79]

16
0.001

-0.081
(-0.47)
[0.64]

X
16
0.124

ECB Working Paper Series No 2584 / August 2021100Panel D: Economic Signiﬁcance for Inﬂation

CB Aﬃliation

Controls
Observations
R2

UK

US

EA

(1)

0.316
(1.34)
[0.25]

16
0.133

(2)

0.349
(1.50)
[0.19]

X
16
0.222

(3)

0.420
(1.86)
[0.11]

20
0.152

(4)

0.406
(1.57)
[0.16]

X
20
0.175

(5)

-0.057
(-0.32)
[0.76]

24
0.003

(6)

0.030
(0.16)
[0.89]

X
24
0.190

ECB Working Paper Series No 2584 / August 2021101Appendix G. Results by U.S. QE Program Studied

Motivated by the strong results we ﬁnd for the U.S. in Appendix F, in this section we

dig deeper into them by considering the three main QE programs in the U.S. separately.

ECB Working Paper Series No 2584 / August 2021102Table G.34: Eﬀects of QE on Output: By U.S. QE Program Results

This table repeats the analysis in Table 2 in the main paper, separately for each of the three U.S.
QE programs studied. Controls include the logarithm of three plus the average author experience
and the number of authors.

Panel A: Peak Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

1.475
(3.09)
[0.02]

12
0.432

(2)

1.431
(2.66)
[0.06]

X
12
0.465

(3)

1.472
(2.78)
[0.03]

12
0.427

(4)

1.490
(2.47)
[0.08]

X
12
0.481

(5)

2.092
(3.40)
[0.05]

4
0.852

Panel B: Cumulative Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

1.206
(1.94)
[0.07]

12
0.206

(2)

1.179
(2.71)
[0.05]

X
12
0.328

(3)

1.240
(2.25)
[0.02]

12
0.388

(4)

1.418
(2.65)
[0.10]

X
12
0.556

(5)

1.978
(1.53)
[0.03]

4
0.538

Panel C: Standardized Peak Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

0.408
(2.57)
[0.10]

12
0.188

(2)

0.403
(2.13)
[0.10]

X
12
0.252

(3)

0.167
(2.06)
[0.02]

12
0.203

(4)

0.147
(1.75)
[0.17]

X
12
0.249

(5)

0.178
(3.40)
[0.05]

4
0.852

(6)

2.170
(4.36)
[0.00]

X
4
0.952

(6)

2.179
(29.54)
[0.00]

X
4
0.999

(6)

0.185
(4.36)
[0.00]

X
4
0.952

ECB Working Paper Series No 2584 / August 2021103Panel D: Standardized Cumulative Eﬀect on Output

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

0.267
(2.48)
[0.04]

12
0.078

(2)

0.267
(2.04)
[0.04]

X
12
0.087

(3)

0.146
(3.07)
[0.01]

12
0.444

(4)

0.145
(2.79)
[0.10]

X
12
0.491

(5)

0.169
(1.53)
[0.04]

4
0.538

(6)

0.186
(29.54)
[0.00]

X
4
0.999

ECB Working Paper Series No 2584 / August 2021104Table G.35: Eﬀects of QE on Inﬂation: By U.S. QE Program Results

This table repeats the analysis in Table 3 in the main paper, separately for each of the three U.S.
QE programs studied. Controls include the logarithm of three plus the average author experience
and the number of authors.

Panel A: Peak Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

1.222
(1.94)
[0.10]

12
0.263

(2)

1.203
(1.99)
[0.07]

X
12
0.324

(3)

1.577
(2.65)
[0.05]

11
0.420

(4)

1.644
(4.26)
[0.11]

X
11
0.599

(5)

1.383
(1.66)
[0.04]

4
0.578

Panel B: Cumulative Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

1.340
(1.75)
[0.13]

12
0.230

(2)

1.285
(1.90)
[0.09]

X
12
0.341

(3)

1.518
(2.36)
[0.08]

11
0.424

(4)

1.776
(3.32)
[0.18]

X
11
0.658

(5)

1.798
(1.57)
[0.17]

4
0.551

Panel C: Standardized Peak Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

0.444
(1.80)
[0.10]

12
0.206

(2)

0.430
(1.85)
[0.06]

X
12
0.238

(3)

0.197
(1.85)
[0.04]

11
0.226

(4)

0.136
(1.31)
[0.38]

X
11
0.326

(5)

0.118
(1.66)
[0.04]

4
0.578

(6)

1.254
(12.58)
[0.00]

X
4
0.997

(6)

1.623
(5.14)
[0.00]

X
4
0.983

(6)

0.107
(12.58)
[0.00]

X
4
0.997

ECB Working Paper Series No 2584 / August 2021105Panel D: Standardized Cumulative Eﬀect on Inﬂation

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

0.400
(1.54)
[0.13]

12
0.152

(2)

0.376
(1.62)
[0.04]

X
12
0.208

(3)

0.156
(3.20)
[0.02]

11
0.505

(4)

0.144
(3.92)
[0.15]

X
11
0.657

(5)

0.153
(1.57)
[0.17]

4
0.551

(6)

0.138
(5.14)
[0.00]

X
4
0.983

ECB Working Paper Series No 2584 / August 2021106Table G.36: Signiﬁcance: By U.S. QE Program

This table repeats the analysis in Table 4 in the main paper, separately for each of the three U.S.
QE programs studied. Controls include the logarithm of three plus the average author experience
and the number of authors.

Panel A: Economic Signiﬁcance for Output

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

0.663
(2.43)
[0.12]

13
0.496

(2)

0.691
(2.88)
[0.04]

X
13
0.587

(3)

0.295
(1.01)
[0.34]

13
0.072

(4)

0.563
(1.74)
[0.22]

X
13
0.223

(5)

0.500
(1.08)
[0.48]

5
0.313

Panel B: Economic Signiﬁcance for Inﬂation

CB Aﬃliation

Controls
Observations
R2

QE 1

QE 2

QE 3

(1)

0.672
(2.38)
[0.13]

11
0.486

(2)

0.613
(3.86)
[0.02]

X
11
0.836

(3)

0.508
(1.45)
[0.19]

12
0.148

(4)

0.559
(1.16)
[0.45]

X
12
0.180

(5)

0.545
(0.97)
[0.40]

4
0.273

(6)

1.388
(1.55)
[0.52]

X
5
0.690

(6)

0.231
(.)
[0.00]

X
4
1.000

Note: Results on statistical signiﬁcance unreported due to zero variation in the dependent variable in several speciﬁcations.

ECB Working Paper Series No 2584 / August 2021107Appendix H. Additional Analyses for Tone Results

This section presents additional analyses to accompany our main results on diﬀerences
in the tone of the papers’ abstracts. Table H.37 presents several alternative speciﬁcations.
We show that our tone results are robust to classifying BIS authors as full central bankers.
They are also robust to classifying researchers at the International Monetary Fund and the
World Bank as 0.5 central bankers. Moreover, our tone results hold also when we replace
the share of central bank authors by an indicator we call Discrete, which is equal to one if
at least one of the authors is aﬃliated with a central bank or the BIS, and zero otherwise.
Table H.37 also reports results from the analysis that computes the abstract’s sentiment
score based on two alternative dictionaries of positive and negative words: the Harvard IV4
semantic dictionary and the Loughran and McDonald (2011) ﬁnancial dictionary.

In Table H.38 we show that the point estimates of β in equation (2) in the main paper
remain similar when we add controls for the magnitudes of the reported eﬀects on output
and inﬂation. In Table H.39, we also conduct a textual analysis of the papers’ conclusions.

ECB Working Paper Series No 2584 / August 2021108Table H.37: Alternative Speciﬁcations for Tone of the Abstract

This table presents robustness tests for our analysis of the tone in the paper’s abstract. All regres-
sions use the same speciﬁcation as in Table 5, Panel A, column (3) in the main paper. For brevity,
we only report coeﬃcients of interest and suppress control variables. Treat BIS as central bank
treats researchers who are aﬃliated with the Bank of International Settlements as aﬃliated with a
central bank. Treat BIS/IMF/WB as 0.5 central banks treats researchers who are aﬃliated with
the Bank of International Settlements, the International Monetary Fund, or the World Bank as
0.5 central bankers. Discrete measure uses an indicator equal to one if at least one of the authors
is associated with a central bank, 0.5 if at least one of the authors is associated with the BIS,
and zero otherwise. Harvard dictionary refers to the Harvard IV4 dictionary. LM dictionary is
the Loughran and McDonald (2011) dictionary. t-statistics are based on robust standard errors.
p-values are obtained using the wild cluster bootstrap procedure (10,000 repetitions).

Baseline
Treat BIS as central bank
Treat BIS/IMF/WB as 0.5 central banks
Discrete measure
Harvard IV4 dictionary
LM dictionary

β

0.056
0.039
0.051
0.038
-0.001
0.003

t-stat

2.60
1.64
2.15
1.69
-0.11
0.53

p-value

0.009
0.100
0.031
0.091
0.912
0.594

ECB Working Paper Series No 2584 / August 2021109Table H.38: Tone of the Abstract: Additional Controls

This table repeats Table 5 in the main paper, after adding controls for the estimated eﬀect on
output and inﬂation, respectively. The other controls include the logarithm of the average author
experience and the number of authors.

Panel A: Sentiment Score

CB Aﬃliation

Country Dummies
Controls
Output Eﬀects
Price Eﬀects
Output Eﬀects (Standardized)
Price Eﬀects (Standardized)
Observations
R2

(1)

0.061
(2.44)
[0.023]

X
X
X

46
0.215

Panel B: Percentage of Positive Adjectives

(1)

0.038
(1.60)
[0.122]

X
X
X

CB Aﬃliation

Country Dummies
Controls
Output Eﬀects
Price Eﬀects
Output Eﬀects (Standardized)
Price Eﬀects (Standardized)
Observations
R2

(2)

0.052
(1.72)
[0.097]

X
X

X

42
0.118

(2)

0.037
(1.37)
[0.189]

X
X

X

(3)

0.056
(2.15)
[0.033]

X
X

X

46
0.175

(3)

0.035
(1.51)
[0.139]

X
X

X

(4)

0.047
(1.54)
[0.130]

X
X

X
42
0.120

(4)

0.033
(1.27)
[0.199]

X
X

X
42
0.109

46
0.172

42
0.112

46
0.156

ECB Working Paper Series No 2584 / August 2021110Panel C: Percentage of Negative Adjectives

CB Aﬃliation

Country Dummies
Controls
Output Eﬀects
Price Eﬀects
Output Eﬀects (Standardized)
Price Eﬀects (Standardized)
Observations
R2

(1)

-0.023
(-1.87)
[0.086]

X
X
X

(2)

-0.015
(-1.01)
[0.337]

X
X

X

(3)

-0.021
(-1.56)
[0.151]

X
X

X

46
0.149

42
0.080

46
0.111

(4)

-0.014
(-0.93)
[0.384]

X
X

X
42
0.088

ECB Working Paper Series No 2584 / August 2021111Table H.39: Tone of the Conclusion

This table repeats Table 5 in the main paper, but uses the tone of the conclusion rather than
the tone of the abstract as the dependent variable. Controls include the logarithm of the average
author experience and the number of authors.

Panel A: Sentiment Score

CB Aﬃliation

Country Dummies
Controls
Observations
R2

Panel B: Percentage of Positive Adjectives

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

0.020
(1.28)
[0.199]

53
0.025

(1)

0.009
(0.68)
[0.499]

53
0.008

Panel C: Percentage of Negative Adjectives

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

-0.011
(-1.52)
[0.134]

53
0.025

(2)

0.024
(1.38)
[0.184]

X

53
0.060

(2)

0.014
(0.90)
[0.375]

X

53
0.050

(2)

-0.010
(-1.36)
[0.176]

X

53
0.053

(3)

0.024
(1.37)
[0.170]

X
X
53
0.060

(3)

0.012
(0.82)
[0.427]

X
X
53
0.056

(3)

-0.012
(-1.59)
[0.094]

X
X
53
0.061

ECB Working Paper Series No 2584 / August 2021112Appendix I. Methodological Choices

This section presents our results on diﬀerences in methodological choices between central
bankers and academics. In Panel A of Table I.40, we repeat the regression from equation
(2) in the main paper, after replacing the dependent variable by an indicator equal to one if
the study uses a DSGE model, and zero otherwise.

In Panel B of Table I.40, columns (1) to (3), the dependent variable is an indicator equal
to one if the paper does not disclose the conﬁdence level, and zero if it does. In columns (4)
to (6), we repeat the same regression, but now the dependent variable is an indicator equal
to one if the study uses a 95% conﬁdence interval, and zero otherwise. Throughout Panel B,
we restrict the sample to papers that assess the statistical signiﬁcance of the eﬀect on either
output or inﬂation. In other words, we exclude all papers for which statistical signiﬁcance
for both output and inﬂation eﬀects is indicated as “N/A” in Table A.4. In Table I.41 we
repeat the analysis after controlling for a dummy variable indicating the Bayesian/frequentist
approach.

ECB Working Paper Series No 2584 / August 2021113Table I.40: Methodological Choices

This table regresses methodological choices on the share of central bank aﬃliated authors. In Panel
A, we regress model choice on the share of central bank aﬃliated authors. The dependent variable
is an indicator equal to one if the paper uses a DSGE model, and zero if it uses a VAR model. In
Panel B, columns (1) to (3), the dependent variable is an indicator equal to one if the paper does
not specify the width of the conﬁdence interval, and zero otherwise. In columns (4) to (6), the
dependent variable is an indicator equal to one if the paper uses a 95% conﬁdence interval, and
zero otherwise. Throughout Panel B, we restrict the sample to papers that assess the statistical
signiﬁcance of the eﬀect on either output or inﬂation. Controls include the number of authors and
the logarithm of three plus the average author experience. t-statistics, reported in parentheses,
are based on robust standard errors. p-values obtained using the wild cluster bootstrap procedure
(10,000 repetitions) are reported in square brackets. The unit of observation is the paper.

Panel A: Choice of the Model (DSGE vs. VAR)

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

0.363
(2.27)
[0.032]

50
0.087

(2)

0.296
(1.71)
[0.103]

X

50
0.155

(3)

0.319
(1.80)
[0.090]

X
X
50
0.170

Panel B: Speciﬁcation of the Conﬁdence Interval

CB Aﬃliation

Country Dummies
Controls
Observations
R2

Unreported CI

95% CI

(1)

(2)

(3)

(4)

(5)

(6)

0.136
(1.02)
[0.350]

31
0.028

0.062
(0.44)
[0.667]

X

31
0.137

0.158
(0.85)
[0.423]

X
X
31
0.230

-0.295
(-1.99)
[0.091]

31
0.167

-0.200
(-2.23)
[0.027]

X

31
0.629

-0.202
(-1.99)
[0.074]

X
X
31
0.651

ECB Working Paper Series No 2584 / August 2021114Table I.41: Choice of Conﬁdence Interval: Control for Bayesian vs. Frequentist

This table repeats Table I.40, Panel A, after adding a control for the type of statistical inference
used (Bayesian vs. frequentist). In columns (1) to (3), the dependent variable is an indicator equal
to one if the paper does not specify the width of the conﬁdence interval. In columns (4) to (6),
the dependent variable is an indicator equal to one if the paper uses a 95% conﬁdence interval,
and zero otherwise. We restrict the sample to papers that assess the statistical signiﬁcance of the
eﬀect on either output or inﬂation. Controls include the logarithm of three plus the average author
experience and the number of authors.

CB Aﬃliation

Country Dummies
Controls
Observations
R2

Unreported CI

95% CI

(1)

(2)

(3)

(4)

(5)

(6)

0.112
(0.88)
[0.574]

30
0.077

0.056
(0.44)
[0.719]

X

30
0.212

0.116
(0.65)
[0.738]

X
X
30
0.239

-0.220
(-2.07)
[0.045]

30
0.304

-0.151
(-2.37)
[0.017]

X

30
0.697

-0.147
(-1.78)
[0.174]

X
X
30
0.748

ECB Working Paper Series No 2584 / August 2021115Appendix J. Additional Analyses for Career Outcomes

This section provides additional graphs and analyses to accompany our main analysis of
career outcomes. Appendix J.1 reports the relationship between career outcomes and other
research outcomes, including the eﬀect on inﬂation and economic signiﬁcance. In Appendix
J.2, we repeat our analysis of the relationship between the estimated eﬀect on output and
subsequent career outcomes after controlling for an indicator equal to one if the paper came
out in a peer-reviewed journal and zero otherwise.
In Appendix J.3, we report results
obtained when we include authors with no career updates, and in Appendix J.4 we treat
departures to academia and the private sector as demotions.

ECB Working Paper Series No 2584 / August 2021116Appendix J.1. Other Research Outcomes

Table J.42: Career Outcomes and Eﬀects of QE on Inﬂation

The table repeats Table 6 in the main paper, after replacing the eﬀects on output with eﬀects on
inﬂation. Controls include the logarithm of the researcher’s experience, the number of authors, the
number of years since the author’s last career update, as well as dummy variables indicating the
author’s rank at the time of the paper’s ﬁrst circulation.

Panel A: Total Program Eﬀect

Eﬀect on inﬂation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Eﬀect on inﬂation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

-0.167
(-0.68)
[0.685]

31
0.023

-0.184
(-0.73)
[0.693]

X

31
0.079

-0.140
(-1.10)
[0.242]

X
X
29
0.482

-0.133
(-0.56)
[0.747]

31
0.016

-0.163
(-0.68)
[0.731]

X

31
0.075

-0.117
(-0.92)
[0.326]

X
X
29
0.478

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.343
(0.60)
[0.732]

31
0.010

0.046
(0.07)
[0.952]

X

31
0.051

0.601
(0.66)
[0.613]

X
X
29
0.483

0.348
(0.61)
[0.720]

31
0.010

0.048
(0.07)
[0.942]

X

31
0.051

0.622
(0.68)
[0.601]

X
X
29
0.484

ECB Working Paper Series No 2584 / August 2021117Table J.43: Career Outcomes and Economic Signiﬁcance

The table repeats Table 6 in the main paper, after replacing the eﬀect on output with the economic
signiﬁcance of the eﬀect on output and inﬂation. Controls include the logarithm of the researcher’s
experience, the number of authors, the number of years since the author’s last career update, as
well as dummy variables indicating the author’s rank at the time of the paper’s ﬁrst circulation.

Economic Signiﬁcance

Country FE
Controls
Observations
R2

Eﬀect on Output

Eﬀect on Inﬂation

(1)

(2)

(3)

(4)

(5)

(6)

-0.327
(-0.72)
[0.508]

34
0.003

-0.110
(-0.28)
[0.761]

X

34
0.047

1.180
(1.10)
[0.269]

X
X
31
0.488

-0.364
(-0.88)
[0.388]

30
0.007

-0.222
(-0.57)
[0.550]

X

30
0.051

0.046
(0.07)
[0.948]

X
X
28
0.468

ECB Working Paper Series No 2584 / August 2021118Appendix J.2. Control for Publication

Table J.44: Career Outcomes and Eﬀects of QE on Output: Control for Publication

This table repeats Table 6 in the main paper, after adding an indicator equal to one if the paper
is published in a peer-reviewed journal to the set of control variables. The other controls include
the logarithm of the researcher’s experience, the number of authors, the number of years since the
author’s last career update, as well as dummy variables indicating the author’s rank at the time of
the paper’s ﬁrst circulation.

Panel A: Total Program Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.264
(2.32)
[0.028]

34
0.030

0.219
(1.85)
[0.035]

X

34
0.066

0.540
(3.13)
[0.008]

X
X
31
0.592

0.204
(1.78)
[0.080]

32
0.027

0.204
(1.25)
[0.229]

X

32
0.076

0.440
(2.13)
[0.015]

X
X
30
0.574

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.407
(1.41)
[0.226]

34
0.044

1.009
(1.15)
[0.359]

X

34
0.062

2.610
(1.98)
[0.064]

X
X
31
0.569

2.311
(2.00)
[0.038]

32
0.051

1.838
(1.45)
[0.137]

X

32
0.081

3.940
(2.22)
[0.012]

X
X
30
0.592

ECB Working Paper Series No 2584 / August 2021119Appendix J.3. Including No Career Updates

Table J.45: Career Outcomes and Eﬀects of QE on Output: Including No Career Updates

The table repeats Table 6 in the main paper, after replacing the dependent variable – change in
the author’s rank – with zero if the author does not experience any career update within ﬁve years
after the paper’s ﬁrst distribution. Controls include the logarithm of the researcher’s experience,
the number of authors, the number of years since the author’s last career update, as well as dummy
variables indicating the author’s rank at the time of the paper’s ﬁrst circulation.

Panel A: Total Program Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.020
(0.25)
[0.823]

64
0.000

0.009
(0.14)
[0.893]

X

64
0.019

0.087
(0.93)
[0.353]

X
X
57
0.234

0.036
(0.54)
[0.607]

62
0.002

0.024
(0.32)
[0.758]

X

62
0.022

0.062
(0.65)
[0.527]

X
X
56
0.223

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.228
(0.58)
[0.592]

64
0.004

0.043
(0.15)
[0.885]

X

64
0.019

0.256
(0.64)
[0.562]

X
X
57
0.229

0.072
(0.27)
[0.798]

62
0.000

-0.049
(-0.19)
[0.853]

X

62
0.022

0.085
(0.22)
[0.831]

X
X
56
0.217

ECB Working Paper Series No 2584 / August 2021120Appendix J.4. Including Exits

Table J.46: Career Outcomes and Eﬀects of QE on Output: Including Exits

The table repeats Table 6 in the main paper, after replacing the dependent variable with the
following variable. The dependent variable is equal to (minus) one if the author’s rank after her
ﬁrst career update following the paper’s ﬁrst circulation is higher (lower) than her rank at the time
of circulation. It is equal to zero if the author remains in the same position, and equal to minus
one if the author leaves to academia or to the private sector. Controls include the logarithm of the
researcher’s experience, the number of authors, the number of years since the author’s last career
update, as well as dummy variables indicating the author’s rank at the time of the paper’s ﬁrst
circulation. We restrict the sample to central bankers who experience at least one career update
after the paper’s ﬁrst public distribution.

Panel A: Total Program Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.195
(2.37)
[0.050]

40
0.085

0.150
(1.94)
[0.083]

X

40
0.153

0.215
(1.79)
[0.114]

X
X
34
0.442

0.180
(2.95)
[0.030]

38
0.105

0.160
(2.32)
[0.050]

X

38
0.180

0.291
(2.90)
[0.012]

X
X
33
0.551

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.277
(0.74)
[0.493]

40
0.014

0.075
(0.21)
[0.848]

X

40
0.107

0.595
(1.09)
[0.285]

X
X
34
0.377

0.324
(0.63)
[0.628]

38
0.016

0.188
(0.40)
[0.789]

X

38
0.105

2.417
(2.75)
[0.025]

X
X
33
0.558

ECB Working Paper Series No 2584 / August 2021121Appendix K. Interaction with Author Seniority

This section presents our results on author seniority.

In Table K.47, we repeat the
analysis from Table 6 in the main paper, after interacting Eﬀectij with the author’s career
rank (Seniority). To simplify the interpretation of the results, we standardize Seniority to
have zero mean and a standard deviation of one. In Table K.48, we repeat the analysis from
Tables 2 through 4 in the main paper, except that we interact CB Aﬃliation with the rank
of the most senior author on the team, Max Seniority. We standardize Max Seniority to
zero mean and unit standard deviation. The results are weaker if we use the average author
rank instead of the rank of the most senior author, suggesting that the seniority of the most
senior author matters more. See Table K.49.

ECB Working Paper Series No 2584 / August 2021122Table K.47: Career Outcomes, the Eﬀects of QE, and Author Seniority

This table repeats the analysis in Table 6, after adding an interaction between the estimated eﬀects
on output and inﬂation and author seniority, which is standardized to have a mean of zero and
t-statistics, reported in parentheses, are based on standard errors
a standard deviation of one.
clustered at the author level. p-values obtained using the wild cluster bootstrap procedure (10,000
repetitions) are reported in square brackets. The unit of observation is the author-paper-country.

Panel A: Eﬀect on Output

Eﬀect on output

Eﬀect on output × Seniority

Seniority

Country FE + Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

Eﬀect on inﬂation

Eﬀect on inﬂation × Seniority

Seniority

Country FE + Controls
Observations
R2

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

0.802
(2.78)
[0.050]
0.364
(2.02)
[0.065]
-1.831
(-2.78)
[0.064]

X
31
0.594

Cumulative
(2)

0.808
(3.22)
[0.006]
0.375
(2.75)
[0.007]
-1.559
(-2.68)
[0.055]

X
30
0.612

Peak
(3)

3.686
(6.42)
[0.003]
2.079
(4.43)
[0.047]
-2.365
(-5.12)
[0.054]

X
31
0.648

Cumulative
(4)

7.380
(4.48)
[0.001]
4.108
(3.15)
[0.005]
-1.999
(-4.79)
[0.009]

X
30
0.676

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

-0.414
(-1.01)
[0.459]
-0.360
(-0.84)
[0.564]
-0.298
(-0.20)
[0.886]

X
29
0.515

Cumulative
(2)

-0.235
(-0.72)
[0.576]
-0.158
(-0.46)
[0.756]
-0.909
(-0.69)
[0.609]

X
29
0.486

Peak
(3)

1.887
(4.84)
[0.113]
1.720
(6.89)
[0.095]
-3.000
(-6.40)
[0.050]

X
29
0.633

Cumulative
(4)

1.912
(5.29)
[0.071]
1.720
(7.53)
[0.055]
-2.997
(-6.72)
[0.046]

X
29
0.639

ECB Working Paper Series No 2584 / August 2021123Table K.48: Author Seniority and the Eﬀects of QE

This table repeats the analysis in Tables 2 to 4 in the main paper, after adding an interaction
between central bank aﬃliation and the rank of the most senior author on the team, which is
t-statistics, reported in
standardized to have a mean of zero and a standard deviation of one.
parentheses, are based on standard errors clustered at the paper level. p-values obtained using the
wild cluster bootstrap procedure (10,000 repetitions) are reported in square brackets. The unit of
observation is the paper.

Panel A: Eﬀect on Output

CB Aﬃliation

CB Aﬃliation × Max Seniority

Max Seniority

Country FE + Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

CB Aﬃliation × Max Seniority

Max Seniority

Country FE + Controls
Observations
R2

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

0.621
(1.66)
[0.121]
0.961
(2.08)
[0.073]
-0.546
(-1.82)
[0.144]

X
56
0.168

Cumulative
(2)

0.492
(1.32)
[0.198]
0.864
(1.57)
[0.190]
-0.584
(-1.71)
[0.256]

X
55
0.140

Peak
(3)

0.117
(1.75)
[0.113]
0.175
(1.79)
[0.092]
-0.073
(-1.20)
[0.226]

X
56
0.264

Cumulative
(4)

0.110
(1.79)
[0.071]
0.145
(1.44)
[0.163]
-0.093
(-1.56)
[0.173]

X
55
0.137

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

1.478
(2.95)
[0.004]
0.811
(1.79)
[0.090]
-0.779
(-2.45)
[0.037]

X
51
0.332

Cumulative
(2)

1.703
(2.04)
[0.049]
0.286
(0.52)
[0.569]
-0.733
(-1.97)
[0.053]

X
51
0.246

Peak
(3)

0.205
(2.80)
[0.006]
0.166
(2.12)
[0.058]
-0.076
(-1.80)
[0.123]

X
51
0.362

Cumulative
(4)

0.206
(2.26)
[0.014]
0.099
(1.17)
[0.278]
-0.069
(-1.69)
[0.093]

X
51
0.242

ECB Working Paper Series No 2584 / August 2021124Panel C: Signiﬁcance

Eﬀect on Output

Eﬀect on Inﬂation

Statistical
(1)

Economic
(2)

Statistical
(3)

Economic
(4)

CB Aﬃliation

CB Aﬃliation × Max Seniority

Max Seniority

Country FE + Controls
Observations
R2

0.381
(2.05)
[0.086]
0.346
(2.53)
[0.050]
-0.232
(-2.33)
[0.136]

X
36
0.478

0.424
(3.57)
[0.008]
0.282
(2.52)
[0.030]
-0.183
(-2.05)
[0.117]

X
61
0.333

0.210
(1.39)
[0.179]
0.315
(2.12)
[0.080]
-0.197
(-1.93)
[0.224]

X
33
0.295

0.235
(1.69)
[0.125]
0.220
(1.75)
[0.159]
-0.134
(-1.34)
[0.320]

X
55
0.203

ECB Working Paper Series No 2584 / August 2021125Table K.49: Mean Author Seniority and the Eﬀects of QE

This table repeats Table K.48, after replacing the rank of the most senior author with the average
author rank (Mean Seniority). For ease of interpretation, we standardize Mean Seniority to have
a mean of zero and a standard deviation of one.

Panel A: Eﬀect on Output

CB Aﬃliation

CB Aﬃliation × Mean Seniority

Mean Seniority

Country FE + Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

CB Aﬃliation × Mean Seniority

Mean Seniority

Country FE + Controls
Observations
R2

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

0.664
(1.61)
[0.128]
0.220
(0.50)
[0.637]
0.036
(0.10)
[0.930]

X
56
0.117

Cumulative
(2)

0.522
(1.31)
[0.189]
0.318
(0.71)
[0.514]
-0.392
(-1.05)
[0.381]

X
55
0.109

Peak
(3)

0.135
(1.84)
[0.096]
0.049
(0.59)
[0.560]
-0.011
(-0.17)
[0.877]

X
56
0.215

Cumulative
(4)

0.116
(1.81)
[0.070]
0.064
(0.97)
[0.303]
-0.073
(-1.22)
[0.232]

X
55
0.120

Total Program Eﬀect

Standardized Eﬀect

Peak
(1)

1.350
(2.60)
[0.014]
0.462
(1.01)
[0.344]
-0.307
(-1.02)
[0.370]

X
51
0.301

Cumulative
(2)

1.573
(1.94)
[0.065]
0.013
(0.02)
[0.985]
-0.528
(-1.02)
[0.345]

X
51
0.243

Peak
(3)

0.200
(2.58)
[0.007]
0.074
(1.39)
[0.187]
-0.020
(-0.53)
[0.581]

X
51
0.316

Cumulative
(4)

0.201
(2.27)
[0.012]
0.009
(0.14)
[0.880]
-0.046
(-0.99)
[0.294]

X
51
0.242

ECB Working Paper Series No 2584 / August 2021126Panel C: Signiﬁcance

Eﬀect on Output

Eﬀect on Inﬂation

Statistical
(1)

Economic
(2)

Statistical
(3)

Economic
(4)

CB Aﬃliation

CB Aﬃliation × Mean Seniority

Mean Seniority

Country FE + Controls
Observations
R2

0.388
(1.97)
[0.077]
0.161
(0.75)
[0.517]
-0.061
(-0.39)
[0.749]

X
36
0.378

0.431
(3.60)
[0.004]
0.137
(1.06)
[0.344]
-0.102
(-1.03)
[0.406]

X
61
0.285

0.259
(1.63)
[0.107]
0.267
(1.52)
[0.137]
-0.207
(-2.04)
[0.131]

X
33
0.279

0.236
(1.71)
[0.118]
0.101
(0.83)
[0.433]
-0.105
(-1.20)
[0.319]

X
55
0.182

ECB Working Paper Series No 2584 / August 2021127Appendix L. Research Quality

This section explores potential diﬀerences in research quality between papers written
by central bankers and academics. Figure L.7 reports the relationship between the share
of central-bank-aﬃliated authors and three measures of paper quality: publication status,
journal impact factor, and the article inﬂuence score. In Table L.50 we show that ﬁnding
larger eﬀects of QE on output increases the odds of publication in a peer-reviewed journal.
Tables C.19 to C.21 document that the diﬀerence in research ﬁndings between central
bankers and academics remains largely unchanged once we condition on papers published
in peer-reviewed journals. We also ﬁnd similar results if we use weighted least squares
regressions, where weights are proportional to the paper’s abnormal citations on Google
Scholar as of September 2019 (see Tables L.51 to L.53).

ECB Working Paper Series No 2584 / August 2021128Figure L.7: Central Bank Aﬃliation and Paper Quality. The ﬁgure plots the coeﬃcient on the share
of central-bank-aﬃliated authors from regressions of four measures of paper quality on the share of central-
bank-aﬃliated authors. Each row is a separate regression, and the dependent variable is standardized to have
mean zero and a standard deviation of one. We also plot the corresponding 95% conﬁdence intervals obtained
using the wild bootstrap procedure (10,000 repetitions) and robust standard errors. The unit of observation
is at the paper level. All regressions include year ﬁxed eﬀects, where the year refers to the year of the paper’s
ﬁrst public distribution. Published is an indicator equal to one if the paper is published in a peer-reviewed
academic journal, and zero otherwise. The impact factor and article inﬂuence score are obtained from the
Clarivate Analytics Web of Science dataset, using the year of the paper’s journal publication. Weak paper
is an indicator equal to one if the paper was publicly circulated before 2014 but remains unpublished.

PublishedLog impact factorLog article influenceWeak paper-2-1012Effect of CB AffiliationECB Working Paper Series No 2584 / August 2021129Table L.50: Research Findings and Publication

The table regresses an indicator equal to one if the paper is published in a peer-reviewed journal,
and zero otherwise, on the eﬀect on output (Panels A and B); the eﬀect on inﬂation (Panels C and
D), and the signiﬁcance of the eﬀect on output (Panel E) and inﬂation (Panel F). Controls include
the logarithm of three plus the average author experience and the number of authors.

Panel A: Total Program Eﬀect on Output

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.064
(1.37)
[0.197]

58
0.025

0.068
(1.39)
[0.176]

X

58
0.037

0.081
(1.77)
[0.113]

X
X
58
0.117

0.071
(1.98)
[0.089]

57
0.032

0.073
(1.88)
[0.089]

X

57
0.036

0.083
(1.99)
[0.088]

X
X
57
0.113

Panel B: Standardized Eﬀect on Output

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.435
(3.02)
[0.031]

58
0.059

0.439
(2.72)
[0.037]

X

58
0.063

0.580
(3.85)
[0.015]

X
X
58
0.169

0.391
(3.34)
[0.044]

57
0.044

0.382
(3.10)
[0.043]

X

57
0.045

0.489
(3.67)
[0.039]

X
X
57
0.137

Panel C: Total Program Eﬀect on Inﬂation

Eﬀect on inﬂation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

-0.036
(-0.65)
[0.529]

53
0.012

-0.027
(-0.45)
[0.662]

X

53
0.024

-0.016
(-0.26)
[0.800]

X
X
53
0.097

0.008
(0.23)
[0.843]

53
0.001

0.009
(0.26)
[0.828]

X

53
0.019

0.014
(0.37)
[0.732]

X
X
53
0.098

ECB Working Paper Series No 2584 / August 2021130Panel D: Standardized Eﬀect on Inﬂation

Eﬀect on inﬂation

Country FE
Controls
Observations
R2

Panel E: Signiﬁcance for Output

Signiﬁcance of output

Country FE
Controls
Observations
R2

Panel F: Signiﬁcance for Inﬂation

Signiﬁcance of inﬂation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.112
(0.45)
[0.688]

53
0.003

0.042
(0.15)
[0.885]

X

53
0.018

0.179
(0.65)
[0.570]

X
X
53
0.101

0.187
(1.01)
[0.433]

53
0.009

0.129
(0.66)
[0.561]

X

53
0.022

0.208
(0.90)
[0.458]

X
X
53
0.105

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

-0.044
(-0.14)
[0.908]

41
0.001

-0.059
(-0.18)
[0.892]

X

41
0.004

-0.176
(-0.58)
[0.637]

X
X
41
0.106

0.087
(0.45)
[0.671]

66
0.004

0.084
(0.42)
[0.687]

X

66
0.006

0.048
(0.25)
[0.813]

X
X
66
0.050

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.396
(2.46)
[0.090]

38
0.083

0.401
(2.40)
[0.090]

X

38
0.084

0.243
(1.44)
[0.222]

X
X
38
0.197

0.112
(0.61)
[0.553]

60
0.007

0.112
(0.61)
[0.561]

X

60
0.010

0.157
(0.92)
[0.382]

X
X
60
0.081

ECB Working Paper Series No 2584 / August 2021131Table L.51: Eﬀects of QE on Output: Weighted Least Squares Regression using Google Scholar
Citations

This table repeats Table 2 in the main paper using a weighted least squares regression, where
the weights are proportional to the paper’s abnormal number of citations on Google Scholar as
of September 2019. The abnormal number of citations is computed as the logarithm of one plus
the number of citations for the paper, divided by the logarithm of one plus the average number of
citations across all papers released in the same calendar year. Controls include the logarithm of
three plus the average author experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.745
(2.24)
[0.032]

57
0.064

(1)

0.162
(2.52)
[0.018]

57
0.057

0.760
(2.20)
[0.035]

X

57
0.091

Peak Eﬀect
(2)

0.154
(2.44)
[0.022]

X

57
0.209

0.686
(1.81)
[0.083]

X
X
57
0.108

(3)

0.139
(2.04)
[0.057]

X
X
57
0.246

0.489
(1.39)
[0.173]

56
0.029

0.465
(1.37)
[0.165]

X

56
0.081

0.451
(1.20)
[0.241]

X
X
56
0.085

Cumulative Eﬀect
(5)

(6)

(4)

0.122
(2.21)
[0.022]

56
0.038

0.112
(2.08)
[0.029]

X

56
0.076

0.107
(1.75)
[0.082]

X
X
56
0.097

ECB Working Paper Series No 2584 / August 2021132Table L.52: Eﬀects of QE on Inﬂation: Weighted Least Squares Regression using Google Scholar
Citations

This table repeats Table 3 in the main paper using a weighted least squares regression, where
the weights are proportional to the paper’s abnormal number of citations on Google Scholar as
of September 2019. The abnormal number of citations is computed as the logarithm of one plus
the number of citations for the paper, divided by the logarithm of one plus the average number of
citations across all papers released in the same calendar year. Controls include the logarithm of
three plus the average author experience and the number of authors.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.577
(3.39)
[0.003]

51
0.171

1.560
(3.30)
[0.003]

X

51
0.249

1.288
(2.83)
[0.011]

X
X
51
0.322

1.500
(2.50)
[0.013]

51
0.133

1.493
(2.46)
[0.016]

X

51
0.136

1.250
(2.20)
[0.024]

X
X
51
0.188

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.237
(2.65)
[0.007]

51
0.123

0.246
(2.73)
[0.006]

X

51
0.279

0.207
(2.75)
[0.005]

X
X
51
0.329

0.209
(2.22)
[0.010]

51
0.096

0.216
(2.23)
[0.010]

X

51
0.183

0.184
(2.37)
[0.004]

X
X
51
0.216

ECB Working Paper Series No 2584 / August 2021133Table L.53: Signiﬁcance of the Eﬀects of QE: Weighted Least Squares Regression using Google
Scholar Citations

This table repeats Table 4 in the main paper using a weighted least squares regression, where
the weights are proportional to the paper’s abnormal number of citations on Google Scholar as
of September 2019. The abnormal number of citations is computed as the logarithm of one plus
the number of citations for the paper, divided by the logarithm of one plus the average number of
citations across all papers released in the same calendar year. Controls include the logarithm of
three plus the average author experience and the number of authors.

Panel A: Eﬀect on Output

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.390
(2.37)
[0.044]

40
0.224

0.380
(2.43)
[0.034]

X

40
0.277

0.362
(2.27)
[0.035]

X
X
40
0.306

0.313
(2.76)
[0.017]

65
0.118

0.318
(2.77)
[0.014]

X

65
0.129

0.366
(3.23)
[0.005]

X
X
65
0.191

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.105
(0.89)
[0.418]

36
0.011

0.132
(1.03)
[0.315]

X

36
0.128

0.087
(0.82)
[0.401]

X
X
36
0.195

0.141
(1.10)
[0.295]

58
0.023

0.144
(1.12)
[0.290]

X

58
0.027

0.177
(1.68)
[0.108]

X
X
58
0.178

ECB Working Paper Series No 2584 / August 2021134Appendix M. External Feedback

After we made our dataset publicly available in October 2020, several readers sent us a
small number of helpful comments on individual data points. It is not clear how we should
treat such feedback given our objective to maximize the accuracy of our results. On the
one hand, changing data in response to external feedback may reduce potential data errors.
On the other hand, changing data may introduce a selection bias because the nature of the
comments we receive is unlikely to be random. For example, all of the feedback we have
received by email as of this writing, in early 2021, came from current or former central
bankers, or from policy economists.

In an eﬀort to strike a balance between maximizing transparency and avoiding the se-
lection bias, we have decided to report the external feedback in this section, and also show
how our main results change if we incorporate this feedback in our dataset. Below, we ﬁrst
review the external comments and then describe how they aﬀect our results. As a preview,
we ﬁnd that our conclusions remain unchanged.

Appendix M.1. Bhattarai et al. (2015)

As we discuss in footnote i of this Appendix, we treat the paper by Bhattarai et al. (2015)
as having 50% central bankers despite all authors being academics, because the paper came
out as a Dallas Fed working paper. A former central banker noted that there may be other
similar cases in our sample that are not treated in the same way as Bhattarai et al. (2015),
such as Peersman (2011). Gert Peersman is an academic who held visiting positions at
multiple central banks, and his 2011 paper came out as an ECB working paper. Although
we believe our treatment of Bhattarai et al. (2015) is justiﬁed, in this section we treat that
paper as having 100% academic authors; i.e., we change the value of CB Aﬃliation for this
paper from 0.5 to 0.

Appendix M.2. Hohberger et al. (2019)

Another former central banker found an earlier version of the working paper by Hohberger
et al. (2019), in which one of the authors, Romanos Priftis, had a non-central bank aﬃliation.
In other words, this author’s primary aﬃliation was a central bank at the time he co-authored
the version we found during our search (and he is still aﬃliated with that central bank, Bank
of Canada, as of early 2021 when we write these words), but there also exists an earlier version
of the paper in which his primary aﬃliation is listed as a diﬀerent policy institution (the
European Commission). We did not use the earlier working paper version of the paper in
our main dataset because it did not appear in our original search results, unlike the working
paper version that we have used. If we use the earlier version, then the value of CB Aﬃliation
for this paper changes from 0.33 to 0. We make that change in this section.

ECB Working Paper Series No 2584 / August 2021135Appendix M.3. Haldane et al. (2016)

The same former central banker also had a diﬀerent interpretation of the output eﬀect
and statistical signiﬁcance of output in the euro area for the paper by Haldane et al. (2016).
We believe that his interpretation is reasonable, but so is ours, as we explain below.

To code the estimated eﬀects for the Haldane et al. (2016) paper, we follow a simple
procedure based on a visual examination of Table 4 in their paper. That table reports the
output eﬀects across our three countries of interest (US, UK, and EA) and four diﬀerent
identiﬁcation schemes. What makes interpreting the numbers from Table 4 diﬃcult is that,
rather than reporting all values and marking those that are signiﬁcant as such, Haldane et al.
(2016) chose to report only statistically signiﬁcant eﬀects and leave all other cells empty. In
our main dataset, we address this challenge as follows. For countries for which the table
reports the average eﬀect on output across all four identiﬁcation schemes (US and UK), we
use that average eﬀect, along with its statistical signiﬁcance. For the one country for which
the table does not report the average (EA), we use the only eﬀect reported in the table,
along with its statistical signiﬁcance.

The former central banker pointed out that the empty cell in the average column in
Table 4 indicate that the ECB’s balance sheet expansion did not have a signiﬁcant eﬀect on
output and prices, which could be interpreted as a zero eﬀect. We do not fully agree with
this interpretation. While the absence of a reported number may indeed indicate that the
average eﬀect is statistically insigniﬁcant, the point estimate could very well be greater than
zero. Since the authors do not report insigniﬁcant eﬀects, we cannot know what the point
estimate would have been. We therefore prefer our original treatment, which uses the only
point estimate that is reported in Table 4.

Nevertheless, for the purpose of this section, we change the output eﬀect of the total
program in the EA from 1.56 (=0.15 × 10.38) to 0.78 (=0.075 × 10.38) percentage points,
and the statistical signiﬁcance of the output eﬀect from 1 to 0.

Appendix M.4. Blattner and Joyce (2016)

A policy economist pointed out that our sample of 54 papers does not include Blattner
and Joyce (2016). This paper did pop up in our search, but it is not included in our sample
because it does not estimate the eﬀect of QE on the euro area as a whole. What the paper
does estimate is the eﬀect of QE on the four largest euro area countries—Germany, France,
Italy, and Spain. The paper uses the aggregate eﬀect on these four countries to approximate
the eﬀect of QE on the whole euro area. This approximation is imperfect because the euro
area includes 19 countries (as of early 2021). By the paper’s own admission, the combined
output of the four countries is equal to only about three quarters of the output of the euro
area. None of the 26 papers in our sample that focus on the euro area makes such a rough

ECB Working Paper Series No 2584 / August 2021136approximation, so it is a judgment call whether to include Blattner and Joyce (2016) in the
sample. For the purpose of this section, we do add this paper, increasing the number of
papers to 55.

The paper was ﬁrst circulated as an ECB working paper in 2016. The only other version
of the paper we could ﬁnd was the published version in the Journal of Money, Credit, and
Banking from September 2020. We therefore use the original working paper version from
2016 for our analysis, since this is the only version of the paper that was publicly available at
the time we collected our dataset. Given that both authors were aﬃliated with the ECB at
the time of the paper’s ﬁrst release, CB Aﬃliation is equal to 1. The country studied is the
EA. The estimated peak eﬀects on output and inﬂation are 0.2 and 0.6 percentage points,
respectively. The cumulative eﬀects on output and inﬂation are 0.0 and 0.6 percentage
points, respectively. The authors do not assess the statistical signiﬁcance of these eﬀects,
but they do claim their eﬀects are “substantial.” Hence, we set economic signiﬁcance equal
to 1 for both output and inﬂation.

Appendix M.5. Wu and Xia (2016)

In the discussion of our paper at the 2021 spring meeting of the NBER Economic Fluc-
tuations and Growth research group, the discussant raised questions regarding our point
estimates for the estimated eﬀects on output in Wu and Xia (2016). In their Figure 6, Wu
and Xia (2016) report two counterfactuals. The ﬁrst counterfactual (I) refers to a scenario
where the Fed follows a historical version of the Taylor rule, whereas the second counterfac-
tual (II) refers to a scenario where the shadow federal funds rate never falls below the lower
bound r (assumed to be equal to 0.25%). The discussant suggested that we use counterfac-
tual II to infer point estimates rather than counterfactual I, which we use in our main dataset.
However, we continue to believe that deviations from counterfactual I provide estimates that
are more appropriate for the purpose of our study, for several reasons.

• The results based on counterfactual I are discussed much more prominently
by Wu and Xia (2016). On page 267, the authors discuss the eﬀect of unconventional
monetary policy on industrial production and inﬂation based on the deviations from
counterfactual I: “In the absence of expansionary monetary policy, in December 2013,
the unemployment rate would be 0.13% higher at the 6.83% level rather than 6.7% in the
data. The industrial production index would have been 101.0 rather than 101.8, [...].
Interestingly, the accommodative monetary policy during this period has not boosted
real activity at the cost of high inﬂation.” The eﬀects on industrial production and
inﬂation based on counterfactual II are not discussed anywhere in the paper.

ECB Working Paper Series No 2584 / August 2021137• Wu and Xia (2016) explicitly state that the results based on counterfactual
II are subject to greater uncertainty and represent an upper bound on the
contribution of unconventional monetary policy measures. Speciﬁcally, on
page 268, they write: “Another question of interest is what would happen if the Fed
had adopted no unconventional monetary policy at all. This question is more diﬃcult
to answer, because it is not clear what the counterfactual shadow rate would be. One
possible counterfactual to consider would be what would have happened if the shadow
federal funds rate had never fallen below the lower bound r. [...] One might view the
diﬀerence between the actual shadow rate and this counterfactual as an upper bound on
the contribution of unconventional monetary policy measures.”

• Wu and Xia (2016) themselves always use their results based on counter-
factual I to compare their estimates to existing studies. On page 268, they
write: “Our estimated eﬀect of unconventional monetary policy on the unemployment
rate is smaller than the ones found in Chung et al. (2012) and Baumeister and Benati
(2013). This is primarily because they assumed that unconventional monetary policy
had a big impact on the yield curve. For example, Chung et al. (2012) assumed that the
large-scale asset purchases reduced the long-term interest rates by 50 basis points and
then translated this number into a 1.5% decrease in the unemployment rate. If we were
to use Hamilton and Wu (2012)’s estimate of 13 basis-point decrease in the 10-year
rate, a simple linear calculation would translate this number into a 0.39% reduction
in the unemployment rate. This is comparable to our estimate.” These statements
suggest Wu and Xia (2016) are using their estimated eﬀect on the unemployment rate
of 0.13% (based on counterfactual I) to compare to existing studies, rather than the
1.0% estimate based on counterfactual II. If they were using 1.0% as their estimate,
then it would be unclear why 0.39% is more comparable to their results than 1.5%.

• Using counterfactual II would make Wu and Xia (2016) a massive outlier in
the dataset. The peak eﬀect on output implied by counterfactual II is ca. 11 percent
— roughly double the largest estimate in our sample. This stands in stark contrast
to statements made by Wu and Xia (2016) about their estimates being smaller than
those by existing studies, such as Chung et al. (2012) and Baumeister and Benati
(2013), which are also in our sample. Wu and Xia (2016) are clearly aware that using
counterfactual II would produce implausible results, as they emphasize their results
based on counterfactual I throughout.

ECB Working Paper Series No 2584 / August 2021138Appendix M.6. Andrade et al. (2016)

The same discussant also suggested that we incorrectly treat the inﬂation rate reported
in Andrade et al. (2016) as a quarterly rate, when in fact the authors report an annualized
rate. The discussant had conﬁrmed this after contacting the authors of that study. Since
the authors do not explicitly state in their paper that they use annualized rates, our treat-
ment of Andrade et al. (2016) is perfectly in line with our approach described on page 6 of
this Appendix (“We assume that authors show quarter-on-quarter growth rates, unless the
authors explicitly state that they use annualized rates”). We believe this approach is reason-
able, because we are trying to capture how a reader of the paper, who can see the paper but
cannot see inside the minds of the paper’s authors, would reasonably interpret the results.
Nevertheless, for the purpose of the exercise in this section, we set the estimated peak and
cumulative eﬀect on inﬂation for Andrade et al. (2016) to 1.07 percent.

Appendix M.7. Results

We repeat our analysis in the main paper after making the above changes to the dataset.
All our conclusions from the main paper continue to hold. Central bank studies report
stronger eﬀects of QE on both output and inﬂation (Tables M.55 and M.56). Central bank
studies are also more likely to report signiﬁcant QE eﬀects on output (Table M.57), and
their abstracts use more favorable language (Table M.58). Moreover, central bankers who
report larger eﬀects on output experience more favorable career outcomes (Table M.59).

In terms of economic magnitude, the diﬀerences become slightly smaller relative to the
main paper, but they remain sizable.
In terms of the level of statistical signiﬁcance, we
observe no change in the level of signiﬁcance in 35 out of 57 speciﬁcations; a decrease in
signiﬁcance in 20 out of 57 speciﬁcations, and an increase in signiﬁcance in 2 out of 57
speciﬁcations. Overall, these results highlight the robustness of our main ﬁndings. That
said, we are only in partial agreement with the comments received, as described above, and
we are also concerned about the selection eﬀect discussed earlier. Due to these concerns, we
stick to our original dataset for the main analysis.

ECB Working Paper Series No 2584 / August 2021139(a) Total Peak Eﬀect

(b) Total Cumulative Eﬀect

(c) Standardized Peak Eﬀect

(d) Standardized Cumulative Eﬀect

Figure M.8: Eﬀects of QE on Output by Central Bank Aﬃliation After External Feedback. The
ﬁgure repeats Figure 2 in the main paper using the alternative version of our dataset that incorporates
external feedback.

0.2.4-1.000.001.002.003.004.005.006.00CB0.2.4-1.000.001.002.003.004.005.006.00Not CB0.25.5.75-2.00-1.000.001.002.003.004.005.006.00CB0.25.5.75-2.00-1.000.001.002.003.004.005.006.00Not CB0.2.4.6-.250.25.5.7511.251.51.75CB0.2.4.6-.250.25.5.7511.251.51.75Not CB0.25.5.75-.250.25.5.7511.251.51.75CB0.25.5.75-.250.25.5.7511.251.51.75Not CBECB Working Paper Series No 2584 / August 2021140(a) Total Peak Eﬀect

(b) Total Cumulative Eﬀect

(c) Standardized Peak Eﬀect

(d) Standardized Cumulative Eﬀect

Figure M.9: Eﬀects of QE on Inﬂation by Central Bank Aﬃliation After External Feedback.
The ﬁgure repeats Figure 3 in the main paper using the alternative version of our dataset that incorporates
external feedback.

0.25.5.75-1.000.001.002.003.004.005.006.007.008.00CB0.25.5.75-1.000.001.002.003.004.005.006.007.008.00Not CB0.2.4.6-9.00-7.00-5.00-3.00-1.001.003.005.007.00CB0.2.4.6-9.00-7.00-5.00-3.00-1.001.003.005.007.00Not CB0.3.6.9-.250.25.5.7511.251.51.75CB0.3.6.9-.250.25.5.7511.251.51.75Not CB0.25.5.75-.75-.5-.250.25.5.7511.251.51.75CB0.25.5.75-.75-.5-.250.25.5.7511.251.51.75Not CBECB Working Paper Series No 2584 / August 2021141Table M.54: Eﬀects of QE on Output and Inﬂation by Central Bank Aﬃliation After External
Feedback

This table repeats Table 1 in the main paper using the alternative version of our dataset that
incorporates external feedback.

All

CB

Not CB

Panel A: Eﬀect on Output
Peak eﬀect on output

Standardized peak eﬀect on output

Cumulative eﬀect on output

Standardized cumulative eﬀect on output

Panel B: Eﬀect on Inﬂation
Peak eﬀect on inﬂation

Standardized peak eﬀect on inﬂation

Cumulative eﬀect on inﬂation

Standardized cumulative eﬀect on inﬂation

Panel C: Signiﬁcance
Statistical signiﬁcance: output

Statistical signiﬁcance: inﬂation

1.52
(1.23)
0.23
(0.16)
0.85
(0.37)
0.13
(0.04)

1.35
(0.92)
0.18
(0.10)
0.83
(0.74)
0.12
(0.08)

0.85
(1.00)
0.84
(1.00)

1.67
(1.27)
0.28
(0.17)
1.01
(0.42)
0.18
(0.05)

1.66
(1.07)
0.23
(0.11)
1.22
(0.80)
0.16
(0.09)

0.95
(1.00)
0.89
(1.00)

1.04
(1.10)
0.11
(0.10)
0.56
(0.11)
0.05
(0.02)

0.65
(0.43)
0.06
(0.04)
-0.05
(0.16)
0.00
(0.02)

0.55
(1.00)
0.78
(1.00)

ECB Working Paper Series No 2584 / August 2021142Table M.55: Eﬀects of QE on Output After External Feedback

This table repeats Table 2 in the main paper using the alternative version of our dataset that
incorporates external feedback.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.664
(1.86)
[0.075]

59
0.051

(1)

0.148
(2.21)
[0.032]

59
0.050

0.665
(1.92)
[0.061]

X

59
0.074

Peak Eﬀect
(2)

0.149
(2.34)
[0.020]

X

59
0.163

0.626
(1.64)
[0.127]

X
X
59
0.082

(3)

0.137
(1.98)
[0.068]

X
X
59
0.198

0.524
(1.39)
[0.178]

58
0.032

0.438
(1.29)
[0.203]

X

58
0.073

0.415
(1.15)
[0.255]

X
X
58
0.079

Cumulative Eﬀect
(5)

(6)

(4)

0.127
(2.04)
[0.030]

58
0.041

0.115
(2.03)
[0.032]

X

58
0.071

0.108
(1.77)
[0.071]

X
X
58
0.099

ECB Working Paper Series No 2584 / August 2021143Table M.56: Eﬀects of QE on Inﬂation After External Feedback

This table repeats Table 3 in the main paper using the alternative version of our dataset that
incorporates external feedback.

Panel A: Total Program Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

CB Aﬃliation

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.129
(2.92)
[0.007]

54
0.102

1.240
(2.86)
[0.006]

X

54
0.226

1.106
(2.53)
[0.018]

X
X
54
0.261

1.411
(1.98)
[0.024]

54
0.097

1.424
(1.95)
[0.028]

X

54
0.097

1.226
(1.87)
[0.065]

X
X
54
0.139

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.167
(2.28)
[0.016]

54
0.084

0.199
(2.46)
[0.011]

X

54
0.241

0.180
(2.49)
[0.010]

X
X
54
0.281

0.175
(2.07)
[0.015]

54
0.083

0.192
(2.10)
[0.012]

X

54
0.171

0.170
(2.21)
[0.014]

X
X
54
0.199

ECB Working Paper Series No 2584 / August 2021144Table M.57: Signiﬁcance After External Feedback

This table repeats Table 4 in the main paper using the alternative version of our dataset that
incorporates external feedback.

Panel A: Eﬀect on Output

CB Aﬃliation

Country FE
Controls
Observations
R2

Panel B: Eﬀect on Inﬂation

CB Aﬃliation

Country FE
Controls
Observations
R2

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.344
(2.08)
[0.069]

41
0.145

0.340
(2.19)
[0.054]

X

41
0.186

0.310
(2.02)
[0.074]

X
X
41
0.245

0.307
(2.67)
[0.020]

67
0.124

0.316
(2.67)
[0.021]

X

67
0.131

0.361
(3.16)
[0.008]

X
X
67
0.230

Statistical Signiﬁcance

Economic Signiﬁcance

(1)

(2)

(3)

(4)

(5)

(6)

0.179
(1.10)
[0.383]

38
0.036

0.188
(1.24)
[0.282]

X

38
0.114

0.151
(1.11)
[0.353]

X
X
38
0.206

0.173
(1.23)
[0.253]

61
0.034

0.182
(1.29)
[0.222]

X

61
0.036

0.209
(1.65)
[0.122]

X
X
61
0.124

ECB Working Paper Series No 2584 / August 2021145Table M.58: Tone of the Abstract After External Feedback

This table repeats Table 5 in the main paper using the alternative version of our dataset that
incorporates external feedback.

Panel A: Sentiment Score

CB Aﬃliation

Country Dummies
Controls
Observations
R2

Panel B: Percentage of Positive Adjectives

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

0.036
(1.56)
[0.131]

55
0.052

(1)

0.026
(1.28)
[0.200]

55
0.036

Panel C: Percentage of Negative Adjectives

CB Aﬃliation

Country Dummies
Controls
Observations
R2

(1)

-0.009
(-0.99)
[0.335]

55
0.020

(2)

0.042
(2.01)
[0.052]

X

55
0.098

(2)

0.033
(1.76)
[0.096]

X

55
0.109

(2)

-0.009
(-0.93)
[0.363]

X

55
0.031

(3)

0.043
(1.98)
[0.055]

X
X
55
0.099

(3)

0.034
(1.79)
[0.084]

X
X
55
0.115

(3)

-0.009
(-0.83)
[0.426]

X
X
55
0.036

ECB Working Paper Series No 2584 / August 2021146Table M.59: Career Outcomes and Eﬀects of QE on Output After External Feedback

This table repeats Table 6 in the main paper using the alternative version of our dataset that
incorporates external feedback.

Panel A: Total Program Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Panel B: Standardized Eﬀect

Eﬀect on output

Country FE
Controls
Observations
R2

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

0.270
(2.51)
[0.026]

35
0.033

0.229
(2.27)
[0.007]

X

35
0.068

0.485
(2.65)
[0.017]

X
X
31
0.553

0.211
(1.93)
[0.063]

33
0.029

0.213
(1.46)
[0.139]

X

33
0.079

0.460
(2.12)
[0.017]

X
X
30
0.550

Peak Eﬀect

Cumulative Eﬀect

(1)

(2)

(3)

(4)

(5)

(6)

1.431
(1.47)
[0.219]

35
0.047

1.060
(1.25)
[0.322]

X

35
0.063

2.661
(1.86)
[0.089]

X
X
31
0.553

2.363
(2.12)
[0.032]

33
0.054

1.912
(1.66)
[0.068]

X

33
0.084

4.095
(2.15)
[0.018]

X
X
30
0.569

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ECB Working Paper Series No 2584 / August 2021151Acknowledgements 

The paper previously circulated under the title “Fifty Shades of QE: Conflicts of Interest in Economic Research.” The views in this paper 
are those of the authors and do not represent the views of the National Bank of Slovakia or the European Central Bank. Pástor is a 
member of the bank board of the National Bank of Slovakia. We are grateful for comments from our discussants José-Luis Peydró and 
Annette Vissing-Jørgensen, and also from Wenxin Du, Mara Faccio, Pavel Gertler, Daniel Gros, Urban Jermann, Sujit Kapadia, Anil 
Kashyap, Luc Laeven, Ľudovít Ódor, Raghuram Rajan, Kasper Roszbach, Bill Schwert, Martin Šuster, Paul Tucker, and Tomasz 
Wieladek, conference participants at the 2021 Spring NBER EF&G Meeting and the 2021 CEPR Conference on the Politics of 
Regulation and Central Banking, as well as to seminar participants at the European Central Bank, Košice Economic Forum, National 
Bank of Slovakia, Purdue University, Toulouse School of Economics, University of Chicago, University of Geneva, University of Miami, 
University of Rochester, and University of Washington. We are also grateful to Livia Amato, Yuliia Kazmina, Fulin Li, Eva Štulrajterová 
and, especially, Bianca He for excellent research assistance. This research was supported by the Fama-Miller Center for Research in 
Finance at Chicago Booth. 

Brian Fabo 
National Bank of Slovakia, Bratislava, Slovakia; email: fabo.brian@nbs.sk 

Martina Jančoková 
European Central Bank and Goethe University Frankfurt, Frankfurt am Main, Germany; email: martina.jancokova@ecb.europa.eu 

Elisabeth Kempf 
University of Chicago Booth School of Business, Chicago, Illinois, United States; CEPR, London, United Kingdom;  
email: elisabeth.kempf@chicagobooth.edu 

Ľuboš Pástor 
National Bank of Slovakia, Bratislava, Slovakia; University of Chicago Booth School of Business, Chicago, Illinois, United States;  
NBER, Cambridge, Massachusetts, United States; CEPR, London, United Kingdom email: lubos.pastor@chicagobooth.edu 

© European Central Bank, 2021 

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