Measuring Economic Policy Uncertainty
Scott R. Baker,
a
Nicholas Bloom,
b
and Steven J. Davis
c
10 March 2016
Abstract: We develop a new index of economic policy uncertainty (EPU) based on newspaper
coverage frequency. Several types of evidence – including human readings of 12,000 newspaper
articles – indicate that our index proxies for movements in policy-related economic uncertainty.
Our US index spikes near tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the
failure of Lehman Brothers, the 2011 debt-ceiling dispute and other major battles over fiscal
policy. Using firm-level data, we find that policy uncertainty is associated with greater stock
price volatility and reduced investment and employment in policy-sensitive sectors like defense,
healthcare, finance and infrastructure construction. At the macro level, innovations in policy
uncertainty foreshadow declines in investment, output, and employment in the United States and,
in a panel VAR setting, for 12 major economies. Extending our US index back to 1900, EPU
rose dramatically in the 1930s (from late 1931) and has drifted upwards since the 1960s.
JEL No. D80, E22, E66, G18, L50
Keywords: economic uncertainty, policy uncertainty, business cycles, fluctuations
Acknowledgements: We thank Adam Jorring, Kyle Kost, Abdulla Al-Kuwari, Sophie Biffar,
Jörn Boehnke, Vladimir Dashkeyev, Olga Deriy, Eddie Dinh, Yuto Ezure, Robin Gong, Sonam
Jindal, Ruben Kim, Sylvia Klosin, Jessica Koh, Peter Lajewski, David Nebiyu, Rebecca Sachs,
Ippei Shibata, Corinne Stephenson, Naoko Takeda, Melissa Tan, Sophie Wang and Peter Xu for
research assistance and the National Science Foundation, the MacArthur Foundation, the Sloan
Foundation, Toulouse Network for Information Technology, the Becker Friedman Institute,
Initiative on Global Markets, and the Stigler Center at the University of Chicago for financial
support. We thank Ruedi Bachmann, Sanjai Bhagat, Vincent Bignon, Yongsung Chang,
Vladimir Dashkeyev, Jesus Fernandez-Villaverde, Laurent Ferrara, Luis Garicano, Matt
Gentzkow, Yuriy Gorodnichenko, Kevin Hassett, Takeo Hoshi, Greg Ip, Anil Kashyap, Patrick
Kehoe, John Makin, Johannes Pfeifer, Meijun Qian, Itay Saporta, John Shoven, Sam Schulhofer-
Wohl, Jesse Shapiro, Erik Sims, Stephen Terry, Cynthia Wu and many seminar and conference
audiences for comments. We also thank the referees and the editors, Robert Barro and Larry
Katz.
a
Kellogg School of Management; [email protected]
b
Stanford; [email protected]
c
University of Chicago Booth School of Business; [email protected]
1
1. INTRODUCTION
Concerns about policy uncertainty have intensified in the wake of the Global Financial
Crisis, serial crises in the Eurozone, and partisan policy disputes in the United States. For
example, the Federal Open Market Committee (2009) and the IMF (2012, 2013) suggest that
uncertainty about U.S. and European fiscal, regulatory, and monetary policies contributed to a
steep economic decline in 2008-09 and slow recoveries afterwards.
1
To investigate the role of policy uncertainty, we first develop an index of economic
policy uncertainty (EPU) for the United States and examine its evolution since 1985.
2
Our index
reflects the frequency of articles in 10 leading US newspapers that contain the following triple:
“economic” or “economy”; “uncertain” or “uncertainty”; and one or more of “congress”,
“deficit”, “Federal Reserve”, “legislation”, “regulation” or “White House”. As seen in Figure 1,
the index spikes near tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the 2011
debt-ceiling dispute and other major battles over fiscal policy. We extend our newspaper-based
approach to measuring policy uncertainty along three dimensions: back in time, across countries,
and to specific policy categories.
To push back to 1900, we rely on archives for six major US newspapers published
throughout the last century. As shown in Figure 2, this long-span EPU index highlights pre-
World War II political developments and shocks like the Gold Standard Act of 1900, the
outbreak of World War I, the Versailles conference in 1919, and a sustained surge in policy
uncertainty from late 1931 when President Hoover, and then President Roosevelt, introduced a
rash of major new policies. The index also shows an upward drift since the 1960s, perhaps due to
rising political polarization or the growing economic role for government (Baker et al., 2014).
Using similar methods, we construct EPU indices for eleven other countries, including all
G10 economies. These indices are particularly helpful in countries with fewer alternative
uncertainty measures. We also develop category-specific policy uncertainty indices for the US
by specifying more restrictive criteria for those articles that contain our triple of terms about the
economy, policy, and uncertainty. As examples, Figure 3 plots indices of healthcare policy
uncertainty and national security policy uncertainty based on the presence of additional terms
1
“[W]idespread reports from business contacts noted that uncertainties about health-care, tax, and environmental
policies were adding to businesses' reluctance to commit to higher capital spending.” (FOMC minutes, 15-16
December 2009) See, also, IMF (2012, pages xv-xvi and 49-53, and 2013, pages 70-76).
2
Our data are available at monthly and daily frequencies on www.policyuncertainty.com and are carried by
Bloomberg, Haver, FRED and Reuters.
2
like “healthcare”, “hospital” or “health insurance” and “war”, “terrorism” or “department of
defense”, respectively. Category-specific shocks and policy initiatives are clearly visible.
Our approach to measuring policy uncertainty raises potential concerns related to
newspaper reliability, accuracy, bias, and consistency. To address these concerns, we evaluate
our EPU index in several ways. First, we show a strong relationship between our measure of
economic policy uncertainty and other measures of economic uncertainty, e.g., implied stock-
market volatility. Second, we compare our index to other measures of policy uncertainty, e.g.,
the frequency with which the Federal Reserve System’s Beige Books mention policy uncertainty.
Third, we find very similar movements in EPU indices based on right-leaning and left-leaning
newspapers, suggesting that political slant does not seriously distort our overall EPU index.
Fourth, we conducted an extensive audit study of 12,000 randomly selected articles
drawn from major US newspapers. Working under our close supervision, teams of University of
Chicago students underwent a training process and then carefully read overlapping sets of
articles, guided by a 65-page reference manual and weekly team meetings. The auditors assessed
whether a given article discusses economic policy uncertainty based on our criteria. We use the
audit results to select our policy term set, evaluate the performance of our computer-automated
methods, and construct additional data. There is a high correlation between our human- and
computer-generated indices (0.86 in quarterly data from 1985 to 2012 and 0.93 in annual data
from 1900 to 2010). The discrepancy between the human and computer-generated indices is
uncorrelated with GDP growth rates and with the level of economic policy uncertainty.
Finally, our indices have a market-use validation: Commercial data providers that include
Bloomberg, FRED, Haver and Reuters carry our indices to meet demands from banks, hedge
funds, corporates and policy makers. This pattern of market adoption suggests that our indices
contain useful information for a range of decision makers.
In Section 4 we provide evidence of how firm-level and aggregate outcomes evolve in the
wake of policy uncertainty movements. Causal inference is challenging, because policy responds
to economic conditions, and it may be forward looking as well. To make progress we follow a
micro and a macro estimation approach. First, the micro approach exploits firm-level differences
in exposure to certain aspects of policy, mainly government purchases of goods and services. We
use micro data from the Federal Registry of Contracts and data on government healthcare
spending to calculate the share of firm and industry revenues derived from sales to the
3
government. Next, in firm-level regressions that include time and firm fixed effects and other
controls, we show that firms with greater exposure to government purchases experience greater
stock price volatility when policy uncertainty is high and reduced investment rates and
employment growth when policy uncertainty rises. Adding the VIX as an explanatory variable
(interacted with firm-level exposure to government purchases), we still find greater stock-price
volatility, and falls in investment and employment with heightened policy uncertainty, which
points to a policy uncertainty channel at work rather than a broader uncertainty effect. We also
find that firms in the defense, healthcare and financial sectors are especially responsive to their
own category-specific EPU measures, confirming their information value.
These firm-level results are suggestive of a causal impact of policy uncertainty on
investment and employment in sectors that rely heavily on government spending and in sectors
like healthcare and finance with strong exposure to major shifts in regulatory policy. However,
the firm-level results offer limited guidance about the magnitude of aggregate effects, in part
because they capture only a limited set of potential policy uncertainty channels.
Our second approach fits vector autoregressive (VAR) models to US data and to an
international panel VAR that exploits our EPU indices for 12 countries. The US VAR results
indicate that a policy uncertainty innovation equivalent to the actual EPU increase from 2005-06
to 2011-12 foreshadows declines of about 6% in gross investment, 1.2% in industrial production
and 0.35% in employment. The 12-country panel VAR yields similar results.
3
While our results
are not necessarily causal, one plausible interpretation of our micro and macro evidence is that
policy uncertainty retards investment, hiring and growth in policy sensitive sectors like defense,
finance, healthcare and construction, and these sectors are important enough for policy
uncertainty to matter at the aggregate level.
This paper relates to at least three literatures. The first is research on the impact of
uncertainty on growth and investment. Theoretical work on this topic dates at least to Bernanke
(1983), who points out that high uncertainty gives firms an incentive to delay investment and
hiring when investment projects are costly to undo or workers are costly to hire and fire.
4
Of
3
Stock and Watson (2012) use our EPU index to investigate the factors behind the 2007-2009 recession and slow
recovery and come to a similar conclusion – namely, that policy uncertainty is a strong candidate to partly explain
the poor economic performance, but causal identification is hard.
4
Dixit and Pindyck (1994) offer a review of the early theoretical literature, including papers by Oi (1961), Hartman
(1972) and Abel (1983) that highlight potentially positive effects of uncertainty. Recent empirical papers include
Bloom (2009), Bloom, Floetotto, Jaimovich, Saporta and Terry (2014), Bachman et al. (2013) and Scotti (2014).
4
course, once uncertainty recedes, firms increase hiring and investment to meet pent-up demand.
Other reasons for a depressive effect of uncertainty include precautionary spending cutbacks by
households, upward pressure on the cost of finance (e.g., Gilchrist et al., 2014, and Pastor and
Veronesi, 2013), managerial risk-aversion (e.g., Panousi and Papanikolaou, 2012), and
interactions between nominal rigidities and search frictions (Basu and Bundick, 2015 and Leduc
and Liu, 2015).
Second, there is a literature focused explicitly on policy uncertainty. Friedman (1968),
Rodrik (1991), Higgs (1997) and Hassett and Metcalf (1999), among others, consider the
detrimental economic effects of monetary, fiscal, and regulatory policy uncertainty. More
recently, Born and Pfeifer (2014) and Fernandez-Villaverde at al. (2015) study policy uncertainty
in DSGE models, finding moderately negative effects, while Pastor and Veronesi (2012, 2013)
model the theoretical links among fluctuations, policy uncertainty, and stock market volatility.
5
Finally, there is a rapidly growing literature on text search methods – using newspaper
archives, in particular – to measure a variety of outcomes. Examples include Gentzkow and
Shapiro (2010), Hoberg and Phillips (2010), Boudoukh et al. (2013), and Alexopoulos and
Cohen (2015). Our work suggests that newspaper text search can yield useful proxies for
economic and policy conditions stretching back several decades, which could be especially
valuable in earlier eras and in countries with fewer data sources.
Section 2 describes the data we use to construct our policy uncertainty indices. Section 3
evaluates our EPU measures in several ways and develops additional evidence about movements
in policy-related uncertainty over time. Section 4 investigates how firm-level outcomes covary
with policy uncertainty and the dynamic responses of aggregate outcomes to policy uncertainty
innovations. Section 5 concludes and offers some thoughts about directions for future research.
5
In other related work, Julio and Yook (2012) find that investment falls around national elections, Durnev (2010)
finds that corporate investment becomes less responsive to stock prices in election years, Brogaard and Detzel
(2015) find that policy uncertainty reduces asset returns, Handley and Limao (2015) find that trade-policy
uncertainty delays firm entry, Gulen and Ion (2016) find negative responses of corporate investment to our EPU
index, Koijen et al. (2016) develop evidence that government-induced uncertainty about profitability generates a
large equity risk premium for firms in the healthcare sector and reduces their medical R&D, and Giavazzi and
McMahon (2012) find that policy uncertainty led German households to increase savings in the run-up to the close
and consequential general elections in 1998.
5
2. MEASURING ECONOMIC POLICY UNCERTAINTY
We build indices of policy-related economic uncertainty based on newspaper coverage
frequency.
6
We aim to capture uncertainty about who will make economic policy decisions, what
economic policy actions will be undertaken and when, and the economic effects of policy actions
(or inaction) – including uncertainties related to the economic ramifications of “non-economic”
policy matters, e.g., military actions. Our measures capture both near-term concerns (e.g., when
will the Fed adjust its policy rate) and longer-term concerns (e.g., how to fund entitlement
programs), as reflected in newspaper articles. We first describe the construction of our monthly
and daily EPU indices for the US from 1985 onwards and then turn to indices for specific policy
categories, indices for other countries, and historical indices for the US and UK.
2.1 US economic policy uncertainty indices from 1985
Our modern monthly EPU index for the US relies on 10 leading newspapers: USA
Today, Miami Herald, Chicago Tribune, Washington Post, Los Angeles Times, Boston Globe,
San Francisco Chronicle, Dallas Morning News, New York Times, and Wall Street Journal. We
search the digital archives of each paper from January 1985 to obtain a monthly count of articles
that contain the following triple: ‘uncertainty’ or ‘uncertain’; ‘economic’ or ‘economy’; and one
of the following policy terms: ‘congress’, ‘deficit’, ‘Federal Reserve’, ‘legislation’, ‘regulation’
or ‘white house’ (including variants like ‘uncertainties’, ‘regulatory’ or ‘the Fed’). In other
words, to meet our criteria, an article must contain terms in all three categories pertaining to
uncertainty, the economy, and policy. We use our audit study to select the policy terms, as
explained in Section 3.1.
An obvious difficulty with these raw counts is that the overall volume of articles varies
across newspapers and time. Thus, we scale the raw counts by the total number of articles in the
same newspaper and month. We standardize each monthly newspaper-level series to unit
standard deviation from 1985 to 2009 and then average across the ten papers by month. Finally,
we normalize the 10-paper series to a mean of 100 from 1985 to 2009. To be precise, let
X
it
denote the scaled EPU frequency counts for newspaper i=1, 2, … 10 in month t, and let and
denote the time intervals used in the standardization and normalization calculations. We
6
Earlier drafts of this paper include index components based on (a) the present value of future scheduled tax code
expirations and (b) disagreement among professional forecasters over future government purchases and consumer
prices. However, to extend our EPU measures over time and across countries, we focus here on the newspaper
approach, while continuing to report the other components at www.policyuncertainty.com.
Τ
1
Τ
2
6
proceed in the following steps: (1) Compute the times-series variance, in the interval for
each paper i. (2) Standardize
X
it
by dividing through by the standard deviation for all t. This
operation yields, for each paper, a series
Y
it
with unit standard deviation in the interval . (3)
Compute the mean over newspapers of
Y
it
in each month to obtain the series (4) Compute M,
the mean value of
Z
t
in the interval . (5) Multiply
Z
t
by (100/M) for all t to obtain the
normalized EPU time-series index. We use the same approach for other countries and indices.
Figure 1 plots the resulting index, which shows clear spikes around the Gulf Wars, close
presidential elections, 9/11, the 2009 stimulus debate, the Lehman Brothers bankruptcy and
TARP legislation in late 2008, the summer 2011 debt-ceiling dispute and the battle over the
“Fiscal Cliff” in late 2012, among other events and developments. Some notable political events
do not generate high EPU according to our index. For instance, our EPU index shows no large
spike in connection with the partial federal government shutdowns from November 1995 to
January 1996, although those shutdowns received much press coverage.
7
In addition to our monthly index, we also produce a daily EPU index using the Newsbank
news aggregator, which covers around 1,500 US newspapers. Newsbank’s extensive coverage
yields enough articles to generate a meaningful daily count. Taking monthly averages of our
daily index, it correlates at 0.85 with our 10-paper monthly index, indicating a high degree of
similarity. Because papers enter and exit the Newsbank archive, and its count of newspapers
expands greatly over time, compositional shifts potentially distort the longer-term behavior of
the daily EPU index. Hence, we focus below on our 10-paper monthly EPU index, but the daily
index provides a useful high-frequency alternative.
8
2.2 EPU indices for policy categories
To create indices for policy categories, we apply additional criteria to those articles that
contain our triple of terms about the economy, policy and uncertainty. The additional criteria
involve the presence of one or more category-relevant terms: “the Fed”, “central bank”, “interest
rate”, “inflation” and so on for the monetary policy category, for example. The appendix reports
the full set of terms that define our eleven policy categories and sub-categories. We use
7
We find more than 8,000 articles about these shutdowns in Newsbank archives, but less than 25% also mention the
economy, less than 2% mention uncertainty, and only 1% mentions both. Thus, politically tumultuous episodes do
not necessarily raise economic policy uncertainty, at least by our measure.
8
We update the daily EPU index at approximately 9am EST each day and post it at www.policyuncertainty.com.
σ
i
2
,
Τ
1
σ
i
Τ
1
Z
t
.
Τ
2
7
Newsbank for the category indices, because its high text density facilitates measurement by time
period and policy category. As seen in Figure 3, the national security EPU index spiked sharply
in connection with the 9/11 attacks, Gulf War I and the onset of Gulf War II. The healthcare EPU
index rose sharply during the Clinton healthcare reform initiative in 1993-94 and has fluctuated
at high levels from 2009 to 2014.
Table 1 reports all eleven category-specific EPU indices.
9
It also reports an overall
Economic Uncertainty (“EU”) index that drops the policy requirement in the EPU index. The
first two rows report average EU and EPU values for the indicated periods, expressed relative to
the average EPU value from 1985 to 2014. For example, the EU value of 218.2 says the (scaled)
frequency of EU articles from 1985:1 to 1990:6 is somewhat more than twice the average
frequency of EPU articles from 1985 to 2014. The next eleven rows report relative frequency
values for specific policy categories and time periods. For example, the 54.1 value for “National
Security” says the frequency of EPU articles during 2001:9 to 2002:12 that mention national
security matters is 54 percent of the 1985-2014 average EPU frequency and 42 percent
(54.1/128.5) of the EPU frequency from 2001:9 to 2002:12.
Fiscal matters, especially tax policy, stand out in Table 1 as the largest source of policy
uncertainty, especially in recent years. The fiscal policy EPU index rose from values near 33 in
the pre-crisis years to 61.5 in 2008:9 to 2009:12 and 78.3 from 2010 to 2013. Healthcare policy
is the second largest source of elevated EPU in recent years. Policy uncertainty related to
financial regulations and entitlement programs also rose sharply after 2008, but from initially
lower levels. Concerns related to sovereign debt and currency crises are up by an order of
magnitude during 2010 to 2013, but from such a low base as to have little impact on the overall
EPU index. EPU concerns related to monetary policy are important throughout the 1985-2014
period, but perhaps surprisingly, they are not elevated in recent years by our measure. We
interpret this result as a reflection of low and stable inflation rates in recent years, which
apparently drive newspaper coverage more than disputes among professional economists about
unconventional monetary policies.
10
9
In contrast to Figure 3, which normalizes each category-specific EPU series to 100, Table 1 expresses each
category-specific EPU series as a percentage of the overall EPU frequency from 1985 to 2014.
10
Other evidence also points to subdued levels of inflation uncertainty in recent years. See Nalewaik (2015) for a
presentation and discussion of evidence based on time-series models, surveys and financial markets data.
8
Several other researchers develop measures related to uncertainty about government
behavior. Marina Azzimonti (2015) constructs a newspaper index of partisan conflict at the
federal level that shows similarities to our EPU index but also notable departures – e.g., war and
national security threats produce declines in partisan conflict but increases in policy uncertainty.
Shoag and Veuger (2015) develop policy uncertainty indices for US states based on newspapers
and other indicators, finding a strong negative link to state-level economic performance.
Fernandez-Villaverde et al. (2015) estimate stochastic volatility processes for US capital taxes,
labor taxes and government expenditures in a DSGE model, finding correlations with our EPU
index of 0.44, 0.31, and 0.67, respectively. Jurado, Ludvigson, and Ng (2015) derive uncertainty
measures from common variation in the unforecastable components of macroeconomic
indicators. Their main uncertainty measure correlates at 0.42 with our EPU index.
2.3 EPU indices for other countries
We also construct EPU indices for eleven other major economies.
11
As with our US
index, we first obtain a monthly count of articles that contain a triple of terms about the economy
(E), policy (P) and uncertainty (U). We then scale the raw counts, standardize each newspaper’s
variation, average across papers in a country by month, and normalize.
12
To help develop
suitable E, P and U term sets, we consulted persons with native-level fluency and economics
expertise in the relevant language and country. Our P term set differs across countries for reasons
both obvious (e.g., using “BOJ” for Japan) and idiosyncratic (e.g., inclusion of “customs duties”
for India). Appendix A lists the term sets and newspapers for each country-level EPU index. We
perform all searches in the native language of the newspaper, drawing on archives for seven
newspapers in India, six each in Canada and South Korea, two each in France, Germany, Italy,
Japan, Spain and the United Kingdom, and one each in China and Russia.
13
Figure 4 displays the EPU index for Russia, and Appendix Figures A1-A10 display the
other country-level indices.
14
The Russian index responds to Russian military conflicts, major
11
We have also assisted other researchers in developing EPU indices for Holland, and we are currently collaborating
with others to develop additional EPU indices for Argentina, Australia, Brazil and Japan and are open to proposals
to develop indices for other countries.
12
For certain papers outside the US, search platform limitations preclude us from scaling by the count of all articles.
In these cases, we instead scale by the count of articles containing the common and neutral term “today”.
13
Censorship and state control of the media present special challenges for Russia and China. For China, we use the
South China Morning Post, the leading English-language newspaper in Hong Kong. For Russia, we rely on
Kommersant, which focuses on financial matters and is reportedly fairly free of government pressures.
14
We provide regular monthly updates of the country-level EPU indices at www.policyuncertainty.com.
9
political developments in Ukraine, the Russian Financial Crisis in 1998, the Lehman Brothers
failure in 2008, the 2013 “taper tantrum” triggered by a perceived shift in US monetary policy,
and other developments. While the Russian index is noisy, reflecting our reliance on a single
paper, it shows that our approach yields useful information even for countries with strong
restrictions on press freedoms. Looking at EPU indices across twelve countries, we see that a
wide variety of global and domestic factors drive movements in our newspaper-based measures
of policy uncertainty.
2.4 Long-span EPU indices for the US and UK
We also construct long-span monthly EPU indices back to 1900 for the United States
(drawing on digital archives for the Wall Street Journal, New York Times, Los Angeles Times,
Boston Globe, Chicago Tribune and Washington Post) and the United Kingdom (Times of
London and the Guardian). Based on informal audits and our review of word usage patterns in
newspapers and other text sources, we expanded the E term set for the historical indices to
include “business”, “industry”, “commerce” and “commercial”. The expanded and narrower E
term sets yield very similar results in recent decades, but the expanded set seems to perform
better in the early decades of the 20
th
century. Based on results of the audit analysis described
below, we also expanded the P term set for the historical indices to include “tariff” and “war”.
Figure 2 and Figure A11 in the appendix display the historical EPU indices for the US
and UK. Indices for these two countries exhibit both similarities and notable differences. For
example, the elevation of EPU levels in the 1930s is dramatic in the US but modest in the UK,
which experienced a less severe output fall during the Great Depression. World Wars I and II are
more prominent in the UK EPU series. Gulf Wars I and II are associated with sharp EPU spikes
in both countries. The mid 1970s stands out as a period of unusually high EPU in the UK, which
suffered severe economic turmoil during the late 1970s and saw the resignation of Prime
Minister Harold Wilson, but not in the US. The post-1960s upward drift of EPU evident for the
US is absent for the UK. This long-span US-UK comparison reinforces our earlier inference that
a broad mix of domestic and international developments influences the extent of policy
uncertainty in any given country.
10
3. EVALUATING OUR POLICY UNCERTAINTY MEASURES
As remarked in the Introduction, using newspaper-based measures of economic policy
uncertainty raises several issues about accuracy and potential bias. This section explains how we
sought to address those issues. We start with a discussion of our audit study, which relies on
human readings of newspaper articles. We use the audit study to select our P term set, compare
the time-series behavior of human and computer-generated EPU indices, and collect other
information about the nature of policy uncertainty. Next, we consider the role of political slant in
our EPU index. Lastly, we compare our newspaper-based index to other measures of uncertainty:
stock market volatility, the frequency of uncertainty and policy uncertainty discussions in the
Beige Books, the share of the “Risk Factors” section in firms’ 10-K filings devoted to
government policies and regulations, and the frequency of large daily stock market moves
triggered by news about government policy.
3.1 Audit Study Based on Human Readings
We spent six months developing an audit process designed to evaluate and refine our US
EPU indices and another 18 months running a large-scale human audit study. During the latter
phase, student teams working under our close supervision read and coded articles drawn from
eight newspapers from 1900 to 2012.
15
We now describe the audit process and results.
Audit process: The authors began by reading a few hundred newspaper articles, typically
in batches of fifty, and comparing notes to develop classification criteria, an audit template in the
form of an Excel file, and the first draft of a guidebook for auditors. Early on, we concluded that
the largest payoff to an audit study involved selecting and evaluating the “policy” or P term set.
Accordingly, the formal audit study described below samples from the universe of articles that
meet our “economy” and “uncertainty” criteria, which concentrates our (expensive) human
resources on samples that are highly germane for our purposes.
16
Next, we conducted a pilot audit. Working with a team of student research assistants, we
read and coded 2,000 randomly selected newspaper articles. To identify coding difficulties and
weaknesses in our training materials, we held weekly review sessions with the auditors and
15
To construct our EPU index, it suffices to recover counts of articles that contain certain terms. In contrast, we
need full-text articles (machine-readable files or images) to carry out the audit study. We could not access full-text
articles for the Boston Globe or USA Today, but we did so for the other eight newspapers.
16
Only 0.5 percent of the articles in our 10 leading newspapers satisfy both the “economy” and “uncertainty”
criteria. Thus, the vast majority of all articles read by our auditors would be useless for selecting and evaluating our
P term set if we were to sample randomly from all newspaper articles.
11
assigned about 20% of articles to multiple auditors. We used the pilot study to develop a training
process and to refine our audit guide. The resulting 65-page guide serves as both a training tool
and reference manual in our full-scale audit. It explains how to assess whether an article meets
our criteria for economic uncertainty and economic policy uncertainty and how to code each
field in the audit template.
17
The pilot study also led to improvements in the audit process. For
example, to ensure that auditor-learning effects are not confounded with differences across
papers or over time, the full-scale audit study presents articles to auditors in a randomized order.
To conduct the full-scale audit, we recruited and trained new teams of research assistants.
Each new auditor underwent a training process that included a review of the audit guide and
template, trial codings of at least 100 articles (not included in the audit sample), a one-on-one
meeting to review the trial codings, and additional trial codings and feedback when needed. We
met with the audit teams on a weekly basis to address questions, review “hard calls” and coding
differences, and maintain esprit de corps. The auditors reviewed 12,009 articles from 1900 to
2012 that we selected using a two-stage approach:
18
First, we specified a target sample size
(higher in 1985-2011 and certain key earlier years), and then we randomly sampled a number of
articles for each newspaper and month. To monitor audit quality and sharpen incentives for
careful work, we randomly assigned about one quarter of the articles to multiple auditors.
Selecting a P term set: When an auditor codes an article as EPU=1, he or she also records
the policy terms contained in the passages about economic policy uncertainty. Using these
records, we identified 15 terms that appear often in newspaper discussions of EPU from 1985 to
2012: “regulation”, “budget”, “spending”, “policy”, “deficit”, “tax”, “federal reserve”, “war”,
“white house”, “house of representatives”, “government”, “congress”, “senate”, “president”, and
“legislation” (and variants like “regulatory”, “taxation”, etc.). We then considered the
approximately 32,000 term-set permutations with four or more of these policy terms. For each
permutation, we generated computer assignments of EPU
C
= 0 or 1 for each article in the sample.
By comparing these computer assignments to the human codings, we obtain a set of false
positives (EPU
C
=0, EPU
H
=1) and false negatives (EPU
C
=1, EPU
H
=0) for each permutation. We
17
The guide includes coding instructions, numerous examples, and FAQs. For example, one of the FAQs asks “Are
remarks about uncertain tax revenues grounds for EPU=1?” and answers “Yes, if the article attributes uncertainty
about tax revenues partly or entirely to uncertainty about policy choices…. No, if the article attributes uncertainty
about tax revenues entirely to uncertainty about economic conditions …” The audit guide is available at
www.policyuncertainty.com/Audit_Guide.pptx.
18
We reviewed more than 15,000 articles across the pre-audit phase, pilot audit, auditor training exercises and full-
scale audit, but we draw only on the 12,009 articles in the full-scale audit for our analysis here.
12
chose the P term set that minimizes the gross error rate – i.e., the sum of false positive and false
negative error rates. This process yields our baseline policy term set for the EPU index in Figure
1: “regulation”, “deficit”, “federal reserve”, “white house”, “congress”, and “legislation”.
Appendix Figures B1 to B6 display alternative EPU indices constructed by dropping the
six baseline terms, one at a time. Inspecting these figures, it is apparent that the time-series
behavior of our EPU index is not particularly sensitive to any single policy term. We also
experimented with compound text filters, e.g., adding {government AND tax} to the baseline
term set. In this regard, we focused on terms that materially lowered the false negative rate
relative to the baseline term set.
19
“Tax” is the leading example in this regard. Somewhat to our
surprise, we were unable to develop simple compound text filters that achieved a lower gross
error rate than our baseline term set.
We repeated this process to obtain the P term set for the historical EPU index in Figure 2,
which makes use of all six terms in the P set for the modern index plus “tariff” and “war”.
Adding these two policy terms accords well with the prominent role of tariffs and tariff revenues
in the first half of the 20
th
century and with US participation in World Wars I and II, the Korean
War and the Vietnam War, all of which involved much greater per capita rates of US military
deployments and casualties than more recent military conflicts.
Time-Series Comparison: We chose the P term set for our computer-automated EPU index
to minimize the gross error rate relative to the human benchmark provided by our audit study. To
assess the time-series performance implied by our automated classifications, we now compare
movements over time in human and computer-generated EPU indices. To do so, we compute the
fraction of audit-sample articles with EPU
H
=1 in each quarter from 1985 to 2012, multiply by the
EU rate for our 10 newspapers, and normalize the resulting human EPU index to 100 over the
period. To obtain the corresponding computer EPU index, we instead use the fraction of audit-
sample articles with EPU
C
=1. Figure 5 compares these human and computer EPU indices. There
are differences between the two series – e.g., a larger spike for the summer 2011 debt-ceiling
dispute in the human EPU index – but they are quite similar, with a correlation of 0.86.
Repeating the same type of comparison using annual data from 1900 to 2010 in appendix Figure
C1, we find a correlation of 0.93 between the human and computer EPU indices.
19
At the cost of even greater increases in the false positive error rate, of course – otherwise, the term in question
would have been part of the baseline set.
13
Figures 5 and C1 provide some assurance that our computer-automated EPU classifications
track the actual time-series variation in the intensity of concerns about EPU, as judged by
intelligent human beings. In this regard, it’s worth stressing that our term-set selection criterion
makes no use of time-series variation. So Figures 5 and C1 offer something of an independent
check on the performance of our automated classification criteria. However, it’s also important
to understand the limitations of these comparisons. They incorporate our computer-automated
EU assignments and, more fundamentally, they rely on the content of newspaper articles. We use
other methods, as discussed below, to assess the reliability of newspaper content for the purposes
of constructing an EPU index.
For downstream econometric applications, we also care about the time-series properties of
net error rates in the computer EPU index. Calculating this net error rate from the series in Figure
5, we find that it is essentially uncorrelated with quarterly real GDP growth rates (correlation of
-0.02) and with the “true” (i.e., human) EPU rate in the audit sample (correlation of 0.004).
Other Audit Results: Our audit study also speaks to several other questions related to our
EPU index. First, only 5 percent of audit-sample articles with EPU
H
=1 mainly discuss actual or
prospective declines in policy uncertainty. Apparently, reporters and editors do not regard falling
uncertainty as particularly newsworthy. Second, 10 percent of EPU
H
=1 articles discuss
uncertainty about who will make future economic policy decisions, 68 percent discuss
uncertainty about what economic policies will be undertaken (or when), and 47 percent discuss
uncertainty about the economic effects of past, present or future policy actions. Third, the share
of EPU
H
=1 articles that discuss who will make future economic policy decisions triples in
presidential election years, as compared to other years, indicating that the nature of policy
uncertainty shifts substantially over the election cycle.
20
Fourth, 32 percent of EPU
H
=1 articles
mention policy matters in other countries, often alongside domestic policy concerns.
20
We also find electoral cycle effects on the level of policy uncertainty in a multi-country setting. In particular, we
merge our country-level EPU indices with data on the timing and closeness of democratic national elections from
Julio and Yook (2012, 2013), updating their data to cover recent elections. This effort yields an unbalanced panel
with 12 countries, 62 national elections (none for China) and 3,263 monthly observations. Using country fixed
effects and an election timing indicator as explanatory variables, EPU is on average 16 log points higher during the
month of national elections (t-statistic of 5.3, clustering errors at the country level). Including ln(1+|percentage
voting gap between first- and second-place finishers|) as an additional regressor, we find statistically significant
evidence that close elections yield a further elevation of policy uncertainty – but the closeness effect is small.
14
3.2 Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for political slant to skew newspaper
coverage of EPU. If right-leaning (left-leaning) newspapers seriously overplay EPU when
Democrats (Republicans) are in power, political slant could distort measured changes in our
index. To investigate this issue, we split our 10 newspapers into the 5 most ‘Republican’ and 5
most ‘Democratic’ papers using the media slant index of Gentzkow and Shapiro (2010). They
assign slant values based on how frequently newspapers use words preferred by one party or the
other in their Congressional speech. For example, a newspaper that frequently uses “death tax”,
“personal accounts” and “war on terror” (terms preferred by Republicans) falls on the right side
of their slant index, and a newspaper that frequently uses “estate tax”, “private accounts” and
“war in Iraq” (terms preferred by Democrats) falls on the left side. Appendix Figure C3 plots the
“left” and “right” versions of our EPU index. They move together closely, with a correlation of
0.92. This finding suggests that political slant does not seriously distort variation over time in
newspaper coverage of EPU and is not a major concern for our index.
3.3 Comparisons to Other Measures of Uncertainty and Policy Uncertainty
Another way to evaluate our EPU index is by comparison to other measures of
uncertainty and policy uncertainty. The most obvious comparator is the VIX, an index of 30-day
option-implied volatility in the S&P500 stock index, available since 1990. As seen in Figure 6,
the VIX and the EPU index often move together (correlation of 0.58), but they also show distinct
variation. For example, the VIX reacts more strongly to the Asian Financial Crisis, the
WorldCom Fraud and the Lehman Brothers collapse – events with a strong financial and stock-
market connection. In contrast, the EPU index shows stronger responses to war in the Gulf
region, the election of a new president, and political battles over taxes and government spending
– events that clearly involve major policy concerns but also affect stock market volatility.
Of course, the two measures differ conceptually in several respects. While the VIX
reflects implied volatility over a 30-day look-ahead period, our EPU index involves no explicit
horizon. The VIX pertains to uncertainty about equity returns, while the EPU index reflects
policy uncertainty, and not just for equity returns. The VIX covers publicly traded firms only,
which account for about one-third of private employment (Davis et al., 2007). To throw some
light on the role of these differences, we create a newspaper-based index of equity market
uncertainty. Specifically, we retain our E and U term sets but replace the P term set with “stock
15
price”, “equity price” or “stock market”. The resulting index, shown in Appendix Figure C2,
correlates with the VIX at 0.73, considerably higher than the EPU-VIX correlation.
21
This result tells us two things. First, it demonstrates that we can construct a reasonable
proxy for an important type of economic uncertainty using frequency counts of newspaper
articles – a proof-of-concept for our basic approach. Second, the stronger correlation of the
newspaper-based equity index with the VIX confirms that differences in topical scope between
the VIX and the EPU index are an important source of distinct variation in the two measures.
Other Text Sources: We also consider uncertainty indicators based on the Beige Book
releases before each regularly scheduled meeting of the Federal Open Market Committee
(FOMC). The Beige Book, published eight times a year, summarizes in roughly 15,000 words
the views and concerns expressed by business and other contacts to the twelve regional Federal
Reserve Banks. We count the frequency of “uncertain*” in each Beige Book, normalized to
account for variation in word count.
22
We also read each passage that contains “uncertain*” to
judge whether it pertains to policy matters and, if so, we record the policy category.
Figure 7 shows the resulting quarterly frequency counts per Beige Book (BB). It
highlights many of the same shocks and policy developments as the EPU index in Figure 1. The
quarterly time-series correlation between the EPU index and the BB policy uncertainty indicator
is 0.54. The BB policy uncertainty indicator shows little immediate response to the financial
crisis but begins to rise in the second half of 2009 and is at highly elevated levels from 2010 to
2013. In a categorical breakdown analogous to Table 1 (not shown), the Beige Books also point
to fiscal policy as the most important source, by far, of elevated policy uncertainty in recent
years. Financial regulation and sovereign debt concerns figure more prominently in the Beige
Books than in newspapers. In sharp contrast to newspapers, the Beige Books almost never
mention monetary policy uncertainty.
Figure 7 also shows a policy uncertainty indicator based on textual analysis of 10-K
filings. For each 10-K filing, we count sentences in the Risk Factors section (mandatory since
fiscal year 2005) that contain one or more of the policy terms listed in Appendix E. We then
divide by the total number of sentences in the Risk Factors section and average over firms by
21
We make no effort here to develop an optimal term set for the news index of equity market uncertainty, something
we are currently pursuing in other work. Instead, Figure C2 reflects our first attempt and can surely be improved.
22
That is, we divide the raw frequency count by the number of words in the Beige Book and rescale to preserve the
average frequency count per Beige Book over the sample period.
16
year to obtain the series in Figure 7.
23
While the temporal coarseness of the 10-K filings
precludes fine-grained comparisons, our analysis reveals a strong upward drift after 2009 in the
degree to which firms express concerns about their exposure to policy-related risk factors.
Appendix Figure C5 reports another 10-K policy uncertainty indicator based on the fact
that firms generally discuss risk factors in order of their importance to the firm. Thus, for each
10-K filing, we calculate the percent of the Risk Factors section one must read before
encountering a discussion of policy-related risks. Averaging across firms by year, the mean
value of this measure falls from 25.2 percent for fiscal year 2005 to 17.0 percent for 2013, and
the median falls from 15.2 to 8.7 percent. In other words, the average firm perceives policy risks
as increasingly important from 2005 to 2013 relative to other risks.
Daily Stock Market Jumps: Finally, following Baker, Bloom and Davis (2015), we
characterize all large daily moves (greater than |2.5%|) in the S&P stock index from 1900 to
2012. In each instance, we locate and read the next-day New York Times and Wall Street Journal
articles that cover the stock move. We record the explanation(s), according to the article, and
classify it as policy-related or not. The idea is that higher policy uncertainty leads to a greater
frequency of large equity market moves triggered by policy-related news. As seen in Figure C6,
we find precisely that. The correlation of the annual frequency count of daily stock market jumps
triggered by policy news and the annual version of the EPU index in Figure 2 is 0.78. The 1930s
and the period during and after the Great Recession stand out in both series.
3.4 Summary
In summary, our audit study and comparison to other text sources and types of data
indicate that our newspaper-based EPU indices contain useful information about the extent and
nature of economic policy uncertainty. Compared to other policy uncertainty measures,
newspaper-based indices offer distinct advantages: They can be extended to many countries and
backwards in time, sometimes by a century or more. For large countries like the US, it is feasible
to construct useful newspaper-based indices at a daily frequency and by region. And newspaper-
based indices are readily disaggregated and parsed to develop category-specific indices.
23
The average length of the Risk Factors section of 10-K filings has grown steadily over time, perhaps because
firms are providing increasingly detailed discussions in this regard. For this reason, we prefer to scale by the total
number of sentences, so as not to overstate the rising importance of policy-related risk factors.
17
4. POLICY UNCERTAINTY AND ECONOMIC ACTIVITY
To investigate whether policy uncertainty matters for economic outcomes, we take two
complementary approaches. The first uses firm-level data, yielding better causal identification
but capturing only a limited set of impact channels – government purchases of goods and
services and certain aspects of regulatory policy. The second uses macro data in VAR analyses,
potentially capturing many channels but offering little assurance about the identification of
causal effects.
4.1 Firm-level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock price volatility, as a proxy for
firm-level uncertainty, and investment rates and employment growth as real activity measures.
We use US panel data on publicly listed firms and an identification strategy that differentiates
firms by exposure to uncertainty about government purchases of goods and services. To measure
this exposure, we draw on two sources of information. For firms in Health Services (SIC 80), we
use the government share of US healthcare expenditures in 2010, which we calculate as 43.8% in
Appendix F. For all other industries, we exploit micro data in the Federal Registry of Contracts
from 2000 to 2013 as follows.
As a first step, we match the federal contracts database to Compustat firms using DUNS
numbers and the names of the parent firm and their US subsidiaries.
24
This match yields the
parent firm’s revenue derived from Federal contracts, which we allocate to 3-digit SIC industries
using industry codes and line-of-business data in Compustat. We then aggregate revenues and
contract awards to obtain the ratio of federal purchases to revenues in each 3-digit industry by
year. To smooth out high-frequency variation from lumpy contract awards, we average these
ratios from 2000 to 2013 to obtain our exposure measure for each 3-digit SIC. At the top end,
firms operating in the Guided Missiles and Space Vehicles and Parts Industry (SIC 376) derive
78% of their revenues (in SIC 376) from sales to the federal government. The corresponding
figure for selected other industries with high exposures to federal purchases is 39% for Ordnance
and Accessories (SIC 348), 27% for Search, Detection, Navigation, Guidance & Aeronautical
Systems (SIC 381), 21% for Engineering Services (SIC 871), 20% for Aircrafts and Parts (SIC
24
We do so using Dunn & Bradstreet’s US database of all public and private firms, which includes a firm name,
DUNS number, industry and ownership information. In this way, we capture federal contracts of the publicly listed
parent firm (e.g. “General Electric”) and contracts with subsidiaries of the parent firm (e.g. “General Electric
Capital Services” and “USA Instruments”).
18
372), 15% for Ship and Boat Building and Repairing (SIC 373), 11% for Blank Books, Loose
Leaf Binders, and Bookbinding (SIC 278), and 9% for Heavy Construction (SIC 160). Direct
sales to the federal government are comparatively small in most other industries.
In a second step, we measure each firm’s exposure to government purchases as its
revenue-weighted mean (across its lines of business) of the industry-level exposure measures
calculated in the first step. If the firm operates in a single 3-digit SIC, then its exposure measure
equals the corresponding industry exposure measure. We prefer this two-step approach because
it may lessen the scope for reverse causality, and because industry-level measures may better
proxy for the firm’s ex ante exposure to uncertainty about government purchases. Our robustness
investigations below consider several other firm-level policy exposure measures.
4.1.1 Implied Stock Price Volatility
Table 2 displays results from regressing firms’ 30-day implied stock-price volatility on
economic policy uncertainty. We obtain the implied volatility measure from Options Metrics,
which calculates the 30-day volatility implied by firm-level equity options. These options have
been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE, 2014),
and our data begin in 1996. We use this volatility measure in quarterly regressions to match the
quarterly company accounts, averaging implied volatility over all trading days in the quarter. We
run regressions on a sample that extends from 1996 to 2012 and weight by firm sales, giving
more weight to the larger firms that also tend to have more actively traded equity options.
Column (1) reports a very basic specification that regresses logged 30-day implied
volatility on our EPU index and the ratio of federal government purchases to GDP, a control for
the first moment of policy. Log(EPU) is highly statistically significant, with the coefficient of
0.432 indicating that a 1% EPU increase is associated with a roughly 0.43% increase in firm-
level implied volatility. To put this magnitude in perspective, our EPU index rose by 85.6 log
points (135%) from 2006 to 2012, which implies an estimated upward shift of 37.2 log points
(45%) in average firm-level implied volatility. The negative coefficient on the control variable in
Column (1) says that, conditional on log(EPU), average firm-level implied volatility is lower
when the ratio of federal purchases to GDP is higher.
Column (2) contains the key result. We add a full set of firm and time fixed effects to
control for unobserved factors that differ across firms and unobserved common factors that vary
over time. The log(EPU) and Federal Purchases/GDP terms drop out, as they are collinear with
19
the time effects. But we now interact these measures with our firm-level measures of exposure to
government purchases. This specification tests whether implied volatility in firms with greater
exposure to government purchases co-varies more strongly with policy uncertainty. We find very
strong evidence for such effects. The coefficient of 0.215 on the log(EPU)*Intensity measure
suggests that for every 1% increase in our policy uncertainty index a firm with, say, a 50%
government revenue share would see its stock volatility rise by 0.11%.
25
Column (3) evaluates to what extent our EPU measure tells us anything different from the
VIX index, the most commonly used proxy for overall economic uncertainty. As noted in
Section 3.3, our EPU index and the VIX have a correlation coefficient of 0.58. Adding the VIX
in a specification without firm or time effects reverses the sign of the EPU term, while the
coefficient on the VIX is large (at 0.734) and highly significant. This result is unsurprising since
the VIX is the 30-day implied volatility on the S&P500 index, and it should be highly correlated
with the average 30-day implied volatility for publicly listed US firms.
Column (4) again adds time and firm fixed effects, and we now interact the EPU, Federal
Purchases/GDP and VIX measures with the intensity of the firm’s exposure to government
purchases. Strikingly, we now find that the EPU index has a large and significant coefficient,
while the VIX drops out entirely. Combining columns (3) and (4) reveals that the 30-day implied
volatility is best explained by the VIX index for the average firm, but the EPU index provides
additional explanatory power for the implied volatility of firms in sectors with high government
exposure – like defense, healthcare, engineering services and heavy construction.
Columns (5) and (6) run a similar evaluation for the Economic Uncertainty (EU) index,
yielding similar results. In column (5) we run a regression with the EPU, EU and Federal
Purchases/GDP measures, but no time or firm fixed effects. The EU index dominates with a
large and highly significant coefficient. Again, this result is not surprising – the EU index
reflects the overall frequency of newspaper articles about economic uncertainty, without any
stipulation that these articles also discuss policy. Column (6) adds time and firm fixed effects,
and we again interact the key measures with each firm’s exposure to government purchases. As
before, the EPU measure dominates the general uncertainty measure in the interacted
specification with controls for firm and time effects. Indeed, the EU measure now takes on the
25
Using a quite different empirical design and source of variation, Kelly, Pastor and Veronesi (2015) find evidence
that policy uncertainty related to election outcomes also raises option-implied stock market volatility.
20
opposite sign. In summary, while the EU index is more closely related to the average firm-level
implied volatility in the specification (5) that excludes firm and time effects, the EPU index
outperforms the EU index in explaining firm-specific movements in option-implied volatility.
Finally, in column (7) we add category-specific EPU measures from Section 2.2 for firms
in the defense, finance and healthcare sectors. These category-specific measures potentially
capture a broad range of impact channels, including ones that involve regulatory policy.
Reassuringly, all three of these measures yield positive, statistically significant coefficients at the
1 to 10 percent level. For example, implied volatility for defense firms responds to the National
Security EPU index, which jumped up in Gulf Wars I and II and after the 9/11 terrorist attacks
(Figure 3). Similarly, implied volatility for firms in the healthcare sector responds to the
Healthcare EPU index, which rose during the Clinton healthcare reform initiative and in response
to uncertainties surrounding the Affordable Care Act. The large, highly significant coefficient on
the Financial Regulation EPU index is especially noteworthy, because direct federal purchases of
goods and services are miniscule in the finance sector. Thus, we see this result as evidence that
regulatory policy uncertainty drives firm-level stock price volatility.
These results imply that policy uncertainty accounts for significant variation in the cross-
sectional structure of stock-price volatilities. To see this point, consider the estimated changes in
firm-level volatilities associated with the change in policy uncertainty from 2006 to 2012. Using
the results in Table 2 Column (7), we calculate these changes as (0.082)*(firm’s exposure to
government purchases)*(change in overall log EPU) plus (coefficient on category-specific log
EPU)*(change in category-specific log EPU). Table A1 implements this calculation for firms in
selected industries, yielding increases of up to 23.8 log points for financial firms and 13.9 log
points for healthcare firms, mainly due to the run up in their respective category-specific EPU
indices; and 3.3 to 4.6 log points for firms in the Ordnance, Aircraft and Engineering Services
industries, mainly due to their strong exposures to government purchases and the rise in overall
policy uncertainty. Comparing July-August 2001 to September-October 2001 (before and after 9-
11) and carrying out the same type of calculations, we find stock-price volatility increases of 14-
15 log points for firms in Ordnance, Aircraft and Engineering Services, 11.2 log points in
Finance sector, 7.5 log points in Healthcare, and tiny responses for firms in most other industries.
Hence, the implied magnitudes are sizable for firms in industries with large policy exposures.
21
Table 3 presents a wide range of additional robustness results for specifications that
include firm and year fixed effects. Columns (1) and (2) consider realized volatility and 182-day
implied volatility to look at longer and shorter uncertainty horizons, yielding very similar results.
Column (3) adds forecasts for government purchases relative to GDP
26
(interacted with firm-
level exposure) as a control, and Column (4) uses actual future government purchases relative to
GDP (again interacted) as a control. Column (5) replaces our preferred firm-level exposure
measure (calculated by the two-step method described above) with a one-step measure calculated
directly from the firm’s own sales to the federal government. Column (6) uses the Belo et al.
(2013) measure of industry-level exposure to government purchases, which exploits the input-
output matrix to capture direct and indirect effects of government purchases.
Columns (7) and (8) in Table 3 consider two entirely different approaches to measuring
firm-level exposure to government policy risks. In column (7), we measure exposure by the slope
coefficient in a regression of the firm’s daily stock returns on our daily EPU index from 1985 to
1995, which pre-dates the sample period in Table 2. Using this “beta” measure of policy risk
exposure, we again find positive and statistically significant effects of EPU on firm-level
volatility. In Column (8), we use the policy risk exposure measure derived from 10-K filings and
plotted over time in Figure 7, but now measured at the firm level (averaging over available
years). We again find sizable effects of EPU on firm-level volatility, but the coefficient on the
log(EPU) interaction term is less statistically significant, partly due to a smaller sample size
27
and perhaps partly because this measure reflects the firm’s perceived exposure to policy risk
factors from 2006 onwards only, whereas the regression sample starts in 1996. Column (9)
restricts attention to firms with at least $500 million in annual sales. These alternative measures
and specifications all yield highly significant results similar to Column (2) in Table 2.
Finally, Appendix Table A2 returns to the baseline specification in Table 2 Column (2)
and replaces the key log(EPU) interaction term by log(EPU/X), where X corresponds to the
newspaper-based E (“Economy”), P (“Policy”), U (“Uncertainty), EP, EU or PU index. These
variants yield slope coefficients on the key log(EPU/X)*intensity variable that are statistically
26
These forecasts come from the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters.
27
The sample shrinks for several reasons. First, the SEC did not mandate a Risk Factors discussion before 2006, so
we cannot obtain this measure for firms that delisted before 2006. Second, some publicly listed firms are exempt
from the Risk Factors disclosure requirement, and some may not comply. Third, our web-scraping and automated
text-reading methods may not capture all relevant 10-K filings, perhaps because some firms present their discussion
of Risk Factors in an unusual format. Fourth, it is not always possible to match data from 10-K filings to Compustat.
Our match rates compare favorably to similar efforts by other researchers. See Appendix E for additional discussion.
22
indistinguishable from the point estimate in Table 2 Column (2). This highlights how it is the
combination of the E, P and U term sets in newspaper articles that drive our results rather than
the frequency of the individual E, P or U terms sets or the precise scaling of the EPU index.
4.1.2 Investment Rates and Employment Growth
Table 4 investigates the contemporaneous relationship between policy uncertainty and
firm-level investment rates and employment growth.
28
We now have data from 1985 to 2012
and, as before, weight by firm sales. We use our preferred measure of the firm’s policy exposure
intensity and a full set of time and firm effects in all Table 4 specifications. Column (1) reports a
regression of the firm-level quarterly investment rate on
Δ log(EPU)
( )
* Intensity
and
Δ Federal Purchases/GDP
( )
* Intensity
. The former has a significant negative coefficient of
−0.032
, and the latter has a significant positive coefficient. These results are in line with
standard predictions of investment-under-uncertainty models, e.g., Bernanke (1983), Dixit and
Pindyck (1994) and Bloom, Bond and Van Reenen (2007).
To assess the magnitude of the estimated policy uncertainty relationship, recall that the
EPU index rose 85.6 log points from 2006 to 2012. For a firm that sells 25% of its output to the
federal government, this EPU change and the coefficient on
Δ
log(EPU)*Intensity in Column (1)
imply a one-time investment rate drop of 0.68 percentage points (=0.856*0.032*0.25*100),
about one-sixth of the median firm-level investment rate of 4.2 percent. While this calculation
rests on a large EPU swing, there were several other large EPU moves during the sample period
– e.g., a fall of 82 log points from 1992 to 1999, a 72 point rise from 1999 to 2001, and a 79
point fall from 2001 to 2006. Hence, for firms with high exposures to government purchases, the
estimates imply that swings in policy uncertainty involve material changes in investment rates.
In column (2) we control for
Δ Forecasted Federal Purchases/GDP
( )
* Intensity
, given the
forward-looking nature of investment decisions, and obtain very similar results on the main
coefficient of interest. Adding controls for cash flow and Tobin’s q in column (2) yields a
28
We focus on simple linear specifications that do not allow for rich response dynamics or interactions between
uncertainty and the responsiveness of outcome variables to first-moment driving forces. More sophisticated
treatments of investment behavior in these respects using other measures of uncertainty include Abel and Eberly
(1996), Guiso and Parigi (1999) and Bloom, Bond and Van Reenen (2007). There is value in applying these more
sophisticated treatments to our policy uncertainty measures, but we leave that task to future research. For a richer
treatment of dynamics in firm-level investment rate responses to our EPU measure, see Gulen and Ion (2016).
23
coefficient of 0.30 (0.10) on
Δ log(EPU)
( )
* Intensity
, again very similar to column (1).
29
In
column (3) we include the average
Δ Federal Purchases/GDP
( )
* Intensity
value in the next 12
quarters as an alternative control for future expectations, and again find a significant negative
coefficient. In column (4) we add the category-specific measures and find statistically significant
negative results for terms involving log changes in the Healthcare EPU index and the Financial
Regulation EPU index. That is, the frequency of newspaper articles about these types of policy
uncertainty has additional explanatory power for the investment rates of firms that operate in
sectors most affected by these types of policy.
Columns (5) to (8) regress annual firm-level employment growth rates on EPU
(Compustat lacks quarterly employment data.) As with investment rates, we find sizable and
statistically significant negative coefficients on policy uncertainty for employment growth rates
for firms with high exposure to government policy. Consider again an 85.6 log point increase in
the EPU index and a firm that sells 25% of its output to the federal government. Given these
values, the coefficient of -0.213 on
Δ log(EPU)
( )
* Intensity
in Column (5) implies a one-time
drop in the annual employment growth rate of 4.6 percentage points, which is large relative to
the mean annual growth rate of 3.4 percent for firms in the sample. The category-specific EPU
variables do not have statistically significant effects on employment growth, in contrast to the
investment results.
In column (9) we consider the impact on sales as a placebo test. While the real-options
literature highlights how uncertainty suppresses demand for input factors with adjustment costs –
the short-run impact on output should be smaller according to this class of theories. Consistent
with this prediction, the estimated effect of
Δ log(EPU)
( )
* Intensity
in column (9) is negative but
not statistically significant, while the government purchases variable remains positive and
significant. Hence, our results suggest that increases in policy uncertainty are associated with
contemporaneous drops in investment rates and employment growth rates for firms in policy-
exposed sectors, but the near-term association with their output growth rates is more muted.
29
Using Compustat data, our cash flow measure is operating income before depreciation expressed as a ratio to the
book value of plant, property and equipment. The numerator of our Tobin’s q measure is the market value of equity
(common and preferred shares) plus the book value of debt less the value of inventories and deferred tax credits, and
the denominator is the book value of plant, property and equipment.
24
Finally, consider the relationship of policy uncertainty changes to the cross-sectional
structure of investment rates and employment growth. To do so, we return to Table A1 and carry
out calculations that parallel the earlier ones for stock-price volatility. Working again with the
policy uncertainty changes from 2006 to 2012, the implied quarterly investment rate changes are
modest except for a 2.9 percent drop for firms in the Healthcare sector, while the annual
employment changes are large in several sectors. Given the change-on-change nature of the
underlying regression specifications, these results are one-time changes associated with the total
change in the policy uncertainty measures from 2006 to 2012.
4.2 Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series variation at the country level.
Drawing causal inferences from VARs is extremely challenging – in part because policy, and
policy uncertainty, can respond to current and anticipated future economic conditions. Despite
the challenges, VARs are useful for characterizing dynamic relationships. At a minimum, they
let us gauge whether policy uncertainty innovations foreshadow weaker macroeconomic
performance conditional on standard macro and policy variables.
We start by fitting a VAR to monthly US data from January 1985 to December 2014. To
recover orthogonal shocks, we use a Cholesky decomposition with the following ordering: the
EPU index, the log of the S&P 500 index, the federal funds rate, log employment, and log
industrial production. Our baseline VAR specification includes three lags of all variables. Figure
8 depicts the model-implied responses of industrial production and employment to a 90-point
upward EPU innovation, equal in size to the EPU change from its average value in 2005-06
(before the financial crisis and recession) to its average value in 2011-12 (a period with major
fiscal policy battles and high EPU levels). Figure 8 shows maximum estimated drops of 1.2% in
industrial production and 0.35% in employment. These responses are statistically significant and
moderate in size, being about one-third as large as a typical business cycle fluctuation. Since
aggregate US investment data are not available at a monthly frequency, we also estimated an
analogous VAR model on quarterly data from 1985 to 2014, using the same type of Cholesky
decomposition to identify shocks. As shown in Appendix Figure C7, gross aggregate investment
exhibits a peak decline of about 6% in response to a 90-point EPU innovation.
Figure 9 shows that the basic character of the impulse response functions is robust to
several modifications of the specification, variable set, causal ordering and sample period: six
25
lags instead of three in the VAR, a bivariate VAR (EPU and industrial production), a bivariate
VAR with reverse ordering, including the VIX (after the EPU index), including the EU index
(after the EPU index), dropping the S&P500 index, including time trends, and using a sample
period that runs from 1920 (when industrial production data become available) until 1984. These
results are in line with the estimated effects of election uncertainty in Julio and Yook (2012) and
Durnev (2010), despite their distinct empirical approaches.
A potential concern is whether, and to what extent, our estimated impulse response
functions reflect bad news generally rather than policy uncertainty shocks in particular. Including
the S&P500 stock market index in the VAR mitigates this concern, given that stock markets are
forward looking and that stock prices incorporate many sources of information. Our baseline
VAR also includes other “first-moment” variables: log employment, log industrial production,
and the fed funds rate. Still, the EPU index will likely embed first-moment information not
captured by these variables. To investigate this issue, we also considered VARs that include the
Michigan Consumer Sentiment Index.
30
When we place the Michigan index after the EPU index
in the causal ordering, the estimated peak effect of a policy uncertainty shock on industrial
production falls by about one-third (Appendix Figure C8). When we place the Michigan index
first in the causal ordering, the peak effect shrinks by about half. These results indicate that,
conditional on the other variables, our EPU index and the Michigan index contain overlapping
information that has value for predicting future output and employment movements.
Perhaps this result is unsurprising. The Michigan index captures a mix of first-moment
and second-moment concerns, as expressed by households in survey data. The relationship
between “confidence” and uncertainty is a murky one, and the two concepts are tightly linked at
a deep level in some theoretical models, e.g., Ilut and Schneider (2014). In any event, the EPU
index has several important advantages relative to consumer confidence indices: EPU indices can
be extended to many countries, pushed back in time by a century or more in some countries,
30
The Michigan index reflects phone surveys of consumers and seeks to determine how consumers view the short-
term economy, the long-term economy, and their own financial situation. It takes the difference between the percent
answering positively and the percent answering negatively for each of 5 questions, then averages these differences
and normalizes by the base period (December 1968) total. The Michigan index has a correlation of -0.742 with our
EPU index. We chose the Michigan index as the more commonly used consumer confidence index, but other
consumer confidence indices are highly correlated with the Michigan Index – for example, the Bloomberg
Confidence index has a correlation of 0.943 with the Michigan index, and the Conference Board Confidence index
has a correlation of 0.912 with the Michigan index.
26
computed in near real-time on a daily basis, and parsed in many ways as illustrated by our
category-specific EPU indices.
Figure 10 shows impulse response functions for a panel VAR fit to monthly data from
1985 to 2014 on the twelve countries for which we have an EPU index. The panel VAR
specification parallels the baseline specification that underlies Figure 8, except that we use the
unemployment rate in place of log(employment). As before, we rely on a Cholesky
decomposition to identify shocks and display responses to an upward 90-point EPU innovation,
which is well within the range of EPU movements experienced by the individual countries. The
twelve-country panel VAR yields results that are similar to the US results in Figure 8. In
particular, the international panel VAR implies that a 90-point EPU innovation foreshadows a
peak drop in industrial production of about 1 percent and a rise in the unemployment rate of
about 25 basis points. Appendix Figure C9 shows that the basic character of the panel VAR
results is robust to a variety of alternative specifications, variable sets, and weighting methods.
Other researchers who use our EPU indices in multi-country time-series analyses also find that
policy uncertainty shocks foreshadow deteriorations in macroeconomic outcomes – examples
include the IMF (2013), Columbo (2013), Klössner and Sekkel (2014) and Nodari (2014).
Broadly speaking, we see three ways to interpret this VAR-based evidence. Under the
first interpretation, an upward EPU innovation corresponds to an unforeseen policy uncertainty
shock that causes the worsening of macroeconomic performance through real options effects,
cost-of-capital effects or other mechanisms. Second, an upward EPU innovation captures bad
news about the economic outlook that is not (fully) captured by the other variables in the VAR
system, and that bad news triggers a rise in EPU that has harmful effects on the economy. Under
this interpretation, EPU amplifies and propagates a causal impulse that originates elsewhere.
Third, EPU has no role as either an impulse or a propagation mechanism; instead, it simply acts
as a useful summary statistic for information missing from the other variables in our system —
log(output), log(employment) or unemployment, the policy rate, log(S&P 500), the VIX, and
consumer sentiment.
31
This third interpretation is hard to fully reconcile with our firm-level
results, which suggests that policy uncertainty has negative causal effects. It’s also worth noting
that our VAR results may understate the importance of policy uncertainty shocks as a driving
31
Stock and Watson (2012) consider many more variables in much larger and richer time-series models. They still
find evidence that EPU innovations precede deteriorations in aggregate performance.
27
force, even under the first interpretation, because other variables in the VAR system may
respond to news about future policy uncertainty shocks before they show up in the EPU measure.
Clearly, there is a need to develop a robust identification strategy for assessing the causal
role of policy uncertainty in macroeconomic performance by, for example, exploiting close,
consequential democratic elections and exogenous sources of variation in policy uncertainty such
as shifts in the outlook for conflict between North and South Korea or events like the UK
“Brexit” vote regarding participation in the European Union. In addition, linear VAR systems
may be overly restrictive in how they model EPU responses to other shocks. Perhaps EPU rises
in the wake of large negative shocks but responds relatively little to small ones. Allowing for this
type of asymmetry may lead to a larger role for EPU in amplifying and propagating the effects of
large negative shocks. It would also be useful to consider stochastic volatility models that allow
EPU shocks to directly influence the future volatility of other shocks, including shocks to policy
variables. We leave these tasks to future research.
At a deeper level, the causal role of policy uncertainty is potentially quite subtle. Sound
institutions and policy regimes foster predictable policy responses, even in the face of large
negative shocks. In this way, good institutions and policy regimes lessen the scope for policy to
act as a source of uncertainty impulses or, through uncertain policy responses, to amplify and
propagate the effects of other shocks.
5. CONCLUSION
We develop new measures of economic policy uncertainty for the United States and eleven
other major economies. We use these new measures to investigate the relationship of policy
uncertainty to firm-level stock-price volatility, investment rates and employment growth and to
aggregate investment, output and employment. Our findings are broadly consistent with theories
that highlight negative economic effects of uncertainty shocks. The results suggest that elevated
policy uncertainty in the United States and Europe in recent years may have harmed
macroeconomic performance. They also point to sizable effects of policy uncertainty on the
cross-sectional structure of stock-price volatilities, investment rates and employment growth.
From a methodological perspective, we show how to tap newspaper archives to develop and
evaluate new measures of interest to macroeconomists, financial economists, economic
historians and other researchers. In this regard, it’s worth stressing that newspapers are available
28
for countries around the world, and they have circulated in similar form for decades in most
countries and for centuries in some countries. This ubiquity, coupled with modern databases and
computers, offers tremendous possibilities for drawing on newspaper archives to deepen our
understanding of broad economic, political and historical developments through systematic
empirical inquiries. As illustrated by our category-specific EPU indices and annotated charts,
newspapers also offer much potential for assessing the role of specific developments and shocks,
at least as perceived by contemporary observers.
29
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32
Notes: Queries run 12 February 2015 on US newspapers in Access World News Newsbank, using the category-specific policy term sets
listed in Appendix B. Except for the last row, all entries are expressed relative to the average EPU frequency from 1985 to 2014. “Overall
Economic Uncertainty” quantifies the frequency of articles that meet our “economy” and “uncertainty” requirements (i.e., dropping the
“policy” requirement) and is also expressed relative to the average EPU frequency from 1985 to 2014. The category-specific index values
sum to more than 100 for two reasons: First, we use a few policy terms in more than one policy category. For example, “Medicaid” appears
in the term sets for both Healthcare and Entitlement Programs. Second, a newspaper article that meets the “economy”, “policy” and
“uncertainty” criteria can refer to more than one policy category.
Table 1: Economic Policy Uncertainty by Policy Category and Time Period, 1985 to 2014
Time period
1985:1-
1990:6
1990:7-
1991:12
1992:1-
2001:8
2001:9-
2002:12
2003:1 –
2007:6
2007:7-
2008:8
2008:9-
2009:12
2010:1-
2013:10
1985:1-
2014:12
Mid 80s
to Gulf
War I
Gulf
War I
1990s
boom
to 9/11
9/11
attacks
2000s
boom
Early
Credit
Crunch
Lehman
collapse &
recession
Fiscal
Policy
Battles
Overall
Average
Overall Economic Uncertainty
218.2%
349.8%
185.9%
326.9%
159.8%
184.8%
370.9%
252.1%
219.3%
Economic Policy Uncertainty
109.6%
141.9%
88.1%
128.5%
71.4%
83.4%
132.1%
127.5%
100.0%
Fiscal Policy
49.6%
59.6%
35.9%
55.4%
32.3%
33.1%
61.5%
78.3%
46.1%
- Taxes
39.9%
48.4%
31.9%
51.2%
30.2%
31.4%
56.9%
68.1%
40.3%
- Government Spending & Other
22.7%
26.8%
12.1%
17.3%
8.5%
6.6%
17.1%
33.2%
17.1%
Monetary Policy
32.7%
41.8%
26.1%
45.2%
22.2%
31.6%
27.8%
26.1%
28.1%
Healthcare
7.0%
15.4%
14.9%
18.4%
13.1%
13.4%
29.3%
39.3%
17.3%
National Security
25.0%
53.6%
18.0%
54.8%
25.4%
15.9%
21.3%
19.8%
23.8%
Regulation
15.7%
23.0%
14.5%
19.6%
11.2%
15.5%
29.2%
28.1%
17.4%
- Financial Regulation
3.3%
7.0%
1.3%
5.3%
1.7%
3.6%
10.2%
6.1%
3.3%
Sovereign Debt & Currency Crises
1.4%
0.6%
2.3%
0.5%
0.4%
0.3%
0.4%
3.9%
1.6%
Entitlement Programs
7.3%
12.6%
11.5%
18.7%
8.8%
8.2%
15.3%
24.7%
12.4%
Trade Policy
3.8%
4.0%
6.3%
2.6%
1.7%
2.0%
1.4%
2.1%
3.8%
Sum of Policy Categories
142.5%
210.7%
129.5%
215.1%
115.2%
120.0%
186.3%
222.2%
150.6%
Ratio of EPU To Overall EU
0.50%
0.41%
0.47%
0.39%
0.45%
0.45%
0.36%
0.51%
0.47%
33
Table 2: Option-Implied Stock Price Volatility and Policy Uncertainty
Dep Var: Log(30-day implied vol)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Log(EPU)
0.432***
-0.044***
-0.752***
(0.010)
(0.013)
(0.027)
Log(EPU)×Intensity
0.215**
0.228**
0.545***
0.082
(0.069)
(0.100)
(0.202)
(0.117)
Log(VIX)
0.734***
(0.016)
Log(VIX)×Intensity
-0.020
(0.117)
Log(EU)
1.080***
(0.027)
Log(EU)×Intensity
-0.301**
(0.177)
Federal Purchases/GDP
-19.30***
-7.75***
-17.40***
(1.50)
(1.49)
(1.49)
(Federal Purchases/GDP)× Intensity
-29.45*
-29.70**
-29.93*
-31.08
(12.72)
(12.36)
(12.66)
(13.24)
National Security EPU*Defense
0.048***
(0.012)
Healthcare EPU*Health
0.071*
(0.043)
Financial Regulation EPU*Finance
0.144***
(0.030)
Firm and Time Effects
No
Yes
No
Yes
No
Yes
Yes
Notes: The sample contains 136,578 observations on 5,460 firms from 1996 to 2012. The dependent variable is the natural log of the
30-day implied volatility for the firm, averaged over all days in the quarter. Intensity is the firm’s exposure to federal purchases of
goods and services computed by the two-step method described in Section 4. Federal Purchases/GDP is from NIPA tables. Log(EU)
is the log of the newspaper-based Economic Uncertainty index. National Security EPU*Defense is the National Security EPU index
from Table 1 multiplied by 1 for firms in defense industries (SICs 348, 372, 376, 379, 381, 871) and 0 otherwise, and analogously for
Healthcare EPU*Health (SICs 800 to 809) and Financial Regulation EPU*Finance (SICs 600 to 699). All regressions weighted by
the firm’s average sales in the sample period. Standard errors based on clustering at the firm level.
34
Table 3: Robustness Checks for Option-Implied Stock Price Volatility and Policy Uncertainty
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Specification
Realized
Volatility
182-day
Implied
Volatility
Add
Purchase
Forecast
Add 12 qtrs
Future
Purchases
Firm-
level
Intensity
Belo et
al. (2013)
Intensity
Beta
Intensity
10-K
Risk
Measure
$500m+
Sales
Firms
Log(EPU)×Intensity
0.346***
0.178***
0.175***
0.258***
0.192***
0.456***
0.283**
0.378*
0.237***
(0.089)
(0.073)
(0.070)
(0.086)
(0.045)
(0.101)
(0.118)
(0.217)
(0.071)
(Federal Purchases/
GDP)×Intensity
-23.72
-27.47***
-58.28***
-7.05
-14.20
-13.60
6.157
27.16
-31.03
(14.71)
(11.77)
(15.35)
(16.74)
(10.03)
(27.64)
(14.97)
(64.17)
(12.40)
(Forecasted Federal
Purchases/GDP)×Intensity
32.61***
(6.27)
Firm and Time Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
136,578
136,578
136,578
73,703
132,628
134,381
133,304
112,023
42,771
Number of Firms
5,460
5,460
5,460
3,070
5,219
5,374
5,328
3,717
1,056
Notes: The sample period is 1996 to 2012. The dependent variable is the 30-day implied volatility for the firm, averaged over all days
in the quarter, except that column (1) uses the realized daily volatility over the quarter, and column (2) uses the average 182-day
implied volatility. See the notes to Table 2 for additional variable definitions. Standard errors based on clustering at the firm level.
35
Table 4: Policy Uncertainty and Firm Level Investment, Employment and Sales
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dependent Variable:
I/K
I/K
I/K
I/K
ΔEmp
ΔEmp
ΔEmp
ΔEmp
ΔRev
Δ Log(EPU)×Intensity
-0.032***
-0.032***
-0.024**
-0.029***
-0.213**
-0.227**
-0.220**
-0.220**
-0.128
(0.010)
(0.010)
(0.011)
(0.010)
(0.084)
(0.089)
(0.118)
(0.094)
(0.096)
Δ(Federal Purchases/
GDP)×Intensity
8.20***
8.04***
12.12***
8.85***
10.79
15.60***
3.19
10.99
20.39**
(2.86)
(2.86)
(3.18)
(2.87)
(7.41)
(8.04)
(12.56)
(7.88)
(9.43)
Δ(Forecasted Federal
Purchases/GDP)×Intensity
1.01
-4.65***
(0.828)
(2.89)
Δ Log(Defense EPU) ×
Defense Firm
0.002
0.018
(0.004)
(0.017)
Δ Log(Healthcare EPU) ×
Health Firm
-0.012***
-0.005
(0.002)
(0.025)
Δ Log(Fin. Reg. EPU) ×
Finance Firm
-0.002***
0.003
(0.001)
(0.005)
Periodicity
Quarterly
Quarterly
Quarterly
Quarterly
Yearly
Yearly
Yearly
Yearly
Yearly
3 Yrs Fed purchase leads
No
No
Yes
No
No
No
Yes
No
No
Observations
708,398
708,398
411,205
708,398
162,006
162,006
107,205
162,006
151,473
Number of Firms
21,636
21,636
13,563
21,636
17,151
17,151
11,505
17,151
15,749
Notes: The sample period runs from 1985 to 2012. All columns include a full set of firm and time effects. I/K is the investment rate
defined as CapEx
t
/(Net Plant, Property and Equipment)
t-1
. ΔEmp is the employment growth rate measured as (emp
t
- emp
t-1
)/
(0.5×emp
t
+ 0.5×emp
t-1
), and ΔRev is the corresponding revenue growth rate. Δ(Federal Purchases/GDP)×Intensity is the change in
(Federal Purchases/GDP) from NIPA tables in the next quarter in quarterly specifications and in the next year in annual specifications,
multiplied by the firm-level policy exposure intensity variable. Δ(Forecasted Federal Purchases/GDP)×Intensity instead uses the
mean forecasted change in (Federal Purchases/GDP) from the Federal Reserve Bank of Philadelphia’s Survey of Professional
Forecasters, drawing on NIPA data for the current values and forecast data for the future values. See the notes to Table 2 for additional
variable definitions. Standard errors based on clustering at the firm level.
50
100 150 200 250
300
1985 1990 1995 2000 2005 2010 2015
Figure 1: Economic Policy Uncertainty Index for the US
Gulf
War I
9/11
Clinton
Election
Gulf
War II
Bush
Election
Stimulus
Debate
Lehman
and TARP
Euro
Crisis
Russian
Crisis/LTCM
Debt
Ceiling
Dispute
Black
Monday
Fiscal Cliff
Govt. Shutdown
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or ‘economy’, and one or
more policy relevant terms: ‘regulation’, ‘federal reserve’, ‘deficit’, ‘congress’, ‘legislation’, or ‘white house’. The series is normalized
to mean 100 from 1985-2009 and based on queries run on 2 February, 2015 for the USA Today, Miami Herald, Chicago Tribune,
Washington Post, LA Times, Boston Globe, SF Chronicle, Dallas Morning News, NY Times, and the Wall Street Journal.
Policy Uncertainty Index
0
100 200
300
400
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Policy Uncertainty Index
Versailles
Conf.
9/11
Debt
Ceiling
OPEC
II
Lehman
and TARP
Great
Depression,
New Deal
and FDR
Truman-
Dewey
election
Great
Depression
Relapse
Gulf
War I
Black
Monday
Start of
WW I
OPEC I
Asian
Financial
Crisis
Wage
& Price
Controls
McKinley
Assassination
Gold
Standard Act
Immigration
Act of 1924
Figure 2: US Historical Index of Economic Policy Uncertainty
Notes: Index reflects scaled monthly counts of articles in 6 major newspapers (Washington Post, Boston Globe, LA Times, NY Times,
Wall Street Journal, and Chicago Tribune) that contain the same triple as in Figure 1, except the economy term set includes
“business”, “commerce” and “industry” and the policy term set includes “tariffs” and “war”. Data normalized to 100 from 1900-2011.
Gulf War II
Fiscal Cliff
Tet
Figure 3: National Security and Healthcare EPU Indices
0
100 200 300 400
1985 1990 1995 2000 2005 2010 2015
Year
National Security Policy Uncertainty
Healthcare Policy Uncertainty
Notes: Indices reflect scaled monthly counts of articles containing the same triple as in Figure 1 and one or more terms pertaining to
national security (e.g., “war”, “terrorism”, or “department of defense”) and healthcare (e.g., “healthcare”, “hospital”, or “health
insurance”), respectively, for the National Security and Healthcare indices. Each series is normalized to mean 100 from 1985-2009
and based on queries run Jan 18, 2015 on Access World News Newsbank newspaper archive, which covers about 1,500 US papers.
Index Value
Gulf
War I
Gulf War II
9/11
Clinton healthcare
reform effort
Affordable
care act
0
50
100
150
200
250
300
350
400
450
1992 10
1993 4
1993 10
1994 4
1994 10
1995 4
1995 10
1996 4
1996 10
1997 4
1997 10
1998 4
1998 10
1999 4
1999 10
2000 4
2000 10
2001 4
2001 10
2002 4
2002 10
2003 4
2003 10
2004 4
2004 10
2005 4
2005 10
2006 4
2006 10
2007 4
2007 10
2008 4
2008 10
2009 4
2009 10
2010 4
2010 10
2011 4
2011 10
2012 4
2012 10
2013 4
2013 10
2014 4
Orange
Revolution
in Ukraine
Duma elections
and protests
against election
fraud
Kizlyar hostage crisis;
PM Chubais resigns
Constitutional Crisis
Russian
financial
crisis
First
Chechen
War
Second
Chechen
War
Acting PM
Gaidar resigns
Russian
military
exits
Chechnya
Timoshenko
resigns;
Terror attack
in Nalchik
Parliament
dismissed
In Ukraine
Terror
attacks in
Nalchik &
Stavropol
Medveded election
Putin
becomes
PM
Lehman Brothers Failure
Ukraine Conflict
Taper Tantrum
Putin election
Kiev
Euromaidan;
Crimea
annexation
!$#
Figure 4: Index of Economic Policy Uncertainty for Russia
Index reflects scaled monthly counts of articles in Kommersant with Russian terms for ‘uncertain’ or ‘uncertainty’, ‘economic’ or
‘economy’, and one or more selected policy terms. The series is normalized to 100 and runs from October 1992 to August 2014.
Nord-Ost
Siege
0 100 200 300
Economic Policy Uncertainty Index
1985 1990 1995 2000 2005 2010
Year
Notes: Index comparison from 1985 Q1 to 2012 Q1 based on 3,723 articles (4,388 audits) in the Chicago Tribune, Dallas Morning
News, LA Times, Miami Herald, NY Times, San Francisco Chronicle, Washington Post and Wall Street Journal. Series are plotted
quarterly to reduce sampling variability, with an average of 33 articles per quarter. Each series is normalized to 100 from
1985-2009. See text for additional discussion of the audit process and this comparison.
Computer
Human
(+ symbol)
Figure 5: Human and Computer EPU Indices
Correlation=0.86
10 20 30 40 50 60
50
100 150
200 250 300
1990 1995 2000 2005 2010 2015
VIX (red)
Economic Policy Uncertainty Index (Blue - - -)
9/11
WorldCom
Fraud
Gulf
War II
Lehman
Failure, TARP
Asian
Financial
Crisis
Gulf
War I
Euro-
zone
Crisis
Debt Ceiling
Dispute
Russian
Crisis,
LTCM
Default
Stimulus
Debate
Clinton
Election
Notes: The figure shows the U.S. EPU Index from Figure 1 and the monthly average of daily values for the 30-day VIX.
Figure 6: U.S. EPU Compared to 30-Day VIX
Bush
Election
Fiscal Cliff
Government
Shutdown
%#
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$#
&'#
&&#
&(#
&!#
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&'#
&)#
('#
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!'#
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&$")#*(#
&$",#*(#
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-./0.#-112#314567/8.9#A17/<>#:;<.4=6/;=>#?1@;=#
A17/<>#BC64.#1D#&'EF#G/H2#I6<=14H#
Figure 7: Policy Uncertainty Measures Based on Textual Analysis of the
Fed’s Beige Books and Part 1A (Risk Factors) of Firms’ 10-K Filings#
#
Notes: The left scale shows frequency counts per Beige Book (normalized by word count) of “uncertainty” and references to policy
uncertainty. The right scale reports the percentage of sentences in Part 1A (Risk Factors) of annual 10-K filings that contain one or
more of the policy terms listed in Appendix E. The correlation between the Beige Book Normalized Policy Uncertainty Count and
the EPU index is 0.54.
#
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K@7D#
L64#M#
$N&&#
K@7D#
L64#MM#
O.C56;#
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#
+(#
-.6 -.4 -.2
0
.2
0 6 12 18 24 30 36
Months
-2 -1
0 1
0 6 12 18 24 30 36
Months
Figure 8: Industrial Production and Employment Responses to EPU Shock,
VAR Fit to Monthly U.S. Data
IP Response, %
Notes: VAR-estimated
impulse response
functions for industrial
production and
employment to an EPU
innovation equal to the
increase in the EPU
index from its 2005-2006
to its 2011-2012 average
value, with 90 percent
confidence bands.
Identification based on
three lags and a
Cholesky decomposition
with the following
ordering: EPU index,
log(S&P 500 index),
federal reserve funds
rate, log employment,
log industrial production.
Fit to monthly data from
1985 to 2014.
Employment Response, %
-1.5
-1
-.5
0
.5
0 6 12 18 24 30 36
Months
Figure 9: U.S. Industrial Production Response to an EPU Shock,
Alternative Samples, Specifications and Identification Assumptions
Notes: The baseline case involves the same sample period, VAR specification and identification as in Figure 8. The other cases
depart from the baseline as indicated. We place EU and VIX after EPU in the ordering. For the “1920-1984” response function, we
use monthly data from 1920 to 1984 on log industrial production and EPU in a bivariate VAR with EPU ordered first.
Industrial Production Response, %
Baseline
(+ symbols)
Bivariate: EPU and log
industrial production
Six months
of lags
1920-
1984
Sample
Adding EU
Adding VIX
Reverse bivariate: log
industrial production, EPU
Adding a
time trend
No S&P
index
Figure 10: Responses to an EPU Shock in a Twelve-Country Panel VAR
Industrial Production, %
Months after EPU shock
Notes: Panel-VAR estimated
impulse response functions
for industrial production and
unemployment to an EPU
innovation equal to the
increase in the average US
EPU value from 2005-2006 to
2011-2012, with 90%
confidence bands.
Identification based on three
lags and a Cholesky
decomposition with the
following ordering: EPU
index, log(stock market
index), unemployment rate,
and log industrial production.
We use own-country data and
a full set of country fixed-
effects in the panel VAR.
Country-level data are
weighted by the square root
of the number of newspapers
used in the EPU index. Fit to
monthly data for Canada,
China, France, Germany,
India, Italy, Japan, Korea,
Russia, Spain, UK and the
US from January 1985 to
December 2014, where
available.
Unemployment Rate Response
-1.5
-1
-.5
0
0 6 12 18 24 30
36
0
.1 .2 .3 .4
0 6 12 18 24 30 36
46
Not Intended for Publication
10 March 2016
Appendices
A. Newspapers, Archives, and EPU Term Sets
United States (1985-, monthly): We search the LA Times, USA Today, Chicago Tribune,
Washington Post, Boston Globe, and Wall Street Journal using Proquest newspaper archives; the
Miami Herald, Dallas Morning News, Houston Chronicle, and San Francisco Chronicle using the
Access World News Newsbank service; and the New York Times using its own online archive.
Because the New York Times archive occasionally yields unstable article counts for recent dates,
we replaced the Times with the Houston Chronicle as of January 2014. In particular, after
rescaling the Chronicle’s EPU index to match the mean and variance of the Times prior to 2014,
we use EPU values from the Chronicle instead of the Times from January 2014 onwards in
constructing our 10-paper monthly index. The full set of policy terms is regulation, deficit,
legislation, congress, white house, Federal Reserve, the Fed, regulations, regulatory, deficits,
congressional, legislative, and legislature. Section 3.1 in the main text explains how we selected
this term set.
United States, Historical (1900-2014, monthly): We rely on archives for the LA Times, Chicago
Tribune, Boston Globe, Wall Street Journal, NY Times and Washington Post from 1900 to 1984
and all ten papers listed above from 1985 to 2010. In constructing the historical US index, we
expanded the economy term set to include “business”, “industry”, “commerce” and
“commercial” in addition to “economy” and “economic”. We expanded the policy term set to
include “war” and “tariff”, as discussed in Section 3.1. We splice the 6-paper and 10-paper series
based on their overlap from 1985 to 1994 as follows: First, we adjust the 10-paper modern series
multiplicatively to match the standard deviation of the historical series. Second, we additively
adjust the modern series to match the level of the historical series during the overlap period.
United States (1985-, daily): We search archives for all daily-circulation US newspapers
available through Access World News Newsbank service. The US daily EPU index uses the
same term sets as the modern US monthly index.
Canada (1985-, monthly): We search the Gazette (from 1985), Globe and Mail (1985), Canadian
Newswire (1999), Ottawa Citizen (1986), Toronto Star (1985), and Vancouver Sun (1987) using
Proquest newspaper archives. The policy term set is spending, policy, deficit, budget, tax,
regulation, and “central bank”.
China (1995-, monthly): We search the South China Morning Post using Proquest. In addition to
meeting E, P and U requirements, an article must meet a “C” requirement to contribute to our
EPU count: in particular, it must contain “China” or “Chinese”. To meet our P requirement for
China, an article must satisfy the following text filter: {{policy OR spending OR budget OR
political OR "interest rates" OR reform} AND {government OR Beijing OR authorities}} OR
tax OR regulation OR regulatory OR "central bank" OR "People's Bank of China" OR PBOC
OR deficit OR WTO. We use this compound filter, because it outperforms simpler alternatives in
47
the audit study that we performed on 500 randomly selected articles that meet the E, U and C
requirements.
France (1987-, monthly): We search Le Monde (from 1987) using Lexis Nexis and Le Figaro
(from 2002) using Factiva. Our term sets are (E) economie OR economique OR economiques;
(P) taxe OR taxes OR impot OR impots OR politique OR politiques OR regulation OR
regulations OR reglementation OR loi OR “lois reglementations” OR depense OR depenses OR
deficit OR deficits OR "banque centrale" OR "BCE" OR "Reserve Federale" OR budget OR
budgetaire; and (U) incertitude OR incertain OR incertitudes OR incertains. To splice the two
newspaper-level sources and construct the EPU index for France, we proceed as follows. First,
we standardize the raw Le Monde EPU series to have unit standard deviation from 1987 to 2009
and normalize to 100 over the same period. Second, using the resulting standardized and
normalized Le Monde EPU series, we compute its standard deviation and mean from 2002 to
2014. Third, we multiplicatively standardize the Le Figaro EPU series from 2002 to 2014 to
match the standard deviation of the standardized/normalized Le Monde series from 2002 to
2014. Then we additively normalize the resulting Le Figaro series from 2002 to 2014 to match
the mean of the standardized/normalized Le Monde series from 2002 to 2014. Finally, we use the
standardized/normalized Le Monde EPU from 1987 to 2001 and the simple mean of the two
standardized/normalized newspapers from 2002 onwards.
Germany (1993-, monthly): We search Handelsblatt and the Frankfurter Allgemeine Zeitung
using each newspaper’s own archives. Our term sets are (E) wirtschaft OR wirtschaftlich; (P)
steuer OR wirtschaftspolitik OR regulierung OR regulierungs OR ausgaben OR bundesbank OR
EZB OR zentralbank OR haushalt OR defizit OR haushaltsdefizit; and (U) unsicher OR
Unsicherheit.
India (2003-, monthly): We search the Economic Times (2003), Times of India (2003),
Hindustan Times (2004), The Hindu (2003), Financial Express (2003), Indian Express (2003),
and the Statesman (203) using Access World News. All are English-language newspapers. The P
term set, which we developed in conjunction with Sanjai Bhagat, Pulak Ghosh and Srinivasan
Rangan, is regulation, deficit, legislation, reform, “fiscal policy”, “monetary policy”, “central
bank”, “RBI”, “Reserve Bank”, parliament, “finance ministry”, policymakers, “finance
minister”, lawmakers, “planning commission”, “economic advisor”, “Prime Minister’s Office”,
“PM Office”, “PMEAC”, “PMO”, “Lok Sabha”, tax, taxes, taxation, “excise duties”, and
“customs duties”.
Italy (1997-, monthly): We search the Corriere Della Sera and La Repubblica using Factiva. Our
term sets are (E) economia OR economico OR economica OR economici OR economiche; (P)
tassa OR tasse OR politica OR regolamento OR regolamenti OR spesa OR spese OR spesa OR
deficit OR "Banca Centrale" OR "Banca d'Italia" OR budget OR bilancio; and (U) incerto OR
incerta OR incerti OR incerte OR incertezza.
Japan (1988-, monthly): We search Asahi and Yomiuri using each newspaper’s own archives.
Leading Japanese newspapers routinely translate some of their articles into English, a practice
that greatly facilitated our development of suitable Japanese-language E, P and U terms. We first
identified Japanese newspaper articles translated into English that meet our EPU criteria. We
48
(i.e., our Japanese research assistants) then reviewed the Japanese-language versions of the same
articles to identify the Japanese terms that correspond to the English-language terms of interest.
Using this approach, we developed the E, P and U term sets set forth in the following table:
Term Sets for Japan EPU Index, with Translations to English
Category
English
Japanese
In Chinese Characters
U
uncertainty OR
uncertain
futoumei OR fukakujitsu
OR
E
economic OR economy
Keizai
経済
P
Policy
Seisaku
政策
Tax
Zei
税
government spending
saishutsu OR kokyo-jigyohi
歳出 公共事業費
OR kokyotoushi OR kokuhi
公共投資 国費
Regulation
Kisei
規制
Bank of Japan OR BOJ
nihonginko OR nichigin
日本銀行 日銀
budget
Zaisei
財政
Deficit
akaji OR fusai
赤字 負債
Federal Reserve
Renpou junbi OR rengin
Russia (1992-, monthly): We search Kommersant’s own online archive from October 1992. We
use Ekonomika (economy) for our E term set and “неопределённый” (uncertain) OR
“неопределённость” (uncertainty) for our U term set. Our P term set is “politika” (policy),
“nalog” (tax). For “spending”, there are several corresponding Russian words. For spending by
the government, we consider a set of three terms: “rashody byudzheta” (budget outflows),
“gosudarstvennye rashody” (government spending), and “rashodovanie” (spending). We
translate “Regulation” as “regulirovanie”; Central Bank of Russia or CBR as “Centralnyj Bank
Rossii” or “CBR”; and “Senate” as “Gosudarstvennaya Duma” (or “Gosduma” or “Duma”). The
Russian counterpart to “White House” is “Kreml”. For “bill”, we use “zakon” or
“zakonodatelnyj akt”, which is a synonym of “zakon” but used in more formal context. We
translate “legislation” as ‘zakonodatelstvo’; “monetary policy” as “denezhnaya-kreditno
politika”; trade policy as “torgovaya politika”; and “interest rate” as “procentnaya stavka”.
South Korea (1990-, monthly): We search Donga Ilbo, Kyunghyang, Maeil Economic (from
1995), Hankyoresh Hankook and Korea Economic Daily (from 1995) using the Mediagon
archives. Our term sets are (E) economy OR economic OR commerce; (P) government OR “Blue
House” OR congress OR authorities OR legislation OR tax OR regulation OR “Bank of Korea”
OR “central bank” OR deficit OR WTO OR law/bill OR “ministry of finance”; and (U)
uncertainty OR uncertain. To construct the South Korean EPU index, we first standardize each
paper’s raw EPU rate to have unit standard deviation from 1995-2014. Using these standardized
and normalized newspaper-level series, we average across papers by month to obtain the overall
South Korean EPU index from January 1990 to December 2014.
49
Spain (2001-, monthly): We search El Mundo and El Pais using Factiva. Our term sets are (E)
económica OR economía; (P) impuesto OR tarifa OR regulacion OR politica OR gastar OR gasta
OR gasto OR presupuesto OR deficit OR "banco central”; and (U) incierto OR incertidumbre.
United Kingdom (1997-, monthly): We search the Times of London and the Financial Times
using the Access World News Newsbank service. Our term sets are (E) economic OR economy;
(P) spending OR policy OR deficit OR budget OR tax OR regulation OR “Bank of England”;
and (U) uncertain OR uncertainty.
United Kingdom, Historical (1900-2010, monthly): We search the Times of London and the
Guardian using the Proquest Historical Newspaper Archive. Our term sets are (E) economic OR
economy OR business OR industry OR commerce OR commercial; (P) spending OR policy OR
deficit OR budget OR tax OR regulation OR "Bank of England” or war or tariff; and (U)
uncertain OR uncertainty.
Figures 1, 2 and 4 in the main text and Appendix Figures A1-A11 display annotated time-series
plots of our country-level EPU indices. In preparing the annotations, we received valuable input
Term Sets for South Korean EPU Index, with Translations to English
Category
English Terms
Korean Terms
In Korean Characters
U
uncertainty OR uncertain
bulhwaksilsung OR
bulhwaksil
불확실성 OR 불확실
E
economic OR economy
commerce
gyeongje OR gyeongjeui
sangup or muyeok
경제 OR 경제의
상업 OR 무역
P
government
“Blue House”
congress
authorities
legislation
tax
regulation
“Bank of Korea”
“central bank”
deficit
WTO
law/bill
“ministry of finance”
jeongbu
Chungwadae
gukhoe
dangguk
jejeong OR jejeongbub OR
ibbub
se
gyuje OR tongje OR
gyujeong
Hankukeunheng OR
Haneun
jungangeunheng
jukja OR bujok
WTO OR Segye muyeok
gigu
bub OR buban
gihwaekjaejungbu OR
gijaebu
정부
청와대
국회
당국
제정 OR 제정법 OR 입법
세금 OR 세
규제 OR 통제 OR 규정
한국은행 OR 한은
중앙은행
적자 OR 부족
WTO OR 세계 무역 기구
법 OR 법안
기획재정부 OR 기재부
50
from many persons including Ruedi Bachmann, Sanjai Bhagat, Vincent Bignon, Yongsung
Chang, Vladimir Dashkeyev, Jesus Fernandez-Villaverde, Laurent Ferrara, Luis Garicano, Yuriy
Gorodnichenko, Takeo Hoshi, Anil Kashyap, Jessica Koh, Meijun Qian, Fabiano Schivardi,
Ippei Shibata, Sophie Wang and Cynthia Wu.
B. Category-Specific Policy Term Sets (Figure 3 and Table 1)
To create the category-specific EPU indices shown in Figure 3 and Table 1, we consider articles
that contain our triple of terms about the economy, policy and uncertainty. We then check
whether the article also contains one or more category-specific policy terms, as listed below.
• Taxes: taxes, tax, taxation, taxed
• Government Spending & Other: government spending, federal budget, budget battle,
balanced budget, defense spending, military spending, entitlement spending, fiscal stimulus,
budget deficit, federal debt, national debt, Gramm-Rudman, debt ceiling, fiscal footing,
government deficits, balance the budget
• Fiscal Policy: Anything covered by Taxes or Government Spending & Other
• Monetary Policy: federal reserve, the fed, money supply, open market operations,
quantitative easing, monetary policy, fed funds rate, overnight lending rate, the fed,
Bernanke, Volker, Greenspan, central bank, interest rates, fed chairman, fed chair, lender of
last resort, discount window, European Central Bank, ECB, Bank of England, Bank of Japan,
BOJ, Bank of China, Bundesbank, Bank of France, Bank of Italy
• Healthcare: health care, Medicaid, Medicare, health insurance, malpractice tort reform,
malpractice reform, prescription drugs, drug policy, food and drug administration, FDA,
medical malpractice, prescription drug act, medical insurance reform, medical liability, part
d, affordable care act, Obamacare
• National Security: national security, war, military conflict, terrorism, terror, 9/11, defense
spending, military spending, police action, armed forces, base closure, military procurement,
saber rattling, naval blockade, military embargo, no-fly zone, military invasion
• Financial Regulation: banking (or bank) supervision, glass-steagall, tarp, thrift supervision,
dodd-frank, financial reform, commodity futures trading commission, cftc, house financial
services committee, basel, capital requirement, Volcker rule, bank stress test, securities and
exchange commission, sec, deposit insurance, fdic, fslic, ots, occ, firrea
• Regulation: Anything covered by Financial Regulation and truth in lending, union rights,
card check, collective bargaining law, national labor relations board, nlrb, minimum wage,
living wage, right to work, closed shop, wages and hours, workers compensation, advance
notice requirement, affirmative action, at-will employment, overtime requirements, trade
adjustment assistance, davis-bacon, equal employment opportunity, eeo, osha, antitrust,
competition policy, merger policy, monopoly, patent, copyright, federal trade commission,
ftc, unfair business practice, cartel, competition law, price fixing, class action, healthcare
lawsuit, tort reform, tort policy, punitive damages, medical malpractice, energy policy,
energy tax, carbon tax, cap and trade, cap and tax, drilling restrictions, offshore drilling,
pollution controls, environmental restrictions, clean air act, clean water act, environmental
protection agency, epa, immigration policy
• Sovereign Debt and Currency Crises: sovereign debt, currency crisis, currency crash,
currency devaluation, currency revaluation, currency manipulation, euro crisis, Eurozone
51
crisis, european financial crisis, european debt, asian financial crisis, asian crisis, Russian
financial crisis, Russian crisis, exchange rate
• Entitlement Programs: entitlement program, entitlement spending, government
entitlements, social security, Medicaid, medicare, government welfare, welfare reform,
unemployment insurance, unemployment benefits, food stamps, afdc, tanf, wic program,
disability insurance, part d, oasdi, Supplemental Nutrition Assistance Program, Earned
Income Tax Credit, EITC, head start program, public assistance, government subsidized
housing
• Trade Policy: import tariffs, import duty, import barrier, government subsidies, government
subsidy, wto, world trade organization, trade treaty, trade agreement, trade policy, trade act,
doha round, uruguay round, gatt, dumping
C. Constructing a Newspaper-Based Measure of Equity Market Uncertainty.
Our newspaper-based measure of policy uncertainty raises a basic question: Can
frequency counts of newspaper articles serve to quantify economic uncertainty in a useful
manner? To shed light on this question, we create a separate newspaper-based index of equity
market uncertainty and compare it to the market-based VIX, a widely used measure of
uncertainty in equity returns that is firmly grounded in option pricing theory.
To construct a newspaper-based measure of equity market uncertainty, we parallel the
approach in Section 3.3 above. Specifically, we use the same newspapers, scaling methods and
search criteria – except for dropping the policy-related term set and, instead, requiring an article
to contain ‘stock price’, ‘equity price’ or ‘stock market’. Figure C2 plots the resulting
newspaper-based index of equity market uncertainty and the monthly average of daily VIX
values from 1990 to 2012. The two series are highly correlated. While the newspaper-based
index is clearly noisier, it picks up every major move in the VIX during the sample period.
D. Constructing Indicators Based on the FOMC Beige Books (Figure 7)
We reviewed all Beige Books from July 1983 to January 2015, covering more than 250
issues. For each instance of “uncertain*” in the Beige Books, we read the surrounding passage to
assess whether it refers to policy-related matters as a source of the uncertainty. If so, we treat the
passage as being about policy uncertainty, at least in part. In these instances, we also classify the
reference to policy uncertainty into one or more categories, similar to the ones in Table 1.
Outside the first few years, each Beige Book contains a statement along the lines of
“Prepared … based on information collected on or before” a specified date. This date is typically
7-12 days before the release date. For the early years, we imputed the specified date based on the
average lag between the specified date and the release date in later years. Since Federal Reserve
System staff gathers information over a period of time on or before the specified date, we
subtract 14 days from the specified date to obtain an “effective” date for the information covered
by each Beige Book. We assign Beige Books to calendar quarters using effective dates.
Given our uncertainty and policy uncertainty counts, we average over Beige Books
within a calendar quarter to obtain our indicators. Figure C4 displays these raw counts. Figure 7
displays normalized counts that adjust for word count differences across Beige Books.
E. Text Analysis of 10-K Filings to Quantify Policy Risk Exposure (Figures 7 and C5)
In 2005, the U.S. Securities and Exchange Commission (SEC) issued a regulation that
requires most publicly held firms to include a separate discussion of “Risk Factors” in Part 1a of
52
their annual 10-K filings. See Campbell et al. (2014) for an extended discussion and analysis of
this regulatory development.
In explaining “How to Read a 10-K” at www.sec.gov/answers/reada10k.htm, the SEC
describes Part 1a as follows:
Item 1A - “Risk Factors” includes information about the most significant
risks that apply to the company or to its securities. Companies generally list the
risk factors in order of their importance. In practice, this section focuses on the
risks themselves, not how the company addresses those risks. Some risks may
be true for the entire economy, some may apply only to the company’s
industry sector or geographic region, and some may be unique to the company.
Our text analysis of Part 1a (Risk Factors) covers 10-K filings in calendar years 2006 to
2014 (fiscal years 2005 to 2013). The vast majority of 10-K filings occur in March, and most of
the rest occur in February or April. We obtained machine-readable 10-K filings from the
EDGAR database, using a Python script with the urllib2 package. We drop filings for which our
automated sentence counter returns a value of less than nine for Part 1a. Visual inspections
reveal that values less than nine reflect routine headings and section separators in 10-K filings
with an empty Part 1a, i.e., with no discussion of Risk Factors. When a firm has more than one
10-K filing in the same calendar year, we retime the “early” (“late”) filing if the firm has no
filing in the prior (next) calendar year.
The following table reports data on the number of firms for which we obtained the Risk
Factors discussion in 10-K filings on the Edgar Database. The second-to-last column shows the
number of firm-level observations per year used in Figure 7. The last column shows the number
of 10-K filings with a non-empty Part 1a that we match to Compustat using the CIK identifier.
Our match rate ranges from 70 to 76 percent across years.
Number of 10-Ks Identified
Number with Risk Factors Section Extracted
Filing
All
Less Same-Date
All
All Non-
Empty
After
Retiming
Matched to
Year
Duplicates
Compustat
2006
8852
8821
7161
5305
5319
4071
2007
8574
8524
6839
4995
4972
3770
2008
8746
8641
7154
5443
5443
4100
2009
9839
9785
7552
6242
6230
4356
2010
9165
9095
7442
6102
6109
4304
2011
8840
8750
8177
6731
6715
4751
2012
8393
8333
7908
6360
6369
4693
2013
8105
7998
7642
6096
6081
4597
2014
8084
7955
7591
6021
6049
4578
For each 10-K filing with a non-empty Part 1a, we count sentences in Part 1a that contain one
or more of the following policy-relevant terms: government, political, legislative, legislation,
congress, white house, regulation(s), regulatory, tax(es), fiscal polic(y/ies), monetary
polic(y/ies), federal reserve, the fed, central bank, tariff(s), trade quota(s), antitrust, competition
polic(y/ies), war(s), national security, government spending, defense spending, defense
polic(y/ies), energy polic(y/ies), healthcare polic(y/ies), affordable care act, tort reform, import
restriction(s), export restriction(s), investment restriction(s), patent polic(y/ies), patent law,
53
trademark polic(y/ies), trademark law, copyright polic(y/ies), copyright law. Dividing this
sentence count by the total number of sentences in Part 1a yields our firm-year measure of
exposure to policy risk factors. Averaging this ratio over firms by year yields the “Policy Share
of 10-K Risk Factors” in Figure 7. Averaging over years for a given firm yields the 10-K policy
risk exposure measure that we use in our firm-level panel regressions.
We also computed another measure of policy risk exposure based on the fact that
“Companies generally list the risk factors in order of their importance” in Part 1a of their 10-K
filings. Specifically, we determined the rank value of the first sentence in Part 1a with one or
more of the policy-relevant terms listed in the previous paragraph. If Part 1a contains no policy
terms, we set the rank value to the total number of sentences in Part 1a plus one. We then scaled
each rank value by the total number of sentences in Part 1a for the same firm-year observation.
The resulting scaled rank value tells us what fraction of Part 1a one must read before
encountering a policy term. Thus, lower values for the scaled rank means greater importance for
policy risks.
Figure C5 reports the resulting mean and median scaled rank values by year. The figure
also shows a regression-based mean that controls for changes over time in the set of firms with a
discussion of Risk Factors, i.e., with a non-empty Part 1a. All three measures in Figure C5 show
a strong downward drift. This finding indicates that firms regard policy risk factors as
increasingly important (relative to the full set of relevant risks) over the period covered by the
10-K filings.
F. Data on Federal Contracts and Government Healthcare Expenditures
We obtain data on federal contracts from 1999 to 2013 at USAspending.gov, a website
mandated by the Federal Funding Accountability and Transparency Act of 2006. The site reports
individual federal contracts and includes information about the originating agency, contract
recipient, contract amount, location of performance, and characteristics of the contract and
recipient. We use information about the contract recipient and its parent company, if applicable,
plus the date and contract amount. Unfortunately, most contract records do not include GV keys,
stock tickers, or other unique firm identifiers that allow easy matching to external sources of data
about the contract recipient. We match contracts to firms using DUNS numbers, when available;
otherwise, we match on standardized names for contract recipients and parent firms.
To standardize firm names, we perform standard cleaning operations: removing
punctuation and abbreviations, deleting excess spacing, replacing common misspellings,
removing parentheticals, and other techniques to standardize firm names. We perform these
operations on the universe of federal contract recipients and on the universe of ORBIS firms. We
then match to ORBIS firm data by, in sequence, own DUNS number, parent DUNS number, own
firm name, and parent firm name. Next, we use stock ticker data from ORBIS to match to
quarterly Compustat data on publicly held firms. Using this procedure, we match about 45
percent of the contract awards and more than 65 percent of aggregate contract volume to
Compustat firms. Imperfect matching accounts for a portion of the residual, but contract awards
to independent privately held firms and public entities are the main reason we do not match all
contract recipients. Public universities, states, and cities are some of the largest recipients of
federal contract awards, and privately held firms account for a large share of private sector
activity in the United States (Davis et al., 2007).
Using the matched dataset, we construct two sets of firm-level measures of federal
contract intensity (hereafter, ‘intensity’) to provide cross-sectional variance in exposure to one
54
aspect of policy uncertainty. The first is a measure at the three-digit SIC code level. Here we
simply take the overall sum of contracts and sum of revenue by three-digit SIC code by year and
take the ratio of the total contracts to total revenue, yielding an annual intensity measure. Finally,
we take the average of these values by three-digit SIC code over time and apply the long-run
average to that industry for all firms and years in the sample.
The second method uses firm segment data from Compustat in order to distribute both
firm revenue and firm contracts to each of their component segments (defined by four-digit SIC
codes). For instance, if one segment of a firm produces 50% of its revenue, we assign that
segment 50% of contracts, as well. With this distribution completed, we sum contract dollars and
revenue across four-digit SIC codes by year, obtaining four-digit SIC code level intensity
measures, as in the three-digit SIC code approach. Using the four-digit SIC code intensities, we
reconstruct a firm’s intensity based on its segment composition (so a firm with 50% of its
revenue in one four-digit SIC code and 50% in another takes the simple average of the two SIC
codes’ intensity levels). This approach yields firm-level variation in cross-sectional intensity of
government contracting based on the four-digit SIC code makeup of each firm.
We obtain data on government and total national health expenditures for the United
States from http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-
Reports/NationalHealthExpendData/Downloads/NHE2013.zip. To obtain the government share,
we sum health expenditures under Medicare, Medicaid, the Department of Defense, the
Department of Veterans Affairs and the Children’s Health Insurance Program for 2010, then
divide by total national health expenditures for 2010. This yields a value of 43.75 percent, which
we assign to firms in SIC 800 (scaled by the firm’s share of revenues in SIC 800).
55
Table A1: Estimated Changes in Stock-Price Volatilities, Investment Rates, and Employment Growth Rates Associated with
Policy Uncertainty Changes from 2006 to 2012 for Firms in Selected Industries
Outcome Measure
And Industry
(1)
Government
Purchases
Share
(2)
EPU
Change,
Log
Points
(3)
Coefficient on
Log(EPU)*
(Government
Purchases
Share)
(4)
Category-
Specific
EPU
Change,
Log Points
(5)
Coefficient on
Category-
Specific
log(EPU)
(6)
Estimated
Outcome Change
(1×2×3)+(4×5),
Log Points or
Percentage Points
Implied Volatility
Healthcare
0.44
85.6
0.082
152.4
0.071
13.9
Ordnance, Accessories
0.39
85.6
0.082
38.7
0.048
4.6
Aircraft, Parts
0.20
85.6
0.082
38.7
0.048
3.3
Engineering Services
0.21
85.6
0.082
38.7
0.048
3.3
Heavy Construction
0.09
85.6
0.082
None
Not applicable
0.6
Finance
0.002
85.6
0.082
160.6
0.144
23.8
Quarterly Investment Rate
Healthcare
0.44
85.6
-0.029
152.4
-.012
-2.9
Ordnance, Accessories
0.39
85.6
-0.029
38.7
0.002
-0.9
Aircraft, Parts
0.20
85.6
-0.029
38.7
0.002
-0.4
Engineering Services
0.21
85.6
-0.029
38.7
0.002
-0.4
Heavy Construction
0.09
85.6
-0.029
None
Not applicable
-0.2
Finance
0.002
85.6
-0.029
160.6
-.002
-0.3
Annual Employment Growth Rate
Healthcare
0.44
85.6
-0.22
152.4
-0.005
-9.0
Ordnance, Accessories
0.39
85.6
-0.22
38.7
0.018
-6.6
Aircraft, Parts
0.20
85.6
-0.22
38.7
0.018
-3.1
Engineering Services
0.21
85.6
-0.22
38.7
0.018
-3.3
Heavy Construction
0.09
85.6
-0.22
None
Not applicable
-1.7
Finance
0.002
85.6
-0.22
160.6
0.003
0.5
56
Notes to Table A1: Column (1) is the federal government purchases share of firms in the indicated industry, computed according to
the two-step method described in Section 4.1. Column (2) is 100 times the change from 2006 to 2012 in the average log(EPU) value,
using the series plotted in Figure 1. Columns (3) and (5) are regression coefficient estimates from Column (7) in Table 2 in the implied
volatility panel, and from columns (4) and (8) of Table 4 in the investment rate and employment growth panels. Column (4) is 100
times the change from 2006 to 2012 in selected category-specific average log(EPU) values. Column (6) is the product of columns (1),
(2) and (3) plus the product of columns (4) and (5), except that Heavy Construction involves the first product only. The implied
volatility results in Column (6) reflect a levels-on-levels specification and should be interpreted as responses that persist for as the long
as EPU remains elevated. In contrast, the Investment Rate and Employment Growth Rate results reflect change-on-change
specifications and should be interpreted as one-time responses.
57
Table A2: Effects on Firm-Level Implied Stock Price Volatility When Scaling EPU by its Components
Dep Var: Log(30-day implied vol)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Log(EPU)×Intensity
0.215***
(0.069)
Log(EPU/E)×Intensity
0.253***
(0.089)
Log(EPU/P)×Intensity
0.206***
(0.069)
Log(EPU/U)×Intensity
0.215**
(0.104)
Log(EPU/EP)×Intensity
0.223**
(0.100)
Log(EPU/EU)×Intensity
0.103
(0.175)
Log(EPU/PU)×Intensity
0.262**
(0.110)
(Federal Purchases/GDP) ×Intensity
-29.449**
-26.932**
-29.171**
-28.825**
-25.747**
-24.629*
-28.50**
(12.723)
(12.926)
(12.742)
(12.755)
(13.090)
(13.276)
(13.00)
F-test EPU coefficient=0.215
n/a
0.673
0.902
0.998
0.936
0.524
0.669
Notes: The sample contains 136,578 observations on 5,460 firms from 1996 to 2012. The dependent variable is the 30-day implied
volatility for the firm, averaged over all days in the quarter. All regressions include a full set of firm and time fixed effects.
Log(EPU)×Intensity is the log of the EPU index, multiplied by the firm’s exposure to federal government purchases of goods and
services computed by the two-step method described in Section 4. Log(EPU/E)×Intensity is the same except that it scales the EPU
index by the newspaper-based index for the frequency of articles that satisfy our E (“Economy”) criteria. Likewise, other rows
consider analogs that scale the EPU index by newspaper-based indices that satisfy our P, U, EP, EU and PU criteria. F-test EPU
coefficient=0.215 tests whether the coefficient on the (scaled) EPU interaction is significantly different from the point estimate for the
baseline regression in column (1). All regressions weighted by the firm’s average sales during the sample period. Standard errors
based on clustering at the firm level.
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or ‘economy’, and one or
more policy-relevant terms: ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, or ‘central bank’. The series is normalized to
mean 100 from 1985 to 2010 and based on the following newspapers: The Gazette, The Globe and Mail, Canadian Newswire,
the Ottawa Citizen, Toronto Star, and the Vancouver Sun.
Figure A1: EPU Index for Canada
Policy Uncertainty Index
0
100 200
300 400
1985 1990 1995 2000 2005 2010 2015
Gulf
War I
Clinton
Election
Quebec
Referendum
Commodity
Price
Slump
Gulf
War II
9/11
Lehman Failure,
Global Financial Crisis,
General Elections
US Fiscal Policy Battles,
Eurozone Crises
Notes: Index reflects scaled monthly counts of articles containing ‘China’ or ‘Chinese’, ‘uncertain’ or ‘uncertainty’, ‘economic’ or
‘economy’ and satisfying the ‘policy’ text filter specified for China in Appendix A. The series is normalized to mean 100 from 1985 to
2011 and based on the South China Morning Post, the leading English-language newspaper in Hong Kong.
Figure A2: EPU Index for China
Policy Uncertainty Index
0
100 200 300
400
1996 1999 2002 2005 2008 2011 2014
China
Stimulus
Deflation and
Deficit Concerns
9/11
Rising
Interest
Rates
Political
Transition
and New
National
Congress
Eurozone and
Protectionism Fears
Inflation and
Export Pressure
Growth
Slowdown
Concerns
Iraq Invasion;
SARS Crisis
Asian
Financial
Crisis;
SOE layoffs
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or ‘economy’, and one or
more policy-relevant terms: ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, or ‘central bank’. The series is normalized to
mean 100 from 1997 to 2009 and based on the following newspapers: Le Monde and Le Figaro.
Figure A3: EPU Index for France
Policy Uncertainty Index
0
200
400 600
1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013
9/11
UMP Routed in
Regional Elections,
Sarkozy Becomes
UMP Leader
European
Currency Crisis,
Fears of Franc
Devaluation
Gulf
War I
Gulf
War II
Bear Stearns,
Northern Rock,
BNP Paribas
French and Dutch Voters
Reject European Constitution
Lehman
Failure
Eurozone Stresses
Greek
Crisis
Greek Crisis
Intensifies;
Concerns
Mount over
French Banks
Le Pen Reaches
2
nd
Round in
Presidential
Election
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or ‘economy’, and one
or more policy-relevant terms: ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, or ‘central bank’. The series is normalized
to mean 100 from 1997 to 2009 and based on the following newspapers: Frankfurter Allgemeine Zeitung and Handelsblatt.
Figure A4: EPU Index for Germany
Policy Uncertainty Index
0
100
200 300 400
1997 2000 2003 2006 2009 2012 2015
Russian
Crisis
9/11
Gulf
War II
2005 German
Elections, Merkel
Becomes Chancellor
Northern Rock
Support/
Takeover
Lehman
Failure
Greek
Bailout
Request,
Rating
Cuts
Italy
Rating Cut
Papandreou
calls for
referendum;
later resigns
Continuing
Eurozone
Stresses
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’ or ‘uncertainties’ or ‘uncertainties’,
‘economic’ or ‘economy’, and one or more of policy-relevant terms listed for India in Appendix A. The series is normalized to mean
100 from 2003 to 2010 and based on the following newspapers: The Economic Times, Times of India, Hindustan Times, The
Hindu, Financial Express, Indian Express, and the Statesman.
Figure A5: EPU Index for India
Policy Uncertainty Index
0
100 200
300
2003 2005 2007 2009 2011 2013 2015
Congress Party Wins
National Elections,
Regains Power
Bear
Stearns
Failure
Congress-Led
Coalition Survives
Confidence Vote
Lehman
Failure
Inflation
Fears
US Debt Ceiling Debate,
Eurozone Crises
Rupee Collapse
and Exchange
Rate Concerns
Iraq
Invasion
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or ‘economy’, and one or
more policy-relevant terms: ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, or ‘central bank’. The series is normalized to
mean 100 from 1997 to 2009 and based on the following newspapers: La Stampa and Corriere Della Sera.
Figure A6: EPU Index for Italy
Policy Uncertainty Index
50
100
150 200 250
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Russian
Crisis
Constitutional
Referendum
Iraq
Invasion
9/11
Attacks
Lehman
Failure
Greek
Crisis
Eurozone Stresses
Berlusconi wins confidence vote
(Oct) but resigns (Nov) amid Italian
debt concerns; Monti’s austerity
package passes in parliament
(Dec); Greek Crisis worsens
Election
yields
political
stalemate
Tremonti
Resignation
Forza exits
ruling coalition,
Letta survives
confidence vote
Figure A7: Index of Economic Policy Uncertainty for Japan
Notes: Index reflects scaled monthly counts of articles in Yomiuri and Asahi containing Japanese-language terms for ‘uncertain’ or
‘uncertainty’, ‘economic’ or ‘economy’, and one or more selected policy terms. The series is normalized to mean 100 from
1985-2009 and runs from June 1988 to January 2015.
0
50
100
150
200
250
Jun-88
Jun-89
Jun-90
Jun-91
Jun-92
Jun-93
Jun-94
Jun-95
Jun-96
Jun-97
Jun-98
Jun-99
Jun-00
Jun-01
Jun-02
Jun-03
Jun-04
Jun-05
Jun-06
Jun-07
Jun-08
Jun-09
Jun-10
Jun-11
Jun-12
Jun-13
Jun-14
!!!!!
New Prime
Minister
Kaifu;
Structural
impediments
initiative
Gulf
War I
LDP wins fewer than half
of seats in Japan's Diet
Electoral
Reform
LDP regains
Prime Ministership
Failure of Major Banks
And Securities Firms
New Prime
Minister Obuchi
Prime Minister
Mori announces
intention to
step down
Gulf War II;
Resona Failure
Diet dissolves &
general election
BOJ
monetary
easing
Diet dissolves
& general
election
Prime
Minister
Fukuda
Resigns;
Lehman
Brothers
Failure
New Prime
Minister Noda
BOJ
Succession
Controversy
Figure A8: EPU Index for South Korea
Policy Uncertainty Index
0 100
200 300
1990 1995 2000 2005 2010 2015
Soviet
Union
Collapse
South
Korean
Presidential
Election
Real-Name
Financial
Transactions
Law Enacted
Death
of Kim
Il-Sung
Asian
Financial
Crisis
Economic
and Financial
Restructuring
9/11
Parliament
Impeaches
President Roh
Moo-hyun(March);
Constitutional
Court Overturns
Move (May)
Lehman
Brothers
Failure,
Global
Financial
Crisis
Eurozone Fears
Notes: Index reflects scaled monthly counts of articles in six South Korean newspapers containing ‘uncertain’ or ‘uncertainty’,
‘economic’ or ‘economy’ or ‘commerce’, and one or more of the policy terms specified for South Korea in Appendix A. The series is
normalized to mean 100 from 1995 to 2014.
Daewoo Motors
Bankruptcy,
Financial Fraud
and Accounting
Scandals
Gulf War II
North
Korea
Renounces
1953 Truce
Surprise
Three-Party
Political
Merger
Large
Anti-FTA
Protests
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or ‘economy’, and one
or more policy-relevant terms: ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, or ‘central bank’. The series is normalized
to mean 100 from 2001 to 2009 and based on queries in the following newspapers: El Pais and El Mundo
Figure A9: EPU Index for Spain
Policy Uncertainty Index
0
100
200 300 400
2001 2003 2005 2007 2009 2011 2013 2015
9/11
General
Strike,
Protests,
Bombings,
Moroccan
Conflict
Gulf War II
Lehman
Failure
Greek
Crisis,
Spanish
Bank and
Fiscal Woes,
Protests in
Catalonia
Protests over
Economic
Conditions
And Policies
Protests Against
Labor Reforms
And Government
Spending Cuts,
Spanish Bank Woes,
Greek Election
Problems at
BNP Paribas,
Sachsen
Landesbank,
Northern Rock
Basque
Region
Autonomy
Push
Catalan
Independence
Crisis
Figure A10: EPU Index for the United Kingdom
Policy Uncertainty Index
0
100 200
300 400
1997 2000 2003 2006 2009 2012 2015
Treaty of
Accession/
Gulf War II
Russian
Crisis/LTCM
Northern
Rock &
Global
Financial
Crisis
General
Election
Eurozone
Crises
Lehman
Brothers
Failure
9/11
Notes: The news-based EPU Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’, ‘economic’ or
‘economy’, and one or more policy-relevant terms: ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, or ‘central bank’. The
series is normalized to mean 100 from 1997 to 2009 and based on queries in the following newspapers: The Times of London
and the Financial Times.
Scottish
Independence
Referendum
Notes: Index reflects scaled monthly counts of articles containing ‘uncertain’ or ‘uncertainty’; ‘economic’, ‘economy’, ‘business’,
‘industry’, ‘commerce’, or commercial’; and one or more of ‘tax’, ‘policy’, ‘regulation’, ‘spending’, ‘deficit’, ‘budget’, ‘Bank of England’,
‘war’, or ‘tariff’. The series is normalized to mean 100 from 1900 to 2008 and based on The Times of London and The Guardian.
Figure A11: UK Historical EPU Index
Policy Uncertainty Index
0 50
100
150 200 250
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
August 1914,
WWI Begins
Paris Peace
Conference
Great
Slump
Germany Invades
Poland, Britain
Declares War
Germany Invades
Norway & Denmark
Feb-May 1946: Bank of England Nationalized; UK-
Soviet Tensions; Withdrawal from Syria and Lebanon;
Parliament Votes to Nationalize Coal Mines
Korean
War
Gulf
War I
Gulf
War II
Onset of
Global
Financial
Crisis
France Vetoes UK Entry to
European Common Market
Oil Shock, Miners’
Strike, Recession
Currency
Crisis
0
100 200 300 400
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Policy Uncertainty Index
Versailles
conf.
9/11
Debt
Ceiling
OPEC II
Lehman
and TARP
Great
Depression,
New Deal
and FDR
Great
Depression
Relapse
Gulf
War I
Black
Monday
WW I
Start
OPEC I
Asian Fin.
Crisis
McKinley
Assassination
Gold
Standard Act
Tet
Offensive
Figure B7: Historical Index of US EPU – ‘All’ Scaling
Notes: We construct this version of the US Historical EPU Index in the same manner as Figure 2, except we scale the raw
newspaper-level EPU counts by the count of all articles in the same paper and month. In contrast, Figure 2 scales the raw EPU
counts by the count of all articles in the same paper or month that contain one of the “economy” terms.
Immigration
Act of 1924
Gulf War II
Fiscal Cliff
0
100
200 300
400
Economic Policy Uncertainty Index
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
year
Computer
Human
Figure C1: Human and Computer EPU Indices
Notes: Index comparison from 1900 to 2010 based on 11,841 articles (15,156 audits) in the Chicago Tribune, Dallas Morning
News, LA Times, Miami Herald, NY Times, San Francisco Chronicle, Washington Post and Wall Street Journal. Series plotted
yearly to reduce sampling variability, with an average of 107 articles per year. Each series normalized to 100 from 1900 to 2010.
Correlation=0.93
0
200 400
600
1990 1995 2000 2005 2010 2015
Figure C2: News-based index of equity market uncertainty compared to
market-based VIX
Notes: The news-based index of equity market uncertainty is based on the count of articles that reference ‘economy’ or ‘economic’,
and ‘uncertain’ or ‘uncertainty” and one of ‘stock price’, ‘equity price’, or ‘stock market’ in 10 major U.S. newspapers, scaled by the
number of articles in each month and paper. The news-based index and the VIX are normalized to a mean of 100 over the period.
Correlation=0.733
Monthly
News-Based
Index of
Equity Market
Uncertainty
Monthly Average
of Daily VIX Values
0
100 200 300
400
1985 1990 1995 2000 2005 2010 2015
Figure C3: Political slant plays little role in our news-based EPU index
Source: Papers sorted into 5 most ‘Republican’ and 5 most ‘Democratic’ groups using the media slant measure from
Gentzkow and Shapiro (2010).
5 most Republican
papers
5 most Democrat
papers
Reagan, Bush I Bush II Clinton Obama
Figure C4: Raw Uncertainty and Policy Uncertainty Counts in the Fed’s Beige
Books and Policy Uncertainty Measure Based on Risk Factors in 10-K Filings
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Notes: The left scale shows frequency counts per Beige Book of “uncertainty” and references to policy uncertainty. The right
scale reports the percentage of sentences in Part 1A (Risk Factors) of annual 10-K filings that contain one or more of the policy
terms listed in Appendix E. The correlation between the BB Policy Uncertainty Count and the EPU index is 0.54.
Gulf
War I
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9/11
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Figure C5: Importance of Policy Risk Factors Based on Their First
Appearance in Part 1a of Firms’ 10-K Filings
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Scaled!Rank!Value!of!First!Sentence!in!!
Part!1a!with!One!or!More!Policy!Terms!
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The vertical scale shows the fraction of Part 1a that must
be read before encountering a discussion of policy risks.
Lower values mean greater importance of policy risks.
Figure C6: What triggers large daily stock market moves?
Correlation of number of policy-
triggered jumps per year with EPU
index is 0.78
Policy increases
Non-Policy increases
Policy decreases
Non-Policy decreases
Reproduced from “What Triggers Large Stock Market Jumps?” by Scott Baker, Nick Bloom & Steven Davis
Based on human readings of next-day news articles
About large S&P Index moves in the New York Times
And the Wall Street Journal. Jump threshold: +/- 2.5%
-8 -6 -4 -2
0
0 6 12
Quarters
-2
-1.5
-1
-.5 0
0 6 12
Quarters
Figure C7: GDP and Investment Responses to EPU Shock, Quarterly VAR
GDP Response, %
Notes: VAR-estimated
impulse response
functions for GDP and
Gross Fixed
investment to an EPU
innovation equal to the
increase in the EPU
i n d e x f r o m i t s
2 0 0 5 - 2 0 0 6 t o i t s
2011-2012 average
value, with 90 percent
confidence bands.
Identification based on
three lags and a
C h o l e s k y
decomposition with the
following ordering:
EPU index, log(S&P
500 index), federal
reserve funds rate, log
gross investment, log
g r o s s d o m e s t i c
product). Fit to data
from 1985 to 2014.
Investment Response, %
-1
-.5
0
.5
0 6 12 18 24 30 36
Months
Figure C8: Adding the Michigan Consumer Sentiment Index to VARs Fit to
Monthly U.S. Data
Industrial Production Response, %
Notes: VAR-estimated impulse response functions for industrial production to an EPU innovation equal to the increase in the EPU
index from its 2005-2006 to its 2011-2012 average value. Identification based on three lags and a Cholesky decomposition. In the
baseline, the VAR has the following ordering: EPU index, log(S&P 500 index), federal reserve funds rate, log employment, log
industrial production. In the “Michigan First” specification the Michigan consumer sentiment index is added first, and in the
“Michigan Second” it is added after the EPU index. Fit to data from 1985 to 2014.
Baseline
Michigan First
Michigan Second
-1.5
-1
-.5
0
0 6 12 18 24 30 36
Figure C9: Robustness of Twelve-Country Panel VAR Response Functions
Months after the policy uncertainty shock
Industrial Production Response, %
Baseline (+ symbol)
Bivariate (EPU and
industrial production)
Six months of lags
Adding stock volatility
Reverse bivariate:
log IP & EPU
Dropping the stock index
Notes: The baseline case involves the same sample period, countries, VAR specification and identification as in Figure 10. The
other cases depart from the baseline as indicated. We place realized stock volatility after EPU in the ordering.
No country
fixed effects
No weighting
