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Policy Uncertainty and Corporate Investment
Huseyin Gulen ∗
Krannert Graduate School of ManagementPurdue University
Mihai Ion†
Krannert Graduate School of ManagementPurdue University
April 4, 2013‡
First Draft: November 2012
Abstract
Using the policy uncertainty index of Baker, Bloom, and Davis (2012), we investigate howcorporate capital investment at the firm and industry level is affected by the uncertainty relatedto future policy and regulatory outcomes. Policy-related uncertainty is negatively related tofirm and industry level investment, and the economic magnitude of the effect is substantial.Our estimates indicate that approximately two thirds of the 32% drop in corporate investmentsobserved during the 2007-2009 crisis period can be attributed to policy-related uncertainty. Moreimportantly, we document that the relation between policy uncertainty and capital investmentis not uniform in the cross-section of U.S. firms. It is significantly stronger for firms with ahigher degree of investment irreversibility, for firms which are more financially constrained, andfor firms operating in less competitive industries. Policy uncertainty is also associated withhigher cash holdings and lower net debt issuance. Overall, these results lend empirical supportto the notion that policy-related uncertainty can depress economic growth through a decreasein corporate investment. This decrease is related to precautionary delays induced by investmentirreversibility and to increases in the cost of external borrowing.
∗Krannert Graduate School of Management, Purdue University, 403 West State Street, West Lafayette, IN 47907.Tel: (765) 496-2689, fax: (765) 494-9658, and e-mail: [email protected].†Krannert Graduate School of Management, Purdue University, 403 West State Street, West Lafayette, IN 47907.
Tel: (765) 494-6501, fax: (765) 494-9658, and e-mail: [email protected].‡We thank John Barron, Utpal Bhattacharya, Nicholas Bloom, David Denis, Diane Denis, Mara Faccio, Andrew
Greenland, John Howe, Mohitosh Kejriwal, John McConnell, Stephen McKeon, Brett Myers, Raghavendra Rau,Stefano Rossi, Toni Whited, Jin Xu, and Deniz Yavuz for helpful suggestions. We are responsible for all theremaining errors.
“Business contacts in many parts of the country were reported to be highly uncertain
about the outlook for the economy and for fiscal and regulatory policies. Although firms’
balance sheets were generally strong, these uncertainties had led them to be particularly
cautious and to remain reluctant to hire or expand capacity...”1
1 Introduction
Politicians and regulatory institutions frequently make decisions that alter the environment in which
firms operate. This is particularly relevant in light of the recent recession and financial crisis, the
European sovereign debt crisis and the rising U.S. fiscal deficit, which have seen politicians take
a very active role in shaping the world economy. There is a significant degree of uncertainty with
regard to what tools policymakers will use to tackle these issues and what impact these policy
changes will have on corporate profitability and investment. Anecdotal evidence shows that when
faced with such uncertainty, U.S. corporations often reduce capital investment, a key component of
economic growth. Consistent with this idea, the financial press often quotes corporate executives
who attribute the recent reduction in corporate investment at least in part to increased political
uncertainty.2
Businesses often face a significant amount of uncertainty related to the timing and content of
government policy changes, as well as the potential impact that these policies will have on firms’
profitability.3 Consequently, the uncertainty associated with future policy decisions can significantly
increase the uncertainty related to firms’ future profitability. Several theoretical studies have
proposed mechanisms through which more uncertain profits can cause lower investment rates. Two
theories in particular have received significant attention in the literature. First, Bernanke (1983),
Rodrik (1991) and others argue that if investment projects are not fully reversible, uncertainty
will increase the value of the option to wait until more information about the profitability of the
projects is revealed. Second, uncertainty can increase the costs of external financing by increasing
the risk of default (e.g. Gilchrist, Sim, and Zakrajsek (2011)) or the equity risk premium (Pastor
and Veronesi (2011)) which can result in lower investment rates. We contribute to this line of
research by empirically investigating the impact of a particular source of uncertainty – the political
1From the minutes of the Federal Open Market Committee (FOMC) meeting in September, 2012.2For example, see “Investment Falls Off a Cliff: U.S. Companies Cut Spending Plans Amid Fiscal and Economic
Uncertainty” (Wall Street Journal, November 19, 2012).3Pastor and Veronesi (2011) investigate the impact of these two types of policy uncertainty on the equity risk
premium. We will not distinguish between the two of them in this paper.
1
and regulatory system – on corporate investment. To do so, we make use of the recently developed
policy uncertainty index of Baker, Bloom, and Davis (2012) to investigate two possible propagation
mechanisms - investment irreversibility and financial frictions - as suggested by the aforementioned
theoretical work.
One of the main challenges in this line of research is finding an appropriate measure of policy
uncertainty. The overall uncertainty faced by firms has been measured using a variety of variables,
such as the volatility of stock returns (realized or implied), input and output prices, total factor
productivity, firm fundamentals, or the dispersion in analyst forecasts.4 However, measuring the
part of that uncertainty attributable to macroeconomic policy is a daunting task. While some
studies have focused on particular types of policy (fiscal, monetary, social security), significantly
less work has been done to measure the overall level of policy uncertainty in the economy.
Several recent studies have used national election years in a cross-country framework as
indicators of times when political uncertainty is likely to be higher.5 The problem with such a
proxy is that, by construction, the election indicator does not capture the variation in policy-
related uncertainty in nonelection years, which may be significant in some countries. This can
strongly bias inferences when studying the country-level effect of policy uncertainty on corporate
decisions, since firm-level investment and financing do exhibit considerable variation in nonelection
years. For example, we find that in the United States capital investment is not significantly lower
in election versus nonelection years. This suggests either that there is no relation between policy
uncertainty and corporate investment in the U.S. (which is at odds with the anectodal evidence in
the U.S. and the cross-country evidence in Julio and Yook (2012)) or that the election year dummy
is not a suitable proxy for policy-related uncertainty in the United States. We argue that gauging
the magnitude (and not just the sign) of the relationship between policy uncertainty and corporate
decisions in a country such as the U.S. requires a more accurate measure of policy uncertainty. At
the very minimum, the measure should also take into account the variation in uncertainty that may
take place between election years.
Baker, Bloom, and Davis (2012) provide a novel measure of the overall level of policy uncertainty
in the economy that exhibits substantial variation over time. Their index is a weighted average of
three components, two of which measure uncertainty related to taxation, government spending and
4See for example Leahy and Whited (1996), Ghosal and Loungani (1996), Minton and Schrand (1999), Bond andCummins (2004), Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry (2012), and Stein and Stone (2012).
5See for example Julio and Yook (2012) and Durnev (2010).
2
monetary policy. The third component is a more comprehensive measure derived from a count of
newspaper articles containing key terms related to policy uncertainty. Deferring a more detailed
discussion of its construction to Section 2, a visual inspection of the index (Figure 1) reveals
that policy uncertainty tends to spike during events that are ex-ante likely to cause increases in
perceived policy uncertainty, such as debates over the stimulus package, the debt ceiling dispute,
major federal elections, wars and financial crashes. It also exhibits considerable time-series variation
in the periods between such major events.
We introduce this measure of policy uncertainty in several reduced-form specifications used in
the investment literature. Controlling for investment opportunities (Tobin’s Q and cash flows),
demand (sales-growth) and overall economic conditions (realized and expected GDP growth,
composite leading indicators, and consumer confidence index, among others), we find evidence
of a persistent negative relationship between policy uncertainty and investment. This effect is
statistically and economically significant at both the firm and industry levels. We estimate that
a one standard deviation increase in policy uncertainty is associated with an average decrease in
quarterly investment rates of approximately 6.3% relative to the average investment rate in the
sample. This is a sizable effect, considering that during the recent recession and financial crisis, the
policy uncertainty index rose by approximately three standard deviations. A counterfactual analysis
performed using our estimates indicates that the increase in policy uncertainty that occurred
between 2007 and 2009 may be accountable for up to two thirds of the 32% fall in capital investments
observed during this period.
As an out-of-sample test, we also investigate whether our main finding holds outside the United
States. To this end, we focus on the countries for which Baker et al. (2012) calculate policy
uncertainty indices analogous to the index for the United States. These countries include Canada,
the United Kingdom, France, Germany and Italy.6 Using annual firm level data from the Thomson
Reuters Worldscope database, we find a significant negative relationship between policy uncertainty
and investment in each of the above countries.
These findings hold up to a battery of robustness tests. First, we verify that the Baker et al.
(2012) index is not simply picking up the election versus nonelection year effects. Second, we run
all our tests using only the news-based component of the index and obtain the same qualitative
results, thereby ensuring that our findings are not driven solely by the components of the index
6Data on country specific indices are available from the authors’ website: http://www.policyuncertainty.com.They also provide an index for Spain, but we excluded it due to limited accounting data availability.
3
related strictly to fiscal or monetary policy. Third, we control for several measures of expected
future economic performance in order to minimize the possibility that the policy uncertainty index
is simply picking up a deterioration of investment opportunities. Fourth, we control for alternative
macroeconomic measures of uncertainty to ensure that the Baker et al. (2012) index is not
proxying for other general sources of risk. Fifth, we address remaining endogeneity concerns using
several different methods of extracting exogenous variation from our policy uncertainty measure
and obtain the same qualitative results. Finally, the results are robust to several econometric
specifications, including simple panel regressions with and without fixed effects, as well as dynamic
panel specifications estimated using system GMM.
In an attempt to identify possible mechanisms through which policy uncertainty propagates
through the economy, we investigate whether the negative effect of policy uncertainty on capital
investment exhibits heterogeneity in the cross-section. This investigation is motivated by the
predictions made by the real options and the financial frictions literatures, which have received
a great deal of attention from both academics and policymakers.7 The real-options literature
emphasizes that if investment projects are (partially) irreversible, uncertainty shocks can increase
firms’ incentives to delay investment until some of the uncertainty resolves (Bernanke (1983), Rodrik
(1991)). If this is the case, the slow-down effect should be stronger for firms with more irreversible
investments. To test this prediction, we use three different proxies for investment irreversibility:
the ratio of fixed to total assets, an indicator variable for whether the firm operates in a “durables”
industry, and a measure of sunk costs based on rent expense, depreciation, and fixed asset sales.
Consistent with the above prediction, we find that the dampening effect of policy uncertainty on
capital expenditures is stronger for firms that, according to these proxies, have a higher degree of
investment irreversibility.
The literature on real options makes another prediction about the relationship between
investment and uncertainty, which is independent of investment irreversibility. This prediction
is based on the observation that firms differ with respect to the expected costs (or lost profits)
7From the minutes of the Federal Open Market Committee, in April, 2008: “Several participants reported thatuncertainty about the economic outlook was leading firms to defer spending projects until prospects for economicactivity became clearer. The tightening in the supply of business credit was also seen as holding back investment,with some firms apparently reluctant to reduce their liquidity positions in the current environment.”From the remarks of Lawrence Summers, director of the White House National Economic Council, at the BrookingsInstitution on the Obama administration’s economic program and the prospects for the American economy on March13, 2009: “...unresolved uncertainty can be a major inhibitor of investment. If energy prices will trend higher, youinvest one way; if energy prices will be lower, you invest a different way. But if you don’t know what prices will do,often you do not invest at all. That is why we must move forwards as rapidly as possible to reduce uncertainty andcarefully create a new cap-and-trade regime.”
4
incurred from delaying investment. Specifically, as argued by Dixit and Pindyck (1994), for firms
operating in more competitive industries in which investment opportunities are short lived and
strategic first-mover advantages are large, the expected costs of delaying investment may grossly
outweigh the benefits of waiting for more information to be revealed. Hence, if policy uncertainty
truly affects investment through its impact on the value of the option to wait, it should do so less
severely for firms in more competitive industries. To test this prediction, we use the Herfindahl
Index to measure industry competitiveness. We find that, consistent with the argument above, the
relationship between policy uncertainty and investment is insignificant for the 10% most competitive
industries, but becomes significantly more negative for firms in less competitive industries.
The third pattern of heterogeneity we study is motivated by the financial frictions literature.
Uncertainty has been argued to increase the risk of default (e.g. Greenwald and Stiglitz (1990),
Gilchrist, Sim, and Zakrajsek (2011)) and the equity risk premium (Pastor and Veronesi (2011)),
which would result in higher costs of external financing. In turn, this should cause a heterogeneous
investment reduction in the cross-section. Specifically, the uncertainty-induced increase in costs of
external financing should have a more negative effect on investment for more financially constrained
firms that have a higher reliance on the external capital market, and a higher chance of being shut
out of this market if the risk of default and the equity risk premium increase. To test this hypothesis,
we use three proxies of financial constraints: an indicator variable for whether the firm has a credit
rating, the Whited and Wu (2006) index and the Kaplan and Zingales (1997) index. We find that
the dampening effect of policy uncertainty on capital expenditures is significantly stronger for firms
that are deemed as being more financially constrained by the above proxies. Overall, our results on
the cross-sectional heterogeneity of the investment-uncertainty relationship support the hypothesis
that policy uncertainty affects investment both through a “wait-and-see” real options mechanism
as well as through a financial constraint channel.
Finally, we investigate whether policy uncertainty also has an effect on firms’ financing policies.
If an increase in uncertainty causes a slowdown in investment, it is interesting to examine what
firms do with the funds that would have otherwise been invested. Introducing the policy uncertainty
index in standard linear specifications for cash holdings, net debt issuance, and net equity issuance,
we find that firms tend to hold significantly more cash and issue significantly less debt when faced
with higher uncertainty. High policy uncertainty does not seem to have a significant effect on equity
issuance. This provides further evidence consistent with the hypothesis that policy uncertainty can
5
both increase the cost of external capital and cause firms to postpone their investment plans until
some of the uncertainty is resolved.
This paper makes two main contributions to the literature on the relationship between
uncertainty and corporate actions. First, it provides empirical evidence that the political and
regulatory system is a significant source of uncertainty, affecting firms’ investment and financing
decisions. In this respect, it is most related to the recent cross-country studies which use national
elections as a proxy for political uncertainty.8 However, unlike the index by Baker, Bloom and
Davis (2012), election years may fail to capture the variation in policy uncertainty which takes
place between elections in a given country. For example, as previously mentioned, in the U.S. there
is no statistical difference between capital investments in election years versus nonelection years.
Our second contribution is to document that the relationship between policy uncertainty and
capital investment exhibits significant heterogeneity in the cross-section, depending on both firm-
level financial constraints and investment irreversibility. We argue that these patterns are consistent
with two mechanisms through which uncertainty has been proposed to affect corporate investment
and financing: real-option-induced delay effects and financial frictions.9 This paper is, to our
knowledge, the first to provide direct empirical support for these propagation mechanisms at the
firm level in the context of overall policy-related uncertainty.
The rest of the paper proceeds as follows: Section 2 describes the data and the methodology
used in the main tests. Section 3 presents the empirical findings, starting with the average effect
of policy uncertainty on investment. This is followed by the analysis of the heterogeneity of this
effect in the cross-section, and the tests on the impact of policy uncertainty on cash holdings, debt
issuance and equity issuance. Section 4 contains robustness tests and Section 5 concludes.
2 Data and Methodology
Our main investment specification takes the following form:
Investmenti,t = αi + βPUi,t−l + γXi,t + δMi,t−l +QRTt + εi,t (1)
where the main dependent variable Investmenti,t is measured as capital expenditures scaled by
lagged total assets:CAPXi,t
TAi,t−1. In all the specifications, i indexes firms or industries, t indexes time
8See for example Boutchkova, Hitesh, Durnev, and Molchanov (2012), Durnev (2010), and Julio and Yook (2012).9See Bernanke (1983) for the real-option mechanism and Stiglitz and Weiss (1981) for the financial frictions
mechanism.
6
(generally calendar quarters), and l ∈ {1, 2, 3, 4} stands for the lead between the investment variable
(left hand side) and the policy uncertainty variable (PUi,t−l).10 The αi are firm or industry fixed
effects and QRTt represents a set of fiscal and calendar-quarter dummies included to control for
seasonality (the base is the fourth quarter).
To control for overall macroeconomic conditions (Mi,t−l) we use lagged quarterly change in
GDP.11 In robustness tests presented in Section 3.2, to capture expectations about future economic
conditions, we also use the following control variables: (i) expected GDP growth calculated
using one-year-ahead GDP forecasts from the Philadelphia Federal Reserve’s biannual Livingstone
survey, (ii) the Conference Board’s monthly Leading Economic Index (iii) the Michigan Consumer
Confidence Index from the University of Michigan, and (iv) the monthly Investor Sentiment Index
from Baker and Wurgler (2007).
In the remainder of this section we describe the firm-level data (Xi,t and the investment variable)
and the policy uncertainty index (PUi,t−l). We then conclude with a more detailed discussion of
the methodology used in our main tests.
2.1 Firm-level Accounting Data
For most of the tests, we use quarterly firm-level accounting data from the COMPUSTAT
database.12 The sample period extends from January 1987 to December 2011.13 To make sure
results are not dominated by large firms, we deflate capital expenditures, cash flows, cash holdings,
depreciation, EBIT, and PPE by total assets at the beginning of the period. Sales growth is
measured as the year-on-year percentage change in sales, and firm size is measured as the natural
logarithm of total assets. We exclude financials (SIC between 6000 and 6999), utilities (SIC between
4900 and 4999), and all observations which have total assets, sales or book equity smaller or equal
to zero. This leaves a sample of 7,861 unique firms over 100 quarters for a total of 309,499 firm-
quarter observations. Finally, we winsorize all variables at the 1st and 99th percentiles in order to
10In this specification, the firm-level controls Xi,t are contemporaneous with the dependent variable. We obtainvery similar results if instead the firm-level controls are contemporaneous with the policy uncertainty variable andlagged with respect to the dependent variable: Investmenti,t = αi + βPUi,t−l + γXi,t−l + δMi,t−l +QRTt + εi,t.
11We find similar results when we use alternative proxies such as the risk-free rate, term spread, default spreadand the dividend yield on the S&P 500 index (results are available upon request).
12Table 2 and Table 5 include results based on annual data. We work with quarterly data to take advantage ofthe variation in the policy uncertainty index which is measured monthly. Nevertheless, our results also hold if we useannual data instead.
13The sample period is chosen to match the availability of the policy uncertainty index.
7
minimize the impact of data errors and outliers. The results are not qualitatively sensitive to any
of the above filters.
2.2 The Policy Uncertainty Index
The main independent variable of our analysis is the policy uncertainty measure from Baker, Bloom,
and Davis (2012). This variable is calculated as a weighted average of three components. The first
component is a count of search results in 10 large newspapers containing at least one of the terms
‘uncertainty’ or ‘uncertain’, at least one of the terms ‘economic’ or ‘economy’, and at least one of the
terms ‘congress’, ‘legislation’, ‘white house’, ‘regulation’, ‘federal reserve’, or ‘deficit’. To control
for the changing volume of news throughout time, for each of the 10 newspapers in each month,
the total number of policy uncertainty articles is normalized by the total number of articles in that
newspaper. The second component of the index relates to uncertainty about expiration of tax code
provisions in the future, using data from the Congressional Budget Office. The third component of
the index is intended to capture uncertainty related to monetary policy and government spending.
It uses data in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters to
measure the forecast dispersion for the CPI and for purchases of goods and services by state, local,
and the federal governments. The overall index of policy uncertainty applies the weights 1/2, 1/6
and 1/3 respectively to the three components described above, and is updated monthly.14
To match the frequency of the monthly policy uncertainty index to the quarterly accounting
data, we take a weighted average of the index in the three months of each calendar quarter, using
the weights 1/2, 1/3, 1/6. Specifically, for any firm i, its accounting data for fiscal quarter t ending
in month m, is lined up with:
PUt =3PUIm + 2PUIm−1 + PUIm−2
6
where PUIm is the value of the Baker, Bloom, and Davis (2012) index in month m. This weighting
scheme accounts for the possibility that more recent levels of uncertainty may have a stronger effect
on investment decisions. Nevertheless, all our results are robust to using a simple average, as well
as using the quarter-end level of the Baker, Bloom, and Davis (2012) index.15
14The reader interested in a more thorough discussion of the methodology used to calculate the policy uncertaintyindex is referred to the original paper by Baker, Bloom, and Davis (2012).
15We report results for the latter specification in Section 4.
8
2.3 Methodology
Our main empirical specification is a simple firm or industry level panel regression similar to those
commonly employed for testing the Q theory of investment. These studies generally use CAPX
deflated by total assets as the measure of investments (the dependent variable) and Tobin’s Q
(market to book value of assets) and cash flows divided by total assets as the explanatory variables.16
The surprising finding is that, contrary to the prediction of the Q theory of investment, the cash
flow variable has strong economic and statistical explanatory power in such specifications. This has
been interpreted as either evidence that financial constraints have a significant effect on investment
(Fazzari, Hubbard, and Petersen (1988)) or that cash flows simply pick up expectations about
future profitability in a way that Tobin’s Q fails to (Alti (2003), Erickson and Whited (2006)).
Even though our study is not concerned with the interpretation of the coefficient on cash flows,
the above criticisms point out the possibility that our measure of policy uncertainty may end up
spuriously picking up changes in expected profitability that are attributable to other structural
forces. To minimize this concern, we control for expectations about future general business
conditions using the macro variables discussed in the beginning of this section. Moreover, we
introduce firm sales growth as a proxy for expected future demand (Bloom, Bond, and Van Reenen
(2007)). The general form of the specification is:17
CAPXi,t
TAi,t−1= αi + β1PUi,t−l + β2TQi,t−1 + β3
CFi,t
TAi,t−1+ β4SGi,t + δMi,t−l +QRTt + εi,t (2)
Once again, i indexes the firms, t stands for a calendar quarter and l ∈ {1, 2, 3, 4} stands for the
quarter lead between the dependent variable and the policy uncertainty variable. The firm-level
controls are: Tobin’s Q (TQ) measured at the beginning of the quarter, operating cash flows (CF),
and sales growth (SG). Investments and cash flows are normalized by beginning of the period total
assets.18 The Mi,t−l term stands for one of the macroeconomic variables discussed in the beginning
of this section (we do not include them all at once because they are highly collinear, although doing
so does not alter the results). For expositional simplicity, we only present the results using GDP
growth as the macro control. The QRTt term contains a set of calendar and fiscal quarter dummy
16Sometimes the normalizing variable is not total assets but gross value or replacement value of capital. Our resultsdo not change significantly if we use any of these alternative normalizations.
17We obtain similar results when we use an alternative specification where control variables are measured at the samequarter as the policy uncertainty variable for all lags:
CAPXi,t
TAi,t−1= α+β1PUi,t−l +γXi,t−l +δMi,t−l +αi +QRTt +εi,t
where X contains the same firm-level controls as in equation 218All results are robust to normalizing by end of the period total assets.
9
variables included to control for possible seasonality in capital investments.19 The αi’s are firm
fixed effects (or industry fixed effect in industry specifications), although they do not show up in
the estimation because they are eliminated using a within-group transformation.
It is important to note that the policy uncertainty variable PUi,t−l has a firm subscript even
though it is based on an economy-wide variable. This is because for each firm i, PUi,t is calculated
as a weighted average of the Baker et al.(2012) index over the three months of the firm’s fiscal
quarter ending in calendar quarter t. Because all firms do not have fiscal quarters ending in the
same month within each calendar quarter t, there will be some cross-sectional variation in PUi,t
for each t.20 However, this variation is minimal, because the vast majority of firms do have fiscal
quarters ending at the same time (the last month of the calendar quarter). Therefore, we do not
include time fixed effects in our specification since doing so would mechanically absorb all the
explanatory power in the policy uncertainty variable.
Equation 2 is used to estimate the unconditional relationship between policy uncertainty and
capital investment (Section 3.1), both in the United States, Canada and several European countries.
The same specification is used to identify the effects of uncertainty on cash holdings, debt issuance
and equity issuance, by changing the dependent variable accordingly (Section 3.4). To study the
degree to which the effect of policy uncertainty on investment depends on investment irreversibility,
financial constraints, and industry competitiveness, we include our proxies for these variables in
equation 2, together with interaction terms with the policy uncertainty index (results are in Section
3.3).
3 Empirical Results
We begin with a discussion in Section 3.1 of the average, unconditional relationship between policy
uncertainty and investment at both the firm level and the industry level. In Section 3.2, we
consider alternative explanations for our findings, and conduct several tests to address potential
endogeneity concerns. In Section 3.3, we explore some of the dimensions of heterogeneity in the
relationship between policy uncertainty and investment as discussed in the introduction. In Section
3.4, we present evidence that policy uncertainty is positively related to cash holdings and negatively
related to net debt issuance.
19Including only fiscal quarter dummies or only calendar quarter dummies does not change the results.20This is also the reason why the macro controls Mi,t−l have a firm subscript.
10
3.1 The Unconditional Relationship between Policy Uncertainty andInvestment
In Table 1 we present summary statistics for the main variables in the paper. In Panel A these
statistics are calculated over the entire sample. Although we have imposed several filters on
the data, we can report that the resulting sample is not significantly different from the entire
COMPUSTAT universe. In Panel B we present non-parametric tests of differences in means and
medians of capital investment between periods of high versus low policy uncertainty. This sample
split is performed based on the median value of the policy uncertainty index. The difference in
means is a simple t-test, while the difference in medians is a Wilcoxon-Mann-Whitney test. Both
show that in this univariate setting, capital investment is significantly lower in periods of high
policy uncertainty.
Before we address multivariate tests, we present a few pieces of evidence to demonstrate why
we believe the index of Baker et al. (2012) adds value to the studies which use election years
as proxies for political uncertainty (Julio and Yook (2012), Durnev (2010)). First, election years
do not capture variation in policy uncertainty that may occur between elections. Intuitively this
variation is likely significant given the infrequency of elections and the many uncertainty inducing
events which happened in nonelection years such as debates over the stimulus package, the debt
ceiling dispute, wars and financial crashes. Figure 1 shows that the policy-related uncertainty index
of Baker et al.(2012) is very responsive to such events and exhibits significant time-series variation
in periods between elections.
Second, as shown in the univariate tests in Panel C of Table 1, capital investment remains
basically unchanged in election versus nonelection years in the United States. Moreover, in Table
2 (columns 1 and 2), using a sample of U.S. firms, we find that in a multivariate setting, elections
do not seem to be related to lower capital investment (specification 1 uses quarterly data as in the
rest of our paper, and specification 2 contains annual data). To control for the possibility that this
result is driven by a lack of power in our tests (our sample period includes only four presidential
elections), we extend our dataset back to 1963 and re-estimate our annual specification on this
longer sample period, which now contains 12 presidential elections (see column 3). Additionally,
to further increase the number of elections considered, in column 4, we use the new 1963-2011
dataset and include midterm elections alongside presidential elections when we build our election-
year indicator variable (a total of 24 elections). Finally, we control for the possibility that the lack
11
of a relationship between election years and capital investments in the United States might be due
to the fact that some of the presidential election outcomes were quite predictable. Specifically, in
column 5, we use an election year indicator which equals one only for “close” elections, defined as
the 25% of all presidential elections (from 1963 to 2011) won by the smallest margin in terms of the
popular vote. As columns 3, 4 and 5 show, we fail to find a significant relationship between capital
investment and elections, even in these extended samples, and even if we restrict our attention to
the least predictable presidential elections.21
Clearly, this does not mean the election variable does not have explanatory power at the global
level, as Julio and Yook (2012) find. We simply point out that in developed, politically stable
countries such as the United States, election years may account for only a small portion of the
time-series variation in policy uncertainty. Our claim is that in such countries, the Baker et al.
(2012) index may prove to be a more useful tool in assessing the impact of policy uncertainty on
the real economy.
Table 3 presents our main results. We run four specifications of equation 2, one for each
l ∈ {1, 2, 3, 4} in order to accommodate for the possibility that the effect of policy uncertainty
on investment may persist over multiple quarters or may manifest itself with a lag (results are in
columns numbered (1) to (4) in each panel.) All four specifications include firm fixed-effects, and
the standard errors are clustered at both the firm and calendar-quarter level to correct for potential
cross-sectional and serial correlation in the error term εi,t (Petersen (2009)).
Panel A contains the firm level results. To facilitate the comparison of economic magnitudes
across covariates, all variables have been normalized by their sample standard deviation. Therefore,
each coefficient can be interpreted as the change in the dependent variable (as a proportion of
its standard deviation) associated with a one standard deviation increase in the right hand side
variable. The results consistently show that policy uncertainty is significantly negatively related to
capital investment, up to four quarters in the future. This effect is also economically significant.
The coefficient estimate in column 1 of Table 3 suggests that a one standard deviation increase in
policy uncertainty is associated with a decrease in investment rates (in the next quarter) equivalent
to 6.3% of the average investment rate in the sample.
21For specifications 3, 4, and 5, we can no longer use the cash flow variable provided by the statement of cashflows as in the rest of the paper, since the cash flow statement was not required prior to 1987. For this reason, inthese specifications we measure cash flows as net income before extraordinary items plus depreciation expense. Inunreported tests, we verify that using this alternative cash flow variable does not alter any of the qualitative findingsin our paper.
12
To compare the magnitude of our estimate to the effect found using election years as a proxy,
consider first that, as mentioned above, in the United States alone, election years are not associated
with significantly lower investment rates. Nevertheless, when using an international dataset, Julio
and Yook (2012) find that, in their entire sample, election years are associated with a 4.8% lower
investment rate relative to nonelection years. On average, this amounts to a 1.2% decrease per
quarter, which is substantially lower than the 6.3% estimate we report above. Even though this
estimate is based on a one standard deviation increase in policy uncertainty, we find that such
large changes are quite common in the sample. For example, we find that 15 out of our 100
calendar quarters exhibit a year-on-year change in policy uncertainty which is at least as large as
the standard deviation of the index over the entire sample.22 This comparison suggests that election
years may significantly understate the magnitude of the relationship between policy uncertainty
and investment.
As an alternative way to gauge the economic magnitude of our policy uncertainty estimate, we
compare the fitted values obtained from our model using realized values of the policy uncertainty
index to the fitted values obtained using the policy uncertainty level observed just prior to the
recent recession. Specifically, we take the firms from our sample in the first quarter of 2007, and
calculate their investment rates as predicted by our model estimates:
CAPXi,t
TAi,t−1= αi + β1PUi,t−l + β2TQi,t−1 + β3
CFi,t
TAi,t−1+ β4SGi,t + δMi,t−l + QRTt
For each calendar quarter from 2004 to 2011, we take a cross-sectional weighted average of these
fitted values using the firms’ total assets in the previous quarter as weights. The resulting time
series is plotted as the solid line in the top panel of Figure 3. We then apply the same averaging
procedure to the the fitted values obtained if the policy uncertainty index remained at the level it
had during the last quarter of 2006. This second time series is plotted as the dashed line in the
top panel of Figure 3. In the bottom panel of the figure, we take annual rather than quarterly
weighted averages for each of the two sets of fitted values described above. Both graphs suggest
that if policy uncertainty would have remained at its pre-2007 levels, the fall in investment from
2007 to 2009 would have been smaller by roughly two thirds.
The policy uncertainty variable also seems to have a strong economic impact when compared to
the rest of the variables in the regression. In our estimations, the only variable that has a stronger
22Furthermore, in the recent crisis (between 2006 and 2011), the policy uncertainty index increased by more thanthree standard deviations.
13
economic effect than policy uncertainty is Tobin’s Q. Finally, in Panel A2, we show that including
an election dummy in our specification does not alter our main finding. This suggests that the
Baker et al. (2012) index captures variability in capital investment in the cross-section of U.S.
firms and that the national election indicator does not.
In Panel B, we test whether the basic result also holds at the industry level. For this, we use
the same basic specification from Panel A (see equation 2 in Section 2.3.) However, now we use
industry means for each variable instead of firm level values. The industries are defined using three
digit SIC codes. This gives us an unbalanced panel of 240 unique industries over 100 quarters for a
total of 22,037 industry-quarter observations. We include industry fixed effects in all specifications,
and standard errors are clustered at the industry and calendar-quarter level.
The results show that policy uncertainty has a strong negative association with capital
investment at the industry level as well. This effect is significant at the 1% confidence level,
and it persists up to four quarters into the future. Once again, we notice that at the industry
level, in economic terms, policy uncertainty is one of the strongest explanatory variables across all
specifications and that introducing an election year dummy does not affect the results. Finally,
in unreported robustness tests, we verify that these industry level results do not change if we use
industries based on the Fama-French 49 classification. They also are unaffected if we use industry
medians, rather than means, for each variable.
In the second part of Table 3, we verify whether the basic result documented above is being
driven solely by one of the components of the Baker et al. (2012) index. This is particularly
important because the main advantage of using this index is to provide a measure for the overall
policy uncertainty faced by firms. Specific sources of uncertainty such as fiscal and monetary policy
can have a significant impact on economic growth.23 Since these sources of uncertainty make up
half of the Baker et al. (2012) index, it is essential to establish that they are not the only driving
force behind our results. More importantly, the news component of the index can be viewed as
an innovation in policy related risk because the flow of news related to political uncertainty is less
likely to be predictable.
To this end, we repeat the analysis in Panels A and B of Table 3, using only the news-based
component of the policy uncertainty index. We find that the results, reported in Panels C and
23See for example Croce, Nguyen, and Schmidt (2012), Fernandez-Villaverde et al. (2011), Gomes, Kotlikoff, andViceira (2011), Hassett and Metcalf (1999) and Hermes and Lensink (2001)
14
D, are only slightly weaker. In fact, in unreported results, we repeat all the analyses using only
this component, and all our results remain valid. Figure 2 plots the news-based index and shows
a striking similarity with the overall index in Figure 1. The largest part of the variation in the
overall index is due to the news component, as suggested by the 89% correlation between them.
Nevertheless, we continue to use the full index, since it is likely a more accurate measure of the
policy uncertainty in the economy.
Finally, we perform an out-of-sample test which verifies whether the negative relationship
between policy uncertainty and capital investment holds outside of the United States. In particular,
we use the policy uncertainty index which Baker et al. (2012) have constructed for Canada and
four European countries: UK, Germany, France, and Italy.24 The index dates back to 1990 for
Canada and 1997 for the remaining countries. We collect data on the relevant accounting variables
from the Thompson Reuters Worldscope Database and GDP data from EuroStat. Throughout, we
use annual data since quarterly data is not available on Worldscope.
In Table 4, we use this data to estimate the same specifications used in Table 3 (see equation 2
in Section 2.3.). We find that, in all the remaining five countries, policy uncertainty is significantly
negatively related to capital investment the following year. Its economic significance is strong when
compared to the other covariates, though slightly weaker than in the United States.25
3.2 Endogeneity and Alternative Explanations
Interpreting our results causally is problematic due to the potential endogeneity of the policy
uncertainty variable. In particular, the general concern is with an omitted variable bias which
arises if increases in policy uncertainty tend to happen at the same time as decreases in expected
profitability/investment opportunities (e.g. during recessions, wars, and financial crises).26 In this
case, if these first moment effects are not properly accounted for by Tobin’s Q or the sales growth,
cash flow, and macro-level controls, their influence on investment may be picked up by the policy
uncertainty variable, biasing its coefficient upward.27
24The authors also provide policy uncertainty data for Spain. However, since imposing all of our data restrictionsresults in a very small sample size for Spain, we had to leave this country out of our tests.
25The results are significantly stronger when Tobin’s Q is not lagged with respect to the investment variable. Inthis specification, the t-statistics for the policy uncertainty variable are −3.66, −4.41, −5.54, −3.29 and −1.96 forCanada, U.K., Germany, France and Italy respectively.
26Henceforth, we loosely use the terms “expected profitability,” “investment opportunities,” and “expected futureeconomic conditions” interchangeably.
27Note however that even though these concerns about omitted variable bias are legitimate, they will likely beof little importance in our specification because we always use lagged values of the policy uncertainty variable with
15
To address this concern, we include in our main specification several variables which may do
a better job at capturing expectations about future economic conditions. First, we use data on
one-year-ahead GDP forecasts from the Philadelphia Federal Reserve’s biannual Livingstone survey
to calculate a proxy for expected GDP growth. Specifically, this variable is measured every June
and December as the percentage change of the mean GDP forecast from the current GDP level.
Second, we use the Conference Board’s monthly Leading Economic Index which is based on 10
indicators that have been shown to have predictive power over future GDP. Our proxy is a year-on-
year log change in this index. Third, we control for consumers’ expectations about future economic
prospects using the Michigan Consumer Confidence Index from the University of Michigan. Finally,
to control for expectations by equity-market participants, we use the monthly Investor Sentiment
Index from Baker and Wurgler (2007).28
In Table 5 we include these control variables one-by-one in the specification from equation 2
with l = 1. The four additional proxies for investment opportunities are all calculated at the
beginning of the quarter in which the dependent variable is measured. Note from Panel A that
when the policy uncertainty index is left out, all of these proxies except for the Investor Sentiment
Index are strongly positively related to firm-level capital investment. This finding is reassuring
since it indicates that these proxies indeed contain information about investment opportunities
which is not captured by Tobin’s Q. However, when we control for policy uncertainty in Panel B,
all of the variables except for the Michigan Consumer Confidence Index lose significance. On the
other hand, our general finding that policy uncertainty is strongly negatively related to investment
remains valid in all specifications. This is the case even when we control for all four proxies at the
same time, as shown in Column 5 of Panel B.
A second potential concern with our results, which is also related to an omitted variables
bias, is that the Baker et al. (2012) index may in fact capture (at least partially) the effect
of other general sources of uncertainty on investment. To address this concern, we control for
several macroeconomic measures of uncertainty as suggested by Bloom (2009). First, we use
the same Livingstone survey mentioned above to calculate a proxy of uncertainty about future
economic growth. Specifically, the proxy is calculated every June and December as the coefficient
respect to the dependent variable. Therefore, we can consider policy uncertainty to be predetermined, which meansthat its effect is estimated consistently in our specifications (see Hayashi (2000), p. 109). This lagging techniquealso helps alleviate any reverse causality concerns. A similar argument is also made by Hennessy, Levy, and Whited(2007). We will discuss this issue further in our robustness tests in Section 4.
28Data on the last two indexes is available only up to December 2010.
16
of variation (standard deviation/mean) in GDP forecasts obtained from the survey. Second, to
proxy for uncertainty about future profitability, we use the within-quarter cross-sectional standard
deviation of firm-level profit growth (quarter-on-quarter change in net profit divided by average
sales). Finally, to capture information about uncertainty as perceived by the equity markets, we use
the monthly cross-sectional standard deviation of stock returns and the VXO (implied volatility)
index from the Chicago Board Options Exchange.29
Following the same methodology as in Table 5, we introduce each of these proxies in our main
specification and find that when we do not control for policy uncertainty (Table 6, Panel A), they all
are significantly negatively related to capital investment. These results provide further empirical
support to theories that postulate a negative relationship between uncertainty and investment.
More importantly, in Panel B, we find that the negative relationship between policy uncertainty
and capital investment is robust to controlling for all of these alternative measures of uncertainty.
In the final part of this section, we investigate several alternative methods of extracting
exogenous variation from our policy uncertainty measure. Our results are shown in Table 7. To
begin with, as explained in Section 2.2, one component of the Baker et al. (2012) index is a measure
of taxation-related uncertainty, which is calculated based on the discounted dollar value of tax code
provisions expiring within the following 10 years. Since Congress often extends temporary federal
tax code provisions at the last minute, these expirations are likely to constitute a significant source
of uncertainty for businesses and households alike. We argue that this component of the policy
uncertainty index is unlikely to be determined by some omitted measures of current or expected
economic conditions, as the expiration of these tax codes is predetermined, having been established
several periods in the past. Hence, this component can arguably be treated as exogenous for the
purpose of our analysis.
In column 2 of Table 7, we estimate our baseline specification substituting our measure of policy
uncertainty with its taxation-uncertainty subcomponent. We find a strong negative relationship
between this measure of uncertainty and capital investment at both a statistical and economic level
(the coefficient estimate is −0.0762 and the t-statistic is −9.28). This result supports the existence
of a strong causal link going from tax-related uncertainty to firm investment behavior.
In our next specification, we address any remaining concerns that our policy uncertainty index
29Following Bloom (2009), to ensure that our proxies are not influenced by time-series changes in the characteristicsof newly listed firms, when we calculate standard deviations of profit growth and returns, we only use firms that arein our sample for at least 20 years.
17
is, to a significant degree, measuring general economic uncertainty not necessarily induced by policy
concerns. If the controls we introduced in Table 6 do not properly account for these non-policy-
related sources of economic uncertainty, then our results may suffer from a measurement error bias.
However, given the ample evidence that the United States and Canadian economies are tightly
interlinked (see for example Romalis (2007)), we expect many of the shocks that affect general
economic uncertainty in the U.S to also affect general economic uncertainty in Canada, albeit to
a lesser extent. If this is the case, and if indeed the Baker et al (2012) index is in part a measure
of non-policy-related economic uncertainty, then we can eliminate this contaminating part of the
index by extracting the component of the U.S. policy uncertainty index that is orthogonal to the
Canadian policy uncertainty index.30
We explore this idea in column 3 of Table 7. Specifically, we run a time-series regression
of the Baker et al. (2012) U.S. policy uncertainty index on their Canadian index. As controls
for observable economic conditions, we include in this time series regression the cross-sectional
means of the firm-level variables used in our baseline panel regression, as well as GDP growth. As
argued above, the residuals from this regression should represent a cleaner measure of U.S policy
uncertainty, as they have been purged of general uncertainty shocks affecting both countries. We use
these residuals as the policy uncertainty measure in our baseline specification and obtain a coefficient
estimate of −0.0363, with a t-statistic of −3.72.31 Hence, the results show a strongly negative
relationship between this new measure of policy uncertainty and corporate capital investment.
Furthermore, as a falsification test, we introduce this same measure of U.S. policy uncertainty in
the Canadian firm-level data and estimate our baseline specification (results are not reported in the
table). We obtain no significant relationship between this new measure and capital investment of
Canadian firms. This reassures us that we have extracted a component of the Baker et al. (2012)
U.S. policy uncertainty index which is orthogonal to any macroeconomic forces common to both
countries, as these forces would likely influence Canadian firms as well.
Finally, in columns 4 and 5, we present results from an IV analysis which uses measures of
30The methodologies used by Baker et al. (2012) to calculate the policy uncertainty indexes in U.S. and Canadaare very similar. The only difference is that the Canadian index does not contain a component based on tax-relateduncertainty.
31The standard errors are bootstrapped to account for the fact that the policy uncertainty measure is estimated andthe estimation error was ignored. Specifically, to also account for the fact that our error term may exhibit correlationboth within firm and within calendar quarter even after we control for firm fixed effects, we use a sequence of clusterbootstraps as suggested by Cameron, Gelbach and Miller (2011): in the first bootstrap we resample with replacementfrom firm clusters, in the second we resample with replacement from quarter clusters and in the third we resamplewith replacement from the entire dataset. The final variance matrix is obtained by adding the variance matricesobtained in the first two bootstraps and subtracting the variance matrix from the last.
18
partisan polarization in the United States House of Representatives and Senate as instruments for
policy uncertainty. Partisan polarisation has been argued to “make it harder to build legislative
coalitions, leading to policy gridlock” and potentially “produce greater variation in policy”
(McCarty (2012))32 Our measures of partisan polarisation are based on the DW-NOMINATE
scores of McCarty, Poole and Rosenthal (1997) which have been widely used in the political science
literature as a method of calculating a legislator’s ideological positions over time. In particular,
we focus on the first dimension of the DW-NOMINATE scores, which can be interpreted as the
legislator’s positions on government intervention in the economy (Poole and Rosenthal (2000)).
Specifically, the polarization measures which we use as instruments are calculated separately for
the House and Senate as differences between the Republican and Democratic party averages in the
first dimension of the DW-NOMINATE scores.33 Holding everything else constant, we expect that
higher levels of polarization in the House or Senate would result in higher uncertainty related to
policy decisions and therefore that our polarization measures satisfy the relevance condition for
an instrument (we verify this empirically below). Moreover, it is difficult to argue that the level
of disagreement between politicians on the liberal-conservative dimension is itself driven by some
omitted measures of firm profitability and investment opportunities. We, thus, feel fairly confident
that these instruments satisfy the exclusion restriction as well.
Since our policy uncertainty variable as well as our instruments are cross-sectionally invariant,
the usual two-stage least squares methodology is not appropriate, since a first stage panel regression
would be using repeated values of the policy uncertainty variable (and its instrument) for all firms
within each time period. Instead, in the first stage, we run a time series regression of the Baker et al.
(2012) index on the instrument, GDP growth and cross-sectional means (in each calendar quarter)
of the firm-level variables included in our baseline regression: Tobin’s Q, cash flow to assets and sales
growth.34 The fitted values from this regression are then used as the policy uncertainty variable
in our baseline panel specification.35 The second stage results are presented in columns 4 and 5 of
32See also Rosenthal (2004), Gilmour (1995), Groseclose and McCarty (2000), and McCarty, Poole, and Rosenthal(2006)
33From McCarty(2011): “[...] DW-NOMINATE scores, are calculated based on a statistical model that uses dataabout who votes with whom and how often to locate legislators on ideological scales. Conservatives are those whogenerally vote with other conservatives, liberals are those who vote with other liberals, and moderates are thosewho vote with liberals and conservatives. The polarization measure for each chamber is simply the average distancebetween Democratic and Republican legislators on this scale.”
34Nevertheless, our results are qualitatively similar if we use the regular two-stage least squares methodology (witha panel regression in the first stage).
35For brevity, we do not report the results from the first stage regressions, but they are available from the authorsupon request.
19
Table 7. The coefficient estimates, −0.0501 if we use polarization in the House as an instrument
(column 4) and −0.0414 if we use polarization in the Senate as an instrument (column 5), are
similar to the ones obtained in our baseline specification (column 1). The t-statistics are −3.37
and −2.83 respectively.36 Therefore, the results remain strongly significant under this alternative
IV specification, both from a statistical as well as an economic perspective. Furthermore, the
F-statistics for the first stage regressions are 12.3 and 13.3 respectively, suggesting that a weak-
instrument problem is not likely.
Overall, the tests presented in this section provide strong evidence that our main result is
unlikely to be driven by an endogeneity bias coming from either omitted variables, reverse causality
or measurement error. We now turn to an alternative identification strategy, which explores the
idea that policy uncertainty may have heterogeneous effects in the cross section of U.S. firms. We
continue to employ our main policy uncertainty measure throughout the rest of the paper, although
one should keep in mind that our results are qualitatively similar if instead we used the above IV
estimation procedure in all of our remaining tests.
3.3 The Heterogeneous Effects of Policy Uncertainty on Investment
In this section, we investigate whether the negative relationship between policy uncertainty and
investment manifests itself heterogeneously in the cross-section in a way that is consistent with
theories that postulate a causal effect of uncertainty on investment. Several such theories have
emerged in the literature, based on considerations such as real options (Bernanke (1983)), financial
frictions (Stiglitz and Weiss (1981)), managerial risk-aversion (Panousi and Papanikolaou (2012)) or
incomplete contracting (Narita (2011)). We focus on the first two, since they seem to have been the
most widely debated in the literature. In the following two subsections, we present empirical results
to support their validity as mechanisms through which policy uncertainty propagates through the
real economy.
3.3.1 Policy Uncertainty and Investment Irreversibility
Real options theories point out that many firms have the option to delay (at least some) investment
projects, and if these investments are even partially irreversible, then the delay options are valuable.
As with financial options, an increase in uncertainty has a positive effect on the value of the option
36We account for the fact that the policy uncertainty variable was estimated by bootstrapping standard errorsusing the same methodology as in the previous test (column 3).
20
to wait, thereby decreasing the incentive to invest today (since investing would eliminate the delay
option). Moreover, this theory predicts that the negative effect of uncertainty is affected by the
firm’s degree of investment irreversibility. Indeed, if all firms are hit with a common positive
uncertainty shock, the ones with more irreversible investments are more likely to delay investments.
After all, they have more to lose if the project proves unprofitable and downscaling is necessary.
At the extreme, firms with completely reversible investment projects would not be affected by
uncertainty shocks because they would not have an incentive to wait.
While (partial) investment irreversibility increases the value of the option to delay investment,
it is important to first acknowledge that for some firms waiting may simply be too costly or
altogether unfeasible. This is particularly likely in very competitive industries in which investment
opportunities are short lived and strategic first-mover advantages are large (Dixit and Pindyck
(1994)). Therefore, if real options are a primary channel through which uncertainty affects
investment, then this effect should be significantly weaker in highly competitive industries in which
firms either do not have the option to wait, or doing so is too costly in terms of forgone cash-flows.
To test this hypothesis, in Panel A of Table 8 we use the Herfindahl Index (HI) as a measure
of industry competitiveness, and we introduce it in our main specification (the one from Table3,
Panel A) together with an interaction with policy uncertainty. To facilitate interpretation, in
the interactive term, we use competitiveness values from 0 to 9 according to industries’ HI cross-
sectional decile ranks (we refer to this transformed variable as “HI Decile”). The coefficient on policy
uncertainty suggests that, indeed, for the most competitive industries (the ones with HI Decile = 0),
the relationship between policy uncertainty and investment is marginal or insignificant. Further,
the coefficient on the interaction term suggests that this relationship becomes significantly more
negative as we move toward less competitive industries.
This finding is consistent with the hypothesis that the negative relationship between policy
uncertainty and investment is attributable to a significant extent to a real-option-induced delay
effect. Moreover, assuming that the impact of deteriorating investment opportunities is not lower
for more competitive industries, these results also cast doubt on the possibility that our policy
uncertainty index is simply proxying for lower expected profitability. For the remainder of this
subsection, we investigate the extent to which investment irreversibility affects the relationship
between policy uncertainty and investment.
Our first measure of investment irreversibility is a variable that has traditionally been used as a
21
proxy for asset tangibility: the ratio of fixed assets to total assets measured as PPE divided by total
assets. The assumption here is that firms that score high on this variable tend to be manufacturing
firms which, when disinvesting, would have to do so in large discrete amounts. On the other hand,
firms with low levels of fixed assets are assumed to have relatively more human capital rather than
physical capital as the main input. This would presumably give them more discretion to scale down
closer to a new optimum level of capital when needed. For this reason, we interpret firms with
higher ratios of fixed to total assets as having higher adjustment costs. However, we acknowledge
that this is a rough proxy, given the fact that it does not take into account other determinants
of adjustment costs such as asset specificity or mobility (Kessides (1990)). For example, costs to
adjusting fixed assets are not as high if there is a very active second-hand market. Similarly, costs
of adjusting human capital may be high if reputational concerns make it difficult to re-hire highly
skilled employees. Consequently, we discuss below two other proxies for sunk costs that are meant
to address these shortcomings.
First, we use an industry-level measure of sunk costs as an indicator of the degree to which
capital investment outlays can be subsequently recovered. Drawing from the industrial organization
literature (Kessides (1990), Farinas and Ruano (2005)), we consider three different proxies for sunk
costs: rent expense, depreciation expense, and sale of PPE. Intuitively, sunk costs are lower for
firms that rent a higher proportion of their physical assets, for firms with rapidly depreciating
capital, and for firms with assets with a more liquid second-hand market. For each firm-fiscal
quarter observation, we measure the rent proxy as the rent expense in that fiscal quarter divided
by PPE at the beginning of the quarter. The depreciation proxy is measured analogously. To proxy
for the liquidity of the second-hand market of the firm’s assets, we use the sum of the firm’s sales
of PPE in the twelve quarters leading up to (and including) the current fiscal quarter, which we
again normalize by the beginning of quarter PPE. We then obtain industry-level measures of these
proxies for each calendar quarter by taking means of the firm-level values for each industry (three
digit SIC). Finally, similarly to Farinas and Ruano (2005), we combine the three proxies into one
sunk-cost index which at any time t takes values 0, 1 or 2 in the following way: 0 for industries
which have all three proxies below their respective cross-sectional medians at time t; 2 for industries
which have all proxies above these medians; and 1 for the rest. Thus, higher values of the index
are associated with higher sunk costs and therefore higher levels of investment irreversibility.
For the third and final measure of adjustment costs, we follow Almeida and Campello (2007)
22
and use a classification of industries into durables and nondurables based on the well-documented
cyclicality of durable goods industry sales. Borrowing from Shleifer and Vishny (1992), the intuition
is that firms in highly cyclical industries will tend to be affected by negative demand shocks
simultaneously. Therefore, they would be less able to sell their assets to the firms that would
otherwise have the best uses/valuations for those assets (the firms in the same industries) because
they are likely disinvesting themselves. Hence, the recovery value of assets in more cyclical industries
should be relatively lower, entailing higher adjustment costs for firms in such industries. Following
this line of reasoning, we construct the durables classification similarly to Sharpe (1994), by first
calculating the correlation between firms’ sales and GNP over the entire sample period. At each
time t, the index takes the value 0 for industries with mean correlations below the sample median
and 1 for the others. Once again, a higher value of the index signifies higher cyclicality and therefore
lower recovery values for used assets, i.e. higher investment irreversibility.
We introduce each of the above proxies into the main specification used to document the average
effect (equation 2 from Section 2.3), together with interactions with the policy uncertainty variable.
The general form of the specification is:
CAPXi,t
TAi,t−1= αi + β1PUi,t−l + β2IRi,t−l + β3PUi,t−l · IRi,t−l + γXi,t + δMi,t−l +QRTt + εi,t (3)
where the IRi,t−l stands for the level of each investment irreversibility proxy described above.
Xi,t contains the same firm-level controls as in equation 2: lagged Tobin’s Q, cash flows and sales
growth. For the PPE proxy, the interactive term IRi,t−l takes a value from 0 to 9, corresponding
to the firm’s PPE decile rank in the cross-section at time t − l. This is done in order to be able
to interpret the β1 coefficient as the marginal effect of policy uncertainty on investment for firms
with the lowest levels of investment irreversibility (i.e. the ones with IRi,t−l = 0 ).
The results are shown in Panel B of Table 8. For expositional clarity, we show only the
coefficient estimates for the policy uncertainty index, the irreversibility proxy, and their interaction
(coefficients β1, β2 and β3 in the equation above). In all three cases (Panels A1 through A3), we
find that higher levels of investment irreversibility are associated with a significantly more negative
effect of policy uncertainty on investment. Once again, this effect seems to persist at least four
quarters into the future (columns (1) to (4)). The results are qualitatively the same if we use an
industry-level specification.
Even though policy uncertainty does not seem to have an effect on investment for firms with
23
extremely low PPE ratios, panels A1 and A2 suggest that it may still have a negative impact
(albeit weaker) on firms with low levels of investment irreversibility. This suggests that real-option-
induced delay effects may not be the only mechanism through which policy uncertainty affects
investment. We turn to this possibility next, by examining financial frictions as an alternative,
perhaps complementary mechanism.
3.3.2 Policy Uncertainty and Financial Frictions
In response to the seminal Modigliani and Miller (1958) irrelevance proposition, many studies have
suggested that agency conflicts and informational asymmetries between borrowers and lenders,
coupled with incomplete contracting, will cause external financing to be costly (relative to internal
funds). Therefore, for firms that cannot entirely finance their investments with internally generated
funds, an increase in the costs of external financing should result in lower investment rates. We argue
that policy uncertainty can contribute to such an increase in external financing costs, particularly
for firms that are more financially constrained.
First, higher policy uncertainty, seen as a mean preserving spread in the distribution of future
cash flows, implies higher likelihoods of default and hence higher costs of debt financing (Greenwald
and Stiglitz (1990)). This effect is particularly acute for firms that are closer to default, since an
increase in uncertainty may be enough to completely shut them out of the debt market. On the
other hand, since equity can be viewed as a call option on the value of the firm, an increase in
uncertainty should reduce the cost of equity financing. However, the degree to which firms can
take advantage of this possible counteracting effect heavily depends on the frictions they face in
the equity market. Hence, the negative effect of policy uncertainty on the cost of external financing
should be stronger for firms that are closer to default and for firms that face stronger frictions in
the equity market. All else equal, these firms tend to be more financially constrained.
Moreover, we do not believe that the positive effect of policy uncertainty on the value of the
equity call option is large enough to completely counteract its effect on the cost of debt. First,
as argued by Pastor and Veronesi (2011), an increase in policy uncertainty can also increase the
equity risk premium, thereby increasing the cost of equity financing. Hence, the net effect of policy
uncertainty on the cost of equity may not necessarily be positive. Second, consistent with the
idea that many firms may face significant frictions in the equity market, Lemmon and Roberts
(2010) find empirical evidence that sharp decreases in the supply of credit are not accompanied by
24
significant substitution to equity financing, but instead result in a nearly one-for-one reduction in
investment rates. Taken together, these arguments lead us to believe that the net effect of policy
uncertainty on the cost of external financing should be positive, but stronger for more financially
constrained firms. Therefore, if policy uncertainty causes a decrease in investment through its
impact on the cost of external financing, then this decrease should be stronger for more financially
constrained firms.
To test this hypothesis, we use three proxies for financial constraints previously proposed by
the literature, and we introduce them in our main specification (equation 2 in Section 2.3) together
with interactions with the policy uncertainty index. For our first proxy, we use data on firms’ credit
ratings from COMPUSTAT (Almeida, Campello, and Weisbach (2005), Denis and Sibilkov (2010)).
Faulkender and Petersen (2006) show that, even after controlling for the possible endogeneity of
having a rating, firms without a rating are significantly rationed by the credit markets. Based on
these findings, our first proxy is an indicator variable which takes a value of 1 for firms which have
never had either a long- or short-term credit rating, but who currently have strictly positive debt.
The results are not significantly affected if instead the indicator variable takes a value of 1 only for
firms that do not currently have a rating, or if we use only long-term ratings or only short-term
ratings to construct the proxy. At the industry level, the index is averaged over all the firms in the
industry in a particular calendar quarter. Hence, financially constrained industries are the ones
with a higher proportion of firms without a debt rating.
The second measure of financial constraints is taken from Whited and Wu (2006). They use
an intertemporal investment model with costs of external financing in which financial constraints
are represented by the shadow cost of raising new equity. They parameterize this shadow cost as a
linear function of firm characteristics whose coefficients are then derived from a GMM estimation
of the investment Euler equation. In its final form, the index is calculated as:
WWit = −0.091CFit−0.062DIV POSit+0.021TLTDit−0.044LNTAit+0.102ISGit−0.035SGit
where CFit is cash flow divided by total assets, DIV POSit is an indicator variable which equals 1
if the firm pays dividends, TLTDit is total long term debt divided by total assets, LNTAit is the
natural logarithm of total assets, ISGit is average industry sales growth at the 3 digit SIC level,
and SGit is sales growth.
The third measure of financial constraints is the index developed by Kaplan and Zingales (1997).
25
The authors use qualitative information in annual reports of 49 firms to place firms on a financial
constraints scale of one to four. They then estimate the effect of various firm characteristics on the
firms’ ranking using an ordered logit. Lamont, Polk, and Saa-Requejo (2001) convert these logit
estimates into marginal effects and use them to construct a financial constraint index for a much
larger sample of firms. We use the same specification as in Lamont et al. (2001):
KZit = 1.1001 CFit + 3.139 TLTDit − 39.367 TDIVit − 1.314 CASHit + 0.282Qit
where TDIVit is cash dividends to total assets, CASHit is cash holdings to total assets and Qit is
Tobin’s Q. For all 3 proxies, lower levels imply higher financial constraints.
We introduce each of these three proxies into the specification from equation 2 in Section 2.3
and also interact them with the policy uncertainty variable. The general form of the specification
is:
CAPXi,t
TAi,t−1= αi +β1PUi,t−l +β2FFLi,t−l +β3PUi,t−l ·FFLi,t−l +γXi,t + δMi,t−l +QRTt + εi,t (4)
where the FFLi,t−l stands for the level of each financial constraints proxy described above. Xi,t
contains the same firm-level controls as in equation 2: lagged Tobin’s Q, cash flows and sales growth.
For the WW and KZ proxies, we follow the same procedure as we did for the PPE index in the
previous section, and let the interactive term FFLi,t−l take a value from 0 to 9, corresponding to
the firm’s respective WW and KZ decile rank in the cross-section at time t− l. Once again, this is
done for ease of interpretation: the β1 coefficient is now the marginal effect of policy uncertainty on
investment for firms with the lowest levels of financial constraints (i.e. the ones with FFLi,t−l = 0).
The results are shown in Panel C of Table 8. For all three proxies (Panels B1 through B3),
we find that higher levels of financial constraints are associated with a significantly more negative
effect of policy uncertainty on investment, an effect which persists at least four quarters into the
future (columns (1) to (4)). The results are not significantly affected if we run these tests at the
three digit SIC level.
3.4 Relationship between Policy Uncertainty and Financing Decisions
Since the financial frictions literature predicts that investment and financing decisions are not
independent, and the results presented above suggest that policy uncertainty has a significant
26
impact on investment, it is natural to ask what kind of implications this effect has for firms’ financing
policies. We focus on three financing variables: cash holdings, debt issuance and equity issuance.
The cautionary effect predicted by the real options mechanism discussed in Section 3.3 suggests that
we should observe firms holding more cash in periods with high policy uncertainty. Additionally,
the increase in the costs of debt predicted by the financial frictions mechanism suggests that we
should see a negative relationship between policy uncertainty and new debt financing. Finally, the
effect of policy uncertainty on new equity financing is not clear. On the one hand, policy uncertainty
may increase the required rate of return on equity (Pastor and Veronesi (2011)), thereby making
equity financing more expensive. On the other hand, due to limited liability and risk-shifting issues
(Jensen and Meckling (1976)), an increase in uncertainty may in fact increase the value of equity
(especially for firm which are closer to default).
In Table 9, Panel A, we regress cash holdings divided by lagged total assets on the policy
uncertainty index, macroeconomic variables and several firm level controls. The specification is
very similar to the one used in the investments equation:
CashHoldingsi,tTAi,t−1
= αi + β1PUi,t−l + γXi,t + δMi,t−l +QRTt + εi,t (5)
The only difference is in the accounting controls Xi,t used: in addition to lagged Tobin’s Q and
operating cash flows, we now have CAPX, lagged size (the natural logarithm of total assets), lagged
book leverage, and an indicator variable for whether or not the firm pays dividends. The cash flow
and CAPX variables are normalized by beginning of the quarter total assets. For simplicity, we
only show results where the macroeconomic control Mi,t−l is the log change in GDP. Panels A1
and A2 contain results at the firm and industry levels respectively.
The results suggest that there is a persistent and statistically significant positive relationship
between policy uncertainty and cash holdings, at both the firm and the industry level, though the
economic significance is smaller than the one we found for the investment effect. This result
is consistent with the hypothesis that when faced with high policy uncertainty, real options
considerations make it more profitable for firms to postpone at least some of their expansionary
plans until some of the uncertainty resolves. A mechanical effect of this precautionary behavior
would be an increase in cash holdings, which is what we find here. Finally, the results suggest
policy uncertainty as a potentially significant driver of the dramatic increase over the years in the
amount of cash firms hold on their balance sheets (Bates, Kahle, and Stulz (2009)).
27
In Panel B we investigate the relationship between policy uncertainty and net debt issuance. The
specification is functionally the same as in Equation 5 above; the only difference is the dependent
variable and the accounting controls used:
NetDebtIssuancei,tTAi,t−1
= αi + β1PUi,t−l + γXi,t + δMi,t−l +QRTt + εi,t (6)
The numerator of the dependent variable is calculated as long-term debt issuance minus long-term
debt reduction (DLTISQ - DLTRQ), following Brown, Fazzari and Petersen (2009). Xi,t contains
the firm-level controls: lagged market to book equity, lagged size (natural log of total assets), EBIT,
lagged PPE to assets and lagged depreciation expense to assets.
In Panel C we regress net equity issuance on the same controls as in the equation above:
NetEquityIssuancei,tTAi,t−1
= αi + β1PUi,t−l + γXi,t + δMi,t−l +QRTt + εi,t (7)
where net equity issuance is measured as sales minus purchases of common and preferred stock
(Brown, Fazzari, and Petersen (2009)). Panels B1 and C1 contain firm level results, while panels
B2 and C2 contain industry level results (three digit SIC).
The results in Panels B and C indicate that there is a significant negative and persistent
relationship between policy uncertainty and net debt issuance, but there is no such relationship
between policy uncertainty and net equity issuance. The results are consistent with the idea that
policy uncertainty may increase the cost of debt financing through its effect on the likelihood
of default (higher left tail risk) without significantly altering the cost of equity financing. Hence,
through this net increase in the cost of external financing, policy uncertainty can lead to a slowdown
in investment activities, an effect for which we found empirical support in Section 3.3. Gilchrist,
Sim, and Zakrajsek (2011) also investigate the possibility that financial frictions are an important
mechanism through which overall uncertainty affects capital investment and find evidence consistent
with the results in this study.
4 Robustness
In this section, we test the robustness of our main result to several alternative methodological
specifications. First, we bring further arguments that our results are unlikely to be driven by
28
endogeneity. Our first efforts towards this goal were made in Section 3.2, in which we showed
evidence that our findings are robust to controlling for various omitted measures of investment
opportunities and alternative sources of uncertainty as well as several methods of extracting
exogenous variation from our policy uncertainty index. To further mitigate the concern that policy
uncertainty may simply be picking up the impact of lower expected profitability (i.e. “first moment”
effects), we exclude NBER recession quarters from our sample, since they are the periods during
which this type of confounding effect is most likely to occur. In column 2 of Table 10, we run our
baseline specification (equation 2, with l = 1) on this restricted sample and find results that are
very similar to the ones obtained from the unrestricted sample (column 1).
Furthermore, even though the policy uncertainty variable may not be strictly exogenous, we
argue that this should not have a significant impact on our estimates. To see this, note that in
our specifications, the policy uncertainty variable is always lagged with respect to the dependent
variable, which means that it can be considered predetermined (i.e. orthogonal to the current error
term: E(PUi,t−l · εi,t) = 0 in equation 2). As a result, the coefficient on the policy uncertainty
variable is consistently estimated (Hayashi (2000), p.109), which means that potential deviations
from the strict exogeneity assumption will have a negligible effect in a large sample such as ours.37
Nevertheless, the within-group transformation which we use to purge the firm-fixed effects could
mechanically induce correlation between lagged policy uncertainty and the current error term,
rendering the above argument invalid.38 However, estimating our baseline specification without the
within-group transformation (i.e. ignoring firm-fixed effects) would not suffer from this problem
and would therefore produce a consistent estimate for the policy uncertainty variable. We do
this in column 3 of Table 10 and we find that the estimate for policy uncertainty remains almost
unchanged (compare with column 1). Therefore, although this may not be true for the rest of the
covariates, we can conclude that controlling for firm fixed effects does not significantly alter the
coefficient estimate for policy uncertainty, and that a possible violation of strict exogeneity will
have a negligible impact on our main result.
Second, we verify that our results are robust to various methods of converting the monthly
Baker et al. (2012) index into a quarterly index. Recall that throughout the paper, this was done
by taking a weighted average of the monthly index for the months within a specific fiscal quarter.
37Hennessy, Levy, and Whited (2007) make a similar argument.38This is because the within-group transformation uses all leads and lags in both the policy uncertainty term and
the error term.
29
Alternatively, in column 4 of Table 10, we use only the quarter-end value of the Baker et al. (2012)
index as a measure of the policy uncertainty the firm faces going into the next quarter(s). Once
again, our main result remains virtually unchanged. In unreported results, we verify that our
results are also robust to using a simple quarter average (arithmetic mean) or quarter median of
the Baker et al. (2012) index as our measure of quarterly policy uncertainty.
Third, we address the concern that our results may be driven by a spurious correlation induced
by a common trend in the policy uncertainty and investment variables. To this end, we linearly
detrend the Baker et al. (2012) index and instead use this as the basis for our policy uncertainty
proxies. In column 5 of Table 10, we show results using a weighted average of this detrended
monthly index to calculate the quarterly policy uncertainty variable (same methodology as in the
baseline specification). The results show that the negative relationship between policy uncertainty
and investment still holds when using these alternative measures, even though it is slightly weaker
than in our baseline model (column 1).
Finally, since several studies estimate investment regressions in dynamic panel format (e.g.
Bloom, Bond, and Van Reenen (2007), Almeida and Campello (2007)), in column 6 of Table 10 we
estimate a dynamic version of the baseline specification from column 1 (i.e. we introduce lagged
investment as a regressor):
CAPXi,t
TAi,t−1= α+ ρ
CAPXi,t−1TAi,t−2
+ β1PUi,t−1 + γXi,t + δMi,t−1 + αi +QRTt + εi,t (8)
Because the within-group and first-difference transformations needed to eliminate the firm fixed
effects mechanically correlate the lagged investment variable with the error term, we estimate this
specification using the “system GMM” methodology of Blundell and Bond (1998). Specifically,
we useCAPXi,t−2
TAi,t−3and
CAPXi,t−3
TAi,t−4as instruments for 4CAPXi,t−1
TAi,t−2in the difference equation and
4CAPXi,t−1
TAi,t−2as an instrument for
CAPXi,t−1
TAi,t−2in the levels equation.
Our results are robust to this alternative specification. The caveat is that since our policy
uncertainty is firm-invariant, we cannot include a time fixed effect, and we have no easy way of
controlling for the possibility that the error terms are correlated cross-sectionally. This is why, for
our baseline model, we chose a static specification in which we could take care of cross-sectional
correlation by clustering standard errors by time.
30
5 Conclusion
In this study, we analyze the effect of policy-related uncertainty on capital investments of U.S.
public corporations, paying close attention to the way this effect manifests itself differently across
firms. To do so, we employ a measure which was developed by Baker, Bloom, and Davis (2012) to
capture the overall level of policy uncertainty present in the economy (as opposed to uncertainty
driven only by one specific type of policy decision). We document a strong negative relationship
between this variable and capital investments at both the firm and industry level, and we find that
this basic effect also holds in Canada, U.K., Germany, France and Italy. These results are robust
to controlling for alternative measures of investment opportunities and macroeconomic uncertainty
as well as to several methods of identifying exogenous variation in policy uncertainty.
Next, we test the predictions of two strands of literature which posit that uncertainty may
influence investments heterogeneously in the cross-section. First, real options theories suggest that
uncertainty increases the benefits from delaying investment until more information reveals itself,
and it does so more severely for firms with a high degree of investment irreversibility and for firms
in less competitive industries in which the cost of waiting is not excessively high. Second, financial
frictions theories predict that uncertainty decreases investments through an increase in the cost of
external capital, and that this effect should be stronger for firms which are ex-ante more financially
constrained. We find evidence that the negative relationship between policy uncertainty and capital
investments is significantly stronger for firms in less competitive industries, with stronger investment
irreversibility and/or higher financial constraints. In so doing, we provide empirical support for
two theories which posit a causal effect of uncertainty on investments, and we shed some light on
the possible mechanisms through which this effect may operate.
Finally, acknowledging that firms’ investment and financing decisions may not be independent
of each other, we also investigate if policy uncertainty has an effect on financing choices. We find
evidence that high levels of policy uncertainty are associated with larger cash holdings and lower
net debt issuance. These results are consistent with the two mechanisms described above as they
suggest that uncertainty works both through a precautionary channel as well as through an increase
in the cost of debt financing.
31
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36
37
The figure depicts quarterly (top panel) and annual (bottom panel) cross-sectional weighted averages offitted values from our baseline model. These fitted values are calculated both using the realized levels ofpolicy uncertainty (solid line) as well as by keeping the policy uncertainty index fixed at the level observedin the last quarter of 2006 (dashed line). The averages are calculated using the previous quarter total assetsas weights. Throughout, we use only the firms that are in the sample in the first quarter of 2007.
38
Table 1Summary Statistics
This table presents summary statistics of the main variables used in our analysis. The data is quarterly and it extends
from January 1987 to December 2011. In Panel A we calculate means, medians and standard deviations over the
entire sample period. Capital investment, cash flow, cash holdings, net equity issuance and net debt issuance are
normalized by total assets at the beginning of the quarter. In Panel B, we split the sample into periods of high or
low policy uncertainty based on the time series median of the Baker et al. (2012) policy uncertainty index, and we
present summary statistics only for the capital investment variable calculated separately over the two subsamples. In
Panel C we do the same, only this time we split the sample into calendar years when there are national elections and
when there are not. The summary statistics in Panels B and C are in percentage points. The z-score for difference
in medians is calculated using the Wilcoxon-Mann-Whitney test.
Panel A: Firm Characteristics
N Mean Median Std. Dev.
Capital Investment/Assets 323,278 0.016 0.009 0.021Tobin’s Q 323,278 1.991 1.459 1.645Cash Flow/Assets 323,278 0.011 0.018 0.062Sales Growth 323,278 0.198 0.083 0.646Total Assets 323,278 1895 176 6710Book Leverage 323,278 0.224 0.188 0.210Cash Holdings 323,278 0.173 0.082 0.211New Equity Issuance/Assets 315,554 0.010 0.000 0.064New Debt Issuance/Assets 293,864 0.004 0.000 0.046Market to Book Equity 311,822 2.964 1.858 3.731EBIT/Assets 323,238 0.005 0.018 0.065PPE/Assets 322,722 0.286 0.217 0.234
Panel B: Capital Investment in High vs. Low Policy Uncertainty Periods
N Mean Median Std. Dev.
High Policy Uncertainty 142,929 0.147 0.084 0.199Low Policy Uncertainty 180,349 0.173 0.103 0.222Difference (High - Low) -0.026 -0.018Diff(t-stat/z-score) -34.91 -45.27
Panel C: Capital Investment in Election vs. Nonelection Periods
N Mean Median Std. Dev.
Election 72,830 0.166 0.097 0.216Nonelection 250,448 0.160 0.093 0.211Difference (Election - Nonelection) 0.0059 0.0040Diff(t-stat/z-score) 6.58 9.11
39
Table 2The Effect of Election Cycles on Capital Investment
In this table we regress firm-level investment (CAPX/Assets) in the United States on an election year indicator as
well as lagged Tobin’s Q, operating cash flows and lagged GDP growth. The sample period extends from January
1987 to December 2011 for the first two specifications and from January 1963 to December 2011 for the last three.
The CAPX and cash flow variables are normalized by beginning of the period total assets. Specifications 2, 3, 4 and
5 are run with annual data and specification 1 is run with quarterly data as in the rest of this paper. Specifications
3, 4 and 5 differ from specification 2 in terms of the sample period (they extend back to 1963). For this reason, in
these specifications, the cash flow variable is measured as net income plus depreciation, as the cash flow statement
was not required before 1987. Specification 4 differs from the rest in terms of the election indicator, which now equals
1 for both presidential as well as midterm elections. Finally, in specification 5, the election indicator is equal to 1
only for “close” elections, defined as the 25% of all presidential elections (from 1963 to 2011) won by the smallest
margin in terms of the popular vote. We include quarter dummy variables (not shown) in specification 1 to control for
seasonality. The table reports within-group estimates (i.e. firm fixed effects are eliminated by de-meaning). Standard
errors are clustered at the quarter and firm level following Petersen (2009); t-statistics are reported in parentheses.
*, ** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Dependent Variable:CAPX/Assets
(1) (2) (3) (4) (5)Frequency Quarterly Annual Annual Annual AnnualElections Included Presidential Presidential Presidential Presid. & Congress Presidential
“Close”
Election Indicator -0.000164 0.00108 -0.000275 0.00106 -0.00113(-0.39) (0.24) (-0.10) (0.46) (-0.33)
Lagged Tobin’s Q 0.00242*** 0.0120*** 0.0140*** 0.0140*** 0.0140***(23.04) (14.64) (16.65) (16.54) (16.69)
Cash Flow/Assets 0.0172*** 0.0416*** 0.0720*** 0.0720*** 0.0720***(14.74) (8.74) (9.17) (9.17) (9.15)
Lagged GDP Growth 0.115*** 0.330*** 0.387*** 0.387*** 0.388***(4.24) (6.38) (9.81) (9.70) (9.71)
N 310,396 73,293 186,659 186,659 186,659R-squared 0.039 0.079 0.092 0.092 0.092
Sample Period 1987-2011 1987-2011 1963-2011 1963-2011 1963-2011Firm Fixed Effects Yes Yes Yes Yes YesQuarter Dummies Yes No No No No
Cluster by Firm Yes Yes Yes Yes YesCluster by Quarter Yes Yes Yes Yes Yes
40
Tab
le3
Policy
Un
cert
ain
tyan
dC
orp
ora
teC
ap
ital
Invest
ment
Inth
ista
ble
we
regre
ssfirm
-lev
elquart
erly
inves
tmen
t(C
AP
X/A
sset
s)on
wel
ldocu
men
ted
det
erm
inants
of
inves
tmen
tand
on
the
policy
unce
rtain
tyin
dex
from
Baker
,B
loom
and
Dav
is(2
012)
(see
Equati
on
2).
The
data
isquart
erly
and
exte
nds
from
January
1987
toD
ecem
ber
2011.
See
Sec
tion
2fo
ra
det
ailed
des
crip
tion
of
how
we
calc
ula
teea
chva
riable
.In
spec
ifica
tion
(1),
the
dep
enden
tva
riable
has
ale
ad
of
one
per
iod
(cale
ndar
quart
er)
wit
hre
spec
tto
the
policy
unce
rtain
tyva
riable
.In
spec
ifica
tion
2it
leads
two
per
iods,
and
sofo
rth
tosp
ecifi
cati
on
(4).
Panel
sA
and
Buse
the
over
all
policy
unce
rtain
tyin
dex
,w
hile
panel
sC
and
Duse
only
the
new
sbase
dco
mp
onen
tof
the
index
.P
anel
sA
and
Cpre
sent
firm
level
resu
lts
and
panel
sB
and
Dpre
sent
indust
ryle
vel
resu
lts
(base
don
a3
dig
itSIC
class
ifica
tion).
To
contr
ol
for
seaso
nality
,w
ein
clude
quart
erdum
mie
sin
each
spec
ifica
tion
(not
show
nin
the
table
).T
he
table
rep
ort
sw
ithin
-gro
up
esti
mate
s(i
.e.
firm
fixed
effec
tsare
elim
inate
dby
de-
mea
nin
g).
To
faci
lita
teth
eass
essm
ent
of
econom
icm
agnit
udes
,all
vari
able
sare
norm
alize
dby
thei
rsa
mple
standard
dev
iati
on.
Sta
ndard
erro
rsare
clust
ered
at
the
quart
erand
firm
level
follow
ing
Pet
erse
n(2
009).
t-st
ati
stic
sare
rep
ort
edin
pare
nth
eses
.*,
**
and
***
indic
ate
stati
stic
al
signifi
cance
at
the
10%
,5%
and
1%
level
,re
spec
tivel
y.
Pan
elA
:F
irm
-Lev
elR
esu
lts
Usi
ng
the
Ove
rall
Policy
Un
cert
ain
tyIn
dex
Dep
enden
tva
riable
:C
AP
X/A
sset
sP
anel
A1:
Wit
hout
Contr
ollin
gfo
rE
lect
ion
Yea
rsP
anel
A2:
Contr
ollin
gfo
rE
lect
ion
Yea
rs
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty-0
.0475
***
-0.0
507***
-0.0
486***
-0.0
400***
-0.0
480***
-0.0
515***
-0.0
509***
-0.0
412***
(-4.9
8)
(-5.3
3)
(-5.2
1)
(-4.2
9)
(-5.0
0)
(-5.2
8)
(-5.0
9)
(-4.3
3)
Tobin
’sQ
0.1
71***
0.1
71***
0.1
70***
0.1
71***
0.1
71***
0.1
72***
0.1
71***
0.1
71***
(21.9
7)
(21.3
8)
(21.0
4)
(20.8
9)
(22.1
7)
(21.7
0)
(21.4
2)
(21.1
4)
Cash
Flo
w/A
sset
s0.0
480***
0.0
475***
0.0
477***
0.0
480***
0.0
479***
0.0
474***
0.0
476***
0.0
479***
(14.2
0)
(14.1
2)
(14.1
8)
(14.4
6)
(14.1
6)
(14.0
7)
(14.1
3)
(14.4
4)
Sale
sG
row
th0.0
506***
0.0
499***
0.0
489***
0.0
478***
0.0
507***
0.0
499***
0.0
491***
0.0
480***
(15.1
5)
(14.5
2)
(14.5
2)
(13.8
2)
(15.1
9)
(14.5
6)
(14.5
7)
(13.8
8)
GD
PG
row
th0.0
115
0.0
188**
0.0
260***
0.0
338***
0.0
119
0.0
208***
0.0
257***
0.0
336***
(1.3
4)
(2.3
1)
(3.2
3)
(4.1
8)
(1.3
8)
(2.6
2)
(3.2
6)
(4.1
7)
Ele
ctio
nIn
dic
ato
r-0
.0143
-0.0
266
-0.0
236
-0.0
141
(-0.7
9)
(-1.4
4)
(-1.2
6)
(-0.7
7)
N309,4
99
298,4
13
288,9
28
280,7
75
309,4
99
298,4
13
288,9
28
280,7
75
R-s
quare
d0.0
44
0.0
45
0.0
45
0.0
45
0.0
44
0.0
45
0.0
45
0.0
45
Fir
mF
ixed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
mY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
41
Tab
le3
Policy
Un
cert
ain
tyan
dC
orp
ora
teC
ap
ital
Invest
ment
(Conti
nu
ed
)
Pan
elB
:In
du
stry
-Lev
elR
esu
lts
(Th
ree
Dig
itS
IC)
Usi
ng
the
Ove
rall
Poli
cyU
nce
rtain
tyIn
dex
Dep
enden
tva
riable
:C
AP
X/A
sset
sP
anel
A1:
Wit
hout
Contr
ollin
gfo
rE
lect
ion
Yea
rsP
anel
A2:
Contr
ollin
gfo
rE
lect
ion
Yea
rs
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty-0
.0931***
-0.0
964***
-0.0
931***
-0.0
828***
-0.0
925***
-0.0
969***
-0.0
936***
-0.0
827***
(-3.7
8)
(-3.4
1)
(-3.2
1)
(-2.9
0)
(-3.7
6)
(-3.3
8)
(-3.1
2)
(-2.8
6)
Tobin
’sQ
0.1
13***
0.1
12***
0.1
19***
0.1
24***
0.1
13***
0.1
12***
0.1
19***
0.1
24***
(3.6
6)
(3.6
6)
(3.7
6)
(3.7
8)
(3.6
6)
(3.6
6)
(3.7
6)
(3.7
8)
Cash
Flo
w/A
sset
s0.0
396**
0.0
390**
0.0
350**
0.0
314**
0.0
397**
0.0
390**
0.0
350**
0.0
314**
(2.5
7)
(2.4
5)
(2.1
6)
(1.9
7)
(2.5
7)
(2.4
5)
(2.1
6)
(1.9
7)
Sale
sG
row
th0.0
970***
0.0
926***
0.0
889***
0.0
906***
0.0
969***
0.0
926***
0.0
890***
0.0
906***
(4.2
7)
(4.4
8)
(4.4
4)
(4.9
2)
(4.2
8)
(4.4
7)
(4.4
5)
(4.9
3)
GD
PG
row
th0.0
418**
0.0
520**
0.0
625***
0.0
722***
0.0
413
0.0
529**
0.0
625***
0.0
722***
(1.9
6)
(2.3
7)
(2.8
3)
(3.4
1)
(1.9
3)
(2.4
0)
(2.8
2)
(3.4
2)
Ele
ctio
nIn
dic
ato
r0.0
173
-0.0
126
-0.0
0487
0.0
0100
(0.3
7)
(-0.2
6)
(-0.1
0)
(0.0
2)
N22,0
25
21,7
69
21,5
19
21,2
72
22,0
25
21,7
69
21,5
19
21,2
72
R-s
quare
d0.0
76
0.0
77
0.0
80
0.0
80
0.0
76
0.0
77
0.0
80
0.0
80
Indust
ryF
ixed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Indust
ryY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
42
Tab
le3
Policy
Un
cert
ain
tyan
dC
orp
ora
teC
ap
ital
Invest
ment
(Conti
nu
ed
)
Pan
elC
:F
irm
-Lev
elR
esu
lts
Usi
ng
the
New
s-B
ase
dC
om
pon
ent
of
the
Policy
Un
cert
ain
tyIn
dex
Dep
enden
tva
riable
:C
AP
X/A
sset
sP
anel
A1:
Wit
hout
Contr
ollin
gfo
rE
lect
ion
Yea
rsP
anel
A2:
Contr
ollin
gfo
rE
lect
ion
Yea
rs
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty-0
.0336***
-0.0
413***
-0.0
429***
-0.0
384***
-0.0
339***
-0.0
420***
-0.0
448***
-0.0
389***
(-4.0
6)
(-4.8
6)
(-5.0
5)
(-4.7
4)
(-4.0
9)
(-4.8
4)
(-4.8
9)
(-4.7
4)
Tobin
’sQ
0.1
73***
0.1
73***
0.1
71***
0.1
71***
0.1
73***
0.1
73***
0.1
72***
0.1
72***
(22.1
8)
(21.5
3)
(21.3
2)
(21.2
1)
(22.3
4)
(21.8
6)
(21.7
0)
(21.3
9)
Cash
Flo
w/A
sset
s0.0
478***
0.0
473***
0.0
477***
0.0
479***
0.0
477***
0.0
472***
0.0
476***
0.0
479***
(14.1
6)
(14.0
6)
(14.1
8)
(14.4
4)
(14.1
1)
(14.0
1)
(14.1
4)
(14.4
4)
Sale
sG
row
th0.0
506***
0.0
495***
0.0
483***
0.0
474***
0.0
507***
0.0
495***
0.0
484***
0.0
475***
(15.1
2)
(14.5
0)
(14.4
3)
(13.8
4)
(15.1
7)
(14.5
3)
(14.4
7)
(13.8
7)
GD
PG
row
th0.0
174
0.0
227**
0.0
280***
0.0
340***
0.0
177
0.0
246***
0.0
278***
0.0
340***
(1.8
7)
(2.5
3)
(3.3
0)
(4.3
9)
(1.9
0)
(2.8
0)
(3.3
3)
(4.4
0)
Ele
ctio
nIn
dic
ato
r-0
.0120
-0.0
258
-0.0
220
-0.0
101
(-0.6
3)
(-1.3
2)
(-1.1
3)
(-0.5
6)
N309,4
99
298,4
13
288,9
28
280,7
75
309,4
99
298,4
13
288,9
28
280,7
75
R-s
quare
d0.0
43
0.0
45
0.0
45
0.0
45
0.0
43
0.0
45
0.0
45
0.0
45
Fir
mF
ixed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
mY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
43
Tab
le3
Policy
Un
cert
ain
tyan
dC
orp
ora
teC
ap
ital
Invest
ment
(Conti
nu
ed
)
Pan
elD
:In
du
stry
-Lev
elR
esu
lts
(Th
ree
Dig
itS
IC)
Usi
ng
the
New
s-B
ase
dC
om
pon
ent
of
the
Poli
cyU
nce
rtain
tyIn
dex
Dep
enden
tva
riable
:C
AP
X/A
sset
sP
anel
A1:
Wit
hout
Contr
ollin
gfo
rE
lect
ion
Yea
rsP
anel
A2:
Contr
ollin
gfo
rE
lect
ion
Yea
rs
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty-0
.0769***
-0.0
816***
-0.0
875***
-0.0
817***
-0.0
764***
-0.0
818***
-0.0
875***
-0.0
813***
(-3.5
2)
(-3.0
7)
(-3.3
4)
(-3.3
0)
(-3.5
3)
(-3.0
6)
(-3.2
5)
(-3.2
8)
Tobin
’sQ
0.1
16***
0.1
14***
0.1
20***
0.1
24***
0.1
16***
0.1
14***
0.1
20***
0.1
24***
(3.7
1)
(3.7
0)
(3.7
9)
(3.8
1)
(3.7
1)
(3.7
0)
(3.7
9)
(3.8
1)
Cash
Flo
w/A
sset
s0.0
386**
0.0
382**
0.0
351**
0.0
313
0.0
387**
0.0
382**
0.0
351**
0.0
314**
(2.4
9)
(2.3
9)
(2.1
5)
(1.9
6)
(2.5
0)
(2.3
8)
(2.1
5)
(1.9
7)
Sale
sG
row
th0.0
968***
0.0
908***
0.0
866***
0.0
889***
0.0
966***
0.0
908***
0.0
866***
0.0
888***
(4.2
5)
(4.3
8)
(4.3
2)
(4.8
4)
(4.2
6)
(4.3
8)
(4.3
3)
(4.8
4)
GD
PG
row
th0.0
493**
0.0
591**
0.0
648***
0.0
717***
0.0
484**
0.0
597**
0.0
648***
0.0
716***
(2.2
6)
(2.5
5)
(2.8
6)
(3.4
4)
(2.2
1)
(2.5
7)
(2.8
5)
(3.4
3)
Ele
ctio
nIn
dic
ato
r0.0
234
-0.0
0852
0.0
00176
0.0
115
(0.4
9)
(-0.1
7)
(0.0
0)
(0.2
3)
N22,0
25
21,7
69
21,5
19
21,2
72
22,0
25
21,7
69
21,5
19
21,2
72
R-s
quare
d0.0
72
0.0
75
0.0
79
0.0
80
0.0
73
0.0
75
0.0
79
0.0
80
Indust
ryF
ixed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Indust
ryY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
44
Table 4Policy Uncertainty and Corporate Investment Outside the United States
In this table, we use data on public firms from Canada, U.K., Germany, France and Italy and we regress firm-
level quarterly investment (CAPX/Assets) on well documented determinants of investment and on the country-level
versions of the policy uncertainty index developed by Baker, Bloom and Davis (2012). The policy uncertainty variable
is lagged one period with respect to the investment variable.The specifications here are the same as those used in
the U.S. (equation 2 with l = 1) with two exceptions: here the data is annual, not quarterly, and it extends from
January 1997 to December 2011. The table reports within-group estimates (i.e. firm fixed effects are eliminated
by de-meaning). To facilitate the assessment of economic magnitudes, all variables are normalized by their sample
standard deviation. Standard errors are clustered at the year and firm level following Petersen (2009); t-statistics are
reported in parentheses. *, ** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Dependent Variable:CAPX/Assets
Canada U.K Germany France Italy
Policy Uncertainty -0.0323*** -0.0204** -0.0571*** -0.0528*** -0.0406**(-2.76) (-2.15) (-3.57) (-3.61) (-2.06)
Tobin’s Q 0.160*** 0.120*** 0.0880*** 0.138* 0.0651**(6.54) (6.24) (3.10) (1.88) (2.19)
Cash Flow/Assets -0.0195 0.0199 0.0916*** 0.0993 0.219***(-0.66) (0.46) (2.93) (1.18) (4.02)
Sales Growth 0.124*** 0.103*** 0.148*** 0.240*** 0.150***(8.43) (7.43) (4.25) (6.17) (4.14)
GDP Growth 0.0262*** 0.0264** 0.0244 -0.0431 0.0293*(2.67) (2.05) (1.37) (-1.17) (1.73)
N 12,626 14,275 6,433 6,638 2,228R-squared 0.089 0.063 0.100 0.104 0.061
Firm Fixed Effects Yes Yes Yes Yes YesCluster by Firm Yes Yes Yes Yes YesCluster by Quarter Yes Yes Yes Yes Yes
45
Tab
le5
Alt
ern
ati
ve
Macro
econ
om
icC
ontr
ols
for
Invest
ment
Op
port
un
itie
s
Inth
ista
ble
we
pre
sent
resu
lts
obta
ined
from
esti
mati
ng
our
base
line
inves
tmen
teq
uati
on
usi
ng
sever
al
alt
ernati
ve
macr
oec
onom
icpro
xie
sfo
rin
ves
tmen
topp
ort
unit
ies.
Thes
eare
:ex
pec
ted
GD
Pgro
wth
calc
ula
ted
bia
nnually
from
the
Liv
ingst
one
surv
eyof
the
Philadel
phia
Fed
eral
Res
erve
Bank
(colu
mn
1),
the
Lea
din
gE
conom
icIn
dex
rele
ase
dby
The
Confe
rence
Board
(colu
mn
2),
the
Mic
hig
an
Confiden
ceIn
dex
dev
elop
edby
the
Univ
ersi
tyof
Mic
hig
an
(colu
mn
3)
and
the
Inves
tor
Sen
tim
ent
Index
from
Baker
and
Wurg
ler
(2007)
(colu
mn
4).
Colu
mns
5in
clude
all
pro
xie
sin
the
sam
esp
ecifi
cati
on.
All
pro
xie
sare
calc
ula
ted
at
the
beg
innin
gof
the
quart
erin
whic
hth
edep
enden
tva
riable
ism
easu
red.
The
only
diff
eren
ceb
etw
een
Panel
Aand
Panel
Bis
that
inP
anel
Aw
edo
not
contr
ol
for
policy
unce
rtain
ty.
See
Sec
tion
3.2
for
det
ails
on
how
each
pro
xy
was
const
ruct
ed.
The
table
rep
ort
sw
ithin
-gro
up
esti
mate
s(i
.e.
firm
fixed
effec
tsare
elim
inate
dby
de-
mea
nin
g).
To
faci
lita
teth
eass
essm
ent
of
econom
icm
agnit
udes
,all
vari
able
sare
norm
alize
dby
thei
rsa
mple
standard
dev
iati
on.
Sta
ndard
erro
rsare
clust
ered
at
the
yea
rand
firm
level
follow
ing
Pet
erse
n(2
009);
t-st
ati
stic
sare
rep
ort
edin
pare
nth
eses
.*,
**
and
***
indic
ate
stati
stic
al
signifi
cance
at
the
10%
,5%
and
1%
level
,re
spec
tivel
y.
Dep
enden
tva
riable
:P
anel
A:
Wit
hout
Contr
ollin
gfo
rP
olicy
Unce
rtain
tyP
anel
B:
Contr
ollin
gfo
rP
olicy
Unce
rtain
tyC
AP
X/A
sset
s
(1)
(2)
(3)
(4)
(5)
(1)
(2)
(3)
(4)
(5)
Policy
Unce
rtain
ty-0
.0500***
-0.0
500***
-0.0
326***
-0.0
562***
-0.0
311**
(-5.6
7)
(-5.7
6)
(-2.7
2)
(-6.1
0)
(-2.4
8)
Tobin
’sQ
0.1
80***
0.1
79***
0.1
73***
0.1
82***
0.1
72***
0.1
71***
0.1
70***
0.1
70***
0.1
72***
0.1
70***
(22.7
6)
(23.0
1)
(21.4
6)
(22.5
0)
(21.7
4)
(22.2
3)
(22.2
7)
(21.3
5)
(21.7
7)
(21.6
0)
Cash
Flo
w0.0
474***
0.0
477***
0.0
482***
0.0
470***
0.0
482***
0.0
479***
0.0
480***
0.0
481***
0.0
476***
0.0
481***
(14.0
7)
(14.1
3)
(14.1
3)
(13.8
4)
(14.0
8)
(14.1
6)
(14.1
6)
(14.0
8)
(13.9
4)
(14.0
5)
Sale
sG
row
th0.0
528***
0.0
517***
0.0
516***
0.0
544***
0.0
511***
0.0
508***
0.0
505***
0.0
510***
0.0
515***
0.0
507***
(14.8
8)
(15.8
7)
(15.0
5)
(15.1
9)
(15.2
9)
(15.0
0)
(15.4
1)
(14.9
7)
(15.3
3)
(15.2
0)
Exp
ecte
dG
DP
Gro
wth
0.0
239***
0.0
0906
0.0
111
0.0
0735
(3.1
8)
(1.1
6)
(1.6
4)
(0.9
8)
Lea
din
gE
conom
icIn
dex
0.0
242***
0.0
0314
0.0
0914
0.0
00570
(2.6
4)
(0.2
9)
(1.1
1)
(0.0
6)
Consu
mer
Confiden
ce0.0
528***
0.0
488***
0.0
323***
0.0
308**
(6.1
8)
(4.7
0)
(3.1
6)
(2.4
4)
Inves
tor
Sen
tim
ent
0.0
0698
-0.0
0108
0.0
0300
-0.0
00201
(0.7
9)
(-0.1
2)
(0.4
1)
(-0.0
2)
N309,4
99
309,4
99
301,5
81
301,5
81
301,5
81
309,4
99
309,4
99
301,5
81
301,5
81
301,5
81
R-s
quare
d0.0
41
0.0
41
0.0
44
0.0
41
0.0
45
0.0
44
0.0
44
0.0
45
0.0
44
0.0
45
Fir
mF
ixed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
mY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
46
Tab
le6
Alt
ern
ati
ve
Macro
econ
om
icM
easu
res
of
Un
cert
ain
ty
Inth
ista
ble
we
pre
sent
resu
lts
obta
ined
from
esti
mati
ng
our
base
line
inves
tmen
teq
uati
on
usi
ng
sever
al
alt
ernati
ve
macr
oec
onom
icpro
xie
sfo
rin
ves
tmen
tunce
rtain
ty.
Thes
eare
:th
eco
effici
ent
of
vari
ati
on
of
the
bia
nnual
GD
Pfo
reca
sts
from
the
Liv
ingst
one
surv
eyof
the
Philadel
phia
Fed
eral
Res
erve
Bank
(colu
mn
1),
the
cross
-sec
tional
standard
dev
iati
on
infirm
-lev
elpro
fit
gro
wth
(colu
mn
2),
the
month
lyV
XO
implied
vola
tility
index
from
the
CB
OE
(colu
mn
3)
and
the
cross
-sec
tional
standard
dev
iati
on
infirm
-lev
elm
onth
lyst
ock
retu
rns
(colu
mn
4).
Colu
mns
5in
clude
all
pro
xie
sin
the
sam
esp
ecifi
cati
on.
All
pro
xie
sare
calc
ula
ted
at
the
beg
innin
gof
the
quart
erin
whic
hth
edep
enden
tva
riable
ism
easu
red.
The
only
diff
eren
ceb
etw
een
Panel
Aand
Panel
Bis
that
inP
anel
Aw
edo
not
contr
ol
for
policy
unce
rtain
ty.
See
Sec
tion
3.2
for
det
ails
on
how
each
pro
xy
was
const
ruct
ed.
The
table
rep
ort
sw
ithin
-gro
up
esti
mate
s(i
.e.
firm
fixed
effec
tsare
elim
inate
dby
de-
mea
nin
g).
To
faci
lita
teth
eass
essm
ent
of
econom
icm
agnit
udes
,all
vari
able
sare
norm
alize
dby
thei
rsa
mple
standard
dev
iati
on.
Sta
ndard
erro
rsare
clust
ered
at
the
yea
rand
firm
level
follow
ing
Pet
erse
n(2
009);
t-st
ati
stic
sare
rep
ort
edin
pare
nth
eses
.*,
**
and
***
indic
ate
stati
stic
al
signifi
cance
at
the
10%
,5%
and
1%
level
,re
spec
tivel
y.
Dep
enden
tva
riable
:P
anel
A:
Wit
hout
Contr
ollin
gfo
rP
olicy
Unce
rtain
tyP
anel
B:
Contr
ollin
gfo
rP
olicy
Unce
rtain
tyC
AP
X/A
sset
s
(1)
(2)
(3)
(4)
(5)
(1)
(2)
(3)
(4)
(5)
Policy
Unce
rtain
ty-0
.0460***
-0.0
329***
-0.0
551***
-0.0
530***
-0.0
371***
(-5.3
0)
(-3.7
7)
(-5.8
0)
(-6.3
2)
(-4.0
8)
Tobin
’sQ
0.1
79***
0.1
71***
0.1
78***
0.1
82***
0.1
69***
0.1
71***
0.1
66***
0.1
71***
0.1
71***
0.1
66***
(22.5
7)
(22.2
8)
(22.9
9)
(23.6
4)
(21.9
7)
(22.2
1)
(21.9
0)
(22.0
9)
(22.1
1)
(21.7
3)
Cash
Flo
w0.0
476***
0.0
483***
0.0
473***
0.0
472***
0.0
486***
0.0
479***
0.0
485***
0.0
478***
0.0
478***
0.0
488***
(14.0
9)
(14.3
7)
(14.0
4)
(13.9
7)
(14.4
1)
(14.1
7)
(14.3
7)
(14.1
6)
(14.1
7)
(14.4
7)
Sale
sG
row
th0.0
526***
0.0
478***
0.0
527***
0.0
532***
0.0
476***
0.0
508***
0.0
472***
0.0
512***
0.0
511***
0.0
472***
(15.0
9)
(15.2
4)
(15.3
7)
(16.0
2)
(15.1
7)
(15.1
7)
(15.0
4)
(15.2
9)
(15.4
8)
(15.0
1)
GD
PF
ore
cast
Dis
per
sion
-0.0
351***
-0.0
220**
-0.0
204**
-0.0
134
(-3.8
8)
(-2.4
7)
(-2.3
1)
(-1.5
4)
Pro
fit
Gro
wth
Std
.D
ev.
-0.0
719***
-0.0
729***
-0.0
588***
-0.0
668***
(-5.2
9)
(-5.7
1)
(-4.1
1)
(-5.2
2)
VX
O-0
.0215**
0.0
0407
0.0
0364
0.0
165*
(-2.5
0)
(0.4
2)
(0.3
8)
(1.7
2)
Ret
urn
Std
.Dev
.-0
.0146*
0.0
0948
-0.0
00773
0.0
113
(-1.8
5)
(1.1
4)
(-0.1
2)
(1.2
0)
N309,4
99
309,4
99
309,4
99
309,4
99
309,4
99
309,4
99
309,4
99
309,4
99
309,4
99
309,4
99
R-s
quare
d0.0
42
0.0
47
0.0
41
0.0
41
0.0
47
0.0
45
0.0
48
0.0
44
0.0
44
0.0
49
Fir
mF
ixed
Eff
ects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
mY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
47
Table 7Mitigating Endogeneity Concerns
In this table we present results obtained from estimating our baseline investment equation using several alternativemethods of extracting exogenous variation from the policy uncertainty measure. In Column 1 we report the baselineresult (in all specifications, policy uncertainty is lagged a single quarter with respect to the dependent variable). InColumn 2, we run the same specification, but replace the policy uncertainty measure with its tax-related componentalone. For specification 3, we regress the U.S. policy uncertainty index on the Canadian policy uncertainty index andseveral U.S. macroeconomic controls, and use the fitted residuals as an alternative policy uncertainty measure in ourbaseline specification. In Columns 4 and 5, we present two-stage least-squares results obtained from using measuresof polarization in the House and Senate as instruments for Policy Uncertainty. Please see Section 3.2 for a detailedexplanation of the way these measures are constructed. The table reports within-group estimates (i.e. industryfixed effects are eliminated by de-meaning). To facilitate the assessment of economic magnitudes, all variables arenormalized by their sample standard deviation. In specifications 1 and 2, standard errors are clustered at the quarterand firm level following Petersen (2009). In specifications 3, 4 and 5, we bootstrap the standard errors to accountfor the fact that the Policy Uncertainty regressor is estimated. We do so using a series of cluster-bootstraps as inCameron, Gelbach and Miller(2011) to account for the possible within-quarter and within-firm correlation in theerror term. t-statistics are reported in parentheses. *, ** and *** indicate statistical significance at the 10%, 5% and1% level, respectively.
Dependent variable: Baseline Tax-related PU Canada House SenateCAPX/Assets Uncertainty Residuals Polarization Polarization
2SLS 2SLS
(1) (2) (3) (4) (5)Policy Uncertainty (PU) -0.0476*** -0.0762*** -0.0363*** -0.0501*** -0.0414***
(-4.98) (-9.28) (-3.72) (-3.37) (-2.83)
Tobin’s Q 0.170*** 0.169*** 0.173*** 0.169*** 0.171***(21.97) (21.74) (30.27) (26.91) (26.69)
Cash Flow/Assets 0.0479*** 0.0484*** 0.0565*** 0.0530*** 0.0528***(14.20) (14.44) (16.62) (19.43) (19.28)
Sales Growth 0.0505*** 0.0499*** 0.0453*** 0.0488*** 0.0489***(15.15) (15.10) (16.05) (19.46) (19.50)
GDP Growth 0.0115 0.0231*** 0.0269*** 0.00391 0.00928(1.34) (3.08) (2.74) (0.29) (0.70)
N 309,499 309,499 242,845 309,499 309,499R-squared 0.044 0.048 0.042 0.042 0.042
First-Stage F-statistic 12.3 13.3
Firm Fixed Effects Yes Yes Yes Yes YesQuarter Dummies Yes Yes Yes Yes Yes
Cluster by Firm Yes Yes Cluster-Bootstrapped Standard ErrorsCluster by Quarter Yes Yes using Firm and Quarter Clusters
48
Table 8The Heterogeneous Effect of Policy Uncertainty on Corporate Investment
In this table, we use the same firm-level specification as in Table 3 (Panel A), to which we add industry competitiveness(Panel A), the investment irreversibility proxies (Panel B) and the financial constraints proxies (Panel C) discussed inSection 3.3 as well as their interaction with the policy uncertainty index from Baker, Bloom and Davis (2012) (PanelB follows Equation 2 and Panel C follows Equation 3). For expositional clarity we only show the coefficient estimatesof the variables of interest. In specification (1), the dependent variable has a lead of one period (calendar quarter)with respect to the policy uncertainty variable. In specification 2 it leads two periods, and so forth to specification(4). The table reports within-group estimates (i.e. industry fixed effects are eliminated by de-meaning). To facilitatethe assessment of economic magnitudes, all variables are normalized by their sample standard deviation. Standarderrors are clustered at the quarter and firm level following Petersen (2009). t-statistics are reported in parentheses.*, ** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Dependent variable: CAPX/Assets Panel A : Conditioning on Industry Competitiveness
(1) (2) (3) (4)
Policy Uncertainty (PU) -0.0140 -0.0176* -0.0166* -0.00982(-1.47) (-1.77) (-1.71) (-1.04)
Herfindahl Index (HI) 0.0541*** 0.0595*** 0.0610*** 0.0544***(5.58) (5.87) (6.11) (6.16)
PU x HI Decile -0.0230*** -0.0232*** -0.0225*** -0.0212***(-8.76) (-8.76) (-7.97) (-7.99)
Tobin’s Q 0.172*** 0.172*** 0.170*** 0.171***(22.53) (21.87) (21.54) (21.37)
Cash Flow/Assets 0.0481*** 0.0476*** 0.0477*** 0.0480***(14.31) (14.15) (14.21) (14.49)
Sales Growth 0.0500*** 0.0494*** 0.0484*** 0.0475***(15.22) (14.53) (14.53) (13.84)
GDP Growth 0.00885 0.0158** 0.0228*** 0.0309***(1.07) (2.05) (3.00) (3.99)
N 309,499 298,413 288,928 280,775R-squared 0.046 0.047 0.047 0.046
Firm Fixed Effects Yes Yes Yes YesQuarter Dummies Yes Yes Yes Yes
Cluster by Firm Yes Yes Yes YesCluster by Quarter Yes Yes Yes Yes
49
Table 8The Heterogeneous Effect of Policy Uncertainty on Corporate Investment
(Continued)
Panel B : Conditioning on Investment Flexibility
Dependend Variable: CAPX/Assets Panel B1 : Durables vs. Nondurables
(1) (2) (3) (4)Policy Uncertainty (PU) -0.0387*** -0.0395*** -0.0383*** -0.0302***
(-4.43) (-4.21) (-4.03) (-3.13)
Durables Indicator 0.0578*** 0.0633*** 0.0577*** 0.0559***(4.29) (5.04) (4.48) (4.11)
PU x Durables Indicator -0.0175** -0.0222*** -0.0205*** -0.0195***(-2.56) (-3.34) (-3.06) (-2.81)
Controls Yes Yes Yes Yes
Dependend Variable: CAPX/Asset Panel B2 : High vs Low Sunk Costs
(1) (2) (3) (4)Policy Uncertainty (PU) -0.0292*** -0.0291*** -0.0317*** -0.0217**
(-3.30) (-2.91) (-3.01) (-2.14)
High Sunk Cost Indicator 0.0421*** 0.0470*** 0.0392*** 0.0352***(3.69) (4.51) (3.73) (3.20)
PU x High Sunk Cost Indicator -0.0177*** -0.0210*** -0.0164*** -0.0179***(-3.17) (-4.36) (-3.39) (-3.51)
Controls Yes Yes Yes Yes
Dependend Variable: CAPX/Asset Panel B3 : Property, Plant and Equipment
(1) (2) (3) (4)Policy Uncertainty (PU) -0.00623 -0.00146 0.00130 0.0123
(-0.79) (-0.15) (0.13) (1.26)
PPE/Assets 0.265*** 0.181*** 0.133*** 0.115***(11.47) (7.93) (5.75) (4.98)
PU x PPE/Assets -0.00938*** -0.0114*** -0.0116*** -0.0121***(-5.37) (-7.63) (-7.50) (-7.59)
Controls Yes Yes Yes Yes
ALL
Firm Fixed Effects Yes Yes Yes YesQuarter Dummies Yes Yes Yes Yes
Cluster by Firm Yes Yes Yes YesCluster by Quarter Yes Yes Yes Yes
50
Table 8The Heterogeneous Effect of Policy Uncertainty on Corporate Investment
(Continued)
Panel C : Conditioning on Financial Constraints
Dependend Variable: CAPX/Asset Panel C1 : Credit Rating
(1) (2) (3) (4)Policy Uncertainty (PU) -0.0382*** -0.0439*** -0.0418*** -0.0340***
(-3.97) (-4.56) (-4.54) (-3.70)
Credit Rating Indicator 0.0723*** 0.0612*** 0.0609*** 0.0579***(7.15) (5.64) (5.74) (5.22)
PU x Credit Rating Indicator -0.0184*** -0.0134** -0.0137** -0.0122**(-3.64) (-2.38) (-2.50) (-2.17)
Controls Yes Yes Yes Yes
Dependend Variable: CAPX/Asset Panel C2 : Whited and Wu (2006) Index
(1) (2) (3) (4)Policy Uncertainty (PU) -0.00197 -0.00658 -0.00541 0.00164
(-0.19) (-0.58) (-0.49) (0.15)
Whited & Wu Index 0.201*** 0.184*** 0.180*** 0.183***(11.04) (10.59) (10.99) (10.29)
PU x Whited & Wu Index -0.00867*** -0.00870*** -0.00870*** -0.00855***(-5.83) (-5.98) (-6.13) (-5.59)
Controls Yes Yes Yes Yes
Dependend Variable: CAPX/Asset Panel C3 : Kaplan and Zingales (1997) Index
(1) (2) (3) (4)Policy Uncertainty (PU) -0.0349*** -0.0327*** -0.0290*** -0.0223**
(-3.35) (-2.99) (-2.66) (-2.06)
Kaplan & Zingales Index -0.0529*** -0.0287*** -0.0162 -0.0102(-4.26) (-2.61) (-1.55) (-0.96)
PU x Kaplan & Zingales Index -0.00273*** -0.00406*** -0.00437*** -0.00391***(-3.01) (-4.62) (-4.94) (-4.59)
Controls Yes Yes Yes Yes
ALL
Firm Fixed Effects Yes Yes Yes YesQuarter Dummies Yes Yes Yes Yes
Cluster by Firm Yes Yes Yes YesCluster by Quarter Yes Yes Yes Yes
51
Tab
le9
Policy
Uncert
ain
tyan
dF
inan
cin
gD
ecis
ion
s
Inth
ista
ble
we
regre
ssquart
erly
cash
hold
ings
(Panel
A),
new
deb
tis
suance
(Panel
B)
and
new
equit
yis
suance
(Panel
C)
on
com
mon
acc
ounti
ng
and
macr
oco
ntr
ols
and
on
the
policy
unce
rtain
tyin
dex
from
Baker
,B
loom
and
Dav
is(2
012).
Panel
sA
1,
B1,
C1
pre
sent
firm
level
resu
lts
and
Panel
sA
2,
B2,
C2
pre
sent
indust
ryle
vel
resu
lts
(base
don
a3
dig
itSIC
class
ifica
tion).
See
Sec
tion
3.3
for
det
ails
on
the
spec
ifica
tion
use
din
each
panel
.T
he
data
isquart
erly
and
itex
tends
from
January
1987
toD
ecem
ber
2011.
Insp
ecifi
cati
on
(1),
the
dep
enden
tva
riable
has
ale
ad
of
one
per
iod
(cale
ndar
quart
er)
wit
hre
spec
tto
the
policy
unce
rtain
tyva
riable
.In
spec
ifica
tion
2it
leads
two
per
iods,
and
sofo
rth
tosp
ecifi
cati
on
(4).
To
contr
ol
for
seaso
nality
,w
ein
clude
quart
erdum
mie
sin
each
spec
ifica
tion
(not
show
nin
the
table
).T
he
table
rep
ort
sw
ithin
-gro
up
esti
mate
s(i
.e.
firm
fixed
effec
tsare
elim
inate
dby
de-
mea
nin
g).
To
faci
lita
teth
eass
essm
ent
of
econom
icm
agnit
udes
,all
vari
able
sare
norm
alize
dby
thei
rsa
mple
standard
dev
iati
on.
Sta
ndard
erro
rsare
clust
ered
at
the
quart
erand
firm
level
follow
ing
Pet
erse
n(2
009);
t-st
ati
stic
sare
rep
ort
edin
pare
nth
eses
.*,
**
and
***
indic
ate
stati
stic
al
signifi
cance
at
the
10%
,5%
and
1%
level
,re
spec
tivel
y.
Pan
elA
:D
epen
den
tV
ari
ab
leis
Cash
Hold
ings
Panel
A1
:F
irm
-Lev
elR
esult
sP
anel
A2
:In
dust
ry-L
evel
Res
ult
s(T
hre
eD
igit
SIC
)
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty0.0
198***
0.0
211***
0.0
198***
0.0
240***
0.0
835***
0.0
861***
0.0
804***
0.0
760***
(3.3
6)
(3.2
6)
(3.2
3)
(3.9
4)
(4.7
0)
(4.8
1)
(4.8
2)
(4.8
0)
Tobin
’sQ
0.1
63***
0.1
64***
0.1
64***
0.1
64***
0.2
22***
0.2
19***
0.2
19***
0.2
18***
(24.4
6)
(24.5
4)
(23.7
4)
(23.8
9)
(8.3
4)
(8.2
1)
(8.2
0)
(8.2
8)
Cash
Flo
w/A
sset
s0.0
763***
0.0
774***
0.0
779***
0.0
786***
0.0
751***
0.0
765***
0.0
786***
0.0
789***
(23.2
4)
(23.8
6)
(23.9
2)
(24.0
6)
(5.9
8)
(6.0
4)
(6.1
8)
(6.1
2)
CA
PX
/A
sset
s-0
.0332***
-0.0
335***
-0.0
343***
-0.0
348***
-0.1
04***
-0.0
988***
-0.0
983***
-0.0
976***
(-10.8
9)
(-10.8
8)
(-11.0
2)
(-11.3
3)
(-5.4
4)
(-4.9
6)
(-4.8
6)
(-4.7
3)
Log
of
Tota
lA
sset
s-0
.0719***
-0.0
671***
-0.0
612***
-0.0
544***
-0.0
687**
-0.0
600
-0.0
535
-0.0
452
(-4.7
1)
(-4.3
0)
(-3.8
8)
(-3.3
9)
(-2.1
3)
(-1.8
7)
(-1.6
4)
(-1.3
6)
Book
Lev
erage
-0.1
83***
-0.1
80***
-0.1
79***
-0.1
77***
-0.2
95***
-0.3
00***
-0.3
00***
-0.3
03***
(-28.8
3)
(-28.0
4)
(-27.5
0)
(-27.0
3)
(-8.8
5)
(-8.7
3)
(-8.5
4)
(-8.4
8)
Div
iden
dP
ayer
-0.0
113
-0.0
0976
-0.0
0717
-0.0
0517
-0.2
22***
-0.2
05**
-0.1
87**
-0.1
93**
(-1.2
3)
(-1.0
4)
(-0.7
6)
(-0.5
4)
(-2.7
8)
(-2.5
2)
(-2.2
6)
(-2.2
9)
GD
PG
row
th0.0
0759
-0.0
0105
-0.0
0791
-0.0
0759
-0.0
000124
-0.0
129
-0.0
232
-0.0
258**
(1.3
1)
(-0.1
6)
(-1.3
5)
(-1.6
1)
(-0.0
0)
(-0.8
5)
(-1.6
0)
(-2.0
3)
N309,4
99
298,4
13
288,9
28
280,7
75
22,0
37
21,7
81
21,5
31
21,2
84
R-s
quare
d0.1
01
0.1
00
0.0
99
0.0
99
0.2
03
0.2
08
0.2
10
0.2
12
Fir
m/In
d.
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
m/In
d.
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Clu
ster
by
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
52
Tab
le9
Policy
Un
cert
ain
tyan
dF
inan
cin
gD
ecis
ion
s(C
onti
nu
ed
)
Pan
elB
:D
epen
den
tV
ari
ab
leis
New
Deb
tIs
suan
ce
Panel
B1
:F
irm
-Lev
elR
esult
sP
anel
B2
:In
dust
ry-L
evel
Res
ult
s(T
hre
eD
igit
SIC
)
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty-0
.0255***
-0.0
345***
-0.0
341***
-0.0
301***
-0.0
630***
-0.0
830***
-0.0
900***
-0.0
767***
(-2.8
8)
(-4.3
8)
(-4.0
6)
(-3.4
7)
(-3.2
5)
(-4.5
7)
(-5.0
9)
(-3.9
8)
M/B
0.0
215***
0.0
207***
0.0
229***
0.0
220***
0.0
213
0.0
201
0.0
249
0.0
293**
(5.5
5)
(5.2
7)
(5.2
1)
(5.1
5)
(1.7
0)
(1.5
5)
(1.9
4)
(2.2
3)
EB
IT/A
sset
s-0
.0420***
-0.0
417***
-0.0
438***
-0.0
435***
-0.0
0943
-0.0
0935
-0.0
0728
-0.0
0519
(-10.2
5)
(-9.7
7)
(-10.4
3)
(-9.8
0)
(-0.6
6)
(-0.6
4)
(-0.5
2)
(-0.3
6)
Log
of
Tota
lA
sset
s-0
.0908***
-0.0
913***
-0.0
918***
-0.0
954***
0.0
137
0.0
175
0.0
162
0.0
0774
(-6.8
7)
(-6.7
0)
(-6.6
5)
(-6.4
6)
(0.7
3)
(0.9
5)
(0.9
3)
(0.4
3)
PP
E/A
sset
s0.0
909***
0.0
883***
0.0
897***
0.0
858***
0.0
643**
0.0
681**
0.0
687**
0.0
664**
(10.7
9)
(10.4
2)
(10.6
1)
(10.1
2)
(2.0
9)
(2.1
8)
(2.1
8)
(2.0
3)
Dep
reci
ati
on
Exp
ense
/A
sset
s-0
.0325***
-0.0
317***
-0.0
340***
-0.0
341***
-0.0
323
-0.0
286
-0.0
253
-0.0
289
(-7.4
0)
(-7.0
4)
(-7.2
7)
(-7.3
7)
(-1.5
9)
(-1.3
9)
(-1.2
4)
(-1.4
1)
GD
PG
row
th0.0
124**
0.0
0989
0.0
135**
0.0
145**
0.0
285
0.0
289**
0.0
290**
0.0
313**
(2.0
5)
(1.6
4)
(2.4
5)
(2.5
3)
(1.8
6)
(2.3
9)
(2.2
8)
(2.3
0)
N251,3
51
242,6
40
234,9
90
228,3
44
21,5
16
21,2
96
21,0
66
20,8
35
R-s
quare
d0.0
05
0.0
05
0.0
06
0.0
06
0.0
09
0.0
11
0.0
13
0.0
11
Fir
m/In
dust
ryF
EY
esY
esY
esY
esY
esY
esY
esY
esQ
uart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
m/In
dust
ryY
esY
esY
esY
esY
esY
esY
esY
esC
lust
erby
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
53
Tab
le9
Policy
Un
cert
ain
tyan
dF
inan
cin
gD
ecis
ion
s(C
onti
nu
ed
)
Pan
elC
:D
epen
den
tV
ari
ab
leis
New
Equ
ity
Issu
an
ce
Panel
C1
:F
irm
-Lev
elR
esult
sP
anel
C2
:In
dust
ry-L
evel
Res
ult
s(T
hre
eD
igit
SIC
)
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Policy
Unce
rtain
ty0.0
0294
0.0
103
0.0
0722
0.0
136
-0.0
124
0.0
0409
0.0
0530
0.0
0690
(0.5
1)
(1.2
6)
(0.9
0)
(1.6
0)
(-0.9
0)
(0.2
3)
(0.3
1)
(0.3
4)
Tobin
’sQ
0.2
20***
0.2
20***
0.2
19***
0.2
17***
0.1
60***
0.1
48***
0.1
47***
0.1
28***
(17.3
0)
(16.9
5)
(16.7
1)
(16.8
4)
(4.2
2)
(4.4
7)
(3.9
4)
(3.9
0)
EB
IT/A
sset
s-0
.0946***
-0.0
922***
-0.0
905***
-0.0
936***
-0.1
53***
-0.1
42***
-0.1
39***
-0.1
27***
(-12.2
7)
(-11.9
6)
(-11.7
3)
(-11.7
8)
(-5.4
0)
(-5.2
1)
(-5.1
3)
(-5.1
7)
Log
of
Tota
lA
sset
s-0
.284***
-0.2
87***
-0.2
87***
-0.2
83***
-0.2
05***
-0.2
15***
-0.2
23***
-0.2
11***
(-17.1
9)
(-17.9
9)
(-17.7
0)
(-16.4
8)
(-9.9
0)
(-11.1
8)
(-10.9
0)
(-10.4
5)
PP
E/A
sset
s0.0
476***
0.0
477***
0.0
516***
0.0
510***
0.0
285
0.0
387
0.0
246
0.0
195
(5.1
2)
(5.1
4)
(5.6
7)
(5.7
3)
(0.9
7)
(1.2
9)
(0.9
0)
(0.7
2)
Dep
reci
ati
on
Exp
ense
/A
sset
s0.0
315***
0.0
310***
0.0
301***
0.0
300***
0.0
417
0.0
450**
0.0
477**
0.0
541**
(5.3
1)
(4.8
8)
(4.8
1)
(4.6
5)
(1.9
3)
(2.0
9)
(2.2
1)
(2.4
7)
GD
PG
row
th0.0
0252
-0.0
0560
-0.0
139**
-0.0
0741
-0.0
0637
-0.0
238
-0.0
291**
-0.0
164
(0.3
2)
(-0.7
6)
(-1.9
8)
(-1.3
7)
(-0.4
5)
(-1.5
2)
(-2.0
2)
(-1.2
3)
N269,3
16
259,9
82
251,7
99
244,7
02
21,6
50
21,4
23
21,1
88
20,9
55
R-s
quare
d0.0
57
0.0
56
0.0
56
0.0
56
0.0
71
0.0
70
0.0
70
0.0
63
Fir
m/In
d.
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Quart
erD
um
mie
sY
esY
esY
esY
esY
esY
esY
esY
es
Clu
ster
by
Fir
m/In
d.
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Clu
ster
by
Quart
erY
esY
esY
esY
esY
esY
esY
esY
es
54
Table 10Alternative Methodological Specifications
In this table we present results obtained from estimating our baseline investment equation using several alternativemethodological specifications. In Column 1 we report the baseline result, where policy uncertainty is lagged a singlequarter with respect to the dependent variable. In Column 2, we run the same specification, but we exclude all fiscalquarters ending in NBER recession months. In Column 3 we run the baseline specification without firm fixed effects.In Column 4 the policy uncertainty variable is measured as the Baker et al. (2012) index in the last month of thequarter prior to the one when the dependent variable is measured. In Column 5 we first linearly detrend the Baker etal. (2012) index and then calculate the policy uncertainty variable the same way we do in the baseline specification(see Section 2.3). In Column 6 we estimate a dynamic version of the specifications in Column 1 (i.e. we includelagged investment on the right hand side) using the system GMM methodology of Blundell and Bond (1998) . SeeSection 4 for details on the instruments used. *, ** and *** indicate statistical significance at the 10%, 5% and 1%level, respectively.
Dependent variable: Baseline Exclude Baseline PU at PU is System GMM:Investment NBER With Quarter Detrended Blundell and
Recessions No FE End Bond (1998)
(1) (2) (3) (4) (5) (6)Policy Uncertainty -0.0475*** -0.0502*** -0.0552*** -0.0445*** -0.0316*** -0.0210***
(-4.98) (-5.21) (-4.31) (-5.24) (-3.47) (-5.18)
Tobin’s Q 0.170*** 0.173*** 0.0841*** 0.171*** 0.173*** 0.119***(21.97) (20.89) (12.91) (22.10) (22.11) (19.59)
Cash Flow 0.0479*** 0.0487*** 0.158*** 0.0478*** 0.0478*** 0.0426***(14.20) (14.01) (24.46) (14.17) (14.18) (14.08)
Sales Growth 0.0505*** 0.0499*** 0.0869*** 0.0506*** 0.0509*** 0.0153***(15.15) (14.81) (14.82) (15.08) (15.19) (4.45)
GDP Growth 0.0115 0.0137 0.0205* 0.0135 0.0188** 0.00695***(1.34) (1.14) (1.81) (1.59) (2.18) (2.89)
Lagged Investment 0.232***(34.98)
Constant 0.811*** 0.554***(13.11) (27.92)
N 309,499 273,475 309,499 309,499 309,499 309,499R-squared 0.044 0.045 0.045 0.044 0.043
Firm Fixed Effects Yes Yes No Yes Yes YesQuarter Dummies Yes Yes Yes Yes Yes Yes
Cluster by Firm Yes Yes Yes Yes No NoCluster by Quarter Yes Yes Yes Yes No No
55