Policy Uncertainty and Corporate Investment

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Policy Uncertainty and Corporate Investment Huseyin Gulen * Krannert Graduate School of Management Purdue University Mihai Ion Krannert Graduate School of Management Purdue University April 4, 2013 First Draft: November 2012 Abstract Using the policy uncertainty index of Baker, Bloom, and Davis (2012), we investigate how corporate capital investment at the firm and industry level is affected by the uncertainty related to future policy and regulatory outcomes. Policy-related uncertainty is negatively related to firm 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 investments observed during the 2007-2009 crisis period can be attributed to policy-related uncertainty. More importantly, we document that the relation between policy uncertainty and capital investment is not uniform in the cross-section of U.S. firms. It is significantly stronger for firms with a higher degree of investment irreversibility, for firms which are more financially constrained, and for firms operating in less competitive industries. Policy uncertainty is also associated with higher cash holdings and lower net debt issuance. Overall, these results lend empirical support to the notion that policy-related uncertainty can depress economic growth through a decrease in corporate investment. This decrease is related to precautionary delays induced by investment irreversibility 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 the remaining errors.

Transcript of Policy Uncertainty and Corporate Investment

Page 1: Policy Uncertainty and Corporate Investment

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.

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“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.

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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).

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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.

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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.”

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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

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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.

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(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.

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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

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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.

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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.

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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.

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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

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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.

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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.

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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)

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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

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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.

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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.

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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.

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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.

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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).

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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

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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)

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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

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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

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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).

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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

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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)).

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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

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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.

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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.

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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.

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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.

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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

Page 41: Policy Uncertainty and Corporate Investment

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

Page 42: Policy Uncertainty and Corporate Investment

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

Page 43: Policy Uncertainty and Corporate Investment

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

Page 44: Policy Uncertainty and Corporate Investment

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

Page 45: Policy Uncertainty and Corporate Investment

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

Page 46: Policy Uncertainty and Corporate Investment

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

Page 47: Policy Uncertainty and Corporate Investment

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

Page 48: Policy Uncertainty and Corporate Investment

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

Page 49: Policy Uncertainty and Corporate Investment

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

Page 50: Policy Uncertainty and Corporate Investment

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

Page 51: Policy Uncertainty and Corporate Investment

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

Page 52: Policy Uncertainty and Corporate Investment

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

Page 53: Policy Uncertainty and Corporate Investment

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

Page 54: Policy Uncertainty and Corporate Investment

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

Page 55: Policy Uncertainty and Corporate Investment

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

Page 56: Policy Uncertainty and Corporate Investment

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

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