Economic Policy Uncertainty, Cost of Capital, andCorporate Innovation

Zhaoxia Xu∗

Abstract

We examine the cost-of-capital transmission channel through which governmenteconomic policy uncertainty (GEPU) can affect corporate innovation activities. Wefind that GEPU increases firms’ cost of capital, which in turn translates into lowerinnovation activities. As economic policy uncertainty rises, firms with more exposureto such uncertainty face a higher weighted average cost of capital and reduce investmentmore. Moreover, GEPU’s impact is stronger on R&D than on fixed-asset investments.Our study provides novel evidence that more uncertainty about economic policy hindersinnovation not only through the investment irreversibility channel, but also throughthe cost-of-capital channel.

Key Words: Economic Policy Uncertainty, Implied Cost of Equity, Cost of Debt, Innovation.

JEL No: G31, G32, O30, E60.

∗I gratefully acknowledge the support of the National Bureau of Economic Research Innovation PolicyResearch Grant. I thank Viral Acharya, Chang-Mo Kang, Qiang Kang, Ron Masulis, and the seminar partic-ipants at Florida International University, New York University, University of New South Wales, the RisingStars Conference Financial Market Workshop for the helpful comments. Department of Finance and RiskEngineering, New York University, 6 MetroTech Center, New York, USA, 11201. Email: zhaoxiaxu@nyu.edu.Phone: (646) 997-3808.

1 Introduction

The intensified uncertainty about government policies in the wake of partisan conflicts in

the recent years highlights the importance of understanding their impact on the real economy

and the underlying transmission channels. Most of the theoretical and empirical studies ana-

lyze the influence of uncertainty on investment in the framework of irreversible investment.1

With costly reversibility, uncertainty changes the optimal timing of investment because of

the real-option feature of investment. In this study, we investigate another crucial trans-

mission mechanism, cost of capital, through which government economic policy uncertainty

(GEPU) affects corporate innovation activities.

The cost of capital is important in transmitting economic policy uncertainty shocks to

investment. Recent studies show that policy uncertainty commands an equity risk premium

because of the undiversifiable political risk (Pastor and Veronesi (2012, 2013)). Since equity

risk premium is a building block for cost of equity, economic policy uncertainty should affect

firms’ cost of capital. The impact could vary across firms because of their varying exposure

to GEPU.

The cost of capital is widely used as a discount rate to evaluate a project when firms

make capital budgeting decisions (Graham and Harvey (2001), Association for Financial

Professionals (2013)). An increase in the cost of capital can turn a positive net present

value project into a negative one. Consequently, firms’ investment decision is affected by the

cost of capital. An increase in the cost of capital may also reduce firms’ incentives to raise

1See, for example, Bernanke (1983), McDonald and Siegel (1986), Abel and Eberly (1994), Dixit andPindyck (1994), and many others for the theoretical framework. Bloom et al. (2007), Bloom (2009), Julio andYook (2012), and Gulen and Ion (2016) among others provide empirical evidence supporting the investmentirreversibility channel.

1

external capital. Firms with less capital available may invest less in innovative activities.

Despite theoretical soundness, no study has empirically linked economic policy uncer-

tainty to individual firms’ financing costs and their subsequent innovation activities.2 We

focus on innovation for several reasons. First, although previous studies show that uncer-

tainty influences firms’ investment and hiring decisions, little is known about its impact

on corporate innovation. Second, increasing evidence shows that economic factors affect

innovation and other investments differently, owing to the unique features of research and

development (R&D).3 Third, R&D investment is more susceptible to uncertainty because it

often takes a long time to yield fruitful results and involves a substantial amount of invest-

ment in human capital, which is hard to recoup.

Although the cost of capital affects investment in physical assets as well, financing costs

are especially important for investment in innovation. Studies have shown that financing is

essential for developing, implementing, and commercializing new technologies (Rajan (2012)

and Kerr and Nanda (2015)). Financing innovation tends to be costlier because of the high

uncertainty of investment outcomes (Hall and Lerner (2010)). Moreover, the uncertainty of

innovation projects is likely to be exacerbated by policy uncertainty.

To analyze the real effects of uncertainty related to the U.S. economic policy, we measure

2Pastor and Veronesi (2012, 2013) develop general equilibrium models featuring government policy uncer-tainty and use S&P 500 index return data and the Baker et al. (2013) index to show that policy uncertaintycommands an equity risk premium at the aggregate level. Based on the time-series analysis of Fama-Frenchsize-momentum portfolio returns, Brogaard and Detzel (2015) show that economic policy uncertainty is animportant risk factor. Kelly et al. (2016) use equity index option prices around national elections and globalsummits to show that political uncertainty is priced. However, none of these studies investigate the impactof economic policy uncertainty on firm-level cost of capital and subsequent investments.

3Market factors, such as stock liquidity, short selling, coverage by financial analysts, and market sentiment,are found to have varying effects on R&D and capital expenditures (Becker-Blease and Paul (2006), Fanget al. (2014), Grullon et al. (2015), He and Tian (2013, 2014), Derrien and Kecskes (2013) and Dang andXu (2015)). Unlike capital expenditures that produce tangible assets, R&D investments generate intangibleassets that are more difficult to serve as collateral. Furthermore, the outcome of innovation investments ismore uncertain.

2

innovation by its inputs (R&D) and outcomes (patents) and estimate each firm’s annual

cost of equity, cost of debt, and weighted average cost of capital (WACC). The cost-of-

capital transmission channel is identified by exploring cross-sectional variation across firms

with varying exposure to economic policy uncertainty. If cost of capital is the underlying

transmission mechanism through which GEPU affects innovation, we expect that the cost

of capital and investments of firms with more exposure to GEPU would be affected more.

One of the main challenges in our analysis is to measure GEPU. Previous studies use

proxies such as volatility in stock returns or the dispersion in analyst forecasts to measure

firm-specific uncertainty.4 However, these measures cannot capture uncertainty attributable

to the government economic policy. Applying a cross-country framework, Julio and Yook

(2012) use national election years to represent periods of political uncertainty. Unfortu-

nately, this proxy does not reflect policy uncertainty in non-election years. Therefore, we

adopt a continuous economic policy uncertainty index recently developed by Baker et al.

(2013) to measure time variation in uncertainty.5 This GEPU index is a weighted aver-

age of uncertainty related to taxation, government spending, and inflation, as well as the

frequency of major newspaper articles discussing uncertainty in economic policy. The in-

dex exhibits substantial variation over time and tends to spike during high economic policy

uncertainty periods, such as debt-ceiling crises, budgetary disputes, and tight presidential

elections. Furthermore, it captures uncertainty about the government’s future policy beyond

electoral uncertainty.

Another challenge is to estimate individual firms’ cost of capital, because the cost of

4See, for example, Bloom et al. (2007) and Bond and Cummins (2004).5The economic policy uncertainty index of Baker et al. (2013) has been used in numerous academic studies

(e.g., Pastor and Veronesi (2012, 2013), Brogaard and Detzel (2015), and Gulen and Ion (2016), among manyothers), as well as by practitioners and policy makers.

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equity and the cost of debt are not observable. Previous studies use average realized returns

to estimate the cost of equity capital (Campbell (1987)). This approach uses ex post realized

stock returns as a proxy for ex ante expected returns. However, realized returns are shown to

be a poor measure of expected returns (Elton (1999)). In addition, the capital asset-pricing

model (CAPM) and Fama and French (1993) three-factor model are found to be imprecise

in measuring the cost of equity (Fama and French (1997), Pastor and Stambaugh (1999)).

Thus, we estimate the cost of equity capital by using an ex ante proxy developed by recent

accounting and asset-pricing research, namely the implied cost of equity.6

The implied cost of equity is defined as the discount rate that equates a stock’s present

value of expected cash flows to its current price. This internal rate of return is viewed as

the market’s expected rate of return on the stock. Recent studies show that the implied

cost of equity capital is a good proxy for a stock’s conditional expected return (Pastor et al.

(2008), Lee et al. (2009), Chava and Purnanandam (2010), and Li et al. (2013)). We derive

the implied cost of equity from Gebhardt et al. (2001) and three alternative models and

estimate a firm’s cost of debt as the actual yield on the debt carried by the firm and two

alternative approaches. The cost of capital is then measured as the weighted average cost of

capital, which comprises a firm’s cost of equity and after-tax cost of debt, with its market

leverage ratio as the weight.7

The third challenge is identification, because confounding factors such as overall eco-

nomic conditions or investment opportunities may cause a spurious relationship. We adopt

6See Easton (2007) for a review of the implied cost of equity estimations in the accounting literature. Inthe asset-pricing literature, implied cost of capital is used as a proxy for expected stock returns to test therisk–return relationship (Pastor et al. (2008), Lee et al. (2009), Chava and Purnanandam (2010)).

7Frank and Shen (2015) estimate the cost of equity by using the Gordon growth model and measurethe WACC through a similar approach. Their study evaluates the impact of WACC on firms’ capitalexpenditures, while this study investigates how economic policy uncertainty affects firms’ financing costs.

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several approaches to isolate the effect of economic policy uncertainty. First, we follow the

recommendation by Bernanke et al. (1996) and investigate cross-sectional differences in the

cost of capital and innovation in response to economic policy uncertainty. Second, we ex-

ploit close congressional elections and close presidential elections as two plausible exogenous

shocks to policy uncertainty. Since the timing of elections is independent from economic

conditions and the outcomes of close elections are difficult to predict, close elections pro-

vide a natural experiment framework to isolate the impact of uncertainty related to general

government policies. Third, we use political polarization as an instrumental variable to ease

the endogeneity concern. Fourth, we control for the time-varying first-moment factors that

capture aggregate-level growth opportunities and control for observable and unobservable

factors that reflect firm-level investment opportunities. This helps disentangle the impact

of second-moment shocks from first-moment effects associated with business cycles and in-

vestment opportunities. Fifth, we perform a fixed-effects estimation to explore the effects of

economic policy uncertainty within each firm over time. Finally, we extract the exogenous

component from the GEPU index by purging the endogenous component of the index.

We find that investments in innovation and the quantity and quality of innovation out-

comes are negatively associated with GEPU. Economic policy uncertainty has an immediate

impact on innovation inputs, while the impact on innovation outcomes becomes significant

in the third year and the influence strengthens in the fourth year. Our robustness analyses

show that investment opportunities or macroeconomic conditions are less likely to be the

main factor driving the relationship between economic policy uncertainty and innovation. In

addition, we find that uncertainty about economic policy has a weaker impact on fixed-asset

investments than on R&D investments, which is consistent with the view that innovation is

5

more sensitive to uncertainty.

We observe that the cost of capital rises as GEPU intensifies. During 1986–2007, the

weighted average cost of capital increased by approximately 129 basis points from the lowest

to the highest levels of economic policy uncertainty. We further show that firms have a lower

propensity to issue equity and debt and raise less capital when economic policy uncertainty

increases. Firms invest less in innovation when the cost of capital rises. The results establish

an explicit link between economic policy uncertainty and financing costs: uncertainty makes

financing innovation costlier for firms.

To identify the cost-of-capital channel, we construct a measure of firms’ exposure to

economic policy uncertainty that captures the sensitivity of a firm’s stock returns to changes

in the GEPU index. Using this measure, we observe that the increase in the WACC of

firms with higher sensitivity to economic policy uncertainty is more than that of the firms

with lower sensitivity when economic policy uncertainty moves from low to high periods.

Economic policy uncertainty has a stronger impact on the cost of capital and investments

for firms with more exposure to uncertainty than those with less exposure. These results

lend support to the cost-of-capital transmission channel perspective. In addition, we find

that the cost-of-capital channel still plays an important role in shaping firms’ investment

under uncertainty after we control for the investment irreversibility mechanism.

This study contributes to the debate on the relation between uncertainty and the real

economy. The theoretical literature offers contradictory predictions on the uncertainty-

investment relationship. On the one hand, the literature suggests that it is optimal for firms

facing uncertainty to defer investment because reversing investment is costly and uncertainty

increases the value of the option to wait (Bernanke (1983), McDonald and Siegel (1986), Abel

6

and Eberly (1994), and Dixit and Pindyck (1994)). On the other hand, Carballero (1991),

Grenadier (2002), and Weeds (2002) argue that the option to wait is less valuable when firms

face competition or when investments can create valuable growth opportunities. Uncertainty

increases investments if firms have an option to resell the assets later (Abel et al. (1996)) or

if the marginal revenue product of capital is convex (Hartman (1972) and Abel (1983)).

Further, the empirical evidence on the real effects of uncertainty is mixed.8 In terms of in-

vestment, Gulen and Ion (2016) and Jens (2016) find that firms delay spending on fixed assets

when facing policy uncertainty or electoral uncertainty. Using implied volatility from equity

options to capture firm-specific uncertainty, Stein and Stone (2013) document that implied

volatility reduces capital investment but increases R&D investment. A contemporaneous

paper, Atanassov et al. (2015), find that firms spend more on R&D during gubernatorial

election years. Differing from Stein and Stone (2013) and Atanassov et al. (2015)’s focus

on uncertainty about individual firms or outcomes of gubernatorial elections, we investigate

uncertainty about government economic policies that affects the general economic environ-

ment within which businesses operate. Such uncertainty goes beyond the potential changes

in local leadership in the midst of elections.9 We find that such aggregate policy risk, which

8Bernanke (1983), Bloom et al. (2007), Baker et al. (2013), Julio and Yook (2012), Gulen and Ion (2016),and Jens (2016) document that uncertainty has a negative impact on fixed-asset investments and employment.However, Ghosal and Lougani (1996) find no impact of uncertainty on investment in U.S. manufacturingindustries, except for competitive industries. Driver et al. (2008) show that the negative effect of uncertaintyon investment is offset by the first-mover advantage.

9For example, events such as debt ceiling crises, budgetary disputes, and debates over the stimuluspackage dramatically elevated uncertainty about government policies, but they were unrelated to elections.Gubernatorial elections take place every four years. Election years do not capture the variation in policyuncertainty that may occur between elections. An election dummy variable also does not take into accountthe fact that elections at different points in time may have different implications for the level of policyuncertainty in the economy. Moreover, Gubernatorial elections may affect firms that operate within thestate. Given that most firms operate and compete in the national or international market, the impacts ofGubernatorial elections might be limited. This is especially the case when firms often have R&D centers inlocations different from the headquarters.

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is hard to diversify, has a negative impact on firms’ innovation activities.

The literature has emphasized that investment irreversibility shapes the relation be-

tween uncertainty and investments. Our study provides novel evidence on another important

mechanism—cost of capital—through which economic policy uncertainty is transmitted to

firms’ innovation activities. We contribute to the literature by explicitly showing that the

firm-level cost of capital is affected by economic policy uncertainty and the impact varies

across firms. To the best of our knowledge, this study is the first to empirically demon-

strate that high uncertainty about government economic policy has a detrimental effect on

innovation by driving up firms’ cost of capital.

Our study also adds to the nascent literature on finance and innovation. The literature

shows that innovation is affected by various economic factors such as the development of fi-

nancial markets (Hsu et al. (2014)), law and legal systems (Acharya and Subramanian (2009),

Acharya et al. (2014), Brown et al. (2013)), institutional ownership (Aghion et al. (2013)),

credit supply (Amore et al. (2013), Chava et al. (2013), and Cornaggia et al. (2015)), and

access to stock markets (Acharya and Xu (2015)), among others. In a cross-country analysis,

Bhattacharya et al. (2015) show that overall uncertainty resulting from national elections

rather than a country’s policy itself affects technological innovation. Unlike their focus on

comparing the relative importance of policy and policy uncertainty, we are interested in un-

derstanding the real effects of time-varying uncertainty related to U.S. economic policy and

identifying the underlying transmission mechanism. We find that the influence of economic

policy uncertainty on innovation through firms’ financing costs is stronger for firms with

higher exposure to such uncertainty.

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2 Data and Measures

2.1 Data

To measure innovation activities, we use patent and citation data from Kogan et al.

(2012) and technology class data from the United States Patent and Trademark Office.10

The stock price data are from the Center for Research in Security Prices (CRSP) monthly

stock database and the firm-level financial data are from the merged CRSP-Compustat

database. The sample period is from January 1, 1986 to December 31, 2007. The sample

ends in 2007 because the average time lag between the patent application date and grant

date is 2–3 years (Hall et al. (2001)). An advantage of focusing on innovation before the

2007–2009 financial crisis is that it minimizes the influence of deteriorating macroeconomic

conditions on firms’ innovation performance.

Firms in financial and utilities industries (SIC code 6000-6999 and 4900-4999) are ex-

cluded. We require firms to have complete data on total assets and a positive value on sales

and cash. Firm years with total assets less than 1 million are excluded. Further, we focus

on R&D firms that invest in R&D during the sample period. Variables are winsorized at

1 percent and 99 percent to avoid the effect of outliers. We merge patent data with our

financial data. Following the innovation literature, the patent and citation counts are set to

zero when no patent and citation information is available. Including firm-year observations

with no patent alleviates the sample selection concern. The final sample consists of 6,167

firms and 54,050 firm-year observations.

10The patent data by Kogan et al. (2012) contain patent and citation data for U.S. firms up to 2010. Wefind similar results using the National Bureau of Economic Research (NBER) Patent Citation database thatcovers patent data until 2006.

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2.2 Innovation Measure

To gauge a firm’s innovation activities, we follow the literature and measure innovation

input by using R&D spending and innovation output by using patent-based metrics (Hall

et al. (2001, 2005)). The quantity of innovation output is measured by the number of patents

applied by a firm in a given year that are eventually granted. The patent application year

rather than the grant year is used because the former is closer to the time of the actual

innovation (Griliches et al. (1987)).

We measure the quality of patents on the basis of the citation count that each patent

receives in the subsequent years. Patent citations are subject to truncation biases because

patents created in later years have less time to accumulate citations than those established

in earlier ones. Additionally, the citation intensities of patents might vary across industries.

To adjust for truncation bias, following Hall et al. (2001, 2005), we scale the raw patent

citation counts by the average citation counts of all patents applied in the same year and

technology class.

2.3 Government Economic Policy Uncertainty Index

The time-varying economic policy uncertainty index developed by Baker et al. (2013) is

a weighted average of a news-based policy uncertainty index, tax expirations, inflation fore-

cast disagreement, and government purchase disagreement. The news-based policy index is a

normalized monthly count of the number of articles that contain terms related to economic

policy uncertainty in 10 of the largest newspapers. The tax expirations measure the dis-

counted dollar-weighted sum of expiring federal tax code provisions. Forecast disagreement

10

about inflation and government purchases captures uncertainty associated with monetary

policy and government spending through forecast dispersion by participants of the Federal

Reserve Bank of Philadelphia’s Survey of Professional Forecasters.

Figure 1 plots the monthly GEPU index and NBER recession periods between January

1986 and December 2011. Uncertainty spikes during the debt-ceiling crisis of 2011, budgetary

disputes, tight presidential elections, the 9/11 terror attack, and recessions. We match the

monthly government economic policy uncertainty index to the annual financial data by the

fiscal year and month.11

Since the GEPU index is likely to be correlated with macroeconomic conditions and/or

investment opportunities, we adopt several identification strategies to minimize the influence

of the confounding factors. Further, it is possible that the GEPU index contains uncertainty

unrelated to the economic policy. Baker et al. (2013) employ various approaches, such as

human audit and comparison with alternative measures of economic uncertainty, to ensure

accuracy and reliability of the index. A potential measurement error in the GEPU index

would result in attenuation bias, which is against finding a statistically significant effect of

economic policy uncertainty and underestimates the magnitude of the effect. We use the

instrumental variable approach to ease the concern about measurement error.

2.4 Cost of Capital

The cost of capital, defined as the weighted average cost of equity and cost of debt, is an

important component in firms’ capital budgeting decisions. We measure the cost of equity

by using the implied cost of equity approach, which estimates the ex ante expected return

11We find similar results by using the annual average economic policy uncertainty index.

11

by using market prices and accounting data (Gebhardt et al. (2001), Pastor et al. (2008),

Lee et al. (2009)). Specifically, the implied cost of equity is the discount rate that equates

the current stock price to the present value of expected future cash flows. According to the

discounted cash flow model, the stock price of a firm at time t is

Pt =∞∑n=1

Et(FCFEt+n)

(1 + re)n, (1)

where Pt is the market value of the stock at time t, Et(FCFEt+n) is the expected free cash

flow to equity at time t+ n, and re is the implied cost of equity capital.

To estimate the cost of equity, we follow the Gebhardt et al. (2001) (GLS) approach to

derive the implied cost of equity. The GLS approach uses the residual income valuation

model under the assumption of clean-surplus accounting.12 This approach has been applied

in several asset-pricing studies, for example, Pastor et al. (2008), Lee et al. (2009), and Chava

and Purnanandam (2010). The stock price in equation (1) can be written as the sum of the

book value and the discounted residual income (economic profits) earned by a firm:

Pt = Bt +∞∑n=1

Et(NIt+n − reBt+n−1)

(1 + re)n= Bt +

∞∑n=1

Et[(ROEt+n − re)Bt+n−1]

(1 + re)n, (2)

where Bt is the book value of equity at time t, NIt+n is the net income for period t + n,

NIt+n − reBt+n−1 is the residual income (economic profit) for period t + n, ROEt+n is the

after-tax return on equity for period t+ n, and re is the cost of equity capital.

We use the Institutional Brokers’ Estimate System (I/B/E/S) consensus analyst forecasts

to predict future earnings per share (EPS). The residual income model is estimated by fore-

casting earnings explicitly for the first three years and then implicitly thereafter, assuming

12Under the clean-surplus assumption, earnings include all gains and losses that affect book value. Thus,the change in the equity book value at t+ 1 equals net income during t+ 1 minus net dividends paid duringt+ 1 (bt+1 = bt +NIt −Dt).

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that the ROE of each firm would revert to the industry median ROE by the terminal period.

The split-adjusted stock price is obtained from CRSP. We solve for the discount rate re in

equation (2).

As a robustness check, we estimate the implied cost of equity capital by using alternative

models: Gode and Mohanram (2003) (GM) and Easton (2004) (PEG). These models rely

on analyst forecasts for future EPS, which are not available for all firms. To circumvent

this disadvantage, Hou et al. (2012) (HVZ) estimate future earnings from cross-sectional

regressions by using total assets, dividends, earnings, and accruals as the independent vari-

ables. Although the HVZ model can provide earnings forecasts for more firms, Gerakos and

Gramacy (2013) and Li and Mohanram (2014) show that the HVZ model underperforms

a naive random walk (RW) model that sets future earnings to past earnings. Li and Mo-

hanram (2014) propose two other cross-sectional models: the earning persistence (EP) and

residual income (RI) models, showing that the RI model outperforms the HVZ, RW, and EP

models in forecasting future EPS. Therefore, we use the Li and Mohanram (2014) RI model

approach to forecast future EPS and estimate the implied cost of equity from the Claus and

Thomas (2001) model. See Appendix for details on estimating the implied cost of equity.

We estimate the WACC as follows:

WACCi,t =Debti,tMVAi,t

CoDi,t(1− TaxRate) + (1− Debti,tMVAi,t

)CoEi,t, (3)

where WACCi,t is the WACC for firm i in year t. DebtitMVAit

is the market leverage ratio. CoDi,t

is the cost of debt for firm i in year t, measured as the actual yield on the debt carried by

the firm, as in Frank and Shen (2015). The actual yield is defined as the ratio of interest

expenses to the sum of long-term debt and debt in current liabilities. Alternatively, the cost

13

of debt is measured using BofA Merrill Lynch US Corporate Effective Yields for different

rating classes and firms’ credit ratings.13 Further, we use the aggregate bond yield from the

Barclays Capital Aggregate Bond Index as a proxy for the cost of debt, following Hann et al.

(2013). The results using this alternative measure are reported in Table 10. TaxRate is the

effective tax rate defined as a ratio of total income tax to taxable income. CoEi,t is the cost

of equity for firm i in year t.

3 Economic Policy Uncertainty and Innovation

Table 1 reports the summary statistics of characteristics and innovation activities by

the sample R&D firms. The average R&D spending as a percentage of total assets at the

beginning of the year is 10.37%. The mean of the natural logarithm of one plus the number

of patents is 0.8790, that is, 1.41 number of patents on an average. The mean of the natural

logarithm of one plus truncation bias adjusted citations is 1.6853.

To test the effect of GEPU on innovation activities of individual firms, we estimate the

following panel data model:

Yi,t+n = α + βUncertaintyt + γXi,t + ηi + εi,t, (4)

where Yi,t+n is the measure of innovation activities including the R&D ratio, natural log-

arithm of one plus the number of patents, and natural logarithm of one plus truncation

bias-adjusted citations for firm i in year t+ n. Since it takes time to generate patents from

R&D investments, we estimate the baseline model from t + 3 to t + 4. Uncertainty is the

GEPU index, which is the uncertainty index by Baker et al. (2013) scaled by 100. The

13Since we are interested in variation in the cost of debt over time within each firm and firms do not issuebonds every year, the actual bond issue data are not suitable for our analysis.

14

coefficient β captures the effect of GEPU on innovation. The impact of economic policy

uncertainty may potentially be confounded by the difference in the first-moment effects of

investment opportunities. Particularly, firms with higher growth opportunities may invest

more in R&D and innovate. Therefore, we use Tobin’s Q to capture variations in investment

opportunities and also control for the distinctness in firm attributes. The control variable

set, X, includes ln(Sales), PPE, CF , Capex, ROA, and Tobin′s Q. Further, equation (4)

includes GDP Growth to control for macroeconomic conditions and a firm’s fixed effect

ηi, which controls for unobserved differences across firms. The firm’s fixed-effect estimation

exploits the time-series variation within each firm.

As shown in Table 2, the coefficients of the uncertainty index in all specifications are

negative. Firms spend less on R&D and generate fewer patents as economic policies become

more uncertain. Further, the number of citations, a proxy for patent quality, drops as

uncertainty intensifies. The coefficient β in the patent specification is −0.0602 in year t+ 3

and −0.1348 in year t+ 4. The coefficient β in the citation specification goes from −0.0207

in year t+3 to −0.1329 in year t+4. Performing the z-test from Paternoster et al. (1998), we

find that the differences in coefficients are statistically significant. The results indicate that

the impact of economic policy uncertainty on the quantity and quality of patents strengthens

with time. In terms of the magnitude of the impact, a doubling of the level of the uncertainty

index is associated with an approximately 1.55% decline in R&D as a percentage of the

beginning-of-the-year total assets in year t+ 1. Given an average R&D ratio of 10.37%, the

impact is also economically significant.

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4 Cost-of-Capital Channel

4.1 Cost of Capital and Economic Policy Uncertainty

To understand the cost-of-capital channel, whereby economic policy uncertainty affects

innovation, we first investigate how the cost of raising external capital varies with economic

policy uncertainty. To this end, we estimate the cost of equity, cost of debt, and WACC for

each firm in each year.

In specifications (1)–(3) of Table 3, we separately regress the cost of equity, cost of debt,

and WACC on the economic policy uncertainty index while controlling for firm-fixed effects.

The reported results are based on the implied cost of equity estimated using the GLS model

and the cost of debt as actual yield. The estimates of the implied cost of equity from the

alternative models and bond yields provide similar results (Section 7.7). The coefficients of

the uncertainty index are positive and significant.

A firm’s cost of capital may be affected by other factors, such as its default risk. In

specifications (4)–(6), we estimate the sensitivity of the cost of capital with respect to eco-

nomic policy uncertainty while controlling for several factors that directly influence the cost

of capital:

Ci,t = α + βUncertaintyt + γXi,t−1 + ηi + εi,t, (5)

where C denotes the cost of equity, cost of debt, and WACC. Uncertainty is the GEPU

index. Xi,t−1 is a set of variables that affects the cost of capital, including ln(Sales), PPE,

CF , Capex, ROA, Tobin′s Q, and Z-Score. The Altman Z-score (Z-Score) is defined as

1.2(WC/TA) + 1.4(RE/TA) + 3.3(EBIT/TA) + 0.6(ME/TL) + 0.999(Sales/TA), where

16

WC is working capital, TA represents total assets, RE denotes retained earnings, EBIT

denotes earnings before interest and taxes, ME is market value of equity, TL is total book

value of liabilities, and Sales is sales. The Altman Z-score measures the financial distress

status of a firm. A higher Altman Z-score indicates a lower probability of default. The

impact of GDP Growth is also controlled. ηi controls for firm-fixed effects.

The coefficients of the GEPU index remain positive and significant while controlling for

other determinants of the cost of capital. The cost of equity, cost of debt, and WACC

increase with rising uncertainty. Given that the uncertainty index has a minimum value of

58 and a maximum of 188 during 1986–2007, the weighted average cost of capital increased

129 basis points. As for the other factors, the negative coefficients for the Z-Score indicate

higher costs of equity and debt for firms with a higher probability of default. Tobin’s Q is

negatively associated with the cost of equity, indicating that firms with high market valuation

face a lower cost of equity.

4.2 Financing Decisions and Economic Policy Uncertainty

As a further investigation, we test whether economic policy uncertainty affects firms’

financing decisions. Since external capital becomes costlier as uncertainty increases, the

cost-of-capital channel predicts that firms are less likely to issue equity and debt when

uncertainty is high. We test this conjecture by examining whether the probability of equity

and/or debt issuance is negatively associated with economic policy uncertainty.

We estimate a probit model with an equity (debt) issuance dummy as the dependent

variable. A firm is considered as issuing equity (debt) if the amount of equity (debt) issuance

as a percentage of total assets at the beginning of the fiscal year is over 5%. Equity issuance

17

is defined as a change in stockholders’ equity minus change in retained earnings divided by

the beginning-of-the-year total assets. Debt issuance is defined as a change in long-term debt

scaled by the beginning-of-the-year total assets. The independent variables are the GEPU

index and factors affecting a firm’s financing decision. Columns (1) and (2) of Table 4 show

that the coefficients of the GEPU index are negative and significant, which is consistent

with the hypothesis that firms are less likely to raise external capital in the case of higher

uncertainty.

Next, we examine whether economic policy uncertainty also affects the amount of securi-

ties issues. Our empirical specification uses equity or debt issuance as the dependent variable

and tests the influence of economic policy uncertainty while controlling for the confounding

factors. Columns (3) and (4) show that the coefficients of the GEPU index are negative

and statistically significant. In other words, as uncertainty increases the cost of capital, we

observe that firms reduce their equity and debt issuances. Overall, the results are consistent

with the financing cost view that firms have a lower propensity to raise external capital when

capital becomes more expensive owing to higher uncertainty.

4.3 Cost of Capital and R&D Investments

The results thus far show that higher GEPU is associated with a higher cost of capital and

fewer innovations. In this subsection, we investigate how cost of capital affects investments

in innovation.

Table 5 Columns (1)–(3) report the estimation results of regressing R&D ratio on cost

of equity (CoE), cost of debt (CoD), and WACC (WACC), while controlling for variables

that affect firms’ R&D investments, GDP growth, and firm-fixed effects. The results show

18

that the coefficients on CoE, CoD, and WACC are all negative and significant, indicating

that firms invest less in R&D when the cost of capital increases.

A potential concern about this test is that more innovative firms may have higher cost of

capital. Although fixed-effects estimations control for time-invariant omitted variables that

affect firms’ cost of capital, it is likely that firms that invest more in innovative projects in the

past tend to face higher financing costs. Past R&D investments is a time-varying confounding

variable that cannot be subsumed in the fixed effects. To ease this concern, we control for

firms’ past R&D expenditures.14 When including the lagged R&D ratio to the regression,

fixed-effect estimation yields biased estimation because lagged R&D is correlated with the

error term (Hsiao (2003) and Baltagi (2005)). To address the endogeneity problem in the

dynamic panel data model, we estimate the model by using system Generalized Method of

Moments (GMM) developed by Arellano and Bover (1995) and Blundell and Bond (1998).

System GMM jointly estimates the dynamic R&D model in differences and in levels, using

lagged R&D variables as instruments for differenced equations and lagged first differences in

R&D as instruments for the level equations. System GMM provides unbiased estimates for

the dynamic panel data model (Blundell and Dias (2000), Bond (2002), and Baltagi (2005)).

The results from system GMM estimation are reported on Columns (4)–(6). The coeffi-

cients on cost of capital remain negative and significant. Given that an average R&D as a

percentage of total assets is 10.37% and an average total assets is $1629.815 million, a one-

percentage increase in WACC could lead to $12.22 million decrease in R&D investment. The

magnitude of the impact is economically significant. To the extent that the change in cost

14See Angrist and Pischke (2008) for differences between fixed effects and lagged dependent variables inestablishing causal inferences.

19

of capital is owing to economic policy uncertainty, the results provide evidence that increas-

ing uncertainty about government economic policy lowers firms’ investments in innovation

through driving up the cost of capital.

5 Heterogeneity in Exposure to Economic Policy Un-

certainty

To provide further evidence for the cost-of-capital transmission mechanism of the eco-

nomic policy uncertainty, we employ the strategy of exploiting the cross-sectional heterogene-

ity in firms’ exposure to GEPU. If economic policy uncertainty affects innovation through

its influence on firms’ financing costs, then the cost of capital and investments of firms with

higher exposure to such uncertainty should be more affected.

5.1 Exposure to Economic Policy Uncertainty

We estimate each firm’s exposure to economic policy uncertainty as in Brogaard and

Detzel (2015) and examine whether firms with higher uncertainty exposure face a higher

cost of capital. We conduct the test by using the monthly stock returns from CRSP’s

monthly return file adjusted for the CRSP delisting return when available. For firms with

more than one class of shares, we maintain the stock returns of class A shares. For each

firm, we estimate the following model by using stock returns from the previous 60 months:

ri,t − rf,t = α + βMKTi (rM,t − rf,t) + βHML

i HMLt + βSMBi SMBt (6)

+βUMDi UMDt + βGEPU

i log(GEPU)t + εi,t,

20

where ri,t is the returns of stock i for month t and rf,t is the risk-free rate measured by the

one-month treasury bill rate. rM,t − rf,t, HMLt, and SMBt are the excess return on the

market, difference between the average returns on small market capitalization portfolios and

large portfolios, and difference between the average returns on value (high book-to-market)

portfolios and growth (low book-to-market) portfolios in the three-factor model (Fama and

French (1993)), respectively. UMDt is the difference between the average returns on the

winners and losers of the past year in the four-factor model (Carhart (1997)). log(GEPU)t

is the natural logarithm of the GEPU index. The coefficient, βGEPUi , measures the exposure

of firm i to uncertainty about economic policy.

According to a firm’s exposure to GEPU, we classify each firm into one of the three

portfolios in each year. A more negative βGEPUi indicates greater exposure to economic

policy uncertainty because it is the least optimal hedge against increases in uncertainty.

Firms in the portfolio with higher exposure to economic policy uncertainty have a more

negative βGEPUi , while those in the portfolio with lower exposure to uncertainty have a

more positive βGEPUi . The portfolio in the middle is omitted. We classify the high (low)

uncertainty state as the time when the GEPU index is above (below) the median.

5.2 Cost of Capital and Heterogeneous Exposure

In Panel A of Table 6, we compare the costs of capital for firms with low versus high

uncertainty exposure in the low versus high uncertainty states. The comparison results show

that the average cost of equity for firms with low uncertainty exposure is 8.13%, while that

for firms with high uncertainty exposure is 8.23% when uncertainty is low. The difference

is 10 basis points but not statistically significant. When uncertainty is high, the difference

21

in the cost of equity between firms with high and low exposure increases to 51 basis points

and becomes statistically significant. We observe that the differences in the cost of debt and

WACC between firms with high and low exposure are also larger in the high uncertainty

state.

We then examine whether uncertainty about economic policy has a stronger impact on

the cost of capital of firms with higher exposure while controlling for the confounding factors.

We regress our proxies for the cost of capital on the GEPU index, an uncertainty exposure

dummy (Exposure), and their interaction. Exposure equals one for firms in a portfolio with

high exposure to GEPU and zero for firms in the portfolio with low exposure. In Columns

(1)–(3) of Panel B in Table 6, we control only for firm-fixed effects. In Columns (4)–(6), we

also explicitly control for other factors affecting firms’ cost of capital. The estimation results

show that the coefficients of the interaction between the uncertainty index and uncertainty

exposure dummy are positive and significant in Columns (1), (3), (4), and (6). The results

indicate that economic policy uncertainty leads to a larger increase in the cost of equity and

WACC for firms with higher uncertainty exposure than for firms with lower exposure. The

coefficients of the interaction term are positive but not statistically significant in the cost

of debt estimations. Overall, these results provide evidence in support of the cost-of-capital

transmission channel.

5.3 Investments and Heterogeneous Exposure

The above results confirm the heterogeneous impacts of economic policy uncertainty on

firms’ cost of capital. In this subsection, we first exploit differences in sensitivity of fixed-

asset investments and R&D to economic policy uncertainty and then examine variations

22

across firms. Recent studies find that economic factors have differential impacts on R&D and

fixed-asset investments owing to the intangibility nature and uncertainty of R&D investment

outcome (Brown et al. (2013), Becker-Blease and Paul (2006), Fang et al. (2014), He and

Tian (2014), Grullon et al. (2015), and Dang and Xu (2015)). We, therefore, investigate

whether economic policy uncertainty affects capital expenditures differently from R&D.

In Column (1) of Table 7 Panel A, we report the result of regressing firms’ capital

expenditures as a ratio of total assets at the beginning of the fiscal year on the GEPU index

while controlling for other confounding factors and firm-fixed effects. The coefficient for

the GEPU index is negative and significant, indicating that uncertainty about government

economic policy also has a negative impact on fixed-asset investments.

To compare the sensitivities of R&D and capital expenditures to GEPU, we compute

the differences between R&D expenses and capital expenditures for each firm in each year

and scale the difference by total assets at the beginning of the fiscal year. We regress the

difference between R&D and capital expenditures on the GEPU index, while controlling for

observable and unobservable factors affecting investments (Column (2)). The coefficient of

the GEPU index is negative and statistically significant, indicating that as uncertainty inten-

sifies, firms reduce R&D investments more than they reduce capital expenditures. The result

indicates that investments in innovation are more sensitive to economic policy uncertainty

than investments in fixed assets.

We further explore cross-sectional heterogeneity in the impacts of uncertainty related to

economic policy on investments among firms with different exposure to economic policy un-

certainty. If the cost-of-capital channel is relevant, the effect of economic policy uncertainty

should be stronger for firms with higher exposure to the uncertainty.

23

To test this conjecture, we regress the measure of investment on GEPU index, Exposure

dummy, interaction with GEPU index and Exposure dummy, and the control variables. Ta-

ble 7 Panel B shows that the coefficients on the interaction term are negative and significant

for the R&D and CAPEX specifications. The results provide evidence that firms with more

exposure to economic policy uncertainty reduce their investments in R&D and fixed assets

more as uncertainty increases. The coefficient on the interaction is negative but insignificant

in the specification of R&D − CAPEX, indicating that R&D investments of firms with

more exposure to economic policy uncertainty are not significantly more sensitive to the

uncertainty than capital expenditures are. Overall, our results highlight the importance of

cost of capital in transmitting uncertainty about economic policy to the real economy.

6 Investment Irreversibility Channel

The results thus far support the prediction that uncertainty about economic policy increases

firms’ cost of capital, which reduces innovation activities. Another possible explanation is

that managers, who face uncertainty, have an incentive to postpone investments until more

information is revealed if investments are costly and (partial) irreversible. It is interesting

to see whether cost of capital affects the economic policy uncertainty and R&D investment

relationship after controlling for the investment irreversibility channel.

To investigate the marginal effect of the cost-of-capital channel, we control for the degree

of irreversibility in investment using several industry-level proxies as in Gulen and Ion (2016).

The first proxy is the asset redeployability measure proposed by Hyunseob and Kung (2014).

We use the 1997 Bureau of Economic Analysis capital flow table to construct the proxy. The

table contains expenditures on 180 asset categories by firms in 123 industries. The first

24

irreversibility measure is constructed in three steps. First, a redeployability score for each

asset category is computed as the number of industries using the asset scaled by 123. An asset

is considered as being used by an industry if the industry’s expenditure on the asset is more

the 0.1% of the total expenditure on the asset by all industries. Second, an industry-level

redeployability index is constructed as a weighted average of the redeployability scores of the

invested asset categories. The weight for each asset category is an industry’s expenditure

on the asset divided by the industry’s total expenditures. Firms in industries with a higher

redeployability index have more redeployable assets and are more likely to recover a higher

fraction of investment. Third, the irreversibility measure for each industry is calculated as

the inverse of redeployability index.

The second irreversibility measure is constructed based on cyclicality of firms’ sales fol-

lowing Sharpe (1994) and Almeida and Campello (2007). We first calculate the correlation

between each firm’s sales and gross national product over the sample period. The firm-level

correlations are then averaged at the three-digit SIC level. The industry-level irreversibility

measure is a dummy variable that is one for industries with correlations above the median,

and zero otherwise. It is harder for firms in cyclical industries to recover their investments

because potential buyers of their assets are likely to be also affected by negative shocks

during economic downturns.

The third proxy of irreversibility is an industry-level measure of sunk costs as in Farinas

and Ruano (2005). Investments in industries with more sunk costs is harder to reverse. We

first measure firm-level sunk costs using three proxies: rent expense, depreciation expense,

and past sale of PPE, all normalized by the beginning-of-fiscal-year PPE. The industry-level

means of these three proxies in each year are then obtained at the three-digit SIC level. We

25

then create three indicator variables that equal one if the firm-level sunk cost is lower than

the industry mean, and zero otherwise. A sunk-cost index is constructed as a combination

of the three indicators. The sunk-cost index takes a value of 2 if the three indicators are

all equal to one; 0 if the three indicators all equals zero; and 1 for the remaining. A higher

value of sunk-cost index indicates a higher degree of investment irreversibility.

We include the investment irreversibility measure and the interaction between economic

policy uncertainty index and cost of capital as the variable of interest to the baseline model.

We replace the GEPU index with time-fixed effects to control for the potential confounding

effect from macroeconomic factors, since the main interest in the test is no longer the average

effect of economic policy uncertainty. Table 8 Panel A reports the estimation results using the

three irreversibility proxies. The coefficients on the interaction between GEPU and cost of

capital are all negative and significant. The results indicate that cost of capital strengthens

the negative impact of high uncertainty related to economic policy on R&D investments,

even after controlling for the investment irreversibility channel.

In Table 8 Panel B, we examine the importance of the cost of capital and investment irre-

versibility by including the weighted average cost of capital and proxies for investment irre-

versibility to the baseline regression. The coefficients on WACC and Irreversibility are both

negative and significant. To evaluate the relative importance of WACC and Irreversibility,

we report the standardized beta coefficients in parentheses. The standardized coefficients are

not reported for the estimation using cyclicality of firms’ sales and sunk costs as proxies for

investment irreversibility, since these two measures are categorical variables. The standard-

ized coefficient on WACC is −0.0892, which is larger in magnitude than the standardized

coefficient on Irreversibility (−0.0420). The result indicates that the cost of capital is rela-

26

tively more important in affecting firms’ R&D investments than the investment irreversibility.

The investment irreversibility channel proposed in the literature cannot fully explain away

the economic policy uncertainty effect.

7 Robustness

7.1 Close Congressional Elections

A potential concern about the economic policy uncertainty index is endogeneity. Bloom

(2014) documents that uncertainty fluctuates counter-cyclically with business cycles. High

policy uncertainty may coincide with diminishing growth opportunities during bad economic

conditions, which could induce fewer innovations. Although we include variables such as

Tobin’s Q and firm-fixed effects to control for firm-level investment opportunities, the effect

of economic policy uncertainty on innovation may be confounded by other factors such

as overall economic conditions or aggregate-level growth opportunities. We adopt several

strategies to further ease this concern.

As the first strategy to mitigate the endogeneity concern, we use close congres-

sional elections to capture policy-related uncertainty. Democrats and Republicans differ

in their ideological positions and economic policies, ranging from R&D spending to regula-

tion. Democrats tend to focus on specific goals, such as producing safe and clean energy

or exploring space, and are also willing to subsidize early-stage innovation in private sec-

tor. Republicans, on the other hand, prefer to focus on the general conditions, such as

low corporate taxes, and create incentives to encourage innovation, such as R&D tax credit

(Kahin and Hill (2010)). Polarization in the Congress reduces congressional productivity

27

in legislation and incentives for bipartisan cooperation, which can have a significant impact

on the economic environment. For example, the fight in the Congress over whether or not

to raise the debt ceiling in August 2011 led to a U.S. government credit rating downgrade

by Standard&Poor’s. Congressional elections contribute to possible changes in government

policies that may directly or indirectly affect firms’ business activities.

Since the timing of elections is independent from macroeconomic conditions, congressional

elections provide a natural experiment framework to disentangle the influences of political

uncertainty from other economic factors on investments. Close elections with unpredictable

outcomes create plausible exogenous shocks to policy uncertainty, which helps establish a

causal effect of policy uncertainty on innovation.

We collect congressional election outcome data from the U.S. Federal Election Commis-

sion. The sample is restricted to general elections for the House of Representatives and the

Senate. We focus on close elections when the difference in the percentage of votes between

the Democrats and Republican is 5% or less. We run the baseline regression with the GEPU

index replaced with a close congressional election dummy (Congressional Electiont) that

equals one if there is a close election in year t, and zero otherwise. We examine the impact

on firms’ R&D investments leading up to the time when uncertainty regarding congressional

election outcomes resolves in November. We also control for election cycles, as congressional

elections occur in even-number years. Panel A in Table 9 reports the estimation results.

The coefficients of Congressional Electiont are negative and statistically significant. To the

extent that close congressional elections capture uncertainty about government policy, firms’

innovation activities are adversely affected by policy uncertainty.

28

7.2 Close Presidential Elections

Following Julio and Yook (2012), we use close presidential elections as another plausible

exogenous shock to government policies. Elections help isolate the impact of uncertainty

associated with national leadership and policies from other confounding factors. The close

precedential elections reflect potential changes in government policies beyond economic poli-

cies.

During the sample period, there were two close presidential elections: the 2000 and 2004

elections. We run the baseline regression with the GEPU index replaced with a close election

dummy (Presidential Electiont) that equals one if there is a close presidential election in

year t, and zero otherwise. We also control for election cycles. Panel B in Table 9 reports the

estimation results. The coefficients of Presidential Electiont are negative and statistically

significant, which is consistent with the finding that high uncertainty in government policy

hinders innovation.

7.3 Instrumental Variable

The third strategy to alleviate the endogeneity problem is the instrumental variable

approach. We use the political polarization in the House of Representatives as an instrument

for policy uncertainty. When the political parties in the Congress are more polarized, it

becomes increasingly difficult for policy makers to reach a consensus, which creates more

uncertainty. Baker et al. (2014) point out that increased political polarization is a reason for

the rise in policy-related economic uncertainty in the United States since 1960. Therefore,

political polarization is likely to satisfy the relevance condition as an instrument. However,

29

it is not obvious that political polarization affects corporate innovation through a channel

other than policy uncertainty, which satisfies the exclusion restriction.

To construct the political polarization measure, we obtain the U.S. House roll-call vote

data from the Political Institutions and Public Choice House Roll-Call Database. We focus

on two types of legislation, bills and joint resolutions, that may affect laws or constitutions.

We measure political polarization as an average disagreement about bills or joint resolutions

considered in the House.15 Specifically, polarization is defined as 1n

∑Nn=1 1 − |Y ean,t% −

Nayn,t%|, where Y ean,t% (Nayn,t%) is the percentage of Y ea (Nay) votes among all votes

for bill n in year t. We look at the appropriation bills and bills regarding economy, taxes, and

budget, defense, foreign policies, and energy and environment issues. N is the total number

of bills or joint resolutions in year t. A higher value of this variable indicates a higher degree

of polarization.

We estimate the baseline panel data model by using the political polarization measure

as an instrument for government policy uncertainty. The second-stage results from the

two-stage least square estimation are reported in Panel C of Table 9. The coefficients of

the economic policy uncertainty measure remain negative and statistically significant. The

coefficients are larger in magnitude than the one in Table 2, since the instrumental variable

approach also addresses the attenuation bias. The estimate would bias towards zero if there

is measurement error in the GEPU index. Our results are robust to the instrument variable

15The dynamic weighted nominal three-step estimation (DW-NOMINATE) scores in Poole and Rosenthal(2007) are also widely used to measure political polarization in the political science literature. The DW-NOMINATE scores locate a legislator’s ideological position in latent ideological space within each Congress.The scores are computed by analyzing the legislators’ roll-call voting behavior through the spatial model. Thedistance between the Democratic and Republican legislators for each chamber denotes political polarization.The DW-NOMINATE scores remain constant within each Congress. We find similar results using thispolitical polarization measure as an instrument.

30

estimation. The first-stage F statistic exceeds 10, indicating that the instrument satisfies

the relevance condition.

7.4 Investment Opportunities and Economic Uncertainty

The fourth approach is to use a set of variables to directly control for macroeconomic

conditions and growth. The first variable is the GDP growth rate, a proxy for economic

growth, from the Federal Reserve Bank of St. Louis. The second variable is the growth

rate in the annual industrial production, which is an indicator of industry growth. Third,

we include the business cycle indicator to disentangle the government economic policy cycle

from the business cycle.

It is also possible that the policy uncertainty index reflects economic uncertainty. We,

therefore, explicitly control for general uncertainty that may affect firms’ innovation activ-

ities. We use the forecast dispersion for the nominal GDP growth rates for the following

year from the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters as

a proxy for uncertainty in economic growth. Forecast dispersion is larger when economic

uncertainty rises.

Stock market uncertainty also potentially affects firms’ financing costs, and thereby,

innovation activities. To isolate the influence of economic policy uncertainty from stock

market uncertainty, we use the standard deviation of value-weighted composite index returns

over the past 12 months as a proxy for uncertainty in the stock market. Monthly returns of

value-weighted index are obtained from the CRSP.

To further minimize the endogeneity concern, we extract the exogenous component from

the GEPU index. Specifically, we purge the component that reflects macroeconomic condi-

31

tions from the economic policy uncertainty index and use the residuals to measure additional

information beyond economic uncertainty. We first regress monthly the GEPU index on six

macroeconomic variables:

Uncertaintyt = α + β1IndProdt + β2DurGrowtht + β3NonDurGrowtht (7)

+ β4ServGrowtht + β5EmpGrowtht + β6Recessiont + εt,

where IndProd is growth in industrial production, DurGrowth is real growth in durable

goods, NonDurGrowth is real growth in non-durable goods, ServGrowth is real growth in

services consumption, EmpGrowth is growth in employment, and Recession is an NBER

recession indicator.16 The residuals from the equation (7) are then used as our exogenous

policy uncertainty index (Exog. Uncertainty). We match this orthogonalized monthly eco-

nomic policy uncertainty index to our sample by fiscal year and month.

We perform robustness tests by including the proxies for investment opportunities and

macroeconomic uncertainty separately and then all together to the baseline equation. The

estimation results using the exogenous component of the uncertainty index as a proxy for

economic policy uncertainty while controlling for macroeconomic conditions and aggregate

uncertainty are reported in Panel D of Table 9. Similar results are obtained in the individ-

ual estimations. The coefficients of Exog. Uncertainty stay negative and significant after

controlling for the confounding effect from investment opportunities or general uncertainty

in economic conditions. The results indicate that the GEPU index captures additional un-

predictability beyond economic uncertainty that adversely affects innovation.

16Baker and Wurgler (2007) use the same set of macroeconomic indicators to orthogonalize their investorsentiment index.

32

7.5 High- versus Low-Growth Industries

We now explore the cross-industries variation in the influences of economic policy uncer-

tainty on innovation. If investment opportunity is the main driver underlying the negative

relationship between the GEPU index and innovation, we expect a difference in the sensi-

tivities of innovation with respect to the uncertainty index between high- and low-growth

industries. To test this conjecture, we augment our model with an interaction between a

HGrowth dummy and the exogenous component of GEPU index. HGrowth equals one if

the firm is in a high-growth industry, and zero otherwise. To construct industry growth, we

first compute the time-series industry-level growth as the median sales growth of firms in

the same three-digit SIC code industries each year. Industry growth is then measured as the

median of the time-series industry-level growth. An industry is considered as a high-growth

industry if its growth is higher than the median growth of all industries.

Panel E in Table 9 shows that the coefficients of Exog. Uncertainty × HGrowth are

all insignificant, indicating no difference between responsiveness of innovation to the GEPU

index between firms in high- and low-growth industries. Thus, investment opportunity is

less likely to be the main factor driving the relationship between innovation and GEPU.

7.6 Lagged R&D Investment

Previous studies show that R&D investments tend to be persistent because of adjustment

costs (Bloom et al. (2007)). We therefore include lagged R&D to the baseline model to

control for the persistency in R&D investment. We estimate the dynamic R&D model using

system Generalized Method of Moments. The estimation result in Table 9 Panel G Column

33

(1) shows that economic policy uncertainty remains to have a negative impact on investment

in innovation, after controlling for persistency in R&D investment.

To address the potential concern that R&D investments may also affect firms’ cost of

capital, we include lagged R&D ratio to equation (5). The estimation results show that

the coefficients on Exog. Uncertainty remain positive and significant. For brevity, we only

report the coefficient in the specification of weighted average cost of capital in Column (2).

More uncertainty about economic policy increases firms’ cost of capital, even after controlling

for the impact of R&D investments.

7.7 Alternative Measures of the Cost of Capital

In this section, we use alternative models to estimate the implied cost of equity and cost

of debt. We estimate equation (5) by using the alternative measures. Columns (1) and (2) in

Table 10 report the estimation results by using the estimates from the models of Gode and

Mohanram (2003) and Easton (2004) (PEG). In Column (3), the future EPS are forecasted

using the Li and Mohanram (2014) approach, and the implied cost of equity is computed

from the Claus and Thomas (2001) model. The coefficients of the GEPU index remain

positive and significant, indicating that the cost of equity goes up as uncertainty surges.

In Column (4), we use corporate bond yields for different rating classes as an alternative

measure for the cost of debt. The yields are BofA Merrill Lynch US Corporate Effective

Yields for AAA, AA, A, BBB, BB, B, CCC, or below ratings. The data are obtained from

the Federal Reserve Bank of St. Louis website. We obtain S&P crediting ratings data from

Compustat and match them with financial data by GVKEY and Datadate. We assume that

firms maintain the same credit rating until a change in rating occurs. For firms without

34

credit ratings, we compute the synthetic rating using the Damondaran (2012) approach.

The interest coverage ratio, defined as earnings before interest and taxes divided by interest

expenses, is used to obtain the synthetic rating.17 We use the yield for each rating category

as the cost of debt for firms in that credit rating class. Since the BofA Merrill Lynch US

Corporate Effective Yields data are only available since 1997, the estimation period is from

1997 to 2007 for this specification. In Column (5), we estimate the cost of debt by using

Barclays Capital Aggregate Bond Index following Hann et al. (2013). The estimation results

show that GEPU still has a positive and significant influence on firms’ borrowing costs using

the alternative measures of the cost of debt.

We then compute the WACC by using the cost of debt estimated with the crediting

rating approach and implied cost of equity estimated with the Li and Mohanram (2014)

approach and the Claus and Thomas (2001) model. Column (6) shows that the coefficient

of the uncertainty index is positive and significant. Our results are robust to alternative

measures of the cost of capital.

8 Conclusions

This study examines how GEPU affects individual firms’ innovation and identifies an

alternate transmission channel—cost of capital. We show that uncertainty about economic

policy has a negative impact on corporate innovation activities. The effect on R&D is

immediate, while the effect on patent portfolios is delayed and strengthens with time. In

17When their market capitalization is over $5 billion, firms with interest coverage ratio greater than 8.5;between 6.5 and 8.5, 3 and 6.5, 2.5 and 3, 2 and 2.5, 1.25 and 2; or below 1.25 is classified as AAA, AA, A,BBB, BB, B, CCC, or below, respectively. When their market capitalization is below $5 billion, firms withinterest coverage ratio greater than 12.5; between 9.5 and 12.5, 4.5 and 9.5, 4 and 4.5, 3 and 4, 1.5 and 3; orbelow 1.5 is classified as AAA, AA, A, BBB, BB, B, CCC, or below, respectively.

35

addition, the adverse impact is stronger on investments in innovation than on investments

in fixed assets. The result is robust to various identification strategies that address the

endogeneity concern.

We provide novel evidence on the cost of capital as a driving force underlying the re-

lationship between economic policy uncertainty and innovation. By estimating each firm’s

cost of equity by using the implied cost of equity approach and cost of debt using actual

yields, we find that the cost of capital rises as uncertainty escalates. Consequently, firms are

less likely to raise external capital when economic policy uncertainty is high. Firms with

more exposure to uncertainty face a larger increase in funding costs and invest less than

those with less exposure during the period of high uncertainty. Since the cost of capital un-

derpins firms’ investment decisions, our results lend support to the view that GEPU affects

innovation through its impact on firms’ cost of capital.

Our results indicate that economic policy uncertainty affects the real economy not only

through the classic investment irreversibility channel but also through the cost-of-capital

channel. An implication of our findings is that efforts by policy makers to reduce policy

uncertainty are desirable to create a good investment environment for corporations.

36

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42

Figure 1: GEPU Index: 1986–2011

This figure presents the monthly GEPU index and NBER recession periods from January 1986to December 2011. The GEPU index data are from Baker et al. (2013) and recession indicatordata are from the Federal Reserve Bank of St. Louis.

Debt Ceiling Dispute

9/11

Clinton ElectionBush Election

Bush Reelection

Black Monday

Government ShutdownGulf War I

Gulf War IIBalance Budget Act

Russian Crisis

Obama Election

5010

015

020

025

0

1985m1 1988m10 1992m7 1996m4 2000m1 2003m10 2007m7 2011m4

Recession Economic Policy Uncertainty Index

43

Table 1:Firm Characteristics and Innovation

This table reports the summary statistics of firm characteristic variables and innovation activities.ln(Sales) is defined as the natural logarithm of sales. PPE is the investment in property, plant, andequipment scaled by total assets. CF is operating cash flow defined as income before extraordinary itemsplus depreciation and amortization scaled by the beginning-of-the-year total assets. Capex is the capitalexpenditures scaled by the beginning-of-the-year total assets. Tobin’s Q is defined as the ratio of themarket value to book value of assets. R&D is the ratio of research and development expenditures to thebeginning-of-the-year total assets. ln(Patent) is the natural logarithm of one plus the number of patentsapplied by a firm in a given year. ln(Citations) is the natural logarithm of one plus citations per patentadjusted for truncation bias. PPE, CF , Capex, and R&D are reported in percentage. The mean andstandard deviation of the variables are reported.

ln(Sales) PPE CF Tobin′s QMean 4.3216 21.1658 -1.2350 2.5609Std. Dev. 2.4378 16.6077 28.8527 3.3803

Capex R&D ln(Patent) ln(Citations)Mean 6.1350 10.3720 0.8790 1.6853Std. Dev. 7.0792 13.6281 1.3245 2.3060

44

Table 2:Impact of GEPU on Innovation

The table reports the effect of GEPU on innovation. The following fixed-effects model is estimated:Yi,t+n = α + βUncertaintyt + γXi,t + ηi + εi,t, where Y denotes the measures of innovation activities:R&D ratio, natural logarithm of one plus the number of patents (ln(Patent)), and natural logarithmof one plus truncation bias adjusted citations (ln(Citations)). Uncertainty is the Baker et al. (2013)economic policy uncertainty index scaled by 100 and X is a set of characteristic variables that affecta firm’s innovation activities, including ln(Sales) (log of total sales), PPE (investment in property,plant, and equipment scaled by total assets), CF (income before extraordinary items plus depreciationand amortization scaled by total assets), ROA (net income divided by total assets), Tobin′s Q (marketvalue to book value of assets), GDP Growth, and ηi controls for firm-fixed effects. Standard errors areclustered at the firm level and corrected for heteroscedasticity. ***, **, and * indicate the 1%, 5%, and10% significance levels, respectively.

R&Dt+1 ln(Patent)t+3 ln(Patent)t+4 ln(Citations)t+3 ln(Citations)t+4

Uncertainty -1.5488*** -0.0602*** -0.1348*** -0.0207 -0.1329***[0.2025] [0.0231] [0.0232] [0.0466] [0.0474]

ln(Sales) -1.3312*** 0.0831*** 0.0635*** -0.2010*** -0.2519***[0.1094] [0.0116] [0.0119] [0.0191] [0.0208]

PPE 0.0587*** 0.0011 0.0011 0.0161*** 0.0149***[0.0087] [0.0008] [0.0008] [0.0017] [0.0018]

CF 0.0386*** -0.0011*** -0.0004 0.0020*** 0.0036***[0.0059] [0.0003] [0.0004] [0.0008] [0.0008]

ROA -0.0812*** 0.0009*** 0.0006** 0.0033*** 0.0032***[0.0053] [0.0003] [0.0003] [0.0006] [0.0007]

Tobin’s Q 1.1832*** 0.0254*** 0.0247*** 0.0349*** 0.0260***[0.0574] [0.0035] [0.0037] [0.0077] [0.0081]

GDP Growth -0.0537* -0.0092*** 0.0031 0.0467*** 0.0866***[0.0301] [0.0032] [0.0034] [0.0065] [0.0071]

N 40,618 31,639 27,899 31,639 27,899Adj. R2 0.7673 0.8538 0.8625 0.7359 0.7474

45

Table 3:Cost of Capital and GEPU

This table presents the estimation results of the impact of GEPU on firm-level cost of capital. We regressthe cost of equity (specification (1)), cost of debt (specification (2)), and WACC (specification (3)) onthe GEPU index. We also control for firm-fixed effects. The cost of equity (CoE) is estimated from theresidual income valuation model using the Gebhardt et al. (2001) approach. The cost of debt (CoD) is theactual yield on the debt carried by the firm. The cost of capital is the WACC with market leverage at thebeginning of the fiscal year as the weight. In specifications (4)–(6), we include factors that influence firms’cost of capital: ln(Sales), PPE, CF , ROA, Tobin′s Q, GDP Growth and Z-Score. The Altman Z-score,Z-Score, is defined as 1.2(WC/TA)+1.4(RE/TA)+3.3(EBIT/TA)+0.6(ME/TL)+0.999(Sales/TA),where WC is working capital, TA is total assets, RE is retained earnings, EBIT is earnings beforeinterest and taxes, ME is market value of equity, TL is total book value of liabilities, and Sales is sales.We also control for firm-fixed effects. Standard errors are clustered at the firm level and corrected forheteroscedasticity. ***, **, and * indicate the 1%, 5%, and 10% significance levels, respectively.

(1) (2) (3) (4) (5) (6)CoE CoD WACC CoE CoD WACC

Uncertainty 1.1261*** 1.7491*** 0.9804*** 1.3540*** 1.5818*** 0.9814***[0.1432] [0.1361] [0.1302] [0.1454] [0.1457] [0.1289]

ln(Sales) -0.0579 -0.4031*** -0.1683[0.1010] [0.0629] [0.1046]

PPE 0.0128* 0.0171*** 0.0081[0.0068] [0.0052] [0.0062]

CF 0.0103 -0.0031 0.0150**[0.0063] [0.0033] [0.0071]

ROA 0.0185*** -0.0055* 0.0133*[0.0066] [0.0030] [0.0073]

Tobin’s Q -0.2006*** 0.0408 -0.3540***[0.0548] [0.0276] [0.0596]

Z-Score -0.0974*** -0.1219*** -0.0580***[0.0172] [0.0145] [0.0188]

GDP Growth 0.5237*** 0.2939*** 0.4360***[0.0237] [0.0227] [0.0215]

N 26,277 44,285 21,829 22,931 33,003 19,064Adj. R2 0.4586 0.2612 0.4767 0.4762 0.3074 0.4980

46

Table 4:Financing Decisions and GEPU

This table presents the estimation results of the impact of GEPU on firms’ decisions of equity and debtissuances. In specifications (1) and (2), we estimate a probit model: DIssuanceit = α+ βUncertaintyt +γXit−1 + εit, where DIssuance is a dummy variable that equals one if a firm issues equity (debt) andzero otherwise. A firm is considered as issuing equity (debt) if the amount of equity (debt) issuance asa percentage of total assets at the beginning of the fiscal year is over 5%. Uncertainty is the GEPUindex developed by Baker et al. (2013). X is a set of characteristic variables that affect a firm’s financingdecision, including ln(Sales), PPE, CF , ROA, Tobin′s Q. In specification (3) and (4), we regressequity issuance and debt issuance on firm characteristics that affect securities issuance. Equity issuanceis defined as a change in stockholders’ equity minus that in retained earnings divided by total assets atthe beginning of the fiscal year. Debt issuance is defined as a change in long-term debt scaled by totalassets at the beginning of the fiscal year. GDP growth and firm-fixed effects are also controlled. Standarderrors are clustered at the firm level and corrected for heteroscedasticity. ***, **, and * indicate the 1%,5%, and 10% significance levels, respectively.

(1) (2) (3) (4)DEquityIssuance DDebtIssuance Equity Issuance Debt Issuance

Uncertainty -0.3251*** -0.1968*** -3.9205*** -2.1668***[0.0356] [0.0345] [0.7839] [0.3437]

ln(Sales) -0.0766*** 0.0197*** -5.2397*** -1.1719***[0.0051] [0.0042] [0.3528] [0.1360]

PPE -0.0037*** 0.0083*** 0.2071*** -0.0161[0.0007] [0.0005] [0.0284] [0.0127]

CF -0.0023*** -0.0011* 0.1718*** 0.0401***[0.0006] [0.0006] [0.0280] [0.0098]

ROA -0.0092*** 0.0006 -0.4075*** -0.0019[0.0006] [0.0005] [0.0263] [0.0084]

Tobin’s Q 0.2453*** 0.0124*** 8.1882*** 0.9589***[0.0073] [0.0042] [0.2771] [0.0802]

GDP Growth -0.0154*** 0.0405*** -0.1702 0.1857***[0.0058] [0.0060] [0.1252] [0.0581]

N 40,479 40,618 40,479 40,618

47

Table 5:Cost of Capital and R&D Investments

This table presents the estimation results of the impact of cost of capital on firms’ R&D investments. Weregress R&D ratio on cost of equity, cost of debt, and WACC, respectively. The cost of equity (CoE) isestimated from the residual income valuation model by using the Gebhardt et al. (2001) approach. Thecost of debt (CoD) is the actual yield on the debt carried by the firm. The cost of capital is the WACCwith market leverage at the beginning of the fiscal year as the weight. We include factors that influencefirms’ R&D investments. In Columns (1)-(3), we use firm-fixed effects to control for time-invariantomitted variable. Standard errors are clustered at the firm level and corrected for heteroscedasticity. InColumns (4)-(5), we include lagged R&D ratio to control for the effects of past R&D investments. Themodel is estimated using system GMM with robust standard error. ***, **, and * indicate the 1%, 5%,and 10% significance levels, respectively.

Fixed Effects System GMM(1) (2) (3) (4) (5) (6)

CoE -0.0226* -0.0730***[0.0119] [0.0112]

CoD -0.0299*** -0.3448***[0.0112] [0.1022]

WACC -0.0386** -0.0841***[0.0162] [0.0148]

Controls Yes Yes Yes Yes Yes YesN 23,368 33,486 19,456 23,368 19,458 19,456Adj. R2 0.7504 0.7756 0.7689

48

Table 6:Cost of Capital and Exposure to Uncertainty

This table reports the cost of capital and firms’ exposure to GEPU. We estimate each firm’s exposureto economic policy uncertainty using the Carhart (1997) four-factor model plus the natural logarithmof the GEPU as the fifth factor. The uncertainty exposure in each year is estimated using the stockreturns from the previous 60 months. In Panel A, we sort each firm into one of the three portfolios onthe basis of its exposure to GEPU in each year. A firm is classified into the high- (low-) exposure groupif its exposure to uncertainty belongs to the third (first) portfolio on the basis of the exposure index.CoE is estimated from the residual income valuation model using the Gebhardt et al. (2001) approach.CoD is the after-tax actual yield on the debt carried by the firm. WACC is the weighted average costof capital with market leverage at the beginning of the fiscal year as the weight. Diff is the differencein the average cost of capital of the low and high exposure portfolios from the t-test. The t-stat is thet-statistics of the t-test. In Panel B, we regress the cost of capital on the GEPU index, Exposure dummy,the interactive term between the policy uncertainty index and the Exposure dummy, control variables,and firm-fixed effects. Exposure is a dummy variable that equals one for firms in the portfolio with highexposure to economic policy uncertainty and zero for firms in the portfolio with low exposure to economicpolicy uncertainty. In the first three specifications, we control only for firm-fixed effect. In specifications(4)–(6), we control for ln(Sales), PPE, CF , ROA, Tobin′s Q, Z-Score, and GDP Growth in additionto firm-fixed effects. Standard errors are clustered at the firm level and corrected for heteroscedasticity.***, **, and * indicate the 1%, 5%, and 10% significance levels, respectively.

Panel A: Cost of Capital under High vs. Low UncertaintyCoE CoD WACC CoE CoD WACC

Low Uncertainty High UncertaintyLow Exposure 8.1295 6.9303 7.7164 8.3764 7.5359 7.9791High Exposure 8.2331 7.1890 7.9729 8.8844 7.8998 8.5466Diff 0.1036 0.3426 0.2564 0.5080 0.3836 0.5675t-stat 0.7513 2.6796 1.9344 2.4669 2.3695 3.0232

Panel B: Cost of Capital and Uncertainty(1) (2) (3) (4) (5) (6)

CoE CoD WACC CoE CoD WACCUncertainty 0.5653* 1.1895*** 0.5130* 0.9433*** 1.1425*** 0.6503**

[0.3391] [0.3204] [0.3001] [0.3472] [0.3182] [0.3099]Exposure -1.2562** 0.3639 -0.9643** -1.1420** 0.3982 -0.9458**

[0.5122] [0.4627] [0.4649] [0.5004] [0.4712] [0.4539]Uncertainty×Exposure 1.0414* 0.0780 0.8416* 1.0235* 0.0650 0.8521*

[0.5518] [0.4744] [0.4856] [0.5256] [0.4831] [0.4613]Controls No No No Yes Yes YesFirm-Fixed Effects Yes Yes Yes Yes Yes YesN 8,192 11,620 6,724 7,899 11,110 6,472Adj. R2 0.4417 0.3200 0.4564 0.4530 0.3242 0.4688

49

Table 7:Investments and GEPU

The table reports the effect of GEPU on capital expenditures and R&D. In Column (1), we estimate thefixed-effects model with the capital expenditure ratio (CAPEX) as the dependent variable and economicpolicy uncertainty index as well as a set of characteristic variables as the independent variables. CAPEXis defined as capital expenditures scaled by total assets at the beginning of the fiscal year. Column (2)shows the differential effect of economic policy uncertainty on R&D and capital expenditures. R&D-Capex is defined as the difference between R&D expenditure and capital expenditure scaled by totalassets at the beginning of the fiscal year. We control for ln(Sales), PPE, CF , ROA, Tobin′s Q,GDP Growth, and firm-fixed effects. Standard errors are clustered at the firm level and corrected forheteroscedasticity. ***, **, and * indicate the 1%, 5%, and 10% significance levels, respectively.

Panel A: Capital Expenditures(1) (2)

CAPEXt+1 R&D − CAPEXt+1

Uncertainty -0.2992* -1.3934***[0.1660] [0.2631]

Controls Yes YesFirm-Fixed Effects Yes YesN 45,757 45,757Adj. R2 0.3868 0.7122

Panel B: Exposure to GEPU(1) (2) (3)

R&Dt+1 CAPEXt+1 R&D − CAPEXt+1

Uncertainty -0.3957 -0.8252** 0.2618[0.4641] [0.3537] [0.5266]

Exposure 1.7028*** 0.6243 1.1626[0.6090] [0.4850] [0.7166]

Uncertainty×Exposure -1.7583*** -0.8735* -1.0092[0.5968] [0.4724] [0.6998]

Controls Yes Yes YesFirm-Fixed Effects Yes Yes YesN 12,186 12,032 12,032Adj. R2 0.7545 0.4182 0.7265

50

Table 8:Investment Irreversibility Channel

The table reports the marginal effect of the cost-of-capital channel, controlling for investment irreversibil-ity. The dependent variable is R&D ratio. Panel A shows the marginal impact of the cost of capital,while controlling for the effect of investment irreversibility. We measure investment irreversibility basedon asset redeployability, cyclicality of firms’ sales, and sunk costs in Columns (1), (2), and (3), respec-tively. We control for ln(Sales), PPE, CF , ROA, Tobin′s Q, GDP Growth, and firm-fixed effects.Panel B shows the effects of the cost of capital and investment irreversibility, while controlling for otherfactors that affect R&D investments. The standardized beta coefficients are reported in parentheses.The standardized coefficients are not reported in the estimations using cyclicality of firms’ sales andsunk costs as proxies for investment irreversibility, since these two measures are categorical variables.Standard errors are clustered at the firm level and corrected for heteroscedasticity and are reported inbrackets. ***, **, and * indicate the 1%, 5%, and 10% significance levels, respectively.

Panel A: Marginal Impact(1) (2) (3)

Uncertainty×WACC -0.1467*** -0.1389*** -0.1275***[0.0215] [0.0203] [0.0197]

Irreversibility -0.4545*** -3.2935*** -2.6279***[0.1712] [0.2407] [0.1400]

Controls Yes Yes YesTime Fixed Effects Yes Yes YesN 18,296 19,438 19,438Adj. R2 0.1894 0.2169 0.2240

Panel B: Cost of Capital vs. Irreversible Investments(1) (2) (3)

WACC -0.1804*** -0.1752*** -0.1558***[0.0254] [0.0237] [0.0229]

(-0.0892)Irreversibility -0.4859*** -3.3608*** -2.7804***

[0.1813] [0.2485] [0.1431](-0.0420)

Uncertainty -0.8464*** -0.9386*** -0.8803***[0.2642] [0.2497] [0.2490]

(-0.0209)Controls Yes Yes YesN 18,338 19,462 19,462Adj. R2 0.1872 0.2154 0.2246

51

Table 9:Robustness Checks

This table reports the impact of electoral uncertainty or economic policy uncertainty on innovation whilecontrolling for overall economic conditions and/or investment opportunities. In Panel A, Congressionalelection is an indicator variable that equals one if the absolute difference in the percentage of votesbetween Democrats and Republicans is less than 5% and zero otherwise. In addition to the controlvariables in the baseline regressions, election cycle indicator variables are included to control for thefact that Congressional elections occur in even-number years. In Panel B, Presential election is adummy variable that is one for close presidential elections and zero otherwise. The control variablesand election cycle indicators are included in the estimation. In Panel C, we estimate the baseline modelusing political polarization as an instrument. In Panel D, we regress the innovation measure on theexogenous component of the policy uncertainty index, macroeconomic variables, economic uncertainty,stock market uncertainty, and firm characteristic variables while controlling for firm-fixed effects. InPanel E, we add an interaction term between the exogenous component of the policy uncertainty indexand an industry growth dummy. HGrowth is a dummy variable that equals one if the firm is in ahigh-growth industry and zero otherwise. Note that a high-growth dummy is not separately includedbecause the model controls for firm-fixed effects. In Panel F Column (1), we include lagged R&D ratioto Equation (4) and estimate the models using system GMM estimations with robust standard errors.In Column (2), we include lagged R&D ratio to Equation (5) and estimate the model using firm fixedeffects with standard errors clustered at the firm level and corrected for heteroscedasticity. ***, **, and* indicate the 1%, 5%, and 10% significance levels, respectively.

Panel A: Close Congressional ElectionsR&Dt ln(Patent)t+3 ln(Citations)t+3

Congressional Election -0.1613** -0.0242*** -0.0997***[0.0715] [0.0057] [0.0126]

N 40,618 31,639 31,639Adj. R2 0.7617 0.8587 0.7734

Panel B: Close Presidential ElectionsR&Dt ln(Patent)t+3 ln(Citations)t+3

Presidential Election -0.3314*** -0.1659*** -0.8048***[0.1276] [0.0139] [0.0284]

N 40,618 31,639 31,639Adj. R2 0.7613 0.8547 0.7444

Panel C: Instrumental Variable ApproachR&Dt+1 ln(Patent)t+3 ln(Citations)t+3

Uncertainty -3.4939*** -0.3813*** -1.6665***[0.4404] [0.0480] [0.1088]

N 40,618 31,639 31,639

52

Panel D: Economic Conditions and UncertaintyR&Dt+1 ln(Patent)t+3 ln(Citations)t+3

Exog. Uncertainty -1.0279*** -0.1216*** -0.5209***[0.2225] [0.0239] [0.0496]

N 40,553 31,585 31,585Adj. R2 0.7675 0.8542 0.7562

Panel E: High vs. Low Growth IndustriesR&Dt+1 ln(Patent)t+3 ln(Citations)t+3

Exog. Uncertainty -0.8163*** -0.0996** -0.5340***[0.2515] [0.0393] [0.0765]

Exog. Uncertainty×HGrowth -0.3360 -0.0356 0.0212[0.3544] [0.0510] [0.0950]

N 40,553 31,585 31,585Adj. R2 0.7675 0.8542 0.7562

Panel F: Lagged R&D Investments(1) (2)

R&Dt+1 WACCExog. Uncertainty -1.2530*** 1.0020***

[0.1960] [0.1594]N 40,618 19,064Adj. R2 0.4974

53

Table 10:Alternative Measures of the Cost of Capital

This table presents the estimation results of the impact of GEPU on the firm-level cost of capital usingthe alternative measures of the cost of capital. We regress the cost of equity (specifications (1)–(3)), costof debt (specification (4)–(5)), and WACC (specification (6)) on the policy uncertainty index. We controlfor firm-fixed effects and factors that affects firms’ cost of capital. The cost of equity is estimated usingthe Gode and Mohanram (2003) (GM), Easton (2004) (PEG), and Claus and Thomas (2001) models.The Claus and Thomas (2001) is estimated using EPS forecasted from the Li and Mohanram (2014)residual income model (RI). See Appendix for the details on computing the CoE. CoDA is aggregatebond yield from the Barclays Capital Aggregate Bond Index following Hann et al. (2013). CoDB isestimated using (synthetic) crediting ratings and BofA Merrill Lynch US Corporate Effective Yields fordifferent rating categories during the period 1997–2007. The cost of capital is the WACC with marketleverage at the beginning of the fiscal year as the weight for the cost of equity (RI) and the cost of debt(CoDB). Standard errors are clustered at the firm level and corrected for heteroscedasticity. ***, **,and * indicate the 1%, 5%, and 10% significance levels, respectively.

(1) (2) (3) (4) (5) (6)GM PEG RI CoDA CoDB WACC

Uncertainty 1.1235*** 1.3391*** 3.2846*** 3.5322*** 2.4167*** 1.5124***[0.1839] [0.2301] [0.2033] [0.2439] [0.1003] [0.1929]

Controls Yes Yes Yes Yes Yes YesFirm Fixed Effects Yes Yes Yes Yes Yes YesN 15,018 14,044 16,771 19,288 36,832 8,817Adj. R2 0.5221 0.5102 0.5306 0.4650 0.0738 0.4980

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Appendix: Implied Cost of Equity Estimation

A.1 Gebhardt et al. (2001) (GLS)

Gebhardt et al. (2001) compute the implied cost of equity capital using the residual income

model:

Pt = Bt +∞∑n=1

Et(NIt+n − reBt+n−1)

(1 + re)i= Bt +

∞∑n=1

Et[(ROEt+n − re)Bt+n−1]

(1 + re)n, (A.1)

where Bt is the book value of equity at time t, NIt+n is the net income for period t + n,

NIt+n − reBt+n−1 is the residual income (economic profit) for period t + n, ROEt+n is the

after-tax return on equity for period t+ n, and re is the cost of equity capital. The residual

income model is estimated using a two-stage approach. In the first stage, we forecast EPS

explicitly for the first three years. We obtain the one- (FEt+1) and two-year-ahead (FEt+2)

EPS median forecasts from the Institutional Brokers’ Estimate System (I/B/E/S).18 The

third-year-ahead forecast is computed as a two-year-ahead EPS forecast multiplied by the

consensus long-term growth rate forecast: FEt+3 = FEt+2(1 +g). In the case of growth rate

unavailable in I/B/E/S, we compute the growth rate using earnings forecasts for years t+ 1

and t + 2. To avoid the influence of outliers, the growth rate is truncated between 0% and

100%.

In the second stage, we implicitly forecast EPS beyond year 3. Specifically, we assume

that ROE in year 3 would mean-revert to the industry median ROE by year t+ T through

linear interpolation. This mean reversion in ROE assumption implies that individual firms

tend to become similar to their industry peers in the long run. The industry median excludes

18I/B/E/S provides monthly forecasts for annual earnings. To ensure that our estimations are based onpublicly available information, we use the last earnings forecasts for the fiscal year.

55

firms with a negative ROE. The industry classifications are based on 48 industries from Fama

and French (1997). The terminal value beyond year t+ T is estimated by the present value

of perpetual residual income. Accordingly, the finite horizon estimate of the stock price for

each firm is as follows:

Pt = Bt +(FROEt+1 − re)Bt

(1 + re)+

(FROEt+2 − re)Bt+1

(1 + re)2+ TV (A.2)

TV =T−1∑n=3

(FROEt+n − re)Bt+n−1

(1 + re)i+

(FROEt+T − re)Bt+T−1

re(1 + re)T−1,

where re is the cost of equity and FROEt+n is the forecasted ROE for year t+n computed as

FROEt+n = FEt+n/Bt+n−1. Bt is the current book value per share from I/B/E/S and from

the Compustat–CRSP merged database if the I/B/E/S value is missing. The book value of

equity evolves according to Bt+n = Bt+n−1 +FEt+n−FDt+n, where FDt+n is the forecasted

dividend per share for year t + n. We estimate the forecasted dividend per share as the

forecasted EPS multiplied by the current dividend payout ratio (k): FDt+n = FEt+n × k.

The dividend payout ratio is defined as the actual dividends divided by earnings in the

most recent fiscal year. If a firm experiences negative earnings, the dividend payout ratio

is estimated as dividends divided by 0.06 × total assets. To avoid the outlier problem, the

payout ratios are set between zero and one. We solve equation (A.2) for re to estimate the

implied cost of equity.

A.2 Gode and Mohanram (2003) (GM)

Gode and Mohanram (2003) compute the implied cost of equity capital using the model in

Ohlson and Juettner-Nauroth (2005) with modified assumptions:

re = A+

√A2 +

FEt+1

P0

(g − (γ − 1)), (A.3)

56

where A = 12

((γ − 1) + DPSt+1

Pt

). Gode and Mohanram (2003) set (γ − 1) to a risk-free

rate minus 3% (rf − 3%). The growth rate g is set as the average of the short- and long-

term growth rate if the short-term growth rate gST = (FEt+2−FEt+1)FEt+1

is greater than the

long-term growth rate from the consensus analyst forecast. The growth rate is set as the

long-term growth rate if the short-term growth rate is smaller than the long-term growth

rate. FEt+1 and FEt+2 are forecasted EPS one- and two-year-ahead, respectively. The

gST can be estimated for firms with FEt+2 higher than FEt+1. DPS is dividend per share

and defined as the dividend ratio times EPS. For firms with positive earnings, the dividend

ratio is the dividends divided by income before extraordinary items. For firms with negative

earnings, the dividend ratio is the dividends divided by 6% of the total assets.

A.3 Easton (2004) (PEG)

Easton (2004) estimates the implied cost of equity capital using a simplified version of the

Ohlson and Juettner-Nauroth (2005) model. Specifically, future dividends and growth in

abnormal earnings changes beyond two years are assumed to be zero. This leads to the

following PEG model:

Pt =FEt+2 − FEt+1

r2e. (A.4)

From equation (A.4), the implied cost of equity capital is derived as

re =

√g ∗ FEt+1

Pt

, (A.5)

where the growth rate g = (FEt+2−FEt+1)FEt+1

. FEt+1 and FEt+2 are forecasted EPS one- and

two-year-ahead, respectively. The PEG model can be estimated for firms with FEt+2 higher

than FEt+1.

57

A.4 Li and Mohanram (2014) (RI)

Motivated by the residual income models in Ohlson (1995) and Feltham and Ohlson (1995,

1996), Li and Mohanram (2014) develop the following RI model:

Et+n = δ0 + δ1NegEt + δ2Et + δ3NegEt × Et + δrBt + δ5TACCt + ε, (A.6)

where Et+n is the EPS in year t+n (n = 1 to 5). NegEt is an indicator variable that equals 1

for negative earnings, and 0 otherwise. Bt is the book value of equity divided by the number

of outstanding shares. TACC is the total accruals defined as the sum of change in non-cash

working capital, change in net non-current operating accruals, and change in net financial

assets divided by the number of outstanding shares. The change in non-cash working capital

is the change in current assets net of cash and short-term investments minus that in current

liabilities net of short-term debt. The change in non-current operating accruals is measured

as the change in non-current assets net of long-term non-equity investments and advances

minus the change in non-current liabilities net of long-term debt. The change in net financial

assets is measured as the change in short- and long-term investments minus the change in

short-term debt, long-term debt, and preferred stock. The missing values of total accruals

are set to zero.

The model is estimated cross-sectionally using the previous ten years of data to ensure

no look-ahead bias. Specifically, one-year-ahead earnings in year t (Et+1) is estimated using

data from year t − 10 to t − 1, two-year-ahead earnings (Et+2) are estimated using data

from year t− 11 to t− 2, and so on. For each firm in each year t, forecasted EPS for years

t + 1–t + 5 (FEt+1–FEt+5) is estimated by the predicted value from equation (A.6). The

model is estimated for firms with non-missing independent variables in year t.

58

Using the forecasted EPS, we estimate the implied cost of equity from the model in Claus

and Thomas (2001). Specifically, the implied cost of equity is estimated by solving r in the

following model:

Pt = Bt +n=5∑i=1

(FEt+n − re ×Bt+n−1)

(1 + re)i+

(FEt+5 − re ×Bt+4)× (1 + g)

(r − g)(1 + re)5, (A.7)

where FEt+n is the forecasted EPS in year t + n. Bt is the current book value per share

and Bt+n−1 is the expected book value per share in year t+ n− 1. As in Claus and Thomas

(2001), the growth rate g is set to a risk-free rate minus inflation rate (rf − 3%).

59