How does Policy Uncertainty Affect Venture Capital?

Xuan Tian

PBC School of Finance

Tsinghua University

tianx@pbcsf.tsinghua.edu.cn

(86) 10-62794103

Kailei Ye

Kenan-Flagler Business School

University of North Carolina at Chapel Hill

kailei_ye@kenan-flagler.unc.edu

(919) 519-9470

Current Draft: March, 2018

* We are grateful for helpful comments from Daniel Bradley, Nitish Kumar, Bibo Liu, Haitian Lu, Christian

Lundblad, and Ronald Masulis, as well as seminar and conference participants at the University of North Carolina at

Chapel Hill, Tsinghua University, and the 2017 China International Conference in Finance. Xuan Tian

acknowledges financial support from the National Natural Science Foundation of China (Grant No. 71790591) and

Tsinghua University Research Grant (Grant No. 20151080451). We remain responsible for all remaining errors and

omissions.

How does Policy Uncertainty Affect Venture Capital?

Abstract

Using a newly developed policy uncertainty index, we find that policy uncertainty is negatively

related to venture capital (VC)’s investment propensity. VCs, however, adjust their investment

strategy quickly in response to policy uncertainty changes. Relying on plausibly exogenous

variation generated by gubernatorial elections, we show that the relation is likely causal. The

negative effect is more pronounced when startups are less mature, have fewer tangible assets, are

more dependent on government spending, and are exposed to severer holdup from entrepreneurs.

Policy uncertainty also adversely affects VCs’ investment outcomes. In response, VCs stage

finance more and cut total investment amount.

Keywords: Venture capital, Policy uncertainty, Investment, Exit outcomes

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1. Introduction

Capital formation starts with the private market, which is of unique importance in

studying the effect of economic policy on the allocation of resources and investment. As a

critical ingredient of the private market, venture capital (VC) has been an important driver of U.S.

entrepreneurship, technological innovation, and economic growth. For example, about 60% of

initial public offerings (IPOs) have been VC-backed since 1999; among the five largest U.S.

companies in terms of market capitalization, three (Apple, Microsoft, and Google) received VC

financing when they were startups. Although a burgeoning strand of literature has examined the

effect of policy uncertainty on the public market (e.g., Gulen and Ion, 2016; Julio and Yook,

2012; Pastor and Veronesi, 2012), there has been no rigorous study that explores how policy

uncertainty affects the private market. The goal of this paper is to fill in this gap by exploring the

effect of policy uncertainty on VC investment and exit outcomes.

We hypothesize that policy uncertainty adversely affects VCs’ propensity to invest in

startup firms. Starting from Bernanke (1983), a number of theoretical models (e.g., Bloom et al.,

2007; and Bloom et al., 2012; Chen and Funke, 2003) show that if an investment is not fully

reversible, investors become cautious and hold back on investment in the face of uncertainty as

uncertainty increases the value of the option to wait. VC investment in startups certainly falls

into this category, because the investees of VCs – startups – are early-stage entrepreneurial firms

that not only have a high growth potential but also are embedded with high failure risk. Startup

firms are typically lack of tangible assets and track records. Their success critically hinges on a

perfect combination of great business ideas, effective management teams, superior market timing

abilities, and oftentimes good luck. As a result, it is very risky for VCs to invest in startups and

about 70% of their investment fails. Once the investment has been made and a startup begins to

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develop, the process is irreversible as the cost is sunk. Moreover, if the whole incubation period

of a startup firm is full of economic policy uncertainty, VCs’ delaying or cancelling their

investment in startups could lead to the miss of investment opportunities, reduced economies of

scale, and the loss of key talent, which adversely affect their investment outcomes. Hence, we

postulate that policy uncertainty negatively affects VCs’ investment and exit outcomes.

There are two major hurdles in our study that we need to clear. The first hurdle is to

appropriately measure policy uncertainty. While existing work has used a variety of firm-specific

proxies, such as stock return volatility, the dispersion in analyst forecasts, and input and output

prices, as well as certain types of macroeconomic policy, such as monetary, fiscal, and social

security policies, to capture the uncertainty exposed to firms and other economic agents, these

measures arguably fail to capture the overall level of policy uncertainty in the economy. To clear

this hurdle, we use an index of aggregate policy uncertainty developed by Baker, Bloom, and

Davis (2016) (hereafter BBD). The BBD index is a weighted average of three different

components. The first component relies on a count of newspaper articles that contain key terms

related to policy uncertainty and this component is most heavily weighted; the second

component captures uncertainty about future changes in the tax code by using the dollar impact

of tax provisions set to expire in the near future; the third component measures uncertainty about

fiscal and monetary policy by using dispersion in economic forecasts of the CPI and government

spending. We discuss in more details about the construction of the BBD index in the next

section.1

We collect startup and VC firm information from the VentureXpert database. Using a

sample of 30,408 entrepreneurial firms that receive VC financing between 1987 and 2015, we

1 Other recent studies, e.g., Gulen and Ion (2016), use the BBD index to capture policy uncertainty as well and

explore its effect on various economic outcomes.

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show that, when policy uncertainty increases, VCs’ propensity to invest in startup firms

significantly drops in the following two quarters. Specifically, VCs are 8.9% and 6.1% less likely

to make an investment in a startup firm in the next two quarters if policy uncertainty increases by

one standard deviation, respectively. VCs also reduce their investment amount in a startup and

are less likely to co-invest in a startup. In addition, unlike the long-lasting effect of policy

uncertainty on investment by public firms (Gulen and Ion, 2016), VCs appear to adjust their

investment strategy more quickly. Specifically, we do not find a significant relation between

VCs’ investment propensity and policy uncertainty beyond two quarters.

Our findings continue to hold in a number of robustness tests. First, we include several

proxies for economic uncertainty to control for other plausible sources of uncertainty in our main

regressions. Our results remain. Second, we include two proxies that capture expectations about

future economy conditions to control for the effect of expected investment returns. Our main

findings are robust to including these two proxies. Third, to remove possible confounding

macroeconomic forces from the BBD index, we regress the BBD index on a measure of policy

uncertainty in Canada that is also developed by BBD, given that the U.S. and Canadian

economies are tightly linked. We then use the residuals from this regression as an alternative

proxy for policy uncertainty, and continue to observe similar results. Fourth, to address the

concern that missing values in VC investment amount between two successive VC financing

rounds may bias our estimation, we aggregate observations to the industry-state level, and our

results continue to hold.

The second hurdle of our study is to establish causality. Our main specifications could be

subject to endogeneity concerns due to both omitted variables and reverse causality:

Unobservable macroeconomic shocks that affect both policy uncertainty and VC investment

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could bias our estimation; expected changes in VC investment propensity could drive current

policy changes that increase policy uncertainty. To clear this hurdle, our identification attempt

relies on plausibly exogenous variation generated by gubernatorial elections. Gubernatorial

elections significantly increase policy uncertainty, as documented by previous literature, and are

staggered across business and economic cycles. A key advantage of using the variation generated

by gubernatorial elections is that it represents multiple shocks that affect different states (and

hence VCs) at exogenously different times. This feature avoids a common identification

difficulty faced by studies using a single shock, i.e., the existence of potential omitted variables

coinciding with the shock could directly affect VC investment. We use a difference-in-difference

(DiD) approach that compares the investment made by the same lead VC in startups located in

states with and without gubernatorial elections surrounding the elections. Year-quarter fixed

effect in our estimation absorbs the effect of investment opportunities and economy environment.

We use VC-year-quarter fixed effect to absorb shocks to VC-level supply of investment. It

mitigates the concern regarding political uncertainty’s effect on individual investor. We also add

VC-state fixed effect to mitigate the concern that the concentration of VC investment on some

specific states and the investment from some large VCs could drive our results. The results in the

dynamic model further mitigate the reverse causality concern. Our results suggest that the

negative relation between policy uncertainty and VC investment is likely causal.

To identify plausible underlying mechanisms through which policy uncertainty affects

VC investment, we explore whether the negative effect of policy uncertainty on VC investment

exhibits heterogeneity in the cross-section along a number of startup characteristics that are

related to their own risk levels. One key advantage of the VC setting used in our study is that it

allows us to directly observe the investment in question: the startup firms and their

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characteristics. This is a unique feature that is different from existing studies that use capital

expenditure of public firms to examine the effect of policy uncertainty, because researchers in

these studies are unable to directly observe the characteristics of the investment made by public

firms. We find that the negative effect of policy uncertainty on VC investment is more

pronounced when startup firms are less mature, have fewer tangible assets, are more dependent

on government spending, and are exposed to more severe holdup from entrepreneurs. These

findings are consistent with the conjecture that VCs are more likely to hold back their investment

when facing higher uncertainty if the underlying risk associated with the startup firms is larger.

In the final part of the paper, we examine a “bottom-line” question, i.e., how policy

uncertainty affects VCs’ investment outcomes, and what VCs can do to mitigate the effect of

policy uncertainty on investment outcomes. We find that a higher degree of policy uncertainty

during a VC’s incubation period of a startup firm is associated with a lower probability that the

startup exits successfully through either going public or an M&A. To mitigate the adverse effect

of policy uncertainty on VCs’ investment outcomes, VCs appear to rely on more staged

financing and lower total investment amount, which help reduce their exposure to the risk

associated with policy uncertainty.

Our paper contributes to two strands of literature. First, it adds to the growing literature

on the effect of policy uncertainty on both a firm’s real investment decisions and stock market

performance. Regarding real investment decisions, Bloom et al. (2007) show that a firm’s

response to stimulus is weaker (by reducing investment) when uncertainty is higher, using

simulated data. Julio and Yook (2012) and Gulen and Ion (2016) find that policy uncertainty is

negatively related to firm-level capital investment. Bonaime et al. (2017) show that policy

uncertainty is negatively related to M&A activities. Bhattacharya et al. (2017) find that policy

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uncertainty adversely affects firm innovation in a cross-country setting. On the capital market

front, Pastor and Veronesi (2012) show that although a policy change might increase the

potential cash flow, it also increases discount rates. Hence, announcements of new policy can

depress stock returns. Our paper is also related to the studies that explore the effect of

government spending on the investment of private sectors (e.g., Cohen et al. 2011; Cohen and

Malloy, 2016; Howell, 2017, etc.)

While existing literature appears to reach a consensus that policy uncertainty reduces

investment by public firms, it is unclear how policy uncertainty affects investment in the private

market. Our paper contributes to this literature by studying the effect of policy uncertainty on

investment by VC investors, an important private market player. To the best of our knowledge,

this paper is the first study that shows VC investment is negatively affected by policy uncertainty

but it recovers much sooner than public firm investment.

Second, our paper contributes to the venture capital literature. This literature mainly

focuses on how VC characteristics affect their investment strategy and outcomes. Specifically,

VCs’ intensive monitoring, reputation, expertise, and network as well as secondary market

conditions all affect their investment strategies, such as syndication and stage financing. This

literature also examines the investment outcomes of VC financing to evaluate the effectiveness

of various VC investment mechanisms, and concludes that VCs generally are able to create value

for their portfolio firms (see, e.g., Da Rin et al., 2013) for an excellent survey of the literature).

Existing studies, however, have largely ignored the role played by the government and its

policies in determining VC investment and exit outcomes. Our paper attempts to fill in this void

by providing evidence that links policy uncertainty and VC investment as well as exit outcomes.

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The rest of our paper proceeds as follows. Section 2 describes the data and summary

statistics. Section 3 provides our main results and discusses a few robustness checks. Section 4

reports cross-sectional results to understand the heterogeneity in the effect of policy uncertainty.

Section 5 presents the results on VC investment outcomes. We conclude in Section 6.

2. Data and Main Variables

2.1 Measure Policy Uncertainty

We use an aggregate index developed by Baker, Bloom, and Davis (2016) to measure

policy-related economic uncertainty. This economic policy uncertainty index, the BBD index, is

a weighted average of three types of underlying components, including the news-based

component, the tax component, and the forecaster disagreement component, with the weight of

one-half, one-sixth, and one-third, respectively.

The news-based component is a normalized index of the coverage of news articles related

to economic policy uncertainty in 10 large newspapers.2 The tax component is measured by the

annual dollar-weighted numbers of federal tax code provisions expiring in the next 10 years,

reported by the Congressional Budget Office. The forecaster disagreement component is a

combination of the government spending uncertainty index and inflation uncertainty index.

Drawing on the Federal Reserve Bank of Philadelphia’s survey, the government spending

uncertainty index is measured by the dispersion among individual professional forecasters’

predictions about the future level of the federal, state, and local expenditures. The inflation

uncertainty index is constructed by consumers’ dispersion of the future price.

2 These newspapers include the Boston Globe, the Chicago Tribune, the Dallas Morning News, the Los Angeles

Times, the Miami Herald, the New York Times, the San Francisco Chronicle, the Wall Street Journal, the

Washington Post and the USA Today.

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The BBD index is released monthly at http://www.policyuncertainty.com/. In our

quarterly-level analysis, we aggregate the BBD index up to the quarterly level by calculating the

average value of the BBD index in a quarter, and use is as a proxy for the U.S. policy uncertainty

in that quarter. We report summary statistics in Table 1 Panel A. In the 116 quarters between

January, 1987 and December, 2015, the average value of the BBD index is 106.3.

2.2 Startup Firms and VC Investment

Our initial sample contains 30,408 U.S. entrepreneurial firms that receive VC financing

between January 1, 1987 and December 31, 2015, provided by the VentureXpert database. From

VentureXpert, we retrieve detailed round investment information, including round date,

estimated investing amount, the number of investing VCs, firm age, and firm development stage.

We also retrieve information on entrepreneurial firms’ characteristics, including founding year

and headquarters location.

The VentureXpert database marks firm exit status as initial public offering (IPO),

mergers and acquisitions (M&A), being written-off, and being under active investment. We

complement the information of IPO exits and the M&A exits with the data from the Securities

Data Company (SDC) Global New Issues database and the SDC’s Mergers and Acquisitions

database. We use the date of IPO and M&A provided by these two databases as the proxy of

entrepreneurial firm’s exit date. VentureXpert, however, does not mark the exit status for write-

off firms, if the VCs do not provide the information. Following the existing literature (e.g.,

Chemmanur et al., 2014), we classify a firm as a written-off firm if it does not receive any

financing within three-year span after its last round of financing, and mark the date of three-year

after its last round of financing as its exit date. Our firm-quarter sample includes all

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entrepreneurial firms during their incubation periods, which are defined as the period between

the first VC financing round date and the exit date.

Table 1 Panel B presents summary statistics of the main VC investment and firm

characteristics variables at the startup firm level. All variables are winsorized at the 1st and 99th

percentiles to minimize the effect of outliers. A typical startup firm in our sample receives a total

VC investment amount of $31.7M from 3.6 VCs across all financing rounds and is 9.8 years old

when it receives the first round of VC financing.

2.3 Control Variables

Because VentureXpert does not provide financial information of entrepreneurial firms,

following Gompers (1995), we use financial variables of public firms in the startup’s industry as

the proxies for those of the startup firms. We obtain public firms’ quarterly accounting data from

Compustat and construct three financial variables, namely, Tobin’s Q, sales growth, and cash

flow, at the quarterly level. Tobin’s Q is calculated as the book value of total assets plus the

market value of common equity minus the book value of common equity, divided by the book

value of total assets. Sales growth is measured by the year-on-year growth rate in quarterly sales.

Cash flow equals operating cash flow normalized by total assets at the beginning of each quarter.

We measure industry Tobin’s Q by taking average of Tobin’s Q in each 3-digit SIC industry at

the quarterly level. We use the same approach to construct industry sales growth and industry

cash flow.

To control the effect of industry technology shocks, we collect information on all granted

U.S. patents, including their filling date, assignee name, and technology class, from Google

USPTO Bulk Downloads (https://www.google.com/googlebooks/uspto.html). Following Hsu et

al. (2014), we map patent technology class to 2-digit SIC codes. To measure industry technology

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shock, we first count the number of patents filed in the past five years in each of the 2-digit SIC

industry. Then we normalize it by the number of patents filed in the past five years in all

industries.

We exclude firm-quarter observations with missing information on policy uncertain, VC

investment, firm characteristics, or industry financial variables. Our final sample contains 30,408

unique startup firms with 616,851 firm-quarter observations.

3. Policy Uncertainty and VC Investment

In this section, we present our main findings on the effect of policy uncertainty on VC

investment. In Section 3.1, we report our baseline results. We then conduct a number of

additional tests to address various concerns regarding our baseline regressions and report the

results in Section 3.2. In Section 3.3, we present our identification attempts.

3.1 Baseline Results

To estimate the effect of policy uncertainty on VC investment, we estimate various forms

of the following model, using either Probit or ordinary least squares (OLS) regression:

𝐼𝑁𝑉𝑖,j,s,𝑡+𝑙 = 𝛼 + 𝛽 × 𝐵𝐵𝐷𝑡 + 𝛾 × 𝐴𝑔𝑒𝑖,j,s,𝑡 + 𝛿 × 𝐼𝑁𝐷𝑗,𝑡 + 𝜑 × 𝑀𝑠,𝑡 + 𝐹𝐸 + 휀𝑖,j,s,𝑡+𝑙 (1)

where i indexes firm, j indexes industry, s indexes state, t indexes quarter, and l indexes the

number of quarters that the dependent variable (INV) leads the independent variables and 𝑙 ∈

{0,1,2,3,4} . Hence, we examine the effect of policy uncertainty on VC investment at the

contemporaneous quarter and the subsequent four quarters. The unit of observation is startup

firm-quarter. We use four VC investment variables as the dependent variable (INV): a funding

dummy, VC funding dummy, that equals one if a startup firm receives VC financing in the

quarter and zero otherwise; the natural logarithm of total VC investment amount a startup firm

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receives in the quarter, VC amount; the number of VCs investing in a startup firm in the quarter,

No. of VCs; total investment amount a firm receives divided by the number of investing VCs in a

quarter, Investment per VC. We report the results with these four dependent variables in Table 2

Panels A – D.

The key variable of interest is the economic policy uncertainty variable (BBD). To

construct this variable, in each quarter t, we use the natural logarithm of monthly BBD index’s

arithmetic average in quarter i. In all regressions, we control for startup firm age (Age) that is the

natural logarithm of firm i’s age in quarter t, considering that startup firm age could significantly

affect VC’s investment. We add one when taking logarithm to avoid losing observations as some

startups receive VC first round financing when they are younger than one year old. In addition,

to address the concern that public markets (that presumably are affected by policy uncertainty as

well) could affect VC investment, we add a set of 3-digit SIC industry corporate financial

variables in IND, namely, Tobin’s Q, sales growth, and cash flow. IND also includes the variable

that captures 2-digit SIC industry technology shocks.

One potential concern is that some macroeconomic conditions cause both the changes in

political uncertainty and the changes in VC investment. To address this concern, we add GDP

growth to capture macroeconomic conditions. GDP growth is measured by the year-on-year

quarterly growth in real GDP. Another concern comes from the demand side. It is possible that,

facing high political uncertainty, entrepreneurs are less likely to start new businesses and hence

the demand for VC investment decreases. To address this concern, we add the number of new

startups, No. of entrepreneurial firms, as a control variable. No. of entrepreneurial firms is

measured by the natural logarithm of the number of all firms aged between 0 and 5 in the same

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state with firm i at each year. We obtain entrepreneurial firm data from Longitudinal Business

Database of Census. Statistics is available during 1977 to 2014.

FE refers to a set of fixed effects to absorb variation that is unrelated to economic policy

uncertainty but may affect our results, including year fixed effect, quarter fixed effect, industry

fixed effect, startup development stage fixed effect, and startup state fixed effect.3 For example,

startup firms tend to cluster in some states, such as California, Massachusetts, and New York

(Chen et al., 2010). We use state fixed effect to capture the effect of startup location cluster on

VC investment. We cluster standard errors at the startup firm level to correct for potential serial

correlations in the error term.

We present regression results estimating equation (1) in Table 2. In columns (1) of each

panel, we report the results with the dependent variable at the current quarter. In columns (2) -

(5), the dependent variables are one to four quarters ahead of the independent variables,

respectively.

In Panel A, the dependent variable is VC Funding Dummy. We estimate equation (1)

using the Probit model and report the marginal effects to facilitate the interpretation of our

results. The coefficient estimates on BBD are negative and significant at the 5% or 1% level in

the first three columns, suggesting that VCs’ propensity to invest startup firms declines

significantly when policy uncertainty increases, and this effect lasts for two quarters. The

economic effect of policy uncertainty on VC investment propensity is sizable: increasing BBD

by one standard deviation (30.2) from its mean value (106.3) is associated with an 8.5% (0.025

×Ln(30.2)) and a 6.1% (0.018×Ln(30.2)) lower probability of VC investment in the following

two quarters, respectively. In columns (4) and (5), the coefficient estimates on BBD are

3 VentreXpert labels startup firms in one of five stages at each round date: startup/seed, early stage, expansion, later

stage and buyout/acquisition.

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statistically insignificant, suggesting that the effect of policy uncertainty on VC investment

decays starting from the third quarter after the change in policy uncertainty. Compared to the

long-lasting effect of policy uncertainty on public firm investment (Gulen and Ion, 2016), our

evidence suggests that VC investors’ response to policy uncertainty recovers quickly.

Regarding controls, younger firms are more likely to receive VC financing. The

coefficient estimates on Tobin’s Q are negative and significant but with the marginal effect very

close to zero. The other two industry variables have insignificant coefficients. The coefficient

estimates on GDP growth are positive and significant at the 1% level in four out of five

specifications, suggesting that economic growth is positively associated with VC investment

propensity. The coefficient estimates on No. of entrepreneurial firms are negative and significant

at the 1% level in all columns, suggesting that when the startups’ overall demand on funding is

higher, the propensity and the amount of VC investment for each startup firm is lower.

In Panel B, we replace the dependent variable with the natural logarithm of total

investment a startup receives in a quarter and estimate equation (1) using OLS. We suppress the

coefficients of all control variables for brevity. The coefficient estimates on BBD are negative

and significant at the 5% level in column (1) and at the 1% level in columns (2) and (3). Because

both the dependent variable and the key independent variable, BBD, are in the form of natural

logarithm, the coefficient estimate on BBD can be interpreted as the elasticity of VC investment

amount with respect to policy uncertainty. Specifically, increasing policy uncertainty by 10% is

associated with a drop in VCs’ investment amount by 1.6% and 1.1% in the next two quarters,

respectively. The coefficient estimates on BBD are statistically insignificant in columns (4) and

(5), suggesting that policy uncertainty’s effect on VC investment amount decays starting from

the third quarter. We observe similar findings in Panels C and D in which No. of VCs and

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Investment Per VC are the dependent variables, respectively, suggesting that fewer VCs tend to

co-invest and they each invest less in startup firms when they are subject to a higher level of

policy uncertainty.

The baseline results reported in Table 2 suggest that policy uncertainty has a significant

and negative effect on VCs’ propensity to invest within two quarters after an increase in policy

uncertainty. VCs, however, appear to be able to adjust their investment strategy quickly as

evidenced that the negative effect vanishes starting from the third quarter. This observation is in

contrast to the long-lasting effect of economic policy uncertainty on investment by publicly

traded firms documented by earlier studies.

3.2 Addressing various concerns

In this section, we undertake additional tests to address a variety of potential concerns of

our main results. The first concern is that our proxy of policy uncertainty, the BBD index, might

capture not only the effect of policy-related uncertainty, but also the effect of other more general

economic factors, such as terrorist attacks, financial crisis, and economic recessions. These

plausible sources of uncertainty could affect VC investment. It is therefore important to control

them for identification purposes.

To address this concern, we follow Bloom (2009) and Gulen and Ion (2016) and

introduce several proxies for non-policy related economic uncertainty. Our first proxy is the

measure of GDP forecaster dispersion published by Livingstone survey. Philadelphia Federal

Reserve conducts this survey every June and December to collect the predictions of future GDP

from individual economists. Our second proxy is the measure of productivity growth volatility.

We first calculate each firm’s quarterly productivity growth as quarter-on-quarter change in net

profit normalized by average sales in these two quarters, using quarterly accounting data from

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Compustat. We then calculate the cross-sectional standard deviation of productivity growth for

each quarter as the productivity growth volatility variable. Our third proxy is the stock market

volatility. We first calculate the cross-sectional standard deviation of stock returns in each month,

using monthly stock return data from CRSP, and then take arithmetic average of the standard

deviation in the three months of each quarter.

The second concern comes from the expected economic conditions. One of the main

factors that VC investors take into consideration when they make investment decisions is

expected returns. When expected returns are low, VC investors may have lower incentives to

invest, thus entrepreneurial firms might not receive VC financing. Meanwhile, policymakers

oftentimes feel increasing pressure to alter their economic policies to boost economy, resulting in

an increase of policy uncertainty. If this is the case, our main findings might in fact capture the

effect of expected investment returns on VC investment.

To address this concern, we add two proxies that previous studies have used to capture

the expectations of future economic conditions from both economists and consumers. The first

proxy we use is GDP growth forecast, published by Livingston survey. Specifically, we use the

12-month expected mean GDP growth, calculated by the percentage change between 12-month

GDP forecasts and current GDP. Second, we include the Michigan consumer expectation index,

to proxy consumer confidence. University of Michigan conducts expectation surveys in

consumers every month. The index is calculated based on their response and reflects their

expectation of future inflation.

We augment our baseline regressions with non-policy related economic uncertainty and

expected economic condition proxies we proposed above. Table 3 presents the regression results

estimation equation (1) with the set of augmented economic control variables (M), including

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GDP growth, No. of entrepreneurial firms, GDP forecast dispersion, productivity growth

volatility, stock market volatility, expected GDP growth, and consumer confidence. Similar to

the structure of Table 2, the dependent variables are VC funding dummy, VC amount, No. of VCs,

and Investment per VC in Panel A to Panel D, respectively. We run Probit regressions in Panel A

and OLS regressions in the other three panels.

In Panel A, we continue to observe negative and significant marginal effects of BBD in

columns (2) and (3), suggesting that the negative effect of policy uncertainty on VCs’ propensity

to invest in startups in the first two quarters is robust to controlling for general economic

uncertainty and economic conditions. The magnitudes of the marginal effect of BBD are

comparable to those reported in Table 2 Panel A. The contemporaneous effect of policy

uncertainty on VC financing, however, becomes insignificant after including these additional

controls. In Panels B, C, and D, when we replace the dependent variable with VC amount, No. of

VCs, and Investment per VC, respectively, we observe similar results. Hence, our results of the

negative effect of policy uncertainty on VC investment in the first two quarters are robust.

To further address the concern that our policy-related uncertainty measure could capture

economic uncertainty that is not policy related but still affects VC investment, we follow Gulen

and Ion (2016) and implement a test with Canada economic policy uncertainty. Romalis (2007)

shows that the Canada-US Free Trade Agreement and later the North American Free Trade

Agreement makes Canada one of the largest US’ trading partners. The large volume of

merchandise trading between Canada and U.S. ties the two economies together closely.

Therefore, important economy characteristics, such as economy shocks and market factors, can

be expected to be shared between Canada and U.S. If the economic policy uncertainty proxy we

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use, the BBD index, does capture omitted economy-related uncertainty, we can extract them by

using the Canada BBD index and thus eliminate their effect on VC investment.

We first extract the common component from U.S. economic policy uncertainty with the

Canada economic policy uncertainty index:

𝑈𝑆𝐵𝐵𝐷𝑡 = 𝛼 + 𝛽 × 𝐶𝐴𝑁𝐵𝐵𝐷𝑡 + 𝛿 × 𝑈𝑆𝐼𝑁𝐷𝑡 + 𝜑 × 𝑀𝑡 + 휀𝑖 (2)

Equation (2) is a quarterly time-series OLS regression. For each quarter t, USBBD and CANBBD

are economic policy uncertainty in U.S. and Canada, respectively. We take the natural logarithm

of average economic policy uncertainty in quarter t for both variables. U.S. market control

variables (USIND) include Tobin’s Q, sales growth, cash flow, and technology shock index. We

measure these variables by taking the average performance among all U.S. firms in each quarter.

The set of augmented economic control variables (M) includes GDP growth, No. of

entrepreneurial firms, GDP forecast dispersion, productivity growth volatility, stock market

volatility, expected GDP growth, and consumer confidence.

Panel A of Table 4 presents the results from estimating equation (2). We first run the

regression with the Canada economic policy uncertainty index as the sole independent variable

and report the results in column (1). In column (2), we then add U.S. market control variables

and the augmented set of macroeconomic control variables into the regression. The results in

both columns suggest that U.S. and Canada economic policy uncertainty have a positive and

significant relation.

Next, we use the residual from the regression in column (2) as an alternative (arguably

cleaner) measure of U.S. policy-related uncertainty. Replacing the BBD index with U.S. BBD

residual, we re-estimate equation (1) and report the results in Panel B. Panels B1 – B4

correspond to the four panels in Table 2 with VC funding dummy, VC amount, No. of VCs, and

18

Investment per VC as the dependent variables, respectively. We continue to observe negative and

significant effect of policy uncertainty on VC investment in the first two quarters following an

increase in policy uncertainty. In addition, the magnitudes of the coefficient estimates are similar

to those reported in Table 2. The contemporaneous effect of policy uncertainty on VC

investment, however, is insignificant in this test.

The third concern is that, because VCs naturally slice their investment in startup firms in

a few rounds to mitigate information asymmetry and agency issues (Gompers, 1995; Tian, 2011),

VCs may not invest in a startup firm in every quarter, which creates missing value in quarters

between two rounds of financing and could bias our estimation. To address this concern, we

aggregate the startup-quarter level data used in our earlier analyses to the 3-digit-SIC industry-

state level data and repeat our baseline results. Note that the VC funding dummy no longer

applies in this test, we report the results on VC amount, No. of VCs and Investment per VC in

Table 5 Panels A – C, respectively.

We continue to observe a negative effect of policy uncertainty on VC amount and

Investment per VC in the first quarter and on No. of VCs in the first two quarters following an

increase in policy uncertainty. In summary, our main findings regarding the effect of policy

uncertainty on VC investment in the first two quarters after uncertainty changes are robust to

including additional controls and using alternative policy uncertainty proxies, and are unlikely to

be driven by the way we construct the data.

3.3 Identification Attempts

In this section, we attempt to address endogeneity concerns and establish causality in a

more direct way. Our identification strategy relies on plausibly exogenous variation generated by

gubernatorial elections. The campaigns before gubernatorial elections trigger political

19

uncertainty, by causing possible changes in political leadership and in government policies

(Colak et al., 2017). The gubernatorial election timings, however, are set by legislations, and are

staggered across business and economic cycles. Thus, a key advantage of using variation

generated by gubernatorial elections is that it represents multiple shocks that affect different

states (and hence VCs) at exogenously different times. This feature avoids a common

identification difficulty faced by studies using a single shock, namely, the existence of potential

omitted variables coinciding with the shock that directly affect VC investment. We use a DiD

approach that compares the investment made by the same lead VC in startups located in states

with and without gubernatorial elections surrounding the elections. Exploring investment made

by the same lead VC in states with different levels of policy uncertainty, we are able to absorb

the effect caused by heterogeneity in lead VCs, which helps to establish causality.

We obtain gubernatorial election information from the Voting and Elections Collection

database in the CQ Press Library. This database provides detailed information on each

gubernatorial election, including the election date, the name of winning candidate and party,

whether the candidate is an incumbent or the challenger, and the voting margin. 370

gubernatorial elections take place in our sample period between 1987 and 2015 in the U.S.

To implement the DiD approach, we first identify the lead VC for each startup firm in our

sample. Following the procedures in the existing literature (e.g. Gompers, 1995), we start with

the VCs that participate in the first round of financing. If there are multiple VCs investing in the

first round, we pick the one that makes the largest amount of investment across all financing

rounds. If there are still multiple VCs satisfying the criteria, we choose the one with the earliest

founding year (i.e., the oldest VC). If there still exists a tie, we pick the one with the longest

investing history. We then group the startups that located in the same state and with the same

20

lead VC together, and the sample now consists of lead VC-state(-year)-quarter observations. We

next estimate various forms of the following model:

𝐼𝑁𝑉𝑣,𝑠,𝑡 = 𝛼 + 𝛽 × 𝐵𝑒𝑓𝑜𝑟𝑒𝑠,𝑡1 + 𝐹𝐸 + 휀𝑣,𝑠,𝑡 (3)

where v indexes VC, s indexes state, and t indexes quarter. We use three VC investment

variables as the dependent variable (INV): the natural logarithm of total investment amount VC v

makes in startups located in state s in quarter t, VC inv amount; the number of startups receiving

investment from VC v in quarter t, No. of startups; total investment amount VC v makes divided

by the number of startups it invests in at quarter t, Investment per startup.4 Before1 dummy

equals one if a gubernatorial election occurs in state s one-quarter after quarter t, and zero

otherwise.

To further control the economy conditions’ and economic uncertainty’s effect on VC

investment, we replace the set of macroeconomic controls with year-quarter fixed effects. Year-

quarter fixed effects absorb all potential changes in investment opportunities and economy

environment. Another concern of our main results is that the decline in VC investment might be

caused by the decline in the supply of money to the VCs, i.e. declines in investment from

institutional investors (who are limited partners, LPs hereafter) into VCs. It is possible that

political uncertainty increases the value of LPs’ option to wait, so that LPs choose to hold back

their investment on VCs. If this is the case, our main results might capture the effect of political

uncertainty on LPs instead of VC investors. To mitigate this concern, we add VC-year-quarter

fixed effects. VC-year-quarter fixed effects control plausible shocks to VC-level supply of

investment. Moreover, we add VC-state fixed effects to mitigate the concern that concentrations

of VC investment on some specific states and the investment from some large VCs could drive

4 Note that the observation unit in this test is lead VC-state-quarter, we cannot use the number of investing VC

investors as the dependent variable. Hence, we use the number of startups receiving investment from the lead VC as

a dependent variable instead.

21

our results. We cluster standard errors at the lead VC level to address potential serial correlations

in the error term. We report the results in Table 6 Panels A.

The coefficient estimates on Before1 are negative and significant at the 10% level in all

columns, suggesting that the amount of VC investment, the number of startups that receive VC

investment, and the amount of investment each startup receives decline significantly one-quarter

before gubernatorial elections, suggesting that policy uncertainty caused by gubernatorial

elections adversely affects VC investment.

One remaining concern regarding our identification attempt is that the results may be

driven by the demand side: the poor local environment conditions, on one hand, reduce the

investment opportunities and thus startups’ demand of investment; on the other hand, cause

tough gubernatorial elections and more policy uncertainty, because incumbent governors could

be blamed for deteriorating economic conditions (Atkeson and Partin, 1995). If this is the case,

poor economic conditions’ adverse impact on VC investment may still exist after gubernatorial

elections resolve the political uncertainty. To address this concern, we examine a dynamic model

of the VC investment surrounding the gubernatorial elections. We construct three new dummy

variables, before2, event, and after1, corresponding to two-quarter before, at the quarter of and

one-quarter after the election, respectively. We then estimate the following model:

𝐼𝑁𝑉𝑣,𝑠,𝑡 = 𝛼 + 𝛽1 × 𝐵𝑒𝑓𝑜𝑟𝑒𝑠,𝑡2 + 𝛽2 × 𝐵𝑒𝑓𝑜𝑟𝑒𝑠,𝑡

1

+𝛽3 × 𝐸𝑣𝑒𝑛𝑡𝑠,𝑡 + 𝛽4 × 𝐴𝑓𝑡𝑒𝑟𝑠,𝑡1 + 𝐹𝐸 + 휀𝑣,𝑠,𝑡 (4)

where v indexes VC, s indexes state, and t indexes quarter. FE is the same as in equation (3). We

report the results in Table 6 Panels B.

The coefficient estimates on event and after1 are not significant, suggesting that state-

level VC investment shows no significant change after gubernatorial elections resolving the

22

political uncertainty. These findings are consistent with the literature (e.g. Jens, 2016). The

coefficient estimates on before1 are negative and significant in all three regressions, suggesting

that VCs significantly reduce the amount of investment, the number of investing startups, and

investment amount per startup when the political uncertainty is high at one quarter before the

gubernatorial elections. The coefficient estimates on before2 are negative but statistically

insignificant, suggesting that political uncertainty’s adverse impact on VC investment only exists

at one quarter before, not long before the elections. This finding further mitigates the concern

that our political uncertainty actually captures the economic uncertainty before the election.

Overall, the results presented in Table 6 Panel B suggest that the adverse impact of policy

uncertainty on VC investment does not exist after gubernatorial elections resolve the political

uncertainty. These results mitigate concerns about the endogeneity concern coming from the

economic conditions and demand side. They support our main findings that political uncertainty

adversely affects VC investment.

In summary, relying on plausibly exogenous variation in policy uncertainty generated by

gubernatorial elections, our results support the notion that policy uncertainty appears to have a

causal, negative effect on VC investment.

4. Cross-Sectional Heterogeneous Effects

In this section, we further explore the effect of economic policy uncertainty on VC

investment by running cross-sectional tests that re-estimate equation (1) in a few cross

dimensions based on startup firm characteristics that are related to startups’ own risk. As the

contemporaneous effect of policy uncertainty on VC investment does not appear to be robust in

Section 3.2, we focus our attention on the effect of policy uncertainty in the subsequent four

quarters starting from this section.

23

Specifically, we add an interaction term between the BBD index and startup

characteristics in the baseline regression to explore how these characteristics alter the effect of

policy uncertainty on VC investment:

𝐼𝑁𝑉𝑖,j,s,𝑡+𝑙 = 𝛼 + 𝜆 × 𝐵𝐵𝐷𝑡 × 𝑆𝐶𝑖,j,s,𝑡 + 𝛽 × 𝐵𝐵𝐷𝑡 + 𝜇 × 𝑆𝐶𝑖,j,s,𝑡 + 𝛾 × 𝐹𝐴𝑖,j,s,𝑡

+ 𝛿 × 𝐼𝑁𝐷j,𝑡 + 𝜑 × 𝑀𝑠,𝑡 + 𝐹𝐸 + 휀𝑖,j,s,𝑡+𝑙 (5)

where i indexes firm, j indexes industry, s indexes state, t indexes quarter, and l indexes the

number of quarters that the dependent variable INV leads the independent variables. In this

analysis, 𝑙 ∈ {1,2,3,4}. The unit of observation in this test is startup firm-quarter. SC represents

startup characteristics. 𝐵𝐵𝐷 × 𝑆𝐶 is the interaction term of economic policy uncertainty and the

startup characteristic that we examine. We use four VC investment variables as the dependent

variable (INV): VC funding dummy, VC amount, No. of VCs, and Investment per VC. All other

control variables and fixed effects are the same as those included in equation (1).

We consider four dimensions of risk associated with startups. In section 4.1, we explore

how firm maturity alters our main results. Section 4.2 shows how our main findings vary with

different degrees of startup firm asset tangibility. Section 4.3 explores the effect of startups’

dependence on government spending on our main results. Finally, we examine how

entrepreneurs’ ability to hold up VCs alters our main findings in Section 4.4.

4.1 Firm Maturity

The first dimension of cross section we explore is firm maturity. Mature entrepreneurial

firms tend to have standardized daily operations and internal structure, clear long-term business

plans, and strong ties with their business partners. Hence, they are less risky and may be less

affected by policy uncertainty, compared to less mature entrepreneurial firms. As a result, we

24

expect to observe a weaker negative effect of policy uncertainty on VC investment for more

mature entrepreneurial firms.

We use startup firm age to capture its maturity. Prior research has shown the positive

relation between firm maturity and firm age. Startup firms need time to standardize routines and

formalize relationships, causing maturity increasing with firm age (Stinchcombe, 1965). We,

therefore, conjecture that the negative effect of policy uncertainty on VC investment is more

pronounced for younger (and less mature) startup firms.

To test this conjecture, we use firm age (Age) as SC, and hence 𝐵𝐵𝐷 × 𝐴𝑔𝑒 is the key

independent variable in equation (5). We report the regression results in Table 7 that has the

same structure as in Table 2. Panel A presents the results regarding VCs’ propensity to invest

when policy uncertainty changes. The coefficient estimates on BBD are negative and significant,

consistent with our main findings in Table 2. More importantly, the coefficient estimates on the

interaction term, 𝐵𝐵𝐷 × 𝐴𝑔𝑒, are positive and significant at the 1% level, suggesting that the

negative effect of policy uncertainty on VC investment is mitigated for more mature firms.

Comparing the results from Table 7 to those reported in Table 2, it is interesting to observe that

the coefficient estimates on BBD are statistically significant in columns (3) and (4) in Table 7

while they are not in Table 2. This finding appears to suggest that policy uncertainty has a

significant and negative effect on VCs’ propensity to invest in younger and less mature firms in

the third and fourth quarter after an increase in policy uncertainty, but this effect is largely absent

in older and more mature firms. Hence, once we pool all firms together in one regression as what

we do in Table 2, the average effect becomes statistically insignificant.

In Panels B, C, and D, we replace the dependent variable with VC amount, No. of VCs,

and Investment per VC, respectively. We only report the coefficient estimates on the interaction

25

term for brevity. We observe similar findings in terms of positive and significant coefficient

estimates on the interaction term, suggesting that the negative effect of policy uncertainty on VC

investment is mitigated for more mature firms.

For robustness, we construct an alternative proxy for firm maturity: a startup firm’s

development stage when it receives the first round VC financing. Early-stage startup firms are

less mature than later-stage firms. Hence, we expect that the negative effect of policy uncertainty

on VC investment is more pronounced for early-stage ventures. To test this conjecture, we

construct an early-stage dummy (ESD) that equals one if the entrepreneurial firm is in

startup/seed or early stage and zero if the firm is in expansion, later stage, or buyout/acquisition

stage when it receives the first-round VC financing. We then replace the startup characteristics

variable, SC, with the early-stage dummy in equation (5). To avoid multicollinearity caused by

the early-stage dummy, we drop stage fixed effects from the regressions. We report the results in

Appendix A. We find that the coefficient estimates on the interaction term are negative and

significant, consistent with our conjecture.

4.2 Asset Tangibility

The second dimension we explore is a startup’s asset tangibility. VCs may lose less when

their investment fails if the startup firm has more tangible assets, because the liquidation of the

startup’s tangible assets allows VCs to recover their losses. As a result, when policy uncertainty

increases, VC investors would feel more comfortable and confident to invest startup firms that

have more tangible assets. Therefore, we expect that the negative effect of policy uncertainty on

VC investment is mitigated if the startup firm has more tangible assets.

Because the VentureXpert database does not provide information about a startup firm’s

asset tangibility, following existing literature (e.g., Gompers, 1995), we use the average asset

26

tangibility (calculated as net property, plant, and equipment divided by total assets at the

beginning of the quarter) as a proxy for that of a startup firm in the same 3-digit SIC industry.

We follow the same approach to construct industry-level asset tangibility as we do for other

industry variables in Section 2.5 We then replace the startup characteristics variable (SC) with

asset tangibility (TAN) in equation (5). We report the results in Table 8.

Panel A presents the results regarding VCs’ propensity to invest. The marginal effects of

the interaction term, 𝐵𝐵𝐷 × 𝑇𝐴𝑁 , are positive but insignificant. In Panel B, we replace the

dependent variable with VC amount. The coefficient estimates on the interaction term are

positive and significant at the 1% level. This finding suggests that the negative effect of policy

uncertainty on VC total investment is mitigated if the startup firm has more tangible assets. In

Panels C and D, we replace the dependent variable with No. of VCs and Investment per VC,

respectively. We observe similar findings in terms of significant coefficient estimates on the

interaction terms. All these findings are consistent with our conjecture that startups with more

tangible assets are less risky, and therefore VC investment in these firms is less affected by

policy uncertainty.

4.3 Dependence on Government Spending

Government spending uncertainty is one of the main components of policy-related

uncertainty. As a result, when the sales of a firm are more dependent on government spending,

this firm’s production and operation would be more sensitive to policy uncertainty. Hence, we

expect that the negative effect of policy uncertainty on VC investment is more pronounced for

firms that are more dependent on government spending.

5 We provide summary statistics of industry-level asset tangibility in Panel B of Table 1.

27

To measure an industry’s dependence on government spending, we first quantify the

proportion of the industry’s total output that is contributed by government spending. Following

Belo et al. (2013), we use Benchmark Input-Output (I-O) data from the Bureau of Economic

Analysis (BEA) to calculate the proportion. The first table we use is the “Use” table, which

contains information about dollar amount of goods and services that are consumed by final users.

For industry j, we construct government purchase 𝑔𝑗 in dollars as the sum of federal, state, and

local government consumption and gross investment. From the Supplementary Industry-by-

Commodity Total Requirements table, we obtain the information on the dollar amount of input

from industry i that is required to produce one dollar of output of industry j, and we donate it as

𝑐𝑖𝑗 . Suppose there are J industries in the economy, the percentage of dollar amount of

commodities produced by industry i that is finally attributed to government purchases is then

𝑓𝑖 = ∑ 𝑐𝑖𝑗 × 𝑔𝑗𝐽𝑗=1 . The ratio of government purchases to total output for industry i is measured

by 𝑓𝑖/𝑜𝑖, where 𝑜𝑖 is the total output of industry i obtained from the “Use” table.

BEA publishes a set of I-O data files every 5 year since 1982, and every year since 1997.

To construct annual ratio between 1987 and 2015, we apply the ratio in 1987 as the proxy for the

ratio in 1988 and 1989, the one in 1992 for the ratio between 1990 and 1994, the one in 1997 for

the ratio in 1995 and 1996, and use the annual ratio between 1997 and 2015. BEA uses I-O

industry codes (1987 and 1992) and NAICS codes (between 1997 and 2015) to identify

industries. We map these two industry codes with the SIC industry code and construct annual

ratio with the 3-digit SIC industry level. If multiple I-O industry codes or NAICS codes are

concord to the same 3-digit SIC code, we take the arithmetic average.

28

We next construct the government spending dependence dummy, GSD, that equals one if

an industry’s ratio is above the median of all 3-digit SIC industries in a year and zero otherwise.6

We then replace the startup characteristics variable (SC) with the government spending dummy

(GSD) in equation (5) and report the results in Table 9.

Panel A presents the results on VCs’ propensity to invest. The coefficient estimates on

the interaction term, 𝐵𝐵𝐷 × 𝐺𝑆𝐷 , are insignificant. In Panels B, we replace the dependent

variable with VC amount. The coefficient estimates on the interaction term are negative and

significant at the 1% level in all columns. This finding suggests that the negative effect of policy

uncertainty on VCs’ total investment is more pronounced if the startup firm is more dependent

on government spending. In Panels C and D, we replace the dependent variable with No. of VCs

and Investment per VC, respectively. We observe similar findings as in Panel B. All these

findings are consistent with the hypothesis that startup firms with high government spending

dependence are riskier, and hence the negative effect of policy uncertainty on VC investment is

more pronounced for these startup firms.

4.4 Holdup from entrepreneurs

Entrepreneurs have incentives to hold up VCs by threatening to leave the startup. In the

Hart and Moore’s (1994) world of incomplete contracting, once the VC has made the investment,

an entrepreneur who recognizes that the cost is sunk cannot contractually commit to staying with

the firm, in which his unique human capital is critical to achieving the venture’s full potential.

This concern is especially severer when there are more outside opportunities around the

entrepreneurs (Tian, 2011). Chen et al. (2010) document three most clustered metropolitan areas

by entrepreneurial firms: San Francisco, Boston, and New York. Close-knit communities where

6 We provide summary statistics of industry-level government spending dependence in Panel B of Table 1.

29

many entrepreneurial firms are clustered together increase an entrepreneur’s outside job options

and the credibility of their threat to leave, which increases their ability to hold up VCs. Hence,

VCs would consider these startups riskier, and be more cautious in making investment decisions

when policy uncertainty increases. As a result, we expect that the negative effect of policy

uncertainty on VC investment is more pronounced for firms that are subject to severer holdup

from entrepreneurs, i.e., located in close-knit communities.

To capture entrepreneurs’ incentives to hold up VCs, we construct an entrepreneur cluster

score variable, CS, which is calculated by the number of startup firms in each state divided by

the number of all startup firms in the U.S. in a quarter. We replace startup characteristics variable

(SC) in equation (5) with CS. We drop state fixed effects from the regressions to avoid potential

multicollinearity caused by the cluster score variable. We report the results in Table 10.

Panel A presents the results regarding VCs’ propensity to invest. The marginal effects of

the interaction term, 𝐵𝐵𝐷 × 𝐶𝑆, are negative and significant at the 5% level, suggesting that the

negative effect of policy uncertainty on VCs’ propensity to invest is more pronounced for firms

located in close-knit communities where entrepreneurs are more able to hold up VCs, i.e. they

have better outside options. In Panels B, C, and D, we replace the dependent variable with VC

amount, No. of VCs, and Investment per VC, respectively. We observe similar findings. These

observations are consistent with our conjecture that entrepreneurs’ holdup exaggerates the

negative effect of policy uncertainty on VC investment.

5. VC Investment Outcomes

In this section, we explore a “bottom-line” question: How does policy uncertainty affect

VCs’ investment outcomes? There are typically three pathways that a VC can exit from their

investment in startup firms: going public, being acquired, and liquidation. While some studies in

30

existing literature (e.g., Bottazzi et al., 2008; Gompers and Lerner, 2000; Sørensen, 2007; and

Tian, 2011) treat both going public and acquisitions as successful exit ways for startup firms,

some other studies show that going public is a more desirable exit way than acquisitions for both

startup firms and VC investors (e.g. Bayar and Chemmanur, 2011; Brau et al., 2003; Sahlman,

1990; Tian and Wang, 2014). In our study, we use two measures to capture VCs’ successful exit

outcomes. The first measure is the IPO exit dummy that equals one if an entrepreneurial firm

goes public, and zero otherwise. The second measure is the Successful exit dummy that equals

one if an entrepreneurial firm either goes public or is acquired, and zero otherwise.

Our initial sample includes 30,408 entrepreneurial firms that receive VC financing

between January, 1987 and December, 2015. 22,147 of them receive the first-round VC

financing after January, 1987 and have exited by December, 2015. To observe the effect of

policy uncertainty on startup exit outcomes, we use the latter subsample and estimate the

following model with Probit regressions.

𝐸𝑥𝑖𝑡𝑖 = 𝛼 + 𝛽 × 𝐴𝐵𝐵𝐷𝑖 + 𝛾 × 𝐹𝑅𝐹𝐴𝑖 + 𝛿 × 𝐴𝐼𝑁𝐷𝑖 + 𝜑 × 𝑀𝑖 + 𝐹𝐸 + 휀𝑖 (6)

where i indexes firm. The dependent variables are IPO exit and Successful exit in column (1) and

(2), respectively. Because the observation unit of this analysis is startup firms, we use the natural

logarithm of average economic policy uncertainty (ABBD) of each startup firm during its

incubation period as key independent variable. FRFA is the natural logarithm of the age of firm i

at the first round of VC financing. Average industry control variables (AIND) include average

industry-level Tobin’s Q, sales growth, cash flow, and technology shock index during startup

firm i’s incubation period. Economic control variables (M) include average GDP growth, No. of

entrepreneurial firms, GDP forecast dispersion, productivity growth volatility, stock market

volatility, expected GDP growth, and consumer confidence during the startup firm i’s incubation

31

period. FE includes industry fixed effect, state fixed effect, first-round development stage fixed

effect, and first-round year fixed effect.

We report the marginal effect of Probit regressions in Table 11 because the raw

coefficients are typically hard to interpret. The marginal effects of policy uncertainty in both

columns are negative and significant at the 1% level, suggesting that a higher level of policy

uncertainty during a startup’s incubation period is negatively related to the startup firm’s

probability of either an IPO exit or a successful exit. The economic significance is sizable. For

example, according to the coefficient estimates reported in column (1), increasing BBD by one

standard deviation (30.2) from its mean value (106.3) is associated with a 57.6% (0.169×

Ln(30.2)) lower probability that a startup venture exits through an IPO.

Given that a higher level of policy uncertainty is associated with worse exit prospects,

VCs may undertake a variety of strategies to mitigate such adverse effect of policy uncertainty.

We next explore two plausible strategies that VCs could use.

First, VCs could use stage financing to mitigate the negative effect of policy uncertainty.

Staging is the stepwise allocation of funds from VCs to entrepreneurial firms. It is a common

tool used by VCs to mitigate potential agency and information asymmetry problems because

staging allows VCs to keep an option to abandon the project and has value as a real option.

When VCs are exposed to policy uncertainty, they may stage finance startup firms more to keep

their “wait and see” option, which helps mitigate the negative effect of policy uncertainty. To

explore this conjecture, we add an interaction term between ABBD and the number of financing

rounds a startup firm receives before it exits, No. of Rounds, in equation (6) and re-run the Probit

model. We report the results in Table 12 Panel A. we find that the marginal effects of ABBD

continue to be negative and significant. More importantly, the marginal effects of the interaction

32

term, ABBD × No. of Rounds, are positive and significant at the 5% or 1% level, suggesting that

the negative effect of policy uncertainty on VC investment outcomes is mitigated if VCs stage

finance their startups more.

Second, VCs could reduce their total investment amount in face of policy uncertainty to

mitigate its adverse effect. To test this conjecture, we add an interaction term between ABBD and

the VC total investment amount across all financing rounds, VC amount, in equation (6). We

report the results in Table 12 Panel B. We find that the marginal effects of the interaction term,

ABBD × VC amount, are negative and significant at the 1% level in both columns, suggesting

that the negative effect of policy uncertainty on VC investment outcomes is mitigated if VCs

invest less total funding in the startup firm.

6. Conclusion

In this paper, we have examined how economic policy uncertainty affects VC investment

and exit outcomes, using the BBD index to capture policy uncertainty. We find that policy

uncertainty has a negative effect on VC investment. Unlike the long-lasting effect of policy

uncertainty on publically traded firms, VCs appear be able to adjust their investment more

quickly and the negative effect of policy uncertainty decays in three quarters. The negative effect

of policy uncertainty on VC investment is more pronounced when startups are riskier, i.e., when

startups are less mature, have fewer tangible assets, are more dependent on government spending,

and are exposed to severer holdup from entrepreneurs. We further show that policy uncertainty

adversely affects VCs’ investment outcomes, and VCs rely on more stage financing and less total

investment amount to mitigate the negative effect of policy uncertainty. Our paper complements

the exiting literature by shedding new light on the effect of policy uncertainty on an important

market that has been largely ignored – the venture capital market.

33

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35

Table 1: Summary statistics

This table reports descriptive summary statistics of main variables used in our study. Panel A

reports the policy uncertainty index and macroeconomic control variables. Macroeconomic

control variables include GDP growth, GDP forecast dispersion, productivity growth

volatility, stock market volatility, expected GDP growth and customer expectation. Panel B

reports the investment, firm and industry variables for entrepreneurial firms that receive VC

financing during January 1987 to December 2015. Firm control variable includes firm age.

The investment variables include VC amount and No. of VCs. Industry control variables

include Tobin’s Q, sales growth, cash flow, technology shock index, tangibility and

government spending. Industry is based on two-digit SIC industry groups for technology

shock index, and on three-digit SIC industry groups for all other variables. Variables in Panel

A are measured at the quarterly level; Investment and firm control variables are at the startup

firm level; Industry control variables are at the startup firm-quarter level. All variables are

winsorized at the 1st and 99th percentiles.

Panel A: Policy Uncertainty Index and Macroeconomic Control Variables

N Mean Median Std. Dev.

Economic Policy Uncertainty Index

(U.S.) Economic policy uncertainty (BBD) 116 106.33 98.88 30.21

Canada economic policy uncertainty 116 117.74 107.58 57.62

Economic Control Variables

GDP growth 116 2.58 2.80 1.76

GDP forecast dispersion 116 0.65 0.61 0.23

Productivity growth volatility 116 209.65 102.84 373.62

Stock market volatility 116 0.08 0.07 0.02

Expected GDP growth 116 2.60 2.64 0.96

Consumer confidence 116 86.92 89.78 11.93

Panel B: Sample Firm Variables

N Mean Median Std. Dev.

Investment Variables

VC amount (million dollars) 30,408 31.65 7.50 120.52

No. of VCs 30,408 3.61 3.00 3.23

Firm Control Variables

Age 30,408 9.78 3.00 18.12

Industry Control Variables

Industry Tobin’s Q 616,851 14.19 4.13 75.22

Industry sales growth 616,851 1.64 0.23 53.99

Industry cash flow 616,851 0.34 0.29 1.30

Industry technology index 616,851 0.06 0.03 0.07

Industry tangibility 616,631 0.38 0.16 17.46

Industry government spending 610,236 0.12 0.12 0.06

36

Table 2: Policy Uncertainty and VC Investment

This table presents the results of estimating equation (1). In each panel, we use VC funding

dummy, VC amount, No. of VCs, and Investment per VC as the dependent variable,

respectively. Policy uncertainty is measured by the natural logarithm of average value of the

BBD index in a quarter. Firm age is measured by the natural logarithm of the age of firm i in

quarter t plus one. Industry control variables include Tobin’s Q, sales growth, cash flow, and

technology shock index. Industry is based on two-digit SIC industry groups for the

technology shock index, and on three-digit SIC industry groups for all other variables.

Macroeconomic control variables include GDP growth and No. of entrepreneurial firms. All

specifications include industry fixed effects, stage fixed effects, state fixed effects, year fixed

effects, and quarter fixed effects. Standard errors are clustered at the firm level. In panel A,

we run the Probit model. In panel B, C, and D, we run OLS regressions. Marginal effects of

independent variables are reported in Panel A. All variables are winsorized at the 1st and 99th

percentiles. Standard errors clustered at the startup level are reported in parentheses. ***, **,

and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Panel A: VC Funding Dummy

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.011** -0.025*** -0.018*** 0.004 -0.000

(0.004) (0.004) (0.004) (0.004) (0.004)

Age -0.069*** -0.024*** -0.026*** -0.026*** -0.024***

(0.001) (0.001) (0.001) (0.001) (0.001)

IND Tobin's Q -0.000 -0.000*** -0.000*** -0.000** -0.000*

(0.000) (0.000) (0.000) (0.000) (0.000)

IND sales growth -0.000 0.000 -0.000 -0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)

IND cash flow 0.006*** 0.001 -0.000 0.003** -0.000

(0.002) (0.001) (0.002) (0.002) (0.002)

IND technology index -0.123*** -0.097** -0.105** -0.111*** -0.087**

(0.044) (0.041) (0.042) (0.043) (0.044)

GDP growth 0.006*** 0.003*** 0.004*** 0.002*** -0.001

(0.001) (0.001) (0.001) (0.001) (0.001)

No. of entrepreneurial firms -0.056*** -0.048*** -0.045*** -0.045*** -0.043***

(0.013) (0.012) (0.013) (0.013) (0.013)

Year fixed effect Yes Yes Yes Yes Yes

Quarter fixed effect Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes Yes

Stage fixed effect Yes Yes Yes Yes Yes

State fixed effect Yes Yes Yes Yes Yes

Pseudo R squared 0.050 0.040 0.040 0.039 0.039

Observations 616,258 592,835 570,053 547,202 524,774

37

Panel B: VC Amount

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.068** -0.155*** -0.109*** 0.027 0.015

(0.033) (0.029) (0.030) (0.030) (0.031)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Macroeconomic controls Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.060 0.035 0.037 0.037 0.036

Observations 616,260 593,801 571,135 548,227 525,013

Panel C: No. of VCs

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.011** -0.029*** -0.022*** 0.000 0.002

(0.005) (0.005) (0.005) (0.005) (0.005)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Macroeconomic controls Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.041 0.028 0.030 0.029 0.029

Observations 616,260 593,801 571,135 548,227 525,013

Panel D: Investment Per VC

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.030 -0.132*** -0.100*** 0.017 0.025

(0.030) (0.026) (0.026) (0.027) (0.027)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Macroeconomic controls Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.054 0.033 0.035 0.034 0.033

Observations 616,260 593,801 571,135 548,227 525,013

38

Table 3: Robustness Check on Economic Uncertainty and Expected Economic Condition

This table presents the results of robustness check estimating equation (1). In each panel, we

use VC funding dummy, VC amount, No. of VCs, and Investment per VC as the dependent

variable, respectively. Policy uncertainty is measured by the natural logarithm of average

value of the BBD index in a quarter. Firm age is measured by the natural logarithm of the age

of firm i in quarter t plus one. Industry control variables include Tobin’s Q, sales growth, cash

flow, and technology shock index. Industry is based on two-digit SIC industry groups for

technology index, and on three-digit SIC industry groups for all other variables.

Macroeconomic control variables include GDP growth, No. of entrepreneurial firms, GDP

forecast dispersion, productivity growth volatility, stock market volatility, expected GDP

growth and customer expectation. All specifications include industry fixed effects, stage fixed

effects, state fixed effects, year fixed effects, and quarter fixed effects. Standard errors are

clustered at the firm level. In panel A, we run the Probit model. In panel B, C, and D, we run

OLS regressions. Marginal effects of independent variables are reported in Panel A. All

variables are winsorized at the 1st and 99th percentiles. Standard errors clustered at the state-

industry level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%,

and 10% levels, respectively.

39

Panel A: VC Funding Dummy

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.004 -0.021*** -0.019*** 0.005 -0.010*

(0.006) (0.005) (0.005) (0.005) (0.005)

Age -0.069*** -0.024*** -0.026*** -0.026*** -0.024***

(0.001) (0.001) (0.001) (0.001) (0.001)

IND Tobin’s Q -0.000 -0.000*** -0.000*** -0.000* -0.000*

(0.000) (0.000) (0.000) (0.000) (0.000)

IND sales growth -0.000 0.000 -0.000 -0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)

IND cash flow 0.005*** 0.001 0.001 0.003** -0.000

(0.002) (0.002) (0.002) (0.002) (0.002)

IND Technology index -0.123*** -0.097** -0.105** -0.111*** -0.087**

(0.044) (0.041) (0.042) (0.043) (0.044)

GDP growth 0.005*** 0.002*** 0.003*** 0.002*** 0.000

(0.001) (0.001) (0.001) (0.001) (0.001)

No. of entrepre. firms -0.056*** -0.047*** -0.045*** -0.045*** -0.043***

(0.013) (0.012) (0.013) (0.013) (0.013)

GDP forecast dispersion -0.009** -0.012*** -0.007** -0.008** -0.007*

(0.004) (0.003) (0.003) (0.003) (0.003)

Productivity growth vol. 0.000** -0.000 -0.000*** 0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)

Stock market volatility 0.059 0.013 -0.041 0.079** 0.062*

(0.038) (0.032) (0.033) (0.034) (0.035)

Expected GDP growth 0.003*** 0.003*** 0.002*** -0.001 -0.003***

(0.001) (0.001) (0.001) (0.001) (0.001)

Consumer confidence -0.000 -0.000 -0.000 0.000 -0.000*

(0.000) (0.000) (0.000) (0.000) (0.000)

Industry fixed effect Yes Yes Yes Yes Yes

Stage fixed effect Yes Yes Yes Yes Yes

State fixed effect Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes

Quarter fixed effect Yes Yes Yes Yes Yes

Pseudo R squared 0.050 0.040 0.040 0.039 0.039

Observations 616,258 592,835 570,053 547,202 524,774

40

Panel B: VC Amount

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.039 -0.117*** -0.095** 0.016 -0.048

(0.045) (0.039) (0.040) (0.041) (0.041)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.060 0.036 0.037 0.037 0.036

Observations 616,260 593,801 571,135 548,227 525,013

Panel C: No. of VCs

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD 0.001 -0.019*** -0.023*** -0.003 -0.010

(0.007) (0.006) (0.006) (0.007) (0.007)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.041 0.028 0.030 0.030 0.029

Observations 616,260 593,801 571,135 548,227 525,013

Panel D: Investment Per VC

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD -0.017 -0.108*** -0.082** 0.009 -0.014

(0.040) (0.035) (0.036) (0.036) (0.036)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.054 0.033 0.035 0.034 0.033

Observations 616,260 593,801 571,135 548,227 525,013

41

Table 4: Robustness Check with Canada Economic Policy Uncertainty

This table presents the results of robustness check with Canada economic policy uncertainty.

Panel A presents the results of estimating time-series regression (2). Policy uncertainty is

measured by the natural logarithm of average value of the BBD index in a quarter. We first

run the regression with Canada policy uncertainty as the only independent variable and show

the results in columns (1). We then add U.S. market control variables, including Tobin’s Q,

sales growth, cash flow and technology shock index, and macroeconomic control variables,

including GDP growth, No. of entrepreneurial firms, GDP forecast dispersion, productivity

growth volatility, stock market volatility, expected GDP growth and customer expectation.

We present the results in column (2). We replace the U.S. economic policy uncertainty by the

residual from column (2) in panel B and estimating equation (1). In panel B1 to B4, we use

VC funding Dummy, VC amount, No. of VCs, and Investment per VC as the dependent

variable, respectively. Firm age is measured by the natural logarithm of the age of firm i in

quarter t plus one. Industry control variables include Tobin’s Q, sales growth, cash flow, and

technology shock index. Industry is based on two-digit SIC industry groups for technology

index, and on three-digit SIC industry groups for all other variables. Macroeconomic control

variables include GDP growth, No. of entrepreneurial firms, GDP forecast dispersion,

productivity growth volatility, stock market volatility, expected GDP growth and customer

expectation. All specifications include industry fixed effects, stage fixed effects, state fixed

effects, year fixed effects, and quarter fixed effects. Standard errors are clustered at the firm

level. In panel B1, we run the Probit model. In panel B2, B3, and B4, we run OLS regressions.

Marginal effects of independent variables are reported in Panel A. All variables are

winsorized at the 1st and 99th percentiles. Standard errors clustered at the startup level are

reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,

respectively.

42

Panel A: U.S. and Canada Economic Policy Uncertainty

(1) (2)

CANBBD 0.378*** 0.214***

(0.040) (0.044)

U.S. Tobin’s Q 0.001***

(0.000)

U.S. sales growth -0.006

(0.014)

U.S. cash flow 0.014

(0.019)

U.S. technology index 16.211

(12.043)

GDP growth 0.010

(0.014)

No. of entrepreneurial firms -1.293**

(0.512)

GDP forecast dispersion 0.006

(0.018)

Productivity growth volatility -0.011***

(0.002)

Stock market volatility 0.249***

(0.078)

Expected GDP growth 0.000

(0.000)

Consumer confidence 2.679***

(0.922)

Constant 2.868*** 23.782***

(0.188) (7.769)

Adjusted R squared 0.432 0.706

Observations 116 110

Panel B1: U.S. BBD Residuals and VC Funding Dummy

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

U.S. BBD residual 0.003 -0.018*** -0.017*** 0.014** -0.007

(0.007) (0.006) (0.006) (0.007) (0.007)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Pseudo R squared 0.050 0.040 0.040 0.039 0.039

Observations 616,258 592,835 570,053 547,202 524,774

43

Panel B2: U.S. BBD Residuals and VC Amount

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

U.S. BBD residual -0.003 -0.130*** -0.095* 0.044 -0.061

(0.057) (0.050) (0.051) (0.052) (0.051)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.060 0.036 0.037 0.037 0.036

Observations 616,260 593,801 571,135 548,227 525,013

Panel B3: U.S. BBD Residuals and No. of VCs

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

U.S. BBD residual 0.009 -0.019** -0.024*** 0.003 -0.011

(0.009) (0.008) (0.008) (0.008) (0.008)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.041 0.028 0.030 0.030 0.029

Observations 616,260 593,801 571,135 548,227 525,013

Panel B4: U.S. BBD Residuals and Investment Per VC

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

U.S. BBD residual 0.010 -0.130*** -0.071 0.036 -0.027

(0.051) (0.044) (0.045) (0.046) (0.045)

Firm age control Yes Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes Yes

Augmented economic

controls

Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.054 0.033 0.035 0.034 0.033

Observations 616,260 593,801 571,135 548,227 525,013

44

Table 5: Robustness Check on Industry-State Level Data

This table presents the results of robustness check estimating equation (1) with data on the

industry-state level. In each panel, we use VC amount, No. of VCs, and Investment per VC as

the dependent variable, respectively. Policy uncertainty is measured by the natural logarithm of

average value of the BBD index in a quarter. Industry control variables include Tobin’s Q,

sales growth, cash flow, and technology shock index. Industry is based on two-digit SIC

industry groups for technology index, and on three-digit SIC industry groups for all other

variables. Macroeconomic control variables include GDP growth, No. of entrepreneurial firms,

GDP forecast dispersion, productivity growth volatility, stock market volatility, expected GDP

growth and customer expectation. All specifications include industry-state fixed effects, year

fixed effects, and quarter fixed effects. Standard errors are clustered at the industry-state level.

In each panel, we run OLS regressions. All variables are winsorized at the 1st and 99th

percentiles. Standard errors clustered at the startup level are reported in parentheses. ***, **,

and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Panel A: VC Amount

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD 0.052 -0.207*** -0.070 0.040 -0.093

(0.075) (0.071) (0.072) (0.071) (0.072)

Industry controls Yes Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.014 0.013 0.013 0.012 0.012

Observations 198,728 196,497 194,181 191,783 189,249

Panel B: No. of VCs

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD 0.015 -0.026** -0.025** -0.003 -0.008

(0.012) (0.011) (0.011) (0.011) (0.011)

Industry controls Yes Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.022 0.023 0.023 0.023 0.022

Observations 198,728 196,497 194,181 191,783 189,249

45

Panel C: Investment Per VC

t

(1)

t+1

(2)

t+2

(3)

t+3

(4)

t+4

(5)

BBD 0.060 -0.192*** -0.062 0.022 -0.058

(0.068) (0.064) (0.066) (0.065) (0.066)

Industry controls Yes Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes Yes

Adjusted R squared 0.012 0.010 0.010 0.010 0.009

Observations 198,728 196,497 194,181 191,783 189,249

46

Table 6: Identification Attempt on Gubernatorial Elections

This table presents the results from estimating equation (3) in panel A, and results from

estimating equation (4) in panel B. In columns (1) – (4), we use VC inv amount, No. of startups,

and Investment per startup as the dependent variable, respectively. Our observation unit is lead

VC-state(-year)-quarter. Before2, before1, event, and after1 dummies respectively equal one if it

is two-quarter before, one-quarter before, at that quarter of, and one-quarter after a

gubernatorial election in that state; otherwise dummies equal zero. All specifications include

lead VC-state fixed effects, VC-year-quarter fixed effects, and year-quarter fixed effect.

Standard errors are clustered at the lead VC level. We run OLS regressions in each column. All

variables are winsorized at the 1st and 99th percentiles. Standard errors clustered at the startup

level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%

levels, respectively.

Panel A: DiD Model

VC Inv

Amount

(1)

No. of Startups

(2)

Investment Per

Startup

(3)

Before1 -0.067* -0.006* -0.066*

(0.039) (0.003) (0.039)

VC-state fixed effect Yes Yes Yes

VC-year-quarter fixed effect Yes Yes Yes

Year-quarter fixed effect Yes Yes Yes

Adjusted R squared 0.227 0.285 0.213

Observations 283,968 283,969 283,968

Panel B: Dynamic Model

VC Inv

Amount

(1)

No. of Startups

(2)

Investment Per

Startup

(3)

Before2 -0.039 -0.003 -0.038

(0.034) (0.003) (0.034)

Before1 -0.075* -0.007** -0.074*

(0.039) (0.003) (0.039)

Event 0.019 0.002 0.019

(0.038) (0.003) (0.038)

After1 0.060 0.004 0.061

(0.039) (0.003) (0.039)

VC-state fixed effect Yes Yes Yes

VC-year-quarter fixed effect Yes Yes Yes

Year-quarter fixed effect Yes Yes Yes

Adjusted R squared 0.243 0.301 0.227

Observations 261,219 261,220 261,219

47

Table 7: Cross-Sectional Heterogeneity on Firm Maturity

This table presents the results of estimating equation (5). In each panel, we use VC funding

dummy, VC amount, No. of VCs, and Investment per VC as the dependent variable, respectively.

Policy uncertainty is measured by the natural logarithm of average value of the BBD index in a

quarter. Firm age is measured by the natural logarithm of the age of firm i in quarter t plus one.

Industry control variables include Tobin’s Q, sales growth, cash flow, and technology shock

index. Industry is based on two-digit SIC industry groups for technology index, and on three-

digit SIC industry groups for all other variables. Macroeconomic control variables include GDP

growth, No. of entrepreneurial firms, GDP forecast dispersion, productivity growth volatility,

stock market volatility, expected GDP growth and customer expectation. All specifications

include industry fixed effects, stage fixed effects, state fixed effects, year fixed effects, and

quarter fixed effects. Standard errors are clustered at the firm level. In panel A, we run the Probit

model. In panel B, C, and D, we run OLS regressions. Marginal effects of independent variables

are reported in Panel A. All variables are winsorized at the 1st and 99th percentiles. Standard

errors clustered at the startup level are reported in parentheses. ***, **, and * indicate

significance at the 1%, 5%, and 10% levels, respectively.

Panel A: VC Funding Dummy

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × Age 0.011*** 0.013*** 0.010*** 0.010***

(0.002) (0.002) (0.002) (0.002)

BBD -0.041*** -0.042*** -0.013** -0.027***

(0.006) (0.006) (0.006) (0.006)

Age -0.074*** -0.086*** -0.074*** -0.069***

(0.008) (0.008) (0.008) (0.008)

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Pseudo R squared 0.040 0.040 0.039 0.039

Observations 592,835 570,053 547,202 524,774

Panel B: VC Amount

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × Age 0.185*** 0.207*** 0.170*** 0.147***

(0.010) (0.011) (0.011) (0.011)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.036 0.038 0.037 0.036

Observations 593,801 571,135 548,227 525,013

48

Panel C: No. of VCs

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × Age 0.028*** 0.032*** 0.029*** 0.027***

(0.002) (0.002) (0.002) (0.002)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.029 0.030 0.030 0.029

Observations 593,801 571,135 548,227 525,013

Panel D: Investment Per VC

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × Age 0.161*** 0.182*** 0.150*** 0.128***

(0.009) (0.010) (0.010) (0.010)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.033 0.035 0.035 0.033

Observations 593,801 571,135 548,227 525,013

49

Table 8: Cross-Sectional Heterogeneity on Asset Tangibility

This table presents the results of estimating equation (5). In each panel, we use VC funding

dummy, VC amount, No. of VCs, and Investment per VC as the dependent variable,

respectively. Policy uncertainty is measured by the natural logarithm of average value of the

BBD index in a quarter. Tangibility is measured by net property, plant and equipment divided

by total asset in the beginning of the quarter. Firm age is measured by the natural logarithm of

the age of firm i in quarter t plus one. Industry control variables include Tobin’s Q, sales

growth, cash flow, and technology shock index. Industry is based on two-digit SIC industry

groups for technology index, and on three-digit SIC industry groups for all other variables.

Macroeconomic control variables include GDP growth, No. of entrepreneurial firms, GDP

forecast dispersion, productivity growth volatility, stock market volatility, expected GDP

growth and customer expectation. All specifications include industry fixed effects, stage fixed

effects, state fixed effects, year fixed effects, and quarter fixed effects. Standard errors are

clustered at the firm level. In panel A, we run the Probit model. In panel B, C, and D, we run

OLS regressions. Marginal effects of independent variables are reported in Panel A. All

variables are winsorized at the 1st and 99th percentiles. Standard errors clustered at the startup

level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%

levels, respectively.

Panel A: VC Funding Dummy

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × TAN 0.004 -0.002 -0.006 -0.005

(0.012) (0.012) (0.012) (0.011)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Pseudo R squared 0.040 0.040 0.039 0.039

Observations 593,501 570,697 547,820 524,571

Panel B: VC Amount

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × TAN 0.186*** 0.188** 0.133* 0.095

(0.072) (0.074) (0.073) (0.073)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.034 0.036 0.036 0.034

Observations 593,585 570,923 548,019 524,810

50

Panel C: No. of VCs

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × TAN 0.037*** 0.038*** 0.027** 0.028**

(0.011) (0.011) (0.011) (0.011)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.026 0.028 0.028 0.027

Observations 593,585 570,923 548,019 524,810

Panel D: Investment Per VC

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × TAN 0.165** 0.157** 0.106 0.071

(0.065) (0.066) (0.065) (0.065)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.032 0.034 0.033 0.032

Observations 593,585 570,923 548,019 524,810

51

Table 9: Cross-Sectional Heterogeneity on Dependence on Government Spending

This table presents the results of estimating equation (5). In each panel, we use VC funding

dummy, VC amount, No. of VCs, and Investment per VC as the dependent variable,

respectively. Policy uncertainty is measured by the natural logarithm of average value of the

BBD index in a quarter. The construction of government spending dependence dummy, GSD,

is based on the ratio of final government purchases to total industry output. For each year, if

the ratio of an industry is higher than the median, dependence dummy equals one; otherwise

zero. Firm age is measured by the natural logarithm of the age of firm i in quarter t plus one.

Industry control variables include Tobin’s Q, sales growth, cash flow, and technology shock

index. Industry is based on two-digit SIC industry groups for technology index, and on three-

digit SIC industry groups for all other variables. Macroeconomic control variables include

GDP growth, No. of entrepreneurial firms, GDP forecast dispersion, productivity growth

volatility, stock market volatility, expected GDP growth and customer expectation. All

specifications include industry fixed effects, stage fixed effects, state fixed effects, year fixed

effects, and quarter fixed effects. Standard errors are clustered at the firm level. In panel A,

we run the Probit model. In panel B, C, and D, we run OLS regressions. Marginal effects of

independent variables are reported in Panel A. All variables are winsorized at the 1st and 99th

percentiles. Standard errors clustered at the startup level are reported in parentheses. ***, **,

and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Panel A: VC Funding Dummy

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × GSD 0.007 0.007 0.005 0.007

(0.005) (0.005) (0.005) (0.005)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Pseudo R squared 0.040 0.040 0.039 0.039

Observations 593,501 570,697 547,820 524,571

Panel B: VC Amount

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × GSD -0.097*** -0.113*** -0.101*** -0.090***

(0.026) (0.027) (0.027) (0.028)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.036 0.037 0.037 0.036

Observations 587,488 565,101 542,473 519,539

52

Panel C: No. of VCs

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × GSD -0.015*** -0.018*** -0.017*** -0.014***

(0.004) (0.004) (0.004) (0.004)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.028 0.030 0.030 0.029

Observations 587,488 565,101 542,473 519,539

Panel D: Investment Per VC

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × GSD -0.077*** -0.092*** -0.080*** -0.071***

(0.024) (0.024) (0.025) (0.025)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.033 0.035 0.034 0.033

Observations 587,488 565,101 542,473 519,539

53

Table 10: Cross-Sectional Heterogeneity on Entrepreneur Holdup

This table presents the results of estimating equation (5). In each panel, we use VC funding

dummy, VC amount, No. of VCs, and Investment per VC as the dependent variable,

respectively. Policy uncertainty is measured by the natural logarithm of average value of the

BBD index in a quarter. Cluster score (CS) is measured by the number of entrepreneurial

firms in each state divided by the number of all entrepreneurial firms in that quarter. Firm age

is measured by the natural logarithm of the age of firm i in quarter t plus one. Industry control

variables include Tobin’s Q, sales growth, cash flow, and technology shock index. Industry is

based on two-digit SIC industry groups for technology index, and on three-digit SIC industry

groups for all other variables. Macroeconomic control variables include GDP growth, GDP

forecast dispersion, productivity growth volatility, stock market volatility, expected GDP

growth and customer expectation. All specifications include industry fixed effects, stage fixed

effects, year fixed effects, and quarter fixed effects. Standard errors are clustered at the firm

level. In panel A, we run the Probit model. In panel B, C, and D, we run OLS regressions.

Marginal effects of independent variables are reported in Panel A. All variables are

winsorized at the 1st and 99th percentiles. Standard errors clustered at the startup level are

reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,

respectively.

Panel A: VC Funding Dummy

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × CS -0.028** -0.031** -0.032** -0.031**

(0.013) (0.013) (0.013) (0.013)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Pseudo R squared 0.040 0.040 0.039 0.039

Observations 593,385 570,573 547,692 525,234

Panel B: VC Amount

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × CS -0.564*** -0.600*** -0.496*** -0.432***

(0.103) (0.104) (0.108) (0.110)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.035 0.037 0.037 0.035

Observations 594,363 571,666 548,727 525,482

54

Panel C: No. of VCs

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × CS -0.103*** -0.107*** -0.102*** -0.100***

(0.017) (0.017) (0.018) (0.018)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.027 0.029 0.029 0.028

Observations 594,363 571,666 548,727 525,482

Panel D: Investment Per VC

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × CS -0.439*** -0.480*** -0.375*** -0.323***

(0.092) (0.092) (0.096) (0.097)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.033 0.034 0.034 0.033

Observations 594,363 571,666 548,727 525,482

55

Table 11: Economic Policy Uncertainty and Investment Outcomes

This table presents the results of estimating equation (6). In each column, we use IPO exit

dummy and success exit dummy as dependent variables, respectively. For firm i, average

policy uncertainty is measured by the natural logarithm of average value of the BBD index

during its incubation period. First-round firm age is measured by the natural logarithm of the

age of firm i at the first round of financing plus one. Average industry control variables and

average macroeconomic control variables are the same as before. All specifications include

industry fixed effects, state fixed effects, first-round stage fixed effects, and first-round year

fixed effects. Standard errors are clustered at the firm level. We run the Probit model in each

panel. Marginal effects of independent variables are reported. All variables are winsorized at

the 1st and 99th percentiles. Standard errors are reported in parentheses. ***, **, and * indicate

significance at the 1%, 5%, and 10% levels, respectively.

IPO Exit

(1)

Successful Exit

(2)

ABBD -0.177*** -0.365***

(0.038) (0.062)

FRFA 0.001 -0.001

(0.002) (0.004)

AIND Tobin's Q -0.000** -0.001**

(0.000) (0.000)

AIND sales growth -0.006*** -0.008***

(0.001) (0.002)

AIND cash flow 0.022 0.120***

(0.022) (0.042)

AIND technology index -0.793*** 0.194

(0.135) (0.286)

Average GDP growth 0.076*** 0.260***

(0.010) (0.017)

Average No. of entrepreneurial firms -0.061 -0.199*

(0.043) (0.111)

Average GDP forecast dispersion -0.027 0.229**

(0.055) (0.115)

Average productivity growth volatility -0.000*** -0.000***

(0.000) (0.000)

Average stock market volatility -0.234 -0.355

(0.318) (0.623)

Average expected GDP growth -0.004 -0.023

(0.010) (0.019)

Average consumer confidence -0.009*** -0.027***

(0.001) (0.002)

Industry and state fixed effect Yes Yes

First-round stage fixed effect Yes Yes

First-round year fixed effect Yes Yes

Pseudo R squared 0.232 0.080

Observations 22,133 22,133

56

Table 12: Cross-Sectional Heterogeneity on Investment Strategies

This table presents the results of estimating equation (6). In each column, we use IPO exit

dummy and success exit dummy as dependent variables, respectively. For firm i, average

policy uncertainty is measured by the natural logarithm of average value of the BBD index

during its incubation period. No. of Rounds is measured by the number of financing rounds a

startup firm receives before it exits. VC Amount is measured by VC total investment amount

across all financing rounds First-round firm age is measured by the natural logarithm of the

age of firm i at the first round of financing plus one. Average industry control variables are

the same as before. Industry is based on two-digit SIC industry groups for technology index,

and on three-digit SIC industry groups for all other variables. Average macroeconomic

control variables include GDP growth, GDP forecast dispersion, productivity growth

volatility, stock market volatility, expected GDP growth, customer expectation, and No. of

entrepreneurial firms. All specifications include industry fixed effects, state fixed effects,

first-round stage fixed effects, and first-round year fixed effects. Standard errors are clustered

at the firm level. We run the Probit model in each panel. Marginal effects of independent

variables are reported. All variables are winsorized at the 1st and 99th percentiles. Standard

errors are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%

levels, respectively.

Panel A: Stage Financing

IPO Exit

(1)

Successful Exit

(2)

ABBD × No. of rounds 0.055** 0.118***

(0.022) (0.042)

ABBD -0.280*** -0.695***

(0.051) (0.090)

No. of rounds -0.226** -0.417**

(0.102) (0.196)

FRFA 0.002 0.003

(0.002) (0.004)

AIND Tobin's Q -0.000** -0.001***

(0.000) (0.000)

AIND sales growth -0.006*** -0.008***

(0.001) (0.002)

AIND cash flow 0.017 0.098**

(0.022) (0.043)

AIND technology index -0.813*** 0.119

(0.133) (0.286)

Average GDP growth 0.075*** 0.267***

(0.010) (0.017)

Average No. of entrepreneurial firms -0.061 -0.212*

(0.044) (0.121)

Average GDP forecast dispersion 0.006 0.446***

(0.057) (0.121)

Average productivity growth volatility -0.000*** -0.000***

57

(0.000) (0.000)

Average stock market volatility 0.011 0.857

(0.322) (0.637)

Average expected GDP growth -0.008 -0.042**

(0.010) (0.020)

Average consumer confidence -0.009*** -0.025***

(0.001) (0.003)

Industry fixed effect Yes Yes

State fixed effect Yes Yes

First-round stage fixed effect Yes Yes

First-round year fixed effect Yes Yes

Pseudo R squared 0.236 0.090

Observations 22,133 22,133

Panel B: Investment Size

IPO Exit

(1)

Successful Exit

(2)

ABBD × VC amount -0.015*** -0.035***

(0.003) (0.004)

FRFA Yes Yes

Average industry controls Yes Yes

Augmented economic controls Yes Yes

Fixed effects Yes Yes

Pseudo R squared 0.263 0.096

Observations 22,133 22,133

58

Appendix A: Cross-Sectional Heterogeneity on Startup Development Stage

This table presents the results of estimating equation (5). In each panel, we use VC funding

dummy, VC amount, No. of VCs, and Investment per VC as the dependent variable,

respectively. Policy uncertainty is measured by the natural logarithm of average value of the

BBD index in a quarter. If the firm is in startup/seed or early stage, early-stage dummy equals

one; if the firm is in expansion, later stage or buyout/acquisition, early-stage dummy equals

zero. Firm age is measured by the natural logarithm of the age of firm i in quarter t plus one.

Industry control variables include Tobin’s Q, sales growth, cash flow, and technology shock

index. Industry is based on two-digit SIC industry groups for technology index, and on three-

digit SIC industry groups for all other variables. Macroeconomic control variables include

GDP growth, No. of entrepreneurial firms, GDP forecast dispersion, productivity growth

volatility, stock market volatility, expected GDP growth and customer expectation. All

specifications include industry fixed effects, state fixed effects, year fixed effects, and quarter

fixed effects. Standard errors are clustered at the firm level. In panel A, we run the Probit

model. In panel B, C, and D, we run OLS regressions. Marginal effects of independent

variables are reported in Panel A. All variables are winsorized at the 1st and 99th percentiles.

Standard errors clustered at the startup level are reported in parentheses. ***, **, and *

indicate significance at the 1%, 5%, and 10% levels, respectively.

Panel A: VC Funding Dummy

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × ESD -0.018*** -0.019*** -0.014*** -0.013***

(0.003) (0.003) (0.003) (0.003)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Pseudo R squared 0.036 0.038 0.037 0.036

Observations 540,925 520,799 500,561 479,889

Panel B: VC Amount

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × ESD -0.263*** -0.284*** -0.207*** -0.173***

(0.029) (0.029) (0.030) (0.031)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.034 0.036 0.035 0.033

Observations 541,009 521,025 500,760 480,128

59

Panel C: No. of VCs

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × ESD -0.044*** -0.046*** -0.038*** -0.035***

(0.004) (0.005) (0.005) (0.005)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.027 0.029 0.028 0.027

Observations 541,009 521,025 500,760 480,128

Panel D: Investment Per VC

t+1

(1)

t+2

(2)

t+3

(3)

t+4

(4)

BBD × ESD -0.235*** -0.258*** -0.199*** -0.160***

(0.026) (0.026) (0.027) (0.028)

Firm age control Yes Yes Yes Yes

Industry controls Yes Yes Yes Yes

Augmented economic controls Yes Yes Yes Yes

Fixed effects Yes Yes Yes Yes

Adjusted R squared 0.031 0.033 0.033 0.031

Observations 541,009 521,025 500,760 480,128