Political Uncertainty and Leverage Adjustments: An International Perspective
Gonul Colak a
, Mark J. Flannery b
, and Özde Öztekin c
January 2014
Abstract
We show that political uncertainty raises financial intermediation costs and slows down firms’ adjustments toward their optimal capital structure. When political uncertainty is high, the transaction costs (fees and underwriter spread) for both new equity capital (SEOs) and new debt capital (bond issuances) increase. This raises the costs of adjustments, reduces the frequency and volume of capital structure changes, and lowers the leverage adjustment speed. Certain institutions and political systems can offset some of the adverse effects of political uncertainty on leverage adjustments.
JEL classification: G30; G32; G35
Keywords: Leverage; Capital Structure; Transaction Costs; Political Uncertainty; International a
Hanken School of Economics, 00101 Helsinki, gonul.colak@hanken.fi b
College of Business Administration, University of Florida, PO Box 117168, Gainesville, Florida 32611-7168, flannery@ufl.edu. b
College of Business Administration, Florida International University, 11200 SW 8th St, Miami, FL 33199, ooztekin@fiu.edu.
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1. Introduction
Uncertain political events can create policy uncertainty that disrupts firms’ regularly planned activities and alters economic outcomes.
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Acrimonious political debates and conflicts mean that an approaching election may substantially alter the government’s attitude toward important components of the business environment. Elevated political uncertainty, therefore, increases the risk of any economic action, which may tend to dampen new developments such as businesses’ investment and security issuance decisions. So, how significant are the economic, financial, and social costs of political uncertainty?
The popular and business press provide many anecdotal examples of how political uncertainty affects firms’ investment and financing decisions.
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For instance, many recent U.S. news articles argue that “Obamacare,” the government shutdown, and the debt-ceiling crisis
“produced a noticeable and fearful effect among Corporate America,” with “unintended consequences,” including “trillions disappearing from capital markets.” That is, money “would be coming out of the stock market and other investments,” adversely affecting the values of stocks and bonds of corporations and making external financing more costly. A March 2014
Wall Street Journal article points out the “high cost of policy uncertainty” in India, and argues
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Many studies focus on the effect of policy uncertainty on firms’ investment activities (see, e.g., Bernanke, 1983 and Rodrik, 1991 for theoretical evidence, and Barro, 1991; Alesina and Perotti, 1996; Leahy and Whited, 1996;
Bloom, Bond, and Van Reenen, 2007; Bloom, 2009; Cohen, Coval, and Malloy, 2011; Durnev, 2012, and Julio and
Yook, 2012 for some empirical evidence.) Other studies present the influences of political uncertainty on market performance (Fisman, 2001; Santa-Clara and Valkanov, 2003; Leblang and Mukherjee, 2005; Bernhard and
Leblang, 2006; Knight, 2006; Snowberg, Wolfers, and Zitzewitz, 2007; Claessens, Feijen, and Laeven, 2008;
Wolfers and Zitzewitz, 2009; Belo, Gala, and Li, 2012; Kim, Pantzalis, and Park, 2012), investments by the stateowned enterprises (Ayyagari and Shashwat, 2013), macroeconomic outcomes (Alesina and Rodrik,1994; Alesina,
Ozler, Roubini, and Swagel, 1996; Blomberg and Hess, 2001; Knack and Keefer, 2006; Chang, 2010), financial development and stability (Erb, Harvey, and Viskanta, 1996; Roe and Siegel, 2011), IPO activities (Colak, Durnev, and Qian, 2014), and foreign direct investment (Julio and Yook, 2013).
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US News, April 2012, “What the presidential election means for the stock market”; Forbes, May 2012, “How will the presidential election impact bond prices?”; WSJ, October 2012, “How the election outcome may affect bonds”;
WSJ, February 2013: “Bond market balks at Italian election results”; CNN, February 2013, “Italian election rattles world markets”, BBC news, March 2014, “Scottish independence: Lloyds and Barclays say referendum a potential risk”; Reuters, December 2013, “Pricing politics will be long, painful for Turkey investors”; WSJ, March 2014,
“Political uncertainty undermines Turkish markets”.
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that “next government will need to reduce policy uncertainty that companies face” in order to
“encourage companies to announce fresh projects.” Similar striking headlines appear in the press emphasizing how the two month election deadlock in Italy, the independence referendum in
Scotland, and approaching elections in Turkey “create financial market instability” and “vastly change the terms for investments and financial products.” In short, the popular press perceives that political uncertainty causes major economic and social losses due to disruption of firms’ investments and financing activities.
Some influential studies (see Bloom, 2009; Julio and Yook, 2012, among others) document a significantly negative impact of political uncertainty on firms’ investment expenditures. While the investment decisions of firms under uncertainty are a heavily researched area, the impact of uncertainty on firms’ financing decisions is a relatively unexplored field of research. More recently, researchers have started to investigate how and whether political uncertainty impacts firms’ financing activities. Pastor and Veronesi (2012, 2013) present a theoretical framework for understanding the adverse effects of policy uncertainty on firms’ securities. The empirical studies in this area mostly focus on the issuance patterns of individual securities and document that the volume of municipal debt issuances, seasoned equity offerings, and initial public offerings all decline during periods of higher political uncertainty (Gao and Qi,
2013; Brogaard and Detzel, 2014; Colak, Durnev, and Qian, 2014). Furthermore, firms have to pay higher costs of equity (Brogaard and Dretzel, 2014; Colak, et al., 2014) and debt (Gao and
Qi, 2013; Francis, et al., 2013) as investors require higher risk premium due to elevated political uncertainty. Since these papers study either debt or equity contracts but not both, they do not directly deal with firms’ leverage choices. Also, they analyze only U.S. firms, which limits the range of financial topics that can be addressed.
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Existing studies reveal two main economic mechanisms through which political uncertainty affects external financing of firms. First, uncertainty makes it more difficult to forecast future cash flows, and thus when faced with political uncertainty, firms are likely to delay new investment (Julio and Yook, 2012) and any external financing that would otherwise be needed to undertake these investments. Second, the aforementioned rise in the costs of debt and equity during high political uncertainty periods would likely discourage new securities issuances.
In addition to these two effects, we find evidence of a new economic channel – the financial intermediation frictions channel – through which policy uncertainty affects firm’s financing activities: the issuance (transaction) costs of new securities are likely to be higher when an uncertain election looms. The higher risk premium demanded by the investors (Pastor and
Veronesi, 2012; 2013) increases the placement costs for the financial intermediaries, who pass through these costs to the issuing firms. This is a novel and previously unexplored channel through which policy uncertainty affects the economy.
Our study is the first to analyze how policy uncertainty affects firms’ capital structure adjustments in a multinational setting. We examine the impact of political uncertainty on many aspects of firms’ capital structure – adjustment costs, financing patterns, and leverage adjustment speeds – using a sample of firms from 38 different countries. We therefore have a wider range of political uncertainties than would be present in any one country. Consequently, we should be able to obtain a more accurate assessment of the impact of political uncertainty on firms’ financing decisions and to study a wider range of questions. For example, we uncover several country-specific characteristics that offset the negative effect of policy uncertainty on firms’ financing activities.
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The study of multiple countries also permits us to utilize several different measures of policy uncertainty. Some of our policy uncertainty measures are election related. They capture the election driven uncertainty prevalent before national elections and are commonly used in the literature (see, Boutchokova, Doshi, Durnev, and Molchanov, 2012; Durnev, 2012; Julio and
Yook, 2012, among others). For the first time in the literature, we employ two new policy uncertainty measures. We use an indicator for the elections that change one or more of the veto players in the country (e.g., the control of the legislature changes hands). We also employ a nonelection based policy uncertainty measure reflecting the elected government’s ability to implement its promised policies. It captures how concentrated the elected government’s power is and whether the government is formed by coalition parties or a more unified policy maker(s) group. Capturing this source of policy uncertainty is important since in many countries (e.g.
Brazil, India, Israel, and many European countries) coalition governments are more common than single party governments. Finally, we confirm our results with the widely used news-based policy uncertainty index of Baker, Bloom, and Davis (2013).
We begin by analyzing the impact of an increase in policy uncertainty on transaction costs. For the first time in the literature, we apply the model of Altinkilic and Hansen (2000) to a multinational sample of firms to analyze the gross spreads charged on seasoned common stock offerings (SEOs) and straight bond offerings (bond issuances) by underwriting firms. The application of this model to a sample of international bonds and SEOs yields valuable insight into a novel transmission channel through which policy uncertainty propagates around the economy. Specifically, we find, depending upon the policy uncertainty indicator we use, that during periods of elevated policy uncertainty, the spread demanded by underwriters is 6-20 basis points and 16-28 basis points higher (relative to the base case of 3.47% and 0.98% under no
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uncertainty) when equity and debt issuance is conducted, translating into an increase of 2%-6% and 16%-28%, respectively.
Next, we confirm that firms are less likely to make voluntary changes to their capital structure as investors’ (and firms’ own) uncertainty increases and the transaction costs become more prohibitive. The frequency and volume of active adjustments to leverage through debt and/or equity issuances are substantially reduced when firms face a politically uncertain environment. This is reflected in the firms’ balance sheets as reduced cash flow from financing.
The economic magnitude of this effect is significant as well. The unconditional frequencies of debt and equity issues are on average 45% and 32%, respectively. Controlling for other determinants of financing decisions, political uncertainty lowers the odds of debt and equity issuances by about nine percentage points, most likely due to higher issuance costs and/or increases in investor and firm uncertainties. Instead, firms rely on passive adjustments (growth in their retained earnings), increase their cash holdings, and reduce the cash flow spent on investments. Our paper is the first to suggest that political uncertainty adversely affects financial intermediation by introducing additional friction into the marketplace, thus preventing firms from implementing their desired capital structure changes.
A widely accepted view in the capital structure literature is that when firms issue debt or equity, they take into consideration not only their observed leverage, but also their target leverage that maximizes firm value (Graham and Harvey, 2001). If leverage adjustment costs were zero, firms would have no incentive to deviate from their target, and the necessary adjustments to leverage would be made instantaneously. However, due to market frictions such as transaction costs, firms may choose to temporarily deviate from their target leverage. To the extent that rebalancing is costly, altering the leverage ratio becomes burdensome, implying a
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slower adjustment to optimal leverage (Hovakimian, Opler, and Titman, 2001; Leary and
Roberts, 2005; Flannery and Rangan, 2006; Strebulaev, 2007; Lemmon, Roberts, and Zender,
2008; Huang and Ritter, 2009). We posit that since policy uncertainty significantly increases leverage adjustment costs and discourages external financing, firms exposed to this uncertainty should exhibit slower adjustment speeds. Our results suggest that a one standard deviation increase in policy uncertainty slows capital structure adjustments from 2% to 8% (compared with an average adjustment speed of 19%), depending upon the uncertainty measure.
Since political systems in many countries are designed to periodically conduct elections, the aforementioned disruptive effect of political uncertainty is unavoidable. However, a country’s institutions, legal traditions, and other properties of its political system can be designed to mitigate the disruptive effects of elections. We report that firms in presidential political systems and in countries with strong institutions (common law legal origin, more disclosure to congress and/or to the public, and higher public sector ethics) are able to counteract some of the adverse effects of policy uncertainty on capital structure adjustments. Within this context, we also analyze the impact of the recent financial crisis. We find that national reactions to the financial crisis are not easily summarized. However, the crisis has significantly altered the relationships among policy uncertainty, adjustment speeds, and a country’s institutions and much more so for countries with weak institutions and parliamentary systems.
The rest of the paper is organized as follows. Section 2 reviews the literature and presents our hypotheses. Section 3 describes the firm-level data, the country-level election sample, and the policy uncertainty measures we use in this study. Section 4 investigates how adjustment costs are affected by policy uncertainty. Section 5 and Section 6 analyze the effects of policy
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uncertainty on firms’ financing patterns and capital structure adjustment speeds respectively.
Section 7 provides our conclusions.
2. Literature review and hypotheses
This article draws on two strands in the political uncertainty and capital structure literature. The first examines the impact of political uncertainty on firms’ financing costs and financing patterns. The second strand is the set of studies that examine the impact of transaction costs on firms’ capital structure. This article contributes to both facets.
2.1. The effect of political uncertainty on firms’ financing costs and financing patterns
Two theoretical studies by Pastor and Veronesi (2012, 2013) claim that political uncertainty commands a risk premium in the prices of securities, which, in turn, could dissuade firms from issuing these undervalued securities to raise external capital. Subsequent empirical studies have found that firms’ financing costs are unfavorably affected by the politically charged atmosphere. For equity, Colak et al. (2014) find that the number of IPOs originating from a state that is scheduled to have a gubernatorial election is significantly reduced, and that the cost of new equity increases around these elections. Similarly, using Baker et al.’s (2013) political uncertainty index (a non-election news-based measure) and a sample of U.S. seasoned stocks,
Brogaard and Detzel (2014) empirically show that an increase in policy uncertainty raises the risk premium of stocks, which, in turn, is likely to increase the cost of equity financing. For debt,
Gao and Qi (2013) find that the cost of financing is higher for U.S. municipal bonds originating from a state when the state is exposed to policy uncertainty due to approaching gubernatorial elections. Francis, Hasan, and Zhu (2013) employ Baker et al.’s (2013) political uncertainty
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index and find that a one standard deviation increase in political uncertainty leads to around ten basis points of additional spread on U.S. bank loans.
Albeit only indirectly, several other studies have also raised the possibility that approaching elections could impede the equity financing efforts of firms. In an international sample of 79 countries, Durnev (2012) demonstrates that political uncertainty reduces the informational value of stock prices. Boutchkova, Doshhi, Durnev, and Molchanov (2012) find that political uncertainty disproportionately increases firms’ stock volatilities in certain industries. Similarly, others have claimed that politics could negatively affect stock market performance (see, e.g., Fisman, 2001; Santa-Clara and Valkanov, 2003; Leblang and Mukherjee,
2005; Bernhard and Leblang, 2006; Knight, 2006; Snowberg, Wolfers, and Zitzewitz, 2007;
Bialkowski, Gottschalk, and Wisniewski, 2008; Claessens, Feijen, and Laeven, 2008; Wolfers and Zitzewitz, 2009; and Belo, et al., 2012). These studies generally point to the reluctance by firms to issue shares during certain politically sensitive periods as they would be unsure of investors’ perceptions of their new equity.
2.2. The effect of adjustment (transaction) costs on capital structure
A widely accepted view in the theory of capital structure is the existence of an optimal target capital structure. A related empirical prediction of this targeting behavior is the convergence of actual leverage toward a target in the long-run. Several studies establish mean reversion in the leverage ratio, both in U.S. and in international samples, and interpret the evidence as convergence toward an optimum over time (Fama and French, 2002; Flannery and
Rangan, 2006; Huang and Ritter, 2009; Öztekin and Flannery, 2012). These studies indicate that
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firms actively pursue target debt ratios, although adjustment (transaction) costs could lead to temporary deviations from the optimum.
Several studies examine firm- and country-level determinants of adjustment speeds to shed more light on the nature of adjustment (transaction) costs. For a sample of U.S. firms,
Faulkender, Flannery, Hankins, and Smith (2012) determine that firms with cash flows significantly exceeding their leverage deviation adjust faster. Warr, Elliot, Koëter-Kent, and
Ȫztekin (2012) find quicker adjustment for firms that are above (below) their target when their equity is overvalued (undervalued). Across an international sample of 37 countries, Öztekin and
Flannery (2012) and Öztekin (forthcoming) illustrate that better institutions are associated with faster adjustment.
2.3. Hypotheses
Our study evaluates the effects of political uncertainty on firm financing costs and financing patterns in a unified framework where a firm has a target capital structure. Based on the financial theory, we posit that managers have discretion over their firms’ leverage choices and that they adopt capital structures with the value-maximizing level of debt and equity. We also acknowledge, however, that firms occasionally deviate from their target leverage ratios, and do not instantly adjust back to their target when there are significant costs associated with rebalancing.
We develop three hypotheses related to the capital structure changes under elevated political uncertainty. First, inspired by the above literature, we hypothesize that the costs of adjusting leverage are likely influenced by political uncertainty. Our transaction cost (financial intermediation frictions ) hypothesis posits that the gross spread demanded by underwriting firms
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increases with policy uncertainty for both debt and equity, because during these periods new securities’ placement risk increases as investors update their Bayesian beliefs about asset valuations (Pastor and Veronesi, 2012; 2013) and the markets are more volatile in general
(Bialkowski et al., 2008; Boutchkova et al., 2012). This adds to the placements costs incurred by these intermediaries and increases their failure (or reputational) risks. For example, for a sample of U.S. firms, Altinkilic and Hansen (2000) find that marginal (placement) costs rise when adverse selection problems, agency problems, and/or marketing problems are exacerbated in the presence of an external shock, such as an election driven uncertainty shock that affects the risk aversion of investors and firms. To test this proposition, we adopt their underwriter spread model, which enables estimations of the fixed and variable (marginal) components of transaction costs incurred by underwriters for their certification, monitoring, and marketing efforts. Through the adaptation of this model, we find a new channel of transmission through which policy uncertainty impacts firms’ activities, namely the financial intermediation frictions channel.
Second, a direct implication of the transaction cost hypothesis is the capital structure adjustment hypothesis , which posits that greater policy uncertainty is associated with a lower frequency and volume of capital structure adjustment, due to the higher costs of transacting in debt and equity markets and/or increases in investor and firm uncertainties. This results in slower speeds of adjustment toward firms’ target leverage ratios. To verify this hypothesis, we conduct a detailed analysis of firms’ observed balance sheet adjustments, financing patterns, and adjustment speeds under political uncertainty.
Third, we hypothesize that certain country features may counteract some of the negative influences of policy uncertainty on capital structure adjustment speed. Democratic countries cannot totally avoid political uncertainty, but institutions can be introduced or designed to
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ameliorate the detrimental effects of politics on economic agents. Weak institutions can have serious social and economic costs. One aspect of these costs is that the adjustments towards optimal capital structure are delayed (Oztekin and Flannery, 2012; Oztekin, forthcoming). Our paper shows that elevated political uncertainty exacerbates social losses due to weak institutions by increasing the risks to the participating economic agents (firms, investors, financial intermediaries, etc.), by imposing additional frictions (transaction costs) in the marketplace, by forcing firms to operate at suboptimal leverage levels for longer periods of time, and by delaying external financing that could otherwise be used to invest in job-creating positive NPV projects.
Put differently, our institutional strength hypothesis claims that there are certain country-specific institutions that can ameliorate the adverse effects of policy uncertainty on capital structure adjustment speed.
3. Sample selection, variables, and data sources
This section provides information regarding the sample selection criteria, the variables used in the empirical analysis, and the data sources used to retrieve various firm and country characteristics.
3.1. Firm-level data
We combine data from several sources for a sample period of 1990-2006. The starting year is determined by the availability of reliable firm-level data and by the lack of elections in many countries prior to the 1989 Berlin Wall collapse. The final sample year is chosen to avoid the financial crisis years from 2007-2009 (We perform additional analyses covering the crisis period in Section 6.). The accounting data for the sample firms in each country is taken from
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Compustat Global Vantage. We apply several selection criteria. First, we eliminate firms headquartered in countries with a one party system (e.g., China and Hong Kong), countries where national elections are non-existent and/or that are ruled by a strong monarchy (e.g.,
Kuwait and Saudi Arabia), or where official elections are conducted, but the country is run by a ruler with “dictator-like power” (e.g., Egypt and Venezuela). Additionally, to reduce short panel bias, we delete firms that report information for fewer than five consecutive years. Moreover, following the previous literature, we eliminate financial firms (SICs 6000-6999) and utilities
(SICs 4000-4999). Finally, to ensure a reasonable cross-sectional variation within each country, we drop countries that have fewer than ten firms reporting the required accounting data.
We obtain information on all of the sample firms’ domestic and global SEOs and straight bond offerings from the Security Data Co. (SDC)'s Global New Issues Database. We follow the sample selection criteria in Altinkilic and Hansen (2000) by excluding unit offerings, rights offerings, shelf offerings; private placements, observations with missing proceeds and gross spread data; and very small (proceeds under $10 million) or very large (over $1 billion) issues.
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This yields a sample of 7,405 SEOs and 1,751 bond offerings.
3.2. Country-level data
We collect information regarding firms’ macroeconomic, institutional, and political environment from various country-level databases. We obtain the macroeconomic data from the
World Development Indicators (WDI) database. The institutional data comes from various sources, primarily the law and finance literature. We rely on several sources to collect the national election data. We begin with the Database of Political Institutions (DPI) developed by
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A shelf offering involves an equity issuance that is spread throughout time, which makes it difficult to pinpoint whether the offering is in a high or low political uncertainty period. It is important to also note that the inclusion of large issues over $1 billion in proceeds does not alter our conclusions.
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the Development Research Group of the World Bank. We verify this data using the Election
Results Archive (ERA) of the Center on Democratic Performance (CDP), the Center for
Systemic Peace’s Polity IV Project dataset, and the Inter-parliamentary Union’s PARLINE database.
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We retrieve the following data items describing each election: the type of political system prevalent in the country at the time of the election (presidential or parliamentary), the election date, the election outcome, the percentage of votes received by the first and the second candidates/parties, the term length for the elected president/representatives, and the percentage of the seats held by the governing party during a given calendar year (as reported by DPI).
Following Durnev (2012), we define national elections as those where the nation’s most powerful politician(s) get elected. For presidential systems, the national elections are defined as those where presidential candidates compete.
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For parliamentary systems, the national elections refer to those where the parliament/congress/assembly is elected.
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We also identify elections that result in one of the political system’s “veto players” changing. Veto players are the actors – politicians or political parties – who can block proposals to move away from status quo.
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These elections are also likely to generate certain level of political uncertainty.
Using the DPI data and the ERA data, we devise several policy uncertainty measures in order to capture the extent of policy uncertainty preceding each election. Our political
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When conflicts arise among the data sources, we also use auxiliary internet resources, such as the ACE Electoral
Knowledge Network, www.electionresources.org, Keesing’s Record of World Events, and Wikipedia, and rely on
“the simple majority rule” among the data sources to resolve a conflict.
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The countries where an assembly elected presidential system is prevalent (e.g., Indonesia) are considered as being run through a presidential system.
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Following Julio and Yook (2012), if a country run with parliamentary system has a bicameral parliament/congress, we consider the elections associated with the more powerful chamber.
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Veto players are the president and the largest party in the legislature for a presidential system, and the prime minister and the parties in the majority government coalition for a parliamentary system. For example, a presidential system changes its veto players, when the majority in Congress shifts from the president’s party to the opposition party. See Table 1, Panel B for further details.
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uncertainty (PU) measures Election Dummy , Shifted Election Dummy (SED) , and Change Veto
Players, (CVP) are meant to capture the uncertainty of economic agents about an election’s outcome. They are similar to the election variables used in Durnev (2012) and Julio and Yook
(2012). Election Dummy indicates whether there was an election during that calendar year. With
SED measure, if the election is in the first-half of the year, the previous calendar year is considered as the one with high election uncertainty. CVP tabulates the percent of veto players who drop from the government in any given year. These measures are described in detail in our
Table 1, Panel B.
We also have variables to measure sources of political uncertainty outside the electoral cycle. Examples of such non-election based events include the 2013 U.S. government shut-down or a parliamentary government’s failure to win a vote of confidence (e.g., Italian government’s vote of confidence in 2013). In a sense, these variables capture the residual policy uncertainty remaining after the elections are over. For example, if the outcome of the election is a coalition government with dispersed power centers (dummy variable Coalition = 1), this outcome would not eliminate all of the policy uncertainty that was prevalent prior to the election. Economic agents would still be unsure which of the promised policies will be implemented. The Economic
Policy Uncertainty Index, ( EPUI ) of Baker et al. (2013) has the advantage of being continuous and is able to be calculated even for non-election years. EPUI has three underlying components: newspaper references to policy uncertainty, scheduled tax code expirations, and disagreement among economic forecasters about monetary policy variables. We employ this index for robustness reasons as it is available for only eight of the countries in our sample and only for a few years in our sampling period limiting its usefulness in cross-country analyses.
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Canada (1990-2006), France (1997-2006), Germany (1997-2006), India (2003-2006), Italy (1997-2006), Spain
(2001-2006), United Kingdom (1997-2006), U.S. (1990-2006).
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Our sample is an unbalanced panel consisting of 16,519 firms from 38 countries. All of the continuous variables are winsorized at the 1% and 99% levels to mitigate the impact of outliers. Table 1 provides information regarding the definition, source, construction, and summary statistics of the firm- and country-level variables. Table 2 provides summary statistics concerning the policy uncertainty indicators for our sample countries.
Insert Table 1 and Table 2 about here.
4. Policy uncertainty and its impact on transaction costs
It has been shown that policy uncertainty increases the cost of equity (Brogaard and
Detzel, 2014; Colak et al., 2014) and the cost of debt (Francis et al., 2013; Gao and Qi, 2013).
However, the speed and frequency of capital structure adjustments also depend heavily upon transaction costs (Strebulaev, 2007; Öztekin and Flannery, 2012). The impact of policy uncertainty on the transaction costs of issuing equity or debt securities is an unexplored area in the literature.
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Thus, we begin our analyses by determining whether and how one of the main components of adjustments costs, namely the transaction costs paid by issuing firms to financial intermediaries, are affected by policy uncertainty.
Policy uncertainty could affect underwriting firms’ gross spreads for several reasons.
First, the intermediaries may perceive equity and debt offerings riskier when political uncertainty is present. Underwriters could realize (or believe) that the demand for securities will be weakened due to investors’ nervousness (Bialkowski et al. 2008) and demand for higher risk premia (Pastor and Veronesi, 2012; 2013) If political uncertainty therefore increases the possibility of a “failed issue,” the underwriters risk losing prestige. Consequently, they will
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One exception is Öztekin and Flannery (2012) who compare the trading costs of debt and equity as reported by
Elkins McSherry across institutional environments for a sample of 34 countries during the last quarter of 2005.
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demand higher compensation for the offering. Second, since demand is likely to be weaker during these periods, underwriters must exert more effort in placing/marketing these securities.
Third, political uncertainty may force the issuing firm to reduce its offering’s size. Spreads tend to be inversely related to offer size (Inmoo, Lochhead, Ritter, and Zhao, 1996).
4.1. Estimation models
A large portion of the transaction costs are the gross spreads paid to intermediaries (or underwriting firms). Pastor and Veronesi (2012) find that political uncertainty increases the required risk premium on securities. Bialkowski et al. (2008) and Boutchkova et al. (2012) document that political uncertainty increases asset price volatilities. It is therefore plausible that underwriters’ costs of certification, monitoring, and marketing will rise with PU. We test this hypothesis by applying, for the first time in the literature, Altinkilic and Hansen’s (2000) model of underwriters’ gross spread to a sample of international SEOs and straight bond offerings.
To estimate the influences of policy uncertainty on SEO underwriters’ spreads, we run a regression model that distinguishes between fixed and marginal costs as in Altinkilic and
Hansen’s (2000) Model 3 (in their Table 2) with the inclusion of country fixed effects to control for the country-specific variation in underwriting services and the corresponding spreads:
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S i
   
1 x i
  x i y i
  j
 j
C j
  i
(1) where S i
is the percentage of underwriter spread for a particular issuance event i as reported in
SDC, x i
represents the gross proceeds raised during the issuance event i , y i
is the market value of
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Instead of including country fixed effects in Equation (1), we could incorporate cross-country heterogeneity by estimating Equation (1) on a country-by-country basis. However, this is not feasible due to the small sample size in some countries.
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the issuing firm’s equity immediately prior to the offering as reported in SDC,
C j
is a dummy variable equal to one if the issue i is in country j , and ε i
is the error term.
For straight bond offerings we apply Altinkilic and Hansen’s (2000) Model 1 (in their
Table 5) with the addition of S&P bond ratings and country fixed effects:
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S i
    ln( x i
)

A

SP

B

SP
R
SP
  j
 j
C j
  i
(2) where R
SP
are four dummy variables indicating whether the issue has an S&P bond rating of B,
BB, BBB, or A, with CCC rated bonds included in the B category. The R
SP
variables measure the firm’s access to public bond markets (Faulkender and Peterson, 2006). The rest of the variables are defined as in Equation (1).
Using one of our policy uncertainty indicators, we group our offerings into issuances during high (High PU) vs. low (Low PU) policy uncertainty periods. Using Election Dummy,
Shifted Election Dummy, and Coalition measures, the grouping is based on whether the indicator variable is zero (Low PU) or one (High PU). For Change Veto Players , the grouping is relative to its median value with above (below) median denoting High PU (Low PU) states. Within each
High or Low PU sample, we estimate the parameters in (1) and (2) by ordinary least squares
(OLS) and construct a fitted spread for each issue. For the SEO issues, we also use the estimated parameters to compute an estimated marginal spread and the marginal spread’s slope.
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Altinkilic
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We rely on their simplest specification due to lack of data for the market values of equity for many firms (36% of the sample) conducting a bond offering. We feel reasonably comfortable that our specification is without loss of generality as Altinkilic and Hansen (2000) note that: 1) bond offerings demonstrate clear economies of scale, 2) bond spreads are much lower than equity spreads, and 3) the role of marginal costs in bond offerings are substantially lower. Nevertheless, in unreported tests, we repeat our analysis with the inclusion of the variable cost proxy and our conclusions remain unchanged. For instance, using Shifted Election Dummy , the marginal spread ($) increases by $1,536 in absolute terms and 9.92% in relative terms and the marginal spread (%) increases 0.18 in absolute terms and 13.64% in relative terms when policy uncertainty is high. This translates into an increase in transaction costs by $670 per $1 million of equity raised, or a 175% increase in relative terms.
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The marginal spread (marginal spread’s slope) refers to the first (second) derivative of the gross spreads with respect to proceeds, i.e. the change in the gross spread (marginal spread) for a change in proceeds. Total fitted
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and Hansen (2000) find that the variable (or marginal) costs of SEO issues form up to 85% of the underwriter’s spread and increase with an issue’s risk. Accordingly, we expect higher (total and marginal) spreads and a steeper marginal spread slope during high policy uncertainty periods.
4.2. Empirical results
The estimation results for the equity gross spreads from Equation (1) are summarized in
Table 3 for issuances taking place during low and high political uncertainty periods.
13
We present the results using four different policy uncertainty indicators ( Election Dummy, Shifted
Election Dummy, Change Veto Players, and Coalition ). In Panel A, the median estimated gross underwriter spread in our international SEOs sample hovers around 3.5%. When policy uncertainty is high, the gross spread is 6-20 basis points higher in absolute terms and (1.85%-
5.71% in relative terms). In Panel C, the estimated marginal spread increases by an even larger amount, suggesting that political uncertainty affects marginal costs of issuing equity more severely. The increase is 13-257 basis points depending upon the policy uncertainty measure. We also report that firms encounter steeper marginal spreads per dollar of proceeds during high policy uncertainty periods. In terms of dollars, the marginal spread for an additional million dollars (Panel B) is $2,620-$51,350 higher under high political uncertainty. This corresponds
(Panel D) to a jump in transaction costs (at the margin) from $83-$1,087 per $1 million of equity underwriter’s spread (%) for SEO and bond issues, the marginal spread (US $), marginal spread (%), and the marginal spread’s slope (US $) are calculated as:



 ˆ   ˆ x
1 i
  ˆ x y i i



;


  ˆ ln( x i
)

A

SP

 ˆ
SP
R
SP
B

 ;



10 , 000



ˆ 
2
 ˆ x y i i








;



 ˆ 
2
 ˆ x y i i



; and



10 , 000



2
 ˆ y
1 i





.
13
To preserve the sample size, we run our estimations on the pooled sample. In unreported tests, our qualitative conclusions are unchanged if we estimate the coefficients separately for the high and low policy uncertainty subsamples.
19

raised. The incremental transaction costs imposed on the firms’ equity financing due to policy uncertainty are economically nontrivial and statistically significant at the 1% level.
Insert Table 3 and Table 4 about here.
The estimation results for straight bond spreads from Equation (2) are presented in Table
4.
14
Panel A reports the mean and median estimated spreads for issuances taking place during low and high political uncertainty periods. The average gross underwriter spread in our international bonds sample is around 1%, and it increases with policy uncertainty by about 16-28 basis points in absolute terms and 16%-28% in relative terms, depending upon the uncertainty measure used. To further analyze the impact of policy uncertainty on bond offerings, in Panels B and C, we report the number of bond offerings per year and the average proceeds per offer for high and low uncertainty periods. The number of bond issuances during the policy uncertainty periods drops by 33%-88%, while the average size of the offerings declines by about 1.63%-
5.30%. This result indicates firms’ aversion in conducting debt offerings of large sizes during periods of elevated policy uncertainty. It is worth noting that coalition results constitute an outlier in Panels A and C, possibly due to the exceptionally low number of observations in High
PU (76 out of 1,751 firm-years) states.
In Panel D, we examine whether the issued bonds’ average debt rating varies with political uncertainty. We quantify S&P bond ratings using a point system similar to the one in
Brisker, Colak, and Peterson (2013), where AAA rated bonds are assigned 20 points and D rated bonds are assigned one point.
15
The results indicate that the average ratings for the bonds issued
14
Bonds have features that are not relevant for equity offerings, but are important characteristics of the bond issuances (such as the credit rating of the issued bond). So, for bond offerings, we report few additional statistics that might describe the reaction of the bond markets to policy uncertainty.
15
More specifically, the mapping of the S&P ratings with numerical values is done as follows: AAA=20, AA=19,
AA-=18, A+=17, A=16, A-=15, BBB+=14, BBB=13, BBB-=12, BB+=11, BB=10, BB-=9, B+=8, B=7, B-=6,
CCC+=5, CCC=4, CCC-=3, CC=2, D=1, and nonrated bonds=0.
20

in High PU periods is about one level higher than the average bonds issued in Low PU periods
(~10 vs. ~9). This suggests that bond issuers who are able or willing to issue during High PU periods need to have higher credit ratings than usual in order to be able to raise debt capital. This, in turn, is a sign of lackluster demand and/or risk averseness by bond investors during politically charged periods.
In summary, our findings suggest that policy uncertainty increases the risks and the placement costs for financial intermediaries who, in turn, pass along these costs to the issuing firms in the form of higher transaction costs for both debt and equity securities. Thus, policy uncertainty introduces additional friction in the marketplace in the form of higher adjustment costs. In the following two sections, we examine the consequences of elevated adjustment costs for firms’ financing patterns and adjustment speeds.
5. The impact of political uncertainty on firms’ capital markets access
5.1. Patterns of capital markets access: Univariate analyses
First, we analyze the patterns of capital markets access under policy uncertainty in a univariate setting. The results thus far indicate that the transaction costs associated with issuing new debt or equity securities increase under policy driven uncertainty. Exposure to such policy risks should alter the frequency and the size of a firm’s capital market access. We define capital market access as any debt or equity issuance (or any debt retirement) that is larger in size than
5% of the firm’s prior year total assets (Leary and Roberts, 2005; Öztekin and Flannery, 2012).
When defining access through equity repurchases, we follow Leary and Roberts (2005) and use
1.25% (instead of 5%) of the prior year assets as our threshold.
Insert Table 5 about here.
21

Table 5 reports the percentage of firm years in which our sample firms access external capital markets (Panel A), the size of the capital structure adjustments (Panel B), how that access breaks down between debt and equity securities and during the election and non-election years, where election year is determined by Shifted Election Dummy.
We also distinguish between active and passive capital structure management. Active management is the outcome of the net issuances of debt and equity securities. Passive management denotes internally funded changes in retained earnings.
Several interesting patterns emerge. First, during election years, firms’ frequency of access (through debt or equity) declines by about 2% relative to the regular accessing pattern of
71.26% during non-election years.
16
In addition and consistent with this phenomenon, we note fewer leverage increases (2.39%) and fewer leverage decreases (1.06%) during election years relative to non-election years. The corresponding magnitudes of access (shown in Panel B) follow a similar pattern. Clearly, firms are reluctant to make any active changes (up or down) in their leverage during a typical election year. However, they alter their leverage more often through passive changes: 52.79% (election years) vs. 51.77% (non-election years), an increase of about 2% relative to non-election years. The magnitude of these passive adjustments, however, is lower during election years, similar to active adjustments. Moreover, while there are fewer debt and equity issuances, the drop in the frequency of equity issuances is larger in magnitude (-
3.62% vs. -2.75%) indicating that equity is more severely affected by policy uncertainty than debt due to the higher variable costs associated with equity issuances. A similar pattern is observed for the incidences of net changes in equity and debt: lower by 3.28% vs. 1.54% in election years, correspondingly. Panel B’s related size results are in line with this pattern and
16
In the text, for brevity, we refer only to relative differences between election and non-election year samples as documented on the fourth row of Panel A and Panel B in Table 5.
22

indicate a drop of 3.71% and 2.75% relative to non-election years. Additionally, only incidences of debt or equity retirement increase with policy uncertainty (by about 1.42% and 1.18%, correspondingly), however the volumes of these retirements are significantly lower (by about
1.06% and 3.66%, respectively) and as reported earlier, they do not result in major changes in leverage. All of the above differences are statistically significant at the 1% level.
To further analyze the financing consequences of policy uncertainty, we use the following identity for cash:
∆𝐶𝑎𝑠ℎ 𝑡
≡ 𝐶𝐹𝐹 𝑡
+ 𝐶𝐹𝐼 𝑡
+ 𝐶𝐹𝑂 𝑡
+ 𝑂𝑡ℎ𝑒𝑟
Insert Table 6 about here.
(3)
Table 6, Panel A, describes what happens to each component of this identity during high election uncertainty periods.
All the accounting variables are scaled by lagged assets to control for possible size differences between the firms in election and non-election subsamples.
17
The change in cash holdings (ΔCASH) increases in magnitude, pointing to a precautionary savings motive under policy uncertainty. More importantly, for the first time in the literature, we report that firms’ cash flows from financing activities (CFF) fall during periods of elevated election uncertainty. This, in turn, leads to the deterioration of financing sources for these firms from
1.32% to 0.66% (of the lagged assets), translating into a decrease of 50%. The magnitude of cash outflows due to investment activities (CFI) also declines, implying reduced investment during election years. Our results on cash holdings and investment are consistent with Julio and Yook’s
(2012) findings. The cash flow from operations (CFO) and the other reconciliatory items (such as exchange rate effects) also change during election years. These changes, however, are less voluntary and less manipulable by the firm, and therefore they may not be directly related to changes in policy uncertainty.
17
The unreported results without any size adjustment (scaling) lead to similar conclusions.
23

Panel B of Table 6 decomposes CFF into its subcomponents (i.e., the accounting items forming CFF).
18
We find that, on average, both the sale (SSTK) and the purchase (PRSTKC) of common and preferred stock decline during election years. The same is true for long-term debt issuances (DLTIS) and retirement (DLTR). These findings are consistent with our results in
Table 5, where we report that the dollar volumes of debt and equity repurchases and issuances decrease. Median dividend spending (DIV) is slightly higher, probably due to a regular uptrend in dividend payments. The change in current debt (DLCCH) is reduced in size implying a lower reduction in the magnitude of current debt.
19
In short, the findings from Table 6 confirm that when facing policy uncertainty, firms adopt a more passive/conservative approach of “wait-and-see” with regard to not only their investment activities, but also their financing activities. This leads to the deterioration of funding sources for these firms. It also encourages cash hoarding behavior, which is typically not a significant value creating activity for the firm. These findings suggest that an increase in policy uncertainty can temporarily lead to socially suboptimal outcomes.
5.2. Patterns of capital markets access: Multivariate analyses
Next, we estimate the determinants of capital market access in a multivariate setting to establish whether policy uncertainty is a significant predictor of the likelihood of access after
18
To save space, the decompositions of CFI and CFO are not tabulated (available upon request). We find some evidence that the decline in CFI during election years is due to slower increase in investments (IVCH) and lower capital expenditures (CAPX). Also, during the election years, firms increase their CFO in order to procure more internal capital by increasing the accounts payables (APALCH) and the accrued taxes (TXACH), and by reducing the cash outflows related to inventories (INVCH). These are items that firms have certain discretion over. Income before extraordinary items (IBC) also increases, but this is likely driven by exogenous forces (e.g., an increase in external demand for certain products by government agencies – through government procurements – during the election years (see Nordhaus, 1975)). The remaining components do not seem to be affected significantly by policy uncertainty.
19
Note that the firms in the election and non-election subsamples may differ. To take this possibility into account, we create a “squared sample” by restricting the sample to the same firms across the election and non-election years.
The results with this sample are, in general, supportive of our main findings, but in some cases the significance tests lack power due to significantly reduced sample size (more than 90% of our firms drop from the sample).
24

controlling for various firms characteristics. Essentially, we estimate a logit model with our proxy variable for policy uncertainty ( Shifted Election Dummy ) and some control variables commonly employed in the capital structure literature: firm size, market-to-book, leverage, profitability, expected capital expenditures, R&D expenses, cash holdings, depreciation, tangibility, selling expenses, and Z-score.
20
We also include country fixed effects to control for any country-specific effects that might influence a firm’s decision to access the capital markets.
All the right hand side variables except expected capital expenditures are lagged one period to alleviate endogeneity concerns. The estimation results are presented in Table 7.
Insert Table 7 about here.
We present the results for each form of capital market access separately: debt issuance, equity issuance, debt retirement, equity retirement, a leverage increasing change, and a leverage decreasing change. Consistent with the univariate results from the previous subsection, debt or equity issuance is significantly negatively affected by a politically charged atmosphere. The odds ratios, presented at the bottom of the table, suggest that the likelihood of debt and equity issuances both decrease by about nine percentage points during an election year (both changes are significant at the 1% level). This implies that firms do not want to make any drastic changes in their capital structure when facing policy uncertainty, possibly due to higher transaction costs and/or increases in investor and firm uncertainties. Indeed, the regressions shown in the last two columns suggest that the likelihood of firms taking any action that will increase (decrease) its leverage is statistically significantly reduced by about ten (nine) percentage points.
21
The odds of retiring (debt or equity) securities increase slightly during election years, but the results are statistically insignificant. This is consistent with the mixed results that we find from our
20
In unreported results, our alternative policy uncertainty indicators lead to similar conclusions.
21
A change in leverage is recognized as non-zero when it exceeds 5% of lagged total assets.
25

univariate analyses in Table 5 regarding the debt or equity retirement choices of firms when policy uncertainty is high. The frequency of access increases, but the volume decreases. Most of the coefficient estimates for the control variables are as expected.
Overall, we find that political uncertainty significantly impedes capital markets access and reduces the probability of active leverage changes. Since policy uncertainty acts as a friction hindering firms’ desired capital structure adjustments, it is likely that it will also cause delays in
(i.e., slow down) firms’ convergence toward target leverage levels. We analyze this issue next.
6. Policy uncertainty and the speed of adjustment towards the target capital structure
If policy uncertainty makes it more difficult to issue debt and equity due to higher transaction costs and/or investor uncertainties, firms exposed to it should exhibit slower adjustment speeds. Therefore, an uncertain political environment could hinder a firm’s adjustment to its target capital structure by affecting the costs of adjustment and making the issuances of debt and equity securities more expensive and less likely. Building on our findings thus far, in this section, we formally analyze how firms’ capital structure adjustment speeds are affected by higher transaction costs under elevated policy uncertainty. First, we present our estimation methodology. Then, we provide the empirical results associated with the impact of policy uncertainty on adjustment speeds.
6.1. Empirical methodology
So far, our analysis has paid no attention to the desired direction of a firm’s adjustment
(except the “Lev Incr” and “Lev Decr” columns in Table 5 and Table 7). But there are capital structure models that take this into account, and which show that deviations from target leverage
26

make a difference in leverage adjustment speeds and patterns. So, now we move on to leverage partial adjustment estimations. Our approach is based on Blundell and Bond (1998) and employs a system generalized method of moments (GMM).
22
The selection of firm-specific variables that describe target leverage is based on the previous literature (Hovakimian et al., 2001; Flannery and Rangan, 2006; Huang and Ritter, 2009; Frank and Goyal, 2009). We use the following firm characteristics: earnings before interest and taxes as a proportion of total assets, book liabilities plus the market value of equity to total assets, depreciation expense as a proportion of total assets, the log of lagged sales to total sales (a measure of firm size), fixed assets as a proportion of total assets, research and development (R&D) expenses as a proportion of total assets where missing values are set equal to zero, a dummy variable that is equal to one if R&D expenditures are not reported and zero otherwise, and the median leverage ratio for the firm’s industry based on the Fama and French (1997) 49 industry categories. In equilibrium, the target leverage for firm i in country j at year t (
𝑇𝐿 𝑖𝑗,𝑡
) is determined by the β coefficient vector to be estimated and
X ij,t−1
, the vector of firm characteristics, including firm fixed effects:
𝑇𝐿 𝑖𝑗,𝑡
= 𝛽𝑋 𝑖𝑗,𝑡−1
. (4)
Confronted with a set of costs and benefits when rebalancing capital structure, managers will determine how quickly to close any gap between the actual (L ij,t-1
) and target capital structure (
𝑇𝐿 𝑖𝑗,𝑡
). A partial adjustment model implicitly allows transaction costs to slow the firm’s rate of adjustment to its optimal leverage:
𝐿 𝑖𝑗,𝑡
− 𝐿 𝑖𝑗,𝑡−1
= 𝜆 𝑗
(𝑇𝐿 𝑖𝑗,𝑡
− 𝐿 𝑖𝑗,𝑡−1
) + 𝜀 𝑖𝑗,𝑡
.
(5)
22
Flannery and Hankins (2013) conclude that this is the right estimator in the presence of an endogenous transformed lagged-dependent variable and a short panel, which is the case in our data.
27

Substituting Equation (4) into Equation (5) yields the following partial adjustment specification to estimate leverage adjustment speeds:
𝐿 𝑖𝑗,𝑡
= (𝜆𝛽)𝑋 𝑖𝑗,𝑡−1
+ (1 − λ )𝐿 𝑖𝑗,𝑡−1
+ 𝜀 𝑖𝑗,𝑡
(6)
The adjustment speed ( λ ) permits the typical firm to move only part way to its target leverage for any given year. If managers have target debt ratios and make proactive efforts to reach them, λ should be strictly greater than zero. In the presence of market friction, the adjustment is not instantaneous and
λ
is less than one.
While the adjustment speed (λ) in (6) is constant for all firms, we are interested in factors that determine this adjustment speed and may vary across time and countries. For this, we use a two-step process. First, we construct GMM estimates of Equation (6) with the inclusion of firm and year fixed effects separately for each country in our sample.
23
A target value for each firmyear can then be extracted as:
𝑇𝐿 𝑖𝑗,𝑡
= 𝛽̂ 𝑗
𝑋 𝑖𝑗,𝑡−1
=
1
λ̂
(𝐿 𝑖𝑗,𝑡
− (1 − λ̂)𝐿 𝑖𝑗,𝑡−1
) + 𝜀 𝑖𝑗,𝑡
.
Each firm’s estimated distance from its target debt ratio is then:
𝐷𝐼𝑆 𝑖𝑗,𝑡 𝑖𝑗,𝑡
− 𝐿 𝑖𝑗,𝑡−1
.
The second step in our process estimates the OLS regression
𝐿 𝑖𝑗,𝑡
− 𝐿 𝑖𝑗,𝑡−1
= 𝜆 𝑖𝑗𝑡
(𝐷𝐼𝑆 𝑖𝑗,𝑡 𝑖𝑗,𝑡
.
(7)
(8)
(9) in which λ is permitted to vary with transaction costs, TC , or policy uncertainty, PU , and control variables,
23
We conduct country-by-country estimations for two important reasons. First, targets and the speed of adjustment differ across countries, with targets ranging from 7.76% to 71.80% and adjustment speeds ranging from 3% to 40%.
In addition, a country’s political environment and institutions affect firm characteristics that, in turn, could affect the speed of adjustment. For instance, firms have fewer growth options, lower depreciation expenses, lower research and development expenses; but are larger in size; and have more tangible assets during election years. We take this into account by allowing different sensitivities for the determinants of target leverage across our sample countries.
28

𝜆 𝑖𝑗,𝑡
= Λ
𝑇𝐶(𝑃𝑈) 𝑖𝑗,𝑡−1
𝑇𝐶(𝑃𝑈) 𝑗,𝑡−1
+ Λ
𝐶 𝑗,𝑡−1
𝐻 𝑗,𝑡−1
.
(10) where Λ
𝑇𝐶(𝑃𝑈) and Λ
𝐶
are coefficient vectors for the adjustment speed function and
H j,t−1
is a set of covariates that could affect the adjustment speed including a constant, GDP growth, bond and stock market capitalization, and country fixed effects.
Substituting Equation (10) into (9) yields a regression that can be estimated with ordinary least squares (OLS):
(𝐿 𝑖𝑗,𝑡
− 𝐿 𝑖𝑗,𝑡−1
) = [ Λ
𝑇𝐶(𝑃𝑈) 𝑖𝑗,𝑡−1
𝑇𝐶(𝑃𝑈) 𝑗,𝑡−1
+ Λ
𝐶 𝑗,𝑡−1
𝐻 𝑗,𝑡−1
̂ ) + 𝛿 𝑖𝑗,𝑡 . (11)
This model isolates the impact of transaction costs or policy uncertainty on capital structure changes. The coefficient
Λ
𝑇𝐶(𝑃𝑈) indicates the magnitude of this impact.
We estimate our model sequentially. In the first step, we estimate our partial adjustment model, Equation (6), country by country using system GMM (Blundell and Bond, 1998). This provides an initial set of estimated
β s and λ that we use to calculate an initial estimated target leverage ratio (
𝑇𝐿 𝑖𝑗,𝑡
) and the deviation from the target leverage ratio (
̂ )
for each firm year through Equation (7) and Equation (8), respectively. We then estimate Equation (11) using OLS with bootstrapped standard errors to account for the generated regressor (Pagan, 1984;
Faulkender, Flannery, Hankins, and Smith, 2012). The vector of estimated coefficients from
Equation (11) allows us to test our hypotheses on the influence of policy uncertainty on the adjustment speed. To ease economic interpretation, transaction cost measures, our continuous policy uncertainty measure, change veto players, and continuous control variables are normalized to a mean of zero and a standard deviation of one.
6.2. Empirical results
29

An important premise of our paper is that policy uncertainty affects the speed of adjustment to target leverage. Our reason for this effect is the impact of policy uncertainty on firms’ cost of transacting in the debt and equity markets. Section 4 shows that policy uncertainty is associated with higher debt and equity transaction costs. Section 5 illustrates that firms engaged in fewer and smaller security transactions during periods of high PU. Since transaction costs significantly influence financing thresholds, variations in policy uncertainty (across countries and over time) that determine these costs should also influence adjustment speeds.
We now discuss how transaction costs and policy uncertainty affect leverage adjustment speeds.
6.2.1. Transaction costs and adjustment speeds
Insert Table 8 about here.
Equation (11) provides a connection between the estimated transaction costs to the speed of adjustment to target leverage. Table 8 reports the marginal impact of debt and equity transaction costs on adjustment speeds using a pooled regression of the change in leverage to deviations from optimal leverage. Specifically, we estimate Equation (11) using our transaction cost estimates, TC . The estimations control for macroeconomic conditions (GDP Growth), financial development (bond and stock market capitalization), and unobserved country fixed effects. The estimated TC coefficients in the first row of Table 8 indicate a significantly negative impact of transaction costs on adjustment speeds, both in statistical and economic terms. A one standard deviation increase in debt (equity) transaction costs reduces the speed of adjustment by 3% (5%) compared with an average adjustment speed of 19%. Internationally,
30

therefore, transaction costs have the predicted correlation with firms’ leverage adjustment speeds.
6.2.2. Policy uncertainty and adjustment speeds
Having established that transaction costs and the speed of adjustment are significantly and negatively related (the transaction costs channel), we turn our attention to measuring how a policy uncertainty shock would affect firms’ adjustment speeds. Table 9 reports the impact of various policy uncertainty measures on adjustment speeds using a pooled regression of the form
(11). We report separate results for four measures of PU: using Shifted Election Dummy,
Election Dummy, Change Veto Players, and Coalition . As before, the estimations control for macroeconomic conditions, financial development, and time-invariant country fixed effects.
Insert Table 9 about here.
All four proxies of political uncertainty matter for capital structure adjustments in the direction hypothesized. Compared with the sample mean (median) of 19% (20%), the impact of political uncertainty on the adjustment speed is large. A one standard deviation increase in political uncertainty decreases the typical firm’s adjustment speed by 2%-8%, capturing 19%-
87% of the cross-sectional standard deviation of the adjustment speed estimates. The negative coefficient estimates in the first row of Table 9 indicate that policy uncertainty influences the convergence to optimal leverage ratios.
To this point, we have demonstrated a significant impact of policy uncertainty on firm capital structure adjustment speed. However, it is plausible that firm characteristics also affect the speed of adjustment. To determine whether the impact of political uncertainty is robust to the inclusion of firm-specific control variables, we re-run our baseline estimations with some firm
31

characteristics that are known to influence firms’ adjustment speeds: dividend payer dummy, firm size, industry market-to-book ratio, and the difference between firm and industry market-tobook ratio (Faulkender et al., 2012). These variables capture firms’ financial constraints and market timing opportunities.
24
We also examine the robustness of our results to another widely used measure of policy uncertainty, EPUI . The results are documented in Table 10.
Insert Table 10 about here.
The first four columns of Table 10 indicate some support for the hypothesis that firm characteristics affect the speed of adjustment. More importantly, however, the sign, magnitude, and significance of the policy uncertainty measures are robust to controlling for the firm-level determinants of the adjustment speeds. All four measures of policy uncertainty continue to have statistically and economically similar effects on the speed of adjustment as in Table 9.
Additionally, we observe in Column 5 that a one standard deviation increase in EPUI , which is a non-election measure of policy uncertainty, hinders the speed of adjustment by about 2%.
Next, we contemplate that some firms are more sensitive to policy uncertainty than others
(Julio and Yook, 2012; Colak et al., 2014). For these firms, we should observe a larger decline in the speed of adjustment. There are anecdotal stories about the potential influence of the industry that the firm operates in on the impact of policy uncertainty. One good example is the analysts’ recommendations before the 2012 U.S. presidential election, advising people to purchase stocks of firms in green energy, hospital, and residential construction if they wanted to bet that Obama was going to win; but buy stocks of firms in defense, utilities, energy drillers, investment banking, and regional banking if they expected a Romney victory.
25
To test our assertion, we
24
Warr et al., (2012) show that equity valuations and leverage adjustment speeds are correlated. The industry market-to-book variable intends to capture such equity valuation effects.
25
See, for example, http://seekingalpha.com/article/983651-the-10-stocks-to-own-for-a-romney-or-obama-win ; http://moneymorning.com/2012/10/08/six-stocks-you-should-own-if-obama-wins-election-2012/ ;
32

follow Julio and Yook (2012) and classify firms in pharmaceuticals, health care, defense, petroleum, natural gas, telecommunications, and transportation as politically sensitive. We then run our adjustment speed estimation with the inclusion of the sensitive indicator and the interaction term between the sensitive indicator and the policy uncertainty measure, again controlling for country fixed effects. The results are reported in Table 11.
Insert Table 11 about here.
As expected, the interaction term between the politically sensitive industry and the policy uncertainty measure is negative and significant in all specifications. Greater political uncertainty leads to 2%-8% slower speed of adjustment and this negative effect is further amplified by 1%-
5% in politically sensitive industries.
6.2.3. The effect of legal and political institutions
Our next empirical analysis considers various institutions that could ameliorate the detrimental influences of policy uncertainty on the speed of adjustment. Although our dataset contains many measures differentiating countries’ legal and political institutions, we focus on the few that seem most likely to protect firms from political uncertainty.
26
The level of development and quality of domestic institutions can be strongly linked to policy uncertainty. Some institutional features are more conducive to hold economic agents and politicians accountable by imposing checks and balances among branches of government, law enforcement, and elections and consequently limiting the range of potential outcomes under higher policy uncertainty. For example, the English legal system may shelter firms from political uncertainty because it is http://moneymorning.com/2012/09/28/if-romney-wins-election-make-sure-you-own-these-six-stocks/ ; http://www.marketwatch.com/story/where-to-put-your-money-if-obama-wins-2012-11-06 .
26
Note that some country features excluded from Table 12 yield similar (untabulated) conclusions. These include lower corruption, better judicial efficiency, and stronger corporate governance.
33

associated with stronger investor protections and better law enforcement (La Porta et al., 1998).
Similarly, the availability and the content of information on politicians’ finances and business activities are central for the accountability of the politicians. More extensive disclosure to the congress and the public leads to higher quality government and better law enforcement (Djankov et al., 2010) and hence is more likely to minimize policy swings. Furthermore, public sector ethics as captured by public integrity and metrics against bribery and favoritism in the public sector is an important aspect of political governance. High ethical standards in the public sector may limit undue changes in public policy (Kaufmann, 2004), especially during politically uncertain times. Finally, the degree of policy uncertainty (before or after the election) heavily depends upon the nature of the political system in a country. In particular, parliamentary systems are characterized by simultaneous changes in control in both the executive and legislative branches of government, which tend to create higher political uncertainty (Julio and Yook,
2012). We include these institutional indices and their interaction terms with the Shifted Election
Dummy to our baseline adjustment speed estimation, along with country fixed effects.
27
The results are reported in Table 12.
Insert Table 12 about here.
The direct effect of better institutions is to increase leverage adjustment speed.
Consistent with the expectation that strong institutions are associated with faster speeds of adjustment due to lower transaction costs, firms from English law origin countries, countries with better disclosure to Congress and the public, and countries with better public sector ethics
27
Although evaluating the country features concurrently is possible, such an approach could obscure valuable information because these indexes are likely to be correlated. For this reason, we estimate equation (11) separately for each country feature. Each column in Table 12 provides a different country effect, controlling for country fixed effects. Note that
𝐷𝐼𝑆
and its interaction terms with the country features are time-varying (leverage targets depend on time-varying firm, industry, and macroeconomic characteristics), allowing us to employ country fixed effects along with the time-invariant country features.
34

adjust faster. Presidential systems are also associated with significantly faster adjustment. Even controlling for these country characteristics, policy uncertainty continues to reduce the typical firm’s speed of adjustment, by 1%-4%. However, as expected, good institutions mitigate the effect of PU. The interaction term between policy variables and institutional variables is significantly positive with a magnitude ranging from 2%-6%. Furthermore, we fail to reject the hypothesis that the sum of the coefficients on PU and the interaction term is zero at the conventional significance levels. That is, strong institutions and presidential political systems ameliorate the destructive influences of political uncertainty, offsetting its negative impact on the speed of adjustment. In unreported results, we obtain similar conclusions with our alternative policy uncertainty indicators.
6.2.4. The recent financial crisis (2007-2009)
Our analyses thus far utilizes pre-crisis period data from 1990-2006. As is well known, the recent financial crisis (2007-2009) was exceptional in the sense that it profoundly affected firms’ capital structure choices across many countries (it was often referred to as “The Balance
Sheet Recession”).
28
Because of this, we analyze the impact of this financial crisis separately.
To conduct this analysis, we extend our sampling period until 2010 using the same data sources described earlier. Then, we run the adjustment speed regressions described in Table 12 by adding a crisis period dummy ( Crisis ) indicating the firm-year observations from 2007-2009.
We also add interaction terms between PU Indicator , Crisis , and country features to disentangle the impact of the crisis period on the previously reported relationship(s) between adjustment speeds, political uncertainty, and a country’s institutions. The results are presented in Table 13.
28
See, among others, the study by Jaime Caruana, “Central banking in a balance sheet recession,” http://www.ijcb.org/journal/ijcb13q0a17.pdf?utm_source=BenchmarkEmail&utm_campaign=BIS_Newsletter_MA
RCH_2013_2nd_resend&utm_medium=email.
35

Insert Table 13 about here.
As previously, we find that policy uncertainty reduces the speed of adjustment even after controlling for the crisis years. Consistent with our prior findings, the coefficients on the country features and their interaction terms with political uncertainty are significantly positive indicating that strong institutions and a presidential system lead to faster adjustment speeds and alleviate the detrimental effects of policy uncertainty on the speed of adjustment.
The negative coefficient on Crisis suggests that during the crisis years, the adjustment speed is slower, possibly because the financial crisis creates its own uncertainty. More interesting results are observed through the interaction terms of political uncertainty and country features with crisis. The PU*Crisis interaction term yields a positive coefficient suggesting that the recent financial crisis changed the usual relationship between policy uncertainty and adjustment speeds. The uncertainty due to the financial crisis, combined with the uncertainty due to political elections, had a profound effect on firms’ leverage adjustments. In this atmosphere, the benefits of moving faster toward “the comfort” of the target leverage ratio seems to outweigh any increases in the adjustment costs due to policy uncertainty (i.e. when both uncertainties combine, the financial distress costs become more material).
The interaction terms between the country features and the crisis indicates that strong institutions, as indicated by English law origin and better disclosure to Congress and public, ameliorate the negative effects of the financial crisis. However, in countries with presidential systems, the adverse effects of the crisis are more pronounced. Our tests indicate that this result is driven by U.S. where the financial crisis was felt very strongly. After removing U.S. firms from the sample, the coefficient on Country * Crisis becomes insignificant. The triple interaction
36

term, PU * Country * Crisis , implies that strong institutions ameliorate the negative impact of the crisis unless the crisis is also accompanied by policy uncertainty.
In summary, our analysis of the recent financial crisis suggests that adjustment benefits outweigh adjustment costs during periods when macroeconomic and policy uncertainty are combined, but this effect is more relevant in weak institutional settings. In strong institutional settings, financial distress costs are alleviated and these adjustment benefits are not enough to counteract the rise in the adjustment costs due to policy uncertainty. The political (presidential) system partly alleviates the adverse effects of the financial crisis when it is accompanied with political uncertainty.
6.2.5. Robustness
We conducted several additional tests, which are not tabulated to save space. First, it is conceivable that political uncertainty affects optimal leverage ( TL ij,t
). We therefore re-estimate our baseline regression (Equation 6) for each country with PU added to the right hand side variables. As one would expect, firms operate with lower leverage in countries where elections are more likely to cause substantive shifts in government policy in the majority of the countries in our sample. Allowing political uncertainty to determine optimal leverage does not alter the economic and statistical significance of our adjustment speed results.
Second, prior literature indicates that capital adjustments depend on the position of the firm relative to its target (Faulkender et al., 2012; Warr et al., 2012). Perhaps political uncertainty also affects the adjustment speed asymmetrically. To test these assertions, we have allowed for asymmetry
29
in the speed of adjustment between 1) under and over levered firms, 2)
29
We split the sample according to 1) the sign of the deviation from the optimal leverage ratio, 2) the sample median of the firm size, and 3) the sample median of profitability in each country and re-run our analyses.
37

small and large firms
30
, and 3) firms with low and high profitability. Our main conclusions continue to hold, i.e. a significantly negative relationship is observed between adjustment speed and political uncertainty in all subsamples.
31
Third, we conduct a cross-sectional test across the elections within each country. To determine how close a particular election in a country is, we collect data (using our data sources described in Section 3.2) on the percentage of votes received by the winning candidate/party and the runner-up candidate/party. We conjecture that, ceteris paribus, the smaller the difference in votes, the higher is the political uncertainty prior to the election, leading to even slower speed of adjustment. We create a binary variable, election closeness , that equals one for the year when the election in a country is closer than the median for that country during the sample period, and zero otherwise. Then, we interact this dummy with our election dummy, SED , and run our baseline regression (Equation 11) again. Consistent with our expectations, we find that the speed of adjustment is reduced even further for the closer elections, i.e. the coefficient of the interaction term between election closeness and SED is significantly negative at 1% level.
Finally, we also test the impact of the dominance of U.S. firms and other large countries in our sample. To avoid a sample bias associated with some countries that have a larger representation in our sample, we rerun our baseline estimation with weighted least squares in which weights are proportional to the inverse number of observations in each country. This
30
Our data do not permit us to directly measure the degree of internationalization of firms. However, the largest firms in every country are typically multinational firms. So, we use the firm’s size as a proxy for its internationalization. Our findings suggest that the multinational firms’ adjustment speeds are not as much affected by political uncertainty in their home country, possibly because of their diversified operation across several countries. Alternative cut offs based on the firm’s size (e.g., largest 50%, 20%, 10% of the firms in each country) yield similar results. We also define multinational firms as firms that report positive foreign income and obtain the same conclusion.
31
As expected, the adverse effect of political uncertainty on the speed of adjustment is stronger among the underlevered, smaller, and/or less profitable firms: 1) debt issuance – an important mean for convergence among the underlevered firms – is more costly under political uncertainty; 2) smaller firms are more sensitive to adverse selection costs which tend to increase under policy uncertainty; and 3) less profitable firms cannot readily rely on their retained earnings to avoid higher external financing (transaction) costs imposed by policy uncertainty.
38

procedure gives equal weight to each country-year combination (i.e., the frequency of the variables of interest). Furthermore, in alternative specifications, we have eliminated U.S. firms and rerun our baseline model. Our results are largely similar.
7. Conclusion
Policy uncertainty introduces additional friction in the marketplace that significantly lowers firms’ capital structure adjustment speeds. This economic channel operates as follows.
Policy uncertainty induces investors to update their beliefs about the valuations of the stock and bond securities and demand a higher risk premium (Pastor and Veronesi, 2012; 2013). This, in turn, increases placement costs and risks for intermediary firms. The investors and intermediaries may become more risk averse, as well. As a result of the increased risk premium demanded by investors and/or their own risk aversion, underwriters raise the transaction costs charged to issuing firms. Higher transaction costs discourage any major adjustments to firm leverage during politically uncertain times. This constitutes a new, previously undocumented channel through which policy uncertainty affects corporate activities.
To uncover the capital structure implications of policy uncertainty, for the first time in the literature, we apply Altinkilic and Hansen’s (2000) model in a multinational setting. We report that the underwriter’s gross and marginal spreads increase due to policy uncertainty. For instance, using one of our political uncertainty measures ( CVP ), gross equity spreads increase for the median firm from 3.47% to 3.60%, an absolute increase of 13 basis points which translates into a relative change of 4%. Equity marginal spreads go up by a larger amount, from 2.99% to
5.56%, an absolute increase of 257 basis points which indicates a relative change of 86%. In dollar terms, the increase is from $35,562 to $86,912 or a spike of $51,350 for an additional
39

million dollars. Gross debt spreads also increase, from 1.02% to 1.30%, an absolute increase of
28 basis points, which implies a relative change of 28%. Furthermore, the bonds issued under policy uncertainty have higher credit ratings and smaller proceeds, further confirming the difficulty of raising external capital during politically uncertain episodes. Increased uncertainty, higher risk aversion and larger transaction costs reduce the frequency and size of corporate security offerings, and slow down the leverage adjustment process. While an average firm takes approximately three years to close half the gap between actual and optimal capital structure, this duration goes up to six years with higher political uncertainty.
32
Furthermore, we identify some country features that counteract the disruptive effects of political uncertainty. These findings could guide firm managers and policy makers concerned with the negative effects of elections and coalition governments on their country’s economy. In this context, we also analyze how the recent financial crisis (2007-2009) affects the relationships among policy uncertainty, adjustment speeds, and a country’s institutional strength. True to its name (“The Balance Sheet Recession”), the financial crisis had a profound negative effect on firm leverage adjustments and has altered the associations between country features and political uncertainty. We find that the combined shock of a financial crisis and policy uncertainty raises leverage adjustment benefits above the costs, leading to faster adjustment under policy uncertainty, especially in weaker institutional settings. Alternatively, we determine that a political system can be designed to remedy the adverse effects of both a financial crisis and political uncertainty.
32
The calculation of life time for an average firm in the sample is LN(0.5)/LN(1 – 0.19), where 0.19 is the sample average of the adjustment speeds. Since political uncertainty slows capital structure adjustments by up to 8%, the upper bound of the half-life under higher political uncertainty is calculated as LN(0.5)/LN(1 – 0.11).
40

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44

Table 1.
Data description, source, construction, and summary statistics
Panel A. Firm, industry, and macroeconomic variables
Variable . Definition. Source.
Equity spread . Total compensation paid to the syndicate on an SEO as a percentage of the proceeds. SDC Global New Issues.
Equity proceeds.
Total proceeds raised in all markets on an SEO in millions of U.S. dollars. SDC Global New Issues.
Market capitalization.
Market value of the issuing firm’s equity prior to the offering (millions of $s). SDC Global New Issues.
Debt spread.
Total compensation paid to the syndicate on a bond offering as a percentage of the proceeds. SDC Global New Issues.
Debt proceeds.
Total proceeds raised in all markets on a bond offering in millions of U.S. dollars. SDC Global New Issues.
S&P score.
The mapping of the S&P bond ratings with numerical values as described in footnote 8. SDC Global New Issues.
Cash flow from financing (CFF).
Net cash flow from financing activities in millions of U.S. dollars. Global Vantage.
Cash flow from investments (CFI).
Net cash flow from investing activities in millions of U.S. dollars. Global Vantage.
Cash flow from operations (CFO).
Net cash flow from operating activities in millions of U.S. dollars. Global Vantage.
Change in cash holdings (ΔCASH).
Change in cash and cash equivalents. Global Vantage.
Sale of common and preferred stock (SSTK) in millions of U.S. dollars . Global Vantage.
Purchase of common and preferred stock (PRSTKC) in millions of U.S. dollars . Global Vantage.
Long-term debt issuance (DLTIS) in millions of U.S. dollars . Global Vantage.
Long-term debt reduction (DLTR) in millions of U.S. dollars . Global Vantage.
Change in current debt (DLCCH) in millions of U.S. dollars . Global Vantage.
Cash dividend (DV) in millions of U.S. dollars . Global Vantage.
Leverage. (Long-term debt+Short-term debt)/Total assets. Global Vantage.
Profitability. (Operating Income+ Interest and related expense+Current income taxes)/Total assets. Global Vantage.
Market-to-book. (Long-term debt+Short-term debt+Preferred capital+Market value of equity)/Total assets. Global Vantage.
Depreciation. Total depreciation and amortization/Total assets. Global Vantage.
Firm size. Log(Lagged sales)/Sales. Global Vantage.
Tangibility. Fixed assets/Total assets. Global Vantage.
R&D dummy. A dummy variable equal to one if R&D expenditures are not reported and zero otherwise. Global Vantage.
R&D expenses. Research and development Expense/Total assets. Global Vantage.
Industry median leverage.
Median leverage for the firm’s industry. Fama and French (1997) industry categories.
Global Vantage.
Expected capital expenditures.
Capital expenditures/Total assets. Global Vantage.
Cash.
Cash and equivalents/Total assets. Global Vantage.
Selling expenses.
Selling expenses/Total assets. Global Vantage .
Z-score.
(3.*Profitability+Sales+1.4*Retained Earnings+1.2*Working capital+0.6*Market value of equity)/Total assets. Global Vantage.
Dividend payer. A dummy variable equal to one if the firm has paid dividends and zero otherwise. Global Vantage.
Industry market-to-book.
Average industry market-to-book ratio. Global Vantage.
Firm minus industry market-to-book.
Firm minus industry market-to-book ratio. Global Vantage.
Bond market capitalization. Outstanding domestic debt securities divided by GDP. Beck, Demirgüç-Kunt, and Levine (2000).
Stock market capitalization. The value of listed shares divided by GDP. Beck, Demirgüç-Kunt, and Levine (2000).
GDP growth. Growth in real GDP. WDI (World Development Indicators), World Bank.
Politically sensitive industry.
A dummy variable equal to one for firms in pharmaceuticals, health care, defense, petroleum, natural gas, telecommunications, and transportation industries. Global Vantage.
0.32
0.38
0.02
0.20
0.06
0.14
0.23
2.05
0.39
0.93
0.29
56.29
98.47
0.02
0.19
Mean
4.74
117.87
Median
5.00
70.00
Std. Dev.
1.20
124.79
1,308.85
1.89
254.54
9.70
444.20 2,096.87
2.13 1.20
187.81
8.00
217.64
4.81
-1,879.81 -3,202.00 18,293.30
-2,643.04 -17.01 14,036.68
3,246.33 20.94 16,475.51
111.26
38.55
8.29
128.42
104.29
-1,521.43
355.62
0.24
0.03
1.22
0.05
0.01
0.20 3,001.41
0.15
0.00
1.00
2.86
0.04
0.01
207.58
84.01
428.41
342.05
0.00 10,719.56
0.82 1,651.83
0.22 0.20
0.06
0.85
0.18
1.33
0.03
0.44
0.28
0.00
0.00
0.20
0.04
0.09
0.16
1.98
0.00
0.83
0.00
49.39
91.80
0.02
0.00
0.22
0.49
0.06
0.10
0.06
0.16
0.24
2.95
0.49
0.36
1.24
31.53
45.10
0.02
0.39
45

Table 1. Continued
Panel B. Political and institutional variables
Variable . Definition. Source.
Election dummy.
It indicates that there was an election during that calendar year. When determining this variable, we focus on the elections where the chief executive of the government is changing. Thus, in the presidential system, it is the presidential elections, while in the parliamentary system, it is the parliamentary elections.
Various sources. World Bank’s Database of Political Institutions (DPI2012).
Shifted election dummy (SED).
If the election is in the first-half of the year (on or before June 30th), the previous calendar year is considered as the one with high election uncertainty.
Various sources. World Bank’s Database of Political Institutions (DPI2012).
Change veto players (CVP).
The variable tabulates the percent of veto players who drop from the government in any given year. The veto players are defined in the following fashion. In presidential systems, if the president does not control the legislature (via closed list and a majority), then veto players are the president and each chamber. If the president gains control of the legislature in time t , then the chambers are counted as no longer being veto players. Similarly, if the president changes and the largest opposition party has a majority in the legislature in time t -1, but not in time t , a change in veto players is again recorded. If the largest government party has a majority in the legislature (and there is no closed list) in time t -1, but not in time t , a change in veto players is again recorded. In parliamentary systems, if members of the government coalition in t -1 are no longer in government in t , that number of veto players changes. Similarly if the prime minister changes and an opposition party has a majority in t -1, but that same party does not have a majority in t , then one veto player is said to have dropped. If parliamentary systems go from no government majority or no closed list to a government majority and closed list in time t , then the chambers are counted as no longer being veto players.
World Bank’s Database of Political Institutions (DPI2012).
Coalition.
The Herfindahl index of the government parties forming the coalition (using their seats in the parliament) is calculated. If the
Herfindahl index is < 1, then Coalition =1; otherwise it is zero.
Various sources. World Bank’s Database of Political Institutions (DPI2012).
Economic policy uncertainty index (EPUI).
A policy-related economic uncertainty index created by Baker et al., (2013). We use the annualized version of this index by taking the average of the monthly values. www.policyuncertainty.com
.
English origin.
Categorical variable equal to unity if the firm operates under English law, and zero otherwise. La Porta, Lopez-de-Silanes,
Shleifer, and Vishny (1998) .
Disclosure to congress.
Sources available to congress. It measures the ratio of all source items contained in the country’s blank disclosure form available to congress over all source items potentially disclosed in the artificial “universal” form. Djankov, LaPorta, Lopez-de-Silanes, and Shleifer (2010) .
Disclosure to public.
Sources publicly available. It measures the ratio of all source items contained in the country’s disclosure form available to the public over all source items potentially disclosed in the artificial “universal” form. Djankov et al. (2010) .
Public sector ethics.
Percentage of firms in the country giving satisfactory ratings to the questions on honesty of politicians, government favoritism in procurement, diversion of public funds, trust in postal office, and the average bribe frequencies for permits, utilities, and taxes.
World Bank’s Ethics Indices, Kaufmann (2004).
Presidential system. A dummy variable taking a value of one if the presidents are elected directly and/or the chief executive of the government is the president (e.g., U.S., Mexico). If the system is parliamentary, this variable is zero (e.g., Canada, Turkey). In a parliamentary system, the prime minister is the head of the government and the president is more of the head of the state (and has veto power, but does not make legislation).
Various sources. World Bank’s Database of Political Institutions (DPI2012).
Mean
0.25
Median Std. Dev.
0.00 0.43
0.25
0.10
0.35
91.47
0.66
0.28
0.25
67.16
0.49
0.00
0.00
0.00
89.99
1.00
0.34
0.33
70.10
0.00
0.43
0.20
0.48
17.17
0.47
0.16
0.14
12.96
0.50
46

Table 2.
Summary statistics of the election sample
The table presents some descriptive statistics of the election sample. For each country, we report the number of firms and firm-years, the number of national elections conducted during our sampling period from1990-2006 [these elections are used to create our Election Dummy and Shifted Election Dummy (SED) ], the average number of years in which the country is under election uncertainty (using our SED indicator), the average percentage of veto players that changed per year (i.e., the average of the CVP indicator across our sampling period), the percentage of years that the country has been run with a coalition government, the average of EPUI [Baker et al. (2013) policy uncertainty index] across the years that are available, and the political system of the country. Countries that switch from presidential to parliamentary political system and vice-a-versa are classified under “Mixed” political system.
Ireland
Israel
Italy
Japan
Malaysia
Mexico
N. Zealand
Norway
Pakistan
Peru
Philippines
Portugal
Singapore
S. Africa
S. Korea
Spain
Country
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
Columbia
Denmark
Finland
France
Germany
Greece
India
Indonesia
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
U.S.
Total
Average
245
166
743
333
60
1,281
5,299
16,506
--
383
173
366
98
74
25
66
43
33
23
168
2,764
723
58
61
113
Firms Firm Years Elections
35 172 3
736
62
4,124
524
6
6
85
65
185
38
646
238
1,568
193
4
4
5
3
10
103
114
501
537
98
460
179
55
871
855
3,556
4,023
468
1,501
1,392
5
5
5
4
5
5
4
3
301
134
1,032
18,864
4,807
414
394
830
341
140
446
276
2,627
1,118
2,330
750
1,725
1,536
2,648
2,128
268
11,128
54,367
128,790
--
4
3
3
4
4
5
3
5
4
3
6
4
3
6
5
6
4
4
4
5
4
5
6
167
4.3947
0.2353
0.3529
0.2941
0.2941
0.2353
0.1765
0.3529
0.2353
0.2353
0.2353
0.1765
0.2941
0.2353
0.1765
0.1765
0.2353
Average
SED
0.1765
0.2941
0.3529
0.2353
0.2353
0.2941
0.1765
0.2353
0.2941
0.2941
0.1765
0.2941
0.2353
0.2941
0.2353
0.2941
0.2353
0.2941
0.3529
0.2353
0.2353
0.2353
--
0.2539
Average
CVP
0.1520
0.0588
Coalition EPUI (years) Political System
35.29%
58.82%
0.0588 100.00%
--
--
--
Presidential
Parliamentary
Parliamentary
0.0588 100.00%
0.1029 100.00%
0.0588
0.1961
0.00%
70.59%
--
--
96.97 (17)
--
Parliamentary
Mixed
Parliamentary
Presidential
0.1912 70.59%
0.1265 100.00%
0.1324 100.00%
0.1667 100.00%
0.0765 100.00%
0.2353 17.65%
0.2863 100.00%
0.1765 41.18%
--
--
--
80.34 (10)
91.93 (10)
--
78.56 (4)
--
Presidential
Parliamentary
Parliamentary
Parliamentary
Parliamentary
Parliamentary
Parliamentary
Presidential
0.0980 100.00%
0.1853 100.00%
0.2912
0.2451
29.41%
64.71%
--
--
101.48 (10)
--
Parliamentary
Mixed
Parliamentary
Parliamentary
0.0706
0.0882
0.2157
0.2235
88.24%
35.29%
70.59%
76.47%
--
--
--
--
Parliamentary
Presidential
Parliamentary
Parliamentary
0.2206
0.1275
0.1716
0.2157
0.0588
0.1765
0.1373
0.1235
46.67%
58.82%
82.35%
17.65%
0.00%
70.59%
35.29%
23.53%
--
--
--
--
--
--
--
98.72 (6)
Mixed
Presidential
Presidential
Parliamentary
Parliamentary
Parliamentary
Presidential
Parliamentary
0.1598 76.47%
0.0588 100.00%
0.0343
0.2471
17.65%
93.75%
0.2549
0.0784
0.0441
--
0.1475
64.71%
0.00%
0.00%
--
0.6174
--
--
--
--
--
78.30 (10)
94.33 (17)
--
--
Parliamentary
Parliamentary
Mixed
Parliamentary
Parliamentary
Parliamentary
Presidential
--
--
47

Table 3.
Equity underwriting costs and political uncertainty
The table summarizes equity underwriter spreads obtained from the estimation of Equation (1) for issuances taking place during low (Low PU) and high (High PU) political uncertainty periods:
S i
   
1
  x i   j
 j
C j
  i
(1) x i y i where S i
is the percentage of underwriter spread for a particular issuance event i as reported in SDC, x i represents the gross proceeds raised during the issuance event i , y i
is the market value of the issuing firm’s equity immediately prior to the offering as reported in SDC, C j
is a dummy variable equal to one if the issue i is in country j , and the error term is shown with ε i
. The estimated underwriter’s spread (%), the marginal spread (US $), marginal spread (%), and the marginal spread’s slope (US $) are calculated using the coefficient estimates (
 ˆ
,
 ˆ
, and
 ˆ ) from Equation (1) and the actually observed values for proceeds and market capitalization as: correspondingly.
*
,
**
, and



 ˆ   ˆ 1 x i
  ˆ x i y i



;



10 , 000



ˆ 
2
 ˆ x i y i








;



 ˆ   ˆ x i y i



; and



10 , 000



2
 ˆ
1 y i





,
***
indicate the significant difference between the Low and High PU groups at the 10%, 5%, and 1% levels, respectively.
Absolute
Difference
Relative (%) Variables
Low PU
Median
High PU
Median
Panel A. Estimated equity underwriter spreads (%)
Shifted Election Dummy
Election Dummy
Change Veto Players
Coalition
3.48
3.47
3.47
3.47
Panel B. Marginal spread (US $)
Shifted Election Dummy 36,788
Election Dummy
Change Veto Players
Coalition
36,788
35,562
36,788
Panel C. Marginal spread (%)
Shifted Election Dummy
Election Dummy
Change Veto Players
Coalition
Panel D. Marginal spread slope (US $)
Shifted Election Dummy 279
Election Dummy
Change Veto Players
Coalition
279
228
279
3.05
3.05
2.99
3.05
3.54
3.54
3.60
3.67
39,408
39,408
86,912
52,522
3.19
3.19
5.56
3.84
405
362
1,315
465
0.06
0.07
0.13
0.20
2,620
2,620
51,350
15,734
0.13
0.13
2.57
0.79
126
83
1,087
186
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
1.85
2.09
3.78
5.72
7.12
7.12
144.40
42.77
4.29
4.29
85.78
25.76
45.16
29.75
476.75
66.67
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
48

Table 4.
Debt underwriting costs and political uncertainty
The table summarizes the number of bond offerings per year, the average proceeds per offer, the S&P score, and the debt underwriter spreads obtained from the estimation of Equation (2) for issuances taking place during low (Low PU) and high (High PU) political uncertainty periods:
S i
    ln( x i
)
 A

SP

B

SP
R
SP
  j
 j
C j
  i
(2) where R
SP
are dummy variables indicating whether the issue has an S&P bond rating ranging from B to A
(B, BB, BBB, A) with CCC rated bonds included in the B category. C j
is a dummy variable equal to one if the issue i is in country j , and the error term is shown with
ε i
. The estimated underwriter’s spread (%) is calculated using the coefficient estimates (
 ˆ
,
 ˆ
,
 ˆ
) from Equation (2) and the actually observed values for proceeds as:


 ˆ   ˆ ln( x i
)

A 
SP

B
 ˆ
SP
R
SP

 . *
,
**
, and
***
indicate significant differences between the
Low and High PU groups at the 10%, 5%, and 1% levels, respectively.
Variables
Panel A. Estimated equity underwriter spreads (%)
Shifted Election Dummy 0.98 1.13
Election Dummy
Change Veto Players
Coalition
0.98
1.02
1.13
1.13
1.30
0.65
Panel B. Number of bond offerings per country per year
Shifted Election Dummy 76 26
Election Dummy
Change Veto Players
Coalition
76
73
93
30
40
5
Panel C. Average proceeds per offer (in millions of U.S. dollars)
Shifted Election Dummy
Election Dummy
Change Veto Players
Coalition
Panel D. S&P Score
Shifted Election Dummy
Election Dummy
Change Veto Players
Coalition
Low PU
Median
233.54
233.54
237.39
233.54
9.28
9.28
9.23
9.28
High PU
Median
221.15
221.15
233.54
245.57
10.05
10.05
9.28
10.33
Absolute
Difference
Relative (%)
0.16
0.16
0.28
-0.48
-50
-46
-33
-88
-12.38
-12.38
-3.85
12.04
0.77
0.77
0.05
1.06
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
15.99
15.99
27.63
-42.68
-65.79
-60.53
-45.21
-94.62
-5.30
-5.30
-1.62
5.15
8.31
8.31
0.51
11.39
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
49

Table 5.
Political uncertainty and financing decisions: A univariate analysis
The table presents information regarding capital market access during election and non-election years. Election years are defined using the shifted election dummy . Panel A presents the incidence of access by reporting the proportion of firms that accessed external capital markets, and how that access breaks down between debt (D.) and equity (E.), and between issuances (Issue) and retirements (Retire). Panel B provides the mean size of capital market access by reporting the magnitude of adjustments (scaled by total assets) either in the form of issuances or retirements. Passive is the change in retained earnings to total assets. Access is defined as a change in the absolute value of outstanding equity or debt exceeding 5% of total assets. An issuance or retirement is defined as having occurred in a given year if the change in equity or debt, normalized by the book value of assets at the end of the previous period, is greater than a certain cutoff. Debt (Equity) is defined as a change in the absolute value of outstanding debt (equity) exceeding 5% of the total assets. Debt issue, debt retirement, and equity issue are each defined as a security issuance or repurchase of at least 5% of the book assets. Equity retirement is defined as a security issuance or repurchase of at least 1.25% of the book assets. Leverage increase (decrease) is defined as the difference between net debt (equity) issued and net equity (debt) issued that is greater in magnitude than 5% of book assets.
*
,
**
, and
***
indicate a significant difference between the groups at the 10%, 5%, and 1% levels, respectively.
Election vs. Non-Election Years
Panel A: Incidence of Capital Market Access
Frequency of Adjustments (%)
Non-election years
Election years
Absolute Difference
Relative Difference
Significance
Passive Access
51.77
52.79
1.02
1.97
***
71.26
69.99
-1.27
-1.79
***
Debt D. Issue D. Retire Equity
64.11
63.12
-0.99
-1.54
***
45.48
44.22
-1.26
-2.75
***
18.63
18.90
0.27
1.42
***
38.79
37.52
-1.27
-3.28
***
E. Issue
32.62
31.44
-1.18
-3.62
***
E. Retire
13.28
13.43
0.15
1.18
***
Panel B: Mean Size of Capital Market Access
Lev Incr
42.86
41.83
-1.03
-2.39
***
Lev Decr
40.88
40.45
-0.43
-1.06
***
Size of Adjustments (%)
Non-election years
Election years
Absolute Difference
Relative Difference
Significance
Passive
1.63
1.59
-0.04
-2.26
***
Debt D. Issue D. Retire Equity
6.79
6.61
-0.18
-2.75
***
7.78
7.50
-0.28
-3.68
***
-5.48
-5.42
0.06
-1.06
***
1.87
1.80
-0.07
-3.71
***
E. Issue E. Retire Lev Incr Lev Decr
2.12
2.06
-0.06
-3.13
***
-1.78
-1.71
0.07
-3.66
***
13.80
13.53
-0.27
-1.91
***
13.73
13.49
-0.24
-1.73
***
50

Variables
ΔCASH
CFF
CFI
CFO
Other
SSTK
PRSTKC
DLTIS
DLTR
DV
DLCCH
Table 6.
Capital structure changes and the related accounting variables.
The table demonstrates what happens to various accounting variables during election years. Election years are defined using the shifted election dummy . We include only the accounting variables that are closely associated with capital structure. Panel A reports the main variables associated with the accounting identity,
ΔCASH = CFF + CFI + CFO + Other
. N indicates the number of firm-year observations used in calculating the corresponding statistics. Panel B displays the decomposition of the CFF into its main components CFF = SSTK - PRSTKC + DLTIS - DLTR- DV
+ DLCCH + Other . The accounting variables are defined as follows. CFF = cash flow from financing, CFI = cash flow from investments, CFO = cash flow from operations, SSTK = sale of common and preferred stock, PRSTKC = purchase of common and preferred stock, DLTIS = long-term debt issuance, DLTR = long-term debt reduction, DV = cash dividends, and DLCCH = current debt changes. All the accounting variables are scaled by lagged assets. When the mean (median) of the election years sample is significantly different from that of the non-election years sample at the 10%, 5%, and 1% significance level it is indicated by
*
,
**
, and
***
, respectively. For the differences in means, we use the t -test. For differences in medians, we employ the Wilcoxon nonparametric rank-sum test.
Non-Election Years
Mean
0.0120
0.0132
-0.0826
0.0690
0.0074
Median N Mean Significance
Election Years
Median Significance
Panel A. Accounting Identity: ΔCASH = CFF + CFI + CFO + Other
0.0016 77,296 0.0141
***
0.0021
**
-0.0113 39,300 0.0066
***
-0.0142
***
-0.0491
0.0744
70,328
77,125
-0.0786
0.0647
*
***
-0.0455
0.0699
***
***
-0.0120 83,432 0.0094 0.0039
***
N
26,395
14,305
24,219
26,346
27,287
Panel B. Decomposition of CFF: CFF = SSTK - PRSTKC - DV + DLTIS - DLTR + DLCCH + Other
0.0562 0.0016 67,698 0.0424
***
0.0013
***
0.0079
0.0522
0.0418
0.0122
-0.0027
0.0000
0.0000
0.0000
0.0025
0.0000
65,824
37,025
82,467
69,295
42,509
0.0073
0.0499
0.0401
0.0114
-0.0021
*
*
*
***
0.0000
0.0000
0.0000
0.0035
0.0000
***
***
***
**
***
20,628
20,692
11,783
27,556
23,589
15,182
51

Table 7.
Policy uncertainty and financing decisions: A multivariate analysis
This table presents estimation results from logistic regressions modeling the firm’s decision to access capital markets. An issuance or retirement is defined as having occurred in a given year if the change in equity (E.) or debt (D.), normalized by the book value of assets at the end of the previous period, is greater than a certain cutoff. Debt (Equity) is defined as a change in the absolute value of outstanding debt (equity) exceeding 5% of the total assets. Debt issue, debt retirement, and equity issue are each defined as a security issuance or repurchase of at least 5% of the book assets. Equity retirement is defined as a security issuance or repurchase of at least 1.25% of the book assets. To minimize the endogeneity problem, all of the control variables, except for Expected CAPX , are lagged by one year. The definitions and the sources of the control variables are provided in Table 1. Robust standard errors are reported beneath the coefficient estimates.
*
,
**
, and
***
indicate significant difference between the groups at the 10%, 5%, and 1% levels, respectively.
D. Issue D. Retire E. Issue E. Retire Lev Incr Lev Decr
Shifted election dummy, SED -0.0912
***
0.0292 -0.0909
***
0.0282 -0.1018
***
-0.0889
***
Market-to-book
Firm size
Profitability
Expected CAPX
R&D expenses
Cash
Depreciation
Tangibility
Selling expenses
(0.015) (0.019) (0.017) (0.021) (0.015) (0.015)
0.1723
***
-0.2190
***
0.3456
***
-0.0019 0.0561
***
0.1341
***
(0.006) (0.011) (0.008) (0.008)
-0.0202 0.1052
***
-0.1792
***
0.0221
(0.006) (0.006)
0.0070 -0.1712
***
(0.021) (0.037) (0.022) (0.026) (0.021) (0.021)
0.1622
***
-0.7211
***
-1.2227
***
1.2466
***
0.4658
***
-1.5787
***
(0.050) (0.065) (0.054) (0.087) (0.053) (0.055)
7.9252
***
-5.8146
***
3.1188
***
-2.2276
***
6.1390
***
-0.8771
***
(0.159) (0.239) (0.139) (0.199) (0.145) (0.136)
-0.8582
***
0.3024 1.1393
***
-1.6389
***
-1.7773
***
0.8282
***
(0.150) (0.186) (0.161) (0.236) (0.158) (0.154)
-0.6003
***
-0.3074
***
0.5660
***
0.3595
***
-0.3834
***
0.5316
***
(0.052) (0.069) (0.055) (0.072) (0.052) (0.052)
-4.3516
***
5.7880
***
-1.8187
***
2.1166
***
-3.7455
***
1.8073
***
(0.261) (0.301) (0.270) (0.333) (0.259) (0.251)
-0.8739
***
-0.8169
***
-0.3859
***
0.0891 -0.6039
***
-0.5371
***
(0.041) (0.051) (0.045) (0.057) (0.041) (0.041)
0.1565
***
0.0873
**
0.1025
***
-0.1914
***
0.1199
***
0.1296
***
Z-score
Leverage
Constant
Country fixed effects
Observations
Implied change in the odds
(0.034) (0.043) (0.038) (0.051) (0.034) (0.035)
-0.0026 -0.0565
***
-0.0500
***
-0.0246
***
0.0203
***
-0.0232
***
(0.003) (0.005) (0.003) (0.004) (0.003) (0.003)
-0.4779
***
2.1137
***
0.2551
***
-0.5995
***
-0.3695
***
1.3209
***
(0.039) (0.047) (0.043) (0.057) (0.039)
0.5013
***
-1.8765
***
-0.2027 -2.4872
***
0.2723
(0.040)
-0.1360
(0.168)
Yes
(0.249)
Yes
(0.166)
Yes
(0.303)
Yes
(0.166)
Yes
(0.166)
Yes
107,789 107,789 107,789 107,789 107,789 107,789
-0.0871 0.0296 -0.0869 0.0286 -0.0968 -0.0851
52

Table 8.
Adjustment speeds and underwriter spreads
The table illustrates the impact of transaction costs on adjustment speeds using a two-stage procedure. In the (unreported) first stage, we estimate the following reduced-form model of leverage, where

is the adjustment parameter,
X
is a set of firm characteristics,
𝐿
is the leverage ratio, and 𝜀
is a random error term, with the inclusion of firm and year fixed effects:
𝐿 𝑖𝑗,𝑡
= (𝜆𝛽)𝑋 𝑖𝑗,𝑡−1
+ (1 − λ)𝐿 𝑖𝑗,𝑡−1
+ 𝜀 𝑖𝑗,𝑡
(6)
We estimate this equation separately for each country using Blundell and Bond’s (1998) GMM estimator.
This estimation provides an initial set of estimated βs and λs that we use to calculate an initial estimated target leverage ratio (
𝑇𝐿
L ij,t−1 ij,t
= β 𝑗
X i,t−1
) and distance from the target leverage ratio (
𝐷𝐼𝑆 ij,t
−
)
for each firmyear. In the second stage, we substitute the estimated deviation from the target leverage ratio into the partial adjustment equation to produce estimates of the determinants of a firm’s adjustment speed:
(𝐿 𝑖𝑗,𝑡
− 𝐿 𝑖𝑗,𝑡−1
) = [Λ 𝑇𝐶 𝑖𝑗,𝑡−1
𝑇𝐶 𝑗,𝑡−1
+ Λ 𝐶 𝑖𝑗,𝑡−1
𝐻 𝑗,𝑡−1
](𝐷𝐼𝑆 𝑖𝑗,𝑡 𝑖𝑗,𝑡
. (11) where
Λ 𝑇𝐶
and
Λ 𝐶
are the vector of coefficients for the adjustment speed function,
𝑇𝐶 j,t−1 is the estimated underwriter spread (i.e., our transaction cost measure of debt or equity), and
H j,t−1
is a set of covariates that affect the adjustment speed including a constant, bond and stock market capitalization, GDP growth, and country fixed effects. The definitions and the sources of the variables are provided in Table 1. We transform continuous independent variables to standard normal variables before interacting them with
𝐷𝐼𝑆 𝑖𝑗,𝑡
.Standard errors are bootstrapped to account for the generated regressor (Pagan, 1984;
Faulkender, Flannery, Hankins, and Smith, 2012). The p-values are reported beneath the coefficient estimates.
*
,
**
, and
***
indicate significance at the 10%, 5%, and 1% levels, respectively.
(1) (2)
Transaction cost ( TC )
Bond market capitalization
Debt
-0.0315
***
(0.000)
-0.0100
Equity
-0.0503
***
(0.000)
0.0481
***
Stock market capitalization
GDP growth
Constant
(0.121)
0.0139
**
(0.035)
0.0693
***
(0.000)
0.2721
***
(0.000)
(0.000)
0.0518
***
(0.000)
0.0367
***
(0.000)
0.1571
***
(0.000)
Country fixed effects
Observations
R-squared
Yes
54,088
0.197
Yes
86,941
0.146
53

Table 9.
Main adjustment speed results
The table notes are identical to those presented for Table 8, except for Equation (11) where
𝑇𝐶 j,t−1
is replaced by
PU j,t−1
, the policy uncertainty measure (SED, Election Dummy, CVP, or Coalition).
Policy uncertainty (
Bond market capitalization
Stock market capitalization
GDP growth
Constant
PU
Country fixed effects
Observations
R-squared
)
(1)
Shifted Election
Dummy(SED)
-0.0448
***
(0.000)
0.0301
***
(0.000)
0.0427
***
(0.000)
0.0395
***
(0.000)
0.1612
***
(0.000)
Yes
118,820
0.137
(2)
Election
Dummy
-0.0453
***
(0.000)
0.0301
***
(0.000)
0.0426
***
(0.000)
0.0395
***
(0.000)
0.1613
***
(0.000)
Yes
118,820
0.137
(3)
Change Veto
Players (CVP)
-0.0186
***
(0.000)
0.0279
***
(0.000)
0.0401
***
(0.000)
0.0375
***
(0.000)
0.1477
***
(0.000)
Yes
118,820
0.137
(4)
Coalition
-0.0843
***
(0.000)
0.0089
***
(0.006)
0.0312
***
(0.000)
0.0447
***
(0.000)
0.1876
***
(0.000)
Yes
118,780
0.143
54

Table 10.
Firm level determinants of adjustment speeds and an alternative measure of policy uncertainty
The table notes are identical to those presented for Table 9, except for
H j,t−1 which now includes a dividend payer dummy, firm size, industry market-to-book ratio, and the firm’s industry-adjusted marketto-book ratio in addition to the control variables in Table 9.
Policy uncertainty (
Bond market capitalization
Stock market capitalization
GDP growth
Dividend payer
Firm size
Constant
Country fixed effects
Observations
R-squared
PU )
Industry market-to-book
Firm minus industry market-to-book
(1)
Shifted Election
Dummy (SED)
-0.0357
***
(0.000)
0.0193
***
(0.000)
0.0347
***
(0.000)
0.0396
***
(0.000)
-0.0652
***
(0.000)
0.0002
(0.955)
-0.0005
(0.767)
0.0160
***
(0.000)
0.1819
***
(0.000)
Yes
118,820
0.148
(2)
Election
Dummy
-0.0364
***
(0.000)
0.0193
***
(0.000)
0.0346
***
(0.000)
0.0396
***
(0.000)
-0.0651
***
(0.000)
0.0002
(0.946)
-0.0006
(0.750)
0.0161
***
(0.000)
0.1820
***
(0.000)
Yes
118,820
0.148
(3)
Change Veto
Players (CVP)
-0.0182
***
(0.000)
0.0166
***
(0.000)
0.0321
***
(0.000)
0.0382
***
(0.000)
-0.0700
***
(0.000)
0.0004
(0.909)
-0.0009
(0.567)
0.0157
***
(0.000)
0.1737
***
(0.000)
Yes
118,820
0.149
(4) (5)
Coalition EPUI
-0.0599
***
-0.0224
***
(0.000)
0.0068
*
(0.000)
-0.0208
***
(0.050)
0.0281
***
(0.000)
0.0429
***
(0.000)
-0.0476
***
(0.000)
0.0072
(0.219)
0.0120
**
(0.022)
0.0067
(0.000)
-0.0004
(0.896)
-0.0029
(0.125)
0.0161
***
(0.000)
0.1922
***
(0.000)
Yes
118,780
0.150
(0.443)
0.0012
(0.696)
-0.0241
***
(0.000)
0.0123
***
(0.000)
0.2870
***
(0.000)
Yes
65,792
0.213
55

Table 11.
Interactions of industry and policy uncertainty
The table notes are identical to those presented for Table 9, except for
H j,t−1
which now includes a constant, a politically sensitive dummy, the interaction term between the politically sensitive dummy and the policy uncertainty measure, and country fixed effects.
Policy uncertainty (PU)
Politically sensitive
PU x Politically sensitive
Constant
Country fixed effects
Observations
R-squared
(1)
Shifted Election
Dummy (SED)
-0.0337
***
(0.000)
0.0230
***
(0.000)
-0.0168
**
(0.030)
0.1281
***
(0.000)
Yes
126,214
0.110
(2)
Election
Dummy
-0.0341
***
(0.000)
0.0233
***
(0.000)
-0.0176
**
(0.023)
0.1282
***
(0.000)
Yes
126,214
0.110
(3)
Change
Veto Players (CVP)
-0.0151
***
(0.000)
0.0175
***
(0.000)
-0.0081
**
(0.024)
0.1187
***
(0.000)
Yes
126,214
0.110
(4)
Coalition
-0.0787
0.0384
***
(0.000)
***
(0.000)
-0.0450
***
(0.000)
0.1601
***
(0.000)
Yes
126,112
0.123
56

Table 12.
Interactions of country characteristics and policy uncertainty
The table notes are identical to those presented for Table 9, except for
PU j,t−1 that denotes the Shifted
Election Dummy , SED, as the policy uncertainty measure and
H j,t−1 that includes a constant, a country feature (English origin, disclosure to congress, disclosure to public, public sector ethics, and presidential system), the interaction term between the country feature and PU, and country fixed effects. P
PU + PU x
Country = 0
is the p-value from the test of the hypothesis that the sum of the coefficients on PU and PU x
Country feature is zero.
Policy uncertainty (PU)
Country
PU x Country
Constant
Country fixed effects
Observations
R-squared
P
PU + PU x Country = 0
(1)
English
Origin
-0.0129
***
(0.000)
0.1907
***
(0.000)
0.0239
***
(0.001)
0.0584
***
(0.000)
Yes
126,214
0.172
0.1388
(2)
Disclosure to
Congress
-0.0294
***
(0.000)
0.3084
***
(0.000)
0.0391
***
(0.004)
-0.0113
***
(0.000)
Yes
126,214
0.165
0.2629
(3)
Disclosure to
Public
-0.0375
***
(0.000)
0.2719
***
(0.000)
0.0552
***
(0.000)
0.0128
***
(0.000)
Yes
126,214
0.159
0.0941
(4)
Public Sector
Ethics
-0.0286
***
(0.000)
0.0465
***
(0.000)
0.0156
**
(0.012)
0.1432
***
(0.000)
Yes
126,214
0.120
0.1552
(5)
Presidential
System
-0.0252
***
(0.000)
0.1267
***
(0.000)
0.0273
***
(0.000)
0.0900
***
(0.000)
Yes
126,214
0.137
0.6751
57

Table 13.
Country characteristics, policy uncertainty, and the financial crisis of 2007-2009
The table notes are identical to those presented for Table 12, except for
H j,t−1 which now includes a crisis dummy; an interaction term between PU and the crisis dummy; an interaction term between the country feature and the crisis dummy; and the triple interaction term involving the PU, the country feature, and the crisis dummy, in addition to the control variables in Table 12. P
PU + PU x Country + PU x Country x Crisis = 0
(P
PU +
PU x Crisis + PU x Country x Crisis = 0
) is the p-value from the test of the hypothesis that the sum of the coefficients on
PU, PU x Country, and PU x Country x Crisis (PU, PU x Crisis, and PU x Country x Crisis) is zero.
Policy uncertainty (PU)
Country
PU x Country
Crisis
PU x Crisis
Country x Crisis
PU x Country x Crisis
Constant
Country fixed effects
Observations
R-squared
P
PU + PU x Country + PU x Country x Crisis = 0
P
PU + PU x Crisis + PU x Country x Crisis = 0
(1)
English
Origin
-0.0210
***
(0.000)
0.1731
***
(0.000)
0.0255
***
(0.002)
-0.0102
***
(0.000)
0.2734
***
(0.000)
0.0449
***
(0.000)
-0.3364
***
(0.000)
0.0552
***
(0.000)
Yes
171,662
0.144
0.0000
0.0603
(2)
Disclosure to
Congress
-0.0442
***
(0.000)
0.3046
***
(0.000)
0.0516
***
(0.001)
-0.0309
***
(0.000)
0.2132
***
(0.000)
0.0620
***
(0.005)
-0.2182
***
(0.000)
-0.0119
***
(0.000)
Yes
171,662
0.144
0.0000
0.0000
(3)
Disclosure to
Public
-0.0522
***
(0.000)
0.2716
***
(0.000)
0.0678
***
(0.000)
-0.0257
***
(0.000)
0.2276
***
(0.000)
0.0359
*
(0.057)
-0.2329
***
(0.000)
0.0108
***
(0.000)
Yes
171,662
0.137
0.0001
0.0000
(4)
Public Sector
Ethics
-0.0450
***
(0.000)
0.0512
***
(0.000)
0.0165
***
(0.002)
-0.0462
***
(0.000)
0.2009
***
(0.000)
-0.0070
(0.284)
-0.0201
(0.148)
0.1389
***
(0.000)
Yes
171,662
0.102
0.0000
0.0000
(5)
Presidential
System
-0.0321
***
(0.000)
0.1649
***
(0.000)
0.0443
***
(0.000)
-0.0183
***
(0.000)
0.1826
***
(0.000)
-0.2353
***
(0.000)
0.0506
***
(0.000)
0.0810
***
(0.000)
Yes
171,662
0.128
0.0000
0.0000
58