Does Economic Policy Uncertainty Impact Firms’ Capital Structure Policy? Evidence from Western European Economies

doi:10.21203/rs.3.rs-2100169/v1

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Does Economic Policy Uncertainty Impact Firms’ Capital Structure Policy? Evidence from Western European Economies

https://doi.org/10.21203/rs.3.rs-2100169/v1

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published 26 Dec, 2022

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This study specifically investigates the impact of economic policy uncertainty (EPU) on the travel and leisure (TL) companies’ debt holdings policy. To do so, the present study selects 92 publicly listed TL companies operating in Western Europe’s top tourist destinations, and performs both the static and dynamic panel data estimation approaches during the 2005–2019 period. The results reveal that the EPU negatively impacts TL firms’ debt holdings, implying that firms tend to decline in debt levels by rising EPU. Consequently, the results highlight that the level of EPU matters and firms’ debt ratios are relatively lower in countries having a higher EPU and vice versa. Besides, the results underscore that the EPU negatively impacts firms’ debt holdings in each sub-sector however the negative effect is most prominent on the debt ratios of the firms, particularly those operating in gambling, hotels, travel and tourism, and also recreational services sub-sectors.

JEL classification: D80, G32, L83

Economic policy uncertainty

Capital structure

Tourism companies

Western Europe

It’s well documented that determining the optimal capital structure is one of the essential decisions for firms’ managers. Generally, firms in an imperfect capital market (e.g., including investment costs, agency costs, and taxes) attempt to select the best combination of capital structure that minimizes investment costs and maximizes shareholders’ worth. Following the trade-off theory, companies should be established the equilibrium point between the advantages (e.g., tax shield) and disadvantages (e.g., financial distress) of debt to the equity financing to determine the optimal capital structure (Jensen and Meckling, 1976, Brealey et al., 2012). This is while, the pecking order theory suggested that firms use equity financing only when all retained earnings and debt financings possibilities are exhausted (Myers and Majluf, 1984).

In the relevant literature, several empirical studies have investigated to explore the significant factors of firms’ debt holdings. Remarkably, the prior studies underscored that the firm-specific determinants including size, effective tax rates, profitability, the tangibility of assets, and growth opportunity are the most significant factors which impact firms’ debt ratios (e.g., Ozkan, 2001, Tong and Green, 2005). Besides, numerous studies showed that the factors at the country-level such as inflation, Gross Domestic Product (hereafter, GDP), and financial market development (Baum et al., 2013, Mokhova and Zinecker, 2014) considerably affect companies’ debt holdings policy. More specifically, some research highlights the significant role of EPU^[1] in determining firms’ debt holdings policy. Their findings showed that EPU by lowering the tendency of the supply side for providing loans (Zhang et al., 2015) and also diminishing the demand side for using external financing (Chen and Hu, 2019) negatively impacts the firm’s debt ratios. The previous studies argued that by increasing a country’s EPU, a level of information asymmetric between borrower and lender increases and firms are likely to encounter higher cash flow volatility and risk of future business failure, driving to rise in the cost of borrowing and use of debt financing as a last resort (Stolbov and Shchepeleva, 2020, Tran et al., 2021).

What about the debt holdings policy in the TL companies? The companies operating in this sector highly need to use external financing to provide enormous amounts for investments, replace, expand, and modernize tangible and intangible assets. Besides, these firms strongly rely on external financing due to having volatile cash flow, low levels of operating margins and stockpiling cash, and being smaller in size (e.g., Yoo, Lee and Bai, 2011, Kuti and Madarász, 2014, Singal, 2014). Considering the unique fundamental financial characteristics of the TL companies, the interests of numerous scholars have been triggered to investigate the significant determinants which impact firms’ debt holdings. Consistently, the findings of studies uncovered the significant effect of factors at both the firm (e.g., size, growth opportunities) and country (e.g., GDP, inflation) levels on TL firms’ debt holdings (Karadeniz et al., 2009, Hua and Upneja, 2012).

Although the empirical research has investigated the determinants of firms’ capital structure in the TL sector, there is less consideration in empirical literature to probe deeply the impact of EPU on debt holdings of TL firms. As discussed, increasing EPU has an adverse effect on both demand and supply sides and imposes corporate executives to revise and take a decision about capital structure in particular for highly levered TL companies that are exposed to financial distress and bankruptcy risks. Besides, there may be found few studies attempting to examine this nexus in both the TL sector and also its subsectors, which are not probably responded similarly to the EPU variations due to having different structures. Hence, the present study contributes by filling this gap and providing new empirical evidence on this nexus by considering Western Europe’s^[2] top tourist destinations and also the most populated countries particularly the United Kingdom, Germany, France, Spain, and Italy. According to the World Bank (2022), the average number of international tourism arrivals for these countries was 86588908 for the period between 2005-2020, which is 11.98 times more and relatively higher than the average for the rest of the world which was 7227007. These countries also had on average 43097600 international tourism arrivals on 2020, which is 7.04 times more and comparatively higher than the average of the rest of the Western European countries which was 6124125. Besides, this study contributes by conducting this nexus for the firms operating in particular the sub-sectors. Moreover, this study contributes by performing both the static and dynamic panel data estimation approaches to find robust results. This may be the first research to examine this association from this view, and the findings could open an original discussion in the empirical literature.

The empirical findings have remarkable policy suggestions. First, the results underscore that, except for the negative effect of profitability, the capital intensity, intangible assets, effective tax rates, growth opportunities, and size positively impact debt holdings of firms operating in the TL sector and also its sub-sectors. Besides, the estimation results reveal that financial market development with positive signs and inflation with negative signs impact firms’ debt holdings policy, correspondingly. This finding suggests that firms’ managers should be considered the important role of significant factors at both firm and country levels in making the debt holdings decision. Second, the results reveal that the EPU negatively impacts TL firms’ debt holdings and firms’ debt ratios are relatively lower in countries having a higher EPU and vice versa. The estimation results also reveal that the EPU negatively impacts firms’ debt holdings in each sub-sector however the negative effect is most prominent on the debt ratios of the firms, specifically those operating in gambling, hotels, travel and tourism, and also recreational services sub-sectors. This finding implies that the policymakers and regularity bodies should be controlled the rising EPU and also has an important message for firms’ managers to take serious practice to shield against the negative impact of EPU.

The paper structure is as follows. Section 2 presents the literature review. Section 3 presents the hypothesis development. Section 4 presents the data and methodology. Sections 5 and 6 present the results and robustness tests. Section 7 presents the conclusion.

[1] Researchers showed a strong responsive behavior of corporations towards volatility in the economic policy including capital structure decisions, cash holdings, and investment decisions (Phan et al., 2019; Li and Qiu, 2021).

[2] The list of Western European Countries in 2022 includes Germany, the United Kingdom, France, Italy, Spain, Netherlands, Belgium, Sweden, Portugal, Austria, Switzerland, Denmark, Finland, Norway, and Ireland (https://worldpopulationreview.com/country-rankings/western-european-countries).

In an influential study, Modigliani and Miller (1958) theoretically stated that capital structure choice is irrelevant in a perfect capital market when there are no investment costs, taxes, and also risks. Modigliani and Miller (1958) also stated that capital structure does not add value to the firm’s worth. However, the presence of investment costs, agency costs, taxes, and information asymmetries violate the above assumption, suggesting the capital market is practically imperfect. Remarkably, this imperfection necessitates investigating the best combination of capital structure (e.g., debt and equity) that minimizes investment costs and maximizes shareholders’ worth. In the literature, two main theories are mainly used by studies to explain the firms’ capital structure policy.

Based on the trade-off theory, firms should be established the equilibrium point and trade-off between the advantages and disadvantages of debt and equity financing. For instance, increasing debt financing has a firm's tax shield advantage while it has financial distress, bankruptcy costs, and agency costs disadvantages (Jensen and Meckling, 1976, Karadeniz et al., 2009, Brealey et al., 2012). Hence, to achieve this optimal point, it needs to alter its capital structures and establish a target debt ratio. Instead, the pecking order theory (Myers and Majluf, 1984) stated that a firm’s capital financing decision first starts with retained earnings, followed by debt, and then use equity as a last resort. Based on the pecking order theory, firms use equity financing only when all retained earnings and debt financings possibilities are exhausted (e.g. Carpenter and Petersen, 2002, Cole, 2013).

Throughout the years, several works have been encouraged to describe the capital structure policy behaviors of companies operating in developed and emerging economies. Findings of previous studies highlighted that the firms’ capital structure policy decisions depend on the specific micro (internal) and macro (external)-level determinants. Focusing on firm-level determinants, the findings of some studies indicated the significant role of the size, tax rate, tangibility, profitability, and investment opportunities. For instance, the studies by Titman and Wessels (1988) and De Jong et al. (2008) showed that there is a significant positive nexus between tangible assets and leverage.

Likewise, the findings of Rajan and Zingales (1995) and Ozkan (2001) indicated that profitability negatively impacts leverage, supporting the pecking order theory. Several studies (e.g., Marsh, 1982, Booth et al., 2001, De Jong et al., 2008) revealed that there is a positive relationship between company size and leverage, confirming the trade-off theory. However, Chen (2004) found a negative nexus between leverage and the size of firms in China, supporting the pecking order theory. The works of Chen (2004) and Tong and Green (2005) underscored that growth opportunity positively impacts the leverage of companies while Slutz (1990) and Frank and Goyal (2009) found an opposite effect. Remarkably, the studies by Hall et al. (2000) and Abor (2007) highlighted that firm’s capital structure policy varies and depends on the type of each industry in which they operate.

More specifically, several studies have triggered to empirically probe the firms’ capital structure determinants operating in the hospitality industry. Distinctively, these companies have an intensive capital nature leading to significantly differing capital structure policies relative to other industries. Based on the pecking order theory prediction, these companies due to bearing the high amounts of fixed costs rely mainly on using internal funds and using debt financing as a last choice. Nevertheless, according to the trade-off theory prediction, it has a likelihood that these companies follow debt financing since the capital-intensive nature may allow for the accessibility of fixed assets which eventually act as the collateral value against debt.

In an empirical study, Delbar and Upneja (2002) uncovered that size is positively related to a higher level of debt ratio while growth opportunities have an opposite effect in the listed restaurant companies in the U.S. They argued that debt ratio is higher as larger companies can better afford the external long-term financing costs. Likewise, the study by Karadeniz et al. (2009) suggested that the tangibility of assets, return on assets, and the effective tax rates determinants adversely impact the debt ratio of lodging companies operating in Turkey. Moreover, Hua and Upneja (2012) discussed that the effects of some determinants (e.g., growth opportunities) on U.S. public restaurant and lodging firms’ debt ratio depend on the type of each sector in the hospitality industry, indicating the capital structure choice may not be homogeneous in the hospitality sectors.

While the above-reviewed studies have explained the firms’ capital structure policy by focusing on the firm-level determinants, some research highlight the significant role of country-level factors. Particularly, the findings of studies revealed that EPU significantly impacted the key strategic decisions of the firm such as capital structure, cash holdings, and investment decisions (Chen et al., 2019, Li and Qiu, 2021). The study by Zhang et al. (2015) showed that a firm’s capital structure is negatively impacted by EPU. The study by Tabash et al. (2022) also underscored that EPU negatively impacts debt financing. In general, the impact of EPU on a firm’s financing decisions could be viewed and assessed from both the supply and demand side effects.

According to the supply side effect, increasing a country’s EPU causes to raise a level of information asymmetric between borrower and lender (Stolbov and Shchepeleva, 2020) and increases the likelihood of default risk (Li and Qiu, 2021). This leads to a lowering tendency of suppliers for providing loans (Zhang et al., 2015), which in turn, diminishes the firm’s debt ratio. Considering the demand side effect, firms have less tendency and usually constraint their demand for external financing sources by rising the EPU as they have concerned about their future cash flow volatility, future business performance, and also the volume of business activities (Tran et al., 2021). Chen et al. (2019) explained that companies during EPU by using less external financing are more probable to forgone their investment opportunities. A rising EPU causes to increase in the cost of external financing and the risk of failure due to increasing cash flow volatility. Moreover, companies will have less profit because of having low investment, leading to reduce the firm’s assets volume. Consequently, fewer assets will exist to offer as security to borrow from banks, which eventually declines the firm’s demand for additional financing. The findings of the study by Lee et al. (2021) documented that there is a negative nexus between EPU and bank financing behavior. Overall, EPU imposes adverse effects on the financing options of firms from both the supply and demand sides and causes firms to boost their cash reserves or use the alternative source of equity financing to meet their funding needs.

Additionally, the findings of some studies underscored that firms’ capital structure policy could be significantly explained by the country’s macro-economic factors. The findings of prior studies (Mokhova and Zinecker, 2014, Baum et al., 2017) indicated interest rate, inflation rate, and financial sector development significantly impact the financing decisions of companies. The findings of the study by Frank and Goyal (2009) also documented the significant role of inflation in explaining a firm’s leverage. As above reviewed, the majority of the empirical studies suggested that the firms’ capital structure policy is explained by determinants at both the firm and country levels. Remarkably, the prior studies underlined that EPU is one of the important factors which impact companies’ financing decisions. Despite the several studies aiming to test the firm’s capital structure determinants in the various sectors, there is a momentous gap to study the capital structure policy in the listed firms operating in the TL sector extensively. More specifically, limited research has been found in the framework of Western European markets to probe the impact of the EPU on firms’ capital structure policy for the TL sector and its sub-sectors. Therefore, this study fills this gap and aims to examine this nexus during the period between 2005 to 2019.

3.1 Firm-level determinants

Based on the trade-off theory, there is a positive nexus between capital intensity and debt ratio. While the level of capital expenditures is high, especially the share of fixed assets (tangible assets) in the hospitality companies, these fixed assets serve as a guarantee for debt financing enabling companies to get debt more easily in particular by increasing the collateral value of fixed-assets (Myers, 1977, Williamson, 1988, Singal, 2015). On the other hand, the relationship can also be negative, as investing in fixed assets can increase fixed costs, which eventually lead to lower profitability and a decline in the borrowing capacity of the companies.

H1: There is a positive or negative nexus between the capital intensity ratio and the debt ratio.

Based on the trade-off theory, a negative nexus exists between the intangibility ratio and debt ratio. This relationship holds as intangible assets have higher asymmetric information and also a lesser amount of collateral value, imposing some restrictions on debt financing and declining the borrowing capacity of the company (e.g., Holthausen and Watts, 2001). On the other hand, it has a likelihood to expect a positive nexus between the two factors for the TL companies. These companies have increased significantly both measurable and unmeasurable intangible assets to gain a competitive advantage and eventually earn more profits (e.g., Krambia-Kapardis and Thomas, 2006). Based on the pecking order theory, higher profitability is likely to increase the borrowing capacity of the companies.

H2: There is a positive or negative nexus between the intangibility asset ratio and the debt ratio.

The prior studies (Upneja and Dalbor, 1999, Bancel and Mittoo, 2004) suggested that the effective tax rate is an important factor in capital structure. Based on the trade-off theory, there is a positive nexus between effective tax rates and debt ratio (e.g., DeAngelo and Masulis, 1980, Haugen and Senbet, 1986). This happens due to the reduction in the effective cost of debt when financial expenses deduct from taxable income. Therefore, there is an advantage of debt financing with an increase in tax rates (Brigham and Houston, 2004).

H3: There is a positive nexus between effective tax rates and the debt ratio.

Based on the pecking order theory, highly profitable companies tend to use internal financing to avoid possible financial distress costs. As a result, a negative nexus between profitability and the debt ratio is expected (Myers and Majluf, 1984). Nevertheless, the trade-off theory expects a positive nexus, implying that companies with higher profitability are more probable to use debt financing due to having a higher level of borrowing capacity.

H4: There is a positive or negative nexus between profitability and the debt ratio.

The pecking order theory implies a positive nexus between growth opportunities and debt ratio. Based on this theory, companies having more growth opportunities incline to rise their borrowing capacity to fulfill their growth opportunities (Myers, 1984). However, the trade-off theory suggests a negative nexus between two variables suggesting that companies with more growth opportunities are more probable to decrease debt financing. Similarly, Jensen (1986) argued the likelihood of debt financing is high in companies with low growth opportunities but rich in cash to alleviate the agency problem between shareholders and managers.

H5: There is a positive or negative nexus between growth opportunities and the debt ratio.

The trade-off theory indicates a positive nexus between company size and debt ratio. This relationship holds because large companies have an advantage of diversification over small companies. Due to the diversification advantage, large companies are most likely to experience lower financial distress. Hence, lower financial distress costs motivate large companies to include high leverage in their capital structure. (e.g., Ang, 1992, Imtiaz et al., 2016). The results of many studies corroborate the trade-off theory and proved a positive nexus (e.g., Dalbor and Upneja, 2002, Sayılgan et al., 2006, Colla et al., 2013, Serrasqueiro and Caetano, 2015). Instead, the pecking order theory implies a negative nexus between company size and debt ratio. As discussed (e.g., Ahmed and Wang, 2011, Kim et al., 2011, Çekrezi, 2013), the cost of capital in large companies is relatively lower due to having less asymmetric information than counterparts, resulting in declining debt ratios.

H6: There is a positive or negative nexus between size and the debt ratio.

3.2 Country-level determinants

Policy uncertainty may rise the cash flow instability of the company which caused in raising financial distress costs. Prior works (e.g., Gilchrist et al., 2014, Brogaard and Detzel, 2015) described that, during the EPU, the cost of external funding surges and assets’ return reduces. According to trade-off theory, companies (demand-side) preferred equity financing to debt financing when EPU is high because of higher financing rates and lower firms’ profitability (Modigliani and Miller, 1958, Athari 2021, Athari and Bahreini, 2021). Besides, since firms have a fixed liability (e.g., interest payment) by the bank financing, the companies are more likely to prefer equity financing during a high EPU to fewer financial liabilities, increase financial stability, and earning a further premium on their shares (Li et al., 2017, Zhang and Gregoriou, 2019). On the other hand, financial firms (supply-side) are unwilling to increase their loans in the period of such economic conditions to curb non-performing loans and maintain financial stability.

H7. There is a negative nexus between EPU and debt ratio.

Previous studies showed that financial market development and inflation significantly impact firms’ capital structure. For instance, Love (2003) revealed that the improvement of the capital market could increase firms’ willingness for debt financing due to accessing firms’ cheaper external capital. In a less capital market development, the cost of capital is higher and firms are likely to forgone profitable investments. Likewise, Taggart Jr (1985) showed a positive effect of inflation on capital structure and argued that a rise in the predicted inflation resulted in reducing the real cost of borrowing, increasing the real value of the tax shield, and eventually triggering the tendency of firms to increase leverage. However, the works by Booth et al. (2001) and Hatzinikolaou et al. (2002) mentioned an opposite relationship as the economic environment deteriorates and borrowing becomes severer for firms by increasing inflation.

H8. There is a positive nexus between financial market development and debt ratio.

H9. There is a positive or negative nexus between inflation and the debt ratio.

4.1. Data description

The sample comprises the listed companies in the TL sector operating in Western Europe’s top tourist destination countries, namely, the United Kingdom, Germany, France, Spain, and Italy. To do so, according to the industry classification benchmark and data availability, an initial sample of 92 publicly traded firms operating in the TL sector is selected between 2005 to 2019. These companies operate in the airlines, gambling, hotels, recreational services, restaurants and bars, and also travel and tourism sub-sectors. In examining the factors of capital structure, the firm-level variables’ data set is gathered from Thomson Reuters’ Datastream database. Besides, the country-level data set for the financial market development and inflation variables are gathered from Word Bank. We also collected the EPU index data from the policy uncertainty website^[3]. Table 1 presents the variables’ descriptions.

4.2 Descriptive statistics

Table 2 displays the results of the descriptive statistics. Panel (A) shows that the debt ratio (DR) has a median (mean) value of 0.60 (0.59), suggesting that TL companies are more likely to use debt financing. Besides, it shows asset growth (AG) and total assets (TA) have a median value of 3.02 and 12.31, correspondingly, indicating that the selected companies have a large size and high potential growth opportunities. Panel (A) also reveals that the median value of EPU uncertainty is 154.91, suggesting that the selected countries have experienced a high level of domestic policy uncertainty during the period of study. Panel (B) also shows that airline companies with a mean value of 0.75 and hotels with a mean value of 0.44 have the highest and lowest debt ratio (DR), respectively. It also shows airline companies are the biggest with total assets (TA) mean value of 13.96 while recreational services and gambling companies are the smallest ones with a mean value of 11.62 and 11.63, respectively. Moreover, Panel (C) shows that TL companies operating in Italy have the highest debt ratio (DR) with a mean of 0.77 while U.K firms have the lowest debt ratio with a mean value of 0.56. Likewise, it shows the U.K with a mean value of 161.56 has relatively the most developed financial market while Italy and Germany with a mean value of 86.10 and 90.11 have the least developed financial market, correspondingly. Moreover, Panel (C) reveals the U.K and France with mean values of 176.68 and 184.20 have the highest EPU scores whereas Spain and Italy with mean values of 105.78 and 108.81 have the lowest scores, respectively.

Figures 1 displays the time series plot of EPU and debt ratio at the country and sub-sector levels between 2005-2019. As shown in the Figures, there is an opposite nexus between EPU and debt ratio and firms reduce (increase) their leverage by increasing (decreasing) the level of EPU.

Table 3 presents the correlation between variables for the entire selected countries. The Pearson correlation matrix results do not show that the multicollinearity problems are significantly serious between the using variables. Table 3 also shows the Variance Inflation Factors (VIF), denoting that there is no serious multicollinearity issue.

4.3. Models and methodology

Equation (1) performs to test the impact of EPU on firms’ financing structure by controlling the firm and country-specific variables. Besides, it’s noteworthy to mention that Equation (1) are included the country, sub-sector, and year dummy variables to control geographic location, sub-sector, and year-specific effects, respectively. However, for parsimony, the coefficients do not provide it. Likewise, the present study follows the work by Tabash et al. (2022) and includes the lagged dependent variable in the estimation equation.

where _it denotes firm and time, respectively. ε_itis an independent error term. DR is debt ratio. DR_it-1 is the lagged debt ratio, ∑Firm_it includes the capital intensity (CIR), intangible assets (IAR), effective tax rate (ETR), profitability (ROA), growth opportunities (AG), and size (TA), FMD is the financial market development, EPU is the economic policy uncertainty, INF is the inflation.

Before conducting estimations, this study winsorized the variables for each year from 1% top and bottom to avoid the impact of outliers (e.g., Athari and Bahreini, 2021). Besides, this study follows the research by Baltagi and Rich (2005) and Hsiao (2014) and uses the panel data approach to control the heterogeneity and multicollinearity problems and also to increase the efficiency of estimations. For estimation of the models, the present study follows the study by Petersen (2009) and performs the Fixed effects (FE) panel data methodology by clustering standard errors at the firm level. Nevertheless, the estimated coefficients by the panel static linear regression are likely inconsistent and biased in the presence of the lagged dependent variable (Roodman, 2009). Using the dynamic panel data methodology could help estimate more consistent and unbiased coefficients as unobserved country-fixed effects and endogeneity problems exist. Specifically, performing the dynamic panel data methodology is more efficient in presence of endogeneity problems among independent variables in the estimation model. Hence, this study used the dynamic panel data approach (GMM), in addition to the FE, to estimate the equations (I=92 > T=15).

More specifically, this study follows the study by Athari et al. (2021) and estimates equations by using the one-step and two-step System-GMM (Sys-GMM) approach (Arellano and Bover, 1995, Blundell and Bond, 1998). Likewise, in examining the validity of the estimated models by the System‐GMM methodology, the serial correlation (AR (2)) and Hansen tests are applied. Moreover, the present study uses the Panel-Corrected Standard Errors (PCSE) approach, as used by Athari and Bahreini (2021), in addition to the FE, to estimate the Equation (1) for each sub-sector because the GMM condition is not valid for some of the subsectors (I < T). It’s noteworthy to mention that the PCSE standard error is also robust to possible contemporaneous correlation and unit heteroskedasticity (Bailey and Katz, 2011).

[3] www.policyuncertainty.com

5.1 Unit root test

Before estimation equations, this study performs the stationarity test for the entire investigated variables to avoid a spurious estimation. Similar to the recent study (Athari et al., 2021a, Athari et al., 2021b), the panel unit root techniques suggested by Levin et al. (2002) and Im et al. (2003) were used. The results are displayed in Table 4 and imply that the examined factors are stationary after the first difference for both trend and cross-sectional dependence alternatives.

5.2 Results and discussion

Table 5 shows the estimation results of Equation (1) for the entire sample countries using both the FE and (Sys-GMM) panel data approaches. The results reveal that the coefficient of the lagged debt Ratio (L.DR) is positive and significant, denoting that Western European tourism firms partially adjust debt levels toward the target debt holdings.

The results reveal that capital intensity (CIR) positively impacts tourism firms’ debt ratios. The finding corroborates the predictions of trade-off theory and the results of Harris and Raviv (1991) and Thornhill et al. (2004) which explained the role of fixed assets in increasing the level of debt financing in companies. The study by Karadeniz et al. (2009) and Singal (2015) argued that the levels of capital expenditures are high in hospitality companies and these companies have high fixed costs at both investment and operating stages, leading to increasing the collateral value of fixed assets to get higher debt. Likewise, Table 5 shows that there is a significant positive nexus between intangibility asset (IAR) and tourism firms’ debt levels. The study by Krambia-Kapardis and Thomas (2006) documented that TL companies by significantly increasing intangible assets attempt to gain a competitive advantage to rise profits. Based on the pecking order theory, companies with higher profitability are more probable to increase their borrowing capacity.

Besides, the results show that effective tax rates (ETR) positively impact firms’ debt ratios, supporting the prior studies (e.g., Haugen and Senbet, 1986). Following the prediction by the trade-off theory, the positive effect happens due to the decline in the effective cost of debt when financial expenses deduct from taxable income (Brigham and Houston, 2004). Table 5 shows a significant negative impact of profitability (ROA) on firms’ debt ratios. The negative effect is supported by the pecking order theory and is in line with the results of previous studies (e.g., Huang, 2006, Karadeniz et al., 2009) that showed profitability negatively impacts leverage. Moreover, consistent with the previous findings, the results imply that growth opportunities (AG) (e.g., Dalbor and Upenja, 2004, lee, 2010) and size (SIZE) (e.g., Huang and Song, 2006, De Jong et al., 2008) positively impact tourism firms’ debt ratios.

Furthermore, the results highlight that there is a significant negative nexus between EPU and firms’ debt ratios, implying that firms are less probable to use debt funding during high EPU. This result is consistent with the predictions of trade-off theory and also the previous works (e.g., Zhang et al., 2015, Athari 2021, Athari and Bahreini, 2021), who explained that, during the EPU period, the cost of external funding rises and profitability of companies reduces. Besides, the results reveal a significant positive and negative nexus between financial market development (FMD), inflation (INF), and firms’ debt ratios, respectively. Love (2003) discussed that the improvement of the capital market could facilitate firms’ access to cheaper external funds and allows companies to take advantage of the high investment opportunities, indicating more willingness for debt financing. Also, it supports the studies by Booth et al. (2001) and Hatzinikolaou et al. (2002), which showed that rising inflation deteriorates the economic system and reduces borrowing capacity for firms.

Table 6 reveals the estimation results of Equation (1) for the firms operating in each sub-sector using both the FE and PCSE approaches. Similar to the results shown in Table (5), the capital intensity (CIR), intangible assets (IAR), effective tax rates (ETR), growth opportunities (AG), and size (SIZE) positively impact firms’ debt ratios at the various sub-sectors though profitability has an opposite effect. Besides, the results show that financial market development (FMD) with positive signs and also economic policy uncertainty (EPU) and inflation (INF) with negative signs impact firms’ debt ratios in the different sub-sectors. Particularly, the results show that the EPU has the most prominent negative effect on the debt ratios of firms operating in gambling, hotels, travel and tourism, and recreational services, respectively. In such sub-sectors, firms are more probable to use internal funding, equity financing, and then debt financing as the last resort. This could be explained as the revenues of these sub-sectors depend on the tourism inflows and the rising of the EPU leads to significantly reducing the borrowing capacity and affordability to repay the loans. As shown in Figure (1), these sub-sectors react more significantly to the variation of EPU relative to others.

5.3 Further Analysis

Table 7 shows the firms’ capital structure policy under the low and high-level of EPU. Although, the results corroborate that EPU negatively impacts firms’ debt ratios it underscores that firms significantly lower debt ratios by raising the degree of EPU. In other words, firms’ debt ratios are relatively lower in countries having a higher EPU and vice versa.

The present study implements numerous robustness analyses to control the reliability of the estimation results. First, this study estimates Equation (1) by using the new alternatives of the “net income to total equities” (ROE) for measuring profitability, “market value to book value” (MV/BV) for measuring growth opportunities, and “country risk index” (CRI)^[4] for measuring a country’s uncertainty. Table 8 shows the estimation results. Consistent with Tables 5 and 6, the results corroborate that the firm and country-specific factors significantly impact TL firms’ debt ratios. Particularly, the empirical results show that CRI significantly impacts capital structure and firms are probable to lower debt ratios by rising a country’s uncertainty.

Second, this study follows the prior studies and performs the Granger causality test to probe the linkage direction between the studied factors and control reverse causality. As shown in Table 9, there is statistically significant evidence of Granger causality from the set of independent variables (capital intensity, intangible assets, effective tax rate, profitability, growth opportunities, size, financial market development, EPU, inflation) to debt ratios in the panel countries. This denotes the historical information of the investigating explanatory factors is capable of suggesting future information about firms’ debt ratios in the panel countries.

^[4] A higher score indicates a lower country’s uncertainty level (www.prsgroup.com)

While several empirical studies have investigated the TL companies’ debt holdings policy behavior, much less consideration has been paid to probing the significant factors of debt holdings of the TL firms operating particularly in Western European countries. More specifically, limited studies attempted to explore broadly the impact of the EPU on the debt ratios of firms operating in the TL sector and also its sub-sectors. These companies have unique characteristics due to their significant dependency on fixed assets and highly need to use external financing to provide sufficient capital for investments, replace, expand, and modernize tangible and intangible assets. Hence, studying the determinants of firms’ debt holdings is essential and it has a pivotal role for policymakers. Consequently, the present study fills the gap by exploring specifically the effects of EPU, as well as traditional determinants, on the debt holdings of the listed TL companies operating in Western Europe’s top tourist destination countries, namely, the United Kingdom, Germany, France, Spain, and Italy. Besides, we examine this nexus for firms operating, in particular, the sub-sectors. To do so, we perform both the static and dynamic panel data estimation approaches during the 2005–2019 period. This may be the first study that conducts this nexus, and the results open an original discussion in the literature.

The results reveal that, except for profitability, the capital intensity, intangible assets, effective tax rates, growth opportunities, and size positively impact the debt holdings of firms operating in the TL sector and its sub-sectors. Besides, the estimation underscores that country-level factors matter and financial market development and inflation have a statistically significant positive and negative impact on firms’ debt holdings policy, correspondingly. Moreover, the results highlight the coefficient of EPU is negative and significant, indicating that firms operating in the TL sector prefer to decline debt levels by rising EPU and using debt financing as a last resort. Especially, the results show that among the sub-sectors, the EPU has the most prominent negative effect on the debt ratios of firms operating in gambling, hotels, travel and tourism, and also recreational services, respectively.

The findings have significant policy implications. First, the results imply that the policymakers and regularity bodies should be controlled the rising EPU to help TL firms have fewer volatile cash flows and also hold less cash for utilizing future investments and firm expansions. A decline in a country’s EPU leads firms to have less cash shortage through easier access to cheaper external financing, which possibly reduces the precautionary motive of firms to stockpile more cash and causing to utilize their cash by considering further investment opportunities. Second, the results imply that the firms’ managers should be taken serious practice by diversifying their assets portfolio to shield against the adverse effect of EPU. These measures could help firms access more capital for exploiting investment opportunities, especially in the sub-sectors such as gambling, hotels, travel and tourism, and recreational services whose revenues are sensitivities to the changes in EPU. Third, the results suggest that firms’ managers should be considered the important role of significant firm and country-levels determinants before setting debt ratios.

For a further study, it would be beneficial to probe the effect of EPU on firms’ debt holdings for other touristic countries to provide a more comprehensive picture. Also, it would be noteworthy to re-estimate this relationship by assessing the impact of geopolitical risks on the debt holdings policy behavior of Western Europe firms.

Ethical Approval: Not applicable

Consent to Participate: The authors all agreed to participate and publish our data here.

Consent to Publish: The authors all agreed to participate and publish our data here.

Authors Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Athari] and [Bahreini]. The first draft of the manuscript was written by [Athari] and [Bahreini] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Availability of data and materials: The data is available upon your request.

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Table 1. Variable’s descriptions
Variable	Definition	Hypothesis	Source
Dependent variable
Debt ratio	Total liabilities to total assets ratio (DR)		Thomson Reuters’ Datastream
Firm-level determinants
Capital intensity	Capital expenditures to total assets ratio (CIR)	+/−	Thomson Reuters’ Datastream
Intangible assets	Total intangible assets to total assets ratio (IAR).	+/−	Thomson Reuters’ Datastream
Effective tax rate	Income tax expenses to earnings ratio (ETR)	+	Thomson Reuters’ Datastream
Profitability	Net income to total assets ratio (ROA)	+/−	Thomson Reuters’ Datastream
Growth opportunities	Asset growth (AG)	+/−	Thomson Reuters’ Datastream
Size	Natural logarithm of total assets (TA)	+/−	Thomson Reuters’ Datastream
Country-level determinants
Financial market development	Domestic credit to the private sector (% of GDP) (FMD)	+	World Bank
Economic policy uncertainty	Economic policy uncertainty index score. This index is constructed based on newspaper articles regarding policy uncertainty (EPU)	−	www.policyuncertainty.com
Inflation	Consumer prices (annual %) (INF)	+/−	World Bank
Note: Table 1 shows the variable’s description.

Table 2. Descriptive summary (2005-2019)
Panel (A): All investigated countries (N=5)
Variables	Mean	Median	St.dev	Minimum	Maximum
DR	0.59	0.60	0.25	-0.11	2.15
CIR	0.06	0.03	0.08	0.00	0.97
IAR	0.17	0.07	0.22	0.00	0.96
ETR	-0.04	-0.01	0.19	-0.59	0.22
ROA	2.18	3.85	18.12	-152.92	108.83
AG	3.73	3.02	0.18	0.74	3.94
TA	12.36	12.31	2.32	5.47	17.25
FMD	112.16	129.59	38.78	70.85	191.41
EPU	160.80	154.91	66.48	59.48	305.43
INF	1.91	2.07	1.05	-0.50	4.07
Panel (B): Average firm and country-level variables for each sub-sector
	Airlines (N=9)	Gambling (N=18)	Hotels (N=12)	Recreational Services (N=21)	Restaurant & Bar (N=19)	Travel & Tourism (N=13)
DR	0.75	0.55	0.44	0.63	0.56	0.65
CIR	0.08	0.04	0.05	0.06	0.08	0.05
IAR	0.11	0.45	0.01	0.15	0.10	0.17
ETR	-0.04	-0.05	0.00	-0.00	-0.05	-0.10
ROA	-2.16	0.69	1.06	1.92	5.00	4.25
AG	7.30	3.33	1.53	0.18	10.10	0.50
TA	13.96	11.63	12.17	11.62	13.04	12.81
Panel (C): Average firm and country-level variables for each country
	United Kingdom (N=47)		Germany (N=18)	Italy (N=7)	Spain (N=4)	France (N=16)
DR	0.56		0.57	0.77	0.73	0.57
CIR	0.06		0.05	0.06	0.05	0.06
IAR	0.18		0.11	0.25	0.06	0.23
ETR	-0.03		-0.06	-0.32	-0.00	0.03
ROA	2.31		3.13	-0.09	2.44	1.74
AG	5.53		0.71	0.20	9.77	2.20
TA	12.34		11.71	12.91	15.00	12.37
FMD	161.56		90.11	86.10	153.21	92.60
EPU	176.68		132.45	108.81	105.78	184.20
INF	2.31		1.49	1.73	1.76	1.33
Note: Table 2 shows the descriptive summary of variables. DR: Debt Ratio, CIR: Capital Intensity Ratio, IAR: Intangibility Asset Ratio, ETR: Effective Tax Rates, ROA: Return on Assets, AG: Asset Growth, TA: Total Assets, FMD: Financial Market Development, EPU: Economic Policy Uncertainty, INF: Inflation rate.

Table 3. Pearson correlation matrix
	CIR	IAR	ETR	ROA	AC	TA	FMD	EPU	INF	VIF
CIR	1.000									1.22
IAR	-0.106^*	1.000								1.19
ETR	-0.128^*	0.028	1.000							1.12
ROA	-0.087	-0.001	-0.059	1.000						1.17
AG	-0.018	-0.033	-0.050	-0.008	1.000					1.26
TA	-0.145^*	0.156^*	-0.023	0.178^*	0.079	1.000				1.05
FMD	0.034	0.001	0.241^*	0.006	0.019	0.032	1.000			1.27
EPU	-0.090	0.043	0.362^*	-0.185	-0.057	-0.014	0.173^*	1.000		1.15
INF	0.027	0.021	0.182^*	0.015	0.009	-0.025	0.343^*	0.103^*	1.000	1.33
Note: Table 3 shows the Pearson correlation matrix and Variance Inflation Factor (VIF) between the variables. * is statistically significant at 1%.

Table 4. Unit root test results
Variables	Panel (A): Levin–Lin–Chu (2002)		Panel (B): Im–Pesaran–Shin (2003)
Variables	With trend	With cross-sectional dependence	With trend	With cross-sectional dependence
Debt ratio	7.892*	-10.214*	-2.233**	-11.434*
Capital intensity	-11.121*	-10.152*	-6.665*	-6.321*
Intangible assets	-8.544*	-7.694*	-5.244*	-5.132*
Effective tax rate	-4.138*	-6.443*	-11.942*	-5.435*
Profitability	-10.146*	-5.756*	-6.322*	-12.524*
Growth opportunities	-18.352*	-10.217*	-5.132*	-1.767***
Size	-9.388*	-5.127*	-4.866*	-6.492*
Financial market development	-12.562*	-7.153*	-2.646**	-4.528*
Economic policy uncertainty	-16.122*	-5.675*	-5.484*	-5.817*
Inflation	-12.323*	-7.832*	-5.248*	-6.225*
Note: Table 4 shows the panel unit root test results of investigated variables. The null hypothesis of Levin–Lin–Chu (LLC) and Im–Pesaran–Shin (IPS) unit root test is panels contain unit roots. The symbols , , and ** denote statistical significance at the 1%, 5%, and 10% levels, correspondingly.

Table 5. The impacts of economic policy uncertainty (EPU) on the TL firms’ capital structure for the entire investigated countries (2005-2019)
	Fixed Effects (FE)		SYS-GMM (One-step)		SYS-GMM (Two-step)
Independent variables	Coefficient	t-value	Coefficient	Z-value	Coefficient	Z-value
L.DR	0.383*	5.75	0.358*	4.71	0.490*	3.23
CIR	0.023	0.32	0.075**	2.09	0.056***	1.94
IAR	0.216**	2.23	0.016	0.83	0.018**	2.26
ETR	0.025*	2.87	0.019*	3.63	0.002	1.21
ROA	-0.002*	-4.73	-0.001*	-2.85	-0.001*	-4.06
AG	0.001	0.21	0.005**	2.19	0.006	1.12
SIZE	0.008**	2.07	0.014*	3.40	0.009**	2.12
FMD	0.004	1.26	0.001*	3.90	0.002**	2.01
EPU	-0.006*	-3.81	-0.043***	-1.95	-0.015*	-3.40
INF	-0.010**	-2.20	-0.004	-1.27	-0.006**	-2.16
CONS	0.286	1.19	-0.023	-0.69	-0.003	-0.12
TSC dummies	YES		YES		YES
F-statistic	74.25*		---		---
Adj. R²	0.54		---		---
AR (2)	--- ---		0.356 0.573		0.496 0.527
Hansen-test	--- ---		0.356 0.573		0.496 0.527
Note: Table 5 shows the impacts of economic policy uncertainty (EPU) on the capital structure for the pool countries using the Fixed Effects (FE), Sys-GMM (One-step), and Sys-GMM (Two-step) between 2005-2019. L.DR: Lagged Debt Ratio, CIR: Capital Intensity Ratio, IAR: Intangibility Asset Ratio, ETR: Effective Tax Rates, ROA: Return on Assets, AG: Asset Growth, TA: Total Assets, FMD: Financial Market Development, EPU: Economic Policy Uncertainty, INF: Inflation rate. TSC dummies: Time, Sub-sector, and Country dummies. , , and ** reveal 1%, 5%, and 10% statistically significance levels, correspondingly.

Table 6. The impacts of economic policy uncertainty (EPU) on the capital structure of the firms operating in each sub-sector (2005-2019)
Independent variable	Airlines		Gambling		Hotels		Recreational Services		Restaurants & Bars		Travel & Tourism
Independent variable	FE	PCSE	FE	PCSE	FE	PCSE	FE	PCSE	FE	PCSE	FE	PCSE
L.DR	0.365***	0.340*	0.278**	0.487*	0.388***	0.518*	0.268**	0.383*	0.298*	0.580 *	0.182**	0.283***
	(1.88)	(5.29)	(2.12)	(4.14)	(1.76)	(4.70)	(2.02)	(3.34)	(4.03)	(4.70)	(2.11)	(1.91)
CIR	0.245*	0.039	0.143	0.030***	0.363*	0.240*	0.314***	0.060**	0.191***	0.595**	0.246***	0.032**
	(3.89)	(0.42)	(0.41)	(1.68)	(2.79)	(0.002)	(1.68)	(2.12)	(1.69)	(2.11)	(1.83)	(2.18)
IAR	0.504	0.060***	0.359***	0.165*	0.106	0.136*	0.184	0.293**	0.814*	0.007**	0.219**	0.008
	(1.30)	(1.79)	(2.33)	(2.87)	(0.55)	(3.33)	(0.70)	(2.03)	(3.45)	(2.22)	(2.07)	(0.34)
ETR	0.201**	0.224*	0.126	0.074**	0.072**	0.087**	0.083*	0.405*	0.024	0.002***	0.040**	0.111**
	(2.04)	(3.52)	(1.20)	(2.20)	(2.17)	(2.15)	(3.99)	(3.67)	(0.66)	(1.93)	(2.01)	(2.15)
ROA	-0.005***	-0.007*	-0.003**	-0.001	-0.002**	-0.003*	-0.003**	-0.003	-0.004*	-0.002***	-0.004*	-0.002**
	(-1.92)	(-5.56)	(-2.11)	(-0.39)	(-2.11)	(-3.21)	(-2.19)	(-0.96)	(-4.03)	(-1.78)	(-3.88)	(-2.17)
AG	0.142	0.068***	0.001	0.006**	0.227*	0.119**	0.022	0.035**	0.058***	0.008	0.126***	0.001
	(1.19)	(1.67)	(0.33)	(2.03)	(4.17)	(2.04)	(0.41)	(2.05)	(1.71)	(0.47)	(1.79)	(1.29)
SIZE	0.137***	0.030*	0.054**	0.021	0.238**	0.029*	0.217*	0.018	0.040**	0.014*	0.064**	0.059*
	(1.98)	(4.16)	(2.16)	(1.56)	(2.03)	(4.86)	(3.23)	(0.98)	(2.12)	(3.42)	(2.16)	(4.42)
FMD	0.001***	0.002	0.006***	0.003***	0.003*	0.001***	0.001*	0.001**	0.004*	0.004**	0.002**	0.007*
	(1.71)	(1.55)	(1.68)	(1.86)	(3.82)	(1.94)	(3.08)	(2.09)	(3.37)	(2.16)	(2.23)	(3.91)
EPU	-0.001*	-0.002**	-0.006**	-0.008*	-0.005*	-0.006*	-0.003**	-0.004**	-0.002**	-0.001***	-0.004***	-0.005***
	(-3.28)	(-2.12)	(-2.04)	(-3.23)	(-3.48)	(-3.37)	(-2.14)	(-2.02)	(-2.05)	(-1.86)	(-1.94)	(-1.68)
INF	-0.025	-0.018*	-0.019**	-0.005**	-0.004	-0.002**	-0.008*	-0.010	-0.008	-0.003*	-0.002	-0.016**
	(-1.49)	(-5.25)	(-2.05)	(-2.15)	(-0.75)	(-2.05)	(-4.68)	(-0.46)	(-1.30)	(-3.92)	(-0.34)	(-2.06)
CONS	0.809**	0.317***	-0.353	0.014	-0.547*	-0.031	0.377	0.219	0.871**	0.167**	-0.328	-0.211**
	(2.65)	(1.71)	(-0.43)	(0.10)	(-4.57)	(-0.56)	(0.91)	(0.82)	(2.17)	(2.34)	(-0.48)	(-2.15)
TSC dummies	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
F-statistic	86.34*	---	76.58*	---	85.48*	---	87.33*	---	78.35*	---	83.68*	---
Adj. R²	0.56	---	0.48	---	0.54	---	0.59	---	0.53	---	0.45	---
Note: Table 6 shows the impacts of economic policy uncertainty (EPU) on the capital structure of the firms operating in each sub-sector using the Fixed Effects (FE) and Panel-corrected standard errors (PCSE). TSC dummies: Time, Sub-sector, and Country dummies. In parentheses, t and Z values are reported for the FE and PCSE, respectively. , , and ** reveal 1%, 5%, and 10% statistically significance levels, correspondingly.

Table 7. TL firms’ capital structure policy under the low and high-level of economic policy uncertainty (EPU)
Independent variables	Fixed Effects (FE)			Sys-GMM (One-step)			Sys-GMM (Two-step)
	Low EPU	High EPU	Low EPU		High EPU	Low EPU		High EPU
L.DR	0.435***	0.359*	0.481***		0.347**	0.379**		0.429*
	(1.68)	(0.000)	(1.71)		(2.15)	(2.05)		(0.000)
EPU	-0.016**	-0.037**	-0.028**		-0.042*	-0.023***		-0.053**
	(0.036)	(0.026)	(0.024)		(0.001)	(0.075)		(0.035)
Firm-specific	Yes	Yes	Yes		Yes	Yes		Yes
Country-specific	Yes	Yes	Yes		Yes	Yes		Yes
TSC dummies	Yes	Yes	Yes		Yes	Yes		Yes
Adj. R²	0.438	0.354	---		---	---		---
AR (2)	---	---	(0.512)		(0.644)	(0.426)		(0.523)
Hansen-test	---	---	(0.467)		(0.572)	(0.674)		(0.469)
Note: Table 7 shows how the EPU level impacts the firms’ capital structure policy decision using the Fixed effects (FE), Sys-GMM (One-step), and Sys-GMM (Two-step) between 2005-2019. TSC Dummies: Time, Sub-sector, and Country dummies. , , and ** reveal 1%, 5%, and 10% statistically significance levels, correspondingly.

Table 8. Robustness test I
	Fixed Effects (FE)		SYS-GMM (One-step)		SYS-GMM (Two-step)
Independent variables	Coefficient	t-value	Coefficient	Z-value	Coefficient	Z-value
L.DR	0.426*	4.55	0.316*	3.18	0.522*	4.16
CIR	0.026**	2.05	0.046***	4.36	0.043**	2.14
IAR	0.194	1.44	0.126***	1.73	0.105	0.89
ETR	0.036***	1.87	0.025*	4.36	0.027**	2.11
ROE	-0.003**	-2.13	-0.002*	-4.56	-0.001***	-1.94
MV/BV	0.001**	2.01	0.003	1.08	0.002	0.88
SIZE	0.007*	4.19	0.006***	1.71	0.004*	4.08
FMD	0.003	1.56	0.002***	1.91	0.011	0.87
CRI	0.025*	5.45	0.036*	4.26	0.053**	2.04
INF	-0.012	-1.22	-0.005**	-2.07	-0.004***	-1.85
CONS	0.344	0.48	0.653	0.54	0.529**	2.14
TSC dummies	YES		YES		YES
F-statistic	55.16*		---		---
Adj. R²	0.46		---		---
AR (2)	--- ---		0.345 0.662		0.534 0.494
Hansen-test	--- ---		0.345 0.662		0.534 0.494
Note: Table 8 shows the impacts of economic policy uncertainty (EPU) on the capital structure for the pool countries using the Fixed effects (FE), Sys-GMM (One-step), and Sys-GMM (Two-step) between 2005-2019. L.DR: Lagged Debt Ratio, CIR: Capital Intensity Ratio, IAR: Intangibility Asset Ratio, ETR: Effective Tax Rates, ROE: Return on Equities, AG: Market Value to Book Value, TA: Total Assets, FMD: Financial Market Development, CRI: Country Risk Index, INF: Inflation rate. TSC dummies: Time, Sub-sector, and Country dummies. , , and ** reveal 1%, 5%, and 10% statistically significance levels, correspondingly.

Table 9. Robustness test II
Null Hypothesis			F-statistics	[Prob. value]	Granger Causality
Capital intensity	→	Debt ratio	2.566^*	[0.001]	Yes
Intangible assets	→	Debt ratio	4.735^*	[0.002]	Yes
Effective tax rate	→	Debt ratio	5.464^*	[0.000]	Yes
Profitability	→	Debt ratio	3.294^**	[0.034]	Yes
Growth opportunities	→	Debt ratio	4.356^*	[0.000]	Yes
Size	→	Debt ratio	4.646^**	[0.024]	Yes
Financial market development	→	Debt ratio	4.247^*	[0.002]	Yes
Economic policy uncertainty	→	Debt ratio	5.436^*	[0.001]	Yes
Inflation	→	Debt ratio	4.835^**	[0.015]	Yes
Note: * and ** denote 1% and 5% statistical significance levels, correspondingly.

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published 26 Dec, 2022

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Does Economic Policy Uncertainty Impact Firms’ Capital Structure Policy? Evidence from Western European Economies

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Abstract

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1. Introduction

2. Literature Review

3. Hypothesis Development

3.1 Firm-level determinants

3.2 Country-level determinants

4. Data And Methodology

4.1. Data description

4.2 Descriptive statistics

4.3. Models and methodology

5. Empirical Results

5.1 Unit root test

5.2 Results and discussion

5.3 Further Analysis

6. Robustness Results

7. Conclusion

Declarations

References

Tables

Status:

Journal Publication

Version 1