Economic policy uncertainty and leverage dynamics: Evidence from an emerging economy
Introduction
The literature on capital structure has emerged from the irrelevance proposition of (Modigliani & Miller, 1958). Since then, this domain has evolved several theories to assist the leverage decisions of firms. Following trade-off theory, a large body of existing literature reinforces that firms establish their target capital structures (Chung, Liu, & Wang, 2018; Drobetz, Schilling, & Schröder, 2015; Graham & Harvey, 2001; Mukherjee & Mahakud, 2012). Both, the static and dynamic versions of the trade-off theory presume a target capital structure. It exists at the balancing point of debt benefits and costs (Fischer, Heinkel, & Zechner, 1989; Kraus & Litzenberger, 1973; Strebulaev, 2007). However, firms may not continually operate at these targets, causing a dent in their valuation. Hence, they attempt to reduce this deviation as soon as the deviation costs surpass adjustment costs. The rate by which the firm reduces this deviation is termed the speed of adjustment. Past research has found that firms' adjustment to targets is partially subject to several adjustment costs, commonly referred to as the partial adjustment framework in the literature (Flannery & Rangan, 2006; Leary & Roberts, 2005).
With the progress of literature, various researchers have tested the heterogeneities in the dynamic capital structure adjustment framework. Studies have documented the impact of firms' financial surplus or deficits (Byoun, 2008), cash flows (Faulkender, Flannery, Hankins, & Smith, 2012), financing activities (Dang, Kim, & Shin, 2012), macroeconomic conditions (Cook & Tang, 2010), institutional differences (Lemma & Negash, 2014; Öztekin & Flannery, 2012), investment decisions (Elsas, Flannery, & Garfinkel, 2014), political patronage (Ebrahim, Girma, Shah, & Williams, 2014) and corporate governance (Buvanendra, Sridharan, & Thiyagarajan, 2017; Chang, Chou, & Huang, 2014) on estimates of the speed of adjustment. However, the impact of economic-policy uncertainties on capital structure adjustments has been scantly explored.
Economic policy uncertainty (EPU) is the economic risk associated with undefined future government policies and regulatory frameworks (Al-Thaqeb & Algharabali, 2019). Economic policies give rise to uncertainties that have far-reaching influences on a firm's financial behaviour (Zhang, Han, Pan, & Huang, 2015). The economic consequences of this phenomenon distort the normal plan of action of firms as well as of investors. It increases the risk such that both firms and investors delay their spending and investments due to market uncertainty. Hence, we argue that EPU aggravates the frictions in the market, causing a change in the behaviours of corporate managers and investors and thereby influencing capital structure and its adjustments. In the past, only a few studies focus on the impact of policy shocks on firms' decision making. Baum, Caglayan, Ozkan, and Talavera (2006) and Baum, Caglayan, and Ozkan (2009) investigated the effect of EPU on firms' cash holdings and lending activities by financial institutions, respectively. After the development of EPU index by Baker, Bloom, and Davis (2013), EPU has been used to examine IPO activities (Çolak, Durnev, & Qian, 2017), asset prices (Ko & Lee, 2015; Pastor & Veronesi, 2013), stock market volatility (Antonakakis, Chatziantoniou, & Filis, 2013; Kang & Ratti, 2013; Liu & Zhang, 2015), investments (Francis, Hasan, & Zhu, 2014; Wang, Chen, & Huang, 2014), banks systemic risk (Calmès & Théoret, 2014), cross-border flows (Gulen & Ion, 2016; Julio & Yook, 2012) and leverage decisions (Çolak et al., 2018; Kotcharin & Maneenop, 2018; Lee, Lee, Zeng, & Hsu, 2017; Schwarz & Dalmácio, 2020; Zhang et al., 2015). However, there is still a dearth of literature focusing on the impact of EPU on the dynamics of capital structure. Moreover, considering the unprecedentedly high level of uncertainty around the globe (Im, Kang, & Shon, 2020), there are only a handful of studies that investigate the association between EPU and corporate financing decisions, particularly for emerging markets. Hence, we aim to examine the impact of EPU on capital structure adjustments in an emerging economy.
We have chosen the Indian economy for this study because, firstly, past research focusing on EPU and leverage decisions has been mostly conducted in developed economies whereas emerging economies with relatively different institutional set-ups (e.g. less developed capital markets, government intervention and dependence, the existence of large business groups, etc.) have not received attention (Erb, Harvey, & Viskanta, 1996; Zhang et al., 2015). Secondly, India is a rapidly growing emerging nation which aims to catch up with developed markets (World Bank Group, 2019). It thus affords an opportunity to investigate the existing findings for an emerging Asian market with unique institutional arrangements that influence firms' decisions. Lastly, the Indian economy has witnessed some major economic reforms over 2009–2018 such as demonetization, the implementation of a goods and services tax (GST), compliance with BASEL III norms, consolidation of public sector banks, and the introduction of the Insolvency and Bankruptcy Code, 2016. The Indian economy is therefore an appropriate choice for analysis.
Our research contributes to the emerging literature that investigates the association between EPU and corporate behaviour in several ways. We firstly extend the work of Kang, Lee, and Ratti (2014) and Lee et al. (2017) to apply the concept of long-run and short-run economic policy uncertainty in the capital structure dynamic framework of Indian firms and evince slower leverage adjustments in the case of high policy shocks. Secondly, we confirm the predictions of Lee et al. (2017), Kotcharin and Maneenop (2018) and Schwarz and Dalmácio (2020) that, during times of uncertainty, expected earnings become uncertain, causing firms to opt for external sources. Debt, being a relatively cheaper source of finance, is preferred, leading to increased leverage. Thirdly, our results are consistent with the adjustment-cost hypothesis that argues that changes in investment and cost of debt shape the role of long-run economic policy shocks on leverage adjustments. Fourthly, we check whether the leverage of firms in industries that are more sensitive to government subsidies could be differentially affected by economic uncertainties; we report a stronger positive association between the two variables. Lastly, we evidence a stronger influence of EPU on leverage and its adjustments regarding how business groups (a unique corporate set-up in India) influence the association of policy shocks and leverage dynamics.
The rest of the paper is structured as follows. Section 2 presents the relevant literature and hypotheses. Section 3 outlines our methodology that presents our empirical model and describes the variables in use. Section 4 covers the data screening and sample selection process. Section 5 furnishes the empirical results of the base model. Section 6 describes an additional analysis using several specifications. Section 7 reports the robustness checks. Lastly, Section 8 makes concluding remarks.
Section snippets
EPU and leverage
Research findings have emerged in different directions in the context of impact of EPU on leverage. Initial research in this domain provides an inverse relation between EPU and leverage. (Cao et al., 2013) and Zhang et al. (2015) advocate supply and demand effects as the two alternate mechanisms in this regard. On the supply side, policy uncertainties hamper the external financing environment. Large policy shocks severely raise the information asymmetry between borrowers and lenders.
Empirical model
We modify the partial adjustment framework to accommodate economic policy shocks. Thus, the target leverage is dependent upon the economic policy uncertainties and firm characteristics from the previous period.
Here αj, β1 ≠ 0.
Notably, there are no adjustment/transaction costs leading to instant reversion to the target level. However, with the existence of these costs in the real business environment, only partial adjustment to the targets is possible. This is called
Data screening and sample selection
We have extracted the annual accounting and financial data of Indian firms listed on the National Stock Exchange (NSE500) from the Bloomberg® database for 2009–2018. Our sample period begins at 2009 to avoid the impact of global financial crises. We use the following common restrictions for deriving the panel data (Marchica & Mura, 2010; Van Hoang et al., 2018). Firstly, we remove financial and utility firms due to their specific accounting considerations and stringent regulatory conditions (
Multivariate estimation of the base model
Table 4 reports the results for the dynamic capital-structure adjustment model using pooled OLS and fixed effect estimation. Col. (1) and Col. (4) report the results without considering the impact of economic policy shocks in the leverage adjustment framework whereas Cols (2), (3), (5) and (6) consider such shocks for long-run and short-run EPU, respectively (refer Eq. (3)). Consistent with Lee et al. (2017) and Kotcharin and Maneenop (2018), EPU exhibits a positive impact on leverage both, in
Interaction of firm-specific factors with EPU
EPU changes the macroeconomic situation, impacting the behaviour of financial managers when making financing decisions (Lee et al., 2017). We posit that it can have an impact on several firm-specific factors such as profitability, growth prospects and R&D opportunities available to firms. As iterated before, leverage is also a function of firm-specific factors. Thus, the impact of EPU on leverage can be moderated through these firm-specific factors. Hence, to delve deeper we explore the
Using an alternative measure of EPU
To check robustness, we re-conduct the analysis of the base model using an alternate measure of EPU. Long-run EPU is calculated as the average of twelve months of the EPU index which is further used to calculate short-run EPU (first difference of EPU_L for period t and t-1). Table 12 presents the results which indicate that both long- and short-run policy shocks are positively associated with leverage. Thus, following the base model, EPU again emerges as a significant factor that impacts
Conclusion
We explored the dynamics of capital structure for Indian firms listed on the NSE500 for 2009–2018 and reported some unique findings. Firstly, we incorporated the economic policy uncertainty (EPU) index developed by Baker et al. (2013), which is a recent and well-accepted measure for policy shocks in the partial adjustment framework. We split EPU into long-run and short-run measures to analyse the separate impacts of both on leverage and its adjustment. EPU positively influences the leverage
Declarations of interest
None.
Acknowledgements
An early version of this manuscript was presented at the 2019 Vietnam Symposium in Banking and Finance (VSBF2019) held at Hanoi, Vietnam from 24-26 October 2019. The authors extend their sincere thanks to Indian Council for Social Science Research i.e., ICSSR (New Delhi) for providing 100 percent funding in regards to the travel, maintenance and registeration fee of the same.
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