Do stronger creditors’ rights and an efficient bankruptcy process affect the speed of adjustment to target capital structure? Evidence from a quasi-natural experiment

Rawal, Dilesh; Mahakud, Jitendra; Mishra, Rohan Kumar Article Do stronger creditors’ rights and an efficient bankruptcy process affect the speed of adjustment to target capital structure? Evidence from a quasi-natural experiment Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Rawal, Dilesh; Mahakud, Jitendra; Mishra, Rohan Kumar (2024) : Do stronger creditors’ rights and an efficient bankruptcy process affect the speed of adjustment to target capital structure? Evidence from a quasi-natural experiment, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-13, https://doi.org/10.1080/23322039.2024.2394490 This Version is available at: https://hdl.handle.net/10419/321577 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Do stronger creditors’ rights and an efficient bankruptcy process affect the speed of adjustment to target capital structure? Evidence from a quasi-natural experiment Dilesh Rawal, Jitendra Mahakud & Rohan Kumar Mishra To cite this article: Dilesh Rawal, Jitendra Mahakud & Rohan Kumar Mishra (2024) Do stronger creditors’ rights and an efficient bankruptcy process affect the speed of adjustment to target capital structure? Evidence from a quasi-natural experiment, Cogent Economics & Finance, 12:1, 2394490, DOI: 10.1080/23322039.2024.2394490 To link to this article: https://doi.org/10.1080/23322039.2024.2394490 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 23 Aug 2024. Submit your article to this journal Article views: 721 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Do stronger creditors’rights and an efficient bankruptcy process affect the speed of adjustment to target capital structure? Evidence from a quasi-natural experiment Dilesh Rawal a , Jitendra Mahakud a and Rohan Kumar Mishra b a Department of Humanities and Social Sciences, Indian Institute of Technology, Kharagpur (IIT Kharagpur), Kharagpur, India; b Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur (IIT Kharagpur), Kharagpur, India ABSTRACT This study investigates the impact of the Insolvency and Bankruptcy Code (IBC) on the capital structure speed of adjustment (SOA) of Indian firms. The IBC, introduced in 2016, significantly enhanced creditors’rights and streamlined the bankruptcy process, providing a quasi-natural experiment to assess its influence on firms’leverage dynamics. Utilising a panel data methodology and propensity score matching-based difference-in-differences (PSM-DID) regression; we categorise firms into over-leveraged (treatment) and under-leveraged (control) groups. Our findings reveal that the IBC significantly increased the SOA for over-leveraged firms, compelling them to reduce debt levels swiftly to avoid financial distress and bankruptcy. Conversely, under-leveraged firms exhibited a decreased SOA, reflecting a strategic shift towards financial stability over leveraging benefits. These results underscore the critical role of regulatory frameworks in shaping corporate financial strategies and align with the dynamic trade-off theory, highlighting firms’active adjustment towards optimal capital structures. The study contributes to the literature on capital structure SOA by providing insights into how legal and institutional changes in an emerging market influence corporate financial behaviour, with significant implications for policymakers, regulators, and corporate executives. IMPACT STATEMENT The objective of this study was to investigates the effect of the Insolvency and Bankruptcy Code (IBC) on the capital structure adjustment speed of Indian firms. The study reveals that the IBC has significantly accelerated the adjustment speed for overleveraged firms, driving them to rapidly decrease debt levels to mitigate financial distress. In contrast, under-leveraged firms exhibited a slower adjustment speed, indicating a strategic focus on financial stability over increased leverage. These findings highlight the transformative effect of regulatory frameworks on corporate financial strategies, offering key insights for policymakers, regulators, and corporate executives. This research not only enhances the understanding of capital structure dynamics in emerging markets but also provides empirical evidence that can guide future regulatory policies and strategic financial decision-making in the corporate sector. ARTICLE HISTORY Received 17 November 2023 Revised 5 August 2024 Accepted 15 August 2024 KEYWORDS Capital structure; speed of adjustment; bankruptcy code; creditor right; dynamic capital structure; IBC; leverage dynamics SUBJECTS Finance; Corporate Finance; Banking & Finance Law 1. Introduction Capital structure refers to the mixture of debt and equity a firm uses to finance its operations and growth. It is a fundamental aspect of a firm’s financial strategy because it influences its risk exposure, cost of capital, capacity for sustained growth, competitive advantage and, ultimately, shareholder value. The static trade-off theory suggests that firms have an optimal capital structure that maximises their value by balancing the tax benefits of debt financing against the financial distress and bankruptcy costs CONTACT Dilesh Rawal [email protected] Department of Humanities and Social Sciences, Indian Institute of Technology, Kharagpur (IIT Kharagpur), Kharagpur, India ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2394490 https://doi.org/10.1080/23322039.2024.2394490 associated with debt (Modigliani & Miller, 1963). According to this theory, transferring a firm’s assets from shareholders to creditors in the event of bankruptcy deters firms from holding higher debt levels. However, this transfer is contingent on the strength of creditors’rights and the effectiveness of their implementation within the country’s legal framework (La-Porta et al., 1997). Consequently, stronger creditors’rights and an efficient bankruptcy process emerge as crucial determinants in shaping a firm’s capital structure (Bose et al., 2021). Before enacting the Insolvency and Bankruptcy Code (henceforth IBC) in 2016, the Indian legal framework presented significant obstacles for creditors seeking to recover debt. Creditors primarily relied on the Debt Recovery Tribunal (DRT) Act and the Securitization and Reconstruction of Financial Assets and Enforcement of Security Interests (SARFAESI) Act to recover their claims. The DRT Act constrained creditors by requiring court or tribunal intervention to advance their priority claims against a defaulting firm. Conversely, the SARFAESI Act empowered creditors to seize a defaulting firm’s assets without court or tribunal involvement. However, this system had limitations, mainly when the debtor’s collateral was insufficient to meet their obligations, compelling creditors to navigate the complex and costly DRT process to recover their dues. To address these shortcomings, the Indian government introduced the IBC on 28 May 2016, ushering in a new era for debt collection. The IBC was a pivotal solution, with one of its key strengths being a time-bound resolution process, in stark contrast to the protracted and complex procedures of the past. The IBC established a strict timeline that allowed for the swift handling of insolvency cases within 330 days, with a possible 90-day extension. This crucial shift ensured creditors could expect a more efficient and expeditious resolution of their claims, significantly reducing the time and cost involved in the debt recovery process (Bose et al., 2021). The implementation of the IBC has significantly transformed the Indian corporate landscape. In contrast to the previous ‘debtor-in-possession’approach, the code has introduced a ‘creditor-in-control’ model, in which the creditors transfer the management of a defaulting firm to a resolution professional. This shift in power dynamics has been crucial in addressing the issue of controlling shareholders or directors’reluctance to initiate the insolvency process, as they now face the prospect of losing control of the firm. Furthermore, the IBC has created dedicated bankruptcy courts, known as the National Company Law Tribunal, to handle insolvency cases, providing a specialised and streamlined legal framework for their resolution. These changes may have a significant impact on the speed at which firms change their capital structures to their optimal levels. Prior studies have investigated the influence of IBC on corporate leverage and borrowing costs, finding that firms utilise less leverage and face reduced borrowing costs following the IBC’s implementation (Singh et al., 2023). However, there is limited research that explores the impact of enhanced creditors’rights and an efficient bankruptcy process, as exemplified by the IBC, on the firm’s SOA of capital structure, except (Kumar, 2024), which investigated how IBC has changed the SOA of financial constrained firm vs unconstrained firm. However there is no study which has investigated the impact of IBC on SOA of over-levered and under-levered firms, although the dynamic trade-off theory (Kraus & Litzenberger, 1973) provides sufficient theoretical support for this impact. Understanding the impact of the IBC on the SOA of capital structure is essential for many reasons. It provides an understanding of how legal and institutional changes affect corporate financial practices, which holds significant implications for policymakers and regulators. Secondly, it adds to the body of research on capital structure by showing how debt recovery and bankruptcy work in an emerging market, which is very different from developed markets. Finally, the outcomes of this research can provide insights to corporate executives regarding the advantages and disadvantages of sustaining various degrees of leverage and changing leverage amidst changing regulatory environments. Economic logic suggests that insolvency and bankruptcy are particularly crucial for over-leveraged firms, characterised by a debt level exceeding their optimal capital structure. Over-leveraged firms facing higher bankruptcy risks are likely more responsive to the regulatory frameworks provided by the IBC, which may lead to quicker adjustments in their capital structure. This contrasts with under-leveraged firms, where the urgency and potential for debt structure adjustment are inherently lower. The IBC’s introduction in 2016 served as a quasi-natural experiment in this study. We used a panel data 2 D. RAWAL, J. MAHAKUD, AND R.K. MISHRA methodology and difference-in-differences regression method to examine the impact of these changes in creditors’rights and the bankruptcy process on the speed of Indian firms’leverage adjustment. This study categorises over-leveraged firms as the treatment group and under-leveraged firms as the control group. This classification facilitates our difference-in-differences analysis to measure the IBC’s varying impacts on these two groups. By examining the convergence of these groups toward their target capital structures following IBC implementation, we gain valuable insights into the effectiveness and influence of such regulatory measures on corporate financial behaviour. The results of this study indicate that the implementation of the IBC has significantly increased the SOA for over-leveraged firms while decreasing the SOA for under-leveraged firms. The magnitude of this relationship is much stronger for over-leveraged firms than under-leveraged firms. The rest of this article is organised as follows: Section 2 reviews the relevant literature and develops the hypotheses. Section 3 describes the data and methodology. Section 4 presents the empirical results and discusses the findings. Section 5 provides the conclusion. Section 6 discusses the implications. Section 7 outlines the limitations and future scope of research. 2. Literature review and hypothesis development This section is divided into four subsections. The first subsection summarises the literature on the determinants influencing the target capital structure. The second subsection comprehensively reviews the factors that impact the SOA in firms’leverage. The third subsection delves into the existing literature on implementing the IBC. The final subsection is dedicated to the formulation of hypotheses. 2.1. Determinants of firm’s target capital structure Three primary considerations emerge when evaluating debt financing against equity financing: tax-deductible interest payments, strict debt obligations, and significant liquidation rights. While debt financing offers tax benefits, it also poses risks of financial distress. A firm determines its target capital structure, balancing the benefits and costs of debt (Hovakimian et al., 2001). Key factors influencing optimal capital structure include depreciation deductions, operating losses and tax credits (DeAngelo & Masulis, 1980; Graham, 1996; Mackie-Mason, 1990). Companies with volatile cash flows or higher systematic risk should opt for less debt (Booth et al., 2001). Financial performance can also influence leverage decisions (Abdullah & Tursoy, 2021). A positive relationship exists. Studies have also shown a positive relationship between firm diversification and debt utilisation (Berger et al., 1995; Comment & Jarrell, 1995). Asset tangibility positively impacts target leverage (Rajan & Zingales, 1995; Titman & Wessels, 1988). Firms with intangible assets may prefer financial flexibility (Harris & Raviv, 1991). Firms with higher growth opportunities have relatively lesser target leverage ratios as they try to conserve their debt capacity (Frank & Goyal, 2009). Firm that has intangible assets try to retain cash reserves for future investments. Hence, the target leverage is less for firms with a lot of R&D expenses (Titman & Wessels, 1988). The industry median leverage ratio influences the target capital structure as firms align their leverage with industry norms (Hovakimian et al., 2001). Furthermore, agency costs can significantly affect the capital structure (Sdiq & Abdullah, 2022). 2.2. Determinants of the speed of leverage adjustment Early empirical evidence by Fischer et al. (1989) on the SOA to target capital structure revealed that market inefficiencies and costs partially lead firms to adjust to long-term financial targets. (Flannery & Rangan, 2006) noted that firms adjust their leverage to balance the difference between actual and desired capital structures. Firms with suboptimal leverage incur legal and investment bank fees, forcing them to change their leverage only when they significantly deviate from their target capital structure. Growing firms have more flexibility in adjusting their capital structure due to various financing options, unlike non-growing firms that face severe signalling consequences (Drobetz & Wanzenried, 2006; Mukherjee & Mahakud, 2010). Larger companies can quickly alter their capital structures due to fewer fixed costs and better access to public information ((Harris & Raviv, 1991). Profitability negatively COGENT ECONOMICS & FINANCE 3 correlates with SOA, as less profitable firms adjust quickly due to higher costs of financial distress (Mukherjee & Mahakud, 2010). Firms with higher leverage adjust faster to minimise financial distress costs ((Byoun, 2008). Corporate governance plays a crucial role in SOA. Strong governance, characterised by accountability, transparency, and reduced agency costs, facilitates faster adjustments (Chang et al., 2014; Liao et al., 2015; Nguyen et al., 2021). SOA is positively influenced by board size, independence, gender diversity, and managerial ownership. In contrast, it is negatively affected by CEO duality. Economic conditions influence SOA, with faster adjustments during economic booms than recessions (Cook & Tang, 2010; Drobetz et al., 2015). High uncertainty hampers the ability to adjust toward optimal capital structures (C¸olak et al., 2018). Business cycle risks also slow the adjustment process (Bajaj et al., 2023). Developed financial markets, efficient legal frameworks, and stronger shareholder protections expedite SOA. Financial liberalisation positively affects SOA, with countries having strong law enforcement and creditor rights adjusting more quickly (Ameer, 2013). Equity mispricing affects SOA, with faster adjustments when stocks are overvalued and slower when undervalued (Warr et al., 2012). Debt maturity misalignment also influences SOA (Zhou et al., 2016). 2.3. Literature on the insolvency and bankruptcy code (IBC) Implementing the IBC has been pivotal in reshaping India’s insolvency resolution framework. The IBC seeks to unify and revise laws concerning the reorganisation and insolvency resolution for corporate entities, partnership firms, and individuals in a prompt manner. Its objectives include maximising asset value, fostering entrepreneurship, and balancing the interests of all stakeholders. The IBC has significantly improved the resolution process in India by providing a structured mechanism for insolvency resolution. It has improved recovery rates and reduced resolution time, contributing to a healthier credit environment. Studies also highlight the challenges faced during implementation, including judicial delays, infrastructural inadequacies, and the need for continuous reforms to address emerging issues (Ghosh, 2022). The IBC framework has facilitated a shift from debtor-in-possession to creditor-in-control, empowering creditors and promoting a more disciplined credit culture (Agarwal & Singhvi, 2023). Empirical evidence suggests a positive impact on the lending environment, with increased willingness among banks to extend credit due to improved recovery prospects. Additionally, the IBC has encouraged corporate borrowers to adopt better financial practices to avoid insolvency proceedings (Bose et al., 2021). The introduction of the IBC has also influenced capital structure decisions, as firms are now more cautious about their leverage ratios. The threat of insolvency proceedings under the IBC is a deterrent against excessive borrowing, leading to more prudent financial management (Kumar, 2024). Moreover, the IBC has fostered a market for distressed assets, attracting investors and facilitating the restructuring of financially stressed companies. Implementing the IBC represents a significant reform in India’s insolvency resolution landscape. It has brought about greater transparency, efficiency, and accountability, contributing to the stability and robustness of the financial system. Continuous monitoring and adaptive reforms are essential to address evolving challenges and ensure the long-term success of the IBC framework. 2.4. Hypothesis development This subsection outlines the hypotheses of our study, rooted in the theoretical framework provided by the dynamic trade-off theory (Kraus & Litzenberger, 1973) and supported by empirical evidence from the literature review. The primary aim of this paper is to investigate how the introduction of the IBC has influenced the capital structure speed of adjustment (SOA) of Indian firms. 2.4.1. The dynamic trade-off theory and capital structure adjustment The dynamic trade-off theory (Kraus & Litzenberger, 1973) complements the static trade-off theory (Modigliani & Miller, 1963) by analysing firms’capital structure over multiple periods and accounting for the adjustment benefits and costs that arise when firms change their leverage towards the optimal level. This theory suggests that firms have a target capital structure. Still, they may not always be at the 4 D. RAWAL, J. MAHAKUD, AND R.K. MISHRA optimal leverage ratio due to varying benefits and costs of leverage adjustments. As a result, firms attempt to partially adjust to their target level (Fischer et al., 1989; Flannery & Rangan, 2006), weighing the benefits of operating at optimal leverage against the incurred adjustment costs. Existing empirical evidence suggests that over 80% of firms actively pursue a target capital structure. When a firm’s actual leverage deviates from its target, it incurs ‘deviation costs,’which incentivise the firm to adjust its capital structure. Overleveraged firms face financial distress and bankruptcy costs, while underleveraged firms forgo the tax advantages of debt. These deviation costs motivate firms to realign their leverage towards the target ratio. However, ‘adjustment costs’impede the process, preventing immediate and complete realignment. This leads to firms exhibiting large, persistent deviations from their target leverage, adjusting only partially over time. Consequently, firms weigh the benefits of achieving optimal leverage (i.e. deviation cost) against the costs involved in making the necessary adjustments (i.e. adjustment cost) to determine the speed of adjustment to the target capital structure. The introduction of IBC in India may have significantly accelerated the pace at which firms change their leverage towards their optimal leverage levels. By strengthening creditors’rights and streamlining the bankruptcy process, the IBC has reduced the time and cost associated with debt recovery. This reduction in recovery time and expense has, in turn, increased the cost of deviating from optimal leverage for over-leveraged firms. Such firms now face a higher risk of bankruptcy, potentially leading to a loss of control by shareholders and a takeover by creditors. Consequently, the increased efficiency and transparency of the bankruptcy process under the IBC incentivise these firms to reduce their debt levels to prevent losing control proactively. The increased efficiency and transparency of the bankruptcy process under the IBC facilitate this proactive adjustment, making it more costly for firms to maintain high debt levels due to the enhanced threat of swift creditor action in the event of default. Therefore, over-leveraged firms are more likely to swiftly realign their capital structures to avoid the adverse consequences of bankruptcy, which may result in a quicker convergence towards their target capital structures. 2.4.2. Impact of IBC on over-leveraged firms Over-leveraged firms with debt levels exceeding their optimal capital structure face significant financial distress and bankruptcy risks. The introduction of the IBC, which enhances creditors’rights and streamlines the bankruptcy process, increases the cost of deviation from the optimal leverage for these firms. The IBC’s efficient and time-bound resolution framework raises the threat of swift creditor action, compelling over-leveraged firms to reduce their debt levels more rapidly to avoid bankruptcy and the consequent loss of control. Hypothesis 1: Implementing the IBC has increased the speed of adjustment towards the target capital structure for over-leveraged firms. This hypothesis is grounded in the expectation that the strengthened legal framework and improved creditor control mechanisms under the IBC will motivate over-leveraged firms to accelerate their capital structure adjustments to mitigate the heightened bankruptcy risks. 2.4.3. Impact of IBC on under-leveraged firms On the other hand, under-leveraged firms are characterised by debt levels below their optimal leverage ratio, thereby forgoing tax benefits associated with debt. Although these firms do not face immediate financial distress, introducing the IBC may influence their capital structure adjustments. However, the urgency and impact are expected to be less pronounced compared to over-leveraged firms, as the primary driver for adjustment in under-leveraged firms is the opportunity cost of not fully utilising debt tax shields rather than the risk of bankruptcy. Hypothesis 2: Implementing the Insolvency and Bankruptcy Code (IBC) has a negative impact on the speed of adjustment towards the target capital structure for under-leveraged firms, and the magnitude of the impact is lesser than over-leveraged firms. This hypothesis suggests that under-leveraged firms may change their leverage ratio in response to the IBC. However, the changes will be less substantial and slower than those of over-leveraged firms, primarily because the immediate risks associated with under-leverage are less severe. COGENT ECONOMICS & FINANCE 5 3. Empirical framework 3.1. Data We use the CMIE Prowess database to gather financial data for all listed firms in India from 2012 to 2019. Following standard practices, we exclude financial and utility firms from the analysis because various regulations govern their capital structure decisions. To ensure meaningful comparisons, we include only firms with complete information across all variables and years during our study period. As a result, we have a balanced dataset comprising 10,416 observations from 1,302 firms. Further propensity scorebased matching identified 645 control firms for 645 treatment firms, yielding a final dataset of 7,722 firm-year observations for 1,290 firms to test the hypothesis. To manage outliers, we winsorised all firmlevel variables at the top and bottom 1% of their distributions. 3.2. Variable definition A firm is defined as over-leveraged if its observed leverage in a particular year exceeds its target leverage for that year. A firm is classified as a treatment firm (over-leveraged) if it remains over-leveraged for at least 3 out of 4 years during the pre-IBC period. We constructed ’OL’as a dummy variable, which takes the value of 1 to over-leveraged firms (treatment group) and zero to under-leveraged firms (control group). Definitions of other variables are detailed in Appendix A. 3.3. Methodology 3.3.1. psm-did To determine the causal relationship between the IBC and the speed of leverage adjustment, we utilise a propensity score matching-based difference-in-differences (PSM-DID)approach. Common support is a critical prerequisite for an effective difference-in-differences analysis, which requires sufficient overlap between the treatment and control firms on relevant characteristics (Singh et al., 2023). However, Panel A of Table 1 indicates statistically significant differences in key firm-level covariates between the treated and control groups at the 1% level, suggesting a lack of overlap. We must perform propensity score matching without such overlap to identify a well-matched sample. To achieve this, we calculate the propensity score using the following logistic regression model OLIt ¼b0þSizeit þProfit þTangit þDepit þMTBit þR&Dit þRD Dummyit þIndustry leverageit þeit (1) In this model, the dependent variable is a binary indicator, taking 1 for over-leveraged firms and 0 for under-leveraged firms. Primary firm-level covariates include Profitability, Tangibility, Size, Depreciation, Growth Opportunity, R&D expenses, R&D Dummy, and median industry leverage. We employed a nearest neighbour matching algorithm with replacement (Singh et al., 2023) to pair treatment and control firms. This resulted in 645 control firms matched with 645 treatment firms. Post-matching diagnostic tests (T value in Panel B of Table 1) confirmed that the propensity score matching method effectively reduced observational differences between the matched treatment and control groups. Table 1. Unmatched (full) sample and matched sample attributes. Panel A: Full sample (without matching) Panel B: Matched sample Variable Treatment Control T-value Treatment Control T-value Size 7.98 8.98 −4.51 8.16 8.19 0.68 Prof 0.09 0.07 −6.51 0.09 0.08 −1.64 Tang 0.32 0.29 −7.72 0.32 0.33 2.10  Dep 0.032 0.031 −3.05 0.033 0.033 −0.02 MTB 2.09 1.79 −4.40 2.05 2.01 −0.52 Median leverage 0.36 0.35 −3.46 0.366 0.371 1.66 R&D expenses 0.002 0.001 −7.14 0.002 0.002 −1.40 R&D dummy 0.64 0.73 9.38  0.64 0.64 −0.04 The mean values of variables affecting target leverage for the full sample (Unmatched Sample) and the Matched Sample. The T Value indicates the significance of the difference in means between the treatment and control groups. Significance levels are denoted by ,, and for 1%, 5%, and 10%, respectively. Definitions of the variables can be found in Appendix A. 6 D. RAWAL, J. MAHAKUD, AND R.K. MISHRA We then examine the parallel trend assumption necessary for the DiD analysis of the matched sample (Kumar, 2024; Singh et al., 2023). Figure 1 illustrates the SOA for treatment and control firms, showing that their leverage speed of adjustment follows a parallel trend during pre-regulation. However, postregulation, the speed of leverage adjustment has increased for over-leveraged firms, a trend not observed in the control firms. 3.3.2. Two-stage dynamic partial adjustment model Drawing from the work of (Cook & Tang, 2010; Flannery & Rangan, 2006), we utilise the basic partial-adjustment model. This model serves as a tool for estimating how quickly a given firm corrects deviations from its target, as articulated by: LEVi,tþ1−LEVi,t,j¼kLEV i,tþ1−LEVi,t  þdi,tþ1(2) Where LEV i,tþ1Represents the target leverage of firm i at year t, kis the Leverage SOA for all firms in the sample; LEVi,tThe leverage ratio of the I firm at a time can be measured either by market leverage (MLEV) or book leverage (BLEV). The above model is estimated using a two-stage process. In the first stage, target leverage has to be estimated. As the target leverage LEV i,tþ1, is unobservable; following Amini et al. (2021) and An et al. (2021), the fitted value of Eq (2) is used as a proxy for the target leverage. LEVi,tþ1¼aiþbjXi,t,jþfi,þti,þti,tþ1(3) Here, Xi,t,jIt is a set of primary firm-level and industry-level covariates like Profitability, Tangibility, Size, Depreciation, Growth Opportunity, R&D expenses, R&D Dummy, and median industry leverage. These control variables are selected based on the literature on determinants of target capital structure. We include firmand year-fixed effects to account for unobserved heterogeneity across firms and years.(Lemmon et al., 2008). Using the target leverage estimated from the 1 st stage, the speed of adjustment is estimated in the second stage using the following Equation. DLEVI,tþ1¼kDEVi,t ðÞ þdi,tþ1(4) Where DLEVI,tþ1is the actual change in leverage (LEVi,tþ1−LEVi,t) made by a firm I, from year t to tþ1, which can be measured by the change in market leverage (DMLEVI,tþ1) or change in book leverage (DBLEVI,tþ1). DEVi,tIs the deviation from target leverage (LEV i,tþ1−LEVi,t) which can be measured by book leverage deviation ðBDEVi,t) or market leverage deviation ðMDEVi,t). The coefficient kmeasures the rate at which firms move their leverage toward the target from year t to year t þ1. A higher ksignifies a quicker speed of adjustment (SOA). Nevertheless, this model presumes that all sample firms adjust uniformly across all years. To analyse the effect of the IBC on the speed of adjustment (SOA) for over-leveraged (treatment) firms and under-leveraged (control) firms using a DID regression framework, we relax the assumption of Figure 1. Variation in capital structure Speed of adjustment. Figure 1 shows the variation in the SOA, derived using Equation (4) from six three-year rolling regressions. The first subsample covers 2012, 2013, and 2014, while the last subsample includes 2017, 2018, and 2019. COGENT ECONOMICS & FINANCE 7