Analysis of the Capital Structure of Latin American Companies in Light of Trade-Off and Pecking Order Theories

Abstract

The study of capital structure is one of the most relevant topics in finance because, despite the various theories that seek to explain it, there is still no consensus on the determining factors or the behaviors of financing decisions in companies. This study empirically analyzes the capital structure decisions of Latin American companies during the period of 2013–2023, in light of trade-off and pecking order theories. A panel data methodology was applied to 62 companies, using fixed and random effects models. The results show that, on average, companies correct around 5.80% of the gap between their current and optimal level of indebtedness per period, partially supporting the trade-off theory. However, the effects of the financial deficit on indebtedness are heterogeneous and, in most cases, inconsistent with the pecking order theory, especially in countries such as Colombia. It is concluded that country risk has a marginal influence on debt decisions, and the need to consider each country’s institutional and market particularities when analyzing the dynamics of capital structure in emerging economies is emphasized.

1. Introduction

Maintaining a precise balance between debt and equity financing sources is undoubtedly one of a company’s main challenges. A company’s capital structure influences its future source of financing, cost of capital, risk, liquidity, return to investors, and company valuation. As a fundamental financial decision, it is a widely researched area, with essential contributions from eminent researchers in the form of capital structure theories (Bajaj et al., 2021).

The role of capital structure is that its correct and optimal determination allows a company’s management to maximize its capitalization and long-term operating objective; thus, how companies finance their investments is a key topic in financial literature. In this context, one of the main works is that of Modigliani and Miller (1958), according to whom the choice of capital structure cannot theoretically alter the value of a company when perfect financial markets and the absence of taxes are assumed. In this context, the value of a company is determined solely by the profitability that its assets can generate (Colombo et al., 2023).

In the literature, various theories that explain the decisions on corporate capital structure can be found. Two of the main theoretical contributions are the trade-off and hierarchical order theories. The first indicates that companies decide on their capital structure by balancing the costs of bankruptcy (Bradley et al., 1984), tax incentives, and corporate control over agency conflicts (Jensen & Meckling, 1976), suggesting the existence of an optimal capital structure to which companies partially adjust. The second predicts companies will establish their capital structure by hierarchically selecting their financing based on information asymmetry costs (Myers & Majluf, 1984). The empirical debate on this issue remains open in both developed and emerging markets, with no clear consensus on which theory best predicts capital structure decisions (Agyei et al., 2020; Dang, 2013; De Jong et al., 2011; Jarallah et al., 2019; Serrasqueiro & Caetano, 2014). More recent studies have also analyzed both approaches together and concluded that they are not mutually exclusive and that their dynamic behaviors would affect the adjustment of the capital structure (Kannadhasan et al., 2018).

This topic is critical in the current socioeconomic context of Latin America, as companies need to plan their economic resources in a way that allows them to manage their obligations in a solvent and adequate manner; implementing models that increase the productivity of their assets means that, based on the optimized profits of companies, the tax burden rises by the fiscal principle of progressivity, i.e., the higher the profit, the higher the level of taxation, which naturally represents an increase in the volume of tax collection by states, which in turn, especially in Latin American regions, allows for improved wealth distribution. Therefore, it is a line of development that starts with economics and ends with social issues.

The objective of this study was to analyze which of the two predominant capital structure theories—the pecking order theory or the trade-off theory—most accurately describes the financing decisions of Latin American non-financial companies listed on stock exchanges during the period of 2013–2023. Unlike many previous empirical studies that focus on a single country or limited time spans, this research provides a comparative, multi-country analysis over a recent ten-year period, offering a broader understanding of regional patterns and structural differences in emerging markets. By examining firms across several Latin American economies, the study captures the diverse financial environments in which these companies operate, enhancing the generalizability and relevance of its findings.

Moreover, the study applies a robust econometric methodology to empirically test the predictions of both the pecking order and trade-off theories, not only evaluating their individual validity but also exploring the extent to which they may coexist or complement each other—an area that remains underexplored in the regional literature. Beyond its theoretical contribution, the research also integrates a reflection on the fiscal and social implications of achieving an optimal capital structure, linking corporate financial behavior to broader themes of economic development and equity. This multidimensional approach expands the interpretive value of the findings and provides actionable insights for both corporate decision-makers and public policy stakeholders in Latin America.

This study fills a notable gap in the specialized literature by offering new empirical evidence on capital structure decisions in Latin American emerging markets, an area still underrepresented in recent comparative research. Unlike previous studies that often focus on developed economies or isolated country cases, this paper provides an integrated regional perspective and highlights how institutional heterogeneity, market imperfections, and contextual fiscal dynamics shape financing behavior. This original approach enriches the current understanding of capital structure by integrating theoretical, empirical, and policy-oriented insights relevant to emerging economies.

The paper is organized into six sections, including this introduction. The second section presents a review of the relevant literature; the third describes the variables used and the econometric methodology applied; the fourth presents the empirical results obtained; the fifth offers an analysis and discussion of these results; and the sixth summarizes the study’s main conclusions.

2. Literature Review

Over the years, various theoretical approaches have emerged to understand and explain how companies choose their optimal financial structure (Miller, 1977; Modigliani & Miller, 1958). These theories offer unique perspectives on the factors and considerations influencing financing decisions, from the impact of taxes and tax benefits to the influence of risk, growth, and the business environment (Pinillos Villamizar et al., 2025).

Several theories exist with two of the most studied in the literature being the trade-off theory (TO), also known as the compensation model or objective leverage model, and the pecking order theory, also identified as the financial hierarchy model. The trade-off theory states that companies finance their investments in exchange for tax benefits. In contrast, the pecking order theory states that companies have an order of priority in obtaining financing (Gómez et al., 2014).

The trade-off and pecking order theories are not alternative views of the same problem but represent complementary approaches to how companies define their capital structures (De Andrés et al., 2018). For this reason, numerous empirical studies in finance have tested many theories on capital structure. As noted above, the pecking order and trade-off theories are among the most influential theories on capital structure (Culata & Gunarsih, 2012). In recent years, these theories have been revisited and tested in emerging markets with updated datasets, confirming their relevance while also identifying their limitations due to institutional and financial frictions (Aybar-Arias et al., 2012; Camara & Sangiacomo, 2022; Prakash et al., 2023; Shakri et al., 2025; Tekin & Polat, 2025).

Many theories attempt to explain financial constraints by focusing on capital structure. Forte et al. (2013) mentioned that, since 1950, capital structure has become a controversial area of research in the field of corporate finance, with one of the most prominent discussions in this area found in the work of Modigliani and Miller (1958), who argued that the market value of a company was not related to its capital structure (Bradley et al., 1984). A few years later, Modigliani and Miller (1963) revised their initial assumptions about perfectly competitive markets and recognized the tax advantage of debt since interest is deductible from income tax. However, this does not mean that companies should always strive to use the maximum amount of debt available.

Introduced in this context is the trade-off theory, which considers the effects that affect the entire industry, such as taxes, bankruptcy costs, and agency problems. This theory also envisages an optimal structure that balances the costs and benefits of issuing debt and equity. From this theoretical approach, leverage is considered advantageous in certain circumstances, and owners and managers tend to prefer using debt even when internal funds are available. This theory assumes that an optimal capital structure is achieved by balancing the benefits of leverage, mainly tax savings, with the costs arising from financial difficulties. Therefore, when companies take on debt, their tax savings are expected to be higher, but their costs associated with the risk of default also increase (Briozzo et al., 2016).

Later, Miller (1977) expanded the model to incorporate taxes on income received by investors, whether in the form of stock income (dividends and capital gains) or interest, in addition to the tax benefits mentioned above (Rivera, 2002). The conclusion reached in this model was that the tax advantage of taking on debt dissipated when both types of taxes were considered. It summarized the thesis that capital structure was irrelevant regarding company value.

As an alternative to previous developments, the Pecking Order (PO) theory was introduced. This theory described a hierarchy of financing options and focused on the asymmetric information between companies and lenders. Since companies have more complete information about their future than lenders, the need for supervision increases borrowing costs, leading companies to initially opt for internal financing. It was argued that companies preferred to reinvest their profits to avoid adverse selection problems (Myers, 1984; Myers & Majluf, 1984).

When these internal funds are exhausted, companies resort to financing through bank debt and, ultimately, turn to the stock market. As the business cycle evolves, the information asymmetries decrease, and access to financing improves in terms of costs and terms. The authors of the pecking order theory explain that this hierarchical order arises due to the greater flexibility and reduced transaction costs associated with using internal resources compared to external resources.

Within this school of thought, leverage is considered less favorable compared to using internal sources of financing. Owners and managers prioritize the use of internal resources in the first instance. However, if these internal funds are exhausted and investment opportunities persist, they opt for debt to avoid missing out on those opportunities. Similarly, once internal funds are available, they prefer to pay off their debt before it matures (Briozzo et al., 2016).

One current theory is the so-called growth cycle theory, which argues that a company’s financial structure evolves based on its size and age (Berger & Udell, 1998). According to this approach, in the early stages, when companies are young or small, they tend to have less transparency in terms of financial information, which leads them to rely mainly on internal sources of financing, such as their resources, investments from family and friends, as well as commercial loans or angel investors. As these companies advance in their growth cycle and reach more significant stages of expansion, their ability to access various external sources of financing increases and may include the participation of venture capital institutions, entry into financial markets for the issuance of shares or bonds, and obtaining financing from commercial banks (Berger & Udell, 1998).

Several authors have concluded that the trade-off (TO) and PO theories have not considered mutually exclusive explanations for financing decisions (Aybar-Arias et al., 2012; Degryse et al., 2012; López-Gracia & Sogorb-Mira, 2008; Serrasqueiro & Caetano, 2014). Along the same lines, it is mentioned that companies tend to adopt a “wait and see” approach when it comes to adjusting their capital structure (Titman & Tsyplakov, 2007). They observe whether changes in investment opportunities or product prices have the necessary impact to achieve an optimal leverage ratio. Consequently, the theories of the financing hierarchy (PO) and trade-off (TO) are not mutually exclusive. Instead, companies choose their leverage ratios based on the benefits of debt financing, as trade-off theory proposes. Still, they may adjust their behavior for the reasons described in the financing hierarchy theory.

Other studies combine the elements of both theories, the trade-off theory and the financing hierarchy theory (Gaud et al., 2007; Hovakimian & Li, 2011; Titman & Tsyplakov, 2007). These studies consider the existence of an optimal leverage target according to the trade-off theory, toward which companies converge over time, allowing for behavior similar to that of the short-term financing hierarchy. Market imperfections, such as transaction and agency costs, can hinder this convergence toward the leverage target, which may explain temporary deviations from the target.

Over time, additional contributions have emerged in the field of corporate capital structure, including theories such as the agency theory (Fama & Miller, 1972; Harris & Raviv, 1991; Jensen & Meckling, 1976), credit rationing (Stiglitz & Weiss, 1981), the corporate strategy theory (Barton & Gordon, 1987; Brander & Lewis, 1986; Mishra & Mcconaughy, 1999), market timing (Baker & Wurgler, 2002), and the matching principle (Brealey et al., 1998), among others. These theories and approaches complement the arguments that describe different perspectives on the capital structure of companies, underscoring the complexity and diversity of the factors that influence corporate financing decisions.

To conclude this theoretical framework, which supports the research topic as well as its objective and is based on classic and recent literature on capital structure,

Table 1. Eminent Studies on Capital Structure.

Sr. No	References	Findings
1	Modigliani and Miller (1958)	It has a significant contribution in the area of capital structure with the origin of the ‘Irrelevance theory’, which states that capital structure has no impact on firm value.
2	Modigliani and Miller (1963)	It analysed the impact of tax shield on interest expense.
3	Kraus and Litzenberger (1973)	The study introduced the classical ‘Trade-off theory’, covering the concept of a trade-off between the cost of financial distress and the benefits derived from the debt tax shield.
4	Stiglitz (1973)	It developed the concept of pecking order. This study states that leverage ratio is the unexpected resultant of profits and investments made by a firm.
5	Jensen and Meckling (1976)	Introduced the ‘Agency cost theory’ and analysed the impact of debtholder–shareholder and manager–shareholder conflicts on capital structure financing.
6	Miller (1977)	Propounds the significance of personal and corporate tax in financial decision making.
7	Ross (1977)	It developed the ‘Signaling theory’ of capital structure and promoted the debt issue as a positive indicator of the performance in capital structure financing.
8	Bradley et al. (1984)	Introduced the well-known ‘Static trade-off theory’.
9	Kane et al. (1984)	It introduced the ‘Dynamic trade-off theory’, which includes the trade-off theory along with the impact of uncertainty, cost, taxes, and tax benefits.
10	Myers and Majluf (1984)	It propounded the ‘Pecking order theory’ and the major role of information asymmetry toward the choice between internal funding, debt, and equity for capital structure financing.
11	Fischer et al. (1989)	It initiated the transaction cost concept and showed its impact on leverage in the capital structure of the firm.
12	Harris and Raviv (1991)	It initiated the concept of the ‘Control-driven theory’.
13	Baker and Wurgler (2002)	It predicted the long-run impact of market value fluctuations on capital structure. It stated that firms issued equity when the market was overvalued and issued debt when it was undervalued.

Source: Sisodia and Maheshwari (2022).

presents the development of the most representative theories and their authors. The authors set themselves the objective of consolidating and generating a comprehensive view of the importance of capital structure. It gives meaning to the need to continue research in this field.

The historical development of the capital structure theory, as outlined in Table 1, highlights the foundational contributions beginning with Modigliani and Miller (1958), who proposed the irrelevance theory, asserting that under ideal market conditions, capital structure did not affect firm value. Their 1963 revision recognized the tax advantages of debt, laying the groundwork for later models.

Kraus and Litzenberger (1973) introduced the classical trade-off theory, emphasizing the balance between the tax benefits of debt and the costs of financial distress. In the same year, Stiglitz (1973) proposed early ideas aligned with the pecking order theory, suggesting that a firm’s leverage resulted from its profitability and investment patterns. Myers and Majluf (1984) later formalized the pecking order theory, arguing that information asymmetry led firms to prioritize internal financing, followed by debt, and lastly, equity.

Other contributions added complexity: Jensen and Meckling (1976) developed the agency theory, Ross (1977) proposed the signaling theory, and Kane et al. (1984) introduced a dynamic trade-off model incorporating uncertainty. Later, Fischer et al. (1989) addressed transaction costs, while Baker and Wurgler (2002) emphasized market timing behavior.

While both the trade-off and pecking order theories have significantly shaped capital structure research, they are not mutually exclusive. Instead, they offer complementary perspectives. The trade-off theory assumes that firms actively optimize their capital structure by weighing the costs and benefits of debt usage. In contrast, the pecking order theory views financing decisions as reactive and path-dependent, shaped by asymmetric information and internal constraints.

In developed markets, where financial systems are stable and access to capital is broader, firms are more likely to engage in optimization behaviors consistent with the trade-off theory. However, in emerging markets—like those in Latin America—firms often operate under financial frictions, institutional limitations, and heightened uncertainty. These conditions amplify information asymmetries and transaction costs, making the pecking order theory more applicable in practice. Firms may prefer internal funds not for tax efficiency, but to reduce risk and avoid the loss of control.

Therefore, the critical evaluation of both theories is essential in understanding corporate behavior in volatile, underdeveloped financial systems. By comparing their predictive power across a decade of data on Latin American firms, this study contributes to the ongoing empirical debate, highlighting the dynamic and context-dependent nature of capital structure decisions.

3. Materials and Methods

The model incorporated methodologies by Shyam-Sunder and Myers (1994) as well as other authors such as Mongrut et al. (2010). The method by Shyam-Sunder and Myers (1994) consists of analyzing capital structure at the company level as a data panel to determine the optimal leverage. The financial hierarchy model in its aggregate version is as follows:

$$D_{it}={\beta }_0+{\beta }_1{DEF}_{it}+{\beta }_2{D}_{protectj}+{\beta }_3{RP}_j+e_{it}$$

(1)

where D corresponds to the amount of debt incurred by company _i in period _t, and DEF is a coefficient of the deficit at the company level. D_protect is a binary variable that takes the value of 1 when there is a debt protection law in the country where the company operates and 0 otherwise. In the case of RP, it refers to the risk indicator as a representation of the risk of investing in a given country. β₀ is the intercept of the proposed model, and the other β are the coefficients for each of the explanatory variables derived from the model.

In the target leverage model specification, some modifications are made to Equation (1). In this case, the gap to optimal leverage is calculated based on the optimal corporate debt multiplied by the company’s net worth, and this is the difference from the previous period (Mongrut et al., 2010) (Appendix A contains a description of each indicator used in the research). In this sense, the specification of the second model is as follows:

$$D_{it}={\beta }_0+{\beta }_1{Ajust}_{it}+{\beta }_2{D}_{protectj}+e_{it}$$

(2)

Ajust is the adjusted version of the gap or the optimal leverage adjustment, analyzed again for company i in period t. Appendix A details the creation of the indicators used to estimate Equations (1) and (2).

The data used in this study were compiled from financial indicators derived from the financial statements of non-financial companies listed on stock exchanges in four Latin American countries. Appendix A presents the variables and formulas used to construct each indicator employed in estimating Equations (1) and (2), based on information available from the Bloomberg platform.

Initially, 5 companies were excluded due to incomplete data, and 14 firms from the financial sector were removed following the methodological recommendation by Maya et al. (2024). Of the 81 companies listed in the region, 62 firms were retained based on these selection criteria. In the second stage, the relevant financial indicators were constructed.

The selected companies operate in countries that are part of the Pacific Alliance. This regional integration initiative promotes economic and political cooperation, particularly in investment projects, thus ensuring the inclusion of emerging economies and making it highly relevant to the dynamics of the business sector. Based on these criteria, the consolidated data set spans the period from 2013 to 2023.

Accordingly, a balanced panel data structure was built using the financial indicators of the selected companies (see

Table 2. Distribution of the sample by country.

Country	Companies
Chile	22
Colombia	7
Mexico	24
Peru	9
Total	62

Source: Own elaboration.

), resulting in a total of 689 firm-year observations. Regarding sample representativeness, it is important to note that the study focuses on publicly traded companies, capturing a formal segment of the corporate landscape characterized by stricter disclosure standards and corporate governance practices. Additionally, it is common in this type of research to encounter constraints in accessing financial data from unlisted firms in emerging markets.

Table 3. Distribution of the sample by economic sector.

Sector	Percentage
Mining	24.20%
Other	21.00%
Service	11.30%
Industry	9.70%
Construction	8.10%
Food and beverages	6.50%
Trade and restaurants	6.50%
Technology and communications	6.50%
Air Transport	6.50%

Source: Own elaboration.

shows the distribution of companies by economic sector, revealing a notable concentration in the mining (24.20%), miscellaneous (21.00%), and service (11.30%) sectors. The prominence of the mining sector underscores its critical role in the region’s economies, suggesting a strong dependence on natural resource exploitation. This reliance can lead to an increased vulnerability to fluctuations in international commodity prices. The relevance of the service sector is also evident, given its contribution to employment generation and its support of complementary activities across other productive areas. Meanwhile, the sectors of food and beverages, trade, technology, communications, and air transport each represent 6.5% of the sample.

4. Results

Table 4. Descriptive statistics.

Country	Variables	Mean	SD
Chile	Amount of debt incurred	0.01	0.06
	Financial deficit	0.09	0.11
	Debt protection	1.00	0.00
	Amount of debt incurred *	97.33	628.77
	Adjust	178.12	1347.89
	RP	1.58	0.26
	Protect	0.98	0.21
Colombia	Amount of debt incurred	0.02	0.06
	Financial deficit	−0.19	1.66
	Debt protection	0.00	0.00
	Amount of debt incurred *	339.08	1439.46
	Adjust	351.22	1899.63
	RP	2.45	0.75
	Protect	−0.38	0.08
Mexico	Amount of debt incurred	0.02	0.06
	Financial deficit	0.07	0.14
	Debt protection	0.00	0.00
	Amount of debt incurred *	200.55	1070.68
	Adjust	317.86	1440.75
	RP	3.08	0.87
	Protect	−0.64	0.14
Peru	Amount of debt incurred	0.02	0.06
	Financial deficit	0.07	0.10
	Debt protection	0.00	0.00
	Amount of debt incurred *	73.76	388.32
	Adjust	117.98	426.36
	RP	1.71	0.25
	Protect	−0.52	0.05

Source: Own elaboration. * Not divided among company assets.

shows the descriptive statistics for the variables used in the model. Regarding the amount of debt incurred, the results are homogeneous, with an average between 0.01 and 0.02. In contrast, the financial deficit showed greater heterogeneity among countries. For example, Colombia stood out for having a negative average deficit (−0.19) and high dispersion, suggesting fiscal imbalances. In contrast, the other countries analyzed had positive financial deficits for low levels and with less variability, indicating more stable management.

The equations were estimated using panel data models, including pooled (POOL), fixed effects (FE), and random effects (RE). Likewise, Hausman tests were applied to determine the best model according to the financial data set used in the research.

Table 5. Aggregate financial ranking results.

	FE (1)	FE (2)
(Intercept)
DEF	−0.009 * (0.004)	−0.009 * (0.004)
Dprotec
RP		0.002 (0.004)
Number of observations	682	682
R2	0.007	0.007
R2 Adj.	−0.093	−0.094
AIC	−1931.9	−1930.1
BIC	−1922.8	−1916.6
RMSE	0.06	0.06

Source: Own elaboration. * p < 0.1.

presents the results of the economic hierarchy model in an aggregated version (1). The leading coefficient of interest in this approach is the financial deficit of companies. It was found that in all models, including the fixed effects version with a single variable, the effects were adverse and significant, indicating that a higher deficit did not mean a proportional increase in indebtedness. Likewise, the results suggested a weak and inverse relationship between the financial deficit and the increase in debt, which questioned the empirical validity of the theory proposed by Shyam-Sunder and Myers (1994) for Latin American companies. As for the variables related to country risk and creditor protection, no statistical significance was found, suggesting that institutional and macroeconomic factors did not systematically affect the relationship between the deficit and the level of indebtedness, at least from the perspective of the aggregate model.

Table 6. Results of disaggregated financial hierarchy.

	FE (1)	FE (2)
DEF	0.135 (0.108)	0.140 (0.107)
DIV	0.000 (0.000)	0.000 (0.000)
I	0.139 (0.117)	0.156 (0.117)
W	0.001 (0.111)	−0.001 (0.110)
R	−0.647 *** (0.135)	−0.658 *** (0.134)
C	0.143 (0.108)	0.148 (0.108)
Dprotec
RP		0.008 * (0.004)
Number of observations	681	681
R2	0.144	0.151
R2 Adj.	0.050	0.057
AIC	−2019.6	−2023.2
BIC	−1987.9	−1987.0
RMSE	0.05	0.05

Source: Own elaboration. * p < 0.1 *** p< 0.01.

presents the results of the disaggregated financial ranking model (1). The current long-term debt (R) portion shows a significant negative relationship. In the fixed effects specification, however, the relationship is more pronounced, suggesting that firms adjust their debt levels based on their prior obligations. This result is consistent with aspects associated with financial sustainability but not with a strict ranking. Regarding the results related to the fixed effects and institutional variables (debt protection and country risk), the fixed effects were shown to present a positive and marginally significant result. Therefore, it is expected that firms will tend to increase their debt levels in the short term in the context of high-country risk.

In the presence of higher country risk, credit is expected to become more expensive and less accessible to firms. For this reason, short-term corporate financing decisions can be considered a precautionary strategy (Rücker & Treibich, 2024). In this scenario, companies can anticipate difficulties in accessing credit, resorting to borrowing before the country’s conditions deteriorate. High levels of country risk in emerging markets do not necessarily reduce access to local debt since alternative financing mechanisms such as subsidies or preferential loans exist.

Regarding the optimal leverage model,

Table 7. Optimal leverage.

	FE (1)	FE (2)
(Intercept)
Ajust	0.056 *** (0.009)	0.057 *** (0.009)
Dprotec		−109.128 (70.751)
Number of observations	682	682
R2	0.054	0.058
R2 Adj.	0.039	0.041
AIC	11,182.4	11,182.0
BIC	11,191.5	11,195.6
RMSE	876.91	875.35

Source: Own elaboration. *** p < 0.01.

shows the results for Equation (2) estimates. The coefficient of the variable “Ajust” is statistically significant, with a value close to 0.05, similar to the forecast made by Mongrut et al. (2010). This result supports the hypothesis of the target leverage model proposed by Shyam-Sunder and Myers (1994), according to which firms adjust their current debt based on the gap between their observed debt level and the desired optimal level; that is, the speed of adjustment points to the optimal leverage. Therefore, it is possible to affirm that, on average, firms correct 5.80% of the gap between their current debt level and their target level for each period.

The results in

Table 8. Models by country.

Country		Model (1)	Model (2)
Chile	(Intercept)	0.0093	−555.44
	DEF	−0.010 (0.03)
	Ajust		0.2698 *** (0.05)
Mexico	(Intercept)	0.0131	−1457.63
	DEF	0.106 *** (0.03)
	Ajust		0.6084 *** (0.09)
Peru	(Intercept)	0.0102	−80.44
	DEF	0.0716(0.07)
	Ajust		0.2036 ** (0.15)
Colombia	(Intercept)	0.0183	−1433.78
	DEF	−0.0111 ***(0.00)
	Ajust		0.7161 *** (0.12)

Source: Own elaboration; model (1): Aggregate financial hierarchy, model (2): Target leverage. ** p < 0.05 *** p < 0.01.

allow us to identify patterns in the capital structure dynamics for each of the countries included in the research. The results are generally heterogeneous concerning the magnitude and significance of the coefficients on the financial deficit and the speed of adjustment. It suggests the presence of institutional factors and market aspects that influence the behavior of the firms in each country.

A positive and statistically significant coefficient was observed in the aggregate financial ranking model for Mexico, suggesting that Mexican firms tend to cover their deficit by increasing their leverage, which is consistent with the financial ranking theory. In contrast, the results for Colombia show an inverse relationship, given that, when faced with a larger deficit, firms may opt for a leverage reduction. Furthermore, Colombia may have restrictions on access to credit, or firms may be opting for domestic sources of financing. The relationship between the financing gap and leverage from the model (2) approach is unclear for Chile and Peru.

The target leverage model’s results are positive and significant for all countries. For companies located in Colombia and Mexico, the speed of adjustment is higher, suggesting greater flexibility in correcting deviations from the target leverage. In contrast, in countries like Chile and Peru, a slower speed of adjustment could be associated with greater market frictions or less pressure to achieve an optimal capital structure in the short term.

Table 9. Models by country.

	FE (1)	FE (2)	FE (3)	FE (4)
DEF	0.135 (0.08)	0.140 (0.08)
DIV	0.000 (0.000)	0.000 (0.000)
I	0.139 (0.145)	0.156 (0.117)
W	0.000 (0.164)	−0.001 (0.128)
R	−0.647 *** (0.129)	−0.658 *** (0.134)
C	0.143 (0.08)	0.148 (0.108)
Dprotec
RP		0.008 * (0.002)
Ajust			0.500 (0.000) ***	0.500 (0.000) ***

Source: Own elaboration; FE (1): Disaggregated Pecking Order Model, FE (2): Disaggregated Pecking Order Model including protection indicators, FE (3): Target Leverage Model, FE (4): Target Leverage Model including protection indicators. * p < 0.1 *** p < 0.01.

presents the model estimates using robust standard errors, aiming to enhance the reliability of the results. The coefficients exhibit only minor variations, indicating no strong evidence of heteroskedasticity, despite the firms operating in countries with diverse economic dynamics. Nonetheless, the potential limitations are acknowledged, particularly those related to the sample size and the use of temporal data as part of the variation in the financial indicators of the firms analyzed.

The results reveal that corporate’s responses to financial imbalances and their ability to adjust toward optimal leverage levels vary significantly across countries. This heterogeneity reflects the substantial differences in institutional environments, the degree of the development of financial markets, and the restrictions on access to financing. Therefore, the findings highlight the need to incorporate each country’s specific economic and institutional context when analyzing the dynamics of capital structure in Latin American economies. This perspective is consistent with the literature that recognizes the role of the institutional and financial environment in determining corporate financing decisions (La Porta et al., 1998; Rajan & Zingales, 1995), as well as the approaches that emphasize the need for differentiated analytical frameworks for emerging economies (Fan et al., 2012).

5. Discussion

The results obtained in this study reveal notable heterogeneity in corporate financing dynamics among the Latin American countries analyzed (Chile, Colombia, Mexico, and Peru). The stability observed in the amount of debt contracted, with homogeneous means between 0.01 and 0.02, contrasts with the significant variability in the financial deficit, especially in Colombia, where a negative average deficit and high dispersion are evident. This pattern suggests the presence of profound fiscal imbalances that could impact debt capacity and corporate financial management.

Panel models consistently show that the financial deficit does not translate into a proportional increase in debt, which challenges the empirical validity of the traditional financial hierarchy theory in the context of the Latin American economies studied (Shyam-Sunder & Myers, 1994). This finding indicates that firms do not use debt as an automatic source to cover deficits, possibly due to credit restrictions, debt aversion, or a preference for self-financing.

Institutional and macroeconomic variables—specifically country risk and creditor protection—do not present significant systematic effects in the aggregate model. However, the disaggregated analysis shows that country risk exerts a marginally substantial positive impact on debt, suggesting that, in contexts of high sovereign uncertainty, firms may anticipate future difficulties and increase their debt as a precautionary strategy (Rücker & Treibich, 2024). This strategy is consistent with emerging market scenarios, where alternative financing mechanisms can cushion credit restrictions.

Regarding the optimal leverage model, the estimated adjustment speed of around 5.80% reflects that firms gradually correct deviations between the observed and target debt levels. However, this adjustment capacity varies by country, as is the case with Mexico and Colombia, which are more flexible than Chile and Peru. This could be attributed to differences in the depth and efficiency of local financial markets.

The country analysis shows that financial deficit and debt relationship varies significantly. At the same time, Mexican companies adjust their debt according to the traditional financial hierarchy. In contrast, Colombia shows the opposite behavior, possibly associated with restrictions on access to credit or a greater dependence on domestic sources of financing. These results emphasize the need to contextualize corporate financial decisions within each country’s institutional and market specificities.

6. Conclusions

This study provides empirical evidence on companies’ complex capital structure dynamics in emerging Latin American economies. Its originality lies in the integration of a multi-country analysis over a recent ten-year period with a focus on the empirical validity and coexistence of dominant capital structure theories, a topic still underexplored in the regional context. Firms’ responses to financial imbalances and their ability to adjust toward optimal capital structures vary significantly across countries, reflecting the influence of institutional factors, market restrictions, and macroeconomic conditions. There is also a weak or inverse relationship between financial deficits and debt, which calls into question the universal applicability of this theory in emerging economies, given the presence of other factors such as access to credit and preferences for self-financing.

Country risk and legal protection from creditors do not systematically affect debt at the aggregate level, although the disaggregated analysis reveals a precautionary effect of country risk on corporate debt. Regarding the adjustment toward optimal leverage, companies tend to gradually correct deviations from their target debt level, albeit with different adjustment speeds that reflect the heterogeneity of the region’s financial markets and institutional structures. Thus, the heterogeneity of the results highlights the need to design financial strategies and policies adapted to each country’s specific context, considering market constraints, the institutional environment, and the characteristics of emerging economies. These insights are particularly relevant for policymakers and corporate executives, as they underscore the importance of developing regulatory frameworks and financial strategies tailored to local realities. Corporate leaders are encouraged to adopt flexible and adaptive financing policies that account for credit access limitations, while public decision-makers should promote institutional conditions that facilitate balanced and sustainable leverage choices over time.

These findings reinforce the importance of incorporating an analysis of economic and institutional contexts in studies on capital structure. They contribute to a better understanding of financial decisions in Latin America and provide a basis for future research in emerging economies. Additionally, given that capital structure remains an evolving and active topic in the financial literature, future studies could explore firm-level dynamics using panel data methodologies, assess the influence of corporate governance or ESG factors on leverage decisions, or carry out comparative analyses across emerging regions to identify structural similarities and differences in financing behavior.