Macroeconomic Conditions, Effective Taxation, and Capital Structure Decisions: Evidence from Indonesian Manufacturing Firms

Abstract

This study examines the associations between macroeconomic conditions, realized effective taxation, firm-specific characteristics, and capital structure among manufacturing firms listed on the Indonesia Stock Exchange during 2021–2025. Using a balanced panel of 97 firms and 485 firm-year observations, the study applies panel-data regression with the Random Effects Model (REM) as the baseline specification based on formal model-selection tests. Robustness is assessed using firm-clustered standard errors, a Fixed Effects specification, and additional ETR sensitivity analyses. The results show that the policy interest rate is negatively and statistically significantly associated with capital structure and remains robust across alternative panel specifications. Firm size is positively associated with capital structure in the baseline REM but becomes statistically insignificant under Fixed Effects, indicating specification sensitivity. In contrast, the Effective Tax Rate (ETR) exhibits a negative but statistically insignificant coefficient under firm-clustered inference and remains insignificant across bounded-ETR, positive-tax-expense, and winsorized specifications. Inflation and the remaining firm-specific variables are also statistically insignificant at the conventional 5% level. Overall, the findings indicate that interest rates exhibit a more stable association with corporate leverage than firms’ realized annual effective tax burden. Given the observational design and limited time-series variation in national-level macroeconomic variables, the results should be interpreted as conditional associations rather than causal effects.

Keywords: capital structure; effective tax rate; interest rate; random effects model; manufacturing firms.

Introduction

Capital structure is a key strategic financial decision because the choice between debt and equity affects firms' financing costs, financial risk, sustainability, and flexibility (Nishihara, 2023; Vinh et al., 2025). Changes in macroeconomic conditions, particularly inflation and interest rates, together with fiscal policy and taxation, increase the complexity of financing decisions (Ali et al., 2022; M. Febrianti & Widhiastuti, 2025). At the firm level, profitability, asset tangibility, investment opportunities, business risk, liquidity, firm size, firm age, and sales growth may further explain differences in financing behavior (Jeffry et al., 2023a; Melananda & Ode Irma Sari, 2024; Widyakto et al., 2024). Trade-Off Theory, Pecking Order Theory, and Agency Theory provide complementary explanations for corporate financing decisions through mechanisms involving tax benefits, financial distress costs, information asymmetry, and agency conflicts (Adair & Adaskou, 2015; Khan et al., 2021). However, empirical evidence on capital structure determinants remains mixed across countries, industries, and institutional settings, indicating that financing decisions are context-dependent and may vary with macroeconomic conditions, taxation systems, and firm characteristics (Fukui et al., 2023; Said, 2025; Salam & Shourkashti, 2019; Tan, 2024; Vintilă et al., 2025) Recent studies have increasingly integrated macroeconomic and firm-specific This study examines the associations between macroeconomic conditions, realized effective taxation, firm-specific characteristics, and capital structure among manufacturing firms listed on the Indonesia Stock Exchange during 2021–2025. Using a balanced panel of 97 firms and 485 firm-year observations, the study applies panel-data regression with the Random Effects Model (REM) as the baseline specification based on formal model-selection tests. Robustness is assessed using firm-clustered standard errors, a Fixed Effects specification, and additional ETR sensitivity analyses. The results show that the policy interest rate is negatively and statistically significantly associated with capital structure and remains robust across alternative panel specifications. Firm size is positively associated with capital structure in the baseline REM but becomes statistically insignificant under Fixed Effects, indicating specification sensitivity. In contrast, the Effective Tax Rate (ETR) exhibits a negative but statistically insignificant coefficient under firm-clustered inference and remains insignificant across bounded-ETR, positive-tax-expense, and winsorized specifications. Inflation and the remaining firm-specific variables are also statistically insignificant at the conventional 5% level. Overall, the findings indicate that interest rates exhibit a more stable association with corporate leverage than firms’ realized annual effective tax burden. Given the observational design and limited time-series variation in national-level macroeconomic variables, the results should be interpreted as conditional associations rather than causal effects. determinants of capital structure. Choi et al., (2024) examine macroeconomic and firm-level determinants among Korean firms, while S. Febrianti, Mustarruddin, et al., (2024) incorporate macroeconomic and financial characteristics of Indonesian listed companies. Saad & Belkacem, (2025) similarly analyze firm-specific and macroeconomic determinants in an emerging-market setting, whereas Egbunike & Okerekeoti, (2018) examine their interaction among manufacturing firms. Despite these contributions, evidence remains limited on whether firms' realized tax burden provides additional information in explaining financing decisions, particularly in Indonesian manufacturing firms, because previous studies generally employ conventional tax measures or do not explicitly incorporate the Effective Tax Rate (ETR) into their empirical models (Garcia-Bernardo et al., 2023; Putri et al., 2025) Unlike statutory tax rates, ETR reflects firms' realized tax burden after considering tax planning, fiscal incentives, tax credits, and accounting adjustments (Andreas & Savitri, 2017a; Garcia-Bernardo et al., 2023; Putri et al., 2025). Thus, ETR provides conceptually different information from statutory tax rates and may capture aspects of firms' actual tax positions that are relevant to financing decisions. Nevertheless, evidence concerning its association with capital structure remains limited in the Indonesian manufacturing context. To address this gap, this study examines the associations of inflation, interest rates, ETR, and firm-specific characteristics—profitability, asset tangibility, investment opportunity set (IOS), business risk, liquidity, firm size, firm age, and sales growth—with the capital structure of manufacturing firms listed on the Indonesia Stock Exchange (Ariefianto et al., 2024a; S. Febrianti, Mustarruddin, et al., 2024; Ibrahimov et al., 2025; Jeffry et al., 2023b; Wahyuni & Kristanti, 2024). The study specifically evaluates whether firms' realized tax burden, measured by ETR, provides additional explanatory information beyond conventional macroeconomic and firm-level determinants. The study contributes to the literature in three ways. First, it provides additional evidence on the applicability of Trade-Off and Pecking Order theories within Indonesia's macroeconomic and institutional environment (Hastutik et al., 2022; Jahanzeb et al., 2013; Yulianto et al., 2016). Second, it incorporates ETR as a measure of firms' realized tax burden, capturing tax-related information not reflected in statutory tax rates (Andreas & Savitri, 2017b; Garcia-Bernardo et al., 2023; Putri et al., 2025). Third, it extends Indonesian evidence by assessing ETR jointly with macroeconomic conditions and firm-specific characteristics in explaining leverage among manufacturing firms (Table 1). The findings may also assist managers, investors, and policymakers in evaluating financing decisions under changing monetary and taxation environments (Gazali et al., 2021; Vintilă et al., 2025).

Literature Review

Capital structure concerns firms' financing choices between debt and equity and their implications for firm value, financing costs, and risk (S. Febrianti, Giriati, et al., 2024; Mazumdar & Mara, 2024). While Modigliani and Miller initially demonstrated capital-structure irrelevance under perfect-market assumptions, subsequent research recognizes that taxation, bankruptcy costs, information asymmetry, and agency conflicts influence financing decisions (Fukui et al., 2023; Wazzan, 2018). These market imperfections provide the basis for integrating macroeconomic conditions, taxation, and firm-specific characteristics when explaining financing behavior, particularly in emerging markets (Yeboah et al., 2024a; Yıldırım & Karabayır, 2024). Trade-Off Theory proposes that firms balance the tax benefits of debt against expected financial distress and bankruptcy costs in determining leverage. (Ai et al., 2021; Cekrezi, 2013). Within this framework, inflation and interest rates affect borrowing conditions, while taxation influences the potential benefits of interest-related tax shields (Ariefianto et al., 2024b; S. Febrianti, Mustarruddin, et al., 2024). However, institutional constraints and macroeconomic volatility may prevent firms from continuously maintaining a single optimal leverage ratio, resulting in adaptive financing behavior (Abdeljawad & Farhood, 2025; Hackbarth & Sun, 2024a). Pecking Order Theory emphasizes financing hierarchy arising from information asymmetry between managers and external investors. Firms generally prefer internal funds, followed by debt, and issue equity as a last resort (Kakouris & Psychoyios, 2025; Leary & Roberts, 2010). Accordingly, profitability and liquidity can reduce dependence on debt, while financing needs may increase external borrowing. This mechanism is particularly relevant in emerging markets characterized by information asymmetry and capital-market imperfections (Abdeljawad & Jaradat, 2025; Saputra & Kusuma, 2025). Agency Theory complements these perspectives by emphasizing conflicts among managers, shareholders, and creditors. Debt can constrain managerial discretion but may also generate risk-shifting and underinvestment problems when leverage becomes excessive (Gifari et al., 2025; Hu et al., 2025). Consequently, firm characteristics such as asset tangibility, business risk, investment opportunities, and firm size can affect debt capacity and creditor–shareholder conflicts (Bishwas & Hossain, 2025; Vengesai, 2023; Zaighum et al., 2024). Together, these theories suggest that capital structure reflects interactions between firm characteristics and the broader financing environment. Macroeconomic conditions, particularly inflation and interest rates, may alter borrowing costs, the real value of liabilities, and financing incentives (Budianto, 2025; Chen et al., 2025; Febrianti et al., 2024; Mustofa & Faizin, 2025). However, mixed empirical findings

Hypothesis Development

Inflation and Capital Structure

Inflation can affect leverage through competing mechanisms. Higher inflation may reduce the real value of nominal debt, potentially encouraging borrowing, but persistent inflation can also increase uncertainty, financing costs, and financial distress risk (Budianto, 2025; Febrianti et al., 2024). Given these competing effects, the study proposes a non-directional relationship: H1. Inflation is significantly associated with capital structure.

Interest Rate and Capital Structure

Trade-Off Theory suggests that higher interest rates increase borrowing costs and reduce the attractiveness of debt, whereas Pecking Order Theory implies that financing hierarchy may moderate firms' responses to borrowing costs (Ai et al., 2021; Cekrezi, 2013; Leary & Roberts, 2010). Consistent with mixed empirical evidence regarding the strength of this relationship (Chen et al., 2025; Kim et al., 2023) the following hypothesis is proposed: H2. Interest rates are associated with capital structure.

Profitability and Capital Structure

Pecking Order Theory predicts that profitable firms rely more on retained earnings and therefore require less debt, whereas Trade-Off Theory suggests that profitability may increase debt capacity and tax-shield benefits. Given the expected dominance of internal financing and evidence from emerging markets (Abdeljawad & Jaradat, 2025; Leary & Roberts, 2010). H3. Profitability is negatively associated with capital structure.

Asset Tangibility and Capital Structure

Tangible assets provide collateral, reduce expected creditor losses, and can mitigate shareholder–creditor agency conflicts, thereby increasing firms' debt capacity (Camisón et al., 2022; Vengesai, 2023). Therefore: H4. Asset tangibility is positively associated with capital structure.

Investment Opportunity Set and Capital Structure

Firms with greater growth opportunities may use less debt because leverage can create underinvestment problems and because future investment opportunities provide limited collateral value. Although growing firms may require external financing, the agency-cost mechanism predicts: H5. Investment Opportunity Set is negatively associated with capital structure.

Business Risk and Capital Structure

Higher business risk increases earnings volatility and financial distress exposure, while creditors may impose tighter financing conditions on firms with volatile operating performance. Accordingly: H6. Business risk is negatively associated with capital structure.

Effective Tax Rate and Capital Structure

Trade-Off Theory predicts that firms facing higher effective tax burdens may have stronger incentives to use debt because interest payments generate tax shields. However, institutional constraints and tax regulations may limit these benefits (Ariefianto et al., 2024b; Cekrezi, 2013). Therefore: H7. Effective tax rate is positively associated with capital structure.

Liquidity and Capital Structure

Pecking Order Theory suggests that highly liquid firms have greater internal resources and therefore rely less on debt, although Trade-Off Theory recognizes that stronger liquidity may increase debt-servicing capacity. Following the internal-financing mechanism: H8. Liquidity is negatively associated with capital structure.

Firm Size and Capital Structure

Larger firms generally have greater diversification, lower perceived bankruptcy risk, and better access to external financing, supporting greater debt capacity under Trade-Off and Agency perspectives. Therefore: H9. Firm size is positively associated with capital structure.

Firm Age and Capital Structure

Older firms generally have longer credit histories, stronger reputations, and established relationships with financial institutions, which may reduce information asymmetry and improve access to debt. Although accumulated retained earnings may reduce financing needs, this study expects the financing-access mechanism to dominate. Therefore: H10. Firm age is positively associated with capital structure.

Sales Growth and Capital Structure

Growing firms require additional resources for working capital, production, and expansion. When internal funds are insufficient, Pecking Order Theory predicts that firms will use debt before issuing equity, although uncertainty associated with growth may constrain excessive borrowing. Accordingly: H11. Sales growth is positively associated with capital structure.

Table 1. Comparison of Closest Empirical Studies and Research Gap

Methods

This study employs a quantitative, explanatory, and associational panel-data design to examine the relationships Table 3. Expected Signs of the Regression Coefficients and Their Theoretical Basis Variable Expected Sign Theoretical Basis Inflation (INF) +/- Trade-Off Theory Interest Rate (IR) - Trade-Off Theory Profitability (PROF) - Pecking Order Theory Asset Tangibility (TANG) + Trade-Off Theory Investment Opportunity Set (IOS) - Pecking Order Theory Business Risk (BRISK) - Trade-Off Theory Effective Tax Rate (ETR) + Trade-Off Theory Liquidity (LIQ) - Trade-Off Theory Firm Size (SIZE) + Trade-Off Theory Firm Age (AGE) + Trade-Off Theory Sales Growth (GROWTH) + Pecking Order Theory between macroeconomic conditions, effective taxation, firm-specific characteristics, and capital structure. Panel data allow the analysis to exploit cross-sectional and time-series variation while accounting for unobserved firm heterogeneity. Given the observational design and the absence of a formal causal identification strategy, the estimated coefficients are interpreted as conditional associations rather than causal effects. The population comprised 153 manufacturing firms listed on the Indonesia Stock Exchange during 2021–2025. Purposive sampling excluded 11 delisted firms, 17 firms with incomplete annual reports, and 28 firms with incomplete research-variable data, resulting in 97 firms. Complete observations for all firms over five years produced a balanced panel of 485 firm-year observations (see Table 2). Data were screened for potentially influential or economically unusual observations, with any alternative treatment reported separately in the sensitivity analyses. Panel-data regression was employed to account for firm heterogeneity and variation across firms and time. The baseline estimator was selected using the Chow/F test, Hausman test, and Breusch–Pagan Lagrange Multiplier (LM) test to compare the Common Effects, Fixed Effects, and Random Effects specifications. The tests supported the Random Effects Model (REM) as the baseline specification. Because REM assumes that unobserved firm-specific effects are uncorrelated with the explanatory variables, an alternative Fixed Effects Model (FEM) with firm-clustered standard errors was also estimated as a robustness check. The general panel-data regression model is specified as: CSᵢₜ = α + β₁INFₜ + β₂IRₜ + β₃PROFᵢₜ + β₄TANGᵢₜ + β₅IOSᵢₜ + β₆BRISKᵢₜ + β₇ETRᵢₜ + β₈LIQᵢₜ + β₉SIZEᵢₜ + β₁₀AGEᵢₜ + β₁₁GROWTHᵢₜ + μᵢ + εᵢₜ where CS denotes capital structure, measured as total debt divided by total assets; INF is inflation; IR is the central bank policy interest rate; PROF is profitability measured by return on assets; TANG is asset tangibility; IOS is the investment opportunity set; BRISK is business risk; ETR is the Effective Tax Rate; LIQ is liquidity; SIZE is firm size; AGE is firm age; and GROWTH is sales growth. The term μᵢ represents the unobserved firm-specific effect and εᵢₜ the idiosyncratic error term. Inflation and interest rates are national macroeconomic variables that vary across years but not across firms within a given year. Their coefficients are therefore identified from time-series variation over the five-year observation period. A complete set of year fixed effects is not included because these national variables would be perfectly or nearly collinear with year indicators. Accordingly, their coefficients are interpreted cautiously as conditional associations rather than isolated causal effects. Firm-clustered standard errors are used to account for within-firm dependence. Given the limited five-year time dimension, procedures requiring a larger time dimension, such as Driscoll–Kraay standard errors, were not adopted. The theoretically expected coefficient signs are summarized in Table 3, while variable definitions and measurements are reported in Table 4. The Effective Tax Rate (ETR) is measured as total tax expense divided by earnings before tax. Because annual ETR may take unusual values due to negative tax expense, deferred-tax adjustments, or other accounting items, its underlying components were verified before estimation. No observations in the final baseline sample had negative earnings before tax; therefore, observations were not mechanically excluded solely because ETR fell outside the conventional 0–1 interval. Three sensitivity analyses were conducted to assess whether the ETR results were driven by unusual tax positions or influential observations. First, the sample was restricted to ETR values between 0 and 1 (423 firm-year observations). Second, the model was estimated using observations with positive reported tax expense (425 observations). Third, ETR was winsorized at the 1st and 99th percentiles while retaining all 485 observations. All baseline and sensitivity specifications used firm-clustered standard errors. Because no observations had negative earnings before tax, an additional loss-firm exclusion based on negative pre-tax income was not applicable.

Table 2. Sample Selection Procedure

Table 3. Expected Signs of the Regression Coefficients and Their Theoretical Basis

Table 4. Measurement

Result and Discussion

Table 5 presents descriptive statistics for 485 firm-year observations from 97 Indonesian manufacturing firms during 2021–2025. Capital structure (CS) averages 0.581, indicating that debt represents approximately 58.1% of total assets on average, although its range of 0.016–2.979 indicates substantial variation across firms. Values above one are retained because they may reflect economically possible conditions in which total debt exceeds total assets rather than data-entry errors. Inflation and interest rates show relatively limited dispersion because they are national-level variables that vary across years but not across firms within the same year. Firm-specific variables exhibit greater heterogeneity, particularly profitability, asset tangibility, IOS, business risk, liquidity, and sales growth. ETR averages 0.212 and ranges from −1.430 to 2.095. Verification confirmed that these unusual values were not associated with negative earnings before tax. Specifically, 60 observations had ETR values below zero and two exceeded one. These observations were retained in the baseline analysis because they represent reported accounting tax positions rather than confirmed data errors, while their potential influence is examined through separate sensitivity analyses. Liquidity also exhibits substantial dispersion, with a maximum of 206.864. Overall, the descriptive statistics indicate considerable heterogeneity, particularly in capital structure, ETR, and liquidity. Table 6 reports the panel-data model-selection tests. The Chow/F test favors FEM over the pooled Common Effects Model, whereas the Hausman test supports REM relative to FEM at the 5% significance level. The Breusch–Pagan LM test also favors REM over the pooled model. Accordingly, REM is retained as the baseline specification. Because REM relies on the assumption that unobserved firm-specific effects are uncorrelated with the regressors, FEM with firm-clustered standard errors is additionally reported as a robustness specification. Table 7 presents the baseline REM estimates for 97 firms and 485 firm-year observations. The model is statistically significant overall (F = 3.0863, p < 0.001), although its explanatory power is modest (R² = 0.0670; adjusted R² = 0.0453). Interest rates are negatively and significantly associated with capital structure (β = −3.8642, p = 0.0009), while firm size is positively significant (β = 0.0671, p = 0.0016). ETR has a negative but statistically insignificant coefficient (β = −0.0399, p = 0.2671). Profitability and liquidity are marginally above the 5% significance threshold, whereas inflation, asset tangibility, IOS, business risk, firm age, and sales growth are statistically insignificant.

Robustness Analysis

Table 8 compares REM and FEM estimates using firm-clustered standard errors. The interest-rate coefficient remains negative and statistically significant in both REM (β = −3.8643, p = 0.0057) and FEM (β = −3.8975, p = 0.0060), indicating that this finding is robust across alternative estimators. Firm size remains positive and significant in the firm-clustered REM (β = 0.0671, p = 0.0039) but becomes statistically insignificant in FEM (β = 0.0754, p = 0.2342), indicating sensitivity to model specification. ETR remains negative and insignificant under both REM (β = −0.0399, p = 0.2042) and FEM (β = −0.0391, p = 0.2272). Thus, the interest-rate result is the most stable across specifications, whereas the firm-size result is specification-sensitive and the ETR association is not statistically supported. Table 9 examines whether the ETR result is sensitive to unusual tax observations. The ETR coefficient remains negative but statistically insignificant in the baseline firm-clustered model (β = −0.0399, p = 0.2042), bounded-ETR sample (β = −0.0088, p = 0.8857), positive-tax-expense sample (β = −0.0068, p = 0.8766), and winsorized specification (β = −0.0584, p = 0.1531). These results show that alternative treatments of ETR do not change the substantive conclusion: the data do not provide statistically significant evidence of an association between firms' realized effective tax burden and capital structure. The negative interest-rate coefficient remains statistically significant across the ETR sensitivity specifications, while firm size remains positive and significant, indicating greater stability of these findings across alternative ETR treatments.

Interpretation of Key Findings

Interest rates exhibit a negative and statistically significant association with leverage, while firm size is positively significant in the baseline REM. In contrast, inflation, profitability, asset tangibility, investment opportunity set, business risk, ETR,

Robustness Analysis

: REM and Fixed Effects with Firm Clustered Standard Errors Variable REM Coef. REM Clustered SE REM p-value FEM Coef. FEM Clustered SE FEM p-value INF 0.784509 0.622013 0.2103 0.824767 0.606728 0.1772 IR -3.864270 1.367650 0.0057 -3.897521 1.386346 0.0060 PROF 0.276773 0.344431 0.4236 0.330885 0.348480 0.3447 TANG -0.043206 0.202415 0.8314 -0.396694 0.241041 0.1031 IOS -0.031959 0.036244 0.3801 -0.033264 0.041195 0.4214 BRISK 0.040948 0.175776 0.8163 0.058701 0.167478 0.7267 ETR -0.039874 0.031194 0.2042 -0.039132 0.032196 0.2272 LIQ -0.002121 0.001579 0.1823 -0.001759 0.001289 0.1754 SIZE 0.067135 0.022691 0.0039 0.075424 0.063002 0.2342 AGE 0.007916 0.018891 0.6761 0.010252 0.020000 0.6094 SG 0.008024 0.005096 0.1187 0.010756 0.010284 0.2982 interest-rate result, the positive firm-size association is specification-sensitive and should not be interpreted as uniformly robust. ETR exhibits a negative but statistically insignificant association with capital structure under firm-clustered inference. This conclusion remains unchanged when ETR is restricted to the 0–1 interval, the sample is limited to positive tax-expense observations, and ETR is winsorized at the 1st and 99th percentiles. Thus, the negative direction of the coefficient does not constitute robust evidence that firms with higher realized tax burdens systematically use less debt. From a Trade-Off Theory perspective, the insignificant ETR result suggests that annual accounting-based ETR may not adequately capture the marginal tax incentives underlying debt-financing decisions. ETR reflects firms' realized tax positions and can be affected by tax planning, incentives, deferred-tax adjustments, and other accounting items that differ from statutory or marginal tax rates. Indonesian evidence similarly indicates that alternative tax measures may capture different dimensions of the taxation–financing relationship (Alfandia, 2018; Kumalasari & Wahyudin, 2020) Accordingly, the findings should be interpreted as a lack of robust statistical evidence for an ETR–leverage association rather than evidence that taxation is economically irrelevant. The remaining firm-specific variables are statistically insignificant at the 5% level. Profitability and liquidity are close to, but remain above, the conventional significance threshold, while asset tangibility, IOS, business risk, firm age, and sales growth are also insignificant. These findings provide selective rather than comprehensive support for the predictions of Trade-Off, Pecking Order, and Agency theories and may reflect the relatively short observation period, heterogeneous firm responses, and institutional factors not fully captured by the contemporaneous specification.

Comparison with Previous Studies

The results provide differentiated support for previous capital-structure research. The robust negative interest-rate association supports the view that monetary conditions influence firms' financing decisions through borrowing costs and access to external debt (S. Febrianti, Mustarruddin, et al., 2024; Yadav & Panda, 2024). The positive firm-size coefficient in the baseline REM is also consistent with previous Indonesian evidence linking larger firm size to greater debt capacity (Tursina, 2024;Shanthana & Basana, 2020), although its loss of significance under FEM indicates that this relationship is sensitive to the treatment of unobserved firm heterogeneity. By contrast, the insignificant ETR coefficient does not provide empirical support for a systematic relationship between realized annual effective taxation and leverage. This result is consistent with the broader observation that accounting-based ETR may capture tax planning, deferred-tax adjustments, incentives, and firm-specific tax positions rather than the marginal tax incentives emphasized by Trade-Off Theory (Andreas & Savitri, 2017b; Garcia-Bernardo et al., 2023). Similarly, the insignificant results for profitability, liquidity, asset tangibility, and IOS differ from studies identifying these characteristics as significant leverage determinants (Bose et al., 2025; Camisón et al., 2022; Chen et al., 2019). Such differences reinforce the context-dependent nature of capital-structure decisions across institutional environments, industries, periods, and empirical specifications (Choi et al., 2024).

Limitations and Cautions

Several limitations should be considered. First, the sample is restricted to manufacturing firms listed on the Indonesia Stock Exchange during 2021–2025, limiting generalizability to other industries, periods, and institutional settings (Hazmi et al., 2023). Second, although the model-selection tests support REM and the results are supplemented with FEM and firm-clustered robustness specifications, REM assumes that unobserved firm-specific effects are uncorrelated with the regressors. More importantly, potential endogeneity between leverage and firm-specific variables such as profitability, firm size, and IOS remains unresolved. Reverse causality, simultaneity, and omitted-variable bias therefore cannot be ruled out, and the coefficients should be interpreted as conditional associations rather than causal effects. Future studies may address these concerns using correlated random effects, instrumental-variable approaches, or dynamic panel estimators such as System GMM or Difference GMM. Third, inflation and interest rates are national-level variables that vary only across the five annual periods. Their coefficients are therefore identified from limited time-series variation and may partly capture unobserved shocks common to firms within the same year. Because a full set of year fixed effects would absorb the variation used to identify these variables, the macroeconomic coefficients should be interpreted cautiously. The short time dimension also limits the reliability of covariance estimators requiring many time clusters. Finally, ETR is an accounting-based measure calculated as tax expense divided by earnings before tax and may be affected by tax planning, deferred-tax adjustments, tax benefits, and other accounting items. Although the underlying data were verified and alternative ETR treatments produced the same substantive conclusion, annual ETR should not be interpreted as a direct measure of the marginal tax incentive to use debt.

Recommendations for Future Research

Future research could extend the sample across sectors, countries, and longer periods to improve external validity and provide greater macroeconomic time-series variation (Yeboah et al., 2024). Alternative tax measures and governance or institutional variables may provide additional insight into financing decisions (Colak & Sarioglu, 2025; Iazzi et al., 2025). Econometrically, correlated random effects, Table 9. Sensitivity Analysis of the Effective Tax Rate Variable / Model Statistic Baseline Full Sample Bounded ETR (0– 1) Positive Tax Expense Winsorized ETR (1– 99%) ETR coefficient −0.0399 −0.0088 −0.0068 −0.0584 Firm-clustered SE (0.0312) (0.0608) (0.0435) (0.0406) p-value 0.2042 0.8857 0.8766 0.1531 Interest Rate coefficient −3.8643 −3.0061 −3.0046 −3.8846 Firm Size coefficient 0.0671 0.0373 0.0383 0.0667 Observations 485 423 425 485 Number of firms 97 96 96 97 R² 0.0671 0.1293 0.1305 0.0685 Adjusted R² 0.0455 0.1060 0.1073 0.0468 instrumental-variable methods, and dynamic panel estimators could be employed to address endogeneity and better capture capital-structure adjustment dynamics (Hackbarth & Sun, 2024).

Table 5. Descriptive Statistics

Table 6. Panel Data Model Selection Result

Table 7. Baseline Random Effects Model Estimates

Table 7 (continued). Model Statistics

Table 8. Robustness Analysis: REM and Fixed Effects with Firm Clustered Standard Errors

Table 9. Sensitivity Analysis of the Effective Tax Rate

Conclusion

This study examines the associations between macroeconomic conditions, effective taxation, firm-specific characteristics, and capital structure among Indonesian manufacturing firms during 2021–2025. The results show that interest rates are negatively and statistically significantly associated with capital structure, and this relationship remains robust across firm-clustered REM and FEM specifications. Firm size is positively significant in the baseline REM but becomes statistically insignificant under FEM, indicating that the firm-size result is sensitive to model specification. The Effective Tax Rate (ETR) exhibits a negative but statistically insignificant association with capital structure. This conclusion remains unchanged across the bounded-ETR, positive-tax-expense, and winsorized specifications. Accordingly, the study does not provide robust statistical evidence that firms’ realized annual effective tax burden systematically influences leverage. Inflation, profitability, asset tangibility, IOS, business risk, liquidity, firm age, and sales growth are also statistically insignificant at the 5% level. Overall, the findings indicate that monetary financing conditions, particularly interest rates, have a more stable association with corporate leverage than realized effective taxation in the Indonesian manufacturing context. The results also highlight the importance of distinguishing robust findings from specification-sensitive relationships when evaluating capital-structure determinants. For managers and investors, changes in borrowing costs should therefore receive particular attention when assessing financing decisions, while ETR should not be interpreted as a stand-alone predictor of leverage. Given the observational design, limited five-year variation in national macroeconomic variables, and unresolved potential endogeneity, the estimated coefficients should be interpreted as conditional associations rather than causal effects. Future research could extend the observation period and sectoral coverage and employ alternative tax measures, correlated random effects, instrumental-variable approaches, or dynamic panel estimators to strengthen identification.

Author Contributions

The first author contributed to conceptualization, research design, data collection, empirical analysis, interpretation, and manuscript drafting. The second author contributed to methodology, supervision, validation, theoretical refinement, and critical review. The third author contributed to the literature review, data verification, presentation of findings, and manuscript editing. All authors reviewed and approved the final manuscript and are accountable for its content.

Acknowledgements

The authors acknowledge Universitas Serang Raya for its academic support and thank the reviewers, academic colleagues, and other contributors whose feedback and assistance supported the completion and improvement of this manuscript.

References

Abdeljawad, I., & Farhood, H. (2025). The trade-off behavior of capital structure in firms within politically unstable emerging countries. Management and Sustainability, (October). https://doi.org/10.1108/MSAR-10-2024-0156

Abdeljawad, I., & Jaradat, M. N. (2025). Revisiting the pecking order theory: insights from an emerging market economy. International Review of Economics, 72(2), 33. https://doi.org/10.1007/s12232-025-00509-7

Adair, P., & Adaskou, M. (2015). Trade-off theory vs. Pecking order theory and the determinants of corporate leverage: Evidence from a panel data analysis upon french SMEs (2002–2010). Cogent Economics and Finance, 3(1). https://doi.org/10.1080/23322039.2015.1006477

Ai, H., Frank, M. Z., & Sanati, A. (2021). The Trade-Off Theory of Corporate Capital Structure. Oxford University Press. https://doi.org/10.1093/acrefore/9780190625979.013.602

Alfandia, N. S. (2018). Taxes and Capital Structure of Manufacturing Companies in Indonesia. Akuntansi Dan Keuangan Indonesia, 03(01), 17–34. https://doi.org/10.20473/baki.v3i1.6822

Ali, S., Rangone, A., & Farooq, M. (2022). Corporate Taxation and Firm-Specific Determinants of Capital Structure: Evidence from the UK and US Multinational Firms. Journal of Risk and Financial Management, 15(2). https://doi.org/10.3390/jrfm15020055

Andreas, Dr., & Savitri, E. (2017a). Determinants of effective Tax Rate of the top 45 Largest listed companies of Indonesia. International Journal of Management Excellence, 9(3), 1183–1188. https://doi.org/10.17722/ijme.v9i3.944

Andreas, Dr., & Savitri, E. (2017b). Determinants of effective Tax Rate of the top 45 Largest listed companies of Indonesia. International Journal of Management Excellence, 9(3), 1183–1188. https://doi.org/10.17722/ijme.v9i3.944

Ariefianto, D., Amanda, C., & Ananto Husodo, Z. (2024a). Term structure of interest rate and macro economy: an empirical study on selected emerging countries sovereign bond. Journal of Capital Markets Studies, 8(2), 195–211. https://doi.org/10.1108/JCMS-05-2024-0022

Ariefianto, D., Amanda, C., & Ananto Husodo, Z. (2024b). Term structure of interest rate and macro economy: an empirical study on selected emerging countries sovereign bond. Journal of Capital Markets Studies, 8(2), 195–211. https://doi.org/10.1108/JCMS-05-2024-0022

Bishwas, P. C., & Hossain, M. S. (2025). Does ownership concentration have an impact on financial performance of firms? Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00491-0

Bose, S., Biswas, K., Ali, M. J., & Saha, A. (2025). Do investment opportunities influence private firms’ performance? International evidence from developing countries. Journal of Accounting Literature, 47(5), 352–387. https://doi.org/10.1108/JAL-08-2023-0149

Budianto, H. (2025). Impact of Inflation and Interest Rate Volatility on Corporate Capital Structure Decisions. Jurnal Informatika Ekonomi Bisnis, 7, 399– 403. https://doi.org/10.37034/infeb.v7i2.1141

Camisón, C., Clemente, J. A., & Camisón-Haba, S. (2022). Asset tangibility, information asymmetries and intangibles as determinants of family firms leverage. Review of Managerial Science, 16(7), 2047–2082. https://doi.org/10.1007/s11846-022-00522-y

Cekrezi, A. (2013). A literature review of the trade−off theory of capital structure.

Iliria International Review, 3(2013), 125–133.

Chen, Harford, J., & Kamara, A. (2019). Operating Leverage, Profitability, and Capital Structure. The Journal of Financial and Quantitative Analysis, 54(1), 369–392.

Chen, L., Zhang, K., & Yang, X. (2025). The long-term and short-term effects of interest rate volatility on corporate bankruptcy risk: An industry and supply chain perspective. PLoS ONE, 20(2 February), 1–24. https://doi.org/10.1371/journal.pone.0317185

Choi, S. S. B., Sauka, K., & Lee, M. Y. (2024). Dynamic Capital Structure Adjustment: An Integrated Analysis of Firm-Specific and Macroeconomic Factors in Korean Firms. International Journal of Financial Studies, 12(1). https://doi.org/10.3390/ijfs12010026

Colak, M., & Sarioglu, M. (2025). The Effect of Corporate Governance on the Quality of Integrated Reporting and ESG Risk Ratings. In Sustainability (Vol. 17, Number 11, p. 4868). https://doi.org/10.3390/su17114868

Egbunike, C. F., & Okerekeoti, C. U. (2018). Macroeconomic factors, firm characteristics and financial performance: A study of selected quoted manufacturing firms in Nigeria. Asian Journal of Accounting Research, 3(2), 142–168. https://doi.org/10.1108/AJAR-09-2018-0029

Febrianti, M., & Widhiastuti, S. (2025). The influence of capital structure, liquidity, and operational cost efficiency on firm value with profitability as an intervening variable in banking companies listed on the Indonesia Stock Exchange for the period 2019–2023. Co-Creation : Jurnal Ilmiah Ekonomi Manajemen Akuntansi Dan Bisnis, 3(4), 291–295. https://doi.org/10.55904/cocreation.v3i4.1443

Febrianti, Mustaruddin, M., & Afifah, N. (2024). Capital Structure, Financial Performance, and Macroeconomic Conditions: Evidence From Indonesia Stock Exchange. AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment), 8(2), 1–8. https://doi.org/10.29165/ajarcde.v8i2.380

Febrianti, S., Giriati, Malini, H., & Wendy. (2024). Determinants of Capital Structure and the Effect on Firm Value: Evidence from Indonesia. Asian Journal of Economics, Business and Accounting, 24(6), 407–421. https://doi.org/10.9734/ajeba/2024/v24i61370

Febrianti, S., Mustarruddin, M., & Afifah, N. (2024). Capital Structure, Financial Performance, and Macroeconomic Conditions: Evidence From Indonesia Stock Exchange. AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment), 8(2), 1–8. https://doi.org/10.29165/ajarcde.v8i2.380

Fukui, T., Mitton, T., & Schonlau, R. (2023). Determinants of Capital Structure: An Expanded Assessment. Journal of Financial and Quantitative Analysis, 58(6), 2446–2488. https://doi.org/10.1017/S0022109022001405

Garcia-Bernardo, J., Jansky, P., & Tørsløv, T. (2023). Effective tax rates of multinational corporations: Country-level estimates. PLoS ONE, 18(11 November), 1–19. https://doi.org/10.1371/journal.pone.0293552

Gazali, A. U., Salle, I. Z., Arifani, A., Hasmawati, H., & Winarti, W. (2021). Insights into Financial Strategy Management: A Qualitative Study of Performance, Investment Decisions, and Strategic Approaches with Literature Review. Atestasi : Jurnal Ilmiah Akuntansi, 4(2), 481–494. https://doi.org/10.57178/atestasi.v4i2.967

Gifari, M., Mukti, A. H., & Triwacananingrum, W. (2025). the Effect of Corporate Governance Mechanisms on Debt Policy: Evidence From Indonesia’S Consumer Noncyclical Companies. Jurnal Riset Akuntansi Dan Bisnis

Airlangga, 10(2025), 27.

Hackbarth, D., & Sun, D. (2024a). Corporate Investment and Financing Dynamics. The Review of Corporate Finance Studies, 13(3), 625–667. https://doi.org/10.1093/rcfs/cfad009

Hackbarth, D., & Sun, D. (2024b). Corporate Investment and Financing Dynamics. The Review of Corporate Finance Studies, 13(3), 625–667. https://doi.org/10.1093/rcfs/cfad009

Hastutik, S., Soetjipto, B. E., Wadoyo, C., & Winarno, A. (2022). Trade-Off and Pecking Order Theory of Capital Structure in Indonesia: Systematic Literature Review. Journal of Positive School Psychology, 2022(5), 5585–5597.

Hazmi, S., Casmi, E., Dwiarti, R., Handayani, S., & Hudin, N. B. S. (2023). Determinants of Capital Structure: Evidence in Indonesia and Malaysia. International Research Journal of Economics and Management Studies, 2(4), 539–545. https://doi.org/10.56472/25835238/irjems-v2i4p163

Hu, Y., Yang, Z., & Zhu, N. (2025). Optimal Ownership and Capital Structure with Agency Conflicts. Journal of Financial and Quantitative Analysis, 1–34. https://doi.org/10.1017/S0022109025101671

Iazzi, A., Vacca, A., Christofi, M., & Stylianou, I. (2025). Tax management as a process of value creation: the influence of corporate governance mechanisms. EuroMed Journal of Business, 1–26. https://doi.org/10.1108/EMJB-12-2024-0330

Ibrahimov, O., Vancsura, L., & Parádi-Dolgos, A. (2025). The Impact of Macroeconomic Factors on the Firm’s Performance—Empirical Analysis from Türkiye. Economies, 13(4), 1–21. https://doi.org/10.3390/economies13040111 Jahanzeb, A., Saif, U.-R., Bajuri, N. H., Karami, M., & Ahmadimousaabad, A. (2013). Trade-Off Theory, Pecking Order Theory and Market Timing Theory: A Comprehensive Review of Capital Structure Theories. International Journal of Management and Commerce Innovations (IJMCI), 1(1), 11–18.

Jeffry, Br Bukit, R., & Bulan Siregar, N. (2023a). The Effect of Profitability, Liquidity, Asset Structure, and Sales Growth on Capital Structure with Firm Size as a Moderating Variable in Automotive Sub-Sector Companies and Their Components Listed on Indonesia Stock Exchange. International Journal of Research and Review, 10(12), 487–498. https://doi.org/10.52403/ijrr.20231253

Jeffry, Br Bukit, R., & Bulan Siregar, N. (2023b). The Effect of Profitability, Liquidity, Asset Structure, and Sales Growth on Capital Structure with Firm Size as a Moderating Variable in Automotive Sub-Sector Companies and Their Components Listed on Indonesia Stock Exchange. International Journal of Research and Review, 10(12), 487–498. https://doi.org/10.52403/ijrr.20231253

Kakouris, K., & Psychoyios, D. (2025). Debt, Equity, and the Pecking Order: Evidence from Financing Decisions of Dividend-Paying Firms. International Journal of Financial Studies, 13(3). https://doi.org/10.3390/ijfs13030161

Khan, M. A., Rehan, R., Chhapra, I. U., & Sohail, A. B. (2021). Capital Structure Theories: A Comprehensive Review. Revista Gestão Inovação e Tecnologias, 11(3), 1562–1574. https://doi.org/10.47059/revistageintec.v11i3.2031

Kim, S. S., Juliana, R., & Handoko, L. (2023). The effects of economic policy uncertainty and large investment project on leverage. Asian Academy of Management Journal, 28(2 SE-Original Articles), 333–369. https://doi.org/10.21315/aamj2023.28.2.12

Kumalasari, D., & Wahyudin, A. (2020). THE EFFECT OF LEVERAGE AND CAPITAL INTENSITY ON THE EFFECTIVE TAX RATE (ETR), WITH PROFITABILITY AS A MODERATING VARIABLE. Jurnal Akuntansi dan Auditing. https://doi.org/10.56696/jaka.v1i2.4281

Leary, M. T., & Roberts, M. R. (2010). The pecking order, debt capacity, and information asymmetry. Journal of Financial Economics, 95(3), 332– 355. https://doi.org/10.1016/j.jfineco.2009.10.009

Mazumdar, M. A. A., & Mara, D. . O. (2024). Analysing the Impact of Capital Structure on Firm Value: A Study in Corporate Finance. International Journal for Research in Applied Science and Engineering Technology, 12(10), 1191–1199. https://doi.org/10.22214/ijraset.2024.64754

Melananda, W., & Ode Irma Sari, W. (2024). The Influence of Company Size, Profitability, And Sales Growth on Capital Structure. Journal of Social Research, 3(2), 539–551. https://doi.org/10.55324/josr.v3i2.1937

Mustofa, H. Z., & Faizin, Moh. (2025). Effect of Macroeconomic Factors on Economic Growth in Indonesia. Journal of Developing Economies, 10(1), 48–72. https://doi.org/10.20473/jde.v10i1.60620

Nishihara, M. (2023). Corporate sustainability, investment, and capital structure. Annals of Operations Research, 1–7. https://doi.org/10.1007/s10479-023-05699-3 Putri, Y. K. W., Setiawati, L. P. E., Putri, I. D. A. O., Yasmini, N. M., & Setiani, N. M.

S. (2025). Effective Tax Rate as a Reflection of Tax Management: An Analysis of Asset Characteristics and the Moderating Role of Institutional Ownership. Journal of International Conference Proceedings, 8(2), 54– 66. https://doi.org/10.32535/jicp.v8i2.4126

Saad, S. Ben, & Belkacem, L. (2025). The impact of economic policy uncertainty on capital structure decisions: Does institutional ownership characteristics matter? International Journal of Disclosure and Governance, 22(1), 81–93. https://doi.org/10.1057/s41310-024-00230-x

Said, S. (2025). The Impact of Capital Structure on Firm Performance: Empirical Evidence From Egypt. OALib, 12(02), 1–16. https://doi.org/10.4236/oalib.1112839

Salam, Z. A., & Shourkashti, R. (2019). Capital Structure and Firm Performance in Emerging Market: An Empirical Analysis of Malaysian Companies. International Journal of Academic Research in Accounting, Finance and Management Sciences, 9(3), 70–82. https://doi.org/10.6007/IJARAFMS/v9-i3/6334

Saputra, I. G. A. A., & Kusuma, P. S. A. J. (2025). The Effect of Profitability and Leverage on Firm Value with Firm Size as a Moderating Variable. American Journal of Economic and Management Business, 1(1), 34–46. https://doi.org/10.58777/rfb.v1i1.34

Shanthana, S., & Basana, S. R. (2020). THE EFFECT OF FIRM SIZE ON LEVERAGE IN INDONESIAN PUBLIC MANUFACTURING COMPANIES, 2013–2017. International Jpurnal of Financial and Invesment Studies, 1(1), 53–65. https://doi.org/10.9744/ijfis.1.1.53-66

Tan, M. (2024). An Empirical Review of Capital Structure Decision-Making. Advances in Economics, Management and Political Sciences, 75(1), 112– 118. https://doi.org/10.54254/2754-1169/75/20241610

Tursina, A. A. (2024). The Influence of Liquidity , Profitability , Firm Size , Tangibility and Growth on Capital Structure. Indonesian Jpurnal of Advanced Research, 3(12), 2035–2054. https://doi.org/10.55927/ijar.v3i12.12820

Vengesai, E. (2023). Unveiling the Role of Investment Tangibility on Financial Leverage: Insights from African-Listed Firms. In Risks (Vol. 11, Number 11, p. 192). https://doi.org/10.3390/risks11110192

Vinh, V. M., Ngoc, D. L. K., & Tran, H. N. B. (2025). When Sustainability Meets Finance: How Esg-Linked Instruments and Capital Structure Optimization Shape Corporate Financial Performance. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 9(4), 1493–1511. https://doi.org/10.22437/jiituj.v9i4.47987 Vintilă, G., Onofrei, M., Vintilă, A. I., & Fometescu, V. I. (2025). Exploring the Key Drivers of Financial Performance in the Context of Corporate and Public Governance: Empirical Evidence. Information (Switzerland), 16(8), 1–23. https://doi.org/10.3390/info16080691

Wahyuni, F. A., & Kristanti, F. T. (2024). The Influence of Profitability, Liquidity, Firm Size, Business Risk, Growth, and Asset Tangibility On Capital Structure. Atestasi : Jurnal Ilmiah Akuntansi, 7(2), 973–985. https://doi.org/10.57178/atestasi.v7i2.888

Wazzan, C. P. (2018). Capital Structure BT - The Palgrave Encyclopedia of Strategic Management (M. Augier & D. J. Teece, Eds.; pp. 204–207). Palgrave Macmillan UK. https://doi.org/10.1057/978-1-137-00772-8_667

Widyakto, A., Widyarti, E. T., Suhardjo, Y., Fresiliasari, O., & Karim, A. (2024). Effect of capital structure, company size, profitability and liquidity on company value: evidence from Indonesia manufacturing companies. Diponegoro International Journal of Business, 7(1), 70–81. https://doi.org/10.14710/dijb.7.1.2024.70-81

Yadav, A. K. K., & Panda, A. K. (2024). Transmission of monetary policy impulses on the capital structure of manufacturing firms: Evidence from an emerging economy. International Review of Economics & Finance. https://doi.org/10.1016/j.iref.2024.103704

Yeboah, M., Yeboah, B., Atuahene, S. O. O., & Appiah Darko, E. (2024a). Macroeconomic and Firm-Specific Determinants of Capital Structure of Listed Firms in Emerging Markets. Risk Governance and Control: Financial Markets and Institutions, 14(4), 136–148. https://doi.org/10.22495/rgcv14i4p13

Yeboah, M., Yeboah, B., Atuahene, S. O. O., & Appiah Darko, E. (2024b). Macroeconomic and Firm-Specific Determinants of Capital Structure of Listed Firms in Emerging Markets. Risk Governance and Control: Financial Markets and Institutions, 14(4), 136–148. https://doi.org/10.22495/rgcv14i4p13 Yıldırım, H., & Karabayır, M. E. (2024). Firm-Specific and Macroeconomic Factors Affecting Capital Structure. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11, 329–345. https://doi.org/10.30798/makuiibf.1383805

Yulianto, A., Suseno, D., & Widiyanto, W. (2016). Testing pecking order theory and trade off theory models in public companies in Indonesia. International Journal of Economic Perspectives, 10(4), 21–28.

Zaighum, I., Aman, A., & Abd Karim, M. Z. B. (2024). Peer effects, industry concentration and capital structure: evidence from emerging market economies. Journal of Applied Economics, 27(1). https://doi.org/10.1080/15140326.2024.2375682