Factors Influencing Capital Structure of Power Sector Companies in India
Study Focus & Core Empirical Findings
“This paper aims to conduct an analysis of the factors influencing capital structure of selected power sector companies in India, during the eleven and twelve plan periods i.e. 01.04.2007 to 31.03.2017. The study suggests that some of the insights from modern finance theory of capital structure are relevant for explaining capital structure in an emerging economy like India. The results of the study conclude that factors such as asset tangibility, profitability, growth, size, cost of debt, tax rate and debt serving capacity have significant impact on capital structure of an organisation in India.”
1. Introduction & The Capital Structure Debate
The growth of corporate sector is crucial for economic development and the pattern of corporate finance plays an important role for the financial well-being of companies in any sector. The issue of corporate capital structure is debatable; some arguments are in favour of its relevance and some are against. All organisations constantly encounter critical questions — e.g. decisions in relation to the reinvestment of retained earnings, dividend decisions, and financing decisions for new ventures by equity or debt funds.
The decisions of corporate finance directly or indirectly affect the various facets of corporate management, which ultimately determines the wealth of investors. In the Indian corporate sector, finance decisions and accomplishments not only affect the financial stability of the concerned private equity but also the financial health of the nation as a whole. In infrastructure industries such as power, these represent public investment decisions by the government and a number of government agencies involved in the planning process.
“In Indian corporate sector, finance decisions and accomplishments not only affect the financial stability of the concerned private equity but also the financial health of the nation as a whole.”
Mainly, there are three conflicting theories of capital structure that have developed following the pioneering work of Modigliani and Miller (1958): static or dynamic trade-off theory, agency cost theory, and pecking order theory. While extensive empirical studies have examined capital structure in developed economies — such as Rajan and Zingales (1995) across G-7 nations, Burgman (1996) in the US, Antonious, Guney and Paudyal (2002), Bevan and Danbolt (2002) in the UK, Akhtar (2005) in Australia, and Akhtar and Oliver (2009) in Japan — developing country evidence remains constrained (e.g. Wiwattanakantang, 1999 in Thailand; Booth et al., 2001 in India; Pandey, 2001 in Malaysia; Chen, 2004 in China; Prahalathan, 2010 in India; Sheikh and Wang, 2011 in Pakistan). As Joshua Abor (2008) emphasized, corporate financing decisions encompass a broad gamut of policy dimensions.
2. Literature Review, Evolution of Theories & Research Gap
MM (1958) demonstrated capital structure irrelevance in perfect markets without tax. MM (1963) incorporated corporate taxes, establishing that the tax shield on debt increases levered firm value. MM (1977) introduced personal taxes on shares versus debt, identifying cases where leverage gain tends to zero, suggesting optimal capital structure exists at the macro but not micro level.
Jensen & Meckling (1976) identified agency conflicts between managers and outside shareholders when managerial ownership is under 100%. Jensen (1986) and Stulz (1990) proved debt mitigates agency costs by reducing free cash flow available to managers. Dybvig & Zender (1989) showed convertible debt eliminates agency conflicts.
Kraus & Litzenberger (1973) balanced tax shields against bankruptcy costs. Haugen & Senbet (1978, 1995) argued rational markets eliminate bankruptcy penalties, while Correia, Flynn, Uliana & Wrmaid (2000) noted bankruptcy costs erode tax shield benefits.
Titman (1984) linked liquidation to customer costs; Brander & Lewis (1986) analyzed oligopoly debt choices; Maksimovic (1988) tied debt capacity to demand elasticity; Sarig (1988) showed transferable employee skills support higher debt; Diamond (1989) proved older firms enjoy lower borrowing costs.
“If market prices are determined by rational investors then bankruptcy costs would not be required. This was argued in a study conducted by Haugen and Senbet (1978) supported by another study conducted by Ronn and Senbet (1995).”
Research Gap: Empirical studies by Bradley, Jarrell and Kim (1984) and Titman and Wessels (1988) confirmed that leverage varies systematically with asset structure, size, and profitability. However, existing research has rarely evaluated the specialized regulated capital structure of the power sector in emerging economies, particularly covering the 11th and 12th Plan periods in India.
3. Research Objectives, Sample & Econometric Model Specifications
The primary objective is to investigate the determinants of capital structure of 20 selected Indian power sector companies across a ten-year horizon (01.04.2007 to 31.03.2017), bridging the 11th and 12th Five-Year Plan periods. Data was retrieved from corporate annual reports and the Accord FinTech database.
| Variable Category | Variable Name & Abbreviation | Operational Definition & Measurement Ratio |
|---|---|---|
| Dependent Variables (Debt Ratios) | Short-Term Debt Ratio (STDR) | Short-Term Debt / Total Assets; indicates capacity to satisfy immediate obligations. |
| Long-Term Debt Ratio (LTDR) | Long-Term Debt / Total Assets; measures permanent debt leverage. | |
| Total Debt Ratio (TDR) | Total Debt / Total Assets; reflects aggregate leverage of the organisation. | |
| Independent Variables (Firm-Specific Factors) | Asset Tangibility (AT) | Net Fixed Assets / Total Assets; proxy for collateralizable asset security. |
| Profitability (PROF) | EBIT / Total Assets; measures operating efficiency and internal funds generation. | |
| Growth (GROW) | Percentage change in Total Assets year-over-year. | |
| Firm Size (SIZE) | Logarithm of total assets; reflects shock-absorption capacity and scale. | |
| Cost of Debt (COD) | Interest before tax / Long-Term Debt. | |
| Tax Rate (TAXR) | Tax Provision / Profit Before Tax. | |
| Debt Serving Capacity (DSC) | EBITDA / Total Interest Expense. | |
| Liquidity (LIQ) | Total Current Assets / Total Current Liabilities. | |
| Financial Distress (FINDIST) | Cash flow volatility proxying bankruptcy vulnerability (Rao & Jijo, 2001). |
Hypotheses Formulation across Models 1, 2, and 3:
4. Empirical Regression Results across Models 1, 2, and 3
Table 1: Coefficients and ‘t’ Values for Model 1 (Dependent Variable: STDR)
| Variable | B (Unstandardized) | Std. Error | Beta | ‘t’ Value | Sig. (p-value) | Null Hypothesis Decision |
|---|---|---|---|---|---|---|
| (Constant) | -.040 | .031 | – | -1.286 | .200 | – |
| Asset Tangibility (AT) | .060 | .018 | .232 | 3.374 | .001 | Rejected (H011) |
| Profitability (PROF) | -.118 | .041 | -.201 | -2.870 | .005 | Rejected (H012) |
| Growth (GROW) | -.001 | .001 | -.084 | -1.302 | .195 | Not rejected |
| Firm Size (SIZE) | .013 | .003 | .280 | 4.317 | .000 | Rejected (H014) |
| Cost of Debt (COD) | -.006 | .004 | -.088 | -1.375 | .171 | Not rejected |
| Tax Rate (TAXR) | .000 | .000 | -.101 | -1.560 | .120 | Not rejected |
| Debt Serving Capacity (DSC) | .000 | .000 | -.098 | -1.482 | .140 | Not rejected |
| Liquidity (LIQ) | .005 | .000 | -.111 | -1.732 | .085 | Not rejected |
| Financial Distress (FINDIST) | .000 | .000 | .083 | 1.283 | .201 | Not rejected |
Table 2: Coefficients and ‘t’ Values for Model 2 (Dependent Variable: LTDR)
| Variable | B (Unstandardized) | Std. Error | Beta | ‘t’ Value | Sig. (p-value) | Null Hypothesis Decision |
|---|---|---|---|---|---|---|
| (Constant) | -.094 | .043 | – | -2.174 | .031 | – |
| Asset Tangibility (AT) | .146 | .024 | .379 | 6.004 | .000 | Rejected (H021) |
| Profitability (PROF) | -.190 | .056 | -.217 | -3.370 | .001 | Rejected (H022) |
| Growth (GROW) | -.002 | .002 | -.081 | -1.379 | .170 | Not rejected |
| Firm Size (SIZE) | .022 | .004 | .311 | 5.225 | .000 | Rejected (H024) |
| Cost of Debt (COD) | -.009 | .006 | -.093 | -1.589 | .114 | Not rejected |
| Tax Rate (TAXR) | .000 | .000 | -.111 | -1.867 | .063 | Not rejected |
| Debt Serving Capacity (DSC) | .000 | .000 | -.107 | -1.760 | .080 | Not rejected |
| Liquidity (LIQ) | -.005 | .000 | -.103 | -1.747 | .082 | Not rejected |
| Financial Distress (FINDIST) | .000 | .000 | .077 | 1.294 | .197 | Not rejected |
Table 3: Coefficients and ‘t’ Values for Model 3 (Dependent Variable: TDR)
| Variable | B (Unstandardized) | Std. Error | Beta | ‘t’ Value | Sig. (p-value) | Null Hypothesis Decision |
|---|---|---|---|---|---|---|
| (Constant) | -.146 | .070 | – | -2.084 | .039 | – |
| Asset Tangibility (AT) | .214 | .039 | .345 | 5.420 | .000 | Rejected (H031) |
| Profitability (PROF) | -.307 | .092 | -.218 | -3.350 | .001 | Rejected (H032) |
| Growth (GROW) | -.004 | .002 | -.087 | -1.460 | .146 | Not rejected |
| Firm Size (SIZE) | .037 | .007 | .321 | 5.340 | .000 | Rejected (H034) |
| Cost of Debt (COD) | -.016 | .010 | -.096 | -1.628 | .105 | Not rejected |
| Tax Rate (TAXR) | .000 | .000 | -.115 | -1.903 | .059 | Not rejected |
| Debt Serving Capacity (DSC) | .000 | .000 | -.109 | -1.774 | .078 | Not rejected |
| Liquidity (LIQ) | -.005 | .000 | -.111 | -1.865 | .064 | Not rejected |
| Financial Distress (FINDIST) | .000 | .000 | .080 | 1.340 | .182 | Not rejected |
5. Detailed Discussion of Regression Results
AT exhibits positive and statistically significant coefficients across all three models: STDR (t = 3.374, p = 0.001), LTDR (t = 6.004, p = 0.000), and TDR (t = 5.420, p = 0.000). Highly tangible fixed assets provide strong collateral security to lenders, lowering borrowing friction in heavy power projects and confirming static trade-off theory.
PROF is consistently negative and statistically significant: STDR (t = -2.870, p = 0.005), LTDR (t = -3.370, p = 0.001), and TDR (t = -3.350, p = 0.001). This strongly validates the Pecking Order Theory: profitable power firms prioritize internally generated retained earnings over debt financing.
SIZE shows a robust positive influence across STDR (t = 4.317, p = 0.000), LTDR (t = 5.225, p = 0.000), and TDR (t = 5.340, p = 0.000). Larger power utilities possess superior credit ratings, diversified asset bases, and enhanced capacity to absorb financial distress shocks, granting greater debt market access.
“Short-term debt raising is greatly influenced by asset tangibility, profitability, growth, cost of debt, tax rate and debt serving capacity whereas long term debt raising is also influenced by size in addition to short-term debt influencer while considering total debt for designing capital structure decisions of the selected Indian power sector companies.”
6. Macroeconomic Dynamics & Policy Implications
Beyond firm-level regressors, the study emphasizes the critical role of systemic macroeconomic determinants:
“There are several macro-economic factors like capital formulation, stock market development, financial instability of country, corporate tax, terrorism threat, foreign direct investment, and so on in influencing capital structure decisions.”
Contributes to the microeconomic and financial economics knowledge base of infrastructure utilities, establishing empirical benchmarks for regulated network industries.
Provides actionable empirical insights for ministries and statutory regulators to structure sustainable debt guidelines and credit guarantees in a sector still developing 75 years post-independence.
Lays an econometric framework for future researchers to examine extended time horizons and additional firm-specific variables such as product uniqueness, carry forwards, and quality spreads.
7. Conclusion & Future Research Agenda
The findings confirm that traditional capital structure determinants play a decisive role in shaping the financing architecture of Indian power sector companies during the 11th and 12th Plan periods. Asset tangibility, profitability, and size are the definitive drivers of short-term, long-term, and total debt leverage, reflecting an interplay between collateral capacity (Trade-off theory) and internal cash accumulation (Pecking order theory).
Future studies should incorporate longer timeline datasets and evaluate specialized micro-variables — including product uniqueness, collateral liquidation value, carry-forward tax losses, discount rates, and quality yield spreads — to further elucidate financial structure choices across India’s core infrastructure landscape. ■■■
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