Does Share Pledging Affect Investment Efficiency? Insights from Financially Constrained and Group-affiliated Firms in India

Abstract

This study examines whether share pledging by controlling shareholders improves investment efficiency in India. Using a panel of 1,167 listed firms from 2009 to 2023, we find that pledging improves investment efficiency by simultaneously reducing overinvestment (12.41%) and underinvestment (3.62%), effects confirmed through channel analyses showing that creditor monitoring drives the overinvestment reduction, while financing access drives the underinvestment reduction. These effects are stronger in financially constrained and group-affiliated firms. Findings are robust to alternative constraint proxies, including the size–age index and size tercile, an augmented investment model, propensity score matching, and Oster omitted variable bias bounds.

JEL Codes: G30, G31, G32, G38, M41, O16

Keywords

Share pledging investment efficiency financial constraints group firms creditor monitoring emerging markets

1. Introduction

Strategic investments help firms optimize resource allocation, improve profitability, and create long-term value. However, optimal investment decisions are often hampered by information asymmetry, financial constraints, and agency conflicts, resulting in overinvestment or underinvestment (Morellec & Schürhoff, 2011). In emerging markets like India, these challenges are exacerbated by concentrated ownership and the dominance of family-controlled firms, which create dual agency conflicts among managers, owners, and minority shareholders (Singla et al., 2014; Sony & Bhaduri, 2021).

One mechanism that may influence investment decisions in such contexts is share pledging, a financing strategy in which promoters use their equity holdings as collateral to secure loans while retaining ownership rights. Share pledging is widespread in emerging markets. In India, around 90% of firms pledging over half their promoter holdings are small-cap firms (Chauhan et al., 2021). Similar patterns appear globally: in the United States, insiders in 26% firms pledged an average of 33.3% equity (Anderson & Puleo, 2020), while in China, most proceeds were diverted toward personal use (Wang et al., 2020). Existing literature links share pledging to firm performance (Cheng et al., 2024), mergers and acquisitions (Duan et al., 2023), and risk-taking (Hao, 2024). However, its impact on investment efficiency, defined as the extent to which firms allocate capital to value-maximizing projects, remains underexplored.

This study examines whether share pledging by controlling shareholders improves investment efficiency. Theoretical perspectives diverge: one stream argues pledging heightens financial risks and enables resource diversion (Raju & Sapra, 2010), while another suggests it relaxes financial constraints and enhances allocation through creditor oversight (Jose & Bhaduri, 2024). The Indian setting is particularly suited to resolving this tension, given high information asymmetry, capital access constraints (Chen et al., 2011), and the prevalence of group-affiliated firms whose complex ownership structures necessitate examining heterogeneity across organizational forms.

Based on a panel of 1,167 Indian listed firms from 2009 to 2023, we find that share pledging is positively linked to investment efficiency, primarily by reducing both overinvestment and underinvestment. The effect is particularly strong among financially constrained firms. In group-affiliated firms, where internal capital markets and complex ownership structures often lead to inefficient resource allocation, share pledging significantly improves efficiency. Standalone firms show only modest gains, mainly through reduced overinvestment.

Our study makes four distinct contributions. First, we provide the first systematic evidence from India on how share pledging affects investment efficiency, disaggregated into overinvestment and underinvestment channels. Contrary to evidence from China (Hao & Lixia, 2023), India’s stricter collateral enforcement, evolving Securities and Exchange Board of India (SEBI) disclosure norms, and creditor-friendly regulatory reforms produce opposite effects, highlighting the primacy of the institutional context (Anderson & Puleo, 2020; Wang et al., 2020).

Second, we demonstrate that pledged collateral simultaneously addresses both financing frictions and agency conflicts, channels that prior literature examines separately (Fazzari et al., 1987; Hadlock & Pierce, 2010; Jensen, 1986). Channel analyses confirm the monitoring effect is the strongest in high free-cash-flow firms, consistent with Jensen’s (1986) free cash flow hypothesis, while the financing effect is the strongest in firms with low institutional ownership, identifying institutional ownership as a boundary condition.

Third, we contribute to the business group governance literature by identifying creditor oversight induced by pledging as a previously unexplored external disciplining mechanism specific to the Indian group context, extending Sony and Bhaduri (2021) and Singla et al. (2014).

Fourth, our findings carry direct policy implications for SEBI and RBI regulations. Rather than restricting share pledging outright, as recent regulatory trends suggest, our evidence favors disclosure-based frameworks that preserve creditor monitoring rights, producing better capital allocation outcomes (Chauhan et al., 2021; Jose & Bhaduri, 2024).

The rest of the article is organized as follows. Section 2 develops the hypotheses. Section 3 presents the data and empirical strategy. Section 4 reports the results. Section 5 provides robustness checks. Section 6 concludes.

2. Background and Hypothesis Development

2.1. Share Pledging in the Indian Corporate Sector

Share pledging involves shareholders, typically promoters, using their equity holdings as collateral to secure loans while retaining ownership rights but transferring cash flow rights to lenders (Hu et al., 2021; Li et al., 2020). In India, the regulatory framework surrounding share pledging has evolved significantly, particularly after the 2009 Satyam scandal. SEBI mandated detailed disclosures on promoter pledging to enhance transparency and mitigate market disruptions caused by abrupt share liquidations. Subsequently, in 2014, the RBI imposed stricter norms on Non-Banking Financial Companies (NBFCs), including a 50% loan-to-value cap, approval requirements for pledged securities, and mandatory exchange disclosures (Reserve Bank of India, 2014).

Despite these efforts, share pledging remains a risky practice. As of 2019, 90% of pledged debts failed to meet required collateral coverage, and the number of firms with pledged promoter shares increased from 607 in 2009 to 728 in 2019 before declining to 602 by 2023 following tightened regulation (see Figure 1). Share pledging is linked to adverse outcomes, including earnings manipulation (Asija et al., 2014; Bhatia et al., 2019), stock price crashes, and lower credit ratings (Chauhan et al., 2021; Singh & Singh, 2022). Nonetheless, it also serves as a liquidity tool for financially constrained firms (Jose & Bhaduri, 2024), giving it a dual nature that is central to ongoing debates on the financial regulation in India.

Figure 1.

Source: Calculated from data available on ProwessIQ.

2.2. Investment Efficiency Challenges in Indian Firms

Investment efficiency refers to the optimal allocation of capital to value-enhancing projects (Salehi et al., 2022). Agency conflicts and information asymmetry disrupt this process, producing overinvestment when managers pursue low-return projects for empire-building motives (Jensen, 1986) and underinvestment when limited external financing and managerial risk aversion cause firms to forgo positive-Net present value (NPV) projects (Morellec & Schürhoff, 2011; Myers & Majluf, 1984). In India, investment efficiency is strained by high information asymmetry (Sony & Bhaduri, 2021) and concentrated family ownership, which intensify agency conflicts between controlling and minority shareholders (Singla et al., 2014). While both inefficiencies are observed, underinvestment remains the dominant concern given pervasive financing constraints (Med Bechir & Jouirou, 2024). In this setting, share pledging may simultaneously ease capital access and impose financial discipline through creditor oversight.

2.3. Share Pledging and Investment Efficiency

Two distinct mechanisms link share pledging to investment efficiency. The creditor monitoring channel operates through financial covenants and mandatory disclosures that discipline managerial behavior. When promoters pledge shares, creditors become exposed to collateral risk and intensify oversight to safeguard their interests (Dou et al., 2019; Huang et al., 2022). This monitoring limits discretionary spending, discourages empire-building, and reduces overinvestment (Jensen, 1986; Jensen & Meckling, 1976). The monitoring effect is expected to dominate when agency problems are the most severe, specifically in high free-cash-flow firms, where empire-building incentives are the strongest, and in firms with weak internal governance, where creditor oversight substitutes for deficient board monitoring.

The financing access channel operates through the collateralization of otherwise illiquid equity. By pledging shares, promoters unlock external capital without diluting ownership, relaxing financing constraints that would otherwise prevent investment in positive-NPV projects (Myers & Majluf, 1984; Stiglitz & Weiss, 1981). Pledging signals commitment to financial obligations, reducing information asymmetry and enhancing creditor confidence (Dou et al., 2019). This channel is expected to dominate in financially constrained environments, specifically in cash-poor firms and firms with limited institutional ownership, where pledging substitutes for the capital access that institutional investors otherwise provide.

Together, these mechanisms predict that share pledging improves investment efficiency by simultaneously reducing overinvestment through monitoring and underinvestment through financing access. This is consistent with Geng et al. (2024) and contrasts with evidence from China (Hao & Lixia, 2023), where weaker creditor rights attenuate the monitoring channel.

H_1₀: Share pledging has no effect on investment efficiency.

H_1₁: Share pledging leads to better investment efficiency.

H_2₀: Share pledging has no effect on overinvestment.

H_2₁: Share pledging reduces overinvestment through creditor monitoring.

H_3₀: Share pledging has no effect on underinvestment.

H_3₁: Share pledging reduces underinvestment by improving financing access.

2.4. Financially Constrained Firms, Share Pledging, and Investment Efficiency

Financially constrained firms face high borrowing costs, limited collateral, and greater sensitivity to economic downturns, leading to increased investment inefficiency (Hadlock & Pierce, 2010; Kaplan & Zingales, 1997). Smaller and younger firms are particularly vulnerable, relying on internal cash flows to meet investment needs (Azeem et al., 2023). Share pledging is especially valuable in this context because by converting illiquid equity into external capital, it eases liquidity constraints and enables investment in value-enhancing projects (Huang et al., 2022). Simultaneously, the creditor oversight associated with pledging reinforces financial discipline, reducing agency-driven inefficiencies (Dou et al., 2019). This dual role, facilitating capital access while enhancing monitoring, makes share pledging particularly effective for constrained firms. We classify financially constrained firms using both dividend payout behavior (Azeem et al., 2023) and the size–age (SA) index (Hadlock & Pierce, 2010), which relies on firm size and age and is less subject to endogeneity concerns than cash-flow-based measures.

H_4₀: Share pledging has no differential effect on investment efficiency in financially constrained firms.

H_4₁: Share pledging improves investment efficiency more strongly in financially constrained firms by mitigating both over- and underinvestment.

2.5. Group Firms, Share Pledging, and Investment Efficiency

Group-affiliated firms are susceptible to investment inefficiencies, including overinvestment in low-return affiliates, underinvestment in high-potential units, and resource misallocation through internal capital markets (Deloof, 1998; Khanna & Yafeh, 2007). Agency conflicts and complex governance structures compound these inefficiencies (Claessens et al., 2002), and Indian group firms exhibit lower profitability despite wider market access, suggesting persistent internal inefficiencies (Bharati & Sahoo, 2022). Share pledging introduces creditors as external monitors who are incentivized to oversee internal decision-making, with oversight often extending beyond the pledging entity to the broader group structure (Stulz, 1990). This monitoring is especially impactful in group structures where governance complexities are higher and internal accountability mechanisms are weaker. Empirical findings confirm that share pledging more effectively enhances investment efficiency in group firms than in standalone counterparts (Geng et al., 2024).

H_5₀: Share pledging has no differential effect on investment efficiency between group-affiliated and standalone firms.

H_5₁: Share pledging improves investment efficiency and mitigates both over- and underinvestment more effectively in group-affiliated firms than in standalone firms.

3. Methodology, Data Sample, and Variable Construction

3.1. Sample Selection and Data Source

The sample comprises non-financial firms listed on the NSE and BSE from 2009 to 2023, with data sourced from the ProwessIQ database Centre for Monitoring Indian Economy (CMIE), which is widely used in Indian corporate finance research (Chauhan et al., 2021; Jose & Bhaduri, 2024; Sony & Bhaduri, 2021). The dataset includes share pledging disclosures, capital expenditure, leverage, firm size, profitability, operating cash flow (OCF), firm age, asset tangibility, and Tobin’s Q. From an initial pool of 5,219 listed firms, we exclude firms in highly regulated sectors (utilities, insurance, and financial services) and firms with majority government ownership. The final sample comprises 1,167 non-financial firms, yielding a 15-year unbalanced panel of 18,672 firm-year observations. All continuous variables are winsorized at the 1st and 99th percentiles to mitigate outlier effects.

3.2. Variables Construction

Investment efficiency captures how effectively firms allocate capital to value-maximizing projects. Following Biddle et al. (2009) and Chen et al. (2011), we estimate investment inefficiency using residuals from an Ordinary least squares (OLS) model regressing investment on lagged sales growth, as specified in Equation 1, where investment is capital expenditure on Property, plant, and equipment (PP&E) and R&D scaled by lagged PP&E (Rao et al., 2022) and sales growth is the annual revenue change. Equation 1 is estimated via pooled OLS with industry fixed effects based on two-digit NIC codes and year fixed effects, ensuring that residuals capture firm-level deviations from industry-year investment norms. Positive residuals indicate overinvestment and negative residuals indicate underinvestment (Rajkovic, 2020; Ren et al., 2025). Investment efficiency is defined as the negative absolute value of residuals, where higher values denote greater efficiency (Ling & Wu, 2022). As a robustness check, we augment Equation 1 with Tobin’s Q to capture investment opportunities, following Richardson (2006), and the resulting efficiency measure yields qualitatively consistent results reported in Table S2.

{Investment}_{i t} = α + β_{1} Sales_{Growth}_{i ​, t - 1} + δ_{i} + γ_{t} + ε_{i t}

(1)

where Investment is capital expenditure on PP&E and R&D scaled by lagged PP&E, Sales_Growth is the annual revenue change, δ_i denotes industry fixed effects based on two-digit NIC codes, and γ_t denotes year fixed effects.

Share pledging is measured as the percentage of promoter-held shares pledged as a loan collateral (Asija et al., 2014; Chauhan et al., 2021). As an alternative measure, we use a dummy variable equal to 1 if any promoter shares are pledged in a given year and 0 otherwise (Chan et al., 2018; Wang & Chou, 2018), with results robust to both specifications as reported in Table S6.

Following established corporate finance literature, we control for firm size measured as the natural log of total assets (Dang et al., 2018), leverage as total debt to total assets (Ahn et al., 2006), Return on assets (ROA) as net income to total assets (Singh et al., 2024), OCF scaled by total assets (Kim & Kross, 2005), firm age as years since incorporation (Coad et al., 2018), Tobin’s Q as market-to-book value of assets (Kim et al., 1993), and asset tangibility as tangible assets to total assets (Lei et al., 2018). One-year lags are used for all independent variables to attenuate reverse causality, as pledging decisions in year t − 1 affect the firm’s financing environment and creditor monitoring intensity in year t (Biddle et al., 2009; Chen et al., 2011). Detailed variable definitions are presented in Table 1.

Table 1.

Variable Definitions.

Variable	Definition	Measurement	Source
Investment efficiency (InvstEff)	Negative absolute value of residuals from Equation 1	−\|ε_it\| from Equation 1; higher = more efficient	Biddle et al. (2009); Chen et al. (2011)
Overinvestment (Over_Invst)	Positive residuals from Equation 1	ε_it > 0 from Equation 1	Richardson (2006); Rajkovic (2020)
Underinvestment (Undr_Invst)	Negative residuals from Equation 1	ε_it < 0 from Equation 1	Bilyay-Erdogan et al. (2024)
Share pledge ratio (Share_Pldg)	Percentage of promoter-held shares pledged as collateral	Pledged shares/total promoter shares	Asija et al. (2014); Chauhan et al. (2021)
Share pledge dummy	1 if any promoter shares pledged; 0 otherwise	Binary indicator	Chan et al. (2018); Wang and Chou (2018)
Firm size (size_ta)	Natural log of total assets	ln(Total assets)	Dang et al. (2018)
Leverage (lev)	Total debt scaled by total assets	Total debt/Total assets	Ahn et al. (2006)
ROA (roa)	Net income scaled by total assets	Net income/Total assets	Singh et al. (2024)
Operating Cash Flow (ocf)	Cash flow from operations scaled by total assets	Operating cash flow/Total assets	Kim and Kross (2005)
Firm age (firm_age)	Years since incorporation	Current year − incorporation year	Coad et al. (2018)
Tobin’s Q (tobinsq)	Market-to-book value of assets; proxy for growth opportunities	(Market cap + Total debt)/Total assets	Kim et al. (1993)
Asset tangibility (tangi)	Tangible fixed assets scaled by total assets	Net fixed assets/Total assets	Lei et al. (2018)
Financial constraint (non_dividend_dummy)	1 if firm does not pay dividends (constrained); 0 otherwise	Binary; non-dividend payers = constrained	Azeem et al. (2023)
SA index	Size-age financial constraint index	−0.737×ln(A) + 0.043 × ln(A)² − 0.040 × Age; top tercile = constrained	Hadlock and Pierce (2010)
Group affiliation (group)	1 if firm is part of a business group; 0 otherwise	Binary; sourced from ProwessIQ group classification	Chauhan et al. (2021); Sony and Bhaduri (2021)

Note: All continuous variables are winsorized at the 1st and 99th percentiles.

3.3. Model Specification

To examine H₁, we estimate a fixed-effects panel regression in which investment efficiency is regressed on the lagged share pledge ratio and lagged controls, with firm fixed effects absorbing time-invariant unobserved heterogeneity and standard errors clustered at the firm level. The use of 1-year lags serves two purposes. First, it mitigates reverse causality, since the current investment efficiency cannot cause prior-period pledging. Second, it reflects the economic timing, whereby pledging decisions in year t − 1 shape the firm’s financing environment and creditor monitoring in year t. This practice is standard in the investment efficiency literature (Biddle et al., 2009; Bilyay-Erdogan et al., 2024; Chen et al., 2011).

Invst_{Eff}_{i t} = α + β_{1} Share_{Pldg}_{i t - 1} + {Controls}_{i ​, t - 1} + α_{i} + γ_{t} + ε_{i t}

(2)

Over_{Invst}_{i t} = α + β_{1} Share_{Pldg}_{i t - 1} + {Controls}_{i ​, t - 1} + α_{i} + γ_{t} + ε_{i t}

(3)

Undr_{Invst}_{i t} = α + β_{1} Share_{Pldg}_{i t - 1} + {Controls}_{i ​, t - 1} + α_{i} + γ_{t} + ε_{i t}

(4)

To test H₂ and H₃, we re-estimate the model using positive residuals from Equation 1 as the overinvestment dependent variable and negative residuals as underinvestment. Equation 1 uses industry and year fixed effects to generate residuals capturing firm-level deviations from industry-year norms, while Equations 2–4 use firm and year fixed effects to absorb time-invariant firm-level heterogeneity. α_i denotes industry fixed effects. This distinction is maintained consistently across all tables.

SA = - 0.737 \times ln (A) + 0.043 \times [ln (A)] ² - 0.040 \times Age

To test H₄, we split the sample by the financial constraint status using two complementary proxies based on fundamentally different firm characteristics. The first classifies non-dividend-paying firms as constrained (Azeem et al., 2023). The second uses the SA index (Hadlock & Pierce, 2010), with firms in the top tercile classified as constrained. To test H₅, firms are categorized as group-affiliated or standalone using ProwessIQ’s group classification (Chauhan et al., 2021; Sony & Bhaduri, 2021), and all models are re-estimated for each subgroup.

3.4. Descriptive Statistics

Table 2 summarizes descriptive statistics for the full sample and, separately, for pledging and non-pledging firms. The average investment-to-lagged-PP&E ratio is 0.085 (SD = 0.356), with substantial cross-firm variations. Mean investment efficiency is −0.177, reflecting widespread deviation from the investment norm. Share pledging (mean = 0.189) is practiced by approximately 50% of sample firms. Pledging firms are significantly larger (ln TA: 8.69 vs 7.92), more leveraged (0.65 vs 0.52), and less profitable (ROA: 1.02 vs 3.50) than non-pledging firms, consistent with the view that capital-constrained promoters are more likely to pledge shares. Pledging firms exhibit greater overinvestment ex-ante (0.211 vs 0.247, p < .001), while the difference in underinvestment is statistically insignificant (p = .128), suggesting pledging is not driven by pre-existing underinvestment. Group affiliation is present in 65% of firms, and 60% are financially constrained.

Table 2.

Descriptive Statistics.

Variable	Observation	Mean	Std. Dev.	Min	Max
Investment efficiency	15,434	−0.177	0.274	−1.477	−0.001
Investment residual	15,434	0.001	0.324	−0.973	1.477
Overinvestment	5,956	0.230	0.354	0.000	1.477
Underinvestment	9,478	−0.143	0.196	−0.973	0.000
Share pledge ratio	15,434	0.189	0.293	0.000	1.000
Firm size (ln TA)	15,434	8.310	1.968	−0.693	14.696
Leverage	15,434	0.585	0.430	0.041	2.691
ROA	15,434	2.249	7.990	−17.47	17.23
Operating cash flow	15,434	0.055	0.085	−0.127	0.227
Firm age	15,434	33.74	20.05	2	160
Tobin’s Q	15,434	1.264	1.356	0.096	5.711
Asset tangibility	15,434	0.290	0.206	0.000	1.000
Panel A: Pledging Firms (Share_Pldg_dmy = 1, N = 7,765)
Investment efficiency	7,765	−0.169	0.258	−1.477	−0.001
Investment residual	7,765	−0.018	0.307	−0.973	1.477
Overinvestment	2,784	0.211	0.331	0.000	1.477
Underinvestment	4,981	−0.146	0.199	−0.973	0.000
Share pledge ratio	7,765	0.376	0.317	0.000	1.000
Firm size (ln TA)	7,765	8.692	1.837	1.686	14.696
Leverage	7,765	0.648	0.451	0.041	2.691
ROA	7,765	1.018	8.092	−17.47	17.23
Operating cash flow	7,765	0.055	0.081	−0.127	0.227
Firm age	7,765	33.05	19.95	2	157
Tobin’s Q	7,765	1.133	1.221	0.096	5.711
Asset tangibility	7,765	0.300	0.211	0.000	1.000
Panel B: Non-pledging Firms (Share_Pldg_dmy = 0, N = 7,669)
Investment efficiency	7,669	−0.185	0.288	−1.477	−0.001
Investment residual	7,669	0.020	0.340	−0.973	1.477
Overinvestment	3,172	0.247	0.372	0.000	1.477
Underinvestment	4,497	−0.139	0.193	−0.973	0.000
Share pledge ratio	7,669	0.000	0.000	0.000	0.000
Firm size (ln TA)	7,669	7.923	2.021	−0.693	14.421
Leverage	7,669	0.521	0.398	0.041	2.691
ROA	7,669	3.495	7.688	−17.47	17.23
Operating cash flow	7,669	0.055	0.089	−0.127	0.227
Firm age	7,669	34.43	20.14	2	160
Tobin’s Q	7,669	1.398	1.468	0.096	5.711
Asset tangibility	7,669	0.279	0.201	0.000	1.000
Panel C: Difference in Means (t-tests)
Variable	Mean (Non-pledge)	Mean (Pledge)	Difference	t-Statistics	p Values
Investment efficiency	−0.185	−0.169	0.015	−3.43	.001***
Investment residual	0.020	−0.018	−0.038	7.35	.000***
Overinvestment	0.247	0.211	−0.036	3.95	.000***
Underinvestment	−0.139	−0.146	−0.006	1.52	.128
Share pledge ratio	0.000	0.376	0.376	−103.76	.000***
Firm size (ln TA)	7.923	8.692	0.769	−24.75	.000***
Leverage	0.521	0.648	0.128	−18.64	.000***
ROA	3.495	1.018	−2.477	19.49	.000***
Operating cash flow	0.055	0.055	−0.000	0.17	.867
Firm age	34.43	33.05	−1.381	4.28	.000***
Tobin’s Q	1.398	1.133	−0.265	12.20	.000***
Asset tangibility	0.279	0.300	0.021	−6.58	.000***

Notes: H₀: difference in means = 0.

Two-sample t-test assuming equal variances.

Group 0 = Non-pledging firms; Group 1 = Pledging firms.

***denotes significance at 1%, respectively.

All continuous variables are winsorized at the 1st and 99th percentiles.

Investment efficiency = Negative absolute value of investment residual from Equation 1; higher values indicate greater efficiency.

Over- and underinvestment are positive and negative residuals from Equation 1, respectively, and are conditional subsamples whose observations do not sum to the full-sample N.

Share pledge ratio = Percentage of promoter-held shares pledged as collateral.

Group 0 = Non-pledging firms; Group 1 = Pledging firms in Panel C.

4. Empirical Results

4.1. Share Pledging and Investment Efficiency

Table 3 presents the results for H₁–H₃. Share pledging is positively associated with investment efficiency (β = 0.0340 without controls; β = 0.0205 with full controls, p < .10) and significantly reduces both overinvestment (β = −0.1608 and −0.1241, p < .01) and underinvestment (β = −0.0472 and −0.0362, p < .01), supporting all three hypotheses.

Table 3.

Share Pledging and Investment Efficiency.

Variable	Investment Efficiency		Overinvestment		Underinvestment
Variable	(1)	(2)	(3)	(4)	(5)	(6)
L.Share_Pldg	0.0340***	0.0205*	−0.1608***	−0.1241***	−0.0472***	−0.0362***
L.Share_Pldg	(0.0107)	(0.0109)	(0.0257)	(0.0254)	(0.0098)	(0.0100)
L.Firm size		0.0489***		−0.0904***		0.0101*
L.Firm size		(0.0051)		(0.0110)		(0.0051)
L.Leverage		−0.0189**		−0.0491**		−0.0463***
L.Leverage		(0.0092)		(0.0237)		(0.0082)
L.ROA		−0.0000		0.0013		0.0015***
L.ROA		(0.0004)		(0.0009)		(0.0004)
L.OCF		0.0173		0.0452		0.0239
L.OCF		(0.0300)		(0.0645)		(0.0283)
L.Firm age		0.0018*		−0.0058**		−0.0016*
L.Firm age		(0.0010)		(0.0024)		(0.0010)
L.Tobin’s Q		−0.0070***		0.0127**		0.0010
L.Tobin’s Q		(0.0028)		(0.0054)		(0.0029)
L.Tangibility		0.2534***		−0.7298***		−0.0265
L.Tangibility		(0.0207)		(0.0519)		(0.0187)
Constant	−0.2170***	−0.7090***	0.3911***	1.3982***	−0.1375***	−0.1545***
Constant	(0.0081)	(0.0427)	(0.0197)	(0.0862)	(0.0072)	(0.0442)
Observations	14,915	14,405	5,851	5,633	9,064	8,772
Adj. R²	0.024	0.043	0.063	0.114	0.087	0.101
Controls	No	Yes	No	Yes	No	Yes
Firm fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Notes: This table reports the effect of share pledging on investment efficiency, over-investment, and underinvestment.

Columns 1, 3, and (5) are partial models without controls; Columns 2, 4, and 6 include full controls.

All models include firm and year fixed effects.

Robust standard errors clustered by firm in parentheses.

***, **, and * denote significance at 1%, 5%, and 10%, respectively.

These findings reflect a dual dynamic. Pledging converts illiquid equity into liquid capital, relaxing the financing constraint that prevents firms from funding positive-NPV projects and thereby reducing underinvestment, an effect particularly relevant in India, where underinvestment dominates (Geng et al., 2024). Simultaneously, because pledged collateral is at risk if the firm value falls, promoters have stronger incentives to ensure firm performance, reducing self-dealing and curbing overinvestment through creditor monitoring (Jensen, 1986). High-pledging firms are thus both better financed and better monitored.

The economic magnitude is meaningful. For median firms with PP&E of ₹1,014 million, a one-unit increase in pledge ratio improves efficiency by ₹21 million annually, reduces overinvestment by ₹126 million, and reduces underinvestment by ₹37 million. Aggregated across 1,167 firms, total improvement amounts to approximately ₹24,270 million annually. The standardized beta (β* = 0.020) is roughly 69% of the leverage’s disciplining effect. The Essel Group episode of January 2019 illustrates how high pledging exposes promoters to creditor discipline, as a sharp fall in Zee Entertainment shares triggered margin calls that forced the promoters to curtail discretionary commitments and divest pledged stakes (Business Today, 2024; Newslaundry, 2021).

Firm size positively predicts efficiency (Dang et al., 2018); leverage reduces both inefficiencies (Lin et al., 2021); Tobin’s Q is negatively associated with efficiency (Cutillas Gomariz & Sánchez Ballesta, 2014); and tangibility improves efficiency (Hamza et al., 2024).

4.2. Financially Constrained Firms, Share Pledging, and Investment Efficiency

Table 4 tests H₄ by splitting the sample into financially constrained and unconstrained firms using dividend payout behavior (Azeem et al., 2023). For financially constrained firms, share pledging significantly improves investment efficiency (β = 0.0330, p < .05) and reduces both overinvestment (β = −0.1599, p < .01) and underinvestment (β = −0.0291, p < .05). The simultaneous reduction in both forms of inefficiency confirms that pledging operates through both channels for constrained firms. Collateral-enabled financing eases liquidity barriers, while creditor oversight disciplines managerial discretion. For financially unconstrained firms, share pledging has no significant effect on aggregate investment efficiency or overinvestment, reflecting their reduced dependence on collateral-based financing. However, it still significantly reduces underinvestment (β = −0.0390, p < .05), suggesting that pledging enhances financial flexibility even for well-capitalized firms.

To assess sensitivity to constraint measurement, we replicate Table 4 using the SA index of Hadlock and Pierce (2010) and a firm size tercile classification following Fazzari et al. (1987). Results are broadly consistent across all three proxies, as reported in Table S1, with share pledging reducing overinvestment in all six subsamples and reducing underinvestment in five of six. The one exception, size-constrained firms under the size-tercile proxy, likely reflects that very small firms face such intense creditor scrutiny that the monitoring channel dominates and suppresses the financing channel. These results confirm that findings are not sensitive to constraint measurement.

Table 4.

Share Pledging and Investment Efficiency: Financial Constraint Subsamples.

Variable	Financially Constrained Firms			Financially Unconstrained Firms
Variable	Investment Efficiency (1)	Over (2)	Under (3)	Investment Efficiency (4)	Over (5)	Under (6)
Panel A: Dividend-based Proxy (Azeem et al., 2023)
L.Share_Pldg	0.0330**	−0.1599***	−0.0291**	−0.0077	−0.0223	−0.0390**
	(0.0145)	(0.0390)	(0.0134)	(0.0210)	(0.0421)	(0.0176)
Constant	−0.7331***	1.5136***	−0.1499**	−0.7059***	1.4082***	0.0114
	(0.0634)	(0.1600)	(0.0637)	(0.0719)	(0.1231)	(0.0699)
Observations	8,242	2,621	5,621	6,163	3,012	3,151
Adj. R²	0.043	0.142	0.084	0.065	0.105	0.232
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Panel B: SA Index: top Tercile Constrained (Hadlock & Pierce, 2010)
L.Share_Pldg	0.0158	−0.1183*	−0.0369*	0.0192	−0.1350***	−0.0409***
	(0.0255)	(0.0660)	(0.0218)	(0.0124)	(0.0332)	(0.0128)
Constant	−0.6746***	1.3586***	−0.0673	−0.7310***	1.5224***	−0.0682
	(0.1225)	(0.2412)	(0.0942)	(0.0765)	(0.1435)	(0.0656)
Observations	4,221	1,523	2,698	10,184	4,110	6,074
R ²	0.034	0.117	0.078	0.052	0.118	0.126
Number of firms	627	491	598	958	908	927
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Panel C: Size Tercile: Bottom Tercile Constrained (Fazzari et al., 1987)
L.Share_Pldg	0.0220	−0.1433**	−0.0203	0.0198	−0.1287***	−0.0385***
	(0.0242)	(0.0680)	(0.0203)	(0.0130)	(0.0310)	(0.0127)
Constant	−0.4735***	1.4519***	−0.0591	−0.8384***	1.5448***	−0.1113
	(0.1117)	(0.2289)	(0.0910)	(0.0768)	(0.1459)	(0.0687)
Observations	4,169	1,415	2,754	10,236	4,218	6,018
R ²	0.035	0.112	0.109	0.053	0.126	0.110
Number of firms	505	425	475	911	859	884
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Notes: Panel A classifies firms as financially constrained if they do not pay dividends.

Panel B uses the SA index (SA = −0.737 ln(A) + 0.043 ln(A)² − 0.040 age; top tercile = constrained).

Panel C uses the bottom size tercile as constrained.

All models include full controls, firm, and year fixed effects.

Control variable coefficients are reported in full for Panel A and suppressed for Panels B and C for brevity; full results available from the authors.

Robust standard errors clustered by firm in parentheses.

***, **, and * denote significance at 1%, 5%, and 10%, respectively.

4.3. Group Firms, Share Pledging, and Investment Efficiency

Table 5 tests H₅. For group-affiliated firms, share pledging is positively associated with investment efficiency (β = 0.0224, p < .10) and significantly reduces both overinvestment (β = −0.1399, p < .01) and underinvestment (β = −0.0478, p < .01). The strong effect in group firms reflects that creditor oversight operates as an independent disciplining mechanism in complex group structures where internal capital markets and tunneling incentives otherwise distort resource allocation (Geng et al., 2024; Khanna & Yafeh, 2007). For standalone firms, share pledging has no significant effect on aggregate investment efficiency; a modest reduction in overinvestment is observed (β = −0.0873, p < .10), and no significant underinvestment effect is found. This limited impact reflects that standalone firms face fewer governance-related investment barriers and benefit less from external creditor oversight given their simpler organizational structures (Yang et al., 2020). Together, these results support H₅ and highlight group affiliation as a key boundary condition.

Table 5.

Share Pledging and Investment Efficiency: Group-affiliated Versus Standalone Firms.

Variable	Group-affiliated Firms			Standalone Firms
Variable	Investment Efficiency (1)	Over (2)	Under (3)	Investment Efficiency (4)	Over (5)	Under (6)
L.Share_Pldg	0.0224*	−0.1399***	−0.0478***	0.0286	−0.0873*	−0.0156
	(0.0132)	(0.0288)	(0.0117)	(0.0199)	(0.0528)	(0.0183)
Constant	−0.4306***	1.4871***	−0.2011***	−0.5388***	1.2424***	−0.0317
	(0.0416)	(0.1037)	(0.0561)	(0.0696)	(0.1595)	(0.0707)
Observations	9,437	4,082	5,691	4,632	1,551	3,081
Adj. R²	0.043	0.120	0.113	0.039	0.133	0.104
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Notes: Group affiliation is based on the ProwessIQ group classification (Chauhan et al., 2021; Sony & Bhaduri, 2021).

All models include full controls, firm, and year fixed effects.

Robust standard errors clustered by firm in parentheses.

***, and * denote significance at 1%, and 10%, respectively.

4.4. Channel Analysis

To provide direct evidence of the two proposed mechanisms, we split the sample along proxies for each mechanism’s predicted boundary conditions. Full results are reported in Table S3. Consistent with Jensen’s (1986) free cash flow theory, share pledging reduces overinvestment more strongly in high-OCF firms (β = −0.144, p < .01) than in low-OCF firms (β = −0.092, p < .10), a difference of approximately 56%. Firms with abundant internal cash face greater empire-building risk, making creditor oversight through pledging more consequential. For the board independence split, pledging significantly reduces overinvestment in both weak-board and strong-board firms with negligible difference in magnitude, reflecting the Indian institutional context where promoter control is concentrated even in formally independent boards (Singla et al., 2014) and creditor monitoring operates as an additional governance layer rather than a substitute for board oversight. Share pledging reduces underinvestment more strongly in low-OCF firms (β = −0.037, p < .05) than in high-OCF firms (β = −0.029, p < .10), consistent with the prediction that cash-constrained firms benefit more from pledging-enabled capital access. The financing channel is significant only in low-institutional-ownership firms, suggesting that when institutional investors are present, they already facilitate capital access and render pledging’s financing role redundant.

5. Robustness Test

We conduct four robustness checks to assess sensitivity to measurement, model specification, and endogeneity concerns.

5.1. Alternative Measure of Share Pledging

Table S6 re-estimates the baseline models using Share_Pldg_dmy, a binary variable equal to 1 if a firm pledges any shares in a given year. Share pledging remains positively associated with investment efficiency (β = 0.0268 and 0.0159, p < .01) and significantly reduces overinvestment (β = −0.0750 and −0.0503) and underinvestment (β = −0.0155 and −0.0113). Results are consistent with the baseline, confirming findings are not sensitive to pledging measurement.

5.2. Alternative Investment Efficiency Specifications

Table S7 extends the Chen et al. (2011) model by introducing a Neg_Growth dummy and a Neg_Growth × Sales_Growth interaction to capture asymmetric investment responses during sales contractions. Share pledging remains positively associated with investment efficiency (β = 0.0456 and 0.0257, p < .05) and significantly reduces overinvestment (β = −0.1279 and −0.0724) and underinvestment (β = −0.0265). We further augment Equation 1 with Tobin’s Q following Richardson (2006); the results in Table S2 remain consistent.

5.3. Omitted Variable Bias—Oster (2019) Test

To address the omitted variable bias, we apply the Oster (2019) method, which estimates confidence intervals [β*, β̂] for the coefficient of interest. Following Oster’s recommendation, δ is set to 1 and R_max to 1.3 × R˜. As reported in Table S8, confidence intervals for investment efficiency [0.0161, 0.0205], overinvestment [−0.0600, −0.0358], and underinvestment [−0.0888, −0.0772] all exclude zero, indicating that estimated effects are not driven by unobserved confounders.

5.4. Endogeneity—Propensity Score Matching

To address selection bias, we employ propensity score matching. Propensity scores are estimated from a logit model regressing the pledging dummy on lagged firm characteristics (size, leverage, ROA, OCF, firm age, Tobin’s Q, and tangibility) with year and industry fixed effects (pseudo-R² = 0.081; LR χ² p < .001). Each pledging firm is matched to the nearest non-pledging firm within a caliper of 0.01 (1-to-1 nearest neighbor, common support enforced). Post-match mean standardized bias falls from 19.4% to 1.9% across all covariates, and pseudo-R² declines from 0.072 to 0.001, confirming covariate balance. Asset tangibility retains a marginal residual bias of 5.5%, well below the 10% threshold of Rosenbaum and Rubin (1985), reflecting that pledging firms genuinely hold more tangible assets. Tangibility is included as a control in all post-match regressions.

Re-estimating baseline models on the matched sample (N = 10,754, Table S4), share pledging continues to significantly reduce overinvestment (β = −0.127, p < .01) and underinvestment (β = −0.039, p < .01). The aggregate investment efficiency coefficient retains its positive sign but is attenuated, consistent with matching equalizing observable firm characteristics that partly explain raw efficiency differences. Regarding reverse causality, firms with higher investment efficiency have lower financing needs and therefore less motivation to pledge shares, making the reverse direction economically implausible. Together with the Oster bounds, firm fixed effects, and 1-year lagged independent variables, these results provide a comprehensive defense against endogeneity concerns.

6. Discussion and Conclusion

This study examines whether share pledging by controlling shareholders improves investment efficiency in Indian listed firms. Analyzing a panel of 1,167 non-financial firms from 2009 to 2023, we find that share pledging enhances investment efficiency by simultaneously reducing overinvestment and underinvestment through two confirmed channels. The creditor monitoring channel constrains managerial discretion and curbs overinvestment, while the financing access channel relaxes capital constraints and alleviates underinvestment.

The monitoring channel is the strongest in high free-cash-flow firms, where empire-building incentives are the greatest, consistent with Jensen’s (1986) free cash flow theory. The financing channel is the strongest in low-OCF and low-institutional-ownership firms, where pledging substitutes for the capital access that institutional investors otherwise provide. Financially constrained firms benefit on both dimensions, while unconstrained firms benefit primarily through reduced underinvestment. Group-affiliated firms exhibit stronger efficiency gains than standalone firms, reflecting that creditor oversight is particularly valuable in complex group structures. Aggregated across the sample, the findings represent approximately ₹24,270 million in improved capital allocation annually.

Our findings make three contributions. First, we provide systematic evidence that share pledging can serve as a governance mechanism in emerging markets, contrasting with negative evidence from China (Hao & Lixia, 2023) and highlighting the primacy of institutional context. India’s creditor rights framework, SEBI disclosure requirements, and regulatory oversight of NBFCs create conditions under which pledging-induced monitoring is effective. Second, we demonstrate that pledging simultaneously addresses both financing frictions and agency conflicts, channels that prior literature examines separately, and identify institutional ownership as a boundary condition of the financing channel. Third, we identify creditor oversight induced by pledging as a previously unexplored disciplining mechanism specific to the business group context.

The findings carry policy implications. Outright restrictions on share pledging may inadvertently eliminate a legitimate source of capital access and external governance, particularly for constrained and group-affiliated firms. A disclosure-based regulatory framework preserving creditor monitoring rights while mandating timely disclosures is likely to produce better outcomes than blanket restrictions, ideally complemented by tiered disclosure requirements and early-warning mechanisms triggered by margin call thresholds.

Several limitations apply. PSM and Oster (2019) bounds address observable selection and omitted variable concerns, but quasi-experimental identification exploiting regulatory changes would provide stronger causal evidence. Channel analyses rely on indirect proxies, and direct measures of covenant enforcement would strengthen the mechanism evidence. The sample is restricted to listed firms, limiting generalizability to unlisted enterprises. Future research could extend the analysis to other emerging markets and exploit SEBI regulatory interventions as natural experiments for causal identification.

Supplemental Material

Supplemental material for this article is available online.

Footnotes

Data Availability Statement

The data that support the findings of this study are available from the ProwessIQ database, but restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data may be made available by the authors upon reasonable request and with permission from the data provider.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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