The Impact of Market Power on Capital Misallocation: A Total Factor Productivity Perspective

Abstract

The proper allocation of corporate capital is critical to sustainable business development, and misallocation of resources can impede sustainable economic growth and competitive markets. This study investigates the relationship between market power and capital misallocation in Chinese A-share listed companies, with a novel focus on the mediating role of total factor productivity (TFP). Using a comprehensive dataset of 20,818 firm-year observations from 2009 to 2021, we employ linear regression analysis to elucidate the mechanisms through which market power influences capital allocation efficiency. The results reveal a significant positive correlation between market power and capital misallocation, with TFP partially mediating this relationship. Specifically, a one-unit increase in the market power index is associated with a 1.106 unit decrease in TFP, and a 0.028 unit increase in the capital misallocation, indicating potential threats to long-term sustainability. This effect is more pronounced in non-state-owned enterprises, firms located in eastern regions, and those without shareholdings in financial institutions. These results contribute to the literature on market structure and resource allocation by providing empirical evidence of the detrimental effects of market power on capital allocation efficiency, operating through the channel of reduced productivity. Our findings have important implications for policymakers and firm managers, suggesting the need for targeted antitrust measures, promotion of market competition, and strategies to enhance TFP. This research advances our understanding of the complex interplay between market power, productivity, and capital allocation in emerging economies, offering valuable insights for addressing market failures, improving allocative efficiency and actively promoting sustainable business and sustainable socio-economic development in the Chinese context.

1. Introduction

Capital misallocation has emerged as a critical issue affecting global economic growth and firm performance. The seminal work by Hsieh and Klenow [1] revealed that resource misallocation can account for more than 50% of the differences in total factor productivity (TFP) across the manufacturing sectors of China, the United States, and India. Subsequent studies by Restuccia and Rogerson [2] and Bils et al. [3] have further confirmed the significant impact of resource misallocation on TFP, with estimates ranging from 20% to 60% in both developed and developing economies. These findings have sparked a wave of academic interest in understanding the causes and consequences of capital misallocation, with the International Monetary Fund (IMF) [4] emphasizing its crucial role in shaping global productivity differences and sustainable economic growth patterns.

The efficient allocation of capital fundamentally shapes economic performance through multiple channels. When resources flow to their most productive uses, they foster innovation, enhance competitiveness, and drive sustainable growth [5]. Ref. Conversely, misallocation manifests in various forms, such as excessive investment in low-productivity sectors and insufficient funding for high-potential ventures, leading to reduced innovation [6] and diminished aggregate productivity [7].

Within this context, market power emerges as a significant but relatively understudied factor. While the existing literature identifies several broad categories of misallocation drivers, including financial frictions and information asymmetries [8,9,10], the relationship between market power and capital allocation efficiency remains unclear. Market power, defined as a firm’s ability to influence market prices and maintain a dominant position, has far-reaching consequences on economic performance. Increased market concentration has been linked to reduced R&D activities [11], while barriers to entry created by powerful incumbent firms have major potential for productivity reductions [12]. These distortionary effects can lead to suboptimal resource allocation, hindering sustainable economic growth and competitive markets [13,14,15].

The “quiet life hypothesis” (QLH) by Hicks (1935) suggests that firms with market power may intentionally allow inefficient resource allocation due to management’s preference for comfort over profit maximization. However, empirical evidence presents mixed findings across different market contexts. In developed markets, public listed firms show higher investment sensitivity to growth opportunities [16], while in emerging markets, higher market power can lead to improved efficiency. For instance, Dai et al. [17] found that higher market power in China’s rice processing firms actually leads to improved cost efficiency, contradicting the QLH.

Two critical gaps emerge from the current literature. First, while studies like Dai et al. [17] provide industry-specific insights, there is a lack of comprehensive firm-level analysis examining how market power affects capital allocation across regions and ownership structures in China’s unique institutional context. Second, the specific mechanisms through which market power affects resource allocation remain unclear, particularly its relationship with total factor productivity (TFP). While previous studies have established that market power can influence resource allocation and that TFP is crucial for efficient capital allocation, the mediating role of TFP in the relationship between market power and capital misallocation has not been systematically examined.

This study addresses these gaps by examining how market power influences capital misallocation in China’s A-share market, analyzing both the underlying mechanisms and institutional constraints. We choose China’s A-share market as our empirical setting because it provides a unique institutional environment where market-oriented reforms coexist with state influence, offering an ideal setting to examine how market power operates under varying institutional constraints.

To measure these relationships, we employ the Lerner index as our primary indicator of market power, complemented by Richardson residuals to capture capital misallocation. This approach provides more precise measures of firm-level market power and investment efficiency than traditional metrics, especially in China’s diverse market environment.

Our research makes three main contributions. First, we develop a novel theoretical framework revealing how market power influences capital allocation through both direct and indirect mechanisms, particularly through its impact on TFP. This integrated approach provides a new perspective on the relationship between market power and resource allocation efficiency, building upon previous studies that typically examined these relationships in isolation [1,5,18,19,20,21].

Second, we uncover significant heterogeneous effects across China’s institutional landscape, showing how state ownership and financial institution shareholdings moderate market power’s impact on capital allocation. Prior research has primarily examined market power’s effects in the global context [22] or focused on China’s industry disparities [23]. Our findings reveal that state-owned enterprises and firms with financial institution shareholdings demonstrate greater resilience to market power’s distortive impacts. Moreover, we document geographical variations, with the effects being particularly pronounced in eastern China’s more developed economic regions.

Third, using a comprehensive firm-level panel dataset spanning 2009–2021, we provide new evidence on how these relationships have evolved during a crucial phase of China’s economic transformation, encompassing major reforms in state-owned enterprises, shifts in industrial policy, and changes in the competitive landscape. This expanded temporal coverage enables a more nuanced and contemporary understanding of these economic dynamics.

Our research advances our understanding of the complex interplay between market power, productivity, and capital allocation, offering valuable insights for policymakers and firms seeking to address market failures and improve allocative efficiency in emerging economies. Our results suggest the need for targeted interventions that consider both institutional context and regional development levels. These may include antitrust measures to promote market competition, reforms to enhance total factor productivity, and firm-level strategies to mitigate the negative effects of market power on resource allocation.

The remainder of this paper is organized as follows. Section 2 develops the research hypotheses. Section 3 describes the data, variables, and empirical models. Section 4 presents the empirical results, robustness tests, and the heterogeneity tests. Finally, Section 5 concludes the paper and offers policy implications.

2. Theoretical Analysis and Hypotheses Development

2.1. Market Power and Capital Misallocation

Market power refers to a firm’s ability to consistently charge prices above competitive market levels and maintain a dominant position [14]. While existing literature has extensively examined market power’s broad economic impacts, the specific mechanisms through which it affects capital allocation efficiency, particularly in transitioning economies, remain understudied [11,19,22].

This paper develops a comprehensive framework linking market power to capital misallocation through three distinct channels:

Firstly, market power affects capital distribution within industries. Firms with substantial market power often command larger production scales and market shares, enabling them to maintain higher profit margins and attract more capital inflows [24]. However, without competitive pressure, these firms lack the intrinsic motivation to improve production efficiency, and the marginal output of capital is difficult to increase [13]. Empirical evidence from emerging markets suggests that these firms frequently exhibit declining marginal returns to capital [25]. Chortareas et al. [26] used found that in South Africa, firms with high market power tended to delay investment under conditions of uncertainty. In contrast, firms with low market power in their growth phase may carry higher risks but often have higher marginal output of capital [27]. If capital excessively flows to large firms, overall, within-industry capital allocation efficiency will decrease.

Secondly, market power influences industry entry and exit barriers, affecting inter-industry capital flows [18]. Recent studies have demonstrated how established firms create entry barriers through technology lock-in and distribution channel control [28]. These implicit barriers weaken potential competition, leading to a lack of dynamism in the industry, and capital cannot flow to more efficient industries. Weterings and Marsili [29] pointed out that new firms in industries with high market concentration are more likely to exit by mergers and acquisitions, significantly impeding efficient capital reallocation.

Thirdly, market power fundamentally alters firms’ innovation incentives, thereby affecting capital allocation efficiency. While the traditional Schumpeterian view suggested that market power might promote innovation through increased resources, recent empirical evidence increasingly supports the opposite view [30]. Monopolistic firms can rely on their existing market position to obtain excessive profits and have little incentive to proactively innovate [31]. Autor et al. [32], based on U.S. patent data, found that for every 1% increase in market concentration, patent applications decrease by 5%. In China’s transitioning economy, where increasing market power across industries has coincided with firms’ strategic efforts to establish dominant positions and create entry barriers. These developments may distort capital allocation through two primary channels: rent-seeking behavior and reduced incentives for innovation and efficiency [33,34,35,36]

Based on the findings of the above-mentioned studies, this paper proposes the following hypothesis:

Hypothesis 1. 

Market power has a significant positive association with capital misallocation.

2.2. Market Power, TFP, and Capital Misallocation

2.2.1. Market Power’s Impact on TFP

Total factor productivity represents the efficiency with which inputs are transformed into outputs. As a key driver of long-term economic growth, TFP growth is closely related to technological progress, innovation, and the quality of human capital [37]. Recent studies have revealed that market power affects TFP through complex mechanisms [38].

Innovation Channel. Firms with significant market power often exhibit reduced innovation incentives due to reduced competitive pressure [11], and preference for maintaining status quo [31], thereby leading to slower TFP growth, as firms fail to improve their production processes and product quality [39].

Resource Allocation Channel. Market power significantly affects how resources are distributed within and across firms, impacting overall TFP through several ways, like distorted factor prices, and inefficient scale economies [40]. Firms with market power may experience higher costs due to organizational inefficiencies, as they allocate resources inefficiently to reach optimal profits [41]. Dai et al. [42] provided empirical evidence to support significant negative correlations between market power and scale economy in all selected sectors in China, and negative correlations between market power and productivity in most of the selected sectors.

Human Capital Channel. Market power also affects TFP through its impact on workforce productivity and human capital development. Firms with strong market power may have less incentive to invest in employee training, as t/hey face less competitive pressure to improve worker productivity [43]. Bagga [44] found that in markets with high concentration, worker mobility and wages were decreased due to reduced value of workers’ outside options. This underinvestment in human capital can lead to slower TFP growth, as the quality of the workforce stagnates or declines [45].

Based on the above arguments, this paper proposes the following hypothesis:

Hypothesis 2. 

Market power negatively affects total factor productivity.

2.2.2. TFP’s Role in Capital Allocation

Differences in TFP across firms and industries also have significant implications for capital allocation efficiency. Recent research has identified that lower TFP can lead to capital misallocation through various channels, such as inefficient investment decisions, survival of inefficient firms, and higher costs of capital leading to underinvestment.

Firstly, TFP could influence investment decision quality. Firms with lower TFP may not have the necessary expertise or information to make optimal investment decisions. They may invest in projects or assets that do not generate the highest returns, leading to capital misallocation [46]. Meanwhile, lower TFP firms may also be more susceptible to agency problems, where management makes investment decisions that benefit themselves rather than maximizing shareholder value, resulting in capital being allocated to projects that do not generate the highest returns [47].

Secondly, firms with lower TFP may survive due to their market power. These surviving firms prevent resources from flowing to the most productive firms, reallocating resources instead to those that succeed at rent-seeking [48].

Thirdly, firms with lower TFP may be perceived as riskier by investors and lenders, leading to a higher cost of capital. This higher cost of capital can discourage these firms from investing in productive assets, even when those investments would generate positive returns. Vachadze [49] found that firms with lower TFP had a higher likelihood of misallocating resources to low-return projects due to imperfection in the credit market. Wu [9] found that financial frictions cause an 8.3 percent of aggregate TFP loss, leading to 30 percent of the capital misallocation observed in China.

Based on the findings of the above-mentioned studies, this paper proposes the following hypothesis:

Hypothesis 3. 

Lower total factor productivity is associated with higher levels of capital misallocation.

Combining the H2 and H3, we propose the following hypothesis:

Hypothesis 4. 

Total factor productivity partially mediates the impact of market power on capital misallocation.

3. Methodology

3.1. Sample and Data Sources

Our study examines A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2021. The 2009–2021 period is specifically chosen as it represents a crucial phase in China’s economic development. Following the 2008 global financial crisis, China implemented significant market-oriented reforms and shifts in industrial policies, making this period particularly relevant for examining market power and capital allocation dynamics. The sample selection follows several criteria. First, we exclude financial industry firms due to their distinct regulatory environment, accounting standards, and capital structure characteristics, which could distort comparisons with non-financial firms. Second, ST and ST* companies are excluded as their financial distress may introduce abnormal patterns in capital allocation. Third, to ensure data consistency throughout our sample period, we exclude firms that went public during 2009–2021, retaining only firms that were listed prior to 2009.

Regarding data quality, we conducted a thorough examination of missing data patterns. Companies with missing data were excluded only after confirming that the missing values were randomly distributed across years and industries, suggesting no systematic bias in our sample selection. After applying these filters, our final sample consisted of 20,818 firm-year observations. All financial and corporate governance data were obtained from the CSMAR database. Industry classifications follow the 2012 Guidelines issued by the China Securities Regulatory Commission. Data processing and empirical testing were conducted using Stata 17.0.

3.2. Variable Settings

(1) Market power (Power), the explanatory variable in this study, was derived from industrial organization theory. Following the approach of most scholars [50], the Lerner Index (also known as Price–Cost Margin, PCM) is used as a proxy for the level of market competition among listed companies. The Lerner Index is the most well-known method for assessing monopoly power among various indicators of market power. The calculation formula is:

$$\mathrm{L}=\left(\mathrm{P}-\mathrm{M}\mathrm{C}\right)/\mathrm{P}$$

(1)

Here, P represents the price, and MC represents the marginal cost. This formula measures the degree of deviation of price from marginal cost, reflecting the strength of monopoly power in the market. The Lerner Index ranges from 0 to 1, with values closer to 1 indicating a market nearing monopoly, and a value of 1 representing a fully monopolized market. Conversely, a Lerner Index approaching 0 indicates a highly competitive market, with a value of 0 representing perfect competition.

Following Peress [51] and Wang [52], this paper uses Price–Cost Margin (PCM) to calculate the Lerner index for individual firms. The specific calculation steps are as follows:

$$\begin{gathered} \mathrm{P}\mathrm{C}\mathrm{M}={\displaystyle \frac{\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}-\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t} \mathrm{M}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}}{\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}}} \\ ={\displaystyle \frac{(\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}-\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s}-\mathrm{S}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{E}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{s}-\mathrm{A}\mathrm{d}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{E}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{s})}{\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}} \end{gathered}$$

(2)

(2) Capital misallocation (mismatch) is the dependent variable in this study. To measure this concept at the firm level, we use investment efficiency as a proxy for capital allocation efficiency, following recent literature [53,54]. Further, we employ the widely-recognized Richardson [55] model to measure investment efficiency, which has been extensively validated in subsequent studies [56,57]. The measurement model is specified in model (3) below:

$$\begin{gathered} {\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}}_{\mathrm{t}-1}+{\mathsf{\alpha }}_2{\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{t}-1}+{\mathsf{\alpha }}_3{\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h}}_{\mathrm{t}-1}+{\mathsf{\alpha }}_4{\mathrm{A}\mathrm{g}\mathrm{e}}_{\mathrm{t}-1}+{\mathsf{\alpha }}_5{\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}}_{\mathrm{t}-1} \\ +{\mathsf{\alpha }}_5{\mathrm{R}\mathrm{e}\mathrm{t}}_{\mathrm{t}-1}+{\mathsf{\alpha }}_6{\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{t}-1}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon } \end{gathered}$$

(3)

Among them, ${\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{t}}$ represents the actual level of new investment by the firm in year t, while ${\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}}_{\mathrm{t}-1}$, ${\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{t}-1}$, ${\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h}}_{\mathrm{t}-1}$, ${\mathrm{A}\mathrm{g}\mathrm{e}}_{\mathrm{t}-1}$, ${\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}}_{\mathrm{t}-1}$, ${\mathrm{R}\mathrm{e}\mathrm{t}}_{\mathrm{t}-1}$ respectively represent the growth rate of operating income, the debt-to-asset ratio, cash holdings, the number of years listed, total assets, and the annual stock return considering reinvested cash dividends in year t−1. ${\mathrm{I}\mathrm{n}\mathrm{v}}_{\mathrm{t}-1}$ represents the new capital investment amount in year t−1. The residual represents the deviation between the actual and expected investment levels, calculated as the difference between the firm’s actual new investment and the predicted new investment level based on various control variables. These controls include factors such as industry and year, which model (3) takes into account to ensure that external influences do not skew the analysis of investment efficiency. The residual value quantifies the degree of capital misallocation by indicating how much the actual investment deviates from what is considered optimal or efficient. A positive residual suggests over-investment, while a negative residual indicates under-investment, both of which reflect inefficiencies in capital allocation.

(3) Total factor productivity (TFP). We employed the Levinsohn–Petrin (LP) method, which has become the standard approach in recent literature. We chose the LP method over alternatives such as Olley–Pakes (OP) for two key advantages. First, it uses intermediate inputs rather than investment as proxy variables, avoiding sample truncation issues that occur with zero investment values. This is especially relevant for our dataset, where intermediate input data were more reliable and consistently available. Second, it effectively addresses endogeneity through a two-stage estimation process, operating under the assumption that intermediate input demand increases monotonically with both capital stock and productivity shock.

Following Sun et al. [58], we implement the LP method as follows:

$$\mathrm{T}\mathrm{F}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathrm{e}\mathrm{x}\mathrm{p}({\mathrm{y}}_{\mathrm{i}\mathrm{t}}-{\hat{\mathsf{\beta }}}_{\mathrm{k}}{\mathrm{k}}_{\mathrm{i}\mathrm{t}}-{\hat{\mathsf{\beta }}}_{\mathrm{l}}{\mathrm{l}}_{\mathrm{i}\mathrm{t}}-{\hat{\mathsf{\beta }}}_{\mathrm{m}}{\mathrm{m}}_{\mathrm{i}\mathrm{t}})$$

(4)

where ${\hat{\mathsf{\beta }}}_{\mathrm{k}}$, ${\hat{\mathsf{\beta }}}_{\mathrm{l}}$, ${\hat{\mathsf{\beta }}}_{\mathrm{m}}$ are the estimated parameters for capital, labor and intermediate inputs respectively, and ${\mathrm{y}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{k}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{l}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{m}}_{\mathrm{i}\mathrm{t}}$ are the logarithms of output, capital input, labor input, and intermediate input respectively. The detailed calculation procedures are provided in the Supplementary File.

This method effectively addresses the endogeneity issue in production function estimation by using intermediate inputs as proxy variables, while avoiding sample loss problems caused by zero investment observations [58].

(4) Control Variables: This study employs several control variables alongside year and industry fixed effects. These include leverage ratio (Lev), firm age (Firmage), managerial ownership (Mshare), ownership balance ratio (Balance), board size (Board), proportion of independent directors (Indep), and management expense ratio (Mfee).

The selection draws from established theoretical frameworks. Higher leverage may intensify financing constraints and capital misallocation [59], while firm maturity typically indicates more developed governance mechanisms [60]. Managerial ownership helps align management–shareholder interests [61], and ownership balance mitigates tunneling behavior [62]. Board characteristics influence corporate governance effectiveness [63] and decision-making efficiency [64], while management expense ratio captures agency costs and their impact on firm efficiency [65] (

Table 1. Variable description table.

	Symbol	Specific Definition
Independent Variable	Power	Measured by model (2)
Dependent Variable	Mismatch	Measured by model (3)
Mediating Variable	Tfplp	Measured by model (4)
Control Variables	Lev	Ratio of total liabilities to total assets
	Firmage	Natural logarithm of company age
	Mshare	Ratio of management shareholding to total shares
	Balance	Ratio of the second largest shareholder’s holdings to the largest
	Board	Natural logarithm of the number of board members
	Indep	Ratio of independent directors to total board members
	Mfee	Ratio of management expenses to operating income

).

3.3. Model Setting

To examine the impact of market power on capital misallocation, this paper constructs the following econometric model:

$${\mathrm{M}\mathrm{i}\mathrm{s}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}+\sum \mathsf{\alpha }\mathrm{C}\mathrm{V}+\mathsf{\epsilon }$$

(5)

${\mathrm{M}\mathrm{i}\mathrm{s}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}$ represents the degree of capital misallocation of the enterprise in period t, $\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}$ represents market power, and $\mathrm{C}\mathrm{V}$ represents control variables.

Next, we will test the direct relationship between market power and capital misallocation (H1) using our baseline linear regression model (Model (5)), then test market power’s effect on TFP (H2), TFP’s effect on capital misallocation (H3), and eventually, TFP’s mediation role (H4).

4. Empirical Results Analysis

4.1. Descriptive Statistics

Table 2. Descriptive statistics results.

Variable	Obs	Mean	Std. Dev.	Min	Max
Power	20,818	0.115000	0.072500	0.012100	0.374000
Mismatch	20,818	0.040800	0.048300	0.000466	0.304000
TFPlp	20,818	9.115000	1.089000	6.842000	12.03000
Lev	20,818	0.443000	0.204000	0.059200	0.888000
Firmage	20,818	2.845000	0.324000	1.792000	3.466000
Mshare	20,818	0.108000	0.180000	0.000000	0.663000
Balance	20,818	0.333000	0.283000	0.008680	0.992000
Board	20,818	2.142000	0.198000	1.609000	2.708000
Indep	20,818	0.374000	0.053300	0.333000	0.571000
Mfee	20,818	0.088800	0.067300	0.008810	0.396000

presents the descriptive statistics to grasp the overall data correlation. As indicated in Table 2, the mean value of Mismatch is 0.0408. Mismatch represents the average degree of capital misallocation among the listed companies in the sample, and its calculation is based on the residuals from a regression model.

In this study, the calculation of mismatch involves the following steps: first, the residuals are calculated by determining the difference between the actual investment levels of each company and their predicted optimal investment levels. These residuals indicate the deviation of the investment from the expected values. Next, to standardize these deviations and reflect the relative extent of misallocation, the absolute values of these residuals are divided by the total assets of the company, resulting in a value expressed as a proportion of total assets. This approach allows Mismatch to measure the extent to which investments deviate from the optimal allocation relative to a company’s total assets. Specifically, a higher Mismatch value indicates a greater degree of deviation from optimal allocation in a company’s investment decisions.

In this context, the mean Mismatch value of 0.0408 indicates that, on average, 4.08% of the total assets of the sampled companies are invested in ways that deviate from the optimal allocation, leading to inefficient use of capital. Similarly, the maximum Mismatch value is 0.304 (or 30.4%), and the minimum value is 0.000466 (or 0.0466%), indicating significant differences in investment efficiency among companies in the sample. These findings are consistent with the findings of Liu et al. [66]. These figures highlight substantial variations in investment strategies and capital allocation efficiency across different companies. Additionally, the maximum value of Power (market power) is 0.374, indicating a relatively strong market influence within the context of any specific industry. It suggests that certain companies may hold significant pricing power, which could lead to monopolistic behaviors. In the broader context of our analysis, this level of market power may have important implications for capital allocation efficiency. Companies with higher market influence may be inclined to invest in ways that reinforce their dominant position rather than optimize resource allocation, potentially leading to inefficiencies and misallocation of capital within the industry.

4.2. Correlation Analysis

A correlation analysis was conducted to examine the relationships among our variables, with results presented in

Table 3. Correlation analysis results and VIF.

	Power	Mismatch	Lev	Firmage	Mshare	Blance	Board	Indep	Mfee
Power	1
Mismatch	−0.021 ***	1
Lev	0.00900	−0.063 ***	1
Firmage	0.135 ***	−0.095 ***	0.115 ***	1
Mshare	−0.073 ***	0.096 ***	−0.309 ***	−0.197 ***	1
Balance	−0.00300	0.035 ***	−0.106 ***	0.018 ***	0.213 ***	1
Board	0.037 ***	−0.032 ***	0.158 ***	−0.00500	−0.193 ***	0.016 **	1
Indep	−0.0100	0.0100	−0.017 **	−0.0110	0.068 ***	−0.022 ***	−0.505 ***	1
Mfee	0.024 ***	0.075 ***	−0.311 ***	−0.069 ***	0.134 ***	0.080 ***	−0.111 ***	0.050 ***	1
VIF	1.02		1.22	1.07	1.22	1.06	1.43	1.35	1.12

Note: ***, **, * Indicates significance at the 1%, 5%, and 10% levels, respectively. The same applies hereafter.

. The correlation coefficients between all pairs of variables are below 0.6, suggesting moderate to weak correlations. To further assess potential multicollinearity, we calculated Variance Inflation Factors (VIF) for all independent variable and control variables. The VIF values range from 1.02 to 1.43, well below the conventional threshold of 10, indicating the absence of severe multicollinearity.

4.3. Main Regression Test

The regression results of the model are presented in

Table 4. Main regression test results.

	(1)	(2)
	Mismatch	Mismatch
Power	0.026 ***	0.028 ***
	(2.86)	(3.03)
Lev		0.000
		(0.03)
Firmage		−0.008 ***
		(−6.00)
Mshare		0.021 ***
		(9.12)
Balance		0.003 ***
		(2.81)
Board		−0.006 ***
		(−3.00)
Indep		−0.004
		(−0.57)
Mfee		0.024 ***
		(4.01)
Year	Yes	Yes
Industry	Yes	Yes
_cons	0.055 ***	0.082 ***
	(10.15)	(9.42)
N	20818	20818
r2	0.047	0.059

Note: t values are reported in parentheses.

. These results reveal a significant positive relationship between market power (“Power”) and resource misallocation (“Mismatch”), with a coefficient of 0.028 (p < 0.01). This finding strongly supports our first hypothesis (H1) that greater market power leads to increased resource misallocation.

This result aligns with the “quiet life” hypothesis by Hicks, which suggests that firms with substantial market power may become less efficient in resource allocation due to reduced competitive pressure. Our findings extend the work of Mortal and Reisel et al., [16], who found similar patterns in developed markets, to the Chinese context, where institutional characteristics might affect this relationship differently.

The economic significance of our findings suggests that a 10% increase in market power is associated with approximately a 0.28% increase in capital misallocation. To put this finding in perspective, consider the work of Hsieh and Klenow [1], which demonstrated that if China were to achieve U.S. level efficiency in capital allocation, its productivity could surge by 50%. Our results suggest that market concentration may be a significant contributor to such allocation inefficiencies.

4.4. Mediation Effect

This study constructs a mediation effect model to empirically examine the impact of market power on capital misallocation and the role of total factor productivity in this relationship. The results are shown in

Table 5. Mediating effect test results.

	(1)	(2)	(3)
	Mismatch	Tfplp	Mismatch
Tfplp			−0.004 ***
			(−9.03)
Power	0.028 ***	−1.106 ***	0.023 **
	(3.03)	(−8.74)	(2.55)
Lev	0.000	1.474 ***	0.006 ***
	(0.03)	(46.82)	(2.82)
Firmage	−0.008 ***	−0.144 ***	−0.008 ***
	(−6.00)	(−7.51)	(−6.46)
Mshare	0.021 ***	−0.556 ***	0.019 ***
	(9.12)	(−18.28)	(8.10)
Balance	0.003 ***	0.017	0.003 ***
	(2.81)	(0.86)	(2.87)
Board	−0.006 ***	0.842 ***	−0.003
	(−3.00)	(24.97)	(−1.36)
Indep	−0.004	2.002 ***	0.004
	(−0.57)	(16.51)	(0.54)
Mfee	0.024 ***	−7.518 ***	−0.005
	(4.01)	(−76.87)	(−0.74)
Year	Yes	Yes	Yes
Industry	Yes	Yes	Yes
_cons	0.082 ***	6.280 ***	0.107 ***
	(9.42)	(47.64)	(11.79)
N	20818	20818	20818
r2	0.059	0.546	0.062

. Column (1) illustrates the main effect of market power on capital misallocation, while columns (2) and (3) evaluate the mediation effect.

The regression results in column (2) reveal a statistically significant negative coefficient of Power at the 1% level, indicating that an increase in market power corresponds to a substantial decrease in total factor productivity. This finding validates Hypothesis 2 (H2). Column (3) demonstrates a statistically significant negative association between TFP and capital misallocation at the 1% level, validating Hypothesis 3 (H3). Concurrently, the coefficient of Power on Mismatch remains significantly positive at the 5% level. These results suggest that market power influences capital misallocation both directly and indirectly through its effect on total factor productivity. TFP serves as a partial mediator in the relationship between market power and capital misallocation, thereby supporting Hypothesis 4 (H4).

4.5. Robustness Tests and Endogeneity Concerns

To further validate the robustness of our findings, we conduct a comprehensive series of robustness tests to validate our findings. These include: (1) varying sample periods to ensure our results are not driven by specific time windows; (2) replacing the Lerner Index with an alternative measure of market power; and (3) using alternative measures of capital misallocation.

1.

Excluding the COVID-19 Period Effects

The COVID-19 pandemic presented unique methodological challenges for measuring the relationship between market power and capital allocation. During this period, traditional market mechanisms were significantly disrupted through multiple channels. Government interventions altered firms’ access to capital and competitive positions. Simultaneously, unprecedented sector-specific demand shocks created unusual patterns of market exit and entry that deviated from normal competitive forces.

To ensure our findings reflect the structural relationship between market power and capital misallocation rather than pandemic-induced anomalies, we conducted additional analysis excluding observations from 2019 onwards. This temporal restriction helps isolate our core relationship from these confounding effects.

Subsequently, we re-estimated our regression model. The results are presented in

Table 6. Robustness test results after excluding outliers.

	(1)	(2)
	Mismatch	Mismatch
Power	0.027 ***	0.028 ***
	(2.74)	(2.88)
Lev		−0.002
		(−0.77)
Firmage		−0.007 ***
		(−5.27)
Mshare		0.021 ***
		(7.82)
Balance		0.004 ***
		(2.72)
Board		−0.007 ***
		(−2.88)
Indep		−0.006
		(−0.74)
Mfee		0.023 ***
		(3.43)
Year	Yes	Yes
Industry	Yes	Yes
_cons	0.056 ***	0.084 ***
	(8.45)	(8.43)
N	17591	17591
r2	0.044	0.056

. Notably, the relationship between market power and capital misallocation remained statistically significant, with a regression coefficient of 0.028 (p < 0.01). This persistent significance, even after controlling for pandemic-related fluctuations, provides robust support for our initial findings (H1). The persistence of our main effect in pre-pandemic data suggests our findings reflect structural rather than crisis-driven relationships.

2.

Alternative Variable Constructions

To validate the robustness of our findings, we employed alternative specifications for both our market power and capital misallocation measures.

For market power, following the approach of Song Chang et al. [67], we replaced our primary Lerner Index with Operating Profit Margin (OPM), calculated as (Operating Income—Operating Cost)/Operating Income. This alternative measure offers a distinct theoretical advantage by capturing “monopoly rent” through a different channel [68]. Columns (1) and (2) of

Table 7. Robustness test results by replacing explanatory and explained variables.

	(1)	(2)	(3)	(4)
	Mismatch	Mismatch	Mismatch-b	Mismatch-b
OPM	0.009 *** (4.00)	0.007 *** (2.98)
Power			0.027 *** (2.84)	0.030 *** (3.19)
Lev		0.002		0.017 ***
		(0.85)		(7.79)
Firmage		−0.007 ***		−0.005 ***
		(−5.88)		(−3.42)
Mshare		0.020 ***		0.014 ***
		(8.92)		(5.57)
Balance		0.003 ***		0.000
		(2.77)		(0.35)
Board		−0.006 ***		−0.007 ***
		(−3.09)		(−3.31)
Indep		−0.004		−0.003
		(−0.59)		(−0.40)
Mfee		0.028 ***		0.018 ***
		(4.50)		(2.86)
Year	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes
_cons	0.058 ***	0.084 ***	0.064 ***	0.081 ***
	(10.84)	(9.66)	(11.99)	(8.91)
N	20818	20818	20818	20818
r2	0.048	0.059	0.046	0.051

present the results using OPM. The coefficients for capital misallocation are 0.009 (p < 0.01) and 0.007 (p < 0.01), respectively, indicating that higher operating margins significantly increase capital misallocation. This supports our main findings.

For our dependent variable, we adopt Chen et al.’s [54] extension of Richardson’s [55] approach to measuring capital misallocation (Mismatch-b). This alternative specification offers critical methodological advantages, particularly in its incorporation of income growth controls to distinguish between efficiency effects and growth-driven investment patterns. Columns (3) and (4) of Table 7 show that our results hold using Mismatch-b, with coefficients of 0.027 (p < 0.01) and 0.030 (p < 0.01).

The consistency of our findings across OPM and Mismatch-b reinforces the robustness of the documented relationship between market power and capital misallocation. The alternative measures address potential mismeasurement concerns and provide convergent evidence supporting our hypothesis (H1).

3.

Endogeneity Test

To address potential endogeneity concerns, we employed two approaches: lagging the independent variable and conducting a Generalized Method of Moments (GMM) analysis.

First, we introduced a one-year lag for the independent variable, market power, to mitigate reverse causality issues. The lagged variable, denoted as L-Power, represents the market power from the previous year. We then re-estimated our model using L-Power as the main explanatory variable for capital misallocation (Mismatch). The results, shown in

Table 8. Robustness test results using lagged market power (L-Power).

	(1)	(2)	(3)
	Mismatch	Mismatch	Mismatch
L-Power	0.038 *** (4.02)	0.039 *** (4.25)
L-Mismatch			0.225 *** (18.49)
Power			0.295 *** (3.76)
Lev		−0.002 (−0.82)	−0.183 *** (−4.40)
Firmage		−0.010 *** (−7.58)	−0.057 *** (−7.61)
Mshare		0.021 *** (8.56)	−0.178 *** (−4.73)
Balance		0.003 ** (2.37)	−0.025 (−1.08)
Board		−0.007 *** (−3.43)	−0.115 *** (−2.67)
Indep		−0.005 (−0.81)	0.004 (0.02)
Mfee		0.020 *** (3.52)	0.380 *** (4.95)
Year	Yes	Yes	Yes
Industry	Yes	Yes	Yes
_cons	0.047 *** (11.12)	0.083 *** (10.34)	0.476 *** (3.14)
AR(1)			−25.150 ***
AR(2)			−1.610
N	17,344	17,344	17,574
r2	0.059	0.074
F			12,382.05

, reveal a statistically significant relationship between L-Power and Mismatch at the 1% level, further supporting hypothesis H1. This finding indicates that the impact of market power on capital misallocation remains robust even when accounting for the lagged effect of market power, enhancing the reliability of our conclusions.

Second, we conducted a GMM analysis by constructing a dynamic panel model with Mismatch as the dependent variable. To test for autocorrelation in the disturbance term sequence, we applied the Arellano-Bond test. The results in Column (3) of Table 8 show that the p-value of AR(1) is 0.000, rejecting the null hypothesis of no first-order serial correlation in the differenced residuals at the 1% significance level. However, the p-value of the Arellano-Bond AR(2) test is 0.108, failing to reject the null hypothesis of no second-order serial correlation in the differenced residuals. These findings suggest the presence of first-order serial correlation but the absence of second-order serial correlation in the differenced residuals.

To account for potential heteroscedasticity, we employed the Hansen test, which is robust to heteroscedasticity. The results indicate that the p-value of the Hansen test for the model exceeds 0.1, supporting the validity of the instrumental variables. Based on these test results, we specified the final dynamic regression model for the impact of market power on capital misallocation by including the lagged variable (L-Mismatch) as an explanatory variable.

Table 8 presents the specific regression results. The coefficient of the lagged Mismatch is 0.225 and statistically significant at the 1% level, indicating the dynamic persistence of capital misallocation. Crucially, the coefficient of the core explanatory variable, market power, remains positive and significant at the 1% level, consistent with the baseline regression results. This finding demonstrates that the conclusion about market power exacerbating capital misallocation is robust, even after controlling for the dynamic persistence of the dependent variable.

4.6. Heterogeneity Tests

(1)

Analysis Based on Financial Institution Share Ownership

China has recently implemented policies encouraging enterprises to invest in the financial sector as a strategy to reduce financing pressures. This has led to the emergence of an “industry + finance” model of industrial–financial integration [69]. Following the approach of Du and Wang [70], this study introduces a binary variable, Stock, to classify firms into two groups: those holding shares in financial institutions (Stock = 1) and those without such holdings (Stock = 0). The regression results are shown in

Table 9. Impact of market power on capital misallocation: financial institution shareholding analysis.

	(1)	(2)
	Stock = 1	Stock = 0
	Mismatch	Mismatch
Power	0.020	0.028 ***
	(1.13)	(2.73)
Lev	0.015 **	−0.001
	(2.30)	(−0.29)
Firmage	−0.006	−0.008 ***
	(−1.62)	(−5.82)
Mshare	0.017 *	0.020 ***
	(1.77)	(8.51)
Balance	−0.003	0.004 ***
	(−0.90)	(2.89)
Board	−0.005	−0.006 ***
	(−0.72)	(−2.74)
Indep	−0.039 **	0.001
	(−2.16)	(0.15)
Mfee	0.011	0.025 ***
	(0.43)	(3.90)
Year	Yes	Yes
Industry	Yes	Yes
_cons	0.071 ***	0.081 ***
	(3.33)	(8.87)
N	2141	18677
r2	0.083	0.062

.

For firms with financial institution shareholdings, we find no significant correlation between Power and Mismatch variables. This suggests that their level of market power has no significant impact on capital misallocation. Two factors likely contribute to this finding. First, these firms typically enjoy more stable financing conditions through their financial institution connections, which may reduce market power’s influence on capital allocation decisions. Second, these firms might be subjected to additional monitoring and governance mechanisms, which could help mitigate the potential negative effects of market power on capital allocation efficiency.

Conversely, firms without financial institution shareholdings show a significant positive correlation (p < 0.01) between market power and capital misallocation. This indicates that when these firms possess greater market power, they are more likely to experience inefficient capital allocation. Without the benefits of holding financial institution shares, these firms may face more volatile financing conditions and fewer external governance mechanisms to guide their capital allocation decisions. Consequently, their market power may more readily translate into capital misallocation.

(2)

Analysis Based on Firm Ownership: Central-SOEs and Non-Central SOEs

The impact of market power on capital allocation may vary across ownership types. To investigate this effect, this study categorizes firms into centrally owned enterprises (central SOEs) and non-centrally owned enterprises (non-central SOEs). Following Niu and Zhang’s [71] methodology,

Table 10. Impact of market power on capital misallocation: ownership heterogeneity analysis.

	(1)	(2)
	Central SOEs	Non−Central SOEs
	Mismatch	Mismatch
Power	0.016	0.024 **
	(0.44)	(2.57)
Lev	−0.004	−0.000
	(−0.25)	(−0.20)
Firmage	0.007	−0.007 ***
	(0.78)	(−5.81)
Mshare	0.074	0.021 ***
	(0.64)	(8.97)
Balance	−0.009	0.004 ***
	(−1.09)	(3.15)
Board	0.000	−0.006 ***
	(0.02)	(−2.87)
Indep	−0.034	−0.003
	(−0.78)	(−0.44)
Mfee	0.053	0.024 ***
	(0.62)	(3.96)
Year	Yes	Yes
Industry	Yes	Yes
_cons	0.055	0.081 ***
	(0.99)	(9.10)
N	422	20396
r2	0.151	0.061

shows that while market power (Power) and capital misallocation (Mismatch) have no significant correlation in central SOEs, they exhibit a strong positive correlation at the 5% significance level in non-central SOEs.

Central SOEs’ resilience to market power’s negative effects stems from two advantages: robust governmental audit systems that control excessive investment, and direct alignment with national policies ensuring strategic investment decisions. These institutional features enable central SOEs to resist the temptation to misuse market power for inefficient investments.

Non-central SOEs, however, face greater vulnerability due to complex institutional and market factors. Local governments often prioritize rapid regional development over operational efficiency, encouraging investments in projects where these enterprises lack comparative advantages. This misalignment, combined with softer budget constraints and weaker corporate governance mechanisms, makes local SOEs more susceptible to market power abuse. Private enterprises face additional challenges, including limited access to financing and key resources, which amplifies the negative impact of market power on their capital allocation efficiency.

(3)

Analysis Based on Firm Location: Eastern and Non-Eastern Regions

The relationship between market power and capital misallocation varies significantly between China’s eastern and non-eastern regions. The eastern region (encompassing Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan) demonstrate a strong positive correlation between market power and capital misallocation, significant at the 1% level (see in

Table 11. Impact of market power on capital misallocation: regional analysis.

	(1)	(2)
	Eastern Region	Non-Eastern Region
	Mismatch	Mismatch
Power	0.031 ***	0.011
	(2.63)	(0.76)
Lev	0.000	−0.000
	(0.15)	(−0.09)
Firmage	−0.008 ***	−0.007 ***
	(−5.20)	(−2.67)
Mshare	0.019 ***	0.024 ***
	(7.06)	(5.39)
Balance	0.003 **	0.003
	(2.38)	(1.56)
Board	−0.005 **	−0.008 **
	(−2.09)	(−2.00)
Indep	0.003	−0.013
	(0.36)	(−1.14)
Mfee	0.032 ***	0.014
	(4.27)	(1.25)
Year	Yes	Yes
Industry	Yes	Yes
_cons	0.074 ***	0.094 ***
	(6.99)	(5.97)
N	14055	6763
r2	0.063	0.081

). This finding can be attributed to the region’s economic advantages, including superior infrastructure, larger markets, and preferential policies, which create conditions conducive to market power concentration.

In the eastern region, dominant firms can leverage their position to influence market dynamics and enjoy preferential access to financial resources and government support. The sophisticated business networks and established relationships with local governments enable these firms to maintain their market power and potentially engage in rent-seeking behaviors. While these firms enjoy better access to financial resources and government support, this advantage paradoxically leads to less efficient capital allocation as they prioritize market dominance over optimal resource utilization.

Conversely, non-eastern regions show no significant correlation between market power and capital misallocation. This can be explained by their more fragmented markets and higher levels of competition. Firms in these regions operate in an environment where market power is harder to accumulate, leading to resource allocation decisions that are more responsive to market forces. The less developed institutional environment may actually promote more efficient capital allocation by limiting individual firms’ ability to distort market mechanisms.

5. Conclusions

5.1. Research Conclusions

Our study reveals that market power significantly distorts capital allocation in firms through reduced competitive pressure. Supporting Hicks “quiet life” hypothesis, firms with substantial market dominance demonstrate less efficient resource deployment as they prioritize organizational comfort over operational efficiency. Our empirical analysis in the Chinese context quantifies this relationship’s magnitude, extending theoretical predictions about how market power impacts investment decisions.

A key contribution of our study is the identification of Total Factor Productivity as a significant mediating channel through which market power affects capital allocation efficiency. Our empirical analysis confirms that market power influences capital misallocation indirectly by reducing TFP. This finding extends beyond the traditional view that market power primarily affects pricing and output decisions, revealing its broader impact on firm productivity and resource allocation patterns.

The impact of market power varies significantly across different institutional contexts, revealing important nuances in how market structure affects firm behavior. Central SOEs demonstrate greater resilience to market power’s negative effects, likely due to their established governance structures and strategic importance. Regional variations show stronger distortionary effects in eastern regions, highlighting the role of local economic conditions in shaping market power’s impact. Additionally, firms with financial institution shareholding show better resistance to capital misallocation, suggesting that better access to financing sources may serve as an important mitigating factor.

5.2. Practical Implications

The findings of this study have significant practical implications for both policymakers and firms in addressing market power and capital misallocation issues.

For policymakers, regulatory authorities should focus on three key areas of reform. First, implement a streamlined antitrust system with clear thresholds based on firm size and industry concentration. This should be complemented by establishing mandatory market share caps in concentrated industries and creating specific incentive mechanisms for new market entrants in oligopolistic sectors. Second, establish region specific interventions. For eastern regions, policies should focus on promoting competition and strengthening market concentration monitoring. In non-eastern regions, the emphasis should be on improving institutional quality while maintaining the competitive market structure that naturally limits capital misallocation. Third, the finding that firms holding shares in financial institutions are less influenced by market power highlights the potential role of financial markets. Therefore, policymakers should focus on developing a robust and efficient financial system, reforming the financial sector through a tiered lending system that favors competitive sectors and supports small enterprises, complemented by regular stress tests and credit allocation reviews.

For firms, different ownership structures require tailored approaches to governance reform. Non-Central SOEs should enhance board independence with a suitable independent director requirement, implement regular auditor rotation, and establish clear performance metrics tied to capital allocation efficiency. All firms, regardless of ownership structure, should undertake regular assessments of their market position and competitive dynamics. This includes developing clear capital allocation strategies aligned with market conditions and implementing robust risk management systems. Regular evaluation of governance practices against industry benchmarks will ensure continuous improvement in operational efficiency and market competitiveness.

These targeted recommendations provide a practical framework for both policymakers and firms to address market power and capital misallocation challenges while promoting sustainable economic growth and market efficiency.

5.3. Limitations and Future Development

While this study provides valuable insights into the relationship between market power and capital misallocation in China, it faces several important limitations. First, China’s unique institutional features may limit the generalizability of our findings. These features include government intervention through SOE ownership, a distinctive dual-track economic system, and state-owned banks’ dominance in financial markets. Regional variations in market development and regulatory enforcement further distinguish the Chinese context from other economies.

Second, our examination of mediating and moderating effects could be expanded. While we investigate TFP and certain heterogeneous effects, the study could benefit from exploring specific corporate governance mechanisms (such as board independence and executive compensation schemes) and macroeconomic conditions (including economic cycles and monetary policy changes). These factors could provide valuable insights into how market power affects capital allocation under different institutional and economic circumstances.

Third, we face certain methodological challenges. While following Richardson’s (2006) approach for measuring capital misallocation, we acknowledge potential measurement limitations and model specification issues. The potential presence of simultaneous causality and omitted variable bias may affect causal interpretation.

For future research, we suggest cross-country analysis examining how institutional features moderate the market power- capital misallocation relationship, investigation of specific governance mechanisms’ effects, and examination of macroeconomic influences. Methodologically, future studies could develop alternative misallocation measures, address endogeneity through advanced econometric techniques, and implement quasi-experimental designs to strengthen causal inference.