Does Earning Management Matter for the Tax Avoidance and Investment Efficiency Nexus? Evidence from an Emerging Market

Abstract

This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ panel data regression to analyze a sample of 58 non-financial firms listed on the Egyptian Exchange (EGX) over the period 2017–2024, yielding 464 firm-year observations. Data are collected from official corporate websites, EGX, and Egypt for Information Dissemination (EGID). Grounded in agency theory, signaling theory, and pecking order theory, this study reveals how conflicts of interest and information asymmetry between managers and stakeholders lead to managerial opportunism. The findings show that tax avoidance undermines the investment efficiency in the Egyptian market. Earnings manipulation further intensified this effect due to the financial statements’ opacity. A closer examination reveals that earnings management exacerbates overinvestment by masking managerial decisions. Conversely, for financially constrained firms with a tendency to underinvest, tax avoidance and earnings management may contribute to improved efficiency by generating internal liquidity and alleviating external financing constraints. These results provide valuable insights for regulators, highlighting that policy should be directed against managerial opportunism and improving transparency, instead of focusing solely on curbing tax avoidance. From an investor perspective, they should closely monitor and understand the tax-planning strategies to ensure they enhance the firm’s value.

1. Introduction

With global economic instability, and especially after the worldwide pandemic, corporate management has increasingly focused on profit maximization and enhancing shareholders’ wealth. The significance of this strategic objective brings important financial decisions that deserve academic interest.

Among these, two practices in particular are of great relevance to corporate financial position: tax avoidance (TA) practices and investment efficiency practices (IE). Tax avoidance represents a strategic decision through which firms reduce their tax burden by exploiting gaps or ambiguities in existing tax laws (Fitriani & Taqi, 2024). While such practices may be beneficial for firms by increasing internal funds, tax avoidance is considered a double-edged sword. It raises ethical and regulatory concerns for governments, especially in developing countries, that collect taxes as a significant means of public financing.

Another dimension that is critical to academics is investment efficiency, which refers to the extent to which firms align capital expenditures with their optimal investment levels. Deviations from this, whether underinvestment or overinvestment, represent inefficient resource allocation (Biddle et al., 2009; K. Chen et al., 2025). Asiri et al. (2020) argued that firms should invest in projects as long as their marginal benefits exceed their marginal costs. However, real-world conditions are far from ideal, and capital limitations, information asymmetry, and managerial incentives often drive firms away from the optimal investment model, leading to either overinvestment in negative return projects or underinvestment by dropping profitable projects.

Complicating this dynamic, the strategy of earnings management arises when managers exercise their discretion to manipulate financial reporting to accomplish objective goals such as meeting strategic goals. Although EM could yield short-term objectives, if used excessively, it diminishes the transparency of financial statements and masks the actual economic performance of the company (Healy & Wahlen, 1999). Accordingly, we hypothesize that EM moderates the association between tax avoidance and investment efficiency, given its potential to influence both financial transparency and the optimal level of resource allocation.

In the Egyptian setting, the relationship between tax avoidance, earnings management, and investment efficiency is especially salient. Egypt, as an emerging market, is characterized by relatively concentrated ownership structures, weak investor protection rules, and capital markets where external financing is often costly or constrained. Such features heighten agency conflicts and information asymmetry between insiders and external capital providers. In this setting, managers face less supervision and are thus more able to divert the cash preserved through tax planning away from shareholders’ interests. At the same time, high information asymmetry grants managers the discretion to manage earnings, which obscures the actual drivers of performance and the risks associated with tax avoidance strategies.

Prior studies have considered the individual effects of tax avoidance and earnings management on investment behavior. Evidence from Egypt, for instance, indicates that managerial characteristics, including competence and overconfidence, influence the relationship between tax avoidance and investment efficiency (Abd El Rehim, 2025). Additional research reveals that tax avoidance can detrimentally affect investment efficiency, with this dynamic being influenced by firm-specific factors, like cash reserves (Elfeky et al., 2024). Furthermore, international studies corroborate that tax avoidance is typically linked to investment inefficiency across various countries (Benkraiem et al., 2024). In addition, Sharaf (2019) reveals that earnings management impairs investment efficiency in firms listed in Egypt. More recently, Eissa et al. (2025) found that institutional ownership mitigates the adverse impact of earnings management on investment efficiency.

However, despite these advances, the interplay between tax avoidance and earnings management and how earnings management may moderate the connection between tax avoidance and investment efficiency has not been extensively researched, especially within the Egyptian context.

To the best of the authors’ knowledge, no empirical study has analyzed how earnings management might moderate the association between tax avoidance and investment efficiency in Egypt, particularly among non-financials listed in the EGX100 index. This paper aims to bridge that gap by examining the association between TA, EM, and IE. This gap is especially due to the country’s institutional features, especially a weak governance structure and elevated levels of information asymmetry (Eissa et al., 2025; Abdel-Meguid, 2021). The findings are expected to contribute to the literature on corporate financial decisions in emerging markets and provide insights for regulators towards the improvement of transparency and governance.

The following sections of the paper will be as follows: Section 2: theoretical background, literature review, and hypothesis development; Section 3: methodology and research design; Section 4: results and discussion; and Section 5: conclusions.

2. Theoretical Background and Literature Review

2.1. Theoretical Background

Many scholars have used a combination of theories to study corporate behavior, with agency theory being first introduced by Meckling and Jensen (1976), which examines the principal–agent relation, where shareholders assign decision-making authority to corporate managers. This delegation frequently leads to conflicts of interest as managers may have more self-serving issues than those of shareholders. Information asymmetry further allow managers to exploit their informational advantage, typically reflected in earnings management whereby they manipulate reported figures to achieve specific performance targets, receive a bonus, or avoid transgressing covenants (Islam et al., 2022). These activities reduce reporting credibility whilst eroding investor confidence.

The signaling theory (Spence, 1973) provides a complementary perspective. It proposes that firms reveal financial information as a means of communicating how well they are performing in the short and long run. Since managers possess a greater understanding of the firm, their disclosures serve to attenuate information asymmetry and guide investors’ decisions. From this perspective, tax avoidance strategies can signal either effective managerial decision-making or self-interested behavior (Abbasi et al., 2024; Khurana et al., 2018; Desai & Dharmapala, 2006). However, in the case of earnings management, these signals can become ambiguous, either veiling firm’s true performance to mask the manager’s intent or, alternatively, making such announcements appear beneficial, by increasing their value relevance to stakeholders (Amidu et al., 2019).

Finally, the pecking order theory (Myers & Majluf, 1984) suggests that firms prioritize financing sources according to cost, with internal funds coming first, followed by debt, and lastly capital (equity). The reason for this hierarchy is due to the higher costs ofexternal financing attributed to information asymmetry. Tax planning is thus a strategic means to generate internal funds, which are the most preferred source in the pecking order. Reducing cash tax outflows allows firms to create internal liquidity that can be crucial for investing, particularly in a financially constrained state (Edwards et al., 2016).

In the context of an emerging market like Egypt, marked by greater information asymmetry and weak corporate governance (Eissa et al., 2025; Abd-Elmageed & Abdel Megeid, 2020), these theoretical issues become more pronounced. From an agency theory perspective, managers benefiting from the information asymmetry may engage in tax avoidance to generate low-cost internal funds. However, the effectiveness of deploying such funds is governed by managerial intentions and transparency. The funds may either be used in value-creating investments, or deployed for empire-building actions that serve managerial priorities and not shareholder interests. This dynamic is moderated by earnings management that obfuscates or enhances the clarity of financial signals to shareholders.

2.2. Previous Literature

2.2.1. Tax Avoidance and Investment Efficiency

There is a lot of literature to review that is focused on tax avoidance and its influence on investment efficiency. However, this relationship is not straightforward, with outcomes that vary considerably across institutional contexts and firm-specific conditions. Prior studies (Dhaliwal et al., 2004; Hope & Thomas, 2008; Armstrong et al., 2015; Jia & Gao, 2021), show that managers exploit high levels ofinformation asymmetry and financial reporting opacity to appropriate the benefits of tax avoidance at the expense of shareholders. Such behavior often results in inefficient resource allocation, particularly managerial empire building in weakly governed environments, ultimately causing a decline in firm value.

One body of literature (Ding, 2019; Widuri et al., 2020; Asiri et al., 2020) suggests that although tax avoidance increases cash availability, managerial opportunism and reporting opaqueness can lead to suboptimal investments, especially where governance mechanisms are ineffective. Additionally, Sukarno et al. (2022) show that effective CSR reporting can alleviate tax avoidance effects on investment efficiency. Macchioni et al. (2024) find that higher financial reporting quality mitigates the negative effects of tax avoidance by reducing information asymmetry.

Benkraiem et al. (2024), in the same vein, report that the harmful effects of tax avoidance are less pronounced in environments with strong investor protection. In the Egyptian context, Elfeky et al. (2024) document that tax avoidance reduces investment efficiency, with cash holding playing a mediating role. Ajmal et al. (2025) reveal the role of short-term debt and ownership concentration in reducing over- or underinvestment due to tax avoidance.

In contrast, other studies indicate that tax avoidance can be beneficial to shareholders by generating an internal source of funds. For example, Mayberry (2012) and Ngelo et al. (2022) show that tax avoidance can alleviate capital constraints and improve investment efficiency. Moreover, Khurana et al. (2018) indicate that strong management ability and sound governance lead to increased efficient investment through tax avoidance. More recent evidence by (Abbasi et al., 2024) underlines that corporate philanthropy combined with tax planning heightens the investment efficiency through improving firm brand image and public perception. Taken together, these findings support the fact that tax avoidance may have a contrasting impact on investment efficiency based on the context and market conditions.

Thus, this study suggests the following hypothesis:

H₁. 

Tax avoidance has a significant impact on investment efficiency among Egyptian firms listed on the EGX100.

2.2.2. Earnings Management and Investment Efficiency

By leveraging the agency role that exists in this relationship between tax avoidance and investment efficiency, an additional line of research underlines the potential relevance of earnings management as an administrative instrument to potentially affect and induce investment decisions. Earnings management is directly linked to the quality of financial statements, which are reported and, therefore, influence future investment decisions. Evidence has consistently revealed that higher earnings quality leads to a higher capital allocation efficiency. Cherkasova and Rasadi (2017) and Biddle and Hilary (2006) demonstrate that improved earnings quality lowers deviations from the optimum level of investment. Linhares et al. (2018) similarly report that earnings management amplifies the risk of investing inefficiently. Corporate governance measures could act as a moderator in the work by Bzeouich et al. (2019), which documents the positive correlation between high quality of financial reporting (indicating lower earnings management) and investment efficiency, particularly enhanced by the independence and diversity of boards. This is in contrast with Sukarno et al. (2022), who focus on situations where earnings management may facilitate access to credit and indirectly support investment efficiency. More empirical evidence is provided by Eissa et al. (2025) and Zhang and Yang (2024), demonstrating that institutional ownership and the degree of board independence can counteract the detrimental effect of earnings management on optimal investment decisions.

2.2.3. Earnings Management and Tax Avoidance

Since EM and TA arise from managerial discretion and agency-driven incentives, we summarize these factors in the context of prior work. TA, however, refers to a legal reduction in tax income (Widuri et al., 2020). EM encompasses the planning and manipulation of a firm’s financial statements (Dechow & Skinner, 2000). In several cases, EM methods intersect, since EM can be used as a means of reducing taxable income, which increases the opportunity for TA (Salah, 2024).

Also, a substantial number of studies have investigated the direct relationship between earnings manipulation and tax avoidance, with EM increasing the opacity of financial statements which in turn facilitate tax avoidance strategies (Wang & Chen, 2012; Amidu et al., 2019; Thalita et al., 2022; Gunawan & Surjari, 2022; Syahfitri & Putri, 2024; Fitriani & Taqi, 2024). In this context, firms engaging in EM often adopt TA strategies as a means of reducing taxable income and increasing funds available under management discretion.

On the other hand, some research reports conflicting findings. Delgado et al. (2023) concluded that tax obligations become heavier for firms that engage in EM. Servantlord et al. (2024) concluded that EM does not have much effect on TA in their sample. Marques et al. (2011) find that in Portuguese private firms, tax rules and external supervision shape EM practices, which typically result in managers reconciling the desire to reduce tax liabilities by deferring their duties compared with maintaining stakeholder credibility.

Combining these threads in that stream, we suggest that earnings management is not just an independent variable, but indeed a major moderating factor. From an agency theory standpoint, the cash flows preserved through tax-planning strategies can be redirected into value-creating investments or inefficient projects that benefit managers (empire building). Earnings management serves either to accentuate this agency problem by diminishing the transparency of financial statements and obscuring how efficiently the firms are utilizing their resources, or to communicate the performance to investors, facilitating the firms’ access to external capital to enhance resource allocation.

Therefore, earnings management may modify the association of tax avoidance and investment efficiency. Based on this reasoning, we propose the following moderating hypothesis:

H₂. 

Tax avoidance’s impact on investment efficiency is significantly moderated by the earnings management among Egyptian firms listed on the EGX100.

This conceptual framework is shown in Figure 1.

3. Methodology and Research Design

3.1. Sample Selection and Data Sources

This study investigates how earnings management moderates the relationship between tax avoidance and investment efficiency using a quantitative approach. The population consists of companies listed on the Egyptian Exchange (EGX) as of the revised listing of August 2024, observed over the period 2017–2024. The final sample was selected based on the following criteria:

• Companies are required to present their financial statements in Egyptian pounds.
• Financial institutions and banks are excluded due to their distinct accounting and regulatory standards.
• To ensure data availability, firms must have been actively traded one year prior to the study period (i.e., in 2016).
• Companies with missing data for the variables being investigated were excluded.

After applying these filters, the final balanced panel dataset consists of 58 non-financial firms, with a total of 464 firm-year observations as shown in

Table 1. The sample selection criteria.

Initial Population	100
Financial banks and institutions	(24)
Companies reporting their financial statements in foreign currencies	(2)
Companies listed after 2016	(14)
Companies with missing data	(2)
Total	58

. Data were retrieved from the Egyptian Stock Exchange, corporate websites, and Egypt for Information Dissemination S.A.E.

3.2. Research Design

3.2.1. Independent Variables

Tax Avoidance

Corporate tax avoidance measurement is one of the challenges faced by academics since it includes different strategies and tactics to reduce the applicable tax burden; hence, one specific metric is inadequate for measuring it (Rui, 2019). In order to address this complexity and to improve the robustness of our findings, we use three independent but complementary measures that are well-established in the literature: Book–Tax Differences (BTD), the effective tax rate (ETR), and the cash effective tax rate (CETR) (X. Chen et al., 2018; Hanlon & Heitzman, 2010; Taylor & Richardson, 2012).

BTD reflects the difference between accounting and taxable income. Arising from the use of distinct accounting standards and tax regulations, these differences create opportunities for tax planning and allow BTD to reflect more persistent and strategic forms of tax avoidance. In contrast, ETR takes an aggregated view, which includes both current and deferred taxes (Ngelo et al., 2022), while CETR focuses on actual cash outflows to tax authorities (Asiri et al., 2020). Such comparisons enable a more comprehensive evaluation of tax avoidance behavior and its influence on investment efficiency.

The first measure, Book–Tax Difference (BTD), was largely employed in previous studies (X. Chen et al., 2018; Qingyuan & Lumeng, 2018). It reflects differences between a corporate firm’s pre-tax accounting income and its taxable income and reflects strategies that reduce its current income tax expense. Large BTD is interpreted as a high indicator of tax avoidance:

$${BTD}_{it}={\displaystyle \raisebox{1ex}{$\left({EBT}_{it}-\{\frac{({income tax expense}_{it} - {DTL}_{it} + DTA)}{{statutory rate}_{it}}\}\right)$}\!\left/ \!\raisebox{-1ex}{${TA}_{it}$}\right.}$$

where

${BTD}_{it}$: Book–tax difference for firm i, in year t. ${EBT}_{it}:$ Earnings before taxes. ${Income tax expense}_{it}$: Current income tax for firm i, in year t. ${DTL}_{it}$: Deferred tax liability for firm i, in year t. DTA_it: Deferred tax assets for firm i, in year t. Statutory rate: Official tax rate applied to companies in Egypt, which is 22.5% TA_it: Total assets for firm i, in year t.

Second, the effective tax rate (ETR) is a standard measure of a firm’s tax burden, calculated as total income tax expense divided by pre-tax accounting income (Hanlon & Heitzman, 2010; Taylor & Richardson, 2012). A higher ETR represents a lower tax avoidance rate, whereas a lower ETR means more tax avoidance (Asiri et al., 2020):

$${ETR}_{it}={\displaystyle \frac{{Current tax expenses}_{it}}{{Earnings before taxes}_{it}}}$$

The third measure is the cash effective tax rate (CETR). The rate, calculated as cash taxes paid divided by earnings before taxes, is well-suited to measuring tax tactics that delay payments of cash taxes with no resulting effect on the income statement tax expense (Macchioni et al., 2024). A lower CETR represents a higher level of tax avoidance:

$${CETR}_{it}={\displaystyle \frac{{Cash tax paid}_{it}}{{Earnings before taxes}_{it}}}$$

3.2.2. Dependent Variable

Investment Efficiency

Investment efficiency is achieved when a firm’s capital expenditures align with its optimal level, while inefficiency arises from deviations from this optimum (Biddle et al., 2009). These deviations are captured by the residuals from Model (3). Negative residuals indicate underinvestment (actual investment below the optimum), and positive residuals indicate overinvestment (actual investment above the optimum).

Following the methodology of (Richardson, 2006; Biddle et al., 2009; Khurana et al., 2018; Bzeouich et al., 2019), total investment is calculated as follows:

$${Total investment}_{it}={R\&D}_{it}+{CAPEX}_{it}+{acquisitions}_{it}-{sales PPE}_{it}$$

(1)

where

R&D: Research and development expenses for firm i, in year t; ${CAPEX}_{it}$: Capital expenditures for firm i, in year t; ${acquisitions}_{it}$: Acquisition of new firms by firm i, in year t; ${sales PPE}_{it}$: Proceeds of PPE sale for firm i, in year t.

Total investment expenditure can then be broken down into two parts: (i) the necessary spending to maintain existing assets, and (ii) the spending on new investments (Richardson, 2006):

I_TOTAL,t = maintenance expenditure + I_new,t

(2)

where

I_TOTAL,t is the total investment calculated from Equation (1); maintenance expenditure is the depreciation and amortization expenses for assets for firm i, in year t; I_new: New investment expenditure. The new investment expenditure is then modeled as a function of growth opportunities and financial constraints. The residual from this regression represents the abnormal, or inefficient, investment. For our primary analysis, we use the absolute value of this residual, multiplied by −1, to create a direct measure of investment efficiency:

$$I_{new,t}={\beta }_0+{\beta }_1{Growthsales}_{i,t-1}+{\beta }_2{cash}_{i,t-1}+{\beta }_3{leverage}_{i,t-1}+{\beta }_4{size}_{i,t-1}+{\beta }_5{age}_{i,t-1}+{\beta }_6{I}_{new,t-2}+{\beta }_7{ROA}_{i,t-1}+{\epsilon }_{i,t-1}$$

(3)

where

I_new,t: Expected new investment in year t. ${Growthsales}_{i,t-1}$: the change in net sales for the period relative to net sales for the period t − 1. ${cash}_{i,t-1}$: cash holdings, calculated as cash and cash equivalents, scaled by lagged total assets. ${leverage}_{i,t-1}$: firm leverage measured by total liabilities and divided by lagged total assets (Richardson, 2006; Hu et al., 2011). ${size}_{i,t-1}$: firm size measured by the natural logarithm of total assets for period t − 1. ${age}_{i,t-1}$ is the firm age, calculated as the natural logarithm of the number of years since the firm was listed on EGX to t − 1. $I_{new,t-2}$: Expected investment in t − 1 divided by total assets for t − 2. ${ROA}_{i,t-1}$: Return on assets measured by net income for t divided by lagged total assets for t − 1 (Khurana et al., 2018). ε_i,t−1: The residual is the abnormal investment, which is the difference between the actual investment and the optimal investment. Positive residual is an indicator for overinvestment, while negative residual is an indicator for underinvestment.

3.2.3. Moderator Variable

Earnings Management

Earnings management is usually measured by discretionary accruals, a subset of total accruals that managers can manipulate for personal or strategic gain, rather than non-discretionary accruals imposed by accounting standards. Discretionary accruals are estimated using the modified Jones model (Dechow, 1995). For this study, which focuses on the moderating effect of earnings management irrespective of its direction, we use the absolute value of discretionary accruals as our proxy (Eissa et al., 2025).

The modified Jones (1991) model is described as follows:

$${TA}_{it}={NI}_{it}-{OCF}_{it}$$

$${TACC}_{it}={NDA}_{it}+{DA}_{it}$$

$${\displaystyle \frac{{NDA}_{it}}{A_{it-1}}}={\beta }_0{\displaystyle \frac{1}{A_{it-1}}}+{\beta }_1{\displaystyle \frac{∆{rev}_{it}-∆{AR}_{it}}{A_{it-1}}}+{\beta }_2{\displaystyle \frac{{PPE}_{it}}{A_{it-1}}}$$

$${\displaystyle \frac{{TACC}_{it}}{A_{it-1}}}={\beta }_0{\displaystyle \frac{1}{A_{it-1}}}+{\beta }_1{\displaystyle \frac{∆{rev}_{it}-∆{AR}_{it}}{A_{it-1}}}+{\beta }_2{\displaystyle \frac{{PPE}_{it}}{A_{it-1}}}+{\epsilon }_{it}$$

where

TACC_it: Total accruals for firm i in year t; calculated as the difference between reported net income (NI) and the cash flows from operating activities (OCF) during year t. A_it−1: Total assets for firm i at the beginning of year t. ∆REV_it: The change in revenues from year t − 1 to year t. ∆AR_it: The change in accounts receivable from year t − 1 to year t. PPE_it: The gross property, plant, and equipment scaled by lagged total asset NDA non-discretionary accruals scaled by lagged total assets. ε_it: Residuals, used as a measure of discretionary earnings management.

To ensure the robustness of our findings, we will also test Hypothesis 2 using the performance-adjusted model developed by Kothari et al. (2005), which controls for firm performance by including return on assets (ROA) measured by the ratio of net income on lagged total assets.

Once again, the residual for this model’s absolute value will be used to compute the earnings management:

$${\displaystyle \frac{{TACC}_{it}}{A_{it-1}}}={\beta }_0{\displaystyle \frac{1}{A_{it-1}}}+{\beta }_1{\displaystyle \frac{∆{rev}_{it}-∆{AR}_{it}}{A_{it-1}}}+{\beta }_2{\displaystyle \frac{{PPE}_{it}}{A_{it-1}}}+{{\beta }_{3}ROA}_{i,t}+{\epsilon }_{it}$$

3.2.4. Control Variables

Following previous studies (Biddle et al., 2009; F. Chen et al., 2011; Mayberry, 2012; Sharaf, 2019; Asiri et al., 2020; Macchioni et al., 2024), we control for explanatory variables that may correlate with investment efficiency by including variables covering firm characteristics such as agency conflicts, financing constraints, liquidity conditions, and operational efficiency, which have previously been documented to influence investment efficiency.

Firm size is calculated as the natural logarithm of total assets. It reflects access to finance and quality of governance, issues that impact investment efficiency. Firm age is measured by the natural logarithm of firm age since listing in EGX. It represents accumulated experience versus organizational rigidity, which can affect firms’ ability to detect and effectively undertake efficient investments. Leverage is measured by the ratio of total liabilities to total assets. It may capture either the disciplinary role of debt or the potential risk of debt overhang, which may cause over- or underinvestment. Profitability is measured by the net income divided by total lagged assets, linked to the ability of generating internal cash, which is pivotal to the tax avoidance–investment efficiency connection. Tangibility is measured by the ratio of property, plant, and equipment to total assets. It refers to firms’ ability to pledge collateral, financing-related constraints, and the financial decisions of firms. Slack is calculated by the ratio of cash balance to property, plant, and equipment. It measures internal liquidity. which can either support efficient investment or support managerial overinvestment. Operating cycle is measured by the natural logarithm of the collection period in days and inventory days outstanding. It shows how liquidity in the company is tied up in operations, thus influencing the agility of investment choice of the company. CFO is measured by the ratio of operating cash flow to sales. It shows firms’ reliance on internal financing and their ability to match investment to growth opportunities.

3.3. Research Models

We estimate the following regression equations to test our hypotheses. The variables used are summarized in

Table 2. Description of variables.

Variables	Name	Measurement	Papers
Independent	Tax avoidance	Tax expense/EBT	Taylor & Richardson (2012), Asiri et al. (2020), Abbasi et al. (2024), Salah (2024)
Dependent variable	Investment efficiency	Residual of the investment equation	Richardson (2006), Biddle et al. (2009), Sharaf (2019), Asiri et al. (2020)
Moderator variable	Earnings management (discretionary accruals)	Residual of the modified Jones model equation	Dechow (1995), Sharaf (2019), Salah (2024), Marques et al. (2011)
Control variables	Sizeit	Natural logarithm of total assets	Biddle et al. (2009), Macchioni et al. (2024), Sharaf (2019), Salah (2024)
Control variables	ROAit	Net income/total lagged assets	Macchioni et al. (2024), Salah (2024)
Control variables	Leverageit	Total liabilities/total assets	Richardson (2006), Sharaf (2019), Eissa et al. (2025)
Control variables	Ageit	The natural logarithm of firm age since listing	Biddle et al. (2009), Macchioni et al. (2024), Salah (2024)
Control variables	Opcycleit	The natural logarithm of operating cycle calculated by the following formula: ln{(average accounts receivables/sales) × 365 + (average inventory/cost of goods sold) × 365}.	F. Chen et al. (2011), Biddle et al. (2009), Asiri et al. (2020), Menshawy et al. (2023)
Control variables	tangit	Tangibility measured by the ratio of property, plant, and equipment to total assets	F. Chen et al. (2011), Biddle et al. (2009), Eissa et al. (2025), Menshawy et al. (2023), Asiri et al. (2020)
Control variables	Slackit	The ratio of cash to PPE.	Biddle et al. (2009), Asiri et al. (2020), Menshawy et al. (2023)
Control variables	${CFO}_{it}$	Operating cash flow divided by sales	F. Chen et al. (2011), Biddle et al. (2009), Asiri et al. (2020)
Parameters	${\beta }_1,{\beta }_2$, ${\beta }_3$, ${\beta }_4$, ${\beta }_5$, ${\beta }_6,{\beta }_7,{\beta }_8,{\beta }_9,{\beta }_{10}$	Coefficients of the equations

.

First, to test the relationship between tax avoidance and investment efficiency, we use the following equation:

$$\begin{array}{l}Investment Efficiency\\ ={\beta }_0+{\beta }_1{Tax avoidance}_{it}+{\beta }_2{ROA}_{it}+{\beta }_3{size}_{it}+{\beta }_4{leverage}_{it}+{\beta }_{5}age+{\beta }_6{opcycle}_{i,t}\\ +{\beta }_7{slack}_{it}+{{\beta }_{7}tang}_{it}+{{\beta }_{8}CFO}_{it}+\epsilon \end{array}$$

Secondly, to investigate the moderating impact of earnings management on the relationship between tax avoidance and investment efficiency:

$$\begin{array}{l}{Investment Efficiency}_{it}\\ ={\beta }_0+{\beta }_1{tax avoidance}_{it}+{\beta }_2{EM}_{it}+{{\beta }_3{tax avoidance}_{it}\ast EM}_{it}+{{\beta }_{4}size}_{it}+{{\beta }_{5}age}_{it}\\ +{{\beta }_{6}leverage}_{it}+{\beta }_7{ROA}_{it}+{\beta }_8{opcycle}_{i,t}+{\beta }_9{slack}_{it}+{{\beta }_{10}tang}_{it}+{{\beta }_{11}CFO}_{it}+{\epsilon }_{it}\end{array}$$

4. Results and Discussion

4.1. Descriptive Statistics

Descriptive statistics of all variables analyzed in this study are shown in

Table 3. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
IE	464	0	0.175	−1.725	1.593
BTD	464	0.014	0.201	−1.677	3.125
ETR	464	0.205	0.278	−2.14	2.501
CETR	464	0.337	1.174	−1.71	10.137
EM MJM	464	0.122	0.26	0.001	4.475
EM Kothari	464	0.117	0.259	0	4.503
CFOsales	464	0.169	0.808	−11.381	6.768
slack	464	11.382	133.139	0.001	2837.674
tangibility	464	0.222	0.213	0	1
Opcycle	464	3.103	0.087	2.813	3.332
Leverage	464	0.566	0.506	0.045	7.939
Size	464	21.772	1.851	16.59	26.6
Age	464	2.92	0.561	0.693	3.664
ROA	464	0.114	0.179	−0.782	1.58

IE is investment efficiency calculated as the residual of Richardson (2006) investment model. BTD is book-tax difference for measuring tax avoidance. ETR is the effective tax rate for measuring tax avoidance. CETR is cash effective tax rate for measuring tax avoidance. EM MJM is earnings management measured as the absolute value of the residual of modified jones model. EM Kothari is earnings management measured as the absolute value of the residual of Kothari model adjusted for performance. CFOsales is the ratio of operating cash flow to sales. Slack is the ratio of cash to plant property and equipment. Tangibility is the ratio of property, plant, and equipment to total assets. Opcycle is the the natural logarithm of operating cycle. Leverage is the ratio of total liabilities to total assets. Size is the natural logarithm of total assets. Age is the natural logarithm of firm age since listing.

. Investment efficiency (IE) is the dependent variable with a mean close to 0, which is in line with its definition of being a deviation from an optimal level. The standard deviation rate of 0.175 and the range between −1.725 and 1.593 support substantial variation in the investment behavior of the sample. Both positive and negative findings confirm the presence of underinvestment and overinvestment, reflecting the common deviations from capital allocation that are optimal amongst the sample companies.

In relation to the three measured tax avoidances, BTD, ETR, CETR, it means 0.014, 0.205, 0.337, respectively, while their standard deviation are 0.201, 0.278, 1.174, with a minimum of −1.677, −2.14, and −1.71, respectively; this suggests that it is not uncommon to find some firms having losses or deferring tax assets; there is a maximum of 3.125, 2.501, and 10.137, respectively, and such disparities are likely due to substantial deferred tax positions or relatively high tax payments compared to pre-tax income

Regarding the moderation variable, the distributions of earnings management (EM) measures are similar. The modified Jones model (EM_MJM)-based measure also has a mean of 0.122 (std. dev. = 0.260), and the performance-adjusted measure (EM_Kothari) indicates a mean value of 0.117 (std. dev. = 0.259). The low average values indicate that the level of discretionary accruals is generally modest. However, the greatest value above 4.4 indicates that a section of firms engages in significant manipulation of earnings. The robust consistency between these two performance measures further supports their reliability as proxies of managerial discretion around financial reporting.

Table 4. Correlation analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
(1) IE	1.000
(2) EM MJM	−0.089 **	1.000
(3) EM Kothari	−0.085 **	0.980 ***	1.000
(4) BTD	−0.041 **	−0.025	−0.028	1.000
(5) ETR	0.010 **	−0.018	−0.017	−0.044	1.000
(6) CETR	0.038 **	−0.011	−0.003	−0.007	−0.017	1.000
(7) ROA	−0.061 **	0.139 **	0.169 **	0.317 **	0.018	0.050	1.000
(8) age	−0.015	0.015	0.016	0.010	−0.103 **	0.039	−0.024	1.000
(9) size	0.005	−0.060	−0.044	0.006	0.089 **	−0.254 **	0.119 **	−0.197 **	1.000
(10) leverage	0.031 **	0.014	0.005	−0.126 **	−0.003	0.007	−0.217 **	0.037	−0.006	1.000
(11) op cycle	0.009	−0.049	−0.036	−0.033	0.085 **	−0.269 ***	0.062	−0.178 ***	0.967 ***	0.242 ***	1.000
(12) tang	−0.036	−0.032	−0.045	−0.068	0.013	−0.019	−0.153 ***	−0.208 ***	−0.136 **	−0.033	−0.143 ***	1.000
(13) slack	0.022 **	−0.010	−0.001	−0.003	0.003	−0.006	0.046	0.036	−0.047	−0.032	−0.052	−0.085 **	1.000
(14) CFO sales	−0.031	0.038	0.031	0.045	0.065	0.033	0.085 *	0.008	0.061	−0.003	0.057	−0.028	0.064

***, ** and * represent statistical significance at the 1% 5% and 10% levels, respectively.

below displays the findings of Pearson correlation analysis between the different variables under study, consisting of the key independent variables (BTD, ETR, and CETR), the dependent variable (investment efficiency, IE) measured by the absolute value of the residual multiplied by −1, the moderator (EM MJM and EM Kothari models) measured by the absolute value of the residual of each model, and control variables (ROA, firm age, firm size, leverage ratio, operating cycle, tangibility, slack, and CFO to sales ratio). Overall, the correlation coefficients show weak-to-moderate relationships between the variables, indicating the lack of serious multicollinearity issues and confirming that all variables were included in the following regression analyses.

The study adopts an empirical design based on a panel dataset of Egyptian firms listed on the EGX100 during the period 2017–2024. To examine the hypotheses, panel data regression methods were applied.

Several diagnostic tests were performed to assess the reliability of the results before the main models were run. Levin–Lin–Chu and Im–Pesaran–Shin unit root tests (Pesaran, 2007) proved that all variables were stationary, and the Variance Inflation Factor (VIF) tested whether multicollinearity was not an issue.

Model selection tests, then, were employed to identify the best estimation approach. The F-test presented in

Table 5. F-Test.

Model	F Statistic	p-Value	Decision	Preferred Model
Pooled OLS vs. Fixed Effects	5.82	0.0001	Reject H0	Fixed Effects

stated that the Pooled OLS model was rejected by the panel structure, and the Hausman test (Hausman, 1978) indicated that the Fixed Effects (FE) model outperformed the Random Effects model, as shown in

Table 6. Hausman Test Results.

Test Statistic	Chi-Square	p-Value	Decision
Hausman	16.27	0.011	Fixed effects preferred

. Moreover, the Breusch–Pagan test revealed heteroskedasticity (Breusch & Pagan, 1979), and the Wooldridge test (Wooldridge, 2002) detected serial correlation. To address these concerns and for robust inference, the analysis relied on a Fixed Effects model with cluster-robust standard errors at the firm level. This specification controls for unobserved, time-invariant firm characteristics and produces more reliable estimates.

Table 7. Impact of tax avoidance on investment efficiency.

Variable	Model 1 BTD	Model 2 ETR	Model 3 CETR
Tax avoidance	−0.082 **	0.067 ***	0.042 **
ROA	0.094 **	0.081 **	0.095 **
Age	−0.007	−0.014	−0.017
Size	0.058	0.021	0.018
Leverage ratio	−0.091 **	−0.012	−0.011
Operating cycle	−1.426	1.386 **	1.218 **
tangibility	0.019	0.211 ***	0.204 ***
slack	0.00031 ***	0.00028 **	0.00029 **
CFO sales	−0.004	−0.002	−0.001
Constant	3.216	−1.176	−1.084
F-test	22.391	8.019	7.851
Prob > F	0.021	0	0
Overall R-squared	0.268	0.214	0.208
R-square adjusted	0.254	0.207	0.205

*** and ** represent statistical significance at the 1% and 5% levels, respectively (two-tailed test).

presents the regression results of our first hypothesis, which tests the effect of tax avoidance on investment efficiency. Model (1) shows a significant negative relationship between BTD and investment efficiency (β = −0.082), which is consistent with prior results (Ding, 2019; Widuri et al., 2020; Ajmal et al., 2025). From an agency theory perspective, this finding suggests that in weak governance environments, conflicts of interest between managers and stakeholders are intensified, enabling managers to divert resources saved through tax planning away from value-creating investments and toward inefficient projects that serve their private interests. Managers benefit from information asymmetry and financial opacity and engage in tax avoidance, which further conceals how saved cash is utilized. This behavior undermines shareholders’ ability to track the investment decisions and evaluate firm performance (Dhaliwal et al., 2011; Khurana et al., 2018).

This result reinforces the existing evidence in the literature that tax avoidance, captured through book–tax difference, is negatively associated with investment efficiency, especially in weak governance settings.

Models (2) and (3) present the effective rate proxies for tax avoidance and optimal investment decisions and demonstrate a positive correlation (β = 0.067 ** and 0.042 **, respectively), suggesting that higher effective rates (the lower the tax avoidance) are associated with greater investment efficiency. In agreement with model (1), this finding reflects the other side of the impact, implying that corporate transparency and a lower level of tax planning are correlated with the firm’s ability to allocate capital efficiently. Aligning with signaling theory, firms that engage less in tax avoidance strategies communicate more reliable and transparent signals about their financial performance and governance quality to external shareholders. This strategy reduces information asymmetry and enhances the managerial focus on routine operational performance, thereby contributing to firm value (Armstrong et al., 2015; Dhaliwal et al., 2011). This finding highlights the importance of the stakeholders’ perception about the tax positions and related disclosures in shaping investment efficiency. We therefore endorse our first hypothesis based on these findings.

Unlike Mayberry (2012) and Ngelo et al. (2022), who argue that tax planning increases investment efficiency by relieving financial constraints, facilitating access to external finance, and reducing the cost of capital, one possible explanation for the difference in results could be contextual considerations, with the positive effects reported in previous studies likely dependent on good governance and sound capital markets. In contrast, the average firm in an Egyptian context appears to face agency costs and informational distortions associated with tax avoidance that outweigh any potential benefit and ultimately impair investment efficiency.

The control variables support the regression framework and provide interesting contextual information. As expected, return on assets (ROA) is positively and significantly related to investment performance. This is consistent with the belief that profitable firms have the advantage of funding and selecting value-adding projects that will allow them to maintain their performance (Bzeouich et al., 2019). The positive coefficient for financial slack also indicates that readily available liquidity enables firms to act quickly on profitable investment opportunities, an observation that also corresponds with previous research (Asiri et al., 2020).

By contrast, leverage shows a significantly negative relationship with investment efficiency. We can assess this finding through the dual lenses of agency theory. The excess of debt can also result in overinvestment as managers may deploy free cash flow on value-destroying ventures (Jensen, 1986). Conversely, it may cause underinvestment problem or the “debt overhang” problem, where managers avoid positive-NPV projects because the returns would primarily accrue to the debt holders rather than their shareholders (Myers, 1977).

An interesting result is the positive coefficient of the operating cycle, often used as a proxy for operational efficiency. This can be attributed to the nature and characteristics of some companies included in the sample, such as real estate and construction companies. Specifically, in these sectors, firms with pre-established and predictable long-term cycles will have more efficient capital allocation processes.

The positive effect of asset tangibility is, however, in contradiction with the literature (Benkraiem et al., 2024; Asiri et al., 2020; Mayberry, 2012). While indicating the contribution of fixed assets as valuable collateral to support external financing at lower cost, the aforementioned studies revealed that these external funds can be used in value-destroying projects. However, in this study, the improved funding capacity enables firms to pursue a wider range of profitable investments.

Table 8. Moderating effect of earnings management on the relationship between tax avoidance and investment efficiency.

Variable	Model 3 MJM	Model 4 Kothari
BTD	−0.083 **	−0.082 ***
ETR	0.094 **	0.087 **
CETR	0.087 **	0.083 **
EM	−0.154 **	−0.142 **
BTD × EM	−0.072 ***	−0.083 **
ETR × EM	−0.053 **	−0.059 **
CETR × EM	−0.038 **	−0.044 **
ROA	−0.087 **	−0.097 **
Age	−0.024	−0.024
Size	0.012	0.011
Leverage	−0.021	−0.021
Operating cycle	0.372	0.372
Tangibility	0.226 ***	0.229 ***
Slack	0.085 **	0.001 **
CFO/sales	−0.003	−0.003
Constant	−1.145	−1.145
F-test	5.992	5.562
Prob > F	0	0
Overall R-squared	0.361	0.401
R-square adjusted	0.352	0.387

*** and ** represent statistical significance at the 1% and 5% levels, respectively (two-tailed test).

presents the regression results of the second hypothesis. Model 3 reports earnings management measured using the modified Jones model, while model 4 applies the performance-adjusted Kothari model, and we observe the increase in the adjusted R-square in model 4 to 38.7%, indicating the higher explanatory power attributable to the Kothari model.

In both models 3 and 4, earnings management significantly moderates the impact of tax avoidance proxies on the optimal allocation of resources.

In the case of the interaction between BTD and EM, earnings management accentuates the negative influence of BTD on the efficiency of investment by exacerbating agency conflicts. This occurs as earnings management further reduces reporting transparency and weakens monitoring mechanisms, allowing managers greater discretion over the allocation of internally generated funds. The results suggest that earnings management and tax avoidance have evolved together as financial “camouflage”, which concealed the use of tax-saved cash and redirected it toward inefficient investments by insulating management from scrutiny (Desai & Dharmapala, 2006). This is in line with (Benkraiem et al., 2024), who show that poor reporting quality amplifies the adverse effects of tax avoidance.

By contrast, in cases when effective rates interact with earnings management (ETR × EM, CETR × EM), earnings manipulation undermines the positive relationship of effective rates with investment efficiency. More importantly, these findings indicate that earnings management acts as a destructive force. Even when companies comply with tax laws and exhibit higher effective tax rates and more transparent tax positions, the earnings management clouds this transparency, distorts stakeholders’ attention, and ultimately negates the investment efficiency gains associated with greater transparency.

To the best of the authors’ knowledge, previous studies have largely examined tax avoidance and earnings management separately, but evidence on their joint effect on investment efficiency remains limited.

The reversal of ROA’s coefficient to a significantly negative value is especially revealing. It indicates that, under a situation of tax avoidance and earnings management, high profits do not indicate high efficiency but instead provide room for managerial opportunism. In such settings, managers in profitable firms may exploit their position for empire-building or value-destroying investments, facilitated by the general opacity created by the combination of tax avoidance and earnings management (Hope & Thomas, 2008). Other control variables remain consistent with the first results.

4.2. Additional Analysis

In order to dig a little deeper, we split the sample into over-investing and underinvesting firms. According to this analysis, the tax avoidance and earnings management impacts are significantly different for the two groups. This asymmetry between over- and underinvesting companies helps to explain why prior studies report mixed results concerning the tax avoidance effects on investment inefficiency.

Table 9. Splitting the sample into two groups: overinvestment and underinvestment.

Moderator	Overinvestment		Underinvestment
	Model 1 MJM	Model 2 Kothari	Model 3 MJM	Model 4 Kothari
Variable	Coefficient	Coefficient	Coefficient	Coefficient
BTD	0.069 ***	0.075 ***	−0.058 ***	−0.059 ***
ETR	0.038 **	0.047 **	−0.037 ***	−0.041 ***
CETR	0.034 **	0.045 **	−0.029 **	−0.030 **
EM	0.050 ***	0.064 ***	−0.087 ***	−0.081 ***
BTD × EM	0.031 **	0.028 **	−0.044 **	−0.047 **
ETR × EM	0.024 **	0.022 **	−0.031 **	−0.034 **
CETR × EM	0.018 **	0.020 **	−0.026 **	−0.029 **
ROA	0.086	0.089	−0.081	−0.087
Age	0.005	0.008	−0.019	−0.021
Size	−0.041	−0.034	0.053	0.033
Leverage	−0.089	−0.082	−0.114 **	−0.122 **
Operating cycle	0.852	0.872	−0.944	−0.964
Tangibility	0.028	0.032	−0.057	−0.052
slack	0.00023 **	0.00015 **	−0.00019 **	−0.00020 **
CFO/sales	0.002	0.001	−0.007	−0.008
Constant	−1.306	−1.302	2.721	2.721
F-test	3.56	3.66	4.15	4.19
Prob > F	0	0	0	0
Overall R-squared	0.179	0.188	0.214	0.224
R-square adjusted	0.178	0.182	0.209	0.219

*** and ** represent statistical significance at the 1% and 5% levels, respectively (two-tailed test).

presents the results across models 1 and 2 for the overinvestment subsample. The positive coefficient of BTD (0.069 and 0.075, respectively) indicates that tax avoidance facilitates the misallocation of capital to negative-NPV projects. This outcome reflects the agency view, whereby managers, supported by the opacity of tax strategies and the availability of free cash flow, are enabled to pursue value-destroying investments and enhance personal prestige. (Jensen, 1986; Richardson, 2006; Desai & Dharmapala, 2009).

On the contrary, the positive coefficients from ETR and CETR (0.038 and 0.047, and 0.034 and 0.045, respectively) are very informative, since they indicate that the reason for overinvestment is not only an issue of tax strategy, but also a matter of fundamental managerial opportunism. Within such weak governance structures, managers disposed to building empires will overinvest regardless of the source of cash, since there are several possibilities for rent extractions without the need for complex tax avoidance (Desai & Dharmapala, 2006). Enhanced taxation transparency could even exacerbate this by reducing the cost of capital, giving a surplus to be used for exploitation. This result is contrary to (Asiri et al., 2020)results, which may be attributed to the existence of severe agency conflicts in the Egyptian market due to the governmental ownership concentration in state-owned companies (X. Chen et al., 2016).

The discrepancy in the outcomes of the tax avoidance proxies in the overinvestment subsample can be due to the method of measurement, where the effective tax rates are sensitive to profitability in their calculations. Consequently, these proxies tend to be distorted in loss years, reducing their usefulness in representing authentic tax-planning behaviors. On the other hand, BTD is scaled by total assets, which is much more stable and less sensitive to swings in profitability. This helps BTD capture more consistently the tax-avoidance behavior, particularly among samples with a high proportion of loss-making firms.

The role of earnings management as a moderator also sheds light on this mechanism. EM intensifies overinvestment both by concealing the abuse of tax-saved funds (0.031 and 0.028) (when interacting with BTD) and by indicating a managerial characteristic of opportunism that manifests itself as resource misallocation independent of the source of finance (in its interaction with ETR/CETR) (0.024 and 0.022, and 0.018 and 0.020, respectively). Ultimately, the evidence points to unchecked managerial empire-building as the primary driver, followed closely by tax avoidance and earnings management as secondary instruments.

The results for the underinvestment subsample in models 3 and 4 illustrate the opposite story. The negative and significant association of BTD level with underinvestment (−0.058 and −0.059) comes in line with the pecking order theory, suggesting that tax avoidance constitutes a vital internal source of capital for financially constrained companies. Companies undertaking different tax avoidance approaches are able to invest in projects that they would otherwise be unable to pursue due to external funding constraints (Asiri et al., 2020; Benkraiem et al., 2024; Elfeky et al., 2024). Therefore, in this context, tax avoidance can be considered a value-maximizing approach that enhances shareholders’ wealth. However, the negative coefficients of ETR/CETR (−0.037 and −0.041, and −0.029 and −0.030, respectively) indicate that greater transparency also reduces underinvestment, albeit from a different perspective. For the less financially restricted firms included in the subsample, greater tax adherence contributes to credibility, lessens information asymmetry, and facilitates access to cheaper external capital. These findings contrast with the agency-based interpretation of tax avoidance and highlight that its effect on investment efficiency is conditional on firms’ financing constraints. Earnings management further clarifies this picture. Combined with high BTD, EM signals perform in a way that helps reduce financing frictions. In the case of high ETR/CETR, earnings management seems to support transparency by strategically managing stakeholders’ perceptions, thereby mitigating remaining investment obstacles. Regarding the control variables, the negative correlation between leverage and underinvestment indicates the disciplinary role of debt in reducing managerial inefficiency (Jensen, 1986; Benkraiem et al., 2024). Likewise, the negative coefficient for financial slack confirms that internal liquidity plays a decisive role in enabling firms to undertake profitable investment opportunities, without resorting to costly external financing.

5. Conclusions

We extend the literature by testing the tax avoidance impact on the investment efficiency and the moderating effect of earnings management in the Egyptian market using different measures.

Our results add additional insights concerning the negative relationship between tax avoidance and investment efficiency. Additionally, earnings manipulation intensifies the negative impact of book–tax difference on optimal investment decisions; however, it outweighs the positive impact of effective tax rates on investment efficiency, as shown through the higher transparency and lower information asymmetry, revealing that the opportunistic behavior of managers is the main factor drawing the investment plan in the weakly governed environments.

Likewise, our findings revealed that when splitting the sample into overinvestment and underinvestment groups, the book–tax difference is positively related to overinvestment, exploiting the free cash flow preserved from the tax burden reduction; nevertheless, the effective tax rates are positively related to overinvestment, indicating that the managers are prone to overinvest, regardless of the source of funds.

Earnings management in this case is employed either to camouflage the poor performance of negative net-present-value projects or to ameliorate the financial position of the companies in order to attract external funds to then be used in exacerbating the overinvestment behavior.

Lastly, the book–tax difference is negatively associated with underinvestment, relaxing the financial constraints and capital rationing problem facing highly constrained firms, while the effective tax rates mitigate the underinvestment behavior through higher transparency and lower information asymmetry, enhancing shareholders’ confidence in management, and the earnings management comes to signal the strength of the firms’ performance and ease the access to financial sources.

Practical implications:

Regulators and Policymakers: They should focus more on regulations and disclosures to enhance the corporate governance mechanisms and to curb managerial opportunism, rather than focusing on reducing the tax avoidance practices.

Investors and Stakeholders: They should consider the tax avoidance techniques and earnings manipulation with caution, as they may be either destructive or beneficial for firm value.

Limitations and Future Research: This study could be extended to a cross-country comparison of emerging markets in the Middle East to strengthen generalizability. Future research should also empirically examine the moderating role of corporate governance mechanisms. The moderating role of real earnings management can be tested in future studies to ensure the impact of managerial discretion on investment decisions. In the Egyptian context, the impact of the pandemic and the devaluation of currency may have an impact on the motive behind the managerial discretion affecting tax avoidance and resource allocation.