VIF Test Research Articles

Effect of Macroeconomic Factors on Economic Growth: A Case Study of Bangladesh

Macroeconomic factors strongly influence the economy. It affects countries and businesses equally. The relationship between different macroeconomic factors is an area of widespread study for policymakers and economists. The ultimate purpose of this study is to assess the impact of macroeconomic factors on the economic growth. This research has taken GDP as a substitute variable for the development of the economy as well as endogenous (dependent) variable and FDI, inflation (INF), money supply (M2), public and private domestic investment (DINVEST), and foreign exchange reserve (FExR) as independent variables. Moreover, this study used time series secondary data starting from 2005-06 to 2022-23 for all econometric analysis. On the other side, we utilized the ADF (Augmented Dickey-Fuller) test to study the unit root (stationary property) of data, the Durbin Watson (DW) statistic technique to verify the autocorrelation of variables, the VIF test technique to examine the multicollinearity among independent variables and multiple linear regression to scrutinize the overall effect of macroeconomic factors on growth of the economy. Study findings show that DINVEST and FDI positively affect the growth of the economy. i.e., if macroeconomic variables- DINVEST and FDI are increased by 1 unit, they increase economic growth by 2.96 units and 0.62 units, respectively. However, inflation, money supply, and foreign exchange reserves have a positive effect on economic growth, but it is statistically insignificant. The study implies that economists, researchers, academicians, and policymakers can use the findings of the study for future research and decision-making regarding the effect of macroeconomic factors on economic growth.

The Intricate Impression of Tourism, Patent, And Renewable Energy on Environmental Sustainability

The growing global attention to indicating climate conversion and plummeting carbon dioxide (CO2) emanations has spurred researchers to examine the influence of several factors on promoting environmental sustainability. This research examines the intricate role of tourism, patent applications, rent from natural resources, and utilization of renewable power in carbon dioxide secretions in Bangladesh. For this purpose, data has been collected from 1995 to 2019 from World Development Indicators (WDI). Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) and the ARDL bound cointegration procedures are employed to determine the existence of stationary data and cointegration among variables respectively. The Autoregressive Distributive Lag (ARDL) procedure is used to consider the effects of the chosen variables on CO2 emissions over the short and long run. VIF test, White's test, and the Breusch-Godfrey test are employed to check multicolinearity, homoscedasticity, and autocorrelation respectively. The research discloses that it is vital to expand the usage of renewable energy sources while simultaneously decreasing natural resource rent by limiting natural resource extraction for a sustainable environment. Although the statistical insignificance of the long-term coefficient of tourism has been observed, the study reveals a negative coefficient for tourism, suggesting the possible efficacy of sustainable tourism practices for mitigating CO2 emissions. The findings also demonstrate the long-term contribution of innovation activities to environmental degradation through the positive coefficient of patent applications. The research results offer significant insights for policymakers and stakeholders involved in the pursuit of a sustainable and ecologically conscious future in Bangladesh.

THE IMPACT OF CLAIM EXPENSES, UNDERWRITING RISK, PROFITABILITY, COMPANY SIZE AND RETENTION RATIO ON SOLVENCY OF INSURANCE INDUSTRY

The main purpose of this research is to analyze Claim Expenses, Underwriting Risk, Profitability, Company Size, and Retention Ratio on Solvency of Insurance Industry. The purpose of this research is to help future investors in choosing the right insurance company. This research was a quantitative descriptive research method. The sample used in this research is secondary data of Insurance Industry on the period from 2015 to 2020. Using SPSS (statistical package for the social sciences), methods of analysis used in this study include tolerance and VIF test, Kolmogorov-Smirnov test, multivariate cointegration tests: Test, SRESID and ZPRED estimation, t-statistical tests, F-statistical test, coefficient of determination (R²), and Pearson Correlation Product Moment. The result of this research shows claim expense, underwriting risk, ROA, and company size have significant influence on insurance industry’s solvency, but retention ratio has no significant influence on insurance industry’s solvency. All the independent variables simultaneously from a good model to explain the solvency since the magnitude of the effect value is 83,4%, while remaining 16,6% is explained by other variables besides claim expense, underwriting risk, ROA, company size, and retention ratio. The linear regression produced a formula to calculate the solvency, so this formula could be used in monitoring the financial health of an insurance company.

Pre and Post–Merger Financial Performance Analysis of Commercial Banks in Nepal

Purpose: This study attempts to examine the effect of merger on the financial performance of Nepalese commercial banks.
 Methodology: This paper employs a descriptive and causal comparative research design to examine the financial performance of commercial banks involved in six merger deals as of mid-July 2022, with a sample of four merger deals completed before mid-July 2018. Mean, standard deviation, paired sample t-test, Pearson correlation, regression analysis, and the VIF test, are utilized for data analysis. Financial performance is evaluated using nine key ratios (EPS, NWPS, P/E ratio, CD ratio, CAR, NPL ratio, ROA, ROE, and NPM). Secondary data covering six years (2010 to 2019), excluding the specific merging year, are analyzed.
 Findings: The analysis of pre- and post-merger financial performance in the sample commercial banks revealed statistically significant differences in EPS, NWPS, ROA, and NPM. Positive correlations were observed between the pre- and post-merger periods for EPS, NWPS, CD ratio, CAR, NPL ratio, ROA, ROE, and NPM, excluding the P/E ratio. The merger proved beneficial for Nepalese commercial banks, leading to improved profitability in terms of EPS, ROA, and NPM in the post-merger phase. However, factors like CD ratio, CAR, and NPL ratio, regulated by the NRB, showed no significant changes post-merger. Although banks experienced increased P/E ratio and ROE, the impact was deemed insignificant. Notably, CAR had the most significant impact on both pre- and post-merger ROA.
 Implication: The results derived from the study are relevant to managers for future merger decisions and can be used for the selection of a suitable partner for future merger activities.
 Value: It contributes to a better understanding of the overall merger scenario in Nepalese banking sector, as well as the relationship between certain restrictions and merger.

The Effect of Working Capital Management on The Profitability of the Textile Industry: Evidence from Pakistan Textile Industry

The working capital management has significant importance in business investment because it impact on company’s Profitability and efficiency. The current study has taken the textile sector of Pakistan comprising of three sectors , that is spinning weaving and composite comprising of 155 companies in total . The aim of the paper is to investigate the effect of working capital management on profitability of the textile firms. A sample of 20 businesses is randomly selected for a period of six years to carry out the current study. In the existing study ROA is taken as a measure of profitability of the firm. While the working capital is taken as the measure of the cash conversion cycle. Account receivable collection period, Account payable time period size of the firm sales growth and current ration. Regression analysis is done and to address the issue of multicollinearity VIF test is done. The study's conclusions demonstrate that working capital utilization matters to Pakistani textile enterprises and that effective working capital utilization affects profitability. Studies show that when a company's inventory turnover period increases, its profitability decreases. The prompt transformation of raw materials into final products has a significant impact on the profitability of the company. The analysis did not uncover any proof that profitability and the cash conversion cycle (CCC) were significantly correlated.

DOES BOARD STRUCTURE AND OWNERSHIP STRUCTURE INFLUENCE THE PERFORMANCE OF LISTED COMPANIES: EVIDENCE FROM PHARMACEUTICALS AND CHEMICAL INDUSTRY OF BANGLADESH?

This study examines the influence of board structure and ownership structure on a firm’s financial performance in the pharmaceutical and chemical industry of Bangladesh. The data of this study is based on all listed companies in the pharmaceuticals and chemical industry on Dhaka Stock Exchange. Data was collected from the annual reports of the concerned industry from 2015 to 2020. To examine the data, the study has applied descriptive analysis, correlation analysis, VIF test, and the twostage least squares (2SLS) estimator using Eviews software. Based on existing empirical studies, seven major attributes (board size, board independence, board gender, managerial ownership, institutional ownership, audit committee size, and frequency of audit committee meetings) have been selected to identify their influence on a firm’s performance. Findings from the study show that there is an insignificant negative relationship among board size, board gender, frequency of meetings, and the firm’s financial performance but a significant relationship with board independence, institutional ownership, and frequency of meetings. The study has proposed that board size can be smaller but should be representative. This study will contribute to the literature on CG and profitability in an emerging economy like Bangladesh.

NAIRU IN UZBEKISTAN: A TRADE-OFF BETWEEN INFLATION AND UNEMPLOYMENT (2016-2022)

Economic analysts frequently encourage the Fed to utilize unemployment rates or wages growth rates as leading measures of inflation and as indicators of whether to loosen or tighten monetary policy. The theory behind this strategy is based on current Keynesian macroeconomics, notably the Phillips curve and the nonaccelerating inflation rate of unemployment (NAIRU). The main purpose of this study to analyze the trade-off between inflation and unemployment of Uzbekistan and calculate NAIRU through Phillips curve approach during 6 years quarterly data from 2016-2021. In this study Hodrick-Prescott filter (HP) filter is used to calculate NAIRU and Ordinary Least Squares (OLS) model to determine the relationship between inflation and unemployment rate. VIF test is used to check collinearity or multicollinearity of determinants and result showed that there is not any collinearity between variables. OLS model indicated that there are positive and significant trade-off between inflation and unemployment, while GDP and inflation showed negative and insignificant relationship.

Comprehensive Evaluation and Stepwise Regression Model of Light Pollution in Topsis Based on Entropy Weight Method

Light pollution is a phenomenon that affects our environment. It is of great significance to make a quantitative analysis of light pollution and establish an evaluation system for energy saving, environment improvement and social and economic benefits. In order to establish a reasonable evaluation system, based on a comprehensive evaluation model of light pollution risk level based on the entropy weight method of Topsis analysis, combined a multiple linear regression model to test the significance of the indicator data and performed the White's test and the VIF test for multiple covariance on the model.Then this paper used the standard inverse stepwise regression model to solve the linear regression model, constructed a comprehensive evaluation model of light pollution.

The probability of choosing the player executing the direct free kick near the box in terms of the differentiation of some harmonic abilities of a football player’s young foot

The importance of the research has emerged by studying the variables of harmonious capabilities in order to be a real indicator of the process of accurate and objective selectio, and to highlight the role of tests and standards that have become an inevitable necessity to avoid mistakes and help in the process of classifying and directing players towards sports that are appropriate for their abilities and capabilities in terms of the diversity of sport, including what needs physical structure and physical abilities And kinetic، in order to accurately choose the players who execute the direct free kick near the penalty area close to the penalty area according to the correct and accurate bases، and since the level of performance depends on many factors, including their physical، motor، mental and skill capabilities, the selection of players and directing them towards the center or the tasks assigned to them It is done according to their own specifications and herein lies the intended benefit of the selection process. Through the researchers’ follow-up to football matches and the performance of the players taking the direct free kick near the penalty area in the matches of local clubs, they noticed a failure in executing the kicks، and this is due to the level of performance of the players, as they find it difficult to perform Kicks near the penalty area، and one of the objectives of the research is to identify the harmonic abilities of young players executing the direct free kick near the penalty area in football, to identify the discriminatory indications of the motor abilities، turmoil and fear of the young players executing the direct free kick near the penalty area in football، to identify the proportions Likelihood of selecting the players executing the direct free kick near the penalty area in terms of the variables of some harmonic abilities. The researchers also dealt with the theoretical framework with everything related to the research variables. The researchers followed the descriptive method in the manner of studies of the current situation. (161) players, the reconnaissance sample was (49) players, and the main application sample was (112) players. The fourth chapter included everything related to data processing and reaching scientific results, among which the researchers reached a conclusion, the most important of which showed the results of the basic research sample (distinguished players and non-distinguished players in executing the direct free kick near the penalty area) were distributed normally. The results showed that there are no self-relationships between the independent variables using the VIF test (amplification factor).

Identification of degradation factors in mountain semiarid rangelands using spatial distribution modelling and ecological niche theory

This paper presents an approach to studying rangeland degradation by using remote sensing data, geographic information systems, and ecological niche theory. The role of environmental factors and land use in the spatial distribution of degraded rangelands in the Central Caucasus was assessed. Degradation stages were modelled in R using ENVIREM predictors, VIF test to select noncorrelated variables, ENMeval package to select the optimal model parameters, and the Maxent method to develop distribution models in the dismo package. Selected models had AUCtest, AUCtrain and CBI values close to 1, deltaAICc and AUCdiff values close to 0, and quite low AICc values. Environmental predictors of climate type (Thornthwaite aridity index and Emberger’s pluviothermic quotient) were explanatory variables for least disturbed rangelands, while topography (Terrain roughness index) largely explained the distribution of most disturbed grasslands. Quantitative (Schoener’s D) and graphical (Kernel density estimation, analysis of predictive maps) assessment revealed a significant overlap of ‘ecological niches’ and potential ranges of grasslands at different degradation stages, which indirectly supports the hypothesis of the important role of overgrazing in their degradation. Livestock management is likely to help restore disturbed mountain meadow steppes to steppe grasslands. Restoration of the arid shrub ecosystems to steppe grasslands or meadow steppes probably requires additional agricultural practices.

Sustainable Cryptocurrency Growth Impossible? Impact of Network Power Demand on Bitcoin Price

Due to the youth of the cryptocurrency sphere, the logic of interaction between investors, users and protocols is not always precisely defined. Analysis of the impact of ESG on cryptocurrencies proves that the demand for bitcoin network capacity (occupies the main market share) is the main factor in predicting the price of this cryptocurrency and the cryptocurrency market as a whole. The choice of the statistical method of analysis is determined by the purpose of statistically justified determination of the relationship of the data under consideration, and the reliability of the analysis is checked using Fischer and Student tests. In this paper, several innovations are proposed to solve the problem of energy dependence of cryptocurrencies: firstly, the analysis of cryptocurrencies in the paradigm of sustainable development (taking into account the consumption of a huge amount of energy for the functioning of cryptocurrency systems); secondly, feedback logic to explain the interaction of subjects, including the following parties: users, developers, network infrastructure and their interaction; thirdly, statistical analysis with the creation of artificial variables from real data and iterative improvement of the model. This paper proves that sustainable cryptocurrency growth is impossible when viewed from the perspective of “Green Economics” by Molly Scott Cato. The author's approach is relevant compared to other methods of linear transformations for creating artificial variables by selecting data using the VIF test. As a result, several versions of models were obtained using various combinations of the initially proposed factors, on the basis of which the nature of the greatest influence on the price of bitcoin was established in the form of technical factors and energy infrastructure needs.

Effect of Renewable Energy and Traditional Energy Production on Economic Growth: The Case of Turkey and Azerbaijan

The aim of this study is to determine the relationship between traditional energy and renewable energy in the economic growth of Turkey and Azerbaijan. Multiple Linear Regression Model was used to measure whether there is a significant or insignificant relationship between dependent and independent variables and how these variables affect economic growth. Data for the years 2005-2015 were used in the study. In order to reveal the importance of the model in the analysis process, a series of assumption tests were conducted. The VIF test was used to measure whether the independent variables were related to each other, the Breusch-Godfrey LM test to examine whether there was autocorrelation between the error terms, and the Shapiro-Wilk W test, which is another assumption, was used to measure the normal distribution of the error terms. As a result of the multiple linear regression analysis performed at the end, no relationship was found between renewable energy and economic growth for Azerbaijan, but a significant result was obtained between traditional energy production and economic growth. When we examine the analysis in terms of Turkey, there is no relationship between renewable energy or traditional energy production and economic growth in this country.

Econometric Analysis of the Relationship Between Tourism Revenues, Inflation and Economic Growth: The Case of Morocco and South Africa

The aim of this study is to determine the relationship between international tourism revenues, inflation rates and economic growth of Morocco and South Africa between. The Multiple Linear Regression Model was used to measure whether there is a significant relationship between dependent and independent variables and how the variables affect economic growth. The data used in the analysis were obtained from the World Bank and include annual data. During the analysis process, a series of assumption tests were made to reveal the significance of the model. The model was established by taking the logarithm of the dependent variable. The VIF test was used to measure that the independent variables were not related to each other, the Breusch-Godfrey LM test was applied to examine whether there was autocorrelation between the error terms, and the Shapiro-Wilk W test, which was another assumption, was applied to measure the normal distribution of the error terms. As a result of the latest multiple linear regression analysis, it was determined that there was a significant relationship between the variables of both countries. In both countries, the increase in international tourism revenues affects economic growth positively, but the increase in inflation affects economic growth negatively.

Effects of Mortgage Interest Rate on Real Estate Investment in Kenya

As an investment and consumer good, real estate is significant. Like many developing countries, Kenya has struggled to give its citizens access to decent housing that is also affordable. More people are in need of housing due to urbanization, fast population growth, and rural-to-urban migration. Housing supply is less than demand as a result of several of macroeconomic and financial issues that affect real estate investment and, consequently, housing. This study looked into the impact of mortgage interest rates on real estate investment. The study, which covered the 11-year period from 2010 to 2020, used a causal research design and regression analytic research methods based on the Vector Error Correction Model. Data on real estate investments in Kenya The Kenya National Bureau of Statistics (KNBS) and Cytonn quarterly investments reports were obtained from the Central Bank of Kenya. The Ministry of Finance's database on national budgets (KNBS) provided information on mortgage interest rates, A pre-estimation test was run to verify the outcomes. The variables were stationary on the first difference. The VIF test result of 1.62 indicated that there was no multi-collinearity. The 1.9122 Durbin test value suggested that there was no serial correlation. Pairwise Correlation analysis showed a moderate correlation between mortgage interest rate on real estate investment (0.578). The report suggests that in order to incentivize real estate investment, the government should offer mortgage loans at reasonable rates, control property tax rates, and promote public-private partnerships in infrastructure development.

Evaluating Risk and Protective Factors for Suicidality and Self-Harm in Australian Adolescents With Traditional Bullying and Cyberbullying Victimizations.

To identify and compare important risk and protective factors associated with suicidality and self-harm among traditional bullying and cyberbullying victims aged 14-17-years in Australia. Cross-sectional population-based study. Young Minds Matter, a nationwide survey in Australia. Adolescents aged 14-17-years (n = 2125). Suicidality and self-harm were outcome variables, and explanatory variables included sociodemographic factors (age, gender, country of birth, household income, location, family type), risk factors (parental distress, family functioning, family history of substance use, child substance use, mental disorder, psychosis, eating disorders, sexual activity) and protective factors (high self-esteem, positive mental health or resilience, school connectedness, sleep) among 2 types of bullying victims-traditional and cyber. Traditional bullying includes physical (hit, kick, push) or verbal (tease, rumors, threat, ignorance), and cyberbullying includes teasing messages/pictures via email, social medial using the internet and/or mobile phones. Bivariate analysis and binary logistic regression models. Statistical metrics include Hosmer-Lemeshow Goodness-of-Fit-test, VIF test, Linktest and ROC curve for model performance and fitness. Overall, 25.6% of adolescents were traditional bullying victims and 12% were cyberbullying victims. The percentages of suicidality (34.4% vs 21.6%) and self-harm (32.8% vs 22.3%) were higher in cyberbullying victims than in traditional bullying victims. Girls were more often bullied and likely to experience suicidal and self-harming behavior than boys. Parental distress, mental disorder and psychosis were found to be significantly associated with the increase risk for self-harm and suicidality among both bullying victims (p < 0.05). While, eating disorder and sexual activity increased the risk of suicidality in traditional bullying victims and self-harm in cyberbullying victims, respectively. Positive mental health/resilience and adequate sleep were found be significantly associated with decreased suicidality and self-harm in both bullying victims. Suicidality and self-harm were common in bullying victims. The findings highlight that the risk and protective factors associated with suicidality and self-harm among adolescent who experienced traditional and cyberbullying victimization should be considered for the promotion of effective self-harm and suicide prevention and intervention programs.

Methodology for Framing Indicators for Assessing Economic-Socio-Cultural Sustainability of the Neighbourhood Level Urban Communities in Indian Megacities: Evidence from Kolkata

This study presents a comprehensive methodological framework for assessing the economic-socio-cultural (ESC) sustainability of neighborhood-level urban communities (NLUCs) in Kolkata, India. The present study aims to propose a set of indicators through a top-down approach that meets certain criteria for assessing ESC sustainability in specific contexts. In the initial phase, the framework has used a set of existing indicators and tools to measure sustainability. Subsequently, the study has categorized the indicators to measure ESC sustainability based on the assessment of an expert opinion for which the Delphi technique has been employed. Grey relational analysis and RIDIT test have been instigated to validate the importance of the selected sustainability indicators and determine the relationships among the indicators. At the decisive stage, the VIF test is conducted followed by employing Random Forest Classifier, a supervised machine-learning algorithm to identify the redundant indicators. The variables that will be contributing positively towards the prediction performance of the model were included in the final list of indicators. This study prepared the base for designing a model for assessing the ESC sustainability of both planned and unplanned NLUCs.

How Financial Development Affects Economic Growth: Evidence from some SAARC Countries

According to Joseph Schumpeter (1911), services provided by financial intermediaries are essential for technical innovation and economic growth. Later, empirical work by Goldsmith (1969) and McKinnon (1973) supported that there were close ties between financial and economic development for a few countries. But numerous other economists, including Robinson (1952) believed that finance was not so important for economic growth; financial development simply follows economic growth. Despite this debate, Levine (1993), among others suggests a positive relationship between financial sector development and economic growth. Moreover, there remains further debate whether the country's financial structure exerts differential impact on economic growth. Empirical studies across the countries (Rajan and Zingales, 1999) suggest that banking sector plays a key role in some countries. In this paper, I intend to investigate whether higher levels of financial development are positively correlated with economic growth using empirical evidence from five South Asian countries namely Bangladesh, India, Nepal, Pakistan and Sri Lanka. I have used Panel data analysis, Linear regression model, Levin-Lin-Chu unit root test, Covariance, Correlation and VIF test based on aggregate annual data from 1993 to 2016. My analysis suggests that development in banking sector has a moderately strong tie to promoting economic growth. The result implies that the policy should focus on banking sector development by enhancing its quality of credit products and offers to private sector as it is the main stimulator for growth in these five South Asian countries.

Financial Sector-Specific Determinants of Foreign Direct Investment in Nepal

This study examines and analyses the impact of financial sector-specific determinants on FDI inflow to Nepal. The Engle-Granger cointegration and error correction models were employed to test the long-run and short-run relationships between the dependent and independent variables using the time series data from 1994 to 2020. The Augmented Dicky Fuller (ADF) test was adopted to test the stationarity of the variables. Similarly, the Breusch-Godfrey serial correlation LM test, the Breusch-Pagan-Godfrey test, the VIF test, and the CUSUM of the square test were employed to detect the presence of autocorrelation, heteroscedasticity, multicollinearity, and the stability of the model. The study's findings revealed a negative significant relation between the NEPSE index and the FDI inflow to Nepal in the short run. All other independent variables are insignificant with FDI inflow in the short run. Similarly, interest rate, broad money supply (M2), and inflation positively correlate with FDI in the long run while the NEPSE index is insignificant in the long run. Broad money supply is found to have the strongest impact on FDI inflow among the independent variables. The study's findings suggest that policymakers should keep increasing the money supply at a stable and sustainable rate maintaining a moderate level of inflation and interest rate in the economy to attract a significant amount of FDI in the fiscal years to come.

Impact of bank-specific internal factors on the profitability of state-owned commercial banks in Bangladesh

Purpose of the Research : The aim of this study is to identify the bank-specific internal factors that affect the profitability of state owned commercial banks of Bangladesh. Methodology: The study applied explanatory research design and carried out based on secondary data only. The main source of data is annual reports (Audited Financial Statements) of the respective banks for the period of 2014 to 2018. Multiple Regression Model & Descriptive Statistics are used for data analysis which is being done through MS Excel 2013 version. In Multiple Regression Model considered ROA as dependent variable and internal factors as an independent variable. VIF test is also employed to check the Multicollinearity problem. Findings: The regression analysis reveals that independent variables (bank- specific internal factors) of the banks are important enough to explain the variation of the dependent variable (ROA) of the study. Moreover, the results show that there is a significant and negative relationship between ROA and NPL ratio & liquidity ratio. On the other hand, ROA and bank size have significant positive relationship. But leverage ratio and solvency ratio have statistically insignificant relationship with ROA. So, it is said that among five internal factors, NPL, liquidity and bank size are the most important bank specific internal factors that affect the profitability of banks. Implications: This study shall provide the regulators, managements, depositors and other concerned parties an important insight about State Owned Commercial Banks profitability and enhance the performance thereby. Originality: This study is a unique one in Bangladesh perspective because there is hardly any study about effect of bank-specific internal factors on the profitability of State Owned Commercial Banks of Bangladesh

The Relationship between Management Ability and Audit Fees by Considering Firm Credit and Auditor’s Dependency during Financial Crises

This paper aims to assess the relationship between management ability and audit fees by considering firm credit and auditor’s dependency during the financial crisis. Despite the presence of extensive studies on determining factors in audit fees, most of the studies have been concentrated on determining factors at the firm level, and little evidence is available to auditors on the information content of special managerial features. This paper aims to examine the relationship between managerial abilities and audit fees by considering firms' financial crisis conditions.In this paper, management abilities are considered to form three firm size indices, the firm's sales share in the market and cash. Moreover, the standard deviation of 3-year sales is used for firm credit, and an abnormal audit fee is employed for calculating auditor dependency. For this purpose, a total number of 91 listed firms on the Tehran Stock Exchange is evaluated for 6 years between 2014 and 2019. The panel regression model is used for hypothesis testing, and Chow and Hausman's tests are used for selecting the appropriate model-fitting method. The VIF test is used for analyzing linearity among explanatory variables. The obtained results indicate that financial crisis conditions contribute to the relationship between management abilities (based on firm size measurement) and audit fees. Further, the results indicate the negative impact of firm credit on the relationship between audit fee and management ability (based on cash). According to the results, audit fee dependency on the relationship between management ability (based on measurement, the firm's sales share, and cash) and audit fee is positive. Moreover, the results suggest that financial crisis conditions contribute to firm credit effectiveness in the relationship between management ability and audit fees.In past research, some factors affecting the auditor fee are debts, management ability, and auditor independence. In this paper, we contribute to the financial crisis as a variable that influences some of these relationships. And we show that relationships make sense by considering this variable.