ABSTRACT This study empirically explores the heterogeneous impacts of agricultural and natural resource‐based activities on CO 2 emissions. This study examines the impact of agricultural, forestry, and fishery value‐added, renewable energy consumption, trade openness and natural resource rents on environmental degradation in BRICS economies over the period 1992–2023. To estimate the models, this study utilizes quantile regression (QR) method, which provides efficient and unbiased estimates across different quantiles of the distribution. The results show that agricultural, forestry, and fishery value added (AGRFFC) and trade openness positively affect CO 2 emissions at all quantiles. However, renewable energy consumption and natural resource rents negatively affect CO 2 emissions at all quantiles. For robustness check, this study uses the Driscoll‐Kraay standard errors method, which corrects heteroscedasticity and autocorrelation in the models. The results of the Driscoll‐Kraay standard errors regression method are consistent with the findings of the quantile regression method. The results of panel causality test show that there is uni directional causality from agricultural and natural resource‐based activities to CO 2 emissions. The findings of this research confirm the existence of a bi‐directional causal link between GDP and CO 2 emissions as well as a bi‐directional causal link between trade openness and CO 2 emissions. However, there is no evidence of a causal link between the REC and CO 2 emissions. The findings suggest that there are interdependent variables (CO 2 and key factors AGRFFC, GDP, TRD) that require an integrated policy response to address the interdependencies. The results show an urgent need for comprehensive strategies to reduce emissions through transforming trade, agriculture, and energy systems from their current state into sustainable future forms while simultaneously addressing emissions at their source.

Asia is a fast-growing emerging market with investment potential, making it essential to investigate the mechanisms by which green finance influences environmental sustainability. For the empirical analysis using data from 2000 to 2022 on twenty-two Asian economies, we applied the fixed- and random-effects model. First, we have verified the model's feasibility and employed diagnostic tests and robustness, which show that the model is appropriate. The key findings of the fixed effect model, suggested by the Hausman test, indicate that there is a negative association between Green Finance and CO2 emissions. The green finance coefficient is - 0.0107, which implies that a 1% increase in green finance leads to a reduction in carbon emissions by 0.0107%. Likewise, population density exhibits a negative relationship with CO2 emissions. The coefficient of population density is - 0.5373, implying that a 1% increase in population density diminishes carbon emissions by 0.5373%, which supports the ecological efficiency theory. The Causality test shows unidirectional causality among the variables. Based on evidence, this study suggests that policymakers of the Asian economies should focus on green finance initiatives to support environmentally sustainable investments, which leads to a decline in CO₂ emissions.

This study examines the relationship between public health expenditure, economic growth, and health outcomes across eight northern states of India over the period of 2000–2021. Using panel data econometric techniques, the analysis employs Pooled Ordinary Least Squares, Random Effects, and Fixed Effects models, with Driscoll–Kraay standard errors to address heteroskedasticity, serial correlation, and cross-sectional dependence. To address potential endogeneity arising from unobserved common factors, an additional robustness check was conducted by estimating a two-way fixed effects model incorporating both state and year fixed effects. In addition, the Dumitrescu–Hurlin panel Granger causality approach is applied to explore the direction of causality among the variables. The core variables include infant mortality which is dependent variable while economic growth, and the growth rate of total public health expenditure are independent variables and gross fixed capital formation, urban population share, and mean years of education of women aged 20 and above are incorporated as control variables to mitigate model misspecification. The findings reveal that increases in public health expenditure, economic growth, and female education are associated with significant reductions in infant mortality. Urban population share, however, exhibits a positive association with IMR, reflecting the adverse health implications of rapid and unplanned urbanization in northern India. Gross fixed capital formation does not exert a statistically significant direct effect on infant mortality. The causality analysis indicates a bidirectional association between economic growth and infant mortality, suggesting mutual feedback effects, while a unidirectional causality runs from public health expenditure to infant mortality. These results underscore the critical role of sustained public investment in health, alongside inclusive economic growth and improvements in female education, in reducing infant mortality.

BackgroundA higher prevalence of behavioral and psychological symptoms of dementia is associated with a greater caregiver burden and increased mortality in people with dementia. Considering the possibility of a reciprocal relationship between sleep disturbances and these symptoms, time series analyses are necessary to explore the associated temporal dynamics.ObjectiveThis study aimed to examine dynamic interdependencies between sleep disturbances and behavioral and psychological symptoms of dementia in older adults.MethodsDaily interactions between sleep patterns and behavioral and psychological symptoms of dementia were analyzed over a 14-day period using a panel vector autoregressive model. Data were collected from June 2018 to June 2020 in community and institutional settings. A total of 154 older adults with dementia wore wrist actigraphy devices continuously for 2 weeks for sleep data, and caregivers recorded behavioral and psychological symptoms of dementia in a daily symptom diary.ResultsUsing a panel vector autoregressive model, we analyzed data from 154 older adults living with dementia and their caregivers. The results showed unidirectional Granger causality running from the number of awakenings on the previous day to irritability (P=.03) and appetite or eating disorders (P=.04) on the following day. Conversely, some of the previous day’s behavioral and psychological symptoms of dementia temporally preceded subsequent changes in sleep patterns. Specifically, delusions had a Granger-causality effect on total sleep time (P<.001), wake after sleep onset (P=.01), and the number of awakenings (P=.006), while irritability had a Granger causality effect on the number of awakenings (P=.007). Notably, bidirectional Granger causality was observed between irritability and the number of awakenings.ConclusionsThis study demonstrates that the relationship between the behavioral and psychological symptoms of dementia and sleep patterns is dynamic and forms a vicious cycle. Consequently, early intervention to alleviate symptoms is imperative, and strategies to enhance sleep quality and address sleep disturbances should be prioritized.

This study investigates the dynamic effects of fiscal policy, inflation, and crude oil prices on Nigeria's economic growth from 2000 to 2023, employing the Autoregressive Distributed Lag (ARDL) bounds testing approach and Granger causality tests. Using annual time-series data from the Central Bank of Nigeria and the Federal Inland Revenue Service, the analysis examines the long-run cointegrating relationships and short-run dynamics between real gross domestic product (RGDP), total tax revenue, government expenditure, inflation rate, crude oil prices, and exchange rates. The bounds test confirms the existence of a long-run equilibrium relationship (F-statistic = 5.74 \textgreater{} 4.18). Long-run estimates reveal that tax revenue, government expenditure, and crude oil prices positively influence economic growth with coefficients of 0.241, 0.198, and 0.412 respectively, while inflation and exchange rate depreciation negatively impact growth ($-0.056$ and $-0.133$). The error correction mechanism shows a rapid adjustment speed of $-0.603$, indicating that about 60.3\% of short-run disequilibria are corrected annually. Granger causality tests demonstrate unidirectional causality running from fiscal variables, oil prices, and exchange rates to RGDP. The study concludes that while fiscal policy remains a potent economic growth driver, its effectiveness is contingent upon macroeconomic stability, efficient public spending, and economic diversification away from oil dependence. Policy recommendations emphasize fiscal discipline, inflation control, exchange rate stabilization, and strategic diversification to foster sustainable economic growth.

This study investigates the inter-temporal growth dynamics of Nigeria with emphasis on the roles of domestic investment, domestic savings, foreign portfolio investment (FPI), economic freedom, and corruption covering a period of 25 years spanning from 1997 to 2022. Data were sourced from IMF International Financial Statistics (IFS), World Development Indicators-WDI (2022), CBN Bulletin (2022). The empirical study adopted the ARDL model and the TDYL causality test. The ARDL test reported that domestic investment, domestic savings, economic freedom, foreign portfolio investment, corruption exerted negative significant effect on economic growth (ECG) in the short and long run validating the prediction of the sand the wheels theory of corruption. This conformed to the uni-directional causality between CORP and RGDP. The ARDL estimate evidenced that DINV has a positive minimal/insignificant effect on ECG of Nigeria both in the short and long run. Similarly, the TYDL granger causality test confirmed that bi-directional relationship exists between DINV and RGDP. Additionally, domestic savings exerted positive significant effect on ECG in Nigeria both in short and long run. Similarly, the TYDL causality test confirmed that uni-directional causality exist between DSAV and RGDP. Comparably, FPI inflows reported higher positive coefficient value in the long run than in the short run. Similarly, the TYDL granger causality test confirmed that bi-directional causality exist between FPI inflows and RGDP. Again, economic freedom improved ECG in both periods. Similarly, the TYDL granger causality test confirmed that uni-directional causality exist between EFCD and RGDP. Lastly, corruption exerted a significantly negative effect on economic growth in both periods. Hence, the study concludes that for the Nigerian economy to experience outstanding growth, Nigerian investment must be used for productive purposes and not for personal gains. Lastly, the current domestic savings rate should be sustained. Lastly, the Nigerian government should encourage more inflows of foreign capital into the Nigeria economy since induces growth.

The study attempts to explore the dynamic interlinkages among foreign direct investment (FDI) inflows, informational globalisation (ING) and global value chain (GVC) empirically in the G20 nations from 1990 to 2019. The trade and growth effects of FDI are widely discussed in theoretical and empirical literature in the context of globalisation in different countries and regions. The noteworthy progress of information and communication technologies (ICT) has also been a substantial factor in the FDI-trade-growth relationship, as observed in various contemporary studies. However, the existing studies cannot provide a suitable answer with explicit scenarios regarding the relationship between FDI and GVC in the context of the latest form of global trade in the era of ING, which encompasses both globalisation and digitalisation. To conduct the empirical exercises examining the dynamic relationships among FDI, ING and GVC, the study chooses G20 nations, which represent around 85% of the global gross domestic product (GDP), over 75% of the global trade and FDI flows, and about two-thirds of the world’s population ( OECD, 2022 , Twenty-eighth report on G20 investment measures ). World Bank (2022 , World Development Indicators (WDI) ) provides country-wise annual data on FDI. The year-wise quantitative measures of the ING for selected countries are obtained from the KOF Globalisation Index (2022 , KOF Globalisation Index 2020 ). The study collects country-wise yearly data on GVC from the UNCTAD-Eora (2023 , UNCTAD-Eora global value chain database ) GVC database. In the panel cointegration and vector error correction mechanism (VECM) framework, the empirical estimations applying the panel fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methods reveal the bidirectional causality between FDI and GVC and FDI and ING, and unidirectional causality between ING and GVC in G20 economies. JEL Codes: F01, F20, F41

This study empirically investigates hierarchical endogeneity and shock transmission among three major international stock markets the S&amp;P 500, FTSE 100, and Nikkei 225 from 2010 to 2023. Utilizing a Vector Autoregression framework and Granger causality test, the research examines the direction and strength of cross-market dependencies. The findings reveal a clear, asymmetric structure where the US market acts as the primary exogenous driver, with unidirectional causality flowing from the S&amp;P 500 to both the FTSE 100 and Nikkei 225. A secondary channel of influence from the UK to Japan was also identified. Forecast error variance decomposition confirms the US market's dominance, explaining over 25% of the movements in the UK's financial market and 18.7% of Japan's forecast error variance, while remaining largely insulated from feedback. The findings of this study suggest that international markets are characterized by hierarchical endogeneity, challenging notions of symmetric interdependence and highlighting significant implications for financial stability frameworks making a noteworthy contribution.

This article explores the bond between agricultural production and money supply within the Central African Economic and Monetary Community between 1991 and 2022. Agricultural production is proxied by the agricultural portion added to the gross domestic product. The exogenous variables include: broad money supply ratio, credit given to the private sector, inflation, capital, and labour are. This study uses the Granger causality and autoregressive distributed lag techniques. The results indicate a negative link between money supply ratio and agricultural value added. A non-significantly negative relationship exists between credit to the private sector and agricultural production. This implies that the credit granted to the private sector is not effectively used for agricultural production; some of it is stifled by inflation while other portions are used in other economic sectors. Inflation, physical capital, and labour positively relate to agricultural production. Unidirectional causality flows from agricultural production to inflation, money supply ratio, and labour. Bidirectional causality oscillates between agricultural production and credit granted to the private sector. The governments should apply monetary policies sweeping market processes to regulate the money supply. The Bank of Central African Economic and Monetary Community (BEAC) should use productive policy to ensure satisfactory credit drift to the productive sector. The agricultural and financial sectors should mutually benefit from credit provided by financial institutions. Literature is conflicting and debating on the nexus between money supply and agricultural production. There are studies that show significantly positive effects of money supply on agricultural production. Others indicate that the money supply has negative effects on agricultural production. Various researches disclose bidirectional causality linking money supply and agricultural production. Others show unit-directional causality among money supply and agricultural production. Also, advanced statistical methods like Granger causality and autoregressive distributed lag techniques are employed which have been rarely used by many past researches. This study reveals a negative correlation between money supply and agricultural value added. The main findings indicate that inflation, physical capital, and labour positively influence agricultural output. Furthermore, the analysis uncovers unidirectional causality from agricultural output to money supply and inflation, alongside bidirectional causality between domestic credit to the private sector and agricultural production. These insights underscore the necessity for effective monetary policies tailored to support agricultural growth in CEMAC zone. The research contributes to understand how monetary policy dynamics affect agricultural productivity, providing a foundation for future investigations into policy frameworks which enhance the agricultural sector's performance in the CEMAC region.

Abstract This study investigates the long-run relationship between economic growth, foreign direct investment (FDI), and financial development (FD) in the Gulf Cooperation Council (GCC) countries over the period 1980–2023. The PMG-ARDL technique is used to estimate an extended panel growth model, which includes trade openness (TO) as control variable and an interaction variable (INT) calculated as the product of FDI and FD. The estimated results of the long-run cointegration relationship reveal negative effects of FD and the interaction variables, whereas FDI and trade openness exert positive effects on GCC countries’ economic growth. Moreover, we apply the Dumitrescu and Hurlin (2012) heterogeneous Granger causality technique and identify a total of six bi-directional short-run links among LRGDP and FD, LRGDP and INT, LRGDP and LFDI, LRGDP and LTO, FD and INT, and LFDI and LTO, whereas three unidirectional short-run causalities are observed among certain variables such as LFDI to FD, LTO to FD, and LFDI to INT. Besides, we identify a long-run bidirectional causality among LRGDP and the error correction mechanism. The study recommends that the GCC countries must make additional efforts to regulate their financial sector, stimulate private investment and private consumption, and diversify their economies to attract FDI.

Diabetic complications can significantly affect the quality of life and prognosis of patients with diabetes. This study employed a systematic approach to elucidate the causal relationship between serum metabolites and six prevalent diabetic complications using a Mendelian randomization (MR) strategy. Serum metabolite data were obtained from genome-wide association studies, and data on six diabetic complications were acquired from the FinnGen consortium. A two-sample MR approach was used to investigate the association between serum metabolites and common diabetic complications. Reverse MR analysis was conducted to investigate potential causal relationships between diabetic complications and serum metabolite levels. Sensitivity analyses were performed to evaluate the robustness of our findings. Analyses included inverse-variance-weighted, MR-Egger, linkage disequilibrium score regression, and colocalization approaches. We identified 81 causal associations, highlighting the significance of serum metabolites in the context of diabetic complications. The results identified significant causal associations: Bilirubin degradation products were inversely linked to diabetic retinopathy, while androstane sulfate and N-succinyl-phenylalanine increased the risk of retinopathy. Caffeine metabolites and adenosine 5'-monophosphate-to-citrate ratios were positively associated with nephropathy. Reverse MR analysis confirmed unidirectional causality, and sensitivity tests ruled out pleiotropy. Colocalization analyses highlighted shared genetic loci, such as rs2991970, between metabolites and hypoglycemia. These findings elucidate metabolite-specific pathways underlying diabetic complications and propose novel biomarkers for risk stratification. The limitations of this study include its European-centric nature and the lack of stratified covariates. This study highlights the value of integrating genetic and metabolomic data to enhance precision medicine in diabetes management.

This research examined the long-run effect of climate change on food security in Malawi, Madagascar, Mozambique and Zimbabwe using the Autoregressive Distributed Lag (ARDL) model. The study used nine variables for quantitative analysis using data for the four countries from the World Bank spanning from 2000 to 2023, using two models. The results were validated using the pooled mean group (PMG) estimator. The results from model 1 show that environmental temperature, fertiliser consumption, credit access, age dependency ratio, urbanisation and land size significantly affect the percentage of crop yields. The model 2 results show that all the aforementioned factors, including cereal temperature and yields, have an effect on the prevalence of malnutrition, which was a proxy for food security in this study. Furthermore, the study used the Granger causality test to indicate a unidirectional causality direction from both models’ independent variables to dependent variables. From the econometric analysis conducted, the findings highlight the urgent need for targeted interventions, such as promoting climate-resilient agriculture, expanding access to credit and social protection policies, to enhance nutritional well-being and improve resilience to climate shocks.

Purpose This study aims to examine the relationship between digital transformation search volume and stock returns in the Vietnamese stock market. Design/methodology/approach The authors collected weekly data from Google Trends and vn.investing.com, spanning from week 33 of 2019 to week 32 of 2023. Using this data set, the authors used various quantitative approaches, including VAR-Granger, Ordinary Least Squares (OLS) and Copula, to test the relationships between variables. Findings The results obtained from VAR-Granger analysis reveal a unidirectional causality from digital transformation search volume to the stock returns of VN-Index, VN-30 and VN-100. Findings from the OLS indicate a negative lagged impact of search volume on digital transformation for stock returns. Moreover, using the Copula approach, the authors determine that the structural dependency between the search volume for digital transformation and the VN-Index follows a normal distribution. This suggests that simultaneous positive and negative changes between the variables are equally likely to occur. Research limitations/implications The study is meaningful further research. Practical implications The study is meaningful for stakeholders: investors and policymakers. Originality/value By offering these insights, this paper contributes to a deeper understanding of the relationship between digital transformation and firm performance within the stock exchange market.

The paper explored the fractal, nonlinear and chaotic dynamics between oil prices, inflation, economic growth and unemployment in Turkiye from 1960 to 2024 and examined how energy market volatility propagated through the macroeconomy via complex, regime-dependent mechanisms. It developed a chaotic regression method and employed entropy-based measures (Shannon, Rényi and Tsallis), Lyapunov exponents, Lorenz and Rössler attractors, Julia set diagnostics and the chaos Granger causality test (Hiemstra–Jones). By nesting entropy, chaos and causality within a unified framework, it contributed methodological innovations and practical insights to the energy–economy literature. The chaotic regression results revealed that oil price shocks generated asymmetric and nonlinear responses in inflation, unemployment and growth that were characterized by chaos and sensitivity to initial conditions and demonstrated that oil shocks act as catalysts for nonlinear propagation and fractal macroeconomic dynamics. Julia set results determined that unemployment can be explained by inflation fractal size. Hiemstra–Jones method determined unidirectional causality from oil to both inflation, economic growth and unemployment. According to the results, adopting nonlinear and chaos-based modeling approaches is essential to understand the macroeconomic consequences of energy shocks. For policymakers, the evidence determined that the costs of disinflation or inflation control are sensitive to energy market volatility. The paper contributed to the energy–economy-econometrics literature by integrating entropy, chaos and causality analyses into the oil price–macroeconomy nexus by offering both methodological innovations and practical insights.

The paper analyses the responsiveness of development expenditure to output across Indian states. The focus is on the government's development expenditure, which is further categorised into social sector expenditure and economics services expenditure. The responsiveness of the development expenditure to output is tested for three groups of states categorised on the basis of income for the period of 1993-94 to 2017-18. Using panel unit root, panel cointegration, and panel Granger causality, the study indicates a longrun relationship between components of development expenditure and output for the group of high-income and low-income states, while the results show a weak relationship between development expenditure and output in the group of middle-income states. The results indicate bi-directional causality between economic services expenditure and output for all three categories of the states in the long run. However, for expenditure on the social sector, unidirectional causality exists for low-income states (causality from output to social sector expenditure). Hence, all Indian states tend to spend on economic services, and only low-income states tend to spend on social services with the increase in output that too in the short run. Therefore, we may conclude that Wagner’s law holds for the economic services expenditures for the Indian states, in the long run.

This study investigates the causal impact of foreign direct investment (FDI) on economic growth in Turkey using annual time series data spanning the period 1980–2020. Economic growth is proxied by real per capita gross domestic product (GDP). The dataset was sourced from internationally reputable institutions such as the World Bank and TurkStat, and all variables were log-transformed to ensure suitability for statistical analysis. Due to the presence of variables with different orders of integration, the Toda–Yamamoto causality approach—robust to variables with mixed integration orders—was employed. The stationarity of the series was tested using the Augmented Dickey-Fuller and Phillips-Perron tests, while the optimal lag length was determined based on information criteria. Diagnostic tests for autocorrelation, heteroskedasticity, and structural stability were conducted to ensure model adequacy. The empirical findings reveal a statistically significant unidirectional causality running from FDI to economic growth at the 1% significance level, with no evidence of reverse causality. These results indicate that FDI functions not only as a source of external capital, but also as a strategic driver of growth by enhancing productivity, facilitating knowledge and technology transfer, and driving structural transformation. By incorporating a long-term dataset and accounting for the macroeconomic structure, this study offers a methodologically robust and contextually grounded contribution to the literature on the FDI-growth nexus in developing economies

This study examines how rainfall and groundwater recharge can help mitigate drought conditions, using the Standardized Precipitation Evapotranspiration Index (SPEI) as the drought indicator. It focuses on the top ten countries experiencing groundwater overexploitation and incorporates a global average perspective to provide deeper insights into these critical relationships. These insights are essential for informed policy-making and integrated decision-making, involving a range of stakeholders from local users to international policymakers on drought mitigation efforts from 1961 to 2022. The analysis employs the novel technique to estimate Dynamic Panel Threshold Regression (DPThR) model. The findings reveal that a 1-millimeter increase in rainfall improves the SPEI by 0.003 units, thereby reducing drought likelihood. The threshold for mitigating drought effects is identified at 614.41 millimeters of annual rainfall, with Pakistan, Iran, and Saudi Arabia being the most at-risk countries when rainfall falls below this level. Conversely, a one-standard-deviation increase in groundwater recharge enhances the SPEI by 5.06 units, indicating a substantial reduction in drought incidence. The threshold for mitigating drought effects is identified at –0.0039 standard deviations, with China, Iran, Mexico, Pakistan, Saudi Arabia, Turkey, and the United States being the most drought-prone when recharge falls below this level. Furthermore, it was found that temperature exerts a consistently negative and highly significant effect, indicating that warming intensifies drought through evapotranspiration and soil moisture depletion. While CO2 emissions show no significant direct impact. Moreover, the study identifies unidirectional causality running from rainfall, groundwater recharge, temperature, and CO2 emissions, reinforcing the dominance of hydro-climatic forces in driving drought variability. Policy recommendations include advancing artificial rainfall, enhancing groundwater recharge, and maintaining country-specific water use thresholds to reduce drought risk and strengthen water and climate resilience in overexploited regions.

Sustainable economic growth is one of the main pillars of sustainable development, together with the environment and society. Therefore, unveiling the factors behind sustainable economic growth is vital for the design of economic, educational, and social policies. This study investigates the role of renewable energy use, gender inequality, human capital, and foreign direct investment (FDI) inflows on sustainable economic growth in the BRICS countries during the period of 2000–2021 by using novel cointegration and causality tests. The findings of the causality test point out a feedback interplay among renewable energy use, gender inequality, and indicators of sustainable economic growth and a unidirectional causality from human capital and FDI inflows to indicators of sustainable economic growth. Furthermore, the consequences of the cointegration test unveil that the use of renewable energy, human capital, and FDI inflows positively impact sustainable economic growth, while gender inequality negatively affects sustainable economic growth. In conclusion, our results highlight the significant roles of renewable energy, human capital, and FDI inflows, along with gender equality, in achieving sustainable economic growth.

The purpose of this paper is to find out the relationship and volatility persistence between price of crude oil, price of natural gas and India?s stock market. The research has been done from 2008 to 2023 considering daily prices of these variables using Granger causality test, ARCH, GARCH, DCC GARCH and Vector Auto Regression models.It is found that there is a unidirectional causality from natural gas prices to crude oil price and from the stock market to natural gas prices. Crude oil, natural gas and stock have Autoregressive Conditional Heteroskedasticity effect. The correlations between financial market and energy market are strongly persistent over time. By providing valuable insights into the linkages between energy prices and the Indian stock market, our findings provide a better understanding of these dynamics, helping investors and financial practitioners make informed decisions.

Private sector business freedom and human capital are the fundamental building blocks of the entrepreneurial ecosystem. Business freedom provides entrepreneurs with the necessary space and opportunities, while human capital provides the knowledge and skills to make the most of these opportunities. Both factors are critical for economic growth, innovation and social welfare. The aim of this study is to determine the causality relationships between private sector job freedom, human capital and entrepreneurship for selected BRICS countries for the period 2002-2023 with the help of Emirmahmutoğlu and Köse (2011) causality test. According to the results of the analysis, unidirectional causality from entrepreneurship to private sector business freedom is determined for the panel. On the other hand, for the country level, unidirectional causality from entrepreneurship to private sector business freedom is found in Brazil, China, South Africa and Russia. Moreover, bidirectional causality was found between GIRSM and BSER at the panel level. At the country level, there is bidirectional causality between BSER and GIRSM for Brazil, China and South Africa, unidirectional causality from GIRSM to BSER for Russia, and no causality for India. Societies that want to encourage entrepreneurship should develop policies to increase business freedom and invest in education and development programs to strengthen human capital.