Generalized Method Of Moments Research Articles

The Influence of Board Characteristics on Corporate Social Performance

This paper explores the relationship between relevant board characteristics and the corporate social performance represented by the Social pillar of ESG scores. By applying the generalised method of moments (GMM) system version of the Arellano-Bond estimator, we analyse the 50 biggest and most traded companies in the Eurozone from 2015 to 2022. Our empirical evidence shows that more gender-diverse and independent boards, as well as the presence of a CSR sustainability committee, enhance corporate social performance. These results are largely confirmed by investigating the four sub-pillars of Social score, i.e. Human Rights, Workforce, Product Responsibility, and Community. To our knowledge, this paper is the first to highlight how high corporate governance standards positively affect the Social pillar in the Euro Stoxx 50 index context. Our findings provide important implications for several stakeholders, including regulators and policymakers. Increasing attention should be paid to board characteristics to improve corporate social performance.

Tourism Activities in the Context of Ecological Footprint: Panel Data Analysis For Mediterranean Countries

Tourism is seen as one of the important sources of economic growth in developing countries. It makes positive contributions to the country's economy, with national income, foreign currency inflow and employment created. While the basis of investments in the tourism sector is primarily to achieve economic gains, today is the sustainable tourism concept emerged and the effects of tourism on the environment have come to the fore. Sustainability in tourism emerges as a new tourism approach that takes environmental damage into consideration. The aim of this study is to examine the relationship between tourism activities and ecological footprint in countries bordering the Mediterranean, using annual data for the period 2001-2021. In the analysis; Generalized Moment Method (GMM), system GMM, constant effects model and random effects models were used. The results of the analysis showed that tourism activities have a significant and positive impact on the ecological footprint of countries bordering the Mediterranean. The findings obtained from the study confirm the expectations that tourism activities increase the ecological footprint by causing environmental degradation.

Icts And Labour Productivity Nexus In Developing Countries: Evidence From Panel Estimation Approach

This study examines the impact of information and communication technologies (ICTs) on labour productivity in developing countries from 2000 to 2019, using the two-step System GMM estimation and dynamic panel quantile regression. The empirical results present new evidence on the moderating effect of ICTs with human capital, financial development and trade openness on labour productivity in developing countries. The interaction terms between ICTs and these three moderators show positive and statistically significant determinants of labour productivity. These three interaction terms have a greater influence on labour productivity than the impact of each variable assessed individually. The dynamic panel quantile regression results revealed that ICTs statistically significant to enhance labour productivity in lower and intermediate quantiles than in the highest quantiles in developing countries. This finding suggested that ICTs play an essential role in improving productivity at the lower and average labour productivity levels. This study can help policymakers develop a long-term strategy in terms of ICTs adoption and usage more intensively in developing countries as they strive to achieve the goals of industrial 4.0.

A Comparative Study between Growth of offering supply chain finance with Non-offering Banks in Pakistan.

This study empirically examines a comparative study between banks growth of offering supply chain finance with Non-offering Banks in Pakistan. This study used dynamic generalized method of moments (GMM) technique, based on 22 Pakistani Banks (10 offer supply chain finance and 12 non-offering supply chain finance) data during 2012-2022. The results show a significant difference between banks growth by offering and non-offering supply chain finance. Furthermore, the results show a significant positive impact of banks growth with supply chain finance on banks financial performance. This study encourages banks to offer supply chain finance that contributes towards their growth and financial performance.

Institutions and economic growth: the role of financial inclusion, public spending on education and the military

PurposeThe paper aims to investigate the relationship between institutions and economic growth in developing countries, considering the role of financial inclusion, education spending and military spending.Design/methodology/approachThe study employs dynamic panel analysis, specifically two-step system generalized method of moments (GMM), on a sample of 61 developing countries over the period 2009–2020.FindingsThe results confirm that weak institutional quality, weak financial inclusion and increased military spending are barriers to economic growth, conversely, increased spending on education and gross capital formation contribute to economic growth in developing countries. Regarding the specific institutional factor, we find that corruption, ineffective government, voice and accountability and weak rule of law contribute negatively to growth.Practical implicationsThe study calls for strengthening institutions so that the financial system supports economic growth and suggests increasing spending on education to improve access to and the quality of human capital, which is an important determinant of economic growth.Originality/valueThe study contributes to scarce literature by empirically analyzing the relationship between institutions and economic growth by considering the role of financial inclusion, public spending on education and military spending, factors that have been ignored in previous studies. In addition, the study identifies the institutional dimension that contributes to reduced economic growth in developing countries.

Climate change impacts on agricultural trade and food security in emerging economies: case of Southern Africa

Climate change has been a significant threat to sustainable agriculture impacting trade and food security. This research investigates the impacts of climate change on agricultural trade and food security in emerging economies focusing on Southern Africa. The research employed the dynamic panel Generalised Method of Moments (GMM) using panel data for the period 2012 to 2021 obtained from a sample of 12 Southern African countries selected based on data availability. The main independent variables to the research were climate change variables (temperature and precipitation). Other control variables included were population growth, food inflation and agricultural growth. The study found significant negative effects of climate change on agriculture trade. Whilst temperature changes were found to have insignificant effects, precipitation changes were found to have significant positive effects. The research recommends concerted efforts towards climate change adaptation and mitigation for sustainable agriculture. Based on the Findings, sustainable food security and agricultural trade Southern African emerging economies may be attained through effective climate change mitigation and adaptation approaches particularly promoting climate-smart agriculture (CSA).

Effect of ICT and Urbanization on Economic Growth: New Insight from OIC Countries

ABSTRACT This paper examines the relationship between ICT, urbanization, and economic development. The context of the analysis is the Organization of Islamic Countries from 1997 to 2019. We develop an original empirical approach based on principal component analysis to generate ICT index. To mitigate any endogenous issue, we utilize the instrumental variable generalized method of moments. Further, we use pooled mean group estimation to check the short-run and long-run relationships between the variables. Our results have two significant implications. First, we demonstrate the favorable influence of ICT on economic growth in OIC nations in both the short and long term. Second, both urbanization and economic growth have a clear and substantial impact in the short and long term. Finally, our analysis supports the feedback hypothesis between ICT and economic growth. These novel empirical findings will help policymakers design ICT development policies to fulfill sustainable economic development in OIC countries.

The interplay of bank competition and institutional quality: Implications for shadow economy

This study investigates the impact of institutional quality and banking competition on shadow economy for 127 economies for the period of 2005–2017. It further explores if institutional quality/competition shapes the relationship differently between banking competition/institutional quality and shadow economy. This paper uses the system GMM estimator to tackle potential omitted variable bias, endogeneity, and simultaneity issues. The findings suggest that overall, greater competition among banks and stricter institutions in the country reduce the size of shadow economy. Furthermore, impact of competition on shadow economy is even stronger in countries with weak institutions and the impact of institutional quality is greater in lower competitive environment. Only severe competition matters for shadow economy in case of developed countries. To sum up the novel findings of this research, competition and institutions complement each other in reducing the size of shadow economy. These findings are robust to different econometric estimators. The findings carry vital policy implications for the governments and regulators to play their part in reducing the shadow economy.

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time series forecasting. With this approach, we've achieved superior predictive accuracy and marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast. The project further extends its machine learning application by creating a random forest classifier model with a precision of 73%, a recall of 78%, and an F1-score of 73%. Building on this, we employed the H2O AutoML process to optimize our model selection, resulting in an XGBoost model that exhibits exceptional predictive power as evidenced by an RMSE of 0.1761205782994506 and MAE of 0.0874235576229789. Factor Analysis was leveraged to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. Scoring history, a tool to observe the model's performance throughout the training process was incorporated to ensure the highest possible performance of our machine learning models. We also incorporated Explainable AI (XAI) techniques, utilizing the SHAP (Shapley Additive Explanations) model to comprehend the contributing factors to accident severity. Features such as Driver_Home_Area_Type, Longitude, Driver_IMD_Decile, Road_Type, Casualty_Home_Area_Type, and Casualty_IMD_Decile were identified as significant influencers. Our research contributes to the nuanced understanding of traffic accident severity and demonstrates the potential of advanced statistical, econometric, machine learning techniques in informing evidence based interventions and policies for enhancing road safety.

Determinants of Local Revenue in West Nusa Tenggara Province

The economic growth of a region can be seen from the contribution of increasing regional original income originating from sources of taxes, regional levies, regional property and other separated regional wealth management results, and other legitimate regional revenues. The Province of West Nusa Tenggara in the last few decades has shown a significant increase in local revenue. In this study, the impact of population, inflation, and Gross Regional Domestic Product (GRDP) on local revenue from 2010 to 2021 is explored. The Generalized Method of Moments (GMM) technique uses district or city annual data in a quantitative panel data regression model for the study. According to the findings of statistical studies, local own-source revenue is significantly positively affected by government spending, population, and gross regional product (GDP), but inflation-related price increases have a negligible impact on local revenue. The money supply determines the rate of inflation, the high rate of inflation is caused by the amount of money in circulation.

Does investor base affect the firm-level ex-ante cost of equity capital?

This research investigates how ownership structures affect the cost of equity capital (COE) in Indian companies, particularly in market conditions with weak investor protection and increased ownership concentration by promoters and families. Using ordinary least squares (OLS), panel fixed effects, and two-step system generalised method of moments (GMM) analysis on data spanning from 2016 to 2021, we discovered that a dispersed ownership structure is linked to a higher ex-ante COE. Our findings indicate that companies with dispersed shareholders have less motivation to monitor managers, which leads to the expropriation of minority shareholders and results in higher costs of equity capital.

The Impact of Economic Openness and Institutional Environment on Technological Innovation: Evidence From China’s Provincial Patent Application Data

Analyzing the impact of economic openness and institutional environment on technological innovation is of great significance for China to build a new system of open economy. This study investigates the short- and long-run effects of economic openness and institutional environment on innovation by applying a dynamic panel regression model and two-step system generalized method of moments (GMM) method to dynamic panel data of China’s 30 provinces, autonomous regions and municipalities directly under the Central Government (referred to as provinces hereafter) from 2008 to 2017. Results indicate that trade openness impedes innovation, whereas foreign direct investment (FDI) significantly promotes China’s innovation particularly in the long-run. The continuous improvement of institutional environment promotes domestic innovation particularly in the long run. The integration of economic openness and institutional environment magnifies the effects of trade openness and FDI on innovation. The effects of economic openness and institutional environment on innovation differ by region with a descending pattern from China’s Eastern to the Central and Western regions.

Utilising AI-based Chatbot for Maximising Value and Profitability of Islamic Financial Institutions

global Islamic finance industry has experienced significant growth in recent years, establishing Islamic financial institutions (IFIs) as prominent players in the international financial landscape. To maintain competitiveness and sustainability, IFIs must embrace innovation and explore avenues for maximising their performance. This research aims to investigate the effect of AI-based Chatbots on the value and profitability of IFIs in Organisation of Islamic Cooperation (OIC) countries. The study has employed quantitative methodology, and the data was collected from the Bloomberg database, Thomson Reuters DataStream, World Development Indicators and annual reports of 104 IFIs across 44 OIC countries from 2020 to 2022. Employing the Generalised Method of Moments (GMM), this study investigates the impact of AI-based Chatbots on the value and profitability of IFIs. The findings show a positive impact of Chatbot on Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q (TBQ). Furthermore, the analysis reveals that variables such as Gross Domestic Product (GDP), institutional size (SIZE), and age (AGE) exhibit positive associations with the value and profitability of IFIs. Conversely, the study identifies Consumer Price Index (CPI) as exerting a negative influence on the value and profitability of IFIs, indicating potential challenges posed by inflationary pressures. IFIs may consider investing in the development and deployment of advanced AI technologies to enhance overall profitability. Keywords: AI-based Chatbot; Profitability; Islamic financial institutions (IFIs); OIC Countries; Dynamic GMM

Factors influencing the participation of Vietnamese enterprises in global value chains: Insights from the manufacturing and processing sectors

This research aims to investigate the factors that impact the participation of companies in Vietnam's Manufacturing and Processing sector in the Global Value Chain (GVC). We employ panel data analysis with a dataset of 387,994 observations from 107,125 enterprises in this industry spanning 2011-2020. Both Ordinary Least Squares (OLS) and Generalized Moment Method (GMM) are used to assess the factors affecting the industry's GVC engagement. The Arellano-Bond test checks for autoregressive processes of order one (AR(1)) or order two (AR(2)) in the model to see if the idiosyncratic error term exhibits serial correlation. The Sargan test checks for over-identifying restrictions in the statistical model. The results reveal that the ratio of large enterprises" exerts the most significant positive influence on the participation of Vietnamese enterprises in the GVCs, closely followed by "Institutional and policy environment." Conversely, HHI, TFP, and GDP growth rates show significant negative impacts. For Foreign Direct Investment (FDI), horizontal spillovers are positive and statistically significant. However, significant results also show a negative coefficient in the Backward GVC and a positive one in the Forward GVC. From these findings, we point out some important policy implications: (1) the government should strengthen favorable conditions for the participation of large enterprises and leading SMEs; (2) It is necessary to improve the institutional and policy environment and refine policies to attract FDI, especially with a focus on promoting forward linkages to facilitate greater engagement of the enterprises in the GVCs.

How export and import concentration influence natural resources and green growth nexus in OECD countries

Green growth (GG) is a multifaceted phenomenon that maintains environmental sustainability with economic growth; however, the resource curse paradox inhibits economic growth. Therefore, it is indispensable to explore the promoting factors of GG, comprising export diversification (EXD), import diversification (IMPD), and natural resources (NR) for 33 selected OECD economies from 2010 to 2022. The system generalized moment method (System-GMM) is employed for empirical investigation, elucidating that EXD associates negatively with GG. However, IMPD significantly encourages the GG by unveiling the positive relationship. NR negatively associated with GG and confirms the resource curse hypothesis. The integrative impact of EXD and IMPD mitigates the resource curse, driving GG by 0.012% and 0.018%, respectively, implying that diversified trade structures in imports and exports convert the resource curse problem into a blessing. Besides, the alternative panel estimator affirms the empirical outcome endorsed by the system-GMM model. Among all variables, EXD, IMPD, and NR reveal the bi-directional causality, while remaining show the unidirectional association. The findings recommend valuable policy ramifications to policymakers of OECD countries in attaining green and low-carbon economies.

Impact of Covid-19 on Income Inequality; a Comparative Analysis for China and Italy

This study examines the impact of covid 19 on income inequality and comparison between China and Italy. Trends in Gini coefficient showed differentiated results for two countries. In this comparison various indicators came to consideration that affected inequality. For empirical investigation from the past data collected from World Development Indicators and Generalized methods of moments (GMM) for panel data analysis. Results indicated that poverty and unemployment have significant positive impact on income inequality while rate of taxes shown negative and significant impact on income inequality. These results supported many other studies but the post covid trend in Gini coefficient shows contradictory results. Rising trend in income inequality in China and falling trend in Italy drives the attention in mitigation policies. China preferred to save its population from corona virus and implemented strict stay at home and isolation policy. In this regard inequality showed rising trend. Whereas Italian government along with isolation and stay at home policies financed poor much and paid less attention to rich. In this way the gap between rich and poor came down. This study pays attention to the country-based policies reflecting government preferences and initiatives taken for betterment.

Construction of a composite fintech index to measure financial inclusion for developing countries

ABSTRACT This study estimates fintech-driven financial inclusion in developing economies across three dimensions: availability, penetration, and usage. To create a new composite fintech index (FI) to measure financial inclusion, we include indicators for mobile money and internet usage, elements often overlooked in prior research. Our dataset, a balanced panel from 2011–2021, is gathered from 40 developing countries. To overcome criticism related to the arbitrary selection of weights in nonparametric methods, we utilize a two-stage Principal Component Analysis (PCA) to construct the FI. We test the robustness of our FI by employing OLS regression and the two-step system generalized method of moments (GMM) estimation techniques. Our proposed index is the first composite fintech index that is robust and powerful across measuring methods, dimensions, and fintech proxies in developing countries. This versatile index is a functional tool for policymakers to identify gaps in financial inclusion and inform policy responses. In addition, remarkable progress in fintech scores between 2011 and 2021, welcoming the mobile, internet, and agent banking era for developing countries. However, the transition to fintech presents substantial challenges, including the requirements for large-scale investment, financial literacy, and customer protection – issues that policymakers, regulators, and financial institutions need to address.

Political Economy of Tourism Growth in Developing Countries: A Comparative Study of Pakistan and Bangladesh

Tourism is a big source of revenue for a country. Political Stability and good governance promote tourism growth of a country by providing a favorable environment for tourism. Present study is empirical evidence of Pakistan and Bangladesh to investigate the role of political stability and governance in tourism growth. Furthermore, along with the principal variable, the study also estimates the role of corruption in tourism growth. Panel times series data, from the year 1996 to 2021, was used for analysis of results. GMM estimation methodology has been employed which show that political stability and governance increase tourism growth in Pakistan and Bangladesh. The finding also shows that variables including in analysis are control of corruption, political economy is also a determinant of tourism. So, finding indicates that political economy along with significant economic growth is desirable for promoting tourism in Pakistan and Bangladesh. They study concludes that in order to promote tourism in Pakistan and Bangladesh. political stability, good governance, role of law, and regulatory quality are significant.

Environment, Institutional quality and corruption impact on Trade in Developing Countries

The objectives of this study are to examine the impact of environment, institutions and corruption on trade in developing countries. For this purpose, the study considers 37 developing countries and data from 1990 to 2021 has been used in this study and it is collected from World Development Indicators and the study has utilized the index for institutions and eight governance indicators developed by World Governance Indicators. Generalized method of moments (GMM) is employed on the panel data for estimation of econometric results. The results conclude that environment and institutions have positive effects on trade while corruption has negative effect on trade in developing countries.

Empirical Analysis of Financial Inclusion's Role in Economic Growth and Poverty Reduction in Sub-Saharan Africa

This research aims to investigate the correlation between financial inclusion (FI), economic growth (EG), and poverty reduction in several Sub-Saharan African (SSA) countries throughout the period of 2014-2021. The research used the generalized method of moments (GMM) econometric methodologies. A financial inclusion (FI) composite index was constructed using principal component analysis (PCA) to include the many attributes of FI indicators. The findings indicate a positive and significant impact of financial inclusion on economic development and a reduction in poverty in Sub-Saharan Africa. These results emphasize the crucial significance of legislators and governments giving priority to policies that enhance the availability, utility, and accessibility of financial goods and services. The outcome will include a reduction in poverty and an increase in equitable economic progress throughout Sub-Saharan Africa.