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Research and experimental development (R&D) is an important driver of economic growth and productivity. Gross expenditure on domestic R&D (GERD) is a country's total expenditure on R&D performed by all sectors of the economy. South Africa's GERD as a percentage of its GDP (GERD/GDP) remains below government targets and has stagnated over the past decade, largely due to declining business sector R&D. This paper aims to identify the drivers of firm-level R&D intensity, defined as the firm's R&D expenditure as a percentage of turnover. It is the first South African study to examine both micro- and macro-economic drivers of firm R&D, with a paucity of literature on this topic for middle-income countries. Using the South African National R&D Survey data, the study utilized the Generalized Method of Moments (GMM) model and found that public financial support for firms, smaller firm-size, firm-level collaboration, political stability, foreign direct investment, and public R&D investment are positively associated with firm R&D intensity. The results highlight the importance of public financial support for smaller firms and investments in higher education and research institutions in promoting firm-level R&D, thereby providing useful policy insights for boosting business sector and economy-wide R&D expenditure and reaching national GERD/GDP targets. [ABSTRACT FROM AUTHOR]

This study examines the impact of audit quality, board of directors' characteristics, and the adoption of International Financial Reporting Standards (IFRS) on the financial performance of firms in the Kingdom of Saudi Arabia. The research is motivated by recent governance and accounting reforms in Saudi Arabia, aligned with Saudi Vision 2030, which aim to develop the financial sector and promote an advanced capital market. The study analyzes a sample of 135 companies listed on the Saudi Stock Exchange Tadawul over a four-year period, covering both pre- and post-2017. The period is significant as it coincides with the introduction of the Saudi Corporate Governance Regulations (SRCGs) and mandatory IFRS adoption in 2017. The study employs the generalized method of moments (GMM) system approach, along with ordinary least squares and fixed effects as robustness checks. The independent variables include market-based indicators (e.g., Tobin's Q) and accounting-based performance indicators (e.g., Return on Assets, Return on Equity). The results from the robust GMM estimator indicate that the characteristics of audit committees, including size, experience, and meetings, do not significantly impact firm performance indicators. This suggests that audit committees in Saudi Arabia may not effectively serve as governance mechanisms. Compliance with the SRCGs appears to prioritize meeting regulatory requirements rather than enhancing the effectiveness of audit committees. The influence of the Big 4 auditing firms is found to have a positive and significant effect on Tobin's Q. Regarding board of directors' characteristics, only independence and meetings show significant effects, but with conflicting impacts. Board meetings have a positive impact on Tobin's Q but a negative influence on ROE. Board independence has a negative impact exclusively on Tobin's Q. Additionally, the mandatory adoption of IFRS is found to have a substantial and negative relationship with all three firm performance measures (Tobin's Q, ROA, ROE). This can be attributed to factors such as transition costs, changes in accounting methods, unique national practices, and a shortage of skilled Saudi accountants. This research contributes valuable insights into the influence of corporate governance and mandatory IFRS adoption on firm performance in an emerging and Islamic economy context, specifically Saudi Arabia. The study addresses a gap in the literature by employing a robust methodology, GMM system, to address endogeneity concerns in the corporate governance-performance relationship. By considering both market-based and accounting-based performance measures, the study offers a comprehensive analysis of the complex dynamics involved. The study suggests that adequate resources are needed to support the long-term implementation of IFRS in Saudi Arabia, such as education and training programs, consultative groups, and stakeholder involvement. The research findings can inform policymakers in designing appropriate governance mechanisms in the emerging market context of Saudi Arabia and the Gulf Corporation Council region, considering cultural and religious specificities. [ABSTRACT FROM AUTHOR]

Existing methods for fitting spatial autoregressive models have various strengths and weaknesses. For example, the maximum likelihood estimation (MLE) approach yields efficient estimates but is computationally burdensome. Computationally efficient methods, such as generalized method of moments (GMMs) and spatial two‐stage least squares (2SLS), typically require exogenous covariates to be significant, a restrictive assumption that may fail in practice. We propose a new estimating equation approach, termed combined moment equation (COME), which combines the first moment with covariance conditions on the residual terms. The proposed estimator is less computationally demanding than MLE and does not need the restrictive exogenous conditions as required by GMM and 2SLS. We show that the proposed estimator is consistent and establish its asymptotic distribution. Extensive simulations demonstrate that the proposed method outperforms the competitors in terms of bias, efficiency, and computation. We apply the proposed method to analyze an air pollution study, and obtain some interesting results about the spatial distribution of PM2.5 concentrations in Beijing. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to contribute to the analysis concerning how inflation forecasts from different economic agents (professional forecasters and consumers) lead to varying levels of central bank credibility and how it affects the monetary policy interest rate and its expectations. Design/methodology/approach: Based on the Brazilian economy data from June 2007 to May 2022, the authors provide evidence that is useful for search mechanisms that improve the conduct of monetary policy through the management of inflation expectations. The authors perform several ordinary least squares and generalized method of moments regressions inspired by the Taylor rule principle. In brief, the benchmark model considers that the monetary policy interest rate and its expectations respond to departures of inflation expectations to the target (a proxy for central bank credibility) and the level of economic activity. Findings: The main result of the analysis is that inflation expectations from professional forecasters and consumers imply different perceptions of central bank credibility that affect the monetary policy interest rate and expectations for horizons until one year ahead. Originality/value: The novelty that the authors bring from the analysis is that the authors calculate central bank credibility by taking into account the "public beliefs" of different economic agents. Furthermore, the authors analyze the effect of central bank credibility from professional forecasters and consumers on the monetary policy interest rate and its expectations. [ABSTRACT FROM AUTHOR]

In the digital age, firms should continually innovate and adapt to remain competitive and enhance performance. Innovation and adaptation require firms to take a holistic approach to their corporate structuring to ensure efficiency and effectiveness to stay competitive. This study examines how corporate restructuring impacts firm performance in Vietnam. We then investigate the moderating role of digital transformation in the corporate restructuring–firm performance nexus. We use content analysis, with a focus on particular terms, including "digitalization," "big data," "cloud computing," "blockchain," and "information technology" for 11 years, from 2011 to 2021. The frequency index from these keywords is developed to proxy the digital transformation for the Vietnamese listed firms. A final sample includes 118 Vietnamese listed firms with sufficient data for the analysis using the generalized method of moments (GMM) approach. The results indicate that corporate restructuring, including financial, portfolio, and operational restructuring, has a negative effect on firm performance in Vietnam. Digital transformation also negatively affects firm performance. However, corporate restructuring implemented in conjunction with digital transformation improves the performance of Vietnamese listed firms. These findings largely remain unchanged across various robustness analyses. [ABSTRACT FROM AUTHOR]

The dynamics of central government funding to regions depend on local investments. In regional autonomy, local governments are encouraged to be more self-reliant from the central government. For regions with high natural resource yields, they will not encounter difficulties in meeting their fiscal needs. Community welfare can be realized through fulfilling basic needs, one of which is infrastructure development. High-quality infrastructure will be able to contribute to further progress in trade, thus enhancing production efficiency. The objective of this research is to analyze the extent of the influence of central government transfer funds, especially the Natural Resource Revenue Sharing Funds (DBH SDA), on local government investments in infrastructure across 508 districts/cities in Indonesia. The method used is dynamic panel regression using the Generalized Method of Moment (GMM) Arellano-Bond approach. This study finds that the role of DBH SDA is still low in infrastructure spending. The role of the central government remains significant in determining infrastructure spending at the district/city level in Indonesia. This indicates that local governments rely more on other sectors in infrastructure investment. By enhancing the role of DBH SDA through technological advancements, it is hoped that the market value of natural resources can be higher through resource downstreaming. This strategy will have broader impacts, as labor needs can be absorbed not only in raw material production activities but also in the processing technology sector. Furthermore, the utilization of natural resources with modern technology can increase extraction efficiency, support sustainable development, and minimize environmental impacts. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I–V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I–V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles' population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I–V dataset and an I–V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach. [ABSTRACT FROM AUTHOR]

The current research project investigates the correlation between economic growth, government spending, and public revenue in seventeen Indian states spanning the years 1990 to 2020. An analysis of the relationship between key fiscal policy variables and economic growth was conducted utilising a panel data approach, the Generalised Method of Moments (GMM), and fully modified Ordinary Least Squares (FMOLS & DOLS) estimation. In our investigation, we assessed the impacts of non-tax revenue, development plan expenditure, tax revenue, and development non-plan expenditure on (i) the net state domestic product (NSDP) and (ii) the NSDP per capita. The findings indicate that the selected fiscal variables are significantly related. The results indicate that expeditious expansion of the fiscal sector is obligatory to stimulate economic growth in India and advance the actual development of the economies of these states. [ABSTRACT FROM AUTHOR]

The goal/purpose/objective of the inquiry is to empirically investigate the impact of capital structure (CS) on firm performance (FP), from an emerging capital market perspective, Ghana Stock Exchange (GSE). Using financial data from 36 listed-firms on the GSE, spanning 2010–2020, and employing a robust dynamic panel System of Generalized Method of Moment (GMM), this inquiry examined CS-FP-nexuses. Results revealed that whilst total-debt-to-total-equity-ratio, total-debt-to-total-assets-ratio, long-term-debt-ratio, and financial risk exhibited negative associations with FP; total-equity-to-total-assets-ratio, short-term-debt-ratio, cash conversion cycle, total assets turnover, tangibility, sales-growth, firm size, and firm age revealed positive associations with FP, suggesting that highly leveraged-firms are susceptible to insolvency due to high debt-financing cost with worsened macroeconomic instability effect via increased sensitivity of the economy to shocks. Recommendations were made to policymakers to revamp Ghana's capital market to encourage equity-investment and also entreat listed-firms to improve upon their profitability and utilize more retained earnings as an internal-financing source to minimize the interest cost on debt-financing. By utilizing data on all the 36 firms listed on the GSE, an uncharted/unexploited path, this current inquiry is one of the few studies that has contributed to the literature on capital structure-performance nexus in Ghana, an emerging West African country south of the Sahara. [ABSTRACT FROM AUTHOR]

This paper represents a pioneering effort to investigate multifractal dynamics that exclusively encompass the return time series of USA Education Group Stocks concerning two non-overlapping periods (before, during, and after COVID-19). Given this, we employ the Multifractal Detrended Fluctuations Analysis (MF-DFA). In this sense, we investigate the generalized Hurst exponent h (q) and the Rényi exponent τ (q) for each asset and quantify their statistical properties, which allowed us to observe separately the contributing small scale (primarily via the negative moments q) and the large scale (via the positive moments q). We perform a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. Also, we shall apply the inefficiency multifractal index to assess the COVID-19 shock for both periods. Our findings show that for both periods, the majority of these assets are marked by multifractal dynamics associated with persistent behavior (α 0 > 0. 5) , a higher degree of multifractality and the dominance of large fluctuations. At the same time, most of these assets show asymmetry parameter (R > 1) for both periods, indicating that large fluctuations contributed more to multifractality in the time series of returns. [ABSTRACT FROM AUTHOR]

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer's Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer's Disease, which is crucial for early disease detection and personalized treatment. [ABSTRACT FROM AUTHOR]

Countries devote a lot of time and effort to develop economically, but they nevertheless struggle to attain that growth consistently. Research into economic growth should have provided some guidance as to which actions these countries should adopt, but multiple studies using different methods and datasets generally have led to an array of outcomes that contradict one another. However, Granger causality analysis can provide better insight into the relationship between some key variables and economic growth to give these countries more direction. Granger causality results establish a stronger connection between variables, even though they do not depict true causation. We therefore use a Vector Auto Regression method with Generalized Method of Moments estimation and then Grangercausality to discover how commonly used variables in the literature individually impact economic growth and in what way for a selection of OECD and non-OECD countries. We choose government expenditure, foreign direct investment (FDI), education (as a proxy for human capital), population growth, urbanization, and trade openness as our variables of interest. We also utilize updated datasets from the World Development Indicators (WDI) ranging from the years 2000-2016, based on the availability of the data. We find that FDI and population growth do Granger-cause economic development for OECD countries but that government expenditures, education, urbanization, and trade openness do not show such Granger causality. However economic growth Granger-causes government expenditures, FDI, and education for these OECD countries. For the non-OECD countries, we find that FDI, population growth, and trade openness Granger-cause growth but Granger causality does not occur from government expenditure, education, and urbanization to economic development. Yet similar to the OECD countries, economic growth does Granger-cause FDI and trade openness for the non-OECD countries as well. The overall results indicate that all countries should encourage the expansion of foreign direct investment to grow their economy, which then in turn can lead to additional investment as well as more resources available for the country as a whole. We also suggest further studies should focus on the economic development of individual countries rather than regions, particularly in terms of how technology can drive growth and impact these other variables. [ABSTRACT FROM AUTHOR]

Foreign Direct Investment is theoretically expected to facilitate the transfer of knowledge from the home country to the host country, however, the empirical evidence on the subject is mixed. Some studies have shown that, on one hand, as competition grows, the incentive to innovate reduces with the decrease in monopoly rents (Schumpeterian effect). On the other hand, market competition can also boost investments in R&D activities incentivized by incremental profits (Escape-Competition effect). Therefore, this study aims to explore which of these two effects dominates in the selected group of countries. This study also identifies the moderators of the relationship between FDI stock and domestic innovation. It examines the role of absorptive capacity, quality of regulations, and property rights protection in the innovative activities of the host countries. Generalized Method of Moments is used to estimate the parameters of the multivariate regression equation. The analysis is based on panel data consisting of 49 countries over 14 years. The results show that FDI has a negative relationship with domestic innovation, indicating the presence of the Schumpeterian effect. The extensions of the main models show that FDI positively affects domestic innovation in countries with higher absorptive capacity, the superior quality of regulation, and stronger protection of property rights. This study shows that the positive relationship between FDI and domestic innovation is conditional on the ability to absorb knowledge and quality of governance in the recipient countries. [ABSTRACT FROM AUTHOR]

This specific research initiative aims to intricately examine the intricate dynamics connecting terrorism, corruption, and capital flight within the context of South Asian economies, encompassing countries including Bangladesh, India, Pakistan, and Sri Lanka. The principal objectives of this study entail a comprehensive investigation into the synergistic impacts of terrorism and corruption on the prevalence of capital flight. To realize these objectives, the study employs longitudinal data from 1990 to 2019, adopting the portfolio choice framework as its theoretical underpinning. In terms of methodology, the empirical inquiry uses the Generalized Method of Moments (GMM) estimation technique. The empirical findings derived from this analysis distinctly establish a statistically noteworthy and positive correlation between terrorism, corruption, and the occurrence of capital flight across multiple South Asian nations. In light of these discerning outcomes, it is strongly recommended that the governments of South Asian countries prioritize and actively pursue the fortification of their institutional governance mechanisms. This strategic approach is deemed crucial in efficaciously counteracting the escalation of capital flight. Specifically, a targeted focus on augmenting institutional governance practices, fostering transparency, fortifying anti-corruption measures, and intensifying counterterrorism efforts could collectively contribute to reducing capital flight tendencies. By undertaking these recommendations, South Asian governments can foster an environment of enhanced economic stability, attractiveness for investment, and sustainable growth, thereby deterring the adverse impact of capital flight while concurrently combatting the underlying challenges posed by terrorism and corruption. [ABSTRACT FROM AUTHOR]

Environmental regulations are important means to influence manufacturing enterprise green development. However, there are two completely different conclusions both in theoretical and in empirical research, namely the "Follow Cost" theory and the "Porter Hypothesis". The nonlinear mechanism needs to be considered. Therefore, this study aims to explain the threshold impact of heterogeneous environmental regulations on enterprise green total factor productivity. Environmental regulations are divided into different sub-categories, then based on the panel data of 1220 Chinese manufacturing listed companies from 2011 to 2020, this paper uses threshold regression model to examine the impact of heterogeneous environmental regulations on Chinese manufacturing enterprise Green Total Factor Productivity. The empirical results show that: (1) Command-controlled, market-incentive and voluntary-agreement environmental regulation all have a significant nonlinear impact on enterprise Green Total Factor Productivity. (2) Enterprise R&D investment plays a threshold role in the impact. (3) There are industry and equity type differences in the impact process. This study focuses on the micro level of enterprises and tests the threshold mechanism, which make some theoretical complement to previous researches. The research results are not only beneficial for the government to propose appropriate environmental regulatory policies, but also for enterprises to achieve green growth through heterogeneous R&D investment. [ABSTRACT FROM AUTHOR]

This work investigates whether investor sentiment influences earnings management, specifically on using discretionary accruals to meet earnings benchmarks. The sample includes all non-financial firms listed on B3 (Bolsa, Brasil, Balcão) between 2010 and 2017. We use a GMM (Generalized Method of Moments) panel estimation to analyze the general relationship between investor sentiment and earnings management, and a logistic regression to analyze whether high sentiment increases the likelihood of management occurring in specific cases. As a result, it was not possible to conclude that investor sentiment influences the general use of earnings management. However, there was a higher probability of using earnings management to outperform the previous period's earnings when the sentiment was pessimistic. Our results are robust to alternative periodicities for the sentiment index and different ways of discretionary accruals estimation. [ABSTRACT FROM AUTHOR]

Energy is a key factor in the production and industrialization process in developing countries. This article aims to analyse the effect of energy policies on industrial development across 25 sub-Saharan African countries from 1996 to 2020. For this purpose, a new industrial development indicator and the Driscoll–Kraay estimation method are used. Results reveal that the implementation of energy policies promotes industrial development. Indeed, public energy policies and, more specifically, public investments in energy capital have a positive impact on the growth rate of the industrial sector's value added per capita. These results remain robust when using the generalized method of moments. In terms of policy implications, the results of this research suggest that the strengthening of existing public energy policies and the implementation of new policies with a focus on investments in energy capital accumulation are necessary to drive sustainable industrial sector development in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the impacts of total subscribers, net investment, mobile data traffic, and mobile number portability on the net sales revenues. In this study, the effects of total subscribers, net investment, mobile data traffic, and mobile number portability on the net sales revenues are tested by using Generalized Method of Moments. The data cover the period of 2010:01-2019:03 (quarterly) for Turkish mobile network market. The Turkish mobile network market is dominated by the three major firms Turkcell, Vodafone, and Avea. According to findings of this study, the most efficient variable that affects net sales revenues is the total mobile data traffic in Turkcell. However, the most efficient variable that affects net sales revenues is the number of total subscribers in Vodafone and Avea. While the number of total subscribers does not have a statistically significant effect on net sales revenues in Turkcell, does significant effect on net sales revenues in Vodafone and Avea. Also, total mobile data traffic does not have effect on the net sales revenues in Avea, but others. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to verify whether the management of taxable income through aggressive tax practices negatively impacts the accuracy of market analysts' forecasts. Originality/value: The central contribution of this study is to relate tax aggressiveness with more significant errors in analysts' forecasts. Thus, the relevance of this paper is in showing that this practice can lead to a greater perception of companies' risks and more significant informational asymmetry, in addition to influencing stock prices and resulting in lower analyst accuracy. Design/methodology/approach: This research paper was developed from regression models with panel data with random effects, also using estimators based on instrumental variables with the application of Jensen's test to verify the validity of the generalized method of moments (GMM) estimation. The data were treated with the winsorization technique at the 1% level. Findings: A sample of companies listed on the B3 between 2010 and 2017 (with a total of 2,805 valid observations) showed that companies with higher tax aggressiveness are subject to a higher analyst forecast error. Thus, it is inferred that, for more aggressive companies, the degree of analyst predictability decreases. With this drop in the quality of analysts' forecasts, the users of their forecasts and reports are more vulnerable to informational asymmetry. [ABSTRACT FROM AUTHOR]

Generalized Pareto distribution (GPD), an asymmetrical distribution, primarily models exceedances over a high threshold in many applications. Within the peaks-over-threshold (POT) framework, we consider a new GPD parameter estimation method to estimate a common tail risk measure, the value at risk (VaR). The proposed method is more suitable for the POT framework and makes full use of data information. Specifically, our estimation method builds upon the generalized probability weighted moments method and integrates it with the nonlinear weighted least squares method. We use exceedances for the GPD, minimizing the sum of squared differences between the sample and population moments of a function of GPD random variables. At the same time, the proposed estimator uses three iterations and assigns weight to further improving the estimated performance. Under Monte Carlo simulations and with a real heavy-tailed dataset, the simulation results show the advantage of the newly proposed estimator, particularly when VaRs are at high confidence levels. In addition, by simulating other heavy-tailed distributions, our method still exhibits good performance in estimating misjudgment distributions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess how some governance dynamics, such as political stability and the rule of law, moderate the incidence of some macroeconomic factors (i.e., domestic investment and trade openness) on tourism development. The focus of this study is on 47 countries in sub‐Saharan Africa with data from 2002 to 2018, and the Generalized Method of Moments is employed as the empirical strategy. From the findings, synergy effects are apparent in the role of the rule of law in moderating domestic investment for tourism development in terms of tourism receipts. It follows that, for the sampled countries, promoting tourism development can be most effective if policies for enhancing domestic investment and promoting the rule of law are implemented simultaneously. [ABSTRACT FROM AUTHOR]

Purpose: The studies that explore the impacts of national intellectual capital on informal economy are scant. Moreover, the effect of an external factor such as institutional quality that moderates this relationship has largely been neglected in previous studies. Institutions are considered important pillars to accumulate national intellectual capital and reduce shadow economy. As such, this paper aims to investigate how institutional quality moderates the effects of national intellectual capital on informal economy in 17 Asian countries from 2000 to 2018. Design/methodology/approach: This paper uses the generalized method of moments techniques, which allow cross-sectional dependence and slope homogeneity in panel data, to examine the moderating role of institutional quality on the relationship between national intellectual capital and informal economy. Various tests are conducted to ensure the robustness of the findings. Findings: Empirical findings from this paper indicate that an increase in national intellectual capital and institutional quality declines the informal economy. Interestingly, better institutional quality aggravates the negative effects of national intellectual capital on reducing the size of informal economy. The author also finds that enhancing international trade and economic growth results in a decrease in the informal economy in Asian countries. Practical implications: Empirical findings offer policymakers an indication of the relationships between national intellectual capital, institutional quality and informal economy, pointing out that national intellectual capital and institutional quality should be strengthened to allow Asian countries to limit the informal economy. Originality/value: This study provides a conceptual model through which the moderating role of institutional quality on the national intellectual capital–informal economy nexus can be recognized. This approach has thus far not been investigated in the existing literature. To the best of the author's knowledge, this study makes an original contribution to the empirical of national intellectual capital and informal economy nexus and produces new insights into the fields of the moderating effects of institutional quality on this nexus. [ABSTRACT FROM AUTHOR]

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine on the ways in which a company's sustainability reporting (SR) differs depending on the types of ownership it has, notably director ownership (DO), institutional ownership (IO), and foreign ownership (FO). This paper uses a sample of 145 firm-year observations from 29 listed banks in the Dhaka Stock Exchange (DSE) of Bangladesh from 2016 to 2020 and regression analysis to test the relationship between different ownership structures and SR. The study also employed the generalized method of moments (GMM) to deal with the endogeneity issue. The findings show that investors have varying perspectives on SR disclosure activity. The findings show that two stockholding patterns, DO and FO, significantly impact SR disclosure in Bangladesh but that there is no significant association between IO and SR disclosure. In constructing SR, this paper depends on only the quantity of disclosure, not the quality, and is solely based on the published annual report. The study findings also provide a comprehensive overview of the SR practice of Bangladeshi banks, which may be helpful to regulators, policymakers, management, creditors, and other stakeholders in making investment and economic decisions. To encourage SR commitment, the government may need to keep an eye on the ownership structure and implement policies that encourage foreign investment. To the best of the authors' knowledge, this is the first study to look at how ownership structure influence SR practices in the banking industry of Bangladesh over five years. [ABSTRACT FROM AUTHOR]

Researchers in the field of sustainable management have recently dedicated significant efforts to understand why organizations exhibit diverse responses to environmental responsibilities. Ethical scholars assert that internal management plays a pivotal role in promoting sustainability because of its attitude toward sustainable issues. In alignment with this perspective, our study formulates a framework for internal monitoring that underscores the significance of independent, female, international, and politically connected directors. We investigate how these internal monitors influence a company's commitment to promote sustainable investments for pollution prevention. By employing fixed effect model and generalized method of moments (GMM) on a dataset obtained from the largest emerging market—China over the period 2012 to 2019, our findings indicate that the mentioned monitors demonstrate a stronger commitment to pollution prevention by promoting corporate sustainable investments. In addition, our analysis reveals that when the government withdraws its involvement in enterprises, it has a notable negative impact on the connection between internal monitors and a company's efforts in pollution prevention. Our results imply that implementation of sustainable policies for pollution prevention fundamentally result from not only internal management, but also from powerful stakeholders (like government involvement). Moreover, our study educates the policy makers regarding the social consequences of governmental withdrawal. [ABSTRACT FROM AUTHOR]

Motivated by the difficulty of ensuring gender equality and the chaotic state of democracy, we analyze the effects of gender political inclusion and democracy on environmental policy performance. The study uses a panel of 45 African countries over the period 2012–2018 and employs the method of moments by quantile regression. The results show that gender political inclusion and democracy positively affect environmental performance in all quantiles. These positive effects tend to be stronger at higher quantiles. The magnitude is larger for gender political inclusion. When performance is decomposed into the sub‐indices of environmental health and ecosystem vitality, positive effects of gender political inclusion and democracy are observed in all quantiles. The effects are larger for the gender dimension than for the democracy dimension, regardless of the sub‐index used. Related Articles: Bingham, Natasha. 2016. "Fighting for Our Cause: The Impact of Women's NGOs on Gender Policy Adoption in Four Former Soviet Republics." Politics & Policy 44(2): 294–318. https://doi.org/10.1111/polp.12155. Hankivsky, Olena. 2013. "Gender Mainstreaming: A Five‐Country Examination." Politics & Policy 41(5): 629–55. https://doi.org/10.1111/polp.12037. Nchofoung, Tii, Simplice Asongu, Vanessa Tchamyou, and Ofeh Edoh. 2022. "Gender, Political Inclusion, and Democracy in Africa: Some Empirical Evidence." Politics & Policy 51(1): 137–155. https://doi.org/10.1111/polp.12505. [ABSTRACT FROM AUTHOR]

This paper analyzes the effects of productive capacities on tax revenue mobilization in 37 Sub‐Saharan African countries. Using the Generalized Method of Moment (GMM) approach, the results show that productive capacities measured by the aggregate Productive Capacity Index (PCI) and its eight components, namely human and natural capital, energy, transport, Information and communication technologies, private sector, institutions and structural change positively and significantly affect total tax revenue mobilization. For example, an increase in one point of the PCI corresponds to an increase in total tax revenues by 0.0016 point. The effect of productive capacities also remains positive and significant on tax revenues, even when disaggregated into direct and indirect taxes. This paper contributes empirically to highlighting the importance of productive capacity in tax revenue mobilization. Hence, strengthening productive capacities is important to enhance tax revenue mobilization in Sub‐Saharan African countries. To meet the current challenges of mobilizing the fiscal resources needed to meet international agendas, Sub‐Saharan African countries must not only work to strengthen overall productive capacity, but also promote the quality of institutions, transport and energy, invest in human capital, create the conditions for real structural transformation, promote good digital governance and a more dynamic private sector. [ABSTRACT FROM AUTHOR]

This study empirically addresses the relevance of public transparency in promoting sustainable development in Sub‐Saharan Africa. Using the generalized method of moments techniques in order to account for concerns of endogeneity and simultaneity, the study utilizes a panel of forty‐eight (48) countries in Sub‐Saharan African countries for the period 2000‐2019. We then establish the subsequent findings. Public transparency maintains overall positive net effects on both the human development index (i.e., social sustainability); adjusted net national income growth (i.e., economic sustainability), and renewal energy consumption (i.e., environmental sustainability). Overall, we establish that public transparency enhances sustainable development. This study recommends that policymakers should ensure that public transparency processes are enhanced in order to achieve the post‐2015 agenda. [ABSTRACT FROM AUTHOR]

The environmental Kuznets curve (EKC) hypothesis assumes the nonlinear relationship between production and its environmental effects, suggesting that the negative effect of production on the natural environment is reversed at higher levels of development. In contrast to the majority of the studies, this paper considers the possibility of non‐linear effects of per capita output on agricultural land use (cropland), as one of the natural resource stocks, and additionally examines the influence of demographic and productivity variables on the agricultural land use. The study draws on panel data from 143 economies over the 1990–2019 period and employs a series of panel econometrics techniques (including panel unit root and cointegration tests, panel ordinary least squares, generalized method of moments, and panel quantile models). The results unequivocally indicate the inverted U‐shaped relationship between the GDP per capita and cropland use, as per EKC hypothesis. The results are robust with respect to employing different techniques, the identified turning points correspond to low and low‐middle‐income levels, and the effects of agricultural productivity and population density on land use are respectively negative and positive. [ABSTRACT FROM AUTHOR]

This study investigates the fiscal implications of natural resource dependence in resource‐rich sub‐Saharan African economies, particularly focusing on the impact of resource revenues on tax revenues, government spending and national savings to assess the sustainability of natural resource reinvestments. Employing a two‐step system Generalized Method of Moments on a dataset with 672 observations, the study identifies significant reductions in non‐resource tax revenues, government current consumption and overall savings associated with an increase in resource revenues. Conversely, a positive coefficient is noted for public investment. The study underscores the importance of high‐quality institutions in mitigating adverse effects and controlling increased public investments. Recommendations include directing resource revenues towards well‐structured taxation frameworks and investing in efficient institutions to manage current consumption, monitor public investment growth and enhance savings. These measures are crucial for curbing procyclical spending and promoting sustainable development in resource‐rich sub‐Saharan African economies. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of older people under the background of population aging has gradually changed the disease spectrum of society, making aging diseases more prevalent, and increasing the demand for health care services, medical and health services, and health insurance among older people, ultimately leading to increasing household and social spending on old age. This study is conducted to assess the impact of those spending burden on the sustainable development of economy and find out some practical and effective solutions. This paper constructs a theoretical model to illustrate the relationship between the old-age dependency ratio and the marginal product of capital (MPK), and then establishes a two-way fixed effect model based on transnational panel data of 81 countries from 1981 to 2017 to verify this relationship empirically. This paper finds that, after controlling a series of variables, an increased burden of old-age dependency leads to a decline in the MPK, a key macroeconomic variable and also a sustainable development criteria, but in which health care, health security systems, and technological innovation play a key and moderating role. The conclusion is also valid after tackling the problem of endogeneity with different methods, like two-stage least squares (TSLS) and the generalized methods of moments (GMM). Overall, before population aging, countries that are old-but-not-rich should encourage more supply-side investments in public health system or technological innovation, and adjust retirement system, or gradually encourage childbearing to strive for time and space for later sustainable development of public health system and economy. [ABSTRACT FROM AUTHOR]

Corruption remains a major challenge to sustainable economic growth, good governance, peace, and stability in both developed and developing countries. However, in developing countries, and particularly in Africa, hunger is another big challenge to inclusive economic development. To date, no empirical study has examined the effects of different types of corruption on hunger. Using three types of corruption (executive, legislative, and judicial corruption dynamics) and a panel of 45 African countries, this study contributes to the literature on the effects of corruption by examining, as a first attempt, the impact of types of corruption on hunger. We address the weak time-variance of our main regressors by using the most recent sequential linear panel dynamic estimator. The results show that countries with higher levels of executive, legislative, and judicial corruption are associated with a higher level of hunger. Moreover, the results show that executive corruption is the most disastrous for hunger in Africa, followed by legislative corruption. Our results remain valid even after using alternative measures of the key variables (hunger and corruption) and after controlling for the dynamic endogeneity using the generalized method of moments. Further analysis provides strong evidence that the political distribution of power across social groups mitigates the effect of corruption on hunger. [ABSTRACT FROM AUTHOR]

This study verified the existence of a non-linear relationship between diversification and the performance of Brazilian credit unions by analyzing panel data from 455 credit unions from June 2012 to December 2019. Through three regression models, estimated by the Systemic Generalized Method of Moments, it was identified that diversification has a non-linear U-shaped relationship with performance. This result demonstrates the existence of a minimum point for diversification, from which increases in diversification of financial products would provide positive effects on performance. This study stands out for bringing a curvilinear model for diversification in credit unions. This finding facilitates the understanding of which level of diversification is beneficial for the performance of credit unions, in addition to favoring the formation of strategies and analysis of the implementation of new financial products in credit unions. [ABSTRACT FROM AUTHOR]

In recent years global hunger has increased across the world. Africa is the main contributor to this and more people in Africa are affected by undernourishment. This issue is likely to be exacerbated by the rapid development of the biofuels industry across the globe. However, it would be very strange to conclude that the production of biofuels should be neglected given that biofuels are intended to preserve the quality of the environment. Hence, the objective of this study is to search for a win‐win solution between biofuels and food security. Dynamic generalized method of moments results indicate that biofuels will have a positive effect on food security if their production can also promote environmental quality, which is favorable to crop production. Thus, it indicates that the significant expansion and consumption of biofuels could contribute to increasing food security and sustaining environmental quality. [ABSTRACT FROM AUTHOR]

In this paper, a compound-Gaussian model (CGM) with the Nakagami-distributed textures (CGNG) is proposed to model sea clutter at medium/high grazing angles. The corresponding amplitude distributions are referred to as the CGNG distributions. The analysis of measured data shows that the CGNG distributions can provide better goodness-of-the-fit to sea clutter at medium/high grazing angles than the four types of commonly used biparametric distributions. As a new type of amplitude distribution, its parameter estimation is important for modelling sea clutter. The estimators from the method of moments (MoM) and the [zlog(z)] estimator from the method of generalized moments are first given for the CGNG distributions. However, these estimators are sensitive to sporadic outliers of large amplitude in the data. As the second contribution of the paper, outlier-robust tri-percentile estimators of the CGNG distributions are proposed. Moreover, experimental results using simulated and measured sea clutter data are reported to show the suitability of the CGNG amplitude distributions and outlier-robustness of the proposed tri-percentile estimators. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to examine the influence of life cycle stages on the corporate decisions of Brazilian firms. Originality/value: The study contributes by presenting a scenario of investment, financing, dividend, and cash decisions, revealing patterns and characteristics of the evolution of Brazilian firms throughout their life cycle stages. For this, we tested an alternative life cycle proxy capable of classifying firms in different stages, even shake-out and decline, not defined by the model of Dickinson (2011), and applied a more robust methodology (GMM-SYS) considering potential endogeneity problems disregarded by previous studies. Design/methodology/approach: The sample consists of 203 traded firms listed on B3 from 2010 to 2018. We collected the data from the Thomson Reuters Eikon database. We estimated the parameters of the models by GMM-SYS (Generalized Method of Moments) to mitigate problems of endogeneity, omitted variables, and heterogeneity. Findings: As our main results, we have that Brazilian firms do not follow a pattern and oscillate between stages and that there is strong evidence of the effect of the life cycle on corporate decisions. In the introduction and growth stages, firms invest more, have more debt, pay fewer dividends, and have greater cash availability. In the mature stage, investments, debts, and the level of cash are lower. In the shake-out stage, no results were significant, requiring further studies to better explore this stage in developing countries, such as Brazil. [ABSTRACT FROM AUTHOR]

Applied researchers often confront two issues when using the fixed effect-two-stage least squares (FE-2SLS) estimator for panel data models. One is that it may lose its consistency due to too many instruments. The other is that the gain of using FE-2SLS may not exceed its loss when the endogeneity is weak. In this paper, an $ L_{2} $ L 2 Boosting regularization procedure for panel data models is proposed to tackle the many instruments issue. We then construct a Stein-like model-averaging estimator to take advantage of FE and FE-2SLS-Boosting estimators. Finite sample properties are examined in Monte Carlo and an empirical application is presented. [ABSTRACT FROM AUTHOR]

The suspension system of the maglev train will inevitably be disturbed by random factors such as track irregularities, which will cause random vibration of the train and even affect the safety of the train. Therefore, the research on the response and reliability of suspension system under random disturbance is crucial to its safe operation. In this paper, the response and the reliability of a suspension system are investigated using the theory and methods of stochastic dynamics. First, the magnetic gap and vertical velocity of the suspension system are random due to the random disturbance. Thus, the stochastic response is investigated through the probability density function (PDF), which is governed by the Fokker–Planck–Kolmogorov (FPK) equation corresponding to suspension system. And the response statistics of the suspension system under different system parameters and disturbance intensities are analyzed by solving the corresponding FPK equation using the finite difference (FD) method. Second, random disturbance may lead to the vibration amplitude of the suspension system exceeding the safety domain and causing safety incident, which is a reliability problem in stochastic dynamical systems. The probability that response is still in the safety domain at a given time is the reliability function of the suspension system, which is governed by the backward Kolmogorov equation. The time that the response first passes through the safety domain is the first-passage time, and its n-order moment satisfies the generalized Pontryagin equation. Reliability of the suspension system is analyzed by solving these governing equations using the FD method. In addition, the results of the FD method in this paper are verified with those of Monte Carlo (MC) simulation, which shows the correctness of FD method. [ABSTRACT FROM AUTHOR]

This article studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain circumstances, because it nests the GLS principle. We first show how our unfeasible weighted-estimator provides a bias-adjusted estimator with the conventional limiting distribution, for situations in which the OLS is affected by a first-order bias. The technical challenge resolved in the article consists of showing how these properties are preserved for the feasible weighted estimator in a double-asymptotics setting. Our theory is illustrated by extensive Monte Carlo experiments and an empirical application that investigates the link between capital accumulation and economic growth in an international setting. for this article are available online. [ABSTRACT FROM AUTHOR]

This research paper aims to investigate. the impact of inflation. on financial sector performance in Western. Balkan. countries. The topic was chosen considering the financial sector's fundamental role and its. impact on sustainable economic growth. This impact is measured through the effect that macroeconomic determinants of financial. performance such. as inflation, GDP. growth, general government final consumption expenditure, trade, and the lending interest rate have on credit to private sector as a share of GDP and broad definition of money as a ratio of GDP, which were used to proxy financial sector development. The data are provided by the World Bank as well as the Central. Banks. of the respective countries, for the period 2002–2021. The research methodology consists of different panel regression models such as OLS, OLS robust, Fixed effects, Random effects, and Generalized method of moments (GMM). Findings indicate that domestic credit to the private sector and inflation are positively correlated, whereas broad money and inflation are negatively correlated. [ABSTRACT FROM AUTHOR]

This study examines the impact of armed conflicts on the fiscal capacity of African governments. It made use of a data set covering 1997–2021 for 50 countries, as well as the system dynamic generalized method of moment estimation technique. The results show that, in the short run, conflicts undermine tax revenue, mount pressure on military expenditure, and force governments to rely more on mineral resource rents for their fiscal needs. As conflicts persist, this fiscal feature changes to a pattern that reflects a decline in mineral resource earnings and an increase in tax revenue. The impact on public health expenditure also changes from an increasing to a decreasing pattern, whereas the positive impact on military expenditure and external borrowing persists over time. The findings suggest that African countries in conflict can address their fiscal challenges by observing these patterns and putting in place policies that protect public resources (e.g., the adoption of digital financial technology protocols to facilitate remote revenue collection and strategic protection of mineral resource‐endowed zones from insurgents' control). Overall, enhancing government effectiveness and strengthening the institutions of governance is important to facilitate a quick return to normalcy in the event of conflict and to prevent future conflicts. [ABSTRACT FROM AUTHOR]

Missing data are a common problem that researchers face in practice. In this article, we focus on the missing response problem for a spatial dynamic panel data (SDPD) model, which allows for both spatial and temporal dependencies. A logistic regression with a set of prespecified covariates is used to model the missingness mechanism, which is assumed to be missing at random (MAR). A weighted maximum likelihood estimator (WMLE) is proposed for parameter estimation in the presence of incomplete data. The associated asymptotic properties are investigated. Thereafter, we develop a novel imputation method, which makes use of the information from spatial dependence, temporal dependence and exogenous regression covariates. Lastly, the performance of WMLE and the proposed imputation method are demonstrated by both simulation studies and a real data example. [ABSTRACT FROM AUTHOR]

We identify sufficient conditions to resolve the identification problem under nonignorable missingness, especially the identifiability of the observed likelihood when some of the covariate values are missing not at random, or, simultaneously, the response is also missing not at random. It is more difficult to tackle these cases than the nonignorable nonresponse case, and, to the best of our knowledge, the simultaneously missing case has never been discussed before. Under these conditions, we propose some parameter estimation methods. As an illustration, when some of the covariate values are missing not at random, we adopt a semiparametric logistic model with a tilting parameter to model the missingness mechanism and use an imputed estimating equation based on the generalized method of moments to estimate the parameters of interest and the tilting parameter simultaneously. This approach avoids the requirement for other independent surveys or a validation sample to estimate the unknown tilting parameter. The asymptotic properties of our proposed estimators are derived, and the proofs can be modified to show that our methods of estimation, which are based on inverse probability weighting, augmented inverse probability weighting, and estimating equation projection, have the same asymptotic efficiency when the tilting parameter is either known or unknown but estimated by some other method. In simulation studies, we compare our methods with various alternative approaches and find that our methods are more robust and effective. [ABSTRACT FROM AUTHOR]

Intellectual property rights (IPRs) have a significant impact on facilitating the economic endeavors of countries. Nevertheless, there exists notable disparity among studies concerning the implications of IPRs within developing countries. Therefore, this study examines how stronger IPRs affect economic activity and moderate two important knowledge channels, domestic and foreign innovation activity. Using a sample of 18 Latin American countries from 2007 to 2018, we employed the Driscoll-Kraay robust standard errors, two-stage least squares (2SLS), and Generalized Method of Moments (GMM) to examine the effects of IPRs. Results confirm an inverted U-shaped relationship between IPRs and economic activity. Hence, the majority of Latin countries continue to vary in the factors of production that suport robust IPRs. Conversely, robust IPRs effectively improve the relation between domestic innovation and economic activity. Similarly, this influence holds true for foreign innovation as well. Based on this evidence, the research suggests implementing an optimal IPR policy. [ABSTRACT FROM AUTHOR]

Modern longitudinal data, for example from wearable devices, may consist of measurements of biological signals on a fixed set of participants at a diverging number of time-points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analysing the cumulatively growing dataset each time new data are collected. We propose a new estimation and inference framework for dynamic updating of point estimates and their standard errors along sequentially collected datasets with dependence, both within and between the datasets. The key technique is a decomposition of the extended inference function vector of the quadratic inference function constructed over the cumulative longitudinal data into a sum of summary statistics over data batches. We show how this sum can be recursively updated without the need to access the whole dataset, resulting in a computationally efficient streaming procedure with minimal loss of statistical efficiency. We prove consistency and asymptotic normality of our streaming estimator as the number of data batches diverges, even as the number of independent participants remains fixed. Simulations demonstrate the advantages of our approach over traditional statistical methods that assume independence between data batches. Finally, we investigate the relationship between physical activity and several diseases through analysis of accelerometry data from the National Health and Nutrition Examination Survey. [ABSTRACT FROM AUTHOR]

The effect of a difference in regions and economic levels, coupled with variations in natural resource rents, total import and export, and economic growth, has been ignored in studies that investigated the link between natural resource rents, trade openness, and economic growth across African countries. To overcome these deficiencies, this study aims to examine the impact of total natural resource rents and trade openness in terms of total import and export on economic growth across regions and the economic levels of African countries. Recent econometric models (CS-DL), variance decomposition, and impulse response were employed for the panel of 48 African countries from 1980 to 2018. Findings revealed total natural resource rent has a long-term significant negative effect on economic growth across regions and economic levels. Trade openness in terms of total export has a significant positive effect on growth at the panel of 48 countries, while the effect of both total import and export is mixed across regions and economic levels. A bi-directional causations relationship between natural resource rents, trade openness, and growth in the panel of involved countries, some subpanels. It was noted that trade openness counted higher variations of GDP than resource rents within 10 years. Empirical results of this study awaken up regional and government policymakers to reflect on negative effects and neutral links noticed between variables across economic levels and regions for reasonably sustainable developments across Africa. [ABSTRACT FROM AUTHOR]