Fixed-effects Model Research Articles

SUKUK: UNVEILING THE STRUCTURAL UNDERPINNINGS, YIELD SPREAD DYNAMICS, AND COMPARATIVE ANALYSIS WITH CONVENTIONAL BONDS IN THE INDONESIAN FINANCIAL LANDSCAPE

This study delves into whether factors influencing credit spreads differ between sukuk and bonds within the vibrant Indonesian secondary market. The research leverages unbalanced panel data encompassing 2017-2023, meticulously obtained from Thomson Reuters Eikon and CEIC. To extract meaningful insights, a Fixed-Effect Model analysis is employed, a robust technique for handling this type of data. The study further conducts a partial analysis to recognize the potential heterogeneity within the sukuk market. This analysis explores explicitly whether the factors driving yield spread exhibit variations between sukuk structured under the ijarah and mudharabah principles. The study's core finding challenges the notion that investors perceive sukuk as a perfect substitute for conventional bonds in terms of the risk-return profile. This is evidenced by the high degree of similarity in the factors that influence the credit spreads of both instruments. However, a fascinating discovery emerges from the partial analysis. It reveals that mudharabah sukuk stands out as a distinct product within the Indonesian secondary debt market compared to its ijarah counterpart. This finding warrants further investigation into the specific characteristics of mudarabah sukuk that contribute to its distinctiveness.

Level of financial disclosures for listed insurance companies using ISO 31000: empirical evidence from Jordan and Palestine

PurposeIn light of the complex risk and transparency regulations, this paper investigates the factors influencing the level of risk management disclosure by insurance firms in Jordan and Palestine. The characteristics examined were ownership structure, which covers public, institutional and management ownership on risk management disclosure (RMD) utilizing ISO 31000, as well as profitability, leverage, liquidity and firm size.Design/methodology/approachTo achieve the aim of this study, a quantitative research methodology was used. Based on the total number of observations, 232 purposeful annual observations for the study sample were collected between 2016 and 2023 for 10 insurance companies listed on the Palestine stock exchange (Palestinian companies) with 80 observations and 19 companies listed on the Amman stock exchange (Jordanian companies) with 152 observations. This study uses panel data regression with fixed effects models. By employing the 2SLS approach, we comprehensively address the main endogeneity concerns and problems in risk management disclosure RMD of insurance firms in Jordan and Palestine.FindingsThe results show that risk management disclosure is significantly influenced by the liquidity and size of an organization. Furthermore, RMD is not significantly affected by profitability, debt, public ownership, institutional ownership or liquidity, whereas business size has a favorable influence.Research limitations/implicationsThe findings of this study may not be generalizable to firms in other countries because of the limitations of insurance firms in Palestine and Jordan. Study replication in future studies should consider the potential for bias and differences in data interpretation when utilizing qualitative methodologies to evaluate RMD.Practical implicationsThe practical implications emphasize how crucial it is for investors, practitioners and stakeholders to choose firms that are large and have little liquidity because this is linked to high levels of risk management transparency. This knowledge can offer investors an important direction for assessing possible risks and transparency in risk management within the insurance sector framework. The study recommends that the governments of Palestine and Jordan enact laws requiring risk management disclosure according to the ISO 31000:2018 standard, especially in the insurance industry.Originality/valueThis study contributes to the literature by illuminating the relationship between firm size, liquidity and risk management disclosure in insurance companies operating in Jordan and Palestine. Therefore, investors should choose large, relatively liquid companies with strong risk management disclosure. This study offers theoretical insights that may be used as a guide for other research, improving the understanding of the variables influencing risk management disclosure in insurance companies and advancing scientific understanding.

Problematizing the Black-White racial dichotomy inherent to high school graduation rate accountability: A mixed methods critical policy analysis

The U.S. Department of Education introduced the four-year adjusted cohort high school graduation rate formula in 2008 to usher high school graduation in as a high-stakes accountability metric. This policy sought to address disparate graduation rates by student race but did not attend to systemic reasons why minoritized students had lower graduation rates. Using a critical structural capital theoretical framework, this mixed methods critical policy analysis analyzes federal and state-level policy on high school graduation rate accountability and state administrative data from North Carolina to assess how policy ignores structural inequality in ways that result in divergent access to traditional forms of capital, dismissing non-traditional forms of capital necessary for college and career readiness. A critical discourse analysis of policy finds language that alludes to college and career readiness but relies on high school graduation alone as the metric. We use these findings to provide context for our quantitative analysis. In school-by-cohort fixed effects models, Black students were more likely to fail courses than similar White students but more likely to graduate high school. These findings indicate federal accountability incentivized schools to push more Black students to graduate but did so at the expense of their college and career readiness.

A study on the potential of higher education in reducing carbon intensity.

The Chinese government has established definitive goals to reach a "carbon peak" by 2030 and achieve "carbon neutrality" by 2060. Investigating the attainment of these emission reduction objectives while simultaneously fostering regional economic growth and enhancing living standards holds critical importance. This study examines the link between higher education and carbon intensity across China's thirty provincial-level administrative regions, employing fixed effects models on provincial panel data spanning 2001-2020. The findings, validated through robustness tests and a mediation effect model, elucidate the mechanisms by which higher education influences carbon intensity. Notably, the results reveal that enhancing higher education markedly lowers carbon intensity; specifically, a 1% increase in the logarithmic transformation of per capita investment in higher education in a province decreases its carbon intensity by 0.219%. Additionally, higher education's output similarly contributes to reductions in carbon intensity. The influence of higher education on reducing carbon intensity is particularly pronounced in the central and western regions of China. Moreover, higher education facilitates the reduction of carbon intensity through mechanisms such as promoting environmental consciousness, advancing industrial structure, and encouraging technological innovation.

Unlocking innovation potential: the impact of artificial intelligence transformation on enterprise innovation capacity

PurposeBuilding upon the resource-based view (RBV) and related research, this paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities. It provides micro-level evidence of AI’s influence on innovation behavior.Design/methodology/approachDrawing upon data from Chinese listed companies spanning the period from 2011 to 2022, this study employs a dual fixed-effects model and a mediation effects model to empirically analyze the influence of enterprise AI transformation on its innovation capability as well as the specific mechanisms involved.FindingsThe research reveals that AI transformation significantly enhances the innovation capability of enterprises. Heterogeneity analysis indicates that AI transformation exerts a stronger promoting effect on the innovation capability of non-technology firms, large enterprises and those within the manufacturing sector. Mechanism analysis further reveals that AI transformation enhances innovation capability by boosting enterprise profits, reducing costs and reinforcing internal control mechanisms. Further examination demonstrates that AI transformation elevates the quality, efficiency and eco-friendliness of enterprise innovation.Originality/valueFirstly, this study employs text analysis methods from machine learning to construct artificial intelligence indicators at the firm level, providing stronger evidence of AI’s impact on corporate innovation capabilities. Secondly, it extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.

Effects of sectoral and aggregate uncertainty on corporate debt structure

PurposeThis paper investigates the differential and interactive effects of sectoral and aggregate uncertainty on the corporate debt structure of credit-constrained and credit-unconstrained firms in India.Design/methodology/approachThe panel fixed-effect model was used to examine the effect of sectoral and aggregate uncertainty on corporate debt structure using annual data of 2,405 firms from 1997 to 2020.FindingsAn increase in sectoral and aggregate uncertainty leads to a greater increase in total and short-term debt and a decrease in long-term debt for credit-constrained firms than in unconstrained ones. However, sectoral uncertainty affects firms’ debt maturity structure more than aggregate uncertainty. Credit-constrained firms use short-term debt to finance capital expenditure during periods of high sectoral uncertainty and working capital requirements during periods of high aggregate uncertainty. When both sectoral and aggregate uncertainty are high, credit-constrained firms decrease total and short-term debt usage more than credit-unconstrained firms do. Our results are robust to a variety of tests, such as different measures of uncertainty, the use of instrument variables and controlling for economic conditions and financial crises.Originality/valueThis study addresses the differential and interactive effects of sectoral and aggregate uncertainty on corporate debt structure.

The effectiveness of Tai Chi for patients with mild cognitive impairment: a systematic review and meta-analysis.

To explore the effectiveness of Tai Chi on cognitive function in patients with mild cognitive impairment (MCI). According to the PRISMA guidelines, randomized controlled trial (RCT) literature on the efficacy of Tai Chi on MCI patients was searched in China National Knowledge Network (CNKI), China Biomedical Literature Database (CBM), Wanfang Data, China Scientific Journal Database (VIP), PubMed, Embase, Duxiu Database, Web of Science and Cochrane Library from their inception to April 2024. The risk of bias in each study was appraised using the Cochrane risk-of-bias tool using Revman 5.4. Random effect model or fixed effect model was used to compare the effects of Tai Chi and control conditions on baseline and post-intervention assessment of cognitive function. Meta-analysis was performed using Stata15.0 software. Nine studies fulfilled the inclusion criteria. Tai Chi significantly improved Montreal Cognitive Assessment (MoCA, SMD, 1.43, p < 0.00001), Delayed Recall Test (DRT, SMD, 0.90, p < 0.00001), verbal fluency test (VFT, SMD, 0.40, p < 0.00001), and Trail Making Test (TMT, SDM, -0.69, p < 0.00001) in MCI patients. Subgroup analyses showed that 24-forms Tai Chi was more effective than 8-forms Tai Chi in improving MoCA (SMD, 1.89, p < 0.00001) and 10-forms Tai Chi was more effective than 24-forms Tai Chi in improving DRT (SMD, 1.53, p < 0.00001). Tai Chi improved cognitive function in MCI patients, and Tai Chi types might be the influence factor on Tai Chi improving the global cognitive function and memory function in MCI patients. https://www.crd.york.ac.uk/prospero/.

Impact of population ageing on fiscal balance in the European Union

In the past two decades, the EU has experienced low fertility rates and declining mortality rates leading to population ageing. Although increasing immigration and longer life expectancy reinforced population growth, these trends could not counterbalance the negative impact of low birth rates on labour supply. Demographic transition towards an aging society is characterised by increase of public health and pension expenditures, as well as decrease of tax revenues that are triggers for fiscal imbalance. The paper aims to analyse the effects of population ageing on government fiscal balance in the EU-27 by applying modern methods of panel data analysis in the period 2001-2021. The dependent variable is general government fiscal balance, while the explanatory variables are related to demographic transition indicators (population aged over 64 in total population, old-age dependency ratio, and health expenditures) and macroeconomic control variables. The results of research show that population ageing has created a significant negative impact on fiscal balance of the EU countries, which was confirmed by all the analysed models. The fixed-effects panel threshold model did not identify a statistically significant threshold of any demographic transition indicator, concluding that population ageing has equally negatively affected fiscal balance, independent of the values of demographic transition indicators.

The driving factors, configuration paths and effects of employment polarization under the development of digital economy.

Taking the development of China's digital economy as a background, the article provides an in-depth analysis and summarises the influencing factors of labour force employment polarisation. The study employs provincial panel data from 2010 to 2020, applies fuzzy set qualitative comparative analysis methods, and constructs a fixed-effects model in order to explore the drivers, paths and their effects of employment polarisation. The results of the study show that economic, social and educational environments together have a significant impact on employment polarisation through interactions and synergistic effects; it also identifies four main paths of labour force employment polarisation, which are numerical-social-environment-driven, numerical-educational-environment-driven, numerical-economic-environment-driven, and types driven by other factors; this study also finds that compared to the summed impact of single elements, these grouping pathways have a more significant impact on employment polarisation. These findings not only provide a key perspective for understanding how the digital economy shapes employment polarisation, but also provide an empirical basis and insights for policies based on the findings.

Research on the Impact of Industry Digitization on Export Technology Complexity: Evidence from China

The rapid development of industrial digitization provides new opportunities for the development of China's real industry and the upgrading of export technical complexity. Based on the balanced panel data of 30 provinces in China from 2011 to 2022, this paper calculates the level of industrial digitalization in each province, and uses the fixed-effect model to discuss the effect of industrial digitalization level on the improvement of export technology complexity. The empirical findings are as follows: (1) The level of industrial digitization in China's provinces is increasing year by year and is related to the level of economic development; (2) China's industrial digitization level is unbalanced in the landing zone; (3) Industrial digitalization can promote the improvement of export technical complexity.

A Study of The Effect of Stock Liquidity on Enterprise Risk-Taking Level

Stock liquidity, as one of the important market characteristics of enterprises, not only affects the financing of listed enterprises, but also affects the risk decision of enterprise management, which in turn affects enterprise risk-taking level. In order to deeply explore the impact of stock liquidity on enterprise risk-taking level, this paper selects all the data of Shanghai and Shenzhen A-share listed companies from 2000 to 2023 in the Cathay Pacific CSMAR database as samples, and eliminates data from financial sector firms, and from companies designated as ST or ST*. Additionally, it excludes any instances where key financial metrics are incomplete or absent.After that, we carry out the descriptive statistics and correlation analysis of the samples, and conduct a analysis of the impact of stock liquidity on enterprise risk-taking level. Meanwhile, descriptive statistics and correlation analysis are carried out on the samples, and a two-way fixed-effects model regression is conducted around the impact of stock liquidity on enterprise risk-taking level, and lagged one period method, replacing the explained variables method and instrumental variables method are chosen to conduct the robustness test. Finally, it is concluded that stock liquidity is significantly positively correlated with enterprise risk-taking level, but there is no difference between the level of significance of the state-owned enterprises and that of the non-state-owned enterprises.And the conclusions obtained are robust.

Towards Green Development: Exploring the Impact of Housing Price Bubbles on Regional Green Innovation Efficiency Based on Chinese Provincial Panel Data Analysis

Innovation is an eternal theme of human development, and green innovation efficiency serves as the basis for achieving innovation-driven development in a country or region, as well as an important aspect of ecological civilization construction. In this context, based on the panel data of 30 Chinese provinces during 2003–2020, this study explores the effect of housing price bubbles on green innovation efficiency by using a global SBM-DEA model with unexpected outputs and a two-way fixed effects model. The results show that housing price bubbles considerably reduced green innovation efficiency, which is also verified by a series of robustness and endogeneity tests. Heterogeneity tests show that housing price bubbles in eastern and high human capital regions have a significantly higher inhibitory effect on green innovation efficiency than that in the central and western regions and low human capital regions. The mechanism test shows that housing price bubbles have reduced green innovation efficiency by intensifying the mismatch of labor and capital between regions. Moreover, high housing prices will further deepen the negative impact of housing price bubbles on green innovation efficiency, while expanding economic openness will help alleviate the negative impact. Therefore, to effectively enhance regional green innovation efficiency, we put forward a series of policy measures in terms of strengthening the adjustment of housing policies, optimizing the resource allocation structure, and implementing differentiated environmental control tools.

Impact of Energy Intensity and CO2 Emissions on Economic Growth in Gulf Cooperation Council Countries

This study investigates the impact of energy intensity and CO2 emissions on economic growth in Gulf Cooperation Council (GCC) countries, aiming to understand the interplay between energy consumption, environmental sustainability, and economic performance. We analyze data from 1990 to 2023 across six GCC countries. The study employs the fixed effects model, random effects model, and pooled regression model to examine the relationships between energy intensity, CO2 emissions, and GDP growth, controlling for factors such as foreign direct investment, trade openness, population, unemployment, and urbanization. Our findings reveal a significant negative impact of energy intensity on economic growth, and an increase in energy intensity is associated with a decrease of approximately 0.2969 units in GDP, indicating that higher energy consumption per unit of output hinders economic performance. While CO2 emissions positively affect growth in GCC countries, a one-unit increase in CO2 emissions is associated with an increase of approximately 0.3961 units in GDP. The study emphasizes the necessity for GCC countries to adopt sustainable energy practices to reduce energy intensity and boost economic growth. By aligning economic strategies with environmental sustainability goals, these nations can achieve long-term growth while effectively addressing the challenges of climate change. This research contributes to the ongoing discourse on sustainable development in the region and underscores the importance of harmonizing economic growth strategies with environmental objectives.

Assessing the Impact of TLR4 rs4986790 Polymorphism on Bacterial Meningitis Risk: A Systematic Review and Meta-Analysis.

Toll-like receptor (TLR2) gene plays an important role in the pathogenesis of bacterial meningitis (such as meningococcal meningitis and pneumococcal meningitis). The association between TLR4 rs4986790 polymorphism and the susceptibility to bacterial meningitis has been extensively studied. However, the results of these studies remain inconsistent. Therefore, we performed a meta-analysis to evaluate the association between TLR4 rs4986790 polymorphism and the susceptibility to meningococcal meningitis and pneumococcal meningitis. Google Scholar, Embase, and PubMed databases were searched for case-control studies on TLR4 polymorphisms and the risks of meningococcal meningitis and pneumococcal meningitis, published up to May 31, 2024. To assess the strength of the association between TLR4 polymorphism and meningococcal meningitis and pneumococcal meningitis, the odds ratios (ORs) with 95% confidence intervals (CIs) were used. The meta-analysis of the associations between the TLR4 rs4986790 polymorphism and meningococcal meningitis and pneumococcal meningitis was carried out under different genetic models. Meta-analyses were conducted using Cochrane RoB 2 tool and Metagenyo to calculate the ORs and 95% CIs. Fourteen published studies with 3599 cases and 7438 controls were included. Overall, there was a strong correlation between TLR4 polymorphisms and meningococcal meningitis observed across three genetic models using a random-effects model: GG + GA vs. AA (OR: 0.34, 95% CI: 0.14-0.79, P = 0.01, I² = 60%); GA vs. AA (OR: 0.34, 95% CI: 0.13-0.91, P = 0.03, I² = 65%); and GG vs. AA (OR: 0.34, 95% CI: 0.14-0.78, P = 0.01, I² = 59%). Conversely, a fixed-effects model also revealed a significant association in the G vs. A model (OR: 0.84, 95% CI: 0.73-0.96, P = 0.01, I² = 38%). In pneumococcal meningitis, a fixed-effects model analysis demonstrated a significant association in the GG vs. AA model (OR: 0.35, 95% CI: 0.14-0.87, P = 0.02, I² = 48%) respectively. This meta-analysis showed a strong correlation between TLR4 rs4986790 polymorphism and susceptibility to meningococcal meningitis and pneumococcal meningitis. Further studies with diverse populations are needed to validate and strengthen these findings.

The impact of artificial intelligence on global energy vulnerability

Investigating the effect of artificial intelligence (AI) on energy vulnerability (EVI) is crucial to understanding how technological advances are changing the resilience and sustainability of energy systems. However, their quantitative relationship still lacks empirical evidence. This study first constructs the EVI of 54 global economies from the perspective of energy security, energy consumption, energy efficiency, and energy availability from 2000 to 2019. Then, a fixed-effect model is employed to investigate the relationship between AI and EVI. Results show that (1) AI can considerably reduce global EVI. The core findings remain reliable after several robustness checks. (2) Mechanism analysis implies that AI can reduce EVI by promoting financial development and technological progress. (3) Heterogeneity analysis implies that the impeding role of AI on EVI is more pronounced in countries with low incomes and industrialization levels. Furthermore, the hindering effect of AI on EVI is strengthened after Industry 4.0 and the financial crisis. Some policy implications are further proposed accordingly to reduce global EVI.

Evaluating safety and efficacy of plastic versus metal stenting in malignant hilar biliary obstruction: a systematic review and meta-analysis of randomized controlled trials.

Stenting malignant hilar biliary obstruction (MHBO) is challenging due to its complex structure, and it is less effective than blockages in the distal bile duct area. Plastic stents (PSs) and metal stents (MSs) are commonly used for stenting MHBO. This study aims to compare the outcomes of PSs and MSs in MHBO patients. We conducted a search of medical databases up to March 2024. Using a fixed-effect model, we analyzed the risk ratios (RRs) of the outcomes between the PS and MS groups. We calculated the RR for clinical and technical success, reinterventions, and adverse events, as well as the hazard ratio (HR) for survival and stent patency. This analysis includes five randomized controlled trials (RCTs) that met the inclusion criteria, comprising a total of 322 patients (156 in the PS group and 166 in the MS group). Significant differences (P < .05) in favor of the MS group were found in the reinterventions (RR 1.80, 95% CI 1.07-3.04), and stent patency (HR 0.54, 95% CI 0.32-0.90). There were no significant differences (P > 0.05) between the PS and MS groups regarding technical success (RR 1.01, 95% CI 0.94-1.09), clinical success (RR 0.86, 95% CI 0.69-1.07), overall survival (HR 0.71, 95% CI 0.47-1.05), stent migration (RR 0.69, 95% CI 0.08-6.02), stent occlusion (RR1.32, 95% CI 0.97-1.81), and adverse events (RR 0.80, 95% CI 0.53-1.20). Both PS and MS are effective for managing MHBO, while MS offers greater efficacy in increased stent patency and lower reintervention rates. Key message What is already known on this topic Metal stents (MSs) and plastic stents (PSs) are used for palliative treatment of malignant hilar biliary obstruction (MHBO). MSs significantly reduced the need for reinterventions compared to PSs in patients with MHBO. What this study adds There were no significant differences between MSs and PSs in terms of technical success, clinical success, overall survival, stent migration, stent occlusion, or adverse events. How this study might affect research, practice, or policy The study's findings may prompt researchers to design more targeted studies to further investigate these specific outcomes in MHBO patients. The results encourage endoscopists to consider patient-specific factors, such as life expectancy and preference for minimizing recurrent procedures, when choosing between MSs and PSs for MHBO.

Does environmental policy stringency promote financial development? New policy insights from BRICS countries

Purpose Over the past few decades, BRICS nations have implemented several environmental legislations to combat environmental issues, along with the development of the financial sector. However, they still face the dual challenge of balancing financial development (FD) and reducing carbon emissions to achieve sustainable development. Hence, this study aims to explore how environmental policy stringency (EPS) influences FD in BRICS nations from 1993 to 2020. Design/methodology/approach The authors performed a panel data analysis to examine the effect of the EPS on FD. Firstly, they conducted a panel unit root test using the Levin–Lin–Chu and the Im–Pesaran–Shin unit root tests to check the stationarity of the data. Then, they used the fixed effect model for analysis. A feasible generalised least squares test is used to ensure the robustness of the results. Findings The EPS positively affects FD through financial markets and intuitions in BRICS nations, indicating that more stringent environmental policies of BRICS countries promote FD in these nations. Practical implications This study suggests that policymakers in BRICS nations should consider strengthening EPS, implying the dual benefit of aligning environmental sustainability goals with economic growth. Originality/value This study provides a deeper understanding by disassembling FD into financial markets and institutions, thereby establishing a nuanced relationship with EPS, which is highly relevant for addressing the distinctive financial systems and environmental challenges of BRICS nations. Therefore, this study fills this gap and offers new insights into the relationship between EPS and FD in these nations.

Cost Effectiveness of Catheter Ablation Versus Antiarrhythmic Drugs for Atrial Fibrillation: A Systematic Review and Meta-analysis.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and is associated with substantial morbidity and mortality. Current international guidelines recommend antiarrhythmic drugs or catheter ablation (CA) as rhythm-control strategies for AF. This study aimed to comprehensively assess economic evaluations (EEs) of the treatment of AF by country income level. Seven electronic databases were systematically searched for EE literature until March 30, 2024, with no constraints on time or language. Two independent reviewers selected the studies, extracted the data, and assessed the quality of the data. Full EEs comparing CA with antiarrhythmic drugs for rhythm-control treatment were included; surgical or rate-control treatments were excluded. The quality of the included articles was assessed using the ECOBIAS checklist. Costs were converted to purchasing power parity US dollars for 2023. A random-effects meta-analysis was applied to pool incremental net benefit (INB) based on a heterogeneity test and its degree (I2 > 25% or Cochran's Q test < 0.1). We also explored heterogeneity and potential publication bias and conducted sensitivity and subgroup analyses. In total, 27 studies across nine countries were eligible, predominantly from high-income countries (n = 25), with a smaller subset from upper-middle-income countries (n = 2). Because of the heterogeneity among the studies, a random-effects model was selected over a fixed-effects model to pool INBs. Most studies (n = 21) favored CA as the cost-effective intervention, yielding an INB of $US23,796 (95% confidence interval [CI] 15,341-32,251) in high-income countries. However, heterogeneity was substantial (I2 = 99.67%). In upper-middle-income countries, the estimated INB was $US18,330 (95% CI - 11,900-48,526). The publication bias results showed no evidence of asymmetrical funnel plots. In this meta-analysis, CA emerged as a cost-effective rhythm-control treatment for AF when compared with antiarrhythmic drugs, particularly in high-income countries. However, economic evidence for upper-middle-income countries is lacking, and no primary evaluations were found for low-middle-income and low-income countries. Further EEs are necessary to expand the understanding of AF treatment globally.

The effect and mechanism of digital economy on green total factor productivity — Empirical evidence from China

In the context of global digitalization, it is of great significance to promote the development of digital economy to reduce carbon emissions and improve green total factor productivity (GTFP). Based on the data of 30 provinces in China from 2011 to 2021, this study constructs a variety of methods, such as Super-SBM model, fixed effect model and intermediary effect model, to empirically test the impact and mechanism of digital economy (DIE) on GTFP. Based on the research findings, the growth of DIE contributes to the improvement of GTFP. Moreover, the impact of DIE on GTFP in eastern China is greater than that in central and western China. There are significant differences between the development level of DIE and GTFP in eastern, central and western regions. Further research shows that digital economy affects the improvement of GTFP through three intermediary variables: economic scale, industrial structure and technological innovation. This study provides empirical evidence to support the effective enhancement of GTFP in developing countries as the digital economy evolves. It provides effective recommendations for developing countries and emerging economies to develop a green economy. However, this study has limitations in data sample, research scope and mechanism analysis depth. Therefore, the conclusions drawn in this study can only provide empirical evidence for identifying the relationship between the DIE and GTFP to a certain extent. Future research should be expanded in these aspects.

How effective are fixed-effects models in fixing the transit supply–demand bidirectional interaction?

ABSTRACT Transit agencies use direct demand models (DDM) to allocate services. Since the service supply – a crucial predictor in DDMs – is endogenous to demand, including it in the model might yield biased estimations. A widely used methodology that is believed to handle this issue is Fixed Effects (FE). However, the underlying assumptions of FE are valid only if service adjustments take a considerable amount of time. This study investigates the performance of FE for estimating transit ridership. We collected 2013–2019 data and constructed 16 DDMs, employing four methodologies with a shared set of variables. We found that FE has significant limitations in handling endogeneity and will result in parameter estimates that significantly differ from those produced by methodologies that are specifically designed to control for endogeneity (such as FE-IV). Moreover, the use of FE leads to the omission of certain predictors and inaccurate ridership predictions, misguiding agencies as to what changes to implement and potentially impacting revenue projections.