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This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.

The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker.

This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

This paper aims to test the empirical validity of the dynamic trade-off theory in its symmetric and asymmetric versions in explaining the capital structure of a panel of publicly listed US industrial firms over the period from 2013 to 2019. It analyzes the existence of an adjustment of leverage toward its target level and whether the speed of this adjustment is influenced by the debt measure, the model specification or/and the fact that the actual debt ratio is higher or lower than its long-term target level.

This paper uses a quantitative research methodology using panel data analysis under the partial adjustment model and the error correction model using the generalized moment method in first differences and in systems to explore the dynamic nature of firms’ capital structure behavior.

The results show that the effects of the conventional determinants of leverage are globally consistent with the trade-off theory predictions. The dynamic versions confirm that firms exhibit leverage-targeting behavior. Although this speed of adjustment (SOA) depends on the debt and model specifications, it is around 60% on average. The estimated SOA is higher for the market leverage measure compared to the book leverage. The asymmetric adjustment model reveals that firms are more sensitive to reducing leverage than increasing it when they are away from their target; overleveraged firms exhibit approximately 5% faster adjustment than underleveraged firms when book leverage is used.

The originality of this research paper lies in its development and test of an asymmetric model to allow the leverage adjustment speed to vary depending on whether the firm’s debt ratio is above or below its target level and the methodological approach as well as the different model specifications used and the insights generated through the application of rigorous econometric techniques.

This study investigates the finance-investment nexus in sub-Saharan Africa using data from 41 countries spanning the period from 2000 to 2022. The central question addressed is whether there is a “too little” or “too much” finance problem in the region.

This study employs a system-generalised method of moments (GMM) approach to analyse the association between finance and private investment. Additionally, a dynamic threshold regression model is used to uncover potential nonlinearities in this relationship.

Initially, the study identifies a negative correlation between increased finance and private investment. However, further analysis using the dynamic threshold regression model reveals a critical threshold level of finance. Specifically, the threshold is found to be 6.52% of domestic credit to the private sector and 23.18% using the financial development index. Below this threshold, finance negatively impacts private investment, while surpassing this threshold leads to positive growth in private investment. These findings indicate an issue of “too little” finance in the finance and private investment nexus in sub-Saharan Africa. The results are robust across different model specifications.

The implications of this study highlight the importance of identifying critical thresholds for financing to enhance investment expenditures in the region.

This study contributes to the literature by uncovering nonlinearities in the finance-investment nexus in sub-Saharan Africa. The identification of critical thresholds provides valuable insights for policymakers, emphasising the need to strengthen the financial sector in countries operating below these thresholds to promote private investment and economic growth.

The purpose of this study is to investigate and compare the dynamic leverage policies of Islamic and conventional banks within selected Organization of Islamic Cooperation (OIC) countries. The study specifically focuses on the concepts of leverage procyclicality and prospect theory.

To achieve the research objectives, the study uses data from three distinct periods: Crisis I (2007–2009), Crisis II (2011–2012) and Crisis III (2020). The analysis uses dynamic panel-data regression, using the generalized method of moments (GMM) technique.

The research findings indicate that both Islamic and conventional banks demonstrate leverage procyclicality. Interestingly, Islamic banks exhibit weaker leverage procyclicality during normal conditions but display stronger procyclicality during crises compared to their conventional counterparts. The application of prospect theory reveals that both bank types exhibit risk-taking or risk-averse behavior through leverage under certain financial and market performance measures as the first-level domain of the gain-vs-loss condition. Furthermore, during crises (as the second-level domain of the normal-vs-crisis condition), both Islamic and conventional banks experience heightened leverage. Notably, Islamic banks, owing to their lower risk exposure and greater shock resilience, demonstrate lesser risk-taking behavior through leverage than conventional banks, both during periods of underperformance and worsening conditions amid crises. These findings validate the extension of prospect theory's applicability in a two-level domain perspective. The dynamic nature of leverage policy, being procyclical and adhering to prospect theory, also varies following different crises specifically.

The study's limitations include the unequal crisis periods (Crises I, II and III), leading to an imbalanced examination of their effects, certain financial and market performance metrics that fail to corroborate the expected hypotheses and the limited generalizability of findings beyond the selected OIC countries.

Understanding the intricate dynamics and behavioral aspects of leverage policy for both Islamic and conventional banks, particularly during crisis scenarios, proves crucial for reviewing banking regulations, making informed financial decisions and managing risks effectively.

This study enriches the current knowledge by presenting two key points. First, it highlights the dynamic nature of leverage procyclicality in Islamic banks, showing a change from weaker procyclicality in normal conditions to stronger procyclicality during crises compared to conventional banks. Second, it expands the application of prospect theory by introducing a dual-level domain context. Examining the comparative leverage policies of Islamic and conventional banks during different crises within OIC countries provides novel insights into leverage procyclicality and behavioral responses.

This study aims to conduct a comprehensive methodological review, exploring the strategies used to address endogeneity within the realms of corporate governance and financial reporting.

This research reviews the application of various methods to deal with endogeneity issue published in the 10 journals covering the corporate governance discipline included in the Web of Science’s Social Sciences Citation Index.

With a focus on empirical studies published in leading journals, the author scrutinizes the prevalence of endogeneity and the methodologies applied to mitigate its effects. The analysis reveals a predominant reliance on the two-stage least squares (2SLS) technique, a widely adopted instrumental variable (IV) approach. However, a notable observation emerges concerning the inconsistent utilization of clear exogenous IVs in some studies, highlighting a potential limitation in the application of 2SLS. Recognizing the challenges in identifying exogenous variables, the author proposes the generalized method of moments (GMM) as a viable alternative. GMM offers flexibility by not imposing the same exogeneity requirement on IVs but necessitates a larger sample size and an extended sample period.

The paper sensitizes researchers to the critical concern of endogeneity bias in governance research. It provides an outline for diagnosing and correcting potential bias, contributing to the awareness among researchers and encouraging a more critical approach to methodological choices, recognizing the prevalence of endogeneity in empirical studies, particularly focusing on the widely adopted 2SLS technique.

Practitioners, including corporate executives and managers, can benefit from the study’s insights by recognizing the importance of rigorous empirical research. Understanding the limitations and strengths of methodologies like 2SLS and GMM can inform evidence-based decision-making in the corporate governance realm.

This study aims to investigate the institutional, macroeconomic and firm-specific determinants of financial leverage in Vietnam and provides new evidence from the dynamic panel fractional estimator.

This study uses a panel dataset of 859 Vietnamese firms from 2008 to 2022 and employs three estimators: Feasible Generalized Least Squares (FGLS), System Generalized Method of Moments (SysGMM) and Dynamic Panel Fractional (DPF), with DPF being particularly suitable for handling fractional dependent variables and the dynamic nature of financial leverage.

The results confirm the dynamic nature of the financial leverage model, with firm-specific factors, institutional factors and macroeconomic factors playing significant roles in shaping firms' financing decisions. The DPF estimator highlights the positive impact of stock market development on leverage. This study contributes to the literature by providing new evidence on the determinants of leverage in Vietnam, using the DPF estimator for more accurate estimation and revealing the significant impact of the size of the banking sector, the size of the stock market, the stock market development index, the financial development index and the corruption perception index on leverage.

This study contributes to the literature by providing new evidence on the dynamic nature of the financial leverage model and the impact of institutional, macroeconomic and firm-specific factors on financial leverage in the context of Vietnam. The use of the DPF estimator allows for a more accurate and reliable estimation of the determinants of leverage, considering the fractional nature of the dependent variable and the persistence of capital structure decisions over time.

This study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.

Dynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.

The short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.

The research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.

The COVID-19 pandemic has affected economic activity both globally and nationally, which also has an impact to banking sector and Islamic banking is no exception. This study aims to see how the impact of Islamic bank financing in seven sectors affected by the COVID-19 to the credit risk of Indonesian Islamic banks. In addition, this study also tries to see whether the proportion of mudharabah-musharaka or profit-loss sharing (PLS) financing also affects credit risk in Indonesian Islamic banks.

This study uses fixed effect panel data regression over the period 2011–2020.

The results of this study show that wholesale and retail trade financing will increase credit risk in Indonesian Islamic banks as a policy implication. In terms of the proportion of PLS financing, it shows that a larger share of PLS financing will reduce credit risk in Islamic banks.

This paper demonstrates that despite the industry’s perception of PLS as riskier than murabaha-based instruments. According to the research, PLS financing will lower credit risk in Islamic banks. This study found that PLS contributes to overall economic stability by shifting the function of Islamic banks from a simple lending body to an active market catalyst/manager/consultant to market players seeking financial aid.

What shapes entrepreneurs’ intention to allocate entrepreneurial effort towards different types of entrepreneurial start-ups? Grounded in a “national business systems” approach, the purpose of this paper is to examine the impacts of institutional dimensions on Schumpeterian and Kirznerian entrepreneurship.

Drawing upon a global context comprising 82 countries over the period between 2007 and 2018, this research applies a dynamic panel modelling approach, namely, the dynamic panel generalised method of moments estimator. This estimator allows to account for unobserved country-specific heterogeneity and to address endogeneity constraints that might occur between institutions and entrepreneurship.

The findings reveal that there are positive relationships between financial capital and both Schumpeterian and Kirznerian entrepreneurship. Educational capital positively affects the allocation of entrepreneurial efforts towards opportunity-based entrepreneurial activities. Moreover, institutional regulatory conditions could hinder the allocation of entrepreneurs’ resources into a Schumpeterian while facilitating the allocation of resources into a Kirznerian type of venture. Finally, a higher level of corruption promotes innovative entrepreneurial activities (i.e. a Schumpeterian type of venture) and leads to constraints on Kirznerian entrepreneurship.

The research findings demonstrate the significance of the macro environment for enacting and implementing policies to reap the benefits of different types of start-ups. It suggests different political actions are needed to motivate highly qualified individuals to engage in Schumpeterian and Kirznerian entrepreneurship, rather than focusing purely on enhancing a country’s overall start-up rates.

By acknowledging the multidimensional nature of entrepreneurship, this research provides greater theoretical exposition and empirical support for the role played by macro-level institutions in determining types of entrepreneurship. It reveals the important role played by macro institutional conditions in influencing choices about different types of start-ups and gives rise to the multidimensional nature of entrepreneurship.