Optimal dynamic tax-transfer policies in heterogeneous-agents economies

Economic Policy Uncertainty in Central America and the Dominican Republic

Understanding how financial development shapes renewable energy investments is essential for designing effective economic policies aimed at ensuring environmental sustainability. This study explores to identify the current status, key literature insights, and evolving trends in the relationship between financial development (FD) and renewable energy (RE). In this context, the data obtained from the WoS database for the period 2015–2024 were analyzed using the bibliometric method. The results indicate rapid growth in the field, particularly since 2018, reflecting an increasing discussion of the role of the FD–RE nexus in climate change and the Sustainable Development Goals. The most productive publisher country, article author’s country, journal, author, and institution are, respectively, the UK, China, Environmental Science and Pollution Research, Ilhan Ozturk from Türkiye, and the Lebanese American University. The most recent trend in the field is the increasing focus on natural resource and innovation-related issues within the FD–RE nexus. The themes formed based on the findings are renewable energy, financial development, economic growth, carbon emissions, and testing. Overall, this study provides a comprehensive overview of recent research on the FD–RE nexus and offers guidance for future studies. • A holistic understanding of the Financial Development–Renewable Energy nexus is vital for formulating effective environmental policies. • Among the most frequently used keywords in the Financial Development -Renewable Energy field, ' natural resources' and “ecological footprint” have emerged as the most recent trend.

Bidirectional spillover effects between China and US short-term interest rate volatility driven by economic policy uncertainty: A BHK-L-MIDAS-Copula analysis

As a result of low attention given towards corporate sustainability initiatives (CSI) which encompass economic, environmental, social, and governance aspects by companies to their host communities, this study examines the relationship between institutional factors such as economic constraints, and competition, and environmental policy towards CSI, as well as firm specific attributes such as company size and leverage on the Nigerian non-financial listed firms. Data were collected from the firms assessed and documented by global consensus rate of corporate social responsibility (CSRHUB) from 2018 to 2023. A total of 300 firm-year observations were considered using the annual reports and accounts of 50 sampled companies. The panel-corrected standard errors (PCSEs) and fixed generalised lease square (GLS) regression models were used to test the hypotheses for this study. The result established that economic constraints measured by access to finance is not significantly related to CSI. In contrast, competition and environmental policy are positive and significantly associated with CSI. Likewise, company size and leverage indicate a positive relationship with CSI. Hence, this study suggests that regulatory authorities should assess how the companies deal with issues relating economic constraints to ensure a good access to finance that could be responsible for sustainability initiative practices among non-financial firms in Nigeria.

Indonesia is currently facing a significant challenge known as the Middle Income Trap (MIT), a condition where economic growth stagnates after reaching middle-income status, hindering progress toward becoming a high-income country. This study aims to identify and model the socio-economic factors influencing MIT at the provincial level in Indonesia during the 2020–2023 period. The Generalized Additive Model (GAM) is employed to estimate nonlinear relationships between predictors and the response variable while capturing complex patterns in panel data. GRDP per capita is used as an indicator of MIT, with six predictor variables: life expectancy, poverty rate, informal employment share, secondary education completion rate, food insecurity prevalence, and population density. The results showed that the best model was obtained based on the minimum GCV and AIC values of the Gaussian family with an identity link function and 5 knot points with the highest correlation of 99,9%. Five variables show nonlinear effects, while food insecurity exhibits a significant negative linear impact. The findings provide a valuable reference for designing inclusive and adaptive eco nomic policies based on each region’s socio-economic characteristics to mitigate MIT risks and also supports the achievement of Sustainable Development Goal (SDG) 8, which promotes decent work and sustained economic growth.

ABSTRACT Latino communities in the U.S. faced disproportionate health and economic consequences during the COVID-19 pandemic. We examined the relationship between pandemic-related financial hardship and symptoms of depression, anxiety, and stress among 1,015 Latino adults surveyed in fall 2020. Mental health symptoms were measured with the DASS-21, and multivariate linear regression models were stratified by gender. Among Latinas, greater financial hardship was significantly associated with higher anxiety and stress but not depression. No significant associations were observed among Latino men. Findings demonstrate the need for culturally responsive, gender-sensitive mental health supports and inclusive economic relief policies for immigrant and mixed-status families.

ABSTRACT This paper examines the dependence between economic policy uncertainty (EPU), market-implied volatility (VIX), and geopolitical risk (GPR) and the dynamic connectedness of financial assets using a novel quantile coherency approach. Different from the existed literature mainly concern single-frequency or single-market state, this paper provides fresh findings: First, the interplay between uncertainty and the return connectedness of financial assets is heterogeneous, varying across different market states and investment horizons. Second, in bear markets, the VIX shows a positive association with total return connectedness across all investment horizons, while the EPU shows a negative one; the GPR is negatively associated only in the long term. Third, EPU and GPR mainly interact with commodity assets (WTI and gold) during bear markets, while the VIX primarily link to investment assets (S&P 500, bonds, and exchange rates). In sum, these results offer crucial implications for policymakers to monitor financial market volatility, as well as for investors in optimizing asset allocation.

ABSTRACT Due to their growth dependency, developed welfare states frequently exceed planetary boundaries and transgress sustainable consumption and production corridors that scholars deem crucial for mitigating climate change. Recognizing that international organizations (IOs) often act as policy entrepreneurs and innovators of new ideas, the article asks whether and how concepts of limits to welfare, which are crucial in recent academic debates on social policy in times of ecological crises, are translated and discussed by IOs. Empirically, it focuses on three key IOs: the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD) and the World Bank. The article maps how IOs frame limits in global eco-social policy and use them strategically. It finds that limits have important implications for social policy: socially, they shift responsibilities to private actors; temporally, they allow a focus on innovation and future change; spatially, they expand policy opportunities into the periphery. These implications are also translated into specific policy proposals: promoting self-limitations rather than regulation, fostering technological and economic change, and exporting economic and social policy models from the Global North worldwide. These results give rise to doubts whether IOs sufficiently acknowledge ecological limits that could orient a transformative agenda of sustainable welfare.

IntroductionOf 1.2 million children and young adolescents (<15 years) developing tuberculosis (TB) yearly, more than 50% are undiagnosed and unreported to national TB programmes (NTPs) and the World Health Organization (WHO). This is mainly due to poor performance of microbiological tests, limited clinical skills and structural barriers for childhood TB diagnosis at decentralised levels of care. Treatment decision algorithms (TDAs) could improve child TB outcomes but require external validation. We aim to evaluate a comprehensive TDA-based approach for childhood TB screening, diagnosis and treatment decision-making at district hospital (DH) and primary health centre (PHC) levels in Mozambique and Zambia.Methods and analysisDecide TB is a pragmatic, hybrid effectiveness-implementation type 2 cluster-randomised trial with a stepped wedge design. The comprehensive TDA-based approach (intervention) will be implemented under programmatic conditions in four districts in each country (each comprising one DH and six PHCs), randomly selected to switch sequentially from the standard of care to the intervention. Evaluations will assess epidemiological, clinical, economic, social sciences, implementation and health policy endpoints. Aggregated and individual data from children with presumptive TB will be extracted from facility registers and individual data will be collected using an electronic medical record (EMR), both data sources will be entered in national Demographic Health Information System 2 databases. Questionnaires and individual/group interviews (among healthcare workers (HCWs), parents/caregivers and key informants), supervision and mentoring reports and quantitative cost tools will be used.Ethics and disseminationEthics approval was obtained from national ethics committees in Mozambique (Instituto Nacional de Saúde review board and National Committee for Bioethics in Health) and Zambia (University of Zambia ethical review board and National Health Research Authority); this includes a waiver for analysing data collected by NTPs (no identifiable information reported, intervention with minimal risk) without individual consent from children’s parents/caregivers. Informed consent will be obtained from HCWs, parents/caregivers and key informants. Results will be openly shared with the scientific community, WHO and national and international stakeholders for translation into policy and practice. Procedures for requesting further use of Decide TB data will be publicly available.Trial registration numberNCT06593080; PACTR202407866544155.

The India-China relationship is defined by a paradox: intensifying geopolitical conflict alongside resilient economic engagement. This paper employs the Coopetition Framework (COOPF) to dissect this dynamic, asking why and how has India pursued unit-level cooperation with China despite system-level geopolitical rivalry, and what are the implications for regional security? This paper examines India’s territorial and economic security policies under the Modi administration since 2014, utilizing a qualitative case study and documentary review. The paper argues that the relationship is driven by two competing forces. At the system-level, India’s alignment with Western powers (e.g. Quad) is a directional counter-balancing strategy against China’s regional influence. However, this relationship’s direction is moderated and behaviour paced by two unit-level coopetition drivers: a liberal-economic driver that necessitates engagement due to critical supply chain dependencies, and the Hindutva-driven constructivist-ideological driver of multialignment that is essential to maintain strategic autonomy while seeking self-reliance in the long-run. This tension confines India-China rivalry to the security realm while ensuring cooperation for sustained national development, defining the relationship as one of managed rivalry and necessary engagement.

ABSTRACT The objective of this article is to analyse the effect of climate vulnerability on energy poverty. To do this, we adopted a Tobit model on 872,469 households in 34 countries in sub-Saharan Africa. The data used for the period 2010–2022 come mainly from three databases: EDS, ND-GAIN and WGI. The results obtained show that climate vulnerability significantly increases the level of energy poverty of households in Sub-Saharan Africa, as well as in regional subgroups and residential areas. The analysis by regional sub-blocks reveals that the marginal effects are more significant in southern and central Africa. However, the analysis by residential area confirms a higher marginal effect in urban areas. We recommend the implementation of economic policies aimed not only at diversifying energy sources with the adoption of renewable energies, but also at using adaptation and mitigation mechanisms that can reduce the levels of climate vulnerability. These recommendations must jointly take into account each regional sub-block and the area where the households are located in order to propose solutions adapted to the needs of the different households.

ABSTRACT This paper examines the relationship between credit risk, credit growth, and economic policy uncertainty (EPU) for a panel of 52 Indian commercial banks from 2005 to 2023 using fixed-effects and panel quantile regression methods. We find a robust U-shaped relationship: moderate credit growth (10–25% annually) reduces credit risk through diversification and improved risk sorting, but growth beyond roughly 36% annually deteriorates asset quality and raises non-performing loans, with thresholds varying across institutions (about 33% for fragile banks at the 90th quantile versus 38.5% for safer banks at the 10th quantile). Long-term credit growth, and to a lesser extent unsecured lending in some distributional specifications, appears more risk-sensitive than short-term and secured lending. EPU further intensifies the credit growth—risk relationship, especially for already-fragile banks, whereas public sector banks show less sensitivity due to mandates and implicit guarantees. These findings suggest the need for dynamic, risk-adjusted supervisory guidance over credit growth, especially for long-term and unsecured lending during high-uncertainty episodes, as well as greater policy predictability through transparent, rules-based monetary and fiscal frameworks.

ABSTRACT This research investigates how supply chain digitization (SCD) influences corporate supply chain resilience (SCR) in China, leveraging the Supply Chain Innovation and Application (SCIA) Pilot Policy as a quasi-natural experiment. Utilizing a longitudinal dataset of Chinese A-share listed companies spanning the period 2013–2023, we implement a difference-in-differences (DID) framework to isolate the causal impact of SCD on SCR. Our empirical findings demonstrate that the implementation of SCIA markedly bolsters firms’ SCR. Mechanism tests suggest that these improvements are driven by accelerated digital integration, heightened technological innovation, optimized investment efficiency, and superior environmental, social, and governance (ESG) outcomes. Heterogeneity analyses further reveal stronger effects for firms facing lower economic policy uncertainty, non-state-owned firms, and smaller firms. By providing robust causal evidence and clarifying the underlying mechanisms, this study contributes to the literature on SCD. These results offer practical guidance for managers seeking to cultivate adaptive supply networks and provide a foundation for policymakers to further encourage digital industrial upgrading.

ABSTRACT Generational equity issues, spanning politics, environmental sustainability, health care, and economic policy, are increasingly visible in public discourse, yet remain underexplored in gerontological education. This article presents an innovative graduate-level social work course that centers intergenerational justice as a framework for engaging students and lifelong learners in aging-related social justice topics. Through thematic modules, experiential activities, and interdisciplinary content, the course invites students to examine justice across and within age groups, challenging ageist assumptions and fostering cross-generational empathy. A unique iteration of the course incorporated older adult learners through partnership with a lifelong learning institute, enriching classroom dialogue and modeling inclusive pedagogy. Evaluation data revealed high student satisfaction, increased engagement, and reduced ageist attitudes across generations. Lessons learned highlight the importance of thoughtful facilitation, institutional support, and curricular adaptability. This course offers a replicable model for integrating aging content into generalist education and specialized courses that address equity across the lifespan. By reframing aging as a dynamic and justice-oriented domain, intergenerational justice education may help attract students with diverse interests to gerontology and address workforce gaps in aging-related fields.

The intensifying global competition among cities necessitates accurate and efficient evaluations of urban business environments to drive economic growth, attract investment, and foster innovation. Traditional assessment methods, often reliant on expert opinions and manual analysis, are prone to subjectivity and inefficiency. To address these limitations, this study introduces an optimized Siamese Neural Network model designed to improve the accuracy and efficiency of urban business environment evaluations. The model leverages feature extraction and multidimensional learning to analyze key indicators, including economic development, infrastructure integrity, policy friendliness, and market entry difficulty, utilizing publicly available datasets. Additionally, the model incorporates emerging technologies, including the Internet of Behaviors and generative artificial intelligence (AI), to bolster capabilities in capturing and analyzing complex behavioral data. The Internet of Behaviors enables the collection of real-time dynamic behavioral data from various urban activities, providing a comprehensive and detailed understanding of the business environment. Generative AI, on the other hand, generates predictive models from existing data, simulating future trends and scenarios, thereby enhancing the accuracy and foresight of decision-making. Performance comparison experiments demonstrate the model's superiority over baseline models across all evaluation metrics. Specifically, the optimized model achieves F1 Scores of 0.874, 0.879, and 0.882 on the Doing Business Indicators, Urban Land Cover Classification, and Open Cities Artificial Intelligence Challenge datasets, respectively, significantly outperforming the Graph Neural Network for Business Environment and Transformer-based Business Environment Evaluation models. Furthermore, the model exhibits exceptional efficiency, with training times of 29.648s, 31.327s, and 32.843s on the respective datasets. In terms of scalability and adaptability, the model achieves Scalability Scores and Generalization Capabilities of 0.821 and 0.876 on the DBI dataset, demonstrating its effectiveness in handling large-scale, multidimensional data. A comprehensive evaluation of urban business environments revealed specific strengths and weaknesses in cities A, B, and C. City A excelled in economic development (8.5) and infrastructure integrity (9.0) but scored lower in market entry difficulty (5.5). City B showed balanced performance across all metrics, while City C demonstrated strengths in policy friendliness (8.5) and market entry difficulty (8.0) but lower scores in infrastructure integrity (6.5). These results highlight the model's utility in identifying areas for improvement and fostering targeted interventions. This study advances the theoretical and practical application of deep learning techniques in urban business environment evaluation, offering city administrators an efficient and objective decision-support tool. By enabling data-driven policy formulation and resource optimization, the proposed model provides a robust strategy for enhancing urban competitiveness.

ABSTRACT The literature shows that cooperation with competitors (i.e. coopetition) is important for firms’ innovation output. However, it remains unclear whether and to what extent coopetition influences firms’ investments in research and development (R&D). We examine the effect of coopetition on firms’ R&D investments, focusing specifically on the tension between knowledge acquisition and misappropriation in coopetition. Drawing on transaction costs theory and a longitudinal dataset of Chinese pharmaceutical firms over a ten-year period (2013–2022), we find a U-shaped relationship between coopetition and firms’ R&D investments. Furthermore, the results indicate that the U-shape is significantly steepened by regional intellectual property rights protection but flattened by economic policy uncertainty. Our findings contribute to understanding the effect of coopetition on firms’ R&D decisions and demonstrate that the effect can be leveraged by considering the subnational intellectual property rights protection and macroeconomic policies.

Purpose This study aims to extend the dynamic conditional correlation (DCC), wavelet coherence (WTC) and Diebold–Yilmaz (DY) spillover index methodologies to investigate the relationship between SJC gold prices, XAU and West Texas intermediate (WTI) crude oil prices. Design/methodology/approach The DCC, WTC and DY spillover index methodologies are used in this study. Findings The results reveal significant dynamic and time-varying relationships among SJC gold, XAU and WTI oil prices. Stronger co-movements and volatility spillovers are observed during the COVID-19 period, indicating heightened market interdependence under uncertainty. The findings also show asymmetric lead–lag effects, with global gold and oil markets playing a dominant role in transmitting shocks to the Vietnamese gold market. In the post-COVID period, the level of connectedness declines but remains significant, suggesting persistent integration. These results highlight the importance of monitoring cross-market linkages for effective risk management. Originality/value The results not only provide insights into financial dynamics but also contribute to the development of risk management strategies and appropriate economic policies, particularly for the Vietnamese gold market in the context of increasingly deep international integration.

This study examines cybersecurity as a critical economic infrastructure and investigates its implications for digital economy growth in Nigeria from 2010 to 2025. Despite the rapid expansion of digital technologies, limited attention has been given to cybersecurity as a productive driver of economic performance, particularly in emerging economies. This study addresses this gap by analyzing the long-run and short-run relationships among cybersecurity investment, digital economic growth, policy coordination, and cyber-risk externalities. Using annual time-series data, the study adopts an Autoregressive Distributed Lag (ARDL) model, Error Correction Mechanism (ECM), and robustness estimators including FMOLS and DOLS. Findings from the bounds test confirm the existence of a long-run equilibrium relationship among the variables. The ARDL estimates reveal that cybersecurity investment and policy coordination exert positive and statistically significant effects on digital economy growth, while cyber-risk externalities negatively affect digital performance. The ECM results indicate a strong speed of adjustment toward long-run equilibrium following short-run shocks. Robustness checks validate the consistency of these findings. The study further shows that digital human capital complements cybersecurity investments in supporting digital transformation. Simulation evidence suggests that increased cybersecurity spending and stronger institutional coordination significantly improve digital economic outcomes. The findings support the argument that cybersecurity is not merely a technical safeguard but a productive form of digital infrastructure essential for economic resilience, innovation, and sustainable digital growth. The study recommends stronger cybersecurity investment, improved governance coordination, and integration of cybersecurity into Nigeria’s broader digital development strategy.

This study aims to identify the short- and long-run macroeconomic determinants of shipping demand in Türkiye and examine whether structural breaks alter these relationships. Monthly data spanning January 2012 to June 2025 are analyzed using an autoregressive distributed lag model and an error correction model. In addition, Augmented Dickey-Fuller, Zivot-Andrews, and Bai-Perron tests were incorporated to identify any structural breaks and assess stationarity. Based on the analysis of empirical results, the Industrial Production Index was determined to be the most significant long-run variable in predicting long-term shipping demand. Conversely, there is a significant negative impact of crude oil prices on shipping activity. The Baltic Dry Index and Global Economic Policy Uncertainty were both determined to be insignificant in predicting long-term shipping demand. However, they each had an immediate negative impact on shipping demand in the short run, exhibiting lagged response effects. The model showed that stable long-run equilibrium exists, which indicates that the model accommodates relatively rapid adjustments. There was evidence of a regime shift occurring in March 2019, which indicates a structural break in the market. This study provides empirical evidence related to maritime policy development through macroeconomic shifts to enhance the understanding of how fluctuations in the macroeconomy affect.