Journal of Public Affairs最新文献

Digitalization is argued to be transformative, where digital platforms are becoming increasingly popular to the point that governments have included digitalization in their blueprints. Regardless of the buzz, it is unclear if digitalization enhances economic growth and lowers volatility. This further becomes more important with the development divide among the economies. We explore whether digitalization enhances economic growth and volatility in global settings. We do this by taking 125 economies as a sample for 2011–2021. We employ the system generalized method of moments estimator for potential omitted variable bias, endogeneity, and simultaneity concerns. It is found that digitalization enhances overall economic growth with no significant impact on volatility. Furthermore, it is found that digitalization slows economic growth and increases volatility in developing economies. For developed economies, the impact of digitalization on economic growth and volatility is precisely the opposite, which is interesting because developed economies are capitalizing on transformative opportunities, and developing economies are yet to explore the potential of digitalization. Moreover, it has been found that economies with more financial development experience extra benefits of digitalization, have higher economic growth, and have lower volatility. These findings are robust to alternative proxies for digitalization, country-year fixed effects, and different econometric estimators. This study carries vital policy implications for governments, policymakers, and digital platforms.

Recent reports modeled the quality of government disaster response via service quality, public trust, perceived justice, and government image in disaster situations. This study explores the mediating role of government image in the relationships between service quality, perceived justice, and public trust, thereby addressing the existing gaps in understanding these complex interactions. Using a cross-sectional survey with non-probability sampling, 860 valid responses were analyzed to test the hypothesized direct and mediating effects related to Typhoon Rai, which hit the Philippines in December 2021. The case was chosen for its significant disaster event, which caused broad spatial and temporal impacts, and the Philippines' frequent exposure to about 20 typhoons annually. Results from Partial Least Squares-Structural Equation Modeling generate insights from the proposed model's five hypothesized paths and two mediation effects. We uncover salient results to serve as inputs for disaster risk reduction and management. All of the main hypothesized structural paths are statistically supported, except for the path from service quality to public trust, whose significance changed after the Gaussian copula analysis was performed to remove possible endogeneity problems. Thus, public trust is developed when there is quality information and fairness in the delivery of services. The government image fully mediates the overall relationship between service quality and public trust, while a partial mediating effect was found between perceived justice and public trust. We discussed insights in light of findings and systematically connected the results to the emerging discussions in the field.

The study investigated how environmental regulation, specifically India's National Action Plan on Climate Change (NAPCC) and the subsequent Paris Agreement, affects the environmental and financial performance of Indian manufacturing firms. Employing a quasi-natural experiment approach, the analysis evaluates how these regulations influence compliance costs, access to external financing, and market valuation for highly polluting firms. The results demonstrate that stringent environmental regulations can incentivize firms to adopt sustainable practices through efficiency-oriented compliance strategies such as process upgradation, leading to reduced abatement expenses and improved access to finance at lower costs. These outcomes provide empirical evidence supporting the weak form of the Porter Hypothesis, showing that well-designed regulations can stimulate innovation and efficiency gains, even when formal research and development (R&D)–led innovation remains limited. The findings further show that the Paris Agreement amplified these effects, facilitating long-term value creation and enhancing market legitimacy. Overall, the study contributes to environmental finance and policy discourse by highlighting how national and global environmental frameworks jointly influence firm adaptation, sustainability, and financial resilience.

As the world's largest employment guarantee scheme, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) serves as a crucial policy experiment in addressing rural poverty and unemployment. While studies have examined its varied impacts on asset creation, financial inclusion, and women's empowerment, a comprehensive understanding of its socioeconomic effects at the household and community levels remains crucial for evaluating such large-scale public employment programs. This study aims to evaluate the socioeconomic impact of the MGNREGA, a large-scale government-sponsored job guarantee scheme. Specifically, we examine the effectiveness of the MGNREGA in alleviating poverty, enhancing consumption, and reducing income inequality. This study utilizes nationally representative longitudinal data from the Indian Human Development Survey (IHDS) to conduct the empirical analysis. We employ logit estimations with district fixed effects to examine the socioeconomic impact of the MGNREGA. The results of the study demonstrate that MGNREGA participation has a significant effect on poverty alleviation and improving household consumption. Furthermore, the results suggest that MGNREGA participation contributes to reducing income inequality at the community level. This study contributes empirical evidence to the broader discourse on government-sponsored large-scale job guarantee schemes as policy tools for addressing socioeconomic challenges at both the household and community levels. This is among the first studies that analyze the impact of MGNREGA at the community level using a dataset comprising over 1400 villages across 29 states in India.

This study examines the impact of Economic Policy Uncertainty (EPU) on Financial Stability (FS) in developed and emerging economies from 2003 to 2022. Using a balanced panel dataset and multiple statistical methods, the results reveal a nonlinear association between EPU and FS. At low to moderate levels, EPU reduces financial stability, but beyond a certain point, higher EPU improves FS. The findings suggest that rising EPU prompts regulators and institutions to respond in order to protect FS. Sub-sample analysis shows that this nonlinear effect becomes more pronounced in emerging economies, where financial systems remain more vulnerable and sensitive to shocks. Additional tests reveal that emerging markets respond more sharply to changes in uncertainty, while developed markets exhibit a weaker response, reflecting the strength of their financial systems. The study findings remain robust across a range of alternative measures and advanced estimators, including the generalized method of moments, spline regression, and panel-corrected standard errors. The study offers crucial policy insights by highlighting the need for tailored regulatory frameworks to mitigate the harmful effects of EPU on FS. By distinguishing between developed and emerging economies, this research enhances our understanding of financial resilience in the face of economic uncertainty and contributes to ongoing debates on macroeconomic stability.

A limited understanding of the role of digital transformation as a moderator in the interaction of institutional quality, economic development, and renewable energy adoption in major economies has significantly held back the progress of sustainable energy transitions. This research focuses on the direct and moderated effects of institutional quality, economic development, urbanization, energy imports dependency, and environmental policy stringency on renewable energy adoption, theoretically grounded in the extension of institutional theory and the resource-based view. The study utilizes Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) and Fully Modified Ordinary Least Squares (FMOLS) techniques on a balanced panel of 15 major economies (1999–2023), with data drawn from the World Development Indicators and Worldwide Governance Indicators. The results reveal that institutional quality is a major factor in the increase of renewable energy adoption (β = 12.678, p < 0.001), and the relationship between them is moderated positively by digital transformation (β = 0.150, p < 0.001). In addition, economic development (β = 2.434, p = 0.005) and urbanization (β = 1.473, p < 0.001) are also responsible for the increase in adoption; however, the short-run urban effects are negative. Moreover, energy imports dependency (β = 0.077, p < 0.001) and environmental policy stringency (β = 0.617, p = 0.006) are the factors that lead to a further advancement of the transitions, while digital tools are the enablers of these effects. The results depict that digital infrastructure enables the effectiveness of governance and economic capacity. Such findings can be applied to theoretical frameworks that explain the role of digital transformation as a strategic enabler and enable purposeful policy focus. The scope of generalizability is hindered by data limitations including the heterogeneity of the region. Future research could contemplate analyzing the subnational processes or AI possibilities in energy systems.

Rising US-China tensions can significantly reshape the worldwide trade dynamics by disrupting established supply chains. Addressing this, this study explores the impact of US-China tension on India's medium-high-tech manufacturing exports by obtaining time series data from India spanning 1997 to 2022. This study also examines the behavior of export market diversification on medium-high-tech manufacturing exports, in the presence of US-China tension. To measure these relationships, the Dynamic Autoregressive Distributed Lag (DARDL) method is employed. The findings reveal that the US-China tension exerts a positive impact on India's MHM exports, while export market diversification deteriorates it. Interestingly, this study discovers that export market diversification exerts a beneficial impact on India's medium-high-tech manufacturing exports in presence of the US-China tension. Additionally, this study shows a beneficial role of domestic bank credit, economic growth, total export demand, government effectiveness, and currency depreciation on India's medium-high-tech manufacturing exports. These findings are consistent with the results from Fully Modified Ordinary Least Squares (FMOLS). The marginal plot and heatmap analysis provide robustness of the interaction effect. Results from the CUSUM and the CUSUM Square confirm the estimated model stability. These findings offer policy recommendations for Indian policymakers to maximize the MHM exports.

This study investigates the impact of foreign aid on social inclusion in Africa using the system Generalised Method of Moments (S-GMM) estimation technique. The results reveal that net official development assistance (ODA) as a percentage of gross national income had an adverse effect on total vulnerable employment. Similarly, net official development assistance per capita, official and received and net official development assistance received only had a negating effect on male vulnerable employment. However, a positive impact exists between net official development assistance as a percentage of gross capital formation and female vulnerable employment. Trade openness has no significant effect across all the models. Therefore, African economies should completely implement social inclusion and enhance workers' rights and working conditions. Inclusive projects ranging from inclusive labour markets, better working conditions and greater social security need to be put in place for efficient delivery of official development assistance. Lastly, the unique challenges that females have in gaining access to resources, particularly as regard to reducing vulnerable employment through foreign aid should be identified and addressed.

Artificial Intelligence (AI) has brought a significant transformation in the labor market, sparking mixed sentiments among individuals regarding their employment prospects. To determine a broader emotional response, individual comments posted on 26 YouTube videos discussing the impact of AI on employment within the Indian labor market were scraped and analyzed using pre-trained VADER and ROBERTA for sentiment analysis; NRCLex for emotion analysis; and topic modeling using Latent Dirichlet Allocation (LDA). Findings indicate an increasing negative sentiment among individuals toward AI and employment from 2017 to 2023, with three dominant emotions being anticipation, trust, and fear. Further, the temporal analysis showcases the trend followed by sentiment and emotion from 2017 to 2023. Using LDA, three major focal points of discussion, namely AI Awareness, AI at workplace and AI and future employment, were identified. Based on our results, relevant managerial and policy implications are offered.