American Journal of Social Sciences and Humanities

Fiscal policy, corruption and economic growth: Evidence from Southern European countries

2026-01-02T00:45:54-06:00

This study investigates the effects of fiscal policy and corruption on economic growth using panel data analysis. Three panel regression techniques were applied: fixed effects, random effects, and pooled OLS. After evaluating the model performance, the Random Effects model and the Pooled OLS model were selected as the most appropriate for the analysis. The research focuses on four Southern European countries, Spain, Italy, Greece, and Portugal, covering the period from 1995 to 2019. GDP growth was used as the dependent variable, serving as a measure of economic performance. The independent variables included the Corruption Perception Index (CPI) to represent corruption, along with government spending and tax revenue to capture fiscal policy. To examine whether corruption influences the effectiveness of fiscal policy, the model also included interaction terms between CPI and the two fiscal variables. The results showed that a one-unit increase in government spending was associated with a 0.24% decrease in GDP growth, while a one-unit rise in tax revenue corresponded to a 0.26% decline. Conversely, a one-point increase in the CPI score, indicating reduced corruption, was linked to a 0.09% increase in economic growth. These findings were statistically significant. However, the interaction terms between corruption and fiscal policy were not significant, suggesting that corruption did not significantly modify the impact of fiscal measures on economic growth within this sample.

Soft values of nature meeting mental needs of wellbeing and profitability gives incentives for improvement planning supporting most sustainability goals

2026-02-12T03:50:59-06:00

Soft natural values are often neglected in planning. One approach has been to aim for a balance of natural resources. However, balancing is no longer sufficient, as global consumption continues to deplete resources, reduce biodiversity, and drive severe climate crises. Instead, every project must actively contribute to improvements. But how can we expect property owners to deliver environmental qualities that exceed previous standards? Achieving this requires economic incentives—a win-win for all stakeholders, from developers to tenants, employees, municipalities, and even nations. Our approach summarizes research findings from the past half-decade on how natural qualities meet fundamental human needs for mental well-being and health. Two resources have been particularly valuable: the Alnarp Rehabilitation Garden and a large Public Health Survey of the Scania Region in southern Sweden, which enabled validation of eight specific sensory dimensions. One key finding is that university productivity is significantly associated with tree cover near campus buildings and with the density of sensory dimensions across an entire campus. These scientific insights led to the development of an assessment protocol for restorative workplaces. A group of property owners and tenant companies formed a mutual partnership for a testbed of an evaluation protocol. Practical implications now show that the tool effectively supports improvements. When stakeholders and staff discuss evaluation questions together, they gain a deeper understanding of how specific characteristics meet specific needs. Achieving social, ecological, and economic goals while improving one’s workplace proves highly motivating for all involved.

From traditional to intelligent bureaucracy: Integrating Al and machine learning into public sector management

2026-02-12T06:29:42-06:00

This research examines the transformation of traditional public sector bureaucracies into intelligent, technology-driven organizations through the integration of Artificial Intelligence (AI) and Machine Learning (ML). Traditional bureaucracies, characterized by hierarchical structures, rigid procedures, and slow decision-making, often face challenges in delivering efficient and responsive public services. By leveraging AI and ML, public sector management can enhance operational efficiency, enable data-driven decision-making, and improve service delivery while reducing costs and administrative bottlenecks. The paper has adopted a qualitative research design based on a Systematic Literature Review (SLR) to address how Artificial Intelligence (AI) and Machine Learning (ML) could be used to change the traditional forms of bureaucracies into intelligent, evidence-based, and responsive forms of government. The study explores key applications of AI and ML, including predictive analytics, automated workflow management, and policy simulation, highlighting their potential to foster transparency, accountability, and citizen-centric governance. It also addresses challenges associated with adoption, such as ethical concerns, data privacy, algorithmic bias, and resistance to change among public employees. Findings indicate that successful integration requires a balanced approach combining technological innovation, human oversight, and institutional reform. The research concludes that intelligent bureaucracies have the potential to create more adaptive, effective, and inclusive public administration systems. Future studies are recommended to investigate empirical outcomes, sector-specific applications, and ethical frameworks, ensuring that AI-driven governance maximizes benefits while mitigating risks.