This study provides a robust empirical investigation into the multifaceted impact of human capital flight on Nigeria's economic output from 1990 to 2020. The persistent exodus of skilled professionals, a phenomenon commonly termed 'brain drain', presents a complex challenge for developing economies. While the inflow of remittances from the diaspora is a notable benefit, its capacity to offset the economic detriments of losing skilled labor remains a critical question. Utilizing annual time-series data, this research employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to co-integration for the dynamic analysis of both the short-run and long-run relationships between real GDP growth, human capital flight (proxied by net migration), remittances, unemployment, and human capital development. The empirical findings reveal a statistically significant and negative long-run relationship between human capital flight and economic growth in Nigeria, indicating that the continuous departure of skilled individuals imposes a substantial drag on the nation's output potential. Conversely, remittances exhibit a positive but primarily short-run impact on economic growth, suggesting they serve more as a temporary economic stimulus than a driver of long-term sustainable development. The results highlighted the inadequacy of remittance inflows to fully compensate for the adverse structural effects of brain drain. The study concludes with targeted policy recommendations, emphasizing the urgent need for the Nigerian government to foster a more conducive domestic environment through strategic investments in infrastructure, education, and security to retain its skilled workforce and harness the full development potential of its human capital.

Economic development policy seeks to attract job-creating companies with the expectation of positive impacts for local workers. However, major warehousing investments in rural counties had no significant impact on wages or unemployment rates. A Comparative Interrupted Time Series (CITS) design exploits a novel quasi-natural experiment—a set of large distribution centers built between 2002 and 2007 by major U.S. retailers. For these warehouses, location choice is exogenous to local policies or economic conditions given that companies are constrained by transportation costs to a narrow spatial window, avoiding reverse causation bias that is common in the literature. A hand-selected sample of 31 counties with large warehouses built by Target, Lowe’s, and Walmart in the mid-2000s are compared to a matched set of counterfactuals. Despite large investments that brought hundreds of jobs, there were few benefits for local workers. Unemployment rates were reduced only temporarily and there was no observed impact on annual average wages. Demand for labor was satisfied through shifting commuting patterns and migration in the short-term and medium term. These results have implications for policymakers who use business attraction policies to promote worker welfare.

This research investigates the impact of digital financial services and financial inclusion indicators on MENA countries' economic growth rate. Using an Autoregressive Distributed Lag (ARDL) model for the period 2004-2021, the study examines the relationship between Gross Domestic Product (GDP) growth and indicators of digital financial services and financial inclusion such as the number of commercial bank branches, borrowers, and ATMs, as well as the number of internet users. The analysis reveals both a long-term equilibrium relationship and a short-term relationship between digital financial inclusion variables and economic growth. Results indicate a positive relationship between the number of bank branches and economic growth in the short and the long term in MENA region. In addition to that, borrowers have a positive association with economic growth in the long run. Moreover, the number of ATMs demonstrates a positive impact on GDP growth in the short run. This finding suggests that providing a sufficient number of ATMs is crucial to fostering economic activity.

This paper investigates how economic inequality shaped Islamic economics as a form of socio-political resistance and identity formation. Using a conceptual analysis approach, it surveys the foundations of Islamic economics within its political context, categorising the literature into four key themes related to minority economic identity and identity theory. The paper explores the clash between Homo Economicus and Homo Islamicus, highlighting the challenges Muslims face in balancing economic pursuits with preserving their economic identity. It examines the role of economic autonomy in empowering marginalised communities and the impact of Islamic economics on these dynamics. Furthermore, the paper discusses the policy implications of the evolving Islamic geo-economic identity in the UK. Migrants from India and Pakistan have established a strong geo-economic identity, embedding Islamic economic principles within Western economies. This transformation has positioned the UK as a leading hub for Islamic finance, prompting significant changes in its legal and regulatory frameworks to support inclusive economic growth. The UK’s experience serves as a model for other Western countries integrating Islamic finance. The paper provides insights into the interplay between Islamic economics, individual identity, and economic pursuits, contributing to theory by identifying self-categorisation as crucial in economic identity formation and its implications for the Islamic finance industry.

This study investigates the efficacy of diverse machine learning survival models, including Cox, Kernel SVM, DeepSurv, Survival Random Forest, and MTLR models, employing the concordance index to assess their predictive abilities. The primary objective of this research is to identify the most accurate model for forecasting the time it takes for a country to witness a 10% surge in unemployment within a 120-month timeframe (2013-2022), utilizing variables from the MVI dataset of 28 American countries. Through the comparative evaluation of complex survival models, we discovered that DeepSurv, a sophisticated machine learning algorithm, excels in capturing intricate nonlinear relationships, while conventional models exhibit comparable performance under specific circumstances. The weight matrix, a pivotal element of our analysis, meticulously assesses the economic repercussions of various risk factors, vulnerabilities, and capabilities.

Broadband availability is generally lower in the Tribal areas of the United States than in other areas and several policies implemented over the years aim to close that gap. Using data on broadband deployment over the years 2014-2020, progress in closing the Tribal Gap is evaluated. Unmatched and matched sample are used, and a sample of census tracts within 30 miles of a Tribal area are also analyzed with and without matching. The gap between Tribal and non-Tribal census tracks has been getting smaller over time and by 2020 the Tribal Gap was near zero in all cases, especially when the deployment differences are conditioned on a few covariates.

In recent years, financial inclusion has taken center stage in policy discussions regarding achieving higher growth rates and reducing poverty levels. The existing literature analyzing the relationship between financial inclusion and GDP growth mostly assumes a one-way relation from financial inclusion to GDP, often ignoring potential reverse causality. Furthermore, the literature commonly adopts a financial inclusion index, or focuses on specific indicators such as the number of bank branches, ATMs or the share of people having an account. Because financial inclusion encompasses multiple dimensions, it is important to analyze the causal linkages between different financial inclusion indicators and GDP. In this paper, we analyze the nature and the direction of the causality between GDP and a large number of financial inclusion indicators for MENA (Middle East and North Africa) countries by adopting the recently developed nonlinear and nonparametric Kernel causality approach. Our analysis suggests that financial inclusion increases as the share of women having bank accounts increases. It also increases with the share of adults with primary education having an account and the share of adults having a mobile account. We also identify the relationship between main barriers to financial inclusion and GDP, and find that affordability and having insufficient funds are associated with GDP.

Based on provincial panel data in China, this study is the first to investigate whether industry-university-research collaborative innovation (IURCI) can help to improve factor misallocation. It is found that IURCI can significantly improve capital misallocation and labor misallocation, and the effect has regional differences, which shows that the improvement effect is obvious in areas with factor under-allocation, such as the central and western regions, but not obvious in areas with factor over-allocation, which conforms to the rule of diminishing marginal returns. A regulatory effect model is built to explore the impact of regional heterogeneity, through which we find that after considering three external environmental conditions, including economic development level, academic research level, and marketization degree, the improvement effect of IURCI on factor misallocation undergoes significant changes. The research results show that to deepen the marketization reform of factor allocation, we can start with IURCI. The government should form a sustainable and normalized industry-university-research collaborative innovation ecological mode through pilot cases and adopt measures according to local conditions to ensure the efficient use and reasonable distribution of capital and human resources of enterprises, universities, and scientific research institutions.

The business world is a competitive place, and enterprises must cultivate sustainable relationships with their clients to gain customer loyalty. Concepts of relationship marketing (RM) are becoming vital in the service industry. Different RM strategies are used to attract customers, but some of these strategies do not achieve customer loyalty as easily as predicted due to customers’ changing behaviour patterns. This paper investigates the impact of RM strategies on customer loyalty through relationship qualities (customer satisfaction and trust) by concentrating on the high-end hairdressing sector in Auckland. Additionally, this research investigates the expectations of customers of high-end hair salons formed by RM strategies, which influence their satisfaction, trust, and customer loyalty to their service provider. The results indicate that brand image shows a significant relevance to trust, satisfaction, and customer loyalty claiming that brand image is an essential element of RM. Additionally, this investigations revels that, hair salons in Auckland are looking forward to ascertaining a sustainable relationship with their clients through a spectrum of strategies such as positive brand image, good service quality, reasonable price, and good value deals to earn optimistic relationship quality, which will ultimately escalate to customer loyalty. Findings of this research will be useful in supporting hair salons to gain a greater understanding of their customers by adopting effective RM strategies.

Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.