Search Results (1,486)

School dropout remains a persistent challenge in developing countries, undermining human capital accumulation and long-term economic development. This paper examines the extent to which Mexico’s PROGRESA conditional cash transfer program influenced school enrollment and reenrollment decisions by analyzing post-program panel data with a Correlated Random Effects probit model. Results indicate that PROGRESA grants significantly increased school enrollment among girls, with the strongest gains observed at the secondary level. Reenrollment effects for girls were positive only when household childcare responsibilities were limited or when secondary schools were located nearby, highlighting the influence of family and community constraints. In contrast, boys exhibited no consistent response in either enrollment or reenrollment outcomes. These findings indicate that although conditional cash transfers can reduce educational inequality, their lasting developmental impact relies on complementary measures—such as childcare provision and improved school access—that mitigate structural barriers to reenrollment and reinforce the connection between education and inclusive growth. Full article

This study proposes a comprehensive framework for evaluating New Quality Productive Forces (NQPF) in forestry, with a focus on human capital, technological innovation, and ecological efficiency as key drivers of sustainable development. Despite moderate growth in NQPF development in China from 2013 to 2022, significant regional disparities persist, with eastern regions outperforming the western regions in terms of forestry productivity and technological adoption. To assess NQPF development, we employ the improved variable-weight matter-element extension model (IVWME), combined with spatial correlation analysis, Gini coefficient measurement, and obstacle degree analysis. The results indicate that, while NQPF development remains stable, eastern regions benefit from superior access to technology and human capital, while western regions face challenges such as slower technological progress and limited labor force development. This study highlights the need for targeted policy interventions that focus on enhancing human capital, promoting technological innovation, and improving regional coordination. The framework provides valuable insights for policymakers in China and other countries facing similar challenges in sustainable forestry development, offering a practical approach to advancing forestry modernization through NQPF. Full article

For a long time, low efficiency in the transfer of rural collective land use rights and the ambiguous attribution of collective land property rights have not only restricted the mobility of rural labor factors but have also hindered the release of vitality in the rural collective economy. This has resulted in lagging growth in the income that rural residents obtain from collective economic factors, contributing to the persistent widening of the urban/rural income gap. As an important institutional innovation to address these issues, the effects of the reform of the rural collective property rights system urgently need to be clarified. The reform of the rural collective property rights system constitutes a major initiative in the transformation of the rural land system. Centered on asset verification and valuation, as well as the demarcation of membership rights and the restructuring towards a shareholding cooperative system, it aims to establish a collective property rights regime characterized by clearly defined ownership and fully functional entitlements. This study takes the national pilot reform of rural collective property rights launched in 2016 as a quasi-natural policy experiment, systematically examining the impact of this pilot policy on the internal income gap within households and its spillover effects on the urban–rural income gap. Based on microdata from the China Household Finance Survey (CHFS) and the China Longitudinal Night Light Data Set (PANDA-China), this study constructs a five-period balanced panel dataset covering 2304 rural households across 25 provinces. A relative exploitation index based on the Kawani index is constructed, and empirical analysis is conducted using a combination of multi-period difference-in-differences (Multi-period DID), discrete binary models, and propensity score matching-difference-in-differences (PSM-DID) models. The results show that: First, the pilot reform significantly reduced the level of income inequality within rural areas in the pilot regions, and its policy benefits further generated positive spillovers via market-driven factor allocation mechanisms, effectively bridging the urban–rural income gap. Second, institutional reforms activated the potential of rural non-agricultural economic factors, establishing new channels for a two-way flow of urban and rural factors, becoming an important path to achieve the goal of common prosperity. Third, the policy effects exhibited significant heterogeneity, specifically manifested in the attributes of major grain-producing regions, initial household income levels, and the human capital characteristics of household heads having significant moderating effects on reform outcomes. This study not only provides theoretical support and empirical evidence for deepening rural property rights reforms under the new rural revitalization strategy, but it also reveals the driving role of institutional innovation in factor mobility, thereby influencing the transmission mechanism of income distribution patterns. This finding offers a China-based solution for developing countries to address the imbalance in urban–rural development and the widening income gap. Full article

The coordinated development of the rural economy and the ecological environment remains a central challenge in China’s rural revitalization agenda. Against this backdrop, the rapid expansion of the digital economy (DE) has the potential to reshape traditional development pathways and ease the longstanding tension between economic growth and environmental sustainability. However, existing studies have predominantly examined the economic or environmental effects of digitalization in isolation, leaving its role in fostering their coordinated development largely unexplored. Using balanced panel data for 30 Chinese provinces from 2011 to 2021, this paper constructs an index of the coupling coordinated development of the rural economy–environment (CREE) and employs a two-way fixed-effects framework, complemented by mediation analysis, panel threshold regression, and a spatial Durbin model, to examine the impact of the DE on CREE and its transmission mechanisms. The results show that the DE significantly enhances CREE on average. This positive effect, however, is non-linear and conditional: it emerges only after rural educational attainment exceeds a critical threshold, and its marginal contribution diminishes as the level of digital development increases. Mechanism analyses indicate that the DE promotes CREE primarily by stimulating technological innovation and advancing urbanization, while improvements in the structure of human capital further strengthen this relationship. Spatial econometric evidence reveals pronounced spillover effects of the DE on CREE across regions, with spillovers based on economic distance outweighing those associated with geographic proximity. By adopting a coupling perspective that integrates economic and environmental dimensions, this paper clarifies the non-linear dynamics, transmission channels, and spatial diffusion processes through which the DE contributes to rural green development. The findings underscore the importance of strengthening rural education foundations, deepening the application of digital technologies, and enhancing regional coordination to fully harness the DE’s role in promoting coordinated economy–environment development. Full article

This study investigates the spatial effects of employment density on the economic, technological, and carbon efficiency of China’s manufacturing sector, using panel data from 30 provinces from 2008 to 2022. A multidimensional performance framework and spatial econometric models are employed to identify both direct impacts and spatial spillovers. The results show that employment density significantly enhances local economic performance while imposing negative spillover effects on neighboring regions. Technological performance exhibits uneven spatial returns, indicating a “technology siphoning” effect in more agglomerated provinces. Carbon efficiency presents a divergent pattern of “local improvement but neighboring deterioration,” highlighting cross-regional ecological externalities. In addition, human capital, capital investment, and regional policy intensity are found to regulate the strength and direction of spatial spillovers across the three performance dimensions. Based on these findings, this study recommends optimizing the spatial layout of manufacturing and population, strengthening interregional innovation collaboration, promoting green transformation, and improving the quality of human capital. These policy implications provide empirical support for advancing sustainable manufacturing development and enhancing regional governance capacity. Full article

This research experimentally investigates the association between multidimensional risk factors and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs). This research experimentally investigates the correlation between multidimensional risk variables and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs) in Saudi Arabia, particularly in alignment with the objectives of Saudi Vision 2030. This study utilizes annual data from 1990 to 2024 and employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to examine the short-run and long-run relationships between economic growth, as measured by GDP, and five key risk dimensions: governance effectiveness, financial development, environmental pressure, human capital, and oil price volatility, which act as proxies for risk dimensions. The main contribution of this study is the integration of these governance, financial, environmental, human capital, and oil price risk factors into a single ARDL framework for Saudi Arabia from 1990 to 2024, using GDP growth as a proxy for progress toward SDGs within the Saudi Vision 2030 context, addressing gaps in prior studies that focus on individual determinants. The empirical evidence indicates a long-term cointegration relationship among the variables. Our findings indicate that government effectiveness and investment in human capital are important positive factors associated with long-term economic growth, thereby validating the importance of institutional improvements and educational expenditures. In contrast, fluctuations in oil prices and environmental pressures are linked to adverse association, highlighting issues related to resource dependency and ecological degradation. Financial development exhibits a negative long-run association, indicating potential inefficiencies or diminishing returns in loan distribution. The study offers essential policy recommendations, such as expediting digital governance reforms, allocating financial resources to non-oil SMEs (SDG 8), aligning educational curricula with labor market demands, and implementing stricter environmental regulations to separate economic growth from emissions. Full article

This research paper focuses on the role of average working hours (AVHs) of the labor force in explaining the variation in QOL across countries, which is an important but unexplored area in the empirical literature. Using data from 62 countries and employing several econometric techniques, we show that long AVHs are detrimental for improved QOL. The sub-sample results demonstrate that AVHs have a significant detrimental impact on the QOL of the population only in the case of developing countries. However, in the case of developed countries, the influence of AVHs is insignificant as these countries are enjoying relatively reduced AVHs as compared to developing countries. Moreover, our results indicate that the labor force participation rate, human capital, government expenditures, internet use, and electricity consumption are the main driving forces behind a better QOL both in developed and developing countries. Finally, we found evidence that trade openness is an irrelevant factor in explaining the variation in QOL as it is insignificant in most of the specifications despite possessing a positive coefficient. Full article

In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article

Digital rural construction (DRC), as a crucial intersection of the rural revitalization strategy and the construction of Digital China, is a key path to addressing the imbalance and inadequacy in the urban–rural income gap (URIG). Based on provincial panel data from 2011 to 2023, this paper systematically examines the relationship and mechanism of action between the two using an econometric model. This study finds that DRC significantly reduces the URIG overall, and this effect is achieved through increasing urbanization levels, accelerating employment, and promoting social consumption. Spatial effect tests indicate that DRC has a spatial spillover effect; construction in one province reduces the URIG in neighboring provinces. Further research shows that, against the backdrop of human capital level acting as a threshold variable, the effect of DRC on the URIG exhibits an inverted “U”-shaped characteristic, first increasing and then decreasing. Therefore, this paper proposes countermeasures and suggestions, including constructing a digital-enabled urban–rural integration mechanism, promoting cross-regional coordinated development of DRC, and implementing a tiered and categorized digital literacy improvement project. Full article

With the ongoing development of the digital economy, the productive function of data as an economic factor has become increasingly salient. Scientifically and rigorously assessing the value of data assets is essential for improving the national economic accounting system and promoting sustainable economic growth. In light of the limitations inherent in existing cost-based and market-based valuation approaches, this paper proposes a comprehensive valuation model that integrates the cost approach with the income approach and applies it to the commercial banking sector. Specifically, text analysis is employed to estimate human capital investment in data assets from the perspective of labor supply and demand, after which total costs are derived based on the proportion of human capital. An ARIMA model is used to forecast future cost inputs and net profits associated with data assets. Furthermore, the income-based approach is adopted to estimate the average present value of data assets, with the results of the two methods serving to validate each other. The comparison of estimation results under the cost approach and the income approach further validates the relationship between input and output in data assets. This also demonstrates that data assets follow the law of diminishing marginal utility, thereby contradicting the notion that data increases in value with greater usage. This study enriches the theoretical framework of data asset valuation, broadens its application scope, and provides meaningful guidance for advancing data asset accounting practices and related research. Full article

Under the background of accelerating global transitions towards low-carbon development, digital intelligence transformation (DIT) has become a critical force that helps companies overcome green technological constraints and translate external green pressures into substantive green innovation. Taking the establishment of China’s NAIIDTZs as a quasi-natural experiment, this study investigates the impact of DIT on corporate green innovation (CGI) from an internal decision-making perspective. Based on a panel dataset of 19,440 samples from Chinese A-share listed companies during 2012–2023, our findings show that DIT significantly enhances both the quantity and quality of CGI. Mechanism analyses indicate that DIT promotes CGI’s quantity through increased R&D human capital input, while improving CGI’s quality through managerial myopia reduction. Heterogeneity analyses further reveal that the positive effects of DIT on CGI are particularly pronounced in firms operating under fierce market competition, in high industrial technological intensity, and in eastern regions. Furthermore, we find that CGI exerts a lagged effect on carbon emission reduction performance, while the effect of CGI’s quality is stronger than that of CGI’s quantity. These findings extend the dynamic capacity theory to digitalization and provide practical and policy implications for promoting CGI through digital intelligence development. Full article

The global maritime and logistics sectors are undergoing rapid digital transformation driven by emerging technologies such as automation, the Internet of Things (IoT), and blockchain. Artificial Intelligence (AI), with its ability to analyze complex datasets, predict operational patterns, and optimize resource allocation, offers a transformative potential beyond the capabilities of conventional technologies. However, mixed results are shown in its implementation. This study examines the current state of AI applications to unlock higher levels of efficiency and competitiveness in logistics firms. A mixed-methods approach was employed, combining surveys from logistics companies with in-depth interviews from key stakeholders in ports and logistics firms to triangulate insights and enhance the validity of the findings. Our results reveal that while technologies such as automation and digital tracking are increasingly utilized to improve operational transparency and cargo management, AI applications remain limited and largely experimental. Where implemented, AI contributes to strategic decision-making, predictive maintenance, customer service enhancement, and cargo flow optimization. Nonetheless, financial conditions, data integration challenges, and a shortage of AI-skilled professionals continue to impede its wider adoption. To overcome these challenges, this study recommends targeted investments in AI infrastructure, the establishment of collaborative frameworks between public authorities, financial institutions, and technology-driven Higher Education Institutions (HEIs), and the development of human capital capable of sustaining AI-enabled transformation. By strategically leveraging AI, Oman can position its ports and logistics sector as a regional leader in efficiency, innovation, and sustainable growth. Full article

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia. Full article

This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers. The study followed a descriptive-analytical approach structured in consecutive methodological phases. The systematic review, conducted following the Kitchenham protocol, identified theoretical factors associated with credit risk, while fieldwork with 300 producers provided the socioeconomic and productive contexts of coffee-growing households. Producer income, cost of living, and farm management expenses were modeled using regression, statistical, and machine learning methods. Subsequently, these variables were integrated to construct a financial risk index, which was normalized using expert scoring. The index was validated using data from 100 additional producers, for whom annual repayment capacity and maximum loan amounts were estimated according to their risk level. The results indicated that incorporating municipal-level economic variables, such as estimated average prices, income, and expenses, enhanced predictive accuracy and improved the rational allocation of loan amounts. The study concludes that credit risk analysis based on variables related to human, productive, and economic capital constitutes an effective strategy for improving access to finance in rural areas. Full article

Human Capital Agility is increasingly recognized as a critical capability for achieving sustainable project success in the highly dynamic construction sector, yet an original and empirically testable Human Capital Agility model rooted in Human Capital theory is still lacking. This study aims to develop and validate a Human Capital Agility framework that integrates Leadership Agility and Knowledge Management and to construct a hierarchical roadmap for the gradual development of Human Capital Agility. Using a multi-method design, survey data from 141 construction professionals were analyzed with Partial Least Squares Structural Equation Modeling to test the structural relationships among Knowledge Management, Leadership Agility, Human Capital Agility, Sustainable Project Success, and the moderating role of Firm Size, while expert judgments from nine practitioners were modeled using Modified Total Interpretive Structural Modeling to derive the internal hierarchy of Human Capital Agility components. The results show that Leadership Agility is a dominant driver of Human Capital Agility and that Human Capital Agility significantly enhances Sustainable Project Success, whereas the direct effect of tacit knowledge on Leadership Agility is not supported. The hierarchical model maps nine key components of Human Capital Agility into six levels, separating foundational drivers such as attitudes and predisposition from higher-level outcome capabilities such as generative behavior, responsiveness, adaptability, and resilience. These findings provide an integrated and empirically grounded Human Capital Agility model that offers both a causal explanation and a practical roadmap for strengthening human capital capabilities in construction projects. Full article