Inefficient Resource Allocation Research Articles

The Effect of Agricultural Subsidies on Rice Productivity and Prices in Southeast Asia: A Panel Regression Study

Agricultural subsidies have become a common instrument used by countries in the Southeast Asian Region to support farmers, increase food production, and control prices. However, the effectiveness and long-term impact of these subsidies are still a hot debate in the economic literature. The purpose of this study is to analyze how agricultural subsidies affect rice productivity and rice prices in three Southeast Asian countries: Indonesia, Thailand, and Vietnam, during the period 2020-2023. The method used in this study is panel data analysis with a fixed effect to control the variation between countries that is not observed. Panel regression analysis was carried out to identify the relationship between agricultural subsidies and the two dependent variables. The F-test and Chi-square test are used to test for the existence of fixed effects, while the cross-section dependency test is used to check the residual correlation between countries. The results of this study show that the regression results of the panel show that agricultural subsidies have a significant negative impact on rice productivity (Y1), which may reflect inefficient resource allocation or the presence of market distortions that hinder productivity increase. In contrast, subsidies have a significant positive impact on rice prices (Y2), suggesting that subsidies can help maintain price stability and provide economic benefits for farmers. From the overall analysis, agricultural subsidies in Southeast Asia have a complex and diverse impact on rice productivity and prices. While subsidies help maintain price stability and support farmers' welfare, their impact on rice productivity still needs to be further eval_uated to ensure more efficient and sustainable allocations. Governments in the region need to continue to review and adjust their subsidy policies in order to strike a balance between supporting smallholders, increasing productivity, and maintaining rice price stability, while minimizing negative impacts on markets and the environment.

Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression.

Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.

Inefficient urban-rural resource allocation and reduced tuition benefits for preschool education: an example from a Chinese county

PurposeTaking urban-rural disparity into account, this research examines the current service provision of preschool education at the local level in China.Design/methodology/approachFollowing a qualitative design, the research is based on a case study on the preschool service provision in Luochuan County in China, with data collected from semi-structured interviews with grassroots officials and residents from the county’s rural and urban regions.FindingsThis research finds that preschool services are inefficiently delivered between the county’s rural and urban areas. While services are oversupplied in rural regions, the popular demand for urban kindergartens is inadequately addressed. Moreover, the tuition subsidy offered by the local government has drastically decreased in recent years.Research limitations/implicationsThis research is confined to a single-case study, so its findings are not necessarily applicable to all Chinese localities. Nevertheless, it proves that these problems in preschool service provision can be a result of the central authority’s tight control.Social implicationsGiven China’s rapid urbanization and fertility decline, this research argues that overinvestment in rural kindergartens can be a widespread phenomenon throughout China. It also suggests a decreasing popular demand for other public services, such as childcare, in Chinese rural regions. This research calls for special attention to the persistent regional discrepancy in preschool service standards and the welfare cuts after the revenue centralization reform.Originality/valueThis research contributes to a clearer picture of the current preschool service delivery by Chinese local governments, which remains largely underexplored by far. It also provides updates on the country’s long-existing urban-rural discrepancy in preschool services.

Semantic guidance incremental network for efficiency video super-resolution

AbstractIn video streaming, bandwidth constraints significantly affect client-side video quality. Addressing this, deep neural networks offer a promising avenue for implementing video super-resolution (VSR) at the user end, leveraging advancements in modern hardware, including mobile devices. The principal challenge in VSR is the computational intensity involved in processing temporal/spatial video data. Conventional methods, uniformly processing entire scenes, often result in inefficient resource allocation. This is evident in the over-processing of simpler regions and insufficient attention to complex regions, leading to edge artifacts in merged regions. Our innovative approach employs semantic segmentation and spatial frequency-based categorization to divide each video frame into regions of varying complexity: simple, medium, and complex. These are then processed through an efficient incremental model, optimizing computational resources. A key innovation is the sparse temporal/spatial feature transformation layer, which mitigates edge artifacts and ensures seamless integration of regional features, enhancing the naturalness of the super-resolution outcome. Experimental results demonstrate that our method significantly boosts VSR efficiency while maintaining effectiveness. This marks a notable advancement in streaming video technology, optimizing video quality with reduced computational demands. This approach, featuring semantic segmentation, spatial frequency analysis, and an incremental network structure, represents a substantial improvement over traditional VSR methodologies, addressing the core challenges of efficiency and quality in high-resolution video streaming.

Consumer willingness to pay (WTP) for beef based on color and price discounts

Discoloration of beef products leads retailers to offer discounts or discard beef products, as consumers tend to reject a brownish beef color. This practice contributes to retail food waste and inefficient resource allocation linked to beef production. The objective of this study was to investigate U.S. consumer preferences for beef color and price discounts. After an initial test for screen acuity regarding color perception using a condensed form the ishihara test, data on meat purchasing behavior as well as demographic information was collected for all participants. An additional part of the study undertook a hypothetical discrete choice experiment using digital photos of beef steaks with different meat color, price, and discount labels in retail packaging to examine consumer preferences, willingness to pay, and the effects of discount pricing. Results of a Mixed Logit Model based on choice data show that beef shoppers perceive, see, and prefer color nearly linearly across days of retail display. Consumers' willingness to pay was negative for 4, 7 and 9 days of retail display relative to day zero and decreased with an increase in discoloration. Results indicate that beef shoppers would have to be financially compensated for choosing a steak that was discolored after 9 days of retail display. However, male beef buyers exhibit weaker preferences for color and could be a target segment for promoting discolored beef products to reduce meat waste.

Sustainable finance analysis of commercial banks in China

The literature generally finds a mixed relationship between corporates' social activities and performance in emerging economies. One possible explanation is that such social activities lack sufficient recognition from financial institutions, leading to inefficient allocation of resources in terms of sustainable development. Therefore, the practice of sustainable finance by commercial banks in emerging economies is crucial to future sustainable development. Our empirical results show that providing loans to corporates engaged in sustainability activities positively contributes to performance in China's banking industry, whereas loans to small- and micro-size firms have an insignificant impact. Hence, the government should actively encourage commercial banks to continuously expand green credits that will not only upgrade sustainable finance, but also enhance future sustainable development.

Dynamic fleet management: Integrating predictive and preventive maintenance with operation workload balance to minimise cost

The optimization of fleet maintenance management is of utmost importance to ensure the efficient and reliable operation of asset fleets. Traditional maintenance strategies are often reactive or rely on predetermined schedules, which can lead to inefficient resource allocation and increased operational costs. The advent of digital technologies has allowed the integration of predictive maintenance into fleet management. This paradigm shift towards a more data-driven approach enables fleet management to dynamically respond to issues identified through sensors and algorithms that detect anomalies and provide prognostic insights regarding the remaining useful life of components. However, a notable deficiency exists in the integration of predictive maintenance with calendar-based preventive maintenance and fleet operational allocation, thereby impeding value creation for businesses. This paper presents an optimisation model to address this issue by incorporating preventive and predictive maintenance while simultaneously striving to balance the workload to meet operational demand and mitigate potential penalties resulting from failure to meet these demands. The paper also examines the role of component criticality as well as the precision of the RUL (Remaining Useful Life) prognosis. Through the experiments conducted, it has been demonstrated that the allocation of the fleet is subject to change depending on the level of criticality of monitored components. These findings reveal the potential risks and penalties that could arise from an insufficient definition of failure impact severity when integrating predictive maintenance with existing preventive approaches and operational workload balance. Additionally, the experiments underscore the impact of RUL distributions precision on total operating costs, showing the confidence intervals through a sensitivity analysis.

Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.

Development of big data assisted effective enterprise resource planning framework for smart human resource management.

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Rescue Relief Agency Management For Disaster Recovery

In the aftermath of a natural or man-made disaster, the swift and coordinated deployment of rescue agencies is critical to minimizing casualties and maximizing the effectiveness of relief efforts. However, the current landscape of disaster response is often hampered by the absence of a centralized platform for communication and collaboration among various rescue agencies. This lack of a unified system often leads to duplication of efforts, inefficient resource allocation, and delays in providing aid to those in dire need. To address these challenges and enhance the efficiency of disaster response, we propose the development of a comprehensive mobile application that would enable rescue agencies to seamlessly register their information, share real-time location updates, and coordinate their operations effectively. This proposed mobile application would serve as a central hub for rescue agencies to register their information, including their location, contact details, areas of expertise, and available resources. This information could be entered manually or automated using GPS or other location tracking technologies. Once the database is populated, the application would provide a user-friendly interface that allows users to visualize the locations of registered rescue agencies on a map. Additionally, users could filter the results based on specific criteria, such as the type of disaster, the resources available, or the time since the last reported activity. Beyond displaying the locations of rescue agencies, the application would also facilitate communication and collaboration among these organizations. Rescue agencies could send alerts or requests for assistance directly through the application, enabling them to coordinate their efforts and optimize resource utilization. Moreover, the application could facilitate the sharing of critical resources such as medical equipment, transportation, and communication infrastructure, ensuring that these resources are deployed where they are most needed. Overall, the proposed mobile application has the potential to revolutionize disaster response by providing a centralized platform for communication, collaboration, and resource management among rescue agencies. By enabling real-time coordination and efficient resource allocation, this application could significantly enhance the effectiveness of disaster relief efforts, saving lives and minimizing the impact of natural and man-made disasters..

A Review of Emergency Resource Allocation under Natural Disasters

The acceleration of urbanization has led to increasingly serious casualties and property losses caused by natural disasters, while also exposing issues such as unreasonable and inefficient allocation of emergency resources. This article elaborates on the location selection of urban emergency resources, prediction of disaster point demand, construction of emergency resource scheduling models, and application of intelligent optimization algorithms by consulting relevant literature. It analyzes some problems in current emergency resource research and summarizes the key points that need to be broken through in future research.

Relay selection in Underwater Acoustic Sensor Networks for QoS-based cooperative communication using game theory

Underwater Acoustic Sensor Networks (UASN) plays a crucial role in monitoring and transmitting environmental information for marine resource exploration. However, underwater communication faces significant challenges such as signal attenuation, noise interference, and unstable underwater environments. Moreover, with the deployment of a large number of nodes, inefficient resource allocation strategies in cooperative communication lead to strained communication resources as well as increased energy consumption. This paper introduces a novel relay selection approach based on game theory that takes into account both competition and cooperation among nodes. By building a potential game model and a utility function, we derive the optimal relay selection strategy that can enhance system Quality of Service (QoS). We also define conditions for the feasibility of pure strategy Nash Equilibria (NE) and develop a low-complexity QoS-based Relay Selection Iterative Algorithm (QoS-RSIA) to procure feasible pure strategy NE for the designed game. Through comparative analysis with other relay selection algorithms, simulation results show that the proposed method not only enhances system throughput and average spectral efficiency by at least 8.3%, but also significantly reduces time complexity compared to exhaustive search algorithms.

Bridging ideas and market success: A framework for managing proof-of-concept projects

While the world is full of potential moonshot projects, there is an ever-growing disconnect between new ideas and their successful transformation into commercialized new products. Companies often struggle with inefficient resource allocation and the challenge of quickly assessing an idea’s feasibility. The burgeoning number of proof-of-concept (PoC) projects reflects the need to mitigate the risk of investing resources in potential failures. This study introduces the PoC Framework, a strategic tool that guides managers through the PoC journey, transforming initial ideas into real-world applications. Importantly, it promotes a mindset shift that equally values the proving and disproving of concepts. This approach saves resources by preventing the further development of non-viable ideas. The PoC Framework has been gaining traction among Silicon Valley firms, underscoring its practical relevance and effectiveness in fostering strategic thinking and overcoming hidden challenges within the PoC process.

Cultivating Household Food Security through Resource – Use Efficiency among Cash Crop Farmers: Evidence from Tobacco Producers in Uganda

Poverty, food insecurity and malnutrition cases have been reported among farmers who prioritize cash crops such as tobacco. This is as a result of inefficient allocation of farm resources to tobacco production, with little or no allocation to staple food crops. To increase food production among such farmers, researchers have recommended optimum allocation of farm resources between tobacco and staple food crops. This study therefore aimed at determining the resource use efficiency for tobacco – food crop production, the optimal farm plans and the factors affecting resource use efficiency for food crops production. Data Envelopment Analysis was used to assess the level of resource use efficiency and the optimal farm plans while Tobit model was used to determine the factors affecting resource use efficiency for the optimal farm plans. The results from data drawn from 200 tobacco farmers in Western Uganda depicted that farmers were 61% efficient in their resource allocation to tobacco and food crops. Results for the optimal allocation plans suggested that farmers should optimally allocate their resources to tobacco and food crops in order to achieve household food requirements. Household size, pesticides use, farm income and support from the tobacco contract companies had a positive association with resource use efficiency for food crop production while input prices, land size and gender had a negative relationship with resource use efficiency. In conclusion, farmers are inefficient in allocating their farm resources between tobacco and other food crops which has far-reaching implications for availability of household foods. The study therefore recommends that the governments of SSA should increase the training and extension services on the adoption of optimum resources for cash – food crop production as well as supporting the farmers through inputs subsidies so as to increase household food production.

Alleviation of Poverty Through Panchayat Raj Institutions: A Critical Study of Challenges and Prospects in Karnataka, India

Purpose: The purpose of this paper is to: Analyse the role of Panchayat Raj Institutions (PRIs) in alleviating poverty in Karnataka, India. Identify the challenges faced by PRIs in implementing poverty alleviation programs. Explore potential solutions to overcome these challenges and improve program effectiveness. Provide recommendations for strengthening the role of PRIs in poverty alleviation efforts. Theoretical reference: This paper draws on several theoretical frameworks, including: heories of poverty alleviation, focusing on the role of local governance and community participation. Theories of decentralization and the devolution of power to local governments. Theories of social justice and equity, emphasizing the need to address the root causes of poverty. Theories of sustainable development, highlighting the importance of integrating economic, social, and environmental considerations. Method: This research is primarily a doctrinal study, relying on a variety of primary and secondary sources: Primary Sources: Statutory enactments: Constitution of India, 1950, Central Government Schemes implemented by PRIs, The Karnataka Gram Swaraj and Panchayat Raj Act, 1993. Policy documents: National Rural Development Policy, Karnataka State Rural Development Policy, Poverty alleviation scheme guidelines. Secondary Sources: Statistical analysis: Government reports and data sets, Research reports and surveys, Research publications: Peer-reviewed articles and books on poverty alleviation, local governance, and development. Case studies: Examples of successful poverty alleviation programs implemented by PRIs. Results: This research identified several key challenges faced by PRIs in implementing poverty alleviation programs in Karnataka: Corruption: Misuse of funds and resources hinders the effectiveness of programs and prevents benefits from reaching the intended beneficiaries. Caste: Deep-rooted social inequalities limit access to resources and opportunities for marginalized communities. Lack of awareness: Many people remain unaware of available schemes and benefits, leading to underutilization of resources. Limited capacity: PRIs often lack the necessary skills and resources to effectively plan, implement, and monitor programs. Lack of coordination: Poor coordination between different levels of government and stakeholders can lead to delays, duplication of efforts, and inefficient resource allocation. Despite these challenges, the research also identified several promising practices and potential solutions: Transparency and accountability: Initiatives like social audits and public hearings can improve transparency and hold PRI officials accountable for program outcomes. Community participation: Engaging communities in program design and decision-making can ensure programs are relevant and address local needs. Capacity building: Training programs can equip PRI officials with the necessary skills and knowledge to manage programs effectively. Technology and innovation: Utilizing technology can enhance program efficiency, data management, and communication with beneficiaries. Partnerships: Collaborations with NGOs, civil society organizations, and private sector can contribute resources, expertise, and innovation. Conclusion: PRIs play a crucial role in alleviating poverty in India. While they face numerous challenges, there are also promising solutions and opportunities for improvement. By investing in capacity building, promoting transparency, fostering community participation, and embracing technology and innovation, PRIs can be empowered to become more effective agents of poverty alleviation in Karnataka and beyond. Implications of research: This research has several key implications for policy and practice: The need for strengthened capacity building for PRI officials to improve program planning, implementation, and monitoring. The importance of enhancing transparency and accountability in the allocation and use of resources for poverty alleviation programs. The necessity for increased community participation in program design and decision-making to ensure programs are responsive to local needs and priorities. The need for better coordination between central, state, and local governments to ensure effective implementation of poverty alleviation programs. Originality/value: This research contributes to the existing body of knowledge on poverty alleviation by: Providing a detailed analysis of the role of PRIs in Karnataka, India, a context that is often overlooked in research. Identifying unique challenges faced by PRIs in implementing poverty alleviation programs. Offering practical recommendations for strengthening the role of PRIs and improving program effectiveness. Contributing to the understanding of how local governance can be leveraged to achieve sustainable poverty alleviation.

The impact of interorganizational collaboration on the viability of disaster response operations: The Gjerdrum landslide in Norway

This study investigates the interorganizational collaboration among agencies that responded to a landslide in Gjerdrum, Norway in 2020. It focuses on the crucial role of communication, coordination, cooperation, and knowledge sharing within organizations, and it examines tensions between centralization and decentralization, professional and administrative leadership, planning and improvisation, and external and internal information sharing. To explore this collaboration, we conducted nine interviews and employed the viable system model (VSM) as a conceptual and methodological framework. Through a systemic diagnosis of the search and rescue (SAR) crisis response system’s viability and by applying the VSM, the structural, communicational, and functional pathologies in interorganizational collaboration were identified. Thus, this diagnostic approach allowed us to determine the pathological features that challenged the SAR system’s effectiveness and viability, including imbalances, inefficiencies in maintaining internal and external interactions, communication breakdowns, and inefficient resource allocation. These insights clarify the structural challenges within the SAR system and underscore the significance of optimizing interconnections, establishing efficient decision-making processes, and improving communication flows to enhance the overall effectiveness of the SAR system.

Dimensional traps in evasion models and their effects on industrial structure

Size-dependent firms’ monitoring by the state leads to sub-optimal results in terms of combating evasion and efficient allocation of investment by firms. While Coppier, Michetti and Scaccia (2022) describe how such a policy can be a source of dimensional trap in a single-firm evasion model, the present paper moves to a heterogeneous context, considering industrial structures composed of many firms with different sizes. We then propose a discrete-time nondeterministic dynamic model to describe the consequences of size-dependent policies and potential dimensional traps on industrial structure and its evolution over time. We show that an unwise choice of policy parameters may determine a long-run equilibrium industrial structure characterized by a small number of large firms and a plethora of small firms, with the latter being marked by inefficient resource allocation and noncompliant behavior with regard to tax regulations. These results are robust to different choices of the initial industrial structure suggesting alternative policy indications.

Going green while getting lean: Decomposing carbon and green total factor productivity

While research on carbon productivity is growing rapidly, the role of structural change in green transition remains unexplored due to the scarcity of firm-level emission data. This study addresses this gap by utilizing unique register-based greenhouse gas emission data from Finland’s energy-intensive manufacturing firms for 2000–2019. Applying a structural change productivity decomposition, we break down the sector’s carbon productivity and green total factor productivity into four components: contributions from non-switching continuing firms, industry-switching continuing firms, the effects of entry and exit, and resource allocation. The findings highlight the important role of structural change in the sector’s productivity. Non-switching continuing firms emerged as the key drivers of both carbon and green total factor productivity growth. The contribution of entry and exit was negative during the financial crisis, while inefficient resource allocation significantly hindered productivity growth throughout the study period. These findings emphasize the importance of public subsidies targeted at environmentally efficient firms to enhance their competitiveness under challenging market conditions. Furthermore, the establishment of a stable yet positive carbon price would incentivize less-productive firms to adopt environmentally friendly technologies.

Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis

Value at risk (VaR) and expected shortfall (ES) have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions, external regulations, and risk capital allocation. However, existing VaR estimation approaches fail to accurately reflect downside risks, and the ES estimation technique is quite limited owing to its challenging implementation. This causes financial institutions to overestimate or underestimate investment risk and finally leads to the inefficient allocation of financial resources. The main purpose of this study is to use machine learning to improve the accuracy of VaR estimation and provide an effective tool for ES estimation. Specifically, this study proposes a VaR estimator by combining quantile regression with “Mogrifier” recurrent neural networks to capture the “long memory” and “clustering” properties of financial assets; while for estimating ES, this study directly models the quantile of assets and employs generative adversarial networks to generate future tail risk scenarios. In addition to the typical properties of financial assets, the model design is also consistent with heterogeneous market theory. An empirical application to four major global stock indices shows that our model is superior to other existing models.

Exploring the effect of International Public Sector Accounting Standards adoption on national resource allocation efficiency in developing countries

International capital providers such as the World Bank suggest that inefficient resource allocation in developing countries remains a major challenge for borrowing countries. Therefore, the purpose of this study is to examine whether the adoption of International Public Sector Accounting Standards (IPSAS) improves the resource allocation efficiency of developing countries. A robust econometric modeling including fixed effect and Two-Step Generalized Method of Moments is employed on a sample of 64 developing countries between 2005 and 2021. The results are not sensitive to potential endogeneity issues. The findings indicate that the IPSAS coefficient is significantly and positively correlated at a 5% level or higher. This suggests a strong and significant relationship between IPSAS adoption and resource allocation, indicating that using IPSAS improves efficient resource allocation. Additionally, the resource allocation coefficient is positive and highly significant at a 5% level or higher. These results are particularly notable in countries with low bureaucratic quality, suggesting that IPSAS adoption strengthens policies and regulations in the public sector’s financial structure, ultimately leading to more efficient resource allocation. Therefore, these findings imply that adopting IPSAS is crucial for developing countries to ensure efficient resource allocation and attract international capital providers.