Pub Date : 2026-01-10DOI: 10.1016/j.seps.2026.102415
Raffaele Mattera , Philip Hans Franses
We propose a new spatio-temporal hierarchical clustering approach that is suitable for clustering African countries based on Gross Domestic Product under measurement error. To accommodate for measurement error, we use slave trade as an instrument. Furthermore, we extend our method to allow for a range of macroeconomic indicators, instead of just GDP. We document that our findings largely agree on the degree of convergence.
{"title":"Analyzing convergence across African economies while allowing for measurement errors","authors":"Raffaele Mattera , Philip Hans Franses","doi":"10.1016/j.seps.2026.102415","DOIUrl":"10.1016/j.seps.2026.102415","url":null,"abstract":"<div><div>We propose a new spatio-temporal hierarchical clustering approach that is suitable for clustering African countries based on Gross Domestic Product under measurement error. To accommodate for measurement error, we use slave trade as an instrument. Furthermore, we extend our method to allow for a range of macroeconomic indicators, instead of just GDP. We document that our findings largely agree on the degree of convergence.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102415"},"PeriodicalIF":5.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1016/j.seps.2026.102416
Xu Zhang , Zhongmin Yan , Abdul Rauf
In the wave of digital transformation, whether artificial intelligence (AI) can drive disruptive innovation in small and medium-sized enterprises (SMEs) has become an important research question. Using data on China's “Specialized, Refined, Distinctive, and Innovative” (SRDI) enterprises from 2014 to 2024, this paper measures the penetration level of AI in enterprises based on large language models (LLMs) text analysis methods, and constructs a large-scale patent text corpus to derive a disruptive innovation index. Results show that the AI adoption significantly enhances the disruptive innovation level of SRDI enterprises, and the conclusion still holds true after robustness tests. Mechanism analysis reveals that AI promotes disruptive innovation by optimizing human capital structures, increasing R&D investment, and facilitating access to policy support. The positive effect of AI on disruptive innovation is stronger for enterprises in eastern regions and high-technology sectors. This study deepens understanding of how AI drives disruptive innovation and provides implications for intelligent manufacturing development.
{"title":"Does artificial intelligence promote disruptive innovation in SRDI enterprises: Evidence from LLM-based text analysis","authors":"Xu Zhang , Zhongmin Yan , Abdul Rauf","doi":"10.1016/j.seps.2026.102416","DOIUrl":"10.1016/j.seps.2026.102416","url":null,"abstract":"<div><div>In the wave of digital transformation, whether artificial intelligence (AI) can drive disruptive innovation in small and medium-sized enterprises (SMEs) has become an important research question. Using data on China's “Specialized, Refined, Distinctive, and Innovative” (SRDI) enterprises from 2014 to 2024, this paper measures the penetration level of AI in enterprises based on large language models (LLMs) text analysis methods, and constructs a large-scale patent text corpus to derive a disruptive innovation index. Results show that the AI adoption significantly enhances the disruptive innovation level of SRDI enterprises, and the conclusion still holds true after robustness tests. Mechanism analysis reveals that AI promotes disruptive innovation by optimizing human capital structures, increasing R&D investment, and facilitating access to policy support. The positive effect of AI on disruptive innovation is stronger for enterprises in eastern regions and high-technology sectors. This study deepens understanding of how AI drives disruptive innovation and provides implications for intelligent manufacturing development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102416"},"PeriodicalIF":5.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-04DOI: 10.1016/j.seps.2025.102413
Yanmei Wang , Enhui Sun , Wenying Yan
As climate change intensifies and ocean resource exploitation continues, the marine environment has gained increasing societal attention. Marine environmental monitoring technologies are crucial for ocean conservation. Collaborative innovation among interdisciplinary organizations is pivotal to technological advancement. However, the mechanisms underlying marine organizational collaborative innovation remain underexplored. This study constructs a collaborative innovation network using Chinese joint patent application data related to marine environmental monitoring buoy technologies. By employing visualization tools, we trace the evolutionary paths of the network and apply the Temporal Exponential Random Graph Model (TERGM) to examine the relationships between key factors and the network's formation and evolution. The findings underscore the roles of endogenous structures, node attributes, external conditions, and time dependence on network formation and evolution. The study also reveals the growing tendency for organizations to collaborate with those possessing similar technological knowledge structures. Identifying these key factors enables environmental advocates and policymakers to tailor strategies effectively in support of marine sustainable development.
{"title":"Marine organizational collaborative network: Enhancing technological innovation for environmental monitoring","authors":"Yanmei Wang , Enhui Sun , Wenying Yan","doi":"10.1016/j.seps.2025.102413","DOIUrl":"10.1016/j.seps.2025.102413","url":null,"abstract":"<div><div>As climate change intensifies and ocean resource exploitation continues, the marine environment has gained increasing societal attention. Marine environmental monitoring technologies are crucial for ocean conservation. Collaborative innovation among interdisciplinary organizations is pivotal to technological advancement. However, the mechanisms underlying marine organizational collaborative innovation remain underexplored. This study constructs a collaborative innovation network using Chinese joint patent application data related to marine environmental monitoring buoy technologies. By employing visualization tools, we trace the evolutionary paths of the network and apply the Temporal Exponential Random Graph Model (TERGM) to examine the relationships between key factors and the network's formation and evolution. The findings underscore the roles of endogenous structures, node attributes, external conditions, and time dependence on network formation and evolution. The study also reveals the growing tendency for organizations to collaborate with those possessing similar technological knowledge structures. Identifying these key factors enables environmental advocates and policymakers to tailor strategies effectively in support of marine sustainable development.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102413"},"PeriodicalIF":5.4,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The real estate industry encompasses sequential sub-processes in operations, including land acquisition, house construction, and house sales and rentals. Investigating the sub-process structure of real estate operations is essential to demystifying and improving the overall operational performance. This study proposes an additive network DEA model to estimate the process-oriented performance of urban real estate operations and capture hidden sub-process performance. The sequential linear programming method is used to address the model's nonlinearity. We further explore the impact of operational performance on housing prices to identify the main underlying driver of China's booming real estate market. The proposed model is applied to assess the operational performance of Chinese urban real estate markets over the past decade. The empirical findings reveal that: (1) performance losses may stem from weaknesses in the housing construction process, with significant improvement potential in overall operational and sub-process performance in most cities. (2) Enhanced performance in the construction process can fuel short-term housing prices increases during market booms. (3) Higher real estate operational performance may initially raise housing prices but ultimately inhibit them in the long term due to limited market demand. Our proposed method framework proves to be an effective tool for policymakers to design wise operational plans for improving real estate operational performance.
{"title":"Operational performance of urban real estate in China: An additive network DEA model","authors":"Hao Zhang , Wattanaporn Nalinrat , Rong Xiang , Anyu Yu , Yue Gao","doi":"10.1016/j.seps.2025.102414","DOIUrl":"10.1016/j.seps.2025.102414","url":null,"abstract":"<div><div>The real estate industry encompasses sequential sub-processes in operations, including land acquisition, house construction, and house sales and rentals. Investigating the sub-process structure of real estate operations is essential to demystifying and improving the overall operational performance. This study proposes an additive network DEA model to estimate the process-oriented performance of urban real estate operations and capture hidden sub-process performance. The sequential linear programming method is used to address the model's nonlinearity. We further explore the impact of operational performance on housing prices to identify the main underlying driver of China's booming real estate market. The proposed model is applied to assess the operational performance of Chinese urban real estate markets over the past decade. The empirical findings reveal that: (1) performance losses may stem from weaknesses in the housing construction process, with significant improvement potential in overall operational and sub-process performance in most cities. (2) Enhanced performance in the construction process can fuel short-term housing prices increases during market booms. (3) Higher real estate operational performance may initially raise housing prices but ultimately inhibit them in the long term due to limited market demand. Our proposed method framework proves to be an effective tool for policymakers to design wise operational plans for improving real estate operational performance.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102414"},"PeriodicalIF":5.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.seps.2025.102408
Sergio Hoffmann , Rita Laura D’Ecclesia
Environmental, Social, and Governance (ESG) metrics have become central to sustainability assessment, yet the link between national conditions and composite ESG performance remains largely unexplored. We develop a bottom-up national ESG rating by aggregating the distribution of listed firms’ ESG scores for twelve developed economies between 2013 and 2022. Several aggregation schemes—mean, median, Sen’s inequality-adjusted index, and a dispersion-adjusted mean—are benchmarked, and the resulting rankings prove highly consistent, supporting the median as the headline measure. National ratings are then compared with World Bank indicators of environmental efficiency, social welfare, and governance quality through panel fixed-effects regressions and four machine-learning models (Random Forest, Gradient Boosting, Support Vector Regression, and CatBoost), assessed via cross-validation and explainability tools. CatBoost achieves the highest predictive accuracy and balanced use of predictors. Energy intensity and under-five mortality consistently act as dominant negative drivers, while gender representation and demographic maturity contribute positively. A pillar-level (E, S, G) panel-VAR analysis reveals strong within-pillar persistence and asymmetric cross-effects led by the social dimension. Overall, the framework provides a transparent bridge from firm-level data to national ESG performance, delivering robust and interpretable evidence for policy evaluation and sustainable investment screening.
{"title":"Measuring national sustainability: ESG scores from corporate data","authors":"Sergio Hoffmann , Rita Laura D’Ecclesia","doi":"10.1016/j.seps.2025.102408","DOIUrl":"10.1016/j.seps.2025.102408","url":null,"abstract":"<div><div>Environmental, Social, and Governance (ESG) metrics have become central to sustainability assessment, yet the link between national conditions and composite ESG performance remains largely unexplored. We develop a bottom-up national ESG rating by aggregating the distribution of listed firms’ ESG scores for twelve developed economies between 2013 and 2022. Several aggregation schemes—mean, median, Sen’s inequality-adjusted index, and a dispersion-adjusted mean—are benchmarked, and the resulting rankings prove highly consistent, supporting the median as the headline measure. National ratings are then compared with World Bank indicators of environmental efficiency, social welfare, and governance quality through panel fixed-effects regressions and four machine-learning models (Random Forest, Gradient Boosting, Support Vector Regression, and CatBoost), assessed via cross-validation and explainability tools. CatBoost achieves the highest predictive accuracy and balanced use of predictors. Energy intensity and under-five mortality consistently act as dominant negative drivers, while gender representation and demographic maturity contribute positively. A pillar-level (E, S, G) panel-VAR analysis reveals strong within-pillar persistence and asymmetric cross-effects led by the social dimension. Overall, the framework provides a transparent bridge from firm-level data to national ESG performance, delivering robust and interpretable evidence for policy evaluation and sustainable investment screening.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102408"},"PeriodicalIF":5.4,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-28DOI: 10.1016/j.seps.2025.102412
Xingjie Yang , Yihang Hu , Huseyin Caliskan , Zhenhong Qi , Qiang Liu
Despite growing emphasis on ecological agriculture, limited attention has been examined how labor endowment and productive services to shape farmers' adoption decisions. This study investigates the synergistic role of labor endowment and productive services in adopting the rice-crayfish co-culture model, using 2023 survey data from small-scale farmers in the middle and lower reaches of the Yangtze River. The results show that labor endowment is a key driver of adoption. Both labor quantity and labor quality increase the likelihood of adoption, by 17.5 % and 3.1 % per additional unit, respectively. Productive services further strengthen these effects. Seedling provision and agricultural supply services mainly amplify the effect of labor quality endowment, while planting and disease prevention services reinforce the overall influence of labor endowment. Marketing services play a distinctive role in enhancing the contribution of labor quality to adoption behavior. Heterogeneity analysis reveals that the positive impact of labor endowment is considerably stronger among production oriented farmers, new business subjects, large-scale grain growers, and farmers with better cultivated land conditions than among subsistence and small-scale farmers. Mechanism analysis shows that labor endowment promotes adoption mainly by improving farmers' ability to learn and master ecological production technologies. The study advances theoretical understanding by demonstrating that productive services in complex ecological agricultural systems operate under a complementarity logic, rather than functioning as substitutes for household labor. These findings provide new empirical evidence on the multidimensional mechanisms linking labor endowment, service provision, and ecological technology adoption.
{"title":"Labor endowment, productive services and farmers' adoption of ecological agriculture: Taking rice-crayfish co-culture model as an example","authors":"Xingjie Yang , Yihang Hu , Huseyin Caliskan , Zhenhong Qi , Qiang Liu","doi":"10.1016/j.seps.2025.102412","DOIUrl":"10.1016/j.seps.2025.102412","url":null,"abstract":"<div><div>Despite growing emphasis on ecological agriculture, limited attention has been examined how labor endowment and productive services to shape farmers' adoption decisions. This study investigates the synergistic role of labor endowment and productive services in adopting the rice-crayfish co-culture model, using 2023 survey data from small-scale farmers in the middle and lower reaches of the Yangtze River. The results show that labor endowment is a key driver of adoption. Both labor quantity and labor quality increase the likelihood of adoption, by 17.5 % and 3.1 % per additional unit, respectively. Productive services further strengthen these effects. Seedling provision and agricultural supply services mainly amplify the effect of labor quality endowment, while planting and disease prevention services reinforce the overall influence of labor endowment. Marketing services play a distinctive role in enhancing the contribution of labor quality to adoption behavior. Heterogeneity analysis reveals that the positive impact of labor endowment is considerably stronger among production oriented farmers, new business subjects, large-scale grain growers, and farmers with better cultivated land conditions than among subsistence and small-scale farmers. Mechanism analysis shows that labor endowment promotes adoption mainly by improving farmers' ability to learn and master ecological production technologies. The study advances theoretical understanding by demonstrating that productive services in complex ecological agricultural systems operate under a complementarity logic, rather than functioning as substitutes for household labor. These findings provide new empirical evidence on the multidimensional mechanisms linking labor endowment, service provision, and ecological technology adoption.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102412"},"PeriodicalIF":5.4,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-27DOI: 10.1016/j.seps.2025.102411
Zahra Abbasi , Mohammad Afzalinejad , Ali Asghar Foroughi
Data Envelopment Analysis (DEA) is a prominent tool used to assess the efficiency of decision-making units (DMUs). While static DEA models measure performance without considering time dependency, dynamic DEA incorporates time as a factor in the modeling. In recent years, environmental concerns have become a significant focus for the world community. In the context of DEA, these concerns are often expressed as undesirable outputs of the production process. Therefore, in addition to evaluating operational efficiency, it is essential to consider environmental efficiency to obtain a comprehensive measurement of DMUs’ performance. This paper presents the assessment of operational and environmental efficiency and their integration into a unified efficiency measure within the dynamic DEA framework. The time dependency of efficiency is taken into account and the links between consecutive time periods are categorized as either good or bad types. Additionally, static environmental models are established to enable comparison of dynamic and static efficiency. The proposed models are used to evaluate the performance of twenty-one countries in the agriculture sector. In this study, GHG emissions and cumulative agricultural loss due to disasters are selected as undesirable factors. Environmental efficiency generally improves over 2016–2018; however, while the static assessment shows steady progress, the dynamic assessment rises until 2017 and then slightly declines in 2018. The number of efficient countries in the operational dimension is much more than those in the environmental dimension, which shows that the economic dimension has a higher priority among countries.
{"title":"Dynamic evaluation of operational, environmental, and unified efficiencies: A DEA application in agriculture","authors":"Zahra Abbasi , Mohammad Afzalinejad , Ali Asghar Foroughi","doi":"10.1016/j.seps.2025.102411","DOIUrl":"10.1016/j.seps.2025.102411","url":null,"abstract":"<div><div>Data Envelopment Analysis (DEA) is a prominent tool used to assess the efficiency of decision-making units (DMUs). While static DEA models measure performance without considering time dependency, dynamic DEA incorporates time as a factor in the modeling. In recent years, environmental concerns have become a significant focus for the world community. In the context of DEA, these concerns are often expressed as undesirable outputs of the production process. Therefore, in addition to evaluating operational efficiency, it is essential to consider environmental efficiency to obtain a comprehensive measurement of DMUs’ performance. This paper presents the assessment of operational and environmental efficiency and their integration into a unified efficiency measure within the dynamic DEA framework. The time dependency of efficiency is taken into account and the links between consecutive time periods are categorized as either good or bad types. Additionally, static environmental models are established to enable comparison of dynamic and static efficiency. The proposed models are used to evaluate the performance of twenty-one countries in the agriculture sector. In this study, GHG emissions and cumulative agricultural loss due to disasters are selected as undesirable factors. Environmental efficiency generally improves over 2016–2018; however, while the static assessment shows steady progress, the dynamic assessment rises until 2017 and then slightly declines in 2018. The number of efficient countries in the operational dimension is much more than those in the environmental dimension, which shows that the economic dimension has a higher priority among countries.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102411"},"PeriodicalIF":5.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1016/j.seps.2025.102410
Jiqiang Zhao , Lijun Cheng , Xianhua Wu
Sustainable agricultural systems are crucial for balancing food security and ecological protection. This study develops a two-stage inverse network data envelopment analysis (DEA) model that incorporates shared inputs and undesirable outputs to evaluate and optimize resource allocation in agricultural production and pollution control. Using data from 31 Chinese provinces (2010–2023), the model estimates optimal resource allocation strategies under constant-efficiency and efficiency-improvement scenarios. Results indicate that although system efficiency is generally improving, notable regional disparities remain. Under constant efficiency, achieving a 5 % output increase requires substantial input growth, particularly in pesticides, whereas efficiency improvement reduces overall inputs by an average of 5.84 %, indicating the role of technological progress in resource conservation. The proposed framework represents a dynamic and practical tool for policymakers to design targeted, forward-looking strategies for sustainable agriculture.
{"title":"Optimal resource allocation estimation of agricultural sustainable systems based on inverse network DEA","authors":"Jiqiang Zhao , Lijun Cheng , Xianhua Wu","doi":"10.1016/j.seps.2025.102410","DOIUrl":"10.1016/j.seps.2025.102410","url":null,"abstract":"<div><div>Sustainable agricultural systems are crucial for balancing food security and ecological protection. This study develops a two-stage inverse network data envelopment analysis (DEA) model that incorporates shared inputs and undesirable outputs to evaluate and optimize resource allocation in agricultural production and pollution control. Using data from 31 Chinese provinces (2010–2023), the model estimates optimal resource allocation strategies under constant-efficiency and efficiency-improvement scenarios. Results indicate that although system efficiency is generally improving, notable regional disparities remain. Under constant efficiency, achieving a 5 % output increase requires substantial input growth, particularly in pesticides, whereas efficiency improvement reduces overall inputs by an average of 5.84 %, indicating the role of technological progress in resource conservation. The proposed framework represents a dynamic and practical tool for policymakers to design targeted, forward-looking strategies for sustainable agriculture.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102410"},"PeriodicalIF":5.4,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.seps.2025.102409
Matheus Pereira Libório, Helena Teixeira Magalhães Soares, Caio Cesar Soares Gonçalves, Marcos Flávio Silveira Vasconcelos D'Angelo, Petr Iakovlevitch Ekel
This study examines the concept of multidimensional welfare, which encompasses multiple aspects across various dimensions. The study introduces an approach that combines ranking normalization, structural equation modeling, and a new method for constructing composite indicators. This approach enables improved differentiation of welfare levels, confirming the multidimensional nature of welfare and representing it through a readily understandable unidimensional measure. This innovative approach fills a gap in methodologies by considering the interrelationships between dimensions, avoiding aggregating dimensions that carry little information into the composite indicator, ensuring the composite indicator's multidimensionality, and avoiding making it predominantly explained by a single dimension. Other advantages of this approach include a rigorous explanation of the conceptual framework of multidimensional welfare, avoiding the assignment of equal weights to the dimensions due to the lack of a clear and consistent weighting scheme, and providing transparency in the objective definition of dimension weights. The results indicate that government efforts to provide social services and protection are insufficient to improve welfare levels in the poorest municipalities. Governments should not allocate resources solely to social assistance and protection; instead, they should generate employment and income opportunities and promote digital inclusion, leisure, culture, and sports. In addition to contributing to the welfare literature and informing the formulation of more effective social policies, this study advances the composite indicators literature by offering an innovative weighting scheme that ensures conceptual compatibility and preserves the composite's multidimensionality.
{"title":"Capturing and representing the multidimensionality of welfare through structural equation modeling and a goal-based composite indicator with multiple constraints","authors":"Matheus Pereira Libório, Helena Teixeira Magalhães Soares, Caio Cesar Soares Gonçalves, Marcos Flávio Silveira Vasconcelos D'Angelo, Petr Iakovlevitch Ekel","doi":"10.1016/j.seps.2025.102409","DOIUrl":"10.1016/j.seps.2025.102409","url":null,"abstract":"<div><div>This study examines the concept of multidimensional welfare, which encompasses multiple aspects across various dimensions. The study introduces an approach that combines ranking normalization, structural equation modeling, and a new method for constructing composite indicators. This approach enables improved differentiation of welfare levels, confirming the multidimensional nature of welfare and representing it through a readily understandable unidimensional measure. This innovative approach fills a gap in methodologies by considering the interrelationships between dimensions, avoiding aggregating dimensions that carry little information into the composite indicator, ensuring the composite indicator's multidimensionality, and avoiding making it predominantly explained by a single dimension. Other advantages of this approach include a rigorous explanation of the conceptual framework of multidimensional welfare, avoiding the assignment of equal weights to the dimensions due to the lack of a clear and consistent weighting scheme, and providing transparency in the objective definition of dimension weights. The results indicate that government efforts to provide social services and protection are insufficient to improve welfare levels in the poorest municipalities. Governments should not allocate resources solely to social assistance and protection; instead, they should generate employment and income opportunities and promote digital inclusion, leisure, culture, and sports. In addition to contributing to the welfare literature and informing the formulation of more effective social policies, this study advances the composite indicators literature by offering an innovative weighting scheme that ensures conceptual compatibility and preserves the composite's multidimensionality.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102409"},"PeriodicalIF":5.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.seps.2025.102406
Aysu Ozel , Karen Smilowitz
In transportation and logistics problems, such as the traveling salesman problem or the vehicle routing problem, the geographic distribution of nodes can significantly impact both the solutions obtained and the performance of solution approaches. Therefore, it is common for researchers to share test instances for meaningful comparisons. In some contexts, this is more challenging when data are protected and cannot be shared. This is particularly true for transportation and logistics problems found in public school operations. Despite growing literature, proposed models and solution approaches are rarely compared across papers because data protection regulations prohibit sharing data. At the same time, randomly generated data can miss critical patterns existing in reality that may impact equitable access to education. In this paper, we introduce a framework to create context-rich data sets for school operations models and methods based on publicly available data that reflect public school district characteristics in the United States.
{"title":"Context-rich data sets for school operations models and methods","authors":"Aysu Ozel , Karen Smilowitz","doi":"10.1016/j.seps.2025.102406","DOIUrl":"10.1016/j.seps.2025.102406","url":null,"abstract":"<div><div>In transportation and logistics problems, such as the traveling salesman problem or the vehicle routing problem, the geographic distribution of nodes can significantly impact both the solutions obtained and the performance of solution approaches. Therefore, it is common for researchers to share test instances for meaningful comparisons. In some contexts, this is more challenging when data are protected and cannot be shared. This is particularly true for transportation and logistics problems found in public school operations. Despite growing literature, proposed models and solution approaches are rarely compared across papers because data protection regulations prohibit sharing data. At the same time, randomly generated data can miss critical patterns existing in reality that may impact equitable access to education. In this paper, we introduce a framework to create context-rich data sets for school operations models and methods based on publicly available data that reflect public school district characteristics in the United States.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"104 ","pages":"Article 102406"},"PeriodicalIF":5.4,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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