Search

Objective: To evaluate the impact on rural Veterans' access to social work services of a Department of Veterans Affairs (VA) national program to increase social work staffing, by Veterans' rurality, race, and complex care needs. Data Sources and Study Setting: Data obtained from VA Corporate Data Warehouse, including sites that participated in the social work program between October 1, 2016 and September 30, 2021. Study Design: The study outcome was monthly number of Veterans per 1000 individuals with 1+ social work encounters. We used difference‐in‐differences to estimate the program effect on urban, rural, and highly rural Veterans. Among rural and highly rural Veterans, we stratified by race (American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White) and complex care needs (homelessness, high hospitalization risk, and dementia). Data Collection: We defined a cohort of 740,669 Veterans (32,434,001 monthly observations) who received primary care at a participating site. Principal Findings: Average monthly social work use was 8.7 Veterans per 1000 individuals. The program increased access by 49% (4.3 per 1000; 95% confidence interval, 2.2–6.3). Rural Veterans' social work access increased by 57% (5.0; 3.6–6.3). Among rural/highly rural Veterans, the program increased social work access for those with high hospitalization risk by 63% (24.5; 18.2–30.9), and for Veterans experiencing homelessness, 35% (13.4; 5.2–21.7). By race, the program increased access for Black Veterans by 53% (6.1; 2.1–10.2) and for Asian Veterans by 82% (5.1; 2.2–7.9). Conclusions: At rural VA primary care sites with social work staffing below recommended levels, Black and Asian Veterans and those experiencing homelessness and high hospitalization risk may have unmet needs warranting social work services. [ABSTRACT FROM AUTHOR]

This article presents the gretl package BayTool which integrates the software functionalities, mostly concerned with frequentist approaches, with Bayesian estimation methods of commonly used econometric models. Computational efficiency is achieved by pairing an extensive use of Gibbs sampling for posterior simulation with the possibility of splitting single-threaded experiments into multiple cores or machines by means of parallelization. From the user's perspective, the package requires only basic knowledge of gretl scripting to fully access its functionality, while providing a point-and-click solution in the form of a graphical interface for a less experienced audience. These features, in particular, make BayTool stand out as an excellent teaching device without sacrificing more advanced or complex applications. [ABSTRACT FROM AUTHOR]

In the context of building a country with a strong transportation network, railway container transportation (RCT) is an important means of reducing costs, increasing efficiency, and adjusting transportation structures. Thus, its impact on regional economic development is important. Based on data from railway container-handling stations and spatial econometric models, this study discusses the differences in the development of RCT and their impact on regional economic development at different leves. This study has three main findings: first, there are significant regional differences in the development of the RCT. The intra-regional differences between the eastern and central regions of China (which do not include Hong Kong, Macao and Taiwan) are gradually narrowing, while the regional differences in the western region are widening. Meanwhile, the intra-regional differences in important economic zones (such as Pearl River Delta Economic Zone (PRDEZ), Chengdu-Chongqing Economic Zone (CYEZ), Bohai Rim Economic Zone (BHEZ), and Yangtze River Delta Economic Zone (YRDEZ)) are narrowing daily. Second, the development differences of RCT in regional level and important economic regions level show different trends. The unbalanced features of large regions are increasingly evident, whereas the differences in economic regions are decreasing. However, the problem of overlapping RCT remains prominent. Third, the transformation of RCT development mode and fierce competition among transportation modes cause RCT to have a restraining effect on the regional economy at three levels. Rational allocation of resources and other means must be used to guide the transformation from inhibition to promotion, and by formulating targeted policies that will promote the development of RCT, which will improve the transportation structure and help construct a country with a strong transportation system. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigates whether and to what extent operating in inner areas affects the profitability of innovative Italian small and medium-sized enterprises (SMEs) over 2012–2018. Design/methodology/approach: Guided by the National Strategy for Inner Areas and the "Investment Compact," this study distinguishes between inner and core innovative SMEs. It employs various econometric models to estimate a regression for the return on assets of SMEs, differentiating between firms operating in inner and non-inner areas of northwest, northeast, centre and south Italy. Findings: Findings reveal that innovative SMEs in inner areas generally exhibit lower profitability compared to those in non-inner municipalities. However, huge heterogeneity in results is observed across the country. Specifically, innovative SMEs in the inner areas of the south register lower profitability than those operating in non-inner zones. Conversely, innovative SMEs located in the inner municipalities of northwest and northeast Italy show higher profitability than their peers in non-inner areas. The results imply that targeted policies for inner areas are crucial. However, due to the diversity of local impacts, a differentiated approach, depending on the geographic context, is necessary. Originality/value: The study aims to explore the relationship between inner areas and the performance of innovative SMEs in Italy. More precisely, it examines the effect of operating in a municipality located within an inner area on the profitability of innovative SMEs. This issue has been overlooked in existing literature. Importantly, we aim to determine whether there is a heterogeneous impact based on geographical localisation, specifically in the Northwest, the Northeast, the Centre and the South of the country. Therefore, this paper contributes to the literature by investigating the factors influencing the performance of innovative SMEs and suggesting new policy recommendations for developing inner areas in Italy. [ABSTRACT FROM AUTHOR]

This study employs econometric models and cross-case analysis to investigate the impact and mechanism of public procurement (PP) on enterprises' green innovation (EGI). Based on the manual identification of government customers, we conduct panel model estimations on Chinese listed company data from 2010 to 2021. Findings reveal that PP has a green innovation effect at the micro level. Moreover, expected market returns, corporate social responsibility (CSR), and financing constraints play a mediating role between PP and EGI. Additionally, we delved into the relationship between PP and EGI through a cross-case study. It not only validates the conclusions drawn from the econometric model but also summarises the heterogeneous characteristics of PP affecting EGI in practice. This study not only contributes to the existing literature on the outcomes of PP and factors impacting EGI but also furnishes additional empirical insights into the mechanisms through which PP enhances EGI. [ABSTRACT FROM AUTHOR]

By using the composite coincident indicator, we document the real-time nowcasting performance of Latvian GDP growth during the COVID-19 period. The Latvian Economic Barometer accurately predicted GDP growth during the COVID-19 pandemic, especially, in the second and third quarters of 2020, a period that proved challenging for more sophisticated econometric models. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to reconcile and address Bowman's paradox empirical criticism from the lens of financial theory, corporate strategy and their econometric adversaries based on three issues, i.e. risk conceptualization, measurement and econometric modeling in Asian emerging countries (AEC). Design/methodology/approach: The study is conducted on panel data sampling from 2,872 firms across four Asian Emerging Countries (AEC) and employs a two-stage least squares (2SLS) estimation technique. We proposed a theoretical framework based on triangulation that outlines four risk-return relationships based on proxies derived from capital market and firm-level data and used different econometric models to answer Bowman's paradox ongoing criticism. Findings: The empirical results negate the empirical artifact viewpoint in AEC. The risk-return relationship estimated on firm accounting-based ratios or its combination with market-based measures supports Bowman's paradox and thus upholds the corporate strategy point of view. Whereas the risk-return relationship based on market-based ratios upholds the financial theory point of view. However, the results are mixed when risk is subdivided into systematic and business risk. Our results are robust across standard deviation and semi-standard deviation-based measures of risk, and there is no evidence of a non-linear relationship. Originality/value: A compelling debate exists that Bowman's paradox is an empirical artifact. We provide an innovative approach that aims to reconcile and address the ongoing debate by employing diverse risk-return proxies and econometric models in Asian emerging countries. Methodological issues such as endogeneity, sample biases, temporal fluctuations, downside risk variations, multiple moments of a variable and model misspecification are also addressed. This triangulation enhances the robustness of our analysis, providing a comprehensive perspective on AEC and laying the groundwork for future researchers to explore Bowman's paradox through alternative measures and models. [ABSTRACT FROM AUTHOR]

This study focused on religious development and explored the impact of cultural and religious tourism policies on local religions. The highlight is the use of econometric models to capture the interrelationship between supply (total number of temples) and demand (total number of followers) on religion. Using Taiwan's Mazu Cultural Tourism Festival Policies in June 2010 as an example, the impact of these policies on Taoism and Buddhism was examined from 2001 to 2021. The results showed that the believer-led model of religious development is superior to the temple-led model. When tourism policies are promoted, they positively impact Taoist temples and believers, but Buddhism does not have such a similar effect. Finally, it was found that after the millennium, when network penetration was greater, local religious beliefs showed signs of weakening. The results of the study indicate that the potential impact of the internet era on local religion deserves subsequent attention. [ABSTRACT FROM AUTHOR]

It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are unable to capture a local relationship between the response and the covariate. The main aim of the paper is the simultaneous consideration of spatial effects as well as nonlinearities through an advanced semiparametric spatial autoregressive econometric model. The paper seeks to contribute to empirical studies of regional science focused on the application of semiparametric spatial autoregressive econometric models. We outline an approach that can be used to correct nonlinearities by incorporating a semiparametric idea within the framework of econometric models. We use an expansion by penalised basis splines that are highly flexible and are able to capture local nonlinearities between variables. In the empirical study, we fit different econometric models that attempt to explain the dynamics of the European Union's regional unemployment. The results show that regional unemployment exhibits significant spatial dependence, indicating interconnectedness among neighbouring regions and suggesting the adoption of a semiparametric spatial autoregressive model for improved modelling flexibility, surpassing traditional parametric approaches. [ABSTRACT FROM AUTHOR]

PurposeDesign/methodology/approachFindingsResearch limitations/implicationsPractical implicationsOriginality/valueThis study postulates that a firm’s level of value appropriation capability (viz. selling capability) plays a pivotal role in influencing a firm’s top management team’s (TMT) sentiment and its perception of future risk and uncertainty. Further, the current study posits that the nexus between firm-level selling capability and the TMT’s sentiment and future risk perception is contingent upon the firm’s industry growth.This research employs a textual analysis-based measure of TMT’s sentiment and perception of future risk and uncertainty and data envelopment analysis (DEA)-based measure of firm-level selling capability, using a sample of 243 USA-based firms during the period 2014 to 2020 and employing an endogeneity-robust econometric modeling technique.The current study demonstrates that selling capability positively affects a firm’s TMT’s sentiment and negatively impacts the TMT’s perception of future risk and uncertainty. Furthermore, the association between selling capability and TMT’s sentiment and future risk perception are accentuated by industry growth.Future studies are warranted to unearth how the association between the focal variables changes depending on the effect of other firm-specific internal boundary conditions.The results demonstrate sales and marketing managers should develop efficient sales organization and management techniques to bolster the overall firm-level selling capability. Managers should focus on enhancing their firm-level selling capability to maximize the positive impact on the TMT’s sentiment.Although studies have shown the effect of managerial sentiment on firm behavior, there is no empirical evidence, to our knowledge, as to what drives TMT’s sentiment and perception of risk and uncertainty. The present treatise shows that a firm’s selling capability and industry growth affect the TMT’s sentiment and the perception of future risk and uncertainty. [ABSTRACT FROM AUTHOR]

Innovation is the source of economic growth. Innovation in a region comes from its own knowledge creation and knowledge spillovers from other regions. Previous studies showed that R&D factors flow benefits knowledge spillover, thereby promoting economic growth. But these studies ignored the impact of a region's knowledge-absorptive capacity on knowledge spillovers. Ignoring the impact of regional absorptive capacity means that the knowledge spillover from the same R&D factors flow is the same, clearly inconsistent with reality. This thesis analyzes the impact of R&D factors flow on economic growth and explores the moderating effect of regional absorptive capacity on the relationship between R&D factors flow and economic growth from theoretical and empirical perspectives. First, we construct a knowledge creation and diffusion model of the new economic geography, including regional absorptive capacity, and analyze the theoretical logic of the flow of R&D factors and regional absorptive capacity influencing economic growth. Second, we employ spatial econometric models to examine the impact of R&D factors flow and regional absorptive capacity on economic growth, utilizing panel data of 30 provinces in China from 2008 to 2021. The results demonstrated a spatial positive correlation between regional economic growth in China. The R&D factors flow could have significantly promoted not just a region's direct economic growth, but also the economic growth of surrounding regions via spatial spillover effects. Furthermore, the stronger the regional absorptive capacity, the greater the direct effects and spatial spillover effects of the R&D factors flow on economic growth. The novelty of this article is to introduce regional absorptive capacity into the theoretical model, refine the methodology for assessing regional absorptive capacity in empirical research, and examine its moderating effect between the inflow of R&D factors and regional economic growth. This article reveals that the positive impact of the inflow of R&D factors on spatial spillovers and economic growth varies depending on regional absorptive capacity. According to the conclusions above, enhancing regional absorptive capacity is equally important as facilitating the flow of R&D factors. Therefore, it is vital for a region to strengthen its absorptive capacity for new knowledge while promoting R&D factors flow. The study provides valuable policy insights for accelerating the flow of innovation factors, enhancing regional absorptive capacity, and consequently promoting long-term sustainable economic development in the region. [ABSTRACT FROM AUTHOR]

Freshwater is an important natural resource that is increasingly stressed due to a changing climate, economic productivity, and population growth, among other factors. Globally, the United States is a top freshwater user, both in total and per capita use. This water is used throughout the U.S. economy, dominated by irrigation and thermoelectric uses. However, analysis to better understand freshwater use is limited by the frequency and timeliness of data available. In this paper, we develop econometric models, forecasts, and mathematical programming models to generate subnational estimates of annual U.S. freshwater use from 1995 to 2021 by water use category. When reviewing the results at the national level, we find that freshwater withdrawals are generally decreasing over time, but the year-to-year and subnational variation differs by water use category. This work combines several methodologies to create a useful, timely dataset on freshwater for applied research applications. The results may also be informative on where and by whom water is being used, indicating areas of potential regional stress or scarcity. [ABSTRACT FROM AUTHOR]

Five national-level urban agglomerations in China, namely the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), middle reaches of the Yangtze River (MRYR), Pearl River Delta (PRD), and Chengdu-Chongqing (CY), have undergone rapid economic development, accompanied by a surge in carbon emissions. Notably, there exists a gap in existing research that hinders comprehensive comparative studies on the carbon emissions of these urban agglomerations during the 12th and 13th Five-Year Plan periods, respectively. In this study, we comparatively analyze the spatiotemporal characteristics and spatial clustering characteristics of carbon emissions and their driving factors across the five urban agglomerations during the 12th and 13th Five-Year Plan periods using spatial autocorrelation and multiple spatial econometric models. The main results are as follows: firstly, the total carbon emissions across the YRD are the highest, while the average carbon emissions in BTH are higher than those across other urban agglomerations. Secondly, during the 12th Five-Year Plan period, the main related socioeconomic factors for carbon emissions of the BTH, PRD, YRD, MRYR and CY are per capita GDP, general public budget expenditure, urbanization rate, population density, and industrial structure, respectively. Thirdly, during the 13th Five-Year Plan period, industrial structure have a close link with carbon emissions across BTH and MRYR; the carbon emissions across PRD have close correlations with urbanization rate and general public budget expenditures; across YRD and CY, the key associated driver was the general public budget expenditures. All in all, these findings offer valuable insights for shaping effective emission reduction policies tailored to the unique characteristics of each urban agglomeration. [ABSTRACT FROM AUTHOR]

The accuracy of GDP per capita growth rate in countries with low statistical capacity has been questioned, which hinders understanding progress of Sustainable Development Goals (SDGs) accurately. This study aims to estimate GDP per capita growth rate in countries with poor statistical quality. To achieve this goal, the study builds an econometric model with processed Visible Infrared Imaging Radiometer (VIIRS) night-time light data and other economic variables and the study further assesses the accuracy of the model by comparing the estimated GDP growth to high-quality economic data from Organization for Economic Co-operation and Development (OECD) countries between 2014 and 2018. First, due to the abnormal fluctuations in the time series generated from yearly composite night-time light images, we propose a trend analysis method for the monthly night-time light data to synthesize a more stable yearly time series. Then, we construct artificially low-quality GDP simulations and combine VIIRS data and official statistics data to construct regression models to correct the simulated GDP. Finally, to assess the model accuracy, we compare its estimates of GDP per capita growth rates to official data from high-quality OECD countries, looking for any deviations. We have two major findings as follows: first, the VIIRS data, which comprise information on human activity, are a valuable resource for estimating GDP growth; second, the GDP estimation model can reduce the artificial bias of the simulated GDP per capita growth rate by using VIIRS night-time light data and the average accuracy of the model with simulated data can reach 89%. [ABSTRACT FROM AUTHOR]

The comprehensive utilisation of solid waste is a critical tool for environmental governance. As a major driver of economic development, industrial structure upgrading and technological innovation are also conducive to environmental governance. As a result, using statistical data from 30 Chinese provinces from 2009 to 2019, this article constructed a spatial econometric model and explored the links between industrial structure upgrading, technological innovation and comprehensive utilisation of solid waste. The results showed that: Industrial structure upgrading has a 'U'-shaped curve relationship that first inhibits and then promotes the comprehensive utilisation of solid waste. In the long run, upgrading industrial structures promotes the comprehensive utilisation of solid waste as a whole. Technological innovation has a significant moderating influence on the impact of industrial structure upgrading on the comprehensive utilisation of solid waste. [ABSTRACT FROM AUTHOR]

The literature on the economics of location regarding creative activities is relatively scarce. Estimations, based on 369 newly created firms operating in creative industries in Portugal, which incorporate spatial effects of neighbouring regions in the location choices, yield the following results: (i) the concentration of creative and knowledge-based activities play an important role in location decisions of new creative establishments; (ii) creative firms tend to favour a diversified industrial tissue and related variety, in order to enjoy from inter-sectorial synergies; (iii) high education at a regional level has a highly significant, positive effect on location decisions, while lower educational levels of human capital negatively affect those decisions; (iv) tolerant/open environments attract creative activities; (v) creative firms tend to favour municipalities where the stock of knowledge and conditions for innovative activity are higher; (vi) municipality's attributes are more important in terms of firms' location decisions than the characteristics of nearby regions. [ABSTRACT FROM AUTHOR]

Value capture theory, or the application of cooperative game theory to theory and pedagogy in strategic management, has been growing in influence, but its potential for empirical research is underexploited. I briefly review value capture theory and then offer thoughts on how it may motivate and inform empirical research that both tests specific elements of value capture theory and uses insights and econometric models based on value capture theory to deepen our understanding of many topics of interest to strategy scholars and practitioners. [ABSTRACT FROM AUTHOR]

The new challenge confronting leaders is investing in improving customer satisfaction or perceived innovativeness. The challenge also reflects the focus of leadership: to make operations more efficient and effective or invest in tomorrow's solutions? Intuitively, both are linked to the financial impact of the firm. In this article, the author explores the mechanisms "behind the scenes" driving perceived quality, and customer satisfaction on one side, and perceived innovativeness and relative attractiveness on the other side. Both routes are linked to customer loyalty, customer lifetime value, customer equity, and firm value. The findings underscore the importance of customer-centric innovation for market performance, suggesting that innovations must resonate with customer needs and expectations to foster market adoption and growth. The article advocates for the extension of this research into global contexts and digital transformation, highlighting the potential for these insights to inform strategi decision-making in an increasingly digitalized business environment. Bridging academic theory with practical application, the article offers a nuanced understanding of how innovation perceptions influence market outcomes. It suggests future research directions, including exploring strategies to enhance customer perception of innovation and leveraging these perceptions for competitive advantage. [ABSTRACT FROM AUTHOR]

The article examines whether expert knowledge improves the estimation results of real estate mass appraisal models. Six econometric models were compared: OLS, mixed, the Bayesian model, the Inequality Restricted Least Squares (IRLS) model, ridge and LASSO regression (with regularization). In three of the models (mixed, Bayesian, and IRLS) prior knowledge was applied. In mixed and Bayesian models priors took the form of intervals for model parameters. In IRLS, restrictions in the form of inequalities were applied. In the empirical example mass appraisal models were applied in the valuation of undeveloped land for residential purposes. Models with prior knowledge turned out to be the best with regard to the consistency of estimates with theory. Also, prediction accuracy was better for models with prior knowledge. In the case of low quality data expert knowledge might significantly improve estimation results of real estate mass appraisal econometric models. [ABSTRACT FROM AUTHOR]

In recent years, research has increasingly focused on the economic value of freshwater ecosystems, which provide essential services such as water supply, climate regulation, and recreation, but are highly vulnerable to human-induced degradation. To examine this body of work, a meta-analysis was conducted on 116 studies regarding the economic value of ecosystem services provided by freshwater ecosystems, which collectively reported 368 valuations. The results indicate that 50.2% of the variation in existing value estimates can be explained by an econometric model that controls for (a) the geographical and socio-economic characteristics of the study site, (b) the valuation method and beneficiary type, and (c) the physical and ecological attributes of the specific biome and ecosystem service. The work illustrates the effect of different freshwater ecosystems on economic valuation estimates, also showing the impact of site and socio-economic characteristics of the study. Furthermore, the study assesses the prospects for using the meta-analytic value function derived from the model, finding an average transfer error of 14.4%. This study effectively summarizes the existing literature on the economic value of freshwater ecosystems, allows for the establishment of stylized facts and provides the foundation for the development of tools to support environmental policy decisions. [ABSTRACT FROM AUTHOR]

One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. The novelty of this paper is the use of the asymmetric Aranda-Ordaz parametric link function to model transition probabilities in DDMS models, instead of the commonly applied logit link. The idea behind this approach is that any incorrect duration choice is compensated for by the parameter in the link, increasing model flexibility. Two Monte Carlo simulations, based on classical applications of DDMS models, are employed to evaluate the methodology. In addition, an empirical investigation is carried out to forecast the volatility of the S&P500, which showcases the capabilities of the proposed model. [ABSTRACT FROM AUTHOR]

Mineral resources are the basic materials for global economic development. Assessing mineral resource supply chain resilience is an important pillar of mineral resource supply chain stability management. The globality, heterogeneity and complexity of supply chain bring challenges to the resilience assessment of global mineral resource supply chain. To solve this problem, a method based on multi-region input-output model, network percolation model and econometric model is proposed, which is able to measure the resilience of global mineral resource supply chain and its influencing factors from the perspective of the whole system. The percolation phase transition is introduced to measure the critical state of global mineral resource supply chain system collapse facing external disruption. Using the proposed method, this paper conducts an empirical study on the evolution of global mineral resource supply chain resilience from 2005 to 2014. The results show that the resilience of global mineral resource supply chain declined by 39.6% in 2005-2014. Most of the critical links that caused the collapse of the global mineral resource supply chain network are the manufacturing sector and its upstream and downstream sectors. The structure of supply chain network plays a key role in network resilience. Increasing the number of linkages in upstream and downstream could improve network resilience, but the increase of linkage strength would deteriorate network resilience. [ABSTRACT FROM AUTHOR]

The aim of this paper is to test the relationship between manufacturing production and economic growth in Portugal from the 1950s to the present day, following Nicholas Kaldor's (1908–1986) thinking, which assumes that manufacturing is the true engine of the economic activity of a country. By estimating a series of econometric models, including a Vector Autoregressive (VAR) model, it was possible to identify a positive relationship between growth and manufacturing production since the 1950s. The results also show that each increase of 1 percentage point (pp) in real growth rate of the production of basic metallurgical industries and manufacture of metallic products in a certain year t−1 for the period 1956–2019, caused an increase of 0.07 pp in the Portuguese economic growth during a following year t on average. We hope that these findings will stimulate new and interesting research regarding the important issues of manufacturing production and economic growth. [ABSTRACT FROM AUTHOR]

Stimulating sustainable FDI through the connection of financial development and its moderating relationships with environmental quality and regulatory quality emerges as a crucial agenda nowadays. This study investigates into this relationship using quarterly data from 1990Q1 to 2022Q4 for Canada; employing ARDL bound tests, Granger Causality, and FM-OLS econometric models. Foreign direct investment is the dependent variable of this study. The findings confirm significant long-run relationships among financial development, stock market development, and the moderating effects of environmental and regulatory quality on FDI inflows in the Canadian economy. Conversely, in the short run, financial development, stock market development, and economic growth exhibit bidirectional causal links with FDI, while environmental quality, regulatory quality, and trade openness demonstrate unidirectional causal links with FDI. The error correction mechanism indicates that all variables quickly return to equilibrium except trade openness. Robustness checks further confirm that all the variables have fully modified co-integrating relationship with FDI inflows including the moderating effects of environmental and regulatory quality which is the innovation in the FDI-Growth existing literature. Thus, policymakers are urged to prioritize environmental quality and regulatory quality, alongside other significant explanatory variables identified in this study to promote sustainable FDI inflows. [ABSTRACT FROM AUTHOR]

The aim of this article is to examine which selected internal factors influence the profitability (ROA) of companies in the energy sector in Poland and how they do so, over the period 2018–2021, taking into account two groups: all types of activities (984 companies) and electricity production (508 companies). This study uses Pearson correlation analysis, Wilcoxon pairwise rank test, descriptive statistics and multiple linear regression to build eight ROA econometric models, four for each group. The research shows that in the energy sector, in particular, variables relating to the capital structure (total equity/total assets, long-term liabilities/total assets, short-term liabilities/total assets and long-term liabilities/short-term liabilities ratios) have a statistically significant impact (positive or negative) on the profitability (ROA). The aforementioned ratios appear in various combinations in all eight ROA models. The use of equity to finance the activities of companies in this sector seems to be particularly beneficial, as the total equity/total assets ratio occurs in as many as seven out of eight models and, moreover, it always has a positive impact on the ROA. The remaining analyzed variables relating to the structure of assets (fixed assets/total assets ratio), financial liquidity (current ratio) and the age of the company appear in the models as statistically significant quite rarely, having a different impact on the ROA (positively or negatively). However, variables such as the fixed assets/current assets and total liabilities/total equity ratios do not have a statistically significant impact on the ROA at all in any of the studied groups of enterprises. The research results suggest that managers, in order to shape profitability (measured by ROA), should pay special attention to the capital structure, i.e., the proportions of the use of equity, long-term liabilities and short-term liabilities to finance the operations of energy companies as these independent variables appear most often in ROA models. Other analyzed factors, such as the assets structure (the share of fixed assets in total assets) or financial liquidity, also have an impact on the return on assets; therefore, their use in energy companies should also be considered. Moreover, the research shows a large diversity of factors shaping ROA in econometric models, the way they affect the dependent variable (positive or negative) and the degree of model fit (R2), both in individual years and in the two groups of companies studied. This proves that it is not possible to clearly and finally determine which factors and how (positive or negative) they affect the profitability. This influence can change over time depending on the circumstances, which indicates the need for the continuous involvement of decision makers in the management process and making decisions based on reliable and appropriate-to-the-situation analyses. [ABSTRACT FROM AUTHOR]

The difference in the results from previous studies on the impact of inflation on poverty, coupled with an increase in the money supply from direct investment, inspired this research. This study aimed to ascertain how poverty, inflation, and foreign investment affect consumption spending in Indonesia based on 1997-2021 time series data. This study used Vector Autoregression estimation, utilizing data sources from the World Bank. The data will be processed with econometric models. From the study’s findings, it may be inferred that overall consumption affects inflation, which can be seen from the Granger causality test that shows that this variable has a one-way causal relationship. The results of the same test also show that FDI has an effect on total consumption and conversely consumption has an effect on FDI. However, the poverty variable does not affect total consumption and vice versa, because according to the causality test, the variable obtains an insignificant probability value. However, the VECM results explain that poverty has an effect on total consumption, and that the effect of consumption on inflation is due to the larger t-statistic value and has a positive relationship. This implies that the rate of inflation will increase the lower the level of consumption. However, the Impulse Reason test shows that poverty has a negative trend, as does the FDI variable. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) has posed a threat to sustainable development, and the identification of the spatial characteristics of UHI is a prerequisite for the mitigation of UHI effects. Taking Kunming City and Guiyang City in the karst region of Southwest China as two examples, this study utilized spatial econometric modeling and random forest regression to identify and compare the spatial distribution and impact of urban heat islands related to topographic and urban morphology. The results indicated that the UHI in Kunming and Guiyang mainly monocentrically and polycentrically distributed, respectively, and the urban heat island intensity (UHII) in Kunming was significantly higher than that in Guiyang. The spatial error model effectively reflected the influence of topography and urban form on UHII, and the random forest regression model objectively measured the contribution of different influencing factors to UHII. Enhanced vegetation index (EVI), population density (PD), percentage of impervious surface (PIS), ground sky view factor (GSVF), building density (BD) and percentage of water bodies (PW) had similar effects and contributions to the UHII in the two cities, while urban surface roughness (USR) contributed less than topographic slope (SLOPE) on UHII in Kunming, and the SLOPE contributed less than USR in Guiyang. In the karst ecologically fragile zone, the morphology of Kunming and Guiyang are mainly shaped by the topography, which can be characterized as monocentric and polycentric, respectively. And the UHI distribution and UHII were also closely related to topographic patterns. EVI, SLOPE and PW, all showed inhibitory effects on UHII, and EVI contributed the most. PD, PIS, USR and BD, exacerbate the UHII, with PD contributing the most. The above conclusions can provide a reference for policymakers and urban planners to optimize urban morphology and mitigate urban heat island. [ABSTRACT FROM AUTHOR]

Scientists are increasingly specializing to manage the vast knowledge bases within their domains, making knowledge-sharing strategies essential for scientific progress. Among these strategies, international scientific collaboration has increased markedly. We analyse the role of thematic specialization as one of its drivers in the context of Chagas research, a neglected disease. Since there is a strong research tradition in Latin America on this topic, it is an interesting context to study centre-periphery research dynamics. Using a novel bibliometric database on Chagas research during 1990–2019, we exploit its panel structure in a gravity-type econometric model. The findings show that the degree of specialization and its proximity among partners boost joint publications. Both effects are particularly important for country dyads involving authors from the periphery linked to authors in the centre. This paper is the first empirical study to assess the role of specialization as a driver of international scientific collaboration. [ABSTRACT FROM AUTHOR]

The digital economy, which boasts general technology, intense penetration, platform ecology, and low marginal cost, is a product of advanced digital technology. This new engine has become a driving force for high-quality economic development. From the three aspects of development momentum, efficiency, and structure, this paper profoundly explores internal mechanisms to lead the high-quality growth of the regional economy. By constructing an econometric model, the influence effect and means of the digital economy on the high-quality development of the regional economy are empirically tested. The digital economy and its three sub-dimensions can significantly promote the high-quality development of the regional economy. However, industrial digitalization has the most vital role in promoting it. The digital economy has shown a more vital promotion role in the central and western regions and provinces with low total factor productivity, and it can indirectly impact high-quality economic development by promoting dynamic, efficient, and structural changes. [ABSTRACT FROM AUTHOR]

This study investigates empirically if land characteristics (especially forests and mountains) are of importance for the local ability to withstand the shock of the Covid-19 on domestic tourism demand during the summer of 2020. A second step of the analysis focuses on the recovery stage in the subsequent summer. Three tourism indicators are considered: arrivals, overnight stays and length of stay. Official data on land use characteristics of 2029 villages in the Federal state of Bavaria (South Germany) are employed for the analysis. Estimations using the Spatial Durbin model combined with the Heckman selection model reveal that there is a significant relationship between the proportion of forest within as well as surrounding the village and the demand for domestic tourism compared with the 2019 baseline. There is also a significant relationship with the altitude of the surrounding areas. The importance of mountains and forests is present in both the initial year of the pandemic and in the recovery year of 2021, although the magnitude is lower in the summer of 2021. Direct and spatial effects can also be found for lakes and rivers on overnight stays and length of stay. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid increase in population and economy, coupled with accelerated urbanization, is placing immense pressure on the water–energy–food (WEF) system. In this context, the water–energy–food nexus framework has emerged, recognizing the interdependencies and interactions among water, energy, and food systems, with the aim of optimizing resource management through cross-sectoral collaboration to promote sustainable development. Understanding the spatio-temporal differentiation patterns of the WEF nexus and elucidating the driving mechanisms behind changes in their coupling relationships is essential. This knowledge is crucial for ensuring the security of each subsystem and enhancing the overall sustainability of interconnected systems through coordinated efforts. To address these challenges, this study first established evaluation indicators for water, energy, and food security to quantify their levels and spatio-temporal dynamics. Subsequently, the degrees of coupling coordination within the WEF nexus were calculated. Finally, the WEF nexus's spatial correlations were analyzed by using a spatial autocorrelation model. Spatial econometric models then identified key factors affecting its coordination. The results revealed significant spatial heterogeneity in water, energy, and food security across mainland China's provinces. From 2002 to 2022, water security improved substantially in 87% of the provinces, while energy security began to improve in the eastern regions following a phase of high consumption. Food security saw significant enhancements, particularly in Inner Mongolia and the northeastern provinces. The overall coupling coordination of the WEF nexus improved across 30 provinces, progressing toward primary coordination. However, Henan and Anhui provinces experienced fluctuations in WEF nexus coordination. Spatial correlation analysis showed upward trends and increased clustering in WEF nexus coordination. Factors such as economic development and population positively influenced coordination, while economic agglomeration, education, and effective irrigation area had negative effects. This study elucidates the complex interconnections and key influencing factors within the WEF nexus, providing a reference framework and practical recommendations for equitable resource management. [ABSTRACT FROM AUTHOR]

This study elaborates on a typology of demographic change and tests this definition at the lowest granular level (LAU2, municipality) with official data. This typology distinguishes between fragile and resilient municipalities based on population dynamics (in terms of duration and intensity) over 1991–2021. This study's second aim is to elaborate a spatial autoregressive econometric model to evaluate to what extent and in which direction the rate of participation of potential beneficiaries of the Rural Development Programmes (RDPs) of 2014–2020 is affected by demographic change and other explanatory variables. Regression models compare the results of the OLS (aspatial) and spatial autoregressive models (SAR) of four types of participation rates (all RDP schemes; all LEADER schemes; sectoral schemes of RDP and LEADER; non-sectoral schemes of RDPs and LEADER). This comparison makes it possible to understand the differences between centralised and decentralised management and between sectoral and broader rural-targeted schemes. The results of the models appear attractive in interpreting the role of RDP instruments in different regions and local areas. First, the rate of participation is strongly dependent on macro-regional differences. Regarding the demographic factors at the local level, this study highlights that demographic fragility does not necessarily hamper the use of RDP measures. Conversely, the participation rate in RDP policy schemes seems particularly significant in very fragile areas, whereas significance has yet to be proved in other demographic typologies. This result holds particularly true for the policy uptake of non-sectoral schemes. Furthermore, LEADER decentralised interventions fit the fragile areas more than resilient and vital ones due to the territorially targeted approach followed by the Local Action Groups. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to study the impact of earnings quality on firms' financial performance. Design/methodology/approach: An unbalanced panel data of 237 small- and medium-sized Portuguese companies from the mold industry, using 2010–2018 yearly data was analyzed. While most studies focus only on earnings management when assessing earnings quality, in this study six proxies for earnings quality are used, namely, accruals quality (a proxy for earnings management), earnings persistence, earnings predictability, earnings smoothness, earnings timeliness and earnings conservatism. Moreover, two proxies of financial performance are considered, the return on assets and the economic value added. An econometric model was estimated using either a fixed-effects or a random-effects specification to account for the individual firm-specific effects and ensure heteroscedasticity corrected estimates. Findings: The results show that managers must be concerned with the quality of reported earnings, as it can affect positively firms' financial performance, especially regarding accruals quality. Persistence, predictability, smoothness, timeliness and conservatism are shown not to exert significant influence on financial performance in the sample. Research limitations/implications: This work contributes not only as a literature review on these thematic but also to firms' managers and stakeholders, who have information that helps them select strategies that guarantee earnings quality and improve firms' financial performance. Originality/value: This study proposed an econometric model that studies the relationship between earnings quality (using several proxies for it) and financial performance that can be applied to all companies. [ABSTRACT FROM AUTHOR]

The increasing trend of transforming fertile land for non-agricultural uses, particularly at the rural–urban interface and city outskirts, has seriously threatened food security in developing countries. However, in an agrarian nation like, Nepal where productive land is minimal due to its challenging geographical terrain, the agricultural land transformation trend and its motivating factors have not been extensively studied. This study emphasizes the shifting land use patterns and factors shaping farmers’ choices for conversion of farmland into non-farm uses in Dhading, Nepal. Land use land cover data analysis revealed that productive agricultural land decreased by 60.28% while non-farm use of land increased by 93.14% in Dhunibeshi Municipality, Dhading between 2013 and 2022. Further analysis using farm-level cross-sectional data shows that 36% of the households are eager to switch from farming to non-farm use. The results from econometric models indicate that socio-economic factors (gender, education, economically active family members, land proportion), vegetable farm attributes (farm income, crop diversification), farm proximity to urban areas, and non-farm firms, and land conversion proposal collectively influence farmers’ decisions regarding agricultural land use change. Promoting widespread adoption of low-cost production technology and making the agriculture sector lucrative could contribute towards sustainable agricultural land management in the future. [ABSTRACT FROM AUTHOR]

We argue that the 40‐year‐old Feldstein–Horioka "puzzle" should have never been labeled as such. We discuss two problems with the literature. First, we show that the series of investment and saving rates typically used in empirical exercises to test the Feldstein–Horioka thesis are not appropriate. The correct series to properly test it are not collected. Second, we show that the Feldstein–Horioka regression is not a model in the econometric sense, that is, an equation with a proper error term (a random variable). The reason is that by adding the capital account to their regression, one gets the accounting identity that relates the capital account, domestic investment, and domestic saving. This implies that the estimate of the coefficient of the saving rate in the Feldstein–Horioka regression can be thought of as a biased estimate of the same coefficient in the accounting identity, where it has a value of 1. Because the omitted variable is known, we call it pseudo bias. Given that this (pseudo) bias is known to be negative and less than 1 in absolute terms, it should come as no surprise that the Feldstein–Horioka regression yields a coefficient between 0 and 1. [ABSTRACT FROM AUTHOR]

Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate. [ABSTRACT FROM AUTHOR]

This paper discusses the relationship between regional resilience and industrial variety from an evolutionary perspective. Using a panel dataset containing information on 285 cities at prefectural and higher levels in China, this paper introduces a spatial econometric model to explain how regional resilience is affected by industrial variety with interregional industrial linkages. I find that industrial variety plays a positive role in regional resilience and this role spills over through industrial linkages bridging regions. Due to the interregional linkages, regions can have greater resilience by utilizing industries of other regions. The effect of industrial variety on regional resilience depends on the position of a region in the network of industrial linkages. This effect is also limited by the types of industrial linkages. [ABSTRACT FROM AUTHOR]

Purpose: In the past, there have been few studies that shed light on the process of how to maintain the right value proposition with retailers. This research aims to examine the factors that play a significant role in the process of keeping the right value proposition with retailers in the B2C sector through a firm-retailer dependency lens. Design/methodology/approach: Longitudinal data was gathered from 700 retailers who deal with the products of two of India's firms in the B2C sector. Three data sets were created, and an econometric model was fitted to each data set separately. Findings: The findings revealed that the firm-centric and retailer-centric variables had positive impacts, whereas competitor-centric variables negatively impacted the value proposition. Furthermore, the intensity of the impact on the value proposition of all types of drivers varied from "quiet-quitting retailers" to "active retailers." Practical implications: This study's findings should assist managers in framing a value-sharing strategy to maintain a "win-win" relationship with retailers. Originality/value: Using real-world data and a panel regression model, this research extends the theory on the relationship between value proposition and its drivers in the B2C sector and, hence, enriches the literature on the interface between business process management, retailing, and marketing. [ABSTRACT FROM AUTHOR]

Food security is a basic guarantee for maintaining social stability, and improving green and healthy grain production is the way to promote food security. China is currently experiencing rapid urbanization that has an impact on grain production and security, with reduced arable land, environmental pollution, insufficient agricultural population, and inadequate resource allocation. How to deal with the relationship between urbanization and grain production becomes the key to solve this problem. Therefore, this study constructs an evaluation system of grain green production efficiency (GGPE) with non-expected output containing carbon emissions, and uses the super-efficient SBM model to measure the level of GGPE, and constructs a spatial econometric model to examine the spatial correlation and spillover effect of urbanization on GGPE; then constructs a panel threshold model to analyze the nonlinear threshold characteristics between urbanization and GGPE. It was found that (1) from 2000 to 2019, China's GGPE showed an overall upward trend, and the performance of GGPE varied among different provinces. (2) According to the results of the spatial econometric model, China's GGPE shows obvious spatial characteristics, and the level of urbanization not only directly affects regional GGPE but also can indirectly affect neighboring regions' GGPE through spatial spillover effects, and the indirect effects among regions are larger than the direct effects. (3) Spatial threshold effect results, there is a significant non-linear threshold characteristic of the impact of urbanization rate on GGPE. In terms of the influence effects of other variables, the influence of each variable on GGPE is roughly in the same direction as the influence under the spatial spillover effect, but the degree of influence is slightly different. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Byline: A. A. L. Santiago, R. G. Vazquez, F. P. Soto, J. H. Ortiz, M. A. L. Santiago and B. I. S. Toledano Keywords: welfare, income, econometric model, elasticities, remittance. [...]

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies. [ABSTRACT FROM AUTHOR]

This paper selects the agricultural mechanization data of 14 prefectures (cities) in Xinjiang from 2012 to 2021 to evaluate the high quality development level of agricultural mechanization. Moreover, a multivariate regression econometric model is constructed to empirically analyze the influence level and degree of urban-rural income ratio on Xinjiang’s high-quality agricultural mechanization development. The results show that the rural-urban income ratio has a positive effect on the development level of Xinjiang’s high-quality agricultural mechanization, and the economic and social condition variables also show a certain lag effect. [ABSTRACT FROM AUTHOR]

New parties are a key mechanism behind party system innovation as voters are offered new choices. To explain party entry (i.e. participation in a lower house election for the first time), the state-of-the-art has typically focused on domestic determinants. This, however, assumes that party entry takes place in an international vacuum. Building on the policy diffusion literature, we explore how party family members abroad influence party entry in the focal country. Based on a new dataset on the evolution of each party family in 17 Western European party systems between 1961 and 2016, including 2191 new parties, our mixed-methods approach combining spatial econometric models with pathway case analysis demonstrates that party entry is influenced by the entry and electoral success of their party family abroad. This has important implications for the literature on party entry and the international diffusion of policy platforms. [ABSTRACT FROM AUTHOR]

Introduction: This article presents a model of political competition in which political parties, through clientelism strategies, vie for control of legislative seats. Parties exercise political violence to prevent potential rivals from gaining power and threatening their position within the hybrid political regime. The theory suggests that the degree of political violence exerted by parties in hybrid regimes will increase (decrease) as they concentrate more (less) power in the legislature. Methods: Using the methodology of analytical narratives, we examine the narrative on political violence in the Colombian political regime to identify key actors, strategies, information sets, and institutional changes. From these identified elements, we construct a theoretical model of political competition within the mathematical theory of games to explain the institutional changes highlighted in the narrative. Finally, we develop an econometric model to find statistical evidence supporting the predictions of the theoretical model derived from the narrative. Results: The narrative and the analytical narrative indicate that, in a hybrid regime, a higher degree of political power concentration causes a higher rate of political violence. The estimation of the Vector Auto-regression model allows us to analyze the response of the violence rate to a shock in the concentration index. Following an unexpected increase in the concentration of political power, the violence rate shows an initial increase of approximately 0.3 percentage points above its initial value. Subsequently, the effect attenuates slightly and stabilizes at around 0.2 percentage points above the baseline, maintaining this level throughout the period. Discussion/Conclusion: The theoretical model proposed in this paper suggests an explanation of political violence that diverges from the explanations offered by traditional theories. We suggest that the theoretical model proposed here captures the historical logic of the relationship between violence, political clientelism, and exclusion in Colombia, a country with a relatively long tradition as a formal democracy (since 1958), leading us to interesting conclusions that have not been proposed so far in the literature on violence in Latin America. [ABSTRACT FROM AUTHOR]

China's reform began in the countryside, and rural reform began in Anhui. This study uses Anhui Province, China as an example in analyzing the impact of agricultural science and technology innovation resource allocation on rural revitalization, particularly by constructing an econometric model. Results indicate that the allocation of agricultural science and technology innovation resources has a steady positive impact on rural revitalization. Improvements in the allocation of agricultural science and technology innovation resources can effectively promote the development of rural revitalization and the spatial spillover of rural development, thereby facilitating the promotion of the spillover of neighboring areas to local rural development. A nonlinear relationship exists between allocation of agricultural science and technology innovation resources and rural revitalization. Lastly, rural revitalization can only be promoted substantially when the allocation of agricultural science and technology innovation resources reaches a certain condition. [ABSTRACT FROM AUTHOR]

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service time uncertainty. We aim to capture the delay tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance sensitivity, monotonicity, and convexity and positive homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the convexity of TAD index ensures a tractable modeling of the collective delay dissatisfaction in the appointment scheduling problem. Using the TAD index, we then develop an appointment model with known empirical service time distribution that minimizes the overall tolerance-aware delays of all users. We analyze the impact of delay tolerance on the sequence and schedule decisions and show that the resultant TAD appointment model can be reformulated as a mixed-integer linear program (MILP). Furthermore, we extend the TAD appointment model by considering service time ambiguity. In particular, we encode into the TAD index a moment ambiguity set and a Wasserstein ambiguity set, respectively. The former captures effectively the correlation among service times across positions and user types, whereas the latter captures directly the service time data information. We show that both of the resultant TAD models under ambiguity can be reformulated as polynomial-sized, mixed-integer conic programs (MICPs). Finally, we compare our TAD models with some existing counterpart approaches and the current practice using synthetic data and a case of real hospital data, respectively. Our results demonstrate the effectiveness of the TAD appointment models in capturing the user delay tolerance with heterogeneity and mitigating the worst-case delays. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], and the Major Program of National Natural Science Foundation of China [Grant 72192843]. S. Wang was also supported by a grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0025) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0025). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The assurance of universal access to electricity refers to not only physical access to electricity, heating, and/or cooling services but also making them affordable to families. This is the case in Brazil, where physical electricity access reaches more than 99% of the urban population, but a high proportion of households are disconnected due to lack of payment. Affordability is a key factor in planning the expansion and maintenance of the electricity grid. In this paper, therefore, we propose the development of a strategy, called the Payment Capability Indicator (PCI), that provides new information about the relationship between energy cost and family income. The classic indicator, the Ten Percent Rule (TPR), was the base, and empirical research was conducted in two low-income neighborhoods in Paraná state, southern Brazil. The results consider variables that add new information to guide local public policies, combining interventions to improve affordability, energy efficiency, alternatives to energy services and consumer behavioral changes. [ABSTRACT FROM AUTHOR]