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This paper introduces a new estimator for the Tobit regression model when the multicollinearity exists. This estimator is called almost unbiased Tobit Liu-Type estimator, which is obtained by a mixture between the Liu-Type estimator and almost unbiased estimator. This estimator avoids the negative effects of multicollinearity on the maximum likelihood estimator. Furthermore, we check the superiority of the new estimator over the maximum likelihood estimator, Liu-Type estimator and almost unbiased estimator according to the simulated mean square error SMSE criteria. In addition, we run the simulation study to investigate the effects of a group of factors on the performance of the new estimator. Finally, to check the benefits of the new estimator via the real data, we use two applications for the Tobit regression, the English Premier League data and Egyptian agricultural GDP data. [ABSTRACT FROM AUTHOR]

Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces' key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed. [ABSTRACT FROM AUTHOR]

This study examined the multidimensional poverty status of households in Nigeria using one of the most recent available household survey data, the 2018 Demographic Household Survey data. The data was analyzed using descriptive statistics, the fuzzy sets measures as well as the Tobit regression model. The results showed that 59.44% of the household heads were between 31 and 50 years of age with the mean age being 43.96 years, implying that most of the household heads were within their economically active years and, as such, should be less vulnerable to poverty than households with older heads. It also showed that the male gender, which is typically favoured over the female in terms of economic opportunities and, hence, is less vulnerable to poverty, accounted for 85.69% of the household heads. Also, 41.53% of the household head population had no formal education while 17.84%, 31.84% and 8.79% had primary, secondary and higher education respectively. This high proportion of household heads in the sample with no formal education as well as the generally low educational attainment presents a significant barrier to poverty alleviation Assets ownership influenced the poverty score of each household head as household heads with fewer assets tended to have higher poverty scores. Moreover, male headed households owned more assets than female headed households. The study concluded that other dimensions to poverty measurement in individuals and households, such as health, education and living standards, were as important as the monetary measures. It was recommended that in order to reduce poverty in households, basic social amenities, healthcare facilities and welfare support needed to be provided for households. [ABSTRACT FROM AUTHOR]

Efficiency analysis of the Maternal, Newborn, and Child Services Network (MNCSN) is crucial for achieving SDG 2030. However, the mortality rates of maternal, newborn, and child have not reduced since 2018, and there have been limited studies conducted in Morocco on this subject. This study aims to analyze the technical efficiency of 82 MNCSN across primary healthcare Establishments Network (PHCEN) in Morocco in 2021 and identify factors influencing their efficiency. Technical efficiency was estimated using the Data Envelopment Analysis (DEA) method with an input orientation. Tobit regression was employed to analyze factors affecting the technical efficiency of maternal and child healthcare. In the initial stage, the average efficiency score of the 82 MNCSN was 0.779 for constant returns, with 36 MNCSN achieving a score of 1. However, the Boujdour MNCSN recorded the lowest efficiency score of 0.033. In the second stage, Tobit regression revealed that Model 2 was more significant than Model 1. Specifically, the explanatory variable Rural Dispensary exhibited strong statistical significance and a positive impact on maternal and child health, followed by variables such as High-risk Pregnancies Supported and High-risk Pregnancies. The study findings suggest that the resources allocated for MNCSN in Morocco are not being efficiently utilized compared to some other African and European countries. Therefore, the Ministry of Health in Morocco should review the functions of MNCSN, aiming to improve the delivery of maternal, newborn, and child healthcare, regardless of the setting, be it urban or rural. [ABSTRACT FROM AUTHOR]

This study attempts to establish the linkage between human development and energy poverty for rural households and evaluate the impact of government schemes such as Pradhan Mantri Ujjwala Yojana, free electricity and unemployment allowance on human development. For the analysis purpose, primary data have been collected from rural areas of two main districts of the Shekhawati region of the state of Rajasthan in India. To pursue the objectives, two measures of energy poverty – energy deprivation and the multidimensional energy poverty index – and one measure of human development – the human development index – have been constructed. The primary survey of 1,000 households is conducted from January to March 2020. For establishing the empirical relationship, the study has used Tobit regression analysis. The findings confirm the hypothesis that the existence of energy poverty adversely affects the level of human development in the region. It also confirms the other side of the relationship, which states that increasing human development reduces energy poverty through various linkages. The study results reveal that the government scheme which directly contributes to the per capita income is also impacting positively human development through an increase in income. Hence, to improve the level of human development and to decline energy poverty, the study recommends policies to improve the overall level of income of households. JEL Classification Codes: I32, O13, O15, Q40 [ABSTRACT FROM AUTHOR]

This study evaluated the efficiency of the logistics industry in Shijiazhuang City by using the DEA-BCC and Malmquist index models to analyse efficiency changes from 2010 to 2019 and compared them with seven logistics hub cities in the eastern region. The results indicate that Shijiazhuang’s logistics efficiency is high, with leading technology and management levels in the eastern region. Additionally, the Tobit regression model was used to explore factors affecting Shijiazhuang’s logistics efficiency, finding that economic development and locational advantages positively influence logistics efficiency, whereas industrial structure has a negative impact. Based on these findings, it is recommended that Shijiazhuang City enhance its logistics efficiency by improving logistics infrastructure, developing multimodal transport, leveraging locational advantages, elevating economic levels and openness, advancing logistics informatisation and nurturing high-quality logistics talent.

Abstract Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces’ key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed.

The current food crisis has become a serious threat to humanity. Other issues such as climate change, farmer regeneration, and excessive use of chemical inputs at the producer level threaten sustainability in agriculture. The sustainability of agricultural practices among farmers remains questionable due to the small number of organic farmers in Indonesia. The purpose of this study was to determine the level of sustainability of rice cultivation and the factors that influence the sustainability of rice cultivation in various types of cultivation in Yogyakarta. Research on farm sustainability that is analyzed in a multidimensional (5 dimensions) and compares 3 types of cultivation has not been widely carried out in Indonesia. This research was conducted in Bantul and Sleman with 90 respondents. The analysis method used Multidimensional Scaling-RAPFISH and Tobit regression. The results showed interesting findings where the 3 types of rice cultivation are in the sufficient (moderately sustainable) category even though the 3 have differences in the use of inputs, especially in the ecological dimension. The factors of education, frequency of attending extension and activity in farmer groups, the use of good agricultural practices, land ownership, and type of cultivation had a positive effect on increasing the sustainability of rice cultivation in various types of cultivation in Yogyakarta. The results of this research have an impact on agricultural extension field education provided by the government to increase the sustainability of rice cultivation in Yogyakarta. It is recommended that the 3 types of rice farmers to pay more attention to the use of Good Agricultural Practices (GAP) to increase sustainability.

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One new factor driving the banking industry towards long-term, high-quality growth is digital marketing, which has arisen within the framework of the digital economy. The purpose of this research is to examine the effect of digital marketing on the financial results of MENA banks from 2010 to 2023. The research examines the impact of digital marketing techniques on the effectiveness of financial institutions through Tobit regression analysis, taking into account and controlling for sustainable practices (ESG), bank-specific characteristics (capital adequacy, bank size, liquidity, and cost efficiency), and macroeconomic variables (GDP and inflation). This empirical paper managed to collect the data for eleven countries in the MENA from the Refinitiv Eikon platform, world bank database, and the annual reports of relevant banks in the different stock markets. The final sample included 78 banks out of 120 listed banks. The results show that there is a clear association between the presence of digital marketing campaigns and improved profitability and market share growth for banks. Aligning digital initiatives with ESG principles is crucial for long-term value development, and sustainable practices increase these beneficial benefits even more. The research also shows that macroeconomic factors and bank-specific characteristics affect how effective digital marketing campaigns are. The significance of digital transformation and ESG integration in promoting competitive advantages and long-term growth in the MENA banking sector is highlighted by these findings, which have important implications for policy, investors, and bank executives. [ABSTRACT FROM AUTHOR]

The demand for livestock products is rising, and China is actively encouraging farmers to increase their livestock production to meet this growing demand. At Shaanxi Province's livestock industry's current production output and growth rate, it appears unfeasible to meet the government's production target for 2025. Inefficiencies within livestock production can significantly impede the development of this industry. Therefore, this research employs the Data Envelopment Analysis (DEA) technique, considering Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) assumptions, to assess the technical efficiency of the livestock industry in Shaanxi Province. The data utilised are secondary data from 2010 to 2019. The findings reveal that the Shaanxi livestock industry has an average technical efficiency of 0.84 (CRS) and 0.92 (VRS), suggesting that there is room for further production growth with the current inputs, breeding scales and technology. Although dairy cows, cattle and goats have achieved full technical efficiency. Technical and scale inefficiencies still exist in hog and layer farming practices, which can be improved to increase production. Notably, hog farming demonstrated the lowest technical efficiency, scoring 0.68. The results of factors affecting inefficiency suggest that increasing spending on disease prevention and raising the selling price can both improve technical efficiency. Additionally, reducing death loss has the potential to improve technical efficiency. Thus, the government is expected to promote farm consolidation and expansion while actively advocating for establishing livestock production cooperatives. [ABSTRACT FROM AUTHOR]

AbstractIn the quest to eradicate poverty, Tanzania has been implementing the productive social safety nets (PSSN), among other efforts. However, despite these well-intentioned efforts, there are valid concerns in the literature highlighting the risk that participants may, in the future, plunge back into poverty and food insecurity. This calls for a nuanced understanding of the vulnerable contexts of social safety net program participants. We draw on the 2017-18 Household Budget Survey data to analyze vulnerability to poverty (VP) and food insecurity (VFI) and their determinants among households enrolled in the PSSN program in Tanzania. We follow the vulnerability as expected poverty approach and estimate the determinants using the Tobit model. We found that 13.9 and 20.6 percent of the PSSN participants were highly vulnerable to poverty and food insecurity, respectively, whereas vulnerability was likely to be lower in male-headed households. Residing in rural areas, ageing, household size, food assistance, credit, and occupation were the most important determinants of VP and VFI. Therefore, there is a need for policy efforts focused on enhancing the effectiveness of SSNs to be cognizant of the vulnerability contexts of participants, as well as the differential implications of safety net programs on various household groups.

Abstract Background With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement. Methods Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China’s healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation. Results The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion. Conclusions Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.

An online survey was conducted from June 2018 to March 2021 to elicit information from commercial farmers and growers on the economic impact of the Brown Marmorated Stink Bug (BMSB) (Halyomorpha halys (Stål)) on agriculture and their attitudes towards adopting various alternative management practices, including use of Trissolcus japonicus as a biocontrol agent. Most respondents reported that BMSB reduced the profitability of their operation, with the largest impacts coming from reduced quality and increased spray cost. Improved monitoring techniques and natural enemies have the greatest probability to be used to manage BMSB, while tactics like attract and kill, border sprays, repellents, trap crops and netting were less likely to be adopted. Further analysis using ordered logistic and tobit regression models shows that the factors that influence potential adoption vary significantly for each management practice. This suggests that adoption efforts for alternate management practices should focus on the attributes of specific commodity groups and targeted educational programmes may be required to overcome the resistance of growers using conventional growing practices. Several variables have a consistent impact on the respondent's willingness to pay for a T. japonicus‐based biocontrol including being a commercial producer (positive impact), use of conventional growing practices (negative) and increased concern about the impact of BMSB (positive). [ABSTRACT FROM AUTHOR]

One of the paramount challenges in natural resource management revolves around the delicate equilibrium between the demand for and the supply of diverse Ecosystem Services (ESs) within a cultural landscape. Recognizing the centrality of cultural landscapes to human well-being, the sustainability of these landscapes hinges upon the health and stability of ecosystems that can effectively provide the required ESs. Over the long term, the sustainable supply of ESs is constrained by the potential supply of ESs. Understanding the potential supply of ESs is crucial for averting compromises to the ecosystems within a landscape. This article introduces a novel perspective on evaluating the ESs of a landscape by means of efficiency analysis. Instead of presenting the potential supply of ESs in absolute terms, we offer a comparative analysis of ESs' relative supply to associated management costs. In principle, the efficiency of Landscape Units (LUs) is defined as the ratio of the potential supply of multiple ESs to the costs associated with land use and land cover management. The resultant efficiency maps serve as hot and cold spot maps, revealing efficient ecosystem compositions that yield multiple ESs. This composition reflects management efforts, incorporating various management costs. Forests emerge as pivotal ecosystems in landscapes, delivering the most ESs at the lowest costs. These efficiency maps offer valuable insights for regional planners, enabling them to enhance the supply of ES in inefficient LUs by studying the ecosystem structure and associated costs of the most efficient LUs. [ABSTRACT FROM AUTHOR]

Globally, karst regions face the dual challenges of urbanization and ecological protection, with the coupling coordination of multi-dimensional urbanization (MDU) and ecological security (ECS) being a necessary condition for achieving sustainable development. This study, based on statistical data on MDU and ECS in the Yunnan–Guizhou Region (the YGR) in China, employs the entropy weight TOPSIS model, degree of coupling coordination (CCD) model, and panel Tobit regression model to explore the coupling relationship between MDU and ECS. The main conclusions are as follows. (1) MDU in the YGR increased from 0.299 to 0.305, indicating low-level and sluggish development. Spatially, it is characterized by a "dual-core" structure centered on Kunming and Guiyang. (2) ECS decreased from 0.456 to 0.423, with a spatial pattern of "high in the east, low in the west". The impact of human activities on ECS increased from 0.579 to 0.631 due to the increase in social and economic activities. (3) CCD increased to 0.579, achieving moderate coordination. The spatial feature evolved into a tri-cluster pattern of "high–low–high" across the "eastern–central–northwestern" regions. (4) Regression results indicate that annual average precipitation has a "both promoting and limiting" dual effect on CCD. The coefficient for the proportion of afforested land area is 0.205, with a significance level of 5%, suggesting that increasing forest cover is a key measure for improving CCD. The study reveals the factors influencing the evolution of MDU and ECS from a negative to a positive correlation, providing a basis for decisions related to sustainable development for urban and ecological management in karst landscapes globally. [ABSTRACT FROM AUTHOR]

In times of prolonged economic doldrums across the globe, multinational firms (MNFs) offshoring blunders can undermine their competitiveness in the marketplace. To help the MNF formulate a more resilient offshoring strategy and identify the most desirable offshoring destination, this article aims to identify dynamic risk factors that significantly hinder the efficiency of offshoring and then measure specific offshoring risks over time using two different versions of data envelopment analysis (DEA) models and Malmquist productivity index (MPI). After assessing the degree of risk resiliency of the offshoring host countries over extended periods and then conducting Tobit regression analysis to identify key factors that significantly influence offshoring risks, we found that the host country's logistics efficiency (i.e., logistics performance index [LPI]) and domestic market size were critical indicators of offshoring success in that country. Since low‐cost sourcing countries (LCCs) tend to have relatively low LPIs and smaller domestic market sizes, they are not attractive offshoring destinations. This finding defies conventional wisdom. This article is one of the first longitudinal studies to assess the comprehensive risk resilience of 87 different offshoring destinations (countries) during multiple periods (6‐year span). [ABSTRACT FROM AUTHOR]

Objective: Identify the livelihoods that contribute to overcoming rural food poverty. Methodology: The research was carried out in three communities in Zacapoaxtla, Puebla. A questionnaire was applied to a sample of 96 households with indigenous characteristics. Multivariate analysis was used to generate an index of livelihoods on a scale from zero to one, which was correlated with the food poverty line. Results: The maximum value of the livelihood index corresponded to physical capital and the lowest, natural capital. The Tobit regression model showed that financial, social, human and physical capitals were significant in explaining food poverty. Initially, the food and non-food poverty lines were considered, but in the sample only a couple of cases reached the latter. Limitations: The measurement of food poverty used an indirect approach, based on household income. However, a multidimensional method could better explain the poverty status of households. Conclusions: Financial capital contributed the most to overcoming the rural food poverty line, due to government support and remittances. Natural capital did not contribute significantly due to the size of the property, rainfed regime and technology. [ABSTRACT FROM AUTHOR]

Background: With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement. Methods: Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China's healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation. Results: The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion. Conclusions: Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts. [ABSTRACT FROM AUTHOR]

The study aims to identify factors influencing the consumption preferences of packaged and unpackaged milk among consumers in Kahramanmaraş province. A face-to-face survey was conducted with 400 consumers selected through proportional sampling in 2020. Tobit regression was employed for data analysis. Results indicate that women prefer packaged milk more than men, and as age increases, consumers tend to prefer packaged milk. Moreover, consumers in higher income groups are more likely to consume packaged milk. Conversely, there is a negative correlation between university education level and unpackaged milk consumption. Additionally, a higher number of family members and the perception of open milk as healthy influence open milk preferences. Advertising campaigns and having children in the family were found to be ineffective in influencing packaging preferences. Thus, decision makers should develop policies to promote open milk consumption among low-income consumers. [ABSTRACT FROM AUTHOR]

Abstract Background Besides the scarcity of resources, inefficient utilization of available health service resources has been the bottleneck to deliver quality health services in Ethiopia. However, Information regarding the efficiency of health service providers is limited in the country. Health service managers and policy makers must be well informed about the efficiency of health service providers and ways of using limited resources efficiently to make evidence-based decisions. This study aimed to assess the level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia. Methods A facility-based cross-sectional study was conducted among 27 randomly selected health centers in East Gojjam zone, Northwest Ethiopia, from October 30, 2022, to April 30, 2023. Using an interviewer-administered questionnaire and document review checklist, health centers’ data was collected and entered to Epi-Data version 4.6. The data was exported to Microsoft office excel and Stata version 14 for analysis. A two-stage output-oriented data envelopment analysis with a variable return to scale assumption was employed to determine the level of technical efficiencies. Finally, the tobit regression model was applied to identify the associated factors at 5% level of significance. Results In this study, 59.3% of the health centers were technically efficient. The mean technical efficiency score of the health centers was 0.899 ± 0.156. Inefficient health centers could provide more 22, 433 outpatient visits, 1,351 family planning visits, 155 referral services, 206 skilled deliveries and 385 fully vaccinations of children if they were technically efficient as their peer health centers for the same year. From the tobit regression, the catchment population and number of administrative staffs were statistically significant determinants of the technical efficiency of health centers. Conclusions The mean technical efficiency of the health centers in East Gojjam zone, Northwest Ethiopia was high. However, nearly half of the health centers were technically inefficient, which indicates the exitance of a space for further improvements in the productivity of these health centers. Employing excess number administrative staffs (above the optimal level) should be discouraged and selecting appropriate sites where the health centers to be constructed (to have large catchment population coverage) could improve the productivity of health centers.

This study examined the relationship between the board characteristics and stock performance of commercial banks. Our analysis is based on a sample of 65 banks across 10 MENA countries and their quantitative data extracted between 2013 and 2022. This research employed pooled OLS, and fixed and random effect regression to confirm the association between board size, board independence, number of board meetings, and CEO duality with stock performance measured by the bank's share price and market-to-book ratio. Further, several control variables were utilized such as the bank's capital adequacy, profitability, and size. The empirical findings reveal that board independence positively affects the bank stock performance while the board size shows a negative relationship. This suggests that banks with fewer board members and high independence levels have their shares outperforming others. However, we found that having frequent board meetings per year and separate roles for the CEO and chairman have no impact on bank stock performance. Moreover, the findings indicate that the bank's capital adequacy, size, and profitability have a positive effect on the stock performance. To test the robustness of our analysis, we implemented a one-limit Tobit model, which enables lower-bound censoring, and obtained similar findings thus confirming our hypotheses. From a practical perspective, our findings highlight the importance of the board size and the directors' independence to MENA regulators and policymakers in an effort to implement an effective corporate governance system. Specifically, MENA banks are advised to decrease the number of board members, and this should reduce the number of annual board meetings which, in turn, should maximize performance. [ABSTRACT FROM AUTHOR]

The literature on what drives crop failure and crop abandonment is scant. This paper explores the interplay between risk factors and crop abandonment. We examine the role of risk sources and risk management strategies in crop abandonment by smallholder maize farmers in Zambia. Specifically, we seek to improve the understanding of risk factors in crop abandonment decisions at the subnational level using the Hellwig synthetic risk measure and tobit regression. Based on data for the years 2009 to 2015, we find variability in the Hellwig Risk Index indicating different levels of crop abandonment risk across provinces, with the Laupula, Southern and Western provinces remaining in the high-risk category (Class III) throughout the period. Maize yield, area planted, area harvested, consumer price index (CPI), maize price and climate were the main drivers of crop abandonment risks among farmers across the country. The government should implement targeted interventions and support programmes to address the specific needs of high-risk provinces such as Laupula, Southern and Western. There is also a need for the government to promote efficient land use and provide support for optimal planting and harvesting practices in order to reduce overextension and abandonment risks. [ABSTRACT FROM AUTHOR]

Background: Besides the scarcity of resources, inefficient utilization of available health service resources has been the bottleneck to deliver quality health services in Ethiopia. However, Information regarding the efficiency of health service providers is limited in the country. Health service managers and policy makers must be well informed about the efficiency of health service providers and ways of using limited resources efficiently to make evidence-based decisions. This study aimed to assess the level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia. Methods: A facility-based cross-sectional study was conducted among 27 randomly selected health centers in East Gojjam zone, Northwest Ethiopia, from October 30, 2022, to April 30, 2023. Using an interviewer-administered questionnaire and document review checklist, health centers' data was collected and entered to Epi-Data version 4.6. The data was exported to Microsoft office excel and Stata version 14 for analysis. A two-stage output-oriented data envelopment analysis with a variable return to scale assumption was employed to determine the level of technical efficiencies. Finally, the tobit regression model was applied to identify the associated factors at 5% level of significance. Results: In this study, 59.3% of the health centers were technically efficient. The mean technical efficiency score of the health centers was 0.899 ± 0.156. Inefficient health centers could provide more 22, 433 outpatient visits, 1,351 family planning visits, 155 referral services, 206 skilled deliveries and 385 fully vaccinations of children if they were technically efficient as their peer health centers for the same year. From the tobit regression, the catchment population and number of administrative staffs were statistically significant determinants of the technical efficiency of health centers. Conclusions: The mean technical efficiency of the health centers in East Gojjam zone, Northwest Ethiopia was high. However, nearly half of the health centers were technically inefficient, which indicates the exitance of a space for further improvements in the productivity of these health centers. Employing excess number administrative staffs (above the optimal level) should be discouraged and selecting appropriate sites where the health centers to be constructed (to have large catchment population coverage) could improve the productivity of health centers. [ABSTRACT FROM AUTHOR]

With environmental degradation and energy shortages, green and low-carbon development has become an industry trend, especially in regards to cold chain logistics (CCL), where energy consumption and emissions are substantial. In this context, determining how to scientifically evaluate the cold chain logistics efficiency (CCLE) under carbon emission constraints is of great significance for achieving sustainable development. This study uses the three-stage data envelopment analysis (DEA) and the Malmquist index model to analyze the overall level and regional differences regarding CCLE in China's four major urban agglomerations, under carbon constraints, from 2010 to 2020. Then, the influencing factors of CCLE are identified through Tobit regression. The results reveal that: (1) the CCLE in the four urban agglomerations is overestimated when carbon constraints are not considered; (2) the CCLE in the four urban agglomerations shows an upward trend from 2010 to 2020, with an average annual growth rate of 1.25% in regards to total factor productivity. However, there are significant spatial and temporal variations, with low-scale efficiency being the primary constraint. (3) Different influencing factors have different directions and exert different effects on CCLE in different urban agglomerations, and the improvement of economic development levels positively affects all regions. [ABSTRACT FROM AUTHOR]

Despite the growing attention paid by consumers and policy makers to health and sustainability issues, contribution analyzing consumers attitude towards both health related and sustainability attributes of foods are lacking. This study aims to investigate consumers’ preferences and willingness to pay for sustainable functional foods, in particular snack bars with high anti-oxidant value and made with a sustainable production process. Results show consumers’ willingness to pay a price premium of 0.30€ for the purchase of such foods. The amount of price premium increases as income increases. Furthermore, women, health-conscious individuals and those more likely to try new foods are willing to pay more for the purchase of sustainable functional snack bars. The preferred attributes are those related to the healthiness, while those related to the sustainability are not considered relevant. The present study enriches literature on functional foods, also providing insights for policy makers and practitioners.

In the current tough business environment, improving total factor productivity (TFP) is essential to the survival and development of China’s thermal power industry. On the basis of a network production structure, this research uses a Data Envelopment Analysis (DEA) approach with cross-efficiency to measure the overall and two-stage (power generation and pollutant treatment) TFPs of this industry in China’s 30 provinces. Moreover, the Tobit regression method is introduced to recognize the impact factors on TFP changes. Empirical results reveal that: (1) The overall TFP of the eastern region is the highest (0.6269), and the TFP levels of the central and western regions are relatively close (0.5182 and 0.5378, respectively). (2) Generally, the TFP of the power generation stage is higher than that of the pollutant treatment stage. The mean values of the two stages are 0.5765 and 0.3998, respectively. (3) Economic development and fuel prices are positively associated with the TFP level, whereas technological progress is surprisingly negatively associated. (4) Major policy directions recommended include establishing the power auxiliary service market, implementing the flexibility transformation of small and medium thermal power units, reducing the cross-subsidy to household electricity consumption, removing interregional barriers of electricity transmission, reforming the electricity price system to mitigate the rebound effect, and reducing inefficient investment in pollution treatment retrofit of thermal power plants.

Purpose. This research aims to analyze the level of economic efficiency of cassava farming and the determinants that affect the efficiency level in the Wonogiri Regency, Indonesia. Methodology / approach. The research location was determined using the stratified random sampling method, and three sub-districts were selected in Wonogiri Regency, namely Ngadirojo, Jatiroto, and Puhpelem. Furthermore, this research used a random sampling method with respondents of 74 monoculture cassava farmers. The analysis method was the Data Envelopment Analysis (DEA) approach to analyze the economic efficiency of cassava farming and Tobit regression to analyze the determinants that affected it. Furthermore, primary data was obtained from respondents through interviews, and secondary data as a complement was obtained from relevant agencies. Results. This research showed that the level of economic efficiency (EE) of cassava farming in the Wonogiri Regency was 68.3 %. From the results of economic efficiency, there was an opportunity to improve efficiency by reducing input inefficiency by 13.1 % and minimizing input costs by 21.1 %. The determinants of factors that affected cassava farming were considered from the socio-economic factors, namely experience, educational level, and participation of farmers in farmer groups. Originality / scientific novelty. Studies on the effectiveness of cassava have never been conducted in Central Java, even though Central Java is one of the three centers of cassava production in Indonesia. The efficiency of farming cassava in this research was not only considered from the technical side but also from the allocative and economic side, as well as the socio-economic character of the farmer’s environment. This research analyzed the efficiency of cassava farming with a nonparametric approach, namely Data Envelopment Analysis. Usually, efficiency research is approached with parametric analysis, namely production function analysis with Stochastic Frontier Analysis (SFA). Practical value / implications. It is recommended to use optimal inputs, especially seeds and fertilizers, which can be applied in cassava farming to reduce inefficiency. The need for fertilizer is related to the land’s condition, where the land in Wonogiri Regency has a steep slope, making it prone to erosion and sedimentation. Then, it affects the decline of the soil layer and the need for fertilizer. The role of the government is required because it is necessary to facilitate access to farmers and provide information about input use.

BACKGROUND AND OBJECTIVES: Profitability is a crucial indicator of insurance company performance, as it reflects their ability to invest and grow. Supervisors also rely on financial characteristics, including profitability, to determine the viability of insurers. The data envelopment analysis (DEA) method has been widely used to evaluate the performance of insurance companies. However, conventional financial ratios are often lacking in such studies, making DEA an effective alternative for measuring profitability. Given the rapid growth of the insurance industry in Iran, this research aims to examine the profitability of non-life insurance companies in Iran and expand coverage analysis. METHODS: The study focuses on 18 insurance companies listed on the Tehran Stock Exchange from 2013 to 2014, with complete and available information during the research period. Profitability is measured using DEA, and the Tobit estimator model is used to investigate the impact of company size, company age, and product variety on profitability. FINDINGS: The results highlight the importance of properly managing expenses and incomes for insurers. Additionally, the study finds that company size, company age, and product variety do not have a significant relationship with profitability. CONCLUSION: Insurance companies need to effectively organize their expenses and various types of business to maximize profit ratios. Optimizing cost structures and diversifying business ventures are key strategies for achieving profitability. This research provides insights into insurance company performance, allowing for a better understanding of their relative profitability within the industry and the formulation of targeted strategies. It also aids the government in developing policies that support the industry’s growth. [ABSTRACT FROM AUTHOR]

Carbon Neutrality goals and Sustainable Development Goals (SDGs), as new requirements for global development at this stage, have raised higher requirements for achieving the coordination of economic efficiency and ecological development of transportation infrastructure, especially highways. To promote the achievement of Carbon neutrality goals and SDGs, this research intends to study the effect of highways on the efficiency of carbon unlocking in each province. In this paper, we take China as an example, use the data of 18 listed highway companies and their provinces from 2010–2021 to conduct the study, measure the static carbon unlocking efficiency by using the super-efficiency SBM model with undesirable outputs, combine with the Malmquist index model for the decomposition of the efficiency and the dynamic analysis, and use the Tobit regression model to analyze the factors affecting the carbon unlocking efficiency. The results show that (1) From a static perspective, the carbon unlocking efficiency shows a steady upward trend, and the carbon unlocking efficiency has been in a state of low efficiency in general, with obvious regional differences. (2) From a dynamic perspective, carbon unlocking efficiency changes are relatively flat, and the carbon unlocking efficiency change index is in the rising stage, relying on technical efficiency can effectively improve the level of carbon unlocking efficiency. (3) The regional economic level, industrial structure, and urbanization level have a significant positive correlation with carbon unlocking efficiency, while the level of scientific and technological development and the level of opening up have a negative effect. [ABSTRACT FROM AUTHOR]

This paper aims to determine the factors affecting cost and profit efficiency of commercial banks in Turkey and to examine the ownership effect on cost and profit efficiency in an emerging market. Another aim of the study is to carry out the most recent and longitudinal (2006-2020) analysis of efficiency in the Turkish banking industry. This study uses an intermediation approach with data envelopment analysis (DEA) as its methodology. A total of 23 commercial banks were selected as the study sample and their quarterly data from 2006- 2020 was collected. In addition, an external two-stage DEA model with Tobit regression was applied to examine the determinants of cost and profit efficiency. The results show that Turkish banks currently work with relatively higher cost efficiency than profit efficiency. On the other hand, foreign banks display a lower cost and profit efficiency performance. The downward trend in profit efficiency in the Turkish banking system sends a warning signal on the health and stability of the banking sector. Multivariate Tobit regression analysis reveals how Total Assets, Deposit Share, Asset Growth, Time Deposits, NPL, and Ownership Structure significantly affect cost and profit efficiency. Ratio of liquid assets to total assets is positively correlated with the efficiency values, in contrast to results from previous studies. Previous studies have mostly been limited to scale and technical efficiency and focused on the cost efficiency of Turkish banks. In this study, the gap in the literature is filled by a comparative examination of the cost and profit efficiency at the scale of bank ownership. The study will look at and discussed these issues at the most stable period and the prepandemic period in the Turkish economy. [ABSTRACT FROM AUTHOR]

Many healthcare education programs recognize the importance of empathy in professional practice and, consequently, include empathy training in their curricula. Unfortunately, the literature has not yet demonstrated that empathy training consistently yields greater levels of empathy. This may partly stem from the fact that empathy is frequently measured using (valid and reliable) self-reported scales. These instruments provide a "snapshot" of a respondent's empathy but provide minimal information about the development of empathy. Applying biometric analyses within the context of empathy training permits collecting additional data that may provide a more developmental evaluation of empathy training. This study empirically examines whether biometric measures are statistically significant predictors of empathy when employed within the context of health-related empathy training. We find that empathy training does not significantly impact self-reported levels of cognitive or affective empathy as measured. However, biometric information collected from individuals who completed empathy training does significantly predict affective (but not cognitive) empathy. [ABSTRACT FROM AUTHOR]

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data.

Promoting the transformation and upgrading of enterprises is an important way to promote the construction of national ecological civilization and achieve high-quality economic development. Meanwhile, the incorporation of party organizations helps private enterprises solve this problem. Based on the network embedding and high-order ladder theory in the VUCA environment, this paper proposes two major hypotheses: firstly, the embedding of party organizations effectively promotes the healthy transformation of private enterprises, and secondly, the heterogeneity of executive teams modifies the healthy transformation of enterprises. We construct an empirical model and define the main variable factors based on relevant studies from previous scholars, taking into account various aspects of enterprise technology transformation. This paper sequentially verifies the hypotheses through empirical analysis. The hypotheses are verified through Tobit regression, and the regression coefficients of models (1)–(4) are 2.578, 1.845, 0.715, and 0.425, respectively, and they are all significant at the 1% level, which shows that the enterprises that set up a party organization will have a better performance in transformation and upgrading, and the hypothesis H1 is verified. In the moderating effect analysis, the three heterogeneity of the executive team’s age, occupational background and educational background, and the coefficient of the interaction term of the heterogeneity of network embedding are −0.091, 0.037, 0.029, respectively, of which the coefficient of the age heterogeneity is negative and insignificant, which indicates that the age heterogeneity of the executive team does not affect the relationship between the network embeddedness of the party organization and the transformation of the private enterprises, and Hypothesis 2a is not valid, The remaining two coefficients are positive and significant at more than 10% level, hypotheses 2b and 2c are valid.

This paper aims to determine the factors affecting cost and profit efficiency of commercial banks in Turkey and to examine the ownership effect on cost and profit efficiency in an emerging market. Another aim of the study is to carry out the most recent and longitudinal (2006-2020) analysis of efficiency in the Turkish banking industry. This study uses an intermediation approach with data envelopment analysis (DEA) as its methodology. A total of 23 commercial banks were selected as the study sample and their quarterly data from 2006- 2020 was collected. In addition, an external two-stage DEA model with Tobit regression was applied to examine the determinants of cost and profit efficiency. The results show that Turkish banks currently work with relatively higher cost efficiency than profit efficiency. On the other hand, foreign banks display a lower cost and profit efficiency performance. The downward trend in profit efficiency in the Turkish banking system sends a warning signal on the health and stability of the banking sector. Multivariate Tobit regression analysis reveals how Total Assets, Deposit Share, Asset Growth, Time Deposits, NPL, and Ownership Structure significantly affect cost and profit efficiency. Ratio of liquid assets to total assets is positively correlated with the efficiency values, in contrast to results from previous studies. Previous studies have mostly been limited to scale and technical efficiency and focused on the cost efficiency of Turkish banks. In this study, the gap in the literature is filled by a comparative examination of the cost and profit efficiency at the scale of bank ownership. The study will look at and discussed these issues at the most stable period and the pre-pandemic period in the Turkish economy.

This study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vaccine hesitancy is a major obstacle in the fight against infectious diseases such as COVID-19 and influenza. Predictors of vaccination intention are identified using the reasoned action approach and the integrated behavioral model. Data from two national samples (N = 1131 for COVID-19 and N = 1126 for influenza) were collected from U.S. Qualtrics panels. Tobit regression models were estimated to predict percentage increases in vaccination intention (i.e., confidence) and the probability of vaccination uptake (i.e., intention reaching 100%). The results provided evidence for the reasoned approach and the IBM model and showed that the predictors followed different patterns for COVID-19 and influenza. The implications for intervention strategies and message designs were discussed. [ABSTRACT FROM AUTHOR]

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Evaluating road safety improvements becomes important because it can assist policymakers in allocating economic resources to improve safety and implementing effective policy interventions. As such, this study aims to estimate the value of road safety risk measures using a new modeling approach for willingness-to-pay (WTP). Specifically, this study integrates a machine learning technique (decision tree) with a correlated random parameters Tobit with heterogeneity-in-means model. The decision tree identifies a priori relationships for higher-order interactions, while the model captures unobserved heterogeneity and the correlation between random parameters. The proposed modeling framework examines the determinants of public WTP for improving road safety using a sample of car drivers from Peshawar, Pakistan. WTP for fatal and severe injury risk reductions is estimated and used to calculate the values of corresponding risk reductions, which can be used for monetizing the cost of road traffic crashes in the country. Modeling results reveal that most respondents are willing to contribute to road safety improvement policies. Further, the model also uncovers significant heterogeneity in WTP corresponding to the safer perception of the overall road infrastructure and perceived risk of accident involvement. Systematic preference heterogeneity is also found in the model by including higher-order interactions, providing additional insights into the complex relationship of WTP with its determinants. Further, the marginal effects of explanatory variables indicate different sensitivities toward WTP, which can help to quantify the impacts of these variables on both the probability and magnitude of WTP. Overall, the proposed modeling framework has a twofold contribution. First, the modeling framework provides valuable insights into the determinants of public WTP, mainly when the heterogeneous effects of variables are interactive. Second, its implementation and consequent findings shall help prioritize different road safety policies/projects by better understanding public sensitivity to WTP. • Examined willingness-to-pay for public policies for road safety enhancement. • Modeled willingness-to-pay using an integrated approach of decision tree and a correlated random parameters Tobit with heterogeneity-in-means model. • Heterogeneous effects of determinants of willingness-to-pay were identified. • Safe perception of road infrastructure and risk perception of traffic accident involvement were significantly correlated. [ABSTRACT FROM AUTHOR]

Marine fishery is an important part of China's maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries. [ABSTRACT FROM AUTHOR]

A significant industrial transformation in China's tourism sector is currently taking place in response to carbon peak and carbon neutrality targets. This paper applies the data envelopment analysis (DEA) model to calculate the efficiency of the tourism industry under carbon emission constraints and further investigates its influencing factors through the Tobit regression. The results are as follows: (1) The tourism efficiency under carbon emission constraints of China from 2000 to 2019 showed a trend of first rising and then declining, and there were obvious regional differences; (2) from 2000 to 2019, the total factor productivity of tourism in China increased significantly, while the contributions of technical progress, pure technical efficiency, and scale efficiency decreased sequentially; (3) the factors of industrial structure, transportation convenience, economic development level, degree of opening to the outside world, and the level of scientific and technological development have varying degrees of influence on tourism efficiency. Based on the analysis results, this paper puts forward several policy suggestions on tourism efficiency and low-carbon development. The findings of this paper have some bearing on developing nations' efforts to boost tourism efficiency and realize high-quality industry growth within the framework of sustainable development. [ABSTRACT FROM AUTHOR]