How signaling and search costs affect information asymmetry in P2P lending:the economics of big data

In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.

Construct Validation by Hierarchical Bayesian Concept Maps: An Application to the Transaction Cost Economics Theory of the Firm

A concept map is a diagram depicting relationships among concepts which is used as a knowledge representation tool in many knowledge domains. In this paper, we build on the modeling framework of Hui et al. (2008) in order to develop a concept map suitable for testing the empirical evidence of theories. We identify a theory by a set of core tenets each asserting that one set of independent variables affects one dependent variable, moreover every variable can have several operational definitions. Data consist of a selected sample of scientific articles from the empirical literature on the theory under investigation. Our “tenet map” features a number of complexities more than the original version. First the links are two-layer: first-layer links connect variables which are related in the test of the theory at issue;second-layer links represent connections which are found statistically significant. Besides, either layer matrix of link-formation probabilities is block-symmetric. In addition to a form of censoring which resembles the Hui et al. pruning step, observed maps are subject to a further censoring related to second-layer links. Still, we perform a full Bayesian analysis instead of adopting the empirical Bayes approach. Lastly, we develop a three-stage model which accounts for dependence either of data or of parameters. The investigation of the empirical support and consensus degree of new economic theories of the firm motivated the proposed methodology. In this paper, the Transaction Cost Economics view is tested by a tenet map analysis. Both the two-stage and the multilevel models identify the same tenets as the most corroborated by empirical evidence though the latter provides a more comprehensive and complex insight of relationships between constructs.

Quantum Model of Decision-Making in Economics

The paper designs a quantum model of decision-making (QMDM) that utilizes neuroscientific evidence. The new model provides both normative and positive implications to economics. First, it enhances the study of decision-making which is an extension of the expected utility theory (EUT) in mathematical economics. Second, we demonstrate how the quantum model mitigates drawbacks of the expected utility theory of today.

Factors affecting farmers'choice to adopt risk management strategies:The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers'adoption of risk management strategies(RMS)in Pakistan during times of uncertainty.The study examines farmers'adoption of RMS using both multinomial probit(MNP)and multivariate probit(MVP).Data were collected from 382 farmers sampled from four districts in KhyberPakhtunkhwa(KP)province of Pakistan via a multistage sampling technique.This study utilizes the MNP model,considering the assumption of Independence of Irrelevant Alternatives(IIA)and incorporating correlated error terms.The objective is to understand farmers'behavior in risky situations and determine if there is heterogeneity.Results are compared with the MVP model to assess robustness and gain deeper understanding of farmers'decisionmaking processes.The research findings reveal that our results are robust,and farmers behave homogeneously in various RMS scenarios.Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood.The risk-averse farmers,who perceive weather-related risks as a threat,access credits and information,and have farms close to a river are more likely to adopt RMS,irrespective of the format of the strategies available.Moreover,the predicted probabilities and correlation of the RMS and RM categories have strengthened our model estimation.These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

Cybersecurity: Integrating Information into the Microeconomics of the Consumer and the Firm

The connectivity of information has changed many things but not the way economists model consumers, firms and government. Information is here newly modeled as a fundamental element of microeconomic choices and utility, cost and tax functions. The results are more clearly defined metrics for losses due to cyber breaches or productivity gains from cyber investments. The integration of information into standard microeconomics also allows use of econometric and other tools to analyze the empirics of the consumer and the firm. In particular, the results identify ways in which losses in the Gordon and Loeb [1] model can be specified in more detail.

Impact of an official accountability audit on industrial structure adjustment:a case study of environmental regulation in China

As a unique environmental regulation in China,the official accountability audit was piloted in 2014.With a focus on prioritizing the ecological environment,officials in pilot districts have implemented economic construction,adjusted industrial structures,and promoted coordinated development between the economy and environment.The effects of implementation have garnered widespread attention from society.However,there is limited research on the impact of an accountability audit on industrial structure adjustments.Using the“Accountability Audit of Officials for Natural Resource Assets(Trial)”released in 2015 as a quasi-natural experiment,this study collected panel data from 279 cities between 2013 and 2017.It then empirically analyzed the impact mechanism and effects of the accountability audit on industrial structure adjustment using the Propensity Score Matching and Difference-in-Differences model.The research findings indicate that the accountability audit directly impacted industrial structure adjustment,promoting the upgrading of the primary industry to the secondary industry and restricting the development of the tertiary industry.In addition,the audit is beneficial for enterprise entry,but not conducive to technological innovation,and has no significant impact on foreign direct investment.This conclusion fills a gap in the existing research and provides valuable insights for policymakers.

Preserving environmental quality of ecotourism sites through community participation in Purulia District of West Bengal,India

The importance of valuing environmental resources,especially in ecotourism sites,has become increasingly important over the last two decades.Ecotourism is now considered as an important source of livelihood of local stakeholders in backward regions.Therefore,the preservation of ecotourism sites through community participation seems very important to maintain continued flow of tourists.This study aimed at recognizing the importance of community participation for the preservation of ecotourism sites.For this,this study executed a survey based on non-probability sampling in two ecotourism sites(Garpanchkot and Baranti)covering 100 respondents in Purulia District,West Bengal of India.The central issue of this study was to assess the tendency of community participation for the conservation of ecotourism sites and find the optimum condition for offering participatory labour time.This study showed that the participation of young people is high,and the majority of respondents are aware of the importance in protecting ecotourism sites.Because respondents were too poor to offer money,the contingent valuation method(CVM)was used to elicit their willingness to pay(WTP)participatory labour time for the conservation of ecotourism sites.Respondents’age,income,education level,caste,and their perceived environmental quality had significant relationship with their WTP participatory labour time by applying the ordinary least square(OLS)model.It was found that the mean WTP participatory labour time of each respondent in a month is approximately 3.64 h.The significance of this study is that community participation can improve the sense of belonging,trust,and credibility of ecotourism sites,making them more appreciative of the value and protection of these sites.

Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Impact of geopolitical risk,GDP,inflation,interest rate,and trade openness on foreign direct investment:Evidence from five Southeast Asian countries

Historically,geopolitical risk(GPR)has posed significant challenges to international economic,social,and political frameworks.This study investigated how internal GPR in the selected five Southeast Asian countries(Indonesia,South Korea,Malaysia,the Philippines,and Thailand)influences foreign direct investment(FDI)during 1996-2019.The stationarity of the data was assessed using the Augmented Dickey-Fuller(ADF)unit root test,which shows that the data became stationary after the first difference.The Kao,Pedroni,and Westerlund cointegration tests were employed to examine long-term cointegration among the selected variables(FDI,GPR index(GPRI),gross domestic product(GDP),inflation,interest rate,and trade openness(TOP)).The results indicated that these variables have a long-term cointegration.Consequently,regression analysis using the Pooled Ordinary Least Squares(OLS)regression,fixed effect,random effect,Arellano-Bond dynamic panel-data estimation,and system generalized moment method(GMM)revealed that GPRI and TOP negatively impacted FDI in the selected five Southeast Asian countries.At the same time,GDP,inflation,and interest rate positively influenced FDI in these countries.Because FDI is crucial to shaping a country’s macroeconomic structure,this study recommends that governments and central banks of the selected five Southeast Asian countries should implement policies and strategies to encourage foreign investments.

Employment and development levels in rural areas of the Russian Federation

The development of agro-industrial complex is important for ensuring national food security and national health.The development of rural areas is subject to the development of agriculture and local infrastructure,as well as the availability of various services.This study selected 15 indicators in 2021 to analyze the employment and development levels in rural areas of 71 regions of the Russian Federation using the analytical grouping method.The results indicated that 20 regions(Group 1)had the highest percentage of rural population(33.10%).The percentage of population engaged in agriculture had the highest value(12.40%)in 31 regions(Group 2).Moreover,20 regions(Group 3)had the highest investments in fixed assets at the expense of municipal budget(11.80 USD/person).Increasing the investments in fixed assets carried out from the budget of the municipality can improve the employment level in rural areas.Then,we used cluster analysis to divide 14 regions of the Volga Federal District in the Russian Federation into 3 clusters.Cluster 1 covered Kirov Region and Republic of Mari El;Cluster 2 included Ulyanovsk Region,Saratov Region,Nizhny Novgorod Region,Perm Territory,Orenburg Region,Chuvash Region,and Republic of Mordovia;and Cluster 3 contained Republic of Tatarstan,Samara Region,Udmurtian Republic,Penza Region,and Republic of Bashkortostan.Results indicated that the 2 regions of Cluster 1 need to increase the availability of resources and natural gas and improve the investment attractiveness of rural areas.The 7 regions of Cluster 2 needed to develop infrastructure,public services,and agricultural production.We found the highest employment level in rural areas,the largest investments in fixed assets at the expense of municipal budget,the largest residential building area per 10,000 persons,and the largest individual residential building area in the 5 regions of Cluster 3.This study makes it possible to draw up a comprehensive regional development program and proves the need for the development of rural areas,which is especially important for the sustainable development of the Russian Federation.

Driving factors of CO_(2)emissions in South American countries:An application of Seemingly Unrelated Regression model

Carbon emissions have become a critical concern in the global effort to combat climate change,with each country or region contributing differently based on its economic structures,energy sources,and industrial activities.The factors influencing carbon emissions vary across countries and sectors.This study examined the factors influencing CO_(2)emissions in the 7 South American countries including Argentina,Brazil,Chile,Colombia,Ecuador,Peru,and Venezuela.We used the Seemingly Unrelated Regression(SUR)model to analyse the relationship of CO_(2)emissions with gross domestic product(GDP),renewable energy use,urbanization,industrialization,international tourism,agricultural productivity,and forest area based on data from 2000 to 2022.According to the SUR model,we found that GDP and industrialization had a moderate positive effect on CO_(2)emissions,whereas renewable energy use had a moderate negative effect on CO_(2)emissions.International tourism generally had a positive impact on CO_(2)emissions,while forest area tended to decrease CO_(2)emissions.Different variables had different effects on CO_(2)emissions in the 7 South American countries.In Argentina and Venezuela,GDP,international tourism,and agricultural productivity significantly affected CO_(2)emissions.In Colombia,GDP and international tourism had a negative impact on CO_(2)emissions.In Brazil,CO_(2)emissions were primarily driven by GDP,while in Chile,Ecuador,and Peru,international tourism had a negative effect on CO_(2)emissions.Overall,this study highlights the importance of country-specific strategies for reducing CO_(2)emissions and emphasizes the varying roles of these driving factors in shaping environmental quality in the 7 South American countries.

Analysis of the effect of the 2021 Semeru eruption on water vapor content and atmospheric particles using GNSS and remote sensing

Mount Semeru,an active volcano in East Java,Indonesia,erupted on December 4,2021,following extreme rainfall that caused an avalanche of hot pyroclastic flows and lava.The tropospheric conditions and dominant particle components in the atmosphere can be monitored using Global Navigation Satellite System(GNSS)technology and remote sensing satellites.GNSS signal propagation delay in Precise Point Positioning(PPP)processing can be used to determine Zenith Tropospheric Delay(ZTD)and Precipitable Water Vapor(PWV)variables so that atmospheric conditions can be generated.In addition,by using remote sensing satellite data,it is possible to obtain rainfall data with high temporal resolution as well as the dominant particle and gas content values during eruptions.During the eruption period,the high value of PWV was dominated by the high intensity of precipitation during the rainy season.High rainfall before the eruption caused activity inside the mountain to increase,which occurred in avalanche type eruption.Apart from that,the atmosphere around Semeru was also dominated by SO_(2)content,which spreaded for tens of kilometers.SO_(2)content began to be detected significantly by remote sensing sensors on December 7,2021.In this study,deformation and atmospheric monitoring were also carried out using low-cost GNSS at the Semeru Monitoring Station on September 9-15,2022.The results of the ZTD and ZWD values show the dominance of the wet component,which is directly proportional to rainfall activity in this period.

Efficiency and sustainability of the tourism industry in Latin America

The tourism industry is economically very important.According to the World Travel Tourism Council,in 2019,the tourism industry accounted for a quarter of all new jobs created worldwide,10.3%of all jobs,and 9.6×1012 USD of the global gross domestic product.This study aimed to calculate the tourism efficiency index for different Latin American countries from 2010 to 2021 using data envelopment analysis,which analyzes the relationships between input variables(including the number of employees in the tourism industry and the number of hotel-type establishments)and output variables(including tourism expenditures in other countries and public social expenditures in recreation and culture per capita).Additionally,this study aimed to identify the countries with greater tourism development and the factors that may affect the development of the tourism industry through the stochastic frontier production function.The results of the tourism efficiency index for Central America(including Costa Rica,Dominica,El Salvador,Honduras,Mexico,and Panama)and South America(including Argentina,Brazil,Chile,Colombia,Ecuador,Paraguay,Peru,and Uruguay)exhibited different trends.However,after the global health crisis,the tourism industry recovered,showing new opportunities to promote sustainability.The results of the stochastic frontier production function demonstrated that countries with higher levels of inbound and outbound tourism,contribution of tourism to the economy,natural resources,and literacy rate exhibited more efficient tourism industry,whereas countries with higher pollution levels exhibited less efficient tourism industry.The findings of this study could allow us to formulate suitable public policies to promote tourism,maintain natural resources,and diversify these sectors with more inclusive programmes that can facilitate growth and benefit vulnerable communities.

Statistical Analysis of Abilities to Give Consent to Health Data Processing

The recent pandemic crisis has highlighted the importance of the availability and management of health data to respond quickly and effectively to health emergencies, while respecting the fundamental rights of every individual. In this context, it is essential to find a balance between the protection of privacy and the safeguarding of public health, using tools that guarantee transparency and consent to the processing of data by the population. This work, starting from a pilot investigation conducted in the Polyclinic of Bari as part of the Horizon Europe Seeds project entitled “Multidisciplinary analysis of technological tracing models of contagion: the protection of rights in the management of health data”, has the objective of promoting greater patient awareness regarding the processing of their health data and the protection of privacy. The methodology used the PHICAT (Personal Health Information Competence Assessment Tool) as a tool and, through the administration of a questionnaire, the aim was to evaluate the patients’ ability to express their consent to the release and processing of health data. The results that emerged were analyzed in relation to the 4 domains in which the process is divided which allows evaluating the patients’ ability to express a conscious choice and, also, in relation to the socio-demographic and clinical characteristics of the patients themselves. This study can contribute to understanding patients’ ability to give their consent and improve information regarding the management of health data by increasing confidence in granting the use of their data for research and clinical management.

Privacy Protection in COVID Data Tracking: Textual Analysis of the Literature

The literary review presented in the following paper aims to analyze the tracking tools used in different countries during the period of the COVID-19 pandemic. Tracking apps that have been adopted in many countries to collect data in a homogeneous and immediate way have made up for the difficulty of collecting data and standardizing evaluation criteria. However, the regulation on the protection of personal data in the health sector and the adoption of the new General Data Protection Regulation in European countries has placed a strong limitation on their use. This has not been the case in non-European countries, where monitoring methodologies have become widespread. The textual analysis presented is based on co-occurrence and multiple correspondence analysis to show the contact tracing methods adopted in different countries in the pandemic period by relating them to the issue of privacy. It also analyzed the possibility of applying Blockchain technology in applications for tracking contagions from COVID-19 and managing health data to provide a high level of security and transparency, including through anonymization, thus increasing user trust in using the apps.

Demographic and socioeconomic determinants of urinary arsenic concentration for elementary school children in Bangladesh

Background:Arsenic(As)is a toxic element that can lead to various health issues in humans.The primary exposure to As is through the consumption of water contaminated with high As levels,particularly in Bangladesh.Previous studies have shown that urinary arsenic(UAs)concentration can reflect As exposure and metabolism in individuals.However,little is known about how other factors,such as age,sex,and socioeconomic status,may affect UAs concentrations in children.Objective:This study aimed to investigate the influence of factors such as age,sex,and socioeconomic status on UAs concentrations in children,in addition to the impact of drinking water arsenic(WAs)levels.Methods:We conducted our study on elementary school-going children aged 8-11 years from rural Araihazar in Bangladesh.We measured UAs(available for a subset of 391 children)and WAs levels and collected information on demographic and socioeconomic characteristics.We employed regression analysis and t-tests to analyze the data.Results:Our findings revealed that younger children(β=197.95,95%confidence interval[CI]:111.97 to 283.94),female children(β=93.95,95%CI:8.49 to 179.40),and children with less educated fathers(β=138.03,95%CI:26.47 to 249.58)had higher UAs levels,particularly when they consumed water with high As content.Conclusion:The study concludes that children's As exposure and UAs concentration may vary depending on their age,sex,and socioeconomic status.Therefore,these factors should be considered when assessing the health risks associated with As.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Economic Burden of Illness of the Cervical Cancer Treatment Protocol in Bangladesh

Background: Cervical cancer is a significant health concern in Bangladesh, with high mortality rates due to limited awareness and costly treatments. The disease stages influence treatment protocols, ranging from surgery and radiotherapy for early stages to chemotherapy and radiation for advanced stages, but survival rates decrease as the cancer progresses. Objective: The objective of this study is to determine the economic impact of the disease and recommend cost-efficient strategies for prevention and treatment. Methods: A population-based, cross-sectional study was conducted with a stratified sample of cervical cancer patients from selected healthcare facilities across Bangladesh. Data collection involved structured interviews and validated questionnaires. The study measured the economic impact, treatment costs, and other related expenses. Quantitative data analysis was performed using SPSS v22, MS-Excel, and R Programming, with Multivariate regression analysis and Post Hoc tests, including the chi-square test, applied to selected indicators. Results: All respondents in the study were female, aged 34 - 75, with 72.3% aged 40 - 50. Most were illiterate (38.6%) and housewives (95.0%). Additionally, 98% were married, 85.10% married before age 18, and 46.50% experienced their first menstruation before age 12. Families typically had a monthly income of 10,000 - 30,000 Taka, spending similar amounts on treatment. Significant relationships were found between educational qualifications, occupation, personal hygiene practices, history of oral contraceptive use, and age of marriage (p Conclusion: The article emphasizes the impact of monthly family income on cervical cancer treatment costs, stressing the need for comprehensive support services to address the financial and emotional burdens faced by patients. Improving access to quality care and implementing measures can enhance outcomes for cervical cancer patients in Bangladesh.

Indirect Water Consumption:The Scope 3 of Water?Estimation of This Indicator in Sectors Relying on Agriculture

The aim of this research project is to assess indirect water consumption,that represents the water consumed in the upstream part of the production life cycle.Estimations are provided for agriculture related companies,as agriculture represents 70%of water consumption on Earth.This consumption varies greatly according to the countries where supply chain is located.The estimation is attempted for 27 European listed companies in sectors relying on agriculture in its supply chain.A data is estimated for 22 companies,showing that indirect water consumption is much more important than direct one.Strategic questions raised through the estimation of indirect water consumption in various subsectors show the interest in this data,which represents the equivalent of Carbon Scope 3 for water issues.

A Structured Method of Teaching Epidural Block reduces the incidence of accidental dural puncture in inexperienced trainees.

Background: The rate of accidental dural puncture is particularly high during the period of training, especially in novices. The structured epidural teaching model (SETM) includes three standardized video lessons, the construction of a 3D epidural module by trainees and practical training by using an epidural simulator with and without the CompuFlo™ Epidural instrument. In this study we report the retrospective analysis of the accidental dural puncture rate of inexperienced trainees during their 6 months clinical practice rotation in obstetrics before and after the introduction of the SETM in our Institution. Method: We evaluated the incidence of accidental dural puncture before the introduction of the SETM methodology and afterwards by analyzing our departmental database from February 2019 to January 2023. All epidural blocks were executed by trainees who had never previously performed an epidural block and were about to begin their obstetrics rotation. Results: We analyzed 7415 epidurals: 3703 were performed before the introduction of the SETM methodology (control group) and 3712 afterwards (study group). The incidence of accidental dural puncture was 0.37% for the control group and 0.13% for the study group (p<.05). The probability of making an accidental dural puncture was 64% (OR: 0.36) lower for trainees who had the training than for those who did not. Conclusions: After the introduction of the structured teaching method, we observed a significant reduction of accidental dural puncture during the training period. We hope that our observation will encourage a constructive discussion among experts about the need to use standardized and validated tools as a valuable aid in teaching epidural anesthesia.