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This article analyzes characteristics of takeover targets in the European market—relatively less studied compared with US and UK markets—to develop a takeover prediction model. Our sample includes 320 European companies with 140 targets and 180 non‐targets over the period 1994–2007, covering two M&A waves. In this study, we test the discriminating power of many relevant variables including new one that could have a discriminating power in potentially determining (value creation). Our results show that European targets are characterized by a growth‐resource imbalance, are less rich in FCF, have growth opportunities, have a higher level of transaction volume of shares prior to the bid, achieve lower economic performance, and destroy value. Furthermore, we develop several predictive models using targets' financial data from 1 year, 2 years, and 3 years before takeover, along with the 3‐year average. The correct classification power in the original sample is 70% (in‐sample). As for predictive ability, the correct classification power in a control sample is 79.4% (out‐of‐sample). We also noted that predictive models using data from 1 or 2 years before the bid appear to display more significant predictive ability. [ABSTRACT FROM AUTHOR]

This study assessed the adoption level of essential oil extraction practices among lemon growers in the Pothwar region of Punjab, Pakistan. A well-structured questionnaire was used to collect data from 138 smallholders using the interview method by trained remunerators. Descriptive statistics and econometrics; logit and Poisson regression were employed for data analysis. Results of farmers' perceptions of adopting new practices indicated that 10.1% of the farmers are not adopting any one, 22.5% are adopting any three, and 26.8% are adopting all four practices. Moreover, farmers' age, farming experience, land holding, skill, training participation, and receptivity proved to be statistically significant determinants in improving the likelihood of adopting all four practices under the logit and Poisson models, respectively. Hence, it is recommended that the provision of an effective package in the form of literature concerning essential oil extraction practices to the doorsteps of smallholders during demonstration sessions, along with adequate professional training, could help in improving farmers' perceptions toward adoption and strengthening the essential oil sector. [ABSTRACT FROM AUTHOR]

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

Chemical analyses of powdered rocks by different laboratories often yield varying results, requiring estimation of the rock's true composition and associated uncertainty. Challenges arise from the peculiar nature of geochemical data. Traditionally, major and trace elements have been measured using different methods, resulting in chemical analyses where the sum of the parts fluctuates around 1 rather than precisely totaling 1. Additionally, all chemical analyses contain an undisclosed mass fraction representing undetected chemical elements. Because of this undisclosed and unknown mass fraction, geochemical data represent a particular kind of compositional data in which closure to unity is not guaranteed. We argue that chemical analyses exist in the hypercube while being sampled from a true composition residing in the simplex. Therefore, we propose an algorithm that generates random chemical analyses by simulating the data acquisition protocol in geochemistry. Using the algorithm's output, we measure the bias and mean squared error (MSE) of various estimators of the true mean composition. Additionally, we explore the impact of missing values on estimator performance. Our findings reveal that the optimized binary log-ratio mean, a new estimator, exhibits the lowest MSE and bias. It performs well even with up to 70% missing values, in contrast to other classical estimators such as the arithmetic mean or the geometric mean. Applying our approach to the GeoPT database, which contains replicate analyses of igneous rocks from numerous geochemical laboratories, we introduce an outlier detection technique based on the Mahalanobis distance between a laboratory's logit coordinates and the optimized mean estimate. This enables a probabilistic ranking of laboratories based on the atypicality of their performance. Finally, we offer an accessible R implementation of our findings through the GitHub repository linked to this paper [subject classification numbers: 10 (compositions) 85 (statistics)]. [ABSTRACT FROM AUTHOR]

Abstract–This article proposes a novel model in the family zero-inflated (ZI) binary models which we named it a ZI Logit Probit (ZILP) model. This model can be employed to analyze the binary data that has an exorbitant number of zero counts. In the scope of this work, we first present the general formula, connected functions, and estimating equation (EE) of the ZILP model. We next rely on some popular regularity conditions to present theory of large-sample for this model. To have many numerical proofs, multiple simulations and a real medical data set were executed in this study. We perform the number of infected blood cells (IBC) data set to test the effectiveness and stability of the maximum likelihood estimation (MLE) method in estimating the parameters for the ZILP model. The results obtained in the analysis of actual medical data are significant and important in practice. The results indicated that smoking status would have no effect on the number of IBCs, however, gender would more or less have an effect on the number of IBCs. Finally, some discussions and conclusions are given in this article. [ABSTRACT FROM AUTHOR]

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit. [ABSTRACT FROM AUTHOR]

Food insecurity has plagued Ethiopia for decades. The objective of this study is determining which personal attributes, household features, income-generating undertakings, and regional location change the food insecurity of Ethiopian households updating past studies. Furthermore, the study quantifies the probability changes in a household being food insecure in response to changes in personal attributes, household features, income-generating undertakings, and regional location. The data from the 2021/2022 Ethiopian Rural Socioeconomic Survey have been applied to estimate coefficients using the logit technique and calculate the effects of explanatory variables. Results show that being a male household head, education, having non-farm enterprise, and access to electricity lower the likelihood of a household being food insecure. In contrast, the likelihood of being food insecure has been associated with the region where a household was located, or dependence on raising livestock. The regional discrepancies have been substantial. Broadening access to education, stimulating non-farm enterprise, and promoting economic development while accounting for regional differences will reduce the risk of household food insecurity.

Taiwan's awareness of environmental conservation and biodiversity has been increasing in recent years. As frog plays a vital role in the environment and recreational activities in Taiwan, this study aimed to quantify the willingness‐to‐pay (WTP) of the public for conserving the selected endangered frog species. Respondents were asked using a semistructured questionnaire with auxiliary audio and video files. After deleting incomplete responses following regular data handling, 585 valid responses were used in the estimation. Using the contingent valuation method and single‐bounded dichotomous choice model, the results showed that people are willing to pay an amount of 32.01 USD (922.85 New Taiwan Dollars) per person annually. Factors affecting the public's WTP include price, age, support for establishing conservation areas, payment through donations, number of trips in ecotourism, and the place of residency in Taiwan. The result of this study can be used as a benchmark for the government for the implementation of the conservation and rehabilitation of the habitat of the endangered frog species in the future. Recommendations for Resource Managers: Involvement of people in recreation like traveling, their location of residency, and their age are critical factors in the government's policy formulation.The government should promote endangered frog species conservation in the Northern part of Taiwan, towns near the conservation areas, and younger and middle‐aged populations for the immediate implementation of rehabilitation.An ecological compensation scheme shall be implemented to mitigate the impact of development on ecosystems and provide aid to those residing near conservation areas.The research focused only on Taiwan's three most representative endangered frog species. Future research direction can also focus on the ecology and quantity of the remaining endangered species subject to the study on willingness‐to‐pay the conservation value. [ABSTRACT FROM AUTHOR]

This article aims to evaluate the financial health of companies operating in the automotive industry in the Slovak Republic and to predict impending bankruptcy using a selected mathematical-statistical method. The research sample consisted of 351 companies from the automotive industry. The basis for the creation of the prediction model is the financial indicators from the previous accounting period. The most significant indicators were selected using LASSO regression. The mathematical-statistical logit method was used to create the model. The success rate of the resulting logit model is assumed to be 78% in predicting the possible bankruptcy of companies in the automotive industry. [ABSTRACT FROM AUTHOR]

This article examined the level of financial literacy among university students in Ghana (N = 3932), and investigated which factors are linked with the different financial literacy components. This article is motivated by the increased drive toward financial inclusion and the critical role of financial knowledge in an increasingly sophisticated financial world. The framework used, assesses students' knowledge in money management, savings and borrowing, investments, and insurance. The research findings show moderate knowledge in savings and borrowing but low level of knowledge in other finance issues among university students. Notably, the findings reveal that gender, work experience, subject of study, and mother's level of education are all important determinants of financial knowledge among the students. The findings further show that students with relatively high financial knowledge are more likely to have sound judgment on financial issues; make the right financial decisions and have sound personal financial management practices. Based on the findings of the study, a national policy on financial literacy and educational programs are urgently needed as intervention to improve the level of financial knowledge of university students. [ABSTRACT FROM AUTHOR]

The MLE of the ATE in the logit model for binary outcomes may have a significant second-order bias if the event has a low probability, which is the case we focus on in this article. We derive the second-order bias of the logit ATE estimator, and we propose a bias-corrected estimator of the ATE. We also propose a variation on the logit model with parameters that are elasticities. Finally, we propose a computational trick that avoids numerical instability in the case of estimation for rare events. [ABSTRACT FROM AUTHOR]

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

This research examines the relationship between objective and perceived relative incomes and health outcomes in Turkey, drawing data from the Life in Transition Survey (LITS) III. The analysis is stratified by gender, employment status, marital status, and educational attainment. Objective income consistently correlates positively with health, with the relationship being particularly strong for employed males. On the other hand, perceived relative income significantly influences health, especially among female subgroups and the unemployed. Disparities in health outcomes based on marital status, age, and education emerge: married males, individuals below 35, and those with below-median education display pronounced associations between objective income and health. The findings emphasize the multifaceted relationships between objective income, perceived economic status, and health, highlighting the need for tailored interventions for health across distinct demographic segments in transitional settings like Turkey. [ABSTRACT FROM AUTHOR]

Financial education plays a significant role in poverty reduction and sustainable economic growth. It also increases individuals' financial well-being. Unfortunately, global financial literacy is low, and in developing countries, it is far below the global average. Nepal is a developing country with low financial literacy. Therefore, this study aimed to analyze the impact of financial literacy training on financial literacy levels and financial decisions among 399 randomly selected rural households in Bhojpur District, a rural area in Nepal. Multiple linear and logit regression models were used to analyze the data. The results revealed that financial literacy training increased the financial literacy level of training participants compared with that of non-participants. Education, financial literacy training, and migration of family members (personal migration history and plans to migrate) were other significant predictors. Respondents' financial and economic behavior was influenced by their financial literacy level, sex, age, education, family size, whether the respondent lives with parental family, and whether the respondent has migrated family members. The study suggested well-organized and inclusive financial literacy training program interventions to improve rural individuals' financial and economic decisions. [ABSTRACT FROM AUTHOR]

Safe standard-oriented shrimp farming is inevitable as the export market accounts for a large proportion and Vietnam has been participating in many international free trade agreements. The study conducted face-to-face interviews with 521 conventional shrimp farmers and 104 safe standard-oriented shrimp farmers in the Mekong Delta. The results from the financial performance comparison suggest that safe standard-oriented shrimp model had higher profit than the traditional shrimp farming. Most of the farmers shifted to safe, standard-oriented shrimp farming due to the expectation of higher profits, few negative effects on the environment, and high-quality harvest. The factors affecting the decision to change the farming practices are: having a flocculation pond, participattion in training, credit access, concern about market risks, usefulness, and ease of use. The research proposed a number of handles to promote the transformation of farming practices.

AbstractResource use efficiency and socioeconomic factors influencing the adoption of vegetables for cultivation were analyzed using cross-sectional data from randomly selected 120 vegetable producers in the Udyapur District of eastern Nepal. A field survey was conducted between October and November 2019. Five major vegetables of the district based on the area of cultivation, namely potato, cauliflower, radish, cabbage, and tomato, were taken for the study. The benefit-cost ratio was highest for cauliflower (2.46), followed by tomato (2.20), radish (2.01), cabbage (1.70), and potato (1.36). The Cobb-Douglas production function revealed that the cost of human labor and the cost of manure and fertilizer had a significant positive impact on income from vegetables, whereas the cost of seed had a significant negative impact on income from vegetables. The resource use efficiency analysis shows that the costs incurred for human labor, tillage, seed, irrigation, and packaging/marketing were overused. In contrast, the costs of manure and fertilizer are underused. The Logit model was used to study various factors affecting the adoption of vegetables for cultivation in the study area, which revealed the relationship and extent of the impact of various socio-economic factors on the adoption of the five different vegetables.

AbstractIn most developing countries, village poultry are raised in a scavenging system which gives the impression that all the birds in the village belong to the same flock. Therefore, actions targeting the entire community (Community-Based Management, CBM) could better contribute to improving the productivity of village poultry, notably through the adoption of technologies similar to those used in intensive poultry. The objective of this paper was to assess the effect of CBM, as well as the socioeconomic and institutional factors, on the adoption of village poultry improvement technologies. This was addressed using a logit model and data collected on 405 village poultry-keepers in Benin. Results indicate that the adoption of village poultry technologies depends on the availability of village poultry vaccinators (VPVs) and breeders’ experience (education level and participation in CBM). Farmers from experimental villages are also more willing to adopt various technologies. This indicates that when farmers have the information and technical support through an approach based on the community, i.e. CBM, they can change their behavior, overcome traditional ways of poultry farming. Furthermore, the adoption of the technologies is successful when the strategies used are based on the involvement of private veterinarians for the supply of VPVs.

This study evaluated the intersectionality thesis, which suggests that the interaction of ethnicity, sex, and socioeconomic status significantly affects achievement in UK higher education. The study used a sample of 135,699 students from the Graduate Outcomes survey for the 2018/19 cohort, the last to leave higher education before the COVID-19 pandemic. Conditioning on a rich set of background characteristics, results from a series of regressions indicated that some interactions are statistically significant predictors of obtaining a good degree. However, the estimated size of these interaction effects, as estimated using marginal effects, appeared to be negligible. Robustness checks included consideration of selection on observables using Inverse Probability Weighted Regression Adjustment (IPWRA), which confirmed the main results’ robustness. Overall, the findings suggest that the data do not support the intersectionality thesis and that markers of disadvantage, when combined, do not have an effect on educational outcomes that is over and above that of the single factors in isolation. These results could have implications for recent policies introduced in England to close ethnic achievement gaps in higher education by 2038. However, the findings should be treated with some caution due to the ethnic categories used in the analysis and the potential bias from omitted and unobserved variables that may lead to an underestimation of ethnic penalties. [ABSTRACT FROM AUTHOR]

Copyright of Mediterranean Journal of Gender & Women's Studies (KTC) / Akdeniz Kadın Çalışmaları ve Toplumsal Cinsiyet Dergisi is the property of Mediterranean Journal of Gender & Women's Studies (KTC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the possible effects of social factors and attitudes towards risk, including financial specialists, media, politicians, religious/spiritual opinion leaders, and business circles, on individuals' preferences for selected traditional, risky, and innovative financial assets. The concrete outputs of the study obtained with the help of the logit model are based on the micro data set of the 2019 Household Financial Perception and Attitude Survey published by the Finance Office of the Presidency of Türkiye. The findings confirm that increases in individuals' attitudes towards risk will increase the probability of choosing risky and innovative assets and diversified portfolio compositions with these assets. On the other hand, individuals' financial asset choices are significantly influenced by social/environmental factors. More strikingly, it should be emphasized that among these factors, the positive effects of financial professionals are lower than those of the business environment, religious/spiritual opinion leaders, and the media. It is essential to carefully monitor the effects of media and religious/opinion leaders, especially when shifting to riskier assets and their combinations in terms of financial assets, in order to formulate policy recommendations. [ABSTRACT FROM AUTHOR]

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

The COVID-19 pandemic has had a massive cascading effect on the entire tourism and hospitality sector, acting as a demand shock, affecting not only customary travellers but also wiping out any transient demand. The upside of these difficult circumstances is that they can be used to test the sector’s resilience. In this context, this paper analyses the deleveraging risk that industry players in India face by employing a qualitative response model, ‘Logit’. The study concludes that the deleveraging risk that sector players face depends upon the amount of debt and leverage ratios, both during the pre-and post-pandemic period. However, the influence of other financial indicators on deleveraging has been different in terms of its intensity and bi-directional impact. Moreover, during COVID-19 deleveraging tendencies were noticed only in 204 firms, compared to 242 firms before COVID-19, discrediting the forced deleveraging as predicted in the literature. [ABSTRACT FROM AUTHOR]

Abstract One of the stated goals of the common agricultural policy reforms has been to provide a fairer distribution of payments across and within member states, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of direct payments. This research aims to investigate the underlying structural factors that contribute to this inequity in Portugal, with a particular focus on farming systems. A logit model was developed using agricultural census data at the commune level, with the percentage of farmers receiving direct payments as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to direct payments between 2009 and 2019. In traditional Mediterranean farming systems, access to direct payments has been restricted to a smaller proportion of farmers. Nevertheless, it is evident that a certain degree of redistribution took place during the previous programming cycle of the common agricultural policy. This redistribution included a shift in support from larger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other farming systems, particularly vineyards.

In the literature several methods have been developed to model ordinal data while considering their natural ordering. However, this study sought to compare two possible link functions for the multilevel ordinal regression using males’ ratings of the police forces in Uganda as an outcome variable. Variables were obtained from the UNGBS database (Uganda National Governance Baseline Survey). The highest proportion of males rated the police as good (40.9%) followed by fair (24.96%), poor (19.1%), and lastly very good (15.1%). The multilevel ordered logistic regression model with both individual and contextual variables had the lowest AIC compared to other models, fitting the data best. All the likelihood ratio test results indicated that there was significant variation in males' ratings of the police forces across districts. Hence, males from the same district were significantly more similar compared to males from another districts. Researchers using data collected by applying multi-stage sampling or any form of nesting should consider multilevel or mixed-effects models.

Objetivo: Identificar os fatores determinantes da Livre Admissão de associados em cooperativas de crédito brasileiras entre 2016 e 2020. Metodologia: Utilizou-se um modelo de regressão logística para estimar tais fatores. A análise considerou tanto variáveis internas específicas das cooperativas (idade, tamanho do ativo, número de cooperados e filiação a sistemas) quanto variáveis que representam o ambiente macroeconômico no qual essas instituições estão inseridas (índice de desenvolvimento municipal, número de habitantes nos municípios que possuem postos de atendimento (PACs) e sede das cooperativas). Resultados: Os principais resultados apontam que quanto menor o número de habitantes dos municípios sede e que contém PACs, maior a probabilidade de ocorrência de Livre Admissão. Além disso, cooperativas com maior número de cooperados e ativos também têm maior probabilidade de adotar a Livre Admissão. Contribuições do Estudo: O estudo destaca a predominância de cooperativas com Livre Admissão em municípios menores e menos desenvolvidos, reforçando o papel dessas cooperativas na inclusão social, redução de desigualdades e oferta de serviços financeiros e de crédito para a população dessas localidades. Além disso, os resultados fornecem subsídios valiosos para formuladores de políticas, instituições financeiras e cooperativas interessadas em promover o desenvolvimento socioeconômico e a equidade em comunidades menos atendidas pelo sistema financeiro.

Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit.

One of the stated goals of the common agricultural policy reforms has been to provide a fairer distribution of payments across and within member states, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of direct payments. This research aims to investigate the underlying structural factors that contribute to this inequity in Portugal, with a particular focus on farming systems. A logit model was developed using agricultural census data at the commune level, with the percentage of farmers receiving direct payments as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to direct payments between 2009 and 2019. In traditional Mediterranean farming systems, access to direct payments has been restricted to a smaller proportion of farmers. Nevertheless, it is evident that a certain degree of redistribution took place during the previous programming cycle of the common agricultural policy. This redistribution included a shift in support from larger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other farming systems, particularly vineyards. [ABSTRACT FROM AUTHOR]

Objective of the study: This study aims to identify and analyze the drivers of innovation in Ecuador. Methodology/Approach: The significance of innovation drivers was investigated through an exploratory systematic review using a mapping innovation approach. Statistical methods were used to apply an empirical analysis based on the Ecuadorian survey on Science, Technology, and Innovation Activities (released 2017). Originality/Relevance: As far as we are aware, this is the first study to investigate the drivers of innovation from a comprehensive approach of mapping innovation in Ecuador. Main results: The management of innovation drivers in Ecuador often presents issues for 98% of non-innovative companies. The most relevant innovation driver in Ecuadorian companies is cooperation with external sources where partner geolocations worldwide play a significant role. Theoretical/ Methodological Contribution: This study extends the framework of innovation drivers. The strengths and limitations of Ecuadorian innovation were identified from four perspectives of innovation drivers: internal innovation drivers, external innovation drivers, knowledge for innovation development, and investment in innovation. Social/Management Contribution: This study contributes to the understanding of innovation drivers in developing countries. [ABSTRACT FROM AUTHOR]

Objective: A review of the factors that determine the consumption of bovine milk in the municipality of Texcoco, State of Mexico. Methodology: Two statistical models of logistic regression (Logit) and probabilistic regression (Probit) were used, which were fed with information from a survey of 400 questionnaires applied to consumers. Results: According to the models carried out, it was found that the most significant variables that explain the consumption of bovine milk are the gender, scholarship, and family income of the consumers. Limitations: Low availability of people to answer the questionnaire. Conclusion: It is recommended that there be more information on the health benefits and harms of bovine milk consumption for better consumption decisions of this product. [ABSTRACT FROM AUTHOR]

Purpose: To identify the determining factors of Open Membership in Brazilian credit unions between 2016 and 2020. Methodology: A logistic regression model was used to estimate these factors. The analysis considered both specific internal variables of the cooperatives (age, asset size, number of members, and affiliation to systems) and variables representing the macroeconomic environment in which these institutions operate (municipal development index, population size in municipalities with service points (PACs) and cooperative headquarters). Results: The main results indicate that a lower population size in the municipalities containing the cooperative headquarters and PACs increases the probability of Open Membership. Additionally, cooperatives with a larger number of members and assets also have a higher likelihood of adopting Open Membership. Contributions of the Study: The study highlights the predominance of cooperatives with Open Membership in smaller and less developed municipalities, reinforcing the role of these cooperatives in social inclusion, reduction of inequalities, and provision of financial services and credit to the population in these locations. Moreover, the results provide valuable insights for policymakers, financial institutions, and cooperatives interested in promoting socioeconomic development and equity in underserved communities by the financial system. [ABSTRACT FROM AUTHOR]

Purpose: To identify the determining factors of Open Membership in Brazilian credit unions between 2016 and 2020. Methodology: A logistic regression model was used to estimate these factors. The analysis considered both specific internal variables of the cooperatives (age, asset size, number of members, and affiliation to systems) and variables representing the macroeconomic environment in which these institutions operate (municipal development index, population size in municipalities with service points (PACs) and cooperative headquarters). Results: The main results indicate that a lower population size in the municipalities containing the cooperative headquarters and PACs increases the probability of Open Membership. Additionally, cooperatives with a larger number of members and assets also have a higher likelihood of adopting Open Membership. Contributions of the Study: The study highlights the predominance of cooperatives with Open Membership in smaller and less developed municipalities, reinforcing the role of these cooperatives in social inclusion, reduction of inequalities, and provision of financial services and credit to the population in these locations. Moreover, the results provide valuable insights for policymakers, financial institutions, and cooperatives interested in promoting socioeconomic development and equity in underserved communities by the financial system. [ABSTRACT FROM AUTHOR]

Is the conflict in Côte d'Ivoire explained by greed or grievance? This study aimed to analyze the most important causes of the rebellion in Côte d'Ivoire. Specifically, it examines the effects of ethnicity on participation in a rebellion and checks the link between education, health and participation in a rebellion. To achieve this goal, a two- step approach is adopted. The first step was the establishment of the basics of an econometric model developed by Ballo and Rocco (2008) to analyze the causes of the civil war in Côte d'Ivoire. The second step was the description of data of 2.214 individuals used in the study, in order to test the hypotheses. By applying logit model, we obtained many significant results. We find that: educated people are less likely to join a rebellion; People from the North of Ivory Coast have more chance to join the rebellion. [ABSTRACT FROM AUTHOR]

Film tourism is one of the fastest developing sub‐segments of creative tourism in the 21st century. Many studies investigated tourists' motivation and behavior towards film tourism in the context of psychological and emotional motives and their involvement and experience. However, we still know very little about the structure of visitors of film sites. Utilizing primary data collected in Slovakia and adopting the econometric approach, the aim of this empirical study is to estimate the relationship between preferred trip parameters and the decision to include a visit to a film location in the travel plan. The results suggest that visits to film sites occur to a significant extent by chance, film tourism is additional, mainly to other segments of tourism, characterized as adventurous and explorative experiences. Visits to film locations occur mainly as trips by younger age groups and longer stays, mostly as self‐organized trips, regardless of budget, frequency of travel, or gender. [ABSTRACT FROM AUTHOR]

This study focuses on testing the efficiency of alternative bankruptcy prediction models (Altman, Ohlson, Zmijewski) and on assessing the possible reasons that led to the confirmation or not of the prevailing model. Data from financial statements of listed (Greek) construction companies before the economic crisis were utilized. The results showed that Altman's main predictive model as well as the revised models have low overall predictability for all three years before bankruptcy. [ABSTRACT FROM AUTHOR]

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In this paper we address the impact of the introduction of the National Greenhouse and Energy Reporting scheme on corporate carbon reporting, and subsequently identify factors that influence the level of voluntary carbon reporting. A review of the literature demonstrates a large number of potential factors have been previously deployed to explain voluntary reporting practices; however, the analytical and empirical methods widely used in the literature have limiting statistical assumptions and confine analysis to a small number of explanatory factors. To address this limitation in prior research we apply advanced machine learning methods, such as gradient boosting machines and random forests, to identify predictive variables through analytical means. We compare the performance of machine learning methods with traditional methods such as logistic regression. We find that machine learning methods significantly outperform logistic regression and provide fundamentally different interpretations of the role and influence of different predictive variables on voluntary carbon reporting. While most variables were not statistically significant in the logit results, a number of key proxies for financial performance, corporate governance, and corporate social responsibility have out‐of‐sample predictive power of the level of voluntary carbon reporting in the machine learning analysis. [ABSTRACT FROM AUTHOR]

Esse trabalho tem como objetivo analisar os efeitos iniciais da COVID-19 sobre a incidência de pobreza e extrema pobreza no Brasil, considerando os segundo e terceiro trimestres de 2018-2020. Para mensurar os níveis de pobreza e extrema pobreza da população, foram utilizados os índices de Foster-Greer-Thorbecke (FGT) para o país e suas macrorregiões, além da estimação de um modelo Logit para avaliar o efeito da pandemia sobre a incidência da pobreza no país a partir dos dados da Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC). Como principal resultado, verificou-se que, na média, houve redução da proporção de pobres e extremamente pobres, o que pode ser atrelado à política do Auxílio Emergencial, implementada pelo governo federal brasileiro, objetivando amenizar a diminuição do nível de renda proveniente das medidas de distanciamento social. No entanto, para mulheres e mais especificamente mulheres negras e indígenas, houve elevação do nível de vulnerabilidade.

Introduction: Ficus pumila and Phtirusa stelis are emerging as a source of natural products. Objectives: Identify the families of chemical compounds present in the species to be studied. Determine the antibacterial and antifungal activity of the crude extracts of F. pumila and P. stelis. Assess the toxicity of the crude extracts of the species against Artemia sp. Determine effects on Aedes aegypti larvae and. Methods: We evaluated the bioactivity of ethanolic, and isopropyl extracts obtained from the fruits, leaves and stems of both species were evaluated for antibacterial activity, antifungal activity, toxic activity against Artemia sp, A. aegypti larvae and phytochemical study. Results: We identificated flavonoids, tannins, and polyphenols in isopropyl and ethanol extracts of leaves, stems and fruits of F. pumila. We detected flavonoids, tannins, anthraquinones, triterpenes, and polyphenols in isopropyl and ethanol extracts of stems and leaves of P. stelis. We detected flavonoids, tannins, anthraquinones, triterpenes, and polyphenols in isopropyl and ethanol extracts of stems and leaves of P. stelis. F. pumila, the isopropyl stem extract demonstrated the most effective activity, since it exerted antibacterial activity, with inhibition halos being observed on B. subtilis (10 mm) and E. coli (7 mm), while the ethanolic extract of the leaves showed effect on Staphylococcus aureus (11 mm). The ethanolic extract of P. stelis leaves exhibited activity against S. aureus (10 mm), B. subtilis (10 mm) and that of stem extract showed similar results with inhibition zone of halos of 11 and 10 mm, respectively. The isopropyl extract of leaves also displayed activity against E. coli (15 mm). None of the extracts from F. pumila and P. stelis, showed any growth inhibitory effect against the pathogenic fungi. The majority of P. stelis extracts showed positive LC results against Artemia sp., with the ethanolic leaf extract exhibiting high toxicity (0,01 µg/mL). The isopropyl leaf extract also showed high toxicity (0,01 µg/mL), as did the isopropyl stem extract (0,02 µg/mL). The ethanolic and isopropyl extracts of F. pumila and P. stelis did not show toxicity to Aedes aegypti larvae. Conclusion: The plant species F. pumila and P. stelis, can be considered as sources of important and promising compounds for therapeutic research. These plants have shown potential in various studies, and further research could lead to the discovery of new treatments and therapies.

The COVID-19 pandemic has had a massive cascading effect on the entire tourism and hospitality sector, acting as a demand shock, affecting not only customary travellers but also wiping out any transient demand. The upside of these difficult circumstances is that they can be used to test the sector’s resilience. In this context, this paper analyses the deleveraging risk that industry players in India face by employing a qualitative response model, ‘Logit’. The study concludes that the deleveraging risk that sector players face depends upon the amount of debt and leverage ratios, both during the pre-and post-pandemic period. However, the influence of other financial indicators on deleveraging has been different in terms of its intensity and bi-directional impact. Moreover, during COVID-19 deleveraging tendencies were noticed only in 204 firms, compared to 242 firms before COVID-19, discrediting the forced deleveraging as predicted in the literature.

Background: The National Health Insurance or Jaminan Kesehatan Nasional (JKN) program is one of the steps taken by the Indonesian government in developing Universal Health Coverage (UHC). However, increased participation in the National Health Insurance is not followed by an increase in met needs for healthcare services. Aims: This study aimed to examine the effect of participation in the National Health Insurance on unmet needs for healthcare services in poor and non-poor population groups. Methods: This study used data from the 2018 National Socio-Economic Survey/ Survei Sosial Ekonomi Nasional (SUSENAS) and Village Potential Survey/ Survei Potensi Desa (PODES). Data were processed using binary logistic model analysis to identify the effect of participation in the National Health Insurance on the unmet needs for healthcare services. Results: Participation in the National Health Insurance, including beneficiary program for poor groups could reduce the risks of unmet needs for healthcare services by 7.7%, while non-beneficiary program could reduce the risks of unmet needs for health services for non-poor groups by 10.4%. Conclusion: Both beneficiaries and non-beneficiaries affect the unmet needs for health services for both poor and non-poor groups. However, the non-beneficiary program is more elastic than beneficiary program to fulfill needs for both sample groups. Keywords: Healthcare services, national health insurance, logit, unmet need

Hidabu Abote, Dera, Wera Jarso and Debra Libanos districts of North Shewa zone are potential in honey production. To enhance this potential, different organizations disseminate improved beehives technologies for the smallholder farmers. However, the impact of the disseminated technologies on household income has not been evaluated. Thus, this study aimed to evaluate the impact of improved beehive adoption on household income. Purposive and two stage sampling technique was used to select 384 sampled households. The study used logistic regression model to identify the determinants of adoption decision of modern beehive technology while propensity score matching to evaluate the impact of modern beehive technology adoption on household income. The result of logistic regression model shows that age of household head, family size, households experience in beekeeping, frequency of extension contact, access to credit services, access to training and access to beehive demonstration site visit had positive and significant effect on household adoption decision of modern beehive technology. The result of propensity score matching indicates that the adopters of improved beehive technology were earned Birr 2690.383 than non-adopter. The difference in household income between the two groups shows that there is considerable room for improvement of household income through increasing the number of adopter of improved beehives technology in the study area. This should be done through provision of training, credit, extension and expansion of beehive demonstration site among the others.