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This study proposes a Bayesian approach to analyze structural breaks and examines whether structural changes have occurred, at the onset of civil war, with respect to economic development and population during the period from 1945 to 1999.

In the Bayesian logit regression changepoint model, parameters of covariates are allowed to shift individually, regime transitions can move back and forth, and the model is applicable to cross-sectional, time-series data.

Contrary to popular belief that the causal process of civil war changed with the end of the Cold War, the empirical analysis shows that the regression relationships between civil war and economic development, as well as between civil war and population, remain quite stable during the study period.

This is the first to develop a Bayesian logit regression changepoint model and to apply it to studies of economic development and civil war.

This study proposes spatial origin-destination threshold Tobit to address spatial interdependence among bilateral trade flows while accounting for zero trade volumes.

This model is designed to capture multiple forms of spatial autocorrelation embedded in “directional” trade flows. The authors apply this improved model to export flows among 32 Asian countries in 1990.

The empirical results indicate the presence of all three types of spatial dependence: exporter-based, importer-based and exporter-to-importer-based. After further considering multifaceted spatial correlation in bilateral trade flows, the authors find that the effect of conventional trade variables changes in a noticeable way.

This finding implies that the standard gravity model may produce biased estimates if it does not take spatial dependence into account.

This paper attempts to offer an improved model of the standard gravity model by taking spatial dependence into account.

The purpose of this paper is to examine a curvilinear effect of legislative constraints on foreign debt.

A cross-sectional, time-series data analysis of 68 developing countries during the period from 1981 to 1999 was performed.

Foreign borrowing is most likely to increase at both low and high levels of legislative constraints, while it is most likely to decrease at moderate levels.

The paper is a first-cut empirical analysis of a curvilinear relationship between legislative constraints and foreign debt.

This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.

The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.

The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.

The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

This study aims to propose a comprehensive conceptual framework for the characterization of agricultural heritage in desert-prone areas, with a focus on Siwa Oasis in Egypt.

The research utilizes a multidimensional approach, integrating perspectives from sustainability principles, stakeholders and the agricultural heritage frameworks of the United Nations Educational, Scientific and Cultural Organization (UNESCO) and Food and Agricultural Organization (FAO). It involves thematic analysis of qualitative data collected through focus group discussions and interviews with a diverse range of participants, including community members, government officials, nongovernmental organizations (NGOs) and researchers.

The study identifies five key elements of agricultural heritage in Siwa Oasis: cultural, ecological, economic, social and scientific. Cultural elements include traditional farming practices, irrigation systems and architectural features, while ecological elements encompass crop biodiversity, wildlife habitats and traditional landraces. Economically, date palm cultivation, olive production and agritourism activities play crucial roles in livelihood security and local economies. Socially, community cohesion, gender roles and cultural practices shape the social fabric of the oasis, with festivals and rituals fostering a sense of belonging. From a scientific perspective, the integration of traditional knowledge with modern agricultural practices enhances sustainability and resilience.

The study focused solely on Siwa Oasis as a case study, limiting generalizability to other desert-prone areas. Further research could explore additional regions to enhance the understanding of agricultural heritage in diverse contexts. Additionally, the qualitative nature of the study may limit statistical analysis. Future studies could employ mixed-methods approaches for a more comprehensive understanding of agricultural heritage dynamics.

Understanding the multifaceted aspects of agricultural heritage in Siwa Oasis can inform sustainable development initiatives, tourism planning and cultural preservation efforts. Local policymakers and stakeholders can utilize these insights to develop policies that support traditional farming practices, ecotourism and community development. Furthermore, the promotion of agricultural heritage can enhance economic opportunities, food security and environmental sustainability in desert-prone regions.

Recognition of the social elements embedded within Siwa Oasis’s agricultural heritage highlights the importance of community cohesion, gender roles and cultural practices. By acknowledging and preserving these aspects, initiatives can be developed to empower local communities, promote gender equality and preserve cultural identity. Such efforts can enhance social cohesion, strengthen cultural bonds and promote inclusivity in the development agenda of desert-prone areas.

This study contributes a novel conceptual framework that bridges multifunctionality concepts, sustainability principles and stakeholder perspectives to characterize agricultural heritage in desert-prone areas. It offers insights into the complex interplay of cultural, ecological, economic, social and scientific dimensions of agricultural heritage systems.

This work examines the impact of institutional agricultural credit on crop productivity of some major crops such as paddy, cotton, wheat and pulses for small and marginal farmers across various social groups.

The cross-sectional field data on socio economic variables was collected from three Indian states from about 400 small and marginal farmers across various social groups using multi-stage stratified random and purposive sampling through a structured questionnaire by interviewing. The method of propensity score matching (PSM) was employed to calculate average treatment effect (ATE) and average treatment effect on the treated (ATET) by categorising sample farmers as treatment group and control group where crop productivity was considered as outcome variable and access to institutional credit was considered as treatment variable.

The PSM estimates reveal that ATE and ATET for all the selected crops are found to be significantly higher for the treated group vis-à-vis non-treated group suggesting that institutional agricultural credit has a statistically and significant positive impact on the crop productivity.

Similar study can be extended for more crops and across regions in India for a universal coverage.

The agricultural credit policy of India has been to increase the access and availability of institutional farm credit. This has led to in general increase in the flow of formal farm credit to agricultural sector. However, the impact of institutional credit and crop productivity especially for small and marginal farmers across social groups is not well recognized in India using field data. Accordingly, this field data study contributes to the existing research by providing fresh evidence from field across social groups for both kharif and rabi crops using recent survey data from small and marginal farmers which has important policy implications.