Does Land Transfer Improve Farmers’ Quality of Life? Evidence from Rural China

The topic of quality of life has long been a focus of global research and the public. The land transfer policy implemented by the Chinese government affects farmers’ quality of life (FQOL); however, the extent of this effect remains unclear. As land transfer may be a self-selection behaviour, it may be subject to selection bias such that traditional measurement methods are unable to effectively estimate its quantitative impact. This study used data from a questionnaire given to 5668 rural households in 25 provinces of mainland China. It sought to quantify the impact of land transfer on FQOL by using endogenous switching regression (ESR) models to correct selection bias. The results show: (1) for farmers who choose to transfer land, if they choose not to transfer land, FQOL may decrease by 64.11%; (2) for farmers who choose not to transfer their land, if they go on to choose to transfer their land, FQOL may increase by 0.75%; (3) land transfer can improve the quality of life of the older generation of farmers but will reduce the quality of life of the newer generation. The results of this study provide research support for China and other countries seeking to effectively implement land policies and improve the FQOL, helping to provide practical strategies for the sustainable development of rural areas.

An Exponential Endogenous Switching Regression with Correlated Random Coefficients

This paper presents a method for estimating the average treatment effects (ATE) of an exponential endogenous switching model where the coefficients of covariates in the structural equation are random and correlated with the binary treatment variable. The estimating equations are derived under some mild identifying assumptions. We find that the ATE is identified, although each coefficient in the structural model may not be. Tests assessing the endogeneity of treatment and for model selection are provided. Monte Carlo simulations show that, in large samples, the proposed estimator has a smaller bias and a larger variance than the methods that do not take the random coefficients into account. This is applied to health insurance data of Oregon.

Distance Traveled in Times of Pandemic: An Endogenous Switching Regression Approach

This paper studies the change in the distance traveled by domestic tourists considering the pre- and post-pandemic outbreak summer periods of 2019 and 2020. Using representative monthly microdata involving more than 31,000 trips conducted by Spanish residents, we examine the heterogeneity in behavioral adaptation to COVID-19 based on sociodemographic and trip-related characteristics. To account for selection effects and the potential change in the population composition of travelers between the two periods, we estimate an endogenous switching regression that conducts separate regressions for the pre- and post-pandemic periods in a unified econometric framework. Our results point to heterogeneous shifts in the distance traveled by domestic travelers after COVID-19 outbreak per sociodemographic group, with notable differences by travel purpose and lower relevance of traditional determinants like income.

A Transmission of Beta Herding during Subprime Crisis in Taiwan’s Market: DCC-MIDAS Approach

The aim of this study is to investigate the herding of beta transmission between return and volatility. We have used the dynamic conditional correlation model with the mixed-data sampling (DCC-MIDAS) model for the analysis. The evidence demonstrates that herding is a key transmitter in Taiwan’s stock market. The significant estimation of DCC-MIDAS explains that the herding phenomenon is highly dynamic and time-varying in herding behavior. By means of time-varying beta of herding based on our rolling forecasting method and robustness check of the Markov-switching regression approach using four types of portfolios, the evidence indicates that there are conditional correlations between betas and herding. In addition, it also reveals that herding forms in Taiwan’s markets during the subprime crisis period.

The Analysis of Groundwater Quality and the Impact of Remedial Measures Adopted by the Wheat Growers: Using Endogenous Switching Regression Model Approach

Groundwater pollution is a serious problem, posing severe problems on many economic activities. The study's main objectives were to access the groundwater quality in the study area and analyze the role of farmers in improving the groundwater quality. Total 108 groundwater samples were collected from different locations along the 11-L distributary located in District Sahiwal, Punjab-Pakistan. Samples were tested to analyze the quality of groundwater for agriculture and livestock. The parameters included pH, Ec, and TDS, were tested. Results showed that 14 samples were found to be fit, 23 were marginally fit and 71 were declared unfit for agricultural consumption. The results of CCME water quality index were also in favour of lab reports. Most wheat-growing farmers were using gypsum as a remedial measure to minimize the side effects of poor groundwater quality. Few farmers were using farmyard manure to improve groundwater quality. There are many factors that influence the adoption of remedial measures to compensate for the poor groundwater. Farmers were facing a few limitations that compelled them to avoid incurring any further costs in order to improve groundwater quality. The financial constraint was the main issue. The endogenous switching regression model was used for data analysis. The findings revealed that family workers, experience, education, and soil quality positively impact remedial measures adoption. The study recommended that proper groundwater quality monitoring is required on a regular basis. Farmers should be educated regarding the proper use of gypsum. The sewerage system was absent in many villages of the study area. To avoid the further leaching of hazardous materials into groundwater, it is critical to construct an effective waste management system.

The impact of geographical indications on farm incomes in the EU olives and wine sector

PurposeGeographical indications (GIs) are expected to stimulate rural development by increasing the viability and resilience of farms in disadvantaged and remote areas. However, little quantitative evidence exists to support this expectation. This study fills this knowledge gap by quantitatively analyzing the effect of GI adoption on farm incomes in the EU olives and wine sectors.Design/methodology/approachThe analysis uses data from the Farm Accountancy Data Network and EUROSTAT and an endogenous switching regression model to analyze the impact of GI adoption on farm incomes for specialized quality wine and olives producers in the year 2014.FindingsThe results show that GI adoption significantly improves farm incomes in both the olives and the wine sector.Research limitations/implicationsThe research uses data from the farm accountancy data network (FADN). This is seen as a limitation of the analysis. The research raises some concerns about the appropriateness of FADN for the assessment of farmers' involvement in food quality schemes and a reconsideration of FADN as a tool for farm performance analysis is advised.Originality/valueThis is one of few quantitative studies of the impact of geographical indications on farm performance. Furthermore, it gives insights into the mechanisms by which GI can affect farm incomes.

Adaptation Implications of Climate-Smart Agriculture in Rural Pakistan

In this paper, we analyze the drivers of the adoption of climate-smart agricultural (CSA) practices and the impact of their adoption on farm net returns and exposure to risks. We use recent farm-level data from three agroecological zones of Pakistan to estimate a multinomial endogenous switching regression for different CSA practices used to reduce the adverse impact of climate change. These strategies include changing input mix, changing cropping calendar, diversifying seed variety, and soil and water conservation measures. The empirical results show that the adoption of different CSA practices is influenced by average rainfall, previous experience of climate-related shocks, and access to climate change information. The findings further reveal that adoption of CSA practices positively and significantly improves farm net returns and reduces farmers’ exposure to downside risks and crop failure. The results also reveal significant differences in the impacts of CSA practice adoption on farm net returns in different agroecological zones. Thus, policies aimed at achieving sustainability in agricultural production should consider agroecological, specific, climate-smart solutions.

Is change worth it? The effects of adopting modern agricultural inputs on household welfare in Rwanda

This study investigates the driving factors that influence farmers’ decisions to adopt modern agricultural inputs (MAI) and how this affects farm household welfare in rural Rwanda. To account for heterogeneity in the MAI adoption decision and unobservable farm and household attributes, we estimate an endogenous switching regression (ESR) model. The findings reveal that size of land endowment, access to farm credit and awareness of farm advisory services are the main driving forces behind MAI adoption. The analysis further shows that MAI adoption increases household farm income, farm yield and equivalised consumption per capita. This implies that adopting MAI is the most consistent and potentially best pathway to reduce poverty among rural farmers. The study hence suggests that policymakers should align the effective dissemination of MAI information and farm advisory services, strengthen farm credit systems and improve market access – most crucially at affordable prices – among small-farmers throughout Rwanda.

Dynamics of Coffee Certifications in Producer Countries: Re-Examining the Tanzanian Status, Challenges and Impacts on Livelihoods and Environmental Conservation

Certification is increasingly becoming necessary for accessing coffee export markets and practicing environmental conservation, especially at this time when many of the farmers in developing countries strive to achieve agricultural transformation. Using data from 400 randomly selected coffee farmers in Tanzania, the study determined the status, constraints, key drivers, and impact of coffee certifications. Descriptive statistics and the endogenous switching regression (ESR) model were used for data analysis. Results indicated that the level of coffee certification is low, being constrained by unawareness and inaccessibility, the prevalence of coffee diseases, failure in realizing price advantages, and certification not being cost effective. Economies of scale, experience, and participation in collective actions are significant factors affecting coffee farmers’ decision to join certification schemes. Additionally, the study rejects the hypothesis of certification to improve household income. However, certification improved awareness and practices of environmental conservation among coffee farmers. It is thus important to embark on awareness creation and make certification services accessible and cost effective to coffee farmers for increased access to niche export markets. Easing transmission of price premiums to coffee farmers will also increase the supply of sustainably grown coffee, improve coffee farmers’ livelihood, and help in the attainment of environmental sustainability goals within the coffee supply chain.