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.

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.

Cotton yield and Climate Change Adaptation in Pakistan: Application of Multinomial Endogenous Switching Regression Model

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Cotton Yield and Climate Change Adaptation in Pakistan: Application of Multinomial Endogenous Switching Regression Model

Purpose:Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach:This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings:Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value:For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Does credit utilization lead to increasing farm outcome? a micro-perspective of tea production from Rwanda

Credit is a crucial factor for tea growers to pay for physical farm inputs mainly input fertilizers, research and development of high yielding tea clones and labour in order to improve the production of green tea leaf and to meet factories’ demand for raw materials. However, mismanagement of accessed credits by farmers has been reported among the snags affecting the sector development. The study analyzed the determinants and impact of credit utilization on farm income among smallholder tea growers in Nyaruguru District, Rwanda. Crosssectional tea household level data were collected from 358 farmers randomly selected from tea cooperatives. The credit utilization and causal effect were estimated using the Endogenous Switching Regression model. Results showed a positive and significant relationship between credit utilization and tea farm income. Precisely, the causal effect of credit is a 7% increase in tea income for farmers who utilised credit for tea production, while its potential effect is up to a 55% decrease in tea income for those who divert credit for out-off tea production uses. Furthermore, training on good agricultural practices and credit management, cost of farm inputs, labour and access to group credit significantly influence utilization of credit for tea production. However, the size of credit (cash) and off-farm businesses significantly increase the diversion of credit and level of tea farm income. Tea farmers are encouraged to use tea credits for planned projects. Sensitizing farmers to procure farm input fertilizers in bulk through cooperatives should be vigorously pursued to discourage credit diversion. Key words: tea credits, tea farming households, farm income, endogenous switching regression

Poverty Reduction Effect of New-Type Agricultural Cooperatives: An Empirical Analysis Using Propensity Score Matching and Endogenous Switching Regression Models

Agricultural cooperatives have been found to effectively alleviate poverty in developing countries because of their specific socioeconomic functions that allow poor households to overcome marketing and production constraints. However, cooperative evaluations are inevitably influenced by other poverty alleviation measures and rarely consider the characteristics of specific ethnic groups. Using cross-sectional surveys in Southwest China and employing propensity score matching (PSM) and endogenous switching regression (ESR) models, this paper analyzed the participation of poor households in New-type Agricultural Cooperatives (NACs) in ethnic areas and assessed the income impacts of NAC membership by eliminating unobserved biases and group heterogeneity. This study detected heterogeneous policy perceptions and behavioral differences between the member and nonmember groups, and the PSM and ESR model results indicated that, overall, participation in the NACs had a positive effect on household income. The ESR model was found to be more plausible as it was able to reveal the significant income gaps under a counterfactual inference framework. Local policymakers need to focus on the policy perception and behavioral and earning capability differences between groups and increase the balanced policy implementation.

Credit Constraint and Rural Household Welfare in the Mezam Division of the North-West Region of Cameroon

The purpose of this study is to examine the effect of the rural household’s head and household characteristics on credit accessibility. This study also seeks to investigate how credit constraint affects rural household welfare in the Mezam division of the North-West region of Cameroon. Using data from a household survey questionnaire, we found that 36.88% of the households were credit-constrained, while 63.13% were unconstrained. A probit regression model was used to examine the determinants of households’ credit access, while an endogenous switching regression model was used to analyze the impact of credit constraint on household welfare. The results from the probit regression model indicate the importance of the farmer’s or trader’s organization membership, occupation, and savings to the household’s likelihood of being credit-constrained. On the other hand, a prediction from the endogenous switching regression model confirms that households with access to credit have a better standard of welfare than a constrained household. From the results, it is necessary for the government to subsidize microfinance institutions, so that they can take on the risk of offering credit to rural households.

Does it pay to switch from free grazing to stall feeding? Impact of stall feeding practice on household welfare in Tigrai Ethiopia

In this paper, efforts were made to the impact of full and seasonal stall feeding technology on households’ economic, ecological, and social welfare outcome indicators in rural Northern Ethiopia using data obtained from the survey of 518 rural farmers. In order to address our primary objective, an endogenous switching regression model was applied. The overall result indicated that SF adoption ensures significant gains in terms of the specified outcome indicators. Using endogenous switching regression models, we estimated different outcome indicators for both adopters from adoption (ATT), and non-adopters had they adopted (ATU). It is identified that there would be a decline of 21% in milk production and productivity if adopters would not have adopted this technology while non-adopters are estimated to increase their milk production and productivity by 100 and 48% if they would adopt this technology. The results further show that SF adoption had a significant increment in the lactation period. An increase of consumption expenditure by 17% from FSF and 44% in the case of SSF could be considered significant on livelihoods for smallholder farmers. On average, adoption of SF increased manure use in the range of 258–294 kg for adopters. The results showed that SF has decreased the propensity of hiring labor by about 29% and purchase of animal feed by 31%. We have found that participation in SF, on average, decreased total cattle stock by 1 TLU but increased the probability of keeping milking cow by 23%. The adoption of SF increased the likelihood of participating in an animal sale market by 29% for adopters and by 47% for non-adopters had they decided to adopt. The adoption of SF leads to a gain in a number of plants of 11 trees and 29 m of physical construction for the typical adopter and 36 trees and 133 m if the typical non-adopter were to adopt the SF technology on their plots. The adoption process also increased the propensity of growing trees by 19% and decreased household animal shock experience by a probability of 19% for adopters and about 15%.