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.

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 Effect of Climate Change Adaption strategies on Household Welfare in Rural Ethiopia: A Panel Endogenous Switching Estimation

Climate change has had a serious effect on farm output, especially in Sub-Saharan Africa. To reduce the effect of climate change farmers require adopting mitigation strategies. Systematic use of crop rotation and improved seed is a pathway to increase farm income, but adoption of these strategies remains low. We investigate the effect of climate change on farm households’ welfare in terms of farm income and explore whether the adoption of crop rotation and improved seed varieties is a strategy to increase household income. In this paper, we use household-plot level data which was collected by World Bank and Central Statistical Agency (CSA) from 2011/12 to 2015/16 from rural farm households in Ethiopia. Using the three-wave panel data, the authors estimate the maximum likelihood endogenous switching regression model (MLESM) to measure the effect of climate change adaption strategies on the farm household’s welfare. The results suggest that the adoption of crop rotation and improved seed varieties helps to improve the well-being of farm households and build a resilient livelihood in rural Ethiopia. Furthermore, beyond average, we find that the effectiveness of crop rotation and improved seed is varied across farm households. Thus, policymakers should consider this heterogeneity and the adoption history of farmers when they aim to improve the practice of crop rotation and improved seed for the particular poor farmers to increase the effectiveness of the strategies.

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