CROP AND LIVESTOCK INSURANCE IN RURAL KARNATAKA: A COMPARATIVE ANALYTICAL FRAMEWORK FOR COVERAGE, CLAIM SETTLEMENT AND FARMER SATISFACTION

Abstract

Agricultural households in rural Karnataka face production and asset-related risks arising from climatic variability, crop loss, livestock mortality and fluctuations in farm income. Crop and livestock insurance address these risks through different forms of financial protection, yet the two forms of insurance are less frequently examined within a common analytical framework. This paper develops a comparative analytical framework for examining crop and livestock insurance in rural Karnataka, with particular emphasis on coverage characteristics, claim settlement and farmer satisfaction. The analytical demonstration uses a simulated dataset of 400 observations, comprising 200 crop-insurance users and 200 livestock-insurance users, and applies descriptive statistics, claim-settlement indicators, Cronbach’s alpha, an independent-samples t-test, Pearson correlation and multiple linear regression. Farmer satisfaction is represented through a ten-item five-point Likert scale, while claim settlement experience, claim transparency, service accessibility, premium affordability and selected socio-economic characteristics are treated as explanatory variables. The illustrative results show a claim settlement rate of 78.0% for crop insurance and 80.3% for livestock insurance. Overall satisfaction is 3.36 for crop insurance and 3.51 for livestock insurance, with the simulated group difference statistically significant (t = −2.53, p = .012). Claim settlement experience, transparency, accessibility and premium affordability also show positive associations with satisfaction in the illustrative regression model. Because respondent-level field data are not available for this manuscript, the numerical findings are presented strictly as a methodological demonstration rather than as estimates from a completed survey. The paper therefore contributes a structured framework that can be applied to verified farmer-level data in future empirical research.