Machine Learning Models for Predicting Romanian Farmers’ Purchase of Crop Insurance

Codruţa Mare (), Daniela Manaţe, Gabriela-Mihaela Mureşan, Simona Laura Dragoş, Cristian Mihai Dragoş and Alexandra-Anca Purcel
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Codruţa Mare: Department of Statistics-Forecasts-Mathematics, Faculty of Economics and Business Administration, and The Interdisciplinary Centre for Data Science, Babeș-Bolyai University, 400591 Cluj-Napoca, Romania
Daniela Manaţe: Department of Statistics-Forecasts-Mathematics, Faculty of Economics and Business Administration, and The Interdisciplinary Centre for Data Science, Babeș-Bolyai University, 400591 Cluj-Napoca, Romania
Gabriela-Mihaela Mureşan: Department of Finance, Faculty of Economics and Business Administration, Babeș-Bolyai University, 400591 Cluj-Napoca, Romania
Simona Laura Dragoş: Department of Finance, Faculty of Economics and Business Administration, Babeș-Bolyai University, 400591 Cluj-Napoca, Romania
Cristian Mihai Dragoş: Department of Statistics-Forecasts-Mathematics, Faculty of Economics and Business Administration, and The Interdisciplinary Centre for Data Science, Babeș-Bolyai University, 400591 Cluj-Napoca, Romania
Alexandra-Anca Purcel: Department of Statistics-Forecasts-Mathematics, Faculty of Economics and Business Administration, and The Interdisciplinary Centre for Data Science, Babeș-Bolyai University, 400591 Cluj-Napoca, Romania

Mathematics, 2022, vol. 10, issue 19, 1-13

Abstract: Considering the large size of the agricultural sector in Romania, increasing the crop insurance adoption rate and identifying the factors that drive adoption can present a real interest in the Romanian market. The main objective of this research was to identify the performance of machine learning (ML) models in predicting Romanian farmers’ purchase of crop insurance based on crop-level and farmer-level characteristics. The data set used contains 721 responses to a survey administered to Romanian farmers in September 2021, and includes both characteristics related to the crop as well as farmer-level socio-demographic attributes, perception about risk, perception about insurers and knowledge about agricultural insurance. Various ML algorithms have been implemented, and among the approaches developed, the Multi-Layer Perceptron Classifier (MLP) and the Linear Support Vector Classifier (SVC) outperform the other algorithms in terms of overall accuracy. Tree-based ensembles were used to identify the most prominent features, which included the farmer’s general perception of risk, their likelihood of engaging in risky behaviour, as well as their level of knowledge about crop insurance. The models implemented in this study could be a useful tool for insurers and policymakers for predicting potential crop insurance ownership.

Keywords: machine learning; crop insurance; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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