The study aims at evaluating crop insurance effects on the farms’ production and investment indicators in Lithuania. The Common Agricultural Policy after 2020 considers measures helping farmers to adapt to climate change. For this reason, it is essential to evaluate existing risk management measures in order to propose appropriate schemes for the next programming period. In order to evaluate crop insurance effects on the farms’ production and investment indicators farm-level, data from Farm Accountancy Data Network dataset and propensity score matching approach was used. Study period was 2008–2017. The study revealed that participation in crop insurance schemes was influenced by the factors such as age of the farmer, wealth, specialization, and location of the farm. The study also demonstrated that crop insurance did not show statistically significant effects on the selected farms’ indicators. The main reason was support from the national and EU funds.

Matched sampling is a methodology used to estimate treatment effects. A caliper mechanism is used to achieve better similarity among matched pairs. We investigate finite sample properties of matching with caliper and propose a slight modification to the existing mechanism. The simulation study compare performance of both methods and show that standard caliper perform well only in case of constant treatment or uniform propensity score distribution. Secondly, in a case of non-uniform distribution and non-uniform treatment the dynamic caliper method outperform standard caliper matching.