Modelling efficiency dynamics and technological progress in the agricultural sector using the Malmquist index

Purpose. The purpose of this study is to model the dynamics of total factor productivity in the agricultural sectors of European Union countries and Ukraine using the DEA-based Malmquist productivity index (MPI), with its decomposition into technical efficiency change (EFFCH), technological change (TECHCH), and scale efficiency change (SECH), to identify the key drivers of productivity growth and assess cross-country differences in efficiency dynamics. Methodology. The study employs a non-parametric Data Envelopment Analysis (DEA) combined with the Malmquist productivity index to assess the dynamics of total factor productivity in the agricultural sectors of EU countries and Ukraine over 2015–2024. The index is decomposed into technical efficiency change, technological change, and, under variable returns to scale, pure (PECH) and scale efficiency change, enabling identification of key productivity drivers. The analysis is based on panel data for 28 decision-making units with multiple inputs and outputs. Estimation is conducted in R using the Benchmarking package, with average changes calculated via the geometric mean. Results. The results indicate a stable increase in total factor productivity in the agricultural sector, with the average Malmquist index exceeding unity for both EU countries (MPI = 1.025) and Ukraine (MPI = 1.021). Productivity growth is primarily driven by technological change (TECHCH > 1), while technical efficiency exhibits more moderate dynamics and declines during 2019–2021. Ukraine demonstrates a pattern where productivity gains are mainly driven by technological progress, accompanied by lower technical and scale efficiency, indicating the presence of structural inefficiencies. Originality. This study represents one of the first attempts to apply an integrated DEA-Malmquist framework to a unified panel of EU countries and Ukraine, enabling a comprehensive decomposition of productivity dynamics and identification of key drivers of total factor productivity growth. It identifies the dominant role of technological progress and reveals structural inefficiencies, particularly in Ukraine. Practical implications. The results provide a quantitative basis for improving agricultural policy and management decisions in EU countries and Ukraine. The findings highlight the need to complement technological modernisation with measures aimed at enhancing technical and scale efficiency, particularly in Ukraine. The study can be used by policymakers and stakeholders to design strategies for increasing productivity, optimising resource use, and strengthening competitiveness in the agricultural sector.