The purpose of this study is to examine the efficiency of the banks in Lithuania by employing the DEA method and evaluate bank performance in a low interest rate environment. The efficiency scores were calculated with a non-parametric frontier input-oriented DEA technique with the variable returns to scale (VRS) and the constant returns to scale (CRS) assumptions. Five alternative models with different input-output combinations were developed, based on production, profitability and intermediation dimensions. The main bank profitability measure—the return on assets (ROA) ratio—was employed to validate the results obtained using the DEA method. The Lithuanian bank’s efficiency analysis based on the VRS assumption shows that better results are demonstrated by the local banks. The technical efficiency analysis based on the CRS assumption shows other results: the banks owned by the Nordic parent group and the branches have higher pure efficiency than local banks and have success at working at the right scale. Based on this, it stated that during the 2012–2016 period the larger Lithuanian banks (subsidiaries) applied a more appropriate business model than smaller (local) banks operating in Lithuania. Additionally, this research contributes to the scholarly literature in the field of determinants of bank business performance in concentrated markets dominated by foreign banks and, in particular, from one region.

This article presents an analysis of the literature on systemic risk measurement methods. Only the recent global crisis has particularly attracted the attention of researchers on systemic risk measurement. Global challenges such as Big Data, AI, IoF, etc. also have an impact on expanding the systemic risk measurement capabilities. The growing number of publications in the last decade opens the door to deeper insights into the systemic risk measurement features, summarizing the contribution of research and analyse the mainstream research on systemic risk, identify the strengths and weaknesses of the studies. Therefore, the main objective of this study is to provide a framework to address the relevant gaps in the current discussion on systemic risk measurement by conducting a wide search in Scopus database to identify the studies that used different systemic risk measurement in the period from 2009 to January 2018. A meta-analysis of scientific articles is performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and using network approach presents the main interconnection of the methods used to measure systemic risk. A critical analysis of these articles addresses some important key issues. The results of this review are important: they will help researchers to develop better research methods and models around systemic risk measurement. Based on the results, it has allowed us to identify the key issues in choosing a method to assess systemic risk and to help researchers avoid pitfalls in using these methods.

The changes in farm structure have been observed in Lithuania as well as in other Central and Eastern European countries. These changes, to a high extent, have been driven by decreasing profitability of the small farms. In this paper, we look into the changes in the profitability of Lithuanian family farms across different farm size groups. Farm size is measured in terms of the standard output. The period covered is 2005–2016. The index decomposition analysis model and Shapley value are adapted for the analysis. The proposed framework ensures complete decomposition among other desirable properties. The decomposition of the changes in profitability was carried out following the DuPont identity. The results suggest that for small (respectively large) farms the asset turnover (respectively profit margin) component appear more important, whereas the leverage effect remained minimal irrespectively of the farm size group.

Income Risk Management in Agriculture using Financial Support

Cooperatives cover a large part of the agricultural sectors and have substantial market shares in agri-food supply chains in the EU Western countries. They account for approximately half of agricultural trade in the EU. By contrast, in the EU Western countries, where farmer cooperatives are widespread and successful, agricultural cooperation in Lithuania has developed intermittently in the last century. We still have very limited knowledge of why the country’s agricultural producers (especially smallholder farmers) are reluctant to cooperate in Lithuania. The aim of this study is to assess the level of the willingness to cooperate among smallholder farmers in Lithuania and to draw up the profiles of small-scale farms that participate in and intend to join cooperatives and, conversely, that do not participate in cooperatives and do not intend to do so. To achieve this goal, a representative survey of small-scale farms was conducted. Results of surveys carried out in 2019 in Lithuania on a group of 1002 small-scale farms showed that only 8% of the surveyed farms participate in producer groups or cooperatives, while another 8% intend to participate. Small-scale farms in Lithuania have weak market integration, with no bargaining power on input and output markets. The vast majority of small-scale farms are reluctant to participate in cooperative activities in Lithuania. Therefore, this study aimed to determine the profile of a small farm that tends to cooperate. The main social characteristics of farm managers and economic factors of farms willing to cooperate have been identified.