Institutional strengthening of export crediting and insurance as a factor in enhancing Ukraine’s agricultural exports
Bibliographic record
Abstract
This article examines the institutional framework for export crediting and insurance in Ukraine’s agricultural sector under martial law, drawing on the international experiences of the EU, the USA, Canada, Brazil, and Poland. The role of export finance instruments in ensuring the resilience of agri-food supply chains, foreign exchange stability, and national economic security is revealed. The current state of the Export Credit Agency of Ukraine is analysed, key barriers to access for agricultural exporters to credit and insurance support mechanisms are identified, and systemic institutional constraints are outlined. The article substantiates policy directions for adapting best practices, including risk guarantee coverage, interest rate compensation, the development of digital services, and incentives for exporting high value-added agricultural products. The purpose of the article is to provide a systematic analysis of the institutional architecture of export crediting and insurance in Ukraine’s agricultural sector in wartime conditions, with the aim of outlining modernization directions based on relevant international experience. Methodology. The study applies structural-institutional analysis, comparative review of national and foreign models for supporting agricultural exports, and a logical-inductive approach to formulating policy proposals. Results. It is established that the current export financing system in Ukraine does not meet the needs of the agricultural sector under conditions of high risk and limited access to bank credit. The paper proposes measures to strengthen the institutional capacity of the Export Credit Agency, integrate war risk insurance mechanisms, and develop a digital platform for agro-exporters. It is concluded that adapting OECD-country practices can serve as a foundation for a new model of agricultural export support in Ukraine’s post-crisis recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".