Barriers to the implementation of education projects funded under the Ukraine Facility
The report summarizes the results of a survey of representatives of educational institutions regarding practical barriers to the implementation of projects funded under the program. The survey was conducted between September and December 2025 among representatives of educational institutions involved in procurement processes within Ukraine Facility projects. Its purpose was to identify practical barriers in the implementation of educational projects. A total of 149 respondents from 21 regions of Ukraine participated in the survey.
Monitoring Education Expenditures Funded under the Ukraine Facility in 2024
The study analyzes the use of Ukraine Facility funds in 2024 to improve access to safe and high-quality education. Fiscal Center conducted a step-by-step collection, systematization, and verification of data on the financing of educational measures through state subventions, additional grants, and co-financing from local budgets in order to demonstrate the real impact of these resources on schools, students, and communities within the framework of indicators 7.12 and 7.13 of the Ukraine Plan for 2024–2027.
Local Budget Expenditures on Recovery in 2024: Shadow Report
Executive Summary In 2024, the pace of recovery increased significantly: the number of projects rose to 987 (up from 481 in 2023), and total expenditures grew to UAH 1.28 billion (from UAH 0.4 billion in 2023). This indicates a shift from crisis response to systematic management of recovery processes at the local level. The structure of expenditures shifted from educational facilities toward housing and infrastructure, reflecting the urgent need of communities for basic infrastructure. At the same time, funding is concentrated in key regions—Kyiv (45% of funds) and Donetsk region (12%)—while other regions show more localized activity and require a differentiated approach to resource allocation. Housing accounts for 75% of all recovery projects, with 50% of the analyzed funds spent on their implementation. At the same time, the average cost of restoring a housing unit is lower than for other types of projects, highlighting the prevalence of numerous small, low-cost repairs. In contrast, educational institutions, healthcare facilities, and critical infrastructure are characterized by higher average project costs, requiring specialized resources and planning. For the majority of projects, between one and three payment orders were issued. Only a small share of projects had more than 10 payments, indicating the complexity of large-scale projects and potential bureaucratic risks. The return of funds (less than 1% of the total volume) is concentrated in large projects and helps identify weaknesses in control and planning. A significant share of funds is concentrated among the top 10 contracting authorities (59%) and contractors (37%), while peak spending occurs in December (41% of the annual total), highlighting the need to revise approaches to budget planning throughout the year. At the same time, the data verification system based on program classification codes TPKVK MB 7372–7377 reveals structural limitations: data is fragmented, incomplete, and inconsistent; sources contain numerous errors and technical discrepancies; and some payments are made outside the treasury system. This complicates oversight, requires manual processing, and underscores the importance of institutional improvements—standardizing payment documents, unifying procurement data, and increasing transparency of processes. This study is the result of a detailed analysis of collected information and serves as an equivalent of a financial audit conducted by an independent analytical center. Its findings can be used by local governments, state authorities, and donor organizations to improve the efficiency of managing the recovery of Ukraine’s damaged infrastructure. Додаткова інформація Дашборд Shadow report 2023
Expenditures of the Fund for eliminating the consequences of armed aggression in 2024: Shadow report
Ukraine is restoring infrastructure destroyed by the war with the support of the Fund for Eliminating the Consequences of Armed Aggression. In our study, we analyze the effectiveness of the use of more than 25 billion hryvnias allocated to 427 facilities across the country. The interactive dashboard we developed allows for detailed tracking of fund distribution — from overall amounts to specific regions and localities.
Local Budget Expenditures on Recovery in 2023: Shadow Report
Ukraine is restoring infrastructure destroyed by the war with the support of the Fund for Eliminating the Consequences of Armed Aggression. In our study, we analyze the effectiveness of the use of more than UAH 25 billion allocated to 427 projects across the country. The interactive dashboard we developed enables detailed tracking of fund distribution — from overall amounts to specific regions and localities.
Expenditures of the Fund for Eliminating the Consequences of Armed Aggression in 2023: Shadow Report
Ukraine is restoring infrastructure destroyed by the war with the support of the Fund for Eliminating the Consequences of Armed Aggression. In our study, we analyze the effectiveness of the use of more than UAH 25 billion allocated to 427 projects across the country. The interactive dashboard we developed enables detailed tracking of fund distribution — from overall amounts to specific regions and localities.
Assessment of Budget Transparency in 2023
Despite the rapid formation and development of Ukraine’s budget system, the issue of transparency in the budget process has unfortunately remained relevant for the third decade.
Despite the progress made, reforms are proceeding slowly, and the practical impact of legislative and policy changes is hardly reflected in everyday budgetary practice.
Salaries in the public sector
Research Description The salaries of civil servants are an important indicator reflecting the effectiveness of public administration, transparency, and fairness in the distribution of state resources. The analysis of civil servants’ salaries makes it possible to assess and understand the dynamics of this indicator, identify gender and territorial differences, and conduct a comparative analysis across different position categories. The purpose of this analytical study is to analyze both the overall dynamics of civil servants’ salaries and the breakdown by job categories and regions, as well as to examine the gender component. The study consists of four main sections. The first section analyzes the dynamics of civil servants’ salaries for the years 2020–2023, providing an overview of the general trends in salary increases or decreases over the analyzed period.The second section examines the differences in salaries between men and women in public service and identifies possible gender disparities. The third section analyzes salaries depending on job categories, which allows for identifying possible differences in pay levels between various groups of positions. The fourth section focuses on the regional dynamics of civil servants’ salaries, revealing territorial differences in payment levels. Data Specifics from Declarations The data for this study were collected via the API of the Unified State Register of Declarations of Persons Authorized to Perform State or Local Government Functions, covering the years 2020–2023.In total, over 688,000 declarations were selected for analysis from individuals who indicated that their positions belong to the “Civil Service Position” category. The declaration data are filled in personally by a large number of individuals and may contain errors, including technical ones, particularly in the section specifying the total salary received by civil servants, as well as in other parts of the declaration. To correct salary data to valid values, 10,000 declarations were manually labeled to identify those containing errors. Based on this labeled dataset, a machine learning algorithm (Random Forest) was used to build a predictive model that estimates the likelihood of errors in declarations. More than 1,500 declarations were identified as containing errors in the reported annual salary amounts of civil servants. Appropriate downscaling coefficients were applied to these declarations. Missing information regarding the region where a civil servant works was filled in when the institution employing the declarant had a defined location within the general dataset. The gender of the declarant was determined based on the person’s first name and patronymic. The presented analysis should be used solely to assess general trends and patterns, as the declaration data may contain errors affecting the final results. However, considering the law of large numbers and the substantial size of the sample, it can be stated that the average values of indicators are likely to reflect reality. For accurate data representation in analysis, verification of all declaration data is required. https://www.youtube.com/watch?v=17SICx8qSmw&t=2241s Share