Context
Ukraine’s micro, small, and medium businesses were taking the full weight of the war: workers gone, supply chains broken, capacity destroyed or unreachable, and no way to predict anything.
The National Bank needed analysis solid enough to guide policy, donor support, and recovery planning.
Diagnosis
The methodology was the entire problem.
Wartime data arrives late, incomplete, and inconsistent between sources, and some of it carries a political interest. Regional numbers don’t reconcile. Government bodies, business associations, and international donors each measure different things for different reasons.
The instinct is to wait for cleaner inputs. That option didn’t exist. Hesitation here costs jobs and closed businesses, so the work had to produce something usable before the data was good enough to justify it.
And nobody needed a vision for Ukrainian MSMEs. They needed to know what still worked, what had broken, and where the money should go first.
Results
Pulling the sources together showed workforce down about a third and capacity down about half: spread very unevenly across regions and sectors.
That unevenness was the useful part. Agriculture, IT, and construction had the strongest recovery potential, which made them the sensible places to concentrate limited resources instead of spreading support thin across everything.
The study fed into national MSME recovery strategy, supported coordination between government, business, and donors, and helped set priorities on credit access and business continuity.
Data quality never got solved. It got managed. Every conclusion went out with its uncertainty attached, which is uncomfortable when the room wants certainty and the situation demands a decision anyway. The output had to be usable by people who were deciding that week, not defensible to an academic reviewer.