Selection Before Treatment: Identifying Programme Effects on Firms
Keywords:
selection bias, programme evaluation, small firms, credit guarantees, causal identificationAbstract
Firms that join a business support programme differ from firms that do not, and the characteristics that put them in the treated group also produce the outcome the programme is credited with. This study examines that selection problem as the central obstacle to identifying programme effects on firms, and asks what the reviewed evidence establishes about its size and its remedies. The method is a structured conceptual review of research published between 2017 and 2024 on credit guarantees, subsidised lending, digital adoption, tax compliance and enterprise performance, classified by whether take-up was self-selected and how the study addressed it. Four findings follow. Selection is documented directly rather than assumed, with one experimental introduction of a financial instrument finding adopters more educated than non-adopters and more likely already to hold a bank account. The characteristic that predicts take-up is frequently the same characteristic that predicts survival, since prediction studies identify staffing quality, industry experience and marketing capability as the discriminators between surviving and failed firms. Aggregate effects can invert once selection or subgroup structure is examined, with one meta-analysis finding a positive published effect becoming small and statistically insignificant after correction for publication bias. And the strongest designs in the corpus obtain identification from variation the firm did not choose, including guarantee eligibility thresholds and branch density that predicts conservative rather than expanded borrowing. The study proposes reporting the selection margin alongside the effect.References
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