Research Evaluation · 2019 · 35 citations · 15 references
Public AffairsBusiness EconomicsInnovation ManagementCorporate TaxationInnovationTaxationInnovation CapabilityInnovation Impact AssessmentInnovation PolicySubstantial Economic EffectTax IncentivesEconomic Performance MeasurementEmployment Effects On Small FirmsEvaluation SynthesisInnovation Incentive SchemesR And D IncentivesTax Incentive EffectivenessTax Incentive EvaluationTurnover Growth
Abstract This article investigates the impact of tax incentives targeting young innovative firms and broader R&D tax incentives where effects on young firms are observed. It examines effects on R&D additionality, R&D wages, employment growth, turnover growth, and sales growth from innovative activities. It draws on academic literature and policy evaluation studies and uses a mixed-method approach based on evaluation synthesis. Evidence on the effectiveness of tax incentives on young and small firms’ employment and economic performance is relatively limited, largely due to a dearth of evaluations. This analysis, based on a limited number of studies, finds that with regard to R&D additionality, generic R&D tax incentives tend to have a larger, or at least as large, effect on young companies, both when compared with companies of average age (with the exception of a study on an Irish tax instrument), and when compared with grants and loans. Some evidence also shows a positive effect on wages. More limited evidence shows that R&D tax incentives targeted at young companies tend to have positive effects on R&D intensity and wages, but that effects decrease relatively if combined with other instruments such as subsidies. With regard to output additionality, generic R&D tax incentives have a limited impact on innovation for all companies and a positive impact on turnover, turnover share of new products or services and labour productivity. There is some evidence of positive effects on employment, productivity, sales and added-value of targeted measures, but these results should be validated using more robust methods.
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