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The COVID-19 pandemic has affected firms in many economies. Exploiting treatment heterogeneity, we use a difference-in-differences design to causally identify the short-run impact of COVID-19 on innovation spending in 2020 and expected innovation spending in subsequent years. Based on a representative sample of German firms, we find that negatively affected firms substantially reduced innovation expenditure not only in the first year of the pandemic (2020) but also in the two subsequent years, indicating ’Long–Covid’ effects on innovation. In 2020, innovation expenditure fell by 4.7 % due to the pandemic. In 2022, innovation spending was even 5.4 % lower compared to the counterfactual scenario without the pandemic. Firms with higher pre-treatment digital capabilities show higher innovation resilience during the pandemic. Moreover, COVID-19 leads to a decrease in innovation spending not only in firms that were strongly negatively affected by the pandemic, but also in those firms that experienced a positive demand shock from the pandemic, presumably to increase production capacity.

The R&D policy instrument mix concept has become increasingly important for understanding how public R&D support drives firm-level R&D. To-date, empirical studies have conceptualised the instrument mix as a static unit, whereby firms receive multiple policy instruments at one point in time. However, firms can also receive multiple instruments in a sequence, over time. While sequencing is well rehearsed theoretically, this remains a major gap in the empirical literature. Our study evaluates, for the first time, how R&D policy instrument mix sequencing impacts firm-level R&D. We construct a unique dataset, containing almost 25,000 firm-year observations over a 17-year period for Ireland. Our analysis focuses on R&D grants, R&D tax credits, and publicly-supported academic-industry collaborations, and develops two novel approaches to measure R&D policy instrument mix sequencing. Our results suggest that R&D policy instrument mix sequencing is highly effective at driving firmlevel R&D, but that some sequences are more effective than others. These findings highlight opportunities to realise superior policy outcomes through targeted sequencing.

Die deutsche Wirtschaft hat die Ausgaben für Innovationen im Jahr 2021 deutlich um 4,7 Prozent auf 178,6 Milliarden Euro gesteigert. Damit wurde der Rückgang aus dem Jahr 2020 - damals sanken die Innovationsausgaben pandemiebedingt um 3,6 Prozent auf 170,5 Milliarden Euro - wettgemacht. Das Vor-Pandemie-Niveau aus dem Jahr 2019 (176,9 Milliarden Euro) wurde allerdings nur leicht übertroffen. Gleichzeitig erhöhte sich die Anzahl der kontinuierlich forschenden Unternehmen um knapp 8 Prozent auf einen neuen Höchststand von etwa 42.000. Zu diesen Ergebnissen kommt die aktuelle Innovationserhebung 2022, die das ZEW Mannheim im Auftrag des Bundesministeriums für Bildung und Forschung (BMBF) jährlich durchführt. German companies increased their innovation spending in 2021 by 4.7 per cent to 178.6 billion euros. This significant increase has made up for the decline in 2020, when innovation expenditure fell by 3.6 per cent to 170.5 billion euros due to the pandemic. However, the pre-pandemic level from 2019 (176.9 billion euros) was only slightly exceeded. At the same time, the number of companies continuously conducting R&D rose by almost eight per cent and reached a new high of around 42,000. These are the findings of the Innovation Survey 2022 (in German only), which ZEW Mannheim conducts annually on behalf of the German Federal Ministry of Education and Research.

Artificial Intelligence (AI) is often regarded as the next general-purpose technology with a rapid, penetrating, and far-reaching use over a broad number of industrial sectors. A main feature of new general-purpose technology is to enable new ways of production that may increase productivity. So far, however, only very few studies investigated likely productivity effects of AI at the firm-level; presumably because of lacking data. We exploit unique survey data on firms’ adoption of AI technology and estimate its productivity effects with a sample of German firms. We employ both a cross-sectional dataset and a panel database. To address the potential endogeneity of AI adoption, we also implement IV estimators. We find positive and significant effects of the use of AI on firm productivity. This finding holds for different measures of AI usage, i.e., an indicator variable of AI adoption, and the intensity with which firms use AI methods in their business processes. ispartof: FEB Research Report MSI_2203 pages:1-36 status: published