approach meant that she could not include NUTS2 fixed effects but only country fixed effects. This leaves her results more susceptible to confounding differences in trend growth within countries, a problem we avoid with the inclusion of subnational (NUTS2) fixed effects. Finally, Coelho uses EU fund commitments as her instrument, which tend to predict levels of spending rather than changes.<sup>12</sup> In contrast, in our paper _changes_ in our instrument are a strong predictor of _changes_ in spending in most specifications.

Durand and Espinoza (2021) use a similar adjusted EU-fund data and IV approach, though they only examine effects at the country level.<sup>13</sup> In contrast with Durand and Espinoza (2021), we produce estimates at the NUTS2 level, which improves the sample size and accuracy of the estimates (though reduces the length of the sample). However, even when we use country-level estimation, our results are still quite different, with those authors finding large (>1) and significant GDP multipliers, whereas we find small (<1) or insignificant multipliers. The different results seem to be mostly explained by the treatment of 2009 (when Europe suffered a severe recession): Durand and Espinoza remove it whereas we control for the average depth of the recession in Europe. When we replicate Durand and Espinoza’s all-Europe results, we get a similarly large IV multiplier of about 1.26 (they get 1.21), but this drops to 0.79 when 2009 is included in the sample (see Section 4.1 for a discussion). While both their approach and ours are reasonable, the fact that a multiplier above one hinges on the modeling assumptions of a single year suggests that that finding should be used with caution in policy discussions.<sup>14</sup> Canova and Pappa (2022) also estimate the effect of EU funds on economic growth at the subnational and national levels, using similar adjusted EU-fund data to ours. There are two main differences between their approach and ours. First, they estimate the effect of two of five EU funds, whereas we examine the combined effect of all spending types.<sup>15</sup> The most important difference is the exclusion of the Cohesion Fund (CF) in their paper, which funds infrastructure and sustainable development in lagging regions, mostly in Central and Eastern Europe. The second difference with Canova and Pappa (2022) is in the estimation methodology and the interpretation of multiplier estimates. They use an “instrumental variable Bayesian local projection approach” that allows for multipliers to vary across regions but does not include time fixed effects. We use a classical panel estimator that is standard in the multiplier literature and does include time fixed effects. Their instrument is the residual of aggregate EU funds regressed on euro area macro variables like aggregate GDP and interest rates, which has some similarities to how shocks are identified in structural vector autoregression models (SVARs). In contrast, we use differences in predicted spending as the instrument.

The different approach means that Canova and Pappa (2022) are estimating a combination of relative and aggregate multipliers, whereas we are only estimating a relative multiplier. A relative multiplier is defined as follows: if region _A_ gets an extra 1€ of spending relative to region B, how much will _relative_ GDP increase in region A relative to region B (other things equal)?<sup>16</sup> The relative multiplier does not incorporate the aggregate response to fiscal spending (or any aggregate shocks, including the effects of monetary policy), as they are

> 12 Coelho (2019) does not report her first-stage estimates, but they are included in her replication file.

> 13 They also disaggregate the multiplier estimates across different sectors and investment types, which we do not.

> 14 However, both our papers are in agreement that the effect on investment can be above one at the country level.

> 15 Canova and Pappa (2022) study the European Regional Development Fund (ERDF) and the European social fund (ESF), but do not consider the Cohesion Fund (CF), the European Agricultural Fund for Rural Development (EAFRD) or the European Maritime and Fisheries Fund (EMFF). Our spending data also includes the Fund for European Aid to the Most Deprived (FEAD), and Youth Employment Initiative (YEI), though these are smaller.

> 16 Coelho (2019) and Durand and Espinoza (2021) also estimate relative multipliers.

5