year before spending. Either way, the estimated anticipation effects presented here represent an interesting area for further investigation.

# **4.4 Multiplier Heterogeneity**

**Results by EU fund.** Several other papers in the literature have found heterogeneous multipliers across different EU funds. Canova and Pappa (2022) investigate the effects of two funds, the European Regional Development Fund (ERDF) and the European Social Fund (ESF). The ERDF focuses on several priority areas, such as innovation, digitalization, support for SMEs, or the “green transition” whereas the ESF mostly invests in employment-focused education and training. Canova and Pappa (2022) find that ERDF has a positive impact multiplier of 1.4, falling to 0.2 after 3 years (cumulatively). In contrast they argue the ESF has a negative short-run multiplier, but a positive and large medium-run multiplier. Coelho (2019) estimates a large multiplier on funds allocated to “Objective 1” regions (regions with low per capita income, to encourage convergence), but an insignificant multiplier for those allocated to “Objective 2” regions (regions with structural changes).

In this section, we disaggregate our main results in Table 1 by the type of fund, with the results presented in Online Appendix Table A6.1.<sup>60</sup> Perhaps due to the smaller sample size, we never get significant multipliers in the country-level sample, so we focus our discussion here only on the NUTS2 sample. The largest economic effects are from the CF, which for the whole of Europe has a significant multiplier of 0.86 (at 1%) for the OLS specification and of 1.21 (at 1%) for the IV specification (with a strong first-stage coefficient). However, CEE multipliers for the CF are insignificant—which is perhaps surprising given spending is focused on CEE regions—and makes us not to put too much weight on the all-Europe CF results. For the ERDF, we get an OLS multiplier of 0.39 for all Europe NUTS2 sample (significant at 1%) and of 1.22 for CEE countries (significant at 1%), but these are not robust in the 2SLS specification. All multipliers for EAFRD and ESF are insignificant at the 5% level.

**Results by programming period/split sample.** The economic situation in Europe has changed substantially over our 2000-18 sample period (or starting in 2002, accounting for lags). Most important has been the European debt crisis and its aftermath, but also the expansion of the European Union to the east. The sample also includes three different programming periods of EU funds: 2000-06; 2007-13 and 2014-20 (though our sample finishes in 2018). In this subsection we test whether multipliers vary over time. Specifically, we either simply split our sample in the middle (allow to vary across the 2000–09 and 2010–18 periods); or split out sample across the three sample periods (while keeping other controls and fixed effects the same): ββ rr, rr, <u>�</u> rr, rr, <u>�</u> for p={2000–09, 2010–18} or  p={2000-06; 2007-13; 2014-20} yyii tt−yyii tt−1 ggii tt−ggii tt−1 rr rr yyii,tt−1 = ∑ββpp pp11pp yyii,tt−1 + δδXXii,tt + γγii + γγtt + eeii,tt

We find little robust evidence of large multipliers in a particular subsample. Online Appendix Table A6.2 reports the results for the mid-sample split. The occasionally significant OLS multipliers in Table 1 seem to be driven by the 2010-18 subsample. However, differences in estimated multipliers are never statistically significant at the 5% level across subsample. The very high 2SLS multiplier that is significant—country-level

> 60 Our variable of interest is now the change in modeled payments for fund _f_ in region _i_ (or country _c_ ) over lagged GDP growth and our instrument is calculated as in equation 5, but with spending for fund f (instead of for all programs). We also use the change in modeled payments for all the other funds except for fund f over lagged GDP growth as a control.

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