importance. This seems consistent with our previous findings that (a) entry barriers seem to matter more for countries exhibiting low levels of institutions which are usually economies which are more reliant on industry rather than services, and (b) entry barriers appear to me most important for sector which are lying further away from the technology frontier which is most likely the case for countries which are less‐ developed and incidentally also more dependent on industry than services.

# **4.2.5  Exogenous US I/O coefficients**

As our discussion on country‐specific or EU‐wide input‐output coefficients indicated, either approach could still run the risk that there could be cases where a higher downstream productivity term of sectors leads to higher input sourcing within or across EU countries. To reduce this endogeneity problem, we use completely exogenous input‐output coefficients from the US. When we do this, the detailed 6‐digit Input‐Output Use Tables from the US Bureau of Economic Analysis (BEA) are employed to compute these sourcing coefficients and are consequently concorded into our NACE classification system. They are then mapped with our regulatory regulations variables and used in the regressions.<sup>27</sup> The results of our regression using these concorded US coefficients are shown in Table 10 where we replicate the baseline model using the Ackerberg and Hsieh and Klenow TFP specifications. The results show a consistent pattern in that conduct regulations are across all columns highly significant. Note that for the Ackerberg measure when entered the various regulatory variables separately or together, conduct regulation is the only regulatory variable that comes out negatively significant. This is somewhat different when looking at the Hsieh and Klenow TFP. In there, both the overall, entry and conduct regulations are highly significant, also when entered jointly. However, looking at the coefficient sizes of all coefficients, conduct regulations appear to have and almost twice as large impact on downstream TFP compared with entry regulations. Both outcomes across the two TFP measures reassures us that our baseline regressions are capturing an important role for conduct regulations and that the potential endogeneity does not form a risk.

# **4.2.6 TFP Distribution**

Our last robustness check takes into account the entire TFP distribution. Figure A1 in the annex gives a graphical representation of the cumulative distribution function of our preferred TFP specification for manufacturing as well as services firms, namely from Ackerberg. As expected, and as shown in many other works on firm‐level productivity, this figure shows that there is a relatively small number of firms with extremely high productivity figures in addition to a low frequency of firms with relatively low productivity while having a large middle range of firms performing in between these two extremes. Hence, the lower quantiles encompass firms with lower productivity distribution whereas the higher quantiles carry firms with a higher TFP distribution. Overall, it means that the entire distribution is not very symmetric. It motivates us to see whether firms in the EU placed in the upper, middle or lower

> 27 To see how we have matched the detailed 6‐digit BEA classification scheme with our NACE classification and to find out how we have classified the regulatory variables with each 6‐digit BEA services sectors, a concordance schemed can be obtained upon request.

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