decreasing with distance to the frontier, i.e. especially significant for industries identified as neck‐and‐ neck.

As in our previous extensions and robustness checks, we prefer to work with the Ackerberg specification in our regressions and so our results show the coefficient outcomes accordingly. The results of interacting our regulatory variables with the technology gap are presented in Table 7. In this table it becomes clear that the separate services barriers overall in the left‐hand panel has the expected negative sign and that the interaction term using this regulatory variable also have a negative and significant outcome in column (1). Splitting up the variable again, it shows that in columns (5) and (6) entry barriers obtain a negative and significant result while conduct regulations obtain a positive and significant result although weakly. This indicates that entry barriers are particularly hurtful for industries which are classified as unleveled, but that conduct barriers have a particularly negative impact for industries that are categorized as neck‐and‐neck.

We have performed alternative specifications to come up with a robust result. For instance, the results remain stable when using the EU‐wide input coefficient results as shown in the right‐hand panel. We also obtain similar results when including a dummy variable that distinguishes between the two types of industries (output omitted). Moreover, recent papers such as McMorrow _et al_ . (2010) and Bourlès _et al_ . (2013) have used alternative technology gaps based on non‐firm‐level data. We follow Bourlès _et al_ . (2013) so that our alternative specification of technology gap is the simple difference of ln(TFP _F_ ) and ln(TFP _i_ ) and then averaged by sector.<sup>26</sup> The results support the idea that entry barriers are particularly important for unleveled sectors whereas conduct regulations are more important for neck‐and‐neck sectors. Additionally, the last two papers use TFP growth rather than levels which we are also able to compute as the log difference of TFP between two years. However, no meaningful results are obtained through this way.

# **4.2.3 Post‐Crises Performance**

Up till now we have selected our time frame to be 2006‐13 in an attempt to exploit more observations and regulatory variance present in the data. However, this time period covers the global financial crisis in which precisely many firm would feel forced to exit the market, although it is unlikely that outgoing firms would represent an equivalent drop of 60 percent which is the difference between our two data sets as previously explained. This is likely to be due to reporting issues. However, we nonetheless would like to check whether our results are robust to including firms as of 2008 only. Admittedly, this will be done by reducing only two years, but it may give us nonetheless extra evidence whether our results of conduct barriers are really the main policy area of concern. Furthermore, the OECD’s NMR point of measurement of regulation for quite some services sector is done in 2008 with previous year of measurement 2003. As such, by selecting our data as of 2006 till latest year available we may also have been picking up any inconsistent real regulatory variation at the time of collecting regulatory data before the year 2008.

26 Of note, although we are able to compute the technology gap at the firm, we nonetheless prefer to compute an average so as to have industry‐specific variation of technology gap in order to be consistent with Aghion _et al_ . (2005) and other papers. Moreover, both McMorrow _et al_ . (2010) and Bourlès _et al_ . (2013) do not work with firm‐ level data and therefore use industry‐specific TFP which automatically creates a technology gap that is industry‐ specific. As a result, none of these papers has used a firm‐specific technology gap in their regressions.

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