investments constrains microenterprises from scaling up operations, especially in capitalintensive sectors, suggesting that smaller firms grow more slowly.<sup>29</sup> _Age_ in years is divided into categories of 1–5 years old, 6–10, 11–20, 21–30, and older than 31. Learning and selection effects imply that younger firms will grow more quickly<sup>30</sup> .

_Ownership type_ is operationalized as a categorical variable denoting whether the firm is a _global headquarters_ with foreign subsidiaries<sup>31</sup> , a _foreign-affiliated_ firm<sup>32</sup> , or a _purely domestic_ firm.<sup>33</sup> The coefficients on ownership categories capture the effects of foreign affiliation. Specifically, the coefficient on _foreign_ captures the productivity benefits that a foreign-owned firm realizes from intra-organizational transfers and integration in global markets. The coefficient on _global headquarters_ captures benefits to firms from investing abroad to expand their consumer base and increase efficiency. We expect that global headquarters will grow most quickly, followed by foreign-affiliates. Purely domestic firms will have the slowest growth.

j j ∆(InwFDI)03−08 measures the change in the inward stock of FDI in country _j._ ∆(OutFDI)03−08 j measures the same for outward stock of FDI. ∆(Credit)03−08 measures changes in the ratio of j private sector credit to GDP. ∆(Skills)03−08 measures changes in the percentage of the workforce j with a tertiary education. ∆(Bus.Reg)03−08 measures changes in business regulation. We predict that better regulations will be positively correlated with more rapid productivity growth.

j ∆(Infra)03−08 measures variation in the quality of infrastructure. The log of productivity in 2003 is included to control for initial firm characteristics: firms that begin with higher productivity levels may realize slower growth rates.<sup>34</sup> We include country and sector fixed-effects, which account for unobserved country- and industry-specific characteristics that might affect productivity growth. _Sector_ m is a vector of sector dummy variables defined at the NACE 1.1 level, while _Country_ is a vector of country dummy variables. j We employ ordinary least squares (OLS) with errors clustered by country in order to allow for possible correlations in growth rates between co-national firms. Regressions are run separately for EU15 and EU12 countries to investigate the sources of the differences between the two

> 29 This is a common prediction in the literature. See Bartelsman, Haltiwanger, and Scarpetta (2009); Ayyagari, Demirguc-Kunt and Maksimovic (2011)

> 30 Various studies have shown that conditional on size and survival rate, young firms tend to grow faster than older firms due to diminishing returns to learning. See Klepper and Thompson, 2007; Dunne, Roberts and Samuelson, 1989.

> 31 Due to an idiosyncrasy of the Bureau van Dijk, co-national affiliates of headquarters firms with foreign subsidiaries are also listed as global headquarters.

> 32 Foreign-owned firms are classified as those which have at least 51 percent foreign ownership. For 34 percent of firms classified as foreign affiliated by Bureau van Dijk, we cannot identify the exact ownership stake. However, as they are mostly small firms, we assume they are not publicly traded firms in which the parent's ownership could be diluted and are therefore managerially fully in control of the foreign parent.

33Given that the sample excludes all firms that were involved in merger and acquisitions operations, the ownership structure of a firm observed in 2009 is assumed to be the same as in 2003. We follow Brown and Earle (2002) in using the latest ownership status to create ownership dummies for 2003. However, it is worth noting that we are not able to control for cases in which the firm ownership structure has changed due to a joint venture.

34 The inclusion of this variable may reflect convergence as proposed by Barro and Sala-i-Martin (1992). We expect the coefficient of baseline productivity level to be negative.

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