**Box 1. Estimating Empirical Employment Growth Equations at the Firm Level**

<mark>The regression analysis is based on empirical firm growth equations (for surviving firms) in the spirit of the</mark> _<mark>Gibrat's law</mark>_ <mark>(see, e.g., Hart 2000 and Coad 2009, for surveys). A cross-sectional</mark> _<mark>Gibrat-law</mark>_ <mark>type of regression can be written as (see,</mark> e.g., Geroski 2005 and Oberhofer and Pfaffermayr 2013): <mark>This equation states that the average employment growth rate g</mark> ij <mark>of firm</mark> _<mark>i</mark>_ <mark>in industry</mark> _<mark>j</mark>_ <mark>is a function of (log) initial firm size S</mark> 0i<sup>6</sup> <mark>(i.e, the number of employees in the first observed period) and other firm- and industry-specific control variables collected in x</mark> ij <mark>and z</mark> j <mark>, respectively. γ and δ are column vectors to be estimated. π</mark> ij <mark>captures the</mark> (conditional) speed of convergence/divergence and is modeled to depend on firm age Aij.

<mark>One standard result in the empirical firm growth literature states that the observed speed of convergence declines with age. This finding would be confirmed in this application if the estimated parameters β</mark> 0 <mark>< 0 and β</mark> 1 <mark>> 0. Economically, such a result would suggest that younger and smaller firms grow faster, while old small firms would not exhibit increased</mark> employment growth dynamics.<sup>7</sup> <mark>The estimation covers the time period 2002 to 2008. x</mark> ij <mark>and z</mark> j <mark>contain firm- and industry-specific variables that may affect job creation in firms located in the EU11 economies. Among the latter, industry dummy variables (based on the classification mentioned in footnote 5) and business environment indicators are included. Based on the BEEPS data, an indicator that measures the average institutional barriers within 2-digit industries and countries is constructed. The overall industry-country specific measure for institutional barriers is based on several questions on perceived obstacles for conducting business that are included in the BEEPS data. In particular, the constructed indicator comprises information on the degree of institutional regulations, access to finance, crime, corruption, taxation and labor regulations. The country averages of this measure are reported as red bars in Figure 2. In the second step, the job</mark> creation effects of these specific business barriers are separately investigated.

<mark>With regard to additional firm-specific controls, (log) firm age and (log) firm's total factor productivity (TFP) are included. TFP is estimated via the approach suggested by Levinsohn and Petrin (2003), which uses a firm’s demand for intermediate inputs (such as materials) in order to overcome the problem of simultaneity when estimating firm level production functions. For more details on the calculation of TFP and corresponding data restrictions see footnote 16.</mark> The interaction effect of firm size with firm age is already incorporated in π.

<mark>For the crisis year 2009, the model is re-estimated. Job creation is defined as the employment growth rate from 2008 to 2009. The firm- and industry-specific characteristics are, consequently, taken from 2008. The only exception refers to the BEEPS data which are not available for 2008. The institutional barriers variable, therefore, is based on the withinindustry survey averages from 2009. Tables 2 and 3 report the corresponding estimation results for both the pre-crisis</mark> period and the year 2009.

> 6 The firm size is a particularly interesting (and debated) characteristic of job creation in the literature. Davidsson et al. 1998 and Neumark et al. 2011 demonstrate that small and medium-sized enterprises (SMEs) are the most important contributors to net job creation. By contrast, Haltiwanger et al.  (2013) highlight the important role of business startups and young firms for job creation in the USA. Huber et al. (2012) document that in Austria large firms irrespective of their firm age positively contribute to (net) job creation, while in small firms more jobs are destroyed than created.

> 7 However, as stressed by Haltiwanger et al (2013), disentangling the role of firm size vis-à-vis age requires more comprehensive data than available for this paper. In particular, census data would allow for the proper estimation of employment shares and hazard rates of non-surviving firms.

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