Selection Variables

The last group of variables are the covariates that are necessary to estimate the propensity score for every establishment. Selection variables serve as the decision criteria according to which management may have decided upon FDI or relocation. Hence, we include the lags of the time varying variables in the propensity score estimation and only the time invariant or persistent selection variables are included with their value contemporaneous to treatment, in order to loose as few observations as possible. Concerning the FDI treatments, selection variables date back to the period of the year 2003, still before treatment starts potentially on January 1^st, 2004. Concerning the relocation treatment, selection variables date from the period of the year 2005 or June 30^th, 2006 still before treatment starts potentially on July 1^st, 2006.

To take into account sample stratication, we always include the stratication variables among the selection variables, i.e. 16 regional dummies and 17 industry dummies, and rm size in terms of employment. In this way we take into account that relocation activity varies by industries, federal state, and rm size (see for instance the descriptive statistics in Statistisches Bundesamt (2008)).

Further, we investigate whether dierent choices of selection variables matter for the re-sults. In particular, we choose the selection variables previously used in the studies of Moser et al. (2015), selection variables MUW henceforth, and of Wagner (2011), selec-tion variables Wagner, henceforth. We use an addiselec-tional specicaselec-tion that explains the probability of the relocation treatment better than the previous two specications. This

specication we call SU henceforth.

Selection Variables MUW

For the FDI variables we use the same selection variables as Moser et al. (2015) do. Their logit estimates show that oshoring is signicantly more likely the larger an establishment in terms of full-time employees is, the more advanced its technology, the higher average wage costs, and the larger the share of high-skilled workers. If we assume these variables to proxy productivity of a rm, their choice is perfectly in line with heterogeneous rms literature following the seminal theoretical contribution of Melitz (2003). Also foreign-owned establishments seem to have a higher probability of oshoring or FDI.¹⁸ These selection variables are measured as follows:

ˆ log total employment: logarithm of total employment at establishment i at time t-1, i.e. before treatment;

ˆ log wage per employee: logarithm of total wage cost per employee at establishment i at time t-1;

ˆ high technology: dummy variable taking value of one if an establishment self reports to employ a technology which is above average or state-of-the-art at time t-1;

ˆ high-skilled: percentage share of high-skilled employees at establishment i at time t-1;

ˆ foreign ownership: dummy variable taking value of one if majority of the establish-ment is held by a foreign investor.

Selection Variables Wagner

Additionally we provide the same variables as Wagner (2011) for all oshoring measures as a robustness check for our results. These selection variables are measured as follows:

ˆ employment: total employment at establishment i at time t-1;

18Similar sets of selection variables are applied for instance by Becker and Muendler (2008) or Barba Navaretti and Castellani (2004). According to the former study the rms that displace their activities internationally, stem from the high technology (manufacturing) sectors and are larger in terms of employment. Additionally, Barba Navaretti and Castellani (2004) nd the size of a rm and its productivity and protability to be relevant covariates for the treatment of international investments.

Becker and Muendler (2008) also identify the domestic employment and the establishment's average wage costs per employee to be signicant selection variables for their measure of foreign employment expansion. Furthermore, they employ variables that describe the skill composition of the workforce at the establishment.

ˆ employment squared: total employment squared;

ˆ employment cubic: cubic term of total employment;

ˆ sales per employee: sales per employee at establishment i at time t-1;

ˆ wage per employee: wage per employee at establishment i at time t-1;

ˆ export share: percentage share of total exports of total sales at establishment i at time t-1;

ˆ employment change: change of total employment at establishment i from time t-2 to t-1.¹⁹

Selection Variables SU

This specication adjusts the estimation of the propensity score to t it better to the relocation case. Apart from the industry and region dummies and the rm size variable in terms of number of employees, we additionally include the export share as in the specication ofWagner (2011) and the technology variable as in the MUW specication.

As a new variable we include an indicator for an establishment that belongs to a corporate group, and an indicator whether an establishment has a works council.

Aliates of a corporate group may be more likely to be relocated, because these often are purely production units which are intensive in production workers and therefore may be relatively cheap elsewhere. Instead, headquarters are intensive in high-skilled labor which is relatively cheap in Germany. Moreover, single establishment corporations are often too small to nance foreign investments, or lack the managerial experience of supervising aliates.

establishments with more than ve employees are eligible in Germany to have a works council if there are employees who desire to have one. In fact, many, even large rms do not have a works council. The decision to close an in-house activity and to dismiss employees is a prototypical situation where a works council takes part in the decision.

Because it is in the interest of the works council to secure domestic employment and works councils can increase the cost of relocation (if not block it at all), its presence is likely to reduce the probability of relocation.

19Note that this selection variable partly accounts for dierent growth paths of the treated and non-treated observations.

Quasi Natural Control Group

In addition to the designed control groups through the matching method we oer a unique quasi natural control group for the relocation variable. Within the 2006's survey (one period before treatment) the establishments were asked if an agreement for employment and location assurance with their workforce or its representation exists and of what con-tent it is. The establishments were asked what promises they make within this agreement and which promise the bargaining workforce makes in turn. One promise of the establish-ments is disclaiming to outsource/relocate any activity of the establishment and possible counterparts of the agreement are typically lower wages or increased hours of work. We assume establishments that disclaim to relocate most likely to be potential oshoring units, because their workforce would not bargain about this probably expensive promise if it is unlikely to happen. We present results for the relocation variable as described above, but with a restricted control group consisting only of such disclaimers.

DESTATIS Data

The second data set we employ is a special purpose survey on relocation in 2006 on behalf of the German federal statistical oce (DESTATIS). The DESTATIS data provide a comparable relocation measure on employment for German rms. These data have also been used in Wagner (2011).²⁰ Here a representative sample of about 20000 German establishments is interviewed about their relocation activities before 2001, between 2001 and 2003 and between 2004 and 2006. Especially they are asked for relocation which implicitly includes a restructuring at home. We merge this information to characteristics of regular reports on manufacturing establishment activities of DESTATIS via a unique establishment identier. We end up with a second data set for a micro-level relocation measure. Again we need three types of variables: treatment, outcome, and selection variables. The outcome variable is equal to the outcome variable before, the dierence in log employment from the period before relocation to the period after relocation. The relocation treatment variable is dierentiated for three time periods: relocation to a foreign country in years (i) 2001-2006, (ii) 2001-2003 and (ii) 2004-2006. Unfortunately, the set of available selection characteristics is limited for this data set. Thus, we include log employment, log sales per employee, and log wage per employee beside 2-digit industry-and 16 regional dummies.

20See Wagner (2011) for a comprehensive description of the data.