The Role of Multinational Enterprises in the Transition Process of Central and Eastern European Economies Marek, Philipp

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1 University of Groningen The Role of Multinational Enterprises in the Transition Process of Central and Eastern European Economies Marek, Philipp IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2015 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Marek, P. (2015). The Role of Multinational Enterprises in the Transition Process of Central and Eastern European Economies. Groningen: University of Groningen, SOM research school. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date:

2 Appendix A.2 Regional Determinants of MNE s Location Choice in Post-Transition Economies A.2.1 Specification of the Coefficients of the Empirical Function The profit function π jk = (1 t j ) [ (σ 1) σ 1 ( ((1 + τj )w jk ) γ ) 1 γ r 2 γ j d 3 δ j S 1 δ jk T 2 δ j H 3 δ 1 σ j E 4 j σ σ M m=1 ] σ 1 MA m σ 1, φ jm can be transformed by taking logs into the following log-linear empirical function with an error term, e jk : ln π jk = (σ 1) ln (σ 1) σ ln σ + ln (1 t j ) + γ 1 (1 σ) ln (1 + τ j ) }{{}}{{}}{{} β 0 β 1 ln t j β 2 ln τ j + γ 1 (1 σ) ln w jk +γ 2 (1 σ) ln r j +γ 3 (1 σ) ln d j +δ 1 (1 σ) ln S jk +δ 2 (1 σ) ln T j + }{{}}{{}}{{}}{{}}{{} β 3 β 4 β 5 β 6 β 7 ( M δ 3 (1 σ) ln H j + δ 4 (1 σ) ln E j + (σ 1) ln }{{}}{{}}{{} m=1 β 8 β 9 β 10 ) MA m + e jk. φ jm The definitions of the coefficients above lead to the profit function serving as the foundation for the empirical analysis. π jk = β 0 + β 1 ln t j + β 2 ln τ j + β 3 ln w jk + β 4 ln r j + β 5 ln d j + β 6 ln S jk + β 7 ln L jk + β 8 ln H j + β 9 ln E j + β 10 ln ( M m=1 ) MA m + e jk. φ jm 163

3 A.2.2 Tables Table A.1: The 33 NUTS-2-regions included in the dataset ID Country NUTS-2 Region Industry Service 1 East Germany DE30 Berlin East Germany DE41 Brandenburg - Nordost East Germany DE42 Brandenburg - Südwest East Germany DE80 Mecklenburg-Vorpommern East Germany DED1 Chemnitz East Germany DED2 Dresden East Germany DED3 Leipzig East Germany DEE0 Sachsen-Anhalt East Germany DEG0 Thüringen Czech Republic CZ01 Praha Czech Republic CZ02 Stredni Cechy Czech Republic CZ03 Jihozapad Czech Republic CZ04 Severozapad Czech Republic CZ05 Severovychod Czech Republic CZ06 Jihovychod Czech Republic CZ07 Stredni Morava Czech Republic CZ08 Moravskoslezsko Poland PL11 Lodzkie Poland PL12 Mazowieckie Poland PL21 Malopolskie Poland PL22 Slaskie Poland PL31 Lubelskie Poland PL32 Podkarpackie Poland PL33 Swietokrzyskie Poland PL34 Podlaskie Poland PL41 Wielkopolskie Poland PL42 Zachodniopomorskie Poland PL43 Lubuskie Poland PL51 Dolnoslaskie Poland PL52 Opolskie Poland PL61 Kujawsko-Pomorskie Poland PL62 Warminsko-Mazurskie Poland PL63 Pomorskie Capital regions highlighted in blackface letters. 1,737 2,

4 Table A.2: Descriptive statistics of the secondary variables Variable East Germany CZ PL Total Relative Agglomeration 0.098* # # spec (0.124) (0.071) (0.047) (0.0962) Diversification 0.148* # # herf (0.033) (0.029) (0.016) (0.031) Sectoral Wage 34.87* # # wage (15.82) (12.76) (9.754) (16.26) Human Resources 28.21* # # hrsto (4.143) (7.540) (3.357) (6.367) Unemployment Rate 16.53* # unemp (2.683) (3.391) (5.205) (5.447) Regional GDP * # # gdp ( ) (6331.1) ( ) ( ) Market Potential * # # mp (2400.1) (2103.0) (1660.4) (2661.9) Population Density 560.6* # popdens (1156.3) (770.1) (75.53) (735.5) Infrastructure-Index 0.889* # # inf (0.446) (0.162) (0.208) (0.298) Corporation Tax 39.81* # # corp (6.984) (4.655) (5.711) (8.988) Tax Wedge 52.99* # # tax (0.762) (0.404) (1.590) (5.069) Patents * # # patents (163.90) (9.909) (5.493) (116.14) Distance * dist ( ) (2126.4) (1772.6) (2117.7) Note: Mean of the referring variable above and the corresponding standard error in parenthesis below. =Significant mean difference compared to the Polish and Czech observations; # =Significant mean difference compared to the German observations. All tests refer to a 5% significance level. The mean and the standard error of the regional values are equally weighted over time, except for the relative agglomeration and wages, which are calculated on the base of the observation of the chosen investments. Table A.3: Correlation table of explanatory variables spec herf patent wage hrsto unemp gdp mp corp taxw popd infra dist spec 1 herf patent wage hrsto unemp gdp mp corptax taxwed popdens infra dist

5 Table A.4: Conditional Logit for the whole sample and subsamples. whole exclusion of obervations from sample East Germany Poland Czech Republic lnspec 0.780*** 0.720*** 0.883*** 0.823*** (0.034) (0.048) (0.049) (0.039) lnherf * * (0.146) (0.257) (0.270) (0.191) lnpatent ** (0.029) (0.033) (0.063) (0.033) capital 0.638*** 0.273* 1.622*** 0.302** (0.098) (0.163) (0.419) (0.127) lnwage 0.603*** 1.012*** 0.670*** 0.800*** (0.083) (0.127) (0.126) (0.096) lnhrsto 0.723*** * 0.459* (0.228) (0.270) (0.405) (0.255) lnunemp 0.534*** *** 0.556*** (0.084) (0.112) (0.145) (0.123) lngdp 0.903*** 1.154*** 0.809*** 1.036*** (0.063) (0.094) (0.153) (0.067) lnmp 0.422** 0.971*** ** (0.182) (0.288) (0.284) (0.233) lnpopdens *** ** *** *** (0.051) (0.062) (0.097) (0.089) lncorp 0.823*** *** 0.957** 2.182*** (0.224) (0.420) (0.455) (0.270) lntax 2.783*** ** *** 8.515*** (0.550) (0.748) (3.737) (0.712) lninfra 0.363*** *** (0.127) (0.184) (0.161) (0.231) lndist_eu *** *** *** *** (0.073) (0.117) (0.105) (0.086) lndist_neu ** (0.507) (0.805) (0.855) (0.542) Investments 4,343 2,633 2,420 3,633 Log-Likelihood -12, , , ,460.0 Hausman-Test χ 2 (18) p-value Conditional Logit Estimation. Dependent Variable: Location choice for Region j. Standard errors in parentheses: ***p 0.01,**p 0.05,*p 0.1. Country dummies, sectoral dummies and company size included in each regression. Hausman Test for systematic differences between the coefficients of the whole sample and the corresponding subsample. 166

6 A.3 Agglomeration and FDI in East German Knowledge-Intensive Business Services Table A.5: Regional distribution of KIBS FDI Name of Raumordnungsregion Investments Altmark 4 Anhalt-Bitterfeld-Wittenberg 8 Berlin 386 Halle/S. 27 Havelland-Fläming 33 Lausitz-Spreewald 20 Magdeburg 34 Mecklenburgische Seenplatte 5 Mittelthüringen 28 Mittleres Mecklenburg/Rostock 25 Nordthüringen 1 Oberes Elbtal/Osterzgebirge 48 Oberlausitz-Niederschlesien 12 Oderland-Spree 9 Ostthüringen 35 Prignitz-Oberhavel 13 Südsachsen 20 Südthüringen 9 Uckermark-Barnim 5 Vorpommern 12 Westmecklenburg 8 Westsachsen 47 Total

7 Table A.6: Correlation table of explanatory variables (log values) LQ MLI herf patent rnd stud hrsto gdp mp wage tax STune dist LQ 1 MLI herf patent rnd stud hrsto gdp mp tax wage STunemp dist