The effect of restrictive measures on cross-border investment in the European Union

Gregori, Wildmer Daniel; Nardo, Michela Working Paper The effect of restrictive measures on cross-border investment in the European Union JRC Working Papers in Economics and Finance, No. 2019/15 Provided in Cooperation with: Joint Research Centre (JRC), European Commission Suggested Citation: Gregori, Wildmer Daniel; Nardo, Michela (2020) : The effect of restrictive measures on cross-border investment in the European Union, JRC Working Papers in Economics and Finance, No. 2019/15, ISBN 978-92-76-14322-2, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/30468 This Version is available at: https://hdl.handle.net/10419/227663 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The effect of restrictive measures on cross-border investment in the European Union Gregori, Wildmer Daniel Nardo, Michela 2020 JRC Working Papers in Economics and Finance, 2019/15 This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. EU Science Hub https://ec.europa.eu/jrc JRC119285 PDF ISBN 978-92-76-14322-2 ISSN 2467-2203 doi:10.2760/30468 Luxembourg: Publications Office of the European Union, 2020 © European Union, 2020 The reuse policy of the European Commission is implemented by the Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of photos or other material that is not owned by the EU, permission must be sought directly from the copyright holders. All content © European Union, 2020. How to cite this report: Gregori, W.D. & Nardo, M. (2020), The effect of restrictive measures on cross-border investment in the European Union, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-14322-2, doi:10.2760/30468, JRC119285. 1 The effect of restrictive measures on cross-border investment in the European Union Wildmer Daniel Gregori, Michela Nardo∗ Abstract This study sheds light on the effect of restrictive policies, such as screening mechanisms, on mergers and acquisitions (M&A) flows into EU Member States in the period 2011-2018, by implementing an augmented gravity model. The results show that different restrictive measures affect crossborder investments unequally, and that the presence of screening mechanisms per se does not negatively affect cross-border investments. When we perform the analysis by sector, results suggest that cross-border investments in manufacturing and non-financial services are negatively by restrictive measures, such as restrictions on foreign personnel being employed in key positions, or restriction on the establishment of branches, land acquisition or profit and capital repatriations. Keywords: cross-border investment, M&A, EU, FDI, statutory restrictions, gravity model. JEL codes: F15, F21, G34, K20. ∗ Wildmer Daniel Gregori: Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, Italy; email: [email protected]. Michela Nardo: Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, Italy, email: miche[email protected]. We would like to thank participants in the JRC-Ispra finance and economy unity seminar. Special thanks are due to Andrea Bellucci and Filippo Pericoli for insightful comments. Responsibility for any errors lies solely with the authors. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission. 2 1. Introduction Despite concerns about an economic slowdown in latest years,1 globalization increased the importance of foreign direct investment (FDI) worldwide. In 2018 inward FDI positions into the EU28 (8.9 tn Euro) represented 54.6% of European GDP, 15 percentage points higher than before the crisis. About 40% of all inward FDI in 2017 are generated from US, first investor in Europe before Switzerland, Canada and Japan (Figure 1). China (considering also Hong Kong) is the 5th investor in Europe, with 3.5% all of FDI positions. Offshores stand up as the extremely important in channelling investments into Europe.2 Economic theories considered FDI as an asset for host countries as foreign takeovers are likely to bring along superior technology, easing technology diffusion, and increase productivity by shifting production toward more sophisticated technologies or goods (Hale and Xu, 2016). The empirical literature on FDI actually points to a positive link between FDI and GDP growth (Iamsiraroj and Doucouliagos, 2015)3. This seems to be related to the degree absorptive capacity of the host country. In particular, the largest impact of FDI on growth is observed for open economies with an educated workforce and developed financial markets (Bodman and Le, 2013). The effects on employment are less clear and go from a decrease in short terms due to the introduction of labour saving technologies (Hijzen at al, 2013) to an increase in the longer term. This is obtained by forcing a change in workforce composition towards more skilled workforce (Dinga and Mnich, 2010). Besides economic theory, in everyday news we see that not all FDIs are equally welcome. The political momentum that perceives as crucial the need to protect domestic technologies, companies and markets from foreign control or from the suspect of forced technology transfer has led to careful scrutiny and occasionally block of foreign investments. Examples are the recent U.S. ban on Chinese acquisition attempts targeted to advanced technologies such as 5G, artificial intelligence, biotechnology, virtual reality, and other dual-use technologies (USCC, 2019), or the German veto in 2018 to the acquisitions of a 20 percent stake in high-voltage energy network operator 50Hertz and of Leifeld Metal Spinning, a company with customers in the aerospace, chemicals and automotive industries. 1 Inward flows dropped 44% with respect to a peak of 3.9% of GDP in 2015. 2 Offshores include Bermuda, Bahamas, Cayman, Gibraltar, Guernsey, Jersey, and UK-Caribbean. 3 There are also dissonant voices claiming a non-significant or even negative link between FDI and growth (see Bermejo Carbonell and Werner, 2018). 3 [Fig. 1 – Non-EU investments in Europe – around here] In Europe, as of December 2019, only 15 out of 28 Member States have FDI review mechanisms in place4, differing widely in scope (e.g., review of intraor extra-EU FDIs, differing screening thresholds, breadth of sector coverage), process (e.g., pre-authorization vs. ex post screening of FDI), review timetables and enforcement. On 5 March 2019, the European Parliament and the Council of the European Union formally adopted a new regulation on foreign direct investment (FDI) screening (EU Regulation 2019/452). The regulation, which will be fully operational in October 2020, introduces a coordination mechanism whereby the European Commission may issue non-binding opinions on FDI reviews performed in an EU Member State when they affect security and public order. A research question, still open, is whether the presence of a screening mechanism (and more generally regulatory barriers) is indeed a deterrent to foreign investments. This issue is the reason behind the initial hesitation of some European Member States to back the new regulation. To address these questions, we estimate a gravity model linking cross-border M&A flows between 2011 and 2018 in 23 European countries to the FDI restrictions as measured by the OECD FDI Regulatory restrictiveness Index. This index aims to capture a plurality of institutional features that could influence FDI, from equity or key personnel restrictions applied to foreign investors, to limitations in the establishment of branches, acquisition of land or clauses on capital/profit repatriation. We find that the presence of a screening mechanism do not negatively affect cross-border investment per se. Instead, other types of restrictions play a central role especially those related to the establishment of branches or repatriation of profits. Our model is in line with Mistura and Roulet (2019). They analyse the effect of FDI restrictions on bilateral FDI positions (from national accounts) and cross-border M&A stocks (from Dealogic data) for the period 1997-2016. They find that a drop in the restrictive index by 10% due to the implementation of liberalization polices, could generate an additional amount of bilateral FDI positions (M&As) as large as 2.1% (3%) on average. Our result is much stronger as a 1 point drop in the Restrictive Index implies a growth of 1,2% in M&As flows on average. The difference is due to the sample used and the smaller time interval encompassing, in the case of Mistura and Roulet, the financial crisis and the plunge of cross-border capital flows. Contrary to Mistura and Roulet (2019) we discard the use of official FDI data coming from national 4 Austria, Denmark, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, Spain, Sweden, United Kingdom. 4 accounts but rather use micro data on M&A deals coming from the Bureau van Djik Zephyr dataset (very similar to Dealogic). Official bilateral FDI data actually measure the link between the declaring country and the first partner country, which not necessarily is the ultimate owner of the investment,5 therefore biasing gravity measures and distorting interpretations. We also refrain from mimicking official investments positions using the cumulated flows of M&As. Even If theoretically possible it presents several shortcomings given current data availability. Among them undeclared M&A deal values, time mismatch between announcements and transactions, and transactions in kind (e.g. transfer of intangibles). A novelty of our paper consists in analysing M&A flows in different economic sectors to account for the sector-specific impact of restrictive policies. Using an augmented gravity framework, we show that different restrictive measures unequally affect cross-border investment in different sectors. While FDIs in the primary sector remains largely unaffected by changes in the restrictive index, M&As in the secondary and tertiary respond to different restrictions. As expected, manufacturing and services are mostly affected by restrictions in the establishment of branches or repatriation of profits, while limitations in equity holdings only affect manufacturing. Furthermore, financial services, a highly regulated sector, behaves differently from the rest of tertiary and remains largely unaffected by changes in the index. Our findings are also in line with and contribute to the literature on the links between regulatory barriers and trade flows. Not surprisingly, trade flows are complements to cross-border capital flows (Ansgar and Clemens, 2018). Van der Marel and Shepherd (2013) analyse the relationship between trade in services and regulation, finding a negative link between regulatory restrictiveness and trade flows, whose strength results to vary across sectors. Nordås and Rouzet (2017) confirm this result. In their gravity model linking the trade in services with regulatory restrictions, tighter restrictions decrease both trade and investments, with the exports of services resulting more sensitive than imports. This work also contributes to the research line in trade models that employ gravity models to analyse FDI determinants (among others, see Carrere, 2006; De Sousa et al., 2012; Heid and Larch, 2016; Nordås and Rouzet, 2017 for a comprehensive view of the extensive literature). The literature has examined a wide range of push and pull factors without offering a clear view on which are the decisive ones (Blonigen, 2005). Blonigen and Piger (2014), identify as main enabling factors the traditional gravity variables such as cultural 5 See OECD BMD4 manual at http://www.oecd.org/investment/investment-policy/fdibenchmarkdefinition.htm. For a pilot study to produce official bilateral FDI statistics based on ultimate ownership, see the concluding document of the Task Force co-chaired by Eurostat and the European Central Bank with the participation of 24 countries, the OECD, the IMF and UNCTAD (https://www.parlament.gv.at/PAKT/EU/XXVII/EU/00/56/EU_05688/imfname_10945651.pdf. 5 distance, the difference in labour endowments and the presence of trade agreements. They find little relevance of pull factors such as multilateral trade costs, host country’s business costs, infrastructure or political institutions. Our results are in line with their findings. In our baseline model contiguity, common language, political links, and trade openness are all enabling factors of cross-border investments, while government effectiveness results non-significant in explaining M&A flows. Different results are found by Bénassy-Quéré et al. (2007). By using gravity equations on national account FDI positions for OECD countries between 1985 and 2000, they obtain a positive impact of institutional quality on bilateral FDI, suggesting that bureaucracy, corruption, information, banking sector and legal institutions are important determinants of inward FDI. These results confirm that of Di Giovanni (2005): institutional factors and domestic financial conditions are important in stimulating cross-border M&A. The link between trade agreements and currency unions and cross-border investments is also an open question. Eicher et al. (2012) find a positive link only under specific conditions, while the potential market opportunities of the host country is identified as a decisive pulling factor. Recently, Economou (2019), focusing on Greece, Italy, Portugal, Spain, confirms the relevance of market size and gross capital formation to attract FDIs, while unit labour costs is found to have a negative impact of cross-border investments. In our estimations, regional trade agreements are not significant while trade openness and the bilateral links with other EU countries of the single market are highly significant. The remainder of the paper is organised as follows. Section 2 illustrates the dataset, while Section 3 specifies the model. Section 4 presents the results and a related discussion. Section 5 focuses on robustness checks, while Section 6 concludes. 2. Data The dataset used has a mixed origin. Cross-border M&A data are from Bureau van Djik Zephyr database, a Moody’s analytics product. Zephyr is widely used in the literature (Reiter 2013, Clo’ et al. 2017; Del Bo et al. 2017, among others) and provides information on M&As, portfolio investments and Joint Ventures deals worldwide starting from 1997 with a daily update. Information come from a wide range of sources, including financial journals, reports, company press releases, and company websites. We focus only on completed cross-border deals where the target company is in EU28, excluding rumoured or uncompleted deals to 6 increase the quality of our dataset.6 For each deal, we have include information on the origin of the investor and target (destination) country of the cross-border investment, year of the agreement, sector of the target company and deal value in nominal terms7. The starting point of our analysis is linked to the availability of FDI regulatory restrictiveness index (RI), used as explanatory variables and published annually by the OECD since 2010.8 RI measures the statutory restrictions on foreign investors or investments for OECD countries. Four types of measures are identified and measured by the index: (1) limits on foreign equity, to account for limits on foreign participation, holdings and ownership; (2) restrictions on foreign personnel being employed in key positions, to account for measures such as time-bound or economic limits on the employment of foreign personnel as managers and requirements related to the nationality of board of directors’ members; (3) other restrictions such that restrictions on the establishment of branches, acquisitions of land for business purposes, profit or capital repatriation, but also reciprocity clauses in specific sectors; and (4) existence of screening and prior approval, to account for screening mechanism applied only to foreign investors. The RI ranges from 0 to 1, and it is calculated as linear aggregation of four sub-indicators corresponding to the four typologies of restrictions considered. RI is also available at the sectoral and subsectoral levels.9 Additional control variables needed to implement the gravity model are the GDP of origin and destination countries and the distance between each country pair, calculated as distance between the biggest cities of those countries (following De Sousa et al., 2012, inter-city distances are weighted by the share of the city in the overall country’s population). GDP and Distance data are taken from the CEPII10 database until 2015 and from the World Bank11 for the last two years of the sample. The augmented gravity model includes a series of country-pair information that, according to the literature, may affect cross-border 6 Actually, Zephyr also reports announced deals and rumors. We exclude these deals form the analysed data to avoid introducing noise. 7 We prefer to use nominal and not real values for a number of reasons. Firstly inflation adjustments are based on subjective elements (baskets or proxies representing the true unknown deflator), and secondly there is no clear empirical evidence that agents base their decisions on real instead of observed market values (Werner, 2013) 8 The FDI Index is also available for many countries for the following years: 1997, 2003, 2006, 2010-2018. For a detailed explanation, see https://www.oecd.org/investment/fdiindex.htm and Kalinova et al. (2010). 9 Sectors are divided as follows: primary, secondary and tertiary, without including real estate. Subsectors are divided as follows. The primary sector is divided into (1) agriculture, (2) forestry, (3) fishing and (4) mining and quarrying. The secondary sector is divided into (5) food and other manufacturing, (6) oil refining and chemicals, (7) metals, machinery and other minerals, (8) electronic, electrical and other instrument and (9) transport equipment. The tertiary sector is divided into (10) electricity, (11) construction, (12) wholesale trade, (13) retail trade, (14) transport land, (15) hotels and restaurants, (16) media, (17) telecommunications, (18) banking, (19) insurance, (20) other finance (including securities and commodities brokerage, fund management, custodial services) and (21) business services. There is also an independent subsector: (22) real estate.For a detailed explanation of the index, see Kalinova et al., 2010. 10 The database is available at http://www.cepii.fr. Mayer and Zignago (2011) offer additional details on the construction of the CEPII’s database on geographical distance. 11 See https://data.worldbank.org/. 13 activities, Tab. 5). As expected, regulatory restrictiveness does not affect financial services while the effect on non-financial services is mainly driven by the variable other restrictions. [Tab. 5 – Focus on Sector 3 – around here] 5. Robustness checks To perform robustness checks we have used the Gamma Pseudo Maximum likelihood estimator (GPML) instead of the Poisson estimator (PPML). The Poisson estimator assumes a constant variance-to-mean ratio of the dependent variable. This estimator could be biased in case that the error term does not follow a Poisson distribution (e.g. log-normal). Therefore, we implement an alternative estimator (GPML) that assigns lower weight to observations with larger means, generating efficiency gains in case these observations are characterised by higher variance. Moreover, as the PPML, a high number of zeros in the dependent variables does not affect the GPML. As suggested by Head and Mayer (2014), if the Poisson and Gamma PML coefficients do not exhibit major divergence, there is no signal of model miss-specification. Tab. A1 in Appendix reports GPML results for the baseline model and show the consistency of estimates between the two estimators. When restrictions are broken down by sector level, GPML confirms sign and significance of equity restrictions and other restrictions, while restrictions on key foreign personnel (weakly significant with the PPML estimator) and the presence of a screening mechanism are not statistically significant any longer. 6. Conclusions We study the effect of restrictive policies on cross-border M&A flows into the EU by implementing an augmented gravity model. Is the presence of a screening mechanism indeed a deterrent to foreign investment? Are other national characteristics important for determining the amount of FDI, such as the legal system or limitations on foreign nationals sitting on company boards? We show that, on average, regulatory restrictions have a negative effect on cross-border investments. Our results show that different restrictive measures affect cross-border investment unequally, while the presence of a screening mechanism per se does not negatively affect cross-border investment. The take-away messages for the policy agenda are therefore two. The first comes from the sector specific analysis: regulatory restrictions 14 influence cross-border investments unevenly, depending on the target sector. Policies addressed to drive up inward investments should tailor regulatory restrictions to the targeted sectors in order to avoid discouraging investments. In particular, manufacturing and non-financial services results negatively affected by restrictive measures, such as restrictions on foreign personnel being employed in key positions, or restriction on the establishment of branches, land acquisition or profit and capital repatriations. 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Dependent variable M&A Orbis 7,294 232.8683 727.4334 0.0071 11,204.5832 Restrictiveness indexes RI - all types of restrictions OECD 7,294 0.0245 0.0772 0 1 RI - equity restrictions OECD 7,294 0.0171 0.0712 0 1 RI - key foreign personnel OECD 7,294 0.0007 0.0066 0 0.0750 RI - other restrictions OECD 7,294 0.0032 0.0095 0 0.1000 RI - screening approval OECD 7,294 0.0034 0.0258 0 0.2000 Bilateral indicators Distance CEPII 7,294 3,786.71 3,933.58 160.93 19,516.56 GDP of the origin country CEPII, World Bank 7,294 3,428.05 5,483.53 0.76 19,390.60 GDP of the destination country CEPII, World Bank 7,294 1,601.03 1,226.36 19.48 3,879.28 Contiguity CEPII 7,294 0.19 0.39 0 1 Common language CEPII 7,294 0.21 0.41 0 1 Colonial links CEPII 7,294 0.17 0.37 0 1 Common legal origins CEPII 7,294 0.35 0.48 0 1 Time difference CEPII 7,294 2.96 3.39 0 12 Regional trade agreements CEPII 7,294 0.67 0.47 0 1 Origin country from EU 7,294 0.53 0.50 0 1 Government indicators Government effectiveness World Bank 7,294 1.40 0.44 0.23 2.24 Regulatory quality World Bank 7,294 1.44 0.41 0.15 2.05 Trade openness World Bank 7,294 0.98 0.60 0.52 4.16 Tax indicator KPMG 7,294 2.15 5.36 -13.00 11.99 Notes: Data for M&A refer to the period 2011-2018 in million EUR (one observation for each country pair, year and subsector), while for all other variables data relate to the period from 2010 to 2017 (they are lagged by 1 year to limit endogeneity issues). GDP values are in billion EUR. The table includes information for observations where the value of M&A is positive. 21 Table 1B. Summary statistics, by sector Variables Sector Obs. Mean SD Min. Max. M&A Primary 326 319.0143 896.8906 0.0461 6002.9072 RI - all types of restrictions Primary 326 0.0189 0.0729 0.0000 0.5500 RI - equity restrictions Primary 326 0.0126 0.0676 0.0000 0.5000 RI - key foreign personnel Primary 326 0.0001 0.0009 0 0.0090 RI - other restrictions Primary 326 0.0060 0.0180 0 0.1000 RI - screening approval Primary 326 0.0003 0.0050 0 0.0900 M&A Secondary 2,865 262.8464 807.5391 0.0070 11204.5800 RI - all types of restrictions Secondary 2,865 0.0023 0.0327 0 1 RI - equity restrictions Secondary 2,865 0.0011 0.0324 0 1 RI - key foreign personnel Secondary 2,865 0.0000 0.0000 0 0 RI - other restrictions Secondary 2,865 0.0012 0.0046 0 0.0230 RI - screening approval Secondary 2,865 0.0000 0.0000 0 0 M&A Tertiary 4,103 205.0909 647.8962 0.0070 7,721.6740 RI - all types of restrictions Tertiary 4,103 0.0405 0.0943 0 1 RI - equity restrictions Tertiary 4,103 0.0288 0.0875 0 1 RI - key foreign personnel Tertiary 4,103 0.0013 0.0086 0 0.0750 RI - other restrictions Tertiary 4,103 0.0044 0.0106 0 0.0680 RI - screening approval Tertiary 4,103 0.0061 0.0344 0 0.2000 Notes: Data for M&A refer to the period 2011-2018 in million EUR (one observation for each country pair, year and subsector), while for all other variables data relate to the period from 2010 to 2017 (they are lagged by 1 year to limit endogeneity issues). Tertiary sector includes also Real estate. 22 Table 2. RIs’ effects and M&A, baseline estimation (PPML, period 2011-2018) Notes: This table shows the baseline results from implementing the PPML model. The dependent variable is the value of bilateral M&A in thousand EUR. All explanatory variables are lagged by 1 year. Robust standard errors are shown in parentheses. Robust standard errors clustered by country pair are shown in parenthesis. The symbols *, ** and *** indicate statistical significance at the 10 %, 5 % and 1 % levels, respectively. (1) (2) (3) (4) (5) (6) (7) (8) RI - all types of restrictions j,t-1 -2.68*** -2.65*** -2.51*** -1.33*** (0.53) (0.53) (0.51) (0.48) RI - equity restrictions j,t-1 -1.18** (0.47) RI - key foreign personnel j,t-1 -9.98** (4.58) RI - other restrictions j,t-1 -20.37*** (4.07) RI - screening approval j,t-1 3.84** (1.60) ln(Distance) ij,t-1 -0.55*** -1.10*** -0.97*** -0.97*** -0.97*** -0.97*** -0.98*** -0.97*** (0.07) (0.16) (0.15) (0.15) (0.15) (0.15) (0.15) (0.15) ln(GDP origin country) i,t-1 0.91*** 0.73*** 0.73*** 0.73*** 0.73*** 0.73*** 0.73*** 0.73*** (0.08) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) ln(GDP destination country) j,t-1 0.82*** 0.77*** 0.83*** 0.83*** 0.84*** 0.84*** 0.81*** 0.86*** (0.07) (0.06) (0.07) (0.07) (0.07) (0.07) (0.07) (0.07) Contiguity ij,t-1 -0.56** -0.50* -0.50* -0.50* -0.50* -0.51* -0.50* (0.26) (0.27) (0.27) (0.27) (0.27) (0.26) (0.27) Common language ij,t-1 0.82*** 0.67*** 0.67*** 0.67*** 0.66*** 0.73*** 0.66*** (0.20) (0.21) (0.21) (0.21) (0.21) (0.22) (0.21) Colonial links ij,t-1 0.70*** 0.82*** 0.82*** 0.82*** 0.83*** 0.79*** 0.83*** (0.19) (0.18) (0.18) (0.18) (0.18) (0.18) (0.18) Common legal origins ij,t-1 -0.01 0.01 0.01 0.01 0.01 0.00 0.01 (0.16) (0.16) (0.16) (0.16) (0.16) (0.15) (0.16) Time difference ij,t-1 0.27*** 0.25*** 0.25*** 0.25*** 0.25*** 0.25*** 0.25*** (0.05) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05) Regional trade agreements ij,t-1 -0.13 -0.06 -0.06 -0.06 -0.06 -0.07 -0.06 (0.21) (0.20) (0.20) (0.20) (0.20) (0.21) (0.20) Origin country from EU i,t-1 0.71*** 0.73*** 0.73*** 0.73*** 0.73*** 0.74*** 0.73*** (0.24) (0.26) (0.26) (0.26) (0.26) (0.25) (0.26) Government effectiveness j,t-1 -0.15 -0.14 -0.15 -0.15 -0.07 -0.14 (0.37) (0.37) (0.37) (0.37) (0.36) (0.37) Regulatory quality j,t-1 0.54 0.55 0.55 0.54 0.48 0.51 (0.38) (0.38) (0.38) (0.38) (0.37) (0.38) Trade openness j,t-1 0.34** 0.34*** 0.35*** 0.36*** 0.34*** 0.39*** (0.13) (0.13) (0.13) (0.13) (0.12) (0.13) Tax indicator j,t-1 0.01 0.01 0.01 0.01 0.02* 0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Year fixed effects no yes yes yes yes yes yes yes Sector fixed effects no no no yes yes yes yes yes Observations 372,416 372,416 372,416 372,416 372,416 372,416 372,416 372,416 Pseudo-R 2 0.03 0.04 0.05 0.05 0.05 0.05 0.05 0.05