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In general equilibrium and under resource constraints at the level of countries, aggregate bilateral foreign investments and foreign affiliate sales are known to be interdependent across host countries for a given parent economy (Baltagi et al. 2007, 2008) as well as across parent countries for investments in a given host economy (Blonigen et al. 2008). Such interdependence leads to the transmission of country-specific shocks in the world investment system, whereby (geographically) more adjacent countries are stronger recipients as well as transmitters of shocks. All existing evidence on interdependent foreign investments, however, seems to pertain to aggregate investment flows or stocks. While such aggregate analysis is useful to understand the relevance of interdependence at the level of countries, theoretical models used to motivate empirical studies of interdependence mainly rely on effects between firms.

This paper contributes to the empirical literature measuring interdependence of foreign direct investment (FDI) with a particular focus on whether such interdependence arises across affiliates within MNEs. To measure whether investment of one particular entity of an MNE depends on investments carried out by other entities of the same MNE, we discern different channels of interdependence such as input-output relationships or mere geographic proximity. This approach relates our paper to a recent literature on the organization of production along the value chain (e.g., Antras and Chor 2013; Costinot et al. 2013), because using weights on input-output dependence allows us to draw conclusions about the relative position (upstream or downstream) of entities (and countries) in the global value chain. Moreover, the approach is related to the literature unveiling vertical international linkages in the productivity (see Bernstein and Mohnen 1997; Keller 2002; Morrison Paul and Siegel 1999; Smarzynska Javorcik 2004), growth and volatility (Burstein et al. 2008; Kleinert et al. 2012; Oberhofer and Pfaffermayr 2013), and, hence, the profitability across units (firms, sectors, and even countries). Comparing input-output-related interdependencies of affiliates' investments to geographic-distance-related interdependencies also permits drawing conclusions about the relative importance of different channels of interdependence. Finally, while there is a large literature on productivity spillovers from FDI on domestic firms or generally across firms, we add to this literature by providing evidence on interdependencies that occur within a firm, but across affiliates and countries.

Our analysis is probably most closely related to the study by Chen (2011). The paper by Chen (2011) is based on subsidiary-level data and suggests horizontal as well as vertical interdependence in the location of subsidiaries of MNEs. While the focus of our study is on the relative importance of different channels (horizontal or vertical linkages) through which investment at a given location is affected by marginal investment decisions at other locations, the paper by Chen (2011) examines the effect of existing production networks on the location of foreign production. That is, Chen (2011) analyzes how horizontal or vertical linkages determine the extensive margin of foreign investment. In contrast, our analysis of interdependencies in the intensive margin of investment is conditional on network location. While our focus is on the intensive margin of investment, we explore the relative importance of adjustments at the intensive margin (changes in investment by old affiliates) versus adjustments at the extensive margin (investment by new affiliates).

Our empirical analysis utilizes census-type panel data of all German MNE parents and their foreign affiliates provided by Deutsche Bundesbank. One obvious advantage of this dataset is that it allows us to control for affiliate- and firm-specific characteristics, whose omission in country-level studies might lead to aggregation bias. Moreover, the availability of a large number of firm-affiliate-host-country-year data points permits identification at relatively great precision in comparison to country-pair-time aggregated data. Also, the census-type data at the firm and affiliate level help avoiding a bias associated with missing data points, which affects virtually all attempts to analyze interdependence at the aggregate foreign investment level from incomplete (e.g., survey-based or otherwise selected) datasets. (1) The same, of course, is true for most firm-level datasets, which cannot provide a similarly complete picture as our dataset can. (2) Given that group effects are at the heart of our analysis, the latter points are particularly important.

We model the stock of FDI invested in a foreign entity as to depend on a weighted function of FDI stock elsewhere within the firm. (3) Our estimation strategy does not condition on the total amount of FDI invested in all units of a firm--by focusing on its allocation only--but it allows aggregate investments to vary. Thus, we do not view investments to be necessarily substitutive across locations but allow them to be substitutive or complementary. For estimation and identification of interdependence effects we specify a spatial autoregressive (SAR) model which allows for fixed effects at the level of foreign affiliates. A novel feature of our analysis is that the structure of interdependence is affiliate-firm-specific to account for differences in MNEs' vertical and geographical (horizontal) organization of production. Moreover, using different weights to distinguish between investments that are relatively more downstream versus upstream versus geographically proximate enables us to provide the following novel insights about the interdependence of investment within firms: (i) what is the relative importance of geographical proximity versus input-output linkages as channels through which interdependencies in investment occur and (ii) what is the relative importance of the upstream versus downstream channel.

The main findings of our analysis can be summarized as follows. First, vertical (input-output) relations between affiliates seem to play a greater role in explaining interdependence than horizontal ones (mere geography). However, conditional on input-output relationships, investment of a given entity declines with bigger proximate investments at other entities of the same MNE. Second, while investments in vertical input-related affiliates exert a complementary impact on investment in a given affiliate (positive interdependence), the opposite is true for investments at vertical output-related affiliates. In other words, input-linked upstream investments of affiliates within an MNE's affiliate network stimulate investments in more downstream affiliates, whereas output-linked downstream investments of affiliates reduce investments in more upstream affiliates. Hence, vertical interdependence is asymmetric between upstream and downstream relationships. Third, while vertical interdependence seems to be driven by adjustments at the extensive margin (investment at other locations by new affiliates), horizontal interdependence seems to be driven by the intensive margin adjustments (changes in investment by old affiliates). Fourth, the relative sensitivity to shocks varies to a relatively large degree across firms (depending on the location and size of their affiliate network) and across host countries (depending on the vertical and horizontal size and structure of the hosted affiliate network).

The finding that more horizontal investments in proximate countries lead to less investments at a given affiliate, conditional on input-output relations, is consistent with the use of foreign affiliates as export platforms (see Ekholm et al. 2007; Tintelnot 2017). Our results regarding vertical linkages are consistent with findings of the literature on spillover effects. Smarzynska Javorcik (2004) finds evidence that positive (productivity) spillovers from foreign affiliates to domestic firms mainly occur through backward linkages (through the intermediate input channel), but not through forward linkages. We find a similar pattern for a given foreign entity which is linked through the input channel to upstream entities anywhere within the same MNE. Our results also suggest a U-shaped pattern of investment along the value chain, similar to the one found in the context of the evolution of countries and industries (see Shin et al. 2012, for an empirical application in the global electronics industry). This suggests that the profitability is particularly high at the root or origin of the production process or product cycle (where research intensity is high) and also at the end of it (where input costs are low and the service intensity is high). In our setting, with Germany as the parent country, we would expect that processes with high research intensity in the first part of the curve are mainly located in the parent country (from where positive technology spillovers are transmitted elsewhere). As Germany is a mature economy, we expect investments to be gradually shifted to other countries with greater growth potential and profits. According to our empirical estimates, for a given foreign affiliate, investments of input suppliers are positively related to a recipient affiliate's own investments, while investments of entities to which output is forwarded are negatively related to an affiliate's own investments. This suggests that investments are shifted downstream towards the end of the production line along the right part of the U-shaped curve.

We may also interpret our results in light of the recent literature on the organization of global value chains. In particular, Alfaro et al. (2015) suggest that upstream or downstream integration choices depend on the relative size of the elasticity of demand for a firm's final product and the elasticity of substitution across sequential inputs. If inputs are not particularly easy to substitute, investments across vertically linked units are found to be positively correlated, in which it is optimal for the firm to integrate only the most downstream stages. This is consistent with our finding of a positive relationship between investment of a given affiliate and investments of all other affiliates within the MNE if the affiliate is linked through the input channel. Moreover, while we cannot model the outsourcing decision, the findings in Alfaro et al. (2015) would suggest that the integrated part of the total value chain for the input-linked affiliates in our data tends to be more downstream. If instead inputs are easy to substitute, investments at different stages are negatively correlated and Alfaro et al. (2015) suggest that a firm finds it optimal to integrate relatively upstream stages. The negative interdependence found for affiliates linked through the output channel is in line with the assumption that the inputs supplied by these affiliates are characterized by a high substitutability, suggesting that the integrated part of the value chain we observe in our data for the output-linked affiliates is more upstream. All this suggests that both goods produced and relationship-specific investments made along the value chain become more specialized and less substitutable, which is again in line with the aforementioned U-shaped curve pattern.

The proposed empirical approach allows us to carry out a number of interesting experiments. For example, we may gauge the relevance of an asset reallocation across existing affiliates in response to location-specific shocks. We are also able to assess the importance of different channels of interdependence depending on location. This includes an identification of particularly shock-prone locations from the viewpoint of German investors (which account for a significant share of world FDI). On average and conditional on a shock of the same size across all existing affiliates there, the United States, China, and Brazil are the most important sources of investment shocks (spillovers) to German affiliates elsewhere. The German affiliates in Botswana, Madagascar, Iceland, and Lebanon are the most important recipients of investment shocks from German affiliates elsewhere. This shows that intra-firm effects on investment are asymmetric with regard to their impact on units upstream versus downstream. The general strength of interdependence among the affiliates in an MNE and its qualitative impact depend on the (vertical and horizontal) structure of an MNE's affiliate network.

Our findings also have policy implications. Most obviously, the degree to which a country is exposed to shocks transmitted from other countries depends on the structure of FDI in such a country. Moreover, the results imply that the interdependence of affiliate-level investment decisions does not permit treating affiliates as independent within the firm in empirical work without encountering biased and inconsistent estimates of parameters and comparative static effects. This is relevant for the analysis of policy effects such as the ones of national or international tax policy, as responses to national and international tax incentives have consequences on investments not only within but also across national borders. Our analysis at the affiliate level suggests that cross-border effects go beyond the (mechanical) interdependence emerging and considered in structural general equilibrium models of multinational firms.

The remainder of the paper is organized as follows. Section II outlines the main theoretical reasons for investment interrelatedness and how our paper relates to this literature. Section III describes the empirical approach as well as the interdependence or linkage measures used. Section IV describes the dataset used in the present analysis, before Section V presents the findings. Section VI states a brief conclusion.


We may distinguish between three different explanations for interdependence of foreign investments within firms but across affiliates and countries: (i) vertical input-output linkages within MNEs; (ii) internal capital markets of MNEs; (iii) correlated learning over sequential investments of MNEs. Point (i) suggests that single entities of the MNE are part of a global value chain and intermediate goods are used by different affiliates at different production stages within and across different countries. Bernard etal. (2010) show that such production leads to tangible assets trade within an MNE. More recently, Atalay et al. (2014) have argued that intra-firm transfers along vertical production lines are often associated with intangible assets. Intra-firm trade of intangible inputs may include transfers related to the simultaneous use of technology and knowledge (McGrattan and Prescott 2009) across locations and entities, which does not necessarily require vertical goods linkages but is also relevant in the context of horizontal FDI (see Markusen's 2002 knowledge capital model of the multinational firm for theoretical arguments along those lines; see Carr et al. 2001 and Markusen and Maskus 2002, for aggregate evidence on vertical versus horizontal MNE activity decisions). Alfaro et al. (2015) analyze outsourcing decisions along the value chain. They argue that organizational decisions at a given stage of the value chain affect all stages of the value chain, because the incentives to make relationship-specific investments depend on investments made by upstream suppliers. Depending on the relative size of the elasticity of demand for the final good and the elasticity of substitution across sequential inputs, investments at different production stages are found to be positively or negatively correlated to each other.

Point (ii) argues that entities of MNEs are linked through an internal capital market. Egger et al. (2014) demonstrate that MNEs may and do use this market to allocate scarce funds. In particular, funds are channeled to those affiliates with the highest excess return on investment. Differences in this excess return are driven by capital market frictions, differences in productivity, taxes, and local institutions. Because the allocation of funds involves lending and borrowing relationships between affiliates, the existence of internal capital markets provides for a natural reason of why there is interdependence across all--vertical and horizontal--entities of an MNE.

Point (iii) recognizes that investments might be connected through complementarities at the extensive margin. Egger et al. (2013) show that correlated learning causes interdependence since information gathered at one location by one affiliate can be used to learn about conditions in other, particularly similar countries. (4)

All of this suggests that an empirical approach modeling investment at a given affiliate needs to consider the interdependence of such investments across all affiliates within a firm. (5) Moreover, the extent of interdependencies may vary between firms as well as affiliates. A natural approach to model the extent of interdependence is geographical distance. For instance, Keller and Yeaple (2013) argue that tangible and intangible transfers from headquarters to affiliates decline with distance from their headquarters. Another measure to capture the degree of interdependence--or the closeness of affiliates--is to model input-output relations between entities. Depending on a firm's specialization, input-output relations determine whether a firm is closer to the core of activities or not, and whether it is further upstream or downstream or not, with potential consequences for the prioritization of its investment plans. The relative importance of horizontal linkages through geography among units in the same sector and of vertical linkages through input-output (or upstream and downstream) relationships among units in different sectors at the level of affiliates is not known.

More formally, we could think of profitability of a foreign investment in affiliate i at some time t, [[PI]], to be a function of stocks of assets at all affiliates of the same firm, [mathematical expression not reproducible] where [N.sub.f] is the number of all affiliates of firm f to which i belongs. Assume that new investments at location i and time t are proportional to their profitability, whereby [mathematical expression not reproducible] Moreover, let us parameterize the latter as [partial derivative][]/[partial derivative][FDI.sub.jt] = [w.sub.ijt][delta], where [w.sub.ijt] is a row vector reflecting channels of influence of investment at j on i (such as input-output channels, horizontal competition and cross-effects on sales, etc.) and [delta] is a conformable column vector of importance weights. The overall level of foreign assets at j and t then induces an overall partial impact on [] of [w.sub.ijt] [delta][FDI.sub.jt]. All other (non-i) affiliates together would have a joint partial impact on [] of [[bar.FDI]] [equivalent to] [summation over (j[not equal to]i[member of][N.sub.ft])] [w.sub.ijt][delta][FDI.sub.jt], where the summation is over affiliates in the same firm f, [N.sub.ft].


This paper considers several channels of interdependence determining individual firms' (intensive) marginal FDIs: one related to mere geography (horizontal proximity) and other ones related to input-output relationships (vertical proximity) between foreign affiliates.

A. Econometric Model

Let us use indices f, i, and j to refer to the ith or jth foreign affiliate of firm f. Altogether, there are F firms, and the fth firm has [N.sub.f] foreign affiliates during the sample period. Because firms enter and exit (and so do affiliates), the number of firms in year t [member of](1 ..., T) is [F.sub.t] [less than or equal to] F and the number of firm f's affiliates in year t is [N.sub.ft] [less than or equal to] [N.sub.f]. Let us denote the set (as opposed to the number) of foreign affiliates in year t and all years as [N.sub.ft] and [N.sub.f] = [N.sub.f1] [union] ... [union] [N.sub.fT]. In year t, the stock of FDI of firm f's affiliate i is determined as

(1) [] = [] [delta] + [], [] = [[[bar.FDI]], ([[bar.FDI]]), []], [delta] = [[[lambda].sup.D],([[lambda].sup.s]),[beta]],

where [] captures exogenous determinants of FDI. Notice that we put parentheses around [[bar.FDI]] and [[lambda].sup.s] in Equation (1). This is to indicate that more than one concept of s may be considered at a time (e.g., input and output relationships may enter separately). We usually refer to [[bar.FDI]] and [[bar.FDI]] as interdependence or linkage terms of [], and they are defined as

(2) [[bar.FDI]] = [summation over (i,j [member of][N.sub.ft])] [w.sup.l.sub.ijt] [FDI.sub.jt], l [member of] {D,s}.

The parameters [w.sub.ijt.sup.l] in Equation (2) are referred to as linkage weights in the literature, and they aggregate other affiliates' investments within a firm and year according to the distance metric indexed by D and the input-output metric indexed by s. They are normalized by a scalar as suggested by Kelejian and Prucha (2010) as

(3) [w.sup.l.sub.ijt] = [w.sup.10.sub.ijt]/max [summation over (i,j[member of][N.sub.ft])][w.sup.10.sub.ijt], [w.sup.10.sub.ijt] [greater than or equal to] 0,

where [w.sup.10.sub.ijt] is the unnormalized counterpart to [w.sup.l.sub.ijt], (6) having the property of [w.sup.10.sub.ijt] = 0 for all units j = i.

B. Channels of Interdependence (Weights [w.sup.10.sub.ijt])

The channels of interdependence, or weighting schemes, considered in this paper are based on geographical distance (l = D) and sectoral, vertical proximity (l = s). While the former is measured by the great circle distance between the countries two affiliates are based in, the latter is based on measures of the intensity of input-output relations consistent with the German input-output table.

Inverse Geographical Distance Weights ([w.sup.D0.sub.ijt]). We define inverse geographical distance weights as

(4) [w.sup.D0.sub.ijt] = [d.sup.-1.sub.ij] [for all]i [member of] [N.sub.ft],j [member of] [N.sub.ft],

where [d.sup.-1.sub.ij] denotes either the great circle distance between the main cities of the (different) countries affiliates i and j locate in, or the average internal distance of the (same) country i and j locate in. (7) If the condition in (4) is not met, for example, if we cross the boundaries of an MNE, [w.sup.D0.sub.ijt] = 0.

In robustness tests, we assume alternative decay functions for the inverse distance. For this, we specify [d.sup.-1.sub.ij] as [([d.sup.-1.sub.ij]).sup.2] and [([d.sup.-1.sub.ij]).sup.0.5]. The reasoning behind these additional tests is That [([d.sup.-1.sub.ij]).sup.2] puts relatively less weight on distant (more weight on close-by) affiliates than in the benchmark with [([d.sup.-1.sub.ij]).sup.1]. And [([d.sup.-1.sub.ij]).sup.0.5] puts relatively more weight on distant (less weight on close-by) affiliates compared with the benchmark (see Baltagi et al. 2007; Bode et al. 2012). In an additional robustness test of the measure of distance within countries, we give all affiliates within a country the same weight across countries. Specifically, we set [w.sup.D0.sub.ijt] = [d.sup.-1.sub.ij] = 1 whenever affiliates i and j are located in the same country (implicitly assuming that intra-national distance plays no role).

Input-Output Weights ([w.sup.s0.sub.ijt]). The second type of interdependence measure, [w.sup.s0.sub.ijt], reflects the amount of intermediate inputs and/or outputs which the sectors of affiliates i and j typically exchange with each other by German standards. (8) The elements [w.sup.I0.sub.ijt],[w.sup.O0.sub.ijt], [w.sup.IO0.sub.ijt] measure the amount of inputs, outputs and inputs plus outputs, respectively, the sector of i typically uses from/provides to the sector of j. Notice that [w.sup.s0.sub.ijt] is time-variant for two reasons: first, the intensity of input-output relationships changes over time; second, the sectors in which i and j mainly operate in might change over time. (9) More precisely, denote the German input-output matrix spanned by the units of firm f in year t as [[OMEGA].sub.ft], and its (ij)th element as [[omega].sub.ijt]. In this case, [[omega].sub.ijt] measures the input of the sector of unit j from the one of unit i at time t. The three measures of sectoral interdependence are defined as

(5) [w.sup.I0.sub.ijt] = [[omega].sub.ijt] [for all]i [member of] [N.sub.ft],j [member of] [N.sub.ft],

(6) [w.sup.O0.sub.ijt] = [[omega].sub.ijt] [for all]i [member of] [N.sub.ft],j [member of] [N.sub.ft]

(7) [w.sup.IO.sub.ijt] = [[omega].sub.ijt] + [[omega].sub.jit] [for all]i [member of] [N.sub.ft], j [member of] [N.sub.f't], [N.sub.ft] = [N.sub.f't].

If the conditions in (5) are not met, that is, if we cross the boundaries of an MNE, [w.sup.I0.sub.ijt], [w.sup.O0.sub.ijt], and [w.sup.IO.sub.ijt] are zero.

The Model Specification in Matrix Notation. For our approach towards intra-firm investment interdependencies, it is important to note that the weighting schemes focus on interdependencies of affiliates i and j within parent firm f in a given year t. Therefore, the typical weights matrix for firm f at time t has size [N.sub.ft] x [N.sub.ft] and is defined as

(8) [w.sup.l.sub.ft] = [[w.sup.l.sub.ijt]] [for all]i,j [member of] [N.sub.ft],

which has zero diagonal elements for all l = {D, s}. Stacking the data for all firms / in year t, we obtain a block-diagonal [N.sub.t] x [N.sub.t] matrix of the form

(9) [W.sup.l.sub.t] = diag ([w.sup.l.sub.ft]).

Thus, using [FDI.sub.t] = ([]), [[bar.FDI].sup.l.sub.t] = [W.sup.l.sub.t] [FDI.sub.t] = ([[bar.FDI].sup.l.sub.t]), and [X.sub.t] = ([]) to denote the corresponding stacked vector of elements across all firms, we may write the model for year t as

(10) [FDI.sub.t] = [[lambda].sup.D] [[bar.FDI].sup.D.sub.t] + ([[lambda].sup.s] [[bar.FDI].sup.s.sub.t]) + [X.sub.t][beta] + [u.sub.t],

where [FDI.sub.t] , and u, are [N.sub.t] x I vectors, [X.sub.t] is a matrix of dimension [N.sub.t] xk and [beta] is a k x 1 vector. (10) Again, we indicate by parentheses in (10) that more than one .v-related interdependence term may be present at a time. In general, the interdependence terms [[bar.FDI].sup.l.sub.t] are endogenous. However, the structure of interdependence of the model delivers valid instruments. This becomes clear by writing the reduced form of the deterministic part of the model,

(11) E ([FDI.sub.t]) = (I - [[lambda].sup.D][W.sup.D.sub.t] - [([[lambda].sup.s][W.sup.s.sub.t])).sup.-1] [X.sub.t][beta],

where several matrices [[lambda].sup.s] [W.sup.s.sub.t] may enter additively the parentheses of the inverse in (11). A Taylor-series expansion together with the properties of [W.sup.l] suggests that [(I - [[lambda].sup.D] - [W.sup.D.sub.t] [[lambda].sup.s][W.sup.s.sub.t]).sup.-1] [X.sub.t] can be approximated well by a polynomial function so that [[bar.FDI].sup.D] and [[bar.FDI].sup.s] can be insgumented well by [bar.[X.sup.D]] = [W.sup.D]X, [bar.[X.sup.s]] = [W.sup.S]X, [[bar.X].sup.Ds] = [W.sup.D][W.sup.S]X, [[bar.X].sup.D] = [W.sup.D] [[bar.X].sup.D], [[bar.X].sup.s] = [W.sup.S][bar.[X.sup.S]], and so on, where it is sufficient in practice to use up to four powers (see Kelejian et al. 2004). We estimate a two-stage least-squares model with affiliate fixed effects (FE2SLS). (11)

Some General Remarks on Interdependence. The parameters on the variables [[bar.FDI]]. with l [member of]{D, I, O, IO) should be interpreted in the following way. A positive effect of [[bar.FDI]] means that, conditional on other determinants of FDI of firm f in affiliate i at time t, an increase in investments in closer affiliates within the same firm (i.e., ones with a bigger inverse distance) stimulates investment at the margin in affiliate i at time t. The latter we dub horizontal complementarity at the intensive foreign investment margin within the firm. A negative effect of [[bar.FDI]] means the opposite, pointing to a substitutive relationship among investments at the intensive margin.

A positive effect of [[bar.FDI]] implies that positive interdependencies are associated with the interdependence in terms of s within a parent's affiliates network. For instance, we might interpret a positive (negative) effect of [[bar.FDI]], on [] as evidence of an upstream vertical complementarity (substitution) in investments. Similarly, we might interpret a positive (negative) effect of [[bar.FDI]] on [] as evidence of a downstream it 'i vertical complementarity (substitution) in investments. A positive (negative) effect of [[bar.FDI]] on [] could then be dubbed evidence of a general vertical complementarity (substitution) in investments. One consequence of a greater such interdependence is the greater vulnerability of affiliate networks in terms of shocks within the network. Whether shocks travel at all, primarily, or more strongly through mere geographic or input-output linkages is a question that only the data can answer.


A. Data on the Dependent Variable

The main source underlying our data is the Microdatabase Direct Investment (MiDi) collected and provided by the German Central Bank (Deutsche Bundesbank). The database represents an annual unbalanced panel with German parent firms' individual affiliates as the unit of observation. The data capture the universe of German MNEs as it is a legal requirement for firms (and even private households) to report FDI above a threshold of 3 million [euro] in their balance sheet and if the participation is at least 10%. Indirect participating interests have to be reported whenever foreign affiliates hold 10% (50% as of 2007) or more of the shares or voting rights in other foreign enterprises with a balance sheet total in excess of 3 million [euro]. (12) For our approach we use the entire panel for the years 1997 to 2009. The dependent variable in our approach are the fixed (and intangible) assets of affiliate i attributable to parent f in year t in logs, [], as available from MiDi and reported in million Euros.

B. Data Underlying the Channels of Interdependence

Data on latitudes (lat,), longitudes ([lon.sub.i]), and geographical area (area,) underlying the geographic weighting scheme, [w.sup.D.sub.ijt], are taken from CEPII's GeoDist database. (13) The data underlying the input-output weighting schemes are taken from annual input-output tables for the German economy over the period 1997-2007, which are publicly available from the German Federal Statistical Office. Because input-output tables as of 2008 are not comparable to the previous ones, (14) we use the one for 2007 for those 2 years. The time variance in input-output shares is minor so that this procedure seems justifiable. The calculation of inputs and outputs by the German Federal Statistical Office is based on the concept of a homogeneous production unit, which is closer to an affiliate (or a production plant) than a firm. The input, output, and input-output matrices are of size sector X sector with altogether 71 sectors of primary (raw material), secondary (manufacturing), and tertiary (services) type of the German economy, based on the so-called CAP classification. We aggregated this format to a 60 X 60 table to match it onto the foreign affiliate statistics as provided by Deutsche Bundesbank, based on the NACE industry classification (see Table A2 for details). The columns of the input-output matrix represent the value of inputs used in a production sector in million Euros, and its rows represent the value of output of intermediate goods (or services) produced in million Euros. (15) Hence, both a bigger number of [w.sup.D.sub.ijt] and of [w.sup.s.sub.ijt] indicates greater proximity between two affiliates i and j at time t.

C. Data on Explanatory Variables

The vector [] in Equation (1) contains firmtime-specific as well as country-time-specific determinants of investment of MNE f at affiliate i and time t. We employ the following affiliate-time-specific variables from the MiDi database. First, [] and [] capture general lagged characteristics of foreign entities affecting investments in t. The former reflects affiliate-specific market size (demand) in logs and the latter an affiliate's supply capacity in terms of employment, also in logs. Second, we include [], the number of German competitors in the same sector and country as of the previous year. We calculate this variable by counting all affiliates j[not equal to]i in a country and year t - 1 by sector. Among the country-time-specific explanatory variables, we include the following. First, we account for the Corporate Income []. Higher corporate taxes require a higher rate of return on investment and, hence, we expect this variable to be negatively related to affiliates' investments. Information on the statutory corporate tax rates is gathered from databases provided by the International Bureau of Fiscal Documentation and annual tax guides issued by Ernst & Young, PwC, and KPMG. Moreover, we include Financial [], as published in the Heritage Indicators Database, which measures the banking efficiency as well as the independence of the financial sector from government control. At the extremes, a value of 100 indicates negligible government interference, whereas a value of 0 indicates repressive government interference. A greater financial freedom is associated with better access to the local capital market and lower costs of external financing. We expect this variable to be positively related to affiliate Vs investments. Also, we employ the local inflation rate Inflation,, from the International Monetary Fund's World Economic Outlook, which reflects aspects of the macro environment affiliate i is operating in. Finally, we include Capital - Labor [], reflecting relative factor endowments in affiliate i's market in year t in logs, and [], the log of real gross domestic product (GDP) at constant U.S. dollars of the year 2000, as a measure for the size of a market at time t. The latter two explanatory variables are taken from the World Bank's World Development Indicator Database, where capital-labor ratios are calculated using the perpetual inventory method to estimate capital stocks. (16)

D. Descriptive Statistics

Our analysis is based on a sample of 21,598 foreign affiliates of 6,059 German MNEs over the period from 1997 to 2009, resulting in an unbalanced panel with 134,781 observations. (17) The German affiliates in our sample are present in altogether 112 countries (for an overview see Table A1). (18) Using darker color to indicate bigger numbers of affiliates, Figure 1 shows that affiliates are highly concentrated in member countries of the European Union, Russia, China, Brazil, Canada, and the United States, with a maximum of 2,443 affiliates for the average year located in the United States. At the other extreme are mainly African and some Asian countries with three or less affiliates in the average year. Using darker color to indicate higher values of average fixed and intangible assets per affiliate, Figure 2 suggests that countries such as China, Brazil, and the United States which host many affiliates also host larger affiliates, on average. However, also countries such as Algeria and Cameroon with on average only 14 and 4 affiliates per annum, respectively, receive similar amounts of fixed and intangible assets per affiliate.

While Figures 1 and 2 consider the number of affiliates and fixed and intangible assets per affiliate in the average year covered by host country, Figures 3 and 4 illustrate the geographic distribution of the number of German parent companies and the fixed and intangible assets per German parent company in the average year by host country. Overall, the number of parent companies at a location tends to be large where the number of affiliates is large, and assets per parent tend to be large where the number of affiliates per parent and/or the assets per affiliate are large in the average year. In countries such as Russia--colored dark-blue in Figure 1 but lighter-blue in Figure 3--a relatively large number of affiliates is held by a relatively small number of parent firms. FDI (fixed and intangible assets) per parent company is on average highest in Cyprus, followed by the United States and China. Overall, most of and the biggest German MNEs mainly invest in the European Union, North America, Brazil, Russia, and China.

In a next step, we describe the closeness or proximity of German foreign affiliates and German FDI per firm and country in terms of three channels of interdependence: input (vertical upstream) proximity, output (vertical downstream) proximity, and geographic (horizontal) proximity. The values in Figures 5, 6, and 7 are calculated by multiplying each parent firm's weights matrix [W.sup.l.sub.ft] by a vector of ones to obtain a measure of pure input, output, or geographic distance within its network of foreign affiliates. This yields a firm-f-specific measure of proximity in year t and dimension (or proximity channel) l. In each country, we then calculate the average of this measure of proximity across all parents weighted by the number of affiliates they hold and the years they are present. This obtains an average measure of proximity of affiliates per country within the German affiliate network in dimension l. In Figure 5 the average affiliate in darker-colored countries is closer in terms of inputs received from other members of its network than the average affiliate in lighter-colored countries. In Figure 6 the average affiliate in darker-colored countries is relatively closer to other affiliates of its network in terms of output delivered than in lighter-colored countries. Figures 5 and 6 suggest that input and output proximity are, in general, relatively similar across countries. Hence, well-connected affiliates through the downstream channel tend to be also well connected through the upstream channel. Nevertheless, there are some interesting differences. For instance, the average German affiliate in the United States is relatively more related to other entities within the average MNE in terms of inputs received than in terms of output delivered. This is consistent, for example, with the global allocation of production of large German car manufacturers present in the United States. It generally makes sense to think about the United States as being a large final market, and not being an intermediate country in the global value chain. A comparison of Figures 5 and 6 with Figure 7 suggests that there is an obvious difference between vertical (input-output) and horizontal (merely geographic) proximity. For instance, while affiliates in Factory Asia (Baldwin 2007) and South America are well connected vertically, their horizontal proximity is relatively low. The opposite seems to be true for European countries, on average. Other interesting examples are countries such as Uruguay or Namibia, which are both more integrated through the output channel. For Uruguay, this is consistent with the fact that many German multinationals provide financial services from Montevideo to affiliated entities located in South and Latin America. For Namibia, this is consistent with the fact that some German firms produce raw materials (e.g., cement) to be exported to other countries in Southern Africa. Figures 8, 9, and 10 suggest a similar pattern for FDI (fixed and intangible assets).


This section reports the parameter estimates and the consequences of counterfactual shocks in the foreign affiliate system based on the estimated model when relying on the specification outlined in Section 3. Table 2 summarizes parameter estimates on the different (endogenous) interdependency terms of FDI stock elsewhere in parent company f's network on the FDI stock at affiliate i. All regressions include affiliate-level (and, implicitly, parent-level) fixed effects as well as a full set of aggregate year effects.

In columns 1 to 5 in Table 2 we use four different variables to capture the channels of interdependence in foreign assets. First, [[bar.FDI]] is the input-weighted FDI stock of other affiliates than i as defined above. Second, [[bar.FDI]] is the output-weighted FDI stock of other affiliates than i. Third, [[bar.FDI]] is the input-plus-output-weighted FDI stock of other affiliates than i. Fourth, [[bar.FDI]] is the inverse-(geographic-)distance-weighted FDI stock of other affiliates than i. The weighting matrices that apply to the interdependence terms are all maximum row-sum normalized to make obvious that the estimated coefficients on interdependence terms are in the admissible parameter space. Notice that this scalar-type normalization preserves the notion of absolute proximity in the affiliate network of any MNE. As suggested in Section 3.1, we use weighted exogenous regressors (applying the respective linkage-channel-specific weight) to instrument the interdependence terms (see Table 1 for those variables and the respective summary statistics). In what follows, the instruments consist of a full set of four instruments per linkage term. That is, input-interdependence, output-interdependence, and geography-interdependence are modeled separately, so that there are 12 instruments based on [[bar.Sales]], [[bar.Employees]], Corporate Income Tax;(, [[bar.Competition]] for l [member of] I,O, D. (19) While it has been shown above that the weighted exogenous variables can be used as optimal instruments, there might be a concern about the exclusion restrictions in the context of weighted variables measured at the level of the firm. We will address this by using lagged weighted affiliate characteristics as instruments for affiliate i but only affiliate characteristics of other multinational firms in the same country and year that are not related to i and belong to another multinational firm. (20) Beside linkage terms and the mentioned affiliate- as well as time-specific effects, we condition on a number of control variables shown and summarized in Table 1.

The control variables affect affiliates' FDI as expected. First, larger sales and a bigger number of employees per affiliate have a positive effect on German MNEs' investment abroad. Second, a higher level of local (corporate) profit tax rates reduces investment. More precisely, a one-percentage-point (1 ppt) increase in the tax rate ceteris paribus reduces local investment by -1.16% for the average German MNE. This magnitude is broadly in line with previous findings (for a meta-analysis see De Mooij and Ederveen 2006). Third, a sound functioning of the financial sector in the host country of the investment, measured by the financial freedom variable, raises investment per affiliate there. Fourth, a higher inflation reduces local foreign investment per affiliate negatively, as does having a German competitor in the same sector and host country. The former reflects adverse temporal (or cyclical) macroeconomic conditions, the latter captures adverse structural (competitive) conditions. While the adverse competitive effect is quantitatively relatively small, it is statistically highly significant. Finally, an increase in a host country's capital-labor ratio (which measures both a relative capital abundance and the relative development of a host country) exerts a positive effect on investment while GDP, as a measure of the size of the host economy is positively related to investments.

A glance at the coefficients on the linkage terms in Table 2 suggests the following conclusions. First, horizontal (geographic) linkages matter to a smaller extent, whereas vertical linkages via input-output relations seem to matter more. There is clear evidence of positive interdependencies to affiliates which are downstream from their upstream network members. For affiliates which are upstream and close to their downstream members the opposite seems to be true. This is consistent with a U-shaped curve pattern in the context of the evolution of countries and industries (see Shin et al. 2012, for an empirical application in the global electronics industry). This relationship suggests that the profitability is particularly high at the root or origin of the production process or product cycle (where the research intensity is high) and also at the end of it (where input costs are low and the service intensity is high). In a developed country such as Germany which is increasingly specializing in services, it is consistent with this pattern that firms seek to shift their activity towards and maximize their profit margins at the end of the production line.

Our findings seem to be also in line with Alfaro et al. (2015). The latter paper suggests that the elasticity of demand for a firm's final product, as well as the relative contractibility vis-a-vis stages located upstream or downstream from a given production stage, determine upstream or downstream integration choices. If inputs are not particularly easy to substitute, Alfaro et al. (2015) suggest that the incentive of a supplier to invest in a relationship-specific input is higher, the larger the investments by upstream suppliers. This is consistent with our finding of a positive relationship between investment of a given affiliate and investments of all other affiliates within the MNE if the affiliate is linked through the input channel. Moreover, while we cannot model the outsourcing decision, the findings in Alfaro et al. (2015) suggest that the integrated part of the total value chain for input-linked affiliates in our data tends to be more downstream. The negative interdependence found for affiliates linked through the output channel is in line with the assumption that the inputs supplied by these affiliates are characterized by a high substitutability, suggesting that the integrated part of the value chain we observe in our data for the output-linked affiliates is more upstream. All this suggests that both goods produced and relationship-specific investments made along the value chain become more specialized and less substitutable.

Our findings finally confirm the results in Smarzynska Javorcik (2004), who shows that (positive) productivity spillovers from FDI to domestic firms mainly take place through backward linkages (through the intermediate input channel), rather than forward linkages. While our results support the view that such interdependencies also exist within MNEs and productive assets, there seem to be even negative effects on affiliates linked through the output or forward channel. (21)

Columns 3 and 4 present specifications with a different decay function about the spatial process and the impact of inverse distance. In particular, in column 3 we use the squared inverse distance, in column 4 the square root of the inverse distance. Once we specify alternative decay functions, the negative effect of [[bar.FDI]] becomes insignificant. This confirms that vertical linkages seem to be more important for interdependence than geography. In column 5 we test the robustness of the internal-distance measure. Figure 7 suggests that average geographic proximity is lowest for affiliates in large countries such as the United States, Canada, Brazil, or China. This may have to do with the fact that the average internal distance is used for affiliates within the same country. For this reason it is of interest to test whether our main results are affected by another weighting of affiliates within the same country. In the specification in column 5, we give all affiliates within a country the same weight, across all countries. Specifically, we set [w.sup.D0.sub.ijt] = [d.sup.-1.sub.ij] - 1 whenever affiliates i and j are located in the same country (implicitly assuming that intra-national distance plays no role). This alternative treatment of affiliates within the same country has no effect on our findings.

Column 6 shows results where we control for country-time effects. These results confirm the impact of the three main variables of interest, [[bar.FDI]], [[bar.FDI]], and [[bar.FDI]]. This shows that the estimated coefficients are not biased through unobserved country-specific variables. Of course, all variables measured at the level of countries and years are not identified in this specification.

While our focus is on the intensive margin of investment (the effect of marginal investment decisions at other locations on the level of investment at a given location), we explore the relative importance of adjustments at the intensive margin (changes in investment by old affiliates) versus adjustments at the extensive margin (investment by new affiliates). Column 6 of Table 2 distinguishes between an extensive and an intensive margin effect. For this, we assume different slope parameters on [[bar.FDI]], [[bar.FDI]], and [[bar.FDI]], respectively, depending on whether an affiliate that contributes to the respective weighted variable is new (an extensive margin adjustment) or not (an intensive margin adjustment). The results suggest that (i) the input-output interdependence is driven by the extensive margin, while the coefficients on the intensive margin estimates show the same signs but are no longer statistically significant; (ii) the geographical interdependence is driven by the intensive margin. This result suggest that the impact of vertical integration decisions on investment across all affiliates in the network is more pronounced than that of horizontal integration decisions.

We finally run regressions (the basic specification shown in column 1) at the level of industries. This might indicate whether there exist heterogeneous spatial effects of horizontal foreign investment across industries. We plot the estimated coefficients on [[bar.FDI]] by way of a kernel density plot (see Figure 11). The figure suggests that there is significant heterogeneity across sectors. Based on those sectors with sufficient observations, the average coefficient estimated [[bar.FDI]] is clearly negative.2- The results also reveal, however, that the effects may be positive, depending on the industry affiliate i is operating in. This is true for about one-third of the sectors we analyze. On average, the negative substitution effect (as in export platform FDI models) dominates the positive effects of information spillovers.


Based on Specification I in Table 2, we may quantify the effect of a 1 ppt decrease, for instance, in the corporate profit tax rate of country r, r = 1, ... R ([DELTA][[tau].sup.r] = -0.01) on the foreign affiliates' FDI stock as follows:

(12) [mathematical expression not reproducible],

where [[??].sub.[tau]] is the estimated coefficient on the corporate tax rate (1,16 in our preferred specification) and [DELTA][[tau].sup.r] has entry (-0.01) only in rows corresponding to affiliates located in r and zeros elsewhere. Notice that the total effect in (12) takes into account that such a shock on i through [DELTA][[tau].sup.r] will not only induce direct (or local) effects on affiliate i, but it will induce indirect effects on other affiliates which will themselves induce indirect effects back on i. This is captured by the inverse [mathematical expression not reproducible] in (12), which accounts for an infinite series of indirect effects within each parent's affiliate network.

The effects consistent with (12) may be visualized as follows. Define [N.sub.rt] and [] as the sets of affiliates located in countries r and m, respectively, in year t. Let [N.sub.rt] and [] be the respective numbers of affiliates in those countries in year t. And denote the total number of countries by R and the total number of affiliates in year t across all countries by [N.sub.t]. Then, the average total effect of [DELTA][[tau].sup.r] on affiliates located in r is

(13) [N.sup.-1.sub.rt] [summation over (i[member of][N.sub.rt])] [DELTA][].

The average indirect effect of [DELTA][[tau].sup.r] on affiliates located outside of country r is

(14) [([[N.sub.t] - [N.sup.-1.sub.rt]).sup.-1] [summation over (m[not equal to]r)] [summation over (i[member of][N.sub.rt])] [DELTA][].

The average indirect effect over all [DELTA][[tau].sup.m], m [not equal to] r on affiliates located in r is

(15) [(R-1).sup.-1][N.sup.-1.sub.rt] [summation over (m[not equal to]r)] [summation over (i[member of][N.sub.rt])] [DELTA][].

For instance, Figure 12 illustrates the geographic pattern of the total effect as in (13) on the average affiliate in a country. Notice that we consider a shock in corporate profit tax rates in one country at a time. Clearly, the direct effect of that shock is always (-1.16) x (-0.01) so that the geographic pattern is entirely due to the heterogeneity in all three dimensions of proximity and the respective parameter weights on the channels in Specification I of Table 2. Figure 13 visualizes the total indirect effect from a shock in tax rates in the country of affiliate i on affiliates of the same parent company that are situated in other countries. The results in this figure are obtained as described in (14), averaging the outcome across all other countries (and firms as well as affiliates). Hence, this figure illustrates which host countries tend to be sources of larger or smaller spillovers on FDI in other countries. Finally, Figure 14 illustrates to which extent country-specific (one-at-a-time) shocks on corporate profit taxes in other countries spill over to affiliates in a given host country, on average. Formally, the results in this figure are obtained as described in (15). This figure illustrates which host countries tend to be recipients of larger or smaller spillovers from other countries on FDI. Due to the asymmetry in input-output tables and the heterogeneity in the sector membership across affiliates within a parent company, the host countries which tend to receive high average spillovers from shocks in foreign taxes abroad are not necessarily also strong sources of spillovers to affiliates in other countries (to see this, compare the shading of Figure 14 with that of Figure 13).

The strongest overall positive impacts of an independent negative shock to local profit tax rates in Figure 12 are found for China, the United States, and Brazil. The smallest impacts are found for Cyprus, Malta, and Luxembourg. Comparing the results of Figures 12 and 7 shows that spillovers are positive and strong mainly to neighboring countries, on average. However, an inspection of Table 2 suggests that the researcher would be misguided to conclude that the source of this pattern is mere geography, since we have seen that the actual channels are downward closeness and upward distance in input-output space. Conditioning on input-output relationships, mere geography has little to contribute to the geographic pattern of spillovers. Figure 13 suggests that shocks are particularly strongly (positively) transmitted by affiliates in the United States, China, and Japan to foreign affiliates and, to a somewhat lesser extent, by affiliates in Italy and Spain. On the other extreme, a reduction in profit tax rates in adjacent countries to Germany--such as Austria, Belgium, The Netherlands, and Switzerland--tends to induce negative effects on the rest of the affiliate network of German MNEs, on average. The main reason for this finding is that affiliates in these countries tend to be upstream and technologically closer to other affiliates downstream rather than to ones further upstream. Also, they tend to be geographically close to other affiliates in the network which, on average, leads to negative spillovers from those countries. Figure 14 suggests that shocks on corporate profit taxes in other countries which spill over to affiliates in a given host country positively affect FDIs in several emerging and South American economies. On the contrary, German MNEs' affiliates in several European countries tend to receive non-positive spillovers from a reduction of profit tax rates abroad.

Note that, when looking at Figures 7 and 10 alone, we could conclude that FDI, from a German perspective, to overseas countries is mainly related to horizontal FDI (because, e.g., we observe a lot of German investments in the United States, but these investments are primarily stand-alone ones). The other figures, however, suggest that there are linkages between affiliates within an MNE's network beyond pure geography (using the example from above, this implies that there is intrafirm shipments to affiliates in the United States from affiliates that are close in terms of industry closeness rather than geographic closeness).


Using a census-type panel dataset of German parent firms and their affiliates from 1997 to 2009, this paper formulated a model to identify several channels of spillovers within the German parent firms' affiliates networks on the affiliate-specific FDIs. Allowing for three channels of interdependence or spillovers--horizontal linkages (mere geography), vertical input linkages, and vertical output linkages--we find that horizontal linkages only matter to a limited extent, whereas vertical linkages are the main source of spillovers. Moreover, we find that spillovers from other affiliates are larger if an affiliate is technologically situated downstream and strongly connected to affiliates further upstream and if it is less strongly connected to affiliates further downstream.

We use the regression results to quantify the magnitude of total effects of shocks, of spillover effects from and to affiliates across countries within the German multinational firm network. For illustrative purposes, we use a reduction in corporate profit tax rates by one-percentage point in one country at a time and calculate its predicted effect on FDIs across affiliates. Overall, the findings are illustrative of non-trivial effects of policy shocks on the investments in a foreign affiliate network. Identical shocks on profit tax rates do not only lead to quantitatively but even to qualitatively different effects, depending on where they occur. The findings in this paper suggest that primarily the technological proximity in input-output space and less so the geography of an average MNE's affiliate network matters for the geographic heterogeneity of spillover and total effects of tax policy on FDI.

TABLE A1 Countries in the Sample by Continent

Continent    Country                    Code    a

Africa       Algeria                    DZA     0
Africa       Botswana                   BWA     1
Africa       Cameroon                   CMR     0
Africa       Democ. Rep. of the Congo   COD     1
Africa       Egypt                      EGY     0
Africa       Ethiopia                   ETH     1
Africa       Gabon                      GAB     1
Africa       Ghana                      GHA     1
Africa       Ivory Coast                CIV     0
Africa       Kenya                      KEN     0
Africa       Madagascar                 MDG     1
Africa       Malawi                     MWI     1
Africa       Mauritius                  MUS     0
Africa       Morocco                    MAR     0
Africa       Mozambique                 MOZ     1
Africa       Namibia                    NAM     0
Africa       Senegal                    SEN     0
Africa       South Africa               ZAF     0
Africa       Swaziland                  SWZ     1
Africa       Tunisia                    TUN     0
Africa       Uganda                     UGA     0
Africa       Un. Rep. of Tanzania       TZA     0
Africa       Zambia                     ZMB     1
Americas     Argentina                  ARG     0
Americas     Barbados                   BRB     1
Americas     Bolivia                    BOL     0
Americas     Brazil                     BRA     0
Americas     Canada                     CAN     0
Americas     Chile                      CHL     0
Americas     Colombia                   COL     0
Americas     Costa Rica                 CRI     0
Americas     Dominican Rep.             DOM     0
Americas     Ecuador                    ECU     0
Americas     El Salvador                SLV     0
Americas     Guatemala                  GTM     0
Americas     Honduras                   HND     0
Americas     Mexico                     MEX     0
Americas     Nicaragua                  NIC     0
Americas     Panama                     PAN     0
Americas     Paraguay                   PRY     0
Americas     Peru                       PER     0
Americas     The Bahamas                BHS     1
Americas     Trinidad & Tobago          TTO     1
Americas     Un. States of America      USA     0
Americas     Uruguay                    URY     0
Americas     Venezuela                  VEN     0
Asia         Armenia                    ARM     1
Asia         Azerbaijan                 AZE     0
Asia         Bahrain                    BHR     1
Asia         Bangladesh                 BGD     0
Asia         China                      CHN     0
Asia         Cyprus                     CYP     0
Asia         Hong Kong S.A.R.           HKG     0
Asia         India                      IND     0
Asia         Indonesia                  IDN     0
Asia         Iran                       IRN     0
Asia         Israel                     ISR     0
Asia         Japan                      JPN     0
Asia         Jordan                     JOR     0
Asia         Kazakhstan                 KAZ     0
Asia         Kuwait                     KWT     1
Asia         Kyrgyzstan                 KGZ     1
Asia         Lebanon                    LBN     0
Asia         Malaysia                   MYS     0
Asia         Oman                       OMN     1
Asia         Pakistan                   PAK     0
Asia         Philippines                PHL     0
Asia         Saudi Arabia               SAU     0
Asia         Singapore                  SGP     0
Asia         South Korea                KOR     0
Asia         Sri Lanka                  LKA     0
Asia         Thailand                   THA     0
Asia         Turkey                     TUR     0
Asia         Un. Arab Emirates          ARE     0
Asia         Uzbekistan                 UZB     1
Asia         Vietnam                    VNM     0
Europe       Austria                    AUT     0
Europe       Belarus                    BLR     0
Europe       Belgium                    BEL     0
Europe       Bulgaria                   BGR     0
Europe       Croatia                    HRV     0
Europe       Czech Republic             CZE     0
Europe       Denmark                    DNK     0
Europe       Estonia                    EST     0
Europe       Finland                    FIN     0
Europe       France                     FRA     0
Europe       Greece                     GRC     0
Europe       Hungary                    HUN     0
Europe       Iceland                    ISL     1
Europe       Ireland                    IRL     0
Europe       Italy                      ITA     0
Europe       Latvia                     LVA     0
Europe       Lithuania                  LTU     0
Europe       Luxembourg                 LUX     0
Europe       Macedonia                  MKD     0
Europe       Malta                      MLT     0
Europe       Moldova                    MDA     0
Europe       Netherlands                NLD     0
Europe       Norway                     NOR     0
Europe       Poland                     POL     0
Europe       Portugal                   PRT     0
Europe       Romania                    ROU     0
Europe       Russia                     RUS     0
Europe       Slovakia                   SVK     0
Europe       Slovenia                   SVN     0
Europe       Spain                      ESP     0
Europe       Sweden                     SWE     0
Europe       Switzerland                CHE     0
Europe       Ukraine                    UKR     0
Europe       United Kingdom             GBR     0
Oceania      Australia                  AUS     0
Oceania      New Zealand                NZL     0

Note: There is a total of 112 countries in the sample. The column
Code refers to ISO-3 codes

(a) Countries indicated by 1 do not appear in Figures 1-11 of the
paper, as they are based on less than three observations and
therefore not released for display according to the confidentiality
regulations of the Deutsche Bundesbank.


Merged Industry Classifications (NACE/CPA)

No.   Economic Sectors                           NACE     CPA
(a)   (Bundesbank)

1     Agriculture, hunting and related           100      1
      service activities

2     Forestry, logging and related              200      2
      service activities

3     Fishing, operation of fish                 500      5
      hatcheries and fish farms;
      service activities incidental to

4     Mining of coal and lignite,                1,000    10
      extraction of peat

5     Extraction of crude petroleum              1,100    11
      and natural gas, service
      activities incidental to oil and
      gas extraction

6     Mining of uranium and thorium ores         1,200    12

7     Mining                                     1,300    13

8     Mining and quarrying, other                1,400    14

9     Manufacturing of food and                  1,500    15.1-
      beverages                                           15.8


10    Manufacture of tobacco products            1,600    16

11    Manufacture of textiles                    1,700    17

12    Manufacture of textile products            1,800    18

13    Manufacture of leather and leather         1900     19

14    Manufacture of wood and wood products      2000     20

15    Manufacture of pulp paper and paper        2,100    21.1

16    Publishing, printing and reproduction      2,200    22.1
      of recorded media

17    Manufacture of coke, refined petroleum     2,300    23
      products and nuclear fuel

18    Manufacture of pharmaceutical products     2,440    24.4

19    Manufacture of chemicals and               2,400    24(ohne
      chemical products                                   24.4)

20    Manufacture of rubber and plastic          2,500    25.1

21    Manufacture of non metallic mineral        2,600    26.1

22    Manufacture of basic metals                2,700    27.1-



23    Manufacture of metal products              2,800    28

24    Manufacture of machinery and equipment     2,900    29

25    Manufacture of office machinery and        3,000    30

26    Manufacture of electrical machinery and    3,100    31
      apparatus n.e.c.

27    Manufacture of radio, television and       3,200    32
      communication equipment and apparatus

28    Manufacture of medical, precision and      3,300    33
      optical instruments, watches and clocks

29    Manufacture of motor vehicles, trailers    3,400    34
      and semi-trailers

30    Manufacture of other transport             3,500    35
      equipment (only until 2004), from 2005
      onwards 3,510,3,520, 3,530,
      3,540, 3,550

31    Manufacure of furniture, manufacturing     3,600    36

32    Recycling                                  3,700    37

33    Electricity, gas, steam and hot water      4,000    40.1,
      supply                                              40.3


34    Collection, purification and               4,100    41
      distribution of water

35    Construction sector                        4,500    45.1-


36    Sale, repair of motor vehicles; retail     5,000    50
      sale of automotive fuel

37    Wholesale trade and commission trade       5,100    51
      (except of motor vehicles and

38    Retail trade, except of motor vehicles     5,200    52
      and motorcycles; repair of personal
      and household goods

39    Hotels and restaurants                     5,500    55

40    Land transport; transport via              6,000    60.2-
      pipelines                                           60.3

41    Water transport                            6,100    61

42    Air transport                              6,200    62

43    Supporting and auxiliary transport         6.300    63
      activities; activities of travel

44    Post and telecommunications (only until    6,400    64
      2004), from 2005 onwards 6,410, 6,420

45    Other credit institutions                  6,560    65

46    Insurance and pension funding, except      6,600    66
      compulsory social security.

47    Activities auxiliary to financial          6,700    67

48    Housing enterprises, other real            7,050,   70
      estate activities                          7,060

49    Renting of machinery and equipment         7,100    71
      without operator and of personal and
      household goods

50    Computer and related activities            7,200    72

51    Research and development                   7,300    73

52    Accounting, book-keeping and auditing      7,412    74
      activities; tax consultancy (2005 on)

53    Federal government, Federal states,        7,560,   75.1-
      local government and local authority       7,570,   75.2
      associations                               7,580

      Social security and employment             7,590    75.3

54    Education                                  8,000    80

55    Health and social work, excluding          8,500    85
      non-profit organizations serving

56    Sewage and refuse disposal, sanitation     9,000    90
      and similar activities

57    Activities of other membership             9,100    91
      organizations, excl. Non-profit
      organizations serving households

58    Recreational, cultural and sporting        9,200    92
      activities, excl. non-profit org.
      serving households (only until 2004),
      from 2005 onwards 9,210, 9,220, 9,230,
      9,240, 9,250, 9,260, 9,270

59    Other service activities n.e.c.,           9,300    93
      excluding non-profit organizations
      serving households

60    Private households with employed           9,550,   95
      persons, Other households                  9,560

No.   Individual Goods
(a)   (Statistisches

1     Erzeugnisse der Landwirtschaft
      und Jagd

2     Forstwirtschaftliche Erzeugnisse
      und DL

3     Fische und Fischereierzeugnisse

4     Kohle und Torf

5     Erdol, Erdgas, DL fur Erdol-,

6     Uran- und Thoriumerze

7     Erze

8     Steine und Erden, sonstige

9     Nahrungs- und Futtermittel

10    Getranke

11    Tabakerzeugnisse

12    Textilien

13    Bekleidung

14    Leder und Lederwaren

15    Holz; Holz-, Kork-, Flechtwaren (ohne Mobel)

16    Holzstoff, Zellstoff,

17    Papier, Karton und Pappe Papier-, Karton-
      und Pappewaren

18    Verlagserzeugnisse

19    Druckerzeugnisse, bespielte Ton-, Bild- und

20    Kokereierzeugnisse, Mineralolerzeugnisse,
      Spalt- und Brutstoffe

21    Pharmazeutische Erzeugnisse

22    Chemische Erzeugnisse (ohne
      pharmazeutische Erzeugnisse)

23    Gummiwaren

24    Kunststoffwaren

25    Glas und Glaswaren

26    Keramik, bearbeitete Steine und Erden

27    Roheisen, Stahl, Rohre und Halbzeug daraus

28    NE-Metalle und Halbzeug daraus

29    Giessereierzeugnisse

30    Metallerzeugnisse

31    Maschinen

32    Buromaschinen, Datenverarbeitungsgerate und

33    Gerate der Elektrizitatserzeugung,
      -verteilung u.a.

34    Nachrtechn., Rundf.- und Fernsehgerate,
      elektron. Bauelemente

35    Medizin-, mess-, regelungstechn., optische
      Erzeugnisse; Uhren

36    Kraftwagen und Kraftwagenteile

37    Sonstige Fahrzeuge (Wasser-, Schienen-,
      Luftfahrzeuge u.a.)

38    Mobel, Schmuck, Musikinstrumente,
      Sportgerate, Spielwaren u.a.

39    Sekundarrohstoffe

40    Elektrizitat, Fernarme, DL der
      Elektrizitats- u.

41    Fernwarmeversorgung

      Gase, DL der Gasversorgung

42    Wasser und DL der Wasserversorgung

43    Vorb. Baustellenarbeiten, Hoch- und

44    Bauinstallations- und sonstige Bauarbeiten

45    Handelsleist. mit Kfz; Rep. an Kfz;

46    Handelsvermittlungs- und

47    Einzelhandelsleistungen; Reparatur an

48    Beherbergungs- und Gaststatten-DL
49    Eisenbahn-DL

50    Sonst. Landv.leistungen,
      Transportleistungen in

51    Schifffahrtsleistungen

52    Luftfahrtleistungen

53    DL beztiglich Hilfs- und Nebentiitigkeiten
      fur den Verkehr

54    Nachrichtenubermittlungs-DL

55    DL der Kreditinstitute

56    DL der Versicherungen (ohne Sozialversicherung)

57    DL des Kredit- und Versicherungshilfsgewerbes

58    DL des Grundstiicks- und Wohnungswesens

59    DL der Vermietung beweglicher Sachen
      (ohne Personal)

60    DL der Datenverarbeitung und von

61    Forschungs- und Entwicklungsleistungen

62    Unternehmensbezogene DL

63    DL der offentlichen Verwaltung, Verteidigung

64    DL der Sozialversicherung

65    Erziehungs- und Unterrichts-DL

66    DL des Gesundheits-, Veterinar- und

67    Abwasser-, Abfallbeseitigungs- u. sonst.

68    DL von Interessenvertretungen, Kirchen u.?.

69    Kultur-, Sport- und Unterhaltungs-DL

70    Sonstige DL

71    DL privater Haushalte

(a) Consecutive sector number introduced for the present analysis.

(b) Consecutive sector number used in the input-output tables
(Statistisches Bundesamt).


FDI: Foreign Direct Investment

FE2SLS: Fixed-Effects Two-Stage Least Squares

GDP: Gross Domestic Product

MiDi: Microdatabase Direct Investment

MNE: Multinational Enterprise

SAR: Spatial Autoregressive

doi: 10.1111/ecin. 12517

Online Early publication November 2, 2017

Bosenberg: Researcher, Swiss National Bank, Zurich 8022, Switzerland. Phone 0041-58631-2168, Fax 0041-586315000, E-mail

Egger: Professor, ETH Zurich, Economics, Zurich 8092, Switzerland. Phone 0041-44632-4108, Fax 0041-446324108, E-mail

Merlo: Professor, ETH Zurich, Economics. Zurich 8092, Switzerland. Phone 0049-7071 -2975421, Fax 0049-7071 295590, E-mail

Wamser: Professor, School of Business and Economics, University of Tubingen, Tubingen 72074, Germany. Phone 0049-178-4303811, Fax 0049-7071295590, E-mail


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* We are grateful to Deutsche Bundesbank for granting access to the MiDi database. The views expressed in this paper are those of the authors and do not reflect those of the Swiss National Bank. We thank Mohammed Mardan as well as seminar participants at the NHH in Bergen, the University of Munich, and the University of St. Gallen for helpful comments

(1.) Notice that, unlike with trade, only few countries provide exhaustive datasets on their FDI. With interdependent observations, omitting some countries or country-pairs from the data leads to measurement error of the regressors and an associated inconsistency of the parameters determining aggregate country-level or country-pair-level FDI.

(2.) Again, much more so than with firm-level exports, only few countries provide exhaustive datasets on their MNEs and the associated affiliates and FDIs. With interdependent observations, omitting some affiliates from the data leads to measurement error of the regressors and an associated inconsistency of the parameters determining investment at the level of affiliates and firms.

(3.) We use the foreign affiliate's stock of fixed and intangible assets attributable to the parent company as a measure of FDI related to production activities.

(4.) While the sequencing of foreign investments determines the direction of learning, this is out of the scope of the present study, as it focuses on the interdependence of investments at the intensive affiliate margin, that is, at a given size and location of the affiliate network, at each point in time.

(5.) On a broader scale, the focus on horizontal versus vertical interdependencies of the present paper relates it to the literature on affiliate networks (see Egger et al. 2013; Oberhofer and Pfaffermayr 2003) and to the recent literature on the value chains and the organization of firms (e.g., Antras and Chor 2013).

(6.) Unlike row-normalized weights, the suggested ones bear the advantage of preserving the notion of absolute proximity in the lth metric.

(7.) The great circle distance is calculated using the haversine formula. The internal distance measure and the coordinates of the main cities are taken from CEPII's GeoDist database (see Mayer and Zignago 2011, for a description).

(8.) It appears plausible to assume that the covered MNEs use German technology standards in their affiliate network. Using a best-practice technology throughout the affiliate network entails one of the major advantages of MNEs relative to stand-alone firms.

(9.) Moreover, [w.sup.s.sub.ijt] and [w.sup.D.sub.ijt] vary only across ijt.

(10.) Note that our estimation approach will also allow for unobserved affiliate heterogeneity (see below).

(11.) Badinger and Egger (2015) and Kelejian (2013) show that FE2SLS provides consistent estimates of [[lambda].sup.l] and [beta] under a broad spectrum of assumptions. As shown above, the estimator we employ suggests using the weighted exogenous variables to instrument for the endogenous spatial lag. There might be a concern about the exclusion restrictions in the context of weighted variables measured at the level of the firm. We will address this by using as instruments lagged weighted characteristics of other affiliates in the same country and year that are not related to i and belong to another multinational firm. We are not concerned that these affiliates may be affected by the same (exogenous) shocks as i. What is important is that neither affiliate i nor the firm controlling i may directly affect the characteristics of other firms in period t - 1. In particular, the latter should be the case as outcome of affiliate i is measured in period t, and the instruments are measured in period t - 1.

(12.) The collection of annual statistics is stipulated by law through the AuBenwirtschaftsgesetz (AWG) (Foreign Trade and Payments Act). The reporting requirements refer to Sections 56a and 58a of the AWG. They were enacted in 2002, but are applied consistently for all years of the panel. For a detailed description of the MiDi database, see 2011.

(13.) See Mayer and Zignago (2011) for a description.

(14.) See Statistisches Bundesamt (2014), p. 4.

(15.) The tables include domestic and imported intermediates used in production. Intermediates are valued at prices which exclude any taxes but include subsidies (see Kuhn 2010, p. 15).

(16.) Following Hall and Jones (1999) the capital stock at time t is generically defined as [K.sub.t] = (1 - [delta])[K.sub.t-1] + [GFC.sub.t]. Here, [GFC.sub.t] is the gross fixed capital formation at constant U.S. dollars of 2000 as reported in the World Bank's World Development Indices Database, and [delta] is the rate of depreciation, set at 0.133 (see, e.g., Learner 1984). Furthermore, we calculate the initial capital stock by [K.sub.0] = [GFC.sub.0]/[delta]+[g.sub.K], where [g.sub.GFK] is the rate of growth of the capital stock, being set at 0.025, as in Bergstrand and Egger (2007).

(17.) On average an affiliate is about 6.2 years in the sample.

(18.) Figures 1-11 include only 92 of the 112 countries in the sample due to the confidentiality regulations of the Deutsche Bundesbank. This results in the deletion of 20 countries with less then 3 affiliates in an average year per country from our graphical representation. Nevertheless, all affiliates and 112 host countries are included in the estimation below.

(19.) In an earlier version of the paper we have experimented more with the set of instruments and have shown that results are robust to alternative specifications and combinations of instruments. Notice that one could increase the instrument set by using higher-order powers of the linkage weights. However, the instrument quality deteriorates with the order of linkage weights and all that is needed here are only at least as many instruments as there are linkages in the model.

(20.) We are not concerned that these affiliates may be affected by the same (exogenous) shocks as i. What is important is that neither affiliate i nor the firm controlling i may directly affect the characteristics of other firms in period t - 1. In particular, the latter should be the case as outcome of affiliate i is measured in period t, and the instruments are measured in period t - 1.

(21.) It is less straightforward to interpret our findings in the light of the learning model as suggested in Egger et al. (2013) to explain international expansion patterns of affiliate networks. On the one hand, Egger et al. (2013) model the extensive instead of, as in this paper, the intensive investment margin. On the other hand, Egger et al. (2013) distinguish between sequential and simultaneous investments and find qualitative differences in their determinants. Since we focus on investments within given affiliates it is not possible to explicitly distinguish between the latter modes. However, as our results suggest interdependencies predominantly through vertical production linkages, the results suggest that proximity on a general level (here, mainly in terms of vertical linkages) matters for marginal investment decisions or for discrete investment project decisions even in a given affiliate network.

(22.) Note that we cannot report more details of the sectorlevel regressions, since the Deutsche Bundesbank has specific requirements regarding the number of observations per firm, per market, and per sector.

Caption: FIGURE 1 Average Number of Affiliates for the Average Year Per Country

Caption: FIGURE 2 Average (Log) Fixed Assets Per Affiliate for the Average Year Per Country

Caption: FIGURE 3 Average Number of Parent-Firms for the Average Year Per Country

Caption: FIGURE 4 Average (Log) Fixed Assets Per Parent-Firms for the Average Year Per Country

Caption: FIGURE 5 Average Input Proximity Across All Affiliates for the Average Firm Per Country

Caption: FIGURE 6 Average Output Proximity Across All Affiliates for the Average Firm Per Country

Caption: FIGURE 7 Average Geographic Proximity Across All Affiliates for the Average Firm Per Country

Caption: FIGURE 8 Average Input-Weighted (Log) Fixed Assets Proximity Across All Affiliates for the Average Firm Per Country

Caption: FIGURE 9 Average Output-Weighted (Log) Fixed Assets Proximity Across All Affiliates for the Average Firm Per Country

Caption: FIGURE 10 Average Geography-Weighted (Log) Fixed Assets Proximity Across All Affiliates for the Average Firm Per Country

Caption: FIGURE 11 Density Plot

Caption: FIGURE 12 Total (Direct Plus Indirect) Effect of a One-Percentage-Point Tax Reduction Per Country on the Average Affiliate There

Caption: FIGURE 13 Total (Indirect) Effect of a One-Percentage-Point Tax Reduction Per Country on Other Affiliates Outside of the Country Reducing the Tax Rate

Caption: FIGURE 14 Total (Indirect) Effect of a One-Percentage-Point Tax Reduction from Other Countries (One at Time) on the Average Affiliate Per Country
TABLE 1 Summary Statistics

Main Variables                                Mean      SD

[]                                 7.725    2.051
[]                            3.109    1.353
[]                         4.475    1.417
Corporate Income []                0.314    0.072
Financial []                   67.703   18.123
[]                           3.343    6.436
Capital - [Labor]               10.553   0.974
[]                                 27.417   1.375
[]                      110.72   156.21

Interdependence Terms                         Mean      SD

[[bar.FDI]]                     0.390    1.134
[[bar.FDI]]                     0.401    1.134
[[bar.FDI]]                     0.241    0.483
[[bar.FDI]]                  0.348    1.071
[[bar.FDI]]                  0.041    0.412
[[bar.FDI]]                  0.358    1.070
[[bar.FDI]]                  0.042    0.414
[[bar.FDI]]                  0.210    0.454
[[bar.FDI]]                  0.030    0.201

Instruments                                   Mean      SD

[[bar.Sales]]                 0.435    1.149
[[bar.Employees]]             0.497    1.330
[[bar.Corporate Income Tax]]    0.014    0.038
[[bar.Competetion]]           2.676    5.659
[[bar.Sales]]                 0.443    1.141
[[bar.Employees]]             0.508    1.322
[[bar.Corporate Income Tax]]    0.014    0.038
[[bar.Competetion]]           2.536    5.335
[[bar.Sales]]                0.439    1.144
[[bar.Employees]]            0.503    1.325
[[bar.Corporate Income Tax]]   0.014    0.038
[[bar.Competetion]]          2.607    5.461
[[bar.Sales]]                 0.311    0.653
[[bar.Employees]]             0.346    0.718
[[bar.Corporate Income Tax]]    0.009    0.017
[[bar.Competetion]]            2.65    5.479
N = 139,696

Note: Minimum and maximum values are not displayed
due to the confidentiality rules of the Deutsche Bundesbank.


Analysis of German MNEs' Foreign Investments

                                 Basic         Basic       Inv. Dist.
                                Spec. I      Spec. II     [Decay.sup.2]

[[bar.FDI]]       0.195 ***                    0.198 ***
                                (0.068)                      (0.068)



[[bar.FDI]]       -0.161 **                    -0.167 **
                                (0.068)                      (0.068)



[[bar.FDI]]       -0.038 *      -0.042 **       -0.057
                                (0.020)       (0.019)        (0.049)

[[bar.FDI]]                    0.033 ***



[]               0.180 ***     0.180 ***      0.180 ***
                                (0.009)       (0.009)        (0.009)

[]           0.335 ***     0.336 ***      0.335 ***
                                (0.012)       (0.012)        (0.012)

Corp. Inc.                    -1.160 ***    -1.164 ***     -1.152 ***
[]                    (0.155)       (0.155)        (0.155)

Financial                      0.002 ***     0.002 ***      0.002 ***
[]]                (0.001)       (0.001)        (0.001)

[]             -0.005 **     -0.005 **      -0.005 **
                                (0.002)       (0.002)        (0.002)

Cap. - Lab.                    0.181 ***     0.177 ***      0.183 ***
[]                  (0.067)       (0.067)        (0.067)

[]                    0.119 *       0.120 *        0.120 *
                                (0.066)       (0.066)        (0.066)

[]        -0.0004 ***   -0.0004 ***    -0.0004 ***
                                (0.000)       (0.000)        (0.000)

Year dummies                      yes           yes            yes

[R.sup.2]                        0.103         0.103          0.103

N                               134,702       134,702        134,702

                               Inv. Dist.     No Int.
                              [square root     Dist.
                              of (Decay)]

[[bar.FDI]]       0.195 ***     0.188 ***
                                (0.068)       (0.068)



[[bar.FDI]]       -0.162 **     -0.154 **
                                (0.068)       (0.068)



[[bar.FDI]]         -0.013      -0.035 **
                                (0.014)       (0.014)




[]               0.180 ***     0.180 ***
                                (0.009)       (0.009)

[]           0.335 ***     0.335 ***
                                (0.012)       (0.012)

Corp. Inc.                     -1.153 ***    -1.153 ***
[]                    (0.155)       (0.155)

Financial                      0.002 ***     0.002 ***
[]]                (0.001)       (0.001)

[]             -0.005 **     -0.005 **
                                (0.002)       (0.002)

Cap. - Lab.                    0.181 ***     0.186 ***
[]                  (0.067)       (0.067)

[]                    0.120 *       0.130 **
                                (0.066)       (0.066)

[]        -0.0004 ***    0.0004 ***
                                (0.000)       (0.000)

Year dummies                      yes           yes

[R.sup.2]                        0.103         0.103

N                               134,702       134,702

                               Country-    Ext. & Int.
                              Time Eff.      Margin

[[bar.FDI]]      0.557 ***

[[bar.FDI]]                    0.105

[[bar.FDI]]                 2.564 ***

[[bar.FDI]]      -0.382 **

[[bar.FDI]]                  -0.072

[[bar.FDI]]                -2.574 ***

[[bar.FDI]]      -0.089 ***


[[bar.FDI]]                 -0.041 **

[[bar.FDI]]                  -0.116

[]              0.244 ***     0.174 ***
                               (0.007)       (0.010)

[]          0.680 ***     0.337 ***
                               (0.009)       (0.012)

Corp. Inc.                                 -1.148 ***
[]                                 (0.155)

Financial                                   0.002 ***
[]]                             (0.001)

[]                          -0.005 **

Cap. - Lab.                                 0.179 ***
[]                               (0.067)

[]                                 0.117 *

[]        -0.003 ***    0.000 ***
                               (0.000)       (0.000)

Year dummies                      no           yes

[R.sup.2]                       0.458         0.093

N                              139,561       134,702

Notes: FE2SLS estimations (see Section 3.1); t-statistics in
parentheses. Our estimation sample (unbalanced panel) includes
21,598 foreign affiliates of German MNEs in 112 different countries
over the time period 1997 to 2009. The dependent variable is
[], reported in logs of million Euros.

* (p<0.05), ** (p<0.01), *** (p<0.001).
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Author:Bosenberg, Simon; Egger, Peter H.; Merlo, Valeria; Wamser, Georg
Publication:Economic Inquiry
Article Type:Author abstract
Geographic Code:1USA
Date:Apr 1, 2018

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