Outward FDI and Total Factor Productivity: Evidence from Germany

Outward FDI and Total Factor Productivity: Evidence from Germany Outward investment substitutes foreign for domestic production, thereby reducing total output and thus employment in the home (outward investing) country. versus Outward FDI enables firms to enter new markets, to import intermediate goods from foreign affiliates at lower costs, and to access foreign technology. Consequently, the entire domestic economy benefits from outward FDI due to the increased productivity of the investing companies and associated spillovers to local firms. 1-1

Outward FDI and Total Factor Productivity: Evidence from Germany There is little evidence concerning the potential macroeconomic consequences of outward FDI for the home countries. The empirical literature regarding outward FDI consists mainly of firm- and industry-level studies on the effects of outward FDI on employment, exports, investment, and productivity. Barba Navaretti et al. (2010), Bitzer and Görg (2009), Braconier et al. (2001), Driffield et al. (2009), Driffield and Chiang (2009) Aggregate country-level studies of the overall impact of outward FDI on the home countries, however, are scarce. 1-2

Outward FDI and Total Factor Productivity: Evidence from Germany The objective is to examine the macroeconomic relationship between outward FDI and total factor productivity using German time series data from 1980-2004. Germany is among the largest outward FDI suppliers in the world, ranking third in terms of outward FDI stocks behind the US and the UK. Foreign enterprise capital of German firms has grown faster in recent years than that of US and UK firms. In Germany, there is an ongoing public policy debate concerning the high labor costs. Given that German firms may achieve major cost reductions by relocating activities to the low-wage countries of Central and Eastern Europe, the concern is that outward FDI replaces domestic production, thus reducing employment in Germany. 1-3

Outward FDI and Total Factor Productivity: Evidence from Germany We find that there is a positive long-run relationship between outward FDI and total factor productivity, and that causality is unidirectional from outward FDI to total factor productivity. Organization Theoretical background / Discussion of the productivity effects of outward FDI Empirical model and data Estimation results 1-4

Outward FDI and Total Factor Productivity: Evidence from Germany We first consider possible interactions between domestic and foreign activities of multinational firms. Then, we consider the possible impact of outward FDI on the domestic economy as a whole. Interactions between domestic and foreign production activities of multinational firms can occur either through the financial side of the firm or through the production process (Desai et al., 2005). 1-5

Interactions through the financial side Interactions through the financial side occur in a situation where fixed investments in different locations compete for (scarce) funds due to costly external financing (Stevens and Lipsey, 1992). The decision to invest scarce resources abroad inevitably reduces the likelihood of concurrent investments at home. Each dollar of outward FDI displaces a dollar of domestic investment. This substitution of domestic for foreign investment, in turn, is likely to also reduce domestic productivity. In particular, when the investments abroad come at the expense of investments necessary to sustain productivity at home (new machinery, worker training, research and development), outward FDI may reduce the domestic productivity of the investing firm in the long run. 1-6

Interactions through the financial side Desai et al. (2004) find that US multinational affiliates substitute internal borrowing for costly external finance stemming from adverse capital market conditions. Desai et al. (2008) find that US parents provide affiliates with additional equity to finance profitable investment opportunities during currency crises. Possible interactions between domestic and foreign activities are less likely to occur through the financial side, but the production process acts as the main source of interdependence. 1-7

Interactions through the production process Because of production interdependence, outward FDI can affect domestic productivity in several ways, each of which depends on the multinational firm s investment motive and the respective investment type. In the following, we distinguish three key types of investment: horizontal FDI vertical FDI technology-sourcing FDI 1-8

Horizontal FDI Horizontal or market-seeking FDI is motivated by market access and avoidance of trade frictions such as transport costs and import protection in the host country. The decision to engage in horizontal FDI is guided by the proximity-concentration tradeoff in which proximity to the host market avoids trade costs but incurs the added fixed cost of building a second production facility. FDI of this type thus occurs when a firm decides to serve foreign markets through local production, rather than exports, and hence to produce the same product or service in multiple countries. 1-9

Horizontal FDI Horizontal FDI may substitute for exports of those goods that were previously produced in the investor s home country. This decrease in domestic export production, in turn, may be accompanied by a decrease in domestic productivity, since export intensity and firm productivity may be linked. 1-10

Horizontal FDI The decline in (export) output and productivity might only be a short-term phenomenon. Because there is rarely a pure case of horizontal production in the sense that there is inevitably some vertical component to a firm, horizontal FDI can boost exports of intermediate goods and services from the home to the host country. Headquarters in the home country provide specialized services to foreign affiliates (such as R&D, design, marketing, finance, strategic management) even if the same final goods are produced in both the home and foreign country (e.g., Kokko, 2006). 1-11

Horizontal FDI Multinational firms combine home production with foreign production to increase their productivity and hence competitiveness both internationally and domestically (e.g., Herzer, 2008; Desai et al., 2009). Furthermore, in the long run, horizontal FDI may allow the firm to raise its competitiveness through access to new markets or successful penetration of existing markets, thereby additionally increasing domestic productivity. 1-12

Vertical FDI Vertical or efficiency-seeking FDI is driven by international factor price differences. It takes place when a firm fragments its production process internationally, locating each stage of production in the country where it can be done at the lowest cost. Such relocations reduce domestic production, at least in the short run (as with horizontal FDI). 1-13

Vertical FDI In the long run, vertical investment may allow the firm to import cheaper intermediate inputs from foreign affiliates and/or to produce a greater volume of final goods abroad at lower cost, thereby stimulating exports of intermediate goods used by foreign affiliates. The new structure of the production chain may be associated with increased efficiency As a result, the firm may be able to improve its competitive position, thus raising its domestic productivity over the long run (e.g., Herzer, 2008). 1-14

Vertical and horizontal FDI If the firm is not able to adjust over the longer term to the reduction in domestic production by failing to raise its competitiveness (e.g., due to labor market rigidities), both vertical and horizontal FDI will substitute foreign activities for domestic activities over the long run, which may also lead to a long-term decrease in domestic productivity (e.g., Bitzer and Görg, 2009). 1-15

Outward FDI may not only affect the productivity and of the investing firms, but also that of the economy as a whole through productivity spillovers to local firms. Local firms may improve their productivity by copying technologies used by domestic multinationals. Domestic producers may benefit from the knowledge and expertise of the outward-investing firms through labor turnover. The increased competition between international firms and their domestically oriented counterparts may force the latter to use their existing resources more efficiently. Outward-investing firms may be able to provide higher quality inputs at lower prices to local producers. If outward FDI allows the investing firms to grow larger than would be possible with production in just one country, both the investing companies and their local suppliers may benefit from economies of scale. 1-16

Outward FDI can also reduce aggregate productivity. Since outward FDI may act as an important vehicle for the transfer of technological and managerial know-how, it is likely to increase the competitiveness of the host economy as well. This may lead to reductions in domestic output and productivity when domestic consumers prefer the foreign competitors. The increased competitiveness may allow domestic firms in the host country to challenge the foreign firms and thereby to capture market shares from the foreign affiliates of the home country s multinationals. Outward FDI may therefore enable competitors in the host country to attract demand away from the home country firms, forcing them to reduce their production and to move up their average cost curve, resulting in productivity losses in the home country. 1-17

Model lntfpt = a + blnofdi t + ε t Assumptions There is a long-run bivariate relationship between permanent movements in the log level of outward FDI and permanent movements in the log level of total factor productivity Total factor productivity is endogenous in the sense that, in the long run, changes in outward FDI cause changes in total factor productivity. However, the establishment or acquisition of foreign affiliates involves the additional costs of overcoming legal, cultural, and social barriers, so that only firms above a certain productivity threshold can cope with these fixed costs and thus engage in outward FDI. 1-18

Data TFP = Y / [K (1-α) L α ] Y is output, measured by German real GDP (in 2000 US$), K denotes the capital stock We construct the capital stock from gross capital formation data (in 2000 US$) using the perpetual inventory equation K t = I t +(1-δ)K t-1 I t is investment and δ is the depreciation rate. We set the initial value of the capital stock equal to K 0 =I 0 /(g+δ) I 0 is the value of the investment series in the first year it is available (1971), and g is the average growth rate of the investment series between the first year with available data and the first year of the estimation period 1-19

Data TFP = Y / [K (1-α) L α ] L stands for labor input L is represented by the labor force (the number of people of working age, defined as being from 15 to 64 years old). 1-α is the capital share of income α is the labor share of income, α = 0.65. lntfp t = lny t 0.65lnL t 0,35lnK t, Data on GDP, labor force, and gross capital formation are from the World Development Indicators (WDI) 2007 CD-Rom Outward FDI data are from the UNCTAD FDI database 1-20

Data Since UNCTAD reports outward FDI stocks as shares of GDP, we multiply the outward FDI-to-GDP ratio by real GDP (from the WDI) to construct real outward FDI stocks (in 2000 US$). Given that the UNCTAD data start in 1980 while the WDI 2007 data end in 2004, the empirical analysis covers the period 1980-2004. 1-21

Results Standard unit root tests as well as Perron structural change tests suggest that lntfp and lnofdi are integrated of order one, I(1). We now tests for the existence of a long-run relationship between outward FDI and total factor productivity and provide estimates of this relationship. Specifically, we use system cointegration methods as well as single equation approaches for this purpose. 1-22

The Johansen approach Lags interval (in first differences): 1 to 1 Hypothesized Trace 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value None ** 0.621833 22.38687 15.41 20.04 At most 1 0.000922 0.021222 3.76 6.65 Trace test indicates 1 cointegrating equation(s) at both 5% and 1% levels *(**) denotes rejection of the hypothesis at the 5%(1%) level Hypothesized Max-Eigen 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value None ** 0.621833 22.36564 14.07 18.63 At most 1 0.000922 0.021222 3.76 6.65 Max-eigenvalue test indicates 1 cointegrating equation(s) at both 5% and 1% levels *(**) denotes rejection of the hypothesis at the 5%(1%) level 1-23

y t p 1 Γi yt k + Πyt 1 i= 1 = µ + + ε t Π = αβ, where the matrices α and β contain the adjustment parameters and the cointegrating vectors We proceed with testing for long-run exclusion and weak exogeneity of the variables. The test of long-run exclusion investigates whether any of the variables can be excluded from the cointegration space, implying no long-run relationship with the remaining variables. The test of weak exogeneity investigates the absence of long-run feedback. A significant adjustment coefficient suggests long-run endogeneity, and thus long-run Granger causality, whereas a non-significant α implies long-run Granger non-causality from the independent to the dependent variable(s), as well as weak exogeneity 1-24

Tests for long-run exclusion Cointegration Restrictions: B(1,1)=0 Convergence achieved after 1 iterations. Not all cointegrating vectors are identified LR test for binding restrictions (rank = 1): Chi-square(1) 22.28691 Probability 0.000002 Cointegrating Eq: CointEq1 LNTFP(-1) 0.000000 LNOFDI(-1) 0.098194 C -2.547168 1-25

Tests for long-run exclusion Cointegration Restrictions: B(1,2)=0 Convergence achieved after 1 iterations. Not all cointegrating vectors are identified LR test for binding restrictions (rank = 1): Chi-square(1) 10.75909 Probability 0.001038 Cointegrating Eq: CointEq1 LNTFP(-1) -60.82435 LNOFDI(-1) 0.000000 C 360.7435 1-26

Tests for weak exogeneity Cointegration Restrictions: A(1,1)=0 LR test for binding restrictions (rank = 1): Chi-square(1) 22.25820 Probability 0.000002 Cointegrating Eq: CointEq1 LNTFP(-1) -89.32116 LNOFDI(-1) 1.568375 C 489.0716 Error Correction: D(LNTFP) D(LNOFDI) CointEq1 0.000000-0.010283 (0.00000) (0.02268) [ NA] [-0.45342] 1-27

Tests for weak exogeneity Cointegration Restrictions: A(2,1)=0 LR test for binding restrictions (rank = 1): Chi-square(1) 0.532422 Probability 0.465590 Cointegrating Eq: CointEq1 LNTFP(-1) -91.13571 LNOFDI(-1) 1.122954 C 511.3878 Error Correction: D(LNTFP) D(LNOFDI) CointEq1 0.008732 0.000000 (0.00157) (0.00000) [ 5.54950] [ NA] 1-28

The Johansen approach After normalizing on lntfp t and imposing weak exogeneity of lnofdi t to the system, we obtain the following equation (t-statistics are given in parentheses; cointegration restrictions: B(1,1)=-1, A(2,1)=0): lntfp t = 5.611+ 0.0123218 (4.023) lnofdi t 1-29

Single equation analysis Cointegration Test - Engle-Granger Date: 07/26/10 Time: 09:27 Equation: TFP_STATIC Specification: LNTFP LNOFDI C Cointegrating equation deterministics: C Null hypothesis: Series are not cointegrated Automatic lag specification (lag=0 based on Schwarz Info Criterion, maxlag=4) Value Prob.* Engle-Granger tau-statistic -5.111306 0.0021 *MacKinnon (1996) p-values. 1-30

Stock approach Variable Coefficient Std. Error t-statistic Prob. C 4.461672 0.869784 5.129631 0.0001 LNTFP(-1) -0.795803 0.153744-5.176166 0.0001 LNOFDI(-1) 0.009806 0.003095 3.168427 0.0056 D(LNOFDI) 0.013027 0.015798 0.824580 0.4210 D(LNTFP(-1)) 0.164887 0.133329 1.236691 0.2330 D(LNOFDI(-1)) 0.021192 0.015681 1.351406 0.1943 Ericsson and MacKinnon (2002) critical value: -4.20 (1% level) ln TFPt = 0.0123221 lnofdi t 1-31

Alternative estimation techniques Method: Fully Modified Least Squares (FMOLS) Variable Coefficient Std. Error t-statistic Prob. LNOFDI 0.010586 0.004541 2.331105 0.0293 C 5.657188 0.118089 47.90612 0.0000 1-32

Alternative estimation techniques Method: Dynamic Least Squares (DOLS) Variable Coefficient Std. Error t-statistic Prob. LNOFDI 0.013258 0.003866 3.429182 0.0032 C 5.583100 0.098793 56.51296 0.0000 1-33

Alternative estimation techniques Method: Canonical Cointegrating Regression (CCR) Variable Coefficient Std. Error t-statistic Prob. LNOFDI 0.010239 0.004587 2.232424 0.0361 C 5.666943 0.118592 47.78538 0.0000 1-34

Granger causality As shown by Toda and Phillips (1993), standard tests for Granger causality based on levels VAR models are asymptotically chi-square distributed if the underlying variables are cointegrated with one cointegrating vector. Since only one cointegrating relationship exists between lntfp t and lnofdi t (because our model is bivariate), we test for causality by applying the standard chi-square-test for the significance of the lagged independent variables. Dependent variable: lna t Dependent variable: lnofd t-1 Conclusion χ 2 (1) 10.26528669381956 0.1837544361582667 lnofdi t-1 (p-values) (0.001355566705812494) (0.6681663949399116) lntfp t The VAR was estimated with one lag. 1-35