1. Empirics of Vertical FDI and off-shoring Lessons 3 and 4 Giorgio Barba Navaretti Gargnano, June, 11-14 2006

2. Objectives OBJECTIVES –Examine if VFDI is indeed a relevant mode of investment –Examine the effects of VFDI (host and home) –Offshoring of services: a different issue?

3. Standard test of the VFDI model (industry i host country j): FDIij = β1Tij + β2ScaleEcoi + β3MKT SIZEj + β4RelFactEndj + β5FactIntensi + εij If β4<0 VFDI rejected But it is the interaction between factor inensities in i and factor endowments in j that matters The model should instead be specified as F DIij = β1Tij + β2ScaleEcoi + β3MKT SIZEj + β4RelF actEndj + +β5F actIntensi + β6RelF actEndj ∗ F actIntensi + εij, (8) It’s the coefficient β6 that matters. Test if β6>0 Testing for the relevance of the VFDI model: the issue

4. Example: the proximity-concentration trade-off (US investments,Brainard, 1997)

5. Testing HFDI vs HFDI Facts in favour of the predominance of HFDI: Dominant negative effect of trade costs Weak evidence on the importance of relative factor endowments Affiliates sales mostly directed to the local market Need to estimates the relative importance of the two (Carr et al. (2001), Markusen and Maskus (2002) – cross country data of activities of US MNEs Horizontal:          Vertical:          RESULTS: support HFDI but note only country variables

6. Testing jointly country and sector specific factors Yeaple, 2003 Sales of US MNEs

7. Testing jointly country and sector specific factors

8. Effects of Fragmenting production: reminding the predictions NOTE: INTRASECTORAL RATHER THAN INTERSECTORAL EFFECTS Skill mix and skill premium –Home: increases, almost uncontroversially –Host: ambiguous Scale and productivity –Home: ambiguous Scale –Negative:  Relocation of production and labour substitution (VFDI)  Substitute export (HFDI) –Positive:  Gain market share  Product complementarity (export of final and intermediate goods). Productivity – Factor mix – Technological sourcing

9. Effects of fragmenting: evidence on skill mix Home: most evidence based on imported intermediates and sectoral level data – no info on source of imported inputs: –Feenstra and Hanson,1996, 1999 offshoring could account for about 15 percent of the observed increase in the relative wage of non-production workers in the US during the 1979-1990 period. –Falk and Koebel 2002, Strauss-Kahn 2004, Hijzen, Görg and Hine 2005, Geishecker and Gorg, 2004 Limited evidence with firm level data –Marin on Austria and Germany: high skilled activities get offshored (questionable) –Barba Navaretti, Bertola, Sembenelli (2006): share of skilled workers rises, Criscuolo 2006 –Whithn MNES skill premium rises (Slaughter, 2000, Hansson, 2001, Head and Ries, 2002 Host –Feenstra and Hanson, (1997) impact of FDI on the demand for skills in maquiladoras in Mexico. FDI account for over fifty percent of the increase in the share of skilled labour in total wages in the late 1980s

10. Effects of fragmenting: evidence on skill mix 1 Geishecker and Gorg, 2004

11. Estimating demand for skills when firms MNEs (IS IT REALLY FRAGMENTATION?): –Slaugther (2000) on US –Hansson (2001) on Sweden –Head and Ries (2002) on Japan Estimate short run labour demands derived from translog cost functions Effects of fragmenting: evidence on skill mix 2

12. Effects of fragmenting: evidence on skill mix 3

13. Survey (Karsten Bjerring Olsen, 2006) –No clear patterns as to how offshoring/outsourcing affects productivity, depends os sector and firm level characteristics Different ways of looking a the matter (industry or firm level evidence??) Key methodological issue: => Define the right counterfactual Evidence on other firm level effects of fragmenting

14. Estimating the effect of fragmentation: Methodological issues Time Average performance t NATIONALs MNEs

15. Time Average performance t NATIONALs SWs (Switching firms) MNEs Estimating the effect of fragmentation: Methodological issues

16. Time Average performance t NATIONALs SWs (Switching firms) MNEs Benchmark: hypothetical trajectory if switching firms had not invested Estimating the effect of fragmentation: Methodological issues

17. Evidence on other firm level effects of fragmenting Barba Navaretti, Castellani and Disdier 2006 Specific question: do firms improve performance at home by investing abroad? –Define the right counterfactual: what would have happened if firms had not invested abroad? –Investing in DCs vs. LDCs

18. Propensity score The effect of investing on performance is: where 1 denotes performance after the investment and 0 the hypothetical performance if firms had not invested But the last term in unobservable  we need to find an observable counterfactual: untreated firms Propensity score matching computes and finds non-investing firms with (almost) identical ex ante probability of investing

19. Estimators Standard matching estimator (SM): Difference-in-difference estimator (DID): DID accounts for further unobserved differences in ex ante performance growth, which were not accounted by matching Multiple treatment: firms can switch both in DCs and LDCs: Counterfact. Treatment Non switching Switching in LDC Multiple treatment Switching in DC

20. Data France: 2002 version of the database “ Enquêtes filiales ” constructed by the Direction of Foreign Economic Relations of the French Ministry of the Economy, Finance and Industry –First time investors between 95 and 2000 (80 in LDCs and 91 in DCs) Italy: Reprint for information on Italian multinationals (stock and newly established subsidiaries) –First time investors between1993 and 2001 (174 in LDCs and 95 in DCs) Amadeus database of Bureau Van Dijck –Balance sheet and employment data –Information on counterfactual

21. Descriptive stat. on national and switching firms (mean) National firms Firms switching to LDC to DC ItalyFranceItalyFranceItalyFrance N. obs.17,219 28,645 174809591 N. of employees7189142241304326 Turnover15'83121'41130'46880'12569'75494'614 TFP1.61.22.21.932.0 Value added per employee50.144.461.858.970.969.4 Cost of labour per employee29.832.029.437.733.641.4 Age22.124.924.231.827.425.6 ROI6.56.76.17.17.58.0 Current ratio1.31.51.31.61.31.7

22. Probability of switching for French and Italian firms. Multinomial logit FranceItaly Switching to LDC Log TFP i, t-1 1.577***(0.421)2.001***(0.264) Log Nb. Employees i, t-1 0.524***(0.138)0.078(0.106) Log Cost of labour per employee i, t-1 0.949(0.644)-1.299***(0.417) Log Age i, t-1 0.326**(0.140)0.256**(0.117) Return on investments i, t-1 0.013(1.312)-3.841***(1.033) Current ratio i, t-1 -0.050(0.146)-0.319**(0.160) Switching to DC Log TFP i, t-1 1.336***(0.396)2.170***(0.401) Log Nb. Employees i, t-1 0.520***(0.117)0.495***(0.141) Log Cost of labour per employee i, t-1 1.176**(0.565)-1.703***(0.635) Log Age i, t-1 -0.090(0.118)0.323**(0.152) Return on investments i, t-1 -0.443(1.196)-2.056(1.543) Current ratio i, t-1 -0.010(0.119)-0.186(0.191) Number of obs2881617488 Pseudo R20.25670.1923 Asterisks denote significance at 1% (***), 5% (**) and 10% (*). Intercept and sector, regional and year dummies not reported

23. Descriptive stat. on switching firms and matched controls (mean) CFT to Sw.Sw. CFT to Sw.Sw. CFT to Sw.Sw. CFT to Sw.Sw. to LDC to DC ItalyFranceItalyFrance N. obs.161718782 N. of empl.89115226.9207.8298.6278.1386.4274.5 Turnover19'83826'70268'760.770'435.6 59 ’ 70363 ’ 080 106'859.984'030.4 TFP1.82.11.7 2.42.61.92.0 Labour prod52.262.254.955.858.563.563.769.7 Wage28.529.337.937.632.633.141.241.1 Age2223.833.031.633.426.432.526.2 ROI6.768.16.97.47.77.98.0 Current ratio1.3 1.6 1.31.41.7

24. The effect of investing abroad on performance at home: France vs Italy FranceItaly Effect sw. in LDCEffect sw. in DCEffect sw. in LDCEffect sw. in DC Coef.Std. ErrCoef.Std. ErrCoef.Std. ErrCoef.Std. Err TFP growth ATT 1-year0.017(0.037)0.047(0.035)0.030*(0.020)0.010(0.033) ATT 2-years0.041(0.038)0.056(0.047)0.058*(0.030)0.011(0.035) ATT 3-years0.020(0.057)0.050(0.061)0.041(0.035)-0.002(0.043) DID 1-year0.126**(0.060)0.001(0.057)0.063**(0.031)-0.075(0.055) DID 2-years0.125(0.092)0.031(0.068)0.086***(0.036)-0.074(0.056) DID 3-years0.103(0.112)0.012(0.079)0.042(0.048)-0.135*(0.081) Value Added growth ATT 1-year0.000(0.037)0.033(0.030)0.017(0.025)0.006(0.032) ATT 2-years0.022(0.045)0.030(0.041)0.063*(0.035)0.055(0.047) ATT 3-years-0.009(0.049)0.039(0.061)0.045(0.037)0.042(0.044) DID 1-year0.059(0.078)-0.007(0.045)0.047*(0.031)-0.063(0.049) DID 2-years0.075(0.100)0.003(0.057)0.112**(0.041)0.010(0.049) DID 3-years0.014(0.102)0.039(0.073)0.090*(0.057)-0.028(0.057)

25. The effect of investing abroad on performance at home (cont.) FranceItaly Effect sw. in LDCEffect sw. in DCEffect sw. in LDCEffect sw. in DC Coef.Std. ErrCoef.Std. ErrCoef.Std. ErrCoef.Std. Err Turnover growth ATT 1-year0.034(0.021)0.029(0.024)-0.009(0.022)0.049**(0.025) ATT 2-years0.094***(0.031)0.097***(0.033)0.006(0.026)0.025(0.037) ATT 3-years0.000(0.052)0.121**(0.049)0.052(0.042)0.068*(0.042) DID 1-year0.008(0.036)-0.040(0.039)-0.044(0.035)0.021(0.039) DID 2-years0.062(0.045)0.034(0.049)0.006(0.041)0.026(0.057) DID 3-years-0.055(0.079)0.061(0.078)0.065(0.055)0.061(0.057) Employment growth ATT 1-year0.049*(0.029)0.024(0.018)-0.022(0.026)-0.033(0.033) ATT 2-years0.047(0.031)0.057**(0.027)0.018(0.036)0.063*(0.035) ATT 3-years0.051(0.039)0.099***(0.039)0.048(0.034)0.040(0.048) DID 1-year0.020(0.029)-0.027(0.024)-0.040(0.038)-0.050(0.050) DID 2-years0.029(0.036)0.013(0.036)0.027(0.051)0.068(0.052) DID 3-years0.012(0.053)0.053(0.045)0.073(0.058)0.068(0.062)

26. Outsourcing material inputs and services: firm level evidence based on imported inputs Gorg, Hanley and Strobl, 2004

27. Off-shoring of high skilled white collars Is it a different issue (Markusen 2006)? –No from the North point of view: outsourcing of lower skilled workers within an industry –South: you need to explain why a scarce factor of production is cheap there (skilled labour) Complementary factors Not cheap relatively to local unskilled labour –Trade expansion at the extensive margin and trade reversals Endless transfer of activities to the South (Trefler 2006): –Remember comparative advantage –Role of R&D institutions

28. Conclusions Fragmentation is an important empirical phenomenon, even when looking at FDI data Effects on skill premium in the North likely important Effects on employment and productivity ambiguous, but likely positive