International services outsourcing and innovation: an empirical investigation

1. International services outsourcing and innovation: an empirical investigation Holger Görg and Aoife Hanley University of Nottingham

2. Why look at services outsourcing? • ‘Fear factor’ • Media attention spurred economists to look into effects of services offshoring (e.g. Amiti and Wei (2006)) • But maybe a good thing: economic restructuring? • Reverse the ‘hollowing out’ of industrial production: ever shifting PPFs through increased innovation sustain a country’s competitive advantage

3. Our research question • We empirically analyse the link between services offshoring and innovation at establishment level • Does offshoring of services raise innovation? • Data for ROI: Amiti and Wei (2006) suggest services more important in ROI than in US • We use data for both services and manufacturing sectors • Time period: 2000 - 2004

4. Background • Empirical work on domestic market effects (Feenstra and Hanson, 1999; Head and Ries, 2002; Hijzen et al., 2005) • Theory: Wage savings prompt North to o/s relatively less advanced intermediates from South (Glass and Saggi, 2001) • Prediction: Northern wages (relative to the South) fall due to outsourcing • What makes Glass-Saggi special: dynamic model with reinvestment of profits

5. Predictions of G-S model • G-S and earlier work allow 2 effects to be identified: • Offshoring negative effect on wage rate • Offshoring raises innovation in offshoring firm • Non G-S effects: • Restructuring • Learning of new technologies

6. Services offshoring in practice (1) • Positive impact of cheaper transport, telecommunications improvements, cheaper computing and liberalisation (Gage and Lesher, 2005) • Overall importance of service inputs small (Amiti and Wei, 2006) • Compelling cost reasons underscoring future growth: • €54 Germany • €44 US • €9.24 Russia • €14 Portugal and China • € India

7. Services offshoring in practice (2) • ‘Services industry’ no longer means strictly services (Gage and Lesher, 2005) • Some trade patterns in services violate textbook predictions: e.g. computer programming from Hyderabad in India

8. Methodology (1) • Innovation measured as R&D intensity • X contains other plant characteristics (lagged) • Time and industry dummies • Error terms: μ (plant specific) and ε (time specific) • To test Glass-Saggi: within transformation i.e. coefficients determined using within plant deviation from the mean in variables

9. Methodology (2) • This equation allows the wage channel (from Glass-Saggi) to work • Expected sign on γ 1t to be negative • Estimate 1) and 2 separately with outs as exogenous • Also try instrumenting outs in both 1) and 2) • If ε and φ are correlated, we estimate using 3SLS

10. Data description • Data from ABSEI collected by Forfásfor 2000 to 2004 • Survey of plants with at least 10 employees • 55 – 60 percent response rate • Spending on R&D in Ireland only 0.8% of GDP (OECD, 1998) • This means Ireland is low on the list of industrialised countries in terms of R&D spending

11. Key variables in the data Table 1

12. Innovation rates for firms that offshore services Table 2

13. Which industries offshore services? Table 3

14. Econometric results (1)

15. Econometric results (2)

16. Econometric results (3)

17. Econometric results (4)

18. Econometric results (5)

19. Conclusions • Recent developments in Web and other media improve scope for offshoring service inputs • Firm should increase innovation with increases in offshored services through the mechanism of wages • Our results somewhat agree with G-S (+ive relationship between offshored services and R&D) • This only holds for manufacturing sector firms • This positive effect does not work through the wage channel, contrary to G-S