Of Flying Geeks and O-Rings: Locating Software and IT Services in India’s Economic Development

1. Of Flying Geeks and O-Rings:Locating Software and IT Servicesin India’s Economic Development David O’Connor OECD Development Centre Paris, France Chennai Conference, Royal Meridien 11-12 Nov 2002

2. Outline • The questions • Analytical framework • Empirics: • software and growth • software and income distribution • Policy for broad-based IT-driven growth

3. Questions • How important has software/IT been to India’s strong 1990s growth performance? • How widely shared have the benefits of software export growth been? • What policies to spread the benefits? • What chances for emulating the Indian model “flying geek” style?

4. Analytical Framework • Explaining the Indian success story • firm strategies • institutional and policy environment • global market and technology trends • Locating software in Indian economy • Assessing its impacts on growth and distribution

5. Flying geeks • Market penetration • body shopping as market entry pricing strategy • accepting lower initial profits to build trust • through F2F interaction with clients • backed by familiarity of early clients with quality of expatriate Indian engineers • Market expansion • role of process quality accreditation (Arora) • Market upgrading?

6. F2F • How important is F2F interaction to the market entry of latecomer “flying geeks”? • Has the internet reduced its importance at the working level of programmers? • What about for Indian companies moving up the value chain -- towards front-end conceptual design?

7. Clustering • Why Bangalore? Chennai? Hyderabad? • Agglomeration economies through: • local knowledge spillovers • rich market for software talent • Where human capital is concentrated, • avg productivity is higher for all workers • may reflect a degree of selection bias • also positive externalities of human capital

8. O-rings • Skill complementarities: • where production process involves multiple, interdependent steps • high skilled workers seek out their kind to maximise their productivity • reduce the risk of failure anywhere along the line • Alternatives: • High skilled Indians seek out high-skilled foreigners by emigrating • Highly skilled Indians have productivity reduced by working in low-productivity environments

9. Questions • Is this the story of the Indian software industry? • Would aggregate productivity be higher/lower in the absence of such skill matching? • Probably lower: e.g. a highly skilled programmer were, say, working in the customs office • But are the positive externalities of human capital diminished by such matching – e.g., fewer spillovers from high to low skilled?

10. Empirics: software and growth • IT sector’s revenues amounted to 2.8 per cent of GDP in 2000-2001; VA share not known but thought to be high. • In 2001, software exports amounted to $ 6.3 bn, i.e., 15 per cent as large as total merchandise exports, roughly on a par with gems and jewellery • India exports more software than “ready-to-wear”

11. Fastest growing states • in terms of real state domestic product (1990/91-1998/99) include several software centres; • in descending order: • Karnataka (7.9% p.a.) • Maharashtra (7.5%) • Gujarat (7.4%) • West Bengal (6.8%) • Tamil Nadu (6.6%) • Andhra Pradesh’s SDP growth rate was slightly below the national average.

12. Indirect contributions • May be equally or more important • E.g., via multiplier effects on other sectors’ output • This may partly explain strong banking growth • Also construction • Demonstration effects (Arora et al.): • Rewards to entrepreneurship • Innovative management practices, esp. in area of human resources and knowledge sharing • Development of equity and venture capital markets

13. Empirics: software and income distribution • Perception: lion’s share of software’s rewards go to grads of elite tech schools • Little in the way of trickle down … • True? • Little hard evidence of productivity spillovers to the less skilled, but • Evidence of strong growth effects on poverty in the “software” states

14. Measuring poverty effects • Datt and Ravallion provide data on: • Inter-state differences in per capita non-agricultural output (NAO) growth, combined with • Inter-state differences in poverty elasticities of NAO growth • Calculations show: • The very high NAO growth rates in software states • More than outweigh relatively low poverty elasticities • Result: During 1990s, NAO growth in TN, Karnataka, Maharashtra had bigger effect on national poverty rate than NAO growth in Bihar, the poorest state.

15. NAO growth was not highest in the states where it would have had biggest impact on poverty based on poverty elasticities, but … The spectacular NAO growth of the “software” states made their contribution to poverty reduction greater than that of the poorest state. Conclusion

16. Caveats • Not proven that software main driver of NAO growth in “software” states, but • hard to imagine it was irrelevant, • esp. where as in TN mfg sector growth was so slow over 1990s • Growth in other sectors has also been strong in some cases, notably banking + insurance • Exact contribution of software blurred by apparent classification with other services (like education, health care) that are large and slow-growing

17. The AP Puzzle • Andhra Pradesh – also a “software” state – underperformed both in • NAO growth and • Poverty reduction effects of growth • Datt and Ravallion suggest possible explanation: • States with poor initial conditions (notably high female illiteracy) see small impact of NAO growth on poverty • 2001 Census shows that AP still has one of highest female illiteracy rates in India.

18. Policy for broad-based IT-driven growth: central level • Maintain reform impetus to stimulate domestic demand for productivity-boosting, cost-cutting technologies • As domestic IT demand grows … • Yawning gap in applications between export and domestic market should narrow • Find more flexible means of protecting workers from vagaries of competition than presently • Review intellectual property regime with view to encouraging domestic software innovators

19. Software export market segments Source: NASSCOM

20. Domestic software market segments Source: NASSCOM

21. Questions • Can the sizeable experience with domestic package/product development be leveraged to develop “global products”? • Or, should India’s heavyweights be aiming instead to move upstream and upscale in the customised software/services market? • Competing/collaborating with the big global brands … • Can both strategies be pursued simultaneously?

22. Policy for broad-based IT-driven growth: state level • Build local talent pools • Broaden literacy first in the poorer states • Develop higher computer skills elsewhere • Liberalise telecoms and ISP services to encourage competition, lower prices, faster diffusion • Take more forceful action to reform electricity sector …

23. Non-policies: where gov’t should steer clear • Promoting IT hardware manufacturing • India’s demonstrated little capacity until now to compete in this area • A mature, commodity business; benefit from low-cost imported hardware to sustain software advantage • Favouring the software industry with fiscal breaks, captive procurement markets, etc.

24. … and the other flying geeks? • Philippines (large English speaking pop, high literacy rate, low-cost college grads) • Phil. ITeS business dates back almost as far as India’s, but • political instability has complicated its growth • also the nature of the talent pool has limited head-on competition in programming, but made it a major competitor (prospective collaborator?)in ITeS • And then there’s China …