IGC-ISI CONFERENCE, DELHI, 2 0TH DECEM B E R 2010

1. IGC-ISI CONFERENCE, DELHI, 20TH DECEMBER2010 Infrastructure And FDI: Evidence From District-level Data In India Rajesh Chakrabarti Krishnamurty Subramanian Sesha Sai Ram Meka Kuntluru Sudershan

2. Motivation FDI forms single largest component of net capital inflows to emerging markets $700 billion into developing economies in 2009 (UNCTAD, 2009) Exceeds official development assistance (OECD, 2002) Government intervention to attract FDI Trade policies (Blonigen, 1997 among others) Tax policies (Hartman, 1995 and others) Provision of public infrastructure In developing countries, public infrastructure offers a comparative advantage: key policy instrument The effect of public infrastructure on FDI inflows remains important to academic scholars and policy makers INTRODUCTION 2

3. Motivation Consensus on this basic question remains surprisingly elusive Accurate measurements not easy (Blonigen, 2005) Cross-country comparisons pose severe identification problems Countries differ along several dimensions Within country changes coincide with other structural changes We cleanly identify effect of infrastructure on FDI inflows Employ a unique district-level dataset of FDI in India India provides an ideal setting BRIC country Preferred destination for FDI INTRODUCTION 3

4. Key Findings The impact of public infrastructure on FDI inflows, though positive, is essentially non-linear FDI inflows remain insensitive to infrastructure till a threshold level is reached Thereafter, FDI inflows increase steeply with an increase in infrastructure INTRODUCTION 4

5. Implications Positive implications Help to explain why marginal improvements in bottom-rung countries fail to excite MNEs to enter them Explains spectacular outcomes in countries like China by creating high infrastructure pockets such as SEZs Normative implications Highlight the need for creating a critical mass of physical infrastructure to attract FDI Quality physical infrastructure matters not just for capital-intensive manufacturing facilities across the board INTRODUCTION 5

6. Data and Proxies District level FDI data: CapEx database created by CMIE As of 2010, CapEx covers over 15,500 projects Total investment of about 2.3 trillion US dollars For each project, CapEx provides information about Exact location (i.e. district) Does the projects involve a Foreign Collaboration (FC) approval? Projects involving FC approval: proxy for FDI Number of projects Value of projects DATAANDSAMPLE 7

7. Data and Proxies District-level socio-economic variables “Indian Development Landscape” put together by Indicus Analytics New dataset Provides two snapshots in time: 2001 and 2008 Education Health Economic Status Infrastructure Demography Empowerment and Crime DATAANDSAMPLE 8

8. Principal Component Analysis To avoid multi-co-linearity and over-parameterization, construct: An index of infrastructure Human Development Index (HDI) Infrastructure variables: Habitations connected by paved roads Households with electricity connection Households with telephone Number of scheduled commercial bank branches Human Development Index: Health Education Empowerment PRINCIPALCOMPONENTANALYSIS 10

9. Figure 3: Non-Linear effect of Infrastructure on FDI EMPIRICALSTRATEGY 14

10. Empirical Strategy Employ a two-pronged strategy that exploits cross-sectional variation among close to 600 districts in India First, we exploit variation among districts within a state after controlling for state level unobserved factors Infrastructurei->s is a vector of variables for infrastructure in district i in state s βs state fixed effects control for States compete with each other to attract FDI Endogenous state-level policies such as tax rates, minimum-wage rates, sops offered to attract FDI Unobserved environmental factors such as availability of skilled labor and other factor endowments EMPIRICALSTRATEGY 15

11. Empirical Strategy Setup ensures direction of causation runs from infrastructure to FDI flows and not vice-versa: First, infrastructure does not change substantially from 2002-07 Correlations between 2001 and 2008: Habitations connected by paved roads: 0.96 Households with electricity connection: 0.91 Households with telephone: 0.88 Number of scheduled commercial bank branches: 0.99 Second, examine effect of infrastructure in 2001 on FDI in 2002-07 Third, exploit cross-sectional variation at the district level Time trends/ structural changes over time less likely to obscure the identification EMPIRICALSTRATEGY 16

12. Results: Table 6 Linear specification in column 1: Quadratic specification in column 2: Piecewise Linear specification in Column 3: High and Low defined as infrastructure being above or below the median value RESULTS 21

13. Table 6: Effect of infrastructure on FDI inflows RESULTS 19

14. Control variables: Actual wage rate in a district FDI inflows greater in districts where wage rates are lower? Minimum wage rates legally set at state level No change => state FE control for the minimum wage rates We do not have information on the actual wages in a district State FE control for average level of wages in the state Actual wage rates should be similar to those in neighboring districts Nevertheless, we attempt to control for wage rates using: Index of human development Population Economic development GDP per capita Level of violent crime Metropolitan city dummy RESULTS 22

15. A theoretical explanation for the threshold effect Canonical FDI-location-choice models predict that higher levels of domestic infrastructure attract uniformly greater FDI See Martin and Rogers 1995 and Baldwin et. al. 2003 Haaland and Wooton (1999): a general-equilibrium model that predicts that a “threshold level of public infrastructure is required to attract FDI” Includes an intermediate goods sector with increasing returns to scale technology More intermediate goods firms => cost of production lower due to spillover benefits Complementarity between finished goods sector (where MNEs operate) and intermediate goods sector RESULTS 25

16. Summary and Conclusions We use a novel district-level dataset of FDI to examine effect of public infrastructure on FDI inflows Our district level dataset enables us to cleanly identify this effect FDI inflows remain insensitive to infrastructure till a threshold level of infrastructure is reached; Thereafter, FDI inflows increase steeply with an increase in infrastructure. Our findings have important positive and normative implications: Explains success of SEZ approach Offer suggestions to policy makers for optimal use of resources in creating infrastructure to attract FDI CONCLUSION 32

17. Thank You!