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

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NFRASTRUCTURE

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FDI: E

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ISTRICT

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NDIA

Rajesh Chakrabarti Krishnamurty Subramanian Sesha Sai Ram Meka Kuntluru Sudershan

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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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Figure 3: Non-Linear effect of Infrastructure on FDI

K R I S H N A M U R T H Y S U B R A M A N I A N

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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  Infrastructure i->s is a district i in state s vector of variables for infrastructure in  β s 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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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Table 6: Effect of infrastructure on FDI inflows

K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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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 K R I S H N A M U R T H Y S U B R A M A N I A N

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Thank You!

K R I S H N A M U R T H Y S U B R A M A N I A N