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IGC-ISI CONFERENCE, DELHI, 2 0TH DECEM B E R 2010
INFRASTRUCTURE AND FDI:
EVIDENCE FROM DISTRICT-LEVEL DATA IN INDIA
Rajesh ChakrabartiKrishnamurty SubramanianSesha Sai Ram MekaKuntluru Sudershan
K R I S H N A M U R T H Y S U B R A M A N I A N
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
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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
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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
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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
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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
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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
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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
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Figure 3: Non-Linear effect of Infrastructure on FDI
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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
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
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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
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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
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Table 6: Effect of infrastructure on FDI inflows
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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
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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
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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
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Thank You!