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

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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!