fdi in india and its growth linkages - Department Of Industrial Policy ...

FDI IN INDIA AND ITS GROWTH LINKAGES
While firms are generally headquartered in relatively large cities, the plants are located in large as well as small
(Class-3) cities.
While investors in industrial plants may not follow a highly structured theory-based model with regard to the choice of
a particular location, there would be some rule-of-thumb considerations for these decisions. Multiple factors are likely
to play a simultaneous role in when a firm makes a decision on where to locate a plant. The decision would be based
primarily on the nature of the plant. For example, an integrated iron and steel plant would prefer to locate close to
regions producing primary inputs (iron ore and coal), whereas a cement plant would be close to limestone quarries.
State-level factors may include the number of SEZs, energy deficit, per capita income, and the number of workers’
unions. City-level factors may include congestion in a city, presence of a port/ airport/ SEZ, distance from the nearest
major city, and the share of FDI plants in total plants, among others. The share of FDI plants in the total number of
plants in a city may be taken as a proxy for FDI plant density/ FDI agglomeration.
The analysis is based on 1,240 plants located in 286 cities (21 Class-1, 20 Class-2 and 245 Class-3 cities) spread over
26 states and Union Territories (Tables 6.52 and 6.53). These 1,240 plants account for 12 per cent of the total plants
(FDI plus domestic) located in the corresponding 286 cities and 26 states / Union Territories. Of the 1,240 FDI plants,
434 plants are located in Class-1 cities, 85 in Class-2 cities and 721 in Class-3 cities. Thus, Class-3 cities account for
58 per cent of the total number of FDI plants. With regard to regional distribution, there are 217 FDI plants in the
north, 184 in the east, 481 in the west and 358 in the south (Table 6.54). In the northern region, about 79 per cent of
the 217 FDI plants are located in Class-3 cities; the corresponding numbers are 71 per cent in the east, 51 per cent in
the west and 49 per cent in the south.
In the model estimation, we have divided city sizes as “large” for Class-1 and Class-2 cities taken together, and “small”
for Class-3 cities. We have used a discrete choice binary logit econometric model to estimate locational decisions of
FDI-enabled plants in India. 15 The discrete choice model facilitates our analysis to reveal the city-level preferences of
FDI-enabled firms which might otherwise be lost in aggregate methodologies (Cheng, 2007). In the estimated
regression equation, the dependent variable is set up as zero if a particular plant is located in a large city (Classes 1 and
2) and 1 if it is located in a small (Class-3) city.
We worked with various alternative model specifications based on independent variables with firm-, city-, state-, and
region-specific characteristics. These characteristics relate to dummy, discrete, and continuous variables. In our final
estimate, we incorporate the firm-level characteristic as export intensity (continuous). The city-level characteristics
include the share of FDI plants in total plants (continuous), airport location in a city (dummy variable) and the distance
from the nearest Class-1 city (distance-wise dummies). The state-level variables include the number of SEZs (discrete),
power deficit to availability ratio (continuous), number of trade unions (discrete) and per capita income (continuous).
Other independent variables refer to the four regions (dummies) as well as regional interaction dummy variables vis-àvis
distance dummies.
It was found that the alternative specifications make some of the independent variables consistent in terms of sign and
significance, while other variables provide fragile results with respect to sign/ significance. Variables, such as the share of
FDI plants in a city and electricity deficit to availability ratio in a state, are robust across various model specifications.
One variable that showed less robust results is per capita income. However, we included this in the final specification of
the model as a proxy for level of development of a state. Two specifications, one showing the base estimate and other
depicting the final accepted specification, are shown in Table 6.55.
15.Technical details of this model are provided in Long and Freeze (2003).
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