chapter 5 determinants of fdi in india: an...

Chapter 5

DETERMINANTS OF FDI IN INDIA: AN ECONOMETRIC

ANALYSIS

The aim of this chapter is to identify the determinants of FDI inflows in vanous

industries and states of India, as well as those for aggregate inflows, by employing tools

of applied econometrics.

We employ panel models for examining the determinants of FDI across industries and

states of India. FDI inflows across industries and states over a period of time are

examined in terms of various industry and state-specific explanatory variables.

Accordingly, panels ofFDI inflows across industries and states are constructed. We also

analyse the determinants of aggregate FDI inflows in India in terms of specific

macroeconomic parameters using simple time series data.

The chapter is organised into four sections. Section 1 sets up the testable hypotheses.

Section 2 specifies the econometric models for estimation, discusses the data and

variables and explains the econometric methodology employed. Section 3 reports and

analyses the results. Section 4 gives a synthesis of the main findings.

5.1 TESTABLE HYPOTHESES

We categorise our hypotheses into three groups. The first group looks at industry

specific determinants of FDI. The second group focuses on state-level characteristics.

The third and final group comprises macro-level determinants of aggregate FDI flows.

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5.1.1 Industry-level hypotheses

Industry size

Among various industry-level characteristics that are likely to be significant

determinants of inward FDI, we identify the size of the industry as a crucial factor.

Larger industries have well-developed markets for final products in the host country,

along with established input suppliers and skilled labour, resulting in several external

economies of scale ( orindustry size). These industries also perhaps belong to sectors in

which the host nation enjoys comparative advantage. Accordingly, we may expect more

FDI to flow into these sectors.

Empirical research on role of industry size as a determinant of FDI has been relatively

limited. This is, because, the thrust of the empirical literature on determinants of FDI

has been largely on macro-level country-specific determinants (e.g. size of the host

country market). Industry-specific studies, however, have found evidence of industry

size being a significant and positive determinant of FDI (e.g. Morgan and Wakelin

(1999), in an empirical study of the determinants of FDI in different categories of the

U.K. food industry).

Labour intensity

Given the intrinsic features of FDI in terms of specific ownership attributes (e.g. money

capital, advanced know-how, managerial expertise, marketing skills etc.), FDI flows are

expected to be directed towards relatively capital-intensive industries for better

exploitation of these ownership advantages. We may, therefore, expect less FDI flow in

relatively labour-intensive sectors. However, it must be clarified that both theoretical

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and empirical literature has pointed out that industries usmg skilled labour more

intensively are also likely to attract more FDI. Thus, the significance of labour-intensity

in determining FDI needs to be judged by making a clear distinction between the skilled

and unskilled components of labour.

The role of relative labour-intensity, as an industry-specific characteristic in

determining FDI, has hardly been empirically examined. There are, however, instances

of empirical studies on India focusing on factors governing entry mode choices for

foreign firms in the pre-economic reform period, which have pointed to greater

concentration ofFDI in skill-intensive industries (Kumar, 1987).

Export-orientation

Empirical studies on the causality between FDI and exports have tried to ascertain

whether FDI moves into export-oriented sectors. Studies on developing countries

identify manufacturing exports as significant and positive determinants of FDI (Narula

and Wakelin, 1995; Singh and Jun, 1995). For India, however, FDI has been found to be

more biased towards the domestic market, rather than exports, as compared to

developing economies attracting high FDI, like China (Guha and Ray, 2001).

Export-orientation of industries as a determinant of FDI should be analysed to gather

insights on the nature of FDI flows, namely, whether they are more of the 'domestic

market-oriented' variety, or of an 'export-oriented' nature.

Import-intensity

Industries with higher import-intensities indicate greater dependence on imported inputs

like raw materials, stores, capital goods, know-how etc. More import-intensive

industries are likely to attract more FDI, since foreign firms have better access to

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imports through global production and marketing networks. In many LDCs, industries

using advanced production techniques· rely heavily on technological imports due to

unavailability of quality indigenous import substitutes. FDI is expected to respond

favourably to these industries due to the oliogopolistic advantages enjoyed by foreign

firms in form of possession of advanced technology. Empirical evidence on t~e effect of

import-intensity of industries on FDI in India is very limited. We hypothesise import

intensity to have a positive impact on FDI.

Profitability

Industries earning higher profits retain larger surpluses for future investment. Moreover,

these industries are also likely to offer greater scope to foreign firms for higher

remittances to home countries. Accordingly, we expect FDI to flow into more profitable

industries.

Advertisement-intensity

Advertisement-intensity is a common feature for industries, where development of

brand loyalties through market promotion assumes considerable significance. These

industries are characterised by product differentiations through innovations. Innovations

and their successful applications are typical attributes of multinational firms.

Advertisement-intensity, therefore, is salient to industries, where ownership advantages

of FDI acquire meaningful dimensions. FDI in India has been found to have a greater

concentration in advertising-intensive industries (Kumar, 1987). We hypothesise FDI

flows to be positively related to advertisement-intensity.

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5 .1.2 State-level hypotheses

Market size

Both theoretical and empirical literature has identified size of the host-country market

as a key determinant of FDI flows. Large markets enable foreign firms to lower their

costs of production by exploiting scale economies. Several empirical studies indicate

market size as a significant and positive determinant of FDI for both developed and

developing countries (Horst, 1972b, Root and Ahmad, 1979; Dunning, 1980, 1998a;

Schneider and Fray, 1985; Wheeler & Mody, 1992; Tsai, 1994; Taylor, 2000;

Chakraborti, 2001 ). Similar conclusions have been reached for India as well (Anand and

Delios, 1996, Guha and Ray, 2001). We propose to examine the effect of market sizes

of states on FDI flows in this study. We expect states with larger markets to attract

greater FDI.

Infrastructure

Availability of quality infrastructure facilities (e.g. rail and road transport, electricity

and telecommunication) plays an important role in attracting FDI flows. Well

developed physical and communication infrastructure helps in efficient distribution of

goods and services by lowering transaction costs. The issue of infrastructure is

particularly significant for low-income countries, which often lack in infrastructural

facilities.

Empirical studies have identified availability of infrastructure as a significant

determinant for FDI in developing countries (Wheeler & Mody, 1992; Collier, 1998,

Urata and Kawai, 1999). Shortage of quality infrastructure services has been identified

as a deterrent to FDI flows for India as well (Sachs and Bajpai, 2001), compared to

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China and other developing economies. We hypothesise that states with better

infrastructural facilities will attract more FDI. We will also try to ascertain the

significance of transport, electricity, and telecommunication, separately, for FDI

inflows.

Industrial relations

The quality of industrial relations, reflected in the number of workdays lost, has been

found to be a significant determinant of FDI flows for developing countries receiving

low FDI (Rana, 1988; Lucas, 1993; Singh and Jun, 1995). Better industrial relations

assure foreign investors about regular participation of labour in production, apart from

indicating fewer disruptions in production schedules. We expect states with better

industrial relations to attract more FDI.

Degree of industrialisation

Ownership advantages of FDI can be exploited more meaningfully in manufacturing

and service sectors. More industrialised locations have larger presence of manufacturing

and service activities. These locations also offer a more enabling environment for FDI.

Accordingly, we hypothesise that FDI will show a higher propensity to flow into the

states that are more industrialised.

Technological capabilities and infrastructure

R&D initiatives undertaken at the state-level reflect the quality of technological

infrastructure available in the states. Higher R&D initiatives not only improve the

quality of existing technological infrastructure, but also enhance technological

capabilities. These are expected to act as 'pull' factors for FDI, since availability of

indigenous technological capabilities can help foreign firms in exploiting their

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ownership advantages better. We hypothesise FDI flows to be positively related to the

level of technological capability and infrastructure of the states.

5.1.3 Macro-level hypotheses

Market size

We have already discussed the importance of host country market size as a determinant

for incoming FDI. Earlier, in chapter 3, we have mentioned various empirical studies

that have identified market size as a significant factor influencing FDI flows for

developed countries, LDCs, as well as the Indian economy. In the present study, we

would like to study the effect of the domestic market size on aggregate FDI inflows into

India. We expect FDI to be positively related to the size of the domestic market.

Returns to Capital

We attempt to study three related determinants ·in this category. These are stock market

returns, FII investment and non-resident deposits.

Stock market returns

Higher returns from domestic stock markets indicate higher returns on the equity capital

invested in the host economy. Though returns from stock markets are not considered

traditional determinants of FDI, neo-classical trade theory identifies differences in rates

of return as the main reason behind movement of capital across nations. In that sense,

stock market returns may reflect the incentive for capital movement. We would like to

study the impact of returns from stock markets on incoming FDI. We expect stock

market returns to have a positive relationship with FDI flows.

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FII inflows

FII inflows into a host economy are intricately linked to returns from stock markets.

Portfolio investments by Fils are determined on the basis of their perceptions regarding

risks and returns from the host economy. These inflows usually increase if the risk··

return expectations are favourable. Higher FII inflows, reflecting positive expectations

about returns from the host economy, can encourage FDI flows. Accordingly, we

hypothesise FDI flows to be positively related to FII inflows.

Inflows of non-resident bank deposits

Bank deposits by expatriates respond favourably to the difference in interest rates

between source and host countries. Host country deposit rates reflect the nature of

returns to capital in that country. Higher inflows of non-resident deposits in response to

higher deposit rates (i.e. higher returns on capital) can motivate FDI flows, according to

the principle of capital arbitrage. We expect FDI flows to be positively related to

inflows of non-resident deposits.

( Export-orientation

Greater export-orientation of the host economy is expected to attract more FDI of the

'export-oriented' variety (Singh and Jun, 1995), Studies on India, however, suggest

greater inflow ofFDI ofthe 'domestic market-oriented' variety, rather than the 'export

oriented' type (Guha and Ray, 2001). We would like to revisit the issue in the present

study.

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5.2 ECONOMETRIC MODEL SPECIFICATION, DATA AND VARIABLES,

AND ECONOMETRIC METHODOLOGY

5.2.1 Econometric model specification

We specify econometric models for testing our three sets of hypotheses.

Industry and state-level hypotheses

For industry and state-level hypotheses, we posit the following panel regression model:

(1) i=l,2, ....... N; t=l,2, ...... T; where

yit: FDI inflow in i-th industry in period 't'(or in i-th state in period 't').

xit: Vector of specific characteristics for i-th industry in period 't' (or i-th state in

period 't').

a1: the individual effect for the ith industry (or state) assumed to be constant

over time.

Eit : the stochastic error term.

Macro-level hypotheses

We propose a simple time series model for our macro-level analysis.

Yt= a+ f3xt + Et; (2) t=l,2, ...... T;,where

Yt : Aggregate FDI inflow in period t .

x1 : Vector of specific macro-economic parameters for period t.

E 1 : the stochastic error terriL

5.2.2 Data and variables

Our study employs panel models for identifying the determinants of FDI at the industry,

state and country levels. The variables are constructed accordingly.

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5.2.2.1 Dependent variable (FDI)

We use actual FDI inflows as the dependent variable for testing our industry-level

hypothese~. Data on annual FDI inflows on an industry-wise basis are compiled by the

Secretariat for Industrial Assistance (SIA), Department of Industrial Promotion and

Policy, Ministry of Industry, Government of India. FDI data has been obtained from

annual issues of the SIA newsletters (2000 and 2002).

Unlike industry, data on actual FDI inflows on a state-wise basis, however, are not

available before the year 2000. Due to this data constraint, we proxy actual FDI inflows

by approved FDI flows. Approvals indicate investment intentions of foreign investors.

These intentions are influenced by the various state-level characteristics that we have

hypothesised. Accordingly, we expect results obtained by using FDI approvals to reflect

accurately the nature of relationship between incoming FDI and various explanatory

variables. Data on FDI approvals is also maintained by the SIA and was obtained from

the same sources mentioned above.

We take aggregate FDI inflows as the dependent variable for our country-level analysis.

Information on aggregate FDI inflows into India is available with both the SIA and the

RBI. On the present occasion, we use the RBI data. 38

5.2.2.2 Explanatory variables

5.2.2.2a Industry-level analysis

Industry size

We employ three variables for capturing industry size. The first of these is share of sales

of a particular industry in total industrial sales (salesxsales) for a given year.

38 See RBI (2002).

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salesxsa/esu = (Sales of i-th industry in year 't') I Total industrial sales in year

't'.

Industry size is also measured by Sales, which reflects the value of total sales for

industry 'i' in year 't'.

The third variable used for industry size is GV AXGVA, which is the share of Gross

Value Added (GVA) by industry 'i' in year 't" in total industry GVA for year 't'.

GVAxGVAu = (GV A for i-th industry in year't')l Total industrial GV A in year

't'.

The data for industry-wise sales39 and GV A 40 have been obtained from the Corporate

Sector (May 2002) report brought out by the Centre for Monitoring Indian Economy

(CMIE).

Labour intensity

We measure labour-intensity of an industry by the share of wages and salaries of a

particular industry, i.e. the total wage bill, in industrial GV A. The variable is expressed

as wsxGVA, where,

wsxGVAit =(Wages and salaries for i-th industry in year 't') I (Total GVA fori-

th industry in year 't')

This variable fails to make a distinction between skilled and unskilled labour intensity.

Due to Jack of disaggregated data on wages and salaries for skilled and unskilled labour,

our variable reflects labour-intensity as a whole. The result, therefore, may be distorted

39 Sales are defined as Income generated from main business activities like sale of goods and services, fiscal benefits, trading income. It also includes internal transfers but excludes expenses capitalized. See CMIE (2002).

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in reporting the essence of the labour-intensity hypothesis. We_would like to clarify that

though we expect FDI to flow more into capital-intensive industries, given the

significance of skilled labour as a determinant of FDI, we should ideally employ

variables that measure labour-intensities separately in terms of skilled and unskilled

components.

Data on wages and salaries have been taken from the CMIE data source cited earlier.

Export-orientation

Export-orientation is measured by the volume of exports from a particular industry as a

proportion of total industrial sales. The variable is expressed as expxsales, where

expxsalesit = (Total export earnings41 for i-th industry in year 't') I (Total sales

for i-th industry in year 't')

Data on export earnings and industrial sales have been obtained from the CMIE data

source.

Import-intensitv

We measure import-intensity by the volume of imports for an industry as a proportion

of total industrial sales. The variable is expressed as impxsales, where

impxsalesit = (Total import expenses42 for i-th industry in year 't') I (Total sales

for i-th industry in year 't')

Data for import earnings have been taken from the CMIE data source.

40 GV A is defmed as the sum of wages and salaries, interest payments, rent paid, profit before tax, and depreciation. Interest and rent payments are net of receipts, while profit before tax is net of non-recurring transactions. See CMIE (2002) 41 Total exports are defined as total forex earnings, including earnings from export of goods on FOB value, as well as forex earnings from services. See CMIE (2002).

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Profitability

Profitability of industries is measured by the share of profit after tax (PAT) (net of non-

recurring transactions) in total sales of an industry. The variable is expressed as

pat.xsales, where,

Pat.xsalesit = (Total profits after tax for i-th industry in year 't') I (Total sales for

i-th industry in year 't')

Data on profit after tax has been taken from CMIE.

Advertisement-intensitv

We use the share of advertising expenses, as a proportion of total industrial sales, as a

measure of advertisement-intensity. The variable is expressed as advtxsales, where

advtxsalesit = (Total advertising expenditure for i-th industry in year 't') /

(Total sales for i-th industry in year 't')

Data on advertising expenses43 has also been collected from the CMIE.

A note on the CMIE data source is given in Appendix 1.

For the industry-level analysis, we estimate the panel regression model specified in the

earlier section for two different data sets. The first data set comprises eighteen

manufacturing industries. These are: metallurgical, fuels, electrical equipment,

transportation, non-electrical machinery, fertilizers, chemicals, drugs &

phmmaceuticals, textiles, paper; sugar, fermentation, food processing, vegetable oi1s,

rubber, soaps & cosmetics, leather and cement.

42 Total import expenses include the CIF value of import of raw materials, stores, import of capital goods and also foreign exchange outgo on royalty, know-how, fees, dividend, interest etc. See CMIE (2002). 43 Advertising cost is defmed as the expenditure incurred on advertising. It also includes marketing expenditure such as rebates, discounts and commissions. See CMIE (2002).

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The second data set includes services, hotel & tourism, and trading, in addition to the

eighteen manufacturing industries. However, due to lack of data on advertising cost for

services, we had to drop advtxls as an explanatory variable from the second variation.

The time period for estimation is 1994-95 to2000-01.

5.2.2.2b State-level analysis

Market size

We capture the size of domestic market for states through annual per capita net state

domestic product (SDP) at current prices. The variable is expressed as pcnsdp. Per

capita net SDP is an income proxy of the market size, reflecting the purchasing power.

Market size can also be measured in terms of the absolute value of net SDP. However,

we have constructed pcnsdp after normalising absolute values ofNSDP by population.

Values of pcnsdp have been obtained from the annual Economic Survey (2002-03)

published by the Ministry of Finance, Government of India.

Infrastructure

In our analysis, we focus specifically upon three major components of infrastructure.

These are transport infrastructure, electricity, and telecommunication, respectively. We

measure transport infrastructure through two variables: raildensity and rddensity. While

raildensity indicates the state-wise density of rail route length per '000 sq. km. of area,

rddensity is state-wise density of road length per '000 sq. km. of geographical area.

Data for both these variables have been obtained from the Infrastructure report44

brought out by the CMIE.

44 See CMIE (2003).

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We measure availability of electricity through per capita electricity consumption in

states. The variable is expressed as pcelecon. Data on state-level per capita electricity

consumption has been taken from the Annual Report (2001-02) on the Working of State.

Electricity Boards & Electricity Departments brought out by the Planning Commission.

Finally, we try to capture the availability of telecommunication services by delciricle,

which measures circle-wise direct exchange lines (provided by BSNL & MTNL) in

'000 numbers for every state. Data on number of circle-wise direct exchange lines has

also been obtained from the CMIE database cited above.

Quality of industrial relations

Quality of industrial relations in various states is measured by number of mandays lost

on account of strikes and lockouts. The variable is expressed as mtmdays. Information

on number of mandays lost annually, state-wise, has been obtained from various issues

of the Handbook of Indhstrial Statistics published by the Department of Industrial

Promotion & Policy (DIP&P), Government oflndia.


We use the share of non-agricultural domestic product in net SDP at current prices, as a

measure for degree of industrialisation. The variable is expressed as nonagrdp.

nonagrdpit = 1- (Net domestic product from agriculture in i-th state in year

't')/(Total net domestic product in i-th state in year 't')

Values of nonagrdp for different states have been calculated on the basis of sectoral and

aggregate estimates for SDPs from the National Accounts Statistics compiled by the

Central Statistical Organisation (CSO).

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Technological Capabilities and Infrastructure

We have tried to quantify the level of technological capabilities and infrastructure at

state-level in terms of share of R&D expenditure as a proportion of per capita net SDP

for each state. The variable is expressed as rdpcnsdp.

rdpcnsdpu = (Total R&D expenditure by state 'i' m year 't') I (Per capita

NSDP for state 'i' in year 't')

The values of rdpcnsdp has been estimated on the basis of data on state-level R&D

expenditure, compiled from statistics released by the Department of Science and

Technology in various issues of the Handbook of Industrial Statistics brought out by the

Ministry of Industry, Government of India.

The panel regression model specified in the earlier section has been estimated for

sixteen states of the country. These are: Andhra Pradesh, Assam, Bihar,, Gujarat,

Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa,

Punjab, Rajasthan, Tamilnadu, Uttar Pradesh and West Bengal.

Due to lack of data for one or more of the explanatory variables, other states and Union

Territories could not be included. The sixteen states figuring in our estimation account

for more than eighty per cent of total FDI approvals during the 'period 1993-94 to 2000-

01. Among the states excluded, only Delhi has sizeable FDI approvals. However, we

could not include Delhi due to lack of data on mandays lost and R&D expenditure.

The time period for the estimation exercise is 1993-94-2000-01.

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5.2.2.2c Macro-level analysis

Market size

Various measures have been used in empirical literature for measuring market size.

These have been discussed in detail in chapter 3. In the present instance, we measure

market size by annual growth rates in real GDP at factor cost, and express the variable

asgdpgr.

Data on GDP growth has been obtained from the Economic Survey (2002-03).

Returns to capital


We measure returns from stock markets by the annual average price-earning ratios for

scrips included in the Sensitive Index (Sensex) of the Bbmbay Stock Exchange (BSE).

The variable is expressed as peratio and reflects the inverse of returns. The data has

been collected :from the RBI data source.

FII inflows

The variable is expressed asjiiinvnet and measures the volume of net annual FII inflows

into India. Data on net FII investment has been obtained from the RBI data source.

Non-resident deposits

The variable expressed as nridepnet, measures net annual inflows under various non

resident schemes. Data has been collected from the RBI .source.

Export-orientation

We measure export-orientation of the Indian economy in terms of share of exports as a

proportion of GDP on BOP basis. The variable is expressed as exportgdp. Data is

obtained from the RBI source.

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We estimate a simple time series model with the above explanatory variables for the

period 1992-93 - 2001-02.

5.2.3 Econometric Methodology

We had posited the following panel regression· model in section 2.1 for examining our

industry and state-level hypotheses:

Yit = ai + f3'xit + Eit; (I)

Depending upon the assumptions made for ai, the model can be analysed under two

different frameworks. These frameworks are referred to as fixed effects and random

effects respectively.

Under fixed effects, a; is assumed to be a group-specific constant term. The assumption

implies that differences across various cross-section units can be captured thro,ugh the

differences in the constant term. It further implies that each aris an unknown parameter

to be estimated. Assuming fixed effects, Yi and Xi as T observations for the i - th unit,

and E i as the associated error term with T x I vector of disturbances, (I) above can be

reformulated as:

Yi = iai + XiJ3 + Ei; (I a), which can be further written as

y = ia + XJ3 + E; (1 b) where y: an X I vector matrix; a: an X 1 vector matrix; X: a n

X 1 vector matrix; E: a n X 1 vector matrix;

After assembling for all hT rows, we can rewrite (I b) as,

Y = Da + XJ3 + E (I c), whereD: a 1 X n matrix of d dummy variables with di being the

dummy variable for the i-th unit. The a;s are estimated as coefficients of the dummy

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variables. This model is popularly referred to as the Least Squares Dummy Variable

Model (LSDV) in econometric literature. The process of estimation in this case,

therefore, becomes similar to . that in the classical regression model. Consistent and

efficient estimates of the coefficients can be obtained by applying Ordinary Least

Squares (OLS) technique.

Under the assumption of random effects, the ais are treated as random variables rather

than fixed constants, and are assumed to be randomly distributed across the different

cross-section units. They are mutually independent and also independent of the error

term i.e. Eit·

For random effects, ( 1) can be reformulated as

y;1 =a;+ f3'xit + u; +Eit (la*); where u; is the random disturbance characterizing the i-th

observation and is unchanged over time.

The presence of the random u;s introduces correlations among the errors of the same

cross-section units, though the errors from the different cross-section units are

independent (Maddala, 2001). This violation of the orthogonality condition creates

some difficulties with OLS estimates. While the fixed effects model can be transformed

into the classical regression model, and OLS can produce consistent and efficient

estimates, under random effects, applying OLS results in consistent, but inefficient

estimators due to correlation of errors within cross-section units. For obtaining efficient

estimators under random effects, it is necessary to apply feasible generalised least

squares (FGLS) technique, instead of OLS ..

There have been debates in applied econometrics literature over the assumption of fixed

effects and random effects in estimating panel data (Mundlak, 1978; Chamberlain,

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1978; Hausman and Taylor, 1981). The commonly accepted point of distinction is that

if inferences are to be confined to only the cross-sectional units included in the study,

and not to the population from which they are drawn, then it is logical to treat the ais as

constant, and assume fixed effects. However, if inferences are also to be drawn about

the population· from which the sample of cross-section units have been selected, then

ais should be taken as randomly distributed, and the random effects model should be

chosen (Greene, 1997; Maddala, 2001).

The decision regarding the application of fixed effects, or random effects, can be

decided on the basis of the results produced by the Breusch and Pagan Test45 and the

Hausman Test46•

Breusch and Pagan Test

The Test devises a Lagrange Multiplier (LM) test for the random effects model based

on OLS residuals (Greene, 1997). It frames the following null and alternative

hypotheses:

Ho: Var(u) = 0,

H1: Var (u) :t:. 0;

I

Under H0, the LM is distributed as a chi-square statistic with one degree of freedom.

Rejection ofH0 implies acceptance of random effects.

Hausman Test

This specification test has the following hypotheses:

Ho: ais are uncorrelated with Xit

45 See Breusch and Pagan (1980). 46 See Hausman (1978)

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H,: ais are correlated with xit

The test proceeds on the notion that under H0, both OLS and FGLS will give consistent

estimates, but OLS will be inefficient. Thus, random effects will be accepted, if H0 is

accepted. The test statistic is asymptotically distributed as a chi-square with k degrees

of freedom (where k relates to the dimensionality of fJ) based on the Wald criterion.

With respect to the panel regression models estimated in this chapter, we have applied

both Breusch and Pagan and Hausman Tests for checking the presence of fixed versus

random effects on each occasion. Accordingly, equation (la) or (la*) has been accepted

for estimation.

We had posited the following model for estimating our macro-level hypotheses:

Yr= a+ fJxr + Er (2)

We have applied OLS to (2), usmg 'robust' estimation method, for correcting

heteroscedastic disturbances.

5.3 RESULTS AND ANALYSIS

5. 3.1 Industry-level analvsis

Before estimating the model, we obtained a matrix of pair-wise correlation coefficients

between the explanatory variables for checking multicollinearity. The correlation matrix

is given in'Table 5.1.

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Table 5 1 · Pair-wise Correlation Matrix (industry) .. Sales Salesxsales GVAx Wsxgv Expxs Impxsal Patxsa Advtxs

GVA a ales es les ales Sales 1.000 Salesxsales 0.9225 1.000

0.0000 GVAxGV 0.7842 0.8856 1.000 A 0.0000 0.0000 Wsxgva -0.2320 -0.3726 -0.2341 1.000

0.0090 0.0000 0.0083

Expxsales -0.2298 -0.2368 -0.1919 0.5405 1.000 0.0096 0.0076 0.0314 0.0000

Impxsales 0.3419 0.3596 0.2845 -0.1139 -0.0651 1.000 0.0001 0.0000 0.0012 0.2040 0.4689

Patxsales 0.1776 0.2412 0.2695 -0.5508 -0.2719 0.0401 1.000 0.0467 0.0065 0.0023 0.0000 0.0021 0.6554

Advtxsales -0.3373 -0.3149 -0.3271 -0.1012 0.1702 -0.1815 0.2488 1.000 0.0001 0.0003 0.0002 0.2594 0.0567 0.0419 0.0050

Note: Values m Itahcs md1cate levels of s1gmficance

The pair-wise correlation values indicate that salesxsales, sales, and GVAxGVA, are

highly correlated. These variables, therefore, were included separately for avoiding

multicollinearity.

We performed Breusch-Pagan and Hausman tests to examme the presence of

fixed/random effects in all the three models. The results of the Breusch-Pagan and

Hausman tests confirm the presence of random effects for all the three models.

The results of our estimation are given in Table 5.2 (manufacturing industries) and

Table 5.3 (manufacturing and services industries).

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Table 5 2· FDI Inflows· Industry (Manufacturing) . Specification Modella Modellb Modellc

FGLS FGLS FGLS Dependent variable FDI (actual) FDI (actual) FDI (actual) Independent variables Salesxsales

Coefficient 3378.207 t-value 4.72***

Sales Coefficient .0028616

t-value 3.97*** GVAxGVA

Coefficient 2143.889 t-value 2.19**

Wsxgva Coefficient 1488.986 1166.425 1092.352

t-value 4.29*** 3.40*** 3.07*** Expxsales

Coefficient -623.8598 -466.9627 -543.5306 t-value -1.75* -1.28 -1.43

Impxsales Coefficient 713.0394 793.2203 993.1239

t-value 2.29** 2.50** 3.06***

Patxsales Coefficient 2737,668 2789.736 2768.908

t-value 2.96*** 2.95*** 2.73*** Advtxsls

Coefficient 6356.21 4974.742 3194.783 t-value 1.69* 1.31 0.79

Intercept Coefficient -576.7105 -440.2317 -367.0364

t-value -3.76*** -2.95*** -2.38*** Log-likelihood -906.9406 -909.7771 -914.8432 Breusch & Pagan chi2 75.66 84.80 97.16 (1) Hausman chi2 ( 6) 15.033** 11.93* 12.61**

Note:***,** and *denote 1%,5% and 10% level ofs1gmficance respectively.

128

Industry size

We find that all the three variables capturing industry size, i.e. salesxsales, sales, and

GVAxGVA, have positive coefficients. Two of the variables, salesxsales and sales, are

statistically significant at 1-% level. The third variable, GVAxGVA, is significant at 5%

level. Thus, there is clear evidence of FDI flows being attracted by larger industries.

Our finding in this regard is consistent with similar conclusions reached by empirical

research elsewhere (e.g. Morgan and Wakelin, 1999).

Labour-intensity

We had hypothesised that FDI is likely to move into more capital-intensive industries.

Accordingly, we expected labour-intensity to be negatively related to FDI flows.

However, we find the coefficient of Wsxgva to be positively significant at 1-% level of

significance in all the three specifications. We feel that this apparently surprising

finding is on account of non-separation of labour intensity into skilled and unskilled

components. The variable captures the effect of combined skilled and unskilled labour

intensity. Indeed, a positive coefficient of this variable perhaps indicates greater

concentration of FDI in industries using skilled labour more intensively. These

industries have higher share of wages in total value added on account of higher rewards

for their skilled labour.

Export-orientation

The coefficient of expxsales is statistically significant only at 10% level of significance

in one of the formulations. The coefficient is also negative. It appears that FDI in India

does not have a distinct 'export-orientation'. Viewed together with the results on

industry size, it appears that FDI in India is more of the 'domestic market-oriented'

129

variety. The finding is consistent with other studies reaching similar conclusions

(Anand and Delios, 1996; Guha and Ray, 2001; Banga, 2003).

Import-intensity

The coefficient of impxsales is positively significant at 5% level of significance in two

formulations and at 1% in another. The results confirm our hypothesis. There is clear

evidence of FDI moving into industries deploying significant volume of imported

inputs. Our data set reveals that industries with relatively higher import-intensities are:

metallurgical, fuels, electrical equipment, transportation, non-electrical machinery,

fertilisers, chemicals, drugs and pharmaceuticals, textiles and food processmg.

Advanced technical know-how is a critical input for these industries. It is obvious that

these industries offer scope for optimal exploitation of the ownership advantages of

FDI.

J>rofitabilitv

The coefficient of patxsales is positively significant at 1-% level in all the three

formulations. The results are on the expected lines of our hypothesis. We find that FDI

has a clear tendency to move into more profitable sectors.

Advertisement-intensity

The coefficient of advtxsales is positively significant at 10% level of significance in

only one of the formulations. Thus, while there is no unambiguous evidence in favour

of our hypothesis, our results do not contradict earlier findings of empirical research,

which suggest that FDI tends to concentrate in advertising-intensive industries (Kumar,

1987).

130

. . . Table 5 3· FDI Inflows· Industry (manufacturing and services) Specification Modella Modellb Modellc

fgls, p(h) fgls fgls, p(h) Dependent variable FDI (actual) FDI (actual) FDI (actual) Independent variables Salesxsales


Sales Coefficient .0025483

t-value 4.59*** GVAxGVA


Wsxgva Coefficient 524.5442 552.8381 400.3735

t-value 3.28*** 2.09** 2.47**

Expxsales Coefficient -375.0712 -478.2772 -394.4834

t-value -3.95*** -1.79* -3.85***

Impxsales Coefficient 287.9373 666.4868 655.3724

t-value 1.23 2.24** 2.53**

Patxsales Coefficient 1268.954 1906.05 948.9522

t-value 3.97*** 2.64*** 3.22***

Intercept Coefficient -148.7607 -126.6658 -95.01934

t-value -2.31 *** -1.10 -1.51 Log-likelihood -973.6492 -1062.789 -977.3279 Breusch & Pagan 99.52 106.07 104.90 chi2 (1) Hausman chi2 (?) 19.95*** 23.94*** 22.33***

Note: ***, **and *denote 1%, 5% and 10% level of s1gmficance respectively.

Like in the previous exercise, we estimated three different model specifications for the

data set comprising manufacturing and services, in order to avoid the problem of

multicollinearity.

131

We also performed the Breusch-Pagan and Hausman tests. The tests were found to yield

contradictory results. While the Breusch-Pagan tests confirmed random effects, the

Hausman tests indicated otherwise. However, given the very strong conclusions of the

Breusch-Pagan 'tests, we assumed random effects, and estimated the models using the

fgls technique. The regressions were found to yield more robust estimations under the

assumption of heteroskedasticity between panels for models 1 a and 1 c.

The results obtained after including services are broadly similar to those obtained

earlier. Industry size, profitability, and import-intensity, emerge as positively significant

determinants of FDI for both manufacturing and services. Our earlier findings with

respect to labour-intensity also remain unchanged.

The coefficient of expxsales is found to be negative and statistically significant on two

occasions at 1-% level. This result can be partly explained by inclusion of non

tradeables ( eg. services, hotels & tourism, and trading) in the data set, which do not

have any export-orientation. However, the thrust of the result points to confirmation of

our earlier finding that FDI in India is largely domestic market-oriented.

5.3.2 State-level analysis

The pair-wise correlation matrix obtained in this case points to high correlation between

pcnsdp,pcelecon and delcircle, as shown in Table 5.4.

132

.. -Table 54· Pair wise Correlation Matrix (States) Pcnsdp Pcelecon Delcirc Rail den Rddens Mdaysl Nonagr Rdpcns

le sity ity ost dp dp Pcnsdp 1.000 Pcelecon 0.7118 1.000

0.0000 Delcircle 0.5756 . 0.3602 1.000

0.0000 0.0000 Raildensit 0.0966 0.1202 -0.0260 1.000 y 0.2799 0.1764 0.7704 Rddensity 0.1409 -0.0761 0.1478 0.1141 1.000

0.1125 0.3931 0.0959 0.1998 Mdayslost 0.0851 -0.1410 0.0285 0.3541 0.0223 1.000

0.3394 0.1124 0.7495 0.0000 0.8030 Nonagrdp 0.4886 0.2511 0.3872 -0.3346 0.1181 0.2354 1.000

0.0000 0.0043 0.0000 0.0001 0.1844 0.0075 Rdpcnsdp -0.2056 -0.0539 0.3576 0.0865 -0.1076 -0.1062 -0.2035 1.000

0.0199 0.5433 0.0000 0.3317 0.2266 0.2327 0.0212 Note. Values m Italics md1cate levels of s1gmficance

We included pcnsdp in one of the model specifications, omitting the other two

variables, since it was felt that the market size variable was too important to be dropped.

Moreover, we also felt that it is possible for the three variables to jointly determine FDI.

Accordingly, a principal component of pcnsdp, pcelecon, and de/circle, which was

named as pcelecle, was constructed for estimation.

A note on the method for constructing principal components is given in Appendix 2.

Both model specifications were initially tested for the presence of fixed effects/random

effects. The results of the Breusch and Pagan and Hausman tests confirmed the presence

of random effects for both the models.

The models were estimated by employing feasible generalised least squares (FGLS)

method for obtaining the most efficient and consistent estimators. However, when we

tried estimation, for one model, we found the regression output to be more robust under

133

the assumption of heteroscedasticity and cross-sectional correlation between panels

(FGLS, p(c)).

Table 5 5· FDI Inflows· States .. Specification Modella Modellb

FGLS, p(c) FGLS Dependent variable FDI (approvals) FDI (approvals) Independent variables Pcnsdp

Coefficient .0109025 t-value 3.30***

Pcelecle Coefficient 41.82739

t-value 3.56*** Raildensity

Coefficient -1.388471 -2.547946 t-value -0.65 -1.43

Roaddensity Coefficient -.0176637 -.0163089

t-value -0.78 -0.89 Mdayslost

Coefficient 0.0006996 0.0092017 t-value 0.10 1.12

Nonagrdp Coefficient 4.859555 4.093727

t-value 2.81 *** 2.42**

Rdpcnsdp Coefficient 135.46 131.4582

t-value 2.35** 3.03**

Intercept Coefficient -340.4687 -151.3078

t-value -2.01 *** -1.06 Breusch & Pagan 45.38 38.59 chi2 (1) Hausman chi2 ( 6) 3.21 3.79

Note:***,** and* denote 1%,5% and 10% level ofs1gmficance respectively

Market size

The coefficient of pcnsdp is positively significant at 1-% level of significance. Thus,

there is evidence that market size is a significant determinant of incoming FDI. Our

134

findings clearly indicate that advantages of scale economies offered by larger domestic

markets positively influence FDI flows.

Infrastructure

The coefficient of the principal component of pcnsdp, pcelecon, and delcircle, viz.

pcelecle, is positively significant at 1-% level of significance. There are two

implications of this finding. Firstly, it reinforces the earlier finding regarding market

size. Secondly, it also indicates that availability of electricity and well-developed

communication networks are important determinants of FDI. It can be further concluded

that states with larger markets are likely to have greater presence of these infrastructural

facilities and are in more advantageous positions for attracting FDI.

Raildensity and rddensity are far from being significant. It appears that physical

infrastructure in terms of rail and road networks are not significant in determining

incoming FDI, as against electricity and telecommunications, which are strongly

significant. The insignificance of road and rail infrastructure probably imply that FDI in

India is geographically locating itself close to final product markets, thereby

overcoming the requirements of physical shipment of goods. The significant presence of .

FDI in non-tradeables can also partly explain the result. However, at the same time, it is

also clear that FDI is concentrating in activities requiring ready availability of electricity

and communication services, which are likely to be more IT -intensive sectors.


As per our hypothesis, the coefficients of nonagrdp appear with positive coefficients.

They are also statistically significant at 1% and 5% level of significance respectively.

·We therefore, conclude that more industrialised states are likely to attract greater FDI.

135

Quality of industrial relations

The coefficient of Mdayslost is not significant. This indicates that the quality of

industrial relations in states is perhaps not a deterrent for incoming FDI. It appears that

despite opinions expressed to the contrary (e.g. Lucas, 1993, Singh and Jun, 199.5),

incidence of strikes and lockouts, as reflected in workdays lost, has not acted as a

deterrent to FDI, at least in India.

Technological Capabilities and Infrastructure

The coefficients of rdpcnsdp are positively significant at 5% level of significance. The

results confirm our hypothesis. There is, thus, evidence of FDI flowing into states that

possess better technological infrastructure and indigenous technological capabilities.

Evidently, these locations offer greater opportunities for exploiting the ownership

advantages of FDI, much of which arise from possession of advanced technology and

know-how.

5.3.3 Macro-/eve/ analysis

We encountered serious multicollinearity problems in estimating the time senes of

' aggregate FDI inflows. The pair-wise correlation matrix is indicated in Table 5.6.

Table 5 6· Pair-wise Correlation Matrix (All-India) Gdpgr Exportsgdp Peratio Nridepnet Fiiinvnet

Gdpgr 1.0000 Exportsgdp -0.0390 1.0000

0.9148 Peratio -0.0171 -0.4635 1.0000

0.9626 0.1772 Nridepnet -0.2144 0.5005 -0.4674 1.0000

0.5520 0.1406 0.1732 fiiinvnet -0.0581 0.7328 -0.2208 0.5016 1.0000

0.8733 0.0159 0.5400 0.1397 Note: Values m Italics md1cate levels ofs1gmficance.

136

In order to avoid multicollinearity, some variables were dropped, and the model was

estimated separately under different specifications. The estimations were carried out on

the basis of Ordinary Least Squares (OLS) technique. Robust estimation was used for

correcting heteroscedastic disturbances.

The results of our estimation are given in Table 5.7.

Table 5 7· FDI Inflows· All-India .. Modella Modellb Modellc OLS, robust OLS, robust OLS, robust

Dependent variable FDI (actual) FDI (actual) FDI (actual) Independent variable Gdpgr

Coefficient -525.9686 -795.5293 -796.9182 t-value -1.00 -1.11 -1.01

Peratio Coefficient -332.3093 -382.6154 -330.3041

t-value -2.86*** -3.60** -3.42**

Nridepnet Coefficient 0.4803497

t-value 1.30 Fiiinvnet

Coefficient 0.3969847 t-value 2.03*

Exportsgdp Coefficient 2686.51

t-value 1.81

Intercept 16236.89 20247.71 -1974.193 F-test F (3,6) =3.63*** F (3,6) =5.33** F (3.6) =7.07**

R-square 0.7568 0.7433 0.7695 RootMSE 3282.6 3320.7 3146.6 Note:***,** and* denote 1%,5% and 10% level ofs1gmficance respectively

137

Market size

We find that the coefficient of gdpgr is statistically insignificant. The result does not

confirm our hypothesis that FDI in India responds positively to growth in domestic

market size and also contradicts the findings of several empirical studies. The short

length of the time series can probably explain the result. Variations in real GDP growth

during this period (1992-93 - 2001-02) have been in the range of only around 3 .. 5

percentage points. Estimation of GDP growth over a longer time series might produce

different results.

Returns to capital


The coefficient of peratio is found to be negatively significant. The result confirms our

hypothesis and indicates that FDI flows respond positively to lower values of peratio,

which arise when earnings from stock rrtarkets increase relative to prices. There is

therefore clear evidence ofFDI inflows being sensitive to returns from stock markets.

FII inflows

The coefficient of jiiinvnet is positively significant confirming our hypothesis. Thus,

there' is evidence that higher FII inflows, which occur from favourable expectations of

returns from the host economy, appear to encourage FDI also, through a 'demonstration

effect'. Since FII inflows are· strongly related to returns from stock markets, the latter

appear to be a major factor behind not only higher FII inflows, but also FDI flows.

Non-resident deposits

The coefficient of nridepnet is statistically insignificant. Thus, higher inflows of NRI

deposits are not a significant determinant for FDI

138

Export-orientation

The coefficient of exportGDP is also insignificant, The result corroborates the earlier

findings from the panel regression analysis, which suggest that export-intensity is not a

significant determinant ofFDI in India. FDI in India, therefore, is not export--oriented.

5.4 A SYNTHESIS

In this section, we try to present a synthesis of the results that we have obtained from

our industry-level, state-level and country-level analyses.

Industry-level analysis

Industry size is clearly a significant determinant of FDI in India. Industries with larger

sizes are seen to attract more FDI.

Although we expected FDI to flow into more capital-intensive industries, the positive

and significant coefficient of labour-intensity perhaps reflects the propensity of foreign

firms to invest in skill-intensive activities. These industries offer foreign firms ample

scope for exploiting their ownership advantages.

Import-intensity also emerges as a significant determinant of incoming FDI. Like skill

intensive industries, industries using more imported inputs in production offer foreign

firms considerable opportunities for better exploiting their ownership advantages again.

On account of better access to foreign inputs like imported raw materials, stores, capital

goods, know-how etc. multinationals are expected to be much more competitive than

domestic firms in import-intensive industrie~ and hence these are the sectors which may

attract larger FDI.

139

We had expected FDI to flow into more profitable industries. The results vindicate our

expectation. Profitability has a positive and significant impact on the level of FDI.

Results appear to be somewhat inconclusive with respect to advertising-intensity. If at

all, our results show that FDI is likely to be attracted to relative advertisement-intensive

· industries in India.

We have, however, obtained fairly clear evidence of FDI in India not being export-

oriented. Our results in this regard appear to confirm earlier findings of empirical

research, which point to FDI in India being more of the 'domestic market-oriented'

variety. The direction of causality between FDI and exports in India remains an

important agenda for future research47.

State--level analysis

The 'domestic market-oriented' nature of FDI in India gathers stronger evidence from

the results obtainkd in our state-level analysis. Market size is found to be a highly

significant locational determinant for FDI flows. It is clear that the advantages of scale

economies offered by large markets are important factors influencing FDI decisions in

India.

We have obtained interesting results regarding the role of infrastructure in motivating

FDI. We find that availability of electricity and communication facilities are critical

factors in deciding FDiflows. Rail and road networks, however, are not significant: Our

findings indicate that FDI has a clear tendency to locate close to markets for final

products. This tendency strengthens the decision to enter large markets and reinforces

47 Though FDI has not been found to be attracted to export-oriented industries, there is empirical evidence indicating that FDI has led to diversification of exports from traditional to non-traditional sectors in India. See Banga (2003).

140

the significance of market size~ We further argue that FDI in India probably has a larger

concentration in non-tradeables, for which, the locations of producers and consumers

are usually the same, thereby reducing the importance of facilities required for physical

shipment of goods. The significance of electricity and communication facilities also

points to the entry of FDI into sectors where the availability of these inputs is vital, i.e.

high technology, knowledge-intensive activities.

We hypothesised that more industrialised states are likely to attract greater FDI. Our

results point to similar conclusions. Degree of industrialisation of states is found to be a

significant determinant of FDI flows.

We did not find FDI flows to be sensitive to the quality of industrial relations.

The role of technological capabilities and infrastructure in attracting FDI is indeed ·a

significant result of our study., The positive significance of R&D expenditure at the

state-level to FDI flows clearly demonstrates the importance of possessing quality

teclmological infrastructure and technological capabilities at the local level for

attracting FDI. Our earlier results from the industry-level analysis, which point to the

inclination of FDI to move into skill-intensive industries, also acquire significance in

this regard. Availability of indigenous technological capabilities certainly enables

foreign firms to better utilise their ownership advantages.

Macro-level analysis

Our study of determinants of aggregate FDI inflows does not indicate market size to be

a significant determinant ofFDI. We attribute this result to the limited span of the time

series. However, it is important to mention in this context that we had employed growth

in GDP as our measure of market size. This measure is an indicator of potential market

141

size, rather than current size. It is possible that the results might have been different had

we used measures for the current size.

With respect to returns to capital, our findings regarding response of FDI flows to stock

market returns are indeed interesting. We find FDI flows to be positively related to

stock market returns. The results indicate that among other things, the returns earned on

equity capital deployed are important factors behind FDI decisions. We also find FII

inflows to positively encourage FDI through a 'demonstration effect'. This finding

corroborates our results on stock market returns. Indeed, buoyant domestic stock

markets and higher FII inflows are likely to encourage greater FDI flows into India. We

do not find inflows of non-resident deposits to be a significant determinant ofFDI.

We also find that FDI in India does not respond to exports. This reinforces our earlier

results that market size acts as a strong 'pull' factor for FDI in India. Our results appear

to confirm that FDI in India is essentially 'domestic market-oriented'.

142

A Note on the Data

Data on various explanatory variables for industry has been obtained from the database of the Centre for Monitoring Indian Econom/8 (CMIE). The CMIE accumulates data on an annual basis for a select sample of companies, which include both public and private companies. The sample varies in terms of number of companies covered each year. The nature of co~panies covered in terms of ownership during the period under consideration (1994-95- 2000-01) is given below:

Distribution of Companies according to Ownership (in numbers) Industry 1994- 1995-96 1996- 1997-98 1998-99 1999-00 2000-01 groups 95 97 Manufacturing 3774 4372 4436 4268 4126 4225 4207

Food 505 616 636 579 551 581 559 &beverages

Textiles 589 682 675 664 638 608 588 Chemicals 887 1055 1069 1042 1017 1016 1002

Non-metallic 227 262 264 247 231 229 217 mineral

products Metals & metal 456 507 485 442 432 451 434

products Machinery 656 737 768 765 755 ' 831 859 Transport 180 187 191 199 193 198 236

equipment Miscellaneous 211 266 286 271 252 251 253 manufacturing

Diversified 63 60. 62 59 57 60 59 Mining 53 60 69 69 69 65 64 Electricity 26 31 32 29 30 34 33 Services 1280 1981 2239 2253 2190 2151 2029

Financial 583 ' 1070 1276 1303 1232 1154 1064 services

Other services 697 911 963 950 958 997 965 Hotels & 67 81 84 84 89 91 88

tourism Trading 348 473 474 456 458 470 443

Overall 5133 6444 6776 6619 6415 6475 6333

The above companies account for 74 per cent of the gross value added in the industrial sector of the country and 78 per cent of the total value of output in the manufacturing sector. There were, however, certain inconsistencies in the classification of industries between the CMIE and the SIA. While the CMIE broadly follows the Annual Survey of Industries (ASI) classification of the Central Statistical Organisation (CSO), the SIA data is classified

48 See CMIE (2002).

143

differently. In order to make the two sets of data compatible, the following matching exercise has been carried out:

Industries: SIA Industries: CMIE Modified format (1) (2) (3)

Metallurgical (ferrous, non- Ferrous metals, non-ferrous The four industries m (2) ferrous, special alloys, metals, mmmg, metals & have been grouped together mining & miscellaneous) metal products under metallurgy. Fuels (power, oil refinery, Petroleum products and The two industries in (2) have fuels) electricity been grouped together under

fuels. Electrical equipment Electrical equipment and The two industries in (2) have (electrical equipment, electronics (including been grouped together under computer software, computer computer) electrical equipment. hardware, electronics) Transportation industry Automobiles (including The two industries in (2) have (air/sea transport, passenger cars) and auto been grouped together under automobiles, passenger cars, ancillaries transportation. auto ancillaries, ports) Industrial machinery, Non-electrical machinery FDI for industries in ( 1) has machine tools, agricultural been accumulated to machinery, earth-moving represent FDI Ill non-machinery, miscellaneous 1 electrical machinery mechanical and engineering, commercial office & household equipment Fertilisers Fertilisers No change Chemicals (other than Chemicals No change fertilisers) Drugs & phannaceuticals Drugs & pharmaceuticals No change Textiles Textiles No change Paper & pulp including paper Paper & paper products No change products Sugar Sugar No change Fennentation industries Beverages & tobacco The industry in (2) has been

represented under fermentation industries

Food processing (food Food products Food products in (2) has been products & marine products) represented under food

processing Vegetable oils & vanaspati Vegetable oils & products No change Rubber goods Rubber and rubber products No change Soaps, cosmetics & .toiletries Soaps and detergents Industry Ill (2) has been

represented as soaps & detergents

Leather, leather goods & Leather products Industry m (2) has been pickers represented as leather. Cement & gypsum Cement Industry in (2) has been

represented as cement Services Services No change Hotel & tourism Hotel & tourism No change Trading Trading No change

144

Some industries from SIA had to. be excluded, as they could not be matched with the CMIE industries. These are: boilers & steam generating plants, prime movers other than electrical, telecommunication, medical and surgical appliances, industrial instruments, scientific instruments, photographic raw film and paper, dye stuffs, glue & gelatine, glass, ceramics and consultancy.

145

Appendix2 A Note on Principal Component Regression Method

The principal component method is a popular method used in applied econometrics for overcoming multicollinearity problems. The basic method (Maddala, 2001) can be outlined as follows: Let there be x 1, x2, .......... xk explanatory variables in a regression model. Let it be further assumed that these variables are correlated. For x~, x2, .......... xk explanatory variables, there can be z1, z2, .......... zk linear functions, which can be represented as:

z, = a,x, + azXz + ............ + akxk .......... (1) Zz = b,x, + bzXz + ............ + bkxk etc.

The a's in (1) can be chosen in such a way so that Var(zl) is maximised subject to: 2 2 2 1 (2) a, +a2 + ............... +ak = .... .

By maximizing the variances of the linear functions (z1, z2, etc) subject to the condition that sum of the squares of the coefficients will be equal to 1, k solutions can be obtained, corresponding to which k linear functions z 1, z2, .......... zk can be constructed. These are the principal components of the k explanatory variables. They can be ordered as:

Var(z1) > Var(z2) > .......... Var(zk) z1, which has the highest variance is the first principal component, followed by z2, the second principal component etc. These principal components are orthogonal or uncorrelated, unlike the x's.

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chapter 5 determinants of fdi in india: an...

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