Chapter – V
ECONOMIC REFORMS AND INDUSTRIAL
CONCENTRATION IN INDIAN MANUFACTURING
SECTOR – AN INTERTEMPORAL ANALYSIS
One of the several objectives of economic reforms process is to achieve
balanced growth of industry in the nation and reduce the inequalities in the
industrial base of the nation. The development of the industry has been identified
as a prime requirement for exploiting backward linkages to agriculture and
forward linkages to overall economy. Thus, for regional development in India, a
need of developing manufacturing or industrial base of each state has been
identified. In this endeavor, government announces different packages over a
period of time to distort the producers’ investment choices in different regions.
Thus, for balanced growth, the concentration of industrial activities must decline
over a period of time and industrially backward states must attract good share in
total output of the nation. Against this backdrop, it becomes mandatory to analyse
the concentration trends in Indian manufacturing sector. The analysis will help to
identify the effectiveness of balanced growth strategy of Indian policy planners.
Market concentration or, more specially, the degree of seller’s
concentration in the market is an important element of the market structure which
plays a dominant role in determining the behavior of a firm in the market. The
market concentration means the situation in which an industry or market is
controlled by a small number of leading producers who are exclusively or at last
very largely engaged in that industry. Two variables that are of relevance in
determining such situation are: (1) The number of the firms in industry, and (2)
their relative size distribution. These two dimensions cause different form of the
market structure having vital consequences for the pricing and output decision of
the firm. In the context of industrial economics, the implications of market
105
concentration are far wider than whatever find in the theory of firm, for example,
concentration in the ownership of the industry, concentration of decision making
power etc. all being elements of market concentration, may have considerable
impact on the market performance of the firm such as profitability, price cost
margin, technical progress etc. These links are to be understood properly, because
all of them are very much relevant from the point of view of decision making and
regulation of industries.
In the present chapter, an attempt has been made to analyze the level of
industrial concentration in Indian manufacturing sector. For analysis purpose, the
chapter has been divided into three sections. Section I provides methods to
measure industrial concentration among the major Indian states. Section II
discusses the empirical results pertaining to the trends in industrial concentration
among different States. The final section sum-up the discussion alongwith relevant
policy implications.
Section – I
Industrial concentration was traditionally summarized by the concentration
ratio, which simply adds the market shares of an industry’s four, eight, twenty, or
fifty largest companies. In 1982, when new federal merger guidelines were issued,
the Herfindal-Hirschman Index (HHI) became the standard measure of industrial
concentration. Suppose that an industry contains ten firms that individually
account for 25, 15, 12, 10, 10, 8, 7, 5, 5, and 3 percent of total sales. The four-firm
concentration ratio for this industry the most widely used number is 25 + 15 + 12
+ 10 = 62, meaning that the top four firms account for 62 percent of the industry’s
sales. The HHI, by contrast, is calculated by summing the squared market shares
of all of the firms in the industry: 252 + 152 + 122 + 102 + 102 + 82 + 72 + 52 + 52 +
32 = 1,366. The HHI has two distinct advantages over the concentration ratio. It
uses information about the relative sizes of all of an industry’s members, not just
106
some arbitrary subset of the leading companies, and it weights the market shares
of the largest enterprises more heavily.
In general, the fewer the firms and the more unequal the distribution of
market shares among them, the larger the HHI. Two four-firm industries, one
containing equal sized firms each accounting for 25 percent of total sales, the other
with market shares of 97, 1, 1, and 1, have the same four-firm concentration ratio
(100) but very different HHIs (2,500 versus 9,412). An industry controlled by a
single firm has an HHI of 1002 = 10,000, while the HHI for an industry populated
by a very large number of very small firms would approach the index’s theoretical
minimum value of zero
Another index which is based on inequality and hence dispersion in the size
of firm in a market can be derived from Lorenz curve. The Lorenz curve shows the
variation in cumulative percentage distribution of market share with cumulative
percentage distribution of firm form smallest to largest in the market as shown in
figure 5.1
If the firms are equal in size the Lorenz curve would then be a straight line
shown by 00 diagonal. If there is inequality in the distribution of the market share
the Lorenz curve would then bend away from the diagonal toward the X-axis. A
coefficient which may be called the ‘Lorenz coefficient’ or the ‘Gini coefficient’
as it is commonly known, is computed by dividing the area bounded between the
Lorenz curve and the diagonal line 00 by the area of the triangle under the
diagonal (00Z). The coefficient varies between 0 to 1 as the degree of inequality in
the distribution increases. Thus, it is used as an index to measure the
concentration. To find the dotted area, one may find the area of the triangle 00Z,
first and then the area under the Lorenz curve either using graphical approximation
or through the use of integral calculus. The difference of these two areas gives the
dotted area and one can then find the Lorenz or Gini coefficient.
107
The Gini coefficient is a measure of statistical dispersion developed by the
Italian statistician Corrado Gini and published in his 1912 paper "Variability and
Mutability" (Italian: Variabilità e mutabilità). The Gini coefficient is a measure of
the inequality of a distribution, a value of 0 expressing total equality and a value of
1 maximal inequality. It has found application in the study of inequalities in
disciplines as diverse as economics, health science, ecology, chemistry and
engineering. It is commonly used as a measure of inequality of income or wealth.
Worldwide, Gini coefficients for income range from approximately 0.23 (Sweden)
to 0.70 (Namibia) although not every country has been assessed.
The figure 5.2 shows that the Gini is equal to the area marked 'A' divided
by the sum of the areas marked 'A' and 'B' (that is, Gini = A/(A+B)). It is also
equal to 2*A, as A+B = 0.5 (since the axes scale from 0 to 1).
The Gini coefficient is usually defined mathematically based on the Lorenz
curve, which plots the proportion of the total income of the population (y axis) that
is cumulatively earned by the bottom x percent of the population. The line at 45
degrees thus represents perfect equality of incomes. The Gini coefficient can then
be thought of as the ratio of the area that lies between the line of equality and the
Lorenz curve (marked 'A' in the figure 5.2) over the total area under the line of
equality (marked 'A' and 'B' in the diagram); i.e., G=A/(A+B).
The Gini coefficient can range from 0 to 1; it is sometimes multiplied by
100 to range between 0 and 100. A low Gini coefficient indicates a more equal
distribution, with 0 corresponding to complete equality, while higher Gini
coefficients indicate more unequal distribution, with 1 corresponding to complete
inequality. To be validly computed, no negative goods can be distributed. Thus, if
the Gini coefficient is being used to describe household income inequality, then no
household can have a negative income.
108
Figure 5.1
LORNEZ CURVE
109
Figure 5.2
GINNI COEFFICIENT
110
When used as a measure of income inequality, the most unequal society
will be one in which a single person receives 100 percent of the total income and
the remaining people receive none (G=1); and the most equal society will be one
in which every person receives the same income (G=0). Some find it more
intuitive (and it is mathematically equivalent) to think of the Gini coefficient as
half of the relative mean difference. The mean difference is the average absolute
difference between two items selected randomly from a population, and the
relative mean difference is the mean difference divided by the average, to
normalize for scale.
The Gini index is defined as a ratio of the areas on the Lorenz curve
diagram. If the area between the line of perfect equality and the Lorenz curve is A,
and the area under the Lorenz curve is B, then the Gini index is A/(A+B). Since
A+B = 0.5, the Gini index, G = A/(0.5) = 2A = 1-2B. If the Lorenz curve is
represented by the function Y = L(X), the value of B can be found with integration
and:
In some cases, this equation can be applied to calculate the Gini coefficient
without direct reference to the Lorenz curve. For example:
•
For a population uniform on the values yi, i = 1 to n, indexed in nondecreasing order ( yi ≤ yi+1):
111
This may be simplified to:
•
For a discrete probability function f(y), where yi, i = 1 to n, are the points
with nonzero probabilities and which are indexed in increasing order ( yi <
yi+1):
Where
and
•
For a cumulative distribution function F(y) that is piecewise differentiable,
has a mean µ, and is zero for all negative values of y:
•
Since the Gini coefficient is half the relative mean difference, it can also be
calculated using formulas for the relative mean difference. For a random
sample S consisting of values yi, i = 1 to n, that are indexed in nondecreasing order ( yi ≤ yi+1), the statistic:
112
is a consistent estimator of the population Gini coefficient, but is not, in
general, unbiased. Like, G, G(S) has a simpler form:
There does not exist a sample statistic that is in general an unbiased
estimator of the population Gini coefficient, like the relative mean difference.
Sometimes the entire Lorenz curve is not known, and only values at certain
intervals are given. In that case, the Gini coefficient can be approximated by using
various techniques for interpolating the missing values of the Lorenz curve. If ( X
k
, Yk ) are the known points on the Lorenz curve, with the X
k
indexed in
increasing order ( X k - 1 < X k ), so that:
•
Xk is the cumulated proportion of the population variable, for k = 0,...,n,
with X0 = 0, Xn = 1.
•
Yk is the cumulated proportion of the income variable, for k = 0,...,n, with
Y0 = 0, Yn = 1.
•
Yk should be indexed in non-decreasing order (Yk>Yk-1)
If the Lorenz curve is approximated on each interval as a line between
consecutive points, then the area B can be approximated with trapezoids and:
More accurate results can be obtained using other methods to approximate
the area B, such as approximating the Lorenz curve with a quadratic function
across pairs of intervals, or building an appropriately smooth approximation to the
113
underlying distribution function that matches the known data. If the population
mean and boundary values for each interval are also known, these can also often
be used to improve the accuracy of the approximation.
The Gini coefficient calculated from a sample is a statistic and its standard
error, or confidence intervals for the population Gini coefficient, should be
reported. These can be calculated using bootstrap techniques but those proposed
have been mathematically complicated and computationally onerous even in an
era of fast computers. Ogwang (2000) made the process more efficient by setting
up a “trick regression model” in which the incomes in the sample are ranked with
the lowest income being allocated rank 1. The model then expresses the rank
(dependent variable) as the sum of a constant A and a normal error term whose
variance is inversely proportional to yk;
Ogwang (2004) showed that G can be expressed as a function of the
weighted least squares estimate of the constant A and that this can be used to speed
up the calculation of the jackknife estimate for the standard error. Giles (2004)
argued that the standard error of the estimate of A can be used to derive that of the
estimate of G directly without using a jackknife at all. This method only requires
the use of ordinary least squares regression after ordering the sample data. The
results compare favorably with the estimates from the jackknife with agreement
improving with increasing sample size (visit http://web.uvic.ca/econ/ewp0202.pdf
for detail on this method).
However, it has since been argued that this is dependent on the model’s
assumptions about the error distributions (Ogwang, 2004) and the independence of
error terms (Reza and Gastwirth, 2006) and that these assumptions are often not
valid for real data sets. It may therefore be better to stick with jackknife methods
114
such as those proposed by Yitzhaki (1991) and Karagiannis and Kovacevic (2000).
The debate continues. The Gini coefficient can be calculated if you know the mean
of a distribution, the number of people (or percentiles), and the income of each
person (or percentile). Princeton development economist Angus Deaton (1997,
139) simplified the Gini calculation to one easy formula:
where u is mean income of the population, Pi is the income rank P of person i,
with income X, such that the richest person receives a rank of 1 and the poorest a
rank of N. This effectively gives higher weight to poorer people in the income
distribution, which allows the Gini to meet the Transfer Principle.
Section – II
In the present study, Annual Survey of Industries (ASI) data over the period
1979-80 to 2006-07 has been utilized to calculate Gini coefficients and HHI for
each year. This section involves the empirical evidences regarding the
concentration of manufacturing sector among different states of India. The
application of the Lorenz curve based method reflects that the inequalities in the
development of the manufacturing sector are high enough among Indian states.
The average Gini index observed for each state over the study period of 1979-80
to 2006-07 ranges between 0.563 and 0.604. It depicts that in terms of the selected
variables, there exists high inequalities among selected states.
To check the sensitivity of the results, an alternative technique of
Hrischman and Herfindal index (HHI) has been applied to work out concentration
115
levels. Table 5.4 provides the hypothesis testing procedure to check whether the
calculated concentration index is robust or not. Two alternative test statistics
namely, Mann-Whitney and Kruskall-Wallis test have been utilized to check
whether the concentration index computed through two different techniques differ
significantly or not. If the difference is significant then it can be concluded that
results are not robust and if the null is not rejected then the results are concluded to
be robust.
In statistics, the Mann–Whitney U test (also called the Mann–Whitney–
Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test) is
a non-parametric test for assessing whether two independent samples of
observations have equally large values. It is one of the best-known non-parametric
significance tests. It was proposed initially by the Irish-born US statistician Frank
Wilcoxon in 1945, for equal sample sizes, and extended to arbitrary sample sizes
and in other ways by the Austrian-born US mathematician Henry Berthold Mann
and the US statistician Donald Ransom Whitney. MWW is virtually identical to
performing an ordinary parametric two-sample t test on the data after ranking over
the combined samples.
The test involves the calculation of a statistic, usually called U, whose
distribution under the null hypothesis is known. In the case of small samples, the
distribution is tabulated, but for sample sizes above ~20 there is a good
approximation using the normal distribution. Some books tabulate statistics
equivalent to U, such as the sum of ranks in one of the samples, rather than U
itself. The U test is included in most modern statistical packages. It is also easily
calculated by hand, especially for small samples. There are two ways of doing this.
For small samples a direct method is recommended. It is very quick, and gives an
insight into the meaning of the U statistic.
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• Choose the sample for which the ranks seem to be smaller (The only reason
to do this is to make computation easier). Call this "sample 1," and call the
other sample "sample 2."
• Taking each observation in sample 1, count the number of observations in
sample 2 that are smaller than it (count a half for any that are equal to it).
• The total of these counts is U.
For larger samples, a formula can be used:
1. Arrange all the observations into a single ranked series. That is, rank all the
observations without regard to which sample they are in.
2. Add up the ranks for the observations which came from sample 1. The sum
of ranks in sample 2 follows by calculation, since the sum of all the ranks
equals N(N + 1)/2 where N is the total number of observations.
3. U is then given by:
where n1 is the sample size for sample 1, and R1 is the sum of the ranks in sample
1. Note that there is no specification as to which sample is considered sample 1.
An equally valid formula for U is:
The smaller value of U1 and U2 is the one used when consulting
significance tables. The sum of the two values is given by:
117
Knowing that R1 + R2 = N(N + 1)/2 and N = n1 + n2 , and doing some
algebra, we find that the sum is
The maximum value of U is the product of the sample sizes for the two
samples. In such a case, the "other" U would be 0. The Mann–Whitney U is
equivalent to the area under the receiver operating characteristic curve that can be
readily calculated as:
Further, Kruskal–Wallis one-way analysis of variance by ranks (named
after William Kruskal and W. Allen Wallis) is also a non-parametric method for
testing equality of population medians among groups. It is identical to a one-way
analysis of variance with the data replaced by their ranks. It is an extension of the
Mann–Whitney U test to 3 or more groups.
Since it is a non-parametric method, the Kruskal–Wallis test does not
assume a normal population, unlike the analogous one-way analysis of variance.
However, the test does assume an identically-shaped and scaled distribution for
each group, except for any difference in medians. The following steps are
followed to find out test statistics:
Rank all data from all groups together; i.e., rank the data from 1 to N
ignoring group membership. Assign any tied values the average of the ranks they
would have received had they not been tied. The test statistic is given by:
118
Where, ni is the number of observations in group I, rij is the rank (among all
observations) of observation j from group I, N is the total number of observations
across all groups
is the average of all the rij.
Notice that the denominator of the expression for K is exactly (N − 1)N(N +
1) / 12 and
Thus,
Notice that the last formula only contains the squares of the average ranks.
A correction for ties can be made by dividing K by:
,
119
Where, G is the number of groupings of different tied ranks, and ti is the number
of tied values within group i that are tied at a particular value. This correction
usually makes little difference in the value of K unless there are a large number of
ties.
Finally, the p-value is approximated by:
If some ni values are small (i.e., less than 5) the probability distribution of
K can be quite different from this chi-square distribution. If a table of the chisquare probability distribution is available, the critical value of chi-square,
χ2α:g-1, can be found by entering the table at g − 1 degrees of freedom and
looking under the desired significance or alpha level. The null hypothesis of equal
population medians would then be rejected if
. Appropriate multiple
comparisons would then be performed on the group medians.
The execution of Mann-Whitney and Kruskal-Wallis tests reveals that there
exists insignificant difference between the concentration indices obtained using
two alternative techniques. Although, for the variable Number of Factories, the
null has been rejected at 10 percent level of significance yet we accept the Null
hypothesis at 5 percent level of significance. However, in economic research,
HHI is most widely used and acceptable index to measure industrial concentration
whereas, for the present analysis purpose, we will prefer the most consistence
index among the two Gini ratios and HHI indices. Hence the coefficient of
variation (CV) has been computed for each variable under evaluation. The index
with less CV assumed to be more efficient. The CV figures in Table 5.1 and 5.2
reveal that the Gini coefficients are having less CV and thus, identified more
120
consistent for the interpretation of trends in Industrial concentration in India. The
reported Gini coefficients depict inequality or concentration of manufacturing
sector activities among some selected states. A Gini coefficient near 1 represents
high inequalities whereas a value near 0 represents weak concentration and high
equality. Using the above methodology, Gini Coefficients have been computed for
20 major industrial state for the variables: i) No of Factories; ii) Fixed Capital; iii)
Total Persons Engaged; iv) Fuel Consumed; v) Material Consumed; and vi) Gross
Output.
Table 5.1 provides the Gini coefficients computed to check inter-temporal
variations in the concentration of manufacturing sector among different states. In
no of factories, it is observed that a statically significant growth rate of Gini
coefficients at the rate of 0.4 percent per annum during the study period under
evaluation. The direct connotation in this fact is that interstate inequalities in term
of no of factories operating under Indian manufacturing sector are raising, hence
the Industrial development policies are biased enough and generating loop sided
development in Indian manufacturing sector. The visualization of figure 5.3 also
supports our inference of rising regional concentration in Indian manufacturing
sector. An upward sloping best fit trend line support this fact.
The same trend has been observed for concentration of Fixed Capital in
Indian manufacturing Sector. The inter-state analysis depict that concentration of
Fixed Capital is rising at a significant rate of 1 percent per annum. It simply means
that the regional inequalities in capital formation in general and investment in
particular are rising over a study period under evaluation. Figure 5.4 represents
trend of concentration of fixed capital at regional level. Regarding the
concentration of employment, stability has been observed given that the growth
121
rate of concentration of total persons engaged observed near 0. Figure 5.5 support
our finding given linear trend approximately parallel to X axis. However, the
concentration fuel consumed and material consumed have been observed rising at
same rate of 0.2 percent per annum and at same rate concentration of gross output
in Indian manufacturing is rising. The analysis thus, supports that each indicator
identity rising inequalities in the Industrial setup among different states of India.
Figures 5.6, 5.7 and 5.8 also represent the trend of concentration of fuel consumed,
material consumed and gross output in Indian manufacturing sector. All these
figures include the upward sloping trend lines which support our earlier findings
that industrial concentration among different states is continuously rising over the
period under study. It can, therefore, be concluded that Indian economy is
operating with high development inequalities, because there exist high inter-state
disparities in the industrial development. High Gini Coefficient obtained for
concentration of industrial activities among different states support this inference.
To analyze the impact of economic reforms, the entire period has been
divided into pre-reform (1979-80 to 1990-91) and post-reform period (1991-92 to
2006-07). The average concentration of each variable for both sub-periods has
been computed along with an application of Kruskaal-Wallis test to test the null
hypothesis of insignificant difference between the average concentrations over two
sub-periods. The results of Kruskaal-Walis test, given in Table 5.3, indicate that
the increase in concentration of fixed capital is statistically significant whereas, the
observed increase in average concentration in case of remaining five variables is
statistically insignificant. Hence, the study proves that the outcome of reforms
process is in opposite direction as desired by the policy planners of India. The
concentration is rising (although at insignificant rate) over a period of time and
thus, does not guarantee equitable distribution of the manufacturing activities.
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TABLE 5.1
INTER-TEMPORAL VARIATIONS IN THE CONCENTRATION OF
INDIAN MANUFACTURING SECTOR’S ACTIVITIES – AN APPLICATION OF
LORENZ CURVE BASED GINI RATIOS
Year
No. of
Factories
Fixed
Capital
1979-80
80-81
81-82
82-83
83-84
84-85
85-86
86-87
87-88
88-89
89-90
90-91
91-92
92-93
93-94
94-95
95-96
96-97
97-98
98-99
99-2000
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
Average
Coefficient
of Variation
0.521
0.549
0.552
0.554
0.544
0.552
0.538
0.555
0.552
0.572
0.602
0.601
0.598
0.604
0.608
0.603
0.61
0.599
0.603
0.587
0.578
0.576
0.569
0.570
0.560
0.580
0.579
0.578
0.575
0.519
0.537
0.544
0.539
0.539
0.537
0.53
0.544
0.53
0.558
0.602
0.61
0.609
0.612
0.622
0.604
0.622
0.616
0.621
0.643
0.628
0.633
0.637
0.636
0.634
0.648
0.624
0.573
0.591
Total
Persons
Engaged
0.514
0.568
0.564
0.555
0.555
0.55
0.554
0.54
0.548
0.562
0.595
0.591
0.593
0.596
0.587
0.601
0.587
0.596
0.336
0.584
0.577
0.577
0.56
0.581
0.590
0.580
0.554
0.564
0.563
4.246
0.4***
(0.001)
7.333
1.0***
(0.000)
8.707
0.000
(0.960)
Growth Rate
p-value
Fuel
Material
Consumption Consumption
0.567
0.585
0.577
0.579
0.577
0.572
0.606
0.594
0.579
0.569
0.615
0.625
0.597
0.623
0.611
0.618
0.605
0.603
0.606
0.606
0.602
0.592
0.578
0.581
0.619
0.623
0.575
0.602
0.596
0.578
0.601
0.599
0.603
0.601
0.595
0.588
0.589
0.575
0.589
0.622
0.63
0.602
0.614
0.619
0.619
0.629
0.603
0.621
0.607
0.6
0.602
0.606
0.623
0.612
0.590
0.621
0.587
0.604
Value Of
Gross
Output
0.573
0.595
0.591
0.591
0.591
0.59
0.586
0.588
0.573
0.588
0.618
0.623
0.600
0.617
0.62
0.62
0.629
0.609
0.611
0.612
0.600
0.604
0.603
0.617
0.614
0.624
0.586
0.614
0.603
3.060
0.2*
(0.053)
2.475
0.2***
(0.010)
2.602
0.2***
(0.001)
Notes: i) The values are Gini Coefficients obtained Using Lorenz Curve Analysis; ii) Growth rates have
been obtained using exponential growth curve; and iii) *, ** and *** represents that the growth rates are
statistically significant at 10 percent, 5 percent and 1 percent levels of significance.
Source: Author’s Calculations
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TABLE 5.2
INTER-TEMPORAL VARIATIONS IN THE CONCENTRATION OF INDIAN
MANUFACTURING SECTOR – AN APPLICATION OF
HRISCHMAN-HERFINDAL INDEX
Year
No. of
Factories
Fixed
Capital
Total
Persons
Engaged
0.602
0.570
0.583
0.556
0.549
0.547
0.545
0.512
0.530
0.530
0.563
0.550
0.557
0.570
0.548
0.591
0.543
0.575
2.023
0.612
0.590
0.593
0.628
0.634
0.601
0.594
0.633
0.621
0.627
Fuel
Material
Consumption Consumption
Value Of
Gross
Output
0.748
0.752
0.736
0.713
0.727
0.727
0.727
0.723
0.662
0.678
0.716
0.759
0.642
0.710
0.731
0.727
0.775
0.686
0.711
0.784
0.737
0.752
0.758
0.818
0.722
0.754
0.702
0.768
0.730
1979-80
0.501
0.486
0.608
0.763
80-81
0.521
0.468
0.616
0.754
81-82
0.550
0.513
0.614
0.754
82-83
0.561
0.490
0.597
0.761
83-84
0.533
0.493
0.607
0.777
84-85
0.559
0.483
0.654
0.752
85-86
0.515
0.474
0.705
0.727
86-87
0.568
0.504
0.663
0.727
87-88
0.554
0.467
0.619
0.675
88-89
0.573
0.521
0.524
0.687
89-90
0.606
0.589
0.649
0.722
90-91
0.602
0.607
0.688
0.785
91-92
0.595
0.602
0.546
0.653
92-93
0.625
0.608
0.678
0.698
93-94
0.655
0.641
0.602
0.731
94-95
0.635
0.576
0.651
0.724
95-96
0.656
0.671
0.578
0.785
96-97
0.621
0.651
0.561
0.690
97-98
0.639
0.691
0.582
0.737
98-99
0.641
0.818
0.692
0.750
99-2000
0.613
0.773
0.685
0.698
2000-01
0.611
0.820
0.658
0.732
2001-02
0.604
0.889
0.629
0.769
2002-03
0.611
0.872
0.633
0.850
2003-04
0.567
0.628
0.602
0.762
2004-05
0.610
0.637
0.634
0.721
2005-06
0.624
0.589
0.595
0.711
2006-07
0.580
0.610
0.612
0.787
Average
0.590
0.613
0.624
0.739
Coefficient
7.219
20.422
43.973
7.189
5.558
5.063
of Variation
Notes: i) The values are Gini Coefficients obtained Using Lorenz Curve Analysis; ii)
Growth rates have been obtained using exponential growth curve; and iii) *, ** and ***
represents that the growth rates are statistically significant at 10 percent, 5 percent and 1
percent levels of significance.
Source: Author’s Calculations
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TABLE 5.3
ECONOMIC REFORMS AND INDUSTRIAL CONCENTRATION
Period
No. of
Fixed
Total
Fuel
Material
Value Of
Factories
Capital
Persons
Consumption
Consumption
Gross
Engaged
Output
PreReform
0.558
0.549
0.558
0.587
0.598
0.592
0.588
0.623
0.566
0.603
0.610
0.611
2.01
4.28**
1.22
2.11
2.001
1.65
(1979-80) to
(1990-91)
PostReform
(1991-92) to
(2006-07)
Kruskal-Wallis
Test
Note: i) *, ** and *** represent that the value is significant at ten, five and one percent
levels of significance, respectively; and ii) The decision about the Null Hypothesis of
insignificant difference between pre-reforms and post-reforms concentration is based
upon 5 percent level of significance.
Source: Author’s Calculations
125
TABLE 5.4
HYPOTHESIS TESTING FOR DIFFERENCE BETWEEN
HHI AND GINI INDICES OF INEQUALITY
Variable
Mann Whitney
Kruskal-Wallis
Hypothesis
U-Test
ANOVA
µHHI=µGini
No.of Factories
1.11
3.804*
Not Rejected
Fixed Capital
1.11
2.03
Not Rejected
Total Persons Engaged
1.08
1.25
Not Rejected
Fuel Consumption
0.11
2.47
Not Rejected
Material Consumption
1.10
3.12
Not Rejected
Value Of Output
1.11
2.55
Not Rejected
Note: i) *, ** and *** represent that the value is significant at ten , five and one percent
levels of significance, respectively; and ii) The decision about the Null Hypothesis is
based upon 5 percent level of significance.
Source: Authors’ Calculations
126
FIGURE 5.3
INTERTEMPORAL VARIATIONS IN CONCENTRATION OF NO. OF FACTORIES IN INDIAN
MANUFACTURING SECTOR
127
FIGURE 5.4
INTERTEMPORAL VARIATIONS IN CONCENTRATION OF FIXED CAPITAL IN INDIAN
MANUFACTURING SECTOR
128
FIGURE 5.5
INTERTEMPORAL VARIATIONS IN CONCENTRATION OF EMPLOMENT IN INDIAN MANUFACTURING
SECTOR
129
FIGURE 5.6
INTERTEMPORAL VARIATIONS IN CONCENTRATION OF FUEL CONSUMPTION IN INDIAN
MANUFACTURING SECTOR
130
FIGURE 5.7
INTERTEMPORAL VARIATIONS IN CONCENTRATION OF RAW MATERIAL CONSUMED IN INDIAN
MANUFACTURING SECTOR
131
FIGURE 5.8
INTERTEMPORAL VARIATIONS IN CONCENTRATION OF GROSS OUTPUT IN INDIAN
MANUFACTURING SECTOR
132
Moreover, rising concentration index indicates the failure of trickle-down
effect by which intentionally created inequalities ensure equity in the long run i.e.
over the period of time the industrial development in some selected states must
disperse to other states. Thus, in India, trickle down and learning by doing process
has failed at industrial front and Indian economy is moving toward loop sided
development.
Section – III
The analysis of the concentration trends in Indian manufacturing sector
reveals the existence of high inequalities in terms of industrial development
among Indian states. Using Annual Survey of Industries (ASI) data over the period
1979-80 to 2006-07, the concentration levels have been worked out using Lorenz
curve based Gini coefficients and Herschman Herfindal index of concentration for
each year. The analysis uses six alternative variables namely, i) No of Factories;
ii) Fixed Capital; iii) Total Persons Engaged; iv) Fuel Consumed; v) Material
Consumed; and vi) Gross Output, for computing concentration levels among
different states.
The analysis has been performed using aforementioned two indices.
However, for interpretation purposes, the Gini coefficients based index of
concentration has been preferred given that the concentration levels are less
133
volatile in terms of Gini coefficients in comparison to HHI index. The use of the
Gini coefficients reflects the existence of high concentration of manufacturing
activities among Indian states. In context of each variable, a rising trend has been
noticed over the period under consideration.
Thus, the facts imitates that concentration in industrial activities is rising
among Indian states over the study period. This increase in the concentration is
towards industrial states such as Maharashtra, Gujarat, Andhra Pradesh and Tamil
Nadu. To analyze the impact of economic reforms, the entire period has been
divided into pre-reform (1979-80 to 1990-91) and post-reform period (1991-92 to
2006-07). The average concentration of each variable for both sub-periods has
been computed along with an application of Kruskaal-Wallis test to test the null
hypothesis of insignificant difference between the average concentrations over two
sub-periods. The application of Kruskaal-Walis test indicates that the increase in
concentration of fixed capital is statistically significant whereas, the observed
increase in average concentration in case of remaining five variables is statistically
insignificant.
Hence, the study proves that the outcome of reforms process is in opposite
direction as desired by the policy planners of India. The concentration is rising
(although at insignificant rate) over a period of time and thus, does not guarantee
equitable distribution of the manufacturing activities. In sum, the agglomerative
134
factors seem to be pulling industrialization towards the industrially developed
states and in the remaining states, industry is disappearing. Thus, Indian planners
will have to design such a fiscal policy through which they can distort the
investor’s choice towards industrially backward states and attract the
industrialization in laggard states. The fact that implications of deregulation for
concentration differ across Indian manufacturing states, strongly supports the need
for a state-specific approach in the post reform period.
************
135