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We used some special measures of poverty under the broad class of measures called the Foster-Greer-
Thorbecke metric[chapter2, globalisation and the poor in asia].
Under this scheme, we use an indicator variable (Ii) to denote the deprivation suffered by the ith
household.
Let xi = income of ith household.
For the ith household, Ii = 1 if xi < Z where Z is the household poverty line.
and Ii = 0 if xi ≥ Z
Let the ith household represent the fraction wi of the population.
The Foster-Greer-Thorbecke measures of poverty are defined as
Pα = Σ(����
�)α (Iiwi)
α = 0 corresponds to the case where only the total fraction of households below the poverty line are
counted, without considering how much deprivation an individual household suffers. It is an extremely
crude measure of poverty, called the headcount ratio.
α = 1 gives the poverty gap ratio, which is a linear measure of the extent to which household incomes fall
below the poverty line.
α = 2 gives the poverty severity index, which measures absolute deprivation suffered by the BPL
households, giving a higher weightage to those households which are further below the poverty line.
In our analysis, we have mostly used this index, to have a truly non-optimistic and unbiased estimate of
India’s poverty situation. Using data on income-distribution of Indian households across different years
[McKinsey], we computed the headcount ratio, poverty gap ratio and poverty severity index of India from
1985 to 2009. The results were as follows.
Year Headcount Ratio (%)
Poverty Gap Ratio (%)
Poverty Severity Index (%)
1985 40 33.6723 28.3456
1986 40 33.6723 28.3456
1987 39 32.8305 27.637
1988 38 31.9887 26.9283
1989 37 31.1469 26.2197
1990 36 30.3051 25.5111
1991 35 29.4633 24.8024
1992 34 28.6215 24.0938
1993 33 27.7797 23.3851
1994 32 26.9379 22.6765
1995 32 26.9379 22.6765
1996 31 26.096 21.9679
1997 28 23.5706 19.8419
1998 26 21.887 18.4247
1999 24 20.2034 17.0074
2000 23 19.3616 16.2987
2001 21 17.678 14.8815
2002 19 15.9944 13.4642
2003 18.6 15.6576 13.1807
2004 18 15.1525 12.7555
2005 17.7 14.9 12.5429
2006 17.2 14.4791 12.1886
2007 16 13.4689 11.3382
2008 14 11.7853 9.921
2009 12 10.1017 8.5037
Next, we tried to assess how much the fruits of India’s growth reach the poor [globalisation and poor in
asia]
Let η be the growth elasticity of poverty, i.e. ������ ������������
������������ �� .
Clearly, η will have two components, namely, the one due to the effect of growth alone, and and the other
due to the effect of changing inequality. These last-mentioned quantities are measured by two indices
denoted by δ and ε respectively.
δ= ������ ��������������������������� �����������������������
������������ ��
We used the index defined by Kakwani and Pernia, 2000, to measure the degree of benefits reaching the
poor, namely, φ = �
�
We note that for positive overall growth rate g, δ is negative, since if all household incomes increased at
g%, poverty would obviously reduce. Hence if φ > 1, we can say that η <0 and |δ| < |η| i.e. actual rate of
decrease of poverty is greater than what it would be if the benefits of growth were equally distributed.
Thus, the growth is strictly pro-poor. On the other hand, if 0<φ<1, η is still less than 0 but this time, |η| <
|δ| i.e. actual rate of decrease of poverty is less than what it would be if the benefits of growth were
equally distributed. Thus, the growth is not pro-poor; the poor don’t receive as much benefits from
growth as they should. This is the most common situation in any country. This kind of growth is called
‘trickle-down’ growth. In some extreme cases, φ may actually become negative, which indicates that δ is
positive, i.e. poverty increases In spite of positive growth. This is an example of anti-poor growth.
All these interpretations are reversed if the economy is in recession. Then, a negative φ is actually good
news for the poor, since this means poverty has reduced despite the recession. This time, the higher the
positive value of φ is, the worse the poor have been hit.
These considerations lead us to conclude that in the general case, if g and φ are of the same sign, the poor
benefit more from a positive growth and are less badly hit by a recession. This conclusion is nicely
captured by the commonly used index called the poverty-equivalent growth rate (PEGR), defined as
g*= gφ
In trying to compute India’s poverty-equivalent growth rate, we found that in certain cases, it gave
inflated figures, sometimes to the tune of 40-50%, leading to an over-optimistic evaluation of India’s
performance in poverty-alleviation. So we used g* = g�φ . We recognise the benefits of this formulation by
noting that
for φ>1, �φ < φ and for φ<1, �φ > φ .
Thus, taking �φinstead of φ reduces the deviations from the actual growth rate, making the PEGR more
down-to-earth and reminding us that a lot of work has still to be done in eradicating poverty.
Following are the results obtained by us from the study in the changes of poverty measures of India from
1985-2009. We have separately determined the δ’s and η’s for poverty gap ratio and poverty severity
index. We have computed both the classical PEGR and the one defined by us, to illustrate how the classical
one tends to overestimate both the positive and the negative trends of poverty alleviation. Real growth
rate of India is as per data from the IMF (www.imf.org). We have used the poverty line of $1.25 at PPP per
day per person, which comes out as INR 44,250 per household per annum.
Year g(%) Eta (Poverty Gap Ratio)
Delta (Poverty Gap Ratio)
phi=eta/delta (Poverty Gap Ratio)
PEGR
= gφ
modified PEGR
=g�φ
1985 5.265
1986 5.027 -0.0015 -0.0019 0.789473684 3.968684211 4.466606713
1987 4.406 -0.0034 -0.0019 1.789473684 7.884421053 5.893959548
1988 8.505 -0.0033 -0.0019 1.736842105 14.77184211 11.20868044
1989 7.238 -0.0036 -0.0019 1.894736842 13.71410526 9.963066491
1990 6.075 -0.0044 -0.0019 2.315789474 14.06842105 9.24476381
1991 2.136 -0.013 -0.0019 6.842105263 14.61473684 5.587224525
1992 4.385 -0.0065 -0.0019 3.421052632 15.00131579 8.110534491
1993 4.939 -0.006 -0.0019 3.157894737 15.59684211 8.776833322
1994 6.199 -0.002 -0.0019 1.052631579 6.525263158 6.360039805
1995 7.351 -0.0029 -0.0019 1.526315789 11.21994737 9.081730733
1996 7.56 -0.0037 -0.0019 1.947368421 14.72210526 10.54983961
1997 4.619 -0.021 -0.0019 11.05263158 51.05210526 15.35609567
1998 5.979 -0.0119 -0.0019 6.263157895 37.44742105 14.96322594
1999 6.916 -0.0111 -0.0019 5.842105263 40.404 16.7162814
2000 5.693 -0.0073 -0.0019 3.842105263 21.87310526 11.15901377
2001 3.885 -0.0224 -0.0019 11.78947368 45.80210526 13.33945947
2002 4.558 -0.0209 -0.0019 11 50.138 15.11717579
2003 6.852 -0.0031 -0.0019 1.631578947 11.17957895 8.752283985
2004 7.897 -0.0041 -0.0019 2.157894737 17.04089474 11.60051489
2005 9.211 -0.0018 -0.0019 0.947368421 8.726210526 8.96532906
2006 9.817 -0.0029 -0.0019 1.526315789 14.98384211 12.12832956
2007 9.295 -0.0075 -0.0019 3.947368421 36.69078947 18.46729239
2008 7.288 -0.0172 -0.0019 9.052631579 65.97557895 21.92783663
2009 4.523 -0.0316 -0.0019 16.63157895 75.22463158 18.44562302
Year g(%) Eta (severity)
Delta (severity)
phi=eta/delta (severity)
PEGR
= gφ
modified PEGR
=g�φ
1985 5.265
1986 5.027 -0.0015 -0.0037 0.405405405 2.037972973 3.200763992
1987 4.406 -0.0034 -0.0037 0.918918919 4.048756757 4.223602996
1988 8.505 -0.0033 -0.0037 0.891891892 7.585540541 8.032124395
1989 7.238 -0.0036 -0.0037 0.972972973 7.042378378 7.139519221
1990 6.075 -0.0044 -0.0037 1.189189189 7.224324324 6.624784545
1991 2.136 -0.013 -0.0038 3.421052632 7.307368421 3.95076435
1992 4.385 -0.0065 -0.0037 1.756756757 7.703378378 5.811997435
1993 4.939 -0.006 -0.0037 1.621621622 8.009189189 6.289466226
1994 6.199 -0.002 -0.0037 0.540540541 3.350810811 4.557595442
1995 7.351 -0.0029 -0.0037 0.783783784 5.761594595 6.507955275
1996 7.56 -0.003704 -0.0037 1.001081081 7.568172973 7.564085383
1997 4.619 -0.021 -0.0037 5.675675676 26.21594595 11.00415623
1998 5.979 -0.0119 -0.0037 3.216216216 19.22975676 10.72262634
1999 6.916 -0.0111 -0.0037 3 20.748 11.97886339
2000 5.693 -0.0073 -0.0037 1.972972973 11.23213514 7.996533332
2001 3.885 -0.0224 -0.0037 6.054054054 23.52 9.559037608
2002 4.558 -0.0209 -0.0037 5.648648649 25.74654054 10.83294659
2003 6.852 -0.0031 -0.0037 0.837837838 5.740864865 6.271874206
2004 7.897 -0.0041 -0.0037 1.108108108 8.75072973 8.312912406
2005 9.211 -0.0018 -0.0037 0.486486486 4.481027027 6.424542003
2006 9.817 -0.0029 -0.0037 0.783783784 7.694405405 8.691143645
2007 9.295 -0.0075 -0.0037 2.027027027 18.84121622 13.23363536
2008 7.288 -0.0172 -0.0037 4.648648649 33.87935135 15.71345642
2009 4.523 -0.0316 -0.0037 8.540540541 38.62886486 13.21810712
From these tables, we observe that India as a whole has performed well consistently in reducing poverty,
even during the recent slowdown.
Plotted below is India’s real growth rate and PEGR from 1985 to 2009, based on our index (g�φ), with
respect to both poverty gap ratio and severity. These graphs show that the ‘moderately poor’ have
benefited the most from India’s growth, as is shown by the graph of the PEGR based on poverty gap ratio,
whereas the acutely poor have been marginalised in some cases, as is shown by the graph of the PEGR
based on severity.
However, these figures do not reflect the performance of India’s different regions.
Data on four different representative states of India (Karnataka, Maharashtra, Punjab and West Bengal) were obtained from “Rural Poverty in India in an era of Economic Reforms” by Devendra Kumar Pant and Kakali Patra. Two such tables, containing data only for 1993-94, are reproduced here for brevity.
0
5
10
15
20
25
1980 1985 1990 1995 2000 2005 2010 2015
Series1
Series2
Series3
Series1 -> Growth Rate
Series2 -> Poverty Equivalent Growth Rate
based on Poverty Gap Ratio
Series3 -> Poverty Equivalent Growth Rate
based on Poverty Severity index
For the different states, we divided the entries in the corresponding columns of the first and second tables
to obtain the relative income-levels of different sections of households. We multiplied this by the average
income per household for that state to obtain the absolute levels of household income for different
sections of society. Based on this data, we computed the severity index for the rural population of these
states over the years. From these, we determined the PEGR (both the classical and ours) for the rural
belts of these states from 1986 to 2009.
PEGR based on the classical formula:
Year PEGR (Karnataka) PEGR(Maharashtra) PEGR(Punjab) PEGR(West Bengal)
1986 1.8039 3.4653 0.2889 0.1441
1987 5.5035 4.614 0.7993 0.1297
1988 4.7434 4.2764 1.1854 0.2365
1989 3.6527 4.1109 1.4258 0.3057
1990 7.9588 5.0485 0.3572 0.1356
1991 6.1802 5.0555 0.1594 0.0929
1992 5.4393 3.5423 0.7837 0.2345
1993 5.4887 3.4927 0.222 0.7854
1994 4.9951 2.6529 0.5998 1.5298
1995 3.2369 2.8839 0.8041 -1.0696
1996 4.5011 4.7718 0.5059 0.3376
1997 4.7795 2.9879 0.6503 0.3138
1998 2.9247 2.6904 1.1512 0.2656
1999 3.5258 2.5234 0.2766 0.2453
2000 4.5576 0.7815 0.5896 0.2547
2001 3.5854 1.1172 0.8001 0.4306
2002 10.3325 2.4238 1.8502 0.2673
2003 4.4742 0.6655 0.6308 0.2843
2004 7.135 1.094 0.9395 0.5707
2005 7.362 1.9444 1.1932 0.3822
2006 12.2045 2.4526 1.7919 0.2077
2007 9.7707 6.1045 1.4317 0.2269
2008 11.8975 5.5006 1.5606 0.0465
2009 4.899 10.8031 2.0378 0.1786
PEGR based on our formula:
Year PEGR (Karnataka) PEGR(Maharashtra) PEGR(Punjab) PEGR(West Bengal)
1986 4.72 7.8759 1.9231 1.7264
1987 8.6642 8.8825 3.2481 1.5945
1988 8.679 8.349 4.0151 2.0252
1989 7.9654 7.9048 4.7718 2.1684
1990 12.1735 8.2861 2.5123 1.2984
1991 10.2771 7.892 1.9207 1.0344
1992 9.4102 6.9536 4.2187 1.5805
1993 8.7722 6.2321 2.1506 2.7056
1994 8.5017 5.4655 3.2176 3.8838
1995 6.8722 5.9968 3.4427 0 - 3.3799i
1996 8.2195 7.5165 2.5743 1.9506
1997 7.9489 6.0598 3.0205 1.783
1998 6.1066 5.6129 3.8581 1.6944
1999 6.3064 5.3369 1.7616 1.4164
2000 7.0126 2.8288 2.3878 1.5538
2001 6.4128 3.3948 2.5802 2.2676
2002 11.0699 5.5633 4.3186 1.8303
2003 7.6295 2.8553 2.5736 1.7111
2004 9.5864 3.9334 3.4407 2.7416
2005 9.4266 5.165 3.8542 2.2394
2006 12.3612 5.8261 4.7649 1.8344
2007 11.0735 8.9049 4.3257 2.1151
2008 12.3357 8.6428 4.5883 0.8958
2009 7.6578 12.1922 5.2934 1.6542
Exactly one entry in the top table is negative, indicating anti-poor growth in West Bengal’s villages around
1995. In general, too, West Bengal’s performance, as far as PEGR is concerned, is seen to be considerably
poorer than Karnataka and Maharashtra. Punjab’s PEGR’s are also low, indicating a possible slowdown
after the effects of the Green Revolution somewhat wore out. We have plotted PEGR of different states
below.
In the above figure, Series1, Series2, Series3, Series4 represent Karnataka, Maharashtra, Punjab and West
Bengal respectively.
We identified three factors on which the severity of poverty of a region can depend[7th chapter
globalisation and the poor in asia]. These are income disparity between urban and rural areas (measured
by the ratio of per capita household incomes of urban and rural areas), per capita expenditure by the
government on poverty alleviation, and globalisation, which in turn has a nonlinear effect on severity. We
have modelled this effect as a cubic. Thus we can write the severity of poverty of a region as
y= α1(income disparity) + α2(poverty alleviation expenditure) + α3(globalisation) + α4(globalisation2) +
α5(globalisation3) +α6
We have measured globalisation by glob= ������ �����!������"#����
$%&∗ 10
Data and Analysis for Karnataka:
Year Income
Disparity
Per Capita
Poverty
Alleviation
Expenditure
(INR)
Glob Glob squared Glob cubed severity
1985 1.31 19.21 1.3 1.69 2.197 18.99925941
1986 1.34 19.4 1.2 1.44 1.728 18.51905126
1987 1.37 21.04 1.3 1.69 2.197 17.0935295
1988 1.4 21.94 1.4 1.96 2.744 15.93152738
1989 1.42 25.13 1.5 2.25 3.375 15.08649541
1990 1.45 21.81 1.6 2.56 4.096 13.33643816
0
2
4
6
8
10
12
14
1980 1985 1990 1995 2000 2005 2010 2015
Series1
Series2
Series3
Series4
1991 1.48 20.41 1.8 3.24 5.832 12.08058961
1991 1.5 19.1 1.9 3.61 6.859 11.07289245
1993 1.53 26.89 2 4 8 10.11783795
1994 1.55 23.43 2 4 8 9.346481852
1995 1.59 25.49 2.3 5.29 12.167 8.880874217
1996 1.61 24.54 2.3 5.29 12.167 8.268060838
1997 1.64 26.78 2.3 5.29 12.167 7.654548146
1998 1.65 29.28 2.4 5.76 13.824 7.303838267
1999 1.69 32.64 2.6 6.76 17.576 6.908827902
2000 1.72 30.21 2.7 7.29 19.683 6.432736301
2001 1.77 34.49 2.7 7.29 19.683 6.094580904
2002 1.81 38.58 2.9 8.41 24.389 5.211147078
2003 1.82 40.14 3.1 9.61 29.791 4.894440651
2004 1.87 42.63 3.8 14.44 54.872 4.426016635
2005 1.88 42.98 4.3 18.49 79.507 3.984054423
2006 1.93 44.56 4.8 23.04 110.592 3.318789142
2007 1.96 48.68 5.2 27.04 140.608 2.859080301
2008 1.98 50.34 5.7 32.49 185.193 2.37738228
2009 1.99 52.34 6.2 38.44 238.328 2.191311011
Following results are obtained from a linear regression analysis of this model.
From this table, we observe that all the factors are significant, since |t| values are greater than 2.
However, somewhat surprisingly, income disparity has a negative effect on severity, implying that
severity decreases as income disparity increases. This may be because of multi-collinearity. To determine
multi-collinearity, we follow the steps:
• We perform a VIF test on the regression. If the VIF values are all less than 5, there is no multi-
collinearity.
• Otherwise, we remove the independent variable which has the highest VIF value and regress
again. Then go to the first step.
Finally, the result of the VIF test was as follows:
Thus, globcubed and income disparity emerge as the only two independent variables.
However, income disparity is still showing a negative effect on severity. We suspected that this might be
due to the variables showing opposite trends with time and thus ran a VAR and unit root test to
investigate time effects.
Following are the results of VAR and unit root test.
VAR:
Dickey-Fuller unit root test:
The Z values of the VAR results show that direct dependence of severity on income disparity is not
significant and the apparent dependence between the two is mainly due to both of them depending on
time. Thus, globalisation is by far the most important factor in determining severity of poverty in
Karnataka. Further, Dickey-Fuller test reveals that globcubed has a unit root.
As per regression, the dependence of severity on globalisation alone for Karnataka can be plotted as
follows.
Thus we see that at low-to-middle levels, globalisation has a somewhat ambiguous effect on poverty, but
as globalisation increases rapidly, poverty declines.
Data and Analysis for Maharashtra:
Year Income
Disparity
Per Capita
Poverty
Alleviation
Expenditure
(INR)
Glob Glob
squared
Glob cubed severity
1985 1.31 16.27 1.3 1.69 2.197 14.39959383
1986 1.32 15.32 1.2 1.44 1.728 13.53799261
1987 1.35 18.55 1.3 1.69 2.197 12.43854352
1988 1.39 20.06 1.4 1.96 2.744 11.4787787
1989 1.42 31.5 1.5 2.25 3.375 10.61215455
1990 1.41 25.47 1.6 2.56 4.096 9.604195171
1991 1.44 21.4 1.8 3.24 5.832 8.672004816
1991 1.47 19.37 1.9 3.61 6.859 8.088149777
1993 1.48 25.46 2 4 8 7.543603608
1994 1.52 27.43 2 4 8 7.162076837
1995 1.57 26.68 2.3 5.29 12.167 6.769315676
1996 1.59 30.45 2.3 5.29 12.167 6.158911147
1997 1.6 32.38 2.3 5.29 12.167 5.817089205
1998 1.61 34.62 2.4 5.76 13.824 5.525645096
1999 1.64 31.26 2.6 6.76 17.576 5.272475571
2000 1.65 36.2 2.7 7.29 19.683 5.197095958
2001 1.69 38.92 2.7 7.29 19.683 5.089094923
2002 1.74 39.41 2.9 8.41 24.389 4.865161931
2003 1.77 41.34 3.1 9.61 29.791 4.80424547
2004 1.78 42.63 3.8 14.44 54.872 4.708173308
2005 1.82 38.52 4.3 18.49 79.507 4.541239534
2006 1.85 44.37 4.8 23.04 110.592 4.337508045
2007 1.91 47.41 5.2 27.04 140.608 3.860512706
2008 1.94 45.22 5.7 32.49 185.193 3.47328211
2009 1.96 48.32 6.2 38.44 238.328 2.786259457
Following results are obtained from a linear regression analysis of this model.
From this table, we observe that glob, globsquared and globcubed are significant, since |t| values are
greater than 2.
However, somewhat surprisingly, income disparity has a negative effect on severity, implying that
severity decreases as income disparity increases. To determine multi-collinearity, we follow the steps as
in the previous case.
Finally, the result of the VIF test was as follows:
Thus, globcubed and percapita poverty alleviation expenditure emerge as the only two independent
variables.
Predictably, per capita poverty alleviation expenditure is showing a negative effect on severity.
So, the results for Maharashtra show no anomaly.
As per regression, the dependence of severity on globalisation alone for Maharashtra can be plotted as
follows.
Thus we see that at low-to-middle levels, globalisation has a somewhat ambiguous effect on poverty, but
as globalisation increases rapidly, poverty declines.
Data and Analysis for Punjab:
Year Income
disparity
Per capita
poverty
alleviation
expenditure
(INR)
Glob Glob
squared
Glob cubed severity
1985 1.01 9.81 1.3 1.69 2.197 1.070472
1986 1.02 10.52 1.2 1.44 1.728 1.054535
1987 1.13 10.68 1.3 1.69 2.197 1.011668
1988 1.21 10.93 1.4 1.96 2.744 0.951229
1989 1.24 10.42 1.5 2.25 3.375 0.886388
1990 1.22 7.09 1.6 2.56 4.096 0.871796
1991 1.31 5.31 1.8 3.24 5.832 0.866234
1991 1.28 8.65 1.9 3.61 6.859 0.838618
1993 1.23 7.26 2 4 8 0.830689
1994 1.32 8.28 2 4 8 0.80751
1995 1.38 10.42 2.3 5.29 12.167 0.775614
1996 1.42 11.94 2.3 5.29 12.167 0.755557
1997 1.38 10.46 2.3 5.29 12.167 0.731031
1998 1.37 12.78 2.4 5.76 13.824 0.687771
1999 1.41 13.45 2.6 6.76 17.576 0.677666
2000 1.44 15.56 2.7 7.29 19.683 0.655472
2001 1.47 20.37 2.7 7.29 19.683 0.625315
2002 1.48 24.67 2.9 8.41 24.389 0.561693
2003 1.46 26.84 3.1 9.61 29.791 0.542319
2004 1.52 30.46 3.8 14.44 54.872 0.51587
2005 1.53 34.27 4.3 18.49 79.507 0.483792
2006 1.61 38.38 4.8 23.04 110.592 0.438761
2007 1.67 41.58 5.2 27.04 140.608 0.406366
2008 1.71 42.36 5.7 32.49 185.193 0.373962
2009 1.72 46.78 6.2 38.44 238.328 0.335209
Following results are obtained from a linear regression analysis of this model.
From this table, we observe that all the factors are significant, since |t| values are greater than 2.
However, somewhat surprisingly, income disparity has a negative effect on severity, implying that
severity decreases as income disparity increases. This may be because of multi-collinearity. To determine
multi-collinearity, we follow the same steps as in earlier cases.
Finally, the result of the VIF test was as follows:
Thus, globcubed and income disparity emerge as the only two independent variables.
However, income disparity is still showing a negative effect on severity. We suspected that this might be
due to the variables showing opposite trends with time and thus ran a VAR and unit root test to
investigate time effects.
Following are the results of VAR and unit root test.
VAR:
Dickey-Fuller unit root test:
The Z values of the VAR results show that direct dependence of severity on income disparity is not
significant and the apparent dependence between the two is mainly due to both of them depending on
time. Thus, globalisation is by far the most important factor in determining severity of poverty in
Karnataka. Further, Dickey-Fuller test reveals that globcubed has a unit root.
As per regression, the dependence of severity on globalisation alone for Punjab can be plotted as follows.
Thus we see that at low-to-middle levels, globalisation has a somewhat ambiguous effect on poverty, but
as globalisation increases rapidly, poverty declines.
Data and Analysis for West Bengal:
Year Income
disparity
Per capita
poverty
alleviation
expenditure
(INR)
Glob Glob
squared
Glob cubed severity
1985 0.92 15.46 1.3 1.69 2.197 1.1176184
1986 0.95 15.54 1.2 1.44 1.728 1.1107106
1987 1.01 15.87 1.3 1.69 2.197 1.1043976
1988 1.07 15.9 1.4 1.96 2.744 1.0923158
1989 1.08 28.47 1.5 2.25 3.375 1.0763762
1990 1.12 21.89 1.6 2.56 4.096 1.0688678
1991 1.09 21.82 1.8 3.24 5.832 1.0637496
1991 1.11 21.86 1.9 3.61 6.859 1.0505355
1993 1.14 22.49 2 4 8 1.0059174
1994 1.25 26.83 2 4 8 0.9233933
1995 1.28 23.54 2.3 5.29 12.167 0.975505
1996 1.31 29.45 2.3 5.29 12.167 0.9582946
1997 1.27 32.31 2.3 5.29 12.167 0.9423298
1998 1.32 38.26 2.4 5.76 13.824 0.9291361
1999 1.35 43.23 2.6 6.76 17.576 0.9164938
2000 1.36 42.76 2.7 7.29 19.683 0.903933
2001 1.41 46.24 2.7 7.29 19.683 0.8838216
2002 1.43 48.85 2.9 8.41 24.389 0.8717958
2003 1.4 45.34 3.1 9.61 29.791 0.8586448
2004 1.44 42.49 3.8 14.44 54.872 0.8340182
2005 1.52 48.76 4.3 18.49 79.507 0.8179942
2006 1.53 53.86 4.8 23.04 110.592 0.809936
2007 1.57 54.61 5.2 27.04 140.608 0.8019572
2008 1.62 57.83 5.7 32.49 185.193 0.8001949
2009 1.61 58.69 6.2 38.44 238.328 0.7933427
Following results are obtained from a linear regression analysis of this model.
From this table, we observe that only income disparity is significant, since |t| value is greater than 2.
However, somewhat surprisingly, income disparity has a negative effect on severity, implying that
severity decreases as income disparity increases. To determine multi-collinearity, we follow the same
steps as in earlier cases.
Finally, the result of the VIF test was as follows:
Thus, globcubed and income disparity emerge as the only two independent variables and even among
them, globalisation is not significant.
However, income disparity is still showing a negative effect on severity. We suspected that this might be
due to the variables showing opposite trends with time and thus ran a VAR and unit root test to
investigate time effects.
Following are the results of VAR and unit root test.
VAR:
Dickey-Fuller unit root test:
The Z values of the VAR results show that direct dependence of severity on income disparity is not
significant and the apparent dependence between the two is mainly due to both of them depending on
time. Thus, globalisation is by far the most important factor in determining severity of poverty in
Karnataka. Further, Dickey-Fuller test reveals that globcubed has a unit root.
As per regression, the dependence of severity on globalisation alone for West Bengal can be plotted as
follows.
Thus we see that for West Bengal, unlike the other states, at low-to-middle levels, globalisation leads to
decrease in poverty, but as globalisation increases rapidly, poverty increases again. This may be due to
other factors like politics. However, we must not look too deeply into this result, as it is merely predicted
and nothing of this sort has been observed yet.