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.