Assessing Poverty, Risk and Vulnerability: A study on the

Flooded Households in Rural Bangladesh

By Md. Israt Rayhan

Md. Israt Rayhan

Ph.D. Student

Center for Development Research Department of Economic and Technological Change (ZEF-B)

University of Bonn

Walter Flex Str.3

D-53113, Bonn, Germany

Email: israt677@yahoo.com / irayhan@uni-bonn.de

Phone: (++49) 228 731852

Fax: (++49) 228 731839

Word count: Abstract (181 words), Introduction to Conclusion (3,200 words).

2

Assessing Poverty, Risk and Vulnerability: A study on the

Flooded Households in Rural Bangladesh

Abstract

Flood is a common catastrophe for Bangladesh. The combination of its geography,

population density, and extreme poverty makes Bangladeshi people vulnerable to flood

risks. This study is set forth to examine the poverty, risk and vulnerability for flood

hazards in the year 2005. Cross sectional household survey was carried out after two

weeks of the flood in four districts and 600 rural households were interviewed through

three stages stratified random sampling. A utilitarian approach is used to assess flood

vulnerability and its components: poverty, idiosyncratic and aggregate risks to capture the

effect of flood on household’s welfare. To estimate the correlates of flood vulnerability, a

set of fixed households’ characteristics are used as explanatory variables. The results

depict that elimination of poverty would increase household welfare and thus lessen

vulnerability mostly amongst its components. Poverty and idiosyncratic flood risk are

positively correlated and highly significant. Households with higher educated members,

male headed and owner of the dwelling place are less vulnerable to idiosyncratic flood

risk. Possession of arable land and small family size can reduce the poverty and

aggregate flood risk.

Key Words: Poverty, Vulnerability, Idiosyncratic risk, Aggregate risk, Flood

3

Introduction

Bangladesh consists mostly of a low-lying river delta with over 230 rivers and tributaries,

situated between the foothills of the Himalayas and the Bay of Bengal. With a population

of 123.85 million and an area of 147,570 square km, Bangladesh is one of the world’s

most densely populated countries (839 persons per square km)1. 40 percent of the

population lives below the poverty line2, and 82 percent live on less than US$ 2 per day.

About 70 percent land of the country is less than 1 meter above sea level. The

combination of its geography, population density, and extreme poverty makes

Bangladeshi people very vulnerable to risks and disasters. Flood is a frequent catastrophe

for Bangladeshi people. In the year 1987, about 40 percent of the country was flooded,

affecting 30 million people and caused about 1800 deaths. The floods in 1988 were even

more serious, covering about 60 percent of the land area, affecting about 45 million

people, and causing more than 2,300 deaths3. In 1998, over 68 percent of the country was

inundated (Ninno D. et al., 2001) and caused about 2,380 deaths. In 2000 and 2002 floods

affected approximately 20 million people. In the year 2004, devastating monsoon flood

submerged two-thirds of the country, 35.9 million people affected, 726 deaths, millions

of people made homeless4.

This study thus is set forth to examine the poverty, risk and vulnerability for flood

hazards in the year 2005, based on the three questions: (a) How vulnerable are the

flooded people? (b) Which sources of risk contribute most to flood vulnerability? (c)

Which types of interventions are most likely to reduce the flood risk and vulnerability?

1Population Census 2001, Bangladesh Bureau of Statistics, July 2003

2 Preliminary Report on Household Income and Expenditure Survey-2005, Bangladesh Bureau of Statistics,

September, 2006 3 Irrigation Support Project for Asia and the Near East (1993: 1). 4 http://www.adb.org/Documents/Economic_Updates/BAN/2004/eco-update-ban.pdf

4

Poverty is an ex-post measure which refers to being deprived of basic levels of economic

wellbeing (absolute income poverty) and human development (Dercon 2001). It also

characterized as the deprivation of capabilities (Sen 1987). Vulnerability, on the other

hand, is an ex-ante measure of household’s wellbeing and concerning about the future

poor (Alwang et al. 2001). Households are vulnerable if a shock (flood) is likely to push

them below a predetermined welfare threshold (poverty line), so vulnerability is a result

of the cumulative process of risk and response. So, the term distinguishes poverty and

vulnerability is risk (Chaudhuri 2003). The risk of a household relates to events possible

occurring, but with less than certainty (Hardarker et al. 2004), which may be upward or

downside for the individuals, households, communities and countries. This study is

focusing more on the downside risk of flood on the households of rural Bangladesh,

albeit very few fishermen or boatmen may increase their income from flooded season.

Downside risk is defined here as the estimate of the potential that a security, income,

expenditure or overall livelihoods might decline in real value if the area is flooded.

There are several papers which define and measures vulnerability to poverty and risk in

different ways. Amin et al. (1999) use panel data from Bangladesh and detect households

whose consumption tends to fluctuate with income, by controlling for household fixed

effects and aggregate variation in mean consumption. This measure is not suitable for

inter-household comparisons. Glewwe and Hall (1995, 1998) measure vulnerability in

Peru and they are interested in the response of households’ consumptions to aggregate

shocks. It seems also difficult to aggregate this measure over time with long periods of

panel data, containing both positive and negative shocks. Chaudhuri (2003), Chaudhuri et

al. (2002), Christiaensen et al. (2000) and Pritchett et al. (2000) use vulnerability to

5

expected poverty methods which suffer from the same shortcomings of headcount

measure of poverty.

This study applies the utilitarian approach to measure flood vulnerability to poverty and

risks, proposed by Ligon and Schechter (2003). Household’s welfare depends not only on

the average income or expenditure or the value of resources, but also the risk it faces. A

household with low income and facing fewer risk, might be in poverty but future well-

being may be higher than, a household with high level of income but facing higher risk.

Data and Methodology

In the year 2005, Bangladesh was affected by two types of floods, a monsoon flood was

occurred during mid August to September and a flash flood was occurred in the northern

areas during November. A cross sectional household survey was carried out after two

weeks of the floods. Four districts were chosen randomly according to the flood

proneness and damage. A three stage stratified random sampling technique was applied

for the survey, where the first stage was district, second one was the mouza (the smallest

administrative unit in rural area) and the third stage was the households. Flooded

households were detected if at least the home or homestead was submerged by flood

water. Sample size was determined by the estimated proportion formula (Cochran W.G.,

1977, p 75). After the Monsoon flood, three districts (Jamalpur, Shirajganj and

Shunamganj) were randomly chosen, a total of 450 rural households were surveyed (150

households from each district). A flash flood affected the northern part of the country in

the month of November and another 150 rural households were surveyed from the

randomly chosen district, Nilphamari. The total number of flooded households from

6

different rural areas amounted to 600. The questionnaires include some fixed household’s

characteristics such as, age, gender, education, occupation; some inter-household

variables such as, monthly income, expenditure, asset value, number of meals taken, cost

to reach market place for both before and after flood periods by recall memory method;

some inter-community variables such as, availability of primary and secondary schools,

public hospital and electricity, flood height and duration.

It is assumed to be a finite population of households, indexed by i = 1, 2,….., n and

denote the state of the world. Households want to stable their welfare overtime,

even the consequent risks occur. Household’s welfare is defined by per capita monthly

income. The distribution of household i ’s income is denoted by: )(iy . If the household

is risk averse, then the utility function will be concave and its slope will be flatter as the

wealth increases. So, the curvature of the utility function measures the household’s

attitude towards risk. Basically, the more concave the utility function, the more risk

averse the household will be (Varian, 2003 p-225). To measure vulnerability and risk for

each household, a strictly increasing and weakly concave function iU is chosen, such as:

mapping income into the real line. Given the utility function, vulnerability of

household i is defined,

)()()( iiii yEUzUyV

Where, z is some certainty-equivalent income, such that if household i had certain

income greater than or equal to this number, the household will not be regarded as

vulnerable. This study considers z as the poverty line. The poverty line is taken from the

7

nationally representative report5, which is 594.60 taka per capita per month. The

properties of utility function imply that vulnerability estimates will include mean and

variance of household’s income.

For better understand, vulnerability measure is decomposed into distinct components,

such as: poverty, aggregate risk, idiosyncratic risk and unexplained risk and measurement

error respectively. The household i ’s income at time t is denoted by i

ty , idiosyncratic

variables as i

tx and the vector of aggregate variables as _

tx .

)]()([ i

t

iii EyEUEyUV (Poverty)

))]|()([_

ti

t

ii

t

i xEyEUEyU (Aggregate Risk)

))],|(())|(([__

i

tti

t

it

i

t

i xxyEEUxyEEU (Idiosyncratic Risk)

)]()),|(([_

i

t

ii

tti

t

i yEUxxyEEU (Unexplained Risk and Measurement Error)

For a suitable choice of }{ iU , the poverty term will satisfy all the axiomatic requirements

enumerated in Foster et al. (1984). The risk terms are consistent with the ordinal

measures of risk proposed by Rothschild and Stiglitz (1970). Some additional

assumptions were taken for estimation, first, }{ iU takes the simple form

)1/()()( 1 yyU i for some parameter 0 ; as increases, the function iU

becomes increasingly sensitive to risk. The shape of utility function is characterized by

the preferences of the households. It is reflected by the curvature of the household utility

function which is defined only up to a positive linear transformation. The parameter

can be interpreted as the household’s relative risk aversion. According the

5 Poverty Monitoring Survey-December, 2004, Bangladesh Bureau of Statistics.

8

microeconomic literatures (Hardaker et al. 2004, Ligon and Schechter, 2002), it is

assumed =2. The justification can be derived from the constant relative risk aversion

(CRRA), usually defined by two functions: (1) Logarithmic: )ln( yU and (2) Power:

1

)1(yU . The power function is commonly preferred over the logarithmic functional

form, because it directly incorporates as the constant coefficient of relative risk

aversion for income, where is called the partial risk aversion coefficient. Anderson and

Dillon (1992) proposed a classification of degree of level of risk aversion, based on the

magnitude of the relative risk aversion coefficient, such as: (i) =0.5, hardly risk averse,

(ii) =1.0, somewhat risk averse, (iii) =2.0, rather risk averse, (iv) =3.0, very risk

averse, (v) =4.0, extremely risk averse. This study considered =2.0, assuming

households are rather risk averse to flood, for decision making on their livelihoods, crop

pattern, education, savings, and overall income-expenditure routine from the previous

experience of flood (downside risk) disastrous. It is also assumed that

i

t

i

tt

ii

tti

t vxxxyE ),|(_

, where ,,( t

i ) a vector of unknown parameters

to be estimated. Here, }{ i shows the influence of household’s fixed characteristics on

predicted per capita income and restricted to sum to zero, }{ t captures the effect of

changes in aggregates and }{ is the vector of parameters for household’s idiosyncratic

variables, i

tv is a disturbance term equal to the sum of both measurement error in income

and prediction error. In a stationary environment, the unconditional expectation of

household i ’s income is estimated by,

T

t

i

t

i

t yT

Ey1

.1

For this analysis, is chosen so

9

as to optimally predict i

ty in a least square application. The utility from perfect equality

in a risk-less society is equal to 1. So, the percentage welfare loss from vulnerability is

equal to the size of vulnerability. After estimating vulnerability measure, the percentage

of welfare loss can be divided to some components of vulnerability, such as, poverty,

aggregate risk, idiosyncratic risk and measurement error. To look at the correlates, some

fixed household characteristics are regressed over each component and bootstrap standard

errors for the coefficients are also measured. This study uses STATA (version 9) software

for data analyses.

Discussion

The study results begin with the poverty level measurements at before and after flood

periods to delineate the affect of flood. Table 1 shows the poverty level in overall sample

and four selected districts using before and after flood per capita income. Above 16

percent of flooded households fall into poverty after flood. The drastic change into

poverty occurs in Jamalpur district by the monsoon flood, where head count poverty rate

fluctuates by 29 percent. Households from Sunamganj district face comparatively less

disastrous effect of flood. Initial period (before flood) poverty level was the highest in

Nilphamari district (71.33 percent) and it is augmented by 15 percent due to flood.

Households those are not currently poor, may also be counted as vulnerable, some events

(such as, flood, a bad harvest, illness of main earner) could push them into poverty. Jalan

and Ravallion (1999), using a six year panel data from rural households of China,

Table 1 could be replaced here

10

investigate chronic and transient poverty with the classifications: persistently poor

(households whose expenditures in each period below the poverty line), chronically poor

(mean expenditures over all periods less than the poverty line but not poor in each

period), transiently poor (mean expenditures over all periods above the poverty line but

experiencing at least one episode of poverty), and never poor. Authors found that

proportion of transient poor is much higher than chronic poor and never-poor.

Table 2 shows that in overall sample 56.38 percent of households were always poor, but

75 percent of the sample experienced at least one episode of poverty between before

flood and after flood. The individual district also shows the higher proportion of falling

into poverty in one episode than the proportions of always poor and never poor.

Summary statistics of some variables, used to examine the correlates of vulnerability, are

given in the table 3. The after flood average income fall below the poverty line (594.60

taka). The inequality also rises due to flood by 20 percent. The average educational year

of the flooded households is up to primary schooling level. Majority of the households

are male headed (89%). Average land holding is quite low for the surveyed households

(less than one acre per capita). On an average each household possesses five members.

Vulnerability is estimated using Ligon and Schechter (2003) methodology. To assess the

correlates and significance of the components of vulnerability, each component is

regressed on a set of fixed household characteristics, such as, educational year of highest

Table 2 could be replaced here

Table 3 could be replaced here

11

educated household member, dummy variable gender of household head (1=male, 0=

female), age and square of age of household head, arable land per capita, ownership of

the dwelling place and number of household members. Linear relationship is assumed

and Ordinary Least Square (OLS) estimates of coefficients are given in the table 4.

To check whether the omitted variables are significant or not, Ramsey RESET test is

performed using powers of the fitted values of vulnerability (Gujarati, 2003, p-521)6. The

null hypothesis is: Ho: model has no omitted variables and the test result shows that F-

statistic is 2.40 with probability value (p-value) 0.671, so the conclusion would be that

the null hypothesis cannot be rejected at 5 percent level of significance. The next step is

to test for heteroscedasticity because the survey was cross-sectional. Breusch-Pagan-

Godfrey test is executed (Gujarati, 2003, p-411)7 for checking heteroskedasticity with the

null hypothesis, Ho: Constant variance, the p-value of chi-square test statistic comes out

as highly significant (0.00). From the test result, it is depicted that there is

heteroscedasticity in the error variance. The next step is to regress vulnerability and its

factors on the fixed set of households’ characteristics resolving heteroscedasticity

problem. This analysis performs the regression with robustness8 and find out the

bootstrap standard errors with 500 replications. The results are given in table 5.

6 Cited in, Gujarati, Damodar N., 2003, Fourth edition, Basic Econometrics, McGraw-Hill.

Ramsey, J.B., Tests for Specification Errors in Classical Linear Least Squares Regression Analysis, Journal

of the Royal Statistical Society, series B, vol. 31, 1969, pp. 350-371.

7 Cited in Gujarati. Breusch, T. and A. Pagan, A Simple test for Heteroscedasticity and Random Coefficient

Variation, Econometrica, vol. 47, 1979, pp. 1287-1294. Godfrey, L., Testing for Multiplicative

Heteroscedasticity, Journal of Econometrics, vol. 8, 1978, pp. 227-236. 8 White’s heteroscedasticity-consistent variance and standard error test (Gujarati, 2003, p-417)

Table 4 could be replaced here

12

Multicollinearity of the regressed variables also checked by Tolerance and variance

inflation factor (VIF), the values are .83 and 5.62 respectively, which illustrates there is

no collinearity among the explanatory variables9. Table 6 is accumulating the information

on the correlates of vulnerability and each of the components, such as poverty, aggregate

risk, idiosyncratic risk and unexplained risk of flooding with the remedial test for

heteroscedasticity and bootstrapping.

It is depicted that the correlates of flood vulnerability are apparently similar to the

correlates of poverty (for significant variables) which is also the noteworthy component

for defining vulnerability. Additionally, the significant variables in poverty and aggregate

risk share the same sign of coefficients. Aggregate shocks from flood are the same for all

households, so the poor households may experience greater impact on their utility from

this part of risk. The household’s idiosyncratic risk is measured by three observed

components from two periods (before and after flood), such as: asset value, number of

meal taken and cost to reach market place. To assess aggregate risk some community

based variables are used, such as: availability of primary and secondary schools, public

hospital, electricity and flood shelter.

Education is the most significant variable to define vulnerability. The households with

higher educated member are less vulnerable. The increase of 1 unit educational year of

highest educated member of household will decrease 24 unit vulnerability to flood, most

of this reduction will appear in poverty, idiosyncratic and aggregate risk also decrease

substantially. The gender of household head has no significant effect on vulnerability, but

9 from the rule of thumb used by Kleinbaum et al. (1988), p-210

Table 6 could be replaced here

13

reduces idiosyncratic risk significantly. The reason may be that male headed households

acquire the proper intra-household resource allocation at household-specific flood risk.

Households with older heads face higher idiosyncratic vulnerability but after a certain

point their experience helps them to reduce such kind of vulnerability (negative

coefficient for age square). Arable land holding shows significant relationship with the

poverty and aggregate risk at diminishing rate. Perhaps the more availability of land leads

the households to rotate and diversify their crop choice, hence lessen poverty and

aggregate risk. It is expected that households with more arable land and involved in

agriculture might face risk from unobservable sources, such as inundation of crops by

flood water, this analysis shows the similar pattern but at insignificant way. Ownership of

the dwelling place also has the significant negative relation with each type of

vulnerability, even reduce unexplained risk considerably. With the increase of family

size, per capita income or allocated resource units will be lower, thus vulnerability,

poverty and aggregate risk may be aggravated significantly, but the goods of common

share may help to minimize idiosyncratic risk.

Poverty and aggregate risk due to flood have strong positive correlation. It can be

described by the diminishing marginal utility principle that the poor are mostly affected

by the aggregate flood risk, which is uniformly distributed into the utility of income of

the households. Poverty and idiosyncratic risk are positively correlated and highly

significant. The poor has less asset and selective ways of earning, if flood ruined their

crops or hinders the way of earning then households would be more in poverty and may

Table 7 could be replaced here

14

fall into the vicious circle of debt. Unexplained risk is negatively correlated with

idiosyncratic risk at 10 percent level of significance.

Conclusion

This study adopts a utilitarian approach to assess flood vulnerability, poverty and risks to

capture the effect of flood on household welfare. Flood in Bangladesh is a common

calamity, even for small scales of monsoon and flash floods in the year 2005, the

surveyed households from four districts have drastic changes in poverty levels. The

estimated vulnerability and risks are also extremely high for flooded households.

The results suggest that in the elimination of poverty would increase household welfare

and thus lessen vulnerability mostly amongst its components; this finding supports the

view from Ligon and Schechter (2002). Education and ownership of dwelling place are

found to be the significant variables to reduce poverty and flood risks. Households with

higher educated member are considerably less poor, and significantly less vulnerable to

both aggregate and idiosyncratic sources of flood risk, as Pritchett et al. (2000) show that

average vulnerability rate gets lower if the educational level of household heads is higher.

Perhaps this is because educated members can acquire better coping strategies during

flood. Ownership of dwelling place also significantly reduces vulnerability and its

components, so Bangladesh disaster management authority could authentically look after

this step while they rehabilitate the flood victims. Male headed households are facing

higher aggregate risk than the female headed households, which resemblance with the

results of Glewwe and Hall (1998). Households which have larger family size are

significantly more vulnerable and poorer, confronting higher level aggregate risk but

15

lower level of idiosyncratic risk having benefited from the share of common goods. This

contrasts with the result of Ligon and Schechter (2002), who find that households with

smaller family size experience lower level of idiosyncratic risk.

Some policy implications to the target groups may mitigate future flood risks. The aid

programs could intend to reach the transient poor rather always poor and provide the

opportunities not only to the current poor but to those households that experience flood

shocks. Social protection, social insurance or micro credit schemes for the landless

households might motivate them to start small scale business or farming. Food for

education policy already implemented in Bangladesh, which supposed to be monitored

properly to enhance the efficiency of flooded households, hence reduces future risks.

16

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19

Table 1: Income poverty (in percentage) for flooded households

Indicators Head count poverty

Before Flood After Flood

Overall 57.38 73.83

Districts Shirajganj 47.06 66.01

Jamalpur 58.67 87.33

Shunamganj 52.67 56.0

Nilphamari 71.33 86.0

n=600, Sample is weighted by household size, Poverty measures followed the formula

proposed by Foster et al. (1984) with =0, Poverty line is the absolute poverty line

based on food energy intake (FEI) method, the changes between before and after flood

unit price of most commonly consumed items do not show significant t-statistic, so per

capita incomes are not weighted by any consumer price index

20

Table 2: Classification of transient and chronic poverty (in percentage) for flooded

households

Chronically poor (mean per capita

income below poverty line)

Transiently poor only

(mean per capita

income above the

poverty line)

Never

poor

Always poor

(before and

after flood)

Not persistently

poor

Overall 56.38 13.27 5.05 25.30

Shirajganj 47.06 11.76 7.2 33.98

Jamalpur 57.33 25.34 6.0 11.33

Shunamganj 50.0 6.0 2.67 41.33

Nilphamari 71.33 10.0 4.67 14.0

21

Table 3: Summary of Variables

Variables Value

Monthly income per capita before flood (mean) in Taka*

750.01

Gini coefficient for income before flood .396

Monthly income per capita after flood (mean) in Taka

545.45

Gini coefficient for income after flood .596

Educational year of highest educated member (mean) 5.02

Male headed households (percentage) 89%

Age of household head (mean) 43.60

Cultivated land per capita in acres (mean) 0.078

Ownership of house (percentage) 53.57%

Family size (mean) 5.24

*80 Taka (Bangladesh currency) =1 Euro (at field survey time, 2005)

22

Table 4: OLS Estimation of vulnerability on some fixed characteristics of households

Covariates Coefficient Standard

Error

t-

statistic

P>t [95% Confidence

Interval]

Education -23.82 5.04 -4.72 0 -33.73 -13.90

Male 35.80 61.94 0.58 0.56 -85.84 157.45

Age -3.03 7.25 -0.42 0.67 -17.27 11.20

Age square 0.008 0.07 0.11 0.91 -0.13 0.15

Arable land per

capita

-24.11 71.83 -0.34 0.73 -165.21 116.96

Ownership of

house

-80.39 37.74 -2.13 0.03 -154.52 -6.26

Family size 10.68 8.74 1.22 0.22 -6.50 27.86

23

Table 5: Correlates of vulnerability in income with bootstrap standard errors and robust

estimates

Covariates Observed

Coefficient

Bootstrap

Standard

Error

z-

statistic

P>z Normal-based [95%

Confidence Interval]

Education -23.82 5.08 -4.68 0 -33.78 -13.85

Male 35.80 52.25 0.69 0.49 -66.60 138.21

Age -3.03 6.74 -0.45 0.65 -16.25 10.19

Age square 0.008 0.06 0.12 0.90 -0.11 0.13

Arable land per

capita

-24.11 38.11 -0.63 0.52 -98.82 50.59

Ownership of

house

-80.39 40.80 -1.97 0.04 -160.78 -0.41

Family size 10.68 6.05 1.76 0.07 -1.18 22.55

24

Table 6: Correlates of vulnerability, poverty, risks

Vulnerability Poverty Aggregate

Risk

Idiosyncratic

Risk

Unexplained

Risk

Variables Coefficient Coefficient Coefficient Coefficient Coefficient

Education -23.82***

-11.80***

-7.89***

-0.31***

-3.81

(5.08) (1.74) (1.33) (0.04) (4.23)

Male 35.80 -13.61 14.85 -2.01**

35.01

(52.25) (20.66) (16.71) (0.89) (44.63)

Age -3.03 2.15 -1.64 0.14**

-3.68

(6.74) (2.23) (1.58) (0.06) (4.99)

Age square 0.008 -0.02 0.02 -0.001*

0.001

(0.06) (0.02) (0.01) (0.001) (0.04)

Arable

Land per capita

-24.11

(38.11)

-48.33*

(25.44)

-23.22**

(11.25)

-0.29

(0.47)

47.73

(48.34)

Ownership of house -80.39**

-33.48***

-24.92***

-1.20***

-20.77*

(40.80) (10.45) (7.97) (0.39) (10.52)

Family size 10.68*

5.86**

4.10**

-0.17*

0.88

(6.05) (2.68) (1.99) (0.09) (5.56)

2R .63 .58 .59 .74 .31

Numbers in parenthesis are bootstrapped standard errors, ***-significant at 1% level, **-

significant at 5% level, *- significant at 10% level, n=600

25

Table 7: Correlations between elements of vulnerability in per capita income

Poverty Aggregate Risk Idiosyncratic

Risk

Unexplained

Risk

Poverty 1.00

Aggregate Risk 0.747***

1.00

Idiosyncratic

Risk

0.388***

-0.291 1.00

Unexplained

Risk

0.042 0.018 -0.148*

1.00

Spearman rank correlations technique is chosen for above table. ***- significant at 1%

level, **- significant at 5% level, *- significant at 10% level