Single vs multiple poverty trap

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Social Indicators ResearchAn International and InterdisciplinaryJournal for Quality-of-Life Measurement ISSN 0303-8300 Soc Indic ResDOI 10.1007/s11205-014-0586-x

Identifying Single or Multiple Poverty Trap:An Application to Indian Household PanelData

Swati Dutta

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Identifying Single or Multiple Poverty Trap:An Application to Indian Household Panel Data

Swati Dutta

Accepted: 16 February 2014� Springer Science+Business Media Dordrecht 2014

Abstract The paper examines the household asset dynamics in India as well as Indian rural

States. The paper contributes to the empirical analysis of poverty trap by investigating the

presence of one potential poverty trap to simultaneous poverty trap. The paper uses the India

Human Development Survey for the year 1993 and 2005. We use the local polynomial regression

with Epanechnikov kernel weights to test the existence of multiple or single equilibrium in asset

poverty dynamics. Moreover, we use the partial linear mixed model to test the impact of illiteracy

trap and under-nutrition trap on asset dynamics process. Across all the States we find only single

dynamic asset equilibrium for rural households. However the nature of the asset dynamics varies

from one state to another. We find that, in most of the States, asset accumulation does not take

place and welfare dynamics is very poor in rural areas. Further, we find under-nutrition trap

uniformly affect the asset accumulation in most of the States. However an illiteracy trap affects

the asset level heterogeneously over the income and regional distribution. We find the most

deprived States (Bihar, Uttar Pradesh, Orissa and Madhya Pradesh) have the multiple poverty

trap compared to richer States. Our result implies that asset dynamics of the household varies in

the long term according to the types of traps. Government and policy makers should take pointed

policy and programme based on whether the poor are trapped and in what ways.

Keywords Poverty trap � Multiple equilibrium � Illiteracy � Under-nutrition � Asset

dynamics � Multidimensional poverty � India

1 Introduction

The distinction between chronic and transient poverty1 is now recognized in discussions on

poverty in the Indian context, although estimates of the incidence of these two types of

S. Dutta (&)Institute for Financial Management and Research, Chennai, Indiae-mail: [email protected]; [email protected]

1 When households are always poor we referred as ‘‘chronic poor’’ and when households are sometimespoor we referred as ‘‘transient poor’’.

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Soc Indic ResDOI 10.1007/s11205-014-0586-x

Author's personal copy

poverty are not common. Studies of poverty have generally focused on the state of being

poor, rather than on the ‘dynamics of poverty’—movement into and out of poverty and the

processes and factors that determine this. According to planning commission estimates of

poverty, in India rural poverty was 50, 42 and 33 % in 1993–1994, 2004–2005 and

2009–2010 respectively (Planning Commission 2009–2010). However these figures do not

tell us anything about the causes of long term persistence poverty. The purpose of this

study is to analysis the poverty dynamics in rural India and to determine why certain

households are more likely than others to remain poor in the long run. In this study we

focus on household assets, rather than consumption or income which has the stochastic

component. The noise from the stochastic part of the income may generate false positives

and false negatives regarding the incidence of chronic poverty and poverty traps (Barrett

et al. 2006). The phenomenon of chronic poverty can be analyzed by examining the nature

of poverty traps. The analysis of poverty trap helps us to understand the causes of persistent

poverty in a long term.

The poverty trap is defined as a situation in which poverty has effects which act as

causes of poverty. There are thus vicious circles, processes of circular and cumulative

causation, in which poverty outcomes reinforce themselves. Moreover, this gives the idea

of a vicious circle of poverty as a ‘‘constellation of forces tending to act and react upon one

another in such a way as to keep a poor country in a state of poverty’’ (Nurkse 1953).

Recent literature has mainly studied the poverty trap to determine the presence of

multiple equilibriums, which provides an opportunity to implement policies to push an

economy into a self sustaining higher equilibrium (Carter and Barrett 2006). Dynami-

cally, it is possible that a section of households remain persistently poor or in poverty

trap because of shortfall in minimum required assets. It is also possible to identify such

asset poor households who have sufficient asset base to move out of poverty, within some

time through self-motivated strategies. Dynamic asset poverty measure draws a line

(known as Micawber threshold) between those structural poor who are likely to persist in

poverty trap and those who can, through various economic strategies, become asset non-

poor in future. The ones who are in the poverty trap need a big push to move out of it

because they lack relevant assets to even feel motivated to adopt to the strategies that

will bring them out of this poverty trap. Those who are in poverty trap are a subset of the

structural poor. A long term poverty trap arises when poor households are faced with two

distinct equilibriums: one below the poverty line and one above it. Households with a

sufficiently low income or asset endowments are trapped in the poor-equilibrium and

small improvements are not enough to escape such poverty trap. This idea was based on

the big-push theory where countries need a big enough inflow of capital to break the

vicious cycle of poverty (Rosenstein-Rodan 1943). However, previous literature has not

systematically analyzed the incidence of multiple dimension of poverty that can be

mutually reinforcing each other. In particular, the very poor are more likely to be trapped

in more than one of the resources. This paper contributes to the empirical analysis of

poverty traps by investigating the presence of one potential poverty trap to simultaneous

poverty trap. The paper will introduce the concept of illiteracy trap and under nutrition

trap along with asset poverty trap. The paper will try to explore what kinds of poverty

trap exist in Indian States and related policy implication will be proposed to overcome

such poverty trap.

Rest of the paper is organized as follows. Section 2 deals with the empirical literature

review on poverty trap. Section 3 builds the theoretical framework on poverty traps.

Section 4 introduces the data sources. Section 5 describes the methodology of our paper.

Section 6 presents the results. Finally Sect. 7 concludes the paper.

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2 Empirical Literature Review

Recent literature on poverty has tended to focus on defining poverty in the asset space and

prominent early asset-poverty measures were offered by Oliver and Shapiro (1990) and

Sherraden (1991). Asset-based approaches (ABA) have been developed to address the

causes and dynamics of longer-term persistent structural poverty primarily in rural Africa

and Asia (Giesbert and Schindler 2012; Carter and Barrett 2006; Francisca and David

2007). Carter and May (1999, 2001) identify the asset poverty line as an extension of

income poverty line. The authors argue that the chronically poor were characterized by a

structurally low asset base, and could only escape poverty temporarily due to some

‘positive shock’ or luck before relapsing. The transient poor were temporarily pushed

below the poverty line by negative shocks to their livelihoods. Empirical research on

multiple equilibria in income and asset poverty trap dynamics has begun recently with

contributions by Jalan and Ravallion (2002), Dercon (2004), Lokshin and Ravallion

(2004), Lybbert et al. (2004), Adato et al. (2006), Barrett et al. (2006), Naschold (2005,

2012), Campenhout and Dercon (2009). Both parametric and non-parametric estimation

methods have been used to estimate the poverty traps.

Jalan and Ravallion (2002) use a 6-year panel of income from four rural provinces of

China and present evidence of a geographic poverty trap, meaning that when the con-

sumptions of identical households living in a better-endowed area rise over time, house-

holds trapped in geographic poverty remain isolated from the ‘rising standard of living’.

Dercon (2004) uses six village data from the Ethiopia Rural Household Survey (ERHS)

from 1989 to 1997 to explore the impact of risk on consumption growth paths using a

linearized empirical growth model. He finds a persistence effect of famine and rainfall

shocks on consumption growth. In addition, road infrastructure is a source of divergence in

growth across villages and households. Lokshin and Ravallion (2004) examine the exis-

tence of poverty traps and distribution-dependent growth using a 4-year household panel

from Russia and 6-year household panel from Hungary. In order to resolve endogenous

attrition to shocks, they use a system estimator based on semi-parametric full information

maximum likelihood. They find evidence of concavity of income for both countries, but

they fail to find convincing evidence of a dynamic poverty trap.

Lybbert et al. (2004) use 17-year cattle herd histories in southern Ethiopia to study

stochastic wealth dynamics. The most important asset for households is a livestock in this

pastoral region. The data they use aggregate heterogeneous livestock into ‘‘Tropical

Livestock Units (TLU)’’. They estimate livestock dynamics using a Nadaraya-Watson

estimator.2 Due to the fact that it is a nonparametric local curvature, they can avoid a local

distortion that parametric regression might arise. A limitation, however, is that a local

constant estimator such as the Nadaraya-Watson estimator is known to suffer from

‘‘boundary bias’’. In addition, they don’t use an optimal bandwidth from a data driven

bandwidth selector such as likelihood cross-validation or plug-in method. Further, Lybbert

et al. allow one asset to explain pastoral welfare, but in asset dynamics literature it is

common to create an aggregate, one-dimensional measure that summarizes a household’s

total asset holdings. To aggregate a portfolio comprised of multiple assets, Adato et al.

(2006), Barrett et al.(2006), and Naschold (2012) estimate asset-based wellbeing indices by

either a regression of expenditure on the household’s productive assets or a factor analysis.

Based on the indices, they expect that households that suffer from income poverty tran-

sitions but not asset losses should not fall into poverty trap. Carter and Barrett (2006) argue

2 They have considered Epanechnikov kernel with arbitrary bandwidth.

Single or Multiple Poverty Trap

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that a dynamic asset poverty threshold should be identified to disaggregate the structurally

poor into those expected to escape poverty on their own over time. If the dynamic asset

poverty line, which is set at unstable dynamic asset equilibrium, is located far above the

level at which it is feasible or rational to accumulate sufficient assets, all the currently

structurally poor and a subset of the non-currently structurally poor would be expected to

gravitate to the low level equilibrium. Some of the studies have identified such a threshold.

Adato et al. (2006) find an evidence of asset poverty trap from KwaZulu-Natal Income

Dynamics Study (KIDS) in South Africa for 1993 and 1998 using bivariate locally

weighted polynomial regression methods (LOESS). Barrett et al. (2006) examine rural

Kenya and Madagascar to see if there is a poverty trap. They distinguish structural welfare

dynamics from stochastic welfare dynamics. They propose a procedure to remove the noise

due to stochastic component of income from total income and estimate both total income

dynamics and structural income dynamics regressions using bivariate quadratic LOESS

with an optimal, variable span based on cross-validation for each village. They find that the

estimated slope is negative from the regression of the total income change on initial

income for each village. However, from the estimated structural income dynamics, the

estimated line does not have a monotonically negative slope for each village. The

dynamics in all five villages have multiple equilibria. In addition, they find multiple

dynamic asset and structural income equilibria by estimating S-shaped curve using both

nonparametric and 4th degree polynomial function of parametric methods using herd size.

Naschold (2005) used the nonparametric and parametric techniques to identify asset

poverty dynamics and asset thresholds in Pakistan and Ethiopia. He found that households

in rural Pakistan and Ethiopia do not face asset poverty traps, but instead would be

expected to gravitate towards one long run equilibrium. By implication, no households

would suffer permanently from short term asset shocks, but would recover over time.

Campenhout and Dercon (2009) explore the existence of livestock asset poverty traps in

Ethiopia using the ERHS from round 1 to round 6. They use GMM estimation and

Threshold Auto-Regression model proposed by Hansen (1999, 2000). They find non-

linearity in dynamics of TLU and multiple equilibria of TLU. One of advantage of their

method is that it allows us to estimate the speed of convergence, which cannot be com-

puted using nonparametric methods. They find that convergence to the low level equi-

librium is almost twice as fast as convergence to the high level equilibrium. From the

literature review, it can be inferred that most of the countries are facing the mixed

experience in the existence of the poverty trap. Both single and multiple equilibrium were

found in terms of poverty dynamics. However the above studies only consider the one

dimensional poverty trap. In this paper we will introduce the concept of multidimensional

poverty trap.

Asset-based poverty evaluation is an important issue in the Indian context and little

rigorous work has been done using nationally representative household data. Research on

poverty in India based on consumption expenditure given by National sample Survey

organization. The survey is done in every 5 years and is a cross section survey and not a

longitudinal survey. Hence it is very difficult to identify the over time persistently poor

households and find out the answer as to how many of the currently poor will likely to

remain poor in the future. This paper will try to fill this gap. Earlier studies by Naschold

(2012) explores household asset poverty traps in rural semi-arid India using semi-para-

metric and nonparametric estimations, using a 27 year panel data set from the International

Crop Research Institute for the Semi-arid Tropics’ (ICRISAT) Village Level Studies

(VLS). He finds a single stable equilibrium in the VLS data rather than the multiple

equilibria. However, his study is based on only few village agricultural households.

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Therefore, these households would have faced the same kind of shocks. Further, VLS data

only contain few richer households since the VLS covers a poor rural population.

Therefore the analysis was biased towards the poorer households. However our study is a

bigger sample size of 13,000 rural households from all over India for a 10 years time span.

We are using household panel data. Time series data on assets will help us to distinguish

the households from deep-rooted persistent poverty from poverty that decreases over time,

based on the understanding of households continuous asset loss or asset accumulation

process. The objective of this study is to identify whether single or multidimensional

poverty trap exists in rural households in India.

3 Theoretical Framework

Many types of poverty traps are thought to exist. In literature, poverty traps has been

studied within consumption and asset space. In addition, poverty trap may occur in various

dimensions such as education, health, geography or any other dimensions of development

indicators. In this paper we will explore the poverty traps in health and education

dimension along with other productive asset dimension. In development economics liter-

ature poverty trap is considered as vicious circle. In this paper we examine poverty traps in

a multidimensional way, given that poverty has multiple aspects (Anand and Sen 1997;

Smith 2005; Alkire and Foster 2009). The following Fig. 1 is built to test the existence of

single or multidimensional poverty trap in the Indian rural households. The horizontal axis

measures the baseline period asset endowment and vertical axis measure the current period

asset endowment of the household.

If households have the livelihood function like aa0, it illustrates the S-shaped dynamics

of the model with two stable equilibrium (AL and AH) and there is an unstable equilibrium

in-between (which is Am in Fig. 1) a ‘‘threshold at which accumulation dynamics bifur-

cate’’ (Carter and Barrett 2006: 190), also referred to as the Micawber threshold (Lipton

1994) or the dynamic asset poverty line in the literature. A household above this threshold

is predicted to accumulate assets over time as more profitable activities and investments

become accessible to the household. Eventually, the household reaches the stable upper

asset equilibrium (AH) and moves out of poverty. In contrast, households below the

threshold (AL) are too poor to accumulate assets. If they also lack the opportunity to

borrow, those households are trapped at low welfare levels. Hence, the dynamic asset-

based approach identifies not only who is poor at a given moment in time, but also allows

forward-looking projections of what types of households lack the productive assets to

escape poverty in the future. Households whose assets place them above the micawber

threshold would be expected to escape poverty over time, while those below would not.

One needs to identify this dynamic asset poverty threshold in order to disaggregate the

structurally poor into those expected to escape poverty on their own overtime through over

time asset accumulation and those expected to be trapped in poverty indefinitely. The

empirical challenge is to find whether such threshold exists, if so where.

By extending the above logic of multiple equilibrium, we can also explain the situation

of single equilibrium framework. For example, households can have the livelihood func-

tion with a one stable equilibrium. For example, if the household livelihood function is cc0

in that case there is no poverty trap and households converge to the equilibrium point A2

which is above the poverty line. In contrast, if household livelihood functions is like bb0,would be expected to reach at point A1, a single steady-state level of equilibrium located

below the poverty line. However, single low equilibrium in more than one asset or welfare


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indicators may predict more persistence causes of poverty trap. Low level of equilibrium in

more than one dimension implies greater difficulty in moving out of poverty. In particular,

the very poor are more likely to be trapped in more than one of the resources. If house-

holds’ income only fulfills their poverty level of consumption, the households are much

likely to be trapped in income and resources, since the households can’t save to invest for

future resources. Naturally, such multidimensional poverty may result in a stagnant low-

level equilibrium in the income and asset dynamics. In this regard, we expand our one

dimensional simple model of income dynamics to a multidimensional model.

4 Data

The paper uses the secondary information mainly from the India Human Development

Survey (IHDS)3 for the year 1994 and 2005. Our analysis is based on the 14 major States in

India which consists of 13,000 common rural households in both the periods. The assets

used in this paper fall into household durable assets, human assets, natural assets.

Household durable assets or productive assets consist of tractor, sewing machine, and

vehicle. Natural assets include land and livestock. Livestock variables are categorized

under dairy, draft animals and others. Household level human capital is measured by

education year of household head. The squared terms of the several variables are included

in order to incorporate the potential diminishing returns on assets. Additionally, the paper

is also considers the household total income which includes family farm income, agri-

cultural wage, non agricultural wage, salaries and net-business income and government

benefits. Also the income variable is needed to be scaled in adult equivalence terms to

3 IHDS is conducted by National Council for Applied Economic Research (NCAER), a well-known appliedeconomics research institution in ‘‘India’’. It is a nationally representative, multi-topic survey across India.Survey included on health, education, employment, economic status, marriage, fertility, gender relations,and social capital. IHDS was jointly organized by researchers from the University of Maryland and theNational Council of Applied Economic Research (NCAER), New Delhi. Various authors have used thesame data for various purposes (Zimmermann 2012; Singh 2011; Pou and Goli 2013, etc.).

Fig. 1 Long-term asset dynamics of the households (Carter and Barrett 2006)

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capture the economics of scale among the households (Srivastava and Mohanty 2011).

Following Atkinson et al. (1995) the paper has used OECD modified scale for calculating

the per capita adult equivalence income. The official poverty line for 1993–1994 and

2004–2005 is taken from planning commission report (2009). Further, for calculating

poverty trap in illiteracy, the paper includes year of education of the family member and

school enrolment of the school age child. Years of schooling acts as a proxy for the level of

knowledge and understanding of household members. Note that both years of schooling

and school attendance are imperfect proxies. They do not capture the quality of schooling,

the level of knowledge attained or skills. Yet both are robust indicators, are widely

available and provide the closest feasible approximation to levels of education for

household member. In terms of deprivation cut-offs for this dimension, the MPI requires

that at least one person in the household has completed 5 years of schooling and that all

children of school age are attending grades 1–8 of school. In addition, the paper has used

z-score of BMI-for age to generate under-nutrition trap. BMI indicates the chronic energy

deficiency or obesity of a family member.

5 Methodology

5.1 A Livelihood-Weighted Asset Index

This study uses Adato et al. (2006) by indexing household livelihood List as household

income divided by the money value of the household’s subsistence needs. In this case study

has used the state specific official poverty line given by the planning commission of India.

The coefficients of the regression give the marginal contribution to livelihood of the j

different assets. Households hold various assets. The asset index is estimated using the key

asset variables including human capital, natural capital, productive capital etc. The

regression also includes household characteristics variables like gender, age of the

household head, cast. To control for the location and time specific effect, village and time

dummies are included.

List ¼ aþX

j

bjðAijstÞ þX

j

X

k

bjkðAijstÞðAikstÞ

þ ciHit þ diVis þ DT þ Ui þ eits where Ui�N 0;r2u

� � ð1Þ

List is the poverty line adjusted income of the household i in the state s at time period t. In

other words, if List = 1 then household is exactly on the poverty line, List \ 1 indicates

household is poor and List [ 1 means the household is non-poor. Aijst indicates ownership

of asset j for household i for state s and time period t. Hence bj is the coefficient of asset j

owned by household i in state s. Since returns from an asset also depend on the interaction

between assets, there is an asset interaction term included in the model. It also takes into

account the rate at which returns from the assets are derived. Aikst referrers to ownership of

asset k for household i for state s and time period t. Therefore bjk is the coefficient of asset

interaction term. Hi includes the household specific demographic characteristics. Vi

includes village specific infrastructure variables. T is the time dummy. The above equation

is estimated through a panel regression framework. Ui indicates the household specific

random effect term and e is random error term. a is the intercept term. D is the coefficient

of time dummy and d is the coefficient of village specific variables.


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The asset index is given by the estimated value of the Eq. (1) after controlling for the

household demographic factor.

Kist ¼X

bj Aijst

� �þX

j

X

k

bjk Aijst

� �Aikstð Þ þ ciHit þ diVi þ dT þ Ui ð2Þ

Here asset index (Kist) is expressed in poverty line units (PLUs). It represents the asset

index for household i for state s in time period t. Hence an asset index below 1 indicates

households which have asset below the poverty line, and an asset index above 1 indicates

non-poor households.

5.2 Analysis of Asset Dynamics

Consumption has been the main wellbeing measure in the previous literature. Barrett et al.

(2006) argue that analysis of solely income hinders us from identifying chronic poverty

because consumption/income addresses stochastic part of income. In order to analyze

chronic poverty and poverty traps, they suggest using the structural part of income (i.e.,

assets).4 In addition, using an asset index helps reduce the multiple dimensions of assets to

a single dimension, which can avoid the ‘‘curse of dimensionality’’ problem in non-

parametric estimation. Using the estimated asset index for 1994 and 2005, the paper will

now explore the pattern of asset dynamics in Indian rural states. The relation between the

current and baseline asset level will be estimated using nonparametric technique.

Ait ¼ f Ait�1ð Þ þ ei ei� iid N 0;r2e

� �ð3Þ

where A represents the asset index of household i, t stands for the current period

(2004–2005) and (t - 1) for the baseline period (1994–1995) and the error term e is

assumed to be normally and identically distributed with zero mean and constant variance.

More specifically, a local polynomial regression with Epanechnikov kernel weights is

used for estimating Eq. 3. At each value of Ait-1, a fitted value is estimated by running a

regression in a local neighborhood of Ai,t-1 using weighted least squares. The neighbor-

hoods are defined as a proportion of the total number of observations. The weight is large if

Ait-1 is close to the fitted value and small if it is not. Therefore the points close to Ait-1

play a large role in determination of the fitted value of Ait while the ones further away play

a smaller role. n weighted local regression would be estimated at each value of Ait-1 in

order to find the smoothed value of Ait.

Finally the estimated asset recursion function (3) will be represented graphically to test

for the existence of a poverty trap.

5.3 Analysis of Multidimensional Poverty Trap

For analyzing the multi dimensional poverty trap we use illiteracy trap and under nutrition

trap. Illiteracy trap implies lack of basic education or skills may lead to low income, and

hence neither the financial nor the knowledge resources needed to acquire better education.

Further, under-nutrition trap means poor nutrition leads to low productivity which leads to

4 Barrett et al. (2006) mentioned three reasons why asset dynamics is better than income dynamics forpoverty analyses. Firstly, Income components are stochastic in nature; hence household may be poor in oneperiod and better off in the next period and vice versa because of stochastic factor such as good luck orreceiving a lucky gift. Secondly, stochastic incomes are likely to exaggerate income inequality in crosssectional analysis and thirdly it generates spurious economic mobility in longitudinal analysis.

S. Dutta

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low income available to improve nutrition. If two traps are present simultaneously, we term

this multidimensional poverty trap. In this paper we have defined illiteracy trap as if year of

education of the family member is\5 years in both the years and if one of the child in the

age of attending school are not enrolled in school in both the periods. For under-nutrition

trap we have used z-score for BMI-for -age and is calculated as

z-score BMI/Ageð Þ ¼BMIijk �MedianBMI

reference populationjk

SDreference populationjk

ð4Þ

where BMIijk represents the BMI of an individual i whose age is k and whose gender

(male/female) is j. We define the under-nutrition trapped household if any members of the

households have BMI-for-age z score less than -25 throughout the periods.

Depending on the status of the poverty traps, households have a difference in the asset

index levels. We use simple t test and Epps–Singleton test for both trapped groups.

To evaluate the impact of various poverty trap (illiteracy and under-nutrition) on asset

index we have used mixed method of semi parametric model (partially linear regression

model).

Ait ¼ b0 þ Ui þ oT þ f Ait�1ð Þ þ Xaþ b1P1i þ b2P2i þ eit

Ui� iid N 0;r2u

� �and ei� iid N 0;r2

e

� � ð5Þ

where Ui is a random household effect, Pji, j = 1; 2 represents the illiteracy trap status and

under-nutrition trap status, respectively, T represent time dummies taking account of time

specific effect and X includes gender of head, age of head, number of dependent person.

6 Results

6.1 Livelihood Asset Index

Equation 1 is estimated through panel livelihood regression to construct the asset index

given by Eq. 2. As seen in Appendix Table 3, the F statistics indicates the joint signifi-

cance of the explanatory variables and all estimated coefficients that are individually

statistically significant. Among the various assets, as expected land size has a positive

significant impact on the household livelihood with a coefficient of 0.03 in pooled OLS

(POLS) and 0.05 in random effect model (REM) respectively. It indicates that as house-

hold’s land size increases it has a significant positive impact on the household income

relative to poverty line income. This implies an increase in land size has a direct positive

increase in the household income. However there seems to be diminishing returns to land

when size of the land increases.

The regression results indicate the importance of livestock for maintaining the house-

hold livelihood. As expected livestock also has a positive significant impact on livelihood.

For example, livestock such as sheep and goat generally perceived as easily disposable

assets were found to be positively associated with household livelihood (Liverpool and

Nelson 2010). A REM shows that a 1 % increase in livestock corresponds to a 0.17 %

increase in income relative to the poverty line. It is also noticed that livestock follows an

expected pattern of diminishing marginal returns with significant and negative coefficients

5 It is a common cut-off to identify abnormal anthropometry (WHO 1995).


123


on the squared terms. Similar to livestock, draft animals and poultry are also considered as

disposable assets, and have positive impact on the household livelihood. It is also noticed

that draft and poultry also follows diminishing marginal returns with significant and

negative coefficients on the squared terms. One of the reasons for the diminishing returns

for both livestock as well as draft animals could be the high maintenance cost associated

with larger numbers of the same (Lybbert et al. 2004). The interesting observation is that

an additional increase in land size yields a higher increase in income for household having

draft animals or tractor/thresher. In other words, there is a positive interaction effect

between land size and draft animals, land size and tractor/thresher. The paper has found

that year of education of male and female has a positive significant impact on household

income relative to poverty line income.

We found that sex of the household head is insignificant in household livelihood pro-

cess. The negative and significant coefficient on the number of dependent people in the

household indicates widespread unemployment which reduced the per capita availability of

income. Household belonging to schedule caste, schedule tribe and other backward caste

has very limited assets (Sundaram and Tendulkar 2003; Meenakshi et al. 2000). Therefore

household belonging to these caste have negative and significant impact on the household

livelihood process.

The paper also includes the village level variables to control the impact of village

infrastructure on the livelihood pattern of the household (Naschold 2012). The paper finds

that distance of the village from the nearest town, distance of district or primary health care

centre, distance of primary school and distance of post office has negative impact on the

household livelihood pattern. For example, if the village is nearer to the town then the

household has more opportunity and will increase their income level. It is also noticed that

after controlling the village level variables, significance of the other variables also

increases.

Multiple specification tests have applied to identify the consistent and efficient estimator

for this study. With a Chi squared statistic of 435.81 (p value = 0.00), we reject the

Breusch and Pagan Lagrange multiplier test for random effects (RE), which indicates that

the variance of the error term is not zero and the RE model is superior to the POLS. With a

Chi squared statistic of 3.85 (p value = 0.45), the Hausman test also indicates that REM is

more efficient than fixed effect model. Therefore REM will be using for further analysis.

Table 1 reports the descriptive statistics of the asset index values. It is seen that the

mean asset index value has increased in 2004–2005 compared to 1994–1995. There is also

increase in the maximum value of asset index in 2004–2005 compared to 1993–1994.

However it is also noticed that there seems to be increase in asset inequality shown by the

increase in standard deviation.

6.2 Analysis of Asset Dynamics

An estimation of the non-parametric bivariate relation between the asset index in the

current and baseline period is displayed in Figs. 2, 3, 4 and 5. The 45� line represents the

perfectly linear relationship between asset endowments in each period as a reference

points. Both axes are scaled in income PLUs. There are no multiple dynamic equilibrium

points. Across all the States we find only single dynamic asset equilibrium for rural

households. However the nature of the asset dynamics varies from one state to another. For

example; Bihar, Rajasthan, Uttar Pradesh, Assam, and Orissa have experienced asset loss

over a period of time. The major reason for asset loss are due to health related factors or

death of the bread earners. It is seen that on an average heath expenditure is high in these

S. Dutta

123


households and some of the bread earners lose their job because of poor health. Asset

loosing households generally have more number of dependent or unemployed family

members. Child dropout rate is also high in these households. Most of the children are

forced to join in the labor force to support their family which actually has a negative

Table 1 Descriptive statistics

Mean SD Maximum Minimum

Asset index 2005 -0.02 1.81 4.06 -1.53

Asset index 1994 -0.30 1.02 2.01 -2.12

-.5

0.5

11.

52

AI_

2005

-2 -1 0 1 2 3

AI_1994

-.5

0.5

11.

52

AI_

2005

-1 -.5 0 .5 1 1.5

AI_1994

-.5

0.5

11.

52

AI_

2005

-1 -.5 0 .5 1

AI_1994

-1 -.5 0 .5 1AI_1994

-.5

0.5

11.

52

AI_

2005

-2 -1 0 1 2 3

AI_1994

-.5

0.5

11.

52

AI_

2005

A B

C

E

D

Fig. 2 Asset losing states. a Asset dynamics_Bihar, b asset dynamics_Uttar Pradesh, c asset dynam-ics_Rajasthan, d asset dynamics_Orissa, e asset dynamics_Assam


123


impact on the future human capital formation at the household level. Over time due to low

education level and low skill level these children do not get an opportunity to get formal

employment. Furthermore, the asset loosing households normally depend on a single

source of income mainly from agricultural wage which tends to be seasonal and poor.

Therefore diversification of income source is also not present in these households. These

households usually have a very small amount of land and small number of livestock. In any

critical situation in their life they end up selling their land or livestock and in the long term

they are in no position to build assets because of various constraints like low skill, large

amount of dependent person and/or sudden job loss.

On the contrary Kerala, Himachal Pradesh and Punjab can be termed as asset accu-

mulating states. Asset accumulating households have various skills to build their assets.

These households have multiple sources of income from various homemade enterprises

and formal sector jobs. These households are able to invest more on livestock business or

can diversify their products like shifting the production to new animals, e.g. shifting from

goats to dairy cows etc. Similarly they have large cultivable land size which they use for

producing different crops instead of a single crop such as paddy. Therefore crop diversi-

fication is also a common characteristic of these households.

Moreover, states like West Bengal, Madhya Pradesh, and Andhra Pradesh are mostly in

a stagnant condition. The characteristics of the asset immobile states are almost similar

with asset loosing states. These households are engaged in low paid or low skill job like

-2-1

01

23

AI_

2005

-.5 0 .5 1 1.5 2

AI_1994

-1-.

50

.51

1.5

AI_

2005

-.5 0 .5 1 1.5 2

AI_1994

-10

12

3

AI_

2005

-.5 0 .5 1 1.5 2

AI_1994

A B

C

Fig. 3 Asset accumulating states. a Asset dynamics_Kerala, b asset dynamics_Punjab, c asset dynam-ics_Himachal Pradesh

S. Dutta

123


causal labor or seasonal labour. Their income stream is very fluctuating and hence they

cannot expect to invest on new assets. They can only manage their day to day food with

their present income stream.

More strikingly, Gujarat, Maharashtra, Haryana and Tamil Nadu have a mixed expe-

rienced in that they are both asset loosing as well as asset accumulating. These households

have common characteristics of both asset loosing and asset accumulating and hence seem

in a process of transformation.

6.3 Analysis of Multiple Poverty Traps

Depending on the status of the poverty traps, households have a difference in asset index

levels. We use t test and Epps–Singleton test for whether both trapped groups have the same

mean and the same distribution compared with non-trapped group, respectively. We found

that except Gujarat, in all other States, household in the illiteracy trap has lower asset index

compared to non-trapped household. However we note that the distribution of asset index in

Gujarat significantly depends on the illiteracy trap status at any conventional level. Moreover

a household in an under-nutrition trap has the lower asset index regardless of the States. In

addition, we find that the illiteracy and under-nutrition traps are positively correlated.6

Table 2 shows the average asset index value according to the illiteracy and under-nutrition

6 By Chi square test we get, v12 = 20.01 and p = 0.00.

-.5

0.5

1

AI_

2005

-.2 0 .2 .4 .6 .8

AI_1994

-.5

0.5

1

AI_

2005

-.5 0 .5 1 1.5

AI_1994

-.2

0.2

.4.6

.8

AI_

2005

-.5 0 .5 1 1.5

AI_1994

-10

12

AI_

2005

-1 -.5 0 .5 1 1.5

AI_1994

A B

C D

Fig. 4 Mixed experienced states. a Asset dynamics_Maharashtra, b asset dynamics_Gujarat, c assetdynamics_Tamil Nadu, d asset dynamics_Haryana


123


trap status. Trapped households have lower asset index than non-trapped households. In

particular, comparing the difference of the average asset index between the illiteracy trapped

group and the non-illiteracy trapped group across States, we found that the difference is

largest in Madhya Pradesh and the lowest is in Gujarat. This finding implies that an illiteracy

trap affects the asset level heterogeneously over the income and regional distribution.

However, comparing the difference of the average asset index between the under-nutrition

trapped group and the non-under nutrition trapped group across states, is less. This implies

that an under nutrition trap affects the asset level uniformly. In addition, comparing the

proportion of households under each poverty trap, we find that Kerala has the lowest pro-

portion of households in each trap and Bihar has the largest proportion in each trap.

In the previous section we find that the asset dynamics differ across states. In this

section we will test the impact of illiteracy trap and under nutrition trap on poverty

dynamics.

6.4 Analyzing the Impact of Poverty Trap Status on Asset Dynamics

We find under-nutrition trap significantly affect the asset accumulation in the relatively

poorer states at 1 % level (Appendix Table 4). For example, Bihar, Madhya Pradesh,

Orissa, Uttar Pradesh has experienced the highest impact of under nutrition trap compared

with other states. In addition, we find evidence that illiteracy trap status only affects the

asset accumulation of the most deprived area, but not that of relatively rich regions. For

example, households residing in relatively rich states may have some assets they can use as

collateral, when they want to invest education even though they don’t have enough current

-.5

0.5

11.

52

AI_

2005

-.5 0 .5 1 1.5 2

AI-1994

-.5

0.5

11.

5

AI_

2005

-.5 0 .5 1 1.5

AI_1994

-2-1

01

23

AI_

2005

-2 -1 0 1 2 3

AI_1994

A B

C

Fig. 5 Asset immobile states. a Asset dynamics_Andhra Pradesh, b asset dynamics_Madhya Pradesh,c asset dynamics_West Bengal

S. Dutta

123


Ta

ble

2A

sset

ind

exw

ith

the

stat

us

of

po

ver

tytr

aps

Un

der

nu

trit

ion

trap

Illi

tera

cytr

ap

No

ntr

app

edT

rap

ped

t-v

alu

es*

Ep

ps–

Sin

gle

ton

test

**

P(T

\t)

No

ntr

app

edT

rap

ped

t-v

alu

esE

pp

s–S

ingle

ton

test

P(T

\t)

All

Ind

ia3

.05

(59

%)

-0

.89

(41

%)

6.1

2(0

.00

)0

.00

3.1

4(6

1%

)-

0.9

1(3

9%

)5

.02

(0.0

0)

0.0

0

An

dh

raP

rad

esh

2.1

4(6

3%

)1

.41

(37

%)

2.1

3(0

.00

)0

.00

12

2.0

4(6

1%

)1

.03

(39

%)

3.1

3(0

.00

)0

.000

2

Ass

am1

.81

(59

%)

1.0

5(4

1%

)2

.09

(0.0

0)

0.0

00

31

.76

(56

%)

-0

.90

(44

%)

2.1

9(0

.00

)0

.01

Bih

ar1

.09

(42

%)

0.3

6(5

8%

)2

.13

(0.0

0)

0.0

06

1.9

(45

%)

0.2

0(5

5%

)3

.13

(0.0

0)

0.0

16

Gu

jara

t2

.24

(54

%)

1.9

1(4

6%

)3

.01

(0.0

0)

0.0

12

.22

(49

%)

1.2

1(5

1%

)0

.41

(0.3

5)

0.0

04

Har

yan

a2

.01

(61

%)

1.1

03

(39

%)

5.1

2(0

.00

)0

.00

82

.22

(58

%)

1.2

1(4

2%

)2

.12

(0.0

0)

0.0

10

Him

ach

alP

rad

esh

2.0

5(5

7%

)1

.09

(43

%)

2.0

9(0

.00

)0

.01

2.1

2(6

3%

)1

.09

(37

%)

4.0

9(0

.00

)0

.001

Ker

ala

3.0

1(7

8%

)2

.29

(22

%)

2.1

4(0

.00

)0

.00

23

.21

(90

%)

2.0

9(1

0%

)3

.14

(0.0

0)

0.0

11

Mad

hy

aP

rad

esh

1.9

0(3

9%

)0

.99

(61

%)

2.5

6(0

.00

)0

.03

1.8

7(4

3%

)-

0.8

0(5

7%

)2

.76

(0.0

0)

0.0

00

3

Mah

aras

htr

a2

.10

(65

%)

1.9

7(3

5%

)2

.45

(0.0

0)

0.0

01

2.1

2(6

8%

)1

.02

(32

%)

2.9

5(0

.00

)0

.00

Ori

ssa

1.0

6(5

3%

)0

.89

(47

%)

3.2

3(0

.00

)0

.03

1.1

3(5

7%

)0

.91

(43

%)

3.1

3(0

.00

)0

.001

Pu

nja

b2

.98

(74

%)

2.3

4(2

6%

)3

.12

(0.0

0)

0.0

12

.45

(78

%)

1.0

2(2

2%

)2

.12

(0.0

0)

0.0

00

2

Tam

ilN

adu

2.6

7(6

9%

)1

.89

(31

%)

3.0

9(0

.00

)0

.02

2.8

7(7

3%

)1

.09

(27

%)

3.1

9(0

.00

)0

.01


123


Ta

ble

2co

nti

nued

Un

der

nu

trit

ion

trap

Illi

tera

cytr

ap

No

ntr

app

edT

rap

ped

t-v

alu

es*

Ep

ps–

Sin

gle

ton

test

**

P(T

\t)

No

ntr

app

edT

rap

ped

t-v

alu

esE

pp

s–S

ingle

ton

test

P(T

\t)

Raj

asth

an1

.78

(60

%)

1.0

5(4

0%

)2

.45

(0.0

0)

0.0

11

.60

(62

%)

0.5

6(3

8%

)2

.35

(0.0

0)

0.0

2

Utt

arP

rad

esh

1.0

1(5

3%

)0

.34

(47

%)

2.6

7(0

.00

)0

.04

1.3

4(5

3%

)-

0.4

5(4

7%

)2

.77

(0.0

0)

0.0

1

Wes

tB

engal

2.4

5(5

8%

)2

.09

(42

%)

2.4

0(0

.00

)0

.01

2.2

3(5

5%

)1

.12

(45

%)

2.5

0(0

.00

)0

.01

Val

ues

inth

ep

aren

thes

isar

eth

ep

rop

ort

ion

of

each

po

ver

tytr

apst

atu

s

*T

he

nu

llh

yp

oth

esis

isth

atth

em

ean

of

no

n-t

rap

ped

asse

tin

dex

iseq

ual

toth

ato

fth

etr

app

ed.

pv

alu

esin

par

enth

esis

are

for

the

on

e-si

ded

test

**

Itte

sts

wh

eth

erth

ed

istr

ibu

tio

nfu

nct

ions

com

ing

from

two

ind

epen

den

tsa

mp

les

are

iden

tica

l

S. Dutta

123


income. However, lower income households in the most deprived states lack assets to use

as collateral, are likely credit constrained, and are more likely to fall into poverty traps.

This may explain why the households in the Bihar, Orissa, Uttar Pradesh, Madhya Pradesh

experience reduced asset accumulation in the presence of an illiteracy trap. Given the

significance of an illiteracy trap and an under-nutrition trap in these states, we infer that the

households in this area sufferer from multidimensional poverty traps.

Comparing the marginal effects of under-nutrition trap and illiteracy trap status, we

found that Bihar has the highest under nutrition trap and Madhya Pradesh has the highest

illiteracy trap. In case of Rajasthan, we found that though illiteracy trap is not significant,

however interaction between under-nutrition trap and illiteracy trap has significant impact

on asset loosing in this state.

7 Conclusion

This paper has examined the household asset dynamics in India as well as Indian rural States.

Naschold (2012) found that no evidence of poverty trap in Indian sample village. However

his study was based on single asset index. In this paper we have used multidimensional

poverty trap framework. The study has used nonparametric regression model to test the

existence of poverty traps in Indian rural states. It is noticed that some of the states like

Rajasthan, Bihar, Uttar Pradesh and Assam continuously losing their assets which was

reflected in sever chronic poverty or high structurally downward mobility. On the other hand,

Kerala, Himachal Pradesh and Punjab have experienced a high asset accumulation because

of that these States are experienced in structurally upward mobility. Maharashtra, Gujarat,

Haryana and Tamil Nadu have experienced both asset loosing and asset accumulation

therefore in these states both structurally downward mobility and structural upward mobility

is high. Moreover, West Bengal, Madhya Pradesh and Andhra Pradesh are almost stagnant in

this 10 years time period. There is no asset dynamics took place. Therefore these states have

the same economic condition in these two periods. From our analysis it is inferred that, in

most of the states, asset accumulation does not take place and welfare dynamics is very poor

in rural areas. As a result households remain at their initial level of the wellbeing. This is

particularly alarming for the large number of households with initial assets holdings below

the poverty line. In addition we have tested the existence of single and multiple poverty traps.

In addition to asset poverty traps, we considered under- nutrition traps as proxies by con-

tinued low BMI-for-age z scores, and illiteracy trap as proxies by continued illiteracy. We

further examined the possibility that there are interlocking traps, in the sense that low levels

of health and education have a negative interaction effect on assets. We find this effect in the

most deprived states (Bihar, Uttar Pradesh, Orissa and Madhya Pradesh). In these states both

illiteracy and under nutrition significantly reduce assets creation; and there is a statistically

significant negative interaction effect of illiteracy and under nutrition on assets. In other

regions a trap has a negative effect on assets, but there is no significant interaction effect. Our

result implies that asset dynamics of the household varies in the long term according to types

of traps. Government and policy makers should take pointed policy and programme based on

whether the poor are trapped and in what ways.

Appendix

See Tables 3 and 4.


123


Ta

ble

3L

ivel

ihood

regre

ssio

nto

der

ive

asse

tin

dex

(pan

eldat

am

odel

)

PO

LS

Fix

edef

fect

mod

elR

and

om

effe

ctm

od

el(1

)R

ando

mef

fect

mod

el(2

)

Co

nst

ant

0.3

5(1

.98

)0

.91

(1.6

7)

0.1

2(2

.01

)0

.45

(1.9

8)

Dep

end

ent

var

iab

le:

adu

lteq

uiv

alen

tin

com

e/p

over

tyli

ne

inco

me

House

hold

char

acte

rist

ics

Hea

d’s

age

0.2

1(2

.01

)0

.07

(1.9

8)

0.1

1(2

.02

)0

.16

(2.0

1)

Ag

e2-

0.0

9(-

2.0

2)

-0

.01

(-1

.99)

-0

.13

(-2

.08)

-0

.24

(-2

.11)

Hea

dm

ale

0.0

8(1

.09

)–

0.0

9(1

.08

)0

.01

(1.0

2)

Sch

edu

leca

st-

0.0

3(-

2.0

1)

–-

0.0

8(-

2.0

9)

-0

.13

(-2

.08)

Sch

edu

letr

ibe

-0

.05

(-2

.02)

–-

0.0

6(-

2.1

1)

0.0

9(1

.08

)

OB

C-

0.0

7(-

1.9

9)

–-

0.0

7(-

2.0

1)

-0

.08

(-2

.09)

Pro

po

rtio

no

fd

epen

den

tp

erso

n-

0.1

2(-

2.0

2)

-0

.10

(-1

.89)

-0

.11

(-2

.04)

-0

.06

(-2

.11)

House

hold

educa

tion

Max

imu

my

ear

of

edu

cati

on

mal

e0

.12

(2.6

7)

0.2

1(2

.01

)0

.27

(2.8

7)

-0

.07

(-2

.01)

Max

imu

my

ear

of

edu

cati

on

fem

ale

0.2

5(2

.06

)0

.21

(1.9

8)

0.3

2(2

.10

)-

0.1

1(-

2.0

4)

Ho

use

ho

ldla

nd

and

liv

esto

ck

Lan

dsi

ze0

.03

(2.8

9)

0.0

1(1

.98

)0

.05

(2.9

0)

0.1

1(2

.09

)

Lan

dsi

zesq

uar

e-

0.0

1(-

2.0

8)

-0

.03

(-1

.98)

-0

.06

(-2

.01)

-0

.09

(-1

.90)

Po

ult

ryn

um

ber

s0

.1(1

.98

)0

.05

(1.9

0)

0.2

1(2

.01

)0

.05

(2.9

0)

Dra

ftan

imal

sn

um

ber

s0

.04

(2.0

9)

0.0

08

(1.7

8)

0.9

(2.1

0)

0.0

6(2

.01

)

Mil

chco

ws

nu

mb

ers

0.1

0(2

.01

)0

.05

(1.7

8)

0.1

1(2

.08

)0

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(2.0

1)

Oth

erli

ves

tock

nu

mb

ers

0.1

2(1

.98

)0

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(1.7

0)

0.1

7(1

.99

)0

.9(2

.10

)

Po

ult

ryn

um

ber

ssq

uar

e-

0.0

4(-

2.0

9)

-0

.000

2(-

1.7

8)

-0

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(-2

.10)

-0

.11

(-2

.08)

Dra

ftan

imal

sn

um

ber

ssq

uar

e-

0.0

7(-

2.1

0)

-0

.06

(-1

.79)

-0

.10

(-2

.11)

0.1

7(1

.99

)

Mil

chco

ws

nu

mb

ers

squ

are

-0

.08

(-1

.89)

-0

.007

(-1

.78)

-0

.09

(-1

.90)

-0

.07

(-2

.10)

Oth

erli

ves

tock

nu

mb

ers

squ

are

-0

.05

(-2

.01)

-0

08

(-1

.89)

-0

.10

(-2

.02)

-0

.10

(-2

.11)

Ho

use

ho

ldd

ura

ble

s

Tra

ctor/

thre

sher

0.1

3(2

.01)

0.0

8(1

.89)

0.1

3(2

.05)

0.0

9(-

1.9

0)

S. Dutta

123


Ta

ble

3co

nti

nued

PO

LS

Fix

edef

fect

mod

elR

and

om

effe

ctm

od

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)R

ando

mef

fect

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el(2

)

Tu

be

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l0

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(1.9

8)

0.0

2(1

.67

)0

.07

(1.9

9)

0.1

0(2

.02

)

Ele

ctri

city

0.1

3(3

.21

)0

.06

(2.0

5)

0.1

5(3

.22

)0

.13

(2.0

5)

Sew

ing

mac

hin

e0

.10

(2.0

9)

0.7

(2.0

1)

0.1

2(2

.11

)0

.07

(1.9

9)

Veh

icle

0.0

6(2

.11

)0

.03

(2.0

1)

0.0

8(2

.13

)0

.15

(3.2

2)

Ho

use

ho

ldem

plo

ym

ent

Dum

my

for

sala

ried

work

er0.0

2(2

.12)

0.0

7(1

.99)

0.1

3(2

.98)

0.1

4(2

.16)

Du

mm

yfo

rn

on

farm

lab

ou

r0

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(2.7

8)

0.0

7(2

.67

)0

.10

(2.7

6)

0.0

8(2

.13

)

Inte

ract

ion

term

Lan

dsi

ze9

dra

ftan

imal

s0

.04

(2.0

8)

0.0

2(1

.89

)0

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(2.1

1)

0.0

7(1

.78

)

Lan

dsi

ze9

trac

tor/

thre

sher

0.0

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5


123


Ta

ble

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trap

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India

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0

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-nutr

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62

0.0

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62

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1

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son

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2-

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20.0

0-

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20.0

0-

0.2

20.0

0-

0.2

12

0.0

0-

0.1

12

0.0

0

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edule

cast

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40.0

0-

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40.0

0-

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0.0

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0

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6.5

5

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4.0

5

p[

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5

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10.3

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20.4

10.3

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90.3

7

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0.0

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0.0

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0.0

16

0.0

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16

0.0

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tera

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09

0.3

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0.

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0.0

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0.0

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0.0

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0.1

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01

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0

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n

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lite

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19

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19

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2-

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19

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of

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S. Dutta

123


Ta

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port

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of

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per

son

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12

0.0

0-

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0.4

20.0

0-

0.3

12

0.0

0-

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12

0.0

0-

0.2

12

0.0

0

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edule

cast

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40.0

0-

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0-

0.1

40.0

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0.0

40.0

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0-

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0

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edule

trib

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0.1

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0-

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0-

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0

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C-

0.0

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0-

0.0

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0.1

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0-

0.2

10.0

0-

0.0

50.0

0-

0.0

40.0

0-

0.1

40.0

0

Sig

nifi

cance

test

of

F(A

it-

1)

V=

5.7

5

p[

|V|

=0.0

0

V=

4.5

5

p[

|V|

=0.0

0

V=

4.8

5

p[

|V|

=0.0

0

V=

4.7

5

p[

|V|

=0.0

0

V=

4.9

5

p[

|V|

=0.0

0

V=

5.1

5

p[

|V|

=0.0

0

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5.2

5

p[

|V|

=0.0

0

R2

0.4

00.5

20.3

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10.4

90.4

7

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est

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gal

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ent

pval

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pval

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ant

1.8

40.0

02.1

40.0

0

Under

-nutr

itio

ntr

ap-

0.2

62

0.0

0-

0.1

02

0.0

0

Illi

tera

cytr

ap-

0.4

50.0

0-

0.1

50.0

0

Under

-nutr

itio

n9

illi

tera

cy-

0.0

40.0

09

-0.0

40.1

19

Age

of

hea

d0.1

90.0

10.0

90.0

1

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10.1

-0.0

10.1

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der

of

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d0.0

03

0.1

04

0.1

13

0.1

04

Pro

port

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of

dep

enden

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son

-0.1

20.0

0-

0.2

20.0

0

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edule

cast

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40.0

0-

0.2

40.0

0

Sch

edule

trib

e-

0.4

00.0

0-

0.1

00.0

0

OB

C-

0.1

90.0

0-

0.1

00.0

0

Sig

nifi

cance

test

of

F(A

it-

1)

V=

5.2

5p

[|V

|=

0.0

0V

=4.9

5p[

|V|

=0.0

0

R2

0.5

20.4

4


123


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