THE DETERMINANTS OF MULTIDIMENSIONAL POVERTY OF THE TEA GARDEN LABOUR COMMUNITY OF DIBRUGARH DISTRICT OF ASSAM (INDIA)
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020 ISSN 2277-8616
THE DETERMINANTS OF MULTIDIMENSIONAL
POVERTY OF THE TEA GARDEN LABOUR
COMMUNITY OF DIBRUGARH DISTRICT OF
ASSAM (INDIA)
Nilakshi Gogoi
Abstract- The main objectives of this paper are to examine the multidimensional poverty situation and to identify the factors that influence the
multidimensional poverty status of the tea garden labour community of Dibrugarh district of Assam. The present study applied the Alkaire-Foster
multidimensional measures on household level primary data of 304 households belonging to the tea garden labour community in order to construct the
Multidimensional Poverty Index (MPI) at the household level. The present study used the Binomial Logistic Regression Model to identify the
determinants of multidimensional poverty status of the tea garden labour community. The value of MPI (0.210) indicates the situation of acute
multidimensional poverty of the tea garden labour community of Dibrugarh district with multidimensional poverty head count ratio of 54.93 percent. The
results of the Logistic Regression Model show that size of the household, gender of the household head, marital status of the household head, level of
education of the household head, employment status of the household head and the number of earning member(s) in the household are the significant
determinants of multidimensional poverty status of the tea garden labour community of Dibrugarh district.
Index Terms- Assam, Community, Determinants, Dibrugarh, Logistic Regression, Multidimensional Poverty, Tea Garden Labour.
————————————————————
The income or consumption measures of poverty assume that
1. INTRODUCTION markets exist for all goods and services but they ignore the
Poverty is one of the most widespread and persistent problem public goods which are not provided through market
for a large number of countries and poverty reduction has mechanism. b) The monetary measures of poverty ignore the
become the key development goal for many governments fact that people have different conversion factors to convert
around the world. The importance of the goal of combating the monetary resources into valuable functions. c) Another
poverty is also reflected in the Millennium Development Goals, limitation of this unidimensional measure is that the certain
as the first Millennium Development Goal is to “eradicate amount of monetary resources does not provide the
extreme poverty and hunger”. However, an agreement about guarantee that they are utilized on valuable goods and
what is the correct definition of poverty and how poverty is services. d) Since income or consumption data are collected
best measured is still missing. Traditionally poverty has been at the household level therefore they cannot provide any
defined and measured only in terms of financial resources information about intra household allocation ofresources. e)
(insufficient income or consumption)(e.g, World Bank 1990). A Income or consumption data are erroneous due to missing
person is considered to be poor if his income (or consumption) observations and misinterpretations. Due to the limitation of
falls below a fixed poverty line. This unidimensional measure traditional unidimensional measure in capturing multiple
of poverty enjoys the advantage of simplicity [19]. The implicit deprivations experienced by the poor, there has been a shift in
assumption of this unidimensional approach is that a person‟s research interest in recent times to understand poverty from
status in one dimension (e.g., income) strongly predicts their its multidimensional nature [22].This broad multidimensional
status in other dimensions [22]. But this may not be always view of poverty developed when Amartya Sen introduced the
considered as true. Moreover poverty which is an indication of Capability Approach. This approach has extended the
insufficient wellbeing depends not only on monetary variables analysis of poverty, inequality and wellbeing from
but also on non-monetary variables [11] and hence money consumption or income based unidimensional approach to the
alone cannot be used to measure poverty. Moreover the capability based multidimensional approach. The capability
recent developments in the literature of poverty measurement approach considers poverty not just as the shortfall of
have highlighted some of the serious limitations of this monetary resources; rather it considers poverty as the
unidimensional approach. Alkaire and Santos [4] highlighted deprivation of several fundamental freedoms that the
the following limitations of income or consumption as a individuals have reasons to value [2]. Assam is famous for its
measure of poverty. a) tea. The tea industry of Assam is more than 160 years old.
The tea cultivation in Assam was first started by the British
________________________ Govt. and it has continued till date with full glory. The tea
garden labour community is referred to as the multi ethnic
Nilakshi Gogoi, M.A., M.Phil in Economics, Department of groups of tea garden labourers in Assam. Their forefathers
Economics, Dibrugarh University, Dibrugarh, Assam, India, E-mail-
kunjaan500@gmail.com, Phn- 9957028969 were brought by the British East India Company as slaves
from tribal and backward class dominated states like
Jharkhand, Andhra Pradesh, Orissa, Chhattisgarh and West
Bengal during 1860s-90s to work in the tea gardens of Assam.
Their population is distributed in almost every district of the
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state but their density varies in accordance with the number of standard of living and these three dimensions are reflected by
tea gardens in different regions of Assam. They are mainly three indicators. Life expectancy at birth is used to reflect the
found in the districts of Dibrugarh, Tinsukia, Jorhat, Sivasagar, health dimension, gross enrolment ratio is used to reflect the
Golaghat, Sonitpur, Nagaon, Kokrajhar and Udalguri. They education dimension and Gross Domestic Product (GDP) per
are perhaps one of the most exploited and socio-economically capita is used as an indicator of standard of living dimension.
backward communities of Assam. The tea community people But one major limitation of HDI was its non responsiveness to
are solely dependent on the tea industry for their livelihood. the changing policy in short time. As a result United Nations
They reside in the labour lines built by the tea planters inside Development Programme (UNDP) created Human Poverty
the tea gardens. Most of the tea gardens are located in the Index (HPI) as an improvement over HDI. The HPI was
remote areas and this is one of the main reasons for their constructed with the objective of measuring deprivation in
backwardness and this also give chance to the tea planters to each dimension of human development. But both HDI and HPI
exploit them. They are paid very low wages and have to live can be constructed at region/country level only. In order to
with the limited facilities provided by the tea garden overcome this shortcoming UNDP introduced
management. They are living in extreme poverty. Moreover Multidimensional Poverty Index (MPI). MPI was constructed to
illiteracy, low standard of living, poor health facilities are the measure poverty both at the individual and at the household
main problems they are facing as a result of the exploitation level by using Alkaire-Foster methodology proposed by
by the tea planters. They are not only deprived in the financial Sabina Alkaire and James Foster in 2007. MPI is the
resources but they are also deprived in some basic composite index of three dimensions namely, health,
capabilities of life. Hence the analysis of their education and standard of living. Health dimension is reflected
multidimensional poverty situation and its determinants is very by nutrition and child mortality. Education dimension is
important from the view of policy formulation.The main reflected by years of schooling and child school enrolment and
objective of the present study is to identify the main factors standard of living dimension is reflected by six indicators
that influence the multidimensional poverty status of the tea namely, electricity, sanitation, drinking water, cooking fuel,
garden labour community of Dibrugarh district of Assam. floor and assets. Using the Alkaire-Foster methodology
several studies attempt to measure poverty from its
2. REVIEW OF LITERATURE multidimensional perspective (For example, [3], [17], [4], [5],
[14], [18], [21], etc.).The existing literature suggests a number
In the literature there are a number of studies that discuss the of variables that leads to multidimensional poverty at the
conceptual and methodological shortcomings of the monetary household level. They are size of the family, gender of the
measures of poverty and need an alternative approach to household head, age of the household head, marital status of
measure poverty from its multidimensional perspective. In this the household head, education of the head of the household
line the first attempt was made in 1990 by Mahbub Ul. Haq and the nature of employment of the household head etc. A
and Amartya Sen who constructed the Human Development summary of existing literature on the determinants of
Index (HDI) with the objective of measuring the level of human multidimensional poverty is shown with the help of the
development of the countries. HDI was constructed by following table-1.
aggregating three dimensions namely health, education and
Table-1: List of Some Existing Literature of the Determinants of Multidimensional Poverty
Study Author (s) Country Analytical Results
Year Technique(s)
2011 Ataguba et al. Nsukka, Probit The major significant variables of multidimensional poverty are
Nigeria Regression household size, employment status, education and health.
Model
2013 Adeoti Nigeria Logistic The factors which are positively related with the poverty status are
Regression female headed households, increases household size, working in
Model agriculture sector and residing in North-East, North-West and South-
South geopolitical zone. On the other hand working in non-
agriculture sector and services, having education and residing in
South-East and South-West geopolitical zones are negatively
associated with the poverty status.
2014 Artha and Indonesia Logistic The household characteristics which are found to be negatively
Dartanto Regression related with the probability of being poor are education of the
Model household head, living in the urban areas and the household size.
On the other hand the household characteristics which are positively
related with the probability of being poor are marital status of the
household head and the number of household members.
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2015 Alkaire et al. West Java, Logistic The increase in the years of education of the household head and
Indonesia Regression living in the urban areas reduces the probability of being poor. On
Model the other hand increase in the household size and the female
headed households increase the probability of being poor.
2017 Amao et al. Nigeria Logistic The increase in household size, female headed households, share of
Regression dependents on household head increases the probability of being
Model poor. While increase in the years of education, land ownership and
non-agricultural wages reduces the probability of being poor.
2018 Megbown South Africa Tobit For the urban areas the significant determinants of multidimensional
Regression poverty are education of the household head, access to electricity
Model and asset stock. While for the rural areas the gender of the
household head, education of the household head, access to
electricity, engaged in agriculture, monthly income, asset stock are
the significant determinants of multidimensional poverty.
3. DATA AND METHODOLOGY age, marital status, educational status, occupational status,
child school attendance details and the reason for school
3.1 Data Source dropout, child labour, body mass index (BMI) of the female
members of the household etc.
The data source of the present study is mainly primary data
collected through field survey. The population chosen for this 3.4 Methods of Analysis
study is the Dibrugarh district of Assam. It is selected because Different types of analytical techniques such Alkaire-Foster
Dibrugarh district has the largest number of tea gardens along method of multidimensional poverty and Binomial Logistic
with the highest concentration of tea garden population among Regression Model were used in the present study. In the
all the districts of the state. Dibrugarh district is famous as the present study multidimensional poverty status of the sample
„Tea City‟ of India. According to the Directorate of Welfare, households were measured by using Alkaire-Foster
Govt. of Assam, the Dibrugarh district has 177 tea gardens methodology as used in the UNDP‟s Human Development
out of 803 tea gardens in Assam. Because of these reasons Reports for measuring multidimensional poverty. In order to
the Dibrugarh district is taken as the area to conduct the measure the multidimensional poverty of the sample
present study. households, four dimensions were used and 12 indicators
were used to reflect these four dimensions. All these
3.2 Sampling Design
dimensions and indicators were chosen on the basis of the
A multistage random sampling method was used in order to existing literature; on the basis of some enduring consensus,
select the samples for the present study. There are seven such as related to human rights, Sustainable Development
community development blocks in the Dibrugarh district. Out Goals (SDGs) and Millennium Development Goals (MDGs).
of these seven community development blocks three blocks The four dimensions that are used in present study are
were selected randomly using computerised random numbers Education, Health, Work and Living Standard. For simplicity of
namely, Lahowal development block, Tengakhat development measurement equal weighting system was used in the present
block and Panitola development block. Then in the next stage study. Under this equal weighting scheme each dimension
of the sampling proceedure three tea gardens from each and each indicator under each dimension are also equally
development block were chosen randomly using computerised weighted. The dimensions and the indicators used in the
random numbers. Thus a total of nine tea gardens were present study to measure multidimensional poverty are shown
selected. In the last step 10 percent of the total households inthetable-2.
were selected randomly from each tea garden. Thus a total of
304 households were sampled.
3.3 Survey Module
The survey questionnaires for the present study are designed
in such a way to collect information on both economic and
non-economic characteristics of the tea community at two
levels that is both at the household level as well as at the
individual level. At household level information was gathered
on type of family, religion, consumption expenditure, type of
housing, type of toilet facilities, drinking water sources,
sources of lighting, sources of energy for cooking, asset
holding etc. On the other hand at the individual level
information was gathered on gender of the family members,
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Table-2: Dimensions, Indicators, Deprivation Cut-offs and Weights
Dimension Indicator Deprived if References of the existing Relative
literature for the choice of
indicators Weight
Years of If no household member has completed five years of UNDP‟s Human 0.125
Schooling schooling. Development Report
Education (2010)
School If any school aged child is not attending grades 1 to 8 of UNDP‟s Human 0.125
Attendance school. Development Report
(2010)
Child Mortality If any child (0-5 years) has died in the family in the five UNDP‟s Human 0.125
year period preceding the survey. Development Report
Health (2010)
BMI of Women If the BMI of any women (15-49 years) in the household Alkaire and Seth (2008), 0.125
2
is below normal (18.5 kg/m ). Naveed and Islam (2010),
Dehury and Mohanty
(2015), Deka (2018)
Occupational If any adult household member with age 15 years or above Alkaire and Seth (2008), 0.125
Status is either unemployed/ irregular wage labour/ agricultural Naveed and Islam (2010),
labour/ temporary tea garden labour. Alkaire and Seth (2013),
Deka (2018)
Work
Child Labour If the household has any incidence of child labour. Alkaire and Seth (2008), 0.125
(according to the definition of United Nations International Santos E.M (2014),
Children‟s Education Fund) Deka(2018)
Electricity If the household does not have electricity. UNDP‟s Human 0.042
Development Report
(2010)
Living Sanitation If the household‟s sanitation facility is not improved UNDP‟s Human 0.042
Standard (according to the MDG guidelines) or if the sanitation facility Development Report
is improved but shared with other households. (2010)
Water If the household does not have access to clean drinking UNDP‟s Human 0.042
water (according to the MDG guidelines) or clean water is Development Report
more than 30 minute walking from home. (2010)
Housing If the household lives in a kutcha house. Alkaire and Seth (2008), 0.042
Alkaire and Seth (2013),
Alkaire and Kumar (2012),
Deka (2018)
Cooking Fuel If the household cooks with dung/wood/charcoal. UNDP‟s Human 0.042
Development Report
(2010)
Assets If the household does not own more than one assets UNDP‟s Human 0.042
(Radio, TV, telephone/mobile phone, bike, motorbike or Development Report
refrigerator) and does not own a car or a truck. (2010)
Source: Normative choice by author on the basis of
existing literature
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The Binomial Logistic Regression Model was used to identify Status of HH 1=Unemployed
the factors that influence the multidimensional poverty of the
sample households belonging to the tea garden labour Number of Continuous Total number of earning
earning members aged 15 years
community. The binomial logistic regression model used in the member(s) in or above in the
present study can be expressed as, the household household
Pi Source: Normative choice by author on the basis of
Zi = ln ( ) = β0 + β1X1 + β2X2 + ............ + βn Xn+ εi existing literature
1 Pi
.........................(1) 4. RESULTS
4.1 Multidimensional Poverty Status of the Sample
In (1) Zi represents the multidimensional poverty status of the Households
„i‟th household and here Zi is a dummy variable; 1 if poor and
0 non-poor. Pi represents the probability that the household is Table-4 shows the results of the multidimensional poverty
multidimensionally poor and 1-Pi represents the probability measurement. From the table-4 it is seen that the
that the household is multidimensionally non-poor. β0 multidimensional poverty rate (or multidimensional head count
represents the constant term in the model. β1, β2,......., βn are ratio) of the sample tea garden labour community for the year
defined as the regression coefficients of the independent 2018 is 54.93 percent. The intensity of deprivation which is
variables X1, X2,...., Xn and εi is the stochastic error term defined as the share of deprivation each poor household
included in the model. experiences on average is 38.19 percent. Since MPI is the
product of the percentage of multidimensionally poor
Based on the existing literature the present study includes households (H) and their intensity of poverty (A) therefore the
size of household, gender of the household head (HH), age of value of Multidimensional Poverty Index (MPI) of the sample
the household head, marital status of the household head, tea garden labour community is 0.210. Thus 21.0 percent of
level of education of the household head, employment status the deprivations poor households‟ experience, as proportion of
of the household head and the number of earning member(s) possible deprivations that would be experienced if all
in the household as the probable determinants of the households were deprived in all dimensions.
multidimensional poverty of the sample households belonging
to tea garden labour community. The explanatory variables Table-4: Multidimensional Poverty of the Sample
used in the logistic regression model are shown in the table-3. Households
Multidimensional Index Value
Table-3: Explanatory variables used in the Logistic
Poverty Cut-off (k)
Regression Model
H (In Percentage) 54.93
Predictor Variable Type Definition
Variables k = 0.33 A (In Percentage) 38.19
Size of Continuous Total number of people MPI 0.210
Household in household
Source: Author’s Calculation
Gender of HH Dummy 0=Male, 1= Female
Age of HH Continuous Age of the head of the While discussing the results of multidimensional poverty it is
household (in years) important to analyze the relative contribution of each indicator
to the MPI. The following table-5 shows the relative
Marital Status of Categorical 1= Never married contribution of the 12 indicators to the MPI of the tea garden
HH labour community of Dibrugarh district. From table-5 we can
2=Divorced/Separated see that the highest contribution to the MPI is from the
3= Widow
indicator occupational status with 26.43 percent followed by
the indicator years of schooling with 19.19 percent and BMI
4= Married (women) with 10.18 percent. On the other hand the lowest
contribution to the MPI is from the indicator water with 0.20
Level of Categorical 1=No education percent followed by child mortality with 0.59 percent.
education of HH
2=Primary
3= Secondary
4=Matriculation or Above
Employment Dummy 0= Employed Table-5: Relative Contribution of Indicators to MPI
(with k = 0.33)
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Indicator Contribution to MPI Employment Status of HH
Years of Schooling 19.19 Unemployed®
School Attendance 2.93 Employed -2.079(*) 0.125
Child Mortality 0.59 Number of Earning Member -0.284(*) 0.752
(s) in household
BMI of Women 10.18
Occupational Status 26.43
Cox & Snell R Square = 0.121
Child Labour 2.93
Nagelkerke R Square = 0.162
Electricity 8.55 Number of Observations =304
Sanitation 2.57
Source: Author’ Calculation
Water 0.20
Note: ® Reference Category
Housing 10.13
***, **, * implies statistical significance at 1 percent, 5
Cooking Fuel 8.62 percent and 10 percent level respectively.
Assets 7.96 The results of the logistic regression model show that
household size is found to be a significant determinant of the
Source: Author’s Calculation multidimensional poverty status of the sample households.
The estimated coefficient of household size is significant at 1
4.2 Determinants of Multidimensional Poverty Status of percent level. The value of coefficient of the variable
the Sample Households household size is 0.331. Thus the coefficient of household
The estimated result of the logistic regression model to size is positively related to the probability of being
identify the determinants of multidimensional poverty is shown multidimensionally poor which means that keeping all other
in the table-6. factors constant the one unit increase in the size of the
household increases the chance of being multidimensionally
Table-6: Estimated Results of Logistic Regression poor by 0.331 times. This result is consistent with the results
Analysis for Determinants of Multidimensional Poverty of [10], [1], and [7] who found that the probability of being
multidimensional poor increases with the increase in the size
Variable Coefficient Odds Ratio
of the household. This is because increase in household size
Size of Household 0.331(***) 1.393
means an increase in the number of dependents on fewer
earners and this may result in the fewer earnings of the
Gender of HH household which in turn may also affects the other non-
income dimensions of wellbeing of the household.Another
Female® significant determinant of multidimensional poverty of the
Male -2.381(**) 0.092
sample households is the variable gender of the household
head and the results show that the male headed household
Age of HH 0.016 1.016 reduces the chances of being multidimensional poor by 2.381
times as compared to their female counterparts. The marital
Marital Status of HH status of the household head is another important determinant
Married®
of multidimensional poverty of the sample households and the
widow headed household increases the chances of being
Divorced/Separated -1.070 0.343 multidimensional poor by 2.262 times. The result of the logistic
regression model establishes a significant effect of education
Widow 2.262(*) 9.602 on household poverty. The household whose head has
Never Married -1.647 0.193
completed primary education reduces the chances of being
multidimensional poor by 1.869 times as compared to those
Level of Education of HH with no education. The household whose head has completed
secondary education reduces the chances of being
No Education® multidimensional poor by 1.395 times as compared to those
with no education. The household whose head has completed
Primary -1.869(***) 0.154
matriculation or above reduces the chances of being
Secondary -1.395(**) 0.248 multidimensional poor by 1.920 times as compared to those
with no education This is because education is considered as
Matriculation or Above -1.920(**) 0.147 an important indicator of wellbeing. Education enables a head
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to acquire the skills and requirements for different employment Moreover the study found that the highest level of contribution
opportunities. Moreover a low level of education of the to the MPI of the tea garden labour community of Dibrugarh
household head reduces the ability to accumulate wealth and district is from the indicator occupational status followed by
this may lead the household to enter into a vicious cycle of
multidimensional poverty [15]. years of schooling and BMI of women. The estimated results
of the logistic regression model show that the main
Another significant variable that influences the determinants of multidimensional poverty status of the sample
multidimensional poverty status of the sample households is households are size of the household, gender of the
the employment status of the household head and the household head, marital status of the household head, level
household whose head is employed have lower chances of of education of the household head, employment status of the
being multidimensional poor by 2.079 times as compared to household head and the number of earning member(s) in the
the household whose head is unemployed. This might be household. The study confirmed that increase in the size of
because of the reason that households whose head are the household increases the probability of being
employed have a regular source of incomes and hence they multidimensional poor. The study also confirmed that increase
are less likely to be deprived in various other non-monetary in the level of education of the household head and the
dimensions of wellbeing [16].The number of earning members households whose heads are employed reduces the
in the household is another important determinant of probability of being multidimensional poor.In view of the
multidimensional poverty status of the sample households. research findings it can be said that since the increase in
The value of the estimated coefficient of the variable number household size increases the chances of being
of earning members in the household is -0.284 and it is multidimensionally poor therefore population control is
significant at 10 percent level. This implies that the one unit important in achieving the objective of poverty reduction.
increase in the earning member in the family lowers the Hence the govt. policies should be designed in such a way
probability being multidimensionally poor by 0.284 times. This that it encourages the poor people to have a manageable
is perhaps because of the reason that increase in earning household size. The education of the household head is also
members of the household reduces the dependency ratio and identified as an important determinant of multidimensional
increases the total incomes of the family and this may help in poverty of the sample households and household whose head
reducing the deprivations in other non-monetary dimensions. has completed primary, secondary and matriculation or above
level of education has lower probability of being
5. CONCLUSION multidimensional poor as compared to those household
whose head has no education. Hence the govt. policies
The present study adopted the Alkaire-Foster methodology to should aim at encouraging education and especially for the
measure the multidimensional poverty of the tea garden adults the govt. policies should provide vocational or skill
labour community of the Dibrugarh district. The present study based training programmes to enhance their skills so that they
used 12 indicators pertaining to four valuable dimensions of can get gainful employment.
wellbeing, namely, education, health, work and living
standard. The results of the present study found that 54.93
percent of the sample households belonging to the tea garden
labour community are experiencing multidimensional poverty.
Survey in India”, Paper presented in OPHI Workshop,
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