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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