Measurement & Comparison of Poverty in Abhujmadiya Tribe: Income-based and Deprivation-based Poverty Approach

Kapil Kumar Chandra^1*, B. L. Sonekar²

 ^1,2School of Studies in Economics Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India.

¹c.kapil09@gmail.com, ²sonekarptrsu@gmail.com

Abstract:

The present paper is attempted to measure and compare the difference and discrepancy between the income-based and deprivation-based poverty measurement in poverty of Abhujmadiya tribes of Chhattisgarh. Two international poverty measurement approach is considered for poverty measurement of Abhujmadiya tribes, first income-based measurement developed by World Bank and second, Multidimensional Poverty Index developed by United Nation Development Program (UNDP). Research study is based on primary data of 80 household collected from 8 village of 2 block of Narayanpur district of Chhattisgarh State. Results shows that 90% Abhujmadiya peoples are poor in income-based poverty whereas, 48.43% Abhujmadiya peoples are poor in deprivation-based poverty measurement. A difference of 41.57% is found in the measurement of poverty by both the poverty measurement approach and this discrepancy is more in income-based poverty as compare to multidimensional poverty measurement. The study also reveals that health facilities, year of schooling, cooking fuel, sanitation and housing conditions are the major contributor in the poverty of Abhujmadiya Tribes.

Keywords: Multidimensional Poverty Index, Income-based Poverty, Abhujmadiya Tribe

Introduction

Poverty, a global phenomenon and biggest hurdle in the path of development. It is a challenge for economists, policymakers, and even government to understand it. Every developing country faces poverty as a big challenge. The effort which governments are taking in different nations to eradicate poverty in rural and urban areas are really appreciable but to reach tribal areas is a big challenge in itself. There are various measures of poverty, however, two international approach of poverty measurement, first income based developed by World bank and second Deprivation based multidimensional poverty index developed by United nation development Program is popularly famous in poverty measurement. But researcher and policy makers are always debate between income-based and deprivation-based poverty measurement because of difference or discrepancy in the results of both the measures.

Sen (1992) in his book “Inequality Re-examined” written that poverty is not due to lack of income but it is deprivation in basic human capabilities. Income poverty seems poverty as a result of inability of the individual or family to congregate their basic needs (world bank, 2000). Still most of the nation developed or developing nation like India consider and using income or consumption expenditure of the people’s to measure poverty (Santos and Alkire,2011). There is negative relationship between income and multidimensional poverty (Wang et.al,2016). There are some other literature studies which argues that the poverty is due to experience of various deprivations and non-monetary measure is complementary to monetary measure for measuring poverty (Alkire and Santos, 2010; Nishimwe-Niyimbanira, R ,2019; Salgotra et.al.,2020; Wang, X.,2022; Roy & Chakraborti, 2023)

The discussion above, revealed that there is some confliction and difference in the measurement value of poverty in both the approach and is related to development policies. This paper tries to measure the poverty in both the approach (income-based and deprivation Based) to find the difference and confliction in values of poverty in Abhujmadiya tribes of Narayanpur district of Chhattisgarh state. In the end this paper gives suggestions to policy makers, governments and researchers through they can reduce poverty significantly.  

The paper is structured in 6 sections as follows: Section 1 presents the problem statement of research, Section 2 presents the objective of the research study, section 3 presents sources and nature of data, Section 4 presents theoretical and empirical Methodology, section 5 explains result & discussion and finally Section 6 deals with conclusion & suggestions.

Objective

The present research has a following objective:

1.     To measure the income-based poverty in Abhujmadiya Tribe.

2.     To measure the deprivation-based (Multidimensional poverty) in Abhujmadiya Tribe.

3.     To compare the income based and deprivation-based poverty of Abhujmadiya tribe.

Sources and Nature of Data

The data for the study chosen is primary by nature collected from Abhujmadiya tribes of Chhattisgarh state. Purposive multistage mixed sampling was used to collect primary data.  80 households from 8 villages (Kundla, Kohkameta, Irakbhatti, Kanagaon, Orcha, Narayanpur, Toke and Basing) from two block Orcha and Narayanpur of Narayanpur district of Chhattisgarh state was chosen as sample for the study. Data was collected through interview schedule and direct personal investigation method. The data was collected between April 2023 to July 2023. The data was analyzed in MS Excel with the help of various statistical tools like average, percentage, scattered diagram etc.

Theoretical and Empirical Methodology:

In order to fulfil the above-listed objectives of the study, exploratory research design have been adopted. To measure the income-based poverty World Bank updated global poverty lines $ 2.15 (₹178.87) per person per day (September 2022) based on 2017 PPP (Purchasing Power parity) is used to measure the income-based poverty. For measuring multidimensional poverty of tribals Multidimensional Poverty Index developed by Oxford Poverty and Human development Initiative (Alkire-Foster Method) is used in which 10 indicators of 3 dimensions of poverty is taken into study namely Education, Health and Standard of living. Each dimension has indicators namely Years of Schooling, School attendance for education. Nutrition and Child Mortality for health. Cooking fuel, Sanitation, Drinking water, Electricity, Housing, Assets for standard of living. Indicators are equally weighted, if a person’s deprivation is 1/3 or higher is considered multidimensionally poor. The 3 statistics used to report multidimensional poverty which are as follows;

Table 1: Dimensions and indicators f Multidimensional Poverty Index

Source: Changes over time MPI Methodology Note -July 2020

For calculating multidimensional poverty each person according to her household’s deprivation assigned a deprivation score in each indicator. The deprivation score of each indicator is summed to identify the household deprivation score and multidimensionally poor people.

The Headcount Ratio is the proportion of multidimensionally poor people in the population which is calculated as below:

The Intensity of Poverty is the average proportion of the weighted component indicator in which multidimensionally poor people is deprived which is calculated as below:

 Multidimensional Poverty Index value is the product of head count ratio and intensity of poverty which is calculated as below:

 Contribution of dimensions (Education, Health and Standard of living) is the measurement of contribution of dimension in MPI, it provides information about the deprivation structure if the population in MPI which is calculated as below:

Contrib_d  = 

Where,

d = Education, Health or Standard of living

n = Total population.

q = the number of people who are multidimensionally poor.

            j = the sum of weights associated with each indicator of d .

            C_ij = weight associated with j indicator of . i^th  multidimensionally poor.

Censored headcount rates of indicators is the proportion of people who are multidimensionally poor and deprived in each indicator which is calculated as below:

Where,

C_i = weight associated with i indicator.

H_i = Headcount rate of i indicator.

   i = Each indicator (1,2,.….,10)

Result & Discussion

Objective 1: Measurement income-based poverty

The first objective of the research study is to measure the income-based poverty of Abhujmadiya tribes for that income of 80 household is collected and analysed. As per world bank global poverty lines $ 2.15 (₹178.87) per person per day (September 2022) based on 2017 PPP. It is converted in per capita monthly income to ₹ 5366 and analysed. Figure 1 show the results of income-based poverty measurement, it reveals that 90% population is found poor in income-based poverty measurement. The poverty value shows 0.9000 household has below poverty line i.e., ₹ 5366 per capita monthly income. Results also shows that the concentration of population is in between the monthly income ₹ 800 to ₹ 2000, only few people is in between the monthly income ₹ 2000 to ₹ 4000 and only 8 household is found above the poverty line.      

Figure 1: Income Based Poverty Measurement

Source: Authors calculation using Primary data.

Objective 2: Measurement of deprivation-based poverty

The second objective is to measure deprivation-based poverty for that Multidimensional Poverty Index (MPI) developed by Oxford Poverty and Human development Initiative (Alkire-Foster Method) is used in which 10 indicators of 3 dimensions of poverty is taken into study namely Education, Health and Standard of living. Total 468 peoples in 80 households are analysed, Figure 2 shows the result of Deprivation Based (MPI) Poverty Measurement it reveals that the MPI value of Abhujmadiya tribes is 0.4843 which means 48.43 % of population is deprived poor or multidimensionally poor and 51.57% is considered less deprived or considered non poor category.

Figure 2: Deprivation Based (MPI) Poverty Measurement

Source: Authors calculation using Primary data.

Figure 3: Percentage wise contribution of dimensions in MPI

Source: Authors calculation using Primary data.

Figure 3 shows the contribution of dimensions (Education, Health & Standard of living) in MPI. The result of deprivation-based poverty measurement shows the MPI value of 0.484 in which the contribution of education is 0.0942 (19.45%), contribution of health is 0.1962 (40.52%) and contribution of standard of living is 0.1938 (40.03%). Result shows that the deprivation in health facilities is contributed highest 40.52 % in MPI followed by Standard of living 40.03% and Education 19.45%.

Table 2 shows the Censored headcount rates for each indicator in MPI. Results shows that, the indicator contribution in MPI are as follows: 0.162 by Nutrition, 0.034 by Child & Adolescent Mortality, 0.060 by Year of Schooling, 0.034 by School Attendance, 0 by electricity, 0.054 by Sanitation, 0 by Drinking water, 0.054 by Housing, 0.054 by Cooking Fuel and 0.031 by Assets. It also reveals the People who are multidimensionally poor and deprived in each indicator, which shows that 454 people deprived in Nutrition, 96 people deprived in Child & Adolescent Mortality, 169 people deprived in Year of Schooling, 95 people deprived in School Attendance, no people deprived in electricity, 454 people deprived in Sanitation, 0 people deprived in Drinking water, 450 people deprived in Housing, 454 people deprived in Cooking Fuel and 262 people deprived in Assets.

Table 2: Censored headcount rates for each indicator in MPI

Source: Authors calculation using Primary data.

Figure 4 shows the Percentage wise contribution of Indicators in MPI, result reveals that Nutrition contributed 33.45%, Child & Adolescent Mortality contributed 7.07%, Year of Schooling contributed 12.45%, School Attendance contributed 7%, Electricity contributed 0%, Sanitation contributed 11.22%, Drinking water contributed 0%, Housing contributed 11.12%, Cooking Fuel contributed 11.22% and Assets contributed 6.47%. result shows that Nutrition and year of schooling contributed highest followed by cooking fuel, housing7 sanitation, assets and school attendance contributed lowest whereas Drinking water 7 electricity has no contribution in multidimensionally poverty.

Figure 4: Percentage wise contribution of Indicators in MPI

Source: Authors calculation using Primary data.

 Table 3: Difference in Income based and Deprivation based Poverty Measurement

    Source: Authors calculation using Primary data.

Objective 3: Comparison of income based and deprivation-based poverty

The third objective of the research study is to compare the income based and deprivation-based poverty of Abhujmadiya tribe. Table 3 & figure 5 shows the analyses results, it reveals that the value of poverty in income-based measurement is 0.9000 which means 90% people of Abhujmadiya tribe lies below poverty line whereas the deprivation-based poverty measurement shows the MPI value of 0.4843 which means the 48.43% people lies in poverty. The result shows that there is 0.4157 or 41.57% difference in poverty values between deprivation based and income-based poverty measurement.

Figure 5: Difference in Income based and Deprivation based Poverty Measurement

Source: Authors calculation using Primary data.

Conclusion & Suggestion

Poverty is very important phenomenon to understand for the development of human lives. In individuals’ life directly or indirectly income play very important role because it influences the standard of living and life, but it fails to give individuals or society outlook in various aspects like health, education etc. whereas non-monetary measure gives deep outlook of individuals life or society. The present research study measures the poverty of Abhujmadiya tribe of Chhattisgarh in both income-based and deprivation-based approach as well as tries to finds the difference between the poverty measurement values in both the approach. The study reveals the in income-based poverty measurement 90% peoples of Abhujmadiya tribes is poor whereas, in deprivation-based poverty measurement only 48.43% peoples of Abhujmadiya tribes are poor. There is difference or discrepancy of 41.57% is found in poverty values between both approaches. This study concluded that there is major conflict persist in both measurement approach, though income-based measurement give an idea of economically deprivation but multidimensional poverty approach give a deep understanding for intensity, magnitude and severity of poverty in Abhujmadiya tribes, it shows that health facilities (40.52%) are major reason for their poverty, as well as in indicators Nutrition contributed 33.45%, Child & Adolescent Mortality contributed 7.07%, Year of Schooling contributed 12.45%, School Attendance contributed 7%, Electricity contributed 0%, Sanitation contributed 11.22%, Drinking water contributed 0%, Housing contributed 11.12%, Cooking Fuel contributed 11.12% and Assets contributed 6.47% for poverty in Abhujmadiya  tribes. On the basis of result of research study, it is suggested that government will have to focus more on health facilities specifically in nutrition, year of schooling, sanitation, cooking fuel and housing conditions to remove poverty from Abhujmadiya e Tribes of Chhattisgarh.

References:

1.     Alkire, S. (2002). Dimensions of Human Development. World Development, 30(2), 181–205. https://doi.org/10.1016/s0305-750x(01)00109-7

2.     Alkire, S., & Foster, J. (2011a). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487 https://doi.org/10.1016/j.jpubeco.2010.11.006

3.     Alkire, S., & Foster, J. (2011b). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487. https://doi.org/10.1016/j.jpubeco.2010.11.006

4.     Alkire, S., & Fang, Y. (2018). Dynamics of Multidimensional Poverty and Uni‐dimensional Income Poverty: An Evidence of Stability Analysis from China. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3845026 .

5.     Alkire, S., Oldiges, C., & Kanagaratnam, U. (2021). Examining multidimensional poverty reduction in India 2005/6–2015/16: Insights and oversights of the headcount ratio. World Development, 142, 105454. https://doi.org/10.1016/j.worlddev.2021.105454

6.     Alkire, S., & Seth, S. (2012). Identifying BPL Households: A Comparison of Methods. SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.2292934

7.     Aprea, M., & Raitano, M. (2023). The income and consumption approach to unidimensional poverty measurement. In Research Handbook on Measuring Poverty and Deprivation (pp. 8–18). Edward Elgar Publishing. http://dx.doi.org/10.4337/9781800883451.00010

8.     Ayala, L., Jurado, A., & Pérez‐Mayo, J. (2011). INCOME POVERTY AND MULTIDIMENSIONAL DEPRIVATION: LESSONS FROM CROSS‐REGIONAL ANALYSIS. Review of Income and Wealth, 1, 40–60. https://doi.org/10.1111/j.1475-4991.2010.00393.x

9.     Bagli, S. (2019). Multidimensional Poverty: An Exploratory Study in Purulia District, West Bengal. Economic Affairs, 64(3). https://doi.org/10.30954/0424-2513.3.2019.7

10.  Chakravarty, S. R., & Silber, J. (2008). Measuring Multidimensional Poverty: The Axiomatic Approach. In Quantitative Approaches to Multidimensional Poverty Measurement (pp. 192–209). Palgrave Macmillan UK. http://dx.doi.org/10.1057/9780230582354_11

11.  Deutsch, J., & Silber, J. (2005). MEASURING MULTIDIMENSIONAL POVERTY: AN EMPIRICAL COMPARISON OF VARIOUS APPROACHES. Review of Income and Wealth, 1, 145–174. https://doi.org/10.1111/j.1475-4991.2005.00148.x

12.  Fahmy, E., Sutton, E., & Pemberton, S. (n.d.). Mixed Methods in Poverty Measurement. In Mixed Methods Research in Poverty and Vulnerability. Palgrave Macmillan. http://dx.doi.org/10.1057/9781137452511.0007

13.  Naveed, T. A. (2022). Investigating the Connections between Income Poverty and Multidimensional Poverty: An Evidence From Punjab-MICS Survey Data. Journal of Development and Social Sciences, II. https://doi.org/10.47205/jdss.2022(3-ii)113

14.  Niephaus, Y. (2009). Multidimensional Deprivation: Income Poverty and Health Care. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1350287

15.  Nishimwe-Niyimbanira, R. (2019). Income poverty versus multidimensional poverty: Empirical insight from Qwaqwa. African Journal of Science, Technology, Innovation and Development, 5, 631–641. https://doi.org/10.1080/20421338.2019.1638585

16.  Nolan, B., & Whelan, C. T. (1996). Income Poverty. In Resources, Deprivation, And Poverty (pp. 39–60). Oxford University PressOxford. http://dx.doi.org/10.1093/oso/9780198287858.003.0003

17.  Roy, A., & Chakraborti, C. (2023). Disparity in income poverty and multidimensional poverty estimates: The Indian scenario with special reference to Salboni and Binpur‐I blocks. Poverty & Public Policy, 1, 98–123. https://doi.org/10.1002/pop4.362

18.  Salgotra, A. K., Kandari, P., & Jena, P. (2020, November). Mixed Approach in Poverty Measurement: A Study of Tribal Communities. International Journal of Economic Research, 17(1), 87–98. https://www.researchgate.net/publication/346057275_Mixed_Approach_in_Poverty_Measurement_A_Study_of_Tribal_Communities

19.  Santos, M. E. (2023). The Alkire and Foster approach to measuring multidimensional poverty. In Research Handbook on Measuring Poverty and Deprivation (pp. 344–354). Edward Elgar Publishing. http://dx.doi.org/10.4337/9781800883451.00046

20.  Sen, A. (1976). Poverty: An Ordinal Approach to Measurement. Econometrica, 2, 219. https://doi.org/10.2307/1912718

21.  Wang, X. (2022). On the Relationship Between Income Poverty and Multidimensional Poverty in China. In Multidimensional Poverty Measurement (pp. 85–106). Springer Nature Singapore. http://dx.doi.org/10.1007/978-981-19-1189-7_5