View of Multi Dimensional Poverty Indexes With Special Reference To Covid 19 Pandemic Crises – Economic Analysis

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Turkish Journal of Computer and Mathematics Education Vol.12 No.10 (2021), 6613 – 6616

6613 Research Article

Multi Dimensional Poverty Indexes With Special Reference To Covid 19 Pandemic Crises

– Economic Analysis

Dasnavis Jeyanthi.Ja_{,Ajitha. K}b

a _{Department of Commerce, A.P.C. Mahalakshmi College for Women, Thoothukudi.} b_{Department of Economics, Manonmaniam Sundaranar University, Tirunelveli}

Article History: Received: 11 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published online: 28 April 2021

Abstract: The COVID-19 pandemic has jeopardized progress in reducing multidimensional poverty. Substantial impacts on

multidimensional poverty are anticipated through two indicators on which the global MPI is based that are being severely affected by the pandemic— nutrition and children’s school attendance. Multidimensional poverty if deprivation across those indicators increases to different extents. The analysis includes 70 countries with 4.8 billion people. The COVID-19 pandemic has also disrupted livelihoods and food supply chains globally. According to the World Food Programme, the number of people facing acute food insecurity may increase by 130 million across 55 countries. The simulations of the impact on multidimensional poverty extend this to all 70 countries covered in the analysis, and the moderate scenario for nutrition anticipates that about 25 percent of multi dimensionally poor or vulnerable people who were not undernourished before the pandemic become undernourished. This study is based on secondary data collected from various government records. In addition, Literacy, access to cooking fuels, access to electricity, access to toilet facilities, access to drinking water and access to pucca houses has been collected from the sources of Census 2011.

Keywords: Multidimensional poverty Index, Standard of living, Health, Education

1. Introduction

The lives of poor people are an intricate balance; their steps out of poverty even more so. Millions of daily labourers, herders and farmers eking out subsistence on rugged terrain have no access to clean drinking water and no electric light at home. Street vendors’ children may be undernourished and entire families illiterate. In tough times many children drop out of school. Improvements may come— an electrification scheme, better water and sanitation, upgraded schools with lunch programmes, and good local health clinics. But conflicts, migrations, disasters and shocks also threaten.

Launched in 2010 by the Oxford Poverty and Human Development Initiative at the University of Oxford and the Human Development Report Office of the United Nations Development Programme for the flagship Human Development Reports, the global Multidimensional Poverty Index (MPI) measures the complexities of poor people’s lives, individually and collectively, each year. This report— released 10 years after that launch— focuses on how multidimensional poverty has declined. It provides a comprehensive picture of global trends in multidimensional poverty, covering 5 billion people. It probes patterns between and within countries and by indicator, showcasing different ways of making progress. Together with data on the $1.90 a day poverty rate, the trends monitor global poverty in different forms.

This is a key moment to study how nonmonetary poverty goes down. It is 10 years before 2030, the due date of the Sustainable Development Goals (SDGs), whose first goal is to end poverty in all its forms everywhere. And it is a year when a pandemic and economic slowdown are pushing many more into poverty, while the spectre of racism still haunts, and environmental threats such as locusts surge. Multidimensional poverty is strongly associated with other SDG challenges. Concentrated in rural areas, multidimensional poor people tend to experience lower vaccination rates and secondary school achievement, insecure work and greater environmental threats. By detailing the connections between the MPI and other poverty-related SDGs, the report highlights how the lives of multidimensionality poor people are precarious in ways that extend beyond the MPI’s 10 component indicators.

The COVID-19 pandemic unfolded in the midst of this analysis. While data are not yet available to measure the rise of global poverty after the pandemic, simulations based on different scenarios suggest that, if unaddressed, progress across 70 developing countries could be set back 3–10 years. The firm hope is that it will not. As Amartya Sen observes, Britain during World War II suffered food shortages and an overall decline in food availability. Yet with judicious rationing and proactive policies, life expectancy rose. In the decade before the war, life expectancy had risen by 1.2 years for men and by 1.5 years for women. But during the war it rose by 6.5 years for men and by 7 years for women. Evidence suggests a similar story in Sierra Leone, which had the fastest reduction in MPI value among all countries with trend data. And this occurred during the Ebola crisis, not after. One by one these stories seem tenuous, even improbable. But the hope is that the information on multidimensional poverty summarized here and detailed online will encourage and empower readers to fight to end poverty during these difficult times, even against all odds. If they do, progress is possible.

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Structure of Multi Dimensional Poverty

Dimensions Indicators

Health

Infant Mortality Rate Higher Order Birth Rate Malnourished Children Education

Drop out in Primary Drop out in Secondary

Standard of Living

Access to Cooking fuel Access to Toilet Facilities Access to Drinking Water Access to Pucca House Access to Electricity

2. Methodology

Computation of Multidimensional Poverty Index

• All the above indicators are negative and positive in nature

.• The index value (in the case of a positive indicator) can be calculated using the formula – • Index Value = (Actual Value – Min. Value) / (Max.Value – Min.Value) Eg: calculations will be based on highest values being assigned highest ranking.

• The index value (in the case of a negative indicator) can be calculated by using the formula –

• Index Value = (Max. Value – Actual Value) / (Max.Value – Min.Value)

Multidimensional Poverty Index The MPI (multidimensional poverty index) measures indicate not only proportion of people deprived that is, the incidence of poverty,but also the degree or intensity of deprivation for each poor household, thus providing us with a better understanding of the dimensions of deprivation. Both the incidence and intensity of these deprivations provide critical information for understanding and intervening in poverty alleviation. Economic growth that does not generate sufficient decent employment is unlikely to foster human development. In addition to money metric measures, efforts have been underway since 2010 so as to come up with additional measures for understanding ways in which the poor face overlapping deprivations across several dimensions, such as health, education and living standards. Such an understanding can help to better address poverty reduction and achievement of millennium development goals (MDGs).

Table – 1: Top and Bottom Five Districts in Multidimensional Poverty Index Top Five Districts with MPI value Bottom Five Districts with MPI value

Kancheepuram 0.34 Ariyalur 0.62 Chennai 0.34 Virudhunagar 0.62 Cuddalore 0.38 Ramanathapuram 0.63 coimbatore 0.41 Perambalur 0.63 Nagapattinam 0.41 Dharmapuri 0.70

Source: Calculated from data provided by the Departments, Government of Tamil Nadu, 2013-14.

MPI can thus help in designing and formulating policies that are more effective in addressing poverty by identifying interconnections, monitoring impacts and allocations of resources effectively. It is evident from the above table that Dharmapuri has the highest multidimensional poverty index and Kancheepuram ranks the lowest. The table also indicates that Dharmapuri, Perambalur, Virudhunagar and Ariyalur are the districts with the largest proportion of population in severe poverty, requiring targeted interventions. Dharmapuri has poor health, education and also standard of living indicators, high infant mortality rate (IMR) and low sanitation coverage.

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Perambalur, Virudhunagar and Ariyalur are also districts which have multiple deprivations in terms of health, education and standard of living. Kancheepuram and Chennai have the least level of deprivations, and both of them appear to be highly urbanized, well connected and have greater access to education and health facilities. The table on MPI values indicates that they are not highly divergent and many of the districts’ values lie close together, indicating that even in the district with the lowest multidimensional poverty, there is scope for improving the values.

Table – 2: MPI Index and Rank: District-wise

District MPI Index MPI Rank

Kancheepuram 0.34 1 Chennai 0.34 2 Cuddalore 0.38 3 Coimbatore 0.41 4 Nagapattiam 0.41 5 Tiruppur 0.42 6 Vellore 0.43 7 Madurai 0.45 8 Thiruvallur 0.46 9 Tirunelveli 0.46 10 Trichy 0.47 11 Thoothukudi 0.49 12 Kanyakumari 0.5 13 Pudukottai 0.51 14 The Nilgiris 0.52 15 Erode 0.52 16 Salem 0.53 17 Thiruvarur 0.53 18 Tiruvannamalai 0.53 19 Sivaganga 0.55 20 Villupuram 0.58 21 Thanjavur 0.59 22 Dindigul 0.59 23 Theni 0.6 24 Nammakkal 0.6 25 Krishnagiri 0.6 26 Karur 0.61 27 Ariyalur 0.62 28 Virudhunagar 0.62 29 Ramanathapuram 0.63 30 Perambalur 0.63 31 Dharmapuri 0.70 32

Source: Calculated from data provided by the Departments, Government of Tamil Nadu, 2013-14.

In the above Table - 2 indicates that Dharmapuri has the highest MPI and Kancheepuram ranks the lower. Dharmapuri, Perambalur, Ramanathapuram, Virudhunagar and Ariyalur districts are very poor health, education and standard of living. These districts have low sanitation coverage and high infant mortality rate. These districts have multiple deprivations. Kancheepuram and Chennai have least level of deprivations. The above 5 top district has highly urbanized, greater education and well connected health facilities.

3.Findings

Our results suggest that about half of India’s population is multidimensional poor, and the estimate of

multidimensional poor varies across states. States with a higher proportion of multidimensional poor also have lower access to improved drinking water, sanitation and cooking fuel. Focusing on states with a high prevalence of multidimensional poverty could help reduce household environmental deprivation of improved water, sanitation and cooking fuel. Poor sanitation is also associated with a vicious circle of disease and linked to poverty and ignorance. It may be mentioned that improving sanitation has been accorded high priority in policy initiatives.

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▪ Across 107 developing countries and 5.9 billion people, 1.3 billion people - 22 percent—live in

multidimensional poverty.

▪ Children show higher rates of multidimensional poverty: half of multi dimensionally poor people (644

million) are children under the age of 18. One in three children is poor compared with one in six adults.

▪ 107 million multi dimensionally poor people are age 60 or older—a particularly importantly figure

during the COVID-19 pandemic.

▪ 65 countries reduced their Multidimensional Poverty Index (MPI) value significantly in absolute terms.

Those countries are home to 96 percent of the population of the 75 countries studied for poverty trends.

4.Conclusion

Worldwide, as the number of Covid-19 cases and resulting deaths continue to rise, there is a growing concern not only about global health but also about a looming economic crisis. Most of the economic impact of the virus results from aversion behavior, the actions people take to avoid catching the virus. Importantly, as the number of Covid-19 cases raise dramatically, especially among developing countries including India, Russia, Brazil, Peru, Chile, and Iran, the pandemic is also having a detrimental impact on the global poor. The analysis used four indicators of economic hardship, namely health, education and standard of living, infant mortality rate and district wise multidimensional index ranking. Instead of simply noting the proportion of individuals deprived in each of the four indicators, the paper employed a multidimensional analysis to measure the overlapping economic deprivations experienced individuals during the early stages of the pandemic in the India.

5. Recommendations

Based on the data obtained as a result of the study, it is seen that the students have problems in the fieldspecific skills such as understanding, interpretation, thinking and reasoning in the new examination system. On the other hand, the opinion is that the textbooks and the exam are not parallel, so teachers have various difficulties. In this direction, various activities can be organized to increase students' motivation and to gain reading habit. By making use of constructivist teaching methods and techniques, learning environments where students can construct knowledge can be designed and studies can be done accordingly. In addition, it is thought that it would be beneficial to provide teachers with in-service training for the exam.

References

1. S. Alkire, J. Foster (2011a), “Counting and multidimensional poverty measurement”, Journal of Public Economics, pp. 476-487.

2. S. Alkire, A. Conconi, S. Seth (2014), “Multidimensional destitution: An ordinal counting methodology for constructing linked subsets of the poor, Oxford Poverty and Human Development Initiative (OPHI) Research in Progress.

3. Alkire S., Foster J., Seth S., Santos M., Roche J., & Ballon P. Multidimensional poverty measurement and analysis. Oxford: Oxford University Press; 2015