Mapping Poverty in India: A Geographical Analysis of the Multidimensional Poverty Index 2016-2021 with GIS

doi:10.21203/rs.3.rs-4942764/v1

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Mapping Poverty in India: A Geographical Analysis of the Multidimensional Poverty Index 2016-2021 with GIS

https://doi.org/10.21203/rs.3.rs-4942764/v1

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Poverty is an enduring challenge in India, persisting through both the current and previous centuries. With a rapidly growing population, the prevalence of poverty continues to escalate. This research undertakes a geographical study of poverty in India, specifically focusing on the Multidimensional Poverty Index (MPI) from 2016 to 2021. Utilizing Geographical Information Systems (GIS), we map and analyze the spatial distribution and trends of poverty across the country. Our study offers a detailed appraisal of the MPI, aiming to visually narrate the complexities of poverty through comprehensive mapping. By integrating MPI data with GIS, we provide valuable insights into the multifaceted nature of poverty in India, facilitating a better understanding for policymakers, researchers, and stakeholders. This approach underscores the importance of targeted interventions to effectively address and mitigate poverty.

Macroeconomics

Geographic Information Systems

Development Economics

Multidimensional Poverty Index

Geographical Information System

Mapping

Poverty

Economics

Geography

Poverty is a complex and pervasive issue that significantly impacts individuals and communities. In India, poverty has been a persistent challenge for centuries, exacerbated by a rapidly growing population. Despite numerous efforts to alleviate poverty, it remains a critical issue that demands continuous attention and innovative solutions.

Our research aims to provide a detailed geographical study of poverty in India, focusing on the Multidimensional Poverty Index (MPI) from 2016 to 2021. The MPI offers a comprehensive measure of poverty, considering various dimensions beyond income, such as education, health, and living standards. By integrating this index with Geographical Information Systems (GIS), our study seeks to map and analyze poverty across different regions of India.

This study is not merely a cursory glance at the issue but a thorough appraisal of the MPI over a five-year period. Using GIS, we aim to visualize the distribution and trends of poverty, providing a clearer understanding of its spatial dynamics. Our approach enables us to tell the story of poverty in India through detailed maps and spatial analysis, highlighting areas that require urgent attention and resources.

By combining the MPI with GIS, we hope to contribute valuable insights to the discourse on poverty alleviation. This geographical perspective can aid policymakers, researchers, and stakeholders in identifying and addressing the root causes of poverty more effectively. Through this research, we endeavor to shed light on the multifaceted nature of poverty in India and advocate for targeted interventions that can drive meaningful change.

Multidimensional Poverty Index 2016 and 2021

Extracting Data: Multidimensional Poverty Index 2016 and 2021

Cleaning the Data

Sorting the Data

Quantifying the Data

Integrating Multidimensional Poverty Index 2016 and 2021 with GIS

Preprocessing Multidimensional Poverty Index 2016 and 2021 with GIS

GIS Processing Multidimensional Poverty Index 2016 and 2021

GIS Analysis Multidimensional Poverty Index 2016 and 2021

Choropleth Mapping Multidimensional Poverty Index 2016 and 2021

Quartile Method for Mapping

Map showing five classes of poverty

Very high, high, moderate, low, very low poverty regions in India 2016

Very high, high, moderate, low, very low poverty regions in India 2021

Change Detection Maps Multidimensional Poverty Index 2016 and 2021

Data Sources India’s poverty and its indices have been extensively studied and reported. To analyze the geographical patterns of poverty in India over two distinct periods—2015-16 and 2019-21—we adopted a comprehensive methodology. The primary data source for this research is the Multidimensional Poverty Index (MPI) published by the National Institution for Transforming India (NITI Aayog). Our objective was to uncover the geographical distribution of poverty and conduct a detailed comparative analysis between these two periods.

Data Collection

Multidimensional Poverty Index 2015-16: Data were collected and extracted from the MPI report for 2015-16.

Multidimensional Poverty Index 2019-21: Data were collected and extracted from the MPI report for 2019-21.

Consolidation: The collected data from both periods were consolidated for comparative analysis.

Data Pre-processing The MPI is a crucial tool used by NITI Aayog to measure poverty. We collected the index data for 2015-16 and 2019-21. The following steps were undertaken to prepare the data for analysis:

Data Cleaning: The data were cleaned to remove any inconsistencies or errors.

Data Sorting: The cleaned data were sorted to facilitate analysis.

Data Quantification: The sorted data were quantified to ensure consistency and comparability between the two periods.

Data Processing with Geographical Information System (GIS) Integrating and preprocessing the MPI data with GIS involved several steps:

Integration: The MPI data for 2015-16 and 2019-21 were integrated with spatial data using GIS.

Shapefile Utilization: A shapefile of India, sourced from the Survey of India, was used for spatial representation.

Spatial Data Integration: The integrated data were checked for topological correctness within the GIS environment.

Data Analysis with GIS The spatial data were analyzed using GIS to examine the poverty indices of 742 districts in India. The steps included:

GIS Processing: The MPI data for both periods were processed within GIS.

GIS Analysis: A detailed analysis was conducted to understand the spatial distribution of poverty.

Quartile Method for Mapping The quartile method was employed to create choropleth maps of the MPI. This method facilitated the classification of regions into different poverty levels.

Mapping with GIS GIS was used to create the following maps:

Map of MPI 2015-16: Showing the distribution of poverty in 2015-16.

Map of MPI 2019-21: Showing the distribution of poverty in 2019-21.

Comparative Map: Highlighting the changes in poverty distribution between 2015-16 and 2019-21.

Map Details The maps classify regions in India into five poverty levels:

Very High Poverty

High Poverty

Moderate Poverty

Low Poverty

Very Low Poverty

Change Detection Change detection maps were created to illustrate the shifts in poverty levels between 2015-16 and 2019-21. These maps help visualize the areas where poverty has increased, decreased, or remained stable over the studied period.

Multidimensional Poverty Index (MPI) for 2015-16

The geographical findings of the Multidimensional Poverty Index (MPI) by NITI Aayog for 2015-16 reveal distinct regional disparities across India, highlighting areas with varying levels of deprivation. Here are some key geographical findings:

1. High Poverty Regions:

Bihar: This state had the highest multidimensional poverty, with a large portion of its population deprived in health, education, and living standards.
Jharkhand, Uttar Pradesh, Madhya Pradesh, Odisha: These states also exhibit high MPI scores, indicating significant deprivation across multiple indicators.

2. Moderate Poverty Regions:

Rajasthan: While still facing considerable multidimensional poverty, Rajasthan fares better than the highest poverty states.
Chhattisgarh and Assam: These states have moderate MPI scores, showing better conditions relative to the highest poverty states but still substantial challenges.

3. Low Poverty Regions:

Kerala: Known for its high literacy rates and better health outcomes, Kerala has one of the lowest MPI scores.
Tamil Nadu, Maharashtra, Gujarat: These states demonstrate lower levels of multidimensional poverty, benefiting from better infrastructure, education, and health services.

4. Least Poverty Regions:

Goa: This state shows minimal multidimensional poverty due to high living standards and better access to basic services.
Sikkim and Union Territories like Delhi: These regions have very low MPI scores, indicating fewer deprivations in health, education, and living standards.

Spatial Patterns:

North-South Divide: There is a noticeable north-south divide, with northern states generally exhibiting higher MPI scores compared to southern states.
Urban-Rural Disparities: Urban areas tend to have lower multidimensional poverty compared to rural areas, reflecting better access to services and infrastructure in cities.
Inter-State Variations: Significant disparities exist between states, necessitating state-specific policies to address the unique challenges each faces.

Multidimensional Poverty Index (MPI) for 2019-21

The geographical findings of the Multidimensional Poverty Index (MPI) for 2019-21 by NITI Aayog provide insights into the distribution of poverty across different regions of India. Here are the key findings:

1. High Poverty States:

Bihar: Continues to have the highest levels of multidimensional poverty, with significant deprivations in education, health, and living standards.
Jharkhand: High MPI score, indicating severe poverty across multiple indicators.
Uttar Pradesh: Remains one of the states with a high proportion of multidimensionally poor people.
Madhya Pradesh and Chhattisgarh: Both states exhibit high levels of multidimensional poverty, particularly in rural areas.

2. Moderate Poverty States:

Rajasthan: Moderate MPI scores, but still facing considerable challenges, especially in rural regions.
Odisha and Assam: These states show moderate levels of poverty with significant regional variations within the states.

3. Low Poverty States:

Kerala: Among the states with the lowest MPI scores, benefiting from better health, education, and living standards.
Tamil Nadu, Maharashtra, and Gujarat: Show relatively low levels of multidimensional poverty, reflecting better infrastructure and service provision.

4. Least Poverty States/Union Territories:

Goa: Very low MPI scores, indicating minimal multidimensional poverty.
Sikkim and Union Territories like Delhi: Exhibit the least poverty, with high living standards and better access to services.

Spatial Patterns:

North-South Divide: The northern and central parts of India continue to show higher levels of multidimensional poverty compared to the southern and western parts.
Urban-Rural Disparities: Rural areas remain significantly more impoverished than urban areas, with higher deprivations in health, education, and living standards.
Inter-State Variations: There are stark differences between states, with some states showing remarkable progress while others lag behind.

Specific Observations:

Rural Areas: High levels of deprivation in health (e.g., child mortality, nutrition) and living standards (e.g., sanitation, housing). Educational attainment remains a major challenge, with high school dropout rates.
Urban Areas: While generally better off, urban poverty is concentrated in slum areas. Issues like lack of adequate housing, sanitation, and access to quality health and education services persist in these areas.

Comparison between Multidimensional Poverty Index (MPI) for 2015-16 and 2019-21

Comparing the Multidimensional Poverty Index (MPI) for 2015-16 and 2019-21 by NITI Aayog reveals trends in poverty reduction and regional disparities across India over time. Here’s a geographical comparison:

General Trends:

Overall Reduction: There has been a general reduction in multidimensional poverty across India from 2015-16 to 2019-21. Many states have seen improvements in health, education, and living standards.
Regional Disparities: While overall poverty has decreased, regional disparities remain significant, with some states showing more progress than others.

High Poverty States:

2015-16: Bihar, Jharkhand, Uttar Pradesh, Madhya Pradesh, and Odisha were among the states with the highest MPI scores, indicating severe poverty.
2019-21: These states continue to have high levels of poverty but have shown some improvements. Bihar and Jharkhand still lead in terms of highest poverty, but the extent of deprivation has slightly decreased.

Moderate Poverty States:

2015-16: Rajasthan, Chhattisgarh, and Assam had moderate levels of multidimensional poverty.
2019-21: These states have seen some reductions in MPI scores, with Rajasthan and Chhattisgarh showing notable improvements. However, Assam still faces moderate poverty with slower progress.

Low Poverty States:

2015-16: Kerala, Tamil Nadu, Maharashtra, and Gujarat had relatively low levels of multidimensional poverty.
2019-21: These states have continued to perform well, with further reductions in poverty levels. Kerala remains a standout with very low MPI scores.

Least Poverty States/Union Territories:

2015-16: Goa, Sikkim, and Union Territories like Delhi exhibited the lowest levels of multidimensional poverty.
2019-21: These regions continue to have minimal poverty, maintaining their positions as areas with high living standards and access to services.

Urban vs. Rural:

• 2015-16:

Rural Areas: Showed significantly higher levels of multidimensional poverty compared to urban areas, with major deprivations in health, education, and living standards.
Urban Areas: Had lower MPI scores, but urban poverty was concentrated in slum areas.

• 2019-21:

Rural Areas: Still exhibit higher poverty levels, but there has been a noticeable reduction in deprivations, particularly in health and education.
Urban Areas: Continue to perform better overall, though challenges in slum areas persist. Improvements in access to services have contributed to poverty reduction.

Spatial Patterns:

• North-South Divide:

2015-16: Northern and central states exhibited higher levels of multidimensional poverty compared to southern states.
2019-21: This pattern persists, though there has been progress in northern states. Southern states continue to lead in poverty reduction.

• Inter-State Variations:

2015-16: Significant disparities between states, with some showing high poverty and others relatively low.
2019-21: While disparities remain, the gap has narrowed in some regions due to targeted interventions and improved governance.

Specific Observations:

Bihar and Jharkhand: Despite improvements, these states still have the highest levels of multidimensional poverty.
Kerala: Continues to perform exceptionally well, maintaining low MPI scores.
Rajasthan and Chhattisgarh: Have shown notable progress in reducing poverty.
Assam: Faces slower progress compared to other moderate poverty states.

Policy Implications:

Continued Efforts: Sustained and targeted efforts are needed to address poverty in high poverty states.
Focus on Rural Development: Enhancing healthcare, education, and infrastructure in rural areas remains crucial.
Urban Slums: Addressing poverty in urban slum areas requires improving living conditions and access to services.

This research underscores the importance of geographical analysis in understanding the distribution and trends of poverty in India. The integration of the Multidimensional Poverty Index (MPI) with Geographical Information Systems (GIS) has enabled us to visualize poverty in a way that highlights the spatial disparities across different regions of the country.

The findings from the MPI for 2015-16 and 2019-21 reveal that poverty in India remains a critical issue, with significant regional disparities. While some states, such as Kerala and Goa, have managed to maintain low levels of multidimensional poverty, others, particularly in the north, continue to struggle with high levels of deprivation.

Our analysis also highlights the persistent north-south divide, the urban-rural gap, and the inter-state variations in poverty. These spatial patterns suggest that targeted, region-specific interventions are necessary to address the unique challenges faced by different parts of the country.

In conclusion, the use of GIS in mapping the MPI has provided a clearer understanding of poverty in India, emphasizing the need for continued efforts to reduce poverty and improve living standards for all. Policymakers, researchers, and stakeholders can leverage these insights to develop more effective strategies for poverty alleviation, ensuring that resources are directed where they are needed most.

National Institution for Transforming India (NITI Aayog). “Multidimensional Poverty Index (MPI) for 2015-16 and 2019-21.” Accessed August 19, 2024. https://www.niti.gov.in/sites/default/files/2023-08/India-National-Multidimentional-Poverty-Index-2023.pdf.
National Institution for Transforming India (NITI Aayog). “Multidimensional Poverty Index (MPI) for 2015-16 and 2019-21.” Accessed August 19, 2024. https://www.niti.gov.in/sites/default/files/2021-11/National_MPI_India-11242021.pdf.
United Nations Development Programme (UNDP). India: The Road to Human Development. New Delhi: United Nations Development Programme, 1997.
United Nations Development Programme (UNDP). Human Development Report. New Delhi: Oxford University Press, 2016.
Survey of India. “Database of Administrative Boundaries up to District Level.” Accessed August 19, 2024. https://onlinemaps.surveyofindia.gov.in/Product_Specification.aspx.
Vineesh, V. “Exploring Spatial Statistics for Cluster and Outlier Analysis in Crime Hotspots: A Geospatial Study of Coastal Zones in Kollam and Thiruvananthapuram.” Eng OA 2, no. 3 (2024): 01–07.
Wikipedia. “Choropleth Map.” Accessed August 19, 2024. https://en.wikipedia.org/wiki/Choropleth_map.
Tobler, Waldo. “Choropleth Maps Without Class Intervals?” Geographical Analysis 5, no. 3 (1973): 262–265. doi:10.1111/j.1538-4632.1973.tb01012.x.

The authors declare no competing interests.

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Version 1

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Mapping Poverty in India: A Geographical Analysis of the Multidimensional Poverty Index 2016-2021 with GIS

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Findings

Multidimensional Poverty Index (MPI) for 2015-16

Spatial Patterns:

Multidimensional Poverty Index (MPI) for 2019-21

4. Least Poverty States/Union Territories:

Spatial Patterns:

Specific Observations:

Comparison between Multidimensional Poverty Index (MPI) for 2015-16 and 2019-21

General Trends:

High Poverty States:

Moderate Poverty States:

Low Poverty States:

Least Poverty States/Union Territories:

Urban vs. Rural:

Spatial Patterns:

Specific Observations:

Conclusion

References

Additional Declarations

Status:

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