Abstract
The major emphasis of this study is showing the district-level inequality in the Standard of Living (SoL) in India. It shows the spatial effects and distribution aspect of SoL in India using various spatial econometric techniques e.g. Moran’s I, LISA, spatial autoregressive model, GWR model and MGWR model to understand spatial inequality in India. Three clusters of districts having high SoL formed in North-western India, Western India and Southern India. These clusters formed due to urbanization, the spread effect of Delhi, the Green revolution in Punjab, the international trade link of Gujarat and Punjab, and impressive social sector development in southern India. The clusters of districts having low SoL mainly formed in the central, eastern and north-eastern parts of India. These are the area dominated by tribal communities having low socio-economic conditions and rural and agricultural populations with a severe resource-population mismatch. However, the Multiscale spatial regression supports that the level of urbanization, workforce structure, human capital, gender empowerment and group identity operates at a different geographic scale in determining the spatial heterogeneity of SoL. This study suggests that the government should focus on the lagging region and that policy responses should be cognizant of the multiple shades of spatial variation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agarwalla, A., & Pangotra, P. (2011). Regional income disparities in India and test for convergence – 1980 to 2006 (W.P. No. 2011-01-04). https://core.ac.uk/download/pdf/6377786.pdf
Anselin, L. (1988). The scope of spatial econometrics. In L. Anselin (Ed.), Spatial econometrics: Methods and models (Vol. 4, pp. 7–13). Springer. https://doi.org/10.1007/978-94-015-7799-1_2
Anselin, L. (1995). Local indicators of spatial organization -LISA. Geographical Research, 27(2), 1–25.
Anselin, L. (2005). Exploring spatial data with GeoDa: a workbook (2nd ed.). Center for Spatially Integrated Social Science.
Anselin, L. (2009). Spatial regression. In A. S. Fotheringham & A. Peter (Eds.), The SAGE handbook of spatial analysis (pp. 255–277). SAGE Publications.
Anselin, L. (2010). Thirty years of spatial econometrics *. Papers in Regional Science, 89(1), 1–25. https://doi.org/10.1111/j.1435-5957.2010.00279.x
Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional Science & Urban Economics, 26(1), 77–104. https://doi.org/10.1016/0166-0462(95)02111-6
Ascani, A., Crescenzi, R., & Iammarino, S. (2012). Regional economic development: a review. In SEARCH Working Paper (1/03). http://www.ub.edu/searchproject/wp-content/uploads/2012/02/WP-1.3.pdf
Barreiro-Gen, M. (2019). Discussing approaches to standard of living. In W. L. Filho, A. M. Azul, L. Brandli, A. L. Salvia, & T. Wall (Eds.), Decent work and economic growth (pp. 1–15). Cham: Springer. https://doi.org/10.1007/978-3-319-71058-7_22-1
Bhunia, A., Sahoo, A., & Chatterjee, U. (2023). Geostatistical analysis of quality of life (QoL) with particular emphasis on the basic amenities and services in urban West Bengal, India. Asia-Pacific Journal of Regional Science, February. https://doi.org/10.1007/s41685-023-00277-8
da Silva, A. R., & Fotheringham, A. S. (2016). The multiple testing issue in geographically weighted regression. Geographical Analysis, 48(3), 233–247. https://doi.org/10.1111/gean.12084
Das, A., Das, M., & Houqe, R. (2020a). Evaluating the quality of living (QOL) of the households in Dandakaranya region, India: A well-being approach. Spatial Information Research, 28(2), 257–272. https://doi.org/10.1007/s41324-019-00286-w
Das, M., Das, A., & Mandal, A. (2020b). Assessing the level of living condition in Bundelkhand region of central India: a households level analysis. GeoJournal, 86, 2723–2745. https://doi.org/10.1007/s10708-020-10220-5
Dreze, J., & Sen, A. (2013). A new India? In J. Drèze & A. Sen (Eds.), An uncertain glory: India and its contradictions (pp. 1–16). Princeston University Press. https://doi.org/10.1353/ens.2015.0019
Dubey, K. N. (1992). Process of socio economic development. Rawat Publications.
Ezcurra, R., & Rodriguez-pose, A. (2017). Does ethnic segregation matter for spatial inequality ? Journal of Economic Geography, 17(6), 1149–1178. https://doi.org/10.1093/jeg/lbx007
Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480
Himanshu. (2019). Inequality in India: a review of levels and trends. In WIDER working paper 2019/42. https://doi.org/10.35188/UNU-WIDER/2019/676-0
Januardi, R. W., & Utomo, A. P. (2017). Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia. Communications in Science and Technology, 2(2), 53–63. https://doi.org/10.21924/cst.2.2.2017.50
Jose, A. (2019). India’s regional disparity and its policy responses. Journal of Public Affairs, 19(4), 302–321. https://doi.org/10.1002/pa.1933
Kanbur, R., Venables, A. J., & Wan, G. (2005). Introduction to the special issue: Spatial inequality and development in Asia. Review of Development Economics, 9(1), 1–4. https://doi.org/10.1111/j.1467-9361.2005.00260.x
Kim, J., & Nicholls, S. (2016). Using geographically weighted regression to explore the equity of public open space distributions. Journal of Leisure Research, 48(2), 105–133. https://doi.org/10.18666/JLR-2016-V48-I2-6539
Kitchen, P., & Muhajarine, N. (2008). Quality of life research: New challenges and new opportunities. Social Indicators Research, 85(1), 1–4. https://doi.org/10.1007/s11205-007-9130-6
Kohli, A. (2012). Introduction. In A. Kohli (Ed.), Poverty amid plenty in the new India (pp. 1–18). Cambridge University Press. https://doi.org/10.1017/CBO9781139015080.002
Krishan, G. (2001). Presidential address: Development. Environment and Decentralized planning. Annals NAGI, 21(1), 3–14.
Kundu, A., Mohanan, P. C., & Varghese, K. (2013). Spatial and social inequalities in human development: India in the global context. https://www.undp.org/content/dam/india/docs/human-development/spatial-and-social-inequalities-in-human-development--india-in-t.pdf
Kundu, A., Pradhan, B. K., & Subramanian, A. (2002). Dichotomy or continuum: Analysis of impact of urban centres on their periphery. Economic and Political Weekly, 37(50), 5039–5046.
Leung, Y. (2010). Discovery of spatial relationships in spatial data. In Y. Leung (Ed.), Knowledge discovery in spatial data (pp. 223–276). Springer. https://doi.org/10.1007/978-3-642-02664-5
Li, Z., & Fotheringham, A. S. (2020). Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), 1378–1397. https://doi.org/10.1080/13658816.2020.1720692
Li, Z., Fotheringham, A. S., Oshan, T. M., & Wolf, L. J. (2020). Measuring bandwidth uncertainty in multiscale geographically weighted regression using Akaike weights. Annals of the American Association of Geographers, 110(5), 1500–1520. https://doi.org/10.1080/24694452.2019.1704680
Liu, Y., Jiang, S., Liu, Y., Wang, R., Li, X., Yuan, Z., Wang, L., & Xue, F. (2011). Spatial epidemiology and spatial ecology study of worldwide drug-resistant tuberculosis. International Journal of Health Geographics, 10(1), 50. https://doi.org/10.1186/1476-072X-10-50
Lombo, L. M., Hooks, G., & Tickamyer, A. R. (2007). Introduction: Advancing the sociology of spatial inequality. In L. M. Lombo, G. Hooks, & A. R. Tickamyer (Eds.), The sociology of spatial inequality (pp. 1–25). State university of New York press.
Mathur, M. (2015). Spatial autocorrelation analysis in plant population: An overview. Journal of Applied and Natural Science, 7(1), 501–513. https://doi.org/10.31018/jans.v7i1.639
Mukhopadhyay, A., & Urzainqui, D. G. (2018). The dynamics of spatial and local inequalities in India. In WIDER working paper 2018/182: http://data.worldbank.org/indicator/SI.POV.GINI
Niranjan, R. (2020). Spatial inequality in human development in India- a case study of Karnataka. Sustainable Futures, 2(100024), 1–10. https://doi.org/10.1016/j.sftr.2020.100024
OCED. (2011). How’s Life? measuring well being. In Housing conditions (pp. 81–100). OECD Publishing. https://doi.org/10.1787/9789264121164-en
Oshan, T. M., Smith, J. P., & Fotheringham, A. S. (2020). Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal of Health Geographics, 19(11), 1–17. https://doi.org/10.1186/s12942-020-00204-6
Planning commission of India_(PCA). (2013). Regional equality. In Twelfth five year plan (2012–2017) faster, more inclusive and sustainable growth (Vol. 1, pp. 302–321). SAGE Publications India pvt Ltd. https://doi.org/10.5005/jp/books/13071_7
Radhakrishna, R., Ravi, C., & Reddy, B. S. (2017). Assessment of well-being in multidimensional perspective in post reform India. In K. L. Krishna, V. Pandit, K. Sundaram, & P. Dua (Eds.), Perspectives on economic development and policy in India (pp. 39–83). Springer. https://doi.org/10.1007/978-981-10-3150-2
Ringen, S. (1991). Households, standard of living, and inequality. Review of Income and Wealth, 37(1), 1–13. https://doi.org/10.1111/j.1475-4991.1991.tb00335.x
Sinha, S., & Basu, A. (2022). Changing levels of disparity in material well being : Highlighting rural urban differentials in West Bengal, India. GeoJournal, 87(S4), 683–701.
Steckel, R. H. (1995). New perspectives on the standard of living. Challenge, 38(5), 12–18. https://doi.org/10.1080/05775132.1995.11471849
Steckel, R. H. (2008). Biological measures of the standard of living life expectancy. Journal of Economic Perspectives, 22(1), 129–152.
Tickamyer, A. R. (2014). Space matters! spatial inequality in future sociology. Contemporary Sociology, 29(6), 805–813.
Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in multiscale geographically weighted regression. Geographical Analysis, 52(1), 87–106. https://doi.org/10.1111/gean.12189
Zhao, C., Jensen, J., Weng, Q., & Weaver, R. (2018). A geographically weighted regression analysis of the underlying factors related to the surface Urban Heat Island Phenomenon. Remote Sensing, 10(9), 1–18. https://doi.org/10.3390/rs10091428
Acknowledgements
The authors would like to express sincere thanks of gratitude to Debottom Saha (Research Scholar, IIT-Delhi, New Delhi, India), Ranajit Ghosh (SACT teacher, Suri Vidyasagar College, Suri, West Bengal, India), and Dr. Niladri Das (Assistant professor, H.B. College, West Bengal, India) for their suggestions and comments during the research.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Author declares that there is no conflict of interest.
Human and animals rights
Not applicable.
Informed consent
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mondal, S., Das, R. & Chakraborty, M. Spatial inequality in standard of living (SoL) in India: a spatial econometric approach. GeoJournal 88, 5305–5329 (2023). https://doi.org/10.1007/s10708-023-10888-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10708-023-10888-5