Skip to main content
Top
Published in: New Generation Computing 1/2023

01-02-2023

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach

Authors: Sandeep Kumar Satapathy, Shreyaa Saravanan, Shruti Mishra, Sachi Nandan Mohanty

Published in: New Generation Computing | Issue 1/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R2 score.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference World Bank Group, Poverty and Shared Prosperity 2020, Accessed: 26 Feb 2022 World Bank Group, Poverty and Shared Prosperity 2020, Accessed: 26 Feb 2022
2.
go back to reference Banks, L.M., Kuper, H., Polack, S.: Poverty and disability in low- and middle-income countries: a systematic review. PLoS One 13(9), e0204881 (2018) CrossRef Banks, L.M., Kuper, H., Polack, S.: Poverty and disability in low- and middle-income countries: a systematic review. PLoS One 13(9), e0204881 (2018) CrossRef
3.
go back to reference Zixi, H.: Poverty Prediction Through Machine Learning, 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT) (2021), pp. 314–324 Zixi, H.: Poverty Prediction Through Machine Learning, 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT) (2021), pp. 314–324
4.
go back to reference Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S.: Combining satellite imagery and machine learning to predict poverty. Science 353(6301), 790–794 (2016) CrossRef Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S.: Combining satellite imagery and machine learning to predict poverty. Science 353(6301), 790–794 (2016) CrossRef
5.
go back to reference Alkire, S., Nogalesa, R., NaïriQuinn, N., Suppaade, N.: Global multidimensional poverty and COVID-19: a decade of progress at risk? Soc. Sci. Med. 291, 114457 (2021) CrossRef Alkire, S., Nogalesa, R., NaïriQuinn, N., Suppaade, N.: Global multidimensional poverty and COVID-19: a decade of progress at risk? Soc. Sci. Med. 291, 114457 (2021) CrossRef
6.
go back to reference Alkire, S., Kovesdi, F., Pinilla-Roncancio, M., Scharlin-Pettee, S.: Changes over time in the global multidimensional poverty index and other measures: towards national poverty reports, OPHI Research in Progress 57a, Oxford Poverty and Human Development Initiative, University of Oxford (2020d) Alkire, S., Kovesdi, F., Pinilla-Roncancio, M., Scharlin-Pettee, S.: Changes over time in the global multidimensional poverty index and other measures: towards national poverty reports, OPHI Research in Progress 57a, Oxford Poverty and Human Development Initiative, University of Oxford (2020d)
7.
go back to reference Anderson, R.M., Heesterbeek, H., Klinkenberg, D., Hollingsworth, T.D.: How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 395(10228), 931–934 (2020) CrossRef Anderson, R.M., Heesterbeek, H., Klinkenberg, D., Hollingsworth, T.D.: How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 395(10228), 931–934 (2020) CrossRef
8.
go back to reference Tavares, F.F., Betti, G.: The pandemic of poverty, vulnerability, and COVID-19: evidence from a fuzzy multidimensional analysis of deprivations in Brazil. World Dev. 139, 105307 (2021) CrossRef Tavares, F.F., Betti, G.: The pandemic of poverty, vulnerability, and COVID-19: evidence from a fuzzy multidimensional analysis of deprivations in Brazil. World Dev. 139, 105307 (2021) CrossRef
9.
go back to reference Huanga, Y., Jiao, W., Wang, K., Li, E., Yan, Y., Chen, J., Guo, X.: Examining the multidimensional energy poverty trap and its determinants: an empirical analysis at household and community levels in six provinces of China. Energy Policy 169, 113193 (2022) CrossRef Huanga, Y., Jiao, W., Wang, K., Li, E., Yan, Y., Chen, J., Guo, X.: Examining the multidimensional energy poverty trap and its determinants: an empirical analysis at household and community levels in six provinces of China. Energy Policy 169, 113193 (2022) CrossRef
10.
go back to reference Alkire, S., Santos, M.E.: Multidimensional poverty index, Oxford Poverty & Human Development Initiative (OPHI) (2010) Alkire, S., Santos, M.E.: Multidimensional poverty index, Oxford Poverty & Human Development Initiative (OPHI) (2010)
13.
go back to reference Uyanık, G.K., Güler, N.: A study on multiple linear regression analysis. Procedia. Soc. Behav. Sci. 106, 234–240 (2013) CrossRef Uyanık, G.K., Güler, N.: A study on multiple linear regression analysis. Procedia. Soc. Behav. Sci. 106, 234–240 (2013) CrossRef
15.
go back to reference Xhafaj, E., Nurja, I.: Determination of key factors that influence poverty through econometric models. Eur. Sci. J. 10(24), 65–72 (2014) Xhafaj, E., Nurja, I.: Determination of key factors that influence poverty through econometric models. Eur. Sci. J. 10(24), 65–72 (2014)
16.
go back to reference Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986) CrossRef Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986) CrossRef
20.
go back to reference Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., Wu, J.: Estimation of poverty using random forest regression with multi-source data: a case study in Bangladesh. Remote. Sens. 11, 375 (2019) CrossRef Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., Wu, J.: Estimation of poverty using random forest regression with multi-source data: a case study in Bangladesh. Remote. Sens. 11, 375 (2019) CrossRef
21.
go back to reference Chen, T., Guestrin, C.: XGBoost: A scalable tree boosting system, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 785–794 Chen, T., Guestrin, C.: XGBoost: A scalable tree boosting system, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 785–794
22.
go back to reference Li, Q., Yu, S., Échevin, D., Fan, M.: Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan. Socio-Economic. Plan.. Sci. 81, 101195 (2021) CrossRef Li, Q., Yu, S., Échevin, D., Fan, M.: Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan. Socio-Economic. Plan.. Sci. 81, 101195 (2021) CrossRef
23.
go back to reference Sharma, A., Rathod, J., Pol, R., Gajbhiye, S.: Poverty prediction using machine learning. Int. J. Computer Sci. Eng. 7(3), 946–949 (2019) Sharma, A., Rathod, J., Pol, R., Gajbhiye, S.: Poverty prediction using machine learning. Int. J. Computer Sci. Eng. 7(3), 946–949 (2019)
25.
go back to reference Alsharkawi, A., Al-Fetyani, M., Dawas, M., Saadeh, H., Alyaman, M.: Poverty classification using machine learning: the case of jordan. Sustainability 13, 1412 (2021) CrossRef Alsharkawi, A., Al-Fetyani, M., Dawas, M., Saadeh, H., Alyaman, M.: Poverty classification using machine learning: the case of jordan. Sustainability 13, 1412 (2021) CrossRef
26.
go back to reference Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (1999) MathSciNetMATH Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (1999) MathSciNetMATH
27.
go back to reference Aguilar, R.A.C., Mahler, D.G., Newhouse, D.: Nowcasting Global Poverty, Policy Research Working Paper 9860, World Bank (2021) Aguilar, R.A.C., Mahler, D.G., Newhouse, D.: Nowcasting Global Poverty, Policy Research Working Paper 9860, World Bank (2021)
28.
go back to reference Cristianini, N., Ricci, E.: Support vector machines, encyclopedia of algorithms, Springer (2008) Cristianini, N., Ricci, E.: Support vector machines, encyclopedia of algorithms, Springer (2008)
29.
go back to reference Henrique, B.M., Sobreiro, V.A., Kimura, H.: Stock price prediction using support vector regression on daily and up to the minute prices. J. Finance. Data. Sci. 4(3), 183–201 (2018) CrossRef Henrique, B.M., Sobreiro, V.A., Kimura, H.: Stock price prediction using support vector regression on daily and up to the minute prices. J. Finance. Data. Sci. 4(3), 183–201 (2018) CrossRef
30.
go back to reference Bienvenido-Heurtas, D., Pulido-Arcas, J.A., Rubio-Bellido, C., Perez-Fargallo, A.: Prediction of fuel poverty potential risk index using six regression algorithms: a case-study of chilean social dwellings. Sustainability 13, 2426 (2021) CrossRef Bienvenido-Heurtas, D., Pulido-Arcas, J.A., Rubio-Bellido, C., Perez-Fargallo, A.: Prediction of fuel poverty potential risk index using six regression algorithms: a case-study of chilean social dwellings. Sustainability 13, 2426 (2021) CrossRef
31.
go back to reference Hoerl, A.E., Kennard, R.W., Baldwin, K.F.: Ridge regression—some simulations. Commun. Stat. 4, 105–123 (1975) CrossRefMATH Hoerl, A.E., Kennard, R.W., Baldwin, K.F.: Ridge regression—some simulations. Commun. Stat. 4, 105–123 (1975) CrossRefMATH
32.
go back to reference Sufian, A.J.M.: An analysis of poverty—a ridge regression approach, IMSCI 2010—4th International Multi-Conference on Society, Cybernetics and Informatics, Proceedings 2 (2010), pp. 118–123 Sufian, A.J.M.: An analysis of poverty—a ridge regression approach, IMSCI 2010—4th International Multi-Conference on Society, Cybernetics and Informatics, Proceedings 2 (2010), pp. 118–123
33.
go back to reference Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal. Statistical Soc. Series B. (Methodological) 58(1), 267–288 (1996) MathSciNetCrossRefMATH Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal. Statistical Soc. Series B. (Methodological) 58(1), 267–288 (1996) MathSciNetCrossRefMATH
35.
go back to reference Afzal, M., Hersh, J., Newhouse, D.: Building a better model: variable selection to predict poverty in Pakistan and Sri Lanka, World Bank Research Working Paper (2015) Afzal, M., Hersh, J., Newhouse, D.: Building a better model: variable selection to predict poverty in Pakistan and Sri Lanka, World Bank Research Working Paper (2015)
36.
go back to reference Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Royal. Statistical. Soc. Series B. (Statistical Methodol). 67(2), 301–320 (2005) MathSciNetCrossRefMATH Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Royal. Statistical. Soc. Series B. (Statistical Methodol). 67(2), 301–320 (2005) MathSciNetCrossRefMATH
37.
go back to reference Sihombing, P.: Regularized ordinal regression with elastic net approach (case study: poverty modeling in Yogyakarta Province 2018). CAUCHY 6, 296–304 (2021) CrossRef Sihombing, P.: Regularized ordinal regression with elastic net approach (case study: poverty modeling in Yogyakarta Province 2018). CAUCHY 6, 296–304 (2021) CrossRef
39.
go back to reference Imandoust, S.B., Bolandraftar, M.: Application of K-nearest neighbor (KNN) approach for predicting economic events: theoretical background. Int. J. Eng. Res. Appl. 3(5), 605–610 (2013) Imandoust, S.B., Bolandraftar, M.: Application of K-nearest neighbor (KNN) approach for predicting economic events: theoretical background. Int. J. Eng. Res. Appl. 3(5), 605–610 (2013)
44.
go back to reference Satapathy S.K., Dehuri, S., Jagadev, A.K., Mishra, S.: EEG brain signal classification for epileptic seizure disorder detection, Elsevier Publication, 1st Eds, ISBN- 9780128174265, Feb 2019 Satapathy S.K., Dehuri, S., Jagadev, A.K., Mishra, S.: EEG brain signal classification for epileptic seizure disorder detection, Elsevier Publication, 1st Eds, ISBN- 9780128174265, Feb 2019
45.
go back to reference Satapathy, S.K., Dehuri, S., Jagadev, A.K.: Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of EEG signal to detect epileptic seizure. Informatica 41, 99–110 (2017) MathSciNet Satapathy, S.K., Dehuri, S., Jagadev, A.K.: Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of EEG signal to detect epileptic seizure. Informatica 41, 99–110 (2017) MathSciNet
46.
go back to reference Satapathy, S.K., Jagadev, A.K., Dehuri, S.: An empirical analysis of training algorithms of neural networks: a case study of EEG signal classification using java framework. In: Jain, L.C. et al. (eds.), vol 309, Advances in intelligent systems and computing. Springer, pp 151–160, (2015) Satapathy, S.K., Jagadev, A.K., Dehuri, S.: An empirical analysis of training algorithms of neural networks: a case study of EEG signal classification using java framework. In: Jain, L.C. et al. (eds.), vol 309, Advances in intelligent systems and computing. Springer, pp 151–160, (2015)
47.
go back to reference Sah, S., Dhanalakshmi, S.R., Mohanty, S.N., Alenezi, F., Polat, K.: Forecasting COVID-19 pandemic using prophet, ARIMA, and hybrid stacked LSTM-GRU Models in India. Computational. Math. Methods. Med. (2022) Sah, S., Dhanalakshmi, S.R., Mohanty, S.N., Alenezi, F., Polat, K.: Forecasting COVID-19 pandemic using prophet, ARIMA, and hybrid stacked LSTM-GRU Models in India. Computational. Math. Methods. Med. (2022)
48.
go back to reference Shome, D., Kar, T., Mohanty, S.N., Tiwari, P., Muhammad, K., AlTameem, A., Zhang, Y., Saudagar, A.K.J.: COVID-transformer: interpretable COVID-19 detection using vision transformer for healthcare. Int J Env Res Public Health 18(21), 1–14 (2021) CrossRef Shome, D., Kar, T., Mohanty, S.N., Tiwari, P., Muhammad, K., AlTameem, A., Zhang, Y., Saudagar, A.K.J.: COVID-transformer: interpretable COVID-19 detection using vision transformer for healthcare. Int J Env Res Public Health 18(21), 1–14 (2021) CrossRef
49.
go back to reference Mangla, M., Sharma, N., Mohanty, A., Satpathy, S., Mohanty, S.N., Choudhury, T.: Geospatial multivariate analysis of COVID-19: a global perspective. Geo J. (2021) Mangla, M., Sharma, N., Mohanty, A., Satpathy, S., Mohanty, S.N., Choudhury, T.: Geospatial multivariate analysis of COVID-19: a global perspective. Geo J. (2021)
50.
go back to reference Shankar, K., Mohanty, S.N., Yadav, K., Gopalakrishnan, T.: Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model. Cogn. Neurodyn. 16(1), 22–34 (2021) Shankar, K., Mohanty, S.N., Yadav, K., Gopalakrishnan, T.: Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model. Cogn. Neurodyn. 16(1), 22–34 (2021)
51.
go back to reference Dash, S., Chakravati, S., Mohanty, S.N., Patnaik, C.R., Jain, S.: A deep learning method to forecast COVID-19 outbreak. N. Gener. Comput. 39(2), 437–461 (2021) Dash, S., Chakravati, S., Mohanty, S.N., Patnaik, C.R., Jain, S.: A deep learning method to forecast COVID-19 outbreak. N. Gener. Comput. 39(2), 437–461 (2021)
Metadata
Title
A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach
Authors
Sandeep Kumar Satapathy
Shreyaa Saravanan
Shruti Mishra
Sachi Nandan Mohanty
Publication date
01-02-2023
Publisher
Springer Japan
Published in
New Generation Computing / Issue 1/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-023-00203-8

Other articles of this Issue 1/2023

New Generation Computing 1/2023 Go to the issue

Premium Partner