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Machine Learning Based Approach for Sustainable Social Protection Policies in Developing Societies

Abstract

Machine learning has been increasingly used for making informed public policy decisions, however, its application in the area of social protection in developing societies has been largely overlooked. We have employed unsupervised machine learning K-means clustering technique for exploring a big data that comprised of 88 attributes and 570 instances for better targeting of households that are in urgent need of welfare from the government. The clusters formed showed common patterns relating to insecurities in terms of loss of income and property, unemployment, disasters and disease etc. faced by households in each cluster. We found that households falling in rural areas jurisdictions face severe insecurities compared to other localities and are in urgent need of social protection interventions. We concluded that by employing K-means clustering unsupervised machine learning approach big data (even if it is limited) can be explored effectively for better targeting of social protection interventions for both developing and smart societies. The unsupervised machine learning technique presented in this study is an efficient approach because it can be used by societies that are facing data constraints and can achieve optimal results for increasing the welfare of poor by using the said approach.

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Notes

  1. 1.

    As the purpose of this paper is not to explain the social policy therefore a very brief explanation is provided here.

  2. 2.

    88 attributes were collected against a household.

  3. 3.

    Australian National University ethics protocol: 2019/377

  4. 4.

    In Pakistan, the Multidimensional poverty Index (MPI) is a way of measuring poverty. MPI combines various deprivations that affect a household across three dimensions: education, health, and living standards and 11 indicators spread across these 3 dimensions. A household is considered multi-dimensionally poor if it is deprived in at least 33% of the weighted indicators [40]. Details of the cities is provided in Table A of the appendix

  5. 5.

    In various ML techniques labels are assigned to instances and trainig data is used for constructing models. However, in k means clustering no training data is used and no labels are assigned to to instances for forming clusters.

  6. 6.

    In DBSCAN dense region is a proximity, where the minimum number of instances are accumulated to establish a new cluster.

  7. 7.

    In Pakistan, rural and urban areas are present within the geographical limits of a city. Rural areas are generally referred to villages where the process the urbanization is limited or has not taken place and people rely on informal employment mechanisms such as agriculture etc. Whereas, urban areas are generally referred to cities where process of urbanization has taken place and there are opportunities of formal employment. For administering rural areas government has formed union councils and for urban areas municipal corporations are present.

  8. 8.

    During the survey, it revealed that in order to fulfill the expenses of marriage some households took loans and some had to use their savings, therefore marriage is considered as a shock.

  9. 9.

    Insurance provided in shape of rotating savings and credit associations where every member contributes towards this fund and get the cash in time of need.

  10. 10.

    Zakat is one of the five pillars of Islam and is mandatory on every Muslim who is financially stable. According to Islamic teachings, zakat is paid @2.5% of the wealth to the poor and needy Muslims as an obligation. It is applicable on every Muslim who owns 613.35 g of silver, or 87.49 g of gold or who owns one or more assets liable, equal in value to 613.35 g of silver or 87.49 g of gold. Zakat is given to Muslims: who are poor and not have any income source etc.

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Acknowledgments

The authors would like to thank Mr. Sohail Sarwar for their valuable contribution in providing technical support for the successful completion of this paper.

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Correspondence to Zahid Mumtaz.

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Mumtaz, Z., Whiteford, P. Machine Learning Based Approach for Sustainable Social Protection Policies in Developing Societies. Mobile Netw Appl 26, 159–173 (2021). https://doi.org/10.1007/s11036-020-01696-z

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Keywords

  • Artificial intelligence
  • Machine learning
  • K-means clustering
  • Big data
  • Social protection
  • Smart and developing societies