Journal of Enterprise and Business Intelligence


Measuring Multidimency Poverty Method



Journal of Enterprise and Business Intelligence

Received On : 22 June 2021

Revised On : 14 August 2021

Accepted On : 20 October 2021

Published On : 05 January 2022

Volume 02, Issue 01

Pages : 033-043


Abstract


The calculation of the poverty rate in Malaysia is measured based on the Poverty Line Income (PGK). Most recently, in 2019, PGK was reviewed. The reassessment of this measurement is in line with current developments as well as Malaysia's developmenttowards developed countries. But to what extent does the poverty measure using PGK reflect the real state of poverty for a household? To overcome poverty, we need to know who is poor, and in what dimension they are poor.To take these into account, we used the multidimensional poverty index measurement method or Multidimensional Poverty Index (MPI) as one of the alternatives to replace poverty measurement using the income method. This multidimensional poverty measurement is more of a measure to identify "who is poor". Apart from calculating the amount of deficiency experienced by a household, this study also conducts two forms of analysis, namely weightlessMPI analysis and MPI analysis with the same weighting. This study uses primary data obtained from face-to-face interviews on 378 heads of households in Sik District, Kedah, in 2013. Selected respondents are the result of random observations conducted by the National Statistics Department. The findings of the study, when the same weighting value is given for each of the eight dimensions, found that if the poverty line k ≥ 4 is set, 34.66 percent or 131 households are poor in various dimensions. Determining the k ≥ 4 separationline in this weighted analysis does not mean that households are deficient in four dimensions, but that households are deficient by four and more dimensions. The studyfound that the average poverty (A) is 31.36 percent, which means that, among the poor households (131), on average, they suffer from a deficit of 31.36 percent of the total poverty dimension (22 dimensions). MPI, the adjusted poverty rate (Mo), is 0.1086, which means that the depth of poor households from the set separation line is 10.86 percent or 11 out of 100 depths or in other words the depth of poverty of households experiencing a deficit from the overall shortfall is 10.86 percent.


Keywords


Poverty; Multidimensional Poverty; Multidimensional Poverty Index (MPI); Poverty Line Income (PGK); Kedah, Malaysia.


  1. R. A. Garrido and F. Allendes, “Modeling the Internal Transport System in a Containerport,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1782, no. 1, pp. 84–91, Jan. 2002.
  2. Z.-C. Li, W. H. K. Lam, and S. C. Wong, “Modeling intermodal equilibrium for bimodal transportation system design problems in a linear monocentric city,” Transportation Research Part B: Methodological, vol. 46, no. 1, pp. 30–49, Jan. 2012.
  3. R. Saha, M. T. Tariq, M. Hadi, and Y. Xiao, “Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling,” Journal of Advanced Transportation, vol. 2019, pp. 1–12, Dec. 2019.
  4. A.Thomas B, “‘Application of Queuing Theory to Larfarge Cement Transportation System for Truck/Loader Optimazation,’” Aspects in Mining & Mineral Science, vol. 2, no. 1, Jun. 2018.
  5. P. Haluzová, “Effective Data Mining for a Transportation Information System,” Acta Polytechnica, vol. 48, no. 1, Jan. 2008.
  6. Z. H. Li and W. Guan, “A Transportation Guidance System Based on Data Mining and GABP,” Applied Mechanics and Materials, vol. 490–491, pp. 914–919, Jan. 2014.
  7. H. M. O. Mokhtar, “HITS: A History-Based Intelligent Transportation System,” International Journal of Data Mining & Knowledge Management Process, vol. 1, no. 2, pp. 34–46, Mar. 2011.
  8. S. Das, X. Sun, and A. Dutta, “Text Mining and Topic Modeling of Compendiums of Papers from Transportation Research Board Annual Meetings,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2552, no. 1, pp. 48–56, Jan. 2016.
  9. J.-P. Lebacque and M. M. Khoshyaran, “Multimodal Transportation Network Modeling Based on the Generic Second Order Modeling Approach,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2672, no. 48, pp. 93–103, Oct. 2018.
  10. A.Xie, J. Lu, and E. Parkany, “Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1854, no. 1, pp. 50–61, Jan. 2003.
  11. J.-H. Lewe, L. F. Hivin, and D. N. Mavris, “A multi-paradigm approach to system dynamics modeling of intercity transportation,” Transportation Research Part E: Logistics and Transportation Review, vol. 71, pp. 188–202, Nov. 2014.
  12. M. Rahimi, M. Amirgholy, and E. J. Gonzales, “System modeling of demand responsive transportation services: Evaluating cost efficiency of service and coordinated taxi usage,” Transportation Research Part E: Logistics and Transportation Review, vol. 112, pp. 66–83, Apr. 2018.
  13. M. Rahimi, M. Amirgholy, and E. J. Gonzales, “System modeling of demand responsive transportation services: Evaluating cost efficiency of service and coordinated taxi usage,” Transportation Research Part E: Logistics and Transportation Review, vol. 112, pp. 66–83, Apr. 2018.
  14. B. Ran, P. J. Jin, D. Boyce, T. Z. Qiu, and Y. Cheng, “Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling,” Journal of Intelligent Transportation Systems, vol. 16, no. 4, pp. 226–242, Jul. 2012.
  15. S. Gokul R. and G. Vikas R., “Multitask TSK Fuzzy System Modeling by Mining Intertask Common Hidden Structure,” International Journal Of Engineering And Computer Science, Jun. 2016.
  16. F. Deflorio and L. Castello, “Traffic Modeling of a Cooperative Charge while Driving System in a Freight Transport Scenario,” Transportation Research Procedia, vol. 6, pp. 325–350, 2015.
  17. Z. TIAN, “Modeling and Implementation of an Integrated Ramp Metering-Diamond Interchange Control System,” Journal of Transportation Systems Engineering and Information Technology, vol. 7, no. 1, pp. 61–69, Feb. 2007.
  18. M. L. TAM and W. H. K. LAM, “Modeling the Market Penetration of Personal Public Transport Information System in Hong Kong,” Journal of Intelligent Transportation Systems, vol. 9, no. 2, pp. 81–89, Apr. 2005.
  19. A.Mohammadian and E. J. Miller, “Estimating Expected Price of Vehicles in a Transportation Microsimulation Modeling System,” Journal of Transportation Engineering, vol. 128, no. 6, pp. 537–541, Nov. 2002.
  20. S. H. Cheon, C. Lee, and S. Shin, “Data-driven stochastic transit assignment modeling using an automatic fare collection system,” Transportation Research Part C: Emerging Technologies, vol. 98, pp. 239–254, Jan. 2019.
  21. X. (Jeff) Ban, L. Chu, R. Herring, and J. D. Margulici, “Sequential Modeling Framework for Optimal Sensor Placement for Multiple Intelligent Transportation System Applications,” Journal of Transportation Engineering, vol. 137, no. 2, pp. 112–120, Feb. 2011.
  22. X. Yang and W. W. Recker, “Modeling Dynamic Vehicle Navigation in a Self-Organizing, Peer-to-Peer, Distributed Traffic Information System,” Journal of Intelligent Transportation Systems, vol. 10, no. 4, pp. 185–204, Dec. 2006.
  23. T. A. Raianov, “Mathematical modeling of a strain gauge measurement system in MATLAB SIMULINK program,” Transportation Systems and Technology, vol. 6, no. 2, pp. 85–93, Jun. 2020.
  24. H. Fricke, M. Schlosser, M. A. Garcia, and M. Kaliske, “Embedding aircraft system modeling to ATM safety assessment techniques,” Transportation Research Interdisciplinary Perspectives, vol. 3, p. 100026, Dec. 2019.

Acknowledgements


Authors thank Reviewers for taking the time and effort necessary to review the manuscript.


Funding


No funding was received to assist with the preparation of this manuscript.


Ethics declarations


Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.


Availability of data and materials


No data available for above study.


Author information


Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.


Corresponding author


Rights and permissions


Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/


Cite this article


Muhammad Asim and Huey Fang Pasha, “Measuring Multidimency Poverty Method”, Journal of Enterprise and Business Intelligence, vol.2, no.1, pp. 033-043, January 2022. doi: 10.53759/5181/JEBI202202005.


Copyright


© 2022 Muhammad Asim and Huey Fang Pasha. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.