Расширения Joomla 3

POVERTY LINE, POVERTY THRESHOLD, POVERTY FORECASTING, METHODS

Hits: 91

UDC: 510+519.6

 

Pejman Jafari

 

In this article, the author focuses on poverty and its consequences. The SBM method was used to calculate the poverty threshold, which helped to determine the upper and lower limits of poverty and forecast the poverty rate for the next three years. The data set entries included income, education, healthcare, and basic needs from 2020 to 2022.

These inputs were used to calculate the poverty threshold. The problem-solving steps included making the dataset dimensionless, finding slacks, calculating variable weights, and then calculating poverty through Y = λ Income * (Norm. Income) + … + λ Basic Needs (Norm. Basic Needs). The poverty lines (Y) were determined to be 0.527, 0.568, and 0.628 from 2020 to 2022, respectively. The next part focused on calculating the poverty limits. The upper limits (UL) were calculated as “Poverty Line + (λ max * Sum of Slacks)” and the lower limits (LL) were calculated as “Poverty Line - (λ min * Sum of Slacks).” The results from 2020 to 2022 were 1.041, 0.087, 1.010, 0.144, and 1.033, 0.300, respectively. The innovation at this stage was to find bandwidth as an indicator of the intensity of poverty. Finally, the last stage focused on forecasting the Poverty threshold. The average annual growth rate (AAGR) was used to calculate the increasing coefficient of the regression line through [(Ending Value / Beginning Value) ^ (1 / Number of Years)] – 1 formula and found to be 0.09 or 9%. By this stage, 0.685, 0.746, and 0.813 were forecasted as the poverty rates for the years 2023-2025. The kind of poverty diagnosed at this stage was progressive.

 

Key words: Slack Based Method, Data bound, Average Annual Growth Rate (AAGR)

 

Information about the author

 

Pejman Jafari – Tajik National University, post-doctoral.

Address: 734025, Dushanbe, Tajikistan, Rudaki Ave., 17.

Phone: (+992)001016655. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it..

 

REVIEWER: Saidzoda I.M., Candidate of Technical Sciences

 

REFERENCES

 

  1. Tone, K. A slacks-based measure of efficiency in data envelopment analysis/European Journal of Operational Research, 2001. 130(3). – Pp.498-509.
  2. Cooper, W.W., ET. Al. Data envelopment analysis: History, models, and interpretations/ Handbook on data envelopment analysis, Springer, 2007. – Pp. 1-39.
  3. Cancian, M., W. W., ET. Al. Changing poverty, changing policies, Russell Sage Foundation, (2009.
  4. Emrouznejad, A., W. W., ET. Al. A survey and analysis of the first-order SBM-DEA models. Omega, 2018, 80. – Pp. 274-284.
  5. Ravallion, M. The economics of poverty: History, measurement, and policy/ Oxford University Press.
  6. Brady, D., W. W., ET. Al. The Oxford Handbook of the Social Science of Poverty/Oxford University Press, 2018
  7. Deaton, A. The analysis of household surveys: A micro-econometric approach to development policy/ World Bank Publications, 1997.
  8. Asgharpour, M. J. Multiple Criteria Decision Making. - Tehran University Publication, 1998.ISBN 96-4-03-9320-7. – P.194.
  9. Fodor, J. Fuzzy preference modeling and multi-criteria Decision-Support. - Kluwer Academic publication, 1995
  10. Chen, S. J., ET. Al. Fuzzy Multiple Attribute decision-making/ Springer- verlag, 1992.
  11. Lai Hwang, Ching, ET. Al. Multi Objective Decision-making-methods and Applications/Springer– verlag. – Berlin Heidelburg,1979
  12. Goedhart, Th., ET. Al, the Poverty Line: Concept and Measurement, Journal of Human Resources, \ Th., ET.AL Goedhart\\ 12, (1977) . – Pp.503-520.
  13. Kapteyn, A., ET. Al, Family Composition and Family Welfare, in: Simon, J. and J. DaVanzo (eds), Research in Population Economics, 11, JAI-Press, Greenwich, \ A., ET.Al \\ (1980). – Pp.77-97.
  14. Jasso, G., Et Al, Distributive Justice and Earned Income, American Sociological Review\ G. Jasso\\ 1977. Vol.42. – Pp.639-651.
  15. Wu, D. D., ET. All..A comparison of three methods for selecting values of input variables in the analysis of output from a computer code/Journal of Statistical Computer Simulation, 2002. 72 (2). – Pp.119–139

 

Article received: 12.10.2023

Approved after review: 24.12.2023

Accepted for publication: 15.04.2024

 

   
© ALLROUNDER