Ranking and grouping of the districts of Sri Lanka based on the expenses of households: A multivariate analysis

Sebastian Reyalt Gnanapragasam


Sri Lanka has twenty-five districts administrated under nine provinces. The cost of living (CoL) diverges among the districts in Sri Lanka like in many parts of the world. The rankings and groupings are useful tools for decision making by stakeholders. Principal component analysis and cluster analysis are used for ranking and grouping the districts, respectively, based on the expenses of a household in Sri Lanka. Sri Lankans spend more for non-food items than food items, particularly for housing and transport, and therefore non-food items are the most influencing factor to decide the CoL in the country. It is concluded that Colombo district is the most expensive district, followed by Gampaha and Kalutara, whereas Kilinochchi and Mullaitivu are among the least expensive districts in Sri Lanka. Districts with moderately high CoL were also identified. These classifications will facilitate investors to make their decision on where to invest more to gain more profit while satisfying the need of customers in that district. Further, this will help people to decide which part of the country will be suitable to settle in depending on their own income level and CoL. Moreover, this grouping will provide some information to policy makers when planning infra-structure development in the country, and it may also provide a direction to a new index to measure CoL in Sri Lanka.

Keywords: Cluster analysis, cost of living, principal component analysis.

Full Text:



Abraham KG. 2003. Towards a cost of living index: Progress and prospects. Journal of Economic Perspectives 17(1): 45-58.

Anderberg MR. 1973. Cluster analysis for applications, Academic Press, New York.

Blashfield RK. 1976. Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. The Psychological Bulletin 83(3): 377–388.

Ferreira L, Hitchcock DB. 2009. A comparison of hierarchical methods for clustering functional data. Journal of Communications in statistics- Simulation and computation 38(9):1925–1949.

Gnanapragasam SR. 2016. Classification of districts in Sri Lanka based on the cost of living: A multivariate approach. Proceedings of the Wayamba University International Conference, Sri Lanka 2016: 18.

Gnanapragasam SR. 2017. A multivariate approach to classify the districts of Sri Lanka based on the cost of living. International Journal of Information Research and Review 4(5): 4128-4132.

Hands S, Everitt B. 1987. A Monte Carlo study of the recovery of cluster structure in binary data by hierarchical clustering techniques. Multivariate Behavioral Research. 22(2): 235–243. DOI:10.1207/s15327906mbr2202_6.

Houweling TAJ, Kunst AE, Mackenbach JP. 2003. Measuring health inequality among children in developing countries: does the choice of the indicator of economic status matter? International Journal for Equity in Health 2(8)

Jacobs D, Perera D, Williams T. 2014. Inflation and the cost of living. RBA Bulletin, Reserve bank of Australia, March Quarter 2014: 33-46.

Jain NC, Indrayan A, Goel LR. 1986. Monte Carlo comparison of six hierarchical clustering methods on random data. Pattern Recognition 19 (1): 95-99.

Johnson RA, Wichern DW. 2002. Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice Hall.

Jorgenson DW, Slesnick DT. 1990. Individual and social cost of living indexes. Contribution to Economic Analysis 196: 155-234. DOI: 10.1016/B978-0-444-88108-3.50009-3

Kaufman L, Rousseeuw PJ. 1990. Finding groups in data. An introduction to cluster analysis, Wiley, New Jersey.

Korale RBM. 2001. The problem of measuring cost of living in Sri Lanka, Research Studies. Macroeconomic Policy and Planing Series, IPS Publication. Retrieved from http://www.ips.lk/wp-content/uploads/2017/01/Problems-of-Measuring-Cost-of-Living.pdf

Kuiper FK, Fisher L. 1975. A Monte Carlo comparison of six clustering procedures.

International Biometric Society 31(3): 777–783. DOI: 10.2307/2529565

Kulatunge S. 2017. Inflation dynamics in Sri Lanka: An empirical analysis, Central bank of Sri Lanka – Staff studies 45 (1 & 2): 31-66.

Manage ABW, Scariano SM. 2013. An introductory application of principal components to cricket data, Journal of Statistics Education. 21(3): DOI:10.1080/10691898.2013.11889689

McKenzie DJ. 2005. Measuring inequality with asset indicators. Journal of population economics 18(2): 229-260.

Messer LC, Laraia BA, Kaufman JS, Eyster J, Holzman C, Culhane J, Elo I, Burke JG, O’Campo P. 2006. The development of a standardized neighborhood deprivation index. Journal of Urban Health 83(6): 1041-1062. DOI:10.1007/s11524-006-9094-x

Muzamhindo S, Kong Y, Famba T. 2017. Principal component analysis as a ranking tool - A case of world universities. International Journal of Advanced Research 5(6): 2114-2135. DOI :10.21474/IJAR01/4650

Peneder M. 2005. Creating industry classifications by statistical cluster analysis, Estudios De Economia Aplicada 23(2): 451-463.

Primpas I, Tsirtsis G, Karydis M, Kokkoris GD. 2010. Principal component analysis: Development of a multivariate index for assessing eutrophication according to the European water framework directive. Ecol Indic 10(2): 178-83. DOI: 10.1016/j.ecolind.2009.04.007.

Salmond C, Crampton P. 2002. NZDep2001 index of deprivation. Department of public health, Wellington School of Medicine and Health Sciences. Retrieved from: https://www.researchgate.net/publication/228798047

Steiner JE. 2007. World university rankings: A principal component analysis. Instituto de Estudos Avançados Instituto de Astronomia, Geofísica e Ciências Atmosféricas Universidade de São Paulo. Retrieved from http://arxiv.org/ ftp/physics/papers/ 0605/0605252.pdf.

World Bank in Sri Lanka. 2018. Retrieved from https://www.worldbank.org/en/country/ srilanka/overview (accessed 22-03-2019).


  • There are currently no refbacks.

Creative Commons Licence
Ruhuna Journal of Science by University of Ruhuna is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

eISSN: 2536-8400

Print ISSN: 1800-279X (Before 2014)