Abstract:
The existing poverty monitoring system in Sri Lanka only measured up to the district level. It has been unable to meet the requirement of the government and other poverty intervention organizations for identifying the extremely poor households and evaluate the selected poverty interventions. However, the extreme poorest household is to be unrecoverable from their present level because they did not receive any assistance not only from the society but also from the responsible authorities. Another major issue arising with this measurement is considering only monetary dimensions of a household. Nowadays most developing countries tend to use the multidimensional well-being index to measure their individual well-being levels which can use for comparing up to international level. According to those reasons, this study is seeking a well-being measuring index to selected case study area and compare the different measurement and their applicability and limitations. To conduct this research, case study selected as Kelani Valley railway line either side household from baseline railway station to Cotta road railway station with 300 sample households. The developed index included 19 indicators and the survey questionnaire designed basing those indicators. With the analysis results, only 11 indicators are significance with the 0.05 level and with the significant indicators, the new index is derived. Furthermore, the derived index, peoples’ perceptions and existing poverty measurement modeling with linear regression (SPSS) and compared each model and indicators to find out the best tool for wealth ranking in the local context. Derived index and existing poverty measurement more significance with the 0.05 level. As the final conclusion though this research tends to intervene in a new well-being index with multi-dimensions the existing measurement more reliable than it. but the well-being index more correlates with the indicators than the existing measurement.so it is good to rethink the existing measurement improvement with new indicators and the monitor system can break down up to the local level which is more useful to knowing well-being levels as individuals or community.
