Wellbeing index avoiding scaling and weights

Abstract

Objective: Existing well-being measures differ in terms of number and format of items, factors being measured, aggregation methods, and are not comparable. A well-being measure involves combining n- number of indicators and quality of the measure depends on properties of combining procedures adopted. The paper proposes two assumption-free aggregation methods to satisfy the desired properties of an index Methods: The paper proposes two indices of well-being in terms of cosine similarity and Geometric mean (GM) avoiding problems associated with scaling of raw data and choosing of weights. Empirical illustration is provided on application of the proposed measures. Results: The proposed indices give better admissibility of operations and satisfy properties like time-reversal test, formation of chain indices, computation of group mean and statistical tests for comparison across time and space. The preferred index can be constructed even for skewed longitudinal data and helps to reflect path of improvement registered by a country/region over time.  Conclusions: The index based on GM is preferred due to wider application areas. The index can further be used for classification of countries, sub-groups and even individuals with morbidity in terms of overall wellbeing values.  Future studies suggested.