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Abstract
Well-being has a multidimensional nature as it depends on multifaceted factors such as material conditions and quality of life. The Organization for Economic Co-operation and Development (OECD) has developed the Better Life Index (BLI) as part of the OECD Better Life initiative to facilitate the better understanding of what drives well-being of people. The BLI is a three-level hierarchical composite indicator that covers several socio-economic aspects. In this paper, considering the entire hierarchical structure of the index, we introduce a bottom-up procedure for the aggregation of the components at each level. We formulate the assessment of BLI as a multiple objective programming (MOP) problem that facilitates the implementation of different concepts to derive different aggregation schemes. We incorporate the data from previous years into the normalization process of the indicators, to take into account the discrepancy on their observed values and smooth their deviations across the years. Also, we consider the public opinion about well-being that is captured from the worldwide responses in the web platform of OECD BLI. We incorporate the public opinion into the assessment models in the form of weight restrictions. In this way, we reduce the effect of compensation that might be imposed by the adopted modelling approach. We apply our methodology to the data of 38 countries (35 OECD and 3 non-OECD economies) for the year 2017. Our findings illustrate that the public opinion in the form of weight restrictions can effectively drive the optimization process and depict the collective preferences to the BLI scores.
Original language | English |
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Article number | 100699 |
Number of pages | 15 |
Journal | Socio-Economic Planning Sciences |
Volume | 70 |
DOIs | |
Publication status | Published - Jun 2020 |
Keywords
- Composite index
- Hierarchical evaluation
- Multiple objective programming framework
- OECD better life index
- Public opinion
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CEGE: Research Center in Management and Economics
Silva, M., Sousa, R., Alves, P., Madsen, A., Lourenço, A., Carvalho, A. S., Andrade, A., Silva, P. D., Vlačić, B., Martins, C., Gomes, C., Gevrek, Z. E., Oliveira, F. G. D., Faria, G., Marreiros, H., Pinho, J., Machado, J., Araújo, J., Pinto, J., Rego, A., Gaspar, J. M., Corbo, L., Costa, L., Fernandes, L., Sottomayor, M., Martins, N., Hernández-Marrero, P., Gonçalves, R., Ribeiro, R., Coelho, S. L., Pereira, S. M., Silva, S., Rodrigues, V., Sotiros, D. G., Valverde, C. J. L., Leitão, A., Julião, J., Tavares, M. F. F., Lages, C. R., Elmashhara, M. G. & Teymourifar, A.
1/01/20 → 31/12/23
Project: Research