Skip to main content
Log in

Constructing Composite Indicators with Individual Judgements and Best–Worst Method: An Illustration of Value Measure

  • Original Research
  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

In practice, a variety of composite indicators are created using the arithmetic average of different sub-indicators. However, this scheme is typically criticized of the possibility of compensation. For this reason, this paper re-constructs these composite indicators by means of a new methodology. Comprehensive individual judgements among the sub-indicators have been considered to determine the maximum total utilities. Then the best–worst method is introduced to determine the preference associated with various individual judgements. An illustration of Value Measure of health systems is presented to demonstrate the validity and usefulness of our methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. https://www.futurehealthindex.com/value-measure/.

References

  • Areal, F. J., & Riesgo, L. (2015). Probability functions to build composite indicators: A methodology to measure environmental impacts of genetically modified crops. Ecological Indicators, 52, 498–516.

    Article  Google Scholar 

  • Badea, A. C., Claudio, M. R. S., Tarantola, S., & Bolado, R. (2011). Composite indicators for security of energy supply using ordered weighted averaging. Reliability Engineering & System Safety, 96(6), 651–662.

    Article  Google Scholar 

  • Barron, F. H., & Barrett, B. E. (1996). Decision quality using ranked attribute weights. Management Science, 42(11), 1515–1523.

    Article  Google Scholar 

  • Becker, W., Saisana, M., Paruolo, P., & Vandecasteele, I. (2017). Weights and importance in composite indicators: Closing the gap. Ecological Indicators, 80, 12–22.

    Article  Google Scholar 

  • Bordley, R. F. (1982). A multiplicative formula for aggregating probability assessments. Management Science, 28(10), 1137–1148.

    Article  Google Scholar 

  • Cherchye, L., Moesen, W., Rogge, N., & Van Puyenbroeck, T. (2007). An introduction to ‘benefit of the doubt’ composite indicators. Social Indicators Research, 82(1), 111–145.

    Article  Google Scholar 

  • Cherchye, L., Moesen, W., Rogge, N., Van Puyenbroeck, T., Saisana, M., Saltelli, A., et al. (2008). Creating composite indicators with DEA and robustness analysis: The case of the Technology Achievement Index. Journal of the Operational Research Society, 59(2), 239–251.

    Article  Google Scholar 

  • Claveria, O., Monte, E., & Torra, S. (2018). A data-driven approach to construct survey-based indicators by means of evolutionary algorithms. Social Indicators Research, 135(1), 1–14.

    Article  Google Scholar 

  • Ding, Y., Fu, Y., Lai, K. K., & Leung, W. J. (2018). Using ranked weights and acceptability analysis to construct composite indicators: A case study of regional sustainable society index. Social Indicators Research, 139(3), 871–885.

    Article  Google Scholar 

  • Ebert, U., & Welsch, H. (2004). Meaningful environmental indices: A social choice approach. Journal of Environmental Economics and Management, 47(2), 270–283.

    Article  Google Scholar 

  • Fusco, E. (2015). Enhancing non-compensatory composite indicators: A directional proposal. European Journal of Operational Research, 242(2), 620–630.

    Article  Google Scholar 

  • Gnaldi, M., & Del Sarto, S. (2018). Variable weighting via multidimensional IRT models in composite indicators construction. Social Indicators Research, 136(3), 1139–1156.

    Article  Google Scholar 

  • Hatefi, S., & Torabi, S. (2010). A common weight MCDA-DEA approach to construct composite indicators. Ecological Economics, 70(1), 114–120.

    Article  Google Scholar 

  • Hochbaum, D. S., & Levin, A. (2006). Methodologies and algorithms for group-rankings decision. Management Science, 52(9), 1394–1408.

    Article  Google Scholar 

  • Karagiannis, G. (2017). On aggregate composite indicators. Journal of the Operational Research Society, 68(7), 741–746.

    Article  Google Scholar 

  • Lauro, N. C., Grassia, M. G., & Cataldo, R. (2018). Model based composite indicators: New developments in partial least squares-path modeling for the building of different types of composite indicators. Social Indicators Research, 135(2), 421–455.

    Article  Google Scholar 

  • Lee, S., & Yu, J. (2013). Composite indicator development using utility function and fuzzy theory. Journal of the Operational Research Society, 64(8), 1279–1290.

    Article  Google Scholar 

  • Luzzati, T., & Gucciardi, G. (2015). A non-simplistic approach to composite indicators and rankings: An illustration by comparing the sustainability of the EU countries. Ecological Economics, 113, 25–38.

    Article  Google Scholar 

  • Morris, P. A. (1977). Combining expert judgments: A Bayesian approach. Management Science, 23(7), 679–693.

    Article  Google Scholar 

  • Munda, G. (2012). Choosing aggregation rules for composite indicators. Social Indicators Research, 109(3), 337–354.

    Article  Google Scholar 

  • Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: A defensible setting. Applied Economics, 41(12), 1513–1523.

    Article  Google Scholar 

  • Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2005). Handbook on constructing composite indicators. Paris: OECD Publishing.

    Google Scholar 

  • Peng, C., Wu, X., Fu, Y., & Lai, K. K. (2017). Alternative approaches to constructing composite indicators: An application to construct a sustainable energy index for APEC economies. Operational Research, 17(3), 747–759.

    Article  Google Scholar 

  • Rezaei, J. (2015). Best–worst multi-criteria decision-making method. Omega, 53, 49–57.

    Article  Google Scholar 

  • Rezaei, J. (2016). Best–worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130.

    Article  Google Scholar 

  • Rogge, N. (2018). On aggregating benefit of the doubt composite indicators. European Journal of Operational Research, 264(1), 364–369.

    Article  Google Scholar 

  • Rogge, N., & Van Nijverseel, I. (2018). Quality of life in the European Union: A multidimensional analysis. Social Indicators Research, 141(2), 765–789. https://doi.org/10.1007/s11205-018-1854-y.

    Article  Google Scholar 

  • Saaty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7), 841–855.

    Article  Google Scholar 

  • Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. European Commission, Joint Research Centre.

  • Saltelli, A. (2007). Composite indicators between analysis and advocacy. Social Indicators Research, 81(1), 65–77.

    Article  Google Scholar 

  • Shen, Y., Hermans, E., Brijs, T., & Wets, G. (2013). Data envelopment analysis for composite indicators: A multiple layer model. Social Indicators Research, 114(2), 739–756.

    Article  Google Scholar 

  • Song, L., & Liu, F. (2018). An improvement in DEA cross-efficiency aggregation based on the Shannon entropy. International Transactions in Operational Research, 25(2), 705–714.

    Article  Google Scholar 

  • Song, L., Fu, Y., Zhou, P., & Lai, K. K. (2017). Measuring national energy performance via energy trilemma index: A stochastic multicriteria acceptability analysis. Energy Economics, 66, 313–319.

    Article  Google Scholar 

  • Van Puyenbroeck, T., & Rogge, N. (2017). Geometric mean quantity index numbers with Benefit-of-the-Doubt weights. European Journal of Operational Research, 256(3), 1004–1014.

    Article  Google Scholar 

  • Verbunt, P., & Rogge, N. (2018). Geometric composite indicators with compromise Benefit-of-the-Doubt weights. European Journal of Operational Research, 264(1), 388–401.

    Article  Google Scholar 

  • Wang, H. (2015). A generalized MCDA-DEA (multi-criterion decision analysis-data envelopment analysis) approach to construct slacks-based composite indicator. Energy, 80, 114–122.

    Article  Google Scholar 

  • Wu, S., Fu, Y., Shen, H., & Liu, F. (2018). Using ranked weights and Shannon entropy to modify regional sustainable society index. Sustainable cities and society, 41, 443–448.

    Article  Google Scholar 

  • Yakowitz, D., Lane, L., & Szidarovszky, F. (1993). Multi-attribute decision making: dominance with respect to an importance order of the attributes. Applied Mathematics and Computation, 54(2–3), 167–181.

    Article  Google Scholar 

  • Zhang, L., & Zhou, P. (2018). A non-compensatory composite indicator approach to assessing low-carbon performance. European Journal of Operational Research, 270(1), 352–361.

    Article  Google Scholar 

  • Zhou, P., Delmas, M., & Kohli, A. (2017). Constructing meaningful environmental indices: A nonparametric frontier approach. Journal of Environmental Economics and Management, 85, 21–34.

    Article  Google Scholar 

  • Zhou, P., & Ang, B. (2009). Comparing MCDA aggregation methods in constructing composite indicators using the Shannon-Spearman measure. Social Indicators Research, 94(1), 83–96.

    Article  Google Scholar 

  • Zhou, P., Ang, B., & Poh, K. (2006). Comparing aggregating methods for constructing the composite environmental index: An objective measure. Ecological Economics, 59(3), 305–311.

    Article  Google Scholar 

  • Zhou, P., Ang, B., & Poh, K. (2007). A mathematical programming approach to constructing composite indicators. Ecological Economics, 62(2), 291–297.

    Article  Google Scholar 

  • Zhou, P., Ang, B., & Zhou, D. (2010). Weighting and aggregation in composite indicator construction: A multiplicative optimization approach. Social Indicators Research, 96(1), 169–181.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yelin Fu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Fu, Y. Constructing Composite Indicators with Individual Judgements and Best–Worst Method: An Illustration of Value Measure. Soc Indic Res 149, 1–14 (2020). https://doi.org/10.1007/s11205-019-02236-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-019-02236-3

Keywords

Navigation