A Robust Approach to Composite Indicators Exploiting Interval Data: The Interval-Valued Global Gender Gap Index (IGGGI)

  • Carlo Drago
  • Andrea GattoEmail author
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


Gender equality is a pillar of the sustainable development agenda. Women empowerment and gender mainstreaming are the bases of sound gender policies, especially in countries where greater gender gaps are observed, e.g. rural areas. This issue becomes particularly relevant in least developed countries, where an effective regulation is compelling. It is convened that gender equality is a powerful driver of economic development and social change, especially for its capacity of facilitating entrepreneurship. The appropriate gauging of the legal, economic, social and cultural factors determining or underlying a potential gender gap is crucial to shape and define such gender policies. Thus, it turns fundamental to attribute more robust bases to measure such phenomenon. With the scientific purpose of measuring gender gap in a more reliable way, this work aims to furnish a robust framework to compute composite indicators in the field of gender economics. We consider the weights of the different components. Thus, we apply an interval data analysis to the World Economic Forum’s Global Gender Gap Index. The results show consistent differences among the rankings of the two indexes, translatable in diverse policy implications.


Gender economics Development policy Interval data Composite indicators Sensitivity analysis Rural entrepreneurship 

JEL Classification

J16 C43 C82  O18 O19 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Rome “N. Cusano”RomeItaly
  2. 2.CED—Center for Economic Development & Social ChangeNaplesItaly
  3. 3.Department of Economic and Legal Studies (DISEG), Palazzo Pacanowski, University of Naples “Parthenope”NaplesItaly
  4. 4.Department of Economics and Business Economics, CREATES, Center for Research in Econometric Analysis of Time Series, Aarhus BSS, Aarhus UniversityAarhus VDenmark

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