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Session 29

Measuring Equality With Quality

4 June 2026
16:30 – 18:00
ŠIBENIK V

Presentation title
Advancing Statistical Quality in Measuring Gender Equality: Evidence from the Refreshed Gender Equality Index
Since the launch of the Gender Equality Index in 2013, Europe has undergone profound social, economic and technological transformations.

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The ways people work, learn, provide care and participate in digital life have evolved significantly, reshaping both opportunities and inequalities between women and men. In response to these changes, the Gender Equality Index developed by the European Institute for Gender Equality (EIGE) has been carefully reviewed and refreshed to ensure it continues to reflect contemporary realities while preserving its original purpose: to measure and monitor progress towards gender equality in the European Union.

This updated Index integrates newly available data sources and aligns with emerging EU policy priorities, allowing for a more accurate and consistent assessment of gender equality over time. While core concepts and the overall framework remain stable to ensure comparability, specific measurement approaches have been refined to better capture changes in areas such as work patterns, care responsibilities, learning pathways and digital participation.

The refreshed Index retains a robust methodological foundation, adhering to the highest quality standards in the development of composite indicators. Particular attention has been paid to transparency, statistical soundness, comparability across countries and over time, and relevance for policy-making. The conference presentation will outline the key methodological updates, discuss how data improvements and policy developments were incorporated, and explain the criteria used to assess the quality and robustness of this composite indicator.

Main author / Presenter
Irene Rioboo Leston
European Institute for Gender Equality

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Dr Irene Rioboo works as a researcher at EIGE, where she conducts studies mainly on the development of the Gender Equality Index and the implementation of the Beijing Platform for Action in the EU. Before joining EIGE, Irene worked at Eurostat and Eurofound in policy-oriented projects on socio-economic inequalities. She also was an academic for more than 15 years, working as a Professor in Applied Statistics and leading research projects on several gender studies.

Presentation title
The quality of data in MICS survey
The Multiple Indicator Cluster Survey (MICS) is an international household survey initiative developed by UNICEF to provide reliable, comparable data across countries.

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It supports evidence-based policymaking by supplying key indicators used in national development planning and in monitoring progress toward the Sustainable Development Goals and other global commitments. Through comprehensive data collection, MICS helps assess the living conditions and well-being of different population groups and evaluates the effectiveness of strategies aimed at improving outcomes for children in Kosovo. Most of the health-related indicators for children and women are produced through MICS, which serves as their primary data source.

The Multiple Indicator Cluster Survey (MICS) is being implemented in Kosovo for the third time, two surveys are conducted at the same time: National Sample and Roma Ashkali and Egyptian sample ensuring the production of high-quality data. The survey generates statistically robust and internationally comparable data.

The survey includes numerous modules, with data collected through five types of questionnaires: the Household Questionnaire; questionnaires for women aged 15–49 and men aged 15–49; and questionnaires for children aged 5–17 and children under five. The survey also includes anthropometry forms and data collection from health facilities. Height and weight measurements are taken for all children under five and for children aged 5–9. Immunization data are collected for all children under two years of age. Data are collected using CAPI methods, with CSPro used for programming. Data cleaning, editing, and analysis follow a structured framework to finalize the MICS dataset.

This paper examines the key phases of the survey and describes the methodological approaches to ensure data quality.

Main author / Presenter
Servete Muriqi Gashi
Kosovo Agency of Statistics

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I work in the Methodology and Sampling Units at the Kosovo Agency of Statistics, where I serve as Quality Manager responsible for preparing quality reports across statistical domains. I am a member of the steering committee supporting the implementation of the European Statistics Code of Practice and contribute to Peer Review activities. My work includes implementing the SIMS metadata system, supporting the preparation of ESMS metadata and ESQRS quality indicators, and contributing to the development of a comprehensive quality management framework aligned with ESS standards. I am the National Coordinator of MICS and am involved in all stages of the survey, including questionnaire customization, programming, sampling, and methodological design, coordinating all activities of survey. My responsibilities also include training interviewers and supervisors, customizing data-processing syntax, and conducting data analysis.

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Presentation title
Inequality Analysis with Quality Control in Sample Surveys with wINEQ R Package
Problem Statement

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One of the core activities in official statistics is conducting sample surveys. An inherent aspect of sample surveys is sampling error, which strongly affects the reliability of estimates in terms of precision. Information about estimated totals, means, and other statistics is incomplete without corresponding measures of precision or confidence intervals.

Inequality is a topic of interest across economics, sociology, and statistics, and many inequality measures have been developed. Nevertheless, for most of them researchers have not proposed consistent and asymptotically unbiased estimators. Reporting the statistical properties of an estimator - such as its precision - is essential for quality control.

Aim

To improve inequality analysis and quality monitoring for data from sample surveys, statisticians may use the recently developed R package wINEQ created by Statistics Poland. It provides more than a dozen inequality measures suitable for variables on ratio, interval, and ordinal scales, with full support for data weighting and reporting bias and precision for all implemented measures.

During the talk, a practical workflow using wINEQ will be presented.

Methods

Bias and precision of inequality measures are assessed using naïve and calibrated bootstrap procedures. At the current stage of development, survey design is not taken into account as explicitly as in the survey package. All inequality measures are implemented with the option of incorporating data weights.

Conclusions

Inequality analysis has been receiving increasing attention over the years. Scientists and statisticians should conduct such analyses with robust quality monitoring to provide comprehensive outputs to data stakeholders. The wINEQ package is an attempt to address this need.

Main author / Presenter
Sebastian Wójcik
Statistics Poland, University of Rzeszów

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Sebastian Wójcik works at Survey Methodology and Quality Division in Statistics Poland and at the Institute of Mathematics of the University of Rzeszów. His main areas of interest include probability theory, data modelling, AI/ML, as well as data analysis and visualization in R.

Presentation title
Intersectionality within Gender Statistics in Political Decision‑Making: Ensuring statistical Quality, Feasibility and Ethical Integrity
Reliable, high quality statistics are essential for monitoring equality commitments and informing evidence based policymaking.

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As EU institutions increasingly integrate an intersectional lens into gender equality strategies, statistical systems face the challenge of extending traditional sex disaggregated data to capture how gender intersects with other identity markers—while preserving quality, comparability and confidentiality.

This paper presents the results of a comprehensive feasibility assessment conducted by the European Institute for Gender Equality (EIGE) on integrating intersectional variables into the Women and Men in Decision Making (WMID) political datasets. Covering over 150,000 political positions across EU countries, the study evaluates policy relevance, data availability, methodological requirements and resource implications for eleven potential variables, including age, country of birth, disability, racial or ethnic origin, sexual orientation and socioeconomic characteristics.

Findings show that while policy relevance is strong across several variables, data availability is highly uneven. Variables such as age, educational level and country of birth show high feasibility, supported by accessible, reliable sources and external reference data. Conversely, sensitive variables—racial or ethnic origin, disability, sexual orientation, and religion—are rarely publicly available, raise serious quality concerns, and pose risks for privacy and identifiability within small populations.

The paper proposes a roadmap built on the principles of the European Statistics Code of Practice, emphasising sustainability, timeliness, confidentiality, and methodological comparability. It highlights the shift from aggregated to micro data collection and outlines the resource and infrastructure needs for integrating intersectionality into existing statistical systems.

By operationalising intersectionality in a measurable, quality assured manner, statistics can better capture structural inequalities, reflecting women and men in al their diversity and support more inclusive democratic decision making across Europe.

Main author / Presenter
Ligia Nobrega, Irene Rioboo
European Institute for Gender Equality

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Ligia Nobrega is a gender statistics expert working at the European Institute for Gender Equality (EIGE) since 2010. She is responsible for managing EIGE's Gender Statistics Database. Her work primarily involves advancing gender-sensitive policymaking, offering technical expertise, and developing and implementing methodologies to monitor frameworks related to gender equality in both conceptual and measurement terms. Prior to her role at EIGE, she worked at Eurostat, where she developed training programmes and capacity-building initiatives in social statistics within the context of international statistical cooperation. Ligia holds two Master's degrees: one in Sociology and Social Analysis, and another in Project Management, both awarded by the University of Coimbra, Portugal. Dr Irene Rioboo is a researcher at the European Institute for Gender Equality (EIGE), where she works on the Gender Equality Index, the CARE survey and the monitoring of the Beijing Platform for Action in the EU. Previously, she worked at Eurostat and Eurofound on policy-oriented research on socio-economic inequalities and has over 15 years of experience in academia as a Professor of Applied Statistics.

Presentation title
What Is Required to Calculate an Accurate Student–Teacher Ratio Indicator?
This paper presents the methodological approach of the Croatian Bureau of Statistics to the compilation of an accurate student–teacher ratio indicator.

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Particular emphasis is placed on the precise definition of key concepts, notably employees in the education sector and teachers. The interpretation of the student–teacher ratio must be approached with caution, taking into account the inherent challenges in its compilation.

While the number of students can generally be measured with a high degree of accuracy, the calculation of the number of teachers presents significant methodological challenges due to the specific characteristics of employment in the education sector. Teachers frequently work in more than one educational institution, which creates a substantial risk of double counting in institutional-level data. It is not feasible to assign a teacher who is employed in multiple schools exclusively to a single institution without introducing distortions into the data.

To address this issue, the use of full-time equivalents (FTEs) is essential. By allocating the proportion of a teacher’s teaching load to each institution in which they are employed, it is possible to derive a more accurate measure of teaching resources. This approach allows for the calculation of the share of the total teaching norm attributable to each institution, although it does not produce an exact headcount of individual teachers.

A well-known methodological issue arises when comparing bottom-up and top-down approaches. In a bottom-up approach, teachers are aggregated across institutional units, often to produce regional statistics. In this process, individuals working in multiple locations are unavoidably counted more than once, as there is no objective basis for assigning them to a single spatial unit. As a result, the same indicator may be compiled and expressed in different ways, depending on its intended analytical or policy use.

The key prerequisite for producing a reliable student–teacher ratio is strict adherence to the methodological guidelines of the UOE Manual. The integration of multiple data sources—statistical, administrative, and, where relevant, private—ensures harmonization, comparability, and consistency throughout the data production process. Cross-checking and integrating data from these sources enable the compilation of the most accurate and robust estimates of teaching staff and, consequently, of the student–teacher ratio.

Main author / Presenter
Marija Gojević
Croatian Bureau of Statistics

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Marija Gojević (born 1972) has over 25 years’ experience in official statistics. Since end of 2019 she has been the head of Education, Culture and Information Society Statistics Department. She is a member of INES and NESL OECD working groups and OECD In-formal working group on Finance, also member of the EUROSTAT Task Force on UOE Education expenditure data, CVTS Task force - Continuing Vocational Training Survey, 2022 AES Task Force - Adult Education Survey and Working Group on Culture Statistics, International Association for Research in Income and Wealth (IARIW) and EGMUS-The European Group on Museum Statistics. Before 2019 she had been working in Croatian National Accounts.

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