A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators

[EN] There is a growing interest in research on the role that space plays in defining and measuring well-being or quality of life. In this paper, we propose to evaluate the regional quality of life using the Multi-Reference Point based Weak Strong Composite Indicator approach, to further enhance the quality of the sub-national analysis. The major motivation is to facilitate assessing the regional quality of life performance at different geographical scales and compensability levels. As an example of application, we compute the composite indicators for 19 Spanish regions to paint a comprehensive picture of the regional quality of life using two different geographical scales: the Spanish and the European ones. Moreover, we provide warning signals to regional, national and European policy-makers on the quality of life dimensions in which each region needs further improvements.This research was partially funded by the Spanish Ministry of Economy and Competitiveness (Project PID2019-104263RB-C42), from the Regional Government of Andalucía (Project P18-RT-1566), and by the EU ERDF operative program (Project UMA18-FEDERJA-065)Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2021). A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators. Social Indicators Research (Online). 1-20. https://doi.org/10.1007/s11205-021-02818-0S120Blancas, F., Caballero, R., González, M., Lozano-Oyola, M., & Pérez, F. (2010). Goal programming synthetic indicators: An application for sustainable tourism in andalusian coastal counties. Ecological Economics, 69(11), 2158–2172.Boggia, A., Massei, G., Pace, E., Rocchi, L., Paolotti, L., & Attard, M. (2018). Spatial multicriteria analysis for sustainability assessment: A new model for decision making. Land Use Policy, 71, 281–292.Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2), 115–151.Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual fund’s environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313–325.Costa, D. S. (2015). Reflective, causal, and composite indicators of quality of life: A conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065.Durand, M. (2015). The OCDE better life initiative: How’s life? and the measurement of well-being. Review of Income and Wealth, 61(1), 4–17.El Gibari, S., Cabello, J. M., Gómez, T., & Ruiz, F. (2021). Composite indicators as decision making tools: The joint use of compensatory and non-compensatory schemes. International Journal of Information Technology and Decision Making, 20(3), 847–879.El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235–1250.El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: A review. Journal of Business Economics, 89(1), 1–24.European Commission: Eurostat quality of life database. (2020). url http://ec.europa.eu/eurostat/data/database.Freudenberg, M. (2003). Composite indicators of country performance.Garcia-Bernabeu, A., Cabello, J. M., & Ruiz, F. (2020). A multi-criteria reference point based approach for assessing regional innovation performance in Spain. Mathematics, 8(5), 797.Goerlich, F. J., & Reig, E. (2021). Quality of life ranking of spanish cities: A non-compensatory approach. Cities, 109, 102979.Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141, 61–94.Greyling, T., & Tregenna, F. (2017). Construction and analysis of a composite quality of life index for a region of South Africa. Social Indicators Research, 131(3), 887–930.Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., et al. (2001). Quality of life indexes for national policy: Review and agenda for research. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 71(1), 58–78.INE: Indicadores de calidad de vida. (2020). url https://cutt.ly/Zj0L0qX.Ivaldi, E., Bonatti, G., Soliani, R., et al. (2014). Composite index for quality of life in italian cities: An application to urbes indicators. Review of Economics and Finance, 4(4)Karagiannis, R., & Karagiannis, G. (2020). Constructing composite indicators with shannon entropy: The case of human development index. Socio-Economic Planning Sciences, 70, 100701.Lagas, P., van Dongen, F., van Rijn, F., & Visser, H. (2015). Regional quality of living in Europe. Region, 2(2), 1–26.Malkina-Pykh, I. G., & Pykh, Y. A. (2008). Quality-of-life indicators at different scales: Theoretical background. Ecological Indicators, 8(6), 854–862.Marchante, A. J., & Ortega, B. (2006). Quality of life and economic convergence across Spanish regions, 1980–2001. Regional Studies, 40(5), 471–483.Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research, 127(3), 983–1003.Mazziotta, M., & Pareto, A. (2020). Composite indices construction: The performance interval approach. Social Indicators Research pp. 1–11.Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on constructing composite indicators.OECD: Handbook on constructing composite indicators: methodology and user guide. (2008). Paris: OECD publishing.Patil, G.R., & Sharma, G. (2020). Urban quality of life: An assessment and ranking for indian cities. Transport Policy.Royuela, V., Suriñach, J., & Reyes, M. (2003). Measuring quality of life in small areas over different periods of time. Social Indicators Research, 64(1), 51–74.Ruiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189–197.Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building ease-of-doing-business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130–140.Ruiz, F., El Gibari, S., Cabello, J.M., & Gómez, T. (2019). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega.Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. Ispra: Joint Research Centre.Stiglitz, J.E., Sen, A., Fitoussi, J.P., et al. (2009). Report by the commission on the measurement of economic performance and social progress

A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain

[EN] The evaluation of regional innovation performance through composite innovation indices can serve as a valuable tool for policy-making. While discussion on the best methodology to construct composite innovation indices continues, we are interested in deepening the use of reference levels and the aggregation issue. So far, additive aggregation methods are, largely, the most widespread aggregation rule, thus allowing for full compensability among single indicators. In this paper, we present an integrated assessment methodology to evaluate regional innovation performance using the Multi-Reference Point based Weak and Strong Composite Indicator (MRP-WSCI) approach, which allows defining reference levels and different degrees of compensability. As an example of application to the Regional Innovation Scoreboard, the proposed technique is developed to measure the innovation performance of Spain¿s regions taking into account Spanish and European reference levels. The main features of the proposed approach are: (i) absolute or relative reference levels could be previously defined by the decision maker; (ii) by establishing the reference levels, the resulting composite innovation index is an easy-to-interpret measure; and (iii) the non-compensatory strong composite indicator provides an additional layer of information for policy-making (iv) a visualization tool called Light-Diagram is proposed to track the specific strengths and weaknesses of the regions' innovation performance.This research has been partially supported by the Spanish Ministry of Economy and Competitiveness (Project ECO2016-76567-C4-4-R), by the Regional Government of Andalucia (research group SEJ-417), and by the ERDF funds (Project UMA18-FEDERJA-065).Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2020). A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain. Mathematics. 8(5):1-21. https://doi.org/10.3390/math8050797S12185Hauser, C., Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2018). Measuring regional innovation: A critical inspection of the ability of single indicators to shape technological change. Technological Forecasting and Social Change, 129, 43-55. doi:10.1016/j.techfore.2017.10.019Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional Innovation Systems: Theory, Empirics and Policy. Regional Studies, 45(7), 875-891. doi:10.1080/00343404.2011.596701Buesa, M., Heijs, J., & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6), 722-735. doi:10.1016/j.respol.2010.02.016Di Cagno, D., Fabrizi, A., Meliciani, V., & Wanzenböck, I. (2016). The impact of relational spillovers from joint research projects on knowledge creation across European regions. Technological Forecasting and Social Change, 108, 83-94. doi:10.1016/j.techfore.2016.04.021Capello, R., & Lenzi, C. (2012). Territorial patterns of innovation: a taxonomy of innovative regions in Europe. The Annals of Regional Science, 51(1), 119-154. doi:10.1007/s00168-012-0539-8Navarro, M., Gibaja, J. J., Bilbao-Osorio, B., & Aguado, R. (2009). Patterns of Innovation in EU-25 Regions: A Typology and Policy Recommendations. Environment and Planning C: Government and Policy, 27(5), 815-840. doi:10.1068/c0884rPinto, H. (2009). The Diversity of Innovation in the European Union: Mapping Latent Dimensions and Regional Profiles. European Planning Studies, 17(2), 303-326. doi:10.1080/09654310802553571Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Hollenstein, H. (1996). A composite indicator of a firm’s innovativeness. An empirical analysis based on survey data for Swiss manufacturing. Research Policy, 25(4), 633-645. doi:10.1016/0048-7333(95)00874-8Gu *, W., & Tang, J. (2004). Link between innovation and productivity in Canadian manufacturing industries. Economics of Innovation and New Technology, 13(7), 671-686. doi:10.1080/1043890410001686806Tang, J., & Le, C. D. (2007). Multidimensional Innovation and Productivity. Economics of Innovation and New Technology, 16(7), 501-516. doi:10.1080/10438590600914585Kumar, S., Haleem, A., & Sushil. (2019). Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework. Benchmarking: An International Journal, 26(6), 1823-1844. doi:10.1108/bij-12-2017-0343Grupp, H., & Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators? Research Policy, 33(9), 1373-1384. doi:10.1016/j.respol.2004.09.007Schibany, A., & Streicher, G. (2008). The European Innovation Scoreboard: drowning by numbers? Science and Public Policy, 35(10), 717-732. doi:10.3152/030234208x398512Kozłowski, J. (2015). Innovation indices: the need for positioning them where they properly belong. Scientometrics, 104(3), 609-628. doi:10.1007/s11192-015-1632-4Carayannis, E. G., Goletsis, Y., & Grigoroudis, E. (2018). Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework. Technological Forecasting and Social Change, 131, 4-17. doi:10.1016/j.techfore.2017.03.008Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zRuiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189-197. doi:10.1057/jors.2009.187Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313-325. doi:10.1016/j.ejor.2013.11.031Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building Ease-of-Doing-Business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130-140. doi:10.1016/j.techfore.2017.06.005El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235-1250. doi:10.1016/j.joi.2018.10.003Mazziotta, M., & Pareto, A. (2017). Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices. Social Indicators Research, 136(3), 967-976. doi:10.1007/s11205-017-1577-5Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12), 1513-1523. doi:10.1080/00036840601019364Cabello, J. M., Navarro, E., Prieto, F., Rodríguez, B., & Ruiz, F. (2014). Multicriteria development of synthetic indicators of the environmental profile of the Spanish regions. Ecological Indicators, 39, 10-23. doi:10.1016/j.ecolind.2013.11.01

A Composite Leading Indicator of Tunisian Inflation

This paper investigates the possibility of constructing a composite leading indicator (CLI) of Tunisian inflation. For doing so, partial information about future inflation rate provided by a number of basic series is analyzed first. Based on the correlation analysis, a few of these basic series are chosen for construction of composite indicator. Empirical results show that the deviation from long‐term trend of two monetary aggregates (M1 and M3), short‐term interest rate (TMM), real effective exchange rate and crude petroleum production, are important leading indicators for inflation rate in Tunisia. Accordingly, based on monthly data on these basic series, one composite indicator is constructed and its performance is assessed by using turning point analysis, granger causality tests, and impulse response functions. The results indicate that our composite indicator is useful in anticipating changes in inflation rates in Tunisia.Tunisia, Inflation, Leading indicators, Composite index

Sustainable Development Indicator Frameworks and Initiatives

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Production Economics,

Predicting Indian Business Cycles-- Leading Indices for External and Domestic Sectors

This paper evaluates the real-time performance of the growth rate of the DSE-ECRI Indian leading index for exports for predicting cyclical downturns and upturns in the growth rate of Indian exports. The index comprises the 36-country real effective exchange rate and leading indices of India’s 17 major trading partners. Leading indices of India’s major trading partners were developed at the Economic Cycle Research Institute and forecast the onset and end of recessions in overall economic activity in these economies. The results show that the real-time performance of the growth rate of the leading index of Indian exports has been creditable in the last seven years since its construction in 2001. In conjunction with the DSE-ECRI Indian Leading Index, designed to monitor the domestic economy, the exports leading index forms a sound foundation for a pioneering effort to monitor Indian economic cycles.

Performance Measures Using Electronic Health Records: Five Case Studies

Presents the experiences of five provider organizations in developing, testing, and implementing four types of electronic quality-of-care indicators based on EHR data. Discusses challenges, and compares results with those from traditional indicators

Romania’s development level comparing with EU countries: The RGS (Relative Gap Scoring) Ranking Index

The main objective of Romania’s post-accession strategy stands for the convergence with the EU Member States. If the nominal convergence (low inflation rate, stability of the exchange and interest rates, contained public debt) seems more easily to be achieved, the real convergence is supposed to catch up structural gap, connected more or less to issues belonging to the development process approach. The study aims at comparative assessment of Romania’s development level within UE 27, proposing a composite index, called Relative Gap Scoring (RGS). This method is based on a scoring calculation depending on the quotient of each indicator level for a certain country and of the country’s ranked first for the respectively indicator, having the advantage to evidence the relative gaps and providing a synthetic image of their resultant. The RGS index has been constructed by the geometric aggregation of scoring resulted for 10 economic and social indicators, considered relevant for the prospective of real convergence. Examining Romania's position within the ranking of EU countries according to the RGS index, the study found that large gap of the current state of economic and social development of our country still remain. Nevertheless, it is worth mentioning that Romania stood at 42.5 percent of the EU average in 2007, while in relation to GDP per capita (PPS) at 40.4 percent, which reveals that, in terms of real convergence, the time horizon of catching up with EU countries could be shorter.Economic and Social Development; International Comparisons; Composite Indexes; Statistical Methods

The psychometric properties of a shortened Dutch version of the consequences scale used in the core alcohol and drug survey

<div><p>Background</p><p>Alcohol and drug misuse among college students has been studied extensively and has been clearly identified as a public health problem. Within more general populations alcohol misuse remains one of the leading causes of disease, disability and death worldwide. Conducting research on alcohol misuse requires valid and reliable instruments to measure its consequences. One scale that is often used is the consequences scale in the Core Alcohol and Drug Survey (CADS). However, psychometric studies on the CADS are rare and the ones that do exist report varying results. This article aims to address this imbalance by examining the psychometric properties of a Dutch version of the CADS in a large sample of Flemish university and college students.</p><p>Methods</p><p>The analyses are based on data collected by the inter-university project ‘Head in the clouds’, measuring alcohol use among students. In total, 19,253 students participated (22.1% response rate). The CADS scale was measured using 19 consequences, and participants were asked how often they had experienced these on a 6-point scale. Firstly, the factor structure of the CADS was examined. Two models from literature were compared by performing confirmatory factor analyses (CFA) and were adapted if necessary. Secondly, we assessed the composite reliability as well as the convergent, discriminant and concurrent validity.</p><p>Results</p><p>The two-factor model, identifying personal consequences (had a hangover; got nauseated or vomited; missed a class) and social consequences (got into an argument or fight; been criticized by someone I know; done something I later regretted; been hurt or injured) was indicated to be the best model, having both a good model fit and an acceptable composite reliability. In addition, construct validity was evaluated to be acceptable, with good discriminant validity, although the convergent validity of the factor measuring ‘social consequences’ could be improved. Concurrent validity was evaluated as good.</p><p>Conclusions</p><p>In deciding which model best represents the data, it is crucial that not only the model fit is evaluated, but the importance of factor reliability and validity issues is also taken into account. The two-factor model, identifying personal consequences and social consequences, was concluded to be the best model. This shortened Dutch version of the CADS (CADS_D) is a useful tool to screen alcohol-related consequences among college students.</p></div

New resonance approach to competitiveness interventions in lagging regions: the case of Ukraine before the armed conflict

Regional competitiveness is considered to be an alternative basis for the determination of regional interventions. However, the composite competitiveness indicator is quite sensitive to the weights of sub-indicators, no matter what methodology is being used. To avoid this uncertainty in the determination of regional interventions, we proposed a new non-compensatory resonance approach that is focused on the hierarchical coincidence between weaknesses of NUTS 1 and NUTS 2 regions measuring the extensive and intensive components of competitiveness. Such a coincidence, being perceived as a resonance effect, is supposed to increase the effectiveness of interventions triggering synergetic effects and stirring up local regional potentials. The components of competitiveness are obtained through synthesising DEA methodology and Hellwig's index, correspondingly focusing on the measurement of technical efficiency and resource level. In analysing Ukrainian regions, no correlation between resonance interventions and the composite competitiveness indicator or GDP per capita was found, pointing toward a completely different direction in resonance approach. In western Ukraine, the congestion of six NUTS 2 regions was defined as a homogeneous area of analogous resonance interventions focused on improving business efficiency.Web of Science171562

Quality assurance of rectal cancer diagnosis and treatment - phase 3 : statistical methods to benchmark centres on a set of quality indicators

In 2004, the Belgian Section for Colorectal Surgery, a section of the Royal Belgian Society for Surgery, decided to start PROCARE (PROject on CAncer of the REctum), a multidisciplinary, profession-driven and decentralized project with as main objectives the reduction of diagnostic and therapeutic variability and improvement of outcome in patients with rectal cancer. All medical specialties involved in the care of rectal cancer established a multidisciplinary steering group in 2005. They agreed to approach the stated goal by means of treatment standardization through guidelines, implementation of these guidelines and quality assurance through registration and feedback. In 2007, the PROCARE guidelines were updated (Procare Phase I, KCE report 69). In 2008, a set of 40 process and outcome quality of care indicators (QCI) was developed and organized into 8 domains of care: general, diagnosis/staging, neoadjuvant treatment, surgery, adjuvant treatment, palliative treatment, follow-up and histopathologic examination. These QCIs were tested on the prospective PROCARE database and on an administrative (claims) database (Procare Phase II, KCE report 81). Afterwards, 4 QCIs were added by the PROCARE group. Centres have been receiving feedback from the PROCARE registry on these QCIs with a description of the distribution of the unadjusted centre-averaged observed measures and the centre’s position therein. To optimize this feedback, centres should ideally be informed of their risk-adjusted outcomes and be given some benchmarks. The PROCARE Phase III study is devoted to developing a methodology to achieve this feedback