281,545 research outputs found

    The composite indicators used in assessing innovation at national level

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    The purpose of present paper is to present some recent developments in constructing composite innovation (or even, science and technology S&T) indicators on a national level. Measuring innovation at the national level is crucial in developing appropriate long term strategies for economic growth, because it is widely believed technological innovation is one of the main drivers of sustained economic-social welfare, if not the single most important driver of economic growth. Our purpose is to present a mapping exercise of metrics – based on composite indicators - found in the STI literature, pointing out those used in practice, with a view to corresponding values in the case of Romania. It has become standard practice to combine several indicators for science, technology, and innovation to form composite numbers. Composite indicators are increasingly being used to make cross-national comparisons of country performance in specified areas such as competitiveness, globalisation, innovation, etc.Innovation, innovation metrics, Science and Technology indicators, composite indicators, National Innovation Systems, Scoreboards.

    Public services: are composite measures a robust reflection of performance in the public sector?

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    A composite indicator is an aggregated index comprising individual performance indicators. Composite indicators integrate a large amount of information in a format that is easily understood and are therefore a valuable tool for conveying a summary assessment of performance in priority areas. This research investigates the degree to which composite measures are an appropriate metric for evaluating performance in the public sector. Do they reflect accurately the performance of organisations? To what degree are they influenced by the uncertainty surrounding underlying indicators on which they are based? Are they robust and stable over time? The construction of composite measures creates specific methodological challenges that make such questions especially pertinent. We address these through a series of quantitative analyses of panel data relating to healthcare (Star ratings of NHS acute Trusts) and local government (Comprehensive Performance Assessment (CPA) ratings of authorities) in England where composites have been widely used. The creation of a composite comprises a number of important steps, each of which requires careful judgement. These include the specification of the choice of indicators, the transformation of measured performance on individual indicators, the specification of a set of weights on individual indicators, and combining the indicators using aggregation methods or decision rules. We use Monte Carlo simulations to examine the robustness of performance judgements to these different technical choices. We show the extent to which composites provide stable performance rankings of organisations over time and assess whether variations are due to genuine performance improvement or merely the result of random statistical variation. The analysis suggests that the judgements that have to be made in the construction of the composite can have a significant impact on the resulting score. Technical and analytical issues in the design of composite indicators have important policy implications. We highlight the issues which need to be considered in the construction of robust composite indicators so that they can be designed in ways which will minimise the potential for producing misleading performance information which may fail to deliver the expected improvements or even induce unwanted side-effects.performance measurement, performance indicators, composite indicators

    Findings of the Signal Approach for Financial Monitoring in Kazakhstan

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    This study concentrates on the signal approach for Kazakhstan. It focuses on the properties of individual indicators prior to observed currency crises. The indicators are used to build composite indicators. An advanced approach uses principal components analysis for the construction of composite indicators. Furthermore, the common signal approach is improved by robust statistical methods. The estimation period reaches from 1997 to 2007. It is shown that most of the composite indicators are able to flag the reported crises at an early stage. In a second step it is checked whether the most recent crisis in 2009 is signalled in advance.currency crises, leading economic indicators, signal approach, Kazakhstan

    Improving quality assessment of composite indicators in university rankings: a case study of French and German universities of excellence

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    Composite indicators play an essential role for benchmarking higher education institutions. One of the main sources of uncertainty building composite indicators and, undoubtedly, the most debated problem in building composite indicators is the weighting schemes (assigning weights to the simple indicators or subindicators) together with the aggregation schemes (final composite indicator formula). Except the ideal situation where weights are provided by the theory, there clearly is a need for improving quality assessment of the final rank linked with a fixed vector of weights. We propose to use simulation techniques to generate random perturbations around any initial vector of weights to obtain robust and reliable ranks allowing to rank universities in a range bracket. The proposed methodology is general enough to be applied no matter the weighting scheme used for the composite indicator. The immediate benefit achieved is a reduction of the uncertainty associated with the assessment of a specific rank which is not representative of the real performance of the university, and an improvement of the quality assessment of composite indicators used to rank. To illustrate the proposed methodology we rank the French and the German universities involved in their respective 2008 Excellence Initiatives.Composite indicators, Rankings, Benchmarking, Higher education institutions, Weighting schemes, Simulation techniques

    Composite indicators for monetary analysis

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    The prominent role assigned to money by the ECB has been the subject of an intense debate because of the declining predictive power of the monetary aggregate M3 for inflation in recent years. This paper reassesses the information content of monetary analysis for future inflation using dynamic factors extracted from a new and richer cross-section of data including the monetary aggregate M3, its components and counterparts, and a detailed breakdown of deposits and loans at sectoral level. Weighting monetary and credit variables according to their signal to noise ratio allows us to downplay those that in recent times contributed significantly to the deterioration of the information content of the M3. Factor-model based inflation forecasts turn out to be more accurate than those produced by traditional competitor models at the relevant policy horizon of six-quarters ahead. All in all, our results support the view that an analysis based on a large set of monetary and credit variables is a more useful tool for assessing risks to price stability than one that simply focuses on the dynamic of the overall monetary aggregate M3.monetary analysis, factor models, forecasting

    New tools for analyzing the Mexican economy: indexes of coincident and leading economic indicators

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    New composite indexes presented in this article could prove useful in analyzing and forecasting the Mexican economy. Keith Phillips, Lucinda Vargas, and Victor Zarnowitz present composite indexes of leading and coincident indexes for Mexico. In constructing the indexes, the economists use an approach similar to that developed by the National Bureau of Economic Research to create the composite indexes of U.S. economic activity. The authors classify peaks and troughs in the Mexican business cycle since 1980. Using these business cycle turning points, the authors determine which indicators consistently turned down prior to recessions and turned up prior to expansions. Eight of the best performing indicators are combined to create a composite index of leading economic indicators.Texas ; Economic indicators

    Macroeconomic Imbalances as Indicators for Debt Crises in Europe

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    European authorities and scholars published proposals on which indicators of macroeconomic imbalances might be used to uncover risks for the sustainability of public debt in the European Union. We test the ability of four proposed sets of indicators to send early-warnings of debt crises using a signals approach for the study of indicators and the construction of composite indicators. We find that a broad composite indicator has the highest predictive power. This fact still holds true if equal weights are used for the construction of the composite indicator in order to reflect the uncertainty about the origin of future crises.macroeconomic surveillance, macroeconomic imbalances, economic governance, signals approach, European Union (EU), European Monetary Union (EMU)

    Coincident and Leading Indicators for the Euro Area: A Frequency Band Approach

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    In the context of a common monetary policy, tracking euro area economic developments becomes essential. The aim of this paper is to build monthly coincident and leading composite indicators for the euro area business cycle. However, instead of looking at the overall comovement between the variables as it is standard in the literature, we show how one can resort to both time and frequency domain analysis to achieve additional insight about their relationship. We find that, in general, the lead/lag properties of economic indicators depend on the cycles periodicity. Following a frequency band approach, we take advantage of this in the construction of the coincident and leading composite indicators. The resulting indicators are analysed and a comparison with other composite indicators proposed in the literature is made.

    Ratings and rankings: Voodoo or Science?

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    Composite indicators aggregate a set of variables using weights which are understood to reflect the variables' importance in the index. In this paper we propose to measure the importance of a given variable within existing composite indicators via Karl Pearson's `correlation ratio'; we call this measure `main effect'. Because socio-economic variables are heteroskedastic and correlated, (relative) nominal weights are hardly ever found to match (relative) main effects; we propose to summarize their discrepancy with a divergence measure. We further discuss to what extent the mapping from nominal weights to main effects can be inverted. This analysis is applied to five composite indicators, including the Human Development Index and two popular league tables of university performance. It is found that in many cases the declared importance of single indicators and their main effect are very different, and that the data correlation structure often prevents developers from obtaining the stated importance, even when modifying the nominal weights in the set of nonnegative numbers with unit sum.Comment: 28 pages, 7 figure

    On the use of Structural Equation Models and PLS Path Modeling to build composite indicators

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    Nowadays there is a pre-eminent need to measure very complex phenomena like poverty, progress, well-being, etc. As is well known, the main feature of a composite indicator is that it summarizes complex and multidimensional issues. Thanks to its features, Structural Equation Modeling seems to be a useful tool for building systems of composite indicators. Among the several methods that have been developed to estimate Structural Equation Models we focus on the PLS Path Modeling approach (PLS-PM), because of the key role that estimation of the latent variables (i.e. the composite indicators) plays in the estimation process. In this work, first we present Structural Equation Models and PLS-PM. Then we provide a suite of statistical methodologies for handling categorical indicators in PLS-PM. In particular, in order to take categorical indicators into account, we propose to use a modified version of the PLS-PM algorithm recently presented by Russolillo [2009]. This new approach provides a quantification of the categorical indicators in such a way that the weight of each quantified indicator is coherent with the explicative ability of the corresponding categorical indicator. To conclude, an application involving data taken from a paper by Russet [1964] will be presented.PLS Path Modeling,Categorical Indicators,Structural Equation Modeling,Composite Indicators
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