Product Market Regulation: Robustness and Critical Assessment 1998-2003-2007

The construction of a composite indicator (CI) involves stages where choices have to be made: the structure of PMR in domains and sub-domains, the normalization of the original data, the weighting of indicators, domains and sub-domains, and the aggregation method. All these choices will affect both the ranking and the message brought by the CI in a way that deserves analysis and corroboration. Robustness analysis is a powerful tool to test the sensitivity of PMR ranking to the different methodological assumptions. In particular we are interested in three questions. Does the use of one construction strategy versus another provide actually a partial picture of the countries¿ performance? Which countries have large uncertainty bounds in their rank (volatile countries)? Which are the factors that affect the countries rankings? We employ two strategies to answer to these questions. We consider each methodological choice individually and we study its effect on PMR ranking and we consider all the possible sources of variability together and study its joint effect on the PMR ranking.JRC.DG.G.3-Econometrics and applied statistic

Product Market Regulation: Robustness and Critical Assessment 1998-2003-2007. Countries' Profiles.

The construction of a composite indicator (CI) involves stages where choices have to be made: the structure of PMR in domains and sub-domains, the normalization of the original data, the weighting of indicators, domains and sub-domains, and the aggregation method. All these choices will affect both the ranking and the message brought by the CI in a way that deserves analysis and corroboration. Robustness analysis is a powerful tool to test the sensitivity of PMR ranking to the different methodological assumptions. In particular we are interested in three questions. Does the use of one construction strategy versus another provide actually a partial picture of the countries¿ performance? Which countries have large uncertainty bounds in their rank (volatile countries)? Which are the factors that affect the countries rankings? We employ two strategies to answer to these questions. We consider each methodological choice individually and we study its effect on PMR ranking and we consider all the possible sources of variability together and study its joint effect on the PMR ranking. This document supplement the main study on PMR indicator by providing countries' profiles.JRC.G.3-Econometrics and applied statistic

The Use of Indicators and Benchmarks in Monitoring the Progress in Education and Training at the European Level

In its monitoring of the progress made towards the Lisbon objectives in education and training, the European Commission proposed a set of 29 indicators divided into three strategic objectives. Among these indicators five benchmarks (‘reference levels of European average performance’) were selected by the Council to help focus the efforts of Member States towards the Lisbon objectives. The following analysis is based on the list of 29 indicators. We attempt to answer two questions: 1. Is the set of five benchmarks a “good” shortlist (in the statistical sense), capable of summarising the information contained in the longer list of 29 indicators? 2. Is it possible to derive from the five benchmarks a composite indicator, and what are the properties of this indicator? In this analysis different statistical techniques were employed to analyse a data set of 24 education indicators. A composite indicator of the five benchmarks is constructed and analysed. Key words: indicators and benchmarks, composite indicatorsJRC.G.9-Econometrics and statistical support to antifrau

Constructing Consistent Composite Indicators: the Issue of Weights

This paper shows that a theoretical inconsistency exists between the real meaning of weights and the meaning that is generally attributed to them by the standard practice in constructing composite indicators. Guidelines to solve this drawback are given.JRC.G.9-Econometrics and statistical support to antifrau

The consumer empowerment index. A measure of skills, awareness and engagement of European consumers

The Consumer Empowerment Index is a pilot exercise, aimed at obtaining a first snapshot of the state of consumer empowerment as measured by the Eurobarometer survey (Special Eurobarometer n. 342). It is neither a final answer on empowerment nor a comprehensive study on all the different facets of consumer empowerment, but instead it is meant to foster the debate on the determinants of empowerment and their importance for protecting consumers. This report describes the steps followed in the construction of the Index of consumer Empowerment. In particular the definition of the theoretical framework, the quantification of categorical survey questions, the univariate and multivariate analysis of the dataset, and the set of weight used for calculating the scores and ranks of the Index. The report also discusses the robustness of the results and the relationship between the Index and the socio-economic characteristics of the respondents in order to identify the features of the most vulnerable consumers. The Consumer Empowerment Index identifies Norway as the leading country followed by Finland, the Netherlands and Germany and Denmark. The middle of the ranking is dominated by western countries such as Belgium, France, and UK, with an average score 13% lower than the top five. At the bottom of the Index are some Eastern and Baltic countries like Bulgaria, Lithuania, Poland, and Romania with a score 31% lower on average (this gap reaches 40% and 38% in Awareness of consumer legislation and Consumer skills). A group of southern countries, Italy, Portugal, and Spain score poorly in the Index, especially in the pillar Consumer skills where the gap with the top performers reaches 30%.Consumer empowerment; composite indicators

A Robust Model to Measure Governance in African Countries

Levels of performance of any government do matter in determining the quality of the civil society. The Ibrahim Index of African Governance developed by the Harvard Kennedy School shows how governance can be measured. The Index assesses governance issues over time (2000, 2002, 2005, 2006) and for 48 African countries south of the Sahara, according to a five-pillar conceptual structure: (a) Safety and Security, (b) Rule of Law, Transparency, and Corruption, (c) Participation and Human Rights, (d) Sustainable Economic Opportunity, and (e) Human Development. This report aims at validating and critically assessing the methodological approach undertaken to build the 2006 Index of African Governance, by raising two key questions: o Is the Index of African Governance internally sound and consistent from a statistical and conceptual point of view? o What scenarios could have been used to build the Index and how do the results from these scenarios compare to the original results? The overall assessment of the 2006 Index by means of multivariate analyses, uncertainty and sensitivity analyses reveals no particular shortcomings in the conceptual structure. Data-driven narratives on governance issues in Africa are also offered in this report with a view to show directions of discussions and messages that stem from an index-based analysis of governance. Overall, the Index of African Governance can be reliably used to identify weaknesses, propose remedial actions, allow for easy spatial and temporal comparisons (benchmarking), to prioritize countries in Africa of relatively low governance content, monitor and evaluate policies effectiveness and ultimately to funnel resources to countries through, for example, multilateral and bilateral agreements between African countries.JRC.G.9-Econometrics and applied statistic

Does web anticipate stocks? Analysis for a subset of systemically important banks

Is web buzz able to lead stock behavior for a set of systemically important banks? Are stock movements sensitive to the geo-tagging of the web buzz? Between Dec. 2013 and April 2014, we scrape about 4000 world media websites retrieving all public information related to 10 systemically important banks. We process web news with a sentiment analysis algorithm in order to detect article mood. We show that web buzz does not seem to lead stock behavior as Granger test fails to support an average association that goes one-way from web to stocks. We nevertheless find a statistically sound anticipation capacity for single banks with gains ranging from 4 to 12%. Hierarchical clustering and Principal Component Analysis suggest that Euro area level decisions/facts do in fact drive stock behaviour, while web news about single banks only episodically make a difference in stock movements. Our analysis confirms that the location of the web source matters. The use of sources with international echo eliminates some of the noise introduced by irrelevant texts at the country level and improves the predictive power of the model up to 27.5%.JRC.G.1-Financial and Economic Analysi

Measuring Financial Integration in Europe: a price-based approach for equity and bond markets

The economic literature, while recognizing the added value of financial market integration, does not offer a clear cut answer on the best measure(s) of this integration. We compute three possible measures based on the sensitivity of domestic European stock markets to global, US or European shocks. The common rationale is to measure the extent to which domestic stock (bond) market volatility incorporates external shocks, following the idea that in more integrated markets shocks transmit more easily. The first method, based on correlation of stock market returns, offers two measures of integration. Firstly, the proportion of shocks generated in EU and US markets that actually hit EU domestic markets and secondly domestic sensitivity to foreign shocks. The third method, based on common factor portfolios, identifies a set of recurrent common patterns in EU and World stock and bond markets. Domestic returns are then matched against these global factors to see the degree of co-movement. This technical report collects JRC contribution to the European Financial Stability and Integration Review (SWD (insert number), Brussels 25 April 2016)JRC.G.1-Financial and Economic Analysi

Tools for Composite Indicators Building

Our society is changing so fast we need to know as soon as possible when things go wrong (Euroabstracts, 2003). This is where composite indicators enter into the discussion. A composite indicator is an aggregated index comprising individual indicators and weights that commonly represent the relative importance of each indicator. However, the construction of a composite indicator is not straightforward and the methodological challenges raise a series of technical issues that, if not addressed adequately, can lead to composite indicators being misinterpreted or manipulated. Therefore, careful attention needs to be given to their construction and subsequent use. This document reviews the steps involved in a composite indicator’s construction process and discusses the common pitfalls to be avoided. We stress the need for multivariate analysis prior to the aggregation of the individual indicators. We deal with the problem of missing data and with the techniques used to bring into a common unit the indicators that are of very different nature. We explore different methodologies for weighting and aggregating indicators into a composite and test the robustness of the composite using uncertainty and sensitivity analysis. Finally we show how the same information that is communicated by the composite indicator can be presented in very different ways and how this can influence the policy message.JRC.G.9-Econometrics and statistical support to antifrau

Finflows: database for bilateral financial investment stocks and flows

Bilateral financial investments are not commonly available from a single source. Our database Finflows aims at centralising the information on bilateral capital financial stocks and financial flows in a single place. It provides estimates of external assets and liabilities flows between around 80 countries including those from European Union, the Organisation for Economic Co-operation and Development, Russia, China, Brazil, India and the largest offshore countries. The database contains yearly data from 2000 to the last available year (in general with a 20 months delay). Initially developed by (Hobza & Zeugner, 2014) under the scope of the macroeconomic surveillance work, we provide bilateral financial investment links broken by class of investment such foreign direct investment, banking flows or portfolio investment following the Sixth Edition of the IMF's Balance of Payments and International Investment Position Manual (BPM6, 2009). The specificity of this database lies in the fact that it includes official intermediated links either via European Central Bank’s funding or other official flows which were providing financial assistance to Euro-area countries in distress helping them to refinance their liabilities. We also consider the investment made during the quantitative easing program of the European Central Bank in 2010 and redirect those inter-banking transactions and customer payments that are settled, in real time, within the Euro-zone system when necessary. Another advantage of the database Finflows relies in the resolution of potential mismatch between countries’ declarations.JRC.B.1-Finance and Econom