carte.png

Welcome to MAPCOMPETE

map.png

“Mapping European Competitiveness” (MAPCOMPETE) is an FP7 proposal of six European universities and research centers (from Germany, France, Italy, Belgium and Hungary) to provide an assessment of data opportunities and requirements for the analysis of comparative competitiveness in European countries.

About Mapcompete

“Mapping European Competitiveness” (MAPCOMPETE) is a FP7 proposal of six European universities and research centers (from Germany, France, Italy, Belgium and Hungary) to provide an assessment of data opportunities and requirements for the analysis of comparative competitiveness in European countries.

Partners of the project are Brussels based think tank Bruegel, Budapest based research center CERS–HAS (coordinator), Milan research centre LdA, Paris School of Economics and Sciences–Po in Paris, and Tübingen research institute IAW. Associate partners are the OECD, the ECB and several central banks in Europe.

Competitiveness is at the heart of policy making at the Union level and specifically within the Eurogroup. Definition of new country–level competitiveness indicators is an essential task. The aim of this project is to provide a thorough assessment of data opportunities and requirements for the analysis of comparative competitiveness in European countries.

Work will examine how to interconnect different approaches to research and policy making in this field at the macro, sectoral and micro–level and consequently map data availability and needs. It will come up with proposals for enhancing standards and consistency of data, with the aim of improving future comparative work on competitiveness. It will analyze data requirement and suggest collection methods for selected topics such global value chains, trade and performance as well as pricing and quality.

Supporting institutions

National Bank of Belgium

Banque de France

Banco de España

Deutsche Bundesbank

Banca d’Italia

Magyar Nemzeti Bank

The Italian National Institute of Statistics (ISTAT)

Carte des régions françaises

Research outline

The project aims to identify gaps in available data sets and key data requirements for constructing better competitiveness indicators at different levels. A key aim is to analyze the combined use of three types of resource: census type quantitative (e.g. national tax authority) data, quantitative survey (e.g. EFIGE survey) data and qualitative (interview based) information. Integrating these approaches may allow a deeper understanding of a wide range of topics related to competitiveness. Indeed, in terms of competitiveness analysis, our primary focus is firm performance. However, we approach this from a wide angle. Hence, data analyzed should cover not only traditional balance sheet figures but include areas such as:

Naturally, the mapping and matching of data cannot be carried out abstractly, i.e. without having clearly in mind (i) the type of research questions that should be addressed by using the data and (ii) the policy indicators that should be constructed by using the data. One of the work packages of the project will be precisely devoted to laying down this conceptual ground, so as to build an analytical framework within which to address all the data issues. The project also aims to create a more systematic connection between research results and the developments of indicators to be used for policy purposes.

The framework for data assessment will have to be derived from a broader analysis of competitiveness and its measures. Proposals will cover three areas: First, how to netter manage existing data (regarding data collections of the past), with special attention to linking and matching datasets. Second, how to better collect data, improve on collection methodology and extended the coverage in terms of new variables. Third, what sort of new types of data shall be collected in the future and what pilot project may be initiated – based on lessons from previous programs such as EFIGE, Innovation surveys, global value chain projects.

Topics:

  1. Mapping existing datasets: screen national sector and micro-level datasets regarding geographical coverage, time span, representativeness with a special focus on areas where no standard set of variables exist, such as non-tangible assets and innovation.
  2. Consistency issues of different datasets: benchmark on existing research to understand the extent to which some country and year-specific competitiveness-related indicators can be derived from data contained within available dataset or from a cross-reference use of available datasets at different levels of aggregation.
  3. Conditions and requirements to match different data sets: on the basis of the pilot indicators identify the extent to which datasets relevant for competitiveness can be matched within country and across countries and map data gaps.
  4. Research directions towards better competitiveness indicators: investigate how novel data or combination of datasets can be used to introduce novel research areas and design new research directions leading towards better competitiveness indicators.
  5. Benchmarking: identify steps to enhance quality and availability of existing data and suggest new methods and sources of data collections

Mapcompete Data

This page will host two webtools:

The MAPCOMPETE Indicators Availability Map

The MAPCOMPETE Indicators Availability Map will be an inbuilt component of the MAPCOMPETE website. Through this webtool, the visitor will search and find information on the most useful indicators of competitiveness for Europe, selected by the MAPCOMPETE team. The information includes:

  1. Description;
  2. Rationale and problems for each indicator;
  3. Different aggregation levels, i.e. country, sector and region;
  4. Country-specific availability of the necessary data, and
  5. For more advanced users, the variables to be used in the construction of the indicator.

The webtool will consist of two components to access information: a map of Europe, that will allow exploring the content country by country, and a search tool that will allow for a refined research (by competitiveness concept, aggregation level etc.). Through the map and the search tool, the user will have the possibility to sail along the MAPCOMPETE database of competitiveness indicators and to easily retrieve all the relevant information, including the original source of the data. The MAPCOMPETEWebtool will boost the dissemination activity of the results of the MAPCOMPETE project. Thanks to its captivating and user-friendly graphic interface, it will be easily usable by the general public as well as by researchers and journalists.

The New MAPCOMPETE Indicators Tool

The New Competitiveness Indicators Tool will offer free access to EU-comparable data on indicators at the frontier of the Competitiveness research. The innovative cutting-edge indicators will probably come from different research strands, mainly from recent firm-level productivity analysis done at the ECB Competitiveness Research Network (CompNet), within the Micro-Dyn project and by SciencesPo.

The webtool will be a user-friendly gateway to ready-made indicators that will serve to disseminate key research results to the wider public. The user will be able to visualize, share and download tables of data as well as maps of Europe where each country will be colored according to the selected competitiveness indicator’s strength.

carte-europe.png

Mapping competitiveness with European data

Europe needs improved competitiveness to escape the current economic malaise, so it might seem surprising that there is no common European definition of competitiveness, and no consensus on how to consistently measure it.

To help address this situation, this Blueprint provides an inventory and an assessment of the data related to the measurement of competitiveness in Europe. It is intended as a handbook for researchers interested in measuring competiveness, and for policymakers interested in new and better measures of competitiveness.

MAPCOMPETE has been designed to provide an assessment of data opportunities and requirements for the comparative analysis of competitiveness in European countries at the macro and the micro level.

Main directions of research towards better assessment of competitiveness

Authors: Maria Bas, Philippe Martin and Thierry Mayer

Non price competitiveness is an important issue for European firms given that they produce in a high wage environment. However, non price competitiveness is difficult to measure, more anyway than price or cost competitiveness. This is true at the firm or at a more aggregate such as sector or country level. Whereas price competitiveness implies low prices,non price competitiveness implies higher demand for a given price. This can come from higher quality, differentiation, better design, brand image, associated services.

This report proposes to use a quality measure which has the advantage of being simple and intuitive. It measures the characteristics of the goods that increase its demand for a given price or similarly that explain why consumers do not decrease their demand even when they have to pay a higher price. This quality measure is based on trade data and can be applied to different sectors and different countries of the EU across time. Our indicator of quality competitiveness seems to be a good proxy of product quality since it is positively correlated with the level of economic development, human capital, physical capital and innovation intensity. Our preliminary results on quality changes across European countries suggest for example that the competitiveness gains of Germany during the 2000-2009 period are, at least in the high-technology (machinery and electronics) sectors we analyze, very much linked to improvement in the quality of goods produced.

Indicators of quality adjusted price competitiveness

Authors: Maria Bas, Philippe Martin and Thierry Mayer

This report reviews the state of the art of the literature on measures of quality adjusted competitiveness. The report starts by an analysis of how the recent theoretical literature on heterogeneous firms’ trade models has introduced product quality on both the demand and the supply side. The first family of models relies on the idea that consumers value quality. The valuation of quality is reflected on their willing to pay a higher price for high quality goods. On the supply side, firms’ quality choice depends on the quality of inputs (skill labor or intermediate goods) used in the production of final goods. Also, producing high-quality goods is costly with marginal costs increasing in the level of quality of the final good but also involves a sunk investment cost in quality upgrading. The paper presents the recent empirical methodologies developed in the literature to estimate product quality using microdata.

The early literature relied on unit values to capture differences in quality at the product level. The more recent literature disentangles price and quality using trade values and quantities to obtain a quality-adjusted measure of unit values. The identification strategy of these works infers quality from a demand function. Quality differentiation across products is then associated to product characteristics that are valued by consumers. The paper then describes alternative measures of non-price competitiveness at the firm level in particular related to R&D and other technological investments. Finally, it analyzes the recent findings of works that focused on how macroeconomic shocks affect firms’ competitiveness.

Theoretical and policy aspects of competitiveness at different aggregation levels

Authors: Lionel Fontagné and Gianluca Santoni

The purpose of this report is to survey the related literature and tentatively clarify the concepts of competitiveness, with a policy perspective. The work revises competitiveness debate from three main points of view: macroeconomic, regional and microeconomics.For Micro-economics, competitiveness usually relates to firm productivity; as well-established concept it is relatively easy to quantify empirically, bothat the firm and sectorallevel. On the other hand, Macro-economic definitions are generally less well-established and more controversial.

The Macroeconomic section is devoted to drivers and measures of competitiveness from an aggregated viewpoint: innovation policy as productivity enhancing factor (especially in economic downturns); relative prices and relative costs approaches to evaluate country performance (Exchange Rates, Unit Labour Costs). A particular focus is dedicated to the external competitiveness, suggesting a newindex of export performance (Export Competitiveness Database) and considering the policy implications from the increasing incidence of international production networks (Global Value Chains).

The debate around regional aspect of competitiveness is faced in section 3, covering the main issues related to regional growth and competitiveness, the effect of agglomeration and local institutions as well as the relevant policies to enhance local economic performance.The Microeconomic perspective is covered in section 4. This section review the main driver of firm competitiveness, either internal to the firms or external (environment) and the most relevant channels through which micro-economic shocks propagate shaping aggregate outcome.

Competitiveness is basically a multidimensional concept that involves economic forces operating at different disaggregation levels. The review of relevant economic literature and empirical evidence proves the interaction strength of such forces and helps drawing sensible policy implications.

Competitiveness indicators report

Authors: Carlo Altomonte, Marco Antonielli, Michael Blanga-Gubbay and Silvia Carrieri

The Competitiveness Indicators Report surveys existing indicators in the economic literature, policy papers and databases, in order to provide a critical assessment and set up a selection of the indicators for the data mapping exercise (WP2).

Technical report describing the state of the art at the industry, regional and aggregate level

Authors: Davide Castellani, Silvia Cerisola, Giulia Felice, Emanuele Forlani and Veronica Lupi

This report provides a mapping of the available data for a list of key indicators of competitiveness at the sectoral, regional and aggregate level, derived from D3.1. For each indicator we have identified the relevant level of disaggregation (aggregate/country, sectoral and regional). Out of a total of 43 indicators, 41 can be computed at the aggregate level, 32 at the sectoral level and 16 at the regional level. Therefore, we have an overall number of 89 indicators for which we need to map data availability.

The indicators are grouped into a smaller number of homogeneous categories, namely:

Overall, the analysis carried out shows that the degree of computability for the macro-indicators is quite good for the EU28 countries. Nevertheless, there are some exceptions. It is possible to group the exceptions in three main categories: i) by country, i.e., there are countries for which data availability is particularly scarce for the majority of indicators; ii) by indicator, i.e., there are indicators on which information is particularly scarce for the majority of countries and levels of aggregation; iii) by level, i.e., there are levels of aggregation on which information is particularly scarce for several indicators for the majority of countries.

As concerning the exception by country, information is scarcer for Croatia and Greece than for the other countries.

As for the second category of exceptions, information on Entry rate and Exit rate indicators belonging to Firms Dynamics is quite heterogeneous across countries and level of aggregation, but in general only half of the countries show the highest level of computability. The indicators belonging to Intangible assets and financial activity are computable for only a few countries and/or for a quite short time interval. The information on R&D Expenditure and Output is in general quite good with the exception of License and patent revenues from abroad as % GDP and EU Summary Innovation Index (SII), which are computed and comparable across countries for all EU countries since only 2004 (2006 for Spain and Greece), as for the former, and since 2008 for the latter.

As for the third category of exceptions, by level of aggregation, it should be mentioned that in general the availability of information for the indicators at the sectoral and regional level is both scarcer and less homogeneous than the one at the aggregate level across countries and indicators. In particular, the indicators’ computability is high at the aggregate level, but quite limited at both sectoral and regional ones for those indicators belonging to Labour Productivity and Total Factor Productivity (Group 3), Innovation activity, SMEs and R&D Expenditure and Output. The indicators’ computability is high at the aggregate level but quite limited at the at the sectoral one for those indicators belonging to Trade Competitiveness, on the one side, while the indicators’ computability is high at the aggregate level, but quite limited at the at the regional one for those indicators belonging to the Innovation activity(all firms), on the other side.

State of the art at the micro-economic level

Authors: Davide Castellani, Silvia Cerisola, Giulia Felice, Emanuele Forlani and Veronica Lupi

This report describes the computability and the availability of a set of competitiveness indicators calculated through a bottom-up approach, which consists in the computation of a competitiveness’ index by aggregating firm-level data for a specific group of firms. For example, aggregation can consist in the computation of averages (but also variances or other moments of the distribution) across all firms in a given sector (or region), or across subsets of firms (such as exporters or multinational firms). For each index, three levels of aggregation are considered: country, sector, and region. The mapping of micro-level databases includes information on firms’ industrial sector (usually NACE Rev. 1.1 or Rev. 2), and geographical location (usually NUTS2 region). The mapping allows users to know if a given competitiveness index is computable at sector, and/or regional level for each country.

This report analyses computability and accessibility for 45 bottom-up indicators, which can be grouped into 4 conceptual areas: (i) productivity, (iii) Firms’ dynamics, (iv) International activities, and (v) R&D and ownership.

Due to some incomplete replies or lack of responses from some of our contacts at the National Statistical Institutes or National Central Banks, this report presents a mapping of existing firm-level databases that can be employed to calculate these indices in 15 EU countries. These countries are: Belgium, Bulgaria, Czech Republic, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Latvia, Portugal, Slovakia, Slovenia, and Sweden

Our main findings can be summarized as follows:

partners.png