SICENTER Ljubljana, Slovenia Time Distance Measure for Analysis and Presentation: Benchmarking and Monitoring of Structural Indicators Professor Pavle Sicherl SICENTER and University of Ljubljana Copyright © P. Sicherl All rights reserved Presented at the 2 nd Meeting of the EPC Task Force on Structural Indicators, Brussels, September 7, 2006

Three issues in the presentation 1.S-time-distance is a novel generic statistical measure (like static difference or growth rate) and an excellent presentation tool 2.Application in comparative analysis and in benchmarking 3.Application in monitoring implementation of Lisbon and Growth and Jobs Strategy SUMMARY: Benefits of immediate operational uses of time distance methodology for Commission services

Example: A Comparison of European and US Economies Based on Time Distances The fact that comparisons should be made in two dimensions has been verified by the world- wide media interest in my analysis for the EUROCHAMBRES Spring Business Forum. The static ratio of 1.41 does not catch much attention, while the time gap of about two decades obviously produced a different perception of reality. The same will be true for comparing within the EU. Source: P. Sicherl, A Comparison of European and US Economies Based on Time Distances, EUROCHAMBRES, Brussels, March 2005

A NEW VIEW IN TIME SERIES ANALYSIS II. a. CONCEPT OF MULTIDIMENSIONAL COMPARISON AND EVALUATION b. PRESENTATION c. VISUALIZATION d. SEMANTICS: POLICY, MANAGEMENT PERCEPTION OF A SITUATION III. STOHASTIC MODELS WITH S-TIME-DISTANCE -e.g. criterion for evaluating forecasting models (Granger and Jeon, 2003) IV. DECISION MAKING MODELS - extension of decision making models FURTHER APPLICATIONS I. DESCRIPTIVE STATISTICAL MEASURE

A new view of the information using levels of the variable as identifiers and time as the focus of comparison and numeraire

The resulting time matrix provides new information from which new generic measures can be derived. Two operators applied to this time matrix lead to the derivation of two novel statistical measures, expressed in standardized units of time.

Source: P. Sicherl, Time Distance: A Missing Link in Comparative Analysis, 28th General Conference of the International Association for Research in Income and Wealth, Cork, Ireland, August

METHODOLOGY: a broader perception, policy and welfare

Importance for European development paradigm: the relations between growth, efficiency and inequality in Lisbon strategy are different with a dynamic concept of overall degree of disparity Static relative measure and time distance lead to different conclusion: higher 4% growth example ratio=1.5, S=10 years; lower 1% growth example ratio=1.5, S=40 years. Per capita income (log scale) Higher growth rates lead to smaller time distances, and thus have an important effect on the overall degree of disparity. This is based on both static disparity and time distance, as both matter. Static measures alone are inadequate.

Static measure and time distance show two very different messages about importance of different components Percentage differences between US and EU15 for employment rate, annual hours worked and productivity per hour are very similar. It seems as if the difficulty of catching up would be similar in the analysed components. S-time-distances are very different, for productivity per hour only 5 years, while for employment rate and annual hours worked are about a quarter of a century. Policy analysis should expect different difficulties of catching up in these fields.

ANALYTICAL AND PRESENTATION TOOL

Comparisons over many indicators can show characteristic profiles across countries, regions, socio-economic groups, firms, etc. Source: Interview with P.Sicherl - Semanario Economico, Lisbon, March 18, 2005

Time distance measure is intuitively understood by policy makers, managers, media and general public and is comparable across different variables, fields of concern, and units of comparison. Source: P.Sicherl, A New Generic Statistical Measure in Dynamic Gap Analysis, The European E-Business Report, 2004 Edition, European Commission, Enterprise Directorate General, Luxembourg, 2004

Time S-time-distance adds a second dimension to comparing actual value with estimated value, forecast, budget, plan, target, etc. and to evaluating goodness-of-fit in regressions, models, forecasting and monitoring Variable X e1e1 S1S1 e2e2 e3e3 e4e4 e5e5 S2S2 S3S3 S4S4 S5S5 The generic idea for many other applications of S-time-distance

Monitoring and goodness-of-fit test in two dimensions The importance of using S-time-distance as a second dimension for monitoring and benchmarking across indicators in many fields is self explanatory, and immediately operational. A more long term scientific assignment is to develop optimizing procedures in models based also on the time distance deviations. E.g. Nobel prize winner Granger and Jeon (1997, 2003) further elaborated S-time-distance for the use as a criterion for evaluating forecasting models of leading and lagging indicators.

Percentage deviation of actual from path to target S-time-distance deviation of actual from path to target (in years) Share of R&D in GDP (%) Employment rate (%) GDP Level Share of R&D in GDP (%) Employment rate (%) GDP Level % 0 years 0.0 years %-0.1%-1.1%0.5 years0.1 years0.4 years %-0.8%-2.9%1.5 years0.8 years1.0 years %-1.7%-4.7%2.6 years1.6 years %-2.0%-5.3%3.9 years2.0 years1.9 years S-time-distance in years: - actual ahead of path to target, + actual behind the path to target Example of monitoring from original Lisbon targets: past deviations of actual from path to target in two dimensions

Share of R&D in GDP (%) Monitoring deviations of actual from path to target in two dimensions Implied path 1 to target 3% Actual EU15 and new target Percentage deviation of actual from path to target S-time-distance deviation of actual from path to target (in years) %0.0 years %0.5 years %1.5 years %2.6 years %3.9 years %3.9 years %3.9 years %3.8 years %3.8 years %3.8 years %3.8 years S-time-distance in years: - actual ahead of path to target, + actual behind the path to target What would be deviations in two dimensions from the original Barcelona target if the new Lisbon 2 targets for EU15 countries would be reached?

Share of R&D in GDP (%) Monitoring deviations of actual from path to target in two dimensions Implied Lisbon 2 path to target 3% Actual Percentage deviation of actual from path to target S-time-distance deviation of actual from path to target (in years) %0.3 years S-time-distance in years: - actual ahead of path to target, + actual behind the path to target Example: monitoring deviations of actual from path to target in two dimensions, Austria, Lisbon 2 target for R&D share in GDP Template for monitoring implementation in two dimensions against NRPs specified targets at relevant levels: national, EU and sub-national ( 25 countries times number of selected indicators )

NEW INSIGHTS FROM EXISTING DATA DUE TO AN ADDED DIMENSION OF ANALYSIS

SUMMARY: Benefits of immediate operational uses of time distance 2.1 A new view in competitiveness issues, benchmarking, target setting and monitoring for economic, employment, social, R&D and environment indicators at the world, EU, country, regional, city, project, socio- economic groups, company, household and individual levels 2.2 A broader dynamic framework for interrelating Lisbon strategy issues of growth, efficiency, inequality and convergence 2.3 Enhanced semantics for policy analysis and public debate 2.4 Additional exploitation of databases and indicator systems 2.5 An excellent presentation and communication tool -among different levels of decision makers and interest groups -for describing of the situations, challenges and scenarios -for proactive discussion and presentation of policy alternatives to policy makers, media, the general public and mobilizing those participating in or being affected by the programs -for communicating the urgent need for change and reforms