Comparing across many indicators and fields is important for perceptions about the overall “position” and “progress” <ul><li>Much effort has been put into developing indicator systems and data coverage but not enough to find new innovative ways to utilise them in the next phases: knowledge building and policy use. </li></ul><ul><li>We have better availability of data and faster computer processing. However, the benefit for better decision making will depend critically on human interface (Sicherl, 2004): </li></ul><ul><li>understanding of the information and </li></ul><ul><li>communication of that in a multidimensional framework </li></ul><ul><li>Time distance concept and the novel generic statistical measure </li></ul><ul><li>S-time-distance contribute two important innovations : </li></ul><ul><li>new intelligible insights from existing time series databases </li></ul><ul><li>and an excellent presentation and communication tool </li></ul>
Two time series can and should be compared in two dimensions: 1. static gap for a given point in time 2. gap in time for a given level of the variable
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 22-28 2004
PROVIDING BETTER UNDERSTANDING: a broader perception, policy and welfare
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-distance s 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 .
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
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= 4 0 years . Per capita income (l og 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.
Variable X e 1 S 1 e 2 e 3 e 4 e 5 S 2 S 3 S 4 S 5 The generic idea for many other applications of S-time-distance 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
S-time-distance: S (X t ) = t(X t ) – T(X t ) S (X t ) = actual time t – time on the line to target T for each actual value of the variable X t S (66.0 2006 ) = 2006 (66.0 2006 ) – 2003.9 (66.0 2006 ) = 2.1 years
Lisbon 1 target of 70% employment rate in 2010 for all countries (deviations in the time dimension from the hypothetical path to target) S-time-distance in years: - actual ahead of path to target, + actual behind the path to target S-time-distance in years
Implementation of national targets for the R&D share in GDP in 2005 (deviations in the time dimension from the hypothetical path to target) S-time-distance in years: - actual ahead of path to target, + actual behind the path to target
SUMMARY: Benefits of immediate operational uses of time distance <ul><li>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, OECD, EU, country, regional, city, project, socio-economic groups, company, household and individual levels </li></ul><ul><li>2.2 A broader dynamic framework for interrelating strategy issues of growth, efficiency, inequality and convergence </li></ul><ul><li>2.3 Enhanced semantics for policy analysis and public debate </li></ul><ul><li>2.4 Additional exploitation of databases and indicator systems </li></ul><ul><li>2.5 An excellent presentation and communication tool </li></ul><ul><li>among different levels of decision makers and interest groups </li></ul><ul><li>for describing of the situations, challenges and scenarios </li></ul><ul><li>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 </li></ul><ul><li>for communicating the urgent need for change and reforms </li></ul>
SICENTER is in the process of developing a WEB TOOL for monitoring implementation of targets with the S-time-distance measure <ul><li>FOR WHOM : possible interested users could be international and national organizations, NGOs, experts, business, educators, students and media: </li></ul><ul><li>FUNCTION : to calculate the lead or lag in time for tracking implementation of targets at the world, regional, national, sub-national or business levels, e.g. </li></ul><ul><li>- Lisbon and NRP targets in the case of EU </li></ul><ul><li>- Millennium Development Goals or other planned, budgeted, or aid disbursement targets </li></ul>
Conclusions for tracking the implementation of Lisbon strategy with time distance measure <ul><li>The time distance information is at least as helpful in providing a proper perception of the progress in implementation or the lack of it as is the percentage difference </li></ul><ul><li>It complements rather than replaces other methods </li></ul><ul><li>It is comparable across variables, fields of concern and units of comparison </li></ul>
<ul><li>4. This innovation provides simultaneous two-dimensional comparisons of time series data: vertically (standard measures of static difference) as well as horizontally (Sicherl time distance) </li></ul><ul><li>Empirically, the perceptions of the degree of disparity may be very different in static terms and in time distance </li></ul><ul><li>Thus the broader conceptual and analytical framework leads to new conclusions and richer semantics important for policy considerations </li></ul><ul><li>THANK YOU </li></ul>