SICENTER Ljubljana, Slovenia TRACKING THE IMPLEMENTATION OF THE MDGs WITH TIME DISTANCE MEASURE Professor Pavle Sicherl SICENTER and University of Ljubljana Copyright © P. Sicherl All rights reserved Presentation prepared for the 2007 International Conference on the Millennium Development Goals Statistics (ICMDGS) 1-3 October 2007, Manila, The Philippines

Comparing across many indicators and fields is important for perceptions about the overall “position” and “progress” 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. 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): understanding of the information and communication of that in a multidimensional framework Time distance concept and the novel generic statistical measure S-time-distance contribute two important innovations: new intelligible insights from existing time series databases and an excellent presentation and communication tool

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

BENEFIT FROM THIS NEW VIEW IN COMPARISONS, BENCHMARKING AND MONITORING S-TIME-DISTANCE AS A NEW GENERIC STATISTICAL MEASURE FOR ANALYSIS AND VISUALIZATION OF TIME SERIES DATA available on More specifically on MDGs MONITORING IMPLEMENTATION OF THE MDGs IN THE TIME DIMENSION, OECD/ISTAT meeting, Rome available on or onwww.gaptimer.eu

PROVIDING BETTER UNDERSTANDING: a broader perception, policy and welfare

Importance for the development paradigm: the relations between growth, efficiency and inequality 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.

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

Numerical example of monitoring progress in reducing under-five mortality (path to target calculated as average absolute rate of decrease) 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 Example for Developing Regions: S ( ) = 2004 ( ) – ( ) = 7.3 years

Monitoring implementation of the Millennium Development Goals in the time dimension for selected indicators: DEVELOPING REGIONS, about 2004 S-time-distance in years: - actual ahead of path to target, + actual behind the path to target

Monitoring implementation of the Millennium Development Goals in the time dimension for selected indicators: CHINA, situation around 2004 S-time-distance in years: - actual ahead of path to target, + actual behind the path to target

Using this methodology at national and sub-national levels can be an important additional information useful also for the MDG Africa Steering Group initiated by the UN Secretary-General

Summary of 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, OECD, EU, country, regional, city, project, socio- economic groups, company, household and individual levels 2.2 A broader dynamic framework for interrelating 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

SICENTER is in the process of developing a WEB TOOL for monitoring implementation of targets with the S-time-distance measure FOR WHOM: possible interested users could be international and national organizations, NGOs, experts, business, educators, students and media: 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. 1.- Millennium Development Goals or other planned, budgeted, or aid disbursement targets 2.- Lisbon and NRP targets in the case of EU

Various options to specify the path to target An important step is how to specify the line to targets between the starting and the target values. 1. Linear path to target 2. Exponential path to target 3. Optional path to target specified by the user The numerical results will depend on this selection by the user A simultaneous two-dimensional implementation evaluation will be provided: static difference and S-time- distance The final qualitative assessment expressed by symbols like traffic lights or smileys users can now be based on broader derived information from the original data on target values and actual implementation

Conclusions for tracking the implementation of MDGs with time distance measure 1.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 2.It complements rather than replaces other methods 3.It is comparable across variables, fields of concern and units of comparison

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) 5.Empirically, the perceptions of the degree of disparity may be very different in static terms and in time distance 6.Thus the broader conceptual and analytical framework leads to new conclusions and richer semantics important for policy considerations THANK YOU