Monitoring Implementation of Lisbon Strategy and NRP in the Time Dimension
Applied Statistics 2019 - Measuring and Analysing Time Series Data in Horizontal Time Dimension PDF Print E-mail
Sunday, 22 September 2019

The presentation of Professor Pavle Sicherl at the international statistics conference Applied Statistics 2019 at Bled (Ribno), organised by the Slovenian Statistical Society, was entitled Measuring and Analysing Time Series Data in Horizontal Time Dimension - Insights Beyond Conventional Static Analysis

Time distance method adds innovative parallel generic system for analysing indicators in both static and time dimensions relevant across many indicators and many domains.

Innovative framework for dynamic indicator analysis beyond GDP PDF Print E-mail
Monday, 25 September 2017

Summary and conclusions

Well-being and development embody multidimensional and long-term experience, going much beyond the GDP. The focus in the media is especially in GDP growth rates. Over time, UNDP, OECD, and European Commission have participated in the conferences, indicator developments, and policy discussions of 'Beyond GDP Initiatives'. In the article, the analyses of beyond the GDP indicators are enriched by the application of the dynamic time distance methodology to complement the results of the usual mostly static tools. 

With time distance methodology, a new perspective related to time does not replace but rather adds a new dimension to existing analysis across many variables, fields of concern, and units of comparison. Section 3 deals with the broadened concept of measuring and evaluating the magnitude of inequalities in two dimensions. LEVEL-TIME matrix in section 4 is an additional option of visualisation of time series data which helped to establish that GDP underestimated the scale of damage of the financial crisis in the EU for selected indicators. Section 5 emphasises the function of the time distance tool for monitoring implementation of targets parallel to other methods, with application to about 150 cases of EU2020 targets; as well as to measuring implementation of the UN Millennium Developments Goals that can be used also for the UN initiative of the 2030 Agenda for Sustainable Development. This transparent and innovative method for monitoring implementation of targets at all levels is available but not yet utilised. It can bring a new easily understandable perception of the magnitude of the gap between the actual implementation and proclaimed targets at many levels: it can help governments, the civil society, and businesses in a broader understanding of continuous policy debate and necessary adjustments. The free software tool is available.

The empirical study exposes that GDP underestimated the scale of damage of the financial crisis as selected time matrices showed deterioration in many indicators:
  • Employment rate fell in 20 EU countries (71% of countries);
  • Risk of poverty as percent of total population increased in 24 EU countries (86%);
  • Income distribution worsened as Gini coefficient and income quartile share ratio increased in 25 EU countries (89% of countries);
  • The most shocking conclusion is that the value of the share of growth fixed capital formation in GDP decreased in all 28 EU countries (100%!). This negatively affected the medium/long-term rate of growth of GDP.

Table 2 shows possible scheme and numerical values for analysing time distance deviations for implementation of five selected headline indicators towards the EU2020 for the entire EU and national targets. It is a clear example of simplicity with an overview of about 150 cases of EU2020 targets, showing the results of 5 selected EU2020 indicators, 28 countries, and the EU aggregate in one single table. Such time distance monitoring supervision could become a standard procedure in numerous other activities of the Commission on the national and local levels, e.g. monitoring and evaluation implementation of budgets, plans, projects, structural funds, etc.

Time distance monitoring of implementation of targets PDF Print E-mail
Wednesday, 18 June 2014

Test application for EU2020 targets by countries and free software tool

Monitoring implementation of targets is an integral part of policy making at many levels and in many domains. The innovation is that implementation of targets is described in two dimensions: static deviation from the line to target at a given point in time and S-time-deviation at a given level of the indicator. Describing the implementation of targets as leading or lagging in time against the line to well-known targets is a very useful application in the policy debate that enhances knowledge, giving data a value beyond spreadsheets. Expressed in time units, S-time-distance is easily understood by policy makers, managers, media and general public thus being an excellent presentation tool for policy analysis and debate. It can help us to form a new perception of the magnitude of the gap between the implementation and proclaimed targets for a given indicator as well as across more indicators.

We measure deviations in two dimensions. Firstly, one can measure the difference in variables at a given point in time. And secondly, discrepancies in time (either time lead or time lag) are measured. Monitoring implementation in time is like comparing train or bus arrivals with the timetable provided for each mode of transport. The statistical chart uses the same identifiers as Formula 1 on TV: drivers who score a minus at time distance are shown in green to signify that they are ahead in time. 

The table for EU 28 countries for 2013 (or 2012) shows the results from 2010 on. Yet the summary results confirm the earlier conclusions. For the headline indicator employment rate 20 countries are behind the schedule, 11 of them had in 2013 values below those in 2010 starting year. For 11 countries there was no progress in the 2010-2013 period for employment rate. The earlier graph that contained also the worse years of the financial crisis showed even a more serious situation. The time distance method, either for monitoring or for benchmarking in the time perspective, brings the second dimension of deviations or disparities that the present state-of-the-art is neglecting. 

For early leavers nine countries were in 2013 already better than their 2020 targets, this holds true for tertiary attainment for 10 countries; with only six countries being behind the schedule for both indicators. The headline indicator renewable energy also more countries are ahead of schedule than behind it, but with fewer cases that already reached the 2020 targets. R&D in GDP indicates a different picture, with 9 countries ahead and 16 countries behind the schedule; overall it is closer with the employment rate situation than with the other three indicators. 

The average for EU28 S-time-distance deviations express the situation with being ahead or behind the track to 2020 targets in simple terms: employment rate is more than 3 years behind, R&D 1.2 years behind, renewably energy 0.6 years, early leavers 2.1 years and tertiary attainment 2.4 years, ahead of  the line to the 2020 target.   

Software for time distance monitoring of targets from your own data: 

For time distance monitoring of implementation of targets, as shown for examples of indicators for EU2020 and UN Millennium Development Goals, SICENTER developed on a software tool to facilitate interested users to use the method for their own data. The tool can be accessed on

Visualisation of MDG implementation with Time Distance Progress Chart PDF Print E-mail
Tuesday, 21 August 2012

Are we ahead or behind in time comparing MDG implementation for 10 selected
indicators for World regions, China and India with the line to the 2015 MDG targets?

Time Distance Progress Chart of Millennium Development Goals implementation

We are using data from the UN, The Millennium Development Report 2012, New York, July 2012 and present the MDG implementation in the time distance perspective. The MDG 2012 Progress Chart (United Nations 2012) gives a quick assessment over 16 selected key targets, as it can deal also with qualitative judgments. For a more restricted number of 10 selected indicators for which numerical estimates are available we complement the UN Progress Chart with Time Distance Progress Chart of monitoring the progress of implementation.

Time distance is first and foremost important as an innovative concept of looking at data in a novel complementary and intuitively understandable way. The application to monitoring is easy to understand and to communicate; it is like comparing actual arrivals with the train (airplane, bus) timetable. S-time-distance measures deviation in time showing whether the actual developments are ahead or behind in time from path to the 2015 MDG targets (+ time lag, - time lead).

Are we ahead or behind in time comparing with the line to the 2015 MDG targets?

The table below examines the situation in more details for Developing Regions, 7 world regions, China, and India. The situation differs among the world regions, but the overall situation shows that for about 26% of cases of 10 selected indicators from all 8 MDG areas the 2015 targets were already achieved, for another 24% of cases the actual developments were ahead of the line to the 2015 targets. From about one half of the cases that were lagging behind about 17% were lagging more than 6 years, especially in Sub-Saharan Africa. China as the most populated country shows excellent results, for six out of ten indicators it already reached their 2015 MDG targets.

Time Distance Progress Chart of MDG implementation for world regions

For each of the analysed units graphical presentation of MDG implementation are provided in the PowerPoint file below. The table above also allows comparisons of implementation across indicators and regions at a glance. From the health domain the three selected indicators stand out as the cases where the MDG targets (with one exception) have not been achieved in any of the world regions. This is true also for the net enrolment ratio in the primary education where the target of full enrolment was set too high in view of the starting positions.

It should be reasonably easy to incorporate the S-time-distance methodology for monitoring implementation of the MDGs in the work of the UN, the World Bank and other agencies or countries on these issues, both at macro and at micro levels.

Where is Slovenia? (Kje je Slovenija?) PDF Print E-mail
Tuesday, 09 August 2011

Interview of Professor Pavle Sicherl in the leading Slovenian newspaper DELO, August 8, 2011

In the interview the time distance innovation and applications in economics and statistics were explained. The main conclusions on the position of Slovenia and Europe are from the article 'Kje je Slovenija?' in the proceedings of the symposium at the Slovenian Academy of Sciences and Arts at the 10th anniverasry of death of Professor Aleksander Bajt.

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