shadow_left
Logo
Shadow_R
   


Monitoring implementation in the time dimension
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 www.gaptimer.eu a software tool to facilitate interested users to use the method for their own data. The tool can be accessed on http://www.gaptimer.eu/s-t-d_monitoring_tool.html


 
MDG implementaion by Gaptimer Progress Chart 2013 PDF Print E-mail
Wednesday, 14 August 2013

Latest update of the article in the Guardian by Professor Pavle Sicherl available also in the wikiprogress ProgBlog


UNDP Report 2013 in the two page summary overview started the first sentence on the first indicator: ‘The world reached the poverty reduction target five years ahead of schedule.’ The first row of the Gaptimer MDG Progress Chart also shows that the 2015 poverty reduction targets have already been achieved even earlier in three world regions (also China was an excellent performer with time lead of even 13 years, reaching the 2015 target in 2002). This is an update of the publication in The Guardian by Professor Pavle Sicherl based on the older data from the 2012 MDG report and reported ealier on this web page. Monitoring implementation with time distance deviation is like comparing train or bus arrivals with the respective timetables. In the context of the MDGs, it amounts to comparing the time of actual implementation with the time stipulated by the schedule to the 2015 target. We are therefore measuring the gap in time.

Source: Own calculations based on data from UN, The Millennium Development Report 2013, New York   © P. Sicherl, 2013

In general the Gaptimer MDG Progress Chart presents in a single table at a glance results for 100 cases across 10 MDG indicators and 10 units (7 world regions, Developing Regions, China, and India) expressed in time lead or time lag providing stories of the situation from the novel time perspective. There are many green colour fields indicating cases where targets have been reached or indicators are ahead of the line to target, to show the many positive developments in the developing countries. The situation differs among the world regions, but the overall situation shows that the number of cases ahead of the line to target (21+15) is exceeding the number of cases behind (18+14). In absolute terms progress has been made in all selected indicators and in all world regions (though it has been quite uneven across regions as well as across countries within the regions). Furthermore, for countries with delays the application of the overall MDG targets at the regional and national cases may be unrealistic. 

For more detailed analysis, below we provide Excel files of results of time distances in which time lead or time lag from the line to the respective MDG 2015 targets are shown for 112-137 developing countries respectively for the five selected indicators. This monitoring method can be applied much more widely. Firstly, world regions can be exchanged with countries, regions within countries, or socio-economic groups, sectors, etc. Secondly, units could be products of an enterprise, budget activities or operational projects, etc., and with e.g. relevant KPIs as horizontal entries. 


EXCEL FILES of S-time-distances for selected developing countries:

IND4.1 Under-five mortality rate 2011.xls
IND5.1 Maternal mortality ratio 2010.xls
IND7.8t improved drinking water 2011.xls
IND7.9t improved sanitation 2011.xls
IND8.16 Internet users 2012.xls

 

Links to wikiprogress:

 


 
2013 Global Forum on Development PDF Print E-mail
Wednesday, 31 July 2013

Intervention of Professor Pavle Sicherl at the Forum

The 2013 Global Forum on Development (GFD) in Paris was designed to promote a better understanding of what the shifting dynamics of poverty means for policies to be pursued by governments, international organisations and others in the post-2015 world.

Within Session 3, Innovative approaches to measuring poverty, well-being and progress, and implications for statistical capacity development; Session 3.2 was dealing with the statistical capacity development in an emerging post-2015 development agenda. Development goals must reflect the realities and priorities of individual countries, but they also need to be measurable.  This implies that statistical capacity development, which was widely neglected when the MDGs were first designed, should have crucial importance for any follow-up framework. Professor Sicherl discussed the evaluation of the MDG implementation in a new way using Gaptimer MDG Progress Chart.

Professor Sicherl also stated that the issue of “how statisticians can take advantage of innovations in data production and dissemination” has to be examined in the broader context; the innovations should include introducing also statistical measures that are transparent and easily understood by everyone. Time distance measure can present one of such measures that produce knowledge and policy messages in a very understandable way to build both objective and subjective perceptions of the overall degree of inequality. The time distance concept can influence the perception and decisions of people when they are assessing their relative position in their surroundings, in the society and across countries over time. In the information age this additional view of the existing data should be evaluated as an important contribution to the more efficient utilisation of the available information in many fields.





 
The Guardian published the time distance method for measuring implementation of MDGs PDF Print E-mail
Monday, 24 June 2013

Gaptimer Progress Chart for World Regions and results for 111-140 countries

The article is available at http://www.guardian.co.uk/ with appropriate links. While setting sensible goals and providing data about implementation are both necessary preconditions for any post-2015 interventions, we also need statistical measures that are transparent and easily understood by everyone. The time dimension of MDG implementation can be presented and analysed in a new complementary way that is very easy to understand and to communicate.

Measuring implementation involves comparing two sets of data: actual developments over time against the implied time path from the starting point to the 2015 MDG target deadline. The discrepancies can be measured in two dimensions; static difference at a given point in time and discrepancies in time (either time lead or time lag). Monitoring implementation is like comparing train or bus arrivals with the timetable provided for each mode of transport. In the context of the MDGs, it amounts to comparing the time of actual implementation with the time stipulated by the schedule to the 2015 target. 

The results in the Gaptimer Progress Chart attached 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; i.e. if the developing world is on track, ahead or behind schedule to achieving MDG goals. It enables the reader to grasp at a glance the world situation for 100 time distance results across 10 MDG indicators and 10 units (7 world regions, Developing Regions, China, and India) to facilitate debate for the past and the post-2015 era.

To facilitate the understanding and use of this method SICENTER has developed a free web tool to monitor implementation of targets with the S-time-distance measure available to international and national organisations, NGOs, experts, businesses, managers, educators, students, interest groups, media, and the general public. It can be used for monitoring implementation in many areas beyond MDGs, like adding a second dimension to comparing actual values with target values, forecast, budget, plan, etc., both at macro and business levels. It is available on http://www.gaptimer.eu/s-t-d_monitoring_tool.html.


 
Upgraded visualisation of the Gaptimer MDG Progress Chart PDF Print E-mail
Monday, 10 September 2012

Gaptimer MDG Progress Chart enables immediate visualisation with 25 graphs


We have upgraded earlier visualisations in two ways. Firstly, by clicking on Gaptimer MDG Progress Chart the readers can reach 25 graphs thus providing user friendly access to better understanding and analysis. Time distances for 100 results for indicators and units are presented in a single summary table. For more detailed analysis we have added 5 graphs and Excel files of calculations in which time distance lead or lag from the line to the respective MDG 2015 targets are shown for 111-140 developing countries respectively. Secondly, interested readers can download the Excel files over the analysed period 1990-2010, analyse all individual country results and select results for those countries that they would like to compare with (e.g. by regions like African countries or simply with neighbouring countries).

Gaptimer Progress Chart of MDG implementation for world regions 



Are we on the track, ahead or behind in time measured by S-time-distance in years 
(+ time lag, - time lead) comparing with the line to the 2015 MDG targets around 2010
Gaptimer MDG Progress Chart   Developing Regions Northern Africa Sub-Saharan Africa Latin America and the Caribbean Eastern Asia Southern Asia South-Eastern Asia Western Asia CHINA INDIA IND 4.1 IND 5.1 IND 7.8t IND 7.9t IND 8.16 Proportion of population living below $1 (PPP) per day (2008) Prevalence of underweight children under-five years of age Net enrolment ratio in primary education Ratio of girls to boys in primary education Under-five mortality rate Maternal mortality ratio Tuberculosis patients successfully treated under short course (2009) Proportion of population using an improved drinking water source, total Proportion of population using an improved sanitation facility, total Internet users per 100 inhabitants (2011) The Millennium Development Report 2012 Gaptimer
1. Click on the name of the world region to view the bar chart over the 10 selected indicators
2. Click on the name of the indicator to view the bar chart over the 10 selected units
3. For 5 selected indicators click on the number of the indicator to view the S-time-distance deviation graph for individual developing countries with appropriate data around 2010: 

IND 4.1, Under-five mortality rate, 137 developing countries
IND 5.1, Maternal mortality rate, 127 developing countries
IND 7.8t, Proportion of population using an improved drinking water source (total), 117 countries
IND 7.9t, Proportion of population using an improved sanitation facility (total), 111 countries
IND 8.16, Internet users per 100 inhabitants, 140 developing countries

For charts of time distances for individual countries on the axis with the country names only every fourth name of the country could be displayed due to lack of space.


Excel files of calculations of time distance deviations from the lines to target for 111-140 individual developing countries for the 5 selected indicators are available below. Thus a more detailed analysis is possible so that interested readers can download the files, analyse all individual country results and select from the results those countries that they would like to compare with (e.g. by regions like African countries or simply with neighbouring countries). 

EXCEL FILES of S-time-distances for selected developing countries:

 
<< Start < Prev 1 2 Next > End >>

Results 1 - 15 of 28
 
 
Related Items