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World Inequalities in Human Development Index (1980-2012)
Tuesday, 04 February 2014

Time Distance Approach

Gaptimer Report No. 1 ‘World Inequalities in Human Development Index’ presents a new way of understanding and discussing development and world inequalities in a new dynamic framework. This manuscript can expand knowledge in two ways. 

Firstly, it offers an innovative approach for looking at disparities over many units and over time. The new time distance measure, expressed in time units, is easy to understand by everybody and offers a novel way to compare situations in economics, politics, business and statistics. 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.

As Sicherl (1973, 1993) proposes … observed time distance is a dynamic measure of temporal disparity between the two series intuitively clear, readily measurable, and in transparent units. It is suggested that one should complement conventional measures with horizontal measures.’ (Granger and Jeon, 1997)
C.W.J. Granger and Y. Jeon, University of California at San Diego 

Secondly, the empirical results for the Human Development Index over the three decades (1980-2012) provide new insights for the post-2015 agenda. S-time-distance measure (calculations based on official UNDP data) estimates HDI inequalities for each of 187 countries within their peer group. Telling new stories includes inequalities within EU27, BRICS countries, and Gulf Coordination Council countries.

These additional insights provide a transparent matter-of-fact message to politicians and the international community about the degree of urgency to tackle wide inequalities between and within countries in formulating and deciding on the post-2015 agenda. 

FULL TEXT: 


 
MDG implementaion by Gaptimer Progress Chart 2013
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
Thursday, 01 August 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.





 
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