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Human Development Index (HDI) in Time Distance Perspective (1980-2011)
Wednesday, 25 April 2012

The time dimension for HDI can be presented and analysed in a new complementary way based on time series data from UNDP Human Development Report 2011


HDI is an established composite indicator. Time series of HDI are now available for selected years for the period 1980-2011 in the UNDP Human Development Report 2011. From the same data a broader dynamic view can be established if the usual static statistical measures are complemented with S-time-matrix format, S-time-distance and S-time-step measures to analyse intertemporal changes in composite indicators. The position is that HDI analysis needs a two-dimensional dynamic framework: static disparity and time distance perspective.

The analysis contains analysis of 187 countries subdivided into the four human development groups: very high, high, medium, and low. For each of the groups the countries are presented in time matrices for selected HDI levels and in the corresponding time matrices for S-time-step estimates, for the available data for the period 1980-2011. In addition to that, summary matrices present the world view over four HD groups and world regions, for BRICS countries and for selected countries from all groups to cover the whole range of 187 countries. The range of HDI values over the three decades is 0.18 - 0.94. There were substantial improvements but disparities remain very large. Time matrix format also provides a first level visualisation tool. China was one the best performers and it is selected as an interesting benchmark to which other countries are compared.
 

China is still nearly 30 years behind Argentina at the lower end of the very high human development group; in turn Argentina is about 30 years behind the leading countries like Norway and Australia. About 20 countries of the low group have still not reached the level of China in 1980. To indicate the magnitude of the time lag between the lowest and the highest countries we can use indicator life expectancy: the two thirds of the low HD group are lagging benchmark Sweden between 90 and 130 years.

The most important conclusion of the analysis is that different measures provide very different perceptions of the order of magnitude of disparities: 1. static disparities in HDI appeared small; 2. time distances in HDI were large. For a more realistic dynamic picture we need both.

Empirical facts e.g. show that the degree of the disparity for the HDI in time perspective is large between and within HD groups: very high group is leading the high group by more than 31 years, medium group is falling behind the high group for 27 years, low group is lagging medium group for 24 years. There are many other empirical conclusions that cannot be mentioned in the summary like the world view over four groups for Health, Education, and Income Index. S-time-step indicates how many years were needed to reach the next level of indicator. The greatest HDI dynamics was in medium group, which needed 1.5 years to increase one level of the HDI; on the average for the other three HD groups S-time-step for HDI was between 2.2 and 2.4 years. 


 
Just published: New book on time distance by Professor Pavle Sicherl
Thursday, 01 March 2012

Book: Time Distance in Economics and Statistics - New Insights from Existing Data


The book on time distance methodology by Professor Pavle Sicherl was published in Vienna. The time perspective, which no doubt exists in human perception when comparing different situations, is systematically introduced in comparative analysis both as a concept and as a quantifiable measure. Time distance is an innovative approach for looking at time-series data, it offers two improvements in the present state-of-the-art of comparative analysis.

The first one is analytical and statistical – two novel generic statistical measures S-time-distance and S-time-step are generalised to complement conventional measures in time series comparisons, regressions, models, forecasting and monitoring, and to provide from existing data new insights due to an added dimension of analysis. Expressed in time units they are intuitively understandable; they can be compared across variables, fields of concern, and units of comparison.

The second component is normative and theoretical, related to subjective perceptions, policy and welfare issues. Time distance concept can influence the perception and decisions of people when they are assessing their relative position in the society and across countries over time. Concept of the ‘overall degree of disparity’ combines static and time distance measures of disparity with the potential to bring new understanding in economics, management, research and statistics. Empirical applications analyse time distance differences between countries in the world, OECD and EU, regional disparities, transition depression, ICT and digital divide, and monitoring implementation of UN MDGs and Lisbon strategy in the EU.


 
OECD Statistics Working Papers, 2011/09 on time distance
Sunday, 08 January 2012

New understanding and insights from time-series data based on two generic measures: S-time-distance and S-time-step

Professor Pavle Sicherl prepared a methodological paper on time distance approach that was published by OECD Publishing. It covers several additional technical points and application examples of the time distance method beyond earlier applications. The time distance approach presents a means of presentation of complex data sets that have universal appeal; it is intuitively understandable and can be usefully applied to a wide variety of substantive fields at macro and micro levels. It represents a new way of presenting and analysing indicators complementing and not replacing existing methods. 

In summary, time distance is an innovative approach for looking at time-series data. Expressed in time units, the approach is easy to understand and provides a useful complement to existing methods. The time distance approach compares time series in the horizontal dimension, i.e. for a given level of the variable, based on two generic statistical measures: S-time-distance and S-time-step. These measures are based on a time matrix that summarises information over many units and years and that provides a first-level visualization tool. The paper also introduces the concept of the ‘overall degree of disparity’, defined as proximity in the indicator space as well as in time, arguing that this concept has the potential to bring new understanding in economics, management, research and statistics. 

While the OECD ‘Your Better Life Index’ is a tool that allows addressing differences in subjective opinions among fields of concern and indicators, the time distance concept opens the question about the weight that people assign to the two dimensions of disparity discussed in this paper (static measure and time distance) to arrive at a overall evaluation of their position in society and globally. 

The empirical examples included in this paper demonstrate how the method could be applied to three indicators (life expectancy at birth, the share of the elderly population, and projections of population growth) drawn from the OECD Factbook. These examples were drawn from an earlier presentation by the author referring to 14 variables (‘Visualisation of 50 years of OECD countries at a glance’, which is available on wikiprogress.org and on ‘50 years of OECD countries at a glance’). The paper has also applied the methodology for monitoring Millennium Development Goals across many indicators, either for the world regions or at the country level. 

 
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