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Sex Differences around the World in Time Distance Watch
Thursday, 21 July 2016

Life Expectancy, Obesity, Mean Body Mass Index, and Diabetes for about 200 Countries

The Gaptimer Report No. 5 offers new insights by analysing gender differences in life expectancy, mean body mass index, obesity and diabetes by using the novel time distance methodology. It combines two developments: firstly, recent availability of gender disaggregated longer time series by NCD Risk Factor Collaboration, 2016 on trends in body mass index and diabetes in 200 countries over 40 (35) years combined with  the UN long time series on life expectancy for about 60 years. As the focus we selected the gender difference in these indicators which can be attractive from both the medical and social standpoint and can be further elaborated with additional studies.    

Secondly, such longer time series make possible creative application of S-time- distance methodology for describing and analyzing indicator differences in the parallel dimension of time. Methodological innovations: parallel additional generic statistical measures S-time- distance, S-time- step and Level-Time Matrix as presentation and visualization tool. Expressed in time units they are comparable across variables, fields of concern and units of comparison. This makes S-time- distance an excellent complementary analytical and presentation tool offering additional insights, intuitive understanding, simplicity, and new semantics to many indicators and issues.  

In the gender difference for life expectancy one can address the question ‘How many years ago did the current level of the male value attained the same level in the past trend for women?’ This makes it possible to describe the gender differences in many indicators in the time distance dimension simultaneously with the static measures, leading to different perception of the extent of disparity than the conventional static measures alone.  

For life expectancy the time distance dimension of the diversion increases the perception of the degree of magnitude of sex difference in the indicator. In percentage terms in 2015 the range for 200 countries varied to about 15 percent for Belarus. The perception of the magnitude of sex differences is very different, as S-time- distances of women being ahead of men ranges up to about 60 years! in Belarus.   

Time lag for males behind the time when female life expectancy already achieved that level is on the world level about 14 years, about 38 years for more developed regions in UN definition and about 11 years for the less developed regions. USA and EU28 are both showing very substantial and persuading differences in favour of women, also at the regional NUTS levels in the EU and for the average of more than 3000 USA counties.   

The analysis of gender differences for the three more indicators, mean body mass index, obesity and diabetes, again shows that there were many cases where the time lead or time lag of one gender were larger than 20 years, which was taken as indication that such gender differences prevailed over longer periods of time (in either direction). For life expectancy and obesity about 100 countries show such female predominance. S-time- distance values range from more than 40 years of mean BMI values for males being ahead of mean BMI for females for Switzerland and Japan to more than 40 years of time lag in the opposite direction for five countries. Gender differences in obesity prevalence are strongly tilted in the female predominance. For Egypt, Turkey and South Africa the gender time distances show large time differences of 28, 24 and more than 40 years, respectively. For USA and for the UK the obesity prevalence is high also for men so that gender time distances are only few years. For diabetes there is predominance of cases for men in two high income regions; it was shown that for 26 countries (out of 27 countries) in the region High Income Western Countries the male values were for more than 20 years ahead of those for women.   

Time distances offer very different perception of the gender disparities as those of percentage differences at given point in time. We need both measures to understand the reality.

 
Innovative framework for dynamic indicator analysis of Beyond GDP initiatives
Friday, 14 August 2015

How well are EU28 countries progressing towards their targets for EU2020


The high-level expert EU conference “Moving ‘beyond GDP’ in European economic governance”, Brussels (October 10, 2014) was intended to discuss recent technical advances in measuring well-being, their current policy implications and how to translate this into EU-level and national policy-making in the future. These issues are discussed in three sections: 

1. Broadened theoretical concept of measuring inequalities and in evaluating the magnitude of inequality. Time, besides money, is one of the most important reference frameworks in a modern society. People have memories of the past and expectations about the future; they compare over many dimensions and over time. The observed distance in time (the number of years, quarters, months, etc.) for given levels of the indicator is used as a temporal measure of disparity between the two series, in the same way that the observed difference (absolute or relative) at a given point in time is used as a static measure of disparity. This innovation opens the possibility for simultaneous two-dimensional comparisons of time series data in two specified dimensions: vertically (standard measures of static difference) as well as horizontally (Sicherl time distance). In the information age this new view of the existing databases should be evaluated as an important contribution to a more efficient utilisation of the existing data. 

2. Time matrix presentation format indicates at a glance that GDP underestimated the scale of damage of the financial crisis, showing the importance of ‘beyond GDP’ initiatives for policy debate. Time matrix organises the same data from Eurostat databases in a way that data are arranged by selected levels of indicators showing in which year these levels of the indicators were achieved by given country. The result is a LEVEL-TIME MATRIX, which is easily understood by everybody. This presents a first level visualisation that usefully complements the details in the original database by showing the easily understandable summary dynamic overview. The study ‘European Union at a Glance’ allows for a quick level comparison for time matrices for 30 selected indicators for 28 countries.

3. Time distance measurement for monitoring of implementation of targets and for goodness-of-fit. It provides new parallel system of monitoring implementation of targets based on deviations in time of actual values from the time on line to the target, complementing (not replacing) the existing mostly static measures of inequality and of implementation of targets. 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. 

Empirical part shows time distance deviations for implementation of five selected headline indicators towards the EU2020 EU and national targets. The Gaptimer progress chart is a clear example of simplicity with the summary story of about 150 cases of EU2020 targets. The additional time distance monitoring supervision can be a standard procedure also in numerous other activities of the Commission and of the national and local levels in hundreds of cases like monitoring and evaluation implementation of budgets, plans, projects, structural funds, etc. 

This presents a complementary possibility to look at indicator differences in the parallel universe of time, adding new vocabulary in the semantics of discussing and analysing differences in the real world.


 
SYSTEM FOR MONITORING IMPLEMENTATION OF TARGETS: Present MDGs and Post-2015 SDGs
Tuesday, 14 July 2015

Results for 10 selected MDG indicators for Developing Regions, 7 world regions, China and India, and for 125-154 countries for four selected indicators


The Millennium Development Goals (MDGs) are coming towards conclusion and the international community is deciding on the scope and the timetable for a set of Sustainable Development Goals (SDGs). The study “SYSTEM FOR MONITORING IMPLEMENTATION OF TARGETS: Present MDGs and Post-2015 SDGs” includes three main parts:

1. Outline of time distance methodology, with S-time-matrix format to present data over many units and over time, two generic statistical measures S-time-distance and S-time-step. The new generic time distance approach, which is easy to understand and to communicate, offers a new view of reality that significantly complements existing mostly static measures of inequality in many domains. In the information age this new view of the existing databases should be evaluated as an important contribution to a more efficient utilisation of the existing data.

2. Empirical part uses the most recent data from the UN 2015 MDG Report Statistical annex and MDG database updated on July 6, 2015 and shows implementation of MDG targets for 

a) 10 selected MDG indicators for Developing Regions, 7 world regions, China and India
b) details for implementation of four MDG indicators for 125-154 countries, year by year. 

It provides new parallel system of monitoring implementation of targets based on deviations of actual values from the line to the target, thus complementing (not replacing) the existing mostly static measures of inequality and of implementation of targets. 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. 

3. This study offers a system for time distance monitoring implementation of targets for many domains and levels (global, regional, national, local, business). The detailed application to current MDGs could be immediately applied for the post-2015 SDGs targets when they are determined. It can be with the help of Sustainable Development Solutions Network facilities further refined and distributed to complement existing methods of monitoring implementation.

We added complementary possibility to look at indicator differences in the parallel universe of time, adding new vocabulary in the semantics of discussing and analysing differences in the real world. Free web monitoring tool is provided. SDG initiative is an important field where this additional dimension could be fruitfully applied making some aspects more transparent and understandable to people as the main potential beneficiaries and participants in the implementation. 

Printed version is available on Amazon.com 

FULL TEXT: 

 
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