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Benchmarking comparison in the time dimension
Recession in USA, EU, Japan - China became the Largest Economy |
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Monday, 04 September 2017 |
Overview of the decade of financial crisis in the world1. The damage of the world financial crisis needs to be studied in the broader perspective of beyond GDP framework. The fall in GDP underestimated the extent of damage of the financial crisis in the Triad (USA, EU, and Japan), as the greater part of the developed world. The deterioration of the employment rate and especially the fall in the share of the capital formation in GDP seriously hindered the medium to long-term capabilities of these economies, not to mention the worsening of inequalities.
Source: own calculations based on OECD (2017a, 2017b) and World Bank data (2017c) The detrimental effect of the world financial crisis beyond that on the GDP level and on the GDP growth rate has been felt with even greater intensity in the deterioration of employment rates, investment share in the GDP, in the increasing risk of poverty, and increasing income inequality. This has diminished the welfare and growth capabilities of these economies during the decade. 2. For the overall magnitude of GDP in constant prices China surpassed EU28 in 2015 and the USA in 2013; for the overall gross capital formation China surpassed EU28 in 2012 and the USA in 2010. The two opposing directions: the decline in the Triad and the high growth rate in China, changed the ranking between them, currently being 1. China, 2. EU28, 3. USA. However, one should add that the level of GDP per capita (as distinct from the overall GDP level) in the Triad is still much higher than in China. Yet, as an effective developing country, China is progressing fast also in this respect. 3. In addition to the usual statistical measures, such as percentages and growth rates, we shall describe the severity of the great recession with statistical measure S-time- distance, which measures distance in time (e.g. years) when the same level of the indicator has been reached. The time distance methodology is available in the large study (Sicherl, 2011a) and on www.gaptimer.eu. The paper (Sicherl, 2011b) published by the OECD Statistics Directorate can be freely downloaded from OECD at http://dx.doi.org/10.1787/5kg1zpzzl1tg-en. It can show how much time has been needed for the indicator to recover to the level before the crisis. The results are: to regain the 2007 level of GDP per capita, Japan needed 6 years, the USA 7 years and EU28 8 years; for the employment rate EU28 needed 8 years, while in 2015 the USA is still below its 2007 level; for the investment effort as the share in GDP the 2006 level has not been yet recovered in Triad: a delay of more than 10 years. This gives politicians and especially the general public unambiguous message that the financial crisis resulted in lost growth potential in this field for more than a decade. 4. While the 2015 and 2016 values of GDP per capita were in all three economies higher than the pre-crisis levels, the situation on the of investment effort is completely different; the investment shares in GDP were distinctly below their respective pre-crisis levels. It seems that the damage done by the financial crisis has in this respect meant a delay of a decade or more. The speed of change was swift. For gross capital formation in constant prices China surpassed the value of the USA in 2010 and the value of EU28 in 2012. In 2006 the magnitude of investment in China was still more than 50% lower than in the USA and EU28. In terms of time distance the 2006 value for China was reached in Japan 10 years earlier, 18 years earlier in the USA and more than 30 years earlier EU28. With great speed China reached the value of the USA in only 3 years and that of EU28 in 6 years. Closing remarksAfter slow recovery, growth may be picking up but we need to know where we start from. The fundamentals need to be improved. As the quality of financial regulation has not improved substantially on either side of the Atlantic, these domains are prone to further deterioration anywhere in the world. Even more so, possible further financial crisis could come around if these financial institutions are not properly regulated. FULL TEXT:
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Sex Differences around the World in Time Distance Watch |
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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.
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European Union at a Glance |
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Thursday, 08 May 2014 |
Statistical portrait with innovative table-graphs for 30 selected indicators over 28 countries in time perspective
European Union at a Glance presents an easily understandable overview of 30 selected indicators over 28 EU countries in time, which is probably the most condensed current summary picture of dynamics and disparities in the EU over many domains over time.
The Gaptimer Report No. 3 is timely publication very useful for discussion of the situation in the EU in light of the forthcoming new European Commission and European Parliament and at the occasion of the 10th Anniversary of the largest EU expansion in 2004.
List of 30 selected indicators
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Indicators
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Data range
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Top country (last year)
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1
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Life expectancy at birth
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1960-2012
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Spain
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2
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Human Development Index
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1980-2012
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Netherlands
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3
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GDP per capita in PPS
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1995-2012
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Luxembourg
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4
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Median income in PPS
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1995-2013
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Luxembourg
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5
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Employment rate (15 to 64 years)
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1992-2012
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Netherlands
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6
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Activity rate (15 to 64 years)
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1992-2012
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Sweden
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7
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Share of gross fixed investment in GDP
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1954-2013
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Estonia
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8
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R&D expenditure (GERD), percent of GDP
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1981-2012
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Finland
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9
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Summary Innovation Index
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2008-2012
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Sweden
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10
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Tertiary attainment for age group 15-64
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2000-2013
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Ireland
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11
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Proportion of population aged 65 years and more
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1961-2013
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Italy
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12
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Old age dependency ratio, projections 2013-2080
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2013-2080
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Slovakia
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13
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Population growth rates, total
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1961-2013
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Luxembourg
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14
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Persons killed in road accidents per million inhabitants
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1990-2012
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United Kingdom
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15
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Death due to homicide, standardised death rate by 100 000 inh.
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1994-2010
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United Kingdom
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16
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Infant mortality rate
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1960-2012
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Slovenia
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17
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At-risk-of-poverty (percent of total population)
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1995-2012
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Czech
Republic
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18
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At-risk-of-poverty (percent of elderly population)
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1995-2013
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Hungary
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19
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Income quintile share ratio S80/S20
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1995-2013
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Slovenia
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20
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GINI coefficient
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1995-2013
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Slovenia
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21
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Early leavers from education and training
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1992-2013
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Croatia
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22
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Healthy life years at birth - females
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1995-2012
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Malta
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23
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Healthy life years at birth - males
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1999-2012
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Malta
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24
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Households with broadband access
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2003-2013
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Finland
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25
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Regular Internet use
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2003-2013
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Luxembourg
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26
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Share of energy from renewable sources
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2004-2012
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Sweden
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27
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Urban population exposure to air pollution by particulate matter PM10
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1997-2011
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Denmark
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28
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Publications per million inhabitants
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1994-2010
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Denmark
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29
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Proportion of seats in national parliaments held by women
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2000-2013
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Sweden
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30
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Current account balance in % of GDP
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1975-2013
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Netherlands
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ADDITIONAL WAY OF PRESENTATION ACROSS MANY UNITS AND MANY YEARS
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It uses innovative time matrix presentation format that enables such
condensed summary visual presentation over many countries and over time.
Secondly, 30 selected indicators from many
Eurostat indicators systems like Quality of life; Sustainable Development
Indicators, Digital Agenda, Headline Indicators, etc. follow the orientation of
Beyond GDP. Annex A1 provides Time Matrix
Calculator to calculate time matrix for your own data. The
30 time matrices give rich food for thought and imagination of readers can find
numerous comparisons and stories in the material. One of them is that the
damage done to countries by the world financial crisis is seen in a much greater scale when we look for 28 countries
beyond GDP and look at employment, investment share, risk of poverty, income
distribution, health, etc. While media and also official organizations
are focusing on discussion of GDP growth rate, such orientation understated the severity of the crisis. Other domains showed a more difficult
situation: - employment rate fell in 20 EU
countries;
- in all 28 EU countries without exception
share of gross investment in GDP decreased;
- risk of poverty as percent of
total population increased in 24 EU countries;
- income distribution worsened as
Gini coefficient and income quartile share ratio increased in 25 EU countries;
- healthy life years at birth
decreased for males and females in 15-18 EU countries.
The
voyage through 30 time matrices for 28 countries compressed a very large amount
of data, expressing multidimensional nature of development and well-being, indicating both visually and in numbers that very large
differences exist between EU countries with respects to levels and dynamics.
Using the innovative approach of time distance methodology the telling power of S-time-matrix provided a good summary overview
at-a-glance over many domains with clear understanding to decision-makers as
well as to the general public. Seeing with new eyes creates new knowledge
and better understanding. FULL TEXT:
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Astonishing Differences in Gender Disparity in Life Expectancy between Countries |
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Wednesday, 07 May 2014 |
How much longer live women than men around the globe?Gender disparities in life expectancy are analysed in Gaptimer Report No. 2 ‘How much longer live women than men around the globe?’ World inequalities are studied by combining two sets of statistical measures: static gap at a given point in time and gap in time for a given level of the indicator, providing a broader picture.
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 concentrate on gender disparity in life expectancy around the globe (at the world level for 196 countries and some aggregates; for EU27 countries with 269 NUTS2 regions). While female life expectancy at birth is higher than that for males for 99.5 percent of the world population, there are astonishing differences among countries. For example, Estonia occupied rank 51 the world for females and 110 for males. On the other extreme, e.g. the rank for Qatar was 65 for females and only 12 for males.
The time distance measure shows the reality with new eyes. The overall life expectancy the static difference between China and Sweden was less than 10 percent (which may appear to be small) while the S-time-distance was 51 years, (which gives a very different perception of the magnitude of the gap). For gender disparity in life expectancy S-time-distance for the world average, i.e. the horizontal time gap between trends of female and male life expectancy amounted to 20 years, 28 years for the EU27 and 35 years for the USA, showing a large and persistent gap in favour of women.
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World Inequalities in Human Development Index (1980-2012) |
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Tuesday, 04 February 2014 |
Time Distance ApproachGaptimer 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:
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