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Recession in USA, EU, Japan - China became the Largest Economy
Monday, 04 September 2017

Overview of the decade of financial crisis in the world

1. 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 remarks

After 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: 

 
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.


 
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