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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 UNDP 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: 

 
Time distance monitoring of implementation of targets
Wednesday, 18 June 2014

Test application for EU2020 targets by countries and free software tool

Monitoring implementation of targets is an integral part of policy making at many levels and in many domains. The innovation is that implementation of targets is described in two dimensions: static deviation from the line to target at a given point in time and S-time-deviation at a given level of the indicator. Describing the implementation of targets as leading or lagging in time against the line to well-known targets is a very useful application in the policy debate that enhances knowledge, giving data a value beyond spreadsheets. 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.


We measure deviations in two dimensions. Firstly, one can measure the difference in variables at a given point in time. And secondly, discrepancies in time (either time lead or time lag) are measured. Monitoring implementation in time is like comparing train or bus arrivals with the timetable provided for each mode of transport. The statistical chart uses the same identifiers as Formula 1 on TV: drivers who score a minus at time distance are shown in green to signify that they are ahead in time. 

The table for EU 28 countries for 2013 (or 2012) shows the results from 2010 on. Yet the summary results confirm the earlier conclusions. For the headline indicator employment rate 20 countries are behind the schedule, 11 of them had in 2013 values below those in 2010 starting year. For 11 countries there was no progress in the 2010-2013 period for employment rate. The earlier graph that contained also the worse years of the financial crisis showed even a more serious situation. The time distance method, either for monitoring or for benchmarking in the time perspective, brings the second dimension of deviations or disparities that the present state-of-the-art is neglecting. 

For early leavers nine countries were in 2013 already better than their 2020 targets, this holds true for tertiary attainment for 10 countries; with only six countries being behind the schedule for both indicators. The headline indicator renewable energy also more countries are ahead of schedule than behind it, but with fewer cases that already reached the 2020 targets. R&D in GDP indicates a different picture, with 9 countries ahead and 16 countries behind the schedule; overall it is closer with the employment rate situation than with the other three indicators. 

The average for EU28 S-time-distance deviations express the situation with being ahead or behind the track to 2020 targets in simple terms: employment rate is more than 3 years behind, R&D 1.2 years behind, renewably energy 0.6 years, early leavers 2.1 years and tertiary attainment 2.4 years, ahead of  the line to the 2020 target.   

Software for time distance monitoring of targets from your own data: 

For time distance monitoring of implementation of targets, as shown for examples of indicators for EU2020 and UN Millennium Development Goals, SICENTER developed on www.gaptimer.eu a software tool to facilitate interested users to use the method for their own data. The tool can be accessed on http://www.gaptimer.eu/s-t-d_monitoring_tool.html


 
European Union at a Glance
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

 

Indicators

Data range

Top country (last year)

1

Life expectancy at birth

1960-2012

Spain

2

Human Development Index

1980-2012

Netherlands

3

GDP per capita in PPS

1995-2012

Luxembourg

4

Median income in PPS

1995-2013

Luxembourg

5

Employment rate (15 to 64 years)

1992-2012

Netherlands

6

Activity rate (15 to 64 years)

1992-2012

Sweden

7

Share of gross fixed investment in GDP

1954-2013

Estonia

8

R&D expenditure (GERD), percent of GDP

1981-2012

Finland

9

Summary Innovation Index

2008-2012

Sweden

10

Tertiary attainment for age group 15-64

2000-2013

Ireland

11

Proportion of population aged 65 years and more

1961-2013

Italy

12

Old age dependency ratio, projections 2013-2080

2013-2080

Slovakia

13

Population growth rates, total

1961-2013

Luxembourg

14

Persons killed in road accidents per million inhabitants

1990-2012

United Kingdom

15

Death due to homicide, standardised death rate by 100 000 inh.

1994-2010

United Kingdom

16

Infant mortality rate

1960-2012

Slovenia

17

At-risk-of-poverty (percent of total population)

1995-2012

Czech Republic

18

At-risk-of-poverty (percent of elderly population)

1995-2013

Hungary

19

Income quintile share ratio S80/S20

1995-2013

Slovenia

20

GINI coefficient

1995-2013

Slovenia

21

Early leavers from education and training

1992-2013

Croatia

22

Healthy life years at birth - females

1995-2012

Malta

23

Healthy life years at birth - males

1999-2012

Malta

24

Households with broadband access

2003-2013

Finland

25

Regular Internet use

2003-2013

Luxembourg

26

Share of energy from renewable sources

2004-2012

Sweden

27

Urban population exposure to air pollution by particulate matter PM10

1997-2011

Denmark

28

Publications per million inhabitants

1994-2010

Denmark

29

Proportion of seats in national parliaments held by women

2000-2013

Sweden

30

Current account balance in % of GDP

1975-2013

Netherlands



AN ADDITIONAL WAY OF PRESENTATION ACROSS MANY UNITS AND MANY YEARS

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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