45IB Revija 1/2017
INNOVATIVE FRAMEWORK FOR DYNAMIC INDICATOR 
ANALYSIS BEYOND GDP1 
dr. Pavle Sicherl, SICENTER and University of Ljubljana 
JEL: C490, C880, I310, N100, O470, O520
UDK: 330.55
Povzetek1
Blaginja in razvoj sta večdimenzionalna in dolgoročna fenomena, ki presegata opazovanje bruto družbenega proizvoda 
(BDP), še posebej medijsko osredotočenost na stopnje rasti BDP. Razvojni program OZN, OECD in Evropska komisija že vrsto 
let pripravljajo konference ter razvijajo nove indikatorje in politike onstran BDP (»Beyond GDP Initiatives«). V tem članku je 
drugi vodilni element uporaba dinamične metodologije časovne distance, ki podaja rezultate v časovni dimenziji, s čimer 
dopolnjuje obstoječe rezultate in metodologije. To lahko pomaga tistim, ki se odločajo (vladi in podjetjem), pa tudi civilni 
družbi – ocenjujem, da bi morala prav ta, pa tudi mediji, pogosteje uporabljati tovrstne nove opisne statistične mere, ki so 
lahko tudi orodje za realnejše ocene.
Točka 3 pomaga razumeti razširjeni koncept merjenja in ocenjevanja velikosti neenakosti v dveh dimenzijah. Točka 4 podaja 
dodatno možnost vizualizacije podatkov časovnih serij z matriko NIVO-ČAS, ki pomaga ponazoriti, kako je enostransko 
opazovanje BDP podcenilo škodo, ki jo je svetovna finančna kriza povzročila v državah EU na drugih področjih. Točka 5 
poudarja orodje časovne distance kot metode za spremljanje uresničevanja ciljev, ki se kot transparentna in lahko razumljiva 
mera lahko uporablja vzporedno z drugimi metodami. V članku je uporabljena za analizo uresničevanja doseganja ciljev 
iniciative EU 2020, katere rezultati so kar za okoli 150 primerov (pet izbranih indikatorjev in 28 držav EU) povzemajoče prikazani 
v eni sami tabeli, kot je tabela 2. Podana je tudi povezava s študijo za oceno doseganja razvojnih milenijskih ciljev OZN (MDG), 
ki sem jo opravil za 10 izbranih indikatorjev za svetovne regije, Kitajsko in Indijo ter za pet izbranih indikatorjev za 125-154 
držav. Metoda je primerna tudi za podobno uporabo za tekoče spremljanje uresničevanja Agende OZN 2030 za trajnostni 
razvoj. Ta transparentna metoda za spremljanja uresničevanja ciljev na vseh nivojih je na razpolago, a ni široko uporabljena. 
Na razpolago je tudi računalniški program na www.gaptimer.eu. 
V empiričnem delu članka so prikazani rezultati, ki poudarjajo, da osredotočenost na BDP podcenjuje velikost škode, ki jo 
je svetovna finančna kriza povzročila na poslabševanju mnogih drugih indikatorjev: 1. Stopnja zaposlenosti je padla v 20 
EU državah (71 % držav); 2. Stopnja tveganja revščine kot delež v celotnem prebivalstvu se je povečal v 23 državah EU (82 
%); 3. Distribucija dohodka se je poslabšala, saj sta se Ginijev koeficient in delež dohodkov kvartilov poslabšala v okoli 25 EU 
državah (okoli 89 %); 4. Najbolj presenetljiv zaključek se nanaša na dejstvo, da se je delež fiksnih investicij v BDP zmanjšal v 
vseh 28 državah EU (100 %). To je seveda negativno vplivalo na srednjeročno in dolgoročno stopnjo rasti BDP. Slovenija pri vseh 
petih izbranih indikatorjih beleži (kot je razvidno iz tabele 1) negativne rezultate, kar zahteva dodatno obravnavo. Razmere se 
sicer delno izboljšujejo, vendar so neodgovorne poteze finančnega sektorja nedvomno negativno delovale na blaginjo večine 
prebivalstva in razvoj v EU.
Empirični rezultati v tabeli 2 obravnavajo drugo situacijo v EU, in sicer z metodologijo spremljanja uresničevanja ciljev 
strategije EU 2020 za celotno EU in nacionalnih ciljev v 28 državah EU za pet indikatorjev za izbrana področja. Med njimi sta 
dve področji, kjer EU za dosego ciljev zaostaja več kot dve in manj kot tri leta; to sta stopnja zaposlenosti in delež investicij v 
raziskave in razvoj v BDP. Pri prvem področju 17 držav časovno zaostaja v času za predvideno linijo do cilja v letu 2020, 10 držav 
pa prehiteva. V letu 2015 ima kar 10 držav nižje vrednosti od tistih iz leta 2010, med njimi tudi Slovenija. Za drugi omenjeni 
indikator je položaj še slabši: 20 držav zaostaja za ciljem, le 10 držav ga presega. Tovrstna dodatna metoda za spremljanje 
uresničevanja ciljev bi bila lahko del običajnega postopka pri mnogih drugih aktivnostih Evropske komisije, v stotinah slučajev 
pa tudi na nacionalnih in lokalnih nivojih (kot tudi v podjetjih), kot so spremljanje in ocenjevanje uresničevanja proračunov, 
planov, projektov, strukturnih fondov, okoljskih zavez itd. 
Ključne besede: S-časovna distanca, onstran BDP, ekonomski in socialni indikatorji, svetovna finančna kriza, spremljanje EU 
2020 ciljev, spletno orodje za spremljanje uresničevanja ciljev
1 Partial financing for the study by the Slovenian Research Agency under Programme P5-0117 is gratefully acknowledged.
46 IB Revija 1/2017
1 Introduction
Progress and well-being are by their nature multi-
dimensional and long-term phenomena. Time is of 
the essence in all domains, both as an operational and 
comparative metric. Perceptions of well-being and 
progress are inherently subjective: one needs measures 
to build the perceptions of the degree of disparity 
regarding the indicators in a dynamic context. 
Measuring well-being and societal progress entities is 
a complex undertaking. In its essence, it involves the 
search how to answer in an agreeable way manner 
two questions transcending fields of concern and 
technicalities: perception about what elements 
(connected to well-being and societal progress) and 
which measures to use to build perception of them 
(measures to present and to communicate the topics 
Abstract
Well-being and development embody multidimensional and long-term experience, going much beyond the GDP. The focus 
in the media is especially in GDP growth rates. Over time, UNDP, OECD, and European Commission have participated in the 
conferences, indicator developments, and policy discussions of 'Beyond GDP Initiatives'. In the article, the analyses of beyond 
the GDP indicators are enriched by the application of the dynamic time distance methodology to complement the results of 
the usual mostly static tools. 
With time distance methodology, a new perspective related to time does not replace but rather adds a new dimension to 
existing analysis across many variables, fields of concern, and units of comparison. Section 3 deals with the broadened concept 
of measuring and evaluating the magnitude of inequalities in two dimensions. LEVEL-TIME matrix in section 4 is an additional 
option of visualisation of time series data which helped to establish that GDP underestimated the scale of damage of the 
financial crisis in the EU for selected indicators. Section 5 emphasises the function of the time distance tool for monitoring 
implementation of targets parallel to other methods, with application to about 150 cases of EU2020 targets; as well as to 
measuring implementation of the UN Millennium Developments Goals that can be used also for the UN initiative of the 2030 
Agenda for Sustainable Development. This transparent and innovative method for monitoring implementation of targets at 
all levels is available but not yet utilised. It can bring a new easily understandable perception of the magnitude of the gap 
between the actual implementation and proclaimed targets at many levels: it can help governments, the civil society, and 
businesses in a broader understanding of continuous policy debate and necessary adjustments.  A software tool is available.
The empirical study exposes that GDP underestimated the scale of damage of the financial crisis as selected time matrices 
showed deterioration in many indicators:
 – Employment rate fell in 20 EU countries (71% of countries);
 – Risk of poverty as percent of total population increased in 23 EU countries (82%);
 – Income distribution worsened as Gini coefficient and income quartile share ratio increased in about 25 EU countries 
(approximately 89% of countries);
 – The most shocking conclusion is that the value of the share of growth fixed capital formation in GDP decreased 
in all 28 EU countries (100%!). This negatively affected the medium/long-term rate of growth of GDP.
Table 2 shows possible scheme and numerical values for analysing time distance deviations for implementation of five selected 
headline indicators towards the EU2020 for the entire EU and national targets. It is a clear example of simplicity with an 
overview of about 150 cases of EU2020 targets, showing the results of 5 selected EU2020 indicators, 28 countries, and the EU 
aggregate in one single table. Such time distance monitoring supervision could become a standard procedure in numerous 
other activities of the Commission on the national and local levels, e.g. monitoring and evaluation implementation of budgets, 
plans, projects, structural funds, etc.
Key words: S-time-distance, beyond GDP, economic and social indicators, world financial crisis, monitoring EU 2020 targets, 
Web monitoring tool 
for policy making). I shall deal with the second issue and 
explore possibilities how the time distance concept and 
two novel generic statistical measures (S-time-distance 
and S-time-step) can enhance knowledge broadening 
and policy implications in the context of Beyond GDP 
initiatives.
Well-being and development embody multidimensional 
and long-term experience, going much beyond the GDP. 
Over time, UNDP, OECD and European Commission have 
become engaged in conferences, indicator developments 
and policy discussions of 'Beyond GDP Initiatives'. 
European Commission (2007, 2014) was dealing with 
the Beyond GDP Initiative in several events, to mention 
two of them. In 2007, the conference ‘Beyond GDP’ was 
organised in Brussels. In October 2014, the high-level 
expert EU conference “Moving ‘beyond GDP’ in European 
economic governance” was organised to discuss recent 
47IB Revija 1/2017
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. 
It is very important how data and indicators are 
understood. New data and indicators are needed, 
together with innovative concepts of looking at data 
and new generic statistical measures to perceive and 
exploit the information available in existing data better. 
These issues are discussed in four topical sections: 
 – Time distance methodology – A new perspective 
related to time;
 – Broadened theoretical concept of measuring 
inequalities and evaluating the magnitude of 
inequality; 
 – Time matrix presentation format indicates that GDP 
underestimated the scale of damage of the financial 
crisis; 
 – Time distance measurement for monitoring the 
target implementation and for goodness-of-fit.
2 Time distance methodology – A new 
perspective related to time
The theoretical starting point of my contribution is 
the fact that in empirical research and in decision-
making the art of handling and understanding different 
views of data is crucial for the discovering of relevant 
patterns and broader understanding of reality from 
several perspectives. The present state-of-the-art 
methodology fails to realise that in principle, there exists 
a theoretically equally universal measure of difference 
(distance) in time when a given level of the variable is 
attained by the two compared time series, in addition 
to the static comparison of time series. The fact that 
Nobel Prize Winner Clive Granger found the concept of 
time distance for a given level of the variable a useful 
addition to the present state-of-the-art methodology 
is proof of the generic capability of the methodology2 
beyond the benchmarking and monitoring examples 
used in this article.
In graphical terms, the usual practice is to compare the 
time series in the vertical dimension, i.e. for a given point 
in time. The time distance approach uses an additional 
perspective: it compares the respective time series in the 
horizontal dimension, i.e. for a given level of the variable 
(Sicherl 1973, 2004a, 2004b, 2007, 2015). For a simple 
description of the methodology on OECD web page 
see Sicherl (2011), for extensive elaboration consult the 
book Sicherl (2012). 
2 The usual metrics for comparing two lines involve differences along 
the vertical axis. This can be an insufficient manner of measuring how 
these trends vary in terms of time, which is on the horizontal axis. When 
one compares trends lines based upon horizontal differences, the time 
distance is measured: that is, the difference between the two lines is 
expressed in a measure of time (such as years or months). 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 vertical measures with horizontal measures.’ 
(Granger and Jeon 1997). 
In brief, the statistical measure S-time-distance is a 
special category of time distance measures. It measures 
the distance (proximity) in time between the points in 
time when the two compared series reach a specified 
level of the indicator X. The S-time-distance for a given 
level of XL is defined as:
Sij (XL) = Δt (XL) = ti (XL) – tj (XL)             (1)
The S-time-step measures the time elapsed between 
two levels of a time series, providing an alternative 
description of its growth rate, measuring the growth of 
a series by using the inverse relation to the conventional 
ΔX/Δt growth rate metrics. The S-time-step is expressed 
in units of time and is defined as: 
Si (ΔXL) = [ti (XL+ΔX) - ti (XL)]/ ΔX             (2)
The range of applications regarding the time distance 
methodology can be examined in two categories. The 
first is the application in statistics in a more narrow 
interpretation, i.e. adding two generic statistical 
measures S-time-distance and S-time-step to the 
literature; with the primary application in descriptive 
statistics but also in goodness-of–fit issues. The strength 
of the time distance concept lies in the fact that it 
enables additional exploitation of data and visualisation 
for time-related databases and indicator systems. 
The second category is the application of time 
distance methodology for a better understanding 
of the information provided in statistical data; for 
building knowledge and for discussing policy and 
business issues in a new perspective. The two generic 
statistical measures S-time-distance and S-time-step 
have in addition to their use as descriptive statistical 
measures the potential to provide new understanding 
of a variety of situations in economics, management, 
research, statistics, asking new questions, formulating 
new hypotheses, establishing new semantics, and 
reaching new conclusions. The concept of time distance 
applies across variables, fields of concern, and units 
of comparison, making it an excellent analytical, 
presentation, and communication tool.
3 Broadened theoretical concept of 
measuring inequalities and evaluating 
the magnitude of inequality
Apart from money, time is one of the most important 
reference frameworks in a modern society. People have 
memories of the past and expectations for the future; 
they compare over many dimensions and over time. This 
explicitly or implicitly introduces the concept of inter-
temporal aspect of well-being. It is also elaborated in 
Sicherl (2014a) in the Springer ‘Encyclopedia of Quality 
of Life and Well-Being Research’, edited by Alex Michalos. 
The inter-temporal aspects of well-being relate both to 
individuals regarding their time preference and in our 
case even to a greater extent to comparing situation 
between countries, regions, gender, socioeconomic 
48 IB Revija 1/2017
groups, individuals, etc., over time. Inter-temporal 
considerations might be of importance even over a 
very long term as e.g. parents might be considering 
the success of their children an important part of their 
well-being. Expectations and hope might be additional 
elements in subjective considerations over time. 
As the perception of well-being is subjective, the concept 
of well-being and progress has to deal not only with the 
categories, measurement, and data availability but also 
with interpersonal and inter-temporal comparisons of 
the chosen constituent elements. The time perspective 
which no doubt exists in human perception when 
comparing different situations has been with the S-time-
distance method systematically introduced in a specific 
way both as a concept and as a quantifiable measure in 
a statistical and comparative analysis. 
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). 
There are two aspects. One is descriptive, i.e. two 
additional statistical measure with clear interpretability. 
The second one is the evaluation of the degree of 
inequality in the broader two-dimensional framework. 
For instance, the perception whether the gender 
difference for the EU average in life expectancy is large 
or small depends on the measure used: large (time 
distance amounts to 27 years, i.e. to about a quarter of 
century) or small (static percentage difference being less 
than 8%), see Sicherl (2014c). Both dimensions - static 
and time distance measures - are required.
In the dynamic world of today, it is not satisfactory to rely 
only on static measures of disparity. The arguments for 
extension in several directions to a broader framework 
in theory and especially in empirical and policy work 
are well established; it has to happen sooner or later. 
If we have a number of various measures of inequality 
such as static absolute or relative difference and time 
distance, this raises the question of weight that people 
attach to each of them when reaching their perception 
of the combined magnitude regarding the degree of 
inequality.3
3 OECD has with the ‘Better life index’ initiative provided a tool to add the 
first and the most important subjective opinions among fields of concern 
and the respective indicators. Yet within each of these indicators, there 
is again an open question of the weight one assigns to the discussed 
two dimensions of disparity, at an instance of time and over time, static 
measure and time distance, to arrive at a subjective overall evaluation of 
their position in the society and in the world. Notionally, one could look 
for the role of subjective weights in two rounds; first among indicators 
of the better life index followed by those among two dimensions of 
disparity.
However, without going into complications of the 
weighting process to arrive at a composite measure 
of inequality, it should be emphasised that analysing 
disparities in economic, social, environmental, 
technological, and business indicators will show that 
the degree of disparities may be very different in static 
terms and in time.4 This means that new insights can 
be provided from existing data. Different indicators 
may have very different growth rates. The greater the 
differences in the growth rates of the indicators, the 
greater is the probability that the degree of the disparity 
measured by time distance or static measures will show 
different conclusions, especially in comparisons among 
indicators (Sicherl 1978, 1980).
The broad concept and measurement of dimensions 
of inequality leads to important policy consequences. 
The understanding of the situation using time 
distance measure may be very different when based 
on static measures of inequality alone. The schematic 
presentation in Figure 1 presents a simple, but not 
simplistic case of comparing two countries, regions 
or social groups for a given indicator, assuming two 
scenarios. Scenario A assumes a growth rate of 4%, and 
scenario B growth rate of 1%, for simplicity reasons both 
units exhibit growth at the same rate. Furthermore, the 
assumption that the future trends continue to grow at 
the same rate as before are used only for the purpose 
to make the scheme as simple as possible. In the two 
compared units, the value of the indicator for region 1 
is in static comparisons kept 50% higher than that of 
region 2 in both scenarios.
The value judgement that people attach to the time 
dimension of disparities and to the static dimension 
of disparity is an open question for interdisciplinary 
research. However, it may be safe to assume that a 
situation with 50 percent static difference and time 
distance of 10 years is preferable to the situation with 
the same static difference and time distance of 40 
years, indicated in Figure 1. The conventional analysis 
based only on static ratios or indexes, percentage 
differences, Gini coefficients or Theil indexes alone does 
not distinguish such situations (scenarios A and B) as 
different degrees of disparity.
It is highly unlikely that one would perceive such 
situations as equal degrees of disparity. Conventional 
welfare theory would need to explain why it would 
4 With the International Telecommunication Union we analysed in the 
time distances between over 200 countries and the benchmark Sweden 
for mobile cellular penetration rate in 2008 (ITU, 2010). Because of the 
indicator’s very high growth rate, the time lag for the lowest Myanmar 
behind Sweden was not larger than 24 years. Though the static difference 
was very large, the level was a few times lower. In contrast, the static 
difference in life expectancy is 20 percent lower, the time distance is 76 
years, and i.e. the level in Myanmar in 2014 was at the level of Sweden 
in 1938. Both types of measures are required. Additionally, other studies 
of digital divide have shown smaller time distances (Vehovar, Sicherl, 
Hüsing, Dolničar 2006; Dolničar 2008).
49IB Revija 1/2017
not be possible to incorporate such broader way of 
thinking, additional generic statistical measure(s), and 
the changed semantics into the present state-of-the-art 
methodology. 
In technical terms, the reduction of relative difference 
(expressed as a ratio of the values of the indicator for the 
two compared units) depends only on the difference 
between the respective growths rates (r2 – r1), while the 
time distance depends on the absolute magnitude of 
the respective growth rates as well.
A program to reduce disparities and alleviate poverty 
must concern itself with the absolute magnitudes of 
the indicator’s growth rates (r2 and r1), and not only 
with the difference in the growth rates (r2 – r1). A higher 
magnitude of growth rates brings a net reduction in 
time distance in addition to whatever reduction in time 
distance has been achieved by the improvement in the 
relative difference. Factors that influence the magnitude 
of overall and sector growth rates also influence the 
overall degree of disparity via time distance if at the 
same time appropriate distributional policies are being 
followed in the general strategic orientation for growth 
and equity (Sicherl 1992). 
If people take into account the time distance as 
one element of their subjective evaluation of the 
(overall) degree of disparity, a new set of hypotheses 
with important economic, social, and political 
Figure 1: Concept of Overall Degree of Disparity: Simultaneously Perceiving and Measuring Differences in Two 
Dimensions (in Indicator Value and in Time)
Source: Sicherl (2007).
consequences follows. An important hypothesis about 
the interrelationship between efficiency, growth, and 
disparity can be formulated. 
In the conventional theory, the trade-off between 
growth and inequality is sometimes emphasised. In 
the framework here, however, a high growth rate (with 
appropriate distribution policy) is not only a means for 
reaching higher levels of need satisfaction faster, but 
can also be a means of reducing disparities, at least in 
the time dimension. Increased efficiency leads to higher 
growth from the same resources, leading further to small 
time distances that in turn could mean greater social 
cohesion, enabling a more conducive environment for 
timely adjustment to changes supporting increased 
efficiency and effectiveness, resulting in a continuation 
of the so-called virtuous circle. 
Thus, higher growth can produce both positive and 
negative effects on social cohesion. The ‘vicious’ 
circle would work in the other direction; inefficiency 
has important negative economic and political 
consequences as far as disparities are concerned (Sicherl 
1992, 1993). 
Lower growth rates should signal to politicians that 
an increase in the degree of disparity may be felt and 
that social tension may be increasing and cohesion 
decreasing. Unfortunately, this relationship might be 
a key element in understanding the broad unrest and 
50 IB Revija 1/2017
dissatisfaction with the consequences of the financial 
crisis in 2008.
On the one hand, many people see that the fall or lower 
growth perspective for future indicators directly affect 
them. On the other hand, their relative position in the 
society or in the world could be at the same time also 
worsened by two factors. One is possible increasing 
the static degree of inequality, the other the expected 
increase in the time distance lag behind others because 
of the lower growth rates. Thus, the time distance 
dimension of the overall degree of disparity provides an 
important component of understanding of the current 
economic and social circumstances beyond what is 
detected by static measures of inequality alone. 
4 Time matrix presentation format 
indicates at a glance that GDP 
underestimated the scale of damage of 
the financial crisis
Time matrix is an innovative possibility of additional 
presentation of time series data. In the usual time series, 
table data of the indicator (e.g. life expectancy) are 
organised in relation to the descriptors, such as units 
(e.g. countries) and time (e.g. years). The time matrix 
presents the original data (or their approximations) in an 
alternative way: descriptors are units and levels of the 
indicator and the value in the field of the table are times 
when such levels were attained. Calculating these times 
by interpolations may pose a small problem regarding 
the degree of accuracy compared to the original data, 
but it offers an additional understanding of the time 
dimension of disparities, a good summary overview over 
many units and years, as well as a first-level visualisation 
tool.
In short, time matrix organises the same data from 
Eurostat databases in a manner that data are arranged 
by selected levels of indicators showing in which year 
these levels of the indicators were achieved by a 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.5 This 
allows for a quick level comparison for time matrices for 
all 30 indicators:
 – Vertical comparison between countries shows the 
situation and disparities across the EU countries over 
time.
5 See report Sicherl (2014b). The level-time matrix is defined for selected 
levels of a given variable and shows in which year various units (countries, 
regions, etc.) achieved these levels. The time matrix for life expectancy in 
the report condenses information for the period of more than 50 years 
(1960-2012), which amounts to more than 1000 entries in the Eurostat 
extended database. The data in the time matrix is condensed to a smaller 
number of entries (228), i.e. nearly five times smaller. 
 –  Horizontal comparison for each country shows how 
many steps over levels of indicators were achieved 
(which is an additional indication of the dynamics in 
the country).
As an example, out of 30 indicators in the report 
(Sicherl, 2014b), the S-time-matrix for Gini coefficient 
is presented in Figure 2. The levels of the indicator on 
the x-axis are oriented in such a way that the lowest 
(most desirable low inequality) values are on the right. 
The year presented in bold show the latest available 
presented year of the indicator for the given country. It 
helps to quickly observe whether there was a noticeable 
decrease in the observed period. It shows that for the 
majority of countries (25 countries or 89% of countries) 
the values in bold are not at the last position to the right, 
which means that there was a decrease for at least one 
step in the Gini coefficient during the analysed period.
Comparing vertically the time matrix for Gini coefficient 
one can observe the range of the situation across EU 
countries, which is very large: from the lowest value 
of 0.23 in Slovenia to the highest values in the range 
of 0.33-0.36 from the United Kingdom, Romania, 
Bulgaria, Greece, Portugal, Spain, and Latvia. Comparing 
horizontally by rows, one can see for each country the 
approximation for the range of values over nearly two 
decades in the period 1995-2013 available in Eurostat.
The examination of 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. The telling power of 
S-time-matrices provided a good summary overview at-
a-glance over many domains with a clear understanding 
to decision-makers as well as to the general public. New 
findings contributed to new information and better 
understanding.
Comparisons of time matrices for a set of indicators 
lead to a major policy conclusion confirming the 
multidimensional nature of development and well-
being in the Beyond GDP initiatives. Furthermore, the 
damage done by the world financial crisis of 2008 can 
be felt in many areas and estimated in many ways. The 
burst of the bubble of unregulated financial instruments 
can be estimated in billions of dollars spent to rescue 
the banking system. Here a modest attempt is made to 
demonstrate how in the world financial crisis the levels 
of five selected beyond GDP indicators decreased in 28 
EU countries. 
It is observed that GDP underestimated the scale of 
damage of the financial crisis. The damage done to 
countries by the world financial crisis is much greater 
when looking at 28 countries in terms of employment, 
investment share, risk of poverty, income distribution, 
51IB Revija 1/2017
Figure 2: Time matrix for GINI coefficient
S-time-matrix: time when specified level of the indicator was achieved (sorted by last available value)
LEVEL 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20
Slovenia 2009 2003
Sweden 2008 2004 1999 1997
Czech Republic 2005 2012
Slovakia 2006 2006 2007 2009 2008
Netherlands 2000 2001 2009 2010 1998
Finland 2001 2009 2000 2000 1999 1998
Belgium 2000 2001 2005 2008
Malta 2000 2002 2010 2007
Austria 2012 2010 2002 2001
Hungary 2006 2006 2006 2006 2007 2013 2012 2011 2010 2002
Luxembourg 2009 2012 2005 2000 1997
Denmark 2012 2010 2009 2007 2006 2002 2001 1999 1997
Germany 2008 2011 2006 2006 2005 2001
Ireland 1998 1999 1999 2007 2011 2009
Croatia 2011 2010 2009 2009 2009
France 2010 2008 2007 2007
EU 28 2010
EU 15 1995 2007 2001
Poland 2005 2006 2006 2008 2012 2001
Cyprus 2012 2011 2008 2004 2003
Italy 2005 2007 2008 2002 2001
Lithuania 2010 2010 2011 2011 2011 2012 2001
Estonia 2004 2004 2005 2005 2007 2011 2008
United Kingdom 2002 2005 2011 2000 1998 1997
Romania 2007 2008 2009 2010 2006 2006 2005 2003 2000
Bulgaria 2011 2012 2006 2006 2006 2006 2006 2005 2005 2005 2005 2003
Greece 1998 2012 2010
Portugal 2005 2007 2008 2009 2011
Spain 2012 2010 2009 2008 2004
Latvia 2006 2009 2010 2002 2000
Source: Sicherl (2014b), p. 34.
health, etc. and not at GDP alone. Table 1 shows much 
greater deterioration in selected indicators: employment 
rate, investment share in GDP, the increasing risk of 
poverty and increasing income inequality across Europe 
(from 71% or 100% of the EU countries):
The policy conclusions are straightforward. The levels of 
inequality and poverty indicators in the last years have 
been less favourable than before the crisis in 82% to 
89% of EU countries. Only about four of 28 EU countries 
escaped the deterioration of inequality. These results 
make it easy to understand that people are disappointed 
twice: with lower GDP growth rate or even decrease 
in GDP level, but also with the simultaneous increase 
in inequality. In other words, needed policy action to 
counteract the inequality effect of the deterioration of 
the general economic situation was not forthcoming.
The other conclusion deals with next two indicators, the 
deterioration of employment rate and of the share of 
growth fixed capital formation in GDP which seriously 
hindered the medium to long-term capabilities of the 
EU economies. Employment rate decreased in 20 EU 
countries and is still one of the most important indicators 
showing delay towards the EU2020 targets analysed in 
section 5. In 2014, 17 EU countries were lagging behind 
52 IB Revija 1/2017
the respective country line to target in 2020; for 10 EU 
countries, the current levels were not higher than their 
2010 levels.
The most shocking conclusion is that the share value of 
growth fixed capital formation in GDP decreased in all 28 
EU countries (100%!). The share of gross fixed investment 
in GDP indicates the relative effort to increase the fixed 
capital as one of the factors of production which together 
with the productivity of capital determines the medium/
long-term rate of growth of GDP. Together with the 
worsened employment situation, this fact demonstrates 
the disastrous effects of the world financial system in the 
current depression on the GDP growth rates. 
Table 1: Effects of the world financial crisis beyond GDP
Number of 
countries where 
indicator levels 
worsened
Employment rate  
(15 to 64 years), 
total
Gross Fixed Capital 
Formation in GDP
At-risk-of-poverty 
(% of total 
population)
GINI coefficient Income quintile share ratio S80/S20
1 Bulgaria Austria Austria Austria Austria
2 Croatia Belgium Belgium Bulgaria Bulgaria
3 Cyprus Bulgaria Bulgaria Croatia Croatia
4 Czech Republic Croatia Croatia Cyprus Cyprus
5 Denmark Cyprus Czech Republic Denmark Denmark
6 Estonia Czech Republic Denmark Estonia Estonia
7 Finland Denmark Estonia Finland Finland
8 Greece Estonia Finland France France
9 Ireland Finland France Germany Germany
10 Italy France Germany Greece Greece
11 Latvia Germany Greece Hungary Hungary
12 Lithuania Greece Hungary Ireland Ireland
13 Netherlands Hungary Italy Italy Italy
14 Portugal Ireland Latvia Latvia Latvia
15 Romania Italy Lithuania Lithuania Lithuania
16 Slovakia Latvia Luxembourg Luxembourg Luxembourg
17 Slovenia Lithuania Netherlands Malta Poland
18 Spain Luxembourg Poland Netherlands Portugal
19 Sweden Malta Romania Poland Romania
20 United Kingdom Netherlands Slovakia Romania Slovakia
21   Poland Slovenia Slovakia Slovenia
22   Portugal Spain Slovenia Spain
23   Romania Sweden Spain Sweden
24   Slovakia   Sweden United Kingdom
25   Slovenia   United Kingdom  
26   Spain      
27   Sweden      
28   United Kingdom      
Percentage 71% 100% 82% 89% 86%
Source: Own calculation based on Sicherl (2014b).
5 Time distance measurement for 
monitoring the target implementation 
and for goodness-of-fit
Monitoring target implementation is an integral part of 
policy making at many levels and in many domains. The 
innovation is that target implementation 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 
53IB Revija 1/2017
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 the general public, thus being an excellent 
presentation tool for policy analysis, and debate. It can 
help 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.
The deviations were measured 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 
and evaluating the degree of implementation of policy 
or business targets are very important. Deviations from 
the line to target both at the public administration and 
at the enterprise levels, i.e. comparing actual values with 
target values, forecasts, budgets, plans, etc. can now be 
measured in two dimensions: static deviation and time 
distance deviation (see Figure 3).
People understand and feel time. The story-telling and 
the interpretation of the deviation of actual development 
from the line to target with S-time-distance measure are 
straightforward and intuitively understandable.
For each unit, the method deals with lead or lag of actual 
development against the line to their own target for the 
selected indicator. It is similar to tracking the actual 
arrivals in comparison with the train or bus timetable, 
Figure 3: Monitoring: time distance deviations from the line to target 
Source: Sicherl (2015), p. 43.
the difference being that the concept of geographical 
space is in this application replaced with the indicator 
space.
The characteristic that time distance monitoring is 
expressed in time units means that the results are 
comparable across variables, fields of concern and 
units of comparison, which makes S-time-distance an 
excellent complementary analytical and presentation 
tool for policy and business debate.
Table 2 shows possible scheme and numerical values 
for analysing time distance deviations regarding the 
implementation of five selected headline indicators 
towards the EU2020 EU and national targets. Such 
results would be available continuously for each year 
of actual development against the respective line to 
the target. In this table, only the latest available year is 
shown. In a single Gaptimer progress chart like Table 2, 
the results are summarized for the EU aggregate as well 
as for individual countries for their national targets for 5 
selected EU2020 targets. The BrainPool report discussed 
indicator factors as well – salience for a broader 
audience and from the very broad discussion we would 
like to mention three elements: simplicity, ability to 
understand and to communicate. The intersection 
between indicator factors from public perspective and 
policy perspective is described with two elements: 1. 
be attractive to the eye and accessible, 2. be easy to 
interpret. Time distance measures are clearly meeting 
these two objectives. 
54 IB Revija 1/2017
Table 2: EU28 time distance monitoring to the line to target EU2020
(S-time-distance: (-) actual ahead or (+) behind the line to target (in years))
  Employment rate - total (2015) R&D in GDP (2014)
Renewable energy 
(2014)
Early leavers from 
education - total 
(2015)
Tertiary 
educational 
attainment - total 
(2015)
EU28 2,6 2,9 -1 -2,4 -3
Belgium > 5 -0,8 -0,4 -2,5 > 5
Bulgaria 2,7 0,5 TA > 5 -0,4
Czech Republic -4,6 TA TA > 5 -3,6
Denmark 3,3 TA -5,1 TA TA
Germany TA -1,6 -1,1 -4,4 2,6
Estonia TA > 4 TA > 5 TA
Ireland -4,6 > 4 -0,1 TA 2,6
Greece > 5 -0,8 -3,3 TA TA
Spain > 5 > 4 -0,3 -1,2 > 5
France 4,6 2,9 1,9 TA 2,1
Croatia > 5 3 TA TA -1,5
Italy > 5 1,5 TA TA -4
Cyprus > 5 -2,1 -1,2 TA TA
Latvia -4,5 2,6 -4,8 TA TA
Lithuania TA 1,1 TA TA TA
Luxembourg 4,1 > 4 0,7 TA 1,5
Hungary -1,2 0,1 2,1 > 5 TA
Malta -2,9 1,7 -2,6 2,1 -0,7
Netherlands > 5 0,4 1,3 -4 TA
Austria 3,7 1,2 -3,5 TA TA
Poland -0,4 0,9 -0,4 3,9 -3,6
Portugal > 5 > 4 -0,4 -3 -0,6
Romania 2,6 > 4 TA 4,8 -3,9
Slovenia > 5 0,1 0,7 TA TA
Slovakia 0,8 -1,5 -1,6 > 5 0,8
Finland > 5 > 4 TA 0,2 TA
Sweden TA > 4 TA TA TA
United Kingdom N/A N/A -0,5 N/A N/A
Target achieved 4 2 9 13 12
Time lead 6 5 14 5 8
Time lag 7 12 5 4 5
Lower than 2010 10 8 0 5 2
Ahead of 
schedule 10 7 23 18 20
Behind schedule 17 20 5 9 7
TA = Target already achieved   Time lead   Time lag
Source: Own calculations based on data from Eurostat (2016a).
55IB Revija 1/2017
The Gaptimer progress chart in Table 2 is a clear example 
of simplicity with an overview of about 150 cases of 
EU2020 targets, showing in one single table the results 
over 5 selected EU2020 indicators, 28 countries, and the 
EU aggregate.
For time distance monitoring of target implementation 
for examples of indicators for EU2020 and UN Millennium 
Development Goals, SICENTER developed a software 
tool to facilitate interested users to use the method for 
their own data in such circumstances. The tool can be 
accessed at http://www.gaptimer.eu/s-t-d_monitoring_
tool.html.
Without going into further details, the summary 
numbers show a very large difference between headline 
indicators. The last three indicators in Table 2 showed 
that about 20 countries were ahead of schedule; for early 
leavers and tertiary education in 2015 in more than 10 
countries even their 2020 targets were already attained. 
For employment rate, these results show another 
quantitative expression of the severe employment 
situation in the EU: 17 countries were behind the 
schedule. What is worse, in 2015, 10 countries were 
below their 2010 starting values; in the language of 
S-time-distance deviation, they were more than 5 years 
behind the line to the 2020 targets. In other words, for 
10 countries there was no progress in the 2010-2015 
period with regards to the employment rate. 
When it comes to the headline indicator renewable 
energy, 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 7 countries ahead and 20 countries behind the 
schedule. Overall, it is closer to the employment rate 
situation compared to the other three indicators. In 
political terms, the implementation analysis is much 
more interesting when it is discussed in relation to 
national targets, which may vary substantively between 
countries. 
In the EU 2020 context, the time distance methodology 
can be applied especially for benchmarking and 
monitoring implementation by complementing 
rather than replacing existing statistical measures. It 
may appear that some politicians, organisations, and 
experts might not like the additional information on the 
implementation of targets as it portrays a clear message 
understandable to everybody. On the contrary, for the 
media, NGOs, independent experts and international 
organisations, the improved transparency provides an 
additional instrument for strengthening the democratic 
debate.
If EU and national institutions would care to assess the 
S-time-distance measure by the same eight criteria 
applied for the selection of structural indicators by 
Eurostat (Munoz 2004) such as 1. Easy to understand, 
2. Policy relevant, 3. Mutually consistent, […] 6. 
Comparable between countries, etc., then for this 
application for monitoring implementation of EU and 
national strategies S-time-distance would pass the test 
with flying colours.
Eurostat periodically publishes reports on sustainable 
development in the European Union covering a broad 
set of indicators beyond GDP. In the methodological 
part of the 2009 monitoring report of the EU sustainable 
development strategy (Eurostat 2009, p. 36), it was 
mentioned that one particular method which has been 
used in the analysis of certain indicators, may need 
explaining. ”The so-called ‘S-time-distance method’ 
(Sicherl 2006) developed by P. Sicherl measures the 
distance to target in terms of time lag or, in other words, 
how many years the indicator lags behind its target 
path.” 
Eurostat published the report with the title “Smarter, 
greener, more inclusive? Indicators to support the 
Europe 2020 Strategy” (Eurostat 2016b), providing 
the situation with many headline indicators and other 
specific groups as well as the picture of the situation at 
the national level in relation to the Europe 2020 headline 
indicators. It provides detailed analysis across relevant 
economic, social, and environmental phenomena. Table 
2 is built on the information of the Eurostat databases 
and it presents a useful addition in two respects: firstly, 
it is expressed in years (time lead or time lag) against the 
line to targets, which makes it easily understandable to 
general public; and secondly, it presents the overview 
of the results of nearly 150 cases in one single table. 
A similar application to monitoring Lisbon targets by 
successive years in the previous decade was given in this 
Review (Sicherl, 2008).  
In addition to the within EU monitoring of targets, the 
new UN initiative of the 2030 Agenda for Sustainable 
Development (UN 2015) will expand these indicator 
activities to the EU countries. In the past, initiative 
Millennium Development Goals (MDGs) concentrated 
on developing countries. Now the setting of targets, 
actions, and monitoring of the implementation of these 
goals will be an important part of policy decisions and 
implementation in all countries, which will increase 
the relevance of the time distance methodology in 
this respect. On their Global development website, the 
Guardian published my article on time distance method 
of measuring implementation of MDGs, where Gaptimer 
progress chart summarised the situation over 7 world 
regions and 10 selected MDG indicators around 2010 
(Sicherl 2013). An updated study “System for Monitoring 
Implementation of Targets: Present MDGs and Post-2015 
SDGs” Gaptimer Reports, Vol. 4, (Sicherl 2015) is now 
available. The study offers a system for time distance 
monitoring implementation of targets for many areas 
and levels. The detailed application to present MDGs 
could be immediately applied for the post-2015 SDGs. 
56 IB Revija 1/2017
This outline offers an improved extended monitoring 
system that could be used in the operational work of 
Commission Services, across countries as well as across 
indicators. The additional time distance monitoring 
supervision can be a standard procedure in numerous 
other activities of the Commission. The same applies to 
national and local levels in hundreds of cases such as 
monitoring and evaluation implementation of budgets, 
plans, projects, structural funds, etc. It is difficult to 
understand why it is so hard to persuade Commission 
services to test and use such innovative methodologies 
in their work. 
The time distance method brings the second dimension 
of deviations or disparities that the present state-of-the-
art methodology has neglected so far. This semantics 
is clear and understandable to everybody and it 
communicates the message in the narrative for a policy 
debate that is beyond the message of the standard 
statistical tables. It can help to form a new perception 
of the magnitude of the gap between the actual 
implementation and proclaimed targets for a given 
indicator as well as across more indicators. 
The time distance information seems to be at least as 
helpful in providing a proper perception of the progress 
in implementation or the lack of it as is the percentage 
difference. Over the years, much effort has been put 
into developing indicator systems and data coverage 
but not enough attention has been paid to finding new 
innovative ways and measures to utilise indicators in 
the next phases: knowledge building and policy use. 
This is a much more transparent way of monitoring the 
implementation of targets for governments and the civil 
society that can help to bring about continuous policy 
debate and adjustments. 
Good governance does not use a setting of targets only 
to specify the vision and the desired direction but it 
also uses the feedback from the implementation as an 
indispensable phase of the policy process to adjust the 
future actions. This transparent innovative method for 
monitoring implementation at all levels is available but 
not utilised.
6 Summary and conclusions
The benefits of the new time distance view in 
comparisons, inequality measurement, competitiveness 
issues, benchmarking, target setting, and monitoring for 
economic, employment, social, R&D, and environment 
indicators at the world, EU, country, regional, city, sector, 
socio-economic groups, company, project, household 
and individual levels could be immediately applied 
to many indicators from a wide variety of substantive 
fields using existing data and indicator systems from 
international, national, regional, business and local 
sources.
In its role as a descriptive statistical measure 
complementing existing approaches, time distance 
can be applied literally to thousands of cases of time 
series comparisons so that additional information 
content embodied in countless databases in different 
fields of concern for socio-economic research is not left 
unutilised. In the information age, this additional view of 
the existing data should be evaluated as an important 
contribution to the more efficient utilisation of the 
information in many fields.
Measurement is costly and this novel generic statistical 
view should be evaluated as an important contribution 
of how additional insights could be more efficiently 
derived from existing data in many fields without 
affecting the current mostly static measures. There is 
no need to collect new data: one can start using the 
existing data and indicator systems from international, 
national, regional, business, and local sources.
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 strength of the time distance 
concept is that it enables additional exploitation of 
data and clear visualization of time series. It is a generic 
concept, in the same way as static difference and growth 
rates; it provides an additional view to many problems 
and applications. Empirically, the degree of disparity 
may be very different in static terms and in time 
distance, which leads to new conclusions and semantics 
important for policy considerations.
The time distance measure can present one of 
the measures that produce knowledge and policy 
messages in a very understandable way to build both 
objective and subjective perceptions of the situation. 
It can influence one’s perception and decisions when 
assessing one’s relative position in the surroundings, in 
the society and across countries over time, which is very 
important. As said; understandable measures enhance 
transparency and encourage participation in dialogue 
and democratic debate (Giovannini 2008). 
By providing an additional dimension of temporal 
disparity, the time distance approach has two 
advantages: Firstly, as it is expressed in time units, it is 
intuitively understood by policy-makers, professionals, 
managers, media, and the general public; secondly, time 
distance measure can be compared across variables, 
fields of concern, and units of comparison.
Especially in dealing with a wider set of fields of 
concern and a greater number of indicators (immanent 
in the Beyond GDP analysis), the additional view of 
the problem provided might be important for a more 
realistic evaluation of the situation, for the improved 
semantics of discussing the policy issues, and for 
57IB Revija 1/2017
progress monitoring. By analogy, this methodology 
could be applied to numerous similar problems in 
business at the micro and corporate levels, for long-
term, medium-term or short-term analysis.
The time perspective which no doubt exists in human 
perception when comparing different situations, adds 
an element of intertemporal aspect of well-being and 
progress. It leaves the static measures of disparity 
unchanged but the notion of the overall degree of 
disparity might change the understanding of the 
situation and the overall conclusion. In this context, 
three conclusions are underlined:
1. In addition to the use of S-time-distance as a 
descriptive statistical measure, the broad conceptual 
framework poses new interesting questions for growth 
and welfare theory and the related policy issues. 
Inequality within and between countries is a major 
challenge in the world and needs to be perceived and 
measured in both dimensions. Higher growth rates 
lead to smaller time distances, and influence the overall 
degree of disparity that is based on both static disparity 
and time distance. Static measures alone are inadequate. 
The relations between growth, efficiency, convergence, 
and perceptions are different in a dynamic concept of 
overall degree of disparity.
S-time-distance relates performance and efficiency with 
inequality in a novel way. It was argued that increased 
efficiency by leading to higher growth from the 
same resources, may at the same time lead to smaller 
time distances that in turn could mean greater social 
cohesion, enabling a more conducive environment for 
timely adjustment to changes supporting increased 
efficiency and effectiveness, and the ’virtuous’ circle 
can continue. For instance, in this framework, the 
improvements through ‘circular economy’ initiative 
could have besides saving resources and fostering 
growth an additional indirect effect by decreasing the 
time distance of inequality. 
Unfortunately, the ‘vicious’ circle works in the other 
direction. Inefficiency has important negative economic 
and political consequences as far as disparities are 
concerned. The consequences of the financial crisis of 
2008 analysed in Section 4 across many domains put 
many EU countries in a situation of the double difficulty 
of diminished levels of indicators and slower growth 
rates or expectations resulting in the perception of 
higher time distances between groups, regions, and 
countries. The broad framework makes it easier to 
understand the wide unrest and dissatisfaction with 
the situation. Since immediate turnaround and sizeable 
substantial growth rates are not in sight the strategy 
at country and EU levels decision makers should in 
the medium term put policy emphasis also on direct 
improvements in the inequality domain to compensate 
for the detrimental effects of past inefficiencies that 
resulted in the deterioration of social cohesion. In the 
short run, the efficiency improvements alone will not 
repair the damage done.
If one does not use the broader framework explicitly 
outlined here, there is a possibility that in political 
debate and policy formulation, various interest groups 
would intentionally look only at the specific statistical 
measure (like static absolute and relative disparities or 
time distance) that will suit their particular interest. For a 
better understanding of the reality, one should take into 
account all these aspects simultaneously.
2. The innovative time matrix presentation format 
enables compressed summary visual presentation over 
many countries and over time. This offers an additional 
understanding of the time dimension of disparities and 
a good summary overview over many units and years 
and also a first-level visualisation tool. SICENTER and the 
Faculty of Social Sciences at the University of Ljubljana 
prepared time matrix calculator tool www.timedistance.
net that enables users to convert conventional 
timetables into time matrices.
The study ‘European Union at a glance’ presents an easily 
understandable overview of 30 selected indicators 
over 28 EU countries in the time leading to a major 
policy conclusion. The time matrix presentation format 
indicates at a glance that GDP underestimated the 
scale of damage of the financial crisis, showing much 
greater effect in the deterioration of employment rates, 
investment share in the GDP, in the increasing risk of 
poverty and increasing income inequality (from 71% or 
100% of the EU countries).
3. A transparent innovative method for monitoring target 
implementation at all levels is available but not utilised. 
Monitoring target implementation is an integral part of 
policy making at many levels and in many domains. The 
two-dimensional measurement of deviations brings a 
new perception of the magnitude of the gap between 
the actual implementation and proclaimed targets in a 
more transparent way, it can help governments and the 
civil society in a broader understanding of continuous 
policy debate and necessary adjustments. 
It resembles tracking the arrivals in comparison with 
the train or bus timetable, the difference being that 
the concept of geographical space is in our application 
replaced with the indicator space. The characteristics 
that time distance monitoring is expressed in time units 
means that the results are comparable across variables, 
fields of concern, and units of comparison, which 
makes S-time-distance an excellent complementary 
analytical, and presentation tool for policy, and business 
debate. The presented progress chart is a clear example 
of combining simplicity with the summary story of 
overview across about 150 cases of EU2020 targets (5 
indicators x 28 countries, and the EU 28 aggregate). It is 
58 IB Revija 1/2017
a joy or nightmare of the possible transparency for the 
Commission, member countries, media, and interested 
public of citizens and entrepreneurs for the policy 
debate in the Beyond GDP framework. The monitoring 
framework can be used both for internal operations 
and/or for publication.
Since S-time-distance is expressed in time units it is 
easily understandable by managers, policymakers, 
professionals, media, and the general public. The time 
distance dimension of the overall degree of disparity 
provides an important component of understanding 
of the current economic and social circumstances at 
the world, national, local, and business levels, beyond 
what is detected by static measures of inequality alone. 
Potential users of the methodology and results are 
very many at various levels: international and national 
organizations, NGOs, experts, businesses, managers, 
educators, students, interest groups, media, and the 
general public. 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. 
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