EUROSTAT Directorate F: Social statistics Unit F1: Social indicators; methodology and development;Relations with users http://ec.europa.eu/eurostat Flash estimates of income inequalities and poverty indicators for 2016 (FE 2016) Experimental results September 2017
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EUROSTAT
Directorate F: Social statistics
Unit F1: Social indicators; methodology and development;Relations with users
http://ec.europa.eu/eurostat
Flash estimates
of income inequalities and poverty indicators for 2016 (FE 2016)
Table 1. Definition of the inequality and income distribution indicators
Indicators Definition
At-risk-of-poverty
rate (AROP)
Share of people with an equivalised disposable income4 (after
social transfer) below the at-risk-of-poverty threshold, which is set
at 60 % of the national median equivalised disposable income
after social transfers.
This indicator shows the percentage of the population whose
income is likely to "preclude them from having a standard of
living considered acceptable in the society in which they live"5.
Income quintile
share ratio (QSR)
The ratio of total income received by the 20 % of the population
with the highest income (the top quintile) to that received by the
20 % of the population with the lowest income (the bottom
quintile). It is a measure of the inequality of income distribution.
Income deciles Income deciles groups are computed on the basis of the total
equivalised disposable income attributed to each member of the
household. Nine cut-point values (the so-called deciles cut-off
points) of income are identified, dividing the survey population
into ten groups equally represented by 10 % of individuals each:
The data (of each person) are sorted according to the value of the
total equivalised disposable income and then divided into 10 equal
groups each containing 10 % of individuals. For example, the first
decile group represents the 10 % of the population with the lowest
income and decile 1 is the cut-off point for this group. Five
representative income deciles have been selected in our analysis to
show the evolution of the different parts of the national income
distribution.
For more details on the calculation of the indicators please see
EU-SILC notes on the calculation of indicators.
Flash estimates should estimate to the extent possible the values captured in the EU-SILC6 survey.
The main target indicators (AROP and QSR) are based on an entire distribution that evolve relatively
slowly, except in times of crisis. Survey based yearly changes can be rather small and/or not
statistically significant. It is therefore relevant
to assess yearly changes together with the trends during a certain period across several years,
to read the whole set of indicators that provide a coherent picture about the evolution of the
underlying income7 distribution in each country. Deciles offer a complementary reading tool
in order to link the decrease of poverty or inequality with the relative movement at different
points of the distribution. Deciles can help in answering better policy questions like: is a
4 The equivalised takes into account the structure of the household. The income is calculated by dividing the total household
income by its size determined after applying the following weights: 1.0 to the first adult, 0.5 to each other household
members aged 14 or over and 0.3 to each household member aged less than 14 years old. 5 See for instance the Joint Report by the Commission and the Council on social inclusion as adopted by the Council
(EPSCO) on 4 March 2004, http://ec.europa.eu/employment_social/soc-prot/soc-
possible decrease of poverty related to a higher increase of the income for poorer people (left
tail of the distribution) or is a possible decrease linked to a decline of the middle class? More
generally, the examination of deciles at different points of the distribution helps to answer the
questions on who is benefiting from the growth and who is affected by the recession.
3. How are the flash estimates on income distribution produced?
The Flash Estimates should anticipate the changes (that will appear later in EU-SILC) based on
auxiliary information already available for the target year. Yearly changes are estimated as described
below and combined with the EU-SILC value for the preceding year, which constitutes the baseline
for the analysis.
A variety of approaches were tested, tailored to each country situation and we have selected the most
robust methodology for a given country. The publication as experimental statistics puts the basis for
receiving feedback from users and the research community and further improving the flash estimates.
The main methodology used for most countries is Microsimulation. It relies on EUROMOD, the
European Union tax-benefit microsimulation model, managed, maintained and developed by the
Institute for Social and Economic Research (ISER) at the University of Essex. For the purposes of the
flash estimates exercise standard EUROMOD policy simulation routines are enhanced with additional
adjustments to the input data to take into account changes in the population structure, the evolution of
employment and main indexation factors. The microsimulation approach in the frame of the flash
estimates exercise is based on previous work done by ISER, University of Essex (Rastrigina, O.,
Leventi, C., Vujackov S. and Sutherland, H. (2016)) and is being further developed by Eurostat in
collaboration with the dedicated Task Force on “Flash estimates on income distribution”. In general,
microsimulation is the preferred approach for both main users and the National Statistical Institutes
(NSIs) given the possibilities for further detailed analyses and the link with policy changes.
For few countries for which microsimulation results were not consistent enough with EU-SILC, a
second methodology is used based on macro-economic time series modelling (METS) with which the
model attempts to nowcast the indicators directly, based on linear regression, with extensions taking
into account the time series nature of the data.
Table 2 summarises the methodological approach chosen by country. Eurostat has produced flash
estimates for 25 countries. For Denmark8 and the Netherlands
9, provisional national register data were
used. Flash estimates for the United Kingdom are not produced as data for income 2016 is already
published10
in EU-SILC.
8 For Denmark, the definitions of the income match the EU- SILC, but there may be small differences in the household
definitions. There is no guarantee for a perfect match with the final SILC caused by statistical uncertainty due to sampling
in SILC compared to the full population in the register. 9 For the Netherlands, the definition of equivalised income is almost equal to the EU-SILC definition except for the inter-
household transfers which are not included. The inter-household transfers form only a small part of the total income, so the
deciles in both statistics are quite comparable. In general, inter-household transfers are paid by the higher income groups,
so the upper deciles may be somewhat actually lower in EU-SILC compared to the national income statistics. 10 SILC 2016 for UK covers current income, therefore, the income reference period is also 2016
5
Table 2. Methodological approach by country
Methodological approach Countries
Microsimulation BG, DE, EE, EL, ES, FR, HR, IT, CY, LV, LT, LU, HU, MT, AT,
PL, PT, RO, SI, SK, FI, SE
METS BE, CZ, IE
National provisional data DK, NL
An essential point in this exercise was the active participation of the Member States at different levels
and the support from the academic community, in particular the University of Essex, in the validation
and improvement of the FE methodology and of the flash estimates 2016. For more details please
consult also the Methodological Note including the description of both microsimulation and METS.
4. How where the flash estimates assessed?
Flash estimates income 2016 are produced by Eurostat (unless specified differently) and published as
experimental statistics.
The publication as experimental statistics puts the basis for receiving feedback from users and the
research community and further improving the flash estimates. However, the accuracy of the
indicators depends on the model assumptions and on several factors explained throughout the quality
assessment. As with any other flash estimate, we cannot expect to capture perfectly changes in the
SILC estimates. Differences can emerge, due to inconsistencies in the input datasets, model errors or
theoretical assumptions underlying the microsimulation techniques.
Developing flash estimates on poverty and income inequalities in the ESS involves that their methods,
sources and output adhere to a common quality framework. This was developed together with the
Member States and validated with the National Statistical Institutes and the academic community.
The quality framework contains two main parts:
1. Quality as an integrated process in the production: Ensure that quality is considered in the
inputs and methods used in all the steps of the production, by analysing inconsistencies in the
input data and performing several intermediate quality checks along the process. It is useful
for identifying possible sources of error and ways of for fixing them.
2. Quality assessment put in place in order to ensure a comparable way to assess results
stemming from different methods and national estimates within this ESS exercise:
2.1 the historical performance of the model is defined as the ability to predict accurately the
past changes in the main target indicators as captured by EU-SILC. Flash estimates were
simulated from 2012 to 2015 and compared with EU-SILC indicators.
2.2 the plausibility of the estimated change is assessed based on the available information
for the target year. Connecting the estimated changes in the income distribution with
observed evolutions in related indicators (e.g. employment trends, total household
income in national accounts, national data) is a key step in building trust in the
estimates. A trusted estimate is a reasonably good stand-in, to be used for drawing
preliminary conclusions until actual data becomes available. Unlike forecasting, for
flash estimates we have several auxiliary sources in the target year which are used either
in the estimation process or for validation checks (for plausibility assessment).
Furthermore, in the case of microsimulation we can disentangle the impact of simulated
policies via EUROMOD and supported with ISER, University of Essex analysis11
.
11 EUROMOD (2017) "Effects of tax-benefit policy changes across the income distributions of the EU-28 countries: 2015-
2016", EUROMOD Working Paper 10/17, Institute for Social and Economic Research, University of Essex
7
5. Flash estimates: communication on magnitude and direction of change
This report presents the figures for the flash estimates relating to the income year 2016 (FE 2016, i.e.
SILC 2017 whose results are expected in summer 2018 for most countries).
Flash estimates refer to the year-on-year (YoY) change rather than the actual levels and they are
calculated as the difference between the model estimates for 2016 and 2015 (or modelled directly in
the case of METS). The point estimate for the flash is calculated by adding the estimated YoY change
to the last observed value in EU-SILC.
Point estimates are subject to several sources of uncertainty: e.g. model bias and variance, the
sampling error in EU-SILC, inconsistencies between the different data sources entering the
estimation. This raises not only a question of quality, but also of communication of the results.
Following in-depth discussions with both users and producers, it was decided that results for this
release of the flash estimates are disseminated according to a magnitude direction scale (MDS) for
the expected change (major/moderate/minor). This dissemination format takes into account that
estimated changes cover a possible range of values, associated with uncertainty12
. Therefore, the FE
will give an indication on the type of change expected in terms of intervals, but not the point
estimates. The MDS is based on absolute thresholds, common across countries and indicator specific,
which makes it straightforward to understand. These were selected based on average values for
changes observed in EU-SILC in the past and in agreement with the Member States. They take also
into account similar exercises13
by policy makers which use thresholds to identify "substantive"
changes. The table below shows the MDS classes chosen by indicator and common across countries.
Table 3. Bounds MDS by indicators (different colour coding by group of indicators for
readability)
MDC AROP(pp) QSR Deciles (%)
Decrease in inequality
indicators=green14
Increase in positional
indicators = green
[---] <-2 <-0.6 <-5
[--] [-2,-1[ [-0.6,-0.3[ [-5,-2[
[-] [-1,0] [-0.3,0] [-2,0]
[+] ]0, 1] ]0, 0.3] ]0, 2]
[++] ]1,2] ]0.3,0.6] ]2,5]
[+++] >2 >0.6 >5
In addition, the magnitude direction class includes also a condition for statistical significance: only
the statistically significant changes are published. At this stage, only the sampling error is considered
for the significance of the change. This translates into a country specific additional "not significant
change" class centered around zero. This implies also that many of the changes mainly in the "minor"
change classes above ([-],[+]) will be not significant. More details are given in Annex 1.
12 UNSD - Handbook on Rapid Estimates (Draft for Global Consultation, 28 October 2016) 13 SPC publications 14 The colour coding used for the QSR is there for readability purpose and does not imply judgements about the desirable
There are 7 countries with different estimated increases across the distribution that didn't impact the
inequality indicators. For Croatia, Finland, France, Netherlands and Austria, there were mostly
similar increases estimated except in the tails (which can be due either to lower estimated increase in
the tail or higher variance for the extreme deciles). For Croatia, the middle deciles are estimated to
increase between 2% and 5% between 2015 and 2016, which is more than the tails (D1 and D9).
There were increases of up to 2% estimated for Finland and France. For Austria, the middle deciles
(D3, MEDIAN and D7) are estimated to increase significantly but not D1 (D9 is not reliable).
A larger change in D1 than in the other part of the distribution is estimated for Malta, Greece and
Italy. For Malta, in 2016, the estimated change in D1 is significant but not the other deciles. In 2015,
all the deciles except D1 are estimated to increase significantly. For Greece, there is a larger increase
estimated in D1 than in the MEDIAN.
For Cyprus, Denmark and Luxembourg there are no significant changes estimated in any of the
indicators. The non-significance can be interpreted in general as a status quo17
. This is also the case
for Cyprus in 2015. For Luxembourg, FE 2015 were not estimated due to a break in series in LFS
2015.
Finally, for Belgium, Sweden and Slovakia, the estimated changes in the income deciles are reliable
only for some indicators. For Sweden, a similar increase in the mid to right part of the distribution is
estimated while for Slovakia, there is an increase between 2% and 5% estimated in the MEDIAN.
From a policy point of view and for microsimulation countries, estimated changes are driven by
several elements that enter the nowcasting model: changes in employment situation and the socio-
demographic structure, change in the evolution of different income components and the impact of
simulated policies. The impact of policies is calculated on the base of EUROMOD policy tool and is
supported with the information collected through the EUROMOD network.18
The models assume
most of the time that these policies are actually implemented.19
Further analysis is therefore possible.
17 To note that in some cases the non-significance can also result from large standard deviations for the specific country (LU) 18 EUROMOD (2017) "Effects of tax-benefit policy changes across the income distributions of the EU-28 countries: 2015-
2016", EUROMOD Working Paper 10/17, Institute for Social and Economic Research, University of Essex 19 Tax evasion (e.g. in Bulgaria, Greece, Italy and Romania) and benefit non-take-up (e.g. in Estonia, France, Greece, Latvia,
Romania, and Finland)
12
8. How to improve the flash estimates?
This is the first publication of flash estimates on income distribution as experimental statistics. The
report contains not only the estimated changes for the target year but also a few elements on the
estimation process, auxiliary sources used to support the analysis of the figures and their reliability. It
is meant to put the basis for a constructive dialogue for further improving the methodology and the
dissemination of these indicators.
To help Eurostat improve these experimental statistics, users and researchers are kindly invited to give
us their feedback:
Would you have comments or suggestions for improvements of the methods applied for this
flash estimate exercise, i.e. based on either microsimulation or METS?
Are there any other factors we should consider?
What other indicators or breakdowns could be useful as early warnings on trends in income
distribution and poverty?
Are there other indicators we should analyse for policy purposes?
Is the magnitude direction scale clear and easy to understand? How to improve it? Would
point estimates be desirable in the future?
Further developments could be envisaged, following also the feedback from users and stakeholders:
improve the dissemination format including the significance and the magnitude-direction
scale;
use of more recent EU-SILC files for microsimulation so that to minimize the impact of
revisions and breaks in series but as well to improve the model;
further develop the plausibility assessment on the line of disentangling the impact of
employment, uprating factors and policies on each indicator;
better take into account the coherence of the estimated changes across the distribution
(especially for METS).
13
Annex 1: Magnitude direction scale and significance
As mentioned the MDS is based on absolute thresholds, common across countries and indicator
specific. It is important to note that the actual MD class will be communicated only if the change is
statistically significant. At this stage, the sampling error is considered for the significance of the
change and is country specific. In specific countries, with large standard deviations, higher values of
yearly changes will be considered not statistically different from zero.
For the main inequality indicators we have used the usual calculation of Eurostat for the standard
deviation of the net change20
. It calculates the variance of the net change based on multivariate linear
regression technique (Berger and Priam , 2016) that reduces non-linear statistics to a linear form and
takes into account the overlap of samples between years. For deciles we have developed a
bootstrapping procedure for computing the variance of the estimates. We have used 1000 subsamples
of the SILC dataset at the target year with each individual having a probability of wj
∑ wjpj=1
to be drawn
where wj denotes the sample weight of the jth individual and the size of the subsamples being equal to
the number of individuals in the SILC dataset . Then we compute all indicators of interest for each
one of these replicated data sets. The collection of computed indicators can then be used to obtain an
estimate of the sampling distribution of the SILC indicators (unweighted). The standard deviation of
the change for deciles is likely to be overestimated as it doesn't consider the overlap of samples
between two consecutive years in EU-SILC. In the future, it is foreseen to apply the same estimation
procedure as for AROP and QSR.
Figure 1 below shows the thresholds for the MD classes, including the significance bounds for AROP
and Median for all countries. It shows also that when we overlap the significance bounds (ns series),
these vary considerably across countries. Therefore, for example a change of 1 percentage point for
AROP will be communicated for certain countries at the right as not significant (ns), while for other
countries with lower SD (at the left of the graph), it would be a change in the interval ]0, 1] pp. It is
very important to note that most of the figures in the minor change classes will be not significant so
Observed changesImpact of simulated social benefits
and taxes34
<-5 [-5,-2[ [-2,0[ ns/= ]0, 2] ]2,5] >5
21
2) Table 8 provides a comparative change of the MD class for the yearly change of the total
disposable income between the FE and Quarterly Sector Accounts29
. We include only countries for
which quarterly data is available for the sector household; non-profit institutions serving households
(S14_S15). This should be read taking into account the underlying comparability of income (trends)
from EU-SILC and National Accounts. For more details on the latter please see also Gregorini et al,
(2016)30
. If we apply the MDS for changes in total disposable income, we notice that the trends and
magnitude are similar in the target year.
Table 8. Comparison with National accounts: evolution total disposable income
COUNTRY MD class total income FE/NA31
PL, RO +++/+++
DE, ES, SE, SI, PT ++/++
AT +++/++
FI, FR, IT, EL =/=
3) The additional SILC variables selected for assessing plausibility are NOT used for producing the
estimates; they are:
Ability to make ends meet (MEM) = % HS120=4÷6 (fairly easily, easily, very easily)
Severe Material Deprivation (SMD) = % Severe Material Deprivation=1
Capacity to face unexpected financial expenses (UXX) = % HS060=1 (yes)
We have used these additional variables to calculate the so-called "external estimates" from 2012
onwards, using a rolling data set. Values of the external estimate are based on a univariate linear
regression, using in turn MEM, SMD, and UXX as predictors. We have also calculated the prediction
interval of the external model, which takes into account only the uncertainty generated by the
estimation model, but not that already present in the input data.
Potential further work might be necessary, insofar as an assessment based on the external estimates
does not allow us to draw strong conclusions regarding the plausibility of the flash estimates, because
the prediction intervals of the external estimates are usually very large, and cover a large part or even
the entire possible range of variation. We are aware of new avenues for assessing the plausibility of
the FE, especially some focused on the coherence of the estimated changes for indicators coming
from the same distribution.
4) In addition to the aforementioned plausibility analysis, all Member States were consulted
concerning the flash estimates and in some cases we have received additional information based on
national sources or models.
29 Source: Eurostat calculations- gross disposable income [nasq_10_nf_tr] 30 http://www.iariw.org/dresden/gregorini.pdf 31 FE/ NA growth rate disposable income ("=" means between -2% and 2%;"++" means between 2% and 5%; "+++" means
The data used in this report for the flash estimates is based on Eurostat estimations. The information
set that entered the estimation includes 1) for microsimulation the EUROMOD model combined with
the latest EU-SILC users' database (UDB) microdata file and/or national SILC microdata32
available
at the time of production33
2) for macro-economic time series modelling (METS) it includes the time
series for EU-SILC until the latest year available34
. This is enhanced with more timely auxiliary
information from the reference period (2016) such as Labour Force Survey (LFS), National Accounts,
etc.
The data used for the target indicators for the year (2012-2015) are primarily derived from data from
EU statistics on income and living conditions (EU-SILC). The reference population is all private
households and their current members residing in the territory of an EU Member State at the time of
data collection; persons living in collective households and in institutions are generally excluded from
the target population.
Main tables
Income and living conditions (t_ilc)
EU-SILC further information
Income, social inclusion and living conditions
EU statistics on income and living conditions (EU-SILC) methodology
32 UDB EU-SILC 2015-1: BG ES FI HR HU LV PT SI
UDB EU-SILC 2015-2: CY EE FR LT LU MT PL RO SE
UDB EU-SILC 2014-1: DE
In addition, for EE LT LU PL LV SI FI, additional national SILC variables were also used
National SILC 2015: EL IT SK AT (+ UDB EU-SILC 2015-1) 33EU-SILC 2015 UDB except DE (2014). In the meantime EU-SILC 2016 is available for most countries but not yet the
UDB and the EUROMOD input file 34 EU-SILC 2009-2016: BE and CZ, EU-SILC 2009-2015: IE