ΙΔΡΥΜΑ ΟΙΚΟΝΟΜΙΚΩΝ & ΒΙΟΜΗΧΑΝΙΚΩΝ ΕΡΕΥΝΩΝ
FOUNDATION FOR ECONOMIC & INDUSTRIAL RESEARCH
Τσάμη Καρατάσου 11, 117 42 Αθήνα, Tηλ.: 210 92 11 200-10, Fax: 210 92 33 977, www.iobe.gr
11 Tsami Karatassou, 117 42 Athens, Greece, Tel.: +30 210-9211 200-10, Fax: +30210-9233 977
Income Breakdown of Respondents to the Consumer Survey:
Methodology and Trends during Crisis in Greece
Michail Vasileiadis -Coordinator, Macroeconomic Analysis and Policy Unit, FEIR /IOBE ([email protected])
Fotini Thomaidou –Research Associate, Macroeconomic Analysis and Policy Unit, FEIR /IOBE ([email protected])
Evangelia Valavanioti-Research Associate, Macroeconomic Analysis and Policy Unit, FEIR /IOBE ([email protected])
EU Workshop on Business and Consumer Surveys 2016
Brussels, 14-15th November, 2016
Methodology of the CS in Greece (Income Distribution)

Non-stratified sampling with respect to income (stratification according to region
and city size).

Income distribution of respondents to household income quartiles.

Cut-offs for the household income breakdown mainly based on official data from
the Hellenic Statistical Authority (EU – SILC survey):

Highest annual personal disposable income per quartile.

Average number of household members aged 16 years and over.

Annual household income cut-offs defined by the combination of these data. Monthly
income cut-offs for the CS based on a hypothesis about the number of payments per
annum, taking into account Christmas, holiday bonuses etc.

Family income distribution from tax statements also taken into account
(Statistical Bulletin, General Secretariat for Information Systems, MinFin).

Income breakdown update every 3 years (FEIR/IOBE first conducted CS in 2008).
Methodology of the CS in Greece (Income Distribution)
Pros and Cons of using data from official sources (ELSTAT,
GSIS) for defining the income cut-offs in Greece:


Pros

Based on a regular survey (SILC), as well as on a population distribution with
respect to family income (GSIS data).

The Survey on Income and Living Conditions (SILC) is part of an EU Statistical
Programme and complies with specific EC and Council Regulations.
Cons

The EU-SILC survey does not provide information on income earners per
household (average number of employed persons, old-age pensioners,
persons receiving benefit(s)).

The latest GSIS data concern the 2011 family income.

The income distribution of the participants to the CS is not taken into
consideration for defining the cut-offs for the income breakdowns.
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Have the economic crises affected the income distribution of the CS
respondents?


Since 2009, Greece was hit by two economic crises

Secondary effects of the 2008 global financial crisis, as the Greek banks had
not purchased “toxic” financial products.

2010 domestic sovereign debt refinancing crisis, Economic Adjustment
Programmes (2010 - ?).

Continuous, severe recession during 2009 – 2013 (-26.3% of GDP), steep rise
of unemployment in the same period (from 7.8% to 27.5% in 2013).
FEIR/IOBE reviewed the income quartiles twice during this period

In 2011, based on 2010 EU-SILC and income tax statements data (early crisis –
practically no effects on personal income cut-offs).

In 2014, based on 2013 EU-SILC data (peak of the crisis, extensive review of
income cut-offs).
4
Have the economic crises affected the income distribution of the CS
respondents?

As expected, the income distribution of the CS respondents shifted
during crisis towards the lowest income quartile.

Highest volatility of respondents during 2009 -2015 in the lowest income quartile
(standard deviation around the period mean: 5%) and vice versa (Table 1).

Highest proportionate decline of respondents up to 2012 in the 3rd quartile.

Constantly high percentage of respondents that do not choose an income quartile
(2009 – 2015 average: 11.7%).
Table 1: CS Respondents distribution to income quartiles
Year
Q1
Q2
Q3
Q4
2009
2010
2011
2012
2013
2014
2015
ST.DEV.
2009 - 2015
41%
42%
43%
53%
51%
45%
43%
29%
27%
28%
25%
26%
26%
27%
14%
14%
14%
11%
12%
16%
16%
3%
2%
2%
1%
2%
3%
3%
Do not know No response
14%
15%
13%
10%
9%
11%
12%
5%
2%
2%
1%
2%
Source: FEIR/IOBE
5
Have the economic crises affected the characteristics of CS
respondents per income quartile?

Steadily increasing proportion of the female respondents in all the income
quartiles but the first one (Figure 1)

Despite the decline in their proportion, male respondents remain a majority in
the quartiles Q2 to Q4
Figure 1: Gender distribution per income quartile*
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
58%
42%
46% 42%
54% 58%
33%
54%
67%
46%
46% 45%
54% 55%
36%
56%
65%
44%
48% 44%
39%
52% 56%
62%
56% 49%
46% 47%
44% 51%
54% 54%
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
2009
2012
MALE
2014
2015
FEMALE
* Selected years: 2009: global recession, 2012: highest uneven distribution of
respondents among quartiles, 2014: extensive review of cut-offs, small growth
in Greece after a 6-year recession. 2015: latest completed year of the CS
Source: FEIR/IOBE
6
Have the economic crises affected the characteristics of the CS
respondents per income quartile?

As expected, the increase of the unemployment rate during 2009 – 2015 was higher in
the two lower income quartiles (+9% & +6% respectively).

The unemployment rate among those that did not know their household income or did
not respond to this question is almost constantly higher than the unemployment rate in
all the other income quartiles.

Conclusively, a high proportion of unemployed persons does not want to state its income.
Figure 2: Unemployment Rate per income quartile
25%
20%
15%
10%
5%
2009
Source: FEIR/IOBE
2012
2014
DNK/NR*
Q4
Q3
Q2
Q1
DNK/NR*
Q4
Q3
Q2
Q1
DNK/NR*
Q4
Q3
Q2
Q1
DNK/NR*
Q4
Q3
Q2
Q1
0%
2015
7
Have the economic crises affected the characteristics of the CS
respondents per income quartile?

Increased participation of the youngest respondents in all the income
quartiles (Figure 3)


Decline of the respondents of 30-49 and 50-64 years of age in all the income quartiles
but Q1, even after the 2014 review of the income cut-offs; a sign of social
marginalisation of the so-called “productive ages”?
On the contrary, higher participation of the oldest respondents (65+) in all the income
quartiles but Q1.
Figure 3: Age distribution, per income quartile
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
15%
11%
28%
31%
30%
7%
31%
23%
31%
18%
32%
32%
16%
25%
42%
44%
13%
15%
14%
37%
36%
15%
22%
8%
9%
10%
20%
22%
36%
37%
24%
26%
38%
32%
36%
37%
25%
26%
35%
23%
7%
20%
21%
32%
31%
19%
42%
48%
30%
25%
22%
30%
41%
10%
18%
22%
31%
11%
16%
22%
30%
22%
32%
7%
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
2009
2012
16-29
Source: FEIR/IOBE
30-49
2014
50-64
2015
65+
8
Effects of economic crises to the Consumer Confidence Index per
income quartile

Consumer Confidence Index (CCI) of respondents from the lowest income
quartile is constantly lower than that of respondents from the other Qs.

The CCI deteriorated during 2010– 2011 mainly in the third income
quartile (-29.6 points), not among respondents of the lowest income
quartile (-27.5 points). A sign of a drastic change in the income status of
the middle class?

During 2009 - 2011, the CCI of respondents from the 3rd quartile
practically did not exceed that of respondents from the 2nd quartile; But
since 2012 the ranking of the income quartiles w.r.t. the CCI level is
analogous to their income level.

Strongest confidence rebound during 2012 - 2014 by Q3, weakest increase
in Q1.

Smallest difference in the CCI level among respondents of all Qs in 2015,
due to high political uncertainty.
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Effects of economic crises to the Consumer Confidence Index per
income quartile
Figure 4: Consumer Confidence Index per income quartile
2009
2010
2011
2012
2013
2014
2015
-49,2
-54,1
-54,5
-57,3
-58,5
-63,8
-67,3
-71,7
-71,7
-72,2
-72,9
-71,7
-77,4
-76,7
-80,4
-83,7
-86,0
1st Quartile
4th Quartile
-71,7
-73,0
-81,3
-82,7
-85,6
-71,7
-62,7
-65,7
-62,4
-64,4
-65,9
-68,7
-71,5
-71,7
-71,7
-75,6
-77,5
-82,8
2nd Quartile
average 2009-2015
3rd Quartile
Source: FEIR/IOBE
10
Effects of economic crises to households’ predictions per income
quartile

Same deterioration during 2010 – 2012 of predictions about the household’s
economic condition in the next 12 months among all the income quartiles

Less pessimistic predictions during 2013 – 2015 mainly from the Q3
respondents (-29 points)
Figure 5: Predictions about household’s economic condition, per
income quartile
2009
-30
-25 -26
2012
2014
2015
-21
-49
-46
-41
-37
-38 -40
-43 -41
-60
-70
-67 -67
1st Quartile
Source: FEIR/IOBE
2nd Quartile
3rd Quartile
4th quartile
11
Effects of economic crises to households’ predictions per income
quartile



Almost same deterioration during 2010 – 2012 of predictions about the country’s
economic condition in the next 12 months among Q1-Q3.
Strongest rebound during 2013 – 2014 in Q3 & Q4, with predictions in the latter
quartile reaching early-crisis levels.
On the other hand, Q4 respondents turned from least to most pessimistic in just
one year (2015).
Figure 6: Predictions about the country’s economic condition, per
income quartile
2009
2012
2014
-41 -40 -39 -39
-49
-72 -73 -71
1st Quartile
Source: FEIR/IOBE
-46
2015
-42
-38
-43
-46 -43
-48
-66
2nd Quartile
3rd Quartile
4th quartile
12
Some ideas for adjusting income cut-offs to real conditions in
times of crisis

More frequent adjustment of the cut-offs for the CS income breakdown
(every 2 years)

Combination for the review of cut-offs of official data and of the
distribution of incomes reported by the respondents.


Use of data from various official sources (if available)


Responses should be classified into relatively “narrow” income groups,
facilitating the monitoring of respondents’ shifts between them and the
effective review of the cut-offs.
Survey on Income and Living Conditions, Labour Force Survey, Tax Statements
data, Pensioners – allowance beneficiaries distributions.
Use of personal income quartiles or quotas


In times of crisis, respondents, especially the younger ones, are more
uncertain about their households’ income.
On the other hand, such a change in income cut-offs would probably be a
source of discontinuity in the income distribution of the CS respondents.
13