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The Fall in Income Inequality during COVID-19 in Four European Countries, Conchita D'Ambrosio
1. The Fall in Income Inequality
during COVID-19 in Four
European Countries
conchita.dambrosio@uni.lu
2. There is a huge interest in knowing and understanding the effects
that the pandemic has had, is having, and will have, on income
inequality both within and across countries.
3. What do the experts say:
inequality increased both between and within countries
• Goldin and Muggah (2020) “Inequality is increasing both within and between
countries.”
• UNDP (2020) “The virus is ruthlessly exposing the gaps between the haves and
the have nots, both within and between countries.”
• Stiglitz (2020) “COVID-19 has exposed and exacerbated inequalities between
countries just as it has within countries.”
4. What do the experts say:
inequality decreased within rich countries
• Deaton (2021) “The COVID-19 pandemic has threatened the lives and
livelihoods of the less-educated and less-well paid more than those of more
educated and better paid, many of whom can stay safely at home and continue
to work. To a lesser or greater extent, the increase in domestic income
inequality has been offset by large scale government income support
programs in the US and in many other countries.”
• Milanovic (2021 talk) “In rich countries the money spent by governments (10%,
20% of GDP) has been targeted, even if not ideally so, and will decrease
inequality.(…) LAC and African countries do not have access to the amount
of money of the rich countries…”
5. What do the data say:
inequality between countries either decreased or increased
Deaton has shown in his webinar (see the working paper):
• Global unweighted inequality has fallen: the dispersion of per capita GDP
between countries taking each country as a unit has continued on its pre-
pandemic downward trend, and has if anything fallen faster as a result of the
pandemic;
• Global weighted inequality has increased: If we weight each country by its
population, between-country income inequality has increased, largely because of
India’s poor performance during 2020 and China’s being no longer a poor country,
so that its positive performance during 2020 did not have a positive effect on
inequality.
6. What do the data say: inequality within countries
• Very little is known on inequality within countries due to lack of data, since the
household surveys we generally use for this purpose will be available with a significant lag.
• Microsimulation studies on rich countries predict an increase in inequality without any
policy response due to the pandemic and a decrease in inequality following policy
interventions (Almeida et al., 2020, Brewer and Tesseva, 2020, O’Donoghue et al., 2020,
Li et al., 2020, Brunori et al., 2020).
7. What do our data say: Inequality within rich countries decreased
Based on COME-HERE, a
real-time longitudinal data
that we have been collecting
since the beginning of the
pandemic.
8. Some of the inequality results (up to September 2020) are in
the following working paper, a revised version of the
ECINEQ working paper:
https://halshs.archives-ouvertes.fr/halshs-03185534
9. More results on different well-being variables
collected in COME-HERE (sleep, gender, well-being
outcomes) are available at:
https://pandemic.uni.lu/
10. COME-HERE stands for: COVID-19, MEntal
HEalth, REsilience and Self-regulation.
It is a study that was initiated by a team of
researchers from the University of Luxembourg in
March 2020, joined in the following months by
colleagues from the Paris School of Economics, U.
Autonoma and IAE-CSIC in Barcelona.
13. COME-HERE Survey
The survey was conducted by Qualtrics.
Format of the survey:
• So far 5 on-line Waves (more planned for 2021), open for two weeks starting:
• April 27, 2020
• June 9, 2020
• August 5, 2020
• November 19, 2020
• March 1, 2021
• Average completion time: approximately 25 minutes.
Ethics approval for our study was granted by the Ethics Review Panel of the
University of Luxembourg.
14. 14
COME-HERE Survey
Who was contacted?
• Around 1,700 individuals per country (France, Germany, Italy and Spain).
• Stratified sampling COME-HERE samples were nationally-representative in
Wave 1 in terms of age, gender and region of residence.
• Specialized recruitment campaigns able to contact groups that may be hard
to reach on the internet (older respondents, for example).
15. COME-HERE Survey
Data-quality Protocols:
• The information supplied by respondents who answer the questionnaire in under
½ of the median survey-completion time is not retained, and a replacement
interview is conducted.
• The IP addresses of the respondents are checked and digital-fingerprinting
technology is used to ensure that observations are not duplicated.
16. COME-HERE Survey
What about the survey itself?
• The COME-HERE survey collects information at the individual and household
levels.
• It is longitudinal.
• It is covers a number of European countries.
17. COME-HERE Survey
• The objective of the COME-HERE survey is to collect sufficient individual
information to describe living and health conditions during COVID-19, but
also to identify recent changes and events that might have affected individuals’
lives.
• Standard sociodemographic characteristics were collected (age, gender,
education, and labour-force and marital statuses).
18. COME-HERE Survey
• The team includes health and clinical psychologists, hence we ask all the
questions from validated scales to measure depression, anxiety, loneliness,
resilience, personality, positive mental health.
• Special survey modules in some waves addressed topics such as risk attitudes,
time discounting, preferences for redistribution, income comparisons, working
conditions, preferences for vaccine allocation and funding.
21. 21
We will focus on one case today:
1. All respondents from the unbalanced panel:
However, our conclusions are similar when we use:
2. All respondents from the unbalanced panel weighted:
a. Using age, gender and region of residence in Wave 1 to construct an IPW measure
i. Attrition falls with age
ii. Effect of gender varies over time
b. Using cross-sectional weights to guarantee national representativeness throughout the waves
3. Balanced panel (only 40% of our sample in Wave 1).
22. We measure income inequality via a question in each survey wave asking respondents about their
household disposable monthly income two to four months prior to the survey, with
responses in the following bands:
• “0 to 1250 Euros”
• “1250 to 2000 Euros”
• “2000 to 4000 Euros”
• “4000 to 6000 Euros”
• “6000 to 8000 Euros”
• “8000 to 12500 Euros”
• “Over 12500 Euros”.
23. 23
0
20000
40000
60000
80000
100000
120000
140000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
Daily Cases (7-days average)
Wave 3 Wave 4 Wave 5
We look at household disposable income at four points in time between Jan.2020 and Jan.2021
(the blue shaded areas is the income month that was asked in the respective Wave in red):
Wave 1
24. As income is measured in bands, we take the mid-point in Euros and in PPP (using 2019 Euros for
household final consumption expenditures as the reference).
We attributed a value of 12 500 Euros to the open-ended top income category: this value produces
the best fit when comparing our relative Gini coefficients in January 2020 to those produced by
Eurostat in 2019.
Each income figure is equivalized using the square root equivalence scale and this income is
assigned to all household members.
25. We first estimate Lorenz curves and calculate four relative measures of inequality: Gini, and
three members of the Generalized Entropy family - Mean Logarithmic Deviation (GE(0)), Theil
(GE(1)) and half the square of the Coefficient of Variation (GE(2)).
These indices differ in their sensitivity to income changes, with the Gini coefficient being more
sensitive to income differences around the mode of the distribution, and Generalized Entropy
measures increasingly to changes affecting the upper tail as the parameter values in parentheses
rise from 0 to 2.
Relative inequality does not change if all individuals lose the same proportion of income, say 2%.
26. We also look at absolute measures of inequality. We consider the absolute Gini coefficient, the
variance of the income distribution, and two versions of the Kolm index with inequality-aversion
parameters of 5x10-4 and 10-4 (the results are very similar with other parameter values).
Absolute inequality does not change if all individuals lose the same amount of income, say 1000€.
27. It is natural to compare COME-HERE to the benchmark dataset used in Europe to monitor poverty
and inequality, EU-SILC.
COME-HERE is not on the same scale as EU-SILC, but has the great advantage of already being
available and offering multiple observations over the pandemic.
Below are relative Gini coefficients from EU-SILC 2019 and COME-HERE January 2020:
France: 0.295 vs. 0.294
Germany: 0.293 vs. 0.302
Spain: 0.331 vs. 0.336
Italy: 0.335 vs. 0.339
We also find that the average equivalised disposable household incomes in COME-HERE in France,
and Germany in January 2020 are very similar to those that can be calculated from the 2019 EU-
SILC wave. The picture is somewhat different in Spain and Italy, where our averages are roughly
20% lower than those in EU-SILC, so that we are missing some observations in the right tail of the
income distributions.
28. 28
Some Descriptive Statistics:
What about the evolution of the average equivalent
disposable household between January 2020 and
January 2021?
• Increase in Germany and in Spain
• Hump-shape in France
• Decrease in Italy
What about the evolution of the median equivalent disposable
household between January 2020 and January 2021?
• Decrease and Increase in Italy and in Spain
• Stable in France and Germany
29. Histograms
Jan. 20-Jan. 21:
Shift to the right, bar
Italy
There was an increase in the
mass in the first bin between
Jan. 20 and May 20 in France,
Italy and Spain; very pronounced
in Italy.
The mass in the first bin then fell
over time everywhere, and to
below its Jan. 20 level by
Sept.20 in France and Spain.
32. Generalized Entropy measures can be additively decomposed in between/within groups (gender,
age, education and partnership status) below the decomposition of GE(0):
Very similar results with GE(1),
GE(2).
• As often, the within component
explains most of the total inequality
• Within inequality decreased
everywhere
• Inequality between groups
increased in Germany
• Inequality between groups
decreased in Spain
34. 34
We performed a battery of robustness checks and found similar results when we:
1. Use different weighting procedures or the balanced sample
Our conclusions are not driven by changes in the sample composition (some exceptions in France
top tail).
2. Use the sample-split histogram technique (Cowell and Metha, 1984)
Our conclusions are not driven by the fact that our income distribution are based on grouped data
3. Infer potential unobserved income changes within bands (using a question on income
losses)
