Aspachs et al., Real-time inequality and the welfare state in motion: Evidence from COVID-19 in Spain (2020-07)
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Economic Working Paper Series Working Paper No. 1734, "Real-time inequality and the welfare state in motion: Evidence from COVID-19 in Spain," by Oriol Aspachs, Ruben Durante, Alberto Graziano, Josep Mestres, Jose G. Montalvo and Marta Reynal-Querol, dated July 2020. The authors propose a method for tracking income inequality after the COVID-19 pandemic using payroll microdata from bank records. The abstract states that without public benefits schemes inequality would have increased dramatically, and that the public benefits activated soon after the start of the pandemic substantially mitigated the crisis's impact on inequality. The introduction states that in Spain close to one million workers lost their job between mid-March and the end of April. The paper closes with figures on the Gini coefficient by country of origin.
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Economic Working Paper
Series Working Paper No. 1734
Real-time inequality and the
welfare state in motion: Evidence
from COVID-19 in Spain
Oriol Aspachs, Ruben Durante, Alberto
Graziano, Josep Mestres, Jose G. Montalvo,
and Marta Reynal-Querol
July 2020
Real-Time Inequality and the Welfare State
in Motion: Evidence from COVID-19 in Spain ∗
Oriol Aspachs† Ruben Durante‡ Alberto Graziano†
Josep Mestres† Jose G. Montalvo§ Marta Reynal-Querol‡
Abstract
Most official economic statistics have a relatively low frequency. The measures of inequality, in
particular, are not only produced with low frequency but also with significant lags. This poses an
important challenge for policymakers in their objective to mitigate the effects of a rapidly moving epidemic
as the COVID-19. We propose a methodology for tracking the evolution of income inequality in the
aftermath of the COVID-19 pandemic using high-frequency, high-quality microdata from bank-records.
Using this approach we study the evolution of inequality since the beginning of the COVID-19 pandemic,
and its effect on different groups of the population. First, we show that the payroll data managed by banks
are an extremely useful source of information to detect, timely and accurately, changes in the distribution
of wages. Our data replicate very closely the distribution of wages from the official wage surveys. Second,
we show that, in absence of public benefits schemes, inequality would have increased dramatically. The
impact of the crisis on inequality is explained mostly by its effect on low-wage workers. Pre-benefits
wage inequality has increased significantly among foreign-born individuals, and regions that have a
heavy economic dependence on touristic activities. Finally, we show that the public benefits activated
soon after the beginning of the pandemic have substantially mitigated the impact of the COVID-19 crisis
on inequality.
Keywords: inequality, COVID-19, administrative data, high frequency
JEL Classification: C81, D63, E24, J31
∗ We want to thank Miguel Angel Barcia for his helpful suggestions. Daniele Alimonti provided excellent research assistance.
† Caixabank Research
‡ ICREA-UPF, IPEG and BarcelonaGSE
§ UPF, IPEG and BarcelonaGSE
1
1 Introduction
The spread of COVID-19 has taken a huge toll on economic activity around the globe. The IMF expects
that global GDP will pencil in the highest contraction on record in 2020, and that it will be way deeper
than the one experienced during the global financial crisis (0% in 2009 vs –5% in 2020). In the Euro area,
the GDP declined by 3.8% in the first quarter of 2020 and is expected to shrink by 8.3% in 2020 according
to the central scenario that the ECB presented in June this year (-12.6% in the severe scenario). The Bank
of Spain predicts that the Spanish GDP will shrink even further, between -11.6% and -15.1% depending
on the scenario. In fact, given the importance for the Spanish economy of the sectors most affected by the
lock-down, and the stringency and the persistence of the restrictions to mobility implemented, it is highly
likely that Spain will struggle more than other countries to recover from the COVID-19 recession. The Bank
of Spain estimates that the GVA of Trade, transport, accommodation and food declined by -71% in Spain
during the lock-down (-44% in the Euro Area), and the weight of these sectors in Spain is 23.8% (19.0% in
the Euro area). The expected lackluster recovery of the Spanish economy is reflected in the medium term
forecasts of most institutions. The central scenario of the Bank of Spain assumes that the Spanish economy
will remain well below pre-crisis levels in 2022 (-1.6%), while the ECB expects the recovery of Euro area to
be more advanced at this stage, with GDP reaching pre-crisis levels at the end of 2022.
The evolution of the labor market also reflects the impact of the lock-down measures. In Spain, between
the 15th of March and the 31st of April, close to one million workers lost their job (4.4% of the active
population). In addition, 4.6 million people was forced to move under different forms of temporary lay-off
schemes. Hence, in just a month and a half, 22% of the active population had either lost their job or was
under temporary leave schemes. The concerns on the impact on inequality have mounted. The new economic
backlash might produce a surge in income inequality right at a time when the large inequalities caused by
the financial crisis were returning to their 2008 levels.
Most governments around the world have tackled the economic consequences of the pandemic using
a combination of family income support and credit facilities for firms and self-employed workers. Family
income support tries to stabilize the income of the families, reducing the impact of the crisis on consumption
and inequality. The recovery of consumption is fundamental for the shape of the economic recovery (V, U,
L, W) since consumption represents the largest component of demand. In Spain, household’s consumption
represents 57% of GDP. In addition, a higher level of inequality could erode the social fabric and, ultimately,
it could call into question the democratic system and promote populism, in a situation where the pandemic
2
is already giving some support to autocratic and illiberal political views. This is one of the most significant
challenges for democratic societies in the years after the pandemic.
However, there is a high degree of uncertainty on the effect of the policy responses and the appropriateness
of the total amount of public support to the economy. Unfortunately, most of the official indicators and
macroeconomic statistics on the effect of the measures to help the families and the economy have a very low
frequency (quarterly or yearly), and are produced with long delays (especially the measures of inequality).
This is an important challenge for policymakers in their efforts to tailor their responses to “flatten the
recession curve” (Gourinchas, 2020).
One characteristic effect of pandemics is its impact on inequality.1 However, inequality measures are
obtained with long lags. We propose an innovative methodology to track inequality, using data at high
frequency, and the evolution of the impact of public subsidies on inequality. Having a granular (by gender,
geography, age, etc.) and frequently updated view of inequality helps tailoring policies to contain the increase
of inequality in general, and by subgroups of the population. Our proposal is related with new international
initiatives to track in real time (or with high frequency indicators) the evolution of economic activity. Cicala
(2020) uses electricity usage from the European grid to proxy the evolution of economic activity since the
consumption of electricity is highly correlated with its entrepreneurial usage. Bick & Blandin (2020) resort
to the a Real Time Population Survey (RPS), following the structure of the government survey (CPS) to
construct high frequency estimates of employment, hours worked and earnings. Chen et al. (2020) shows
that these high-frequency measures of energy consumption and hours worked are strongly correlated with
the mobility indicators from the Google Community Mobility Report. Chetty et al. (2020) have built an
economic tracker to measure economic activity at high-frequency in the US. They use anonymized economic
information from private companies to measure consumer spending (card-based transactions from Affinity
Solutions); change in small business open (business making transactions on a given day from Womply); time
spent at work (GPS data provided by Google); and hours worked at small business (provided by Homebase).
In Spain, since the outbreak of the pandemic, CaixaBank Research has published weekly notes analysing
credit cards usage at POS, online transactions and cash withdrawals to estimate the impact of the confinement
measures on consumption. Carvalho et al. (2020) also uses credit card transactions reported by the POS
of BBVA (1,300 million transactions) to measure the evolution of consumption by categories. This type of
information is also routinely exploited by other banks like Abanca (Observatorio Abanca by IESIDE) or the
1 For instance Wade (2020) or Scheidel (2017).
3
Banc Sabadell (Pulso).2
S. R. Baker et al. (2020) goes a step further and analyze the effect of income support on consumption.
Following a recent literature that examines, using data from Personal Finance Websites, the effect of income
on consumption at daily frequency.3
However, the new literature on high frequency and real-time data has not proposed a methodology to
construct an indicator of inequality that provides a high frequency view of its evolution. In this paper we
propose a new source of high quality payroll administrative data, and a methodology, to calculate inequality
indicators at high frequency.
2 Methodology
As in many other countries, the Spanish government approved several measures to support income and
revenues of workers and companies to mitigate the impact of the COVID-19 crisis. One of the most important
measures adopted in Spain to support employment and salaries are the ERTEs, a mechanism to provide
income to workers temporarily (or partially) layed off from work. ERTEs also aim at keeping the match
employer-employee in the aftermath of exogenous and temporary shocks. But it is important to evaluate
their effectiveness. In fact, the implementation of ERTEs has been surrounded by complaints about delays
on the transfers and errors on the administrative procedures.4
Researchers have recently started to analyze the impact of the economic stimulus packages to maintain
employment and wages. Very recently Autor et al. (2020) have analyze the Paycheck Protection Program
of the US (PPP), a program of guaranteed loans for small business to support employment and wages.
Using anonymized and aggregated payroll administrative data from ADP (one of the largest providers of
cloud-based human resources management services), and the Small Business Administration, they conclude
that PPP increased employment approximately by 3.25%. Our objective is to use high-quality microdata to
evaluate the evolution of wage inequality in Spain at high frequency. Without this type of data the impact of
COVID-19 on financial vulnerability by groups of population can only be estimated indirectly using sectoral
2 The Bank of Spain has also used POS to track expenditure during the pandemic (Gonzalez et al. (2020)).
3 For instance Gelman et al. (2014), S. Baker (2018), Olafsson & Pagel (2018) or Kueng (2018).
4 The problem of management of the large number of public measures adopted to mitigate the impact of the COVID19 on
the economy is not specific of Spain. Public agencies in charge of managing these programs are overwhelmed by the size and
complexity of the different relief programs. For instance, there are also reports in the US on the reception by large corporations
of large amounts of financial aid directed to small and medium size firms. There is also evidence that the Paycheck Protection
Program has been disproportionately directed to areas scarcely affected by the COVID19.
4
information of workers by characteristics (gender, age, etc.) like the recent work of Alvargonzález et al.
(2020).
However, proper inequality indices are produced with important lags. While monetary policy has many
high frequency indicators available to check its performance (short term interest rates, multiple indicators
for credit and financial markets tensions, etc.), fiscal policy does not have the advantage of being able to rely
on high frequency data. This is particularly troublesome to evaluate the impact of fiscal policies to mitigate
the effect of the COVID-19 pandemic and, in particular, to fight against the increase of inequality. For
instance, the latest indicators of wage inequality in Spain, produced by the National Institute of Statistics,
were presented at the end of June of 2020, but refer to the situation in 2018. This is not unusual in this
type of indicators. The World Bank reports in July of 2020 the 2018 Gini indices of 10.6% of the countries,
and for 25% of the countries the latest Gini index refers to 2017. But even when we only consider advanced
economies, the lags in the production of inequality indicators are very significant. Eurostat reports only
40% of the Gini indices for 2019 for countries of the EU. Therefore, inequality measures are produced with a
significant lag. However, many programs are designed to have a direct impact on the distribution of income.
In fact, public transfers are an important political issue and attention has been given to evaluate their effect
on the distribution of income, both before and after policy implementation.
Since the outbreak of the coronavirus pandemic researchers have developed online surveys to track the
evolution of the labor market in several countries. Bick & Blandin (2020) construct a Real-Time Population
Survey (RPS) that follows closely the Current Population Survey of the BLS of US. Adams-Prassi et al.
(2020) presents also a real time labor survey for the UK, US and Germany. This is an interesting approach
but relies on online surveys, which required access to Internet, and produce relatively small, and potentially
biased, samples. Our approach is different and relies on large administrative data from financial institutions
like recent research by Gelman et al. (2014) or S. R. Baker et al. (2020). Cajner et al. (2020) uses information
by ADP, a large international provider of payroll processing, benefit management and tax services.
In this paper we show the utility, and validity, of bank’s information to produce high frequency indicators
that can help to track the evolution of wage inequality at high frequency. For this purpose we use aggregated
and anonymized data5 from the second largest Spanish financial institution (CaixaBank) on payroll and
5 The treatment of information uses anonymized data to generate groups of individuals who are extracted from the company’s
Data Warehouse without any field identifying the clients.
5
benefits, for more than 3 million wage earners6 . The computation of inequality in Spain cannot rely on
administrative data and that is the reason why, until now, it has been based on surveys. The data of the
tax authority is compiled yearly, and the Social Security information, although in principle is updated every
month, does not have information on salaries but on the contribution base which is capped.
Our methodological proposal aims at tracking the evolution of wage inequality and the impact of public
support on its attenuation. The information of banks is well suited for this type of exercise since wages and
subsidies are very well identified in their information systems. For this purpose we will analyze the monthly
change in wages by level of salary in the reference period. The evolution of these distributions (over time
and across wage intervals) will allow following the impact of the crisis on the distribution of salaries over
time and across the wage distribution. We also provide a methodology to calculate monthly Gini indices and
Lorentz curves, before and after accounting for public benefits, to analyze if the schemes to support workers
temporarily out of the labor market (including potentially the future system of minimum income guarantee)
is working well enough to keep inequality at a low level.
Obviously, it is interesting to analyze not only the evolution of total inequality but also the effect of the
COVID-19 crisis on inequality for different subgroups of the population. The information systems of banks
allow calculating inequality for different criteria like gender, age, region, or country of birth7 . Using this
information we calculate the monthly evolution of gender inequality. We also calculate the monthly evolution
of intergenerational inequality. Economic research shows that young people in transition to the labor market
have a worse labor market experience if they access the labor market in the middle of a recession. Therefore,
this is a particularly important group of the population. We also analyze the evolution of wage inequality
by regions (CCAA).8
3 Data
In this section we present the criteria to construct the sample and discuss its representativeness with
respect to the population of Spanish workers.
6 At the end of 2019 Caixabank had 27,1% of the payroll receipts in the financial sector. In Spain, differently from other
countries like the US, the payment of the salary using checks is a very rare event. Almost all the payments of wages and salaries
use direct deposits.
7 We leave the effect of the sector of activity for future research.
8 In future work we will also distinguish between rural and urban areas.
6
3.1 Sample construction
We use data on monthly payrolls and unemployment benefits paid in personal bank accounts in Caixa-
Bank. CaixaBank is the second largest financial institution in Spain and has the highest market share in
payrolls paid in a bank account in the country (27.1%).
Our base sample comes from anonymized personal bank accounts and includes all payroll income and
labor market related benefits received in that account. We restrict our sample to accounts with either
only one account holder or with multiple account co-holders but only one employer paying-in wages. This
way, we ensure that payrolls or transfers recorded correspond to only one individual and avoid recording
multiple payrolls or transfers from multiple account holders.9 . In addition, we exclude from the sample
those individuals who died during our period of study or who did not use the bank account for their usual
financial transactions during the period.10 Finally, to ensure some stability on the sample of individuals
studied, we require observing either wages or public benefits during two months (that is, in December 2019
and in January 2020) prior to the period of study (starting in February 2020). No other filters are applied, in
particular we do not restrict our sample by income level, neither minimum nor maximum. We will only do so
in the following section for comparability reasons with other public datasets to show the representativeness
of our sample.
This way, our base sample includes individuals aged 16-64 who received either wages or unemployment
benefits in December of 2019 and January of 2020. We follow those individuals in the following months
starting in February 2020 (lockdown measures started on March 14th 2020 when the state of alarm was
declared in Spain) until May 2020 (the lockdown measures started to be lifted that month) and we observe
wage income and unemployment benefits received or the absence of any of those. Salaries in month t are
defined as all wage payments received in a particular bank account from the 16th of that month until the
15th of the following month.11 Public benefits paid by the Social Security in month t cover the same time
period, and include those workers in furlough schemes (known as ERTE in Spain).12
9 Around 5% of individual bank accounts had multiple holders receiving two or more payrolls or subsidies and were excluded
from the sample.
10 In particular, we identify each month those clients who are actively using their bank account and perform at least two
non-automatic transactions (payments, etc) during the last two months. Those represent around 0.7% of the sample.
11 In Spain, employees’ payments are usually deposited monthly, towards the end of the month, but they can also be paid
fortnightly, weekly, etc. and they are very rarely paid in cheques.
12 ERTE refers to “Expediente de Regulación de Empleo” and is a temporary layoff program in which workers remain affiliated
to the Social Security but receive benefits while they are not working, or they are working a lower number of hours than in
regular times). In addition, we know for each client her gender, age, region and country of birth.
7
3.2 Sample representativeness
Since our main source of data is related with holding a bank account it is important to start analyzing
the level of financial inclusion in Spain. The data of the Global Findex, the index of financial inclusion of
the World Bank, shows that 97.6% of Spanish people over 15 years old holds a banking account.13 There is
no gender difference in holding bank accounts in the Spanish case. Men and women have exactly the same
proportion.
Given our data source we restrict our sample to people working or getting some kind of subsidy related
with their job market participation. We exclude the self-employed from the sample since it is difficult to
calculate their monthly income from bank account information. However, it is important to note that the
proportion of wage earners among the Spanish working population was 84.4% in the first quarter of 2020
(Labor Force Survey of Spain EPA).14
Since most of the individuals in the sample are workers, to analyze the representativeness of the sample
in terms of the distribution of salaries we compare our data with the data of the Encuesta de Estructura
Salarial (EES, Spanish Wage survey). Therefore, we consider the individuals in our sample who were
working in February of 2020. We should notice that the wages received by workers in their accounts are
net of tax withholds and Social Security. In order to compare our data with the EES we need to calculate
the distribution of net salaries. For that purpose we have transformed the gross salaries of the EES in net
salaries by subtracting social insurance payments and taxes withheld.
Figure 1 shows the distribution of the monthly wages of our sample, corresponding to February of 2020,
compared with the distribution of monthly net salaries15 for the Spanish Wage Survey. Unfortunately the
latest microdata available for the quadrennial survey on the distribution of wages corresponds to 2014.16
Since there is a time difference with our data we have adjusted the figure moving the whole distribution by
the increase in the average salary between 2014 and 2018 (the last available EES). Figure 1 compares both
distributions. We can see that the histogram of the net wages of our sample is very well adjusted by the
density estimation of the adjusted distribution of net salaries in the official wage survey.
13 The average in high income countries is 93.7%.
14 The relevance of wages as the main source of income can also be seen in the similarity of the inequality measures using
income or gross wages. For instance, for the last period for which both measures are available, income inequality in Spain,
measured by the Gini index, was 34.5 while wage inequality was 34.3. The average absolute difference in the Gini index for
income and wages over the period 2008-2016 was 0.3.
15 Base salary plus extraordinary payments plus payment for extra hours minus tax withholds minus Social Security contri-
butions.
16 The microdata for the EES 2018 are not yet available at the time of writing this version.
8
[Include Figure 1 here]
Since the distributions are so similar it is not surprising to see that the quantile ratios used regularly to
describe inequality are very similar in both distributions as shown in Table 1.
Table 1: Inequality: basic quantile ratios (net wages)
Our sample (CBK) EES 2014
P90/P10 4.24 4.12
P90/P50 1.88 1.87
P10/P50 0.44 0.45
P75/P25 1.85 1.83
Notes - EES stands for Encuesta de Estructura Salarial (Spanish Wage Survey).
To run an additional check on the representativeness of our sample we compare the distribution of
individuals by gender and age with other sources. Table 2 summarizes the comparisons. In our sample 54%
of the individuals are male. This compares satisfactorily with the 52% of males in the sample of the EES
of 2014. In order to compare with more recent estimates, columns 3 and 4 include the proportions of males
among employees in the Labor Force Survey of the last quarter of 2019 and first quarter of 2020.We want
to compare with February of 2020, which is in the middle of the first quarter. The proportion of males
is identical to the one in the EES of 2014 and very close to the one in our sample. With respect to age
we also find that the proportions in our sample are very similar to the age distribution of workers. These
distributions of characteristics confirm the appropriateness of our sample.
4 Results
In this section we present the basic results of the paper. One of the objectives of the paper is to analyze
the role of public benefits on inequality. For this reason, all the exercises present two situations: pre-public
benefits and post-benefits. The pre-benefits scenario considers the wages of the individuals in the reference
sample before including unemployment benefits and furlough schemes. The post-benefits scenario considers
unemployment benefits, subsidies and furlough schemes perceived by the individuals of the reference sample.
9
Table 2: Check our data versus labor surveys
Our sample (CBK) EES 2014 EPA4T19 EPA1T20
N 3,028,204 209,473 ≈ 200, 000 ≈ 200, 000
Gender
Male 0.54 0.52 0.52 0.52
Female 0.46 0.48 0.48 0.48
Age
≤ 19 0.01 0.00 0.008 0.007
20-29 0.18 0.12 0.145 0.142
30-39 0.25 0.31 0.246 0.243
40-49 0.28 0.30 0.305 0.304
50-59 0.21 0.21 0.233 0.237
≥ 60 0.07 0.05 0.060 0.063
Notes - EES stands for Encuesta de Estructura Salarial (Spanish Wages Survey); EPA4T19 refers to the sample of
employees in the Spanish Labor Survey (EPA) in the last quarter of 2019; EPA1T2020 refers to the sample of employees in the
Spanish Labor Survey in the first quarter of 2020.
4.1 Changes in the distribution of wages
Figure 2 shows the changes in wages from the period before the beginning of the lockdown (February) to
April for different levels of wages in the reference period (February) before accounting for public schemes.
We include not only the distribution of the change in 2020 but also in 2019.17 In the x-axis we show the
percentage change in salaries while in the y-axis we include the distribution. To simplify the figure we include
only three levels of initial wages, measured in February of 2020, and the average for all the individual in the
sample of reference. We consider five representative levels of wages. The interval from 900 to 1,000 euros
includes the 25 percentile of the distribution of wages. The median is included in the interval from 1,200
to 1,300 euros. The interval from 1,700 to 1,800 euros includes the 75 percentile. The interval from 2,900
euros to 3,000 euros includes to the 95 percentile. Finally, the interval 4,700 to 4,800 represents to top 1%.
The comparison of the changes between April and February in 2019 and 2020 show several interesting facts.
First, in general there is a reduction in the proportion of salaries that are unchanged between both months
in 2020 versus 2019. Second, we observe that the movement to the category no income is much larger in
2020 than in 2019. Finally, Figure 2 shows a large increase in the probability mass around a large reduction
of wages for the group of high salaries (top 5% and top 1%). The reason for this bump is the payment of
bonuses in February, which implies that the difference of wages between April and the reference month is a
sudden reduction.18 This is the reason why analyzing the evolution of the distribution of wages only in 2020
17 The same figures are available for March and May but the trends are essentially the same.
18 As we explained before and it is the standard in official statistics, our definition considers all the payments to employees
including extraordinary payments and bonus.
10
would not provide the right answer.
Figure 3 shows the difference in wages between April and February after accounting for the public support
schemes. In this case the movement to the no income category has been largely mitigated with respect to the
pre-benefits situation (Figure 2). There is still the concentration around 50% to 75% reduction of salaries
for the high wages segments.
[Insert figure 2 here]
[Insert figure 3 here]
Figure 4 provides the same information of the previous two graphs but comparing in the same figure
all the interval of wage of reference. Panel (a) shows the pre-benefits situation. Most of the distribution
is concentrated around the no change, or small changes, in wages. There are two interesting parts of the
distribution. First of all a significant portion of the probability mass is concentrated around the no income
extreme. More interestingly, the lower is the salary in the period of reference, the higher is probability of
losing the job. This monotonic behavior is not only present in the groups of income included in Figure 4
Panel (a) but it is a feature common to all the levels of wages of the distribution of reference. The second
noticeable feature of the figure is a large bump in the reduction of wages around a 50% that we already
discussed above. In fact, for the highest level of wages in our sample (top 0.01%) this bump reaches over
30%. Not surprisingly for low level of wages there is no bump in the distribution at that salary change.
[Insert figure 4 here]
This means that wages for high income individuals are drastically reduced from February to April.
However, this is a seasonal effect that is observed every year. To eliminate this seasonality we will discuss
the results of the diff-in-diffs distribution.19 Figure 4 Panel (b) includes the same distribution calculated
after considering public benefits. In this case the concentration of mass around the no income situation
is largely reduced while part of the probability mass is moved to increases in wages. In the upper limit
we observe the same monotonic classification: the highest proportion of increase corresponds to the lowest
wages and increases with the salary of reference.
19 The Economist has recently cautioned against putting too much weight in the measurement of economic activity using
new real-time indicators claiming that seasonality is an important problem that cannot be addressed most of the time when
using real-time indicators. See ”Real-time danger”, 25th of July, 2020.
11
To correct for seasonality Figure 5 presents the difference in the proportion of changes in salaries between
April and March of 2020 versus 2019, separating by different levels of wages. Logically, the effect of the
February bonus in the high wages has been eliminated. It is interesting to notice that the transmission of
the shock to the shape of the wave is very different for low salaries and high salaries in the pre-subsidy
situation. In the case of the wages below 1,300 euros the reduction of the no salary change category has
push part of the probability mass to the no income category. For wages above 1,700 euros the vibration of
the wave sends part of the no salary change to a small reduction of wages.
To facilitate the comparison across wages groups, Figure 6 shows the changes for all wage categories in
the same figure. The movements are similar, although less pronounced than in Figure 4. The pre-benefits
figure, Panel (a), reproduces the same monotonicity of increase in the proportion of no income in function
of the wages of the month of reference. Panel (b) shows the post-benefits case where, as before, the increase
in the no income category has been largely reduced.
[Insert Figure 5 here]
[Insert figure 6 here]
4.2 The evolution of inequality
One important objective of this research is to evaluate the evolution of inequality after the impact of the
COVID-19 and the mitigating effect of the measures approved by the government. Figure 7 Panel (a) shows
the evolution of the Gini index from February to May of 2020 and the same months for 2019. The pre and
post-benefits curves are basically parallel until April of 2020 were there is a large increase of the Gini index
pre-benefits while the post-benefit curve shows a more moderate increase. In May of 2020 the pre-benefits
Gini index still stays very high while the post-benefits has returned to the pre-COVID situation.
[Include Figure 7 here]
We can check the evolution of the Gini index with respect to alternative measures of inequality. For
instance, Figure 7 Panel B shows the evolution of the Theil index, which is an inequality measure related
with the concept of entropy and Shannon’s index. The Generalized entropy family
N α
1 X yi
GE(α) = − 1 f or α 6= 0, 1 (1)
N α(α − 1) i=1 ȳ
12
where ȳ is the mean of y. If α = 1 then we obtain the Theil index
1 X yi yi
GE(1) = T heil = ln (2)
N ȳ ȳ
Figure 7 Panel B confirms the evolution of inequality during this period, showing that the Theil index
evolves very similarly to the Gini index. There is a large increase in inequality in March in both, pre and
post-benefits, that continues in April in the pre-benefits situation but is tamed in the post-benefits case.
Figure 8 depicts the Lorentz curve in February of 2020, our reference month, before and after considering
benefits. Obviously the effect of the public action moves the curve upward, mostly for the low percentiles.
Figure 9 shows the change between the pre and the post-benefits Lorenz curve for April of 2020. In this
case, as already seen in the Gini index, the movement between the pre-benefits Lorenz curve and the post-
benefits is much larger. Figure 10 shows the movement of the pre-benefits Lorenz curve every month from
February 2020 until May 2020. It shows, as expected after analyzing the evolution of the Gini index, that
the downward movement accelerates in April and it stabilizes in May. The post-benefits movements are
smoother than the ones of the pre-benefits Lorenz curves (Figure 11)
[Include Figure 8 here]
[Include Figure 9 here]
[Include Figure 10 here]
[Include Figure 11 here]
5 The evolution of inequality by subgroups and geographies
In this section we analyse the evolution of inequality measured by the Gini index in different subgroups of
the population, differentiating by gender, age, regions, and country of origin. This allows us to analyse how
the crisis is affecting the different segments of the population and, ultimately, the impact it might be having
on the most vulnerable groups. Our preliminary analysis of decomposition between and within groups20
20 We have used the Theil index which, differently from the Gini index, provides an additively decomposable measure of
inequality.
13
shows that the between groups contribution is very small in all the cases, and inequality is dominated by
the within component. Additionally, the change in the importance of the between component with respect
to the situation in the reference month is also small. For this reason in the rest of the section we consider
the evolution of the within group inequality21
5.1 Gender and inequality
Figure 12 shows the evolution of the Gini index by gender, before and after the outbreak of the crisis,
and before and after public subsidies. As it can be seen, the evolution of the Gini index was relatively stable
during 2019. In February 2020 its level was similar to the one observed in the previous year. We do not
observe major differences between males and females before the shock. The magnitude of the increase in the
Gini index after the declaration of the state of alarm is similar across gender types before public transfers,
but slightly higher for females in the post-benefits case.
[Include Figure 12 here]
5.2 Inequality within age groups
Figure 13 shows the evolution of the Gini index by different age groups, before and after the shock,
and pre and post-benefits. In this case, the differences across groups are remarkable. In the pre-transfers
case, we observe a large increase in the Gini index among the youngest cohorts (between 16 and 29 years
old). For the other age groups the impact is also notable, but substantially smaller, specially for the oldest
cohorts (between 50 and 64 years old). The disbursement of the public transfers partially mitigates the large
increase in the Gini index among the young. Nonetheless, the increase in the Gini index post-benefits is still
remarkable both in absolute and in relative terms. The increase in the Gini index post-benefits for the older
cohorts is relatively minor.
[Include Figure 13 here]
21 Recent research (Liao (2016)) argues that the within component may contain part of the between group inequality if there
is a part of the within distribution that is not shared by the different groups. This could be particularly relevant for the case
of gender inequality. We plan to address these issues in future research.
14
5.3 Inequality and regions
The change in the Gini index across the different Spanish regions is also very different. The jump of the
index pre-benefits is specially pronounced in the Balearic Islands and Canarias, where the weight of tourism
is specially high, and the economic impact of the restrictions to international mobility is likely being specially
intense. The increase in the Gini index post-benefits is significantly smaller, and the regional variation is
also significantly reduced once public benefits are taken into account. Note that the variation in Gini index
post-benefits is not only related to the variation in the Gini index pre-benefits but also to the coverage and
processing speed of support programs. We leave this for further research.
[Include Figure 14 here]
5.4 Nationality and inequality
The internal data of CaixaBank also allows us to analyse the impact of the crisis depending on the
country of origin of the population. The Gini index pre-benefits increases significantly more for foreign-born
individuals than for native-born. The increase in the Gini index for foreign-born is smaller in the post-
benefits case, but still significantly higher than for natives. If we further classify foreign-born individuals
by the GDP per capita level of their country of origin, we observe significant differences between different
income groups. For example, the Gini index post-benefits is not reduced over time for foreign-born from
low income countries while for foreign-born from high-income countries inequality post-benefits is reduced
(as it is the case for native-born individuals). These higher increases in inequality post-benefits for migrants
show their vulnerability in times of crisis as their social welfare net is thinner. We can also aggregate all the
foreign born population. Figure 16 shows the large increase in inequality of the foreign born sample and the
improvement, although incomplete, after accounting for public benefits.
[Include Figure 15 here]
6 Concluding remarks
In this paper we study the evolution of inequality since the beginning of the COVID-19 pandemic, and
its effect on different groups of the population. First, we show that the payroll data managed by banks are
15
an extremely useful source of information to detect, timely and accurately, changes in the distribution of
wages. Our data replicate very closely the distribution of wages from the official wage surveys. Second, we
show that, in absence of the large public scheme activated soon after the beginning of the COVID-19 crisis,
inequality would have increased dramatically. The impact of the crisis on inequality is explained mostly by
its effect on low-wage workers. Pre-benefits wage inequality has increased significantly among foreign-born
individuals and regions that have a heavy economic dependence on touristic activities. Finally, we show
that the public benefits activated soon after the beginning of the pandemic have substantially mitigated the
impact of the COVID-19 crisis on inequality.
16
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18
.0008
.08
.0006
.06
Density
.0004 .04 Distribution
.0002 .02
0 0
0 2000 4000 6000 8000 10000
Euros
EES CABK
Figure 1: Distribution of monthly net wages: our sample (CABK) versus the sample of the Wage Survey
(EES)
19
Percentage Percentage Percentage
N 0 10 20 30 40 N 0 10 20 30 40 N 0 10 20 30 40
o o o
-7 in -7 in -7 inc
5% co 5% co 5% o
m m m
or e or e or e
-5 mo -5 mo -5 mo
0 0 0
& re & re & re
-4 75 - -4 7 5 - -4 75 -
0 0 0
& % & % & %
-3 -50 -3 -5 0 -3 -50
0 0 0
& % & % & %
-2 -40 -2 -4 0 -2 -40
5 5 5
& % & % & %
-2 -30 -2 -3 0 -2 -30
0 0 0
& % & % & %
-1 -25 -1 -2 5 -1 -25
5 5 5
& % & % & %
-1 -20 -1 -2 0 -1 -20
0 0 0
to % to % to %
- - -
-5 15% -5 1 5 % -5 15%
to to to
-1 -1 -1
-5 0 % -5 0 % -5 0%
to to to
N N N
o -1 % o -1 % o -1%
ch ch ch
a a a
+1 ng +1 ng +1 ng
to e to e to e
+5 +5 +5 +5 +5 +5
to % to % to %
+1 +1 +1 +1 +1 +1
0 0 0 0 0 0
(e) From 4700€ to 4800€ (c) From 1700€ to 1800€ (a) From 900€ to 1000€
to % to % to %
+1 +1 +1 +1 +1 +1
5 5 5 5 5 5
to % to % to %
+2 +2 +2 +2 +2 +2
0 0 0 0 0 0
to % to % to %
Figure 2: Changes in payments between April and February by level of wages in the reference period.
+2 +2 +2 +2 +2 +2
5 5 5 5 5 5
+5 to + % +5 to + % +5 to + %
0% 50 0% 50 0% 50
or % or % or %
m m m
or or or
Pre-19 e e e
20
Percentage Percentage Percentage
N 0 10 20 30 40 N 0 10 20 30 40 N 0 10 20 30 40
o o o
-7 in -7 in -7 inc
5% co 5% co 5% o
m m m
or e or e or e
-5 mo -5 mo -5 mo
0 0 0
& re & re & re
-4 -75 -4 -7 5 -4 -75
0 0 0
& % & % & %
-3 -50 -3 -5 0 -3 -50
0 0 0
& % & % & %
-2 -40 -2 -4 0 -2 -40
5 5 5
& % & % & %
-2 -30 -2 -3 0 -2 -30
0 0 0
& % & % & %
-1 -25 -1 -2 5 -1 -25
5 5 5
& % & % & %
Pre-20 -1 -20 -1 -2 0 -1 -20
0 0 0
to % to % to %
- - -
-5 15% -5 1 5 % -5 15%
to to to
-1 -1 -1
-5 0 % -5 0 % -5 0%
to to to
N N N
o -1 % (f) Total o -1 % o -1%
ch ch ch
a a a
+1 ng +1 ng +1 ng
to e to e to e
+5 +5 +5 +5 +5 +5
to % to % to %
+1 +1 +1 +1 +1 +1
0 0 0 0 0 0
(d) From 2900€ to 3000€ (b) From 1200€ to 1300€
to % to % to %
+1 +1 +1 +1 +1 +1
5 5 5 5 5 5
to % to % to %
+2 +2 +2 +2 +2 +2
0 0 0 0 0 0
to % to % to %
+2 +2 +2 +2 +2 +2
5 5 5 5 5 5
+5 to + % +5 to + % +5 to + %
0% 50 0% 50 0% 50
or % or % or %
m m m
or or or
e e e
Pre-benefits. Comparing 2020 and 2019
Percentage Percentage Percentage
N 0 10 20 30 40 N 0 10 20 30 40 N 0 10 20 30 40
o o o
-7 in -7 in -7 inc
5% co 5% co 5% o
m m m
or e or e or e
-5 mo -5 mo -5 mo
0 0 0
& re & re & re
-4 75 - -4 7 5 - -4 75 -
0 0 0
& % & % & %
-3 -50 -3 -5 0 -3 -50
0 0 0
& % & % & %
-2 -40 -2 -4 0 -2 -40
5 5 5
& % & % & %
-2 -30 -2 -3 0 -2 -30
0 0 0
& % & % & %
-1 -25 -1 -2 5 -1 -25
5 5 5
& % & % & %
-1 -20 -1 -2 0 -1 -20
0 0 0
to % to % to %
- - -
-5 15% -5 1 5 % -5 15%
to to to
-1 -1 -1
-5 0 % -5 0 % -5 0%
to to to
N N N
o -1 % o -1 % o -1%
ch ch ch
a a a
+1 ng +1 ng +1 ng
to e to e to e
+5 +5 +5 +5 +5 +5
to % to % to %
+1 +1 +1 +1 +1 +1
0 0 0 0 0 0
(e) From 4700€ to 4800€ (c) From 1700€ to 1800€ (a) From 900€ to 1000€
to % to % to %
+1 +1 +1 +1 +1 +1
5 5 5 5 5 5
to % to % to %
+2 +2 +2 +2 +2 +2
0 0 0 0 0 0
to % to % to %
Figure 3: Changes in payments between April and February by level of wages in the reference period.
+2 +2 +2 +2 +2 +2
5 5 5 5 5 5
+5 to + % +5 to + % +5 to + %
0% 50 0% 50 0% 50
or % or % or %
m m m
or or or
Post-19 e e e
21
Percentage Percentage Percentage
N 0 10 20 30 40 N 0 10 20 30 40 N 0 10 20 30 40
o o o
-7 in -7 in -7 inc
5% co 5% co 5% o
m m m
or e or e or e
-5 mo -5 mo -5 mo
0 0 0
& re & re & re
-4 -75 -4 -7 5 -4 -75
0 0 0
& % & % & %
-3 -50 -3 -5 0 -3 -50
0 0 0
& % & % & %
-2 -40 -2 -4 0 -2 -40
5 5 5
& % & % & %
-2 -30 -2 -3 0 -2 -30
0 0 0
& % & % & %
-1 -25 -1 -2 5 -1 -25
5 5 5
& % & % & %
-1 -20 -1 -2 0 -1 -20
Post-20 0 0 0
to % to % to %
- - -
-5 15% -5 1 5 % -5 15%
to to to
-1 -1 -1
-5 0 % -5 0 % -5 0%
to to to
N N N
o -1 % (f) Total o -1 % o -1%
ch ch ch
a a a
+1 ng +1 ng +1 ng
to e to e to e
+5 +5 +5 +5 +5 +5
to % to % to %
+1 +1 +1 +1 +1 +1
0 0 0 0 0 0
(d) From 2900€ to 3000€ (b) From 1200€ to 1300€
to % to % to %
+1 +1 +1 +1 +1 +1
5 5 5 5 5 5
to % to % to %
+2 +2 +2 +2 +2 +2
0 0 0 0 0 0
to % to % to %
+2 +2 +2 +2 +2 +2
5 5 5 5 5 5
+5 to + % +5 to + % +5 to + %
0% 50 0% 50 0% 50
or % or % or %
m m m
or or or
e e e
Post-benefits. Comparing 2020 and 2019
N Percentage N Percentage
o 0 10 20 30 o 0 10 20 30
in in
-7 co -7 co
m m
5% e 5% e
or or
m m
-5 or -5 or
0 e 0 e
& &
-7 -7
-4 5% -4 5%
0 0
& &
-5 -5
-3 0% -3 0%
0 0
& &
-4 -4
-2 0% -2 0%
5 5
From 900€ to 1000€
& &
From 2900€ to 3000€
Figure 4: Change in payments received by workers between April 2020 and February 2020.
-3 -3
-2 0% -2 0%
0 0
& &
-2 -2
-1 5% -1 5%
5 5
& &
-2 -2
-1 0% -1 0%
0 0
to to
-1 -1
5% 5%
-5 -5
to to
22 -1 -1
0% 0%
-5 (b) -5 (a)
to to
-1 -1
N % N %
o o
ch ch
a ng an
+1 e +1 ge
to to
From 4700€ to 4800€ From 1200€ to 1300€
+5 +5
+5 % +5 %
to to
+1 +1
+1 0% +1 0%
0 0
to to
+1 +1
+1 5% +1 5%
5 5
to to
+2 +2
+2 0% +2 0%
0 0
to to
+2 +2
+2 5% +2 5%
5 5
to to
+5 +5 +5 +5
0% 0% 0% 0%
or or
m m
or or
(a) Pre-transfers. (b) Post-transfers
e e
From 1700€ to 1800€
Difference in percentage points Difference in percentage points Difference in percentage points
N -20 -10 0 10 20 N -20 -10 0 10 20 N -20 -10 0 10 20
o o o
-7 in -7 in -7 inc
5% co 5% co 5% o
m m m
or e or e or e
-5 mo -5 mo -5 mo
0 0 0
& re & re & re
-4 75 - -4 7 5 - -4 75 -
0 0 0
& % & % & %
-3 -50 -3 -5 0 -3 -50
0 0 0
& % & % & %
-2 -40 -2 -4 0 -2 -40
5 5 5
& % & % & %
-2 -30 -2 -3 0 -2 -30
0 0 0
& % & % & %
-1 -25 -1 -2 5 -1 -25
5 5 5
& % & % & %
-1 -20 -1 -2 0 -1 -20
0 0 0
to % to % to %
- - -
-5 15% -5 1 5 % -5 15%
to to to
-1 -1 -1
-5 0 % -5 0 % -5 0%
to to to
N N N
o -1 % o -1 % o -1%
ch ch ch
a a a
+1 ng +1 ng +1 ng
to e to e to e
+5 +5 +5 +5 +5 +5
to % to % to %
+1 +1 +1 +1 +1 +1
0 0 0 0 0 0
(e) From 4700€ to 4800€ (c) From 1700€ to 1800€ (a) From 900€ to 1000€
to % to % to %
+1 +1 +1 +1 +1 +1
5 5 5 5 5 5
to % to % to %
+2 +2 +2 +2 +2 +2
0 0 0 0 0 0
to % to % to %
Figure 5: Diff-in-diffs in payments for each level of salaries in the reference month. April vs February - 2020
+2 +2 +2 +2 +2 +2
5 5 5 5 5 5
+5 to + % +5 to + % +5 to + %
0% 50 0% 50 0% 50
or % or % or %
m m m
or or or
e e e
Pre
23
Difference in percentage points Difference in percentage points Difference in percentage points
N -20 -10 0 10 20 N -20 -10 0 10 20 N -20 -10 0 10 20
o o o
-7 in -7 in -7 inc
5% co 5% co 5% o
m m m
or e or e or e
-5 mo -5 mo -5 mo
0 0 0
& re & re & re
-4 -75 -4 -7 5 -4 -75
0 0 0
& % & % & %
-3 -50 -3 -5 0 -3 -50
0 0 0
& % & % & %
-2 -40 -2 -4 0 -2 -40
5 5 5
& % & % & %
-2 -30 -2 -3 0 -2 -30
0 0 0
& % & % & %
-1 -25 -1 -2 5 -1 -25
Post 5 5 5
& % & % & %
-1 -20 -1 -2 0 -1 -20
0 0 0
to % to % to %
- - -
-5 15% -5 1 5 % -5 15%
to to to
-1 -1 -1
-5 0 % -5 0 % -5 0%
to to to
N N N
o -1 % (f) Total o -1 % o -1%
ch ch ch
a a a
+1 ng +1 ng +1 ng
to e to e to e
+5 +5 +5 +5 +5 +5
to % to % to %
+1 +1 +1 +1 +1 +1
0 0 0 0 0 0
(d) From 2900€ to 3000€ (b) From 1200€ to 1300€
to % to % to %
+1 +1 +1 +1 +1 +1
5 5 5 5 5 5
to % to % to %
+2 +2 +2 +2 +2 +2
0 0 0 0 0 0
to % to % to %
+2 +2 +2 +2 +2 +2
5 5 5 5 5 5
+5 to + % +5 to + % +5 to + %
0% 50 0% 50 0% 50
or % or % or %
m m m
or or or
e e e
vs 2019
N Difference in percentage points N Difference in percentage points
o -20 -10 0 10 20 o -20 -10 0 10 20
in in
-7 co -7 co
m m
5% e 5% e
or or
m m
-5 or -5 or
0 e 0 e
& &
-7 -7
-4 5% -4 5%
0 0
& &
-5 -5
-3 0% -3 0%
0 0
& &
-4 -4
-2 0% -2 0%
5 5
From 900€ to 1000€
& &
From 2900€ to 3000€
-3 -3
-2 0% -2 0%
0 0
& &
Figure 6: Diff-in diffs in payments received by workers: April vs February comparing 2020 vs 2019
-2 -2
-1 5% -1 5%
5 5
& &
-2 -2
-1 0% -1 0%
0 0
to to
-1 -1
5% 5%
-5 -5
to to
24 -1 -1
0% 0%
-5 (b) -5 (a)
to to
-1 -1
N % N %
o o
ch ch
a ng an
+1 e +1 ge
to to
From 4700€ to 4800€ From 1200€ to 1300€
+5 +5
+5 % +5 %
to to
+1 +1
+1 0% +1 0%
0 0
to to
+1 +1
+1 5% +1 5%
5 5
to to
+2 +2
+2 0% +2 0%
0 0
to to
+2 +2
+2 5% +2 5%
5 5
to to
+5 +5 +5 +5
0% 0% 0% 0%
or or
m m
or or
e e
From 1700€ to 1800€
(a) Pre-transfers. (b) Post-transfers
20 .2 .25 .3 .35 .4 .45 .5 .55 .6 .65 .7 20 .3 .35 .4 .45 .5 .55 .6
19 19
O O
2 2
20 20
19 19
O O
3 3
20 20
19 19
O O
4 4
20 20
19 19
O O
5 5
Figure 7: Inequality measures
... ...
Theil-pre Gini-pre
25
(b) (a)
... ...
20 20
20 20
O O
2 2
Thail-post Gini-post
(a) Gini index (b) Theil index (α = 1)
20 20
20 20
O O
3 3
20 20
20 20
O O
4 4
20 20
20 20
O O
5 5
Figure 8: Lorenz curve (February 2020)
26
Figure 9: Lorenz curve (April 2020)
27
Figure 10: Movements of the Lorenz curve. Pre-benefits, 2020
28
Figure 11: Movements of the Lorenz curve. Post-benefits, 2020
29
20 .3 .35 .4 .45 .5 .55 .6 20 .3 .35 .4 .45 .5 .55 .6
19 19
O O
2 2
20 20
19 19
O O
3 3
20 20
19 19
O O
4 4
20 20
19 19
O O
5 5
Figure 12: Evolution of the Gini coefficient by gender
Gini-pre ... ...
30
(b) Female (a) Male
... ...
20 20
20 20
O O
2 2
Gini-post
20 20
20 20
O O
(a) Males (b) Females 3 3
20 20
20 20
O O
4 4
20 20
20 20
O O
5 5
20 .3 .35 .4 .45 .5 .55 .6 20 .3 .35 .4 .45 .5 .55 .6 20 .3 .35 .4 .45 .5 .55 .6
19 19 19
O O O
2 2 2
20 20 20
19 19 19
O O O
3 3 3
20 20 20
19 19 19
O O O
4 4 4
Figure 13: Evolution of the Gini coefficient by age
20 20 20
19 19 19
O O O
5 5 5
Gini-pre ... ... ...
31
... (c) From 50 to 64 ... (b) From 30 to 49 ... (a) From 16 to 29
20 20 20
20 20 20
O O O
2 2 2
Gini-post
20 20 20
20 20 20
O O O
3 3 3
20 20 20
20 20 20
O O O
4 4 4
(a) From 16 to 29 years old (b) From 30 to 49 years old (c) From 50 to 64 years old
20 20 20
20 20 20
O O O
5 5 5
0 5 10 15 20
Ba
le
ar
ic
Is
l an
ds
C
an
ar
y
Is
C l an
om ds
m
un
ity
of
M
ad
rid
C
at
al
on
ia
La
R
io
ja
Va G
al
l en ic
ia
ci
an
C
om
m
un
Ba ity
sq
ue
C
ou
nt
rie
s
Pre-transfer
An
C da
32 om lu
m si
un a
ity
of
N
av
C ar
ra
as
til
la
-L
a
M
an
ch
a
Ar
ag
on
C
as
til
Post-transfer e
an
d
Le
R ón
eg
io
n
of
Pr M
in ur
ci
ci
p a
al
ity
of
Figure 14: Changes in the Gini coefficient by regions
As
tu
r ia
s
C
an
ta
b ria
Ex
tre
m
ad
ur
a
20 .3 .35 .4 .45 .5 .55 .6 20 .3 .35 .4 .45 .5 .55 .6 20 .3 .35 .4 .45 .5 .55 .6
19 19 19
O O O
2 2 2
20 20 20
19 19 19
O O O
3 3 3
20 20 20
19 19 19
O O O
4 4 4
20 20 20
19 19 19
O O O
5 5 5
Gini-pre
... ... ...
... ... ... (a) Spain
20 20 20
20 20 20
O O O
2 2 2
Figure 15: Evolution of the Gini coefficient by country of origin
(e) High income
20 20 20
20 20 20
O O O
(c) Upper middle income
3 3 3
20 20 20
20 20 20
O O O
4 4 4
20 20 20
20 20 20
O O O
Gini-post
5 5 5
33
20 .3 .35 .4 .45 .5 .55 .6 20 .3 .35 .4 .45 .5 .55 .6
19 19
O O
2 2
20 20
19 19
O O
3 3
20 20
19 19
O O
4 4
20 20
19 19
O O
5 5
... ...
... ...
20 20
20 20
O O
2 2
(d) Low income
20 20
20 20
O O
(b) Lower middle income
3 3
20 20
20 20
O O
4 4
20 20
20 20
O O
5 5
(a) Spain (b) Lower middle income (c) Upper middle income (d) Low income (e) High income
.6
.55
.5
.45
.4
.35
.3
2 3 4 5 ... ... 2 3 4 5
O O O O O O O O
19 19 19 19 20 20 20 20
20 20 20 20 20 20 20 20
Pre - Natives Post - Natives
Pre - Foreign-born Post - Foreign-born
Figure 16: Evolution of the Gini coefficient: natives versus foreign born
(a) Natives (b) Foreign born
34
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