22nd September 2020, IMF Institute for Capacity Development Lunchtime Series

Tracking the Impact of COVID-19 on Economic Inequality at High Frequency

Oriol Aspachs (CaixaBank Research), Ruben Durante (ICREA-UPF, IPEG, Barcelona GSE), Jose G. Montalvo (UPF, IPEG, Barcelona GSE),Alberto Graziano (CaixaBank Research), Josep Mestres (CaixaBank Research), Marta Reynal-Querol (ICREA-UPF, IPEG, Barcelona GSE).

Roadmap

2

1. Motivation

2. Objectives

3. Related work and contribution

4. Data & Representativeness

5. Results

6. Conclusions & Next steps

Motivation

COVID-19 has taken a heavy toll on the European economy. GDP of Spain expected toshrink by over 10% in 2020 (IMF, 2020; Bank of Spain, 2020)

Concerns that the economic impact of the pandemic may hit the most vulnerablesegments of the population disproportionately, leading to a surge in economic inequality

Governments are investing vast resources to support families' income and provide creditto firms (IMF, 2020; ILO 2020)

How effective these policies are remains unclear due to the difficulty to track economicinequality at a high frequency and with a high degree of spatial granularity

Official statistics on inequality only available at yearly frequency and often with longdelays. Not useful to allow policymakers to quickly evaluate and adjust their responses

Objectives

We propose a new methodology to track the evolution of income inequality at ahigh frequency using anonymized data from bank records

We focus on Spain and use data from CaixaBank - Spain’s second-largest bank bytotal assets and first by direct payroll deposits (i.e., 27.1%)

Data on all account holders receiving payroll payments from a private or publicemployer and/or government transfers

Overall sample includes over 3 million retail depositors

Period of interest: February to June 2020 (compared to 2019), i.e., before, during,and after national lockdown

Ongoing effort: update analysis regularly as more data become available

Related work and contribution

1. Increasing use of real-time data to track economic activity:

Data on electricity usage (Cicala, 2020)

Data on hours worked and mobility from phone records (Chen et al., 2020)

Data on payroll, benefits, and taxes (Cajner et al., 2020)

Data on credit card use, payroll, job postings, financial transactions (Chetty et al., 2020)

Contribution: first attempt to use high-frequency data to study inequality

2. Measuring inequality using real-time labor surveys (Bick-Blandin, 2020; Adams-Prassl etal., 2020)

Contribution: use administrative data to overcome the limitations of (online) surveys

3. Use of bank data to study the response of consumption to income shocks (Bick-Blandin,2020; Adams-Prassl et al., 2020)

Contribution: focus on pre- and post-tax inequality using data on payroll and subsidies

Data

SAMPLE DEFINITION

Individuals aged 16 to 64 years old / not self-employed / positive wages or benefits previous 2 months

Salaries and unemployment benefits in month t: from the 16th of month t to the 15th of month t+1

Restrictions (1) one account holder or multiple account co-holders but only one

employer paying-in wages (around 5% of initial sample excluded) (2) using the bank account for usual financial transactions (0.7% of initial

sample excluded)

No other filters applied (income level, etc.)

About 3 million individuals followed each month

Sample Representativeness

Net wages comparison: CaixaBank vs. Official Wage Survey (EES) 2014

CABK EES

P10 564.66 577.01

P25 958.59 956.7

P50 1273.6 1274.45

P75 1777.06 1752.76

P90 2395 2380

EES 2014(net wages)

CABK

Gini Index 28.8 30.7

P90/P50 1.87 1.89

P50/P10 2.21 2.25

P90/P10 4.12 4.26

Sample Representativeness

(%)EES

(2014)EPA

(4T 19)CABK(2020)

Gender

Male 52 52 54

Female 48 48 46

Age

15-19 - 0.8 1

20-29 12 14.5 18

30-39 31 24.6 25

40-49 30 30.5 28

50-59 21 23.3 21

60+ 5 6.0 7

Sample characteristics comparison:CaixaBank sample (CABK) vs. Official Wage Survey (EES) and Labour Force Survey (EPA)

Impact of COVID-19 on labour earnings

Changes in labor earnings (pre-tax): April vs. Feb 2020 - April vs. Feb 2019

Impact of COVID-19 on labour earnings

Changes in labor earnings: April vs. Feb 2020 - April vs. Feb 2019

From 900€ to 1000€ (25th percentile) From 2900€ to 3000€ (95th percentile)

Impact of COVID-19 on labour earnings

Changes in labour earnings: April vs Feb (diff-in-diff) - Total

Impact of COVID-19 on labour earnings

Changes in labour earnings: April vs Feb (diff-in-diff) - Total

From 900€ to 1000€ (25th percentile) From 2900€ to 3000€ (95th percentile)

Impact of COVID-19 on Economic Inequality

Gini IndexLevel (0-100)

30

35

40

45

50

55

60

feb.-19 mar.-19 abr.-19 may.-19 jun.-19 feb.-20 mar.-20 abr.-20 may.-20 jun.-20

Pre-transfers Post-transfers

Importance of seasonalityadjustments

Source: CaixaBank Research

Impact of COVID-19 on Economic Inequality in Spain

30

35

40

45

50

55

60

feb.-20 mar.-20 abr.-20 may.-20 jun.-20

Pre-transfers Post-transfers

Gini Index (seasonally-adjusted)Level (0-100)

Note: seasonally-adjusted data

Source: CaixaBank Research

Economic Inequality by Age

0

2

4

6

8

10

12

14

16

18

20

16-29 30-49 50-64 16-29 30-49 50-64

Mar-20 Apr-20 May-20 Jun-20

Monthly Evolution of the Gini IndexChange with respect to February Level

Pre-transfers Post-transfers

Note: seasonally-adjusted data

Source: CaixaBank Research

Economic Inequality by Country of Birth

0

2

4

6

8

10

12

14

16

18

Native-born Foreign-born Native-born Foreign-born

Mar-20 Apr-20 May-20 Jun-20

Monthly Evolution of the Gini IndexChange with respect to February Level

Pre-transfers Post-transfers

Note: seasonally-adjusted data

Source: CaixaBank Research

Economic Inequality in Spain by region

0 5 10 15 20

April June

0 5 10 15 20

Balearic Islands

Canary Islands

Catalonia

Murcia

Basque Countries

Valencian Community

Galicia

Community of Madrid

Andalucia

La Rioja

Aragon

Castille and Leon

Castilla-La Mancha

Cantabria

Community of Navarra

Principality of Asturias

Extremadura

April June

Gini IndexChange with respect to February Level Pre-transfers Post-transfers

Note: seasonally-adjusted data

Source: CaixaBank Research

Inequality tracker

New inequality tracker available on CaixaBank Research website:

Tracking economic inequality at high frequency and the impact of social transfers

Interactive graphs and the possibility to download data

Access to published articles

Direct access from the main menu at www.caixabankresearch.com

Agenda

Decompose changes in inequality by age, gender and country of birth

Study impact of unlocking down the economy using the time variation between geographical areasand sectors

Explore the relationship between earnings inequality and consumption (linking earnings data with expenditure data) :

• How public subsidies affect households propensity to consume

• How uncertainty has affected consumption of those individuals whose employment or earnings has not been affected by the crisis

Explore the spatial dimension of inequality (using data from electronic payments and POSs):

• Study how the pandemic affected mobility and the geographical distribution of consumption

• Key to understand how loss of earnings from residents in a specific area spreads to other areas

Conclusions

COVID-19 has produced a massive increase in pre-tax income inequality

Most of it has been driven by job losses and wage cuts to low income earners

Government intervention has been effective at containing inequality

Reopening of economic activity has reduced inequality, especially among the most vulnerable workers (young, foreign-born)

New reliable methodology to track economic inequality in quasi-real time

Useful to evaluate policy responses and adapt them to the evolution of events and specific local needs

www.caixabankresearch.com

ANNEX

Sample Representativeness

Lorenz Curves