Prepared with SEVISLIDES

National Accounts in a World of

Naturally Occurring Data:

An Application to Consumption

Gergely Buda (Barcelona GSE),

Vasco Carvalho (University of Cambridge),

Stephen Hansen (Imperial College),

Alvaro Ortiz (BBVA Research),

Tomasa Rodrigo (BBVA Research),

Jose V. Rodrıguez Mora (University of Edinburgh)

June 14, 2021

** Work in Progress! **

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Summary ß à ê

• Financial Transaction Data ßà• Evolution of Household Consumption ß• Evolution of the Channel of Payment ß• Evolution of Sector Composition ßà• Income and Consumption Inequality ßà

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Financial Transaction Data (1/2) ãß ê

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�Pre-COVID: growing inter-est, relatively small-scaledatasets.

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�COVID: explosion of datathat uses payments datafor tracking activity.

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�Post-COVID: use of newdata sources for nationalaccounts construction?

Our project: takes near-complete record of financialtransactions from BBVA and asks whether they canbe used to build national statistics.

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• Real time, high frequency Þtracking impact of shocks on national accounts.• Huge samples Þgranular measures (e.g. geography, income)

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...but Not intended for research: many potential sources ofbias and noise.

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Financial Transaction Data (2/2) âß ê

Partnership with Big Data division of BBVA Research.

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��Universe of transactions.�

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�Includes both• card transactions• transactions from/to accounts

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BBVA card transactions from 2015 Q2-2021 Q1.

Number of transactions 4,739,263,647Total volume of transactions 254,862,281,174Number of distinct customer IDs 7,224,844a

Number of ‘Active Customers’b 1,921,652

a717,514 self-employedbAt least one transaction in each quarter

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Evolution of Household Consumption ß ê

• Aim to reproduce Non-Durable consumption

• Data is ”naturally occur-ring”, it is prone to pro-duce biases and perhaps toamplify fluctuations.

• Clean data with criterialike• Removal of non-

consumption MCCs.

• Include cash withdrawals.

• Active clients filter for mini-mum of 10 transactions perquater

• Exclude self-employed.

• Weight by INE demographiccharacteristics.

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Evolution of the Channel of Payment ß à ê

Trend towards more online and less cash predates Covid.

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Evolution of Sector Composition (1/2) ãß à ê

Massive swings in composition of consumption during lockdown

In previous paper we did show:'

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That the timing of lockdownand consumption restrictions(closures, etc.) determines toa large extent the movementsof consumption.(using differences in timingof the restrictions acrossprovinces)

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That the restrictions affectedmore the pattern of consump-tion of the better-off (eval-uated at zipcode income),whose consumption decreasedmore during lockdown

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Evolution of Sector Composition (2/2) âß à ê

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Consumption of utilities is morestable

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most likely the same for hous-ing ... ongoing work

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... we have not yet computedthis for durables

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Income and Consumption Inequality (1/5) ã ò ê

Use account data linking directly the income of the customer to her expenditurein order to create distributional national accounts pre and post covid

Growth Rate of Income'

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Sample restricted to activeclients who receive at least onesource of income (salary, bene-fits or pensions) in each monthof 2017-2019.No unemployed!�

�Slight decrease in inequality�

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�In Covid times very low in-come growth (nominal).Lower growth for richer

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Income and Consumption Inequality (2/5) âã ò ê

Spending per Income groups

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�Non-Durables

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Income and Consumption Inequality (3/5) âã ò ê

Growth Rates Before Covid��

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Income grows more thanspending.

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�More equality of income.

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Spending of rich grows slightlymore than of poor.

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Income and Consumption Inequality (4/5) âã ò ê

Covid Year�

�Equalization of Consumption

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Massive decrease of consump-tion of the rich'

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... Because restrictions makethe rich unable to consume thethings they like to do: no trav-eling, no services, etc.

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�Massive Forced private savings

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Income and Consumption Inequality (5/5) â ò ê

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Prepared with SEVISLIDES

National Accounts in a World of

Naturally Occurring Data:

An Application to Consumption

Gergely Buda (Barcelona GSE),

Vasco Carvalho (University of Cambridge),

Stephen Hansen (Imperial College),

Alvaro Ortiz (BBVA Research),

Tomasa Rodrigo (BBVA Research),

Jose V. Rodrıguez Mora (University of Edinburgh)

June 14, 2021

** Work in Progress! **

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