DG ECONOMICS, STATISTICS AND RESEARCH – BANCO DE ESPAÑA

HOUSEHOLD INDEBTEDNESS IN THE SPANISH SURVEY

OF HOUSEHOLD FINANCES AND THE CENTRAL CREDIT

REGISTER: 

A COMPARATIVE ANALYSIS

Olympia Bover, Laura Crespo and Sandra García-Uribe

Presenter: Laura Crespo

Survey of Household Finances Unit

Microeconomic Studies Division

2022 FCSM CONFERENCE

27th October 2022
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 2

THIS PAPER

• The goal of this study is to evaluate the quality of household indebtedness information provided by the Spanish

Survey of Household Finances (EFF) at the micro level.

• The EFF collects detailed information on household wealth, debt, income and expenditure for a representative

sample of householdsfrom 2002, every three years.

• To compare with, we use admin data from the Central Credit Register (CIR) of Spain which registers monthly

information on all outstanding debts (over 6,000 euros) that individuals have with financial institutions.

• This exercise is useful to:

• Document whether there exist important differences between the debt data provided by each source at the

household level (micro approach).

• Learn about potential bias in how households report information on their debts in the survey.

• Learn about which household characteristics are associated to those biases or discrepancies.
DG ECONOMICS, STATISTICS AND RESEARCH – BANCO DE ESPAÑA

OUTLINE

3

1. Introduction

2. Linkage and Comparability methodology

3. Indebtedness measures

4. Empirical analysis of extensive and intensive margin discrepancies: 

5. Conclusions
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 4

• We build a household-interview date level database with comparable measures of indebtedness across EFF and CIR.

We pool data from EFF 2002, 2005, 2008 and 2011 and CIR 2002-2011.

• To link both databases at the household level, we use a blind linkage based on:

• Scrambled ID numbers for the individuals registered at the interviewed household addresses according to the

population municipality registers (provided by Spain-Statistics).

• Scrambled ID numbers from the Central Credit Register.

• Using the scrambled (based on the same algorithm) numbers from both sources and the date of the interview we

merge the individual debt data from CIR and the households who participated in the survey.

INTRODUCTION
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 5

CIR contains: Scrambled

numbers of risk holders at 

financial institutions.

ID codes for

people having

risks at 

financial

institutions

THE BLIND LINKAGE PROCEDURE

Spain-Statistics sends

BdE receives: Scrambled

numbers for people

registered at the EFF 

participant addresses

ID numbers for

individuals

registered at 

EFF HH 

addresses at 

the time of the

sample

selection

Financial institutions send to CIR

MERGE BY SCRAMBLED NUMBERS AND DATE
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 6

• This linkage is not free of problems: for some households we find a discrepancy between the number of household

members reported by the survey and the registered inhabitants(for which we have the ID numbers).

• Given this:

• We quantify the relative importance of these discrepancies for each wave.

• We compare indebtedness measures from EFF and CIR for a comparable sample of households with no

discrepancies between HH members and registered inhabitants.

• We perform our empirical analysis using this sample.

THE BLIND LINKAGE PROCEDURE
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 7

• Different sources of discrepancies:

1. For some regions (Navarre and Basque Country) there is no available municipal register data for the years 2002

and 2005:

• LINKABLE HH: 21,396 / 23,415  adjust sample weights.

2. Municipality registers do not always have updated and exact register of people living at each particular address.

3. The definition of a household: the EFF measures the number of members belonging to the household, which does

not need to be equal to the number of people registered/living at a particular address:

• E.g. there can be more than one household registered or living at an address and the survey only interviews

one per address.

4. The number of inhabitants according to the register, even if coinciding at the first interview date with household

members, can differ in succesive survey waves due to changes in household composition across time.

THE BLIND LINKAGE PROCEDURE
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 8

THE BLIND LINKAGE PROCEDURE

(percentage points) Total Panel Refreshment

At least four more 1.3 1.3 1.3 

Three more 3.4 4.1 2.9 

Two more 9.4 10.8 8.3 

One more 13.9 14.3 13.7 

No difference 42.8 36.0 48.0 

One less 16.7 18.5 15.2 

Two less 7.5 9.5 6.0 

Three less 2.9 3.4 2.5 

At least four less 2.1 2.1 2.0 

Number of 

observations 21,396 9,319 12,077

Household members reported in the EFF minus registered inhabitants

Distribution of cases (all waves together):

• To control for the discrepancies between household members and registered inhabitants, we analyze

the subset of households with no discrepancy between the number of adult household members and

registered inhabitants.

• This is a comparable sample and covers 50.75% of the original sample of linkable households.
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 9

• From the EFF we use all the following debts that households report to hold with all type of institutions (except for

debts held with non-financial corporations and with relative and friends).

• Mortgage loan — Other collateralised loans — Personal loan — Credit line — Instalment loan — Current account 

overdrafts — Advances — Reverse mortgage — Credit card debt

• From the CIR side, five variables are used to define each risk: type, currency, maturity, guarantee, and default status.

• Based on these variables, we select debts classified as “finance credit or credit transferred to third parties” and 

debts associated to “renting and leasing” since they are the comparable ones to those in the EFF.

COMPARABLE MEASURES OF DEBT
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 10

• To control for the presence of differences between HH members and inhabitants we provide the proportions of 

indebted HH for the comparable, refreshment and full samples separately:

• The difference between EFF and CIR is 5.5 pp in the “comparable” sample, much lower than the difference observed

in the “total” sample of linkable households (12.7 pp).

• Proportions in the “total” sample are affected by members discrepancies with the inhabitants register.

EXTENSIVE MARGIN RESULTS: PERCENTAGE OF INDEBTED HOUSEHOLDS

Total Comparable Refreshment

EFF 37.0 36.3 37.0 

(0.33) (0.46) (0.44) 

CIR 49.7 41.8 47.9 

(0.34) (0.47) (0.45) 

Test statistic -36.0 -13.3 -18.1 
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 11

Mortgage debt Other debt

Total Comparable Refreshment Total Comparable Refreshment

EFF 25.2 25.8 25.6 19.5 18.5 18.9 

(0.30) (0.42) (0.40) (0.27) (0.37) (0.36) 

CIR 37.0 29.9 34.8 29.1 23.5 27.6 

(0.33) (0.44) (0.43) (0.31) (0.41) (0.41) 

Test statistic -37.6 -11.9 -19.4 -27.0 -11.2 -14.6 

• Sample proportion of indebted households by type of debt:

Note: In CIR data, “mortgage debt” is defined as financial credit that is fully collateralised and with a maturity of over five years. 

• In the comparable sample, the difference betwen CIR and EFF is 4.1 pp for mortgage debts and 5 pp for otherdebt.

EXTENSIVE MARGIN RESULTS: PERCENTAGE OF INDEBTED HOUSEHOLDS BY TYPE OF DEBT
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA 12

• We analyze household indebtedness differences between EFF and CIR by demographic and debt characteristics.

• These differences are of two types: 

1. Householdsindebted in CIR but not in EFF.

2. Householdsindebted in EFF but not in CIR.

• For each type, we estimate logit average marginal effects (AME) of a list of demographic and debt characteristics

on the probability of discrepancy.

EMPIRICAL ANALYSIS OF INDEBTEDNESS DIFFERENCES
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 13

HH INDEBTED IN CIR BUT NOT IN EFF

Comparable

Type of debts (base = only 

mortgages)

Mortgages and other -0,05***

(0.02)

Only other debts 0,06**

(0.02)

Solidarity debt w non-member 0,08***

(0.03)

Demographic characteristics

Education of reference person

Higher secondary education -0,01 

(0.02)

University education -0,01 

(0.02)

The omission of indebtedness statusin the survey is:

• Less likely when households have an important source of debt (mortgages) and also when they have various different types of debts,

maybe because they are more aware of their debts.

• More likely when households share a debt with non-hh members.

• More likely for elder households.

• More likely when the reference person is a woman.

Age of the reference person

Between 35 and 44 years old 0,02 

(0.02)

Between 45 and 54 years old 0,08***

(0.03)

Between 55 and 64 years old 0,16***

(0.03)

Between 65 and 74 years old 0,33***

(0.04)

Over 75 years old 0,40***

(0.07)

Gender of the reference person

Women 0,05***

(0.02)

Comparable
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 14

Percentile of the wealth distribution

Between 20 and 40 -0,23***

(0.05)

Between 40 and 60 -0,25***

(0.04)

Between 60 and 80 -0,24***

(0.05)

Between 80 and 90 -0,21***

(0.05)

Between 90 and 100 -0,25***

(0.05)

Percentile of the income distribution

Between 20 and 40 -0,07 

(0.04)

Between 40 and 60 -0,13***

(0.04)

Between 60 and 80 -0,13***

(0.05)

Between 80 and 90 -0,12**

(0.05)

Between 90 and 100 -0,10**

(0.05)

HH INDEBTED IN CIR BUT NOT IN EFF

Comparable

The omission of indebtedness status in the survey

is:

• More likely among the poorest HHs.

• Less likely for higher income HHs.
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 15

Number of adults

Two -0,01 

(0.02)

Three 0,05 

(0.03)

Four or more 0,01 

(0.03)

At least one self-employed 0,01 

(0.02)

Wave (baseline = wave 2002)

2005 -0,08***

(0.02)

2008 -0,06**

(0.03)

2011 -0,09***

(0.02)

Comparable

Total observations 4,539

Dependent variable mean 0.20

HH INDEBTED IN CIR BUT NOT IN EFF

Comparable

The omission of indebtedness status in the survey

is:

• More likely for households in EFF 2002 than

subsequent waves (learning effect). 
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 16

HH INDEBTED IN EFF BUT NOT IN CIR

Type of debts (base = only mortgages)

Mortgages and other -0,15***

(0.02) 

Only other debts 0,09***

(0.03) 

Debts under 6000 euros 0,27***

(0.03) 

Comparable

The presence of debts in EFF which are not in CIR is:

• More likely for households with only non-mortgage debts.

• More likely for debts under six thousand euros.

• Less likely for larger households.

• More likely when reference person is a woman.

• More likely for households in EFF 2002 than subsequent waves

(learning effect). 

Gender of the reference person

Women 0,05***

(0.02) 

Number of adults

Two -0,05**

(0.03)

Three -0,10***

(0.03)

Four or more -0,11***

(0.03)

At least one self-employed 0,03 

(0.02)

Wave (baseline = wave 2002)

2005 -0,09***

(0.02)

2008 -0,07***

(0.02)

2011 -0,06**

(0.02)

Total observations 3,936

Dependent variable mean 0.18

Comparable
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 17

We have also explored other factors which could affect the chances of discrepancy between CIR and EFF, conditional

on demographic and debt factors:

• Reference period: No significant effect for the probability that the hh is indebted in CIR but not in EFF. 5.3 pp

larger probability of an indebted hh in EFF but not in CIR if hh interviewed the last week and 4.7 pp larger if

interviewed the third week wrt to the first week of the month.

• Time restrictions to answer the survey: No significant effects for the reference person working more than 45

hours a week or for the presence of children under 16 years old in the HH.

• Other household composition effects: siblings that no longer belong to the household or households where a

sibling is more than 45 years are not significant characteristics.

• Robustness check of the kind of institution providing the loan has been also implemented. Results remained

unchanged.

ROBUSNESS CHECKS: OTHER POTENTIAL FACTORS
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 18

• We also look into the differences between the amounts of outstanding debts for indebted households reported in

both sources.

• We present results in relative terms (differences relative to outstanding debt according to CIR).

INTENSIVE MARGIN DISCREPANCIES

(in percentage points) 10% pctile 25% pctile Median 75% pctile 90% pctile

All types of debts

Comparable sample -75.8 -36.7 -3.8 9.4 83.7 

Mortgage debts

Comparable sample -56.3 -18.4 -0.5 11.3 94.6 

Other debts

Comparable sample -85.6 -54.9 -10.2 12.1 95.1 

• Negative values stand for larger outstanding volumesin CIR than in EFF.

• Positive values stand for lower outstanding volumes in CIR than in EFF.

• We see that the differences are larger in relative terms for non-mortgage debt.
DG ECONOMICS, STATISTICS AND RESEARCH-BANCO DE ESPAÑA USO INTERNO 19

• We provide a micro-level comparison for debt data between the Spanish Survey of Household Finances and the CIR

register information.

• We document the presence of linkage problems from the use of population municipality registers to link both sources.

To avoid these problems, we construct a comparable sample of HHs between the municipality register and the survey

data.

• Using the comparable sample, we observe that the difference in the proportion of indebted HHs is only of 5.5 pp,

which is even smaller for mortgage debts.

• This shows that the EFF data provide a good measurement of the proportion of indebted HH and also, to a large

extent, of the outstanding amountsfor indebted HH.

• HHs’ characteristics which are more clearly associated to discrepancies between both souces are the wealth and

income level of the household, the age of the reference peson, being a woman, debt type, and the survey edition.

CONCLUSIONS
DG ECONOMICS, STATISTICS AND RESEARCH – BANCO DE ESPAÑA

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