Transcript The vulnerability of indebted households during the

The vulnerability of indebted households
during the crisis:
evidence from the euro area
L.Bartiloro, V.Michelangeli and C.Rampazzi
“The Bank of Italy’s Analysis of
Household Finances”
Rome, December 4th 2015
Outline
•
The financial crisis has shown that households’ financial
vulnerability plays a pivotal role for financial stability.
•
We analyse the characteristics that are correlated with
vulnerability for the euro area households.
•
We focus a standard indicator of financial vulnerability and we
also compare our findings with those obtained using with the
other indicators.
•
Policy implications of our results
What’s new
Related literature:
•
Household vulnerability in a country over time (IMF, 2011, 2012, 2013;
ECB 2013b; Magri and Pico, 2012; Michelangeli and Pietrunti, 2014)
•
Household vulnerability in a country with different indicators of financial
fragility or over-indebtedness (Bartiloro and Rampazzi, 2013; D’Alessio
et al., 2013, among others).
•
Household indebtedness in the euro area (Bover et al., 2013)
•
Household mortgage choice in the euro area controlling for macro and
financial variables (Ehrmann and Ziegelmeyer, 2013)
In this paper:
•
Focus on household vulnerability in the euro area countries (Financial
stability)
•
We also evaluate how the main mortgage characteristics are related to
household vulnerability (Policy implications)
Household vulnerability
• Standard indicator of vulnerability:
DSR=Debt service payments/Income
• Low income households may find it difficult to face other general
expenses and to accumulate savings in order to offset unexpected
negative economic shocks
• A household is vulnerable if
1) its DSR ≥ 40 per cent and
2) its income is below the median of the population
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Household Finance and Consumption Survey
• Fist harmonized survey on households’ wealth, debt, income and
consumption in the euro area
• It is voluntary conducted by national central banks of the euro area
member states and coordinated by the ECB
• Data are so far available for just one wave and they mostly refer to
year 2010.
• The total sample of the first edition consists of about 62,000
households and covers 15 euro area countries
• We excluded Finland from our sample because of lack of
information on debt service and Slovenia because of the very limited
sample size. Therefore, our euro area aggregate includes 13
countries.
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Indebted households: a comparison in the euro area
Vulnerable households: a comparison in the euro area
Share of debt held by vulnerable households
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The model
Main regression:
Pr vuln = 1 = β0 + β1 𝑠𝑒𝑥 + β2 𝐻𝑀𝑅 + β3 𝑎𝑔𝑒 + β4 𝑒𝑑𝑢𝑐 + β5 𝑁𝑐ℎ𝑖𝑙𝑑 +
β6 𝑁𝑒𝑚𝑝𝑙 + β7 𝑤𝑜𝑟𝑘 + β8 𝑓𝑖𝑛𝑎𝑠𝑠 + β9 𝑚𝑜𝑟𝑡𝑔 + β10 𝑐𝑜𝑛𝑠𝑐𝑟𝑒𝑑 + β11 Yj + β12 Lij
Where:
sex: gender of the head of the household
HMR: dummy variable equal to 1 if the household owns her main residence
age: age class of the head of the household (<35, 35-44, 45-54, 55-64, >=65)
educ: education level of the head of the household (low, middle, high)
Nchild: n. of dependent children (0, 1, 2, or greater than 2)
Nempl: n. of hh members in employment (0, 1, 2, or greater than 2)
work: work status (employee, self-employed, unemployed, retired, other)
finass: quintile of financial assets based on each country distribution
mortg: dummy equal to 1 if the household has only mortgage debt
conscred: dummy equal to 1 if the household has only consumer credit
Yj : controls (Model1: country dummies, Model 2: macro variables, Model 3:
banking variables, Model 4: macro and banking)
Lji :variables related to mortgages on the home main residence (LTV, number
of mortgage loans, refinancing, mortgage length, type of interest rate)
Odds ratios
• We estimate the odds of being vulnerable as a function of sociodemographics variables and mortgage characteristics.
• Odds of being vulnerable=
probability of being vulnerable
probability of not being vulnerable
• For any explanatory variable, an estimated coefficient higher than 1
implies that the probability of being vulnerable is higher for the
category defined by the explanatory variable with respect to the
baseline household; while a coefficient smaller than 1 implies that
the probability of being vulnerable is lower than for the baseline
household.
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Baseline household
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•
•
•
•
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the head of the household is aged between 35 and 44
employee
medium level of education
two members in employment
no dependent children
first quintile of financial assets
both a mortgage and loans for consumption purposes.
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Benchmark logistic regressions
Sex: female
Tenure of house: non owner
Age <35
Age 45-54
Age 55-64
Age ≥65
Education: Low (0-2 ISCED)
Education: High (5-6 ISCED)
N° of children: 1
N° of children: 2
N° of children: 3+
N° of empl. members: 0
N° of empl. members: 1
N° of empl. members: 3+
Working status: Self-employed
Working status: Unemployed
Working status: Retired
Working status: Other
Financial asset quintile: 2°
Financial asset quintile: 3°
Financial asset quintile: 4°
Financial asset quintile: 5°
Only mortgage debt
Only non-mortgage debt
Model 1
Model 2
Model 3
Model 4
Odds ratio
Odds ratio
Odds ratio
Odds ratio
1.15
0.88
1.34
1.06
1.04
0.72
1.41
0.75
1.27
1.62
2.11
3.95
1.93
1.05
2.67
1.18
0.75
1.25
0.72
0.47
0.43
0.36
0.73
0.23
**
**
***
***
***
***
*
***
***
***
*
***
Inflation
GDP growth rate
Unemployment rate
1.15
0.82
1.35
1.07
1.04
0.76
1.48
0.78
1.29
1.63
2.12
3.85
1.92
1.07
2.57
1.25
0.72
1.30
0.69
0.46
0.42
0.37
0.74
0.23
0.55
0.76
1.02
***
**
***
***
***
***
**
***
***
***
*
***
***
***
No. of observations
20,603
***
*
***
***
***
*
***
20,603
20,603
Growth rate of bank loans
yes
0.03 ***
***
***
***
***
no
0.19 ***
Bank deposits/GDP
Constant
***
1.01
1.00
1.03
no
0.03
Bank concentration
Country fixed effects
1.14
0.80
1.33
1.03
1.02
0.81
1.61
0.80
1.34
1.65
2.02
3.80
1.92
1.08
2.53
1.36
0.67
1.36
0.72
0.48
0.43
0.37
0.73
0.22
**
***
**
***
1.16
0.84
1.32
1.07
1.04
0.75
1.45
0.76
1.28
1.63
2.05
3.71
1.92
1.07
2.68
1.26
0.75
1.33
0.70
0.46
0.43
0.36
0.74
0.24
0.65
0.87
1.04
1.01
1.00
1.00
no
0.05
**
**
***
***
***
***
**
***
***
***
*
***
**
*
**
**
***
***
20,603
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Benchmark logistic regressions
only hh with mortgage
Model 1
Odds ratio
10% ≥ LTV <30%
30% ≥ LTV <50%
50% ≥ LTV <80%
LTV ≥80%
N° of mortgage loans
Refinancing
Rate on HMR mortgage: fixed
HMR mortgage lenght
Only mortgage debt
Inflation
GDP growth rate
Unemployment rate
Bank concentration
Bank deposits/GDP
Growth rate of bank loans
Country fixed effects
Constant
No. of observations
1.83
1.76
2.43
2.92
0.98
1.16
1.07
0.99
0.74
***
**
***
***
*
yes
0.02 ***
8,583
Model 2
Odds ratio
Model 3
Odds ratio
Model 4
Odds ratio
1.81
1.69
2.28
2.84
0.99
1.24
0.87
0.99
0.76
0.75
0.87
1.04
1.82
1.69
2.29
2.77
0.96
1.28
0.80
0.99
0.74
1.86 ***
1.80 **
2.51 ***
3.12 ***
0.99
1.21
0.94
0.99
0.75
0.85
1.00
1.08 ***
1.02 ***
1.00 ***
1.00
no
0.01 ***
8,583
**
**
***
***
**
**
***
***
*
**
no
0.03 ***
8,583
1.01
1.00 ***
1.04 *
no
0.01 ***
8,583
All the results with respect to the demographics are confirmed
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Other indicators of vulnerability
• debt to income ≥3: long-run ability of repaying accumulated debt
given the future stream of income
• net wealth < 0: long-run ability of repaying accumulated debt given
the accumulated savings
• income - debt payments < food expenses: short-run ability to face
expected expenses
• financial assets < 2 months of income: short-run ability to face
unexpected expenses
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Other indicators of vulnerability (Cont.)
Heterogeneity across euro area countries ….
memo:
hh with
any debt
Austria
Belgium
Cyprus
France
Germany
Greece
Italy
Luxembourg
Malta
Netherland
Portugal
Slovakia
Spain
EURO AREA
35.6
44.8
65.4
46.9
47.4
36.6
25.2
58.3
34.1
65.7
37.7
26.8
50.0
43.4
DSR>=40% & Debt/income
income<median
>=3
2.8
6.0
13.6
3.3
2.2
6.7
4.9
5.1
1.2
7.8
10.2
6.7
10.9
5.0
9.2
15.0
31.5
12.4
11.3
12.2
11.0
20.6
10.5
35.2
28.3
11.4
24.2
16.0
Net wealth<0
14.8
6.0
4.4
8.3
15.7
6.9
5.7
6.5
2.4
17.7
6.8
4.4
6.9
10.9
Income-debt
Liquid asset< N° indicators
payments<food 2 months of >Euro area
expenses
income
mean
2.9
8.0
13.6
1.2
2.3
5.5
5.1
3.2
3.2
8.7
9.1
6.0
6.6
4.1
36.8
44.3
56.8
47.5
49.0
72.4
50.5
44.1
20.8
42.9
53.8
70.7
57.0
49.9
1
2
4
0
1
3
2
2
0
4
4
3
4
… but the main results are confirmed: being self-employed, a reduction
in the n. of employed members, lower financial assets increase the
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odds of vulnerability in a significant way.
Building a unique indicator of vulnerability
•
Combine the different indicators of vulnerability
•
Weights obtained using the principal component analysis (First
component)
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We obtain a continuous variable (OLS regression)
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Provide a synthetic representation of vulnerability
Vulnerability index (PCA weights):
OLS regression
Sex: female
Tenure of house: non owner
Age <35
Age 45-54
Age 55-64
Age ≥65
Education: Low (0-2 ISCED)
Education: High (5-6 ISCED)
N° of children: 1
N° of children: 2
N° of children: 3+
N° of empl. members: 0
N° of empl. members: 1
N° of empl. members: 3+
Working status: Self-employed
Working status: Unemployed
Working status: Retired
Working status: Other
Financial asset quintile: 2°
Financial asset quintile: 3°
Financial asset quintile: 4°
Financial asset quintile: 5°
Only mortgage debt
Only non-mortgage debt
Model 1
Model 2
Model 3
Coef.
Coef.
Coef.
0.07
-0.08
0.19
-0.01
-0.11
-0.19
0.10
-0.05
0.03
0.11
0.18
0.55
0.25
-0.08
0.37
0.09
-0.19
0.00
-0.18
-0.32
-0.35
-0.40
-0.18
-0.60
*
*
***
*
**
**
**
**
***
***
***
**
***
***
***
***
***
***
Inflation
GDP growth rate
Unemployment rate
0.08
-0.08
0.19
-0.02
-0.11
-0.16
0.12
-0.03
0.03
0.09
0.16
0.53
0.25
-0.06
0.35
0.13
-0.21
0.06
-0.20
-0.33
-0.35
-0.37
-0.17
-0.62
-0.32
-0.12
0.00
**
**
***
*
*
***
*
*
***
***
***
***
***
***
***
***
***
***
***
***
Bank concentration
Bank deposits/GDP
Growth rate of bank loans
Country fixed effects
Constant
No. of observations
yes
0.23 ***
20,597
no
1.06 ***
20,597
0.07
-0.09
0.18
-0.02
-0.10
-0.13
0.15
-0.03
0.04
0.10
0.14
0.52
0.25
-0.06
0.35
0.15
-0.23
0.08
-0.19
-0.32
-0.35
-0.39
-0.17
-0.62
**
**
***
*
***
*
***
***
***
***
***
***
***
***
***
***
0.005 ***
0.000 ***
0.003
no
0.06
20,597
Model 4
Coef.
0.08
-0.09
0.18
-0.01
-0.11
-0.16
0.11
-0.04
0.03
0.10
0.15
0.51
0.25
-0.07
0.37
0.13
-0.19
0.06
-0.20
-0.34
-0.36
-0.41
-0.17
-0.60
-0.30
-0.12
0.01
0.004
0.001
-0.013
no
0.76
**
**
***
*
*
**
*
*
***
***
***
**
***
***
***
***
***
***
***
***
**
***
***
**
***
20,597
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Vulnerability index (PCA weights):
OLS regression - only hh with mortgage
Model 1
10% ≥ LTV <30%
0.18
30% ≥ LTV <50%
0.28
50% ≥ LTV <80%
0.70
LTV ≥80%
1.00
N° of mortgage loans
0.05
Refinancing
0.15
Rate on HMR mortgage: fixed -0.04
HMR mortgage lenght
0.00
Only mortgage debt
-0.13
Inflation
GDP growth rate
Unemployment rate
Bank concentration
Bank deposits/GDP
Growth rate of bank loans
Country fixed effects
yes
Constant
-0.47
No. of observations
8,582
***
***
***
***
**
**
Model 2
0.18
0.28
0.69
1.01
0.05
0.17
-0.15
0.00
-0.12
-0.14
-0.07
0.01
no
-0.10
8,582
***
***
***
***
*
***
*
Model 3
0.19
0.29
0.71
1.02
0.05
0.18
-0.17
0.00
-0.13
***
***
***
***
**
***
**
*
0.00
0.00
0.02
no
-0.60
8,582
**
***
*
***
All the results with respect to the demographics are confirmed
Model 4
0.19
0.29
0.72
1.03
0.05
0.16
-0.12
0.00
-0.12
-0.13
-0.01
0.03
0.01
0.00
0.00
no
-0.67
8,582
***
***
***
***
*
**
**
***
***
***
*
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Conclusion
• Large heterogeneity in the euro area with respect to the share of
indebted households and of the debt at risk.
• Some common aspects relevant for financial stability:
1) Strong correlation between self-employment and vulnerability,
which may suggest the implementation of some policy initiatives to
support the liquidity of self-employers (financial education, efficient
mortgage insurance market)
2) Higher LTV is associated with higher vulnerability, while no effect
has been detected for the number of mortgages or their duration.
Main policy implication: this analysis provides support for the
introduction of limits to LTV ratios for macroprudential
purposes.
• Our benchmark indicator is a good measure of financial distress in
the household sector. It is therefore crucial that macro-prudential
authorities work on a unique, correct and exhaustive way of using
the DSR in order to identify vulnerable households.
Thank you
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