Name Content Type Application Characteristics of the loan contract
NPL Sept-2022 Is the loan non-performing in September 2022? cat. basel. an.
Moratorium type Participation of the loan in the general moratorium: left before the end of the pro-
gramme, left at the end of the programme, did not participate in the programme cat. basel. an.
Morat. spells Has the loan entered the general moratorium at least twice? (We only apply the
products of this variable with the bank fixed effects.) cat. basel. an.
NPL Oct-2021 Is the loan non-performing in October 2021? cat. basel. an.
Previous FX loan
It takes the value of 1 if the loan was foreign currency denominated previously, 2 if the debtor ever had another foreign currency denominated loan, 3 if the loan was foreign currency denominated previously and the debtor had another foreign currency denominated loan, 0 otherwise.
cat. basel. an.
Net transfer Difference in net present values of cash flows regarding the loan contract from full participation and no participation in the general moratorium using a 3 per cent
discount rate, as a percentage of the outstanding debt in October 2021. cont. rob. ch.
Remaining maturity Remaining maturity in October 2021, unit of measure is month disc. rob. ch.
Loan type Loan type: housing, home equity, prenatal baby support, personal, vehicle, hire
purchase, overdraft, credit card, other cat. basel. an.
Delinquency Delinquency in October 2021, unit of measure is day disc. basel. an.
No. of debtors Number of debtors in the loan contract, its highest value is 11. disc. basel. an.
Int. rate period Interest rate period, its values are the following. 1: below 12 months, 2: 12 months, 3: between 12 and 60 months, 4: 60 months, 5: between 60 and 120 months, 6: 120
months, 7: between 120 and 240 months, 8: 240 months, 9: above 240 months cat. rob. ch.
Debt Outstanding debt in October 2021, unit of measure is HUF million cont. basel. an.
Interest rate Applicable interest rate in October 2021, unit of measure is per cent cont. basel. an.
Fixed effects
Year of contr. Year of contracting disc. basel. an.
Bank Credit institution ID cat. basel. an.
District District of the primary borrower’s residence cat. basel. an.
Settlement type Settlement type of the primary borrower’s residence. There are 5 categories: commu-nities, large commucommu-nities, towns and districts in the capital, county seats and cities
with county rights, other. cat. basel. an.
Note: Abbreviations: category: cat.; discrete: disc.; continuous: cont.; baseline analysis: basel. an.;
robustness check: rob. ch. Category variables are discrete variables whose finite values are used to construct indicator variables with two possible values. The variable takes the value of 1 if the answer to the yes-or-no question in the column “Content” is “yes” and 0 if the answer is “no”.
Table 4
Main results of extended linear probability models estimated on the same sample
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 Moratorium type
(reference: never in morat.)
Dropped out at the end 0.0787*** 0.0686*** 0.0386*** 0.0233***
Voluntarily left –0.0060*** 0.0014*** –0.0019*** –0.0068***
Moratorium intensity (reference: 0%)
0–10% 0.0190*** 0.0052*** 0.0010 –0.0018***
10–20% 0.0221*** 0.0083*** 0.0042*** 0.0010
20–30% 0.0185*** 0.0058*** 0.0048*** 0.0013*
30–40% 0.0236*** 0.0109*** 0.0068*** 0.0018**
40–50% 0.0302*** 0.0144*** 0.0080*** 0.0038***
50–60% 0.0370*** 0.0203*** 0.0122*** 0.0041***
60–70% 0.0452*** 0.0271*** 0.0167*** 0.0113***
70–80% 0.0514*** 0.0318*** 0.0194*** 0.0136***
80–90% 0.0619*** 0.0399*** 0.0239*** 0.0163***
90–100% 0.1020*** 0.0762*** 0.0438*** 0.0301***
Sample size (thousand pcs) 876 876 876 876 876 876 876 876
R2 0.037 0.109 0.180 0.298 0.063 0.107 0.179 0.298
Fixed effects: year of contr.,
bank, district, settlement type N Y Y Y N Y Y Y
Debtor and loan
characteristics N N Y Y N N Y Y
Non-performance
in October 2021 N N N Y N N N Y
Note: We use household loans existing in October 2021, exited the payment moratorium until the end of October 2021 permanently or never participated in it, and including observations for each of the variables in each model specification. The dependent variable in each specification is the September 2022 non-performing classification (non-performing: 1, performing: 0). In addition to the debtor and loan characteristics used in Table 2, we include also the following: (1) average monthly income before the pandemic, i.e. between March and December 2019, (2) annual change in income between March and December 2020 compared to the same period in 2019, (3) whether income decreased by at least 10 per cent during this period, (4) whether income was missing for at least 6 months between March and December 2020, (5) the remaining maturity of the loan, (6) the length of the interest rate period, (7) the amount of the net financial transfer that can be achieved by participating in the general moratorium.
Standard errors are clustered at the client level. *p<0.10, **p<0.05, *** p<0.01.
Table 5
Main results of extended linear probability models estimated on the largest possible samples
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 Moratorium type
(reference: never in morat.)
Dropped out at the end 0.0660*** 0.0590*** 0.0386*** 0.0233***
Voluntarily left –0.0054*** –0.0021*** –0.0019*** –0.0068***
Moratorium intensity (reference: 0%)
0–10% 0.0059*** 0.0027*** 0.0010 –0.0018***
10–20% 0.0109*** 0.0083*** 0.0042*** 0.0010
20–30% 0.0085*** 0.0059*** 0.0048*** 0.0013*
30–40% 0.0122*** 0.0078*** 0.0068*** 0.0018**
40–50% 0.0136*** 0.0079*** 0.0080*** 0.0038***
50–60% 0.0232*** 0.0175*** 0.0122*** 0.0041***
60–70% 0.0309*** 0.0248*** 0.0167*** 0.0113***
70–80% 0.0397*** 0.0320*** 0.0194*** 0.0136***
80–90% 0.0529*** 0.0444*** 0.0239*** 0.0163***
90–100% 0.0854*** 0.0730*** 0.0438*** 0.0301***
Sample size (thousand pcs) 4,456 4,456 876 876 4,456 4,456 876 876
R2 0.022 0.056 0.180 0.298 0.024 0.058 0.179 0.298
Fixed effects: year of contr.,
bank, district, settlement type N Y Y Y N Y Y Y
Debtor and loan
characteristics N N Y Y N N Y Y
Non-performance
in October 2021 N N N Y N N N Y
Note: We use household loans existing in October 2021 exited the payment moratorium until the end of October 2021 permanently or never participated in it. The dependent variable in each specification is the September 2022 non-performing classification (non-performing: 1, performing: 0). We always use the largest sample available for a given model. In addition to the debtor and loan characteristics used in Table 2, we include also the following: (1) average monthly income before the pandemic, i.e. between March and December 2019, (2) annual change in income between March and December 2020 compared to the same period in 2019, (3) whether income decreased by at least 10 per cent during this period, (4) whether income was missing for at least 6 months between March and December 2020, (5) the remaining maturity of the loan, (6) the length of the interest rate period, (7) the amount of the net financial transfer that can be achieved by participating in the general moratorium. Standard errors are clustered at the client level. *p<0.10, **p<0.05, *** p<0.01.
Table 6
Main results of the estimated logit models
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 Moratorium type
(reference: never in morat.)
Dropped out at the end 0.0760*** 0.0751*** 0.0446*** 0.0362***
Voluntarily left –0.0092*** –0.0020*** 0.0025*** 0.0029***
Moratorium intensity (reference: 0%)
0–10% 0.0049*** 0.0063*** 0.0041*** 0.0046***
10–20% 0.0077*** 0.0098*** 0.0092*** 0.0101***
20–30% 0.0053*** 0.0083*** 0.0100*** 0.0106***
30–40% 0.0106*** 0.0126*** 0.0125*** 0.0130***
40–50% 0.0109*** 0.0130*** 0.0117*** 0.0121***
50–60% 0.0219*** 0.0244*** 0.0209*** 0.0202***
60–70% 0.0307*** 0.0319*** 0.0251*** 0.0234***
70–80% 0.0390*** 0.0385*** 0.0290*** 0.0263***
80–90% 0.0519*** 0.0492*** 0.0343*** 0.0295***
90–100% 0.0996*** 0.0864*** 0.0507*** 0.0428***
Sample size (thousand pcs) 2,384 2,381 2,381 2,381 2,384 2,381 2,381 2,381 Fixed effects: year of contr.,
bank, district, settlement type N Y Y Y N Y Y Y
Debtor and loan
characteristics N N Y Y N N Y Y
Non-performance
in October 2021 N N N Y N N N Y
Note: We use household loans existing in October 2021 exited the payment moratorium until the end of October 2021 permanently or never participated in it, and including observations for each of the variables in each model specification. The dependent variable in each specification is the September 2022 non-performing classification (non-performing: 1, performing: 0). The explanatory variables are the same as those used in the baseline analysis (see Table 3). Standard errors are clustered at the client level. *p<0.10, **p<0.05, *** p<0.01.
Table 7
Detailed results of the estimated linear probability models
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 Moratorium type
(reference: never in moratorium)
Dropped out at the end 0.0992*** 0.0824*** 0.0473*** 0.0315***
(0.0004) (0.0005) (0.0005) (0.0004) Voluntarily left 0.0141*** 0.0072*** 0.0031*** –0.0010***
(0.0002) (0.0003) (0.0003) (0.0003) Moratorium intensity
(reference: 0%)
0–10% 0.0245*** 0.0070*** 0.0018*** –0.0004
(0.0005) (0.0006) (0.0005) (0.0005)
10–20% 0.0273*** 0.0107*** 0.0050*** 0.0032***
(0.0006) (0.0006) (0.0006) (0.0006)
20–30% 0.0249*** 0.0100*** 0.0065*** 0.0047***
(0.0006) (0.0007) (0.0006) (0.0006)
30–40% 0.0302*** 0.0131*** 0.0065*** 0.0042***
(0.0007) (0.0008) (0.0008) (0.0007)
40–50% 0.0305*** 0.0136*** 0.0047*** 0.0022***
(0.0007) (0.0008) (0.0008) (0.0007)
50–60% 0.0414*** 0.0244*** 0.0138*** 0.0080***
(0.0009) (0.0009) (0.0009) (0.0008)
60–70% 0.0503*** 0.0318*** 0.0183*** 0.0137***
(0.0010) (0.0010) (0.0010) (0.0009)
70–80% 0.0586*** 0.0390*** 0.0226*** 0.0175***
(0.0010) (0.0010) (0.0010) (0.0009)
80–90% 0.0715*** 0.0506*** 0.0305*** 0.0224***
(0.0010) (0.0010) (0.0010) (0.0009)
90–100% 0.1190*** 0.0941*** 0.0569*** 0.0420***
(0.0006) (0.0006) (0.0006) (0.0005)
NPL Oct-2021 0.5230*** 0.5230***
(0.0020) (0.0020)
Table 7
Detailed results of the estimated linear probability models
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 ISCO_1
1 –0.0025*** –0.0019*** –0.0030*** –0.0022***
(0.0006) (0.0005) (0.0006) (0.0005)
2 –0.0074*** –0.0059*** –0.0077*** –0.0059***
(0.0004) (0.0004) (0.0004) (0.0004)
3 –0.0083*** –0.0068*** –0.0085*** –0.0069***
(0.0005) (0.0004) (0.0005) (0.0004)
4 –0.0078*** –0.0071*** –0.0081*** –0.0072***
(0.0008) (0.0007) (0.0008) (0.0007)
5 –0.0035*** –0.0030*** –0.0040*** –0.0033***
(0.0006) (0.0006) (0.0006) (0.0006)
6 0.0044 0.0064** 0.0038 0.0059**
(0.0030) (0.0028) (0.0030) (0.0028)
7 –0.0035*** –0.0023*** –0.0042*** –0.0029***
(0.0006) (0.0006) (0.0006) (0.0006)
8 –0.0012** –0.0006 –0.0018*** –0.0010*
(0.0006) (0.0005) (0.0006) (0.0005)
9 0.0236*** 0.0197*** 0.0229*** 0.0192***
(0.0008) (0.0008) (0.0008) (0.0008)
Natural person 0.0008 –0.0574*** 0.0075 –0.0529***
(0.0053) (0.0053) (0.0053) (0.0053)
Previous FX loan
1 –0.0167*** –0.0123*** –0.0173*** –0.0127***
(0.0003) (0.0003) (0.0003) (0.0003)
2 0.0375 0.0253 0.0374 0.0255
(0.0380) (0.0175) (0.0375) (0.0170)
3 0.0054* –0.0043** 0.0035 –0.0055***
(0.0028) (0.0020) (0.0028) (0.0020)
Previous delinquency 0.0811*** 0.0500*** 0.0812*** 0.0499***
(0.0007) (0.0006) (0.0007) (0.0006)
Table 7
Detailed results of the estimated linear probability models
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 Delinquency
31–60 days 0.2070*** 0.2010*** 0.2060*** 0.2010***
(0.0065) (0.0065) (0.0065) (0.0065)
61–90 days 0.2310*** 0.2110*** 0.2310*** 0.2120***
(0.0089) (0.0087) (0.0089) (0.0087)
91–180 days 0.2610*** 0.0986*** 0.2610*** 0.0990***
(0.0065) (0.0062) (0.0065) (0.0062)
181–360 days 0.2690*** 0.0982*** 0.2700*** 0.0987***
(0.0053) (0.0046) (0.0053) (0.0046)
361 days or more 0.2830*** 0.1220*** 0.2840*** 0.1220***
(0.0022) (0.0020) (0.0022) (0.0020)
DSTI 0.0142*** 0.0049*** 0.0138*** 0.0045***
(0.0005) (0.0004) (0.0005) (0.0004)
Debt cap * DSTI 0.0037*** 0.0254*** 0.0011 0.0232***
(0.0011) (0.0010) (0.0011) (0.0010)
No. of debtors
2 0.0009** –0.0008** 0.0003 –0.0012***
(0.0004) (0.0003) (0.0004) (0.0003)
3 –0.0010 –0.0006 –0.0023*** –0.0016***
(0.0007) (0.0006) (0.0007) (0.0006)
4 –0.0027** –0.0014 –0.0041*** –0.0025**
(0.0014) (0.0011) (0.0014) (0.0011)
5 0.0039 0.0020 0.0016 0.0002
(0.0047) (0.0035) (0.0047) (0.0035)
6 –0.0006 –0.0043 0.0003 –0.0037
(0.0085) (0.0051) (0.0085) (0.0051)
7 –0.0035 –0.0062 0.0008 –0.0030
(0.0261) (0.0128) (0.0261) (0.0128)
8 –0.0426** –0.0291*** –0.0436** –0.0301**
(0.0182) (0.0109) (0.0215) (0.0132)
9 –0.1270*** –0.0796*** –0.1330*** –0.0860***
(0.0043) (0.0040) (0.0043) (0.0041)
Table 7
Detailed results of the estimated linear probability models
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022
Age –0.0007*** –0.0005*** –0.0007*** –0.0005***
(0.0000) (0.0000) (0.0000) (0.0000)
Debt –0.00007*** –0.0002*** –0.00004** –0.0002***
(0.00002) (0.00002) (0.00002) (0.00002)
Loan type
home equity 0.0051*** 0.0033*** 0.0059*** 0.0036***
(0.0006) (0.0005) (0.0006) (0.0005)
prenatal baby support 0.0051*** 0.0014*** 0.0064*** 0.0024***
(0.0005) (0.0004) (0.0005) (0.0004)
personal 0.0258*** 0.0192*** 0.0247*** 0.0179***
(0.0006) (0.0005) (0.0006) (0.0005)
vehicle –0.0149*** –0.0192*** –0.0147*** –0.0189***
(0.0012) (0.0011) (0.0012) (0.0011)
hire purchase 0.0353*** 0.0395*** 0.0355*** 0.0391***
(0.0013) (0.0012) (0.0013) (0.0012)
overdraft 0.0033*** 0.0077*** 0.0088*** 0.0115***
(0.0007) (0.0007) (0.0007) (0.0007)
credit card –0.0213*** –0.0057*** –0.0193*** –0.0038***
(0.0010) (0.0009) (0.0010) (0.0009)
other –0.0122*** 0.0033 –0.0031 0.0093**
(0.0045) (0.0040) (0.0045) (0.0040)
Interest rate 0.0341*** 0.0223*** 0.0322*** 0.0203***
(0.0029) (0.0027) (0.0029) (0.0027)
Add. loan:
housing –0.0241*** –0.0156*** –0.0280*** –0.0180***
(0.0005) (0.0005) (0.0005) (0.0005)
personal –0.0102*** –0.0021*** –0.0156*** –0.0059***
(0.0005) (0.0005) (0.0005) (0.0005)
vehicle –0.0235*** –0.0137*** –0.0246*** –0.0142***
(0.0011) (0.0010) (0.0011) (0.0010)
hire purchase –0.0310*** –0.0139*** –0.0308*** –0.0136***
Table 7
Detailed results of the estimated linear probability models
(1) (2) (3) (4) (5) (6) (7) (8)
Non-performance in September 2022 No. of add. loans
1 0.0621*** 0.0374*** 0.0653*** 0.0390***
(0.0007) (0.0005) (0.0007) (0.0005)
2 0.0780*** 0.0465*** 0.0823*** 0.0488***
(0.0009) (0.0007) (0.0009) (0.0007)
3 0.0953*** 0.0560*** 0.1030*** 0.0604***
(0.0012) (0.0010) (0.0012) (0.0010)
4 0.1110*** 0.0639*** 0.1210*** 0.0703***
(0.0017) (0.0015) (0.0017) (0.0015)
5 0.1250*** 0.0710*** 0.1380*** 0.0791***
(0.0029) (0.0027) (0.0030) (0.0027)
6 0.1450*** 0.0810*** 0.1570*** 0.0890***
(0.0100) (0.0096) (0.0099) (0.0095)
7 0.1980*** 0.1280*** 0.2130*** 0.1370***
(0.0340) (0.0340) (0.0341) (0.0341)
Settlement type
county seats –0.0077*** 0.0008 0.0005 –0.0076*** 0.0007 0.0006
(0.0006) (0.0006) (0.0005) (0.0006) (0.0006) (0.0005) large communities 0.0000 0.0017** 0.0016** –0.0001 0.0017** 0.0016**
(0.0009) (0.0008) (0.0007) (0.0009) (0.0008) (0.0007) towns –0.0066*** –0.0014*** –0.0014*** –0.0066*** –0.0014*** –0.0014***
(0.0005) (0.0004) (0.0004) (0.0005) (0.0004) (0.0004)
other –0.0004 –0.0051 –0.0032 0.0001 –0.0046 –0.0028
(0.0042) (0.0040) (0.0039) (0.0042) (0.0040) (0.0039) Sample size
(thousand pcs) 2,384 2,384 2,384 2,384 2,384 2,384 2,384 2,384
R2 0.064 0.068 0.169 0.321 0.068 0.068 0.170 0.322
Fixed effects: year of contr., bank, district,
settlement type N Y Y Y N Y Y Y
Debtor and
loan characteristics N N Y Y N N Y Y
Non-performance
in October 2021 N N N Y N N N Y