1 / 35

Household Debt and Credit Constraints: Comparative Micro Evidence From Four OECD Countries

Household Debt and Credit Constraints: Comparative Micro Evidence From Four OECD Countries. Jonathan Crook (U Edinburgh) Stefan Hochguertel (VU Amsterdam). od1. Introduction. Debt holding has increased over last 15 yrs

virote
Download Presentation

Household Debt and Credit Constraints: Comparative Micro Evidence From Four OECD Countries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Household Debt and Credit Constraints: Comparative Micro Evidence From Four OECD Countries Jonathan Crook (U Edinburgh) Stefan Hochguertel (VU Amsterdam) od1

  2. Introduction • Debt holding has increased over last 15 yrs • Empirical literature on liquidity constraints has not come up yet with internationally comparative figures: how important are they? • Arguably, importance of both debt holding and constraints differs across countries • Institutional differences between countries will matter od2

  3. Household Debt in OECD Countries ============================================== Debt/YD Mortg/GDP 1995 2005 % 1992 2002 % ---------------------------------------------- DK 112.9 155.2 37.5 63.9 74.3 16.3 NL 63.4 134.1 111.5 40.0 78.8 97.0 PORT 46.8 112.6 140.6 12.8 49.8 289.1 US 78.8 111.1 41.0 45.3 58.0 28.0 SP 47.4 93.5 97.3 11.9 32.3 171.4 GER 74.3 83.2 12.0 38.7 54.0 39.5 SW 54.7 78.3 43.1 37.5 40.4 7.7 FR 47.8 65.2 36.4 21.0 22.8 8.6 BEL 45.7 54.2 18.6 19.9 27.9 40.2 FIN 47.2 58.6 24.1 37.2 31.8 -14.5 GR 8.6 44.9 422.1 4.0 13.9 247.5 IT 24.6 43.1 75.2 6.3 11.4 81.0 ============================================== Source: OECD od3

  4. This Paper • Use micro data from four OECD countries (Italy, US, NL, Spain) and estimate equations for ‘demand for debt’ and ‘credit constraints’ • Consider various selection issues • Provide comparable estimates and relate this, where possible, to differential incentives and constraints arising out of differing institutional designs between countries od4

  5. Find: pronounced differences as regards effects of incomes, net worth, age, household composition • Selection effects of various kinds appear unimportant for debt holding equations • DYYP [= difference between current and ‘permanent income’] has differential effects for credit applications, credit constraints, and conditional debt holding: • applications: “–” NL, “0” IT, US, SP • constraints: “0” NL, “+” IT, US, “0”SP • debt holding: “0” IT, “–” NL, US, “0”SP od5

  6. Simulation Results • Assume • CRRA utility function; R=1; bequests=0. • Individuals retire at fixed date tR when income drops to fraction •  of last earned income • Permanent income follows an AR(1) process, grows at rate G, • subject to permanent shocks • Current income subject to transitory shocks • Solution method follows Deaton 1991 od13

  7. Generalisations • Debt/Y hump shaped and reaches peak at age 40 • Debt incidence is monotonically decreasing with age • If cash on hand< 110% of consumption at given age  credit constrained then: • 18% constrained at age 30, 2.5% at age 40, 0% thereafter • If decrease replacement rate fraction constrained decreases at all ages • If increase growth rate of permanent income fraction constrained decreases at all ages • If increase time preference rate fraction constrained increases at all ages • If increase interest rate fraction constrained decreases at all ages od13b

  8. Empirical Literature on Debt and Constraints International comparisons • Jappelli & Pagano 1989 AER Bacchetta & Gerlach 1997 JME Micro Data US • Jappelli 1990 QJE Cox & Jappelli 1993 JMCB • Duca & Rosenthal 1993 JFinInterm Crook 1996 & 2001 App Fin Ec • Gropp, Schultz & White 1997 QJE Jappelli et al 1998 REStats • Ferri & Simon 2002 UB WP Lyons 2003 JCA • Grant 2005 EUI WP Italy Australia • Fabri & Padula 2004 JBF La Cava & Simon BoA WP 2003 • Magri 2002 BoI WP od14

  9. Institutional Aspects • Income insurance (SS, pensions, UI, EPL) • Bankruptcy • Usury • Judicial efficiency, information sharing • Asset prices (homes), homeownership • Mortgage market institutions • Taxation of incomes and wealth • Preferences • Organization of financial markets od14b

  10. Institutional Differences NL ITUSSp Unemployment benefit insurance against income shocks greater in NL and Sp than IT and US Unemployment spend/GDP (%) 2.30.90.42.2 Duration of unemployment benefit 606624 (months) Employment protection 2.32.40.73.7 (OECD index 0 to 6) (rank out of 28) 1211?4 ----------------------------------------------------------------- Bankruptcy protection higher in US than IT and NL Bankruptcy discharge repaymnoyes possibleplan ---------------------------------------------------------------- od15

  11. -------------------------------------------------------------------------------------------------------------------------------------- Judicial efficiency and information sharing between lenders highest in NL and US than IT and SP NLITUSSp Judicial enforcement days to collect bounced check 3964554 147 days to evict delinquent tenant 5263049 183 Time to repossess (months)660-84 8 7-9 #reports issued/person 0.640.0462.3 na by private credit bureau. ----------------------------------------------------------------- Homeownership (%) 1990 45686478 2002 53806885 House price increases in 90’s rapid declinerapidrapid Home equity withdrawal highnonehighnone ----------------------------------------------------------------- od15b

  12. NLITUSSp --------------------------------------------------------------------------------------------------------------------- Highest downpayment % and lowest LTV values in IT, mid values for NL and SP, lowest downpayment % for US Downpayment (%) 25401120 Loan to value Typical(%) 90557870 Max (%) 11580na100 Typical term (years) 30153015 ---------------------------------------------------------------- Tax deductibility higher in NL and US than IT and SP Tax deductibility for mortgage on main residence Yes cappedcappedcapped C20% of$100k Є9k interest --------------------------------------------------------------- Italian banks are higher cost that elsewhere. Competition in all European markets increasing due to entry. od16

  13. Summary ----------------------------------------------------- Greatest Min Credit Income insurance NL, Sp > IT, USIT, US Bankruptcy US > IT, NLIT, NL Usury na Judicial efficiency, NL, US> IT, SPIT, SP information sharing Asset prices inflation(homes), NL, US > IT, SPIT, SP Homeownership IT, SP > NL, USNL, US Downpayment IT, NL > US, SPIT, NL Taxation deduction NL, US > IT, SPIT, SP Organization of financial Markets (Bank costs) IT > US, NL, SPIT ------------------------------------------------------------------- od16b

  14. Micro Data • US: Survey of Consumer Finances, Federal Reserve Board, SCF • Italy: Survey of Household Income and Wealth, Bank of Italy, SHIW • Netherlands: DNB Household Survey, Central Bank of the Netherlands, DHS • Spain:Spanish Survey of Household Finances, Bank of Spain,EFF od17

  15. US Data • SCF: 1992, 1995, 1998, 2001 • Repeated cross section; household level • Triennial; 4000-4500 hh per year • Large variety of questions on wealth and debt holding (account-level) • Oversampling of high wealth households • Multiple imputations od18

  16. Italian Data • SHIW: 1991, 1993, 1995, 1998, 2000, 2002, 2004 • Cross section with panel component (> 40%); household level • Biennial; 5000-8000 hh per year • Large variety of questions on wealth and debt holding (account-level) • Some missing values imputed, only one implicate od19

  17. Dutch Data • 1993-2004 DHS • Panel data; household level • Annual frequency; 2000-3000 hh per year • Large variety of questions on wealth and debt holding (account-level) od20

  18. Spanish Data • 2002 EFF • Cross section household level • 5000 hh • Large variety of questions on wealth and debt • holding (account level) • Oversampling of wealthy • Multiple imputations od21

  19. Permanent income od22

  20. Incidence of Debt ================================================================ Mortgages Other Total NLITSpUSNLITSpUSNLITSPUS ----------------------------------------------------------------------- 1991 11.014.123.0 1992 41.864.7 73.5 1993 40.612.546.015.064.725.1 1994 39.243.664.1 1995 41.213.443.443.314.166.264.624.674.7 1996 42.944.466.5 1997 43.343.966.3 1998 43.69.145.343.516.463.766.822.974.3 1999 42.142.867.2 2000 45.39.241.316.568.323.1 2001 42.646.640.064.265.675.5 2002 43.610.226.741.313.924.467.021.4 43.6 2003 41.040.565.4 2004 42.411.9 36.215.0 65.2 23.5 ======================================================================= NL: DHS, IT: SHIW, SP: EFF, US: SCF od23

  21. Mean Debt Holding (1992 Euros, 1000's) ================================================================ Mortgages Other Total NL ITSpUS NL ITSpUS NL ITSpUS ---------------------------------------------------------------- 1991 1.751.443.19 1992 35.376.8242.19 1993 29.57 2.18 3.75 1.72 33.32 3.90 1994 26.61 3.28 29.90 1995 27.66 2.2535.51 3.45 1.657.65 31.11 3.9043.16 1996 29.00 3.43 32.42 1997 27.91 3.26 31.20 1998 25.77 1.6641.28 2.77 2.9210.35 28.54 4.5851.62 1999 27.28 2.59 29.86 2000 27.45 2.21 3.24 2.38 30.11 4.59 2001 33.63 45.24 3.90 9.55 37.90 54.79 2002 36.76 2.568.26 3.21 2.01 2.04 40.23 4.56 10.30 2003 34.18 4.21 38.02 2004 35.20 3.73 3.99 2.31 39.136.04 ================================================================ NL: DHS, IT: SHIW, SP: EFF, US: SCF od24

  22. Median Debt Holding (1992 Euros, 1000's)if positive ================================================================ Mortgages Other Total NL ITSpUS NL ITSpUS NL ITSpUS ---------------------------------------------------------------- 1991 10.864.895.97 1992 53.155.4021.26 1993 59.90 11.37 2.72 3.21 34.49 6.43 1994 57.36 2.65 30.96 1995 55.99 11.3056.55 2.48 3.166.20 32.30 6.8724.14 1996 56.58 2.55 33.99 1997 53.76 2.07 29.57 1998 49.36 12.5564.05 2.23 4.18 7.78 28.32 6.2733.96 1999 51.57 2.38 29.75 2000 43.08 16.04 2.33 4.01 18.05 6.42 2001 61.13 70.18 3.71 7.86 37.12 36.96 2002 64.44 18.4424.46 3.08 4.43 4.29 36.56 7.38 16.01 2003 67.90 2.70 35.92 2004 68.01 24.37 2.76 4.21 34.76 8.78 ================================================================ NL: DHS, IT: SHIW, SP: EFF, US: SCF od25

  23. Applicants and Rejections ============================================================== % Apply % Reject % Reject|Apply NLITSPUSNLITSPUSNLITSPUS -------------------------------------------------------------- 1991 0.9 1992 22.5 1993 22.2 0.81.14.3 1995 19.95.663.60.90.920.44.416.232.0 1998 21.46.063.60.80.521.83.97.734.2 2000 21.95.41.90.45.68.0 2001 26.164.91.519.93.730.7 2002 24.94.2 20.82.50.5 1.19.111.7 5.1 ============================================================== ================================= %Rejected or Discouraged NLITSPUS --------------------------------------------------------- 1993 2.33.0 1995 2.92.328.6 1998 3.02.828.4 2000 3.11.7 2001 2.326.9 2002 3.52.23.4 ================================= NL: DHS, IT: SHIW, SP, EFF, US: SCF od26

  24. Observational regimes od27

  25. Selection mechanisms • Debt holding • Wants debt • Applies • Accepted od28

  26. Empirical modeling • Observability rule: • ML (normality, random effects, simulation) • Various versions • Non-convergence, partial convergence, etc od29

  27. SHIW: no random effects in selection equations, but also no selection • DHS: random effects, but no selection effects • EFF: cross section, Tobit • SCF: pooled cross section, no selection effects found (consistent two- • stage estimator with double selection rule) • Shall focus on single equation models for comparability od30

  28. Prob(Credit Application) – marginal effects =========================================================== NLITUSSP ----------------------------------------------------------- wealth -0.0064**-0.0030**-0.0023* -0.0172** income1 0.0037 0.00330.0212** 0.0361 income2 0.1316**0.0516**0.2897** 0.1050* income3 0.2743**-0.03110.0729 0.0908 income4 0.0164 0.0211 0.0332 0.0906 income5 0.1739**0.0237-0.0810** 0.0242 income6 0.04560.0082-0.0367** 0.0286 DYYP -0.0028**0.00010.0011°-0.0014 unemployed -0.0680*0.0003-0.1683** 0.0426 selfempl 0.00250.0035 0.0077 0.0020 age < 30 0.0091-0.0006-0.0062°-0.0024 30/39 -0.0093**0.0002-0.0041° -0.0075 40/49 -0.0052*-0.0009*-0.0075** 0.0001 50/64 -0.0094**-0.0018**-0.0117** -0.0038** 65+ -0.0089**-0.0023**-0.0194** -0.0100** kids <=6yrs -0.00100.0036-0.00630.0091 kids 7-12 0.00730.0066**-0.00570.0188 kids 13-19 -0.00440.0067** 0.0134 0.0145 kids 20+ 0.01080.0062** 0.0102 0.0237** single -0.0822**-0.0051-0.0875** -0.0377* =========================================================== od31

  29. Prob(Rejection | Application) – marg. effects ============================================== NLITUS ---------------------------------------------- wealth -0.0002**-0.0001-0.0089** income1 0.0035-0.0074 0.0093 income2 -0.0033-0.0497-0.0832* income3 -0.0082 0.0073-0.1585** income4 -0.0061-0.0445 -0.2362** income5 0.00180.0008 -0.0975** income6 -0.0088-0.0375 -0.0117 DYYP 0.00010.0015* 0.0058** unemployed 0.00280.0468 -0.0222 selfempl 0.0064°0.00130.0241° age < 30 0.0012°0.0015-0.0033 30/39 -0.00020.0004-0.0093** 40/49 0.00030.0006-0.0006 50/64 -0.0004°0.00030.0002 65+ 0.0001 0.0009-0.0069* kids <=6yrs 0.00030.0049 0.0142 kids 7-12 -0.00020.00950.0354** kids 13-19 -0.00010.00290.0420** kids 20+ 0.00130.0036-0.0152 single -0.00060.1114* -0.0086 ============================================== od32

  30. Prob{(Rejection | Application) or (Discouraged | No Application)} marg. effects =========================================================== NLITUS SP ----------------------------------------------------------- wealth -0.0001**-0.0011**-0.0080** -0.0037** income1 0.0004-0.0011 0.0042 -0.0081 income2 -0.0013-0.0026 -0.0158-0.0159 income3 -0.0032-0.0127-0.1227** -0.0005 income4 -0.0001-0.0093-0.1585** -0.0004 income5 0.00300.0057 -0.0780** -0.0227 income6 -0.0017-0.0077-0.0150-0.0069 DYYP 0.00000.0003**0.0040** -0.0003 unemployed 0.00060.0160**-0.02380.0216* selfempl 0.0028**0.00170.0182-0.0006 age < 30 0.0001-0.0002-0.0031-0.0016 30/39 -0.0001*0.0003-0.0074** 0.0001 40/49 0.0000-0.0006*-0.00170.0001 50/64 -0.0001-0.0002-0.0032** 0.0001 65+ -0.0001 -0.0008**-0.0115**-0.0003 kids <=6yrs -0.00010.00260.0070 0.0033 kids 7-12 0.00010.0035**0.0262** 0.0019 kids 13-19 0.00010.00210.0346** -0.0048 kids 20+ 0.00000.0027**-0.00200.0020 single -0.0006*0.0030 -0.0186-0.0022 ========================================================== od33

  31. E(Debt | Debt> 0) - coefficients =================================================================== NLITUS SelCorr SP(Tobit) ------------------------------------------------------------------- wealth -0.024**-0.033**-0.008* 0.010 -0.125 income1 -0.088-0.113*-0.080** -0.0750.291 income2 0.829**0.718**1.940** 1.988** 5.658** income3 2.524**0.3621.372** 1.427** 4.176° income4 0.369 0.3321.221** 1.219** 0.840 income5 1.259**0.3060.973** 0.967** 2.324 income6 0.175 0.651**0.571** 0.521** -0.191 DYYP -0.014**0.001-0.023** -0.021** 0.039 unemployed -0.149-0.058-0.073 -0.1090.136 selfempl 0.141°0.657**0.416** 0.382** -0.336 age < 30 0.192**-0.0100.051** 0.042**0.215 30/39 0.030**0.0140.006 0.008-0.202* 40/49 -0.008-0.023**-0.007 -0.014* -0.198* 50/64 -0.036**-0.017*-0.008° -0.008 -0.327** 65+ -0.031**-0.011-0.055** -0.066** -0.459** kids <=6yrs 0.120**-0.0210.053* 0.063*1.661** kids 7-12 0.063°0.0260.047° 0.061** 0.974* kids 13-19 -0.005-0.0400.069** 0.061* -0.137 kids 20+ -0.0510.0260.063° 0.0470.891** single -0.670**-0.353**-0.138* -0.114-3.642** ================================================================= od34

  32. Conclusions • Credit behavior differs across countries: • Much greater percentage apply for credit in US than in NL or • Spain, Italy least. (Italy consistent with greater social insurance). • Of those who apply a much higher percentage are rejected in the • US than Italy or the NL. The percentage in Spain is tiny. (Consistent • with more credit bureau data available in NL and US) • Percentage who are rejected or discouraged much larger in the US, • about the same in NL, Italy and Spain. • Re Application • Less wealthy more likely to apply in all countries • Prob of application follows life cycle model wrt age • Unemployed less likely to apply in US & NL (low insurance & high • insurance respectively) • Effect of permanent income consistent with PIH only for NL. od35

  33. Re Credit Constraints • Less wealthy rejected or discouraged in all 4 countries • Income above permanent income increases chance of rejection or of being • discouraged in Italy & US • Unemployed more likely to be rejected or discouraged in Italy and Spain • Self employed more likely to be rejected or discouraged in NL • Re Debt Outstanding • Income above permanent income reduces debt outstanding in NL and US • Debt outstanding follows simulated precautionary savings model • Self employed increases debt outstanding in Italy and US (consistent with • less social insurance) od36

  34. Combined • Difference between income and permanent income • reduces both the chance of application and the volume for those who demand debt in NL • has no effect on the chance of application or on the volume in IT or SP • increases the chance of application but reduces the volume in the US od37

More Related