MODELLING HOUSEHOLD BEHAVIOUR: RESPONSE TO MACROECONOMIC SHOCKS IN THE UK PAULO ARANA UNIVERSITY OF ESSEX 28 TH OF JUNE 2017
INTRODUCTION AND MOTIVATION Insolvency Service Agency shows an increment of 9.8% in the proportion of poor households on debts about the previous year (2016).
Financial Stability Report (2016) comments on how to reduce vulnerability to households by increasing caps on loan to income lending
RESEARCH QUESTION How do households allocated their assets in response to macroeconomic shocks?
OBJECTIVES OF THE STUDY Identify patterns of asset allocation within the WAS Determining debt relative to their income Examining whether this debt creates exposure when taking into account financial shocks
LITERATURE REVIEW (1) Data mostly gathered from the US and Europe given the availability of data (SFC, PSID, HFCS) Literature divided into determinants of allocation of wealth, household debt and macroeconomic shocks
LITERATURE REVIEW (2) Studies focused asset allocation (invest or not) Study Journal Variable of interest Main finding Ameriks and Zeldes (2004) Campbell and Cocco (2007) Canner et al (1997) Cooper and Zhu (2016) Lahey et al (2003) Mariotti et al (2014) Journal of Financial Instability Journal of Monetary Economics The American Economic Review Review of economic dynamics Financial services review Discussion paper (Institute for the Study of Labor) Age and portfolio changes (US) Investing wealth (US) Willingness to hold risky assets (US) Influence of age and education for households on stock market participation (US) Retirement and allocation of wealth (US) Wealth diversification across markets (Australia) As age increases, households become more adamant to include more stocks into their portfolios. Long-term investors seek to consider opportunities to reinvest wealth at a reasonable rate of return. Popular advice given by financial advisors does not follow rational behaviour Higher amounts of education reduces the costs of participation Newly retirees held a greater amount of fixed income securities before retiring compared to others, signalling a greater degree of risk aversion Higher amounts of education increases disposition to diversify.
LITERATURE REVIEW (3) Studied focused on household debt Study Journal Variable of interest Main finding Ampudia et al (2016) Journal of Financial Stability Shocks to income on debt (EU) Debt to income ratios positively affected by income shocks, particularly to poorer households Anderloni et al (2012) Research in Economics Vulnerability indicator researched (Italy) DTI ratios are particularly influenced by financial hardship, whereas house ownership decreases the value of the vulnerability indicator Dynan and Kohn (2007) Discussion paper (Division of Research & Statistics and Monetary Affairs) Determinants of indebtedness in the US Increases in age and education seem to increase the sample s DTI ratios, mortgages also contributing to this increase Studies focused on Macroeconomic Shocks Study Journal Variable of interest Main finding Cloyne and Surico (2014) Working paper (Bank of England) Effect of income taxes on household consumption and indebtedness (UK) Mortgages are primarily affected by changes to income tax than outright house owners Kick et al (2014) Working paper ( European Central Bank) Impact of economic downturn on both European households and firms (Germany) Shocks decrease the amount of asset concentration for households, primarily risky assets 8 Tiongson et al (2010) Book, The World Bank. Identification of transmission channels Three channels through which households are affected are the labour market, product market and the financial
EMPIRICAL FRAMEWORK Based on Brown ands Taylor (2016) Random effects probit estimation. Assets Home ownership Deposit/savings accounts Cash/Investment ISAs Liabilities Credit card use Overdraft account Mortgage loans Fixed term investment bonds Company shares Life insurance
EMPIRICAL FRAMEWORK: FIRST STAGE (1) By identifying the main instruments that households used, I computed the following equation: where: Y i k = 1 X i β + ε i > 0, with k = 1,2, 9. Y i k represents the indicator variable corresponding to the probability of the household using the instrument, i representing the household and k representing the instrument X i β is a vector of demographic related variables which accounts for different characteristics of both the household and the household reference person (HRP). ε i is the regression error term.
EMPIRICAL FRAMEWORK: FIRST STAGE (2) HRP Age (banded) Demographic variables Gender Earnings (gross) Degree Married
EMPIRICAL FRAMEWORK: SECOND STAGE (1) Debt to income ratios (DTI) DTI i,t = tdebt i,t tincome i,t where DTI i,t represents the DTI ratio for household i in time t ( which corresponds to each wave respectively), which is defined as the total amount of unsecured debt tdebt i,t over the total amount of income per household tincome i,t.
DATA WEALTH AND ASSETS SURVEY Linkage within wave and across waves (cross sectional approach vs. longitudinal approach) Cross sectional weights used for calibration Cross sectional approach used to obtain an aggregate amount of instruments per household
DESCRIPTIVE STATISTICS: ALLOCATION STATISTICS Variables Wave 1 Wave 2 Wave 3 Wave 4 Unsecured debt Credit card 41.67% 11.81% 19.88% 17.46% Overdraft accounts 5.89% 5.64% 5.66% 5.00% Secured debt Mortgage loan 43.82% 42.06% 40.37% 38.74% Housing assets Home ownership 87.84% 89.26% 84.29% 88.69% Financial assets Deposit/Savings account (Ind) 28.08% 31.23% 27.08% 28.48% Cash/Investment ISA 28.16% 35.64% 33.98% 35.32% Fixed term investment bonds 5.77% 9.37% 8.85% 7.36% Shares (Both UK and foreign listed) 10.25% 11.73% 9.29% 9.62% Another type of assets Life insurance 14.33% 11.58% 9.59% 9.28%
ESTIMATION RESULTS: ASSET ALLOCATION ( WAVE 1) Financial instrument Credit card use Overdrawn account Mortgage Contract Savings/Dep osit accounts Individual Savings Account Fixed investment Bonds Shares Insurance Observations: 4369 Variables (M.E. and S.E.) Degree Male married Accom age1 age2 age3.1378085.023939 -.0137627 -.0236791 -.0604323 -.018985.0256666 (.0155859) (.0148634) (.0153479) (.0273907) (.0238218) (.0204237) (.0219797) -.0257398 -.0227651 -.0317027.0401495.1083711.0847494.052225 (.0134186) (.0130229) (.0131871) (.0224449) (.0218014) (.0194034) (.02056).0426555.0132785.0547737 -.1428532.4426007.4143738.3072365 (.0165193) (.0161132) (.0164192) (.029545) (.023244) (.0174944) (.0196782).11696 -.019953 -.1295698 -.064947 -.0982806 -.0829845 -.0690879 (.0180301) (.0178136) (.018209) (.0332484) (.0293676) (.0247288) (.0259804).0532211 -.0594673.0259719 -.0330253 -.1431474 -.190092 -.0995285 (.018912) (.0185489) (.0192114) (.0353633) (.0302186) (.024902) (.0264737).0327963.0111553 -.0046231 -.0169042 -.1434827 -.1009398 -.0587217 (.0105629) (.0104785) (.0108298) (.0218228) (.0202584) (.0136564) (.0131341).121543.0940361 -.0049569 -.0381271 -.2819949 -.142679 -.0519386 (.0163374) (.0163131) (.0171724) (.0329846) (.0281694) (.0216554) (.0225835) -.0173924.0385822.0585415.004888.2773688.24208.1382457 (.0183145) (.0179296) (.0183568) (.0341927) (0284188) (.0231857) (.0248088)
ESTIMATION RESULTS: ASSET ALLOCATION (WAVE 2) Financial instrument Credit card use Overdrawn account Mortgage Contract Savings/Deposi t accounts Individual Savings Account Fixed investment Bonds Shares Insurance Observations: 902 Variables Degree Male married accom age1 age2 age3.1746617.0934278.1479068 -.1075901 -.0359165.0275575.068041 (.0335945) (.0317391) (.033522) (.0676962) (.0520709) (.0454169) (.0482817) -.0704858.002388.0149211.1205721.0982217.0844741.0372432 (.0252579) (.0218496) (.02367) (.0378751) (.0366437) (.0333078) (.0362341).0117141.0621037.1015206 -.095159.4302827.3949629.2701792 (.0309901) (.0285492) (.0301983) (.0569048) (.0402539) (.0329539) (.037769).083008 -.0540921 -.0756939 -.0477867 -.1055212.0303186 -.0324495 (.0351855) (.0324919) (.0351355) (.0659609) (.0521841) (.0460826) (.0487918).1144313 -.0624114.0227938 -.1300866 -.078604 -.0787167 -.0642499 (.0340502) (.0321705) (.0350263) (.0700141) (.0521075) (.0451778) (.0482118).0739036.0108701.0066391 -.0860016 -.1743109 -.1046042 -.0885621 (.0197396) (.0192131) (.0212218) (.0539869) (.0362957) (.0241132) (.0254674).1447098.0497331.030367 -.0889873 -.2684404 -.1206342 -.0404922 (.0233041) (.0238167) (.0266223) (.0580268) (.0465875) (.030645) (.0316229) -.0758046 -.0168418.0611452 -.0002403.2276711.1907947.0897643 (.0318707) (.0301393) (.0321679) (.0593516) (.0473164) (.0401719) (.0429996)
ESTIMATION RESULTS: ASSET ALLOCATION (3) Financial instrument Credit card use Overdrawn account Mortgage contract Savings/Deposit accounts Individual Savings Account Fixed investment Bonds Shares Variables Degree Male married accom age1 age2 age3.1846797.134704.1236406.2732648 -.0333663 -.0023902.1161003 (.0422918) (.0402354) (.0442294) (.0953061) (.0679471) (.0583267) (.0583487).0063725 -.0193317.004848 -.0740031.1929625.0936537.0579415 (.0285112) (.0272329) (.029481) (.0716151) (.0470811) (.0453059) (.0469097).0307208 -.0413584.13395 -.2561061.4321105.4071593.3078242 (.0408681) (.0380418) (.0394475) (.0862111) (.0557852) (.0450981) (.048186).2322183 -.0145666 -.0558986.0108579 -.0329882 -.0020891 -.0618397 (.0420103) (.0422423) (.0457101) (.0975332) (.0694019) (.0595266) (.0602135).1933476 -.0670176.086001 -.0510977 -.0826042 -.1327542 -.2088722 (.0404376) (.0406516) (.0444001) (.094585) (.0655962) (.0560079) (.0565288).0514182.001186.0166822 -.0177927 -.1661474 -.1077486 -.096924 (.0238156) (.022776) (.0257593) (.056592) (.047197) (.0296401) (.0293761).0648646.0693087 -.0654805 -.0713388 -.2434416 -.0631456 -.1073558 (.0277834) (.0269939) (.0283882) (.0751109) (.0610364) (.0332502) (.0357697) -.0430561 -.0504775 -.0379392.0213879.1364777.1626272.1317854 Insurance (.0408417) (.0384909) (.0421448) (.090376) (.0617091) (.0521673) (.052862) Observations: 541
ESTIMATION RESULTS: ASSET ALLOCATION (4) Financial instrument Credit card use Overdrawn account Mortgage Contract Savings/Dep osit accounts Individual Savings Account Fixed investment Bonds Shares Insurance Observations: 602 Variables Degree Male married accom age1 age2 age3.1879534.131137.1867185 -.0038731 -.0240231 -.063891.0282569 (.0378968) (.0377877) (.0415819) (.0666216) (.0699616) (.0565773) (.0566038) -.0572599.0016053 -.0175197 -.0870026.0745224.0803969.0231172 (.0290998) (.0269652) (.0294297) (.0589011) (.0497955) (.041612) (.043022).04624.0552967.0678983 -.1770564.3868818.3822321.2945896 (.0379007) (.0362241) (.0392849) (.058146) (.06027) (.045583) (.0472698).1598241 -.0539461 -.018606 -.0478786 -.0433379 -.0612053 -.0483008 (.0402696) (.0401625) (.0445143) (.0677934) (.0722954) (.0586716) (.0586145).1330649 -.0445459.102522 -.0504833 -.0326238 -.1274564 -.1526584 (.0392126) (.0390915) (.0433284) (.0667315) (.0694445) (.0559693) (.0558138).0175493.011899.0510203.0363446 -.1060881 -.1105654 -.0437617 (.0170271) (.0169296) (.0230459) (.0229416) (.0393649) (.0292611) (.0191437).0819318.0494611.0483492.0352762 -.273355 -.0791615 -.0158422 (.0261809) (.0262899) (.0307484) (.0434815) (.083004) (.0359081) (.0341476) -.075526.0084192 -.044142 -.0813933.2751836.1907191.0894597 (.0366922) (.0361057) (.0403058) (.0601111) (.0652722) (.0493977) (.0492604)
ESTIMATING THE EFFECT OF MACROECONOMIC SHOCKS Stage 3 estimation: DTI i,t = β 0 + β 1 infl t + β 2 irate t + ε i,t where DTI i,t is defined as the DTI ratios obtained in the previous step regressed with the general inflation level at time t infl t and the interest rate level irate t. ε i,t represents the regression s error term.
ESTIMATION RESULTS: IMPACT OF MACROECONOMIC SHOCKS (2) Fig1: DTI ratio frequency over the sample.
ESTIMATION RESULTS: IMPACT OF MACROECONOMIC SHOCKS (3) Variables Coefficient Std. Error z P> z Inflation Interest rate Constant -.0164325.0468366-0.35 0.726 -.0001662.0081979-0.02 0.984.9722944.1416598 6.86 0.000 σ μ =.31697399 σ e =.60688668
CONCLUDING REMARKS Refusal of life-cycle hypothesis. Marriage doesn t influence asset market participation. More complex financial instruments overlooked. No effect of inflation and interest rates on households.
NEXT STEPS Secured Access data (longitudinal purposes) Exploring other transmission channels Including financial literacy and exploiting possible regional differences
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