Saving behaviour and health: A high-dimensional Bayesian analysis of British panel data

We develop a two-part high-dimensional Bayesian modelling approach to analyse the relationship between saving behaviour and health. In contrast to the existing literature, our approach allows different data-generating processes for the decision to save and the amount saved, and therefore unveils a more detailed picture of the relationship between financial behaviour and health than previous work. We explore different measures of saving, including monthly saving behaviour and the stock of financial assets held. Further, we exploit British panel data, which includes an extensive range of biomarkers. Our second contribution lies in comparing the effects of these objective measures of health with commonly used self-assessed health measures. We find that health is a significant determinant of saving behaviour and financial asset holding, and that biomarker measures have differential impacts on saving behaviour compared to self-reported health measures.

[1]  A. Yoshida,et al.  Estimating Saving Functions in the Presence of Excessive-Zeros Problems , 2002 .

[2]  Sarah Brown,et al.  Debt and distress: Evaluating the psychological cost of credit , 2005 .

[3]  S. Pudney,et al.  Concordance of health states in couples: Analysis of self-reported, nurse administered and blood-based biomarker data in the UK Understanding Society panel. , 2017, Journal of health economics.

[4]  Pulak Ghosh,et al.  Modelling household finances: A Bayesian approach to a multivariate two-part model , 2015, Journal of empirical finance.

[5]  T. Seeman,et al.  Age differences in allostatic load: an index of physiological dysregulation , 2003, Experimental Gerontology.

[6]  J. Brazier,et al.  The Estimation of a Preference-Based Measure of Health From the SF-12 , 2004, Medical care.

[7]  Pulak Ghosh,et al.  Household Finances and Social Interaction: Bayesian Analysis of Household Panel Data , 2016, SSRN Electronic Journal.

[8]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[9]  H. Schmitz,et al.  Broke, Ill, and Obese: Is There an Effect of Household Debt on Health? , 2014 .

[10]  R. Collins,et al.  Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55 000 vascular deaths , 2007, The Lancet.

[11]  Pulak Ghosh,et al.  The Existence and Persistence of Household Financial Hardship , 2014 .

[12]  R Peto,et al.  Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. , 1998, JAMA.

[13]  B. Singer,et al.  Price of adaptation--allostatic load and its health consequences. MacArthur studies of successful aging. , 1997, Archives of internal medicine.

[14]  T. Okura,et al.  The Associations Between Smoking Habits and Serum Triglyceride or Hemoglobin A1c Levels Differ According to Visceral Fat Accumulation. , 2016, Journal of epidemiology.

[15]  C. Seplaki,et al.  A comparative analysis of measurement approaches for physiological dysregulation in an older population , 2005, Experimental Gerontology.

[16]  Gary K Grunwald,et al.  Analysis of repeated measures data with clumping at zero , 2002, Statistical methods in medical research.

[17]  Mariachristina Rossi Examining the Interaction between Saving and Contributions to Personal Pension Plans: Evidence from the BHPS , 2009 .

[18]  C-reactive protein and the risk of developing hypertension. , 2003 .

[19]  Karl Taylor,et al.  Household debt and financial assets: evidence from Germany, Great Britain and the USA , 2008 .

[20]  M. Rossi,et al.  Private medical insurance and saving: evidence from the British Household Panel Survey. , 2003, Journal of health economics.

[21]  Catherine P. Montalto,et al.  Loss Aversion and Saving Behavior: Evidence from the 2007 U.S. Survey of Consumer Finances , 2011 .

[22]  G. Willemsen,et al.  The association of alcohol intake with γ-glutamyl transferase (GGT) levels: evidence for correlated genetic effects. , 2014, Drug and alcohol dependence.

[23]  T. Chandola,et al.  Re-employment, job quality, health and allostatic load biomarkers: prospective evidence from the UK Household Longitudinal Study , 2017, International journal of epidemiology.

[24]  A. Kapteyn,et al.  Health, Wealth, and the Role of Institutions , 2003, The Journal of Human Resources.

[25]  T. Lyons,et al.  Biomarkers in diabetes: hemoglobin A1c, vascular and tissue markers. , 2012, Translational research : the journal of laboratory and clinical medicine.

[26]  Burton H. Singer,et al.  Social Relationships, Gender, and Allostatic Load Across Two Age Cohorts , 2002, Psychosomatic medicine.

[27]  A. Lusardi On the Importance of the Precautionary Saving Motive , 1998 .

[28]  A. Guariglia Saving behaviour and earnings uncertainty: Evidence from the British Household Panel Survey , 2001 .

[29]  Eva H. Telzer,et al.  Daily family assistance and inflammation among adolescents from Latin American and European backgrounds , 2009, Brain, Behavior, and Immunity.

[30]  R. C. Campbell,et al.  The influence of individual health outcomes on individual savings behavior , 2013 .

[31]  E. Haynes,et al.  Biomarkers of obesity and subsequent cardiovascular events. , 2007, Epidemiologic reviews.

[32]  Sophia T. Anong,et al.  Household Savings Motives , 2007 .

[33]  Catherine P. Montalto,et al.  Effect of saving motives and horizon on saving behaviors , 2010 .

[34]  M. White,et al.  Personal Bankruptcy and Credit Supply and Demand , 1996 .

[35]  A. Belloni,et al.  SPARSE MODELS AND METHODS FOR OPTIMAL INSTRUMENTS WITH AN APPLICATION TO EMINENT DOMAIN , 2012 .

[36]  P. Lavrakas,et al.  Over the limit: the association among health, race and debt. , 2000, Social science & medicine.

[37]  Xin-Yuan Song,et al.  Financial literacy and household finances: A Bayesian two-part latent variable modeling approach , 2019, Journal of Empirical Finance.

[38]  Joseph L Schafer,et al.  A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data , 2001 .

[39]  E. Frees,et al.  Household Life Insurance Demand , 2010 .

[40]  M. Levine,et al.  A comparison of methods for assessing mortality risk , 2014, American journal of human biology : the official journal of the Human Biology Council.