1 / 18

Dean R. Lillard 1,3 , Richard V. Burkhauser 2,3,4 , Markus H. Hahn 4 and Roger Wilkins 4

Does Early-Life Income Inequality Predict Later-Life Self-Reported Health? Evidence from Three Countries. Dean R. Lillard 1,3 , Richard V. Burkhauser 2,3,4 , Markus H. Hahn 4 and Roger Wilkins 4

trista
Download Presentation

Dean R. Lillard 1,3 , Richard V. Burkhauser 2,3,4 , Markus H. Hahn 4 and Roger Wilkins 4

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. Does Early-Life Income Inequality Predict Later-Life Self-Reported Health?Evidence from Three Countries Dean R. Lillard1,3, Richard V. Burkhauser2,3,4, Markus H. Hahn4 and Roger Wilkins4 1Ohio State University, 2Cornell University, 3DIW-Berlin, 4Melbourne Institute, University of Melbourne July 2013 www.fbe.unimelb.edu.au

  2. Introduction – What’s the link between inequality and health? (And why does it matter?) Hypothesised effects (Leigh, Jencks and Smeeding, 2009) Absolute income hypothesis (health concave in income) Relative income (or relative deprivation) hypothesis (“status anxiety” – chronic stress from relative deprivation) Violent crime (including second-order effects on stress) Public spending (not necessarily only health-related) Social capital and trust (“income inequality hypothesis” of Wilkinson (1996) – various mechanisms, including effects on demands for public spending) (Matters for both health policy and redistribution policy) www.fbe.unimelb.edu.au

  3. Empirical evidence – Earlier studies (mostly 1980s and 1990s) (+) Infant mortality (–) Life expectancy (–) Average age at death (+) Mortality risk (–) Self-reported health www.fbe.unimelb.edu.au

  4. Shortcomings of older literature Cross-sectional data Health usually measured by aggregate statistic for whole country Not always comparable across countries Often for single or limited number of years Failure to account for substantial heterogeneity (lack of controls) Weak theoretical support Relates current health to current inequality www.fbe.unimelb.edu.au

  5. More recent studies Individual-level data – better controls Better / Alternative health measures Better / Alternative inequality measures  More mixed results These include studies: Using panel data on self-reported health (Weich, Lewis, and Jenkins 2002; Lillard and Burkhauser 2005; Lorgelly and Lindley 2008; Bechtel et al. 2012) Using alternative measures of inequality Including data from tax records (Leigh and Jencks 2007) Examining lagged effects(Blakely et al.,2000; Mellor and Milyo, 2003; Karlssonet al. 2010) www.fbe.unimelb.edu.au

  6. Our contribution Combine: Panel data on self-rated health from three countries Australia, Great Britain, United States New long-run country-level inequality measure from administrative tax records Investigate whether there is a link between early-life inequality (average in first 20 years of life) and later-life self-reported health What is the potential mechanism? Public spending / immunisation etc. most important when young – that is, health investments when young an important determinant of health in adulthood. www.fbe.unimelb.edu.au

  7. Data (other than for early-life inequality) US Panel Study of Income Dynamics (PSID) British Household Panel Study (BHPS) Household, Income and Labour Dynamics in Australia Survey (HILDA) Sample selection PSID: 1984 to 2009 BHPS: 1991 to 2008 HILDA: 2001 to 2011 Native-born individuals aged 21 and older Born after tax data first observed Britain: 1908 US: 1913 Australia : 1921 www.fbe.unimelb.edu.au

  8. Health measure 5-point scale in all countries: PSID: Would you say your health in general is excellent, very good, good, fair or poor? BHPS: Please think back over the last 12 months about how your health has been. Compared to people of your own age, would you say that your health has on the whole been excellent, good, fair, poor or very poor? HILDA: In general, would you say your health is excellent, very good, good, fair or poor? Limitations: Not entirely certain what is being measured, especially by HILDA and PSID (time frame, reference point) Potential endogeneity (eg, Johnston et al., 2009) www.fbe.unimelb.edu.au

  9. Health measure distribution (%) (GB categories in parentheses)

  10. Inequality data Tax records  Income share of the top 1% Available from early 20th century to present day Data for AU from Burkhauser, Hahn and Wilkins (2013) Data for GB and US from Top Incomes Database on the Paris School of Economics web site Excludes capital gains in AU and US; some of GB series includes some capital gains Inequality variable: Average income share of the top 1% over the first 20 years of life Each birth cohort has the same value. Identification comes from temporal variation. Age can be controlled for because we have multiple years of data on self-reported health www.fbe.unimelb.edu.au

  11. Inequality data – Limitations Pre-tax income Sensitive to the personal income tax base Tax unit differs across countries and time: Australia – individual GB – family until 1989, individual after US – family Top income share is correlated with measures of overall income inequality such as the Gini coefficient, but it’s not the same thing (Leigh, 2007) www.fbe.unimelb.edu.au

  12. www.fbe.unimelb.edu.au

  13. Empirical strategy Estimate ordered probit models Start with parsimonious model and progressively add controls M1: Early life inequality and time/period controls only M2: M1 + age controls M3: M2 + permanent household income M4: M3 + Father’s education and occupation Permanent income: Log of average equivalised income over all years up until two years before health measured Also control for the number of years over which permanent income measured Father’s education and occupation: Proxies for early-life economic resources Cluster on birth year www.fbe.unimelb.edu.au

  14. Results (coefficient estimates) – Men www.fbe.unimelb.edu.au

  15. Results (coefficient estimates) – Women www.fbe.unimelb.edu.au

  16. Mean marginal effects of early-life inequality – US (Model 3) www.fbe.unimelb.edu.au

  17. Robustness checks and caveats Restrict to the 2001-2009 period for all countries Restrict to the 1991-2009 period for US and GB Alternative specifications of time effects Alternative specifications of age effects Yet to examine whether US result robust to inclusion of measures of early-life income. www.fbe.unimelb.edu.au

  18. Discussion Focus on current inequality and current health theoretically weak We find evidence that early-life inequality matters in the US Permanent income and early-life income also appear to matter Further work: Early-life income measure for US Consider differences in effects of early-life income inequality by level of early-life income Consider inequality at other ages Explore other (objective?) measures of health (but data limitations) www.fbe.unimelb.edu.au

More Related