Inequalities in the older population: Evidence from ELSA. James Banks, Carl Emmerson, Alastair Muriel and Gemma Tetlow 18 th November 2008

Transcription

1 Inequalities in the older population: Evidence from ELSA James Banks, Carl Emmerson, Alastair Muriel and Gemma Tetlow 18 th November 2008

2 ELSA and inequalities in the older population Multidimensional outcomes Economic, social, biological, psychological, environmental and key dimensions of capabilities e.g. Life; Bodily health, Bodily integrity; Senses, imagination and thought; Emotion; Practical reason; Affiliation; Other species; Play; Control over one s environment (Nussbaum, 2000) over time 6-10 years of panel data now available Early life circumstances, including SES and geography Selected life-histories, including employment and housing

3 ELSA and inequalities in the older population Strengths Multidimensionality means can measure correlation between dimensions Breadth versus depth of poverty and inequalities Targeting Subjective and objective measures of the same thing Link to mortality records (even for non-responders) Links to admin records, e.g earnings history, benefit take-up Weaknesses Older population only Only available since 2002

4 Overarching framework: A life-course approach to inequalities in the older population Short-medium term horizons Address current pensioner inequalities Alleviation of immediate pressures Address current employment/disability of older workers Quality of life and wellbeing more generally Health care and social services Medium-long term horizons Address the causes of poor health, disability, social exclusion and material poverty in the elderly Very long term horizons The long shadow of early life circumstances Considering past trends in returns to education, within-pensioner inequalities may well increase over next 20 years

5 Simulating pensioner income to per week Mean p10 p25 Median p75 p90 FRSMean FRSp10 FRSp25 FRSMedian FRSp75 FRSp90 Source: Brewer, et al (2007).

6 Net income distribution: 2003 Number of individuals (thousands) % Poverty Threshold 2003/04, ,000 per week, 2003/04 prices Note: net incomes shown under White Paper policy baseline Source: Brewer, et al (2007).

7 2017 Number of individuals (thousands) % Poverty Threshold 2003/04, ,000 per week, 2003/04 prices Note: net incomes shown under White Paper policy baseline Source: Brewer, et al (2007).

8 2017 Number of individuals (thousands) % Poverty Threshold 2017, ,000 per week, 2003/04 prices Note: net incomes shown under White Paper policy baseline Source: Brewer, et al (2007).

9 Correlation between pension wealth and nonpension wealth Pension wealth/ 000's Correlation between pension wealth and 300 non-pension wealth Total pension wealth Mean p25 p75 Median 0 Poorest Richest All Total non-pension wealth decile Source: Banks, Emmerson, Oldfield and Tetlow (2005).

10 Distribution of predicted retirement resources 100% % of the population 80% 60% 40% 20% 0% 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 Pension wealth Non-housing wealth Total wealth (incl. expected inheritances). plus Pension Credit Gross equivalised family income ( p.a.) Note: Assumes baseline (probabilistic) likelihood of working to SPA. Source: Banks, Emmerson, Oldfield and Tetlow (2005).

11 Health and wealth at older ages 100% Percentage of population 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Fair or poor health Good health Excellent or very good health 0 250, , ,000 1,000,000 1,250,000 1,500,000 Total w ealth ( ) Source: Banks et al (2005) calculations from 2002 ELSA, Individuals aged 50 to SPA

12 Survival after wave 1 (months) by wealth quintile; age adjusted 1.00 Male Richest Female 2 Poorest Survival probability 0.80 Survival probability Months since wave 1 Months since wave 1 Source: Nazroo, Zaniotto and Gjonca (2008), in Banks et al (2008), ELSA wave 3 report.

13 Odds for 5-year mortality after wave 1 Hazard ratio 95% ci Separated/Divorced 1.39 [1.11,1.74] Widowed 1.18 [1.02,1.37] Never Married 1.49 [1.18,1.89] p-value <0.001 <0.001 <0.001 Wealth quintile [0.82,1.31] Wealth quintile [0.92,1.45] Wealth quintile [1.17,1.82] <0.001 Wealth quintile [1.24,1.95] <0.001 Educ: Intermediate 0.91 [0.70,1.18] Educ: No qualifications 0.90 [0.68,1.18] Class: Intermediate Class: Routine/manual [0.88,1.28] [0.98,1.38] Additional controls not reported: Sex, age (5-year dummies), Physical activity, Smoking, drinking Reference group: yr old, male, non-smoker; high physical activity, non-drinker, highest wealth quintile, education: degree, class: managerial/professional Source: Nazroo, Zaniotto and Gjonca (2008), in Banks et al (2008), ELSA wave 3 report.

14 Per cent without any CVD-related disease by age-specific wealth quintile, sex and age Angina, myocardial infarction, stroke, heart failure heart murmur, abnormal heart rhythm, diabetes Poorest Richest Source: Breeze et al (2006), in Banks et al (2006), ELSA wave 2 report.

15 Per cent without any of six selected chronic physical diseases by age-specific wealth quintile, sex and age Chronic lung disease, asthma, arthritis, osteoporosis, cancer (excl. primary skin cancer), Parkinson s disease Poorest Richest Source: Breeze et al (2006), in Banks et al (2006), ELSA wave 2 report.

16 Incidence of at least one major condition between 2002 and 2004 by gender and age-specific wealth quintile in 2002 Men Men Women Women Poorest Richest Source: Breeze et al (2006), in Banks et al (2006), ELSA wave 2 report.

17 Perceptions of work disability by wealth quintile e.g. Geoffrey suffers from back pain that causes stiffness in his back especially at work, but it is relieved with low doses of medication. He does not have any pains other than this general discomfort. How much is Geoffrey limited in the kind or amount of work he could do? Percentage in paid work Extremely limited Severely limited Moderately limited Mildly limited Not limited 0 Poorest Richest Quintile of total non-pension wealth Source: Banks and Tetlow (2008), in Banks et al (2008), ELSA wave 3 report.

18 Odds of work disability by wealth quintile 5 4 Poorest Richest Unadjusted Adjusted for sex, age and education Adjusted for sex, age, education and reporting behaviour Source: Banks and Tetlow (2008), in Banks et al (2008), ELSA wave 3 report.

19 Probability of work disability onset; by work status and wealth Percentage who experience the onset of a work disability between waves 2 and 3 Percentage experiencing onset of work disability Working Not working Poorest 2nd 3rd 4th Richest Source: Banks and Tetlow (2008), in Banks et al (2008), ELSA wave 3 report.

20 Average annual real earnings growth over ten years Excludes those with earnings truncated at the UEL in either year and those with no earnings above LEL in either year Data removed awaiting approval for external dissemination

21 Earnings mobility: Men aged in 1975, transitions from 1975 to Data removed awaiting approval for external dissemination

22 Ongoing ELSA work Some analysis already underway: Late life consequences of previous employment/earnings differences Health and mortality differences and links to education/income/wealth Further understanding of disability and employment prior to SPA Other possible issues: More work on multi-dimensional inequalities Link between objective inequalities and subjective well-being measures Within-household spillovers, e.g. caring Household versus individual financial inequalities Pensioner specific equivalence scales

23 Inequalities in the older population: Evidence from ELSA James Banks, Carl Emmerson, Alastair Muriel and Gemma Tetlow 18 th November 2008

24 Well-being and quality of life (GHQ and CASP) Factors associated with self-reported well-being/quality of life: Income (+) Physical functioning (+) Being above retirement age (+) Marital status: Women in couples report highest quality of life but men in couples report highest level of well-being. Divorced/separated/widowed individuals report lowest quality of life (It is not better to have loved and lost...?) Source: Emmerson and Muriel (2008), in Banks et al (2008), ELSA wave 3 report.