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We estimate the labour market and health effects of a long-term unemployment (LTU) subsidy targeted to middle aged disadvantaged workers. In order to do so, we exploit a Spanish reform introduced in July 2012 that increased the age eligibility threshold to receive the subsidy from 52 to 55. Using a within-cohort identification strategy, we show that men ineligible for the subsidy were more likely to leave the labour force. In terms of health outcomes, although we do not report impacts on hospitalizations when considering the whole sample, we do find significant results when we separate the analysis by main diagnosis and gender. More specifically, we show a reduction by 12.9% in hospitalizations due to injuries as well as a drop by 2 percentage points in the probability of a mental health diagnosis for men who were eligible for the LTU subsidy. Our results highlight the role of long-term unemployment benefits as a protecting device for the health (both physical and mental) of middle aged, low educated men who are in a disadvantaged position in the labour market.

Background and Aims: The impact of tobacco control on European older adults has not been studied, despite evidence that smoking cessation at old age can bring significant life expectancy gains. Our aim was to evaluate the impact of tobacco control policies on smoking among older adults in Europe from 2004 to 2013. Design: We used longitudinal data from the Survey of Health, Ageing and Retirement in Europe (SHARE, aged 50+ years) from four waves from 2004 to 2013. We used logistic regression models with clustered standard errors to determine whether the implementation of tobacco control policies was associated with changes in smoking status. Furthermore, we studied whether these associations varied by socio-demographic characteristics. Regression coefficients were converted to changes the probability of smoking [marginal effects (ME)]. Measurements: Smoking status was the dependent variable, and the Tobacco Control Scale (TCS) was the explanatory variable, overall and by its main policy components (pricing and smoke-free policies). Covariates included age, sex, education and country and wave fixed-effects. Findings: A 10-point increase in TCS was associated with a lower probability of smoking by 1.6 percentage points [95% confidence interval (CI) = −3.208, −0.056] for those aged 50–65, but not for older Europeans. Among those with primary school or no education, the associated drop was of 1.5 percentage points (95% CI = –2.751, −0.253). By contrast, no significant relation between TCS and smoking was observed among those with high education. Higher TCS scores for pricing (ME = –0.636, 95% CI = –0.998, −0.275) and smoke-free policies (ME = –0.243, 95% CI = –0.445, −0.041) were associated with a significantly lower probability of smoking (P = 0.001 and P = 0.018, respectively). Conclusion: Increases in tobacco taxes and smoke-free policies are significantly related with a reduction in smoking among European older adults, suggesting potential health gains for this rising share of the population. These policies may be more effective among the lowest educated. publishersversion published

BACKGROUND: In a context of population ageing, it is a priority for planning and prevention to understand the socioeconomic (SE) patterning of functional limitations and its consequences on healthcare needs. This paper aims at measuring the gender and SE inequalities in functional limitations and their age of onset among the Southern European elderly; then, we evaluate how functional status is linked to formal and informal care use. METHODS: We used Portuguese, Italian and Spanish data from the Survey of Health, Ageing and Retirement in Europe (SHARE) of 2011 (n = 9233). We constructed a summary functional limitation score as the sum of two variables: i) Activities of Daily Living (ADL) and ii) Instrumental Activities of Daily Living (IADL). We modelled the functional limitation as a function of age, gender, education, subjective poverty, employment and marital status using multinomial logit models. We then estimated how functional limitation affected informal and formal care demand using negative binomial and logistic models. RESULTS: Women were 2.3 percentage points (pp) more likely to experience severe functional limitation than men, and overcame a 10% probability threshold of suffering from severe limitation around 5 years earlier. Subjective poverty was associated with a 3.1 pp. higher probability of severe functional limitation. Having a university degree reduced the probability of severe functional limitation by 3.5 pp. as compared to none educational level. Discrepancies were wider for the oldest old: women aged 65-79 years old were 3.3 pp. more likely to suffer severe limitations, the excess risk increasing to 15.5 pp. among those older than 80. Similarly, educational inequalities in functional limitation were wider at older ages. Being severely limited was related with a 32.1 pp. higher probability of receiving any informal care, as compared to those moderately limited. Finally, those severely limited had on average 3.2 hospitalization days and 4.6 doctor consultations more, per year, than those without limitations. CONCLUSION: Functional limitations are unequally distributed, hitting women and the worse-off earlier and more severely, with consequences on care needs. Considering the burden on healthcare systems and families, public health policies should seek to reduce current inequalities in functional limitations. publishersversion published

Coefficients of the negative binomial models of healthcare use. Robustness check (I) of Table 5. Standard errors in parentheses *** p

Marginal effects for functional limitation from the multinomial model, by age group. Robustness check (II) of Table 3. Standard errors in parentheses *** p

Marginal effects for functional limitation from the multinomial model, by age group. Robustness check (I) of Table 3. Standard errors in parentheses *** p

Marginal effects from the logistic model of informal care, among those limited. Robustness check of Table 4. Standard errors in parentheses *** p

Coefficients of the negative binomial models of healthcare use. Robustness check (II) of Table 5. Standard errors in parentheses *** p

Marginal effects for functional limitation from the multinomial model. Robustness check (II) of Table 2. Standard errors in parentheses *** p

Marginal effects for functional limitation from the multinomial model. Robustness check (III) of Table 2. Standard errors in parentheses *** p