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Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Mortality and Life Expectancy in Post-Communist Countries: -- What Are the Lessons for Other Countries? -- Introduction -- 1. Stylised Facts -- 2. Literature Review -- 3. Puzzles and Hypotheses -- References -- Chapter 2 -- A Theory of Why Potentially Favourable Political and Economic Changes May Lead to Mortality Crises -- 1. Introduction -- 2. Sources of Future Favourable Changes -- 3. Progressive Political Reforms and Rising Mortality -- 3.1. The Abolition of Slavery in the US, 1850-1880 -- 3.2. The South African Transition to Democracy, 1989-2004 -- 4. Rural-Urban Migration, Inadequate Public Urban Infrastructure, and Rising Mortality -- 4.1. Rapid Urbanisation and Mortality Increases in 19th-Century Britain -- 4.2. Rising Male Mortality during the Urbanisation of Sweden, 1810-50 -- 5. Contact and Contagion between Heterogeneous Populations -- 6. Liberal Market Reforms and Health Crises -- Conclusion -- Acknowledgments -- References -- Chapter 3 -- Mortality Crises in High-Income Countries: Evidence from the United States, the United Kingdom, Italy, and Greece -- 1. Introduction -- 2. 'Health Reversals': The Unexpected Decline of Life Expectancy at Birth in High-Income Nations In 2015 -- 2.1. Potential Explanations for the Decline -- 2.1.1. Peak Longevity -- 2.1.2. Rise of Infant Mortality -- 2.1.3. Worsening Elderly Health -- 2.1.4. Drug Overdose in Working-Age Men -- 2.2. The Need for a Comprehensive Approach in the Study of Mortality Crises -- 3. 'The Liberalisation of Desperation': The Rise of Suicides, Alcohol Deaths, and Drug Overdoses Among Middle-Aged White Americans After Trade Liberalisation Policies in 2000 -- 3.1. Distal (Economic and Political) Determinants -- 3.1.1. Globalisation and Trade Liberalisation Policies.

Knowledge of trends in life expectancy is of major importance for policy planning. It is also a key indicator for assessing future development of life insurance products, substantiality of existing retirement schemes, and long‐term care for the elderly. This article examines the feasibility of decomposing age‐gender‐specific accidental and natural mortality rates. We study this decomposition by using the Lee and Carter model. In particular, we fit the Poisson log‐bilinear version of this model proposed by Wilmoth and Brouhns et al. to historical (1975–1998) Spanish mortality rates. In addition, by using the model introduced by Wilmoth and Valkonen we analyze mortality‐gender differentials for accidental and natural rates. We present aggregated life expectancy forecasts compared with those constructed using nondecomposed mortality rates.

AbstractWe investigate the relationship between eudaimonic wellbeing (sense of life) and subjective survival probability (SSP), a proxy for self‐assessed life expectancy. Our econometric analysis uses 220,601 observations of SHARE panel data from 2006 to 2015. We find evidence of a robust and strong positive relationship between eudaimonic wellbeing and subjective survival probability after controlling for self‐assessed health, coupled with a negative effect of sense of life on mortality. The magnitude of the first effect is relevant, since the minimum difference (adjusted for fixed effects) between individuals declaring the highest versus the lowest sense of life is a 7‐point higher self‐assessed probability of being alive at the target age. Together, our two main findings imply that when respondents declare a high sense of life, they self‐report a lower mortality risk and their predictions are correct.