@article {LEE2022100381,
	title = {Returning to work: The role of soft skills and automatability on unretirement decisions},
	journal = {The Journal of the Economics of Ageing},
	volume = {22},
	year = {2022},
	pages = {100381},
	abstract = {Many retirees are seeking to return to work after retirement. In light of growing unretirement, we explore the linkages between unretirement decisions and individuals{\textquoteright} skill-specific automatability, i.e., the degree to which machines can replace their skills. The automatability is measured by how routinized individuals{\textquoteright} skills are and how little non-routine analytical adaptability, non-routine social intelligence, non-routine physical adaptability, and non-routine interpersonal adaptability their skills encompass. Using data from the 2000{\textendash}2016 Health and Retirement Study, we find that retirees whose skills are more automatable are less likely than others to return to work. Among unretirees, those whose skills are more automatable tend to be hired into jobs whose task-contents are also more automatable. This work provides empirical evidence that retirees{\textquoteright} high automatability poses work disincentives and discourages productive aging.},
	keywords = {Automation, Post-retirement employment, productive aging, Soft skills, technological change, unretirement},
	issn = {2212-828X},
	doi = {10.1016/j.jeoa.2022.100381},
	author = {Zeewan Lee}
}
@article {10189,
	title = {On the Rise of Health Spending and Longevity},
	year = {2009},
	month = {12/2009},
	pages = {1-50},
	institution = {RAND},
	address = {Santa Monica, United States},
	abstract = {We use a calibrated stochastic life-cycle model of endogenous health
spending, asset accumulation and retirement to investigate the causes
behind the increase in health spending and life expectancy over the period
1965-2005. We estimate that technological change along with the increase
in the generosity of health insurance may explain independently 53\% of the
rise in health spending (insurance 29\% and technology 24\%) while income
explains less than 10\%. By simultaneously occurring over this period, these
changes may have lead to a {\textquotedblleft}synergy{\textquotedblright} or interaction effect which helps
explain an additional 37\% increase in health spending. We estimate that
technological change, taking the form of increased productivity at an annual
rate of 1.8\%, explains 59\% of the rise in life expectancy at age 50 over this
period while insurance and income explain less than 10\%. },
	keywords = {demand for health, health spending, Insurance, Longevity, technological change},
	url = {https://www.rand.org/content/dam/rand/pubs/working_papers/2010/RAND_WR722.pdf},
	author = {Raquel Fonseca and Pierre-Carl Michaud and Galama, Titus and Arie Kapteyn}
}
