Wednesday, 19 December 2018

Value of Unpaid Work and Leisure time

Oftentimes, cost effectiveness analyses of new treatments measure health benefits less medical costs.  However, getting treatment often involves a significant amount of time cost.  Some cost-effectiveness analyses take into account take into account lost productivity amount individuals who work. However, these analyses rarely take into account lost time that could be used for unpaid work or leisure time.  

Consider the case of renewing your driver’s license at the DMV.  If you were measuring the cost and benefits of enforcing driver’s license, you may measure the benefits in terms of fewer accidents from making sure individuals have good eyesight, and are decent drivers and the costs would be the costs of administering the system.  As anyone who has visited the DMV knows, however, this cost-benefit analysis would greatly underestimate the cost of the system.  If people are working and the DMV is only open during work hours, individuals need to take time off of work to go to the DMV to renew their license.  However, if someone is retired, should we count the lost time waiting at the DMV as part of the cost-benefit analysis?  I would argue certainly so.  This DMV wait time certainly imposes disutility, even among retired individuals.

If we want to include the value of this time among retired or nonworking individuals, what value should we place on it?  A paper by Verbooy et al. (2018) conduct a contingent valuation survey to measure both willingness to accept a reduction in leisure time or unpaid work and willingness to pay for additional time for leisure or unpaid work.  Using this approach, the authors find: 

The average WTA value for unpaid work was €15.83, and the average WTA value for leisure time was €15.86. The mean WTP value for leisure time was €9.37 when traded against unpaid work, and €9.56 when traded against paid work. Differences in monetary values of unpaid work and leisure time were partly explained by respondents’ income, educational level, age, and household composition.


With this information from  Verbooy et al. (2018) we can now apply this cost into standard cost benefit analysis in the medical sector.  For instance, if there is an intervention that is more expensive, but imposes significant wait times on patients, we can now estimate the value of this interventions among both working and non-working individuals.

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