Calculating marginal tax rates when near a cliff When using tax and transfer microsimulation models, a common approach for calculating marginal tax rates is to recalculate the tax liability when adding $1 to earnings. This will take into account various tax laws that interact in complex ways.

However, this will generally miss the effects of cliffs, such as losing Medicaid, ACA subsidies, or SSDI, unless a household is $1 below the threshold.

Is there a best practice to deal with this? A couple ideas come to mind:


Recalculate the tax liability for multiple changes to earnings, such as $1, $100, $1,000, etc., and weigh the results by the distance (e.g. 1/x).
Make copies of the underlying data, varying the initial earnings within some range, e.g. +/- $1,000, possibly oversampling near the true earnings (e.g. triangular distribution).
The marginal tax rate is supposed to reflect incentives for the individual. In order to derive a meaningful metric, the calculation should hence try to reflect the actual decision an individual is facing. An increase in \$1 pay is probably not something people are considering. If, however, someone earning \$10 per hour considers to increase his/her labor supply from 35 to 37 hours per week, you will end up with a pay increase of sth like \$1,000 per year. So I'm rather sympathetic to the \$1,000 step than to the $1 step.  

The OECD also applies a change in hours worked to obtain the change in gross income used for calculating marginal tax rates.