02173cam a22002297 4500001000600000003000500006005001700011008004100028100002200069245010200091260006600193490004100259500001800300520126500318530006101583538007201644538003601716710004201752830007601794856003701870856003601907w0788NBER20160501182609.0160501s1981 mau||||fs|||| 000 0 eng d1 aHausman, Jerry A.10aStochastic Problems in the Simulation of Labor Supplyh[electronic resource] /cJerry A. Hausman. aCambridge, Mass.bNational Bureau of Economic Researchc1981.1 aNBER working paper seriesvno. w0788 aOctober 1981.3 aModern work in labor supply attempts to account for nonlinear budget sets created by government tax and transfer programs. Progressive taxation leads to nonlinear convex budget sets while the earned income credit, social security contributions, AFDC, and the proposed NIT plans all lead to nonlinear, nonconvex budget sets. Where nonlinear budget sets occur, the expected value of the random variable, labor supply, can no longer be calculated by simply 'plugging in' the estimated coefficients. Properties of the stochastic terms which arise from the residual or from a stochastic preference structure need to be accounted for. This paper considers both analytical approaches and Monte Carlo approaches to the problem. We attempt to find accurate and low cost computational techniques which would permit extensive use of simulation methodology. Large samples are typically included in such simulations which makes computational techniques an important consideration. But these large samples may also lead to simplifications in computational techniques because of the averaging process used in calculation of simulation results. This paper investigates the tradeoffs available between computational accuracy and cost in simulation exercises over large samples. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web.2 aNational Bureau of Economic Research. 0aWorking Paper Series (National Bureau of Economic Research)vno. w0788.4 uhttp://www.nber.org/papers/w078841uhttp://dx.doi.org/10.3386/w0788