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- • As Karahan and Ozkan (2013), we distinguish three education groups: individuals without high school degree, high school graduates and college graduates. The education dummies take on the values 9.570346, 9.91647 and 10.26789 respectively. Based on all these parameters, one can now simulate the evolution of the earnings distribution. We simulated millions of lives such that a law of large numbers applies. For each simulated life, we then have the income for each year, which allows us to calculate the average income of one individual for all three parts of his life. For our simulations these are the age groups 24-36, 37-49 and 50-62 – see main text. We set the initial share of non high-school graduates to 0.15, for high-school graduates to 0.60 and for college graduates to 0.15. This matches well US numbers – see, for example, the NLSY97.
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- B Details on Calibration We use the empirical model from Karahan and Ozkan (2013), who estimate their model using PSID-data. yi h,t denotes log income of individual i at age h in period t. To obtain residual log incomes ˜ yi h,t, the authors regress log earnings on some observables (age and education): yi h,t = f(Xi a; θt) + ˜ yi h,t, where f(Xi a) is a function of the observable characteristics. Residual income is then decomposed into a fixed effect (αi ), an AR(1) component (zi h,t) and a transitory component (Æti h): ˜ yi h,t = αi + zi h,t + Æti h,t, where the AR(1) process is given by zi h,t = ÃÂh−1zi h−1,t−1 + Àtηi h, and where the error term ηi h captures persistent shocks, Àt is a time dependent loading factor and ÃÂh−1 measures the persistence of these shocks.
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- Based on non-parametric estimates, Karahan and Ozkan (2013) divide individuals into three age groups: 24-33 (young), 34-52 (middle age) and 53-60 (old). In the following, we list the values they obtain for the different parameters, where the indices Y, M, O correspond to the three age groups from their paper.
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- We next discretize the earnings distribution. Thus for each simulated life, we then have 3 grid points; one for each period. With a standard kernel smoother (bandwidth of $2,500), 0 20 40 60 80 100 120 140 160 180 200 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 Densities Income, 1000s 24−36 37−49 50−62
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