Dissertation

Table 2.6: Effect of population aging on household behavior and factor prices under different values of capital’s share in total income.

(a) Effect of population aging with α = 0.3.
Baseline Case 1 Case 2 Case 3 Case 4

Capital stock 38.78 - 48.51 - 48.79 Retirement age (actual) 64.2 - - 65.12 67.12 Labor supply 10.43 - 11.6 - 12.13 Rate of return 0.0452 - 0.0358 - 0.0388 Wage rate 1.04 - 1.08 - 1.06

(b) Effect of population aging with α = 0.4.
Baseline Case 1 Case 2 Case 3 Case 4

Capital stock 48.11 - 58.47 - 59.15 Retirement age (actual) 64.36 - - 64.2 67 Labor supply 10.45 - 11.63 - 12.11 Rate of return 0.0857 - 0.0774 - 0.0801 Wage rate 1.11 - 1.15 - 1.13
The next step is to verify the sensitivity of the simulation results with respect to the

leisure share of total time endowment parameter (l_W ). According to 2001 CPS data on hours

worked by production workers in the U.S., 34 hours per week on an average were spent on

market work. Accounting for 8 hours per day as sleep time, this roughly implies that

30.36% of the total weekly time endowment is spent on market work. However, to account

for vacations, holidays, weekends and sick days, the value of the leisure share parameter

needs to be revised downward. Therefore, to verify the sensitivity of the simulation results

with respect to the leisure share of total time endowment parameter (l_W ), I compute new calibrated baseline equilibria with the values l_W = 0.575 (based on a five-day working week) and l_W = 0.6458 (based on a six-day working week), and then re-compute the impact of

population aging on the projected retirement benefits. The unknown preference parameters

and the associated model-generated target values for the calibrated baseline equilibria with

l_W = 0.575, 0.6458 and 0.6964 are reported in Table 2.7. It is clear from the table that calibrated baseline equilibria of the model under l_W = 0.575 and l_W = 0.6458 provide

reasonable fits to the data targets, with the values for the unknown parameters falling

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