Experimental Design Details
The experiment reflects a ‘lifetime’ of 25 periods, 17 ‘working’ periods (WPs) and 8 ‘retirement’ periods (RPs). In each period, the participants decide on how much to consume, c_t, or save, s_t, given a token inventory of w_t=(1+r)s_(t-1)+y_t, the sum of last period’s savings next to interest and this period’s income (during WPs only). During WPs, we enforce a compulsory consumption of c ̅ . The consumed tokens convert to eurocent via the ‘consumption utility’ function u_c (c_t )=C_t=a_c×ln(b_c (c_t-c ̅)+d_c ), i.e. every additional token consumed has a lower eurocent value. The parameters a, b, and c shape the utility function in a meaningful way. The compulsory consumption yields no euro cents. During the RPs, the token inventory reduces to w_t=(1+r)s_(t-1). In the very last RPs, c_T=w_T holds. The income y_t equals the excerpted effort e_t∈{0,E} times a wage factor w(h_t) depending on a human capital level, h_t, such that y_t=w(h_t )e. We see E as the period capacity that can be used for working (e_t) or leisure l_t=E-e_t. Hence, the period capacity not used for work yields euro cents via the ‘leisure utility’ u_l (l_t )=L_t=a_l×ln(b_l (E-e_t)+d_l ), i.e. every additional leisure unit has a lower eurocent value. Within a working period, the participant first decides on e_t, then learns about w_t, and finally decides on c_t. The environment described so far reflects the baseline treatment (B) without unconditional basic income (UBI) and human capital accumulation (HCA). The following treatment adds the additional features.
Treatment U (the UBI treatment)): We add the unconditional basic income to treatment B in that we add a fixed payment ubi_t=ubi to the period token inventory such that w_t= y_t+(1+r) s_t+ubi. The RPs earn no UBI.
Treatment H (the HCA treatment): We add human capital accumulation as follows. Participants can opt to undertake ‘training’ to improve human capital level h_t∈\{1,2,…\}, and thus their wage level w(h_t )=w×h_t. A training period needs the entire capacity such that neither income nor leisure utility can be generated, i.e. y_t=0 and L_t=0. Moreover, not everyone will make it to the next level; the training is a memoryless stochastic process, with a 2/3 chance of improving to the next level, during the RPs, y_t=0 and L_t=0.
Treatment UH combines the treatments U and H, i.e. participants earn ubi and can train to increase their human capital level.
Setup: In a between-subjects design, the participants go through two lifetimes. The first one is practice; the second one translates to their Euro payment. The participant's payment equals the show-up X plus the earnings from the second lifetime, i.e. Total=X+ [∑ _(t=1)^25 C_t ] + [ ∑ _(t=1)^17 L_t .
We will run the experiment in an in-person lab setting, recruiting students via ORSEE (Greiner, 2015) who will sit at a computer cube submitting decisions using zTree software (Fischbacher, 2007).
Based on simulations with our model adjustment of the Duffy & Li (2019) setup, we set the value in line with the optimal decision path {c_ip^(**),e_ip^(**),h_ip^(**) \}_(p=1)^P such that optimal players will earn the average student per-hour salary.