The impact of financial literacy on risk seeking and patient attitudes of university students

Figures & data

Table 1. Typical payoff matrix for the time preference experiments.

	Lottery A	Lottery B	Choose A or B
row	Payment in one week	Payment in one month and one week
1	R250	R250+ 10% interest = R252.09	A B
2	R250	R250 +20% interest = R254.20	A B
3	R250	R250+30% interest = R256.33	A B
4	R250	R250+40% interest = R258.47	A B
5	R250	R250+50% interest = R260.63	A B
6	R250	R250+60% interest = R262.81	A B
7	R250	R250+70% interest = R265.00	A B
8	R250	R250+80% interest = R267.22	A B
9	R250	R250+90% interest = R269.45	A B
10	R250	R250+100% interest = R271.70	A B

Table 2. Typical payoff matrix for the risk preference experiments.

	Lottery A				Lottery B
row	p	Rands	p	Rands	p	Rands	p	Rands	EVA in rands	EVB in rands	Choose A or B
1	0.1	60	0.9	50	0.1	100	0.9	25	51	32.5	A B
2	0.2	60	0.8	50	0.2	100	0.8	25	52	40	A B
3	0.3	60	0.7	50	0.3	100	0.7	25	53	47.5	A B
4	0.4	60	0.6	50	0.4	100	0.6	25	54	55	A B
5	0.5	60	0.5	50	0.5	100	0.5	25	55	62.5	A B
6	0.6	60	0.4	50	0.6	100	0.4	25	56	70	A B
7	0.7	60	0.3	50	0.7	100	0.3	25	57	77.5	A B
8	0.8	60	0.2	50	0.8	100	0.2	25	58	85	A B
9	0.9	60	0.1	50	0.9	100	0.1	25	59	92.5	A B
10	1	60	0	50	1	100	0	25	60	100	A B

Table 3. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
age	192	22.28	3.2346	18	44
income	192	1543.49	1189.03	500	10 000
literacy	192	40.00%	16.32	3.3%	80%

Table 4. Expected utility theory ML estimates Homogenous risk preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r	0.679	0.067	10.06	0.0000	0.5464 0.811
Error $\left(\mu \right)$	−0.394	0.031	−12.61	0.0000	−0.788 −0.332
N = 4431
log pseudolikelihood = −2657.12

Results account for clustering at the individual level.

Table 5. Expected utility theory ml estimates heterogeneous risk preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r
literacy	−0.009	0.012	−0.77	0.439	−0.032	0.014
constant	0.784	0.163	4.82	0.000	0.465	1.103
Error $\left(\mu \right)$	−0.394	0.031	−12.60	0.000	−0.456	0.333
N = 4151
log pseudolikelihood = −1912.66

Table 6. Expected utility theory ml estimates heterogeneous risk preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r
female	0.0304	0.039	0.78	0.434	−0.046	0.107
urban	0.059	0.045	1.32	0.186	−0.028	0.146
african	−0.018	0.093	0.2	0.844	−0.163	0.200
Asian	−0.054	0.123	−0.44	0.663	−0.295	0.187
colored	−0.029	0.085	−0.34	0.734	−0.195	0.138
non_decision_maker	−0.077	0.046	−1.67	0.095	−0.167	0.013
joint_decision_maker	−0.082	0.042	−1.98	0.048	−0.164	−0.001
literacy	−0.007	0.004	−1.61	0.107	−0.015	0.001
age	−0.025	0.012	−2.16	0.031	−0.047	−0.002
income	0.0001	0.000	2.91	0.004	0.0000	0.0001
_cons	3.321	0.297	11.18	0.000	2.739	3.903
Error $\left(\mu \right)$	−0.108	0.014	−7.69	0.000	−0.135	−0.080
N = 4146
log pseudolikelihood = −1987.70

Results account for clustering at the individual level.

Table 7. Discounting ml estimates expected utility theory and homogenous preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r	0.674	0.079	8.5	0.000	0.519 0.830
delta _cons $\left(\delta \right)$	0.731	0.111	6.57	0.000	0.513 0.950
noiseRA _cons $\left(\mu \right)$	−0.233	0.019	−12.54	0.000	−0.467 −0.197
noiseDR _cons $\left(v\right)$	−10.902	6.035	−1.81	0.071	−22.730 0.926
N = 8802
log pseudolikelihood = −6134.23

Results account for clustering at the individual level.

Table 8. Discounting ml estimates expected utility theory and heterogeneous preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r_cons	0.632	0.081	7.83	0.000	0.474 0.790
delta $\left(\delta \right)$
female	0.059	0.148	0.4	0.692	−0.231 0.348
urban	−0.106	0.157	−0.67	0.5	−0.414 0.202
african	−0.019	0.332	−0.06	0.955	−0.669 0.632
Asian	−0.208	0.677	−0.31	0.758	−1.536 1.119
colored	0.027	0.479	0.06	0.955	−0.911 0.965
non_decision_maker	−0.218	0.188	−1.16	0.247	−0.587 0.151
joint_decision_maker	−0.197	0.192	−1.02	0.306	−0.5738 0.1800
literacy	−0.012	0.013	−0.9	0.369	−0.0378 0.0140
age	0.022	0.018	1.22	0.221	−0.013 0.057
income	0.000	0.0001	−0.64	0.524	−0.0001 0.0001
_cons	0.613	0.632	0.97	0.332	−0.626 0.851
noiseRA _cons $\left(\mu \right)$	−0.230	0.019	−12.17	0.000	−0.267 −0.193
noiseDR _cons$\left(v\right)$	−7.780	4.499	−1.73	0.084	−16.597 1.038
N = 8802
log pseudolikelihood = −4929.03

Results account for clustering at the individual level.

Table 9. Expected utility theory ML estimates Homogenous risk preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r _cons	0.761	0.147	5.18	0.000	0.473 1.049
noise_cons $\left(\mu \right)$	−0.294	0.035	−8.49	0.000	−0.361 −0.226
N = 2050
log pseudolikelihood = −1145.25

Results account for clustering at the individual level.

Table 10. Expected utility theory ML estimates Heterogeneous risk preferences.

	Coefficient	Std. Err. z	Z	P > z	95% Conf. Interval
r
female	0.035	0.050	0.7	0.482	−0.063	0.134
urban	−0.006	0.063	−0.1	0.922	−0.129	0.117
Asian	−0.040	0.078	−0.51	0.609	−0.194	0.114
colored	−0.122	0.097	−1.26	0.207	−0.311	0.067
non_decision_maker	−0.036	0.072	−0.49	0.621	−0.178	0.106
joint_decision_maker	−0.058	0.064	−0.9	0.366	−0.183	0.067
literacy	−0.057	0.019	−3.01	0.003	−0.094	−0.020
age	−0.030	0.018	−1.67	0.095	−0.065	0.005
income	0.0001	0.00003	3.74	0.000	0.00005	0.0002
cons	4.097	0.496	8.26	0.000	3.124	5.069
noise _cons $\left(\mu \right)$	−0.139	0.024	−5.79	0.000	−0.186	−0.092
N = 1890
log pseudolikelihood = −1016.85

Results account for clustering at the individual level.

Table 11. Discounting ml estimates expected utility theory and homogenous preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r _cons	0.765	0.145	5.29	0.000	0.482	1.048
delta _cons $\left(\delta \right)$	0.824	0.222	3.71	0.000	0.388	1.259
noiseRA_cons $\left(\mu \right)$	−0.294	0.035	−8.49	0.000	−0.361	−0.226
noiseDR_cons$\left(v\right)$	−24.164	24.148	−1	0.317	−71.494	23.166
N = 4501
log pseudolikelihood = −2944.64

Results account for clustering at the individual level.

Table 12. Discounting ml estimates expected utility theory and heterogeneous preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r _cons	0.655	0.150	4.36	0.000	0.361	0.950
delta $\left(\delta \right)$
female	−0.215	0.214	−1.00	0.316	−0.635	0.205
urban	−0.227	0.321	−0.71	0.48	−0.857	0.403
colored	−0.207	0.321	−0.64	0.52	−0.836	0.422
non_decision_maker	−0.372	0.325	−1.14	0.252	−1.008	0.265
joint_decision_maker	−0.168	0.317	−0.53	0.596	−0.790	0.453
literacy	−0.132	0.060	−2.21	0.027	−0.250	−0.015
age	0.042	0.052	0.81	0.418	−0.060	0.145
income	0.00002	0.0001	0.19	0.846	−0.0002	0.0002
_cons	1.346	1.255	1.07	0.283	−1.113	3.806
noiseRA _cons $\left(\mu \right)$	−0.283	0.034	−8.25	0.000	−0.351	−0.216
noiseDR _cons$\left(v\right)$	−9.771	10.582	−0.92	0.356	−30.511	10.970
N = 4071
log pseudolikelihood = −2430.57

Results account for clustering at the individual level.

Figure 1. Fraction of Large Later choices and the interest rate offered.

Table A1. Expected utility theory ML estimates Homogenous risk preferences.

	Coefficient	Std. Err.	z	P > z	95% Conf. Interval
r _cons	0.594	0.093	6.37	0.000	0.411 0.776
noise_cons $\left(\mu \right)$	−0.189	0.021	−8.85	0.000	−0.230 −0.147
N = 2381
log pseudolikelihood = −1023.05

Results account for clustering at the individual level.

Table A2. Expected utility theory ML estimates Heterogeneous risk preferences.

	Coefficient	Std. Err. Z	Z	P > z	95% Conf. Interval
r
female	0.008	0.061	0.31	0.895	−0.111	0.127
urban	0.053	0.056	0.95	0.343	−0.056	0.162
African	−0.025	0.090	−0.27	0.786	−0.201	0.152
Asian	−0.042	0.197	−0.21	0.831	−0.428	0.344
colored	−0.077	0.080	−0.96	0.336	−0.233	0.079
non_decision_maker	−0.088	0.071	−1.24	0.216	−0.228	0.052
joint_decision_maker	0.023	0.043	0.55	0.584	−0.060	0.107
literacy	−0.004	0.008	−0.58	0.563	−0.019	0.011
age	−0.010	0.013	−0.78	0.437	−0.037	0.016
income	0.00003	0.00002	1.17	0.241	−0.00002	0.0001
_cons	2.929	0.365	8.02	0.000	2.213	3.644
noise _cons $\left(\mu \right)$	−0.069	0.010	−6.93	0.000	−0.088	−0.049
N = 2256
log pseudolikelihood = −943.32

Results account for clustering at the individual level.