Figures and data in Parameterising a detailed dynamic programming model of savings and labour supply using cross-sectional data

Cite this article as: J. W. van de Ven; 2017; Parameterising a detailed dynamic programming model of savings and labour supply using cross-sectional data; International Journal of Microsimulation; 10(1); 135-166. doi: 10.34196/ijm.00152

2 tables

Tables

Table 1

Calibrated model parameters adjusted to match behaviour reported for the British population cross-section in 2011.

	singles	couples
relative risk aversion, γ	1.55	1.55
intratemporal elasticity, ε	0.6	0.6
utility price of leisure, α	2.2	1.034
d1scount factor, δ	0.97	0.93
bequest mot1ve, ζ	30000	10000
factor effects of pens1on take-up
at age 55	1	1
from age 65	0.6	0.6
rents	lower	upper
shared	63.89	63.89
1 bedroom (from spa)	55.00	100.00
1 bedroom (pre spa)	101.48	106.82
2 bedrooms	69.25	132.54
3 bedrooms	81.82	156.59
4 bedrooms	107.12	205.01

Note: ‘spa’ refers to rents from ‘state pens1on age’.

Table 2

Effects of model parameters on simulated moments considered for calibration.

	high alpha		no retirement effects		low delta		no bequest		low epsilon		high gamma		high rents
	singles	couples	singles	couples	singles	couples	singles	couples	singles	couples	singles	couples	singles	couples
proportions of population not employed
age 18–29	0.005	0.003	0.000	−0.001	0.008	0.008	0.006	0.005	−0.003	0.001	0.001	−0.002	0.000	−0.001
age 30–54	0.018	0.037	0.002	0.005	0.037	0.076	0.090	0.096	−0.015	−0.006	0.035	−0.004	0.000	0.000
age 55–74	0.024	0.028	−0.098	−0.117	0.004	0.026	0.159	0.141	−0.038	−0.023	0.052	0.002	0.000	0.000
geometric mean consumption (£2011 per week)
age 18–29	−2.871	−7.022	0.228	0.741	10.279	36.642	4.593	15.421	−4.252	−2.864	9.681	8.327	8.155	11.605
age 30–54	−2.754	−12.967	1.248	8.072	13.229	68.993	25.536	92.553	−9.079	−34.765	19.582	21.433	30.011	14.241
age 55–74	−1.560	−4.169	3.168	22.309	12.742	68.909	139.796	340.303	−10.600	−21.884	29.616	2.075	16.485	7.192
proportions of population participating in private pensions
age 18–29	0.004	0.014	−0.006	−0.004	−0.159	−0.112	−0.053	−0.002	0.015	0.016	−0.158	−0.II7	−0.001	−0.002
age 30–54	−0.013	−0.012	−0.002	−0.010	−0.104	−0.197	−0.072	−0.039	0.022	−0.002	−0.132	−O.278	−0.001	−0.003
age 55–64	−0.009	−0.016	−0.003	−0.026	−0.043	−0.031	−0.048	−0.034	0.000	−0.003	−0.081	−0.0é2	−0.012	0.000
equivalised consumption to leisure ratio of graduates to non-graduates – aged 55–60
	−0.066		−0.015		−0.025		0.127		−0.181		0.123		−0.062
proportion of population below 60% of median income (%)
	all	retirees	all	retirees	all	retirees	all	retirees	all	retirees	all	retirees	all	retirees
BHC	−1	−2	1	−1	−2	−4	−2	−4	0	1	−2	−3	−2	−1
AHC	0	−1	0	−1	−1	−2	−1	−2	0	1	0	−2	−1	1

Source: Author’s calculations on simulated data.
Notes: All simulations based on the calibrated model parameters, except where explicitly indicated.
‘high alpha’ simulation increases calibrated values of alpha by 20 percent.
‘no retirement effect’ simulation suppresses wage penalties of pension take-up (from 55).
‘low delta’ simulation reduce exponential discount factors by 3 percentage points.
‘no bequest’ simulation omits warm glow bequest motive.
‘low epsilon’ simulation reduces intratemporal elasticity by half.
‘high gamma’ simulation assumes: gamma = 2.0, delta(single) = 0.95, delta(couple) = 0.89, alpha reduced by 12%, zeta (single) = 3300000, zeta (couple) = 2650000.
‘high rents’ simulation increases all simulated rental charges by 50%.

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