Table 7.

Influence of WFH on number of weekly one-way modal commuter trips.

	Wave 1		Wave 2
	Car	Public Transport	Car	Public Transport
Constant	0.468 (3.64)	−2.114 (−9.1)	0.7060 (6.27)	−0.2421 (−5.39)
Annual household income ($000 s)	0.0035 (4.49)	−0.0207 (−15.6)	0.0032 (12.95)	−0.0036 (−4.59)
Professionals (ABS 8 classes)		0.498 (5.37)	0.1675 (4.74)	0.3321 (4.03)
Metro Location (Sydney, Brisbane, Melbourne) (1,0)	–	–	–	1.4878 (5.86)
Other capital cities (1,0)	–	–	–	0.4782 (3.80)
Urban (including metro and capital cities) (1,0)	–	–	0.0690 (1.99)	–
Male (1,0)	–	1.231 (12.23)	–	–
Health risk to me personally (10 = extremely high)	–	–	−0.0014 (−1.61)	–
Probability WFH 0 days per week	1.094 (5.97)	−1.9408 (−9.96)	1.4043 (24.29)	0.4303 (3.53)
Probability WFH 1 day per week	–	–	1.6014 (4.40)	–
Tau (ZIP)	–	–	−0.3265 (−25.20)	−0.8627 (−2.93)
Sigma (latent heterogeneity)	1.322 (19.9)	3.5322 (18.96)	0.7274 (38.62)	1.9176 (27.40)
Goodness of fit
Pseudo R2	0.404	0.671	0.369	0.681
Vuong stat vs Poisson	9.33	4.605	24.25	6.485
Partial effects
Annual household income ($000 s)	0.031 (4.28)	−0.378 (−6.1)	0.008 (12.9)	−0.0004 (−6.31)
Professionals (ABS 8 classes)	–	9.12 (4.54)	0.408 (4.72)	0.0394 (4.12)
Urban (including metro and capital cities) (1,0)	–	–	0.1680 (1.98)
Metro Location (Sydney, Brisbane, Melbourne) (1,0)	–	–	–	0.177 (8.44)
Other capital cities (1,0)	–	–	–	0.0568(5.03)
Male (1,0)	–	22.54 (7.41)	–	–
Health risk to me personally (10 = extremely high)	–	–	−0.003 (−2.2)	–
Probability WFH 0 days per week	9.81 (5.22)	−35.53 (−5.24)	3.418 (23.1)	0.0511 (4.92)
Probability WFH 1 day per week	–	–	3.897(4.41)	–

Note: Vuong test favours extended model; Murphy and Topel correction of standard errors. The constants in the models were calibrated to match the predicted average trips to the actual average trips in the sample.

Note: t-values are provided in brackets within each table and the 95% confidence intervals for each parameter estimate are available on request.