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CPS2128 Wlodzimierz Okrasa et al.

= + + + + ( 0 + + 1 1 + 2 2 ) (7)

1 1

- where is a 2-level predictor. i.e. the index of local deprivation. .
1
1
The following model was calculated using data from Time Use Survey 2013 (22
695 and 24 065 persons surveyed for weekdays and for weekends/holidays.
respectively):
( − ) = 00 + + + +
10

20

1

∗ + ∗ + + +
2

11

1

21

0 + (8)

[It is assumed that] Such a specification of cross-level (between individual and
community/gmina measures of well-being) with cross-level interaction effect
should ensure robust estimation (e.g.. Subramanian. op. cit.. p. 521; Hox et al..
2018
e. Spatial aspects - checking for spatial dependence Estimation of the spatial
regression model parameters: (notation for individual observation i):
= ∑ = 1 + ∑ = 1 + (9)

ℎ: – the dependent variable for observation ; – explanatory

variables. r = 1. …. k with associated coefficient ; is the disturbance term;

is parameter of the strength of the average association between the
dependent variable values for region/observations and the average of them
for their neighbours (eg.. LeSage and Pace. 2010. p. 357). The above
specification of the spatial regression model assumes that is meant as the

spatially lagged term – versus spatial error formulation - for the dependent
variable (which is correlated with the dependent variable). that is: =

. + + . Both types of models are used below to check how and why

‘place’ and ‘space‘ matter.

3. Result
Some at a glance results and observations [comments to be added]
3.1 Impact of income vs. work time (acc. to 'well-being equation'): Opposite
directions of influence of income and time-in-work on wellbeing , according
to U-index: while greater income is positive for individual wellbeing, the
increased amount of time spent on work is negative (U- index increases) -
question arises about the point of balance (trade-off between the two factors
of wellbeing). (See Kahneman and Deaton 2010 for comparison of income
effect)

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