Future Society in 2024: 21st Century 'Good Life' - Rural Economy ...

So, for example for the driver ‘the proportion of people working in agriculture’ we gave three possible
outcomes for 2024 – to increase, to stay the same or to decrease. If the proportion in agricultural
employment decreased then we felt, in terms of the relative importance of the typologies, that deep
rural, peripheral amenity and dynamic rural would decline while retirement retreats, transient rural,
dynamic commuter, and settled commuter would all gain, in relative terms. The model ensured that
the result was a zero sum game i.e. the gains and losses for the various typologies would be offset
each other.
The table below shows the direction of impact that each of the drivers has on each of the typologies
and an indication of the size of that impact (a scale running from –3 to +3).
Drivers and Relative Impact on typologies
Peripheral
Amenity
Deep Rural
Retirement
retreats
Transient Rural
Dynamic
commuter
Dynamic Rural
Settled
commuter
1 Proportion of over 65s (with attention to 45-64s) ++ + +++ - --- -- 0
2 Housing - laxer housing policy - - - -- +++ + +++
3 Transport and comms - improved transport links - -- -- 0 +++ + +++
- improved ITC provision and infrastructure --- + --- - + +++ +
4 Political and economy - proportion of people in agriculture + +++ -- - --- 0 --
5 - proportion of people in knowledge economy -- -- - - +++ +++ ++
6 - proprtion of people in managerial and professional -- - -- -- +++ ++ +++
7 - successful political initiatives towards rural areas + +++ -- 0 --- 0 ---
8 Leisure /tourism - authentic ++ +++ - -- --- + ---
- allegoristic -- - ++ + +++ - +
9 Positive attitudes and values towards the 'uses' of the typology 0 ++ - --- + + 0
As we noted above for each driver we have a range of possible outcomes for 2024 e.g. for the driver
‘the proportion of people of people working in agriculture’ the outcomes were: to increase, to stay
the same and to decrease. We then gave the probability of each outcome occurring. In this instance
we said that across all rural areas there was a 2.5% probability that agricultural employment would
be higher in 2024 than in 2001, a 7.5% probability that it would be broadly the same and a 90%
probability that it would be lower.
A technique known as a ‘Monte Carlo simulation, was then used to allow us to run a large series of
simulations of the model, with the drivers taking on different values dependent on the probabilities
associated with that driver (so, for instance, if we were to make 1,000 runs of the model in 25 of
those runs agricultural employment would increase, in 75 it would remain the same and in 900 it
would decline).
The use of the ‘Monte Carlo’ approach therefore not only gives us a central (or most likely) forecast
for the amount of land accounted for by each typology, but it also gives a probability distribution
around that central view.
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