Guidelines for designing and implementing
transferable non-market valuation studies:
A multi-country study of open-access water
quality improvements
Bateman , I.J., Brouwer, R., Ferrini, S., Schaafsma, M., Barton, D., Binner, A.,
Day, B.H., Dubgaard, A., Fezzi, C. Hasler, B., Hime, S., Liekens, I., Navrud, S.,
De Nocker, L., Ščeponavičiūtė, R. and Semėnienė, D.
Presented at the Water Economics Preconference to the 17th Annual Conference of the
European Association of Environmental and Resource Economists, Vrije Universiteit,
Boelelaan 1105, Amsterdam, Room 2A-00, 2.00pm, 24th 2009
Presented by Ian Bateman, University of East Anglia, UK.
De
Outline
•
Background issues
•
New principles for value function transfer
•
Case study: Common design
•
Results
•
Recommendations
EAERE 2009
Value transfers
Growth in:
EAERE 2009
- policy demand for value transfers
- academic scepticism about their robustness
Two alternative approaches:
1. Transfer mean values from study site (SS) to policy site (PS)
2. Transfer value functions:
Study site value function:
WTPSS = αSS + β1SSX1SS + β2SSX2SS + ... + βkSSXkSS
Transferred policy site value function:
WTPPS = αSS + β1SSX1PS + β2SSX2PS + ... + βkSSXkPS
Methodological development: sample at both sites to allow actual
versus predicted comparison and calculate transfer errors
Value transfer paradox
EAERE 2009
Expectation: Value function transfers attempt to control for the
variety of factors that determine value - so they should
generate lower errors than just transferring mean values
Empirics: Function transfer errors are SIMILAR or HIGHER than
mean value transfer errors
Paradox: Including determinants seems to increase errors!
Value function transfers
EAERE 2009
Study site value function:
WTPSS = αSS + β1SSX1SS + β2SSX2SS + ... + βkSSXkSS
Transferred policy site value function:
WTPPS = αSS + β1SSX1PS + β2SSX2PS + ... + βkSSXkPS
• Value function transfers assume that the same set of
variables determine values at both sites
• However, many valuation studies use 'best-fit' statistical
principles to build value functions. These include study site
specific variables which may not apply at all policy sites (or
at least not with the same coefficients)
Transferable value functions
EAERE 2009
• We need to construct value functions specifically for transfer
purposes.
• Rather than pursuing statistical 'best fit' principles, we
construct models using theory driven variables that should
apply to all study and policy sites
• Economic expectations suggests a range of such variables.
Transferable value functions
EAERE 2009
• WTP for (say) improvements in water quality at open-access
sites should be related to:
– Size of the improvement
– Income
– Accessibility (e.g. travel time to site)
– Availability of substitutes
• Omit ad-hoc variables (e.g. age) as these may be site specific
• Many SP studies fail to fully specify the actual location of
improvements. This is odd as it really matters. For example:
– Is the river 5km away from my home or 10km?
– Is there an alternative river 2km from my home?
– Is it in a location with lots of existing high quality sites?
• Spatial SP approach addresses this
Case study areas
EAERE 2009
5 countries:
Belgium
Denmark
Lithuania
Norway
UK
Main issue is
water quality
(e.g. eutrophication)
Common study design
EAERE 2009
Main objective: Transfer tests
Common valuation design:
- Common spatial SP approach
- Common WFD water quality ladder
- Common valuation scenario
- Common WTP payment card
- Common tests for scope sensitivity
- Common tests for procedural invariance
- Common sampling approach for spatial variation
levels
Water quality ladder
BLUE
Chemical
Need
to: indicators
Physical indicators
EAERE 2009
GREEN
YELLOW
Chemistry
BOD Limit < 4mgl-1
4mgl-1 <BOD Limit< 6mgl-1
Ammonia < 0.6 mgNl-1
Ammonia < 1.3 mgNl-1
BOD Limit >= 6 and <
8mgl-1
Ammonia < 2.5 mgNl-1
RED
BOD > 8mgl-1
Amm.> 2.5mgNl-1
Physical state
• Understand the relation between existing national
Patches of faster flow
Lower flow rate; no fast
patches
Small gravel and sand
substrate; little algae
Low flow rate
physical environment measures and water quality
Gravel substrate; No algae
Mud; algae on rocks
Very low flow rate
Mud; algae on rocks
Aquatic plants
• Convey water quality to survey respondents
Biological
objectives
No algae;
Water plants
Good clarity
Greater amount of aquatic
plants; Slight increase in
water turbidity
Less aquatic plants,
increase in algae, turbidity
and green hue, Small
number of algal mats
Large degree of
siltation; Turbid water
with a brown hue;
Algal mat cover
Tot. Veg. cover = 50%
Tot. Veg. cover = 60%
Tot.veg. cover = 70%
Tot. Veg. cover = 85%
Rhynchostegium riparoides;
Myriophyllum alterniflorum ;;
etc.
Apium nodiflorum;
Leptodictyum riparium; etc
Apium nodiflorum;
Leptodictyum riparium;
etc.
algae Cladopora etc.
Lower coarse fish, no
game fish.
Bream
Common Carp (low)
Roach (high)
Rudd (low)
Pike (v. low)
Stickle Back (mid)
Very few fish
Fish
Game and coarse
Value related
outcomes
Brown trout (mid)
Minnow (high)
Vendace (mid)
Barbel (mid)
Chub (mid)
Pike (v. low)
Same or higher coarse
numbers, few game fish
Bream
Common Carp (mid)
Perch (less) mid-water
Roach (mid) mid-water
Rudd (mid) mid-water
Pike (v. low) mid-water
Game and coarse fishing
Swimming
Canoeing & boating
Bird watching
Coarse fishing
Swimming
Canoeing & boating
Bird watching
Use and non-use values
10/7/2010
Restricted coarse fishing
Canoeing & boating
Bird watching
Restricted bird
watching
Valuation scenarios
EAERE 2009
Example: UK case study
STATUS QUO
ALTERNATIVE
(smaller
(larger improvement)
improvement)
InSpatially
each country
half sampling
theinsample
to valued
capture
the
real
smaller
world
variation
in the
before
availability
the larger
and
Looking
atdispersed
two changes
provision
allows
shows
usimprovement
how marginal
WTP
diminishes
improvement
while
quality
the remainder
of
the improvement
valued the larger
siteof
and
improvement
substitutes
then the values
smaller.
with increased
provision
- avoids
overstatement
multiple
improvement
Descriptives
EAERE 2009
Belgium
Denmark
Lithuania
Norway
Belgium
Denmark
Lithuania
NorwayUK UK Total
Total
Sample characteristics
Respondents’
characteristics
Number of respondents
768
754
500
1133
434
3589
Income (in € PPP/year/hh)
40877
34854
9531
24884
26686 28310
Protest bids (% of
5%
2%
8%
12%
2%
7%
Belgium
Denmark
Lithuania
Norway
UK
Total
Distance
to the
21
30
20
22
10
22
WTP
values
(in improved
€ PPP) site
country
sample)
(in km)
Average WTP- Small
47
25
6
42
19
31
Distance to the unimproved
3
24
1
7
5
9
substitute site (km)
Average
WTP- Large
48
36
8
47
26
37
Age
45
50
48
45
50
47
Urban (% urban)
45%
79%
63%
41%
78%
58%
Estimated value function
EAERE 2009
• We have two WTP values from each respondent (for the
•
smaller and larger improvement) in each country - therefore
a random effects panel Tobit model is applied.
We initially specify our transfer model to include only those
variables which economic expectations suggest should apply
in all countries.
Results from value
function
estimated from
data pooled for
all five EU
countries
Variable
Estimated β
Size of
improvement
Income
+
+
+
Distance to
site
Distance to
substitutes
Conforms to
expectations
Significant
YES
YES
YES
YES
YES
YES
YES
YES
Transfer errors
Belgium
Lithuania
EAERE 2009
Denmark
Norway
UK
Average
Mean value transfer
(adjusted for size of improvement)
Error
47
430
13
40
50
116
1
38
Value function transfer
Size of improvement, income
Error
42
62
25
60
Size of improvement, income, distance to site, distance to substitute
Error
47
40
17
65
2
35
Size of improvement, income, distance to site, distance to substitute, age, urban
Error
62
86
11
71
50
56
Conclusions
EAERE 2009
• Economic expectations are that value function transfer
should outperform mean value transfers. But this only
works if we use those expectations to guide the
specification of transfer functions.
• In this multi-country, cross-EU study, theory driven value
functions yield errors which are less than one-third of
those from mean value transfers.
• Discussions with policy makers suggest these would be
suitable for practical decision purposes.