Economic Valuation of Environmental Impacts in ... - ARCHIVE: Defra

Economic Valuation of Environmental 

Impacts in the Severely Disadvantaged 

Areas 

Final Report 

Submitted to 

Department for Environment, Food and Rural Affairs 

3 rd January 2006 

Economics For The Environment Consultancy Ltd (eftec) 16 Percy Street London W1T 

1DT, tel: 02075805383, fax: 02075805385, eftec@eftec.co.uk, www.eftec.co.uk

Economic Valuation of Environmental Impacts in the Severely Disadvantaged Areas 

This report has been prepared by 

Ms Helen Johns, eftec 

Ms Ece Ozdemiroglu, eftec 

Professor Nick Hanley, Stirling University 

Dr Sergio Colombo, University of Stirling / IFAPA Junta de Andalusia 

Dr Alistair Hamilton, Stirling University 

Dr Tony Hyde, Socio-Economic Research Services 

The fieldwork for this report was undertaken by Carrick James Market Research and Socio- 

Economic Research Services. 

Acknowledgements 

Preliminary work on the forecast changes to upland attributes by eftec was reviewed and 

expanded by Cumulus Consultants Ltd. in association with the Institute for European 

Environmental Policy and the Countryside and Community Research Unit. This work is 

quoted extensively in Section 2. 

The study team would like to thank Professor Ian Bateman (University of East Anglia) who, 

as the peer reviewer, provided valuable comments at various stages of the work, and Dr 

Stavros Georgiou (CSERGE, University of East Anglia), who moderated the focus groups. The 

study team would also like to thank the Steering Group for their valuable comments 

throughout the study. 

eftec January 2006


Executive Summary 

E.1 Introduction 

This report summarises the methodology and findings of a study to estimate the economic 

value of changes in environmental features associated with the Severely Disadvantaged 

Areas in England, which could arise from changes to the Government support for agriculture 

in Less Favoured Area. 

Severely Disadvantaged Areas (SDAs) and Disadvantaged Areas (DAs) together comprise the 

English Less Favoured Areas (LFA). These are areas where farming is more difficult because 

of poor climate, soils and terrain. These lead to lower yields and higher production and 

transportation costs. LFAs are located mainly in upland hill-farming areas. They include 

almost all of the upland areas in the North of England (including the Pennines, Lake District 

and North York Moors), the Peak District, some (from an English perspective) of the English- 

Welsh border, Exmoor, Dartmoor, and parts of Cornwall. Farming plays a crucial role in 

maintaining the distinctive landscape of such areas. 

The instrument for support of upland farming, the Hill Farm Allowance (HFA), is currently 

being revised as part of the new Rural Development Regulation which covers the period 

2007-2013. The ultimate policy objective of this revision is to generate the maximum public 

benefit provided by upland farmers. 

The principal stages of this study can be summarised as follows: 

• Identify a number of different policy scenarios which could arise in relation to the 

revision of the HFA (initially identified by the eftec team and later expanded upon by 

Cumulus et al. (2005), which was subsequently used in the economic analysis); 

• Predict the likely effects – predominantly in terms of changes in quantity rather than 

quality - on fourteen different upland environmental attributes of these different policy 

scenarios; 

• Estimate the economic value of the changes in some of these attributes through a stated 

preference survey (the principal method being choice experiment); 

• Estimate the economic value of the changes in the rest of the attributes through benefit 

transfer; and; 

• Aggregate the economic values per attribute to estimate the total welfare change for 

different LFA policy revision scenarios. 

The economic valuation aspect of this study has been accomplished by means of a stated 

preference survey of the six Government Office Regions containing SDAs (North West, North 

East, Yorkshire and Humber, West Midlands, East Midlands and South West), as well as one 

survey in a region not containing SDAs (the South East). The principal valuation method 

used was the choice experiment approach, but the survey also contained an element of 

contingent valuation. 

E.2 Upland attributes and policy scenarios 

The long list of upland attributes considered for inclusion in the survey was: heather 

moorland and bog, improved grassland, rough grassland, hay meadows, bracken, gorse, 

arable & set aside land, broadleaf and mixed woodland, coniferous woodland, field 

boundaries (hedgerows and drystone walls), cultural heritage, water quantity, water 

quality, and greenhouse gas emissions. Of these, heather moorland and bog, rough 

grassland, broadleaf and mixed woodland, field boundaries and cultural heritage were 

chosen as the five attributes to be used in the choice experiment. Cultural heritage may be 

taken to include the visual presence in the landscape of traditional farm buildings; less 

visual aspects may be the presence of animals on the hill, traditional breeds, or traditional 

farming practices such as shepherding with sheep dogs. Given the shortlist of attributes 

eftec i January 2006


chosen, only changes to quantity, rather than quality, of the attributes (except for cultural 

heritage) were considered. 

Four different possible policy scenarios were identified and described. These are 

summarised as follows: 

Scenario 0 – Baseline: there is no change in the strategic aims, structure and level of 

upland support. Support remains focused on maintaining upland farming systems with 

environmental enhancement being a secondary objective. In other words the Hill Farm 

Allowance scheme remains the same as it is presently. 

Scenario 1 - Environment-agri: the strategic policy aims to reflect the importance of 

environmental and conservation objectives over production and the maintenance of upland 

farming regimes. The same amount of LFA-type support still exists, but is targeted more 

towards achieving environmental goals. 

Scenario 2 - Environment only: the strategic aims for upland support are focused solely on 

achieving environmental goals. It is assumed that existing support is maintained, but it is 

incorporated into the agri-environment scheme budget and ring-fenced for the uplands. 

The LFA scheme disappears. 

Scenario 3 – Abandonment-intensification: under this scenario, upland support is 

withdrawn entirely. Existing funding of £27 million per year disappears and is ‘lost’ from 

the uplands. On many farms dominated by poorer, higher ground, farming would be 

completely abandoned. 

CAP reform and Environmental Stewardship are assumed to have been implemented in all 

scenarios. 

The possible effect of each scenario on the fourteen attributes in the long list was 

determined by Cumulus et al. (2005). However it should be emphasised that these need to 

be considered not as definitive predictions, but as examples of possible outcomes. Further 

information on attributes and scenarios can be found in Section 2 of the main report. 

E.3. Valuation Methodology 

Two stated preference techniques are used in the report: choice experiment and 

contingent valuation. The potential for benefits transfer is explored for upland attributes 

that could not (or need not) be included in the stated preference survey. 

A choice experiment approach is used for the main part of the survey to elicit willingness to 

pay (WTP) estimates for the five attributes included in the survey, namely: heather 

moorland and bog, rough grassland, broadleaf and mixed woodland, field boundaries and 

cultural heritage. The survey questionnaire consisted of the following sections: (A) 

attitudes, opinions and uses; (B) choice experiment valuation section for SDAs in the 

respondent’s own GOR; (C) contingent valuation question about SDAs in the rest of England; 

and (D) follow-up and socio-economic questions. 

In the choice experiment section, respondents were asked to choose their most preferred 

option in each of the six choice sets they were given. Each choice set contained three 

options: the current policy (baseline) and alternatives A or B, which on the whole reflected 

the possible outcomes of Scenarios 1-3 with randomly allocated price tags (increases in 

annual tax payments of the household). In the six GORs with SDAs, respondents were 

presented information about and asked to make their choices for the SDAs in their own 

region. Respondents in the South East were asked the same question, but for attributes in 

all SDAs in England. 

eftec ii January 2006


A contingent valuation question was also added (in all questionnaires except the South 

East) to gauge respondents’ WTP for a single improvement scenario for SDAs in all other 

regions except their own. 

The survey design underwent focus group and pilot survey stages before the main survey 

was undertaken. Valuation workshops were also undertaken after the survey as part of 

validity testing and to examine issues such as respondents’ understanding of the choice 

experiment tasks, the scope of the geographical area and environmental change, and 

generally the motivations behind their choices. 

Further details of the valuation methodology and initial testing of the questionnaire can be 

found in Section 3. This study is the first of its kind in the UK in that it examines (a 

selection of) the individual upland attributes separately identified but jointly provided, 

rather than as a bundle under the general heading of ‘landscape’. Other studies that look 

at individual attributes on their own or together as landscape are also reviewed in the 

report (see Section 4). 

The other nine attributes in the long list which had not been included in the choice 

experiment were considered for their suitability for benefit transfer. Of the nine, only hay 

meadows were found to be a suitable candidate for transfer of amenity benefits, from their 

inclusion in the Environmental Landscape Features (ELF) model (IERM/SAC, 2001). 

Greenhouse gas emissions could be used for benefit transfer if forecasts for how they may 

change under the different policy scenarios were quantitative. Other features either had 

not been previously valued (e.g. bracken, gorse), or had been valued, but were too 

complex as goods to be transferable (e.g. water quality). Section 5 contains a detailed 

assessment of the suitability of the evidence from current literature for benefits transfer. 

E.4. Results of the Stated Preference Survey 

Questions on attitudes and habits showed that respondents in general considered 

environmental policy to be important compared to other policy areas, but did not consider 

protecting the countryside to be the most pressing environmental issue. A quarter of 

respondents were members of an environmental, recreational, heritage or farming 

organisation. A substantial majority of respondents were familiar with the SDAs in their 

area and visited them for recreational purposes. 

Overall, 84% percent of respondents said that they visited the countryside, and 79% SDAs, 

for recreational purposes (these are the sums of those visiting for recreational purposes 

only and those who visit for both work and recreation). There was therefore a high level of 

awareness and experience of SDAs amongst respondents. 

Table E.1 shows the WTP estimates from the choice experiment survey for each GOR region 

(except for the North East, for which WTP figures were not derived). 

WTP results for which the confidence intervals include zero are not significantly different 

from zero at the 5% level. The South East and North West show the highest number of 

significant results. For further detail on the analysis of average WTP responses using 

different econometric models, see Section 7 and Annex 5. 

The analysis of the contingent valuation question in the surveys for the six regions with 

SDAs revealed an average WTP across all six regions of £49–105 per household per year for a 

change from a ‘worst case’ to a ‘best case’ scenario. 

eftec iii January 2006


Table E.1: WTP results (£ per household per year per 1% improvement for the first four 

attributes) derived from the choice experiment for each region (except the North East). 

NW YH WM EM SW SE 

Model used 1 : A&S A&S AO A&S AO A&S 

HMB 0.78 0.30 0.80 1.04 0.92 0.81 

(0.45-1.11) (-0.06-0.65) (0.42-1.18) (-0.03-2.31) (0.37-1.54) (0.36-1.25) 

RG 0.74 0.31 0.25 0.08 -0.06 0.50 

(0.45-1.05) (0.01-0.60) (-0.05-0.53) (-0.99-0.91) (-0.56-0.39) (0.14-0.86) 

BMW 0.61 0.15 0.43 0.97 0.39 1.21 

(0.30-0.91) (-0.16-0.48) (0.07-0.81) (0.03-2.46) (-0.01-0.78) (0.81-1.66) 

FB 0.00 0.04 0.02 0.06 -0.04 0.06 

(-0.03-0.04) (0.01-0.08) (-0.02-0.05) (-0.06-0.18) (-0.11-0.02) (0.02-0.11) 

CH 

(small 2 ) 

1.03 

(-1.84-4.14) 

3.08 

(-0.24-6.71) 

-0.40 

(-4.27-3.03) 

7.92 

(-1.96-22.62) 

5.48 

(-0.11-11.59) 

0.81 

(-3.22-4.96) 

CH 

(big 3 ) 

4.89 

(1.52-8.43) 

11.93 

(8.47-15.44) 

6.56 

(2.49-10.73) 

22.51 

(11.84-37.24) 

7.68 

(1.24-15.03) 

15.79 

(11.47-20.64) 

Figures in brackets are the 95% confidence interval. Note that if the confidence interval spans zero 

then the WTP is not significantly different from zero. 

HMB = heather moorland and bog, RG = rough grassland, BMW = mixed and broadleaf woodland, FB = 

field boundaries, CH = cultural heritage. 

1 

A&S = attributes and socio-economic variables; AO = attributes only 

2 

from “rapid decline” to “no change” 

3 

from “rapid decline” to “much better conservation” 

E.5. Validity Testing 

With regards to stated preference studies, there are three types of validity which should be 

considered: content, construct and convergent validity. Content validity assesses whether a 

study asked the right questions in a clear, understandable, sensible and appropriate manner 

with which to obtain a valid estimate of the WTP measure under investigation. Construct 

validity examines whether the relationships between choices or WTP estimates and factors 

likely to affect those are in accordance with expectations based on economic theory. 

Convergent validity examines whether the quantitative results are broadly comparable with 

the results of other studies valuing similar goods. 

The validity tests involve the analysis of responses about attitudes towards the survey, 

econometric analysis of the WTP function and comparison of the findings against prior 

expectations and comparison of the results from this study to previous studies from the 

literature. Section 7 of the main report presents all three types of validity testing. Here it 

suffices to summarise the findings of these assessments. 

Content validity 

• Income non-response: the income non-response rate was relatively high, particularly in 

Yorkshire and Humber and the East Midlands. This precluded the use of income as an 

explanatory factor in the regression models of four regions. 

• Protest bids: the number of protest bids was also relatively high, and in the case of the 

North East, may have been the main reason for the model to return insignificant 

coefficients for all upland attributes. Protest bids were at similar levels for both 

sections of the questionnaire while genuine zero WTPs were noticeably higher for 

Section C (CV) than Section B (CE). This should be expected, as respondents are more 

likely to value SDAs in their own region than elsewhere in the country. 

• Attitudes: 69% of the survey respondents found the survey easy to understand while only 

20% found it difficult to understand. 

Construct validity 

• The most important finding of the construct validity analysis is that for both regression 

models (an attributes only model and an attribute plus socio-economic variables model) 

for each region, the coefficient on tax is both negative and significant at the 5% level. 

eftec iv January 2006


This indicates that respondents were not making random or arbitrary choices, but 

considered the cost of options before making their choices. 

• Income was not found to be a significant determinant of WTP in the choice experiment 

for the three regions where it could be included in the regression models. This could be 

largely due to the high non-response rate, particularly as income was shown to be a 

significant determinant of WTP in the contingent valuation question, where all regions 

were pooled together for the analysis. 

• In all regions except the North East, at least two upland attributes positively affect 

respondents’ choices. 

• In general, other characteristics affect respondents’ choices as one would expect: 

people who consider environmental policy to be important, people who are members of 

environmental or recreational clubs, more educated people, and people who visit SDAs 

more often (except in the North West, where many people visit them for work) are all 

more likely to be willing to pay for upland landscape improvements. 

• Age, gender and rural/urban location, however, do not appear to consistently affect 

choices. 

Convergent validity 

• Comparison with the mean WTP estimate range supplied by the ELF model (suitably 

adjusted for appropriate comparison) shows that this study’s regional WTP estimates are 

broadly similar for rough grassland, broadleaf and mixed woodland and field boundaries, 

but dissimilar (generally higher) for heather moorland and bog. 

• Other studies valuing landscapes which cover roughly comparable areas to those valued 

by respondents in this study show similar (or in one case higher) WTP for improvements 

in landscape attributes. 

E.6: Aggregation 

The study results indicate a fair degree of heterogeneity between regions. The differences 

appear to be greater than could be explained by socio-economic factors alone. Therefore 

we would suggest that there is some regional difference which cannot presently be 

explained, which in particular may give rise to a significant degree of transfer error in a 

potential benefits transfer exercise. For this reason, we do not think it advisable to conduct 

a benefits transfer exercise from the South East results to the other two non-SDA regions of 

England. 

For the purposes of aggregation, the valuation results should be aggregated across 

attributes, across the relevant population, and across time. 

Aggregating across attributes yields estimates for the compensating surplus (CS) for each 

region. This is the change in income from an individual’s original level that keeps the 

individual at the original utility level given a new quantity level for a good. The CS 

estimates in this study therefore represent a measure of welfare in the form of 

respondents’ average WTP to move from the state of the world given in Scenario 0 to the 

state of the world given in Scenarios 1 to 3. 

These CS estimates are then aggregated across the relevant regional populations (Table 

8.4) and across time (Table 8.8, E.2) through discounting. Values found for the only 

transferable attribute (hay meadows) are also added. Table E.2 shows the final benefit 

estimate aggregated across attributes, population and time. 

eftec v January 2006


Table E.2: Aggregation of benefits across attributes, population and time 

Scenario 1 

Scenario 2 

Scenario 3 

Env-agri 

Env only 

Aband-intern 

Estimated annual net benefit of eventual change (£ per year) 

Choice experiment 

attributes – 

256,830,000 

301,860,000 

-14,690,000 

compensating 

(132,480,000 – (169,600,000 – (-50,740,000 – 

surplus (Table 8.4) 

Hay meadows 

421,700,000) 486,490,000) 

22,500,000) 

(Table 8.7) 62,000 156,000 -94,000 

Estimated discounted benefit over the period 2007-2013 (£ million) 


897 

1,054 

-51 

attributes 

(463 – 1,473) (592 – 1,699) 

(-177 – +79) 

Hay meadows 0.2 0.5 -0.3 

TOTAL 897 

1,055 

-51 

(463 – 1,473) (593 – 1,700) 

(-177 – +79) 

Figures in brackets indicate 95% confidence intervals. 

E.7 Conclusions 

This study is one of two interlinked studies seeking to generate monetary valuation 

evidence to inform changes to Government support for agriculture in the LFAs (the other 

being Cumulus et al., 2005). The revision of this policy will affect land use management in 

the SDAs, and as a result the current quality and quantity of various upland attributes or 

features. Cumulus et al. (2005) establish the baseline changes in these attributes due to 

CAP and other currently planned reforms, and analyse the potential changes in the upland 

attributes under three policy revision options. The policy options presented in that report 

and used in this study are not definitive predictions but possible outcomes. The conclusions 

of this study should be interpreted with that caveat in mind. 

The objective of this study was specifically to provide monetary estimates of the relative 

economic value of upland attributes in each GOR with SDAs so that the allocation of funds 

can benefit from this information (e.g. more funding for the protection or improvement of 

upland attributes valued more highly by the GOR population). Some of this information is 

collected through a choice experiment survey applied separately in each GOR with SDAs (as 

well as in the South East GOR) while some is estimated through benefits transfer. It was not 

possible to provide any quantitative monetised estimates for the changes in a number of 

attributes due to lack of data. 

Individual preferences for upland attributes are only one part of the evidence needed to 

inform the policy revision. The conclusions presented here are based only on the findings of 

this study and may not necessarily be the conclusions reached at the end of the policy 

appraisal process which should consider other evidence (e.g. expert opinion). 

On the basis of the stated preference survey, we can make the following general 

conclusions: 

• On the whole, people are willing to pay to contribute to the improvements in Severely 

Disadvantaged Areas and upland attributes associated with them. The exception to this 

are the results for the North West GOR which displayed a significant negative constant 

term in one of the regression models (that which considered the upland attributes 

alone), indicating that respondents in the North West may actually prefer the current 

policy option to scenarios offering alleged improvements. This fits in with attitudinal 

responses from the North West, and the fact that the policy cost (increases in annual 

tax payments) attribute showed the most negative coefficient in the North West, 

indicating an unwillingness to pay. The North West results, along with the high 

willingness to pay of South Eastern respondents, to some extent indicates that people 

either value what they do not already have in abundance, or that people in the North 

eftec vi January 2006


West are less willing to pay alone for what they consider to be a national asset (the 

Lake District). 

• There are large variations in the values individuals place on landscape features across 

different regions that are not possible to explain fully on the basis of socio-economic 

differences between populations. 

• Notwithstanding the regional differences, on the whole, changes in the cultural 

heritage (if a large improvement) and policy cost attributes seem to have played a role 

in the choices respondents made. The consistently large coefficients for a large 

improvement in cultural heritage could be purely because a large discrete change (from 

“rapid decline” to “much better conservation”) was on offer. If we had been able to 

measure the cultural heritage attribute quantitatively, and hence vary it continuously, 

it may not have been as consistently important in affecting respondents’ choices. That 

said, it was clear from the focus groups, valuation workshops and reasons respondents 

gave for being willing to pay for landscape improvements that cultural heritage is 

something that is highly valued. Therefore, the implications of any agricultural or 

environmental improvement scheme on this attribute should be considered carefully. 

• In terms of the other attributes, woodland was the next most likely to affect 

respondents’ choices, followed by heather moorland and bog, rough grassland and field 

boundaries; the latter did not appear to be highly valued. It is possible that 

respondents saw field boundaries as an attribute which could be rebuilt or replanted, 

and which was not gone forever if lost. 

• There is evidence of preference heterogeneity within each region. 

• There is evidence (from the contingent valuation question in the survey) that 

individuals in one GOR with SDAs are likely to value the SDAs in the rest of England, 

albeit to a lesser extent than they do the SDAs in their own region. However, this 

result is derived from a context where respondents were asked to choose between the 

upper and lower ranges which attributes took under the choice experiment; 

respondents would presumably have been willing to pay much less if they had been 

asked to pay for a much smaller improvement. 

• On the basis of the South East survey, there is also evidence that those who live in 

regions without SDAs are likely to have positive preferences for the improvement of 

upland attributes in the SDAs. However, due to significant but not completely 

explainable differences in preferences across GORs, we cannot transfer the results of 

the South East to London and East of England GORs which also do not have SDAs. 

• A final piece of evidence for non-use values comes from the finding that many 

respondents in SDA GORs who said that they never visited SDAs were nevertheless 

willing to pay for improvements. 

On the basis of the choice experiment and benefits transfer results, the “environment only” 

scenario (Scenario 2) appears to yield the greatest benefits. This is because it provides 

superior quantities of all attributes except rough grassland, which was not valued 

particularly highly by respondents. On the basis of the quantitative assessment by Cumulus 

et al. (2005), Scenario 3 (“Abandonment-intensification”) was likely to generate 

disbenefits. The economic assessment in this study also comes up with a negative benefit 

estimate for this scenario, i.e. a disbenefit. However, it should be noted that this estimate 

is not significantly different from zero for any of the regions. Scenario 1 (“Environmentagri”) 

gives a benefit which is about 85% of that in Scenario 2. 

Other policy scenarios can be analysed using the results of the choice experiment as long as 

the magnitude of changes in the attributes forecast for the scenarios is within the set up 

changes covered by the choice experiment. The economic value of any changes smaller or 

larger than the set covered in the experiment cannot be estimated using the results of this 

study. 

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eftec viii January 2006


Table of Contents 

1. INTRODUCTION.................................................................................. 1 

1.1 

1.2 

POLICY BACKGROUND ............................................................................... 1 

STUDY OUTLINE ..................................................................................... 1 

2. THE UPLAND ATTRIBUTES AND THEIR FORECAST CHANGES ...........................5 

2.1 

2.2 

2.3 

UPLAND ATTRIBUTES ................................................................................ 5 

POLICY SCENARIOS .................................................................................. 9 

ATTRIBUTE LEVEL CHANGES RESULTING FROM POLICY SCENARIOS ..................................11 

3. VALUATION METHODOLOGY ................................................................ 17 

3.1 

3.2 

3.3 

INTRODUCTION .....................................................................................17 

CONCEPTUAL BASIS.................................................................................18 

IMPLEMENTING THE STATED PREFERENCE SURVEY ...................................................20 

4. VALUATION OF LANDSCAPE – LITERATURE REVIEW.................................... 26 

4.1 

4.2 

4.3 

4.4 

OVERVIEW..........................................................................................26 

REVIEW STUDIES....................................................................................26 

INDIVIDUAL VALUATION STUDIES ....................................................................27 

ISSUES ARISING .....................................................................................28 

5. VALUATION OF ATTRIBUTES NOT INCLUDED IN THE CHOICE EXPERIMENT........ 30 

5.1 

5.2 

BENEFITS TRANSFER USING THE ENVIRONMENTAL LANDSCAPE FEATURES MODEL .....................30 

OTHER ATTRIBUTES ................................................................................31 

6. STATED PREFERENCE SURVEY: SUMMARY AND NON-MONETARY RESULTS ........ 33 

6.1 

6.2 

6.3 

MAIN QUESTIONNAIRE SUMMARY ....................................................................33 

SOCIO-ECONOMIC REPRESENTATIVENESS OF THE SAMPLE ............................................34 

ATTITUDES, OPINIONS AND OTHER INFORMATION ...................................................37 

7. VALIDITY TESTING ............................................................................ 40 

7.1 

7.2 

7.3 

7.4 

7.5 

CONTENT VALIDITY.................................................................................40 

CONSTRUCT VALIDITY: WTP FUNCTIONS ...........................................................45 

CONVERGENT VALIDITY: COMPARISON WITH PREVIOUS STUDIES .....................................52 

VALUATION WORKSHOP FINDINGS ..................................................................54 

VALIDITY TESTING - SUMMARY......................................................................54 

8. WILLINGNESS TO PAY RESULTS, AGGREGATION, POLICY IMPLICATIONS AND 

CONCLUSIONS ........................................................................................... 56 

8.1 

8.2 

8.3 

WTP RESULTS .....................................................................................56 

AGGREGATION......................................................................................58 

CONCLUSIONS ......................................................................................62 

REFERENCES ............................................................................................. 64 

Annexes 

(in separate documents) 

ANNEX 1 – FINAL FOCUS GROUP PROTOCOL 

ANNEX 2 – FOCUS GROUP REPORT 

ANNEX 3 - PILOT SURVEY REPORT 

ANNEX 4 - FINAL QUESTIONNAIRE 

ANNEX 5 – TECHNICAL ANNEX 

ANNEX 6 – VALUATION WORKSHOP PROTOCOL 

ANNEX 7 - VALUATION WORKSHOP – RESULTS AND DISCUSSION 

ANNEX 8 - PEER REVIEWER REPORT 

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eftec x January 2006


1. Introduction 

1.1 Policy Background 

This report covers the research, design and data analysis stages of a study to estimate the 

economic values of environmental features associated with the Severely Disadvantaged 

Areas in England, and the likely changes in these features that could result from the 

redesign of the Less Favoured Area policy. 

Severely Disadvantaged Areas (SDAs) and Disadvantaged Areas (DAs) together comprise the 

English “Less Favoured Areas”, a land classification instigated at the EU level in 1975. All 

uplands are covered by the LFA. Farmers in Less Favoured Areas receive compensatory 

allowances for the fact that the nature of the surrounding physical landscape results in 

higher production and transportation costs, as well as to acknowledge the role farmers play 

in maintaining the landscape and rural communities. SDAs are often remote upland hillfarming 

areas which face particular difficulty in this respect. They include almost all of the 

upland areas in the North of England (including the Pennines, Lake District and North York 

Moors), the Peak District, some of the English-Welsh border, and Exmoor and Dartmoor. 

The policy on support for upland farmers, i.e. the Hill Farm allowance (HFA), is currently 

being revised as part of the new Rural Development Regulation which covers the period 

2007-2013. However, what the new policy will be, including for the Severely Disadvantaged 

Areas, is currently not known. Consequently, despite some preliminary work in this area, 

the implications for agricultural practice and hence the landscape and environment are also 

not known with any great certainty. Notwithstanding the current uncertainty, the ultimate 

policy objective of the LFA revision is to reward sustainable land management and the 

provision of public benefits. 

1.2 Study Outline 

1.2.1 Study objectives 

The principal objective of this study is to estimate the changes in external environmental 

values in the SDA associated with the land use changes that are expected to follow from 

potential revisions of the LFA. Other studies examining the possible economic effects of 

policy design will consider the estimation of the social benefits of hill farming and analysis 

of the financial position of farmers in the SDAs. 

A number of different environmental and landscape features associated with SDAs are 

included in the study (referred to as ‘upland attributes’ in this report). It is assumed that 

each of these attributes could potentially be changed – for better or worse – by any changes 

in support to upland farming. Given the current uncertainty, as mentioned above, different 

scenarios of policy change are also examined. Thus, the principal stages of the study can be 

summarised as follows: 

• Identify a number of different policy scenarios which could arise in relation to the 

revision of the LFA; 

• Predict the likely effects – predominantly in terms of changes in quantity rather than 

quality - on fourteen different upland environmental attributes of these different policy 

scenarios; 

• Estimate the economic value of the changes in some (see Section 1.2.2 below) of these 

attributes through a stated preference survey (the principal method being choice 

experiment); 

eftec 1 January 2006


• Estimate the economic value of the changes in the rest of the attributes (where 

possible) through benefits transfer; and 

• Aggregate the economic values per attribute to estimate the total welfare change for 

different LFA policy revision scenarios. 

The overall objective of the study is to provide evidence for informing the allocation of the 

existing upland support within each region, at the current level. It is not about 

undertaking, say, a cost benefit analysis to decide whether LFA support should be abolished 

or the funds allocated to LFA should be decreased or increased. In order to inform the 

allocation decisions (both between different environmental attributes and across the 

regions with SDAs), the relevant information is the relative economic value associated with 

each upland attribute. This regional focus has influenced the design of the stated 

preference study, and in reading the results for individual upland attributes (in Sections 6 

and 7) and the aggregated estimates (Section 8), this objective should be kept in mind. 

1.2.2 Scope 

The scope of the project can be thought of in terms of: environmental impacts, 

geographical coverage and regional level disaggregation. 

In terms of environmental impacts, the long list of fourteen upland attributes to include in 

the study was decided jointly by the project team and the Steering Group. These 

attributes, which are described in more detail in Section 2.1, are: 

• heather moorland and bog*; 

• improved grassland; 

• rough grassland*; 

• hay meadows; 

• bracken; 

• gorse; 

• arable & set aside land; 

• broadleaf and mixed woodland*; 

• coniferous woodland; 

• field boundaries*; 

• cultural heritage*; 

• water quantity; 

• water quality; and 

• greenhouse gas emissions. 

The attributes marked with an asterisk are included in the stated preference questionnaire. 

This selection was suggested by the study team on the basis of the initial research and 

focus group findings and approved by the Steering Group prior to the pilot survey. 

The overall geographical scope of the study is the SDAs in England, namely in six 

Government Office Regions (GORs) (see Fig 1.1): the North West, North East, Yorkshire and 

the Humber, East Midlands, West Midlands, and the South West. SDAs in other parts of the 

UK and Disadvantaged Areas are explicitly excluded. 

The stated preference study was designed and implemented separately in each of the 

above mentioned GORs in order to estimate the economic value of SDAs within each region 

as expressed by the population of that region. In addition, we also investigated the 

economic value of the attributes of the SDAs as held by the population living in GORs which 

do not contain SDAs. In order to save time and budget, instead of undertaking a stated 

preference survey in each of the three GORs without SDAs, we implemented a survey in one 

of those, namely, the South East GOR. The aim was to transfer the results from the South 

East to the two other regions without SDAs (London and the East). The results of the 

surveys show that such a transfer is not advisable given the regional differences. This is 



further discussed in Section 8. Respondents in the six SDA-GORs were also asked a less 

detailed question about their value for SDAs in the regions of England other than their own. 

The feasibility of estimating (through benefits transfer) the economic values for the 

attributes excluded from the stated preference study using the evidence in the current 

literature is discussed in Section 5. 

Fig 1.1: Map of England showing the Severely Disadvantaged Areas in pink and GOR 

borders in green. 

1.2.3 Study and report structure 

A schematic showing the study structure is given in Figure 1.2. The ‘policy – impact – 

economic values’ structure forms the basis of the study. The impact analysis was 

undertaken by Cumulus et al. (2005) and their findings on the upland attributes of interest 



in the survey and the possible land use changes brought about by different potential policy 

scenarios are summarised in Section 2. Section 3 describes the conceptual bases of the 

valuation methodologies used and the stages in their practical implementation. Section 4 

discusses the literature review conducted as initial research for the stated preference 

survey and the benefits transfer exercise. Section 5 discusses the estimation of value 

changes of the upland attributes which were not included in the choice experiment. Section 

6 discusses the main survey results, and Section 7 the validity testing of these results. 

Section 8 aggregates the survey results with these results to gain estimates of the total 

value change, discusses the policy implications and outlines the conclusions. 

CE 

SDA 

GORs 

(residents 

for own 

GOR) 

Policy 

scenarios 

Policy Impact Economic 

Values 

1 non-SDA 

GOR 

(nonresidents 

for other 

GORs) 

CV 

SDA 

GORs 

(residents 

for other 

GORs) 

Impact 

analysis 

SP survey 

Aggregation 

Workshops 

Upland 

attributes 

Benefits 

transfer 

Figure 1.2: Flowchart showing the structure of the study. Blue ellipses indicate 

processes, while green boxes indicate components of economic valuation. Italics indicate 

inputs. CE and CV are short for Choice Experiment and Contingent Valuation – the two 

valuation methods used (see Section 3 for details). 



2. The Upland Attributes and Their Forecast Changes 

This Section presents the upland attributes examined in the study, in terms of their 

definition and current trends (Section 2.1), and how they may be expected to change under 

four potential policy scenarios. The policies themselves are outlined in Section 2.2, and the 

expected changes in attribute levels in Section 2.3. 

Although eftec performed preparatory work on the current trends and forecasts in attribute 

levels at the beginning of this study, this work was reviewed and extended in a parallel 

study by Cumulus et al. (2005). Consequently, the Cumulus report is used as the basis of 

the rest of this report. For full details on current trends and forecasts in each of the 

attributes, the reader is referred to Cumulus et al. (2005). 

2.1 Upland Attributes 

2.1.1 Attribute long list 

The long list of upland attributes for use in the study was decided jointly by the project 

team and steering group. These attributes were chosen as they are most likely to be 

affected by changes in LFA policy. 

For those attributes that are essentially a habitat type, care has been taken to standardise 

the definitions as much as possible to fit with other studies and allow use of other data. In 

particular, reference has been made to UK Biodiversity Action Plan (UKBAP) broad habitat 

definitions 1 and the Countryside Survey (Haines-Young et al., 2000). Differences in 

definitions are discussed later. Note that in the uplands, particularly in unfenced areas 

used for extensive grazing, many of these habitats can occur together in an intricate 

mosaic. 

A description of the long list of attributes follows (numbered A1-A14). 

A1. Heather moorland and bog: Calluna vulgaris (heather) dominates heather moorland, 

although the extent of that domination varies from very high in dry heath (typically 

on well-drained nutrient-poor acid soils) to lower in wet heath (found on wetter, 

peaty soils). In areas of high rainfall and over flatter areas and in hollows, bog 

habitat may be found. This high conservation value habitat is often characterised by 

a high cover of Sphagnum mosses. These habitats are all typically used as extensive 

grazing, particularly for sheep. All of these habitats can be found over peat or peaty 

podsol soils, and peat erosion is often a problem when the habitat is over-grazed or 

subject to nutrient enrichment. 

A2. Improved grassland: This usually occurs on fertile soils, is dominated by fast-growing 

plant species preferred by livestock, and is used mainly for either pasture or silage. 

Improved grassland is typically found on the valley floors, or on the less steep slopes. 

These areas will be enclosed to keep in (or keep out) grazing animals. Many 

improved grassland areas would have previously consisted of smaller fields, but in 

places these have been amalgamated to make it easier to manage (applying fertiliser 

and herbicides, ploughing and re-seeding) and to reduce boundary maintenance. 

A3. Rough grassland: This definition applies to areas typically used for extensive 

grazing, usually by sheep, and in this study is taken to include acid grasslands 

(including grass dominated moorland) and calcareous grasslands. In areas of poor soil 

quality (acidic, nutrient poor), acid grasslands may often contain some heather, or 

may be intermixed with heather moorland. As with heather, the colour of this 

1 see http://www.ukbap.org.uk/habitats.aspx 



habitat changes with season (and with mix of species) – in spring and summer green 

is predominant, but in autumn and winter some plants die back to give a ‘white 

moor’ appearance. In contrast, calcareous grassland is found on richer soils, often 

subdivided into fields and is typically grazed a lot shorter than the poorer quality 

acid grassland. 

A4. Hay meadows: Typically found on soils that are neither too acidic nor too alkaline, 

these grasslands reflect a land use that is much less important than previously. Hay 

meadows are periodically cut for a supply of hay, but are managed without the 

inputs associated with improved grassland. This management encourages and 

maintains high plant diversity, and this is therefore a high conservation value 

habitat. 

A5. Bracken dominated: Bracken (Pteridium aquilinum) is recognised as a pest species 

in many open upland habitats, as it is not eaten by livestock and can be hard to 

control once established. Spread of bracken can be encouraged on moorland by 

inappropriate management (e.g. burning), and the historic reduction in cattle on the 

hill is also thought to have assisted its spread. Bracken also harbours sheep ticks 

which spread Lyme disease and louping ill. 

A6. Gorse dominated: Gorse (Ulex europaeus) is a prickly leguminous shrub with 

conspicuous yellow flowers, usually found on lighter (not deep peat) soils on moors 

and rough grassy habitats. Like bracken, gorse is considered an invasive species into 

some habitats and control is often attempted by burning, but it recovers well after 

fire. However, gorse can provide a good habitat for nesting birds, and new growth 

can be eaten by browsing animals. 

A7. Arable/set aside: In the uplands this land use is found only on the flatter/gently 

sloping and more fertile areas. Crops may be grown in rotation with improved 

grassland, sometimes to provide supplementary or winter feed for livestock. Under 

agri-environment schemes, some fields may be left as stubbles or have a reduced 

cropping area due to uncultivated buffer strips, beetle banks or field corners. 

A8. Broadleaf and mixed woodland: In upland areas this habitat may consist of a mix of 

native species, or be dominated by one. In different parts of the country different 

species may dominate or be of higher conservation importance. Broadleaf woodland 

is often newly established by planting young trees (usually with a stake for support 

and a plastic tube for protection). The spread of established woodland can be 

assisted by reduced grazing and ground preparation (e.g. scarification). The most 

common species found naturally establishing in upland areas is birch (Betula spp). 

Note that this habitat category is taken to include woodland habitats that may 

contain native conifers such as yew (Taxus baccata) and juniper (Juniperus 

communis). 

A9. Coniferous woodland: The species used in forestry plantations are usually exotic, 

with probably the commonest being sitka spruce (Picea abies). Although there are 

still many remaining examples of unsightly rectangular blocks on hillsides, guidelines 

now dictate that modern coniferous forestry plantations are planted in a more 

landscape-friendly fashion. This includes having irregular boundaries, with the sides 

of watercourses being left unplanted, and a minimum area of native broadleaf trees 

also planted, most likely around the outside/lower part of the plantation to reduce 

visible impact. 

A10. Field boundaries: Included in this attribute are hedges, stone walls, ditches, banks 

and lines of trees, but not modern fences. Field boundaries prevent the passage of 

animals and demarcate fields, as well as being part of the visual landscape. In many 

upland areas, field boundaries are made up of traditional dry-stone walls. However, 

hedges can also be common and these can be a valuable habitat for many species. 

Hedges and stone walls are costly to maintain and have often been replaced or 

supplemented by modern fences, and there has also been a move towards bigger 

field sizes resulting in removal of boundaries altogether. 



A11. Cultural heritage: This attribute may be taken to include the visual presence in the 

landscape of traditional farm buildings. Less visual aspects may be the presence of 

animals on the hill, traditional breeds, or traditional farming practices such as 

shepherding with sheep dogs. 

A12. Water quantity: This attribute may be taken to mean a range of conditions from 

drought to excess of water leading to flooding, either in upland catchments or 

further down the river system. The important parameter in determining whether 

flooding occurs is not overall precipitation but the frequency and severity of 

individual precipitation events, combined with variables such as soil moisture. It is 

thought that such flooding is also exacerbated by lack of suitable vegetation in the 

uplands to slow down the flow of water into the rivers. 

A13. Water quality: Water quality in the uplands is determined to a great extent by 

agricultural pollution. This pollution, referred to as DWPA (diffuse water pollution 

from agriculture) consists of nitrogen, phosphorous, organic wastes, pesticides, 

veterinary medicines, micro-organisms and silt. The relative contributions of these 

different pollutants varies by agricultural activity and intensity, and therefore also 

by farm type and geographical region. 

A14. Greenhouse gas emissions: There are several main gasses that contribute to the 

greenhouse effect, with probably the most important being carbon dioxide. In order 

to summarise the overall effect and make comparisons possible, a given quantity of 

each greenhouse gas is usually converted to a carbon equivalent value which 

summarises its ‘global warming potential’. The habitat or activity in question can 

either be a greenhouse gas ‘source’ or ‘sink’. In the uplands, certain activities can 

be a source (e.g. carbon released by peat erosion, methane from livestock), or can 

be a sink (e.g. via good condition ‘active’ bog habitat or through tree growth, both 

of which sequester carbon). 

2.1.2 Estimates of current trends 

For all habitat type attributes, estimates of recent rates of change in the extent of these 

habitats were made. This was done using the data classified as ‘Environmental Zone 3’ from 

the Countryside Survey 2000 (CS2000), which is essentially the upland areas of England and 

Wales. As can be seen in Figure 2.1, Zone 3 corresponds very closely with the area of 

England classified as SDA, but with the important addition of the uplands of Wales. As the 

data for England only is not readily available it was decided that the Zone 3 estimates of 

habitat changes were acceptable as a broad-brush representation for the whole country. 

For most habitat types the amount of habitat loss/gain between 1990 and 1998 (final 

column, Table 2.1) were obtained from the main report of the CS2000 (Haines-Young et al., 

2000), however for bracken and field boundaries Zone 3 information was obtained from the 

survey website 2 . There are some definition differences between the habitats as defined in 

this study, those in the CS2000, and those in the Land Cover 2000 data set supplied by 

Defra. Table 2.1 compares these habitat definitions and indicates where habitat types were 

amalgamated. For example, ‘Heather Moorland and Bog’ in this study corresponds to ‘Dwarf 

Shrub Heath’ and ‘Bog’ from CS2000. In this case the areas and changes in area were 

combined to calculate a revised single percentage change. A similar process was necessary 

with the ‘Linear Boundary Features’ of CS2000. These data were re-analysed to give a rate 

of change for all boundary types (walls, hedges, lines of trees/shrubs, bankings) except 

fences. 

2 See http://www.cs2000.org.uk/M01_tables/index.htm 



A B 

Figure 2.1: Map A shows the LFA Severely Disadvantaged Areas in England (pink 

shading). Map B shows the area covered by Environmental Zone 3, as used by CS2000. 

Note that this Zone, whilst substantially corresponding to the SDAs in England, also 

includes much of Wales. 

In the case of Hay Meadow, this attribute is considered a part of the Neutral Grassland 

CS2000 category, and even though this habitat contains two sub-habitats (neutral grassland 

and herb-rich), neither seemed to correspond closely to Hay Meadow. The change in area 

figure of -15 % quoted is therefore a compromise derived from the data for these two 

categories, and reflects an ongoing loss of this habitat type. Quantitative estimates of rates 

of change in gorse area over the English SDA are not available. It has therefore been 

decided to adopt a current rate of change of zero, considering gorse as being controlled by 

browsing animals and other management measures. 

Cumulus et al. (2005) reviewed whether the trends identified over the period 1990-1998 

should be modified in the light of any further information, but concluded that such further 

information was not available. 



Table 2.1: Comparison of attributes with the equivalent categories from the Land Cover 

2000 and Countryside Survey 2000, and the estimated % change over an 8 year period 

(used to define the current trend). 

Name of attributes in 

this study 

Heather moorland 

and bog 

Improved grassland 

Rough grassland 

Hay meadow 

Bracken 

Gorse 

Arable (including setaside 

and fallow) 

Broadleaf and mixed 

woodland 

Coniferous woodland 

Field boundaries 

2.2 Policy Scenarios 

Categories in LC2000 Categories in CS2000 Estimated change 

(1990-1998) 

Moorland heath and 

bog 

Dwarf Shrub Heath 

Bog 

+1% 

Improved grassland Improved Grassland 

+7% 

Rough grass 

Acid grass 

Calcareous grass 

Improved grassland 

(subdivided by Defra) 

Acid Grassland 

Calcareous Grassland -13% 

Neutral Grassland (a 

part of) 

Bracken Bracken 

Not included as a category 

Arable / set-aside Arable and 

horticultural 

Broad-leaved/mixed Broadleaved, Mixed 

woodland 

and Yew Woodland 

Coniferous woodland Coniferous woodland 

Not included as a 

category 

Boundary and Linear 

Features 

The following describes the policy scenarios (or “policy options”) used in this study. 

According to Cumulus et al. (2005), “all scenarios cover the seven year period 2007 to 2013, 

which is the period for any revised system of support for upland farmers”. 

In all scenarios, the hypothesised effects of the policies outlined are superimposed on 

current trends. CAP reform and the Environmental Stewardship Scheme are assumed to 

have taken place in all scenarios. Scenarios 0, 1 and 2 assume that there is continued and 

adequate funding to support varying degrees of upland farming in the SDA, and these policy 

options therefore describe differing emphasis (in direction and degree) of available 

funding. Option 3 (Abandonment-intensification) is a ‘no subsidy’ scenario, assuming that 

support for upland farming has been withdrawn. 

According to Cumulus et al. (2005): 

“It should be noted that these scenarios and assumptions are intended to show only the 

impacts of changing forms of upland support, not the impacts of other possible changes 

(e.g. market changes, further reductions in the value of the Single Payment, or lower than 

anticipated agri-environment scheme funding).” 

The scenario descriptions are interpretations of ways in which the policy options could be 

implemented and do not represent government policy, nor do they necessarily reflect how 

the policies would actually be implemented. 

The following descriptions of the policy scenarios are taken from Cumulus et al. (2005). 

eftec 9 January 2006 

-15% 

+8% 

0% 

-5% 

+4% 

-7% 

+9%


2.2.1 Scenario 0 - Baseline 

Under the baseline scenario, there is no change in the strategic aims, structure and level of 

upland support. Support remains focused on maintaining upland farming system. In other 

words the Hill Farm Allowance (HFA) scheme remains broadly the same as it is presently. 

It is assumed that CAP reform has been implemented from the beginning of 2005 and that 

the level of the Single Payment will reduce by about 20% over the period given anticipated 

deductions for the national reserve, modulation and financial discipline. 

It is also assumed that the agri-environment scheme budget will increase from its present 

level of £180 million per year to £400 million per year by 2013. This includes an increase in 

the coverage of Entry Level Stewardship (ELS) to 60% of all farmland by the end of 2007 and 

75% by 2013. 

2.2.2 Scenario 1 - Environment-agri 

Under Scenario 1, the strategic policy aims reflect the importance of environmental and 

conservation objectives and the importance of maintaining upland farming systems to 

achieve these objectives. HFA-type support amounting to around £27 million per year still 

exists but it is focused on SDA land only. Furthermore eligibility for support is dependent 

upon producers having all or part of their holding in an existing or new agri-environment 

scheme. Existing schemes include Environmentally Sensitive Area (ESA) or Countryside 

Stewardship (CSS) schemes. New schemes refer to Environmental Stewardship (ES) schemes 

including Entry Level Stewardship (ELS), Organic Entry Level Stewardship (OELS) or Higher 

Level Stewardship (HLS). Farmers would be paid for all their land in the SDA (not just land 

in an agri-environment scheme). 

The aims of the changes are firstly to target existing resources where they will have most 

impact in terms of maintaining and enhancing the upland environment (there is a 

concentration of environmental assets in SDAs and farming practices which support these 

assets are particularly under threat in these areas) and secondly to provide a further 

incentive to farmers to join Environmental Stewardship. The result would be more land 

being entered into ELS, OELS and HLS than otherwise and the associated delivery of greater 

environmental outcomes. This scenario can be considered as the inverse of the baseline 

(agri-environment), as it is essentially a switch in emphasis from agriculture to 

environment. 

Other changes under this scenario include targeting upland support at all livestock farmers 

(as opposed to beef and sheep farmers only) and removing the current system of enhanced 

HFA payments if certain environmental criteria are met. This latter system is superseded by 

the requirement for producers to have all or part of their land in an agri-environment 

scheme in order to be eligible for HFA payments. 

The CAP reform and agri-environment scheme assumptions (e.g. the agri-environment 

scheme budget) remain the same as under the baseline scenario. 

2.2.3 Scenario 2 - Environment only 

Under Scenario 2, the strategic aims for upland support are focused solely on achieving 

environmental goals. It is assumed that existing support amounting to £27 million per year 

is maintained but it is incorporated into the agri-environment scheme budget and ringfenced 

for the uplands, specifically the SDAs. It is assumed that such a ring fence would be 

demanded by stakeholders to ensure that the funding was not simply subsumed into 

broader budgetary planning and used to displace other funds sought from Treasury for 

scheme growth. The HFA scheme disappears. 



In practice it is assumed that the funding is used for an Upland Entry Level Stewardship 

Scheme (UELS) and would take the form of annual payments targeted at enhancing specific, 

valuable or threatened upland habitats or features alongside ESA, CSS, ELS, OELS and HLS. 

Examples of UELS options might include payments for: 

• active grazing and/or cutting management according to specific tailored plans for 

individual upland areas; 

• mixed grazing, including cattle (an upland equivalent of existing ELS Option EK5 ‘mixed 

stocking’); 

• shepherding or maintaining hefted flocks on the poorest grazing areas; 

• the management of archaeological sites through grazing; 

• moorland grip blocking and rewetting of upland agricultural areas; 

• increasing vegetation through low intensity land management; 

• preparing management plans e.g. Moorland Management Plans and Manure, Nutrient and 

Waste Management Plans. 

Re-focused support of this type is likely to lead to more significant redistributive effects 

than under scenario 1. 

The CAP reform and agri-environment scheme assumptions remain the same as under the 

baseline scenario. 

2.2.4 Scenario 3 – Abandonment-intensification 

Under Scenario 3, upland support is withdrawn entirely. Existing funding of £27 million per 

year disappears and is ‘lost’ from the uplands. The CAP reform and agri-environment 

scheme assumptions remain the same as under the baseline scenario. In other words, there 

will be still be more agri-environment scheme funding, some of which will be used in the 

uplands, despite the loss of the HFA scheme. 

2.3 Attribute Level Changes Resulting From Policy Scenarios 

The difficulty in making predictive assessments of CAP reform are acknowledged (GFA-RACE 

& IEEP, 2003), and the difficulties in predicting the impacts of further policy changes 

considered here are even greater. Given the uncertainty attached to all predictions at this 

stage, those contained in this report should be considered as examples of possible 

outcomes, rather than definitive predictions. 

With regards to climate change, the UKCIP scenarios (Hulme et al., 2002) for low and 

medium-low emissions for the year 2020 have been consulted for possible impacts. 

Although the predicted climate changes will have different regional impacts (e.g. Holman 

et al., 2002), and will affect different habitats to different rates and extents, these 

impacts are considered minor within the timescale considered, compared to policy-induced 

changes. The one exception to this is precipitation changes, which are taken into account 

in predicting flood risk (water quantity). 

Cumulus et al. (2005) provide predictions of the likely effects of the policy scenarios upon 

the different attributes. These are summarised in Table 2.2 at the end of this Section. 

Edited versions of Cumulus et al. derivations of these changes are given for each 

attribute below 3 . However, for a full, detailed explanation, the reader is referred to 

the Cumulus report. 

3 In places where Cumulus et al. were in broad agreement with eftec’s initial report for this study, some of the 

text used here may be taken from the initial report. 



A1. Heather moorland and bog: 

Scenario 0: A forecast reduction in cattle numbers and a greater emphasis on sheep 

production have significant implications for heather moorland and bog. Reduced grazing 

pressure may well result in heather re-establishing itself in areas where it has been 

suppressed by grazing (e.g. on rough grassland). In addition, HLS will be targeted at 

extensive SSSI (Sites of Special Scientific Interest) heather moorland areas. Much of what 

happens will depend on the actual management of heather moorland as well as the 

absolute grazing pressure. Practices such as shepherding, heather burning and bracken 

control are all important factors in the management of upland areas, heather moorland in 

particular. The expected decline in labour and the loss of traditional upland management 

skills could have a significant bearing on what happens to heather moorland. 

Other Scenarios: Small increases in the extent of this attribute are estimated under 

Scenarios 1 (+3%) and 2 (+5%), as grazing pressure reduces. However these increases are 

limited due to the loss of cattle and increases in some places of rough grassland and 

bracken. There should be some improvements in the quality of heather moorland as ELS 

and HLS kick in – especially towards 2008/9 and beyond (and more so in Scenario 2 than 1). 

In Scenario 3, loss of LFA support could tip the economic balance for some farmers and 

result in further reduction in grazing resulting in a decline in this attribute, although agrienvironment 

schemes will help to counter this effect. 

A2. Improved grassland: 

Scenario 0: As a result of the 2003 CAP reforms, some upland farmers are likely to place 

greater emphasis on finishing animals. There will be pressure to farm intensively on lower, 

better land (compared to the extensification and switch to agri-environment management 

that will occur elsewhere) and grassland improvement is likely to continue, mainly on areas 

of neutral and rough grassland. Land taken out of arable cropping is also likely to be 

reverted to improved grassland; the extent of this area will be relatively small, however, 

given that arable cropping only occurs on 1% of upland farms. Given that grassland 

improvement was a trend already being witnessed before the reforms, it is estimated that 

the extent of improved grassland will increase. The expansion of agri-environment schemes 

will help to counter the intensification trend. 

Other Scenarios: Scenarios 1 and 2 are likely to slow the intensification trend identified 

under Scenario 0. More modest increases are expected under Scenario 1 (+1%) and no 

increase expected under Scenario 2. In Scenario 3, loss of LFA support will affect all 

farmers and the trend towards intensification is likely to be reduced to the extent that 

improved grassland declines by 2%. 

A3. Rough grassland: 

Scenario 0: As discussed in relation to heather moorland, the expected reduction in cattle 

numbers and the withdrawal of labour resulting in less shepherding, heather management 

etc. may result in the loss of some heather moor and an increase in rough grassland over a 

period of time. Much will depend on the absolute grazing pressure, the management of 

livestock, the demand for improved grassland further up the hill and the countering factor 

of conservation and agri-environment strategies. Increases in bracken and woodland will be 

countering factors to the expansion of rough grassland. Scenario 0 could result in either 

small decreases or larger increases depending particularly on the change in heather 

moorland. 

Other Scenarios: Changes in the extent of rough grassland are linked to changes in 

attributes such as heather moorland, intensive grassland, bracken, gorse and woodland, 

and are influenced by what happens to cattle numbers and overall grazing pressure. 

Scenarios 1 and 2 are likely to result in small declines (1% and 3% respectively) in the 

extent of rough grassland as agri-environment schemes and LFA funding promote the 

extension and management of other habitats such as heather moorland and woodland. 

Scenario 3 is likely to result in a reversed trend with an increase in rough grassland (5%) as 

the loss of LFA funding begins to be felt. 



A4. Hay meadows: 

Scenario 0: It is anticipated that species-rich hay meadow will continue to be protected 

under agri-environment scheme agreements and will be targeted for future HLS agreement. 

Many meadows are also protected by SSSI legislation and are designated Special Areas of 

Conservation under the EU Habitats Directive. However, the value of hay meadows to 

farmers will continue to decline, with the loss of cattle in particular; agri-environment 

payments would need to be increasingly attractive to sustain meadow management. 

Other Scenarios: Continuing the explanations for scenario 0, the expected net results would 

be a slow decline in the area of hay meadow under Scenario 1 (-3%) and maintenance under 

Scenario 2. Greater losses (-10%) would occur under Scenario 3. 

A5. Bracken dominated: 

Scenario 0: The extent of bracken will be determined by the grazing pressure and the type 

of grazing as well as the use (or not) of control strategies. The decline in cattle, (expected 

as a result of the 2003 Mid-Term CAP reforms) and the reduction in grazing pressure overall 

suggests bracken may become difficult to control and will continue to spread. Agrienvironment 

schemes and uptake will be important factors in relation to control. 

Other Scenarios: Cattle control bracken, so reductions in their number will lead to bracken 

spread. In Scenario 1 animal numbers fall more than under the baseline, and whilst there 

are still control subsidies, the rate of spread increases slightly as upland habitats are 

managed less. This rate drops in Scenario 2 as there is more money available for control, 

probably through spraying and/or cutting. The rate of spread jumps dramatically in 

Scenario 3 due to a lack of animals on the hills and a lack of incentive to undertake control. 

A6. Gorse dominated: 

Scenario 0: Gorse is not specifically encouraged for conservation purposes although can be 

a valuable component of upland habitats. Fewer livestock are likely to create conditions 

under which gorse will spread. 

Other Scenarios: Under Scenarios 1 and 2 gorse will increase, but less than in Scenario 0, 

due to active management. There is likely to be a higher percentage increase under 

Scenario 3. 

A7. Arable/set aside: 

Scenario 0: Only 1% of upland farms are classified as cereal or general cropping farms. 

These farms will probably decline under all scenarios, including Scenario 0, and those that 

remain will make more use of set-aside and fallow. However this is still technically 

classified as arable land so the overall area may not decline significantly, rather its 

management will change. Where arable land use does change it is likely to become 

improved grassland. Getting arable cropping back in the uplands is a key objective for some 

environmental interests (e.g. RSPB) and agri-environment schemes already offer some 

options. In the future, arable will be a target habitat in the uplands and there is likely to 

be some small increase in extensive arable cropping for conservation purposes 

Other scenarios: In continuation of the explanation for Scenario 0, under Scenarios 1 and 2, 

losses in arable land will be reduced by the measures discussed (-3% and -1% respectively). 

Under Scenario 3 there is likely to be a slightly greater decline than the Scenario 0 (-5%). 

A8. Broadleaf and mixed woodland: 

Scenario 0: Broadleaf and mixed woodland creation is policy driven and much depends on 

the EARDF (European Agriculture Rural Development Fund) rules and budgets post 2007. 

Woodland cover will increase through a combination of schemes/grants, demand for short 



rotation coppice for energy and natural succession but increases due to natural succession 

are likely to be curtailed by cross-compliance requirements and the relatively short 

timescale (to 2013). 

Other Scenarios: This attribute increases successively through Scenarios 1 and 2, again 

because of the increasing importance and success of environmental/conservation schemes. 

However, the rate then drops in Scenario 3 due to a reduction in active management for 

this woodland type, and although native herbivores (deer, rabbits) contribute to some 

control, there is still natural invasion, particularly by birch onto moorland habitats. 

A9. Coniferous woodland: 

Scenario 0: The current policy emphasis is on broadleaf planting/expansion but 

opportunities under new EAFRD for afforestation could result in more coniferous planting. 

This may appeal to some non-farming owners although the tax advantages of forestry 

investment are much more limited than previously and the present and likely future 

markets for softwood timber would not encourage significant expansion. Single Payment 

would also be forfeited. As such we consider that there is unlikely to be an overall increase 

in coniferous woodland but instead a slow decline. 

Other Scenarios: Cumulus et al. (2005) do not provide any discussion of how the other 

scenario figures are arrived at apart from to repeat the above paragraph and to add: “This 

may be stemmed under Scenario 3 given the lack of other alternatives, particularly on 

marginal land.” 

A10. Field boundaries: 

Scenario 0: We would suggest that there is likely to be a steady improvement in the quality 

and extent of traditional field boundaries as a result of cross compliance, the continuation 

of ESA agreements in large parts of the uplands for the next 5-10 years and the introduction 

of ELS and HLS. Negative influences include loss of farm profitability (required to pay for 

the balance of costs), simplified farming systems, less labour and the loss of traditional 

skills such as hedge-laying and dry-stone walling. In addition ELS will not provide payments 

for boundary restoration but only maintenance and even then this will tend to be targeted 

at selected boundaries. 

Other Scenarios: It is suggested that there is likely to be a steady improvement in the 

quality and extent of traditional field boundaries as a result of cross compliance, the 

continuation of ESA agreements in large parts of the uplands for the next 5-10 years and 

the introduction of ELS and HLS. Scenario 1 is likely to see steady increases in the extent of 

field boundaries (+5%) while Scenario 2 will result in greater increases (+10%) as LFA 

funding is put to more targeted use. Scenario 3 is likely to result in no change; a reduction 

in farming activity brought about by loss of LFA funding would result in less attention to 

boundary features but the timescale is too short to see any losses. 

A11. Cultural heritage: 

Scenario 0: It is anticipated that there will be a continuing decline in cultural heritage 

under the baseline scenario due to reduced use/function of buildings, reduced profitability 

and manpower to maintain buildings, reduced availability of grants and increasing damage 

to the archaeological resource due to bracken and tree growth. Conservation strategies and 

agri-environment schemes will help but are unlikely to be adequately resourced to deal 

with the extent of the problem. 

Other Scenarios: The decline in cultural heritage identified under the baseline scenario is 

likely to be less under Scenario 1. Selective management and enhancement of priority 

features through agri-environment schemes will bring about no change under Scenario 2. 



Scenario 3 is likely to see a decline in this attribute. Under all scenarios, changes in hill 

farming – especially fewer farmers and a decline in labour - may well result in the loss of 

traditional skills from the uplands such as shepherding. However diversification should be 

able to safeguard some traditional buildings. 

A12. Water quantity: 

Scenario 0: The water quantity attribute represents a range from drought to flooding. The 

possibility of drought will be largely driven by climate change and little impacted by 

changes in LFA policy. However, with regard to flood risk, climate change is predicted to 

reduce rainfall overall, but to make winters wetter and most importantly to increase the 

incidence and severity of storm events. A modelling study by CEH Wallingford (2001) 

indicated that for their eight study rivers across the UK, high discharge events would 

increase in the future. The results of Price & McKenna (2003), who determined an increased 

flood risk for different areas of Scotland in the future, are applied here. Based on their 

results from eastern and south-west Scotland, the predictions given here are for the return 

period for a ‘once in a hundred year flood’. The different predictions in the scenarios 

reflect the different degrees to which the vegetation and management in the uplands may 

influence flooding. As agriculture decreases in Scenario 0, to be replaced by different 

vegetation types (in particular trees), the incidence risks of flooding decrease. 

Other Scenarios: As agriculture decreases through Scenarios 1 to 3, to be replaced by 

different vegetation types (in particular trees), the incidence risks of flooding decrease. 

There may be localized effects, for example, areas where intensification occurs may be more 

vulnerable to flooding. 

A13. Water quality: 

Scenario 0: Water quality in and from the uplands has been generally improving since the 

1990s, due to tighter controls over the use of chemicals and less spillage allowed into 

waterways. However, erosion leading to sedimentation continues to be a problem in some 

areas (e.g. the Peak District). In future, there may be localized problems where 

intensification occurs. The development of riparian habitats, through natural succession as 

well as through agri-environment scheme targeting, will help improve water quality. 

Improper use and disposal of sheep dip may remain a problem in some areas especially with 

the anticipated reduction in labour. 

Other Scenarios: Water quality is predicted to continue to improve through Scenarios 1 to 3 

even though there may be localized problems where intensification occurs. As in relation to 

flooding, targeting of agri-environment schemes and catchment management approaches 

could bring greater improvements. 

A14. Greenhouse gas emissions: 

Scenario 0: greenhouse gas emissions from agriculture are currently decreasing. This 

reduction is predicted to continue under Scenario 0. This is due to the cumulative effects of 

fewer livestock (reducing methane emissions), more young tree growth, re-vegetation 

slowing soil (particularly peat) erosion, and peat habitats becoming active sinks again for 

carbon. However, there may also be some displacement effects, for example, as farming 

intensifies in adjacent lowland areas. 

Other Scenarios: The reduction in greenhouse gas emissions is predicted to continue in all 

scenarios, but with the rate increasing with the reduction in the importance of agriculture, 

for reasons explained above. 

Attempts to give quantified predictions for reductions in this attribute have been omitted. 

The predicted changes for each policy scenario are summarised in Table 2.2 



Table 2.2: Summary of the current trend and hypothesised future changes in the upland 

attributes according to Cumulus et al. (2005). Percentage changes in the 3 rd to 6 th 

column are relative to the extent of the habitat before the period 2007-2013. 

Attribute Estimated 

change 

(1990-1998) 

Heather 

moorland/bog 

Improved 

grassland 

Rough 

grassland 

Policy 0 

Baseline 

(2007-2013) 

Policy 1 

Env-agri 

(2007-2013) 

Policy 2 

Env only 

(2007-2013) 

Policy 3 

Aband-Inten 

(2007-2013) 

+1% +1% +3% +5% -2% 

+7% +1% 0% 0% +2% 

-13% +1% -1% -3% +3% 

Hay meadow -15% -5% -3% 0% -8% 

Bracken 

dominated 

+8% +7% +6% +3% +9% 

Gorse 


0% +5% +4% +2% +7% 

Arable (& set 

aside / fallow) 

Broadleaf and 

-5% -5% -4% -4% -5% 

mixed woodland 

+4% +3% +4% +6% +5% 

Coniferous 

woodland 

-7% -5% -5% -6% -5% 

Field 

boundaries 

+9% +5% +6% +10% 2% 

Cultural 

heritage 

Slight loss Decline Slow decline No change Decline 

Water quantity 

(flood risk) 

More floods 

100 year 

flood every 

85 years 

100 year 

flood every 

90 years 

100 year 

flood every 

90 years 

100 year 

flood every 

95 years 

Water quality Improving Improving 

Improving 

more 


much more 


but localised 

deterioration 

Greenhouse 

gas emissions 

Reducing Reducing Reducing Reducing Reducing 



3. Valuation Methodology 

3.1 Introduction 

This section explains the methodology employed to value the changes to the attributes 

described in the previous section. 

The stated preference part of this study could only include a limited number of attributes 

(up to five), for reasons explained in Section 3.2.1 below. The rest of the attributes must 

be valued by benefits transfer – by transferring valuation estimates for the attributes found 

by other studies and adjusting them to the context of this study. This, in turn, is limited by 

the available literature. Table 3.1 summarises how each of the upland attributes is to be 

valued. 

Table 3.1: Attribute list and valuation method to be used 

Upland attribute Valuation method 

A1 Heather moorland/bog Choice experiment 

A2 Improved grassland n/a 

A3 Rough grassland Choice experiment 

A4 Hay meadow Benefits transfer 

A5 Bracken dominated n/a 

A6 Gorse dominated n/a 

A7 Arable (inc. set aside / fallow) n/a 

A8 Broadleaf and mixed wood-land Choice experiment 

A9 Coniferous woodland n/a 

A10 Field boundaries Choice experiment 

A11 Cultural heritage Choice experiment 

A12 Water quantity (flood risk) n/a 

A13 Water quality n/a 

A14 Greenhouse gas emissions 

Benefits transfer would be 

possible if data on SDA 

emissions were available 

and scenario forecasts 

were quantitative 

n/a: shows attributes which were not include in the stated preference 

questionnaire and about which either there are no estimates in the literature 

or previous estimates are not transferable. 

Stated preference techniques estimate values by asking people directly to state how much 

they would be willing to pay (WTP) for non-market goods, or willing to accept (WTA) to 

forgo them, or by asking them to choose between alternative sets of goods with different 

associated price tags. 

The conceptual bases of the two stated preference techniques used (choice experiment and 

contingent valuation) and benefits transfer are explained in Section 3.2. Although the 

choice experiment method is the main technique used in the survey to value the selected 

upland attributes, contingent valuation is also used to estimate the value that survey 

respondents had for SDAs outside of their own region. The practical implementation of the 

various stages of survey design is outlined in Section 3.3. 



3.2 Conceptual Basis 

3.2.1 Choice experiment 

Most of the upland attributes included in this study are examples of non-market goods – 

goods which, while contributing towards people’s welfare, are not traded in markets and 

therefore if they are to be valued, must be valued by methods other than those using 

market data 4 . The main economic valuation technique used in this study is the choice 

experiment (CE), which is one of a group of survey based valuation techniques referred to 

as stated preference techniques. CE shows a series of ‘choice cards’ containing alternative 

sets of a goods with different associated price tags and asks people to state their most 

preferred choice among the alternatives. The choice cards always include the ‘current 

situation’ at no extra cost in order to enable the respondents to choose the ‘no change’ 

option. Thus, respondents are asked to imagine that they face a situation where it is 

possible to, in effect, trade off the different attributes of a good against each other and 

against the cost of securing the provision of those attributes. From their choices, the 

analyst can infer the respondents’ willingness to pay to secure a unit improvement or to 

avoid a decline in any of the non-cost attributes. 

In this study, the ‘good’ presented to the respondents was different policy options including 

the current situation. This good was defined in terms of the impacts of the policy options 

on the upland attributes, i.e. the level of each upland attribute in each policy option. The 

cost attribute was zero in the baseline and took random values (out of a set of six) in other 

policy options. While no policy option would cost extra in reality, associating cost with each 

policy option was necessary in order to create a trade off between changes in upland 

attributes and money and hence infer WTP estimates. An illustration of how the choice sets 

were presented to respondents is shown in Figure 3.4 in Section 3.3.3. Respondents do not 

all face the same choice sets, and there are many more choice sets than any one 

respondent sees. 

For more on the theory and practice of choice experiments see Bateman et al. (2002) and 

Louviere et al. (2000). 

3.2.2 Contingent valuation 

The other commonly used stated preference technique, contingent valuation (CV), elicits 

WTP or WTA directly by asking respondents to express their maximum WTP/WTA for a 

hypothetical change in the provision of the good of interest. Respondents are presented 

with alternative scenarios, usually one(s) where an environmental good is offered a certain 

level of protection or guaranteed accessibility, and one where it isn’t. Here the trade off is 

between money and the good as a bundle of attributes as opposed to the CE which offers 

trade off between the attributes and between attributes and money, as mentioned above. 

The WTP and WTA question can be asked in a variety of different so-called elicitation 

formats. In this study, the dichotomous choice elicitation format is used, whereby 

respondents are asked if they are willing to pay a given amount per year for an alternative 

policy option. The amounts are randomly varied between respondents. In the CV survey, 

the same set of six amounts used in the choice experiment were applied. Finally, as with 

the CE, if a respondent preferred the current situation (or baseline scenario), they would 

face no additional cost. 

For more on the theory and practice of contingent valuation see Bateman et al. (2002). 

4 Note that while the land itself can be bought and sold, the amenity value associated with it cannot. 



3.2.3 Choosing between choice experiment and contingent valuation 

Contingent valuation is typically suited to investigating changes to the whole resource in 

question, i.e. overall quantity or quality changes. A CE approach was chosen in preference 

for the main valuation in this study for two key reasons. Firstly, it allows the presentation 

of individual attributes of the good, allowing more information about preferences for 

specific upland attributes than CV could. Secondly, CE can accommodate a larger 

uncertainty about future scenarios than CV. So long as the upper and lower limits of the set 

of potential future scenarios are included in the CE design, other alternative policy 

scenarios that fall within this set can be assessed using the results of the CE. With CV, on 

the other hand, the survey results are limited to the policy scenarios presented in the 

survey and cannot be extrapolated beyond these. Therefore, in this study a simpler CV 

question was only used to test respondents’ willingness to pay for upland attributes in SDAs 

outside of their own region. 

3.2.4 Benefits transfer 

Changes to those attributes that are not included in the choice experiment are analysed 

through benefits transfer. Benefits transfer involves transferring valuation estimates for 

environmental goods from a previous study(ies) to a new context. The context of a previous 

study is referred to as the ‘study site’ and the new context as the ‘policy site’. 

Previous valuation studies covering the environmental good(s) in question are identified and 

examined for whether they are sufficiently similar to the current context in terms of the 

similarities between the environmental goods, the change examined and the affected 

population at the very least. Therefore, the success and reliability of benefits transfer 

depends on the similarity of the goods in the two contexts, the level of detail known about 

the socio-economics of the affected populations and the equivalence of expected changes 

to the good(s) in question (e.g. a study valuing a change in the quality of a good will not be 

applicable to a context where its quantity is changing). 

Benefits transfer can involve simply taking a ‘value-per-unit’ estimate from the study site 

and applying it unadjusted to the policy site. However, this is likely to be inaccurate apart 

from in cases of goods with high spatial equivalence. For example, the value of a tonne of 

carbon sequestered (in terms of climate change damages) is the same wherever it occurs, 

but the value of a clean stream varies according to geographical and local population 

characteristics. 

The ‘value-per-unit’ estimate can be adjusted to reflect the differences between study and 

policy sites. The most common adjustment factor is income since this is usually a significant 

determinant of WTP or WTA and is relatively easy to find data for both study and policy 

sites. However, a more conceptually accurate approach is to take the function linking the 

study site value to various explanatory factors and to plug in the appropriate numbers for 

the explanatory variables for the policy site. For example, if the original study finds that 

WTP in the study site appears to be a function of age, income and frequency of use of the 

good, then it is assumed that this function can be transferred to the policy site with the 

appropriate average age, income and frequency of use (if available) estimates inserted into 

the function. 

It should be noted, however, that this approach is not always so straightforward, depending 

on the method used to elicit WTP. If, for example, a survey has asked people “would you 

be willing to pay £X, yes or no?”, then the econometric modelling of the results has to take 

the form of modelling which factors were more likely to make people say “yes” – as 

opposed to modelling which factors caused people to state higher WTP. This form of 

modelling makes benefits transfer more difficult. In addition, not all studies report full 

functions and it is not always possible to find policy site data for all explanatory variables. 

For a more detailed review of the benefits transfer see Bateman et al. (2000). 



3.3 Implementing the Stated Preference Survey 

This section lays out the practical steps taken to implement the methodology. The 

following steps are best practice in implementing a stated preference study (see Bateman 

et al., 2002): 

• Initial research (see Section 2 for attribute and policy definitions. A literature 

review is presented in Section 4 as it also applies to benefits transfer); 

• Focus groups (Section 3.3.1); 

• Pilot survey (Section 3.3.2); 

• Main survey (Section 3.3.3); 

• Data analysis (Section 6 and Annex 5); 

• Validity testing (Section 7), and 

• Aggregation (Section 8) 

The stated preference questionnaire was revised several times during the focus group stage 

and after the pilot survey to reflect the lessons learnt. The final version of the 

questionnaire can be seen in Annex 4. In addition to the above stages, valuation workshops 

were also held to analyse preferences for upland attributes in greater detail (see Sections 

3.3.4 and 7.3). 

Throughout this report, the socio-economic classifications laid out by the Market Research 

Society are used in defining the sample and assessing its representativeness of the regional 

populations: 

A: professionals, very senior managers in business or commerce or top-level civil servants; 

B: middle management executives in large organisations, with appropriate qualifications; 

principal officers in local government and civil service; top management or owners of 

small business concerns, educational and service establishments; 

C: junior management, owners of small establishments, and all others in non-manual 

positions; jobs in this group have very varied responsibilities and educational 

requirements; 

D: all skilled manual workers, and those manual workers with responsibility for other 

people; 

E: all semi-skilled and un-skilled manual workers, and apprentices and trainees to skilled 

workers; 

F: all those entirely dependant on the state long-term, through sickness, unemployment, 

old age 5 or other reasons; those unemployed for a period exceeding six months 

(otherwise classify on previous occupation); casual workers and those without a regular 

income. 

As it is very difficult to select samples that can represent each socio-economic group 

separately, standard combinations of groups and sub-groups are used (e.g. ABC1, BC1C2 

etc.) in focus group recruitment and pilot and main survey samples. 

3.3.1 Focus groups 

Focus groups are semi-structured discussion groups led by a moderator, in which 

participants are presented with a topic. In this way, attitudes about the study issues can be 

reflected in the design of the questionnaire so as to make it credible, meaningful and easily 

understood. Each group consists of 6-8 people and typically lasts one and a half hours. 

Participants are paid for their time. 

5 

This does not include pensioners with pensions from their previous jobs, or widows receiving a pension from a 

husband’s previous job. 



Three focus groups were held for this study: one in Manchester and two in Kendal. The 

details of the focus groups are given in Table 3.2. 

Table 3.2: Focus Group Participants and Logistics 

Groups 1. Manchester 2. Kendal 3. Kendal 

Number of participants 8 8 6 

Gender 5F 3M 4F 4M 4F 2M 

Age 18-51 18-40 40-65 

Socio-economic group BC1C2 ABC1 C2DE 

Date 21/06/05 22/06/05 23/06/05 

Time 19:00 19:30 19:30 

Moderator Stavros Georgiou assisted by Helen Johns 

The main points of interest to note from the Focus Groups were that participants in 

general: 

• showed fairly marked and consistent preferences for certain landscape and 

environmental features; 

• generally preferred attributes associated with maintaining attractive, “natural-looking” 

scenery and maintaining cultural heritage; 

• were generally very familiar with SDAs, showed a strong appreciation of their amenity 

value and recreational opportunities, and visited SDAs regularly; 

• were influenced by the amount of information they had on certain attributes and 

sometimes revised their preferences in the light of others’ opinions; 

• mostly supported the principle of subsidising hill-farming, but disagreed over the 

appropriate means to do this; and 

• showed a greater willingness to subsidise hill farming in their own immediate locality 

than further afield, indicating that use values are likely to be greater than non-use 

values. 

The protocols were changed between groups, but the protocol used in the final group is 

presented in Annex 1. A more detailed report on the findings of the focus groups is provided 

in Annex 2. 

3.3.2 Pilot survey 

Only a limited number of attributes can feasibly be contained in a choice set, otherwise 

respondents are required to trade off against too many different attributes. The five upland 

attributes used in the survey were chosen from the initial long list (described in Section 

2.1.1) of fourteen through the results of the focus groups and through consultation with the 

Steering Group. The upland attributes chosen were: 

• heather moorland and bog 

• rough grassland 

• broadleaf and mixed woodland 

• field boundaries (particularly hedgerows and dry stone walls) 

• cultural heritage 

These attributes were chosen because they were shown to be thought to be important in 

the focus groups, and because they would be impacted by the policy options. The cost 

attribute was defined as “Increase in tax payments by your household each year”. A more 

specific ‘payment vehicle’ such as council tax or a new national tax was not used as a 

general increase was seen both by the study team and the Steering Group as more realistic. 

The pilot survey questionnaire was conducted over the period 4 th -13 th July 2005. The pilot 

survey sample comprised 25 respondents in Manchester and 25 in the Lake District, and was 



sampled according to gender, age and socio-economic characteristics representative of the 

North West. The survey mode was face-to-face, door-to-door personal interviews. 

The pilot survey worked well enough to continue with the same broad questionnaire design 

and the same upland attributes listed above. See the pilot survey report provided in Annex 

3 for a discussion of the results and conclusions drawn from the pilot survey. 

3.3.3 Main survey and questionnaire 

The populations affected by the presence and upkeep of SDAs are: (i) local/regional 

residents; (ii) visitors to upland areas; and (iii) the non-user population living further away 

from the areas of concern. Three-hundred respondents in each of the six GORs which 

contain SDAs, as well as 300 respondents in the South East GOR, were interviewed. The 

respondents in each GOR with SDAs were of type (i) as far as their own SDA was concerned, 

but were of either type (ii) or (iii) with respect to other regions’ SDAs. Each sample was 

chosen according to quotas for age, gender, socio-economic group and also whether 

respondents resided in an urban or rural area. The survey mode was face-to-face, door-todoor 

personal interviews. 

The questionnaire consisted of four sections: (A) attitudes, opinions and uses; (B) choice 

experiment valuation section for SDAs in the respondent’s own GOR; (C) CV question for 

SDAs in the rest of England; and (D) follow-up and socio-economic questions. The 

questionnaires in all versions followed the same format except for two different changes to 

the South West and South East questionnaires. In the South West survey, half of the 

respondents received a questionnaire with a different upper limit for increases in broadleaved 

and mixed woodland, as a validity test to see if different upper limits to the 

attributes would make any difference to responses. The questionnaire used in the South 

East GOR had the same structure but no CV section, since the choice experiment section 

already asked for a value for all of England. 

Further details on each of the questionnaire sections are provided in Section 6.1. Here the 

design of the choice experiment is discussed as it affected the design of the sub-samples 

and the different versions of the questionnaire. 

Each choice card shown in Section B of the questionnaire presented three possible 

scenarios: the first scenario represents the ‘current policy’, which is identical on all the 

choice cards. The other two scenarios present different levels of change over the baseline, 

described by different levels of the upland attributes. Note that the attribute levels 

appearing on the choice cards are not exactly the same as any of the policy scenarios 

outlined in Section 2.2, including the current situation (which is not the same as ‘Scenario 

0’). The purpose for selecting the levels in this way was to include the highest and the 

lowest estimates of change for each attribute (from Table 2.2) and a variation in between 

these two. 

The same baseline is used for every choice card, but the other levels are used in different 

combinations, chosen from one of three levels for each upland attribute (six levels for cost) 

presented in Table 3.3. The appropriate range of values to use was decided in consultation 

with the Steering Group, considering the range of predictions for each attribute made by 

eftec’s Initial Report and by Cumulus et al. (2005). 



Table 3.3: The levels of each attribute which were used in the choice experiment. 

Attribute Baseline Bottom level Middle level Top level 

Heather moorland/bog -2% -12% -2% +5% 

Rough grassland -10% -10% +5% +10% 

Broadleaf and mixed wood-land +3% +3% +10% +20% 1 +30% 2 


(For every 1km, Xm is restored) 

X = 100 X = 50 X = 100 X = 200 

Cultural heritage Rapid decline Rapid decline No change 

Cost £0 £2, £5, £10, £17, £40, £70 

1 For all GORs other than the half of the South West survey. 

2 For half of the South West survey only. 

Much better 

conservation 

Statistical design theory was used to combine the levels of the attributes into a number of 

alternative landscape scenarios to be presented to respondents. Combination of the six 

attributes at their various levels created a total of 1,458 (levels to the power of attributes: 

five attributes at three levels and one attribute at six: 3 x 3 x 3 x 3 x 3 x 6) possible 

scenario combinations. It is obviously too difficult for a respondent to make an informed 

choice from such a large number of scenarios. A fractional factorial design 6 was therefore 

used to reduce the number of scenarios, while still maintaining the possibility of estimating 

‘main effects’, i.e. the effects of the attributes on respondents’ choices (Louviere et al., 

2000). The fractional factorial design selected 36 “alternative policy” scenarios, which 

were then grouped into choice sets to be presented to respondents. 

This process resulted in 18 choice cards (two “alternative policy” scenarios per card) for 

each region (each with current policy and two future policy options). Given that it would 

not be practical for an individual to make even that many choices, each respondent was 

shown six out of the total set of 18 cards 7 . Therefore, the sample (300) in each region was 

divided into three: 100 respondents for cards 1-6, 100 for cards 7-12 and 100 for cards 13- 

18. In each sub-sample, the sequence of cards was rotated between respondents (e.g. 

respondent 1 saw cards 1-6 in order, respondent 2 saw cards 2,3,4,5,6,1 and so on), in 

order to avoid any ‘ordering effect’. 

Table 3.4 presents an example of a choice card used in the survey. 

6 The ‘full factorial’ is the set of all possible combinations of attribute levels. A ‘fractional factorial’ refers to a 

selection of this set. The fractional factorial must be chosen so as to maintain the property that there is no 

correlation between attributes, but if it is too small “it may not be capable of driving a model that accurately 

represents the relationships existing between choice probabilities and attribute levels” (Bennett and Adamowicz, 

2001). 

7 Different numbers of choice cards were tested in the pilot survey to asses the optimum number of cards a 

respondent could cope with. 



Table 3.4: Example of a choice card used in the survey 

Policy Option Current 

Policy 

Change in area of Heather 

Moorland and Bog 

Change in area of Rough 

Grassland 

Change in area of Mixed and 

Broadleaf Woodlands 

Condition of field boundaries 

Change in farm building and 

traditional farm practices 

Increase in tax payments by 

your household each year 

A loss of 2% 

(-2%) 


(-10%) 

A gain of 3% 

(+3%) 

For every 1km, 

100 m is 

restored 

Rapid decline 

Policy 

Option A 


(+5%) 


(+10%) 


(+20%) 


200 m is 

restored 

Much better 

conservation 

Policy 

Option B 


(-2%) 


(-10%) 


(+10%) 


50 m is 

restored 

No change 

£0 £70 £10 

A contingent valuation scenario was also presented to respondents (in all regions except the 

South East) to provide evidence on respondents’ values for SDAs in the rest of England. This 

presented respondents with a choice between two options: the same zero-cost Current 

Policy as used in the choice experiment, and a ‘best case’ alternative policy scenario using 

the best level for each landscape attribute. The cost varied between the same six levels 

that were used in the choice experiment. Table 3.5 shows how the contingent valuation 

question was presented to respondents. 



Table 3.5: How the contingent valuation section of the questionnaire was presented to 

respondents. 

CURRENT POLICY FUTURE POLICY OPTION 

CHANGE IN AREA OF 

HEATHER MOORLAND AND BOG 


ROUGH GRASSLAND 


MIXED AND BROADLEAF WOODLANDS 

CHANGE IN 

RESTORATION OF FIELD BOUNDARIES 

CHANGE IN 

FARM BUILDINGS AND TRADITIONAL FARM 

PRACTICES 

INCREASE IN TAX PAYMENTS BY YOUR 

HOUSEHOLD EACH YEAR 

Loss of 6,600 ha 

(2% loss) 


(10% loss) 

Gain of 2,800 ha 

(3% gain) 

For every 1km, 100m is 

restored 








restored 

Rapid decline Much better 

£0 

Hectare figures shown are appropriate for the East Midlands survey (i.e. they represent the national 

total areas minus the East Midlands area for each attribute). X was one of the same six cost amounts 

used in the choice experiment. 

3.3.4 Valuation workshops 

Two valuation workshops of seven and twelve participants were held in Knaresborough and 

Harrogate on the 19 th and 20 th October 2005, respectively. The valuation workshops were 

intended to provide a supplementary approach to deal with complex impacts for which 

participants may not have had pre-determined preferences. Participants answered the 

same questionnaire as that used in the main survey (for the Yorkshire & the Humber GOR) 

to gain qualitative insights into the relative values which respondents place on the different 

attributes, and to hear participants explain their reasoning behind their choices. Following 

the discussions, the participants were asked to reconsider their choices and make changes 

if they wished. 

The workshop protocol is presented in Annex 6 while a full valuation workshop report is 

presented in Annex 7. Some key findings of the valuation workshops are discussed in 

Section 7.3. 


£X


4. Valuation of Landscape – Literature Review 

4.1 Overview 

The following literature review covers valuation studies of agriculturally-dominated 

landscapes and other studies which may usefully inform on the survey design and can be 

used for benefits transfer. Although this valuation study relates only to SDAs in England, 

many of the relevant studies are from Scotland. As some of the upland areas under 

consideration are in many respects similar to upland areas in Scotland rather than the 

gentler countryside in England, these studies are very much relevant. Studies of very 

different landscapes (e.g. coastal or East Anglian flat) have been omitted by the literature 

review; as have studies which deal only with woodlands or biodiversity, or those which seek 

values for access, path maintenance or visitor facilities. 

The type of goods valued in the relevant literature varies. Typical landscape attributes 

examined by the studies reviewed include the physical extent of broad habitat categories 

(moorland, wetland, broad-leaved woodland, heather moorland, scrub) and diversity of 

habitat components, either the species diversity of habitats, or of some other consideration 

(e.g. age diversity of woodlands). Some studies do not discuss specific landscape attributes 

with respondents, but show them pictorial representations of different landscapes between 

which they are asked to choose (e.g. Bullock and Kay, 1997, and Willis and Garrod, 1993). 

In common with this study, most previous studies were subject to a conventional series of 

design stages, using focus groups to determine whether the questions asked in the survey 

were credible and to find out how much the general public already knew about the survey 

subject. Small scale pilot surveys were also generally used to test the credibility of the 

survey as well as survey logistics. 

Two of the studies reviewed contain literature reviews. These are summarised in Section 

4.2. The other studies that deal with individual goods or changes are presented in Section 

4.3. Issues arising from this review are discussed in Section 4.4. 

4.2 Review Studies 

4.2.1 The Environmental Landscape Features model 

The Environmental Landscape Features (ELF) is a benefits transfer model to estimate the 

value of various landscape features in different regions of England (IERM/SAC, 1999; 

IERM/SAC, 2001). It was originally constructed from 14 original valuation studies to obtain 

value estimates for rough grassland, heather moorland, salt marsh, woodland, wetland and 

hay meadow. Hedgerows and field margins (in the form of arable headlands) were added to 

the model after original contingent valuation surveys were conducted in 2001. The ELF 

model was updated by David Oglethorpe in 2005 (see Oglethorpe 2005 for details). 

The benefits encapsulated in the ELF model for environmental features in each region are 

intended to accrue only to those living in the region and not residents of other regions or 

visitors. The ELF is also intended to describe the diminishing marginal utility of the 

features, i.e. unit values are lower when the features are plentiful and higher when they 

are scarce. Transferred benefits are estimated through function transfer. 

The original ELF model was validated by comparing values calculated through benefits 

transfer with the results of two specially designed pilot surveys: one for heather moorland 

in Northumbria and one for rough grazing in the North Pennines. The observed mean per 

household per annum WTP for the rough grazing survey was found to be within the range 

predicted by the model; that for heather moorland was higher (although the 95% 



confidence interval overlapped with the predicted range). The authors attributed this to 

the survey sample having a slightly higher average income than the regional average. 

The values obtained in the ELF model for rough grassland, heather moorland, woodland and 

hedgerows are compared with those found in the survey in Section 7.4. The ELF is also used 

for benefits transfer for hay meadows in Section 5.1. 

4.2.2 Framework for Environmental Accounts for Agriculture 

This study (eftec and IEEP, 2004) was intended to develop a framework of sectoral 

accounting in order to include the economic costs and benefits of environmental impacts of 

agriculture in the national agricultural accounts. The study reviewed both existing similar 

frameworks and relevant literature, and provided a useful summary of valuation studies 

relating to the impacts of agriculture on landscape, habitats and species, and a range of 

other environmental impacts. 

The report notes that at least seven Environmentally Sensitive Areas (ESAs) have been the 

subject of valuation studies. Only two of these are described below since the other studies 

were situated in areas irrelevant to this study, such as the coast or Norfolk Broads. Studies 

focussing on ESAs typically encompass their range of landscape, biodiversity and heritage 

features rather than just landscape features as such. 

4.3 Individual Valuation Studies 

4.3.1 Willis and Garrod (1993) – Yorkshire Dales 

Willis and Garrod (1993) used CV to assess preferences for a range of different landscapes 

in the Yorkshire Dales which could result from future subsidy changes. These were 

described in the survey as: intensive and semi-intensive agricultural, planned, conserved, 

sporting, wild and abandoned. The respondents consisted equally of residents of Craven 

local authority district in North Yorkshire and visitors to the area. Changes were illustrated 

to respondents pictorially. No attempt was made to estimate WTP for separate attributes, 

although preferences were examined by asking respondents whether they would prefer 

less/more/the same of features. The outcome of such questions revealed fairly predictable 

preferences; most people would prefer more wild flowers and broadleaved woodland, 

fewer modern sheds and less wire fencing. Other attributes such as numbers of dry stone 

walls and grazing animals were deemed to be at the right level by most people. There were 

fairly minor differences in preferences between residents and visitors. Overall, the status 

quo landscape was the most popular, with nearly 50% of respondents choosing it, with the 

conserved landscape also popular. The survey revealed strong preferences against intensive 

and semi-intensive landscapes. Willingness to pay for the preferred landscape ranged from 

£18-35 per household per annum. An interesting finding is that residents who felt the Dales 

to be overcrowded had a lower WTP than other residents, suggesting that perceived 

overcrowding can reduce the benefits of living in a national park. 

4.3.2 Hanley et al. (1998) – Breadalbane ESA 

Hanley et al. (1998) study was designed to assess the economic value of the conservation 

benefits of the Breadalbane Environmentally Sensitive Area (ESA) in Highland Perthshire. 

This was achieved by using both the CV and CE methods for comparison purposes. The 

relevant populations were considered to be the UK general public, local residents and 

visitors. In the choice experiment, the attributes were broadleaved woodland, moorland, 

wetland, dry stone dykes and archaeological sites, which are all features which farmers 

receiving ESA payments are obliged to conserve. The policy scenarios pursued were simply 

‘policy on’ and ‘policy off’, i.e. a world with ESA payments and one without. Respondents 



expressed positive WTP for all the attributes at the ‘policy on’ levels, showing distinct 

preferences for greater levels of broad-leaved woodland, heather moors and wet 

grasslands. Dry stone walls and archaeology commanded lower WTP. 

4.3.3 Bullock and Kay (1997) – Central Southern Uplands 

Bullock and Kay (1997) also performed a CV study on upland attributes in Scotland, but in 

the Central Southern Uplands south of Edinburgh and Glasgow. They were testing for WTP 

for agricultural policies that produced landscapes associated with extensive grazing. Three 

policy options were presented: business-as-usual, extensive and very extensive. The 

landscapes were illustrated pictorially (using symbols indicating the abundance of wildlife), 

and no attempt was made to elicit values for individual landscape attributes. Local 

residents, birdwatchers and visitors to local beauty spots were surveyed. The results 

showed a clear preference for landscapes with more extensive grazing and with more tree 

cover than at present. 

4.3.4 White and Lovett (1999) – North York Moors 

White and Lovett (1999) performed a CV study on a particular estate (Levisham) in the 

North York Moors to measure willingness to pay to maintain or enhance the estate. WTP for 

different landscape features was not sought, although qualitative preferences for habitat 

types associated with or reduced by sheep farming, such as heather moorland, semi-natural 

woodlands and unimproved pasture were sought. Many more respondents preferred either 

heather moorland or semi-natural woodlands compared to unimproved pasture, and 

indicated different motivations for these choices (heather moorland for landscape, 

woodland for biodiversity and scarcity value). Fewer than half of participants indicated a 

positive WTP to maintain or enhance the estate, with half of the zero WTP bids being 

protest bids stating that National Parks should pay for themselves. 

4.4 Issues Arising 

Overall, the studies indicate that the landscapes of upland areas and other agricultural 

areas are valued by the public. Although by no means all of those interviewed said that 

they would be willing to contribute financially, this does not indicate a lack of value, as a 

large proportion of protest bids are often observed. 

Hanley et al. (1998) note that both contingent valuation and choice experiments could 

estimate the value of landscapes, but that the choice experiment approach “is more suited 

to measuring the (marginal) value of the individual landscape and wildlife characteristics” 

that make up natural and semi-natural areas. They also note that there is a very large set 

of such characteristics available and the welfare measure may be influenced by which ones 

are chosen. CE is also more useful for benefits transfer because individual features are 

valued. 

The studies reviewed do not agree on whether respondents tend to prefer a particular 

landscape because it has always been familiar to them, i.e. attachment to the status quo. 

Willis and Garrod (1993) report a clear preference for the status quo. By contrast, Bullock 

and Kay (1997) tested specifically for this bias by actually asking respondents which 

landscape they found the more typical. They found that respondents did not necessarily 

prefer this landscape, and that overall respondents preferred a more extensified landscape. 

These different findings could reflect regional differences; or they could reflect a more 

conservative attitude by residents in a designated National Park. 

A further issue is that respondents’ preferences for particular landscape features can be 

sensitive both to the amount and type of information that they are given and on the way 



that features are presented to them pictorially. Information about the wider ecological 

impact of a feature could alter preferences. Willis and Garrod (1993) note that 

“respondents unfamiliar with the aesthetic good being valued by a [CV study] can easily be 

influenced by information contained in the definition of the good". Further, Shelby and 

Harris (1985) note that the presence of animals or time of year that the picture was taken 

can unduly influence respondents. It is therefore important that any survey using images 

should treat different features consistently and also be designed so that respondents are 

expressing preferences for features being examined, and not just for the pictures. This is 

why, pictures that are representative of typical features of the upland attributes were used 

in the survey for this study as well as verbal descriptions of the attributes. 



5. Valuation of Attributes Not Included in the Choice 

Experiment 

This Section describes the scope for valuing the upland attributes not included in the choice 

experiment via benefits transfer. For a description of the principle behind benefits transfer 

see Section 3.3. In all but one case, benefits transfer is not able to be used in this study. 

5.1 Benefits transfer Using the Environmental Landscape Features 

Model 

5.1.1 Attributes considered 

Six of the upland attributes considered in this study have exact or rough equivalents in the 

ELF model: hay meadows, rough grassland, heather moorland and bog, broad-leaved and 

mixed woodland, field boundaries, and arable land. However, each of these upland 

attributes could only be applied to benefits transfer or compared with new values found in 

this study with the following caveats: 

• The actual area coverage of hay meadows in SDA areas is not known with precision. 

Defra use a simple rule of thumb of assuming that hay meadows make up 35% of 

improved grassland (pers. comm., Defra, 2005). 

• In the ELF model, the heather moorland attribute does not explicitly include bog. Bogs 

were included in the “wetland” feature of the ELF model; 

• The “farm woodlands” feature in the ELF model again does not perfectly overlap with 

the “broad-leaved and mixed woodland” attribute. Farm woodlands is intended to cover 

mostly ash, oak, beech and alder woodlands, but does also include “recently planted” 

coniferous woodland; 

• In the ELF, only hedgerows, not drystone walls, are considered as field boundaries; and 

• Only arable headlands and field margins are considered in the ELF. These are likely to 

be valued much more highly by the public than a whole field of arable crops without 

field margins. 

Therefore, there is only one upland attribute, hay meadow, whose per hectare value can be 

entirely estimated through benefits transfer from the ELF model. The values of four others 

– rough grassland, heather moorland and bog, broad-leaved and mixed woodland, and field 

boundaries – are estimated in the choice experiment and can be compared with values 

found in the ELF model, and eventually, can be used to update the ELF model. 

5.1.2 Transfer of hay meadow value 

The ELF model provides value estimates based on 10%, 30%, 50%, 70% and 90% reductions in 

abundance. From the predictions laid out in Section 2.3, it can be seen that the 10% 

reduction is the most appropriate to use. The estimated per hectare values by the 2005 

update of the ELF in each region are presented in Table 5.1. It is important to remember 

that these are within-region values only. For this reason, it should be kept in mind that the 

estimates for the South East, which does not have any SDAs, are not applicable to SDAs. 



Table 5.1: Estimated annual per hectare values (£) 

from the ELF model (2005 update) for each GOR 

region. 

Region Lower bound Upper bound 

North West 20 34 

North East 20 33 

Yorkshire & Humber 26 44 

West Midlands 19 32 

East Midlands 24 40 

South West 8 13 

South East 29 48 

5.2 Other Attributes 

5.2.1 Improved grassland 

According to eftec and Entec (2002), there has only been one study which explicitly values 

improved grassland, in the context of urban development in Canada (Bowker and Diychuck, 

1994). However, this is not of relevance to improved grassland in SDAs in the UK. Eftec and 

IEEP (2004) note that “studies on the value of greenbelt land value agricultural landscape 

against the threat of development reflect both the disamenity of housing development and 

the value of the landscape without housing”. At this time, there does not appear to be any 

quantitative evidence on the amenity value of improved grassland, and this attribute will 

have to be excluded from benefits transfer. 

5.2.2 Bracken or gorse dominated 

There is no evidence in the literature on the valuation of bracken and gorse. On the 

evidence of the focus groups, it is unlikely that either attribute would have a high positive 

value, and may even have a negative value, in terms of landscape amenity. However, gorse 

has some ecological benefits which may contribute to biodiversity provision. 

5.2.3 Arable / set aside 

The amenity value of arable and set aside land is unknown, and is likely to be very low (or 

possibly even negative). There do not appear to be any studies on the amenity value of 

arable land from which to transfer benefits. 

5.2.4 Water quantity 

Information relating to flooding and the costs of flooding is sketchy. Eftec and IEEP (2004) 

note that the Environment Agency estimates that at least 14% of all flood costs (or about 

£153 million per annum) should be attributed to agriculture (EA, 2002). However, the 

contribution of different types of agriculture is not detailed. Relating the causal factors 

which cause a greater flooding risk in any particular area to the costs which may be 

attributable to flooding is highly complicated and beyond the scope of this project. 

5.2.5 Water quality 

Eftec and IEEP (2004) cite Georgiou et al. (2000) as “the only study undertaken that has 

linked WTP for improvements in water quality indices”. The study sought WTP for 

improvements in total ammonia, biochemical oxygen demand and dissolved oxygen in the 



River Tame in the West Midlands, relating these to “large”, “medium” and “small” 

improvements in overall water quality. This study estimated that the annual welfare impact 

on society from loss of amenity associated with rivers classed as ‘fair’ or ‘poor’ was £5,600 

per km of river. However, the River Tame is an urban river, so it is not appropriate to use it 

for comparison purposes with rivers in SDAs. 

Furthermore, given that the policy–attribute impact analysis has not been able to provide 

quantitative estimates for this attribute (see Table 2.2), it would not be possible to take 

this analysis further even if a unit economic value estimate existed. 

5.2.6 Greenhouse gas emissions 

In the case of greenhouse gas emissions, the appropriate figure to use is the ‘social cost of 

carbon’, i.e. the estimated damage (in the form of damage inflicted by extreme weather 

events, sea level rises, etc.) caused by an extra tonne of carbon dioxide equivalent (tCO2e) 

emitted. Defra have adopted 8 a range of estimates for the social cost of carbon of £35-£140 

per tC, with a single point estimate of £70 per tC. In terms of carbon dioxide equivalent, 

these figures translate into £9.50 to £38 per tCO2e, with a single point estimate of £19 per 

tCO2e. 

However, greenhouse gas emissions will not be able to be included in the benefits transfer 

as no quantitative estimate of their likely change under the different policy scenarios has 

been made. Furthermore, the amount of greenhouse gas emissions for which agriculture in 

SDAs is responsible is not known. 

8 See http://www.defra.gov.uk/environment/climatechange/carbon%2Dcost for details. 



6. Stated Preference Survey: Summary and Non- 

Monetary Results 

This section provides a summary of the questionnaire used in the main survey (Section 6.1) 

and the headline results from the survey including socio-economic representativeness of 

the samples (Section 6.2), and summary of attitudes, opinions and other related 

information (Section 6.3). Willingness to pay estimates from the choice experiment and 

contingent valuation are presented in Section 8.1. The actual questionnaire and the 

statistical analysis of the results can be found in Annexes 4 and 5, respectively. 

6.1 Main Questionnaire Summary 

The survey contained four sections as follows: 

A. Attitudes 

B. Choice experiment questions 

C. Contingent valuation question (not for the South East survey) 

D. Socio-economic characteristics 

6.1.1 Section A 

Section A contains three brief questions on attitudes to the environment and countryside. 

Question A1 asked whether respondents considered environmental policy to be important in 

relation to other areas of government expenditure. Question A2 asked respondents to rank 

the four environmental priorities presented to them (controlling air pollution, tackling 

climate change, protecting the countryside and protecting the quality of rivers, lakes and 

the sea) from the most important to the least. Finally, question A3 asked respondents 

whether they ever visited the countryside for recreational or work purposes or both. 

6.1.2 Section B 

This section contains the choice experiment. In the preamble to the experiment the 

valuation scenario, the location (within the region) and character of SDAs were explained 

to respondents. A map showing all SDAs in England and a smaller one showing the specific 

region are presented as a show card. The valuation scenarios also described the five chosen 

upland attributes (heather moorland and bog, rough grassland, broadleaf and mixed 

woodland, field boundaries and traditional farm buildings and farming practices). The need 

for the hill farming policy and alternative policy options was presented to respondents in 

order to clarify the institutional set up through which policy changes would be affected. 

Respondents were given an example choice card to show how the experiment will proceed. 

Before the experiment commenced, respondents were assured that the survey was 

interested in their views and not that of ‘experts’, and they were reminded of other 

demands on their household budget. Finally, they were shown a card to explain the 

absolute amounts of each upland attribute as it exists in their region and what percentage 

changes shown in the choice cards may mean in absolute terms. 

During the choice experiment each respondent was asked to state their most preferred 

option out of three policy options on each of the six choice cards they were given (question 

B1). Some immediate follow up questions aimed to find out the motivations behind 

choosing the current situation (zero WTP) and an alternative policy option (positive WTP) 

(questions B2-B4). The section ended with question B5 which asked the level of difficulty 

the respondents faced in answering the choice questions. 



6.1.3 Section C 

This section contains the contingent valuation part of the questionnaire. Two scenarios 

were presented to respondents detailing the possible level of attributes under both a 

costless ‘current policy’ scenario and an improvement scenario, with one of six different 

price levels attached. The improvement scenario was constructed using the best possible 

outcome of all upland attributes. This time, however, the attribute levels presented were 

for the other five SDAs in England as this question aimed to elicit respondents’ preferences 

for SDAs that are not in their own region. The South Eastern survey did not include a 

Section C because, as the South East does not contain any SDAs, the choice experiment 

section had already asked about the rest of England. 

After the contingent valuation question (question C1), those who were not willing to pay 

the stated amount were asked a follow-up question to explain the reason for their answer 

(question C2). It was assumed that those who were willing to pay something towards SDAs 

in other regions had similar reasons for doing so as they did when they answered the choice 

experiment questions for their own region. Therefore, respondents were not asked a 

follow-up question in order to minimise the size of the questionnaire. 

6.1.4 Section D 

This section contains follow up questions on respondents’ habits regarding actually visiting 

SDAs (questions D1 and D2), their previous and expected residency in the region (question 

D4 and D5) as well as standard socio-economic characteristics including the size and 

composition of their household (question D3), their membership of environmental and other 

organizations (question D6), their education (question D7), employment status (question 

D8) and income (question D9). The final question (D10) asked respondents what they 

thought of the questionnaire. 

6.2 Socio-economic Representativeness of the Sample 

The survey sample was selected according to appropriate quotas for sex, age and socioeconomic 

group for each GOR in order to ensure that responses were representative. Table 

6.1 summarises for each region the proportions of survey respondents of each gender, age 

bracket and socio-economic group, along with (in brackets) comparative proportions 

according to the 2001 Census (or ONS data provided by Defra in the case of the rural/urban 

split). The table shows that in each region, more than 300 respondents were sampled in 

order to account for possible unusable surveys. 

With the odd exception (particularly in the North West), the survey quotas have mostly 

been fulfilled and these figures indicate that samples have been as far as possible 

appropriately representative of gender, age, socio-economic background and location. 

Adjusted mean gross household income figures in Table 6.2 are in some regions fairly 

different to official regional estimates (in brackets). This is probably due to the 

approximate nature of the mean estimation (generally it is not accepted practice to ask 

respondents for their exact income), an approximate McClement’s income adjustment (due 

to uncertainty over household composition), and a high income non-response rate (see 

Section 6.1.1). 

It was originally planned to have three survey quotas based on location: urban, rural non- 

SDA and SDA. However, figures provided by Defra showed that the proportion of the 

population actually living in SDAs in each region is too small for this to be feasible or 

necessary: overall only 37 out of 1,800 respondents in SDA GORs (2%) would have been 

interviewed, and only five or fewer respondents in most GORs. The definition of ‘rural’ and 

‘urban’ is the new definition created by the Office of the Deputy Prime Minister in 2002 9 . 

9 See Office of National Statistics http://www.statistics.gov.uk/geography/urban_rural.asp for more details. 



Other socio-economic characteristics of the final survey respondents which are important 

but which were not specified in the form of sample quotas are presented in Table 6.2. 

There was quite a high non-response rate for the education question in some areas. 

Table 6.1: Principal socio-economic characteristics of survey samples 

NW NE YH WM EM SW SE 

Total no. 

respondents 

Gender (%) 

335 300 312 318 314 301 345 

Males (%) 48(48) 49(49) 50(49) 49(49) 46(50) 46(50) 48(50) 

Females (%) 

Age brackets (%) 

52(52) 51(51) 50(51) 51(51) 54(50) 54(50) 52(50) 

18-34 42(33) 34(32) 33(32) 33(33) 39(32) 30(30) 35(32) 

35-54 32(42) 42(43) 43(42) 38(42) 36(42) 41(42) 40(43) 

55-70 26(25) 24(36) 24(25) 29(25) 26(25) 29(28) 25(26) 

Socio-economic group 1 (%) 

ABC1 35(48) 43(37) 40(38) 48(46) 46(43) 47(48) 57(53) 

C2DE 65(52) 56(63) 59(62) 52(54) 54(57) 52(52) 42(47) 

Distribution of sample (and population) between rural and urban areas 

Rural 11(12) 12(19) 23(20) 15(16) 39(30) 32(34) 20(22) 

Urban 89(88) 88(81) 77(80) 85(84) 61(70) 68(66) 80(78) 

1 

Definitions of socio-economic groups are provided in Section 3.3. 

General notes: The numbers in brackets are comparative figures from the 2001 Census. The region 

abbreviations will be used throughout the rest of the report: NW = North West, NE = North East, YH 

= Yorkshire and the Humber, WM= West Midlands, EM = East Midlands, SW = South West, SE = South 

East. 



Table 6.2: Further socio-economic characteristics of the survey samples 


Adjusted mean 

gross household 

income 1 (£ pa) 

Employment (%) 

32,800 

(23,300) 

20,000 

(21,400) 

18,000 

(23,000) 

28,900 

(25,100) 

18,400 

(25,300) 

21,000 

(26,000) 

26,500 

(33,300) 

Self-employed 9 6 7 13 6 8 8 

Full-time 52 43 40 32 37 35 33 

Part-time 11 12 12 19 14 18 12 

Student 4 4 7 6 9 3 10 

Unemployed 2 10 6 3 7 7 4 

Looking after 

home full time 

3 6 10 5 7 10 10 

Retired 13 15 14 21 15 15 16 

Unable to work 2 Education (%) 

2 4 3 1 4 2 4 

Primary 16 14 11 10 8 10 5 

O Levels / GCSE 

or equivalent 

A level / HSE or 

(vocational) 

equivalent 

Professional 

qualification of 

degree level 

44 39 55 23 57 41 41 

19 19 18 17 18 17 26 

4 8 6 16 7 4 8 

First degree level 11 8 8 26 4 11 14 

Higher degree 

(masters or PhD) 

5 3 1 8 3 4 7 

No. children 3 in household (%) 

None 84 66 55 69 69 64 62 

One 9 17 16 16 13 14 13 

Two 5 14 19 12 13 14 18 

Three or more 3 4 9 4 5 8 7 

Percentages may not add up to exactly 100% in all cases because of small numbers of non-responses 

to certain questions or because of rounding. 

1 

Figures in brackets are inflation-adjusted regional average gross annual income figures provided by 

ONS Regional Trends for the period 1999-2002. Survey incomes have been adjusted according to the 

McClement’s equivalence adjustment, so that they are comparable with official statistics. However 

as we do not have detailed data on household composition this is approximate. The average shown 

here is the average of mid-points of intervals. The mid-point of the top unbounded income interval 

“£125,000 or more” is taken to be £125,000, though this may under-estimate average income. 

2 from sickness or disability. 

3 

defined as 16 or under. Please note that this is the number in the household, not in the family – i.e. 

grown up children who have left, or children living with a divorced partner, are not counted. 



6.3 Attitudes, Opinions and Other Information 

This section presents the results of the questions not presented in Section 6.2, i.e. 

questions on attitudes, habits and residence in the GOR. The questions are paraphrased; for 

exact phrasing see the full main questionnaire in Annex 4. 

6.3.1 Attitudes towards the environment, countryside and SDAs 

The results relating to the following questions are presented in Table 6.3: 

Question A1: How important would you say that environmental policy is, in relation to 

other things that government is concerned with, such as law and order, or education? 

Question A2: What do you think should be the main concern for environmental policy in 

this country over the next 10 years? Ranked from 1 - most important) to 4 - least 

important). 

Question D6: Are you or is anyone in your household a member of an environmental, 

heritage, outdoor recreation or farming organisation? 

The percentages reflect the aggregated sample of all seven GORs surveyed. 

Table 6.3: Responses to attitudinal questions on the environment 

and countryside 

A1 response % response 

Very important 45 

Quite important 43 

Not all that important 8 

I really don’t care 3 

A2 response % saying most 

important 1 

average rank 

(high means 

less important) 

Controlling air pollution 36 2.1 

Tackling climate change 32 2.4 

Protecting the countryside 18 2.7 

Protecting water quality 13 2.8 

D6 response % respondent 

RSPB 

members 

5 

Other environmental group 11 

National Trust 13 

NFU or other farming group 2 

Outdoor recreational 

4 

organization 2 

1 

These are percentages of those who answered the question fully (see text). 

2 

e.g. Ramblers’ Association. 

It was clear that most respondents were environmentally aware and concerned. However, 

protecting the countryside did not rank highly as an environmental concern compared to 

controlling air pollution or tacking climate change. These rankings did not change much 

between regions, with all samples citing air pollution as the most important problem, and 

all but one citing climate change as the second most important. As a generalisation, 

samples with higher educational levels were more environmentally aware. Not all 

respondents completed the ranking question, with a large proportion in the West Midlands 

saying that all were of equal importance. 



Overall, 24% of respondents (or members of their household) belonged to some kind of an 

environmental, heritage, outdoor recreation or farming organisation, with the single most 

important group being the National Trust. There were large regional variations in group 

membership, with 35% in the South West, but only 11% in Yorkshire and the Humber. These 

figures could be higher than national proportions for individual membership because 

respondents were also asked about members of their household. 

6.3.2 Countryside and SDA visiting habits 

The results relating to the following questions are presented in Table 6.4 and Figure 6.1: 

Question A3: Do you ever visit the countryside for recreation, for work or for both? 

Question D1: Do you ever visit SDAs in your region for recreation, for work or for both? 

Question D2: How often to you visit SDAs in your region? 

Table 6.4: responses to questions on visiting SDAs and 

countryside in general. 

A3/D1 response A3 (% response) D1 (% response) 

Yes, for recreation 71 67 

Yes, for work 2 2 

Yes, for both 13 12 

No, never 13 17 

D2 response 1 

% response 

More than once a week 23 

More than once a month 21 

More than once a year 25 

Less than once a year 15 

Never 14 

1 

There were actually ten different frequency groupings for this question, but 

these are put into fewer groupings here for succinctness. 

Question A3 was asked of respondents before it was apparent that SDAs were the subject of 

the survey, while Questions D1 and D2 were asked afterwards. 

A high proportion (84%) of respondents visited the countryside for recreational purposes, 

and a high proportion (79%) visited SDAs for recreational purposes (these percentages are 

the sum of the “Yes, for recreation” and “Yes, for both” responses). More people said they 

never visit SDAs in question D1 than in D2. This could be because Question D2 contained a 

“have visited at least once in the past” response, whereas a “Never” in Question D1 

probably meant recent and current intent. 

Respondents in most regions said that they visited the countryside more than they visited 

SDAs, as would be expected (see Figure 6.1). Unsurprisingly, this was especially true in the 

South East which does not have any SDAs. Oddly, in the North West region, only 69% of 

respondents said that they visited the countryside for recreational purposes, compared to 

85% who said that they visited SDAs for recreation. It is possible that in a region like the 

North West, where SDAs dominate the landscape, the initial term “countryside” conjured 

up flatter, less wild countryside to respondents. 



Percentage of respondents 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Yes 

(recreation) 

Yes (work) Yes 

(both) 

Response 

Countryside 

SDAs (in region) 

Figure 6.1: Number of respondents who visited either “the countryside” or SDAs for 

recreation, work or both 

6.3.3 Length of residence in region 

The results relating to the following questions are presented in Table 6.5: 

D4: Approximately, how long have you been living in your region? 

D5: Thinking ahead, how long do you think you will remain living in your region? 

Table 6.5: To date and expected future 

residency in respondents’ GORs 

D4 response % response 

10 years or less 14 

11-20 years 16 

21-30 years 19 

31-40 years 19 

41-50 years 10 

More than 50 years 20 


Less than 1 year 2 

Greater than 1 year 5 

Greater than 5 years 7 

Greater than 10 years 5 

No intention of moving 70 

Don’t know 10 

The majority of respondents were long term residents in their region and the overwhelming 

majority expected to continue living there for the foreseeable future. Because 

respondents’ exact ages are not known, it is not possible to determine how many were so 

far lifelong residents, although it is possible to determine how many were definitely not 

lifelong residents (because they had lived in the region less time than the minimum age of 

their age bracket). Overall 38% of respondents were definitely not lifelong residents; this 

varied considerably between regions, from only 23% in the North East to 54% in the West 

Midlands. 


No


7. Validity Testing 

The following general approaches to test the validity of stated preference study results are 

commonly used: 

• Content validity: assesses whether the stated preference study asked the right 

questions in a clear, understandable, sensible and appropriate manner with which 

to obtain a valid estimate of the WTP measure under investigation; 

• Construct validity: examines whether the relationships between measures produced 

by a stated preference study and other measures are in accordance with 

expectations. This involves assessing how far the coefficients of the variables used 

in the WTP functions show the relationships expected based on the economic 

theory and examining whether coefficients on certain attitudinal and 

socioeconomic variables have the expected signs; and 

• Convergent validity: examines whether the quantitative results are broadly 

comparable with the results of other studies valuing similar goods. 

These tests are outlined in Sections 7.1 to 7.3, respectively, with a brief summary of the 

findings of the valuation workshops in Section 7.4. 

7.1 Content Validity 

As part of the content validity tests, the following has been investigated: 

• Non-response rate to the income question: one of the signs that the questionnaire 

has worked well is generally accepted as a high response rate to individual 

questions. In terms of increasing the ability of the econometric analysis to explain 

the WTP responses or the most preferred choices, income is an important factor. 

Therefore, response rate to the income question is seen as one of the indicators of 

validity. This is discussed in Section 7.1.1. 

• The reasons behind positive and zero WTP responses (or implied WTP responses 

from choices made) can also be indications of validity. Those reasons that 

correspond to the change in the good presented in the questionnaire are termed as 

‘valid’ while those that do not are termed as ‘invalid’ or ‘protest’ answers. These 

are investigated in Sections 7.1.2 and 7.1.3. 

• Attitudes to the survey on the whole can also be indicators of validity. These are 

reported in Section 7.1.4. 

7.1.1 Income non-response 

The analysis of the results is to some extent dependent on respondents’ willingness to 

divulge their approximate level of household income. Table 7.1 indicates the percentage of 

respondents in each region unable or unwilling to answer the income question D9. 

Table 7.1: Non-response to the income question D9 (either refusal or “don’t know”) 


Income nonresponse 

(%) 

8 21 37 18 37 32 30 



The non-response rate varied considerably across regions, with very high levels in Yorkshire 

and the Humber and the East Midlands. Unfortunately, the income non-response rate was 

high enough in four regions for income to have to be excluded from the regression analysis 

(see Section 7.2.1). 

7.1.2 Reasons for having a positive Willingness to Pay 

Those respondents who chose at least one policy option in the choice experiment other 

than the ‘current policy’ indicated that they had non-zero willingness to pay for 

improvements to the upland attributes. The proportion of respondents who were willing to 

pay for improvements in the upland attributes, i.e. those who preferred an alternative 

policy at least once in the choice experiment, and those who chose the alternative scenario 

in the CV section, are presented in Table 7.2. 

Table 7.2: Percentage of respondents willing to pay for improvements to upland 

attributes 

Respondents 

willing to pay (%) 

Section B 

(choice 

experiment for 

own region) 

Section C 

(contingent 

valuation for all 

other SDAs) 


Overall 

56 48 60 81 65 75 65 64 

54 36 44 62 56 51 n/a 51 

Unsurprisingly, more respondents were willing to pay for upland attributes in their own 

region than in other regions. Some of the responses to Question B1 are noted in Box 7.1: 

Box 7.1: reasons for being willing to pay for upland attribute improvements: 

B1. What were the main reasons you were willing to contribute to the funding of future 

policy options? 

“I want to have a nice place to go at weekends and have a lovely countryside for holidays” 

“To make sure the woodlands and countryside in general are well maintained and also help 

out the farmers” 

“It would be such a loss otherwise – I want to keep it for my children and grandchildren” 

“To maintain Britain’s natural beauty and preserve it as long as we can” 

“The improvements sustain quality of life” 

“Any effort to retain some tradition should be applauded” 

“So that when my son grows up he has a better quality of life and still has a countryside to 

visit or live in” 

“It’s a small amount of money per year but gains in good things - grassland, woodland” 

“If we don’t pay things will just decline” 



Figure 7.1 shows the proportion of respondents giving each of the different categories of 

pre-identified reasons for having positive WTP. Note that respondents were not presented 

with a list of pre-coded reasons: therefore their answers could be coded by the interviewer 

in multiple categories, with the result that these percentages do not sum to 100. 

Percentage of respondents with positive WTP 

45% 

40% 

35% 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

ENJ 

ALT 

BEQ 

FIN 

OFIN 

HER 

ENV 

OBL 

WGLW 


NCR 

Reason given for having positive WTP 

Figure 7.1: Proportion of respondents giving different reasons for having positive WTP. 

Percentages are as a percentage of those with positive WTP, not as a percentage of all respondents. 

ENJ = personal/family enjoyment; ALT = others’ enjoyment; BEQ = future generations’ enjoyment; 

FIN = personal financial gain; OFIN = others’ financial gain; HER = heritage / communities; ENV = 

environmental concern; OBL = moral obligation; WGLW = warm glow; NCR = not credible (e.g. “I 

don’t have to pay, so it doesn’t matter”); DKN = don’t know; NOA = none of the above. 

7.1.3 Reasons for having a zero Willingness to Pay: protest bids and scenario 

credibility 

In every stated preference survey, a certain number of respondents will state zero WTP 

because they object to the manner in which they are being asked to pay (‘the payment 

vehicle’), or they do not find the scenario credible enough, rather than because they have 

no value for the good itself or can’t afford to contribute more. These respondents must be 

identified by asking all who state zero WTP why they have done so (Questions B4 and C2). 

These so-called ‘protest bids’ may then be excluded from the survey econometric analysis. 

Table 7.3 indicates the percentage of respondents in each region who were either deemed 

to be protest bidders or indicated genuine zero WTP for the choice experiment in Section B 

and the contingent valuation question in Section C separately. 

DKN 

NOA


Table 7.3: Percentage of respondents in each region who indicated zero WTP, the 

proportion indicating a genuine zero WTP, and the proportion seemingly indicating a 

protest bid. 

Identified respondents (%) 

Over 


-all 

Section B (choice experiment for own region) 

Zero WTP indicated 44 52 40 19 35 25 35 36 

Genuine zero WTP indicated 18 9 9 5 12 8 8 10 

Protest zero indicated 26 44 31 14 23 17 26 26 

Section C (contingent valuation for all other SDAs) 

Zero WTP indicated 46 64 56 38 44 49 n/a 49 

Genuine zero WTP indicated 24 22 26 21 18 22 n/a 22 

Protest zero indicated 22 43 30 17 25 27 n/a 27 

Again, the number of protest bids varies widely across regions, with a very high level in the 

North East and a relatively low number in the West Midlands. The level of protest bids are 

at the high end or slightly in excess of the range (6-25%) found in the comparable studies 

detailed in Section 4. The proportion of protest bids in the North East is particularly high. 

This could be because the other studies detailed were valuing much more specific areas, 

such as a particular Environmentally Sensitive Area or even a particular estate. 

It should be expected that the proportion of protest bids for each region should be similar 

for both sections of the questionnaire. This is mostly the case, with the exception of the 

South West, where protest bids for Section C were quite a bit higher. It is likely that the 

South West respondents felt very separate from the rest of the country’s SDAs, so more 

thought that it was alright for their taxes to be spent protecting their own SDAs, but not 

those in the rest of the country. Surprisingly, more respondents in the North West protested 

in Section B than in Section C. This brings to mind comments made by focus group 

participants in the Lake District that they viewed the Lake District as a national asset and 

did not see why people in their region alone should pay for it. 

Table 7.4 contrasts some actual reasons for not being willing to pay given in the survey 

which indicate protest bids with reasons which indicate valid responses. 

Figure 7.2 shows the proportion of respondents giving each of the different categories of 

pre-identified reasons for having zero WTP. Note again that respondents were not 

presented with a list of pre-coded reasons: therefore their answers could be coded by the 

interviewer in multiple categories, with the result that these percentages do not sum to 

100. 



Table 7.4: Reasons for not being willing to pay 

Reasons indicating a protest bid Reasons indicating a ‘valid’ zero 

Relevant to either section 

“I’m sick of taxes going up” 

“I’m willing to pay for the countryside but not 

for farmers” 

“I feel that the council should do more to help 

the environment” 

“We don’t get value for our council tax as it is” 

“God gave it to us for nothing – why do we have 

to pay higher taxes?” 

“I don’t trust the government to spend my 

money wisely” 

Specific to section C 

“People in Birmingham don’t pay to keep our 

beaches clean, so why should we pay for their 

hills?” 

“The rest of the country don’t support our high 

water rates” 

Percentage of respondents with zero WTP 

45% 

40% 

35% 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

AFFRD 

N.IMP 

SLDM 

NVR 

OBJCT 

GOVT 

FARM 

VISTR 

“I never go to these areas” 

“Seventeen pounds is too much” 

“We only go there occasionally” 

“I can’t afford it” 

“These areas aren’t important to me” 


CRED 

Reason given for not having positive WTP 

Figure 7.2: Proportion of respondents giving different reasons for having zero WTP. 

Percentages are as a percentage of those with zero WTP, not as a percentage of all respondents. 

AFFRD = inability to afford; N.IMP =areas aren’t important to respondent; SLDM = seldom visits 

areas; NVR = never visits areas; OBJCT = objects to paying for environmental improvements; GOVT = 

the government or council should pay; FARM = farmers should pay; VISITR = visitors should pay; CRED 

= doesn’t think policies will happen; DKN = don’t know; NOA = none of the above. 

DKN 

NOA


7.1.4 Attitudes towards the survey itself 

Answers to the following two questions are presented in Table 7.5. 

B2: How easy or difficult did you find it to make your decisions about which policy 

option to choose? 

D10: What did you think of this questionnaire? 

Table 7.5: attitudes towards the survey 

B5 response % response 

Very easy 32 

Fairly easy 37 

Neither easy nor difficult 10 

Fairly difficult 15 

Very difficult 5 


Interesting 53 

Too long 16 

Difficult to understand 16 

Educational 12 

Not credible 9 

It is encouraging that a majority of respondents found the survey interesting, although a 

certain proportion did find it rather too long or difficult to understand. Sixty-nine percent 

of respondents found the survey very or fairly easy to understand. 

7.2 Construct Validity: WTP functions 

The purpose of construct validity is to gauge whether respondents’ choices are internally 

consistent, and whether the relationships expected based on economic theory appear. The 

latter is difficult in this study (at least in the choice experiment analysis) because the 

income non-response rate was high, so income had to be excluded from regression models 

in most regions. However, a negative coefficient for the tax attribute would also be in line 

with economic theory. Respondents should be expected to be more likely to be willing to 

pay for environmental goods if they indicate by answers to other questions that they are 

concerned about environmental issues or make use of the goods in question. 


The probability of respondents making particular choices were initially analysed using a 

conditional logit (CL) model, firstly using only the levels of attributes as explanatory 

variables, and then introducing socio-economic and attitudinal variables to the model to 

test the effect of relevant individual characteristics on choice. Typically, a nested logit 

model would also be tested, i.e., a model which attempts to explain the factors behind a 

respondent having either zero or non-zero WTP, and then model respondents’ choices given 

that they have non-zero WTP. However, the survey dataset did not contain enough zero 

WTP observations that were not protest results to run a nested logit model. 

An important implication of the model specification used in analysing the CE responses is 

that selections from the choice set must obey the ‘independence from irrelevant 

alternatives’ (IIA) property. This property states that the relative probabilities of two 

options being selected are unaffected by the introduction or removal of other alternatives. 

If a violation of the IIA hypothesis is observed, then more complex statistical models are 

necessary that relax some of the assumptions used. There are numerous formal statistical 



tests than can be used to test for violations of the IIA assumption. In this study we 

employed the test developed by Hausman and McFadden (1984), being the most widely 

used. 

If the data of a specific region did not pass the IIA test a random parameter logit (RPL) 

approach was chosen for the analysis. Using this model specification it is also possible to 

circumvent the limitation of the conditional logit model that assumes that preferences are 

homogenous amongst respondents. The random parameters logit model allows for variation 

in preferences across individuals. Application requires assumptions be made about the 

distribution of preferences. Here it is assumed that preferences relating to the attributes 

are heterogeneous and follow a normal distribution while preferences towards cost are 

assumed to be homogenous. Therefore, separate parameters are estimated for each 

individual for all attributes along with a single parameter for all respondents for cost. The 

result is that each random attribute has a mean value (interpreted as the average 

preference of respondents for the attribute) and a standard deviation value (interpreted as 

the magnitude of differences in respondents’ preferences for the attribute). 

The details of each model run are presented in the statistical Annex 5. Table 7.6a and 7.6b 

presents the coefficients for both the attributes only models and models that include socioeconomic 

variables as well as the attributes. Table 7.6c explains the variable abbreviations 

used. Following the tables, the main points of interpretation are presented. 

Table 7.6a: Attributes only models: random parameter or conditional logit model 

coefficients for each region. Coefficients found to be statistically significant at the 5% 

level are indicated in bold. 


model 1 

RPL RPL CL RPL RPL RPL RPL 

const -1.162 0.681 1.196 1.019 

HMB 0.048 0.005 0.030 0.026 

RG 0.041 -0.001 0.009 -0.002 

BMW 0.043 0.016 0.016 0.011 

FB -0.001 0.001 0.001 -0.001 

CH1 0.082 -0.044 

CH2 0.215 0.171 

TAX -0.066 -0.019 

Standard deviation values 

Did not 

pass IIA 

test 

-0.015 

0.248 

-0.038 

Model did 

not 

converge 

0.158 

0.222 

-0.029 

HMB 0.096 0.068 0.054 0.073 

RG 0.099 0.067 0.050 0.065 

BMW 0.063 0.103 0.074 0.033 

FB 0.009 0.007 0.004 0.008 

CH1 0.554 0.473 0.522 0.672 

CH2 1.056 0.982 

0.601 

1.014 

1 

RPL – random parameter logit model applied; CL – conditional logit model applied. 

Model 

did not 

converge 



Table 7.6b: Attributes and socio-economic variables: random parameter or conditional 

logit model coefficients for each region. Coefficients found to be statistically significant 

at the 5% level are indicated in bold. 


model 1 

RPL RPL CL RPL RPL RPL RPL 

const -0.485 0.248 2.915 3.880 2.225 1.463 2.828 

HMB 0.062 0.006 0.011 0.035 0.016 0.030 0.025 

RG 0.059 0.004 0.011 0.008 0.001 -0.002 0.016 

BMW 0.049 0.005 0.005 0.023 0.015 0.009 0.038 

FB 0.000 0.000 0.001 0.001 0.001 -0.001 0.002 

CH1 0.082 -0.079 0.109 0.033 0.119 0.187 0.025 

CH2 0.391 0.139 0.423 0.214 0.338 0.291 0.493 

TAX -0.080 -0.021 -0.035 -0.039 -0.015 -0.029 -0.031 

AGE -0.463 0.121 -0.031 0.342 -0.175 0.155 -0.047 

GENDER 0.079 0.727 0.231 -0.497 0.261 0.903 -0.122 

ENVIMP -0.899 -1.070 -0.599 -0.585 -0.794 -0.255 -0.381 

VISFREQ 0.153 0.085 -0.235 -0.222 -0.044 -0.125 -0.250 

LIVING -0.022 -0.021 -0.006 -0.012 0.009 0.003 -0.001 

REMAIN -0.150 0.064 -0.196 -0.204 -0.446 -0.127 -0.204 

MEMBER 0.936 0.019 0.437 0.564 -0.059 -0.006 0.554 

EDU 0.482 0.275 0.267 0.126 0.393 0.281 0.286 

EMPLOY 1.343 1.046 0.148 -0.230 0.321 -0.059 0.089 

RURAL -1.083 2.862 0.673 -1.559 0.325 -0.394 1.481 

INCOME 0.166 n/a 2 

n/a 0.109 0.095 n/a n/a 


HMB 0.089 0.072 0.054 0.000001 0.062 0.00002 

RG 0.080 0.064 0.050 0.000020 0.049 0.00002 

BMW 0.031 0.097 Not 0.078 0.000008 0.031 0.00003 

FB 0.010 0.007 relevant 0.003 0.0000002 0.008 0.000004 

CH1 0.479 0.183 0.199 0.000056 0.471 0.00030 

CH2 0.667 1.034 

0.760 0.000121 0.900 0.00027 

1 RPL – random parameter logit model applied; CL – conditional logit model applied. 

2 The income variable had to be omitted from these models due to the high non-response rate. 

Table 7.6c: Explanation of variable abbreviations used in preceding tables. 

const constant term (= 0 if the current policy is chosen, = 1 for alternatives A or B) 

HMB percentage change in area of heather moorland and bog 

RG percentage change in area of rough grassland 

BMW percentage change in area of broadleaf and mixed woodland 

FB change in the length of field boundaries (in metres restored) 

CH1 small change in cultural heritage indicated (1 = yes, 0 = no) 

CH2 large change in cultural heritage indicated (1 = yes, 0 = no) 

TAX tax amount indicated in pounds 

AGE respondent’s age in years 

GENDER respondent’s gender (1 = male, 0 = female) 

importance of environmental policy to respondent (1 = very important, 4 = not 

ENVIMP important) 

respondent’s frequency of visits to severely disadvantaged areas (1 = every day, 

VISFREQ 10 = never) 

LIVING number of years respondents have been living in the region 

respondent’s expected residence in the region (1 = less than 6 month, 5 = 

REMAIN indefinite) 

whether respondent belongs to an environmental, recreational, etc. 

MEMBER organization (1 = yes, 0 = no) 

EDU respondent’s education level 1= primary, 6= higher degree) 

EMPLOY whether respondent is an active worker (1 = yes, 0 = no) 

RURAL whether respondent is a rural-dweller (1 = yes, 0 = no) 

INCOME household income per head 



A significant positive coefficient (at the 5% level, indicated in bold in Tables 7.6a and 7.6b) 

indicates that the likelihood of a respondent choosing an alternative policy option is greater 

the higher the level of the variable. A significant negative coefficient indicates that the 

higher the level of the variable, the more likely the respondent is to choose the zero-cost 

current policy. A coefficient which is not significant indicates that it is not possible to 

determine that the variable affected the respondents’ choices. The significance and sign of 

the constant indicates whether, all things being equal, respondents are willing (positive 

coefficient) or not (negative coefficient) to pay hill-farmers for improvements to upland 

attributes. 

Note that two of the variables – ENVIMP (importance of environmental policy to respondent) 

and VISFREQ (respondent’s frequency of visit to SDA) - have questionnaire rankings in 

reverse order of magnitude, therefore extra care must be taken over the interpretation of 

their coefficients. More specifically, a lower variable value implies that someone is more 

concerned about environmental policy, or that they visit SDAs more often. This means that 

a negative (rather than a positive) coefficient for VISFREQ, for example, means that 

respondents are more likely to be willing to pay for alternative policy options if they visit 

SDAs more often. 

Some general findings emerge from the analysis of the model results. While the same 

questionnaire and specification of attributes are used in all regions, the factors that 

influence the choices regional respondents made are different. Heather moorland and bog, 

mixed and broadleaved woodland and a large change in cultural heritage attributes are 

shown to be significant factors in respondents’ choices in most regions. On the other hand, 

rough grassland, field boundaries and a small change in cultural heritage attributes are 

generally not significant factors in the choices. The only evidence that respondents might 

prefer what is already abundant in their region is that respondents in Yorkshire and the 

Humber were the only SDA GOR respondents to have their choices significantly affected by 

field boundaries. However, this GOR also has the greatest abundance of heather moorland 

and bog, and respondents were not shown to be particularly responsive to that attribute. 

In terms of socio-economic variables, the level of education seems to be the variable that 

has a significant influence over respondents’ choices most commonly across the regions. 

Where income could be included in models, it does not appear to have been a significant 

factor, a potential result of the high non-response rate to the income question resulting in 

fewer observations for that variable. However, among other factors such as awareness, 

education could also embody at least part of the impact income would have, given that 

education level is generally associated with income level. 

The full list of factors that influence the choices in the models using both attributes and 

other (socio-economic) variables is discussed below with respect to each region. The only 

universally significant finding was that the cost of a policy option was a significant factor in 

making respondents less likely to choose an alternative policy the higher its cost, all else 

being equal. 

North West 

Respondents were more likely to choose an alternative policy if it expressed an increase in 

heather moorland and bog, rough grassland, broadleaf and mixed woodland, or a large 

improvement in cultural heritage, but their choices were unaffected by field boundaries. 

Being younger, more educated, in employment or an urban dweller made respondents more 

likely to be willing to pay. Respondents who had lived in the area a shorter time were also 

more willing to pay, as were respondents who considered environmental policy to be 

relatively important or who belonged to an environmental or recreation organisation. 

Respondents who visited SDAs more often were less willing to pay. The North West is the 

GOR which has the greatest proportion of SDAs relative to total area; it is also the GOR with 

the highest proportion of respondents visiting SDAs for work reasons and the lowest 

proportion who visit only for recreation; and has nearly twice the average proportion of 

respondents who has been there only 0-2 years. These results paint a picture of the North 

West as an area where relative newcomers (or seasonal workers), perhaps attracted by 



access to the Lakes, attribute more value to improvements in upland attributes than more 

established residents, more rural residents, and residents visiting SDAs regularly for work 

purposes. The North West region also had a far greater than average proportion of 

respondents who felt that the environment was not all that important to them, or not 

important at all. 

North East 

In the North East survey, none of the upland attributes were shown to be significant 

determinants of respondents’ choices. This may be because of the very high number of 

protest bids in the North East, or it may be due to respondents having “opposite” 

preferences offsetting each other, as explained in Section A5.2.6 in Annex 5. However, cost 

was shown to negatively influence willingness to pay. Being male, more educated, in paid 

employment or a rural dweller increased the probability that respondents would be willing 

to pay for improvements. Considering environmental policy to be relatively important and 

having lived in the region for a shorter time period also made respondents more willing to 

choose alternative policy options. The North East had higher than average numbers of very 

long term residents in the sample population – as these have been identified as a group 

more indifferent to improvements, this could also explain the seeming indifference of the 

regional results towards the upland attributes. The North East is also the least well off of 

all the GORs according to official statistics. 

Yorkshire and the Humber 

A significant positive constant term for this region shows more emphatic evidence that 

respondents were willing to pay for landscape improvements. Increases in rough grassland, 

field boundaries and cultural heritage made respondents more likely to pick an alternative 

option over the current policy. It was also the only SDA GOR to be more likely to choose 

options which showed improvements in field boundaries. More educated respondents and 

those living in rural areas were more likely to be willing to pay, as were respondents who 

rated environmental policy as important, those who visited SDAs more frequently and those 

who expected to remain in the area a shorter time. According to census data, the region 

has the highest proportion of the population actually resident in SDAs – twice as many as 

the next highest (the North West). There was also the highest proportion of survey 

respondents in this region who visited SDAs solely for recreation. However, overall WTP 

results for Yorkshire and the Humber were small, possibly due to the low average income of 

the region. 

West Midlands 

The high positive constant term shows that West Midlands respondents were the most likely 

of all to favour paying for alternative policy options over the current policy. Increases in 

heather moorland and bog, broadleaf and mixed woodland and large improvements in 

cultural heritage, were shown to increase respondents’ likelihood of selecting an 

alternative scenario. Being older, female and urban-dwelling also made respondents more 

likely to choose alternative policy options. Considering environmental improvements to be 

relatively important, visiting SDAs more often, being a member of an environmental or 

recreational society, or expecting to live in the area for a shorter time also made 

respondents more likely to be willing to pay. 

The West Midlands sample had by far the highest proportion with positive WTP for both the 

choice experiment and contingent valuation questions. From the market researchers’ 

comments, the West Midlands sample appears to have been exceptionally environmentallyconcerned 

and well-informed, with many respondents refusing to rank the environmental 

problems by importance in question A2 because they said that they were “all important”. 

Despite the small size of SDAs in the West Midlands, and their distance from urban centres, 

the sample had the second smallest proportion of people who said they never visited their 

region’s SDAs (after the North West). The sample had the highest combined proportion of 

people saying that environmental policy was “very” or “quite” important, the highest 

environmental or recreational group membership, and the highest proportions of people 



with first or higher degrees or equivalent professional qualifications. It was also one of the 

regional samples with the most transient residence and had the highest proportion of 

people who were definitely not life-long residents 10 . The picture gained of the West 

Midlands sample is of a geographically mobile and educated population attracted to the 

region by its economic opportunities, but also highly appreciative of and concerned for the 

factors contributing to its quality of life. 

East Midlands 

Overall, respondents in the East Midlands were in favour of payments for alternative 

policies. Increases in broadleaf and mixed woodland were more likely to make respondents 

willing to pay, as were large improvements in cultural heritage. More educated 

respondents, those who considered environmental policy to be relatively important, and 

those who expected to remain in the area for a shorter time were also more likely to 

choose alternative policy options. The region sample was overall one of the least educated 

and least geographically mobile. 

South West 

Respondents in the South West were not emphatically likely to choose alternative policy 

options (as denoted by the statistically insignificant constant term). Out of the upland 

attributes, only increases in heather moorland and bog and in cultural heritage (if a large 

improvement) made them more likely to opt to pay for alternative policies. Male or more 

educated respondents were also more likely to be willing to pay, as were respondents who 

visited SDAs more frequently. Respondents in the South West were much more likely to cite 

heritage reasons and concern for farmers’ livelihoods for being willing to pay for 

improvements. The South West also had the greatest proportion of people who belonged to 

farming organizations. 

South East 

The interpretation of the South East results should be seen in the light of South East being 

the only GOR without its own SDAs included in the survey. The South East was the only 

region where all upland attributes were significant, i.e. improvements in all made 

respondents more likely to choose alternative policies over the current policy (although 

cultural heritage only did so if a large improvement was noted). Being more educated, a 

rural dweller or a member of an environmental or recreational group made respondents 

more likely to be willing to pay. Considering environmental policy to be relatively 

important, visiting SDAs more frequently and expecting to live in the region for a shorter 

amount of time also made respondents more willing to pay. Unsurprisingly, compared to 

other GORs included in the survey, respondents in the South East were most likely to say 

that they never visit SDAs. However they were the most likely of all respondents to cite 

altruistic, bequest, environmental concerns and moral obligation reasons for being willing 

to pay. This ties in with the finding that they were indeed willing to pay for landscapes 

which they did often directly experience, and shows that they had considerable non-use 

value for the SDAs in other parts of the country. 


The main goal of contingent valuation survey was to test if regional populations had 

preferences for SDAs in other regions. Respondents were asked for their WTP for a ‘best 

case’ policy over the current policy, as detailed in Table 3.5 as it would not have been 

possible to undertake a choice experiment for other regions considering the cognitive 

burden that doing two choice experiments would impose on the respondents. 

10 This is difficult to ascertain with certainty as we did not ask for respondents’ exact ages. However 

those who are definitely not life-long residents of the region can be marked out by comparing their 

age bracket with their stated number of years’ residence. 



Given the similarity of the valuation exercise in all regions, data from all regions with SDAs 

were analysed together. Respondents that stated a protest answer or said that they did not 

know if they were willing to pay the proposed bid were excluded from the analysis. All 

variables initially used to model the CE results were initially applied, and those which were 

not significant at the 10% level were then dropped from the model. The results of the final 

logit model are presented in Table 7.7. The logit model explains the probability of a 

respondent answering ‘yes’ to a WTP bid they are asked to pay for the alternative policy 

option. 

Table 7.7: Logit model responses for WTP for the CV alternative scenario over the 

current policy. Coefficients found to be statistically significant at the 5% level are 

indicated in bold. 

Independent Coefficients Standard error Significance 

Variables 

constant 4.619 .805 .000 

SW -0.537 .375 .152 

YH -1.075 .350 .002 

NE -1.360 .355 .000 

NW -0.914 .373 .014 

EM -0.626 .355 .078 

ENVIMP -0.658 .138 .000 

BID -0.015 .004 .000 

VISFREQ -0.169 .038 .000 

REMAIN -0.348 .112 .002 

MEMBER 0.254 .082 .002 

INCOME 0.356 .091 .000 

-2Log-Likelihood 739.863 

Significance level 0.000 

% of correct 80% 

predictions 

No. observations 820 (418 missing cases) 

constant constant term 

SW = 1 if respondent is resident in the South West, = 0 otherwise 

YH = 1 if respondent is resident in Yorkshire and the Humber, = 0 

otherwise 

NE = 1 if respondent is resident in the North East, = 0 otherwise 

NW = 1 if respondent is resident in the North West, = 0 otherwise 

EM = 1 if respondent is resident in the East Midlands, = 0 otherwise 

ENVIMP respondent’s frequency of visits to severely disadvantaged areas 

(1 = every day, 10 = never) 

BID bid amount indicated (£) 

VISFREQ respondent’s frequency of visits to severely disadvantaged areas 

(1 = every day, 10 = never) 

REMAIN respondent’s expected residence in the region (1 = less than 6 

month, 5 = indefinite) 

MEMBER whether respondent belongs to an environmental, recreational, 

etc. organization (1 = yes, 0 = no) 

INCOME household income per head 

The proportion of protest bids for the CV question is reported in Table 7.3 above. The 

results show that people living in the West Midlands region (used as a reference for the 

region variable) were more willing to contribute to the change in policy than those in any 

other region. This is in accordance with the high positive constant for CE regression model 

for the West Midlands. Having a larger household, a higher income, being a longer term 

resident, visiting SDAs more frequently, and considering environment policy to be relatively 

important were all significant determinants of probability of being willing to pay 

(remember again that the ENVIMP and VISFREQ variables have counterintuitive rankings). 



Expected future long term residence and being presented with a higher cost for the 

alternative policy reduced the likelihood of being willing to pay for it. 

7.3 Convergent Validity: Comparison with Previous Studies 

The mean WTP figures found by the study for a 1% change in each attribute (except cultural 

heritage, which can only be considered in discrete changes) and the compensating surplus 

estimates are presented in Section 8 (in Tables 8.1 and 8.4 respectively). However, this 

section compares the WTP results with those found by the ELF model, and the 

compensating surplus estimates with values for landscapes found in other studies. 

Comparison with previous studies in the case of landscape attributes is difficult. As 

described in Section 4, studies rarely cover identical goods or comparable geographical 

scopes. In the case of contingent valuation, different studies will frequently cover a change 

which is unique to the particular context and difficult to apply elsewhere. However both of 

the comparisons given below can give an indication that at the very least, the order of 

magnitude of values found is consistent. 

7.3.1 Comparison with ELF 

Comparison with the regional WTP values provided by the ELF model outlined in Section 

4.2.1 is not straightforward. The habitats valued in the ELF are defined differently from 

those in this study: heather moorland or heathland, rather than heather moorland and bog; 

woodland rather than broadleaved and mixed woodland; and hedgerows rather than field 

boundaries. The ELF model values attributes wherever they occur in a region – not solely in 

SDAs. It should also be remembered that for the South East region, the ELF model values 

attributes within that region, while in this study values found in the South East are for all 

other regions of England. 

In addition, this study values a change in attributes, whereas the WTP values included in 

the ELF model are taken to value the entirety of the attribute within any given region 

(pers. comm., Oglethorpe, 2005). For this reason, in the comparison given in Table 7.8, the 

per household WTP figures given in the ELF model for each attribute are divided by 200 to 

make them comparable to WTP for a 1% change found in this study. The reason why this is 

the appropriate factor to use is explained in Annex A5.4; briefly, it relates to the fact that 

marginal WTP is higher at greater scarcity, therefore WTP for the totality of the attribute is 

greater than one hundred times the WTP to prevent a 1% change. 

The grey cells indicate where the 95% confidence intervals for WTP found by this study 

overlap the mean WTP range given by the ELF model (note that this is an estimated mean 

range, not a 95% confidence interval range, which is not provided). 

It is encouraging that over half of the estimates from this study appear to show confidence 

intervals which overlap with the mean range given by the ELF model. The values for field 

boundaries show the most similarity, with only the values in the South West markedly 

differing. The rough grassland and broadleaved and mixed woodland values are for the most 

part similar, with the ELF figures appearing at the low-to-mid end of the SDA study 

confidence intervals for all areas except the North West and South East. However, as the 

ELF model values attributes within the South East, and this study values South Eastern 

respondents’ values for attributes outside the South East, it is not surprising that the South 

East value are fairly different. The heather moorland and bog values appears to fit less 

well, with the SDA study generally estimating significantly higher values than the ELF 

model. 

Overall, though, the results of this study are not too dissimilar from the results of the ELF 

model, which is encouraging. 



Table 7.8: Comparison of the 95% confidence intervals for per household WTP found 

by this study with the range for mean WTP found by the ELF model (divided by 200 for 

comparison purposes). 

NW Y&H WM EM SW SE 

Heather moorland and bog 

SDA 0.45 - 1.11 -0.06 - 0.65 0.42 - 1.18 -0.03 - 2.31 0.37 - 1.54 0.36 - 1.25 

ELF 0.02 - 0.05 0.02 - 0.04 0.02 - 0.05 0.02 - 0.05 0.02 - 0.05 0.02 - 0.05 


SDA 0.45 - 1.05 0.01 - 0.60 -0.05 - 0.53 -0.99 - 0.91 -0.56 - 0.39 0.14 - 0.86 

ELF 0.04 - 0.10 0.04 - 0.10 0.04 - 0.10 0.04 - 0.10 0.04 - 0.10 0.04 - 0.10 

Broadleaf and mixed woodland 

SDA 0.30 - 0.91 -0.16 - 0.48 0.07 - 0.81 0.03 - 2.46 -0.01 - 0.78 0.81 - 1.66 

ELF 0.06 - 0.08 0.05 - 0.08 0.06 - 0.08 0.06 - 0.08 0.06 - 0.08 0.05 - 0.08 


SDA -0.03 - 0.04 0.01 - 0.08 -0.02 - 0.05 -0.06 - 0.18 -0.11 - 0.02 0.02 - 0.11 

ELF 0.03 - 0.04 0.03 - 0.04 0.03 - 0.04 0.03 - 0.04 0.03 - 0.04 0.03 - 0.04 

Key: SDA = 95% confidence intervals for WTP for a 1% change in the attribute found by this study. 

ELF = estimated mean range given in the ELF model for WTP for the whole attribute, 

divided by 200. Note that this is not a 95% confidence interval. 

7.3.2 Comparison with other studies 

The compensating surplus (CS) figures presented in Section 8.4 for Scenarios 1 and 2 range 

from £7-48; those for Scenario 3 are mostly negative. The £7-48 range can be compared 

with the WTP ranges found in the other studies summarised in Section 4.3 – these are shown 

in Table 7.9. 

Table 7.9: Summary of per household WTP figures found by the landscape valuation 

studies examined in Section 4.3. 

Study / 

Geographical Change valued WTP per 

Location scope 

household 

per annum 1 

Willis & 

Yorkshire Dales A range of alternative landscape £18-35 

Garrod (1993), National Park changes, ranging from 

Yorkshire Dales (177,000 ha) intensive/agricultural to 

wild/abandoned. 

Hanley et al. An ESA 

(1998), Breadalbane 

2 

Change to the landscape from £22-27 

covering removing ESA subsidies. 

179,000 ha 

Bullock & Kay An ESA 

Landscapes brought about by 

£41-82 

(1997), Scottish covering extensive or very extensive grazing 

Central Southern 

Uplands 

273,000 ha 

White & Lovett The Levisham Landscapes and habitat types brought Mean WTP 

(1999), North Estate (1,359 about by reduced sheep grazing £3.10 

York Moors ha) 

This study SDAs in a given Landscape changes brought about by £7-48 

GOR 

hypothecated policy Scenarios 1-3 

net of the baseline 

1 

Ranges are for different scenarios or sub-samples 

2 

Environmentally Sensitive Area 

The only study with figures considerably lower than the CS figures found in this study is 

White and Lovett (1999); this is unsurprising as it valued only one small estate, whereas the 

other studies valued large upland areas comparable to the regional SDA areas valued in this 

study. The CS figures are broadly comparable to the figures found in Willis and Garrod 

(1993) and Hanley et al. (1998); they are on the low side compared to Bullock and Kay 

(1997). 



7.4 Valuation Workshop Findings 

A brief explanation of the workshops was provided in Section 3.3.4 and a full discussion of 

the valuation workshop findings is presented in the Valuation Workshop Report in Annex 7. 

In summary, the main findings of the workshops were: 

• Respondents considered all the attributes in the policy options presented. They 

maintained various strategies of ‘simple summing’ (all increases considered with equal 

weight), ‘weighted summing’ (the level of the attribute is valued incrementally), 

‘personal weighting of attributes’ (a selected attribute has greater weight than others) 

and finally a logical deduction. The more complex strategies tended to be applied to the 

more difficult choices. 

• The geographical regional limit of the survey was maintained in choice decisions, in that 

respondents were considering only the SDAs in their own GOR when asked to. 

• There was no evidence of bias due to scope. The wider context of the national and 

global environment was discussed but was considered to be beyond the scale of the 

survey, and respondents did not change their choices as a result of the discussion. 

• A broader ‘choice set’ than that presented in the study was discussed, for example, the 

possibility of ending all agricultural subsidies. Respondents considered such matters to 

be beyond the scale of this study and did not change their choices as a result of the 

discussion. 

• Of the attributes that were not in the CE questionnaire, only hay meadows might have 

had any impact on WTP. The scale of this potential impact is not known but discussion 

of hay meadows affected four (out of 19) participants. All other attributes not originally 

included in the CE had little or no effect as they were viewed largely as components of 

the attributes already included in the survey. 

• Discussion, information and a period of reflection did not cause respondents to change 

their original choices. 

• Respondents considered the tax in policy choices appropriately. Some chose to set 

themselves a cost threshold beyond which they would not accept policy options and 

increases beyond their threshold WTP were rejected 

There is therefore a good deal of evidence in the behaviour of the participants which 

indicates the theoretical validity of this study, and the technical information presented was 

apparently sufficient to support the choice making of individuals. 

7.5 Validity Testing - Summary 

7.5.1 Content validity 

• Income non-response: the income non-response rate was relatively high, particularly in 

Yorkshire and Humber and the East Midlands. This precluded the use of income as an 

explanatory factor in the regression models of four regions. 

• Protest bids: the number of protest bids was also relatively high, and in the case of the 

North East, may have been the main reason for the model to return insignificant 

coefficients for all upland attributes. Protest bids were at similar levels for both 

sections of the questionnaire, while genuine zero WTPs were noticeably higher for 

Section C (CV) than Section B (CE). This should be expected, as respondents are more 

likely to value SDAs in their own region than elsewhere in the country. 

• Attitudes: 69% of the survey respondents found the survey easy to understand and only 

20% found it difficult to understand. 



7.5.2 Construct validity 

• The most important finding of the construct validity analysis is that for both regression 

model versions for each region, the coefficient on tax is both negative and significant at 

the 5% level. This indicates that respondents were not making random or arbitrary 

choices, but considered the cost of options before making their choices. 

• Income was not found to be a significant determinant of WTP in the choice experiment 

for the three regions where it could be included in the regression models. This could be 

largely due to the high non-response rate, particularly as income was shown to be a 

significant determinant of WTP in the contingent valuation question, where all regions 

were pooled together for the analysis. 

• In all regions except the North East, at least two upland attributes positively affect 

respondents’ choices. 

• In general, other characteristics affect respondents’ choices as one would expect: 

people who consider environmental policy to be important, people who are members of 

environmental or recreational clubs, more educated people, and people who visit SDAs 

more often (except in the North West, where many people visit them for work) are all 

more likely to be willing to pay for upland landscape improvements. 

• Age, gender and rural/urban location do not appear to consistently affect choices. 

7.5.3 Convergent validity 

• Comparison with the mean estimate range supplied by the ELF model (suitably adjusted 

for appropriate comparison) shows that this study’s regional WTP estimates are broadly 

similar for rough grassland, broadleaf and mixed woodland and field boundaries, but 

dissimilar for heather moorland and bog. 

• Other studies valuing landscapes which cover roughly comparable areas to those valued 

by respondents in this study show similar (or in one case higher) WTP for improvements 

in landscape attributes. 



8. Willingness to Pay Results, Aggregation, Policy 

Implications and Conclusions 

Average willingness to pay results are presented in Section 8.1. The aggregation process is 

described in Section 8.2. Policy implications and conclusions are presented in Section 8.3. 

8.1 WTP Results 

This section reports the WTP estimates from the choice experiment (Section 8.1.1) and 

contingent valuation (Section 8.1.2). The estimates from the choice experiment are for the 

individual upland attributes. Those from the contingent valuation question are for the 

improvement scenario presented for all SDAs not in the region the respondent lives. The 

functions that explain these estimates are reported in Section 7. 


Table 8.1 shows the implicit prices for the attributes and the respective 95% confidence 

intervals, calculated using the Krinsky and Robb (1986) procedure. The results are the 

estimates from the willingness to pay functions that include both attributes and a selection 

of socio-economic and attitude questions (see Section 7.2 for the explanation of different 

models). The implicit prices for most attributes are positive, implying that respondents 

have a positive willingness to pay for increases in the quality or quantity of each of these 

attributes. 

In the case of quantitative attributes the implicit prices represent the WTP per household 

per year to achieve one unit more (1% more in this study) of the attribute considered. For 

example, in the North West, respondents are, on average, willing to pay £0.78 per 

household per year for a 1% increase in heather moorland and bog beyond the ‘current 

policy’ baseline. For field boundaries, the results show WTP per metre increase in the 

length of boundaries conserved per km. For cultural heritage, the implicit price reflects the 

WTP for a discrete change in the attribute's level, for example to improve farm building 

and traditional farm practices from ‘rapid decline’ to ‘much better conservation’. For 

example, in the North West, respondents are, on average, willing to pay £4.89 per 

household per year for the cultural heritage attribute to change from “rapid decline” of 

farm buildings and traditional farm practices to “much better conservation” of these. 

Table 8.1 summarises the results for all regions except for the North East. As Section 7.2 

and statistical Annex 5 show, the function that explains the WTP estimates for the North 

East returned insignificant coefficients for all upland attributes. The coefficient for the 

cost attribute alone was significant. This precludes estimation of a WTP per attribute for 

this region and similarly aggregation across different policy options is not presented for 

North East. However, in the “attributes only” model for the North East, the constant term 

was positive and significant. This indicates that, all else being equal, respondents were in 

favour of the payments. The lack of significance for the attribute coefficients does not 

indicate zero WTP. 



Table 8.1: WTP results (£ per household per year per 1% improvement for the first 

three attributes, £ per 1 metre increase in the case of field boundaries) derived from 

the choice experiment for each region (except the North East). 

NW YH WM EM SW SE 

Model used 1 : A&S A&S AO A&S AO A&S 

HMB 0.78 0.30 0.80 1.04 0.92 0.81 

(0.45-1.11) (-0.06-0.65) (0.42-1.18) (-0.03-2.31) (0.37-1.54) (0.36-1.25) 

RG 0.74 0.31 0.25 0.08 -0.06 0.50 

(0.45-1.05) (0.01-0.60) (-0.05-0.53) (-0.99-0.91) (-0.56-0.39) (0.14-0.86) 

BMW 0.61 0.15 0.43 0.97 0.39 1.21 

(0.30-0.91) (-0.16-0.48) (0.07-0.81) (0.03-2.46) (-0.01-0.78) (0.81-1.66) 

FB 0.00 0.04 0.02 0.06 -0.04 0.06 

(-0.03-0.04) (0.01-0.08) (-0.02-0.05) (-0.06-0.18) (-0.11-0.02) (0.02-0.11) 

CH 

(small 2 ) 

1.03 

(-1.84-4.14) 

3.08 

(-0.24-6.71) 

-0.40 

(-4.27-3.03) 

7.92 

(-1.96-22.62) 

5.48 

(-0.11-11.59) 

0.81 

(-3.22-4.96) 

CH 

(big 3 ) 

4.89 

(1.52-8.43) 

11.93 

(8.47-15.44) 

6.56 

(2.49-10.73) 

22.51 

(11.84-37.24) 

7.68 

(1.24-15.03) 

15.79 

(11.47-20.64) 

Figures in brackets are the 95% confidence interval. Note that if the confidence interval spans zero 

then the WTP is not significantly different from zero. 

HMB = heather moorland and bog, RG = rough grassland, BMW = mixed and broadleaf woodland, FB = 

field boundaries, CH = cultural heritage. 

1 

A&S = attributes and socio-economic variables; AO = attributes only 

2 


3 

from “rapid decline” to “much better conservation” 

It should be remembered that while the WTP figures for cultural heritage appear large 

compared to the others, as cultural heritage was a qualitative attribute, it could only be 

considered as changing discretely. All other WTP figures are marginal changes (a 1 metre 

increase in the case of field boundaries and 1% change in all others), while the cultural 

heritage WTP figures are for a large improvement in the attribute. 

These results show a fair degree of heterogeneity between regions, which could be due to 

the following factors: (i) different abundances of attributes in different regions; (ii) 

different regional cultural preferences; and (iii) socio-economic differences. The first of 

these has not been included in the regression model; it is not obvious how the second could 

be included; while the differences appear to be greater than could be explained by socioeconomic 

factors alone. Therefore there are regional differences which cannot presently be 

explained or included in a benefits transfer function (Section 3.2.4). This could make the 

transfer error in a possible benefits transfer exercise unacceptably large. For this reason, 

we do not think it advisable to conduct a benefits transfer exercise from the South East 

results to the other two non-SDA regions of England. 


As described in Section 3.3.3, the contingent valuation section asked respondents in each of 

the GORs containing SDAs to choose between two scenarios for all other GORs containing 

SDAs in England: the same current policy that was used in the choice experiment and a 

‘best case’ alternative policy option. 

Mean WTP was estimated for all regional results together, both parametrically and nonparametrically. 

The results are presented in Table 8.2. 

Table 8.2: Comparison of parametric and non-parametric estimates of WTP for the 

alternative policy over the current policy presented in Table 3.5. 

Estimation method Estimated mean 95% confidence Standard error 

WTP 

interval 

Non-parametric £49.27 47.56 – 50.99 0.88 

Parametric £104.92 72.17 – 137.67 16.71 

WTP is expressed in units of £ per household per year. 



It is likely that the non-parametric method estimate is conservative, as the method is not 

capable of taking into consideration the fact that many respondents may well have been 

willing to pay more than £70 per household per year, which was the highest amount offered 

in the survey. 

The CE and CV results are not directly comparable or compatible for joint aggregation, as 

the CV results value a change from a different baseline to the baseline used in the choice 

experiment and cover a different geographical scope. 

8.2 Aggregation 

Results from a valuation study such as this can be aggregated across several dimensions. In 

this case across: the potential policy scenarios (for CE); population (for CE and CV) and 

time (for CE and CV). The separate aggregation of the strands of this study is presented 

below, for choice experiment (Section 8.2.1), contingent valuation (8.2.2) and benefits 

transfer (Section 8.2.3). In the final Section (8.2.4), results of the CE and benefits transfer 

results are brought together. 

It is worth emphasising that while aggregation is interesting and has policy relevance, the 

advantage of the CE is that it gives us the relative importance of upland attributes through 

the unit WTP estimates per upland attribute. The relative importance of the upland 

attributes could be use to determine allocation of the existing LFA, i.e. allocating higher 

funds to attributes that have higher WTP. The aggregate values for policy options, on the 

other hand, could be an input to deciding whether the existing LFA constitutes an under- or 

over-spend, by comparing the aggregate values to the LFA funds. However, this is not the 

objective of the study. 

We also aggregated the CE results across regions as an illustration. However, again this has 

caveats as the survey was designed to elicit respondents’ preferences for the attributes of 

the SDAs in their own regions. Therefore, possible substitution effects of SDAs in different 

regions on the preferences of the respondents in a given region are not taken into account. 

8.2.1 Choice experiment results 

The choice experiment undertaken for this study produces unit values in terms of £ per 

household per year per unit change of upland attribute. Here, the aggregation is across LFA 

scenarios compared to the baseline. Table 8.3 sets out the forecast levels of the attributes 

used in the choice experiment under the different scenarios, as outlined in Table 2.2. 

Table 8.3: Predictions to the five CE landscape attributes under the four policy 

scenarios, according to Cumulus et al. (2005). 

Upland 

Scenario 0 

attribute 

Baseline 1 

Scenario 1 Scenario 2 Scenario 3 

Env-agri Env only Aband-intern 

Heather 

moorland and 

bog 

+1% +3% (+2%) +5% (+4%) -2% (-3%) 

Rough grassland +1% -1% (-2%) -3% (-4%) +3% (+2%) 

Mixed and 

broadleaved 

woodland 

+3% +4% (+1%) +6% (+3%) +5% (+2%) 

Field boundaries +5% +6% (+1%) +10% (+5%) +2%(-3%) 

Cultural 

heritage 

Decline Slow decline No change Decline 

Figures in brackets indicate the change of policies 1 to 3 over the baseline. 

1 

For simplicity, the lower bound of the Cumulus estimates has been used for heather moorland and 

bog and rough grassland. 



Table 8.4 summarises the compensating surplus (CS) results and their aggregation for each 

of the above scenarios for each GOR per household and per GOR population. All attributes 

are included in the compensating surplus estimates. The North East results are not 

presented for the reason explained in Section 8.1. 

What is sought it is a measure of the value to an individual of an increase in the quantity of 

a public good (i.e. landscape attributes). From underlying economic theory, the 

appropriate measure for this change in an individual’s welfare is that of compensating 

surplus. This measure asks what compensating payment (in practical terms what change in 

income/expenditure) will make an individual indifferent between their original level of 

utility and the opportunity to consume the new quantity of the public good. Given the 

implied property rights (that the individual is entitled to their current level of utility/status 

quo), the appropriate metric for measuring the compensating surplus is that of willingness 

to pay, which is the amount of money an individual would be willing to give up to obtain 

the change in the provision of the public good and still be as well as off as their situation 

prior to the change (for further, see Mitchell and Carson, 1989; Freeman, 1993). 

The CS estimates in this study therefore represent respondents’ average WTP to move from 

the state of the world given in Scenario 0 to the state of the world given in Scenarios 1 to 

3. Ultimately they are a measure of the welfare associated with different scenarios. The 

calculation of the CS estimates is explained in Section A5.1.2. 

Assuming the same level of impact (% change), the higher the coefficient, the greater the 

share of the attribute in the CS. Assuming the same coefficient, the larger the change, the 

greater is the impact. So the attributes that have higher coefficients and higher changes 

are likely to dominate. 

Table 8.4: Compensating surpluses of the different policy options over the current 

policy: per household per year and aggregated across all households 

NW YH WM EM SW SE Total 

Compensating surplus (£ per household per year) 

Scenario 

1 

Scenario 

2 

Scenario 

3 

7.68 

(2.59 - 

13.33) 

9.17 

(3.60 - 

15.22) 

0.21 

(-1.41 - 

1.88) 

18.64 

(12.28 - 

25.56) 

20.54 

(14.16 - 

27.59) 

-1.20 

(-2.78 - 

0.63) 

7.44 

(0.39 - 

14.42) 

10.04 

(2.58 - 

17.51) 

-1.50 

(-3.36 - 

0.42) 

41.81 

(22.27 - 

81.34) 

47.97 

(26.45 - 

94.88) 

-2.73 

(-9.06 - 

3.83) 

20.59 

(9.28 - 

32.83) 

21.74 

(9.84 - 

34.64) 

-0.92 

(-3.76 - 

2.20) 

19.85 

(12.47 - 

27.66) 

25.40 

(17.72 - 

34.17) 

-0.89 

(-3.12 - 

1.15) 

No. households (million) 

2.81 2.07 2.15 1.73 2.09 3.29 

Compensating surplus (aggregated across all households, £ million per year) 

Scenario 

1 

Scenario 

2 

Scenario 

3 

21.58 

(7.28 - 

37.46) 

25.77 

(10.12 - 

42.77) 

0.59 

(-3.96 - 

5.28) 

38.58 

(25.42 - 

52.91) 

42.52 

(29.31 - 

57.11) 

-2.48 

(-5.75 - 

1.30) 

16.00 

(0.84 – 

31.00) 

21.59 

(5.55 - 

37.65) 

-3.23 

(-7.22 - 

0.90) 


72.33 

(38.53 - 

140.72) 

82.99 

(45.76 - 

164.14) 

-4.72 

(-15.67 - 

6.63) 

43.03 

(19.40 - 

68.61) 

45.44 

(20.57 - 

72.40) 

-1.92 

(-7.86 - 

4.60) 

65.31 

(41.03 – 

91.00) 

83.57 

(58.3 - 

112.42) 

-2.93 

(-10.26 - 

3.78) 

256.83 

(132.48 - 

421.70) 

301.86 

(169.6 - 

486.49) 

-14.69 

(-50.74 - 

22.5) 

Table 8.4 shows that the CS results are in line with what might be expected from looking at 

the physical changes under each policy scenario in Table 8.3. For example, Scenario 2 may 

be expected to yield the highest benefits based on the physical change levels and it indeed 



does. Similarly, Scenario 3 may be expected to produce net disbenefits on the basis of the 

physical changes. This is the case in Table 8.4 but note that the CS estimates for Scenario 3 

are in fact not significantly different from zero, in that the 95% confidence intervals 

contain zero. 

It is worth noting that the compensating surplus figures for the West Midlands are not 

especially high compared to other regions, despite the apparent enthusiasm of West 

Midlands respondents to payments for upland landscape improvements noted in Section 

7.2.1. In the regression analysis, the constant term for the West Midlands was fairly high; 

this could indicate that respondents in the West Midlands were more in favour of 

environmental improvements without preferences for particular upland attributes. 

8.2.2 Contingent valuation results 

The contingent valuation results cannot be added to the choice experiment results, 

because the change valued by the contingent valuation scenario is different to the change 

expressed by any of the policy scenarios - in that the baseline used in the contingent 

valuation section was different to that used in the choice experiment. It should be 

remembered that this WTP was measuring a change from a worst case scenario to a best 

case scenario, and that WTP for smaller changes would be considerably less. 

However, it should be noted that the fact that respondents expressed quite a substantial 

WTP for landscape improvements in GORs other than their own is evidence that the withinregion 

aggregated benefits expressed in this chapter are likely to be an underestimate of 

total value. 

As a stand alone result, the aggregated CV WTP results using the lower bound result of 

£49.27 per household is presented in Table 8.5. 

Table 8.5: Aggregation of WTP for landscape improvements in SDA GORs other than 

respondents’ own. 

Per household average WTP estimate £49.27 (47.56 – 50.99) 

Total no. households 1 

10.85 million 

Aggregate CV result £535 million 

1 

I.e. the total number of households covered by English SDA GORs, not in England overall. 

Figures in brackets represent the 95% confidence intervals. 

8.2.3 Benefit transfer results 

As explained in Section 5 and Table 3.1, there is only one other attribute from the long list 

which can be included in the aggregation: hay meadow. The expected changes to this 

attribute under the four policy scenarios, according to Cumulus et al. (2005), is presented 

in Table 8.6. 

Table 8.6: Predictions of changes in hay meadow under the four policy scenarios, 

according to Cumulus et al. (2005). 

Upland 

Policy 0 Policy 1 Policy 2 Policy 3 

attribute 

Baseline Env-agri Env only Aband-intern 

Hay meadows -5% -3% (+2%) 0% (+5%) -8% (-3%) 

Figures in brackets indicate the change of policies 1 to 3 over the baseline. 

As described in Section 5.1.2, values for hay meadows are taken from the Environmental 

Landscape Features model, and are taken from estimates in the model where hay meadows 

are reduced in abundance by 10%. The derivation of the estimated net benefits (using a 

midpoint estimate) of policy options 1-3 over the baseline scenario with respect to hay 

meadows is presented in Table 8.7. 



Table 8.7: Derivation of estimated annual net benefit in 2013 of the different policy 

scenarios with respect to hay meadows. 

NW NE YH WM EM SW Total 

A. Present 

area in 

SDAs 1 (ha) 

39,318 27,144 20,309 6,290 7,487 22,268 122,816 

B. Changes net of the baseline brought about by policy scenarios (ha) – 

multiplying A with % changes from Table 8.6 

Scenario 1 

change 786 543 406 126 150 445 2,456 

Scenario 2 

change 1,966 1,357 1,015 315 374 1,113 6,141 

Scenario 3 

change -1,180 -814 -609 -189 -225 -668 -3,684 

C. ELF value estimates (£ per ha per annum) 

LB value 20 20 26 19 24 8 

UB value 34 33 44 32 40 13 

mid-point 27 26 35 25 32 11 

D. Estimated annual benefits net of the baseline of the different policy scenarios (£) 

(B x C (mid point)) 

Scenario 1 21,000 14,000 14,000 3,000 5,000 5,000 62,000 

Scenario 2 53,000 35,000 36,000 8,000 12,000 12,000 156,000 

Scenario 3 -32,000 -21,000 -21,000 -5,000 -7,000 -7,000 -94,000 

LB = lower bound; UB = upper bound. 

1 Data provided by Defra 

8.2.4 Overall aggregation 

Overall aggregation necessitates summing the different elements of value outlined above, 

and also aggregating over time (but appropriately discounting future values). The estimated 

values presented above, aggregated over population, are the values for the eventual 

change – e.g. for a state of affairs where heather moorland and bog is 5% higher in 2013 

than it would have been under the current policy baseline scenario. Clearly, the predicted 

changes to the landscape attributes will not happen overnight. In the aggregation of time, 

the (dis)benefits associated with changes to the attributes are assumed to accrue linearly. 

The justification of this is detailed in Section A5.5 in the Annexes. Therefore, it should be 

borne in mind that benefits have been both adjusted to account for the fact that the 

impacts are not immediate and subjected to discounting. 

The overall aggregation across benefits and across the period 2007-2013 are presented in 

Table 8.8, using 2007 as the base year and a 3.5% discount rate, as recommended by the 

Treasury Green Book (HM Treasury, 2003). 

It is important to remember that the total aggregation figures for England do not include 

the North East, London or East regions. 



Table 8.8: Aggregation of benefits across attributes, population and time 

Scenario 1 

Scenario 2 

Scenario 3 

Env-agri 

Env only 

Aband-intern 

Estimated annual net benefit of eventual change (£ per year) 


attributes – 

256,830,000 

301,860,000 

-14,690,000 

compensating 

(132,480,000 – (169,600,000 – (-50,740,000 – 

surplus (Table 8.4) 

Hay meadows 

421,700,000) 486,490,000) 

22,500,000) 

(Table 8.7) 62,000 156,000 -94,000 

Estimated discounted benefit over the period 2007-2013 (£ million) 


897 

1,054 

-51 

attributes 

(463 – 1,473) (592 – 1,699) 

(-177 – +79) 

Hay meadows 0.2 0.5 -0.3 

TOTAL 897 

1,055 

-51 

(463 – 1,473) (593 – 1,700) 

(-177 – +79) 


8.3 Conclusions 

This study is one of two interlinked studies seeking to generate monetary valuation 

evidence for revision of the LFA policy (the other being Cumulus et al., 2005). The revision 

of this policy will affect land use management in the SDAs, and as a result the current 

quality and quantity of various upland attributes or features. Cumulus et al. (2005) 

establish the baseline changes in these attributes due to CAP and other currently planned 

reforms, and analyse the potential changes in the upland attributes under three policy 

revision options. The policy options presented in that report and used in this study are not 

definitive predictions but possible outcomes. The conclusions of this study should be 

interpreted with that caveat in mind. 

The objective of this study was specifically to provide monetary estimates of the relative 

economic value of upland attributes in each GOR with SDAs so that the allocation of funds 

can benefit from this information (e.g. more funding for the protection or improvement of 

upland attributes valued more highly by the GOR population). Some of this information is 

collected through a choice experiment survey applied separately in each GOR with SDAs (as 

well as in the South East GOR) while some is estimated through benefits transfer. It was not 

possible to provide any quantitative monetised estimates for the changes in a number of 

attributes due to lack of data. 

Individual preferences for upland attributes are only one part of the evidence needed for 

the policy revision. The conclusions presented here are based only on the findings of this 

study and may not necessarily be the conclusions reached at the end of the policy appraisal 

process which should consider other information (e.g. expert opinion). 

On the basis of the stated preference survey, we can make the following general 

conclusions: 

• On the whole, people are willing to pay to contribute to the improvements in Severely 

Disadvantaged Areas and upland attributes associated with them. The exception to this 

are the results for the North West GOR which displayed a significant negative constant 

term in one of the regression models (that which considered the upland attributes 

alone), indicating that respondents in the North West may actually prefer the current 

policy option to scenarios offering alleged improvements. This fits in with attitudinal 

responses from the North West, and the fact that the policy cost (increases in annual 

tax payments) attribute showed the most negative coefficient in the North West, 

indicating an unwillingness to pay. The North West results, along with the high 

willingness to pay of South Eastern respondents, to some extent indicates that people 

either value what they do not already have in abundance, or that people in the North 



West are less willing to pay alone for what they consider to be a national asset (the 

Lake District). 

• There are large variations in the values individuals place on landscape features across 

different regions that are not possible to explain fully on the basis of socio-economic 

differences between populations. 

• Notwithstanding the regional differences, on the whole, changes in the cultural 

heritage (if a large improvement) and policy cost attributes seem to have played a role 

in the choices respondents made. The consistently large coefficients for a large 

improvement in cultural heritage could be purely because a large discrete change (from 

“rapid decline” to “much better conservation”) was on offer. If we had been able to 

measure the cultural heritage attribute quantitatively, and hence vary it continuously, 

it may not have been as consistently important in affecting respondents’ choices. That 

said, it was clear from the focus groups, valuation workshops and reasons respondents 

gave for being willing to pay for landscape improvements that cultural heritage is 

something that is highly valued. Therefore, the implications of any agricultural or 

environmental improvement scheme on this attribute should be considered carefully. 

• In terms of the other attributes, woodland was the next most likely to affect 

respondents’ choices, followed by heather moorland and bog, rough grassland and field 

boundaries; the latter did not appear to be highly valued. It is possible that 

respondents saw field boundaries as an attribute which could be rebuilt or replanted, 

and which was not gone forever if lost. 

• There is evidence of preference heterogeneity within each region. 

• There is evidence (from the contingent valuation question in the survey) that 

individuals in one GOR with SDAs are likely to value the SDAs in the rest of England, 

albeit to a lesser extent than they do the SDAs in their own region. However, this 

result is derived from a context where respondents were asked to choose between the 

upper and lower ranges which attributes took under the choice experiment; 

respondents would presumably have been willing to pay much less if they had been 

asked to pay for a much smaller improvement. 

• On the basis of the South East survey, there is also evidence that those who live in 

regions without SDAs are likely to have positive preferences for the improvement of 

upland attributes in the SDAs. However, due to significant but not completely 

explainable differences in preferences across GORs, we cannot transfer the results of 

the South East to London and East of England GORs which also do not have SDAs. 

• A final piece of evidence for non-use values comes from the finding that many 

respondents in SDA GORs who said that they never visited SDAs were nevertheless 

willing to pay for improvements. 

On the basis of the choice experiment and benefits transfer results, the “environment only” 

scenario (Scenario 2) appears to yield the greatest benefits. This is because it provides 

superior quantities of all attributes except rough grassland, which was not valued 

particularly highly by respondents. On the basis of the quantitative assessment by Cumulus 

et al. (2005), Scenario 3 (“Abandonment-intensification”) was likely to generate 

disbenefits. The economic assessment in this study also comes up with a negative benefit 

estimate for this scenario, i.e. a disbenefit. However, it should be noted that this estimate 

is not significantly different from zero for any of the regions. Scenario 1 (“Environmentagri”) 

gives a benefit which is not much less than Scenario 2. 

Other policy scenarios can be analysed using the results of the choice experiment as long as 

the magnitude of changes in the attributes forecast for the scenarios is within the set up 

changes covered by the choice experiment. The economic value of any changes smaller or 

larger than the set covered in the experiment cannot be estimated using the results of this 

study. 



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Areas 

Final Report – Annexes 1-4 





Annexes 


ANNEX 1 – FINAL FOCUS GROUP PROTOCOL ........................................................1 

ANNEX 2 – FOCUS GROUP REPORT ....................................................................6 

A2.1 OVERVIEW ......................................................................................... 6 

A2.2 FOCUS GROUP 1 FINDINGS ......................................................................... 7 



A2.5 MAIN FINDINGS....................................................................................10 

ANNEX 3 - PILOT SURVEY REPORT .................................................................. 13 

A3.1 INTRODUCTION ....................................................................................13 

A3.2 MAIN RESULTS ....................................................................................14 

A3.3 LESSONS FOR THE MAIN SURVEY ...................................................................17 

ANNEX 4 - FINAL QUESTIONNAIRE................................................................... 19 

eftec A-i January 2006


Annex 1 - Focus Group Protocol 

Annex 1 – Final Focus Group Protocol 

Introductions: Participants and discussion topic 

• Introduction 

o Good evening and welcome to the session today. Thank you for taking the 

time to join our discussion on a topic of environmental interest. 

o My name is Stavros, I work at the University of East Anglia. My colleague is 

Helen from eftec, an environmental consultancy. 

• Inform about purpose of focus group 

o Part of a government funded research project we are currently working on. 

o We are holding a series of discussions such as today’s to help us understand 

public attitudes towards environmental features. 

o Basically we are here to learn from you and your discussion! 

• Instructions for the group 

o Lots of issues to discuss, so we will have refreshments as we go along 

rather than take a break! 

o We would like everyone to participate, with no-one dominating the 

discussion. We want to hear as many different things from as many of you 

as time allows. 

o There really are no right or wrong answers, we are here to learn from 

your views and experiences. 

o If your experience/opinion is different from what others are saying, please 

let us know as that is valuable information. 

o On the other hand, if you think your experience is just like everyone 

else’s that is still important information for us and we want to hear your 

story. There is always something unique to each person’s own 

experience/views. 

• We will be on a first name basis tonight and in our reports no names will be 

attached to comments. You may be assured of complete confidentiality. 

• We would like to record the session: it will help us a lot to register your views in 

an accurate way. 

o As we are recording the session please try to speak one at a time and no 

side conversations between neighbours please! 

• Please turn off your mobiles at this point. 

• The discussion should last 1.5 hours. 

• Opening / ice-breaker question: Ask participants to briefly introduce themselves 

– Name, occupation, any children, how long have lined in the area, etc. 

eftec A- 1 

January 2006



Discussion points 

A. Visits to the countryside 

1. Do you visit the countryside near here or elsewhere in England and if so for what 

purpose? (Brainstorming) 

� Flipchart: list events mentioned 

A ‘check-list’ of activities could include: hill-walking / rambling 

Fishing / bird-watching / other nature observation / cycling (esp. mountain-biking?) / 

just enjoying scenery or peace and quiet in an inactive way / painting / photography / 

water sports / rock-climbing / touring in car / other 

1 

a. Of the activities listed, which would you say is the most important to you and 

the second most important? 

b. Approximately how often do you visit the countryside around here? 

B. SDA specific discussion 

What we would like to discuss in the next little while concerns the areas shaded 

pink in these two maps. 

� SHOW the map of England with SDA areas and the map of the GO region, 

on which the hill areas and SDAs are shaded using different colours and 

the location of the FG is marked. 

� Show pictures of Lake and Peak Districts 

2. How familiar are you, if at all, with areas shaded on these maps (England and the 

GO Region)? How would you describe them? (Brainstorming) 

� Flipchart: list expressions, thoughts 

3. How familiar are you with this area of the Yorkshire Dales which Helen is indicating 

compared to the rest of the Yorkshire Dales? 

� indicate bit of the Yorkshire Dales in NW GOR 

(note – this question was testing whether participants could identify parts of trans-boundary 

SDAs in their own GOR more than the parts in other GORs. A Peak District example was used 

in Manchester) 

eftec A- 2 

January 2006



4. These pink areas are called ‘severely disadvantaged areas’ because farming is much 

more difficult than in the lowlands, and because they are less accessible by 

transport. 

a. Approximately how often do you visit other SDAs in the North West 

beyond the Lake District? 

b. What for? 

c. Do you visit SDAs in other regions? 

d. Are you familiar with the blue areas on the fringes of the pink areas? [if 

familiar] What are the differences? Can you describe what they are? 

(note – testing recognition of Disadvantaged Areas) 

5. Thinking only of the SDAs (the shaded areas on the map) in the NW region, what do 

you think is attractive or unattractive about these? 

� Flipchart: list expressions, thoughts 

6. These (SDA) areas have been used for farming for a very long time. I’d like to 

discuss the impacts that these uses have had on the land? 

a. In your view, what are the positive impacts that farming might have had 

on the environment in SDAs (shaded areas on the map)? (Brainstorming) 

� Flipchart: list 

b. In your view, what are the negative impacts that farming might have had 

on the environment in (SDAs – shaded areas on the map)? (Brainstorming) 

� Flipchart: list 

c. If farming stopped or was significantly reduced in these areas, what effect do 

you think that would have? 

7. Do you think there are any particular issues concerning these areas in your own 

region (SDA) that require changes in government policy or require government 

support? If there are, what are these? 

� Flipchart: list aspects that require policy change or support 

8. Do you have any questions about the information presented so far? Has the 

discussion been clear? 

C. Attributes section 

9. Helen will now pass out some cards. 

� Distribute attribute cards 

eftec A- 3 

January 2006



The cards have some of the different features associated with hill farming areas written 

on them. Please sort these cards in order of how important each feature is to you, how 

much each feature adds or would add to your enjoyment or appreciation of upland 

areas. We have some pictures showing some of these features to help you. 

Is there anything written on the cards that isn’t clear? 

� Distribute pictures 

� Give them a few minutes to do this. 

� Helen to distribute bits of paper and ask each to note ranks – also to 

write their names on 

10. Now please keep your top five cards and put the rest to one side. We are interested 

in finding out how you would allocate public money to these features if you were in 

charge of spending decisions to protect them. 

We are going to give you twenty counters each. 

� Helen to hand out counters 

Imagine that your twenty counters represent a sum of public money which you can 

distribute towards protecting the five features you have chosen. Please allocate your 

counters between your features. However, please only consider your personal feelings 

on these features, without trying to second guess what you would need to do to keep 

other people happy. 

� Ask each to note counter scores on their bits of paper 

� Helen to collect cards 

11. In principle, are you willing to pay towards subsidising farmers in upland areas to 

provide some of the environmental benefits that we have been discussing? 

12. Through what mechanism could such a payment be made? 

13. How credible/clear were these questions on ranking and allocating money? Was 

there anything that was not clear or confusing? 

eftec A- 4 

January 2006



D. Follow up questions 

14. For those who were willing to pay something: can you tell me your main reasons for 

agreeing to pay? 

15. If you were NOT prepared to pay anything: can you tell me why you were not 

prepared to pay anything? 

TO ALL: 

16. When you were ranking the features in order of importance, what kind of factors 

were you considering? 

17. Did you think that any of the features were not at all important? 

18. When you were choosing to allocate public money to protecting the different 

features, what kind of factors were you considering? 

19. [If cultural heritage appears to be important] When you hear the phrase ‘cultural 

heritage’ applied to hill farming areas, what does this mean to you? 

20. Do you think we could have told you about other features? If so, what are these? 

Non-use question 

21. How would you have responded to the question asking if you were willing to pay if 

we asked you about similar areas elsewhere in the country? 

If their answers would be the same, this would be sign of embedding. You need to 

ask why they would not change their answers. If their answers would be different, 

ask why? 

That is the end of our discussion! 

Thank you very much for your participation and for sharing your views with us. 

eftec A- 5 

January 2006


Annex 2 – Focus Group Report 


A2.1 Overview 

This is a report on the focus groups conducted as part of a stated preference analysis 

undertaken by eftec, with Carrick James Market Research, for the Department of 

Environment, Food and Rural Affairs. The overall aim of the analysis is to provide an 

economic measure of the environmental impacts of hill-farming in Severely Disadvantaged 

Areas. This report is accompanied by an initial report, which further details the policy 

background and overall study approach. 

Focus groups are semi-structured discussions led by a moderator among a small group of 

participants, typically six to eight people. The moderator uses a script called the ‘protocol’ 

to guide the discussion and thereby gain insights about the issue at hand as well as inform 

particular questionnaire design characteristics. Focus groups are the most important 

qualitative research procedure and an essential stage in the design of a stated preference 

survey. The participants of focus groups are paid and are normally chosen to be 

homogenous on particular characteristics. In this case socio-economic group, age and 

location of residence in the North West Government Office Region were the defining 

characteristics of groups. Overall the three focus groups cover the range of characteristics. 

Three focus groups were undertaken between the 21 st and the 23 rd of June 2005, one in 

Manchester and two in Kendal. The focus group locations and socio-economic details of the 

focus-group attendees are provided in Table A2.1. Each was moderated by Stavros Georgiou 

of the University of East Anglia with assistance from Helen Johns of eftec. 

Table A2.1: Focus Group Participants and Logistics 

Groups 1. Manchester 2. Kendal 3. Kendal 

Number of 

participants 

8 8 6 

Gender 5F 3M 4F 4M 4F 2M 

Age 18-51 18-40 40-65 

Socio-economic 

group 

BC1C2 ABC1 C2DE 

Date 21/06/05 22/06/05 23/06/05 

Time 19:00 19:30 19:30 

Moderator Stavros Georgiou assisted by Helen Johns 

The learning process from focus groups is iterative, and means that the focus group 

protocol should be altered between groups to reflect what was learnt from the previous 

group and to either refine the design or to test different aspects of design in subsequent 

groups. Sections 2 to 4 of this report describe the key findings from each of the focus 

groups and changes made to subsequent protocols. The main findings are provided in 

Section 5. 

The finalized version of the protocol for the focus groups is provided in the previous Annex. 

However, the protocol can be briefly summarised as follows. The first section invited 

participants to talk very generally about their visits to the English countryside. The second 

section introduced the SDAs, asked participants to describe them, say what they found 

attractive and unattractive about them, and discuss the impacts of farming on the 

landscape and environment. The third section consisted of an experiment whereby 

participants were asked to rank a long list of upland attributes in order of how much they 

contributed to their appreciation/enjoyment of upland areas. They were then asked to 

allocate twenty counters (representing a sum of public money) between their top five 

ranked attributes. They were also asked whether they were, in principle, in favour of 

eftec A- 6 

January 2006



subsidizing hill-farming. The final section consisted of follow up questions, asking 

participants what had been on their minds in the previous section. 

Lessons learned from the focus groups will be instrumental in arriving at the questionnaire 

design that enters into the pilot survey testing phase in the week beginning the 4th of July. 

A2.2 Focus Group 1 Findings 

A2.2.1 Summary points from Focus Group 1 

Overall the first focus group discussion went well. Participants expressed a strong 

familiarity with and appreciation of the countryside in the North West GOR (as well as the 

Peak District), and mentioned the Lake District as a favourite destination without 

prompting. When shown the map of SDAs in England, participants had very positive 

associations with the areas shown, mentioning factors such as their lack of development, 

purity, beauty and peace and quiet. A wide range of outdoor activities in SDAs were 

engaged in fairly regularly; the SDAs were very much the areas that participants had in 

mind when talking about their use of the countryside. However, familiarity with SDAs in 

other parts of England seemed to tail off with reduced accessibility, with hardly any 

participants familiar with the South Western SDAs. 

The main difficulty was trying to get participants to narrow down what they found 

attractive about the SDAs. They saw the landscape as an indivisible good, and did not 

mention particular landscape features (apart from obvious geographical features such as 

mountains, lakes and reservoirs) unprompted. The impacts of farming generally had 

negative (but invisible) associations, such as pesticides and soil nutrient depletion; 

however, the participants also acknowledged that farming had preserved the land from 

development. It was considered that if upland farming were stopped or reduced that this 

would benefit wildlife and lead to a more natural, more rugged look, which was preferred 

to landscapes which seemed more man-made. 

When we asked the participants to rank the long list of attributes, we found that the most 

popular attributes (on average and in order) were field boundaries, water quality, rough 

grassland, cultural heritage and water quantity. There was not unanimity on which 

attributes were preferred. Some attributes, such as hay meadows, heather moorland and 

coniferous woodland were deemed attractive by some but ranked lowly by others. The only 

universally unpopular feature was improved grassland. This may have been prejudiced by 

earlier discussion where some participants said that they couldn’t see why it was called 

‘improved’ grassland compared to rough grassland, which was deemed more naturallooking, 

and therefore preferable. Arable and gorse were not universally unpopular, but 

were the only other two attributes not to appear in anyone’s top five. Although the concept 

of carbon sequestration was explained, it was only ranked in the top five by the one person 

with prior knowledge of the concept. Broadleaf woodland was not much preferred to 

coniferous woodland on average, but unlike coniferous woodland, was not as actively 

disliked. 

In terms of spending allocation, field boundaries, water quality and water quantity each 

attracted between an eighth and a sixth of the total pot. However, carbon storage, 

broadleaf woodland and water quantity were all valued highly by those who chose them. 

Cultural heritage was defined by participants to mean old buildings (not necessarily just 

agricultural buildings), landscapes, intact rural communities with an alternative outlook 

and different way of life, and local dialects. Very early on in the discussion participants 

mentioned visiting National Trust properties as something they liked to do in the 

countryside, indicating that cultural heritage is an important factor in their enjoyment. 

Participants were emphatically not amenable to the idea of paying higher taxes to protect 

upland areas. However, they emphasized that this was not because they did not think that 

eftec A- 7 

January 2006



it was important to spend money on protecting upland areas – but because of a resistance 

to contributing more to central funding. Some participants said they would be willing to 

pay into a very specific local scheme; others said that voluntary payments to bodies such as 

the National Trust should pay for it, or that upland areas should pay for themselves. 

A2.2.2 Suggested changes to the next protocol 

This version of the protocol had two separate questions asking participants (a) what was 

attractive or unattractive about SDAs in the NW and (b) asking them to identify the 

“positive” and “negative” aspects of the environment in SDAs, drawing them towards 

farming impacts if necessary. We found that this second question did not draw anything 

more specific than the general impressions found in the first, so we altered it to ask more 

specifically and directly about the positive and negative impacts of farming. 

We also changed the question about taxation. This previously asked participants whether, 

in principle, they would be willing to see their taxes increase to pay for the environmental 

benefits we had been discussing. This drew such an immediate and strongly negative 

response that it was likely that participants were envisaging a fairly large and/or arbitrary 

tax increase (e.g. a penny on income tax). We changed the question to ask (a) whether in 

principle participants were willing to subsidise upland farmers and if so (b) what kind of 

payment mechanisms would be acceptable to them. 



The principle differences between Focus Groups 1 and 2 were that Focus Group 2 actually 

lived in an SDA and were more affluent. Due to their location, the participants engaged in 

recreation in SDAs (specifically the Lake District) much more regularly. Other SDAs in the 

North West were visited fairly regularly, and those much further afield only rarely. Their 

descriptions of the characteristics of SDAs (“hilly”, “rural”, “less accessible”, “wild”), 

while still appreciative, were less enthusiastically positive than those in Manchester, 

presumably because of the lack of contrast with their own surroundings. 

Consideration of the impacts of farming drew both positive and negative responses. 

Participants were much more aware that farming affects the look of the landscape, and 

mentioned the impact of sheep grazing on keeping areas “open”, i.e. not obscured by 

woodland, unprompted. They also mentioned dry stone walls unprompted. Other positive 

impacts mentioned were local food production, keeping development at bay, and 

maintaining cultural identity. However, some conceded that farming could also sometimes 

have a negative visual impact; pesticide pollution and reduced water quality were also 

mentioned as negative impacts. The prospect of reduced farming drew a much more 

concerned response – participants were very aware of how that would affect the identity, 

employment prospects and community structure of their area. 

In terms of ranking, the most popular attributes (in terms of average rank) were water 

quality, field boundaries, broadleaf woodland, rough grassland and heather moorland and 

bog, representing an overlap of three attributes with the previous group. There were no 

features that were universally unpopular; arable and improved grassland were jointly least 

popular. Water quality and cultural heritage both attracted about 20% of the public 

spending scores, with field boundaries, broadleaf woodland and carbon sequestration each 

attracting about 10%. Again, carbon sequestration was highly valued by one or two 

environmentally-conscious individuals. Participants commented that they found it difficult 

to rank those attributes which they didn’t much care about. 

eftec A- 8 

January 2006



Cultural heritage was partly valued so highly because one participant allocated all twenty 

of her counters to it, declaring that to her the phrase cultural heritage was an umbrella 

term that encompassed all of the other attributes. However other people’s definitions of 

cultural heritage were less all-encompassing, mentioning components such as “the farming 

way of life”, farm buildings and dry stone walls. 

Participants agreed in principle with spending public money on subsidizing hill farmers 

(subject to controls), but the attitudes towards taxation were markedly different from the 

Manchester group. Participants were much more supportive of funding through national 

taxation, and felt that a more localized tax would unfairly penalize them for living on the 

edges of what they viewed as a national asset. They also suggested lottery money and taxes 

on supermarkets and visitors to the national park as payment mechanisms which would be 

acceptable to them. They were slightly less inclined to subsidise SDAs further afield than 

those nearer home, but this concern was minor compared to their preference for a national 

funding mechanism. 

A2.3.2 Suggested changes to the next Protocol 

It was felt that both the questions and the ranking/scoring exercises had gone well in this 

focus group, and that no further changes to the protocol were necessary. 



The third focus group differed in that they were in general less affluent and on average 

older than either of the previous two, had the longest experience of living in an SDA, and 

had more personal contact with hill farmers (either socially or professionally). The 

participants naturally were very familiar with the Lake District, but were less familiar than 

other groups with other SDAs in the North West and other regions. Being older, they were 

also less physically active, and the group contained the only two participants in any of the 

focus groups not to engage in any recreational activities in SDAs. However, those that did 

engage in recreational activities did so fairly regularly. They were the only group not to 

mention the attractiveness of SDAs unprompted, presumably because of habituation with 

the surroundings. This did not mean, however, that they were unappreciative of the 

amenity and other environmental benefits of SDAs once specifically asked about them. 

Consideration of the impacts of farming again drew positive and negative responses similar 

to those mentioned previously. Again, participants were aware that farming had an impact 

on the landscape (“the whole landscape is man-made”), and that sheep-farming in 

particular kept the levels of larger vegetation down. The prospect of reduced hill-farming 

produced the most concerned response, with participants very concerned that the 

character of their local area was already irrevocably changing for the worse. They were 

very aware that the social and economic fabric of certain hamlets and market towns were 

highly dependent on hill-farming. 

In terms of ranking, the most popular attributes (in terms of average rank) were cultural 

heritage (which was – significantly – in everyone’s top three), water quality, field 

boundaries, carbon sequestration and hay meadows (however, the latter was ranked last by 

two participants). Gorse was almost universally unpopular. Global warming was a much 

more widely spread concern than in the previous groups, despite the fact that participants 

did not otherwise claim to be particularly environmentally conscious (this may be because 

more of them had children or grandchildren). Uniquely, rough grassland was less 

appreciated than improved grassland and broadleaf woodland less appreciated than 

coniferous woodland. In terms of public spending allocation, cultural heritage was again by 

far the most significant recipient (27%), and water quality the second most (19%). This huge 

eftec A- 9 

January 2006



preference for cultural heritage is very much in accordance with casual comments 

participants made throughout the session on the perceived disintegration of local 

communities and the farming way of life. Other cited components of cultural heritage 

included hunting, old farming techniques and other old trades associated with farming, 

such as blacksmithing. 

Participants expressed very mixed views about subsidizing hill-farming and its associated 

benefits. They initially said “no” outright, but after discussion expressed the idea that 

expenditure of public money to protect the landscape (to “keep the countryside as it is”) 

for future generations is important, but expressed skepticism that such subsidies would do 

good other than “line the pockets of farmers”. Again, there was very much a preference for 

transparent taxation rather than contributing more to a national pot that they would not 

know directly benefited the landscape. There was also a perception that urban and 

southern areas received a disproportionate amount of public money. A tourist tax was again 

suggested as a preferable payment mechanism. When asked about subsidizing SDAs in other 

regions, participants were split between those who said they would be willing to pay 

exactly the same as in the Lake District, and those who said that they would be much less 

willing to subsidize other areas. It was considered that farmers further south did not need 

as much support because the winters are milder; however, it is possible that during some of 

the discussion it was not clear that the subsidies would just be for SDA farmers and not 

lowland farmers. 

A2.5 Main Findings 

A2.5.1 Ranking and allocation results 

The overall findings of the ranking exercise are presented in Table A2.2. 

Table A2.2: Overall rankings of attributes from the focus groups. 

Attribute Overall Urban/Manchester Rural/Kendal 

Average 

rank 

Rank of 

average 

rank 

Average 

rank 


average 

rank 

Average 

rank 


average 

rank 

Heather 

moorland and 

bog 

Improved 

6.7 6 6.1 6 7.1 5 

grassland 

Rough 

10.7 14 12.3 14 9.9 13 

grassland 6.4 4 4.8 2 7.3 8 

Hay meadows 

Bracken 

8.0 9 7.6 8 8.2 9 


Gorse 

9.5 11 9.8 10 9.3 10 

dominated 10.5 13 10.4 13 10.6 14 

Arable 

Broadleaf 

9.5 12 9.8 10 9.4 11 

woodland 

Coniferous 

6.8 7 6.3 7 7.1 6 

woodland 9.0 10 8.3 9 9.4 11 

Field boundaries 4.2 1 3.4 1 4.6 2 

Cultural heritage 4.9 3 5.3 4 4.7 3 

Water quantity 6.5 5 5.3 5 7.1 6 

Water quality 

Carbon 

4.2 1 4.8 2 3.9 1 

sequestration 7.7 8 10.1 12 6.3 4 

The top five in each column are highlighted in bold. 

eftec A- 10 

January 2006



According to the overall average rank, the most popular upland attributes appear to be 

field boundaries, water quality, cultural heritage, rough grassland and water quantity. 

Many of the rankings remain very similar between the urban and rural groups – rough 

grassland and carbon sequestration being the obvious exceptions. The consistency between 

the groups’ choices is encouraging and indicates that the rankings were not wholly 

arbitrary, but do reflect genuine preferences. 

The results of the spending allocation exercise are presented in Table A2.3. Note that each 

respondent allocated counters to their personal top five only. 

Table A2.3: Overall sum of public spending counters for individual attributes 

given by the focus groups (absolute sum an percentage). 

Attribute 

Overall 

Urban / 

Manchester 

Rural / Kendal 

Sum % Sum % Sum % 

Heather moorland and 

bog 36 8.2 14 8.8 22 7.9 

Improved grassland 3 0.7 0 0.0 3 1.1 

Rough grassland 25 5.7 14 8.8 11 3.9 

Hay meadows 31 7.0 14 8.8 17 6.1 

Bracken dominated 12 2.7 3 1.9 9 3.2 

Gorse dominated 0 0.0 0 0.0 0 0.0 

Arable 5 1.1 0 0.0 5 1.8 

Broadleaf woodland 34 7.7 15 9.4 19 6.8 

Coniferous woodland 13 3.0 11 6.9 2 0.7 

Field boundaries 58 13.2 27 16.9 31 11.1 

Cultural heritage 76 17.3 15 9.4 61 21.8 

Water quantity 36 8.2 20 12.5 16 5.7 

Water quality 72 16.4 21 13.1 51 18.2 

Carbon sequestration 39 8.9 6 3.8 33 11.8 

The overall top five (or six where there is a tie) in each column are highlighted in bold. 

Again, with some attributes there is a certain consistency between groups. However, the 

notable exceptions here are field boundaries, cultural heritage, carbon sequestration and 

water quantity and quality. As the top attributes in this case are not the same as under the 

ranking exercise, it is likely the participants were making some budgetary assumptions in 

their allocation decisions; i.e. considering which attributes may actually need funding, and 

which (such as rough grassland) could fend for themselves. Cultural heritage was allocated 

over twice as many counters by the rural groups as the urban groups (and attracts 

significantly more even without the outlier represented by the lady for whom the term was 

an umbrella for all attributes). It is not surprising that the participants who actually reside 

in an SDA should be more aware of, and concerned for, the role that hill-farming has to 

play in cultural heritage and attribute a higher spending allocation to it. 

It should be noted that the preferences for water quantity and quality may be suffering 

from a regional bias because of the fact that the focus groups took place either in the Lake 

District or (in Manchester) amongst people who said they preferred to go to the Lakes 

rather than any other SDA. Several participants said that either they or their children liked 

to go swimming in the lakes; one or two others liked to go canoeing on them. It is not 

known whether water attributes would be as highly favoured elsewhere in the country. 

A2.5.2 Other general findings 

When shown the map of the North West, participants were not generally able to readily 

distinguish Disadvantaged Areas from Severely Disadvantaged Areas from their own 

experience of travelling through such areas. This was probably due to the fact that 

Disadvantaged Areas are usually thin fringes on the SDAs. Descriptions of them (“a bit more 

eftec A- 11 

January 2006



built up”, “less remote”, “more populated”) were partially based on local knowledge but 

were mostly based on inferences from the map itself. However, the low ranking of 

improved grassland by participants, coupled with the fact that remarks were made during 

the sessions on preferring more rugged, more “natural-looking” terrain, suggest that the 

landscape attributes of DAs are likely to be much less valued by the public than those of 

SDAs. 

As expected, on the whole people identified with national parks (or geographical features 

such as the Pennines) much more than with the North West Government Office Region. 

Despite the fact that the Kendal groups said that they recognised a qualitative difference 

between the part of the Yorkshire Dales in the North West GOR and the part in the 

Yorkshire and Humber GOR, it should be remembered that this is not likely to be true for 

most respondents. Respondents in the North West, North East and Yorkshire and Humber 

will probably not be able to clearly demarcate between SDAs in their GOR and those in 

neighbouring GORs. 

Participants cited different reasons for ranking and allocating public spending between 

attributes. With respect to the ranking, some thought very specifically about what they 

liked to see on days out; others considered altruistic and bequest motives; others the 

contribution the attributes make to their recreational activities. With respect to allocation, 

some participants gave their allocation seemingly in proportion to the contribution to their 

appreciation; others considered how much funding the attributes would actually need 

compared to others. 

All of the focus groups showed that participants can be swayed towards certain preferences 

by having more information or hearing others’ opinions. In all three, at least on person 

said, after hearing why others had ranked the attributes as they did, that they would have 

ranked theirs differently in the light of the subsequent discussion. Although the concept 

was explained during each session, carbon sequestration in particular was valued more by 

people who were better informed about global warming. Cultural heritage would also have 

been more highly valued by at least two participants if they had heard others’ descriptions 

of what it meant to them first. 

The last focus group also shows that both the payment vehicle and the fact that payments 

would only be given to hill-farmers must be stressed in the survey. Participants will be 

willing to pay less if they imagine that payments will go to lowland farmers who they 

perceive as not needing it. Comments made during the taxation and payment parts of the 

session also suggest that a distance decay effect is likely to be observed, and that use 

values will be likely to be much higher than non-use values. 

When asked if we could have included any other attributes in the exercise, participants 

cited air quality, wildlife, farm animals and industries that might replace farming, such as 

wind farms. It was also suggested that images of habitats could have contained information 

about which species live there. 

In conclusion, the focus groups indicated that while some participants expressed difficulty 

in disaggregating the different parts of the landscape which contributes to their enjoyment 

of SDAs, participants did in fact show fairly marked and consistent preferences for certain 

landscape and environmental features. Whether the exact top five overall attributes should 

be used in the choice experiment merits further discussion, (a) because of the likely 

regional bias towards water quality and quantity; and (b) because urban and rural 

preferences are slightly different. This is in particular need of consideration as rural 

participants outweighed urban participants in the focus groups, whereas urban respondents 

will outweigh rural respondents in the main survey. 

eftec A- 12 

January 2006


Annex 3 – Pilot Survey Report 

Annex 3 - Pilot Survey Report 

A3.1 Introduction 

The objective of the pilot surveying a stated preference questionnaire is to test the 

coverage, wording, length and the design of the questionnaire in a small sample of 

respondents before applying it to the main survey sample. While some level of population 

representativeness is expected from the pilot survey sample, high levels of 

representativeness and statistical significance are not sought at this stage. 

The pilot survey for this study had a sample of 50 respondents and took place in the North 

West GOR between 5 th and 13 th July 2005. The sample was divided 25-25 in rural (Kendal 

and Ambleside in the Lake District) and urban (Stockport, Middleton and Blackley in 

Manchester). This is not representative of the rural-urban split of the North West GOR 

population but again representativeness of this split was also not sought. The socioeconomic 

characteristics of the pilot survey sample are given in Table 1. 

Table 1: Pilot Survey Respondents 

Number of respondents 

No. (%) used in survey 

50 

Gender 

Male 29 (58%) 

Age 

Socio-economic group 

Residence 

Female 21 (42%) 

18-34 10 (20%) 

35-54 20 (40%) 

55-70 20 (40%) 

ABC1 24 (48%) 

C2DE 26 (52%) 

Rural 25 (50%) 

Urban 25 (50%) 

It appears that the actual survey process went well, with 68% of respondents saying that 

they found the survey interesting, and only 4% saying that they found it unrealistic or not 

credible (question D8). Fifty four percent of respondents said that they found the choice 

experiment part of the survey either very or fairly easy, while 30% said it was fairly or very 

difficult. This means that the questionnaire provided the necessary information and was 

thought to be accessible and clear. 

Each interview took 15-45 minutes to complete depending on the interviewer and version of 

the questionnaire they were using. They did not report any particular problems in 

implementing the questionnaire. 

The next section reports the main results from the pilot survey. These results should be 

read for what they are – pilot survey results. They are not expected to be representative of 

the sampling points let alone the North West GOR or England. 

eftec A- 13 

January 2006



A3.2 Main Results 

A3.2.1 Section A: Attitudes 

Respondents appeared to be both environmentally aware and to have an appreciation of 

the countryside. Ninety two percent of respondents said that they considered 

environmental policy to be an important consideration for the Government, with climate 

change drawing the most concern. Eighty four percent of respondents said that they visited 

the countryside for days out. About 60% of respondents engaged in general sightseeing or 

gentle walking in the countryside, while about a quarter engaged in hill walking or visiting 

historic buildings. 

A3.2.2 Section B: Valuation scenario 

A choice experiment does not make for an easy questionnaire to complete. In this case, the 

questionnaire is even more difficult since unlike most other surveys, the attributes here 

could get better as well as worse under alternative policy options in comparison to the 

current situation depending on the respondent’s interpretation. For example, the area of a 

particular attribute can be larger in the current situation compared to policy option A but 

can be smaller compared to policy option B. Whether one change (current situation to A) is 

better than the other (current situation to B) depends on whether the respondent finds the 

attribute a pleasant one to start with (so that he would like to have more of it). This is 

why it is important to word the example choice set in the scenario as clearly as possible. 

Encouragingly, as responses to question D8 shows on the whole, respondents were happy 

with the questionnaire (see below). 

The pilot data were analysed using a Conditional Logit model. In this model, the probability 

that a particular scenario is chosen by a respondent is explained as a function of the levels 

of attributes. In its simplest form only the attributes are used as explanatory variables. In 

more comprehensive models some of the relevant socio-economic characteristics of the 

respondents are also included in the model. But for the pilot survey analysis, the simplest 

form is sufficient to test the appropriateness of the questionnaire. 

Overall the model is significant. All coefficients had the expected signs: cost had a negative 

sign (the higher the cost of a policy option, the less likely is a given respondent to choose 

that option) and all other attributes had positive signs (the more there is of an attribute, 

the more likely is a given respondent to choose that option). However, moorland, grassland 

and field boundaries attributes did not affect the choice of the preferred option, i.e. these 

attributes were insignificant. On the other hand, the quality of woodland area, the 

conservation of cultural heritage and the tax played a significant role in the choice of the 

policy option. In particular the conservation of cultural heritage is significant only when a 

“much better conservation” of the current condition is offered, being no significant when 

only a better conservation is presented. 

The constant term embodies all considerations that the respondent may have in mind other 

than what is presented and hence included in the model. The significance and positive sign 

of the constant term show that respondents are, everything else equal, in favour of the 

payments to farmers for the “environmental services” they are required to provide. 

Incidentally, a negative and significant constant term would have showed a current 

situation bias or a tendency to keep the current situation and not make any more 

payments. 

In summary, the model says respondents value programmes which result in higher woodland 

area, much better conservation of cultural heritage and the less these programmes cost the 

better. Table 2 presents the model. Note that only two respondents stated zero WTP by 

choosing the current situation in each choice set. While some of the reasons given in the 

eftec A- 14 

January 2006



follow up questions for having positive or zero WTP can be classified as invalid or protest 

responses, these have not been excluded from the analysis at this stage. 

Table 2: Discrete choice (multinomial logit) model; Maximum Likelihood Estimates 

Dependent variable Choice 

Weighting variable None 

Number of observations 298 

Iterations completed 5 

Log likelihood function -276.5660 

Log-L for Choice model = -276.56601 

R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj 

Constants only -309.3028 .10584 .09368 

Response data are given as ind. choice. 

Number of obs.= 298, skipped 0 bad obs. 

Variable Coefficient (b) Standard Error b/St.Er. P[|Z|>z] 

CON .8636212639 .27686916 3.119 .0018 

MOOR .1280057635E-01 .16458674E-01 .778 .4367 

GRASS .1400712558E-01 .26885885E-01 .521 .6024 

WOOD .3293236416E-01 .16320005E-01 2.018 .0436 

FB .2286595058E-01 .15864743E-01 1.441 .1495 

CH1 .1192586984 .12056125 .989 .3226 

CH2 .4631175946 .13857153 3.342 .0008 

TAX -.2877372103E-01 .45331256E-02 -6.347 .0000 

CON: Constant term 

MOOR: Change in area of Heather Moorland and Bog 

GRASS: Change in area of Rough Grassland 

WOOD: Change in area of Broadleaf and mixed woodlands 

FB: Change in length of field boundaries (hedges and stone walls) 

Cultural heritage (Change in farm building and traditional farm practices): as a qualitative 

indicator has to be coded so that 

CH1: shows change in cultural heritage attribute from ‘worse’ to ‘better’ 

CH2: shows change in cultural heritage attribute from ‘worse’ to ‘much better’ 

Tax: Increase in tax payments per annum 

Finally, in this section of the scenario, three versions of the questionnaire were tested: (1) 

each respondent was given four choice cards; (2) each respondent was given six choice 

cards and (3) each respondent was given eight choice cards. The analysis shows that there 

are no signs of fatigue effects. With a such a small sample size it is not possible to estimate 

separate models for each groups of choice cards used (4, 6 and 8 in the pilot) and test for 

differences in parameter estimates. But we compared the responses to question D8 “what 

did you think of this questionnaire” with the number of choice cards presented. This 

comparison shows that the only one respondent who found the questionnaire too long was 

presented only 4 card. As well, interviewers reported that respondents found the survey 

fairly difficult of very difficult in the case of only 6 people. These people were given with 

4, 6 or 8 choice cards so that the difficult seems to be independent from the number of 

choice cards presented. It can be seen that it is highly correlated with respondents’ age (5 

out of 6 people were older than 55 years) as expected. 

eftec A- 15 

January 2006



A3.2.3 Section C: Follow-up questions 

Comparing the model results to what respondent stated in the follow-up question C1: “in 

making your choices, which of the features on the cards concerned you the most” shows 

some interesting consistencies. The three most important attributes were broadleaf/mixed 

woodland (38% of respondents), cultural heritage (20%) and cost (20%). It can be seen that 

the three attributes that were significant in the choice model are the ones that concern 

respondents the most. That shows a consistency between the choice model and 

respondents’ preferences. 

There are two further aspects are of interest here. The first is about the insignificance of 

heather moorland and bog and field boundaries attributes. The heather moorland and bog 

is a feature in which respondents are interested, but it is not a significant variable in the 

choice model. The same applies to the field boundaries attributes, although respondents 

are even less concerned in this attribute. In the choice experiment design, both of these 

attributes have a current situation policy that offers an intermediate level, i.e. policy 

options A and B sometimes contain a value that is worse than the one that is shown in the 

current option. This may have caused confusion to respondents, so that the no significance 

of the attributes might be due to this confusion. Probably, by including a short explication 

that only the overall policy A and B options are always better than the current situation 

policy and not in each attribute level and by training interviewers to point out any 

inconsistencies in responses may help in the understanding (or acceptance) of the 

insignificance of this attributes in the choice model. 

Secondly, the rough grassland area attribute was indicated as the leading attribute when 

choosing the preferred option by only one respondent. Also, it was not significant in the 

choice model. That does not mean the attribute is not of concern, but it shed some doubts 

about it. Furthermore, the positive and significant value of the model constant says that 

there are some other systematic reasons that “push” respondents choosing policy options A 

and B respect to the current policy. Considering that and the fact the rough grassland 

attribute was deemed not very important, leads to the question if it would be better to 

change this attribute. This decision cannot be taken on the basis of this pilot survey alone 

but it may be worth keeping in mind when the new study on the baseline and policy options 

considers the issue of the attributes list. 

Other follow up questions asked respondents why they were / were not willing to pay for 

the future policy options, where having zero WTP means selecting the current situation (at 

zero cost) in all choice sets and having a positive WTP means selecting one future policy 

option at least on one of the choice sets. 

Whether they have a positive or zero WTP, all respondents were asked the relevant openended 

questions about the reasons behind their choices. The interviewers were given a precoded 

answer list, which was not shown to the respondents, and asked to record the 

answers both verbatim and mark them on the pre-coded list. 

The comparison of the verbatim and coded answers of those who had a positive WTP shows 

that we will need to extend the pre-coded sheet to reflect some of the common reasons 

people gave. These include more general feelings such as “I care very much about how 

farms look and are managed”, “it’s about time things were turned around” and “it’s the 

right thing to do”. Note that some of these results could in fact signify ‘invalid’ or protest 

reasons for a positive WTP. For example, it is not clear if the WTP of someone who pays 

‘because it is the right thing’ should be excluded on the grounds that what they get is not a 

benefit specific to the scenario described to them but a ‘warm glow’ unrelated to the 

scenario; or it should still be included because in addition to these policies being a right 

thing to do the respondent also derive direct benefits from the improvements. In order to 

be able to decide whether to include the responses from such individuals in the analysis, 

we need to understand the motives behind these reasons in more detail. 

eftec A- 16 

January 2006



Only two respondents were not willing to pay for any alternative options presented on their 

choice cards, citing the reasons: “the farmers should pay” and “the government or council 

should pay – we pay enough taxes”. These would in fact classified as protest votes rather 

than genuine refusals due to lack of receiving any benefits and we would tend to exclude 

them from the analysis. 

A3.2.4 Section D: Socio-Economic Characteristics 

Most of the respondents were long term, or life-time, residents of the North West. The 

average length of residency was 36 years, and more than half of respondents had been 

resident for more than 30 years. Eighty percent had no intention of leaving. 

The range of educational level was fairly restricted: 82% of respondents had a maximum 

level of educational attainment of A-levels, O-levels or primary school; 46% of respondents 

were in full-time or part-time or self-employed, and 34% were retired. Household income 

ranges were fairly evenly spread. However, 28% of respondents did not know or refused to 

disclose their income. Given that income is an important determinant of WTP and hence an 

important variable in explaining the choices people make, collection of data on income 

levels of the respondents is crucial. Therefore, we will instruct the interviewers to be more 

insistent in getting more respondents to answer this question. 

Forty percent of respondents belonged to a heritage, environmental, recreational or 

farming association; the most common of these was the National Trust, with 24% of the 

survey. 

Urban-rural differences 

The attitudes of urban and rural respondents towards the environment and countryside did 

not vary much, apart from in the ranking of environmental issues: on average, urban 

respondents ranked water quality as the most important issue, while rural respondents 

ranked it last and climate change first. Cultural heritage appeared to be a more important 

attribute to urban respondents, and cost to rural respondents. This is unsurprising, as the 

rural sub-sample contained more people on low household incomes (less than £10,400 per 

annum). Urban respondents were more likely to cite altruistic, bequest or “national 

heritage” reasons why they were willing to pay, while rural respondents were less likely to 

be pinned down. 

A3.3 Lessons for the Main Survey 

The pilot survey is carried out mainly to fine-tune the questionnaire and to train the 

interviewers. The questionnaire format, wording, editing and length are fine. People found 

it interesting and not too long. The format (number of attribute and levels) of the choice 

set can be considered adequate. The number of choice sets that can be shown to 

respondents may be high (up to 8 or 9). That will allow the collection of more data and will 

confer more statistical power to the models. 

However, there are some aspects pointed out in this report that may be used to improve 

the questionnaire. Of particular relevance are the high number of people that did not 

declare their income, the description of the valuation scenarios and the attributes that 

should be used in the final survey. The interviewers’ training is a crucial point when fixing 

these aspects. These are further listed below. 

Section A - Attitudes: 

• In question A1, it will be useful to add a reminder to the respondents that every 

pound spent on environmental policy cannot be spent on anything else thereby 

eftec A- 17 

January 2006



introducing the idea of a trade-off. Otherwise, it is expected that a large 

proportion of respondents will say environmental protection is important. 

Section B – Valuation Scenario 

• In order to ensure that people are thinking about only the SDAs in their own GOR as 

marked on the map of England they are shown, we may want to revise the wording 

of the scenario to emphasise this point. This, however, remains to be seen through 

further work over the period between now and the start of the main survey. This 

has been a point we have had in mind since the beginning of the design process 

rather than as a result of any particular finding of the pilot survey. 

• If an inconsistency is detected between the respondent’s choices and the reasons 

given in the follow up questions (e.g. respondent says ‘visitors should pay’ but still 

chooses policy options implying a cost to his/her household as well as to visitors), 

the interviewer may be required to check that the earlier choices were valid. We 

will think about how this instruction could be given to interviewers in an 

appropriate way. 

• In order to better test for the sensitivity of the respondents to different levels of 

the attributes, we will also include a test in the choice set design by using different 

estimates for the maximum levels of 1-3 attributes in at least one of the surveys. 

Comparison of the results will tell us more about the construction of respondent 

preferences. 

• Having tested different number of choice sets per person and found that most 

respondents are comfortable with eight choice sets, we are happy to use this 

number of choice sets to implement different tests of consistency and validity. 

Section C – Follow up Questions 

• Half the respondents were classified as “none of the above” in response to question 

C2 on why they were willing to pay. Our range of pre-coded answers will need to be 

expanded, and clearer guidance will be given to the interviewers on how to 

interpret responses. 

• In order to clarify and/or reduce the motivations like ‘it’s the right thing to do’ or 

‘warm glow’, we will also ensure that the wording of the valuation scenario and the 

necessary tests will be put in place. 

Section D – Socio-economic Characteristics 

The pilot survey was not meant to be fully representative of the North West GOR. However, 

the market research companies are required to ensure full representativeness in the main 

survey according to socio-economic group, age and gender distribution at the level of the 

GOR. 

eftec A- 18 

January 2006


Annex 4 – Final Questionnaire template 

Annex 4 - Final Questionnaire 

Market_Research_Company_Name 

TIME TAKEN TO FIND RESPONDENT: 

RECORD TIME OF INTERVIEW: 

Region – Version Rx 

Start (24hr Clock): : End (24hr Clock): : 

INTERVIEWER DECLARATION: 

This interview was conducted face to face with a respondent who is unknown to me. 

Signature: ………………………………… (Print Your Name: …………………………………….) 

Date: …… / …... /2005 

Introduction 

Hello! I am [GIVE NAME AND SHOW IDENTIFICATION] from Market_Research_Company_Name. 

We are conducting a survey about an issue of general regional interest for the UK Government 

Department of Environment, Food and Rural Affairs. I’d be grateful if you would answer a few 

questions concerning your opinions about this issue. The interview should last about half an hour. 

For your piece of mind, we adhere to the Code of Conduct of the Market Research Society, and 

anything you say is confidential. Under no circumstances will your answers be linked to your name. 

First, may I check some details about you. 

Quota Controls 

A. How old are you? 

Exact Age: 

AGE MALE FEMALE 

1 18-34 years 01 11 

2 35-54 years 02 12 

3 55-70 years 03 13 

B. Where do you live? 

Town/village: 

C. What is the Occupation of the chief 

wage earner in your Household? 

Job: 

Industry: 

D. Please record in CAPITALS: 

Name: 

VERSION Questionnaire No. 

Full Postal Address: 

Full Post Code: 

Tel. Number: 

Sampling 

Point No. 

AB 1 

C1 2 

C2 3 

DE 4 

eftec A- 19 

January 2006



A. ATTITUDES 

A1. How important would you say that environmental policy is, in relation to other things that 

government is concerned with, such as law and order, or education? 

CARD A1: CIRCLE ONE ONLY 

Very important 1 

Quite important 2 

Not all that important 3 

I really don’t care about the environment at all 4 

A2. What do you think should be the main concern for environmental policy in this country 

over the next 10 years? Please rank the options on this card from 1 (most important) to 4(least 

important) 

CARD A2: WRITE DOWN RANKS HERE – 1 (MOST IMPORTANT) TO 4 (LEAST IMPORTANT) 

Controlling air pollution 

Tackling climate change 

Protecting the countryside 

Protecting the quality of rivers, lakes and 

the sea 

A3. Do you ever visit the countryside for recreation, for work or for both? 

IF THEY JUST SAY “YES” FIND OUT IF RECREATION, WORK OR BOTH. 

SHORT VISITS (E.G TO WALK THE DOG), DAYS OUT, WEEKENDS AWAY OR LONGER 

HOLIDAYS AND VISITING FRIENDS AND FAMILY ALL COUNT AS RECREATION. 

YES - RECREATION 1 

YES - WORK 2 

YES - BOTH 3 

NO, NEVER visit the countryside 4 

eftec A- 20 

January 2006



B. CHOICE EXPERIMENT VALUATION SCENARIO 

This survey is mainly concerned with your opinions about hill farming areas in the Region [of 

England]. This map shows your region. 

SHOW MAP ERROR! REFERENCE SOURCE NOT FOUND.1 AND POINT OUT THE DETAILED 

MAP OF THE REGION 

[FOR INFORMATION – PURPLE AREAS ON MAP ARE URBAN AREAS – IGNORE THESE] 

The green line shows the regional border and the pink shaded areas shows the hill farming areas of 

interest. You can see that the [main] areas of concern in the Region [of England] is/are the specific 

area name(s)[, but there are other areas, such as …]. 

We would like you to think in particular about the different landscape features in these areas, such 

as farm woodlands, hedgerows and moorland. These features are affected by the way in which 

farming is carried out. If there are fewer working hill farms in an area, or if they change their 

practices, then some of these landscape features may change. You may get more or less of them, 

or their quality might alter. 

In these areas, farmers receive special payments from the Government to make up for the fact that 

farming is more difficult, because of the steep ground, and because these areas are far away from 

the majority of customers. 

The Government may change how it pays farmers in these hilly areas. If this happened, the main 

aim would be to try and reduce the bad impacts of future changes in farming on the landscape, and 

to increase any good impacts. However, this policy change would come at a cost to people like you, 

either through higher national or local taxes, or even perhaps through charges on people visiting 

the areas. The government would like to know what people think of as good and bad impacts and 

whether the cost of the policy change is right. This is why we are conducting this survey. 

So what are these “Landscape Features” that I have been talking about? Well, the landscape 

features we are looking at in this survey are the following. 

SHOW CARD B0a. 

Please read this card carefully - it will help you to make decisions in the next section of the 

questionnaire. 

eftec A- 21 

January 2006

CARD B0a 




These are areas of heather moorland in drier areas and on steeper slopes, but with less heather and 

more bog in wetter areas. Bogs contain peat, with bog mosses and sometimes bog pools. All of these 

areas are typically used for sheep grazing, and may contain many different kinds of birds, insects and 

plants. 


These areas can often look untidy where the soil is poor, but can be heavily grazed by sheep where 

soil is better. They may look a bit brown or pale at some times of the year, but at other times can look 

very green. Birds also like these areas. 


These woodlands usually consist of a mix of native tree species such as ash, oak and hazel. Unlike 

conifer plantations, these woodlands have an irregular shape when seen from a distance (so they 

don’t look like square blocks on the hillside!). Some of these woods are very old, others are more 

recently planted. 


These are the traditional stone walls (or dykes) and hedgerows seen across the upland landscape. 

Modern wire fences are NOT included in this feature, although they have often replaced, or run 

alongside, traditional stone walls and hedges. 

Traditional farm buildings and farming practices 

Here we mean the traditional farm buildings that can be seen in the uplands, and their associated 

barns and sheds. But we also include traditional farm practices such as shepherding. 

We would now like you to think about a number of possible options for future government policy in 

hill-farming areas. These options are shown in the “Choice Cards” which I am going to show you. In 

each one, you will see there are three choices for you to make: the Current Policy, Policy Option A 

or Policy Option B. You’ll notice that if you choose the current policy then this comes at no extra 

cost to your household, but some landscape features will get worse in time according to 

predictions. Policy Option A and Policy Option B come at an additional cost to your household every 

year, but also give you some improvements in the landscape features of these hill-farming areas. 

SHOW EXAMPLE CARD B0b 

Here’s an example which we have worked through for you. As you can see in the “current policy” 

(POINT), the condition of many of the landscape features in the Region is getting worse: the 

amounts of heather moorland and rough grassland are both falling, there is some restoration of 

field boundaries and there is a rapid decline in the condition of farm buildings and traditional 

farming practices. However, the area of woodland is increasing, and sticking with this option would 

not create any extra cost for your household. 

In Option A (POINT) the area of heather moorland increases, the area of rough grassland falls by 

less, and more field boundaries are restored. There is also more woodland compared to the current 

policy. However, your household would face an additional cost of £20 a year to pay for these 

landscape improvements. 

In Option B (POINT), the extra cost is lower - £10 per year - and you still get some landscape 

improvements over the current policy, but not as much as with Option A. 

EXAMPLE CARD B0b 

eftec A- 22 

January 2006




policy 




Grassland 

Change in area of Broadleaf 

and mixed woodlands 







(-2%) 


(-10%) 


(+3%) 

For every 1 km, 

100m is 

restored 

Policy 

Option A 


(+2%) 


(-2%) 


(+20%) 


200 m is 

restored 

Rapid decline no change 

Policy 

Option B 


(-2%) 


(-2%) 


(+10%) 

for every 1 km, 

50 m is 

restored 

Much better 

conservation 

£0 £20 £10 

So, which would you prefer? The current policy with no extra costs but some losses in most 

landscape features; Option A with an extra cost of £20 per year to you, but some improvements; or 

Option B, with fewer, but less expensive improvements? 

There are no right or wrong answers. On each choice card you just pick the option that you would 

prefer to see happen in the Region. You’ll notice that sometimes in the cards you are shown things 

get better, and sometimes things get worse, but that is the most likely range of possibilities for 

what might happen in the future. Now you might think that you do not have enough “expert 

knowledge” to make these kinds of choices, but we are interested in what ordinary people think 

about what the Government should be doing. 

Please do consider only the hill-farming areas, and only those areas in your region, the Region, as 

the survey is being conducted in other regions separately. Each time, just think about what you 

would most like to happen. But remember that if you opt to pay extra by choosing Option A or B on 

any card, then this means your household would have less money available to spend on other 

things, or to save. Take your time and make your best choice for each of these six cards I am going 

to show you. Please think about which you would prefer. 

On each card, you will see that these landscape features can take a number of possible values. On 

the cards we show these as % changes over how much there is of each feature at the moment – for 

example, “a 2% loss”. You might like to know what these % changes mean for landscape features in 

the Region. This card shows you. You might find it helpful to think of “1 hectare” as a couple of 

football pitches! 

SHOW CARD B0c AND READ THROUGH IT WITH THEM. LET THEM KEEP HOLD OF IT WHILST 

THEY ARE GOING THROUGH THE CHOICE CARDS. 

eftec A- 23 

January 2006

CARD B0c 



Heather 

moorland and 

bog 

Rough 

grassland 

Broadleaf and 

mixed 

woodlands 

Current policy: Effects of possible future policy changes 

A 2% loss is equal 

to losing 380 

hectares out of the 

current total of 

19,000 hectares 


to losing 3, 200 




A gain of 3% is 

equal to having an 

extra 190 hectares 

in addition to the 

current area of 



to losing 2,300 


current total 

A 5% gain is equal 

to having an extra 




630 hectares 



960 hectares 







Note: Page: 24 

These are the figures for East Midlands – these figures were appropriate for each GOR and changed in each 

version. 

B1. 

GO THROUGH THE SIX CHOICE CARDS AND FOR EACH ONE ASK THE RESPONDENT FOR 

THEIR PREFERRED OPTION. 

THE ORDER OF THE CHOICE CARDS IN YOUR SET SHOULD BE ROTATED EACH TIME. 

WRITE THE ANSWERS IN THE TABLE BELOW. TICK THE CHOICE CARD USED AS A STARTING 

POINT AND CIRCLE ONLY ONE RESPONSE PER CHOICE CARD IN THE OTHER COLUMNS. 

Choice card Tick starting Current Policy Policy 

number point 

policy Option A Option B 

B1 1 2 3 

B2 1 2 3 

B3 1 2 3 

B4 1 2 3 

B5 1 2 3 

B6 1 2 3 

Note: three different sets of choice cards were used: B1-6, B7-12 and B13-18. 

eftec A- 24 

January 2006



B2. In making your choices, which of the these features concerned you the most? 

CARD B2: CIRCLE ONE ONLY 

Heather moorland and bog 1 

Rough grassland 2 

Broadleaf and mixed woodlands 3 

Field boundaries (hedges and stone walls) 4 

Farm buildings and traditional farm practices 5 

Cost 6 

QUESTION ROUTING: 

*ALL WHO CHOOSE POLICY OPTION A OR POLICY OPTION B AT LEAST ONCE 

� QUESTION B3 

*ALL WHO CHOOSE ‘CURRENT POLICY’ EVERY TIME � QUESTION B4 

B3. What were the main reasons you were willing to contribute to the funding of future policy 

options? 

PROBE FULLY AND WRITE IN, THEN CODE ON NEXT PAGE 

……………………………………………………………………………………………………………………… 

……………………………………………………………………………………………………………………… 

eftec A- 25 

January 2006



Category Phrases / reasons Tick Coding 

Personal / 

Family 

enjoyment 

Others’ 

enjoyment now 

– altruism 

I/we like / enjoy / love going to the countryside 

I/we care about the countryside 

The countryside is lovely / beautiful 

Other personal/family enjoyment reason 

It should be conserved / protected / improved 

to benefit us all / for everyone 

Others’ 

Other altruistic reason 

It should be conserved / protected for 

future generations 

enjoyment – I want future generations / to enjoy it 

3 

future 

my (grand)children 

Personal 

financial gain 

Other future enjoyment reason 

Many/most of my customers are farmers 

I’m a farmer myself 

My business depends on tourists 

Other personal financial gain reason 

4 

� Mix � Farming should be maintained to keep prices down 

Preserve (small) farmers’ livelihoods 

Safeguard livelihoods / employment 

4 and 5 

Others’ financial 

gain 

It would be good for the farming industry 

Tourism industry is important for those/this area(s) 

Farming industry deserves support 

Other reason supporting others’/farmers’ livelihoods 

5 

� Mix � 

Keep (small / hill) farms going / here / in the 

hill-farming areas 

Keep countryside as it is / keep what we have 

5 and 6 

Preserve 

heritage / 

communities / 

way of life 

Preserve/maintain our (national) heritage 

Protect / stop decline of the country / hill-farming 

way of life 

Protect rural / local communities 

Stop / prevent decline of countryside 

Other communities / heritage reason 

6 

� Mix � Preserve/protect the countryside / landscape 

Improve the environment / I care about environment 

6 and 7 

Environmental 

concern 

Countryside needs looking after 

The environment is under threat 

Protect wildlife / animals / plants / birds / trees 

Other environmental reason 

7 

� Mix � We have a duty to protect environment 

It’s the right thing to do / we have a duty 

7 and 8 

Moral obligation We have to pay for conservation / to get benefits 

Other moral obligation reason 

We all have to give sometimes / do our bit (for the 

8 

Warm glow 

environment / for others) 

The money isn’t important 

Other warm glow reason 

9 

Scenario not 

credible 

I am not paying so it does not matter 

Other “scenario not credible” response 

10 


None of the above 12 

GO TO QUESTION B5 

eftec A- 26 

January 2006 

1 

2


Annex 4 – Final Questionnaire 

B4. What were the main reasons you were not willing to contribute to the funding of 

future policy options? 

PROBE FULLY AND WRITE IN, THEN CODE BELOW 

…………………………………………………………………………………………………………… 

……… 

I can’t afford to pay 

The improvements aren’t important to me/ current policy is OK 

I seldom see / use the areas described 

I never see / use the areas described 

I object to paying in this way for environmental improvements in these areas 

The government or council should pay for this 

The farmers should pay for this 

Visitors to these areas should pay 

I don’t believe the policies would actually happen 

Don’t know 

None of the above 

GO TO QUESTION B5 

B5. How easy or difficult did you find it to make your decisions about which policy 

option to choose? 

CARD B5: CIRCLE ONE ONLY 

Very easy 1 

Fairly easy 2 





eftec A- 27 

January 2006 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11



C. Contingent Valuation Section 

The policy choices I showed you up to now were about the landscape features of the hillfarming 

areas in the Region region. For the final choice, please consider the landscape 

features of the hill farming areas in the rest of England. These are the shaded areas on the 

map of England. 

SHOW MAP R1 AGAIN AND INDICATE THE PINK AREAS ON THE MAP OF ENGLAND 

ONE OF SHOWCARDS C1a TO C1f SHOULD HAVE BEEN SELECTED BEFORE THE 

START OF THE INTERVIEW (ROTATE THE SHOWCARDS SO THAT ALL ARE USED 

EQUALLY OFTEN). 

SHOW THE SHOWCARD C1# SELECTED. 

This card shows the likely changes in other regions under the most likely government policy 

for hill farming. It shows the current policy contrasted with an alternative future policy 

option. 

C1. In addition to the amount you said you would (or would not) be 

willing to pay for alternative policies in the Region, would you be willing to 

pay this amount here (POINT TO THE SHADED CELL ON SHOWCARD 

WHERE AMOUNT IS SHOWN) in increased taxes per year, towards the 

cost of the future policy shown on the card in other regions of England? 

Or would you prefer not to pay and maintain the current policy? 

TICK THE SHOWCARD/AMOUNT USED AND CIRCLE THE ANSWER GIVEN 

Amount 

C1a £2 

C1b £5 

C1c £10 

C1d £17 

C1e £40 

C1f £70 

Tick if used 

Willing to pay 

for future 

policy 

1 

GO TO 

SECTION D 

Not willing to 

pay – prefer 

current policy 

2 

GO TO 

QUESTION C2 

Don’t know 

3 

GO TO 

SECTION D 

eftec A- 28 

January 2006



C2. What were the main reasons you were not willing to contribute to the funding of 

this future policy option? 

PROBE FULLY AND WRITE IN, THEN CODE BELOW 

…………………………………………………………………………………………………………… 

……… 

I’ve already been asked to pay for my own area 

I am not (as) interested in other regions of the country 

I can’t afford to pay (more) 

The improvements aren’t important to me/ current policy is OK 

I seldom see / use the areas described 

I never see / use the areas described 

I object to paying in this way for environmental improvements in these areas 

The government or council should pay for this 

The farmers should pay for this 

Visitors to these areas should pay 

I don’t believe the policies would actually happen 

Don’t know 

None of the above 

D. SOCIO-ECONOMIC CHARACTERISTICS 

D1. Going back again just to the Region - do you ever visit any of the areas shaded 

pink on the map in the region? For recreation, for work or for both? 

SHOW THE DETAILED REGIONAL MAP AGAIN ON MAP ERROR! REFERENCE SOURCE 

NOT FOUND.1 – – INDICATE PINK AREA(S) 

IF THEY JUST SAY “YES” FIND OUT IF RECREATION, WORK OR BOTH. 

SHORT VISITS (E.G TO WALK THE DOG), DAYS OUT, WEEKENDS AWAY OR LONGER 

HOLIDAYS AND VISITING FRIENDS AND FAMILY ALL COUNT AS RECREATION. 

YES - RECREATION 1 

YES - WORK 2 

YES - BOTH 3 

NO, NEVER visit these areas 4 

eftec A- 29 

January 2006 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13



D2. How often to you visit this/these areas in your region? 

CARD D2: CIRCLE ONE ANSWER ONLY 

Every day 1 

More than once a week 2 

Once a week 3 

More than once a month 4 

About once a month 5 

At least once every six months 6 

At least once a year 7 

Less than once a year 8 

Have visited at least once in past 9 

Never 10 


D3. Including yourself, how many people in your household are: 

READ OUT AND INSERT ANSWERS 

Over 60 years old ................... 

Between 17 years and 60 

years old 

................... 

Between 5 and 16 years old ................... 

Below 5 years old ................... 

D4. Approximately, how long have you been living in the Region? 

RECORD ANSWER ……………………………years 

D5. Thinking ahead, which of these phrases best describes how long you think you will 

remain living in the Region? 

CARD D5: CIRCLE ONE ONLY 

Less than 6 months 1 

At least 1 year 2 

At least 5 years 3 

At least 10 years 4 

I have no intentions of moving out of 

this area 

5 

Don’t know / Not sure 6 

eftec A- 30 

January 2006



D6. Are you or is anyone in your household a member of any of these organisations? 

CARD D6: CIRCLE ALL THAT APPLY 

Royal Society for the Protection of Birds 1 

The Ramblers’ Association 2 

National Trust 3 

Friends of the Earth / Greenpeace 4 

A local wildlife trust or environmental organisation 5 

A local recreational club (e.g. angling or walking club) 6 

National Farmers’ Union 7 

Other environmental organisations 

Specify…………………………………………………….. 

8 

Other farming organisations 9 

Specify……………………………………………………… 

D7. At what level did you complete your education? 

(IF STILL STUDYING): 

Which level best describes the highest level of education you have obtained until now? 


Primary 1 

O levels/ GCSE/ CSE/ School Cert./ Intermediate GNVQ / or 

equivalent 

2 

A levels Advanced/ Vocational training 

(HNC/ HND) (BTEC) or equivalent / Advanced GNVQ 

3 

Professional qualification of degree level 4 

College/ University/ First degree level 5 

Higher degree (MA, MSc, PhD, etc.) 6 

eftec A- 31 

January 2006



D8. What is your current work status? 


Self-employed 1 

Employed full-time 

(30 hours plus per week) 

2 

Employed part-time 

(under 30 hours per week) 

3 

Student 4 

Unemployed 5 

Looking after the home full-time / housewife 6 

Retired 7 

Unable to work due to sickness or disability 8 

D9. Could you estimate approximately your total household annual, monthly or weekly 

income before tax? Choose from one of the categories on this card. Remember to 

include all your sources of income, for instance, pensions, benefits, income from 

savings etc. Your answer is completely confidential. It will be used only for statistical 

analysis. 


Weekly gross Monthly gross Annual gross 

K Up to £99 Up to £429 Up to £5,199 1 

X £100-£199 £430-£869 £5,200-£10,399 2 

A £200-£299 £870-£1,299 £10,400-£15,599 3 

L £300-£399 £1,300-£1,699 £15,600-£20,799 4 

Z £400-£499 £1,700-£2,199 £20,800-£25,999 5 

F £500-£599 £2,200-£2,599 £26,000-£31,199 6 

P £600-£799 £2,600-£3,499 £31,200-£41,599 7 

C £800-£999 £3,500-£4,299 £41,600-£51,999 8 

M £1,000-£1,499 £4,300- £6,299 £52,000-£74,999 9 

O £1,500-£1,999 £6,300-£8,299 £75,000-£99,999 10 

S £2,000-£2,399 £8,300- 

£10,399 

H £2,400 or £10,400 or 

more 

more 

£100,000- 

£124,999 

11 

£125,000 or more 12 


Refused 14 

IF RESPONDENT ANSWERS ‘DON’T KNOW’, PLEASE ASK HIM/HER TO GIVE AN 

ESTIMATE IF RESPONDENT REFUSES TO ANSWER, PLEASE TELL HIM/HER THAT THE 

eftec A- 32 

January 2006



ANSWERS ARE COMPLETELY CONFIDENTIAL AND THAT INCOME IS A VERY 

IMPORTANT EXPLANATORY FACTOR FOR THE RESEARCHERS. 

D10. Finally, what did you think of this questionnaire? 

CARD D10: CIRCLE AS MANY AS APPLY 

Interesting 1 

Too long 2 

Difficult to understand 3 

Educational 4 

Unrealistic / not credible 5 

Other (please specify) 6 

That’s all the questions I have. Thank you very much for your time. 

QUESTION TO YOU, THE INTERVIEWER. 

In your opinion, how easy or difficult did the respondent find it to make decisions 

about which options to choose? 

Very easy 1 

Fairly easy 2 





eftec A- 33 

January 2006



Areas 

Final Report – Annexes 5-8 





Annexes 


ANNEX 5 – TECHNICAL ANNEX........................................................................ 34 

A5.1 CHOICE EXPERIMENT: INTRODUCTION..............................................................34 

A5.2 REGIONAL ANALYSIS ...............................................................................35 

A5.3 CONTINGENT VALUATION..........................................................................64 

A5.4 COMPARISON OF ELF WTP WITH STUDY WTP.....................................................68 

A5.5 THEORETICAL BASIS OF LINEAR TIME TREATMENT OF BENEFITS.....................................69 

A5.6 COMPENSATING SURPLUS AGGREGATION ESTIMATES (ADJUSTED VARIANT) .........................70 

ANNEX 6 – VALUATION WORKSHOP PROTOCOL................................................... 72 

ANNEX 7 - VALUATION WORKSHOPS – RESULTS AND DISCUSSION ............................ 85 

A7.1 INTRODUCTION ....................................................................................85 

A7.2 WORKSHOP METHODOLOGY........................................................................85 

A7.3 RESULTS ..........................................................................................86 

A7.4 CONCLUSION ......................................................................................92 

ANNEX 8 - PEER REVIEWER REPORT ................................................................ 94 

eftec A-i January 2006


Annex 5 – Technical Annex 


A5.1 Choice Experiment: Introduction 

A5.1.1 Introduction 

The data were initially analyzed using only the levels of attributes as explanatory variables 

in a Conditional Logit model. In this model, the probability that a particular scenario is 

chosen by a respondent is explained as a function of the levels of attributes. Subsequently, 

socio-economic and attitudinal variables were also added to the model to test the effect of 

relevant individual characteristics on choice. 

An important implication of this model specification is that selections from the choice set 

must obey the ‘independence from irrelevant alternatives’ (IIA) property (or Luce’s Choice 

Axiom; see Luce, 1959). This property states that the relative probabilities of two options 

being selected are unaffected by the introduction or removal of other alternatives. This 

property follows from the independence of the error terms across the different options 

contained in the choice set. If a violation of the IIA hypothesis is observed, then more 

complex statistical models are necessary that relax some of the assumptions used. These 

include the multinomial probit (Hausman and Wise, 1978), the nested logit (Hensher and 

Greene, 1999), the random parameters logit model (Train, 2003) and the heterogeneous 

extreme value logit (Allenby and Ginter, 1995). There are numerous formal statistical tests 

than can be used to test for violations of the IIA assumption. In this study we employed the 

test developed by Hausman and McFadden (1984) being that it is the most widely used. 

If the data of a specific region did not pass the IIA test, a random parameter logit approach 

was chosen for the analysis. Using this model specification it is also possible to circumvent 

the limitation of the conditional logit model that assumes that preferences are homogenous 

amongst respondents. The random parameters logit model allows for such variation in 

preferences across individuals. Application requires assumptions being made about the 

distribution of preferences. Here it is assumed that preferences relating to the attributes 

are heterogeneous and follow a normal distribution, while preferences towards price are 

assumed to be homogeneous. Therefore, separate parameters are estimated for each 

individual for all attributes along with a single parameter for all respondents for price. It 

results estimate for each attribute a mean value (interpreted as the average preference of 

respondents for the attribute), and a standard deviation value (interpreted as the 

magnitude of differences in respondents’ preferences for the attribute). 

A5.1.2 Compensating surplus calculation 

What is sought it is a measure of the value to an individual of an increase in the quantity of 

a public good (i.e. landscape attributes). From underlying economic theory, the 

appropriate measure for this change in an individual’s welfare is that of compensating 

surplus. This measure asks what compensating payment (in practical terms what change in 

income/expenditure) will make an individual indifferent between their original level of 

utility and the opportunity to consume the new quantity of the public good. Given the 

implied property rights (that the individual is entitled to their current level of utility/status 

quo), the appropriate metric for measuring the compensating surplus is that of willingness 

to pay, which is the amount of money an agent would be willing to give up to obtain the 

change in the provision of the public good and still be as well as off as their situation prior 

to the change. 

eftec A- 34 

January 2006



Compensating surplus welfare measures relative to different environmental scenarios can 

be obtained by applying the following formula: 

1 

CS = Y − 

β 

M 

( V V ) 

X 

where VX is the utility associated with the “initial state”, VY the utility associated with an 

improved state, and βM the monetary attribute coefficient. 

Vi is a conditional indirect utility function assumed to be linear in parameters: 

Vi = C + ∑kβk Xik +∑m γm Smn (2) 

where C is the regression constant, Xik is the value of attribute k under alternative i; βk are 

the regression coefficients associated with attribute k, Smn is the vector of socio-economic 

characteristics of individual n, and γm is the vector of the coefficients associated with the 

m individual socio-economic characteristics. 

The following formula can be used to estimate the difference in utility between any two 

alternatives and inserted into equation (1) to obtain the compensating surplus estimate: 

VB – VA = C + ∑kβk XBk +∑m γm Smn – (C + ∑kβk XAk +∑m γm Smn) 

= ∑kβk XBk - ∑kβk XAk = ∑kβk (XBk - XAk) (3) 

A5.2 Regional Analysis 

A5.2.1 West Midlands 

In the West Midlands region 318 respondents were interviewed. Of the total number of 

responses 45 expressed a protest answer regarding the proposed project; these protest bids 

were removed from the sample. All respondents that displayed a genuine zero WTP (WTP) 

by always choosing the current policy option (5%), and those that chose either alternative A 

or B at least once were considered in the analysis, giving a total number of 1638 [(318-45) x 

6)] observations for model estimation. 

Analysis of West Midlands data showed that the simple conditional logit model specification 

cannot be used since both models (attributes only and attributes plus socioeconomic 

variables) suffer from IIA violations. Table A5.1 shows the attributes only RPL model. 

eftec A- 35 

January 2006 

(1)



Table A5.1: Random parameters logit model; Maximum Likelihood Estimates 

Dependent variable: CHOICE 

Number of obs.: 1638, skipped 1 bad obs. 

Iterations completed: 43 

Log likelihood function: 1553.938 

Number of parameters: 14 

Restricted log likelihood: -1798.428 

Chi squared: 488.9815 

Degrees of freedom: 14 

Prob[ChiSqd > value] = .0000000 

Variable Coefficient (b) Standard Error t values Significance 

Mean values 

K 1.196 0.137 8.733 0.000 

HMB 0.030 0.007 4.280 0.000 

RG 0.009 0.006 1.559 0.119 

BMW 0.016 0.008 2.134 0.033 

FB 0.001 0.001 0.824 0.410 

CH1 -0.015 0.071 -0.215 0.830 

CH2 0.248 0.076 3.255 0.001 

TAX -0.038 0.003 -13.038 0.000 


NsHMB 0.054 0.011 4.834 0.000 

NsRG 0.050 0.007 6.931 0.000 

NsBMW 0.074 0.010 7.701 0.000 

NsFB 0.004 0.002 2.760 0.006 

NsCH1 0.522 0.110 4.741 0.000 

NsCH2 0.601 0.098 6.132 0.000 

Clarification: 

K: Constant term, = 0 in the current policy and 1 in alternatives A and B. 

HMB: Change in area of Heather Moorland and Bog 

RG: Change in area of Rough Grassland 

BMW: Change in area of Broadleaf and mixed woodlands 

FB: Change in length of field boundaries 

Cultural heritage as a qualitative indicator has to be coded so that: 

CH1: shows change in cultural heritage from ‘rapid decline’ to ‘no change’ 

CH2: shows change in cultural heritage from ‘rapid decline’ to ‘much better conservation’ 


Overall the model is highly significant, although the fitting to data is moderate (ρ 2 = 0.14) 1 . 

Turning to the attribute coefficients, the change in the area of rough grassland and the 

change in the length of field boundaries did not affect respondents’ choices. Neither did 

the prospect of improvement in cultural heritage from “rapid decline” to “no change”. The 

significance and positive sign of the constant term shows that respondents are, all else 

being equal, in favour of the payments to farmers for the “environmental services” they 

provide. A negative and significant constant term would have suggested a current situation 

bias – i.e. a tendency to keep the current situation and prefer not to make payments. 

Increasing the areas of heather moorland and bog and broadleaf and mixed woodland are 

changes positively evaluated by respondents. The same is true for the cultural heritage 

attribute when the change being evaluated is an improvement from a “rapid decline” 

situation to “much better conservation”. Cost is highly significant and has a negative sign, 

1 Simulations by Dominick and McFadden (1975) compare values of ρ 2 between 0.2-0.4 to values 

between 0.7-0.9 of the R 2 in the case of the ordinary linear regression. 

eftec A- 36 

January 2006



showing that the higher the cost associated with a policy option, the less likely a given 

respondent is to choose that option. 

Interestingly, all standard deviation terms are significant, indicating preference 

heterogeneity does indeed exist. Under these circumstances, it is not surprising that the 

conditional logit model did not fully describe respondents’ preferences. There is no clear 

direction of preferences amongst respondents. For instance, from the mean and standard 

deviation values of the heather moorland and bog attribute it can be seen that 27% of 

respondents have negative preferences for increases in the area of this attribute 2 . It is 

noteworthy that the standard deviation of the rough grassland attribute is five times 

greater than its mean, showing an extreme heterogeneity in people’s preferences. In this 

case 43% of respondents prefer a reduction of the area of rough grassland and 57% an 

increase. 

In summary, the model indicates that respondents value programs which result in greater 

heather moorland and bog area, an increase in woodlands area, much better conservation 

of cultural heritage, and the less these programs cost the better. But within the sample a 

high degree of heterogeneity exists. 

By including individual socioeconomic and attitudinal variables it is possible to explain a 

part of respondent heterogeneity. For instance, people that give a high value to landscape 

conservation or that use it regularly for personal purposes (recreation, work, etc.) should 

be more likely to choose policy options in favour of the payments to farmers to increase the 

quality of landscape. 

Table A5.2 presents a random parameter logit model in which the socioeconomic and 

attitudinal characteristics of respondents have been added. 

2 This is the probability at zero of a normal distributed random variable with mean = 0.03 and 

standard deviation = 0.05. Zero represents the cut-off between positive and negative preferences for 

an attribute. 

eftec A- 37 

January 2006



Table A5.2: Random parameters logit model (socio-economics) 


Number of obs.: 1638, skipped 481 bad obs 


Log likelihood function: -1059.020 







Mean values 

K 3.880 0.861 4.507 0.000 

HMB 0.035 0.008 4.206 0.000 

RG 0.008 0.007 1.167 0.243 

BMW 0.023 0.010 2.393 0.017 

FB 0.001 0.001 1.265 0.206 

CH1 0.033 0.074 0.452 0.651 

CH2 0.214 0.098 2.176 0.030 

TAX -0.039 0.004 -10.913 0.000 

AGE 0.342 0.188 1.821 0.069 

GENDER -0.497 0.232 -2.141 0.032 

ENVIMP -0.585 0.204 -2.871 0.004 

VISFREQ -0.222 0.052 -4.263 0.000 

LIVING -0.012 0.008 -1.550 0.121 

REMAIN -0.204 0.113 -1.801 0.072 

MEMBER 0.564 0.238 2.373 0.018 

EDU 0.126 0.082 1.527 0.127 

EMPLOY -0.230 0.250 -0.920 0.357 

RURAL -1.559 0.435 -3.583 0.000 

INCOME 0.109 0.091 1.199 0.231 


NsHMB 0.054 0.014 3.951 0.000 

NsRG 0.050 0.009 5.664 0.000 

NsBMW 0.078 0.012 6.574 0.000 

NsFB 0.003 0.002 1.570 0.117 

NsCH1 0.199 0.169 1.179 0.238 

NsCH2 0.760 0.099 7.671 0.000 

Clarification of socioeconomic variables: (for attributes see Table A5.1). 

AGE: Respondents’ age (1 = 18-34; 2 = 35-54; 3= 55-70) 

GENDER: Respondents’ gender (0=female; 1= male) 

ENVIMP: Ranking of environmental policy relative to other policies (1=very important; 4= not 

important) 

VISFREQ: Respondents’ frequency of visits to severely disadvantaged areas (1= every day; 10 = 

never) 

LIVING: number of years respondents have been living in the area 

REMAIN: time period respondents thing are staying in the area (1=less than 6 month; 5=for 

ever) 

MEMBER: dummy = 1 if respondents’ belong to any “environmental” or trust organization and 0 

otherwise 

EDU: Respondents’ education (1= primary, 6= higher degree) 

EMPLOY: dummy = 0 if respondent is not an active worker; 1 otherwise 

RURAL: dummy = 0 if respondent is an urban dweller, 1 if he/she is a rural dweller. 

INCOME: respondents’ per capita incomes. 

eftec A- 38 

January 2006



Before turning to a discussion of the model coefficients, it is worth noting that this model is 

estimated by using 480 fewer observations than the attributes only model, due to missing 

data in the socioeconomic characteristics. Hence adding individual characteristics has a 

cost in terms of missing information. 

The interpretation of the coefficients is exactly the as in the attributes only model, save 

that the field boundaries attribute and the cultural heritage change from “rapid decline” to 

“no change” are no longer heterogeneous in preference. This implies that the 

socioeconomic variables now capture this heterogeneity. 

Turning to the interpretation of individuals’ characteristics 3 , it can be seen that females 

and older people are more likely to choose option A or B. Consistently, people who assigned 

a high importance to environmental policy relative to other policies are more likely to 

choose alternative A or B over the current policy. Along the same line, people that visit the 

region more often are in favour of the payments to farmers. The same is true for people 

that belong an environmental, heritage, recreation or farming organization. However, the 

longer respondents think they will remain in the region, the less likely they are to be 

willing to contribute to alternative policies. Finally, rural people tend to prefer the current 

policy. 

The random parameter model with socioeconomic information provides a useful insight into 

the effects of individual characteristics on choices, but in this dataset, it makes little 

difference to attribute significances and values. It can be seen that the attribute values are 

very similar in the two models. For statistical parsimony, the attributes only random 

parameter logit model will be used when estimating implicit prices and compensating 

surplus measures. 

Table A5.3 shows the implicit prices for the attributes and the respective 95% confidence 

intervals, calculated using the Krinsky and Robb (1986) procedure. The implicit prices for 

all significant attributes are positive 4 , implying that respondents have a positive willingness 

to pay (WTP) for increases in the quality or quantity of each attribute. In the case of 

quantitative attributes, the implicit prices represent the WTP to achieve one unit more 

(one percent more in this study) of the attribute considered. For cultural heritage, the 

implicit price reflects the WTP for a discrete change in the attribute's level, from ‘rapid 

decline’ to ‘much better conservation’. These implicit prices afford some understanding of 

the relative importance of each attribute, and can be used by policy makers to assign more 

resources to improving those attributes which have higher implicit prices. 

3 Socio-economic characteristics cannot be introduced alone into the model. This is because Hessian 

singularities would arise in the model estimation. To circumvent this problem in this study the 

individual socio-economic characteristics are introduced as interactions with the constant. The 

resulting interaction coefficients have to be interpreted as the effect that socio-economic 

characteristics of individuals have on the probability of choosing the alternative A or B over the 

current policy option. For instance, a positive coefficient for “age” means that older people are more 

likely to choose alternative A and B than the current policy. 

4 If a coefficient is not significantly different from zero neither is its implicit price, as can be 

observed in the 95% confidence intervals of Table A5.3. 

eftec A- 39 

January 2006



Table A5.3: Implicit prices and 95% confidence interval. 

Attributes Implicit price 

95% lower 

bound 

95% upper 

bound 

Heather moorland and bog 0.80 0.42 1.18 

Rough grassland 0.25 -0.05 0.53 

Broadleaf and mixed woodlands 0.43 0.07 0.81 

Field boundaries 0.02 -0.02 0.05 

Cultural heritage: 



from “rapid decline” to “much better 

conservation” 

-0.40 -4.27 3.03 

6.56 2.49 10.73 

Using the choice model parameters it is also possible to obtain compensating surplus 

estimates for the required range of policy scenarios, as detailed in Table 2.2 of the main 

report. Table A5.4 shows the appropriate mean compensating surpluses and their 

corresponding 95% confidence intervals. As expected, the compensating surplus for the 

change from the current policy to the scenarios considered in the example increases with 

better environmental conditions. 

Table A5.4: Compensating surplus to change from the baseline scenario to each of the 

alternative policy scenarios, and 95% confidence intervals 

Scenario 

Compensating surplus 

(£) 

95% lower 

bound 

95% upper 

bound 

Scenario 1 

7.44 0.39 14.42 

Scenario 2 

10.04 2.58 17.51 

Scenario 3 

-1.5 -3.36 0.42 

In the survey follow-up questions 53% of respondents declared the attribute that concerned 

them the most when making choices was the cost, followed by broadleaf and mixed 

woodland (19%). The attribute that concerned respondents the least was rough grassland, 

chosen by only 3% of respondents as most important. 

Regarding respondents’ cited reasons for being willing to pay for landscape improvements, 

environmental concerns followed by personal or family enjoyment and the enjoyment of 

future generations in the countryside were the most commonly cited reasons (see Fig. 

A5.1). Note that these percentages are approximate since responses could be coded under 

more than one category. 

eftec A- 40 

January 2006



23% 

9% 2% 

17% 

16% 

7% 

1% 

4% 

21% 

Personal family enjoyment 

Other enjoyment altruism 

Other enjoyment future 

Personal financial gain 

Other financial gain 

Preserve heritage 

communities 

Environmental concerns 

Moral obligation 

Warm glow 

Figure A5.1: Pie chart showing the break down of why respondents in the West 

Midlands were willing to pay for alternative policies over the current policy. 

A5.2.2 North West 

In the North West region 335 respondents were interviewed. Of the total number of 

responses, 88 expressed a protest answer regarding the proposed project; these protest 

bids were removed from the sample. All respondents that displayed a genuine zero WTP by 

always choosing the current policy option (17%), and those that chose either alternative A 



Analysis of North West data showed that the simple conditional logit model specification 



eftec A- 41 

January 2006














Mean values 

K -1.162 0.194 -5.994 0.000 

HMB 0.048 0.011 4.248 0.000 

RG 0.041 0.011 3.790 0.000 

BMW 0.043 0.011 3.894 0.000 

FB -0.001 0.001 -0.716 0.474 

CH1 0.082 0.113 0.725 0.469 

CH2 0.215 0.141 1.527 0.127 

TAX -0.066 0.005 -12.944 0.000 


NsHMB 0.096 0.016 5.834 0.000 

NsRG 0.099 0.010 9.604 0.000 

NsBMW 0.063 0.016 3.849 0.000 

NsFB 0.009 0.002 5.674 0.000 

NsCH1 0.554 0.152 3.636 0.000 

NsCH2 1.056 0.141 7.486 0.000 











Overall the model is highly significant and the fitting to data is high (ρ 2 = 0.29). Turning to 

the attribute coefficients, the change in the length of field boundaries and the effect that 

future policies might have on cultural heritage did not affect respondents’ choices. The 

significance and negative sign of the constant term shows that respondents are, all else 

being equal, in favour of maintaining the current policy. This reveals the presence of a 

status quo bias, a phenomenon that has been noted before in environmental applications of 

choice experiments (e.g. Adamowicz et al., 1998; Hanley, Adamowicz and Wright, 2005). 

These authors identified three possible reasons to explain status quo bias: (i) some kind of 

endowment effect; (ii) mistrust of the government to actually undertake programmes 

effectively; and (iii) people finding the choice task to be too complex, and picking the 

current policy as an easy opt-out. Considering that in the region 26% of the sample 

expressed a protest answer, and 17% always chose the current policy option, both points (i) 

and (ii) can reasonably explain the status quo bias. On the other hand, we do not have 

enough information (see the end of this section) to say how difficult respondents found the 

choice task, since 55% reported a “don’t know” answer to the question “how easy or 

difficult did you find it to make your decisions?”. 

eftec A- 42 

January 2006



Increasing heather moorland and bog, rough grassland and broadleaf and mixed woodlands 

are changes positively evaluated by respondents. Cost is highly significant and has a 

negative sign, showing that the higher the cost of a policy option, the less likely a given 

respondent is to choose it. 

Interestingly, all the standard deviation terms are highly significant, indicating preference 

heterogeneity does indeed exist. Under these circumstances it is not surprising that the 

conditional logit model did not fully describe respondents’ preferences. There is not a clear 

direction of preferences amongst respondents. For instance, from the mean and standard 

deviation values of the heather moorland and bog attribute it can be seen that 31% of 

respondents have negative preferences for increases in the area of that attribute 5 . The 

same is valid for the rough grassland and broadleaf and mixed woodland attributes where 

34% and 25% respectively of respondents had negative preferences for increasing the area 

of these attributes. 

In summary, the model suggests that respondents value programs which result in greater 

areas of heather moorland and bog, rough grassland area and woodland. Again, the less 

these programs cost the better. But within the sample a high degree of heterogeneity 

exists. 

By including individual socioeconomic and attitudinal variables it is possible to partially 

explain this heterogeneity. For instance, people that give a high value to landscape 

conservation or that encounter it regularly for recreational or work reasons should be more 

likely to choose policy options in favour of the payments to farmers to increase the quality 

of landscape. 



Before turning to a discussion of model coefficients, it is worth noting that this model is 


data in the socioeconomic characteristics. So adding the individual characteristics has a 

cost in terms of missing information. 

The interpretation of the coefficients is quite different to the attributes only model, so 

that the heterogeneity explained from the socioeconomic variables matter in this model. 

Here comments are only made on differences with respect to the previous model; the 

constant is now no longer significant, and that implies the addition of individual 

characteristics explains part of the unexplained variance that was caught by the constant in 

the attributes only model. The change from “rapid decline” to “no change” in cultural 

heritage is also now significant. 

5 This is the probability at zero of a normal distributed random variable with mean = 0.03 and 

standard deviation = 0.05. Zero represents the cut-off between positive and negative preferences for 

an attribute. 

eftec A- 43 

January 2006














Mean values 

K -0.485 0.933 -0.519 0.604 

HMB 0.062 0.014 4.584 0.000 

RG 0.059 0.012 4.948 0.000 

BMW 0.049 0.012 4.133 0.000 

FB 0.000 0.002 0.257 0.797 

CH1 0.082 0.121 0.680 0.497 

CH2 0.391 0.143 2.736 0.006 

TAX -0.080 0.007 -11.733 0.000 

AGE -0.463 0.223 -2.082 0.037 

GENDER 0.079 0.276 0.285 0.776 

ENVIMP -0.899 0.192 -4.679 0.000 

VISFREQ 0.153 0.064 2.399 0.016 

LIVING -0.022 0.009 -2.434 0.015 

REMAIN -0.150 0.125 -1.202 0.230 

MEMBER 0.936 0.407 2.296 0.022 

EDU 0.482 0.109 4.413 0.000 

EMPLOY 1.343 0.336 4.001 0.000 

RURAL -1.083 0.514 -2.108 0.035 

INCOME 0.166 0.105 1.579 0.114 


NsHMB 0.089 0.021 4.309 0.000 

NsRG 0.080 0.012 6.534 0.000 

NsBMW 0.031 0.020 1.528 0.127 

NsFB 0.010 0.002 4.466 0.000 

NsCH1 0.479 0.167 2.863 0.004 

NsCH2 0.667 0.163 4.086 0.000 




ENVIMP: Ranking of environmental policy relative ot other policies (1=very important; 4= not 

important) 

VISFREQ: Respondent’s frequency of visits to severely disadvantaged areas (1= every day; 10 = 

never) 



ever) 


otherwise 





eftec A- 44 

January 2006




the effects of individual characteristics and it is used in the estimation of implicit prices 

and compensating surplus measures. 


intervals, calculated using the Krinsky and Robb (1986) procedure. The implicit prices for 

all significant attributes are positive, implying that respondents have a positive WTP for 

increases in the quality or quantity of each attribute. In case of quantitative attributes the 

implicit prices represent the WTP to achieve one unit more (one percent more in this study) 

of the attribute considered. For cultural heritage, the implicit price reflects the WTP for a 

discrete change in the attribute's level, for example to improve farm building and 

traditional farm practices from ‘rapid decline’ to ‘much better conservation’. 



95% lower 

bound 


0.78 0.45 1.11 


0.74 0.45 1.05 

Broadleaf and mixed woodlands 

0.61 0.30 0.91 



0.00 -0.03 0.04 



1.03 -1.84 4.14 


conservation” 4.89 1.52 8.43 

95% upper 

bound 




corresponding 95% confidence intervals. 



Scenario 


(£) 

95% lower 

bound 

95% upper 

bound 

Scenario 1 

7.68 2.59 13.33 

Scenario 2 

9.17 3.6 15.22 

Scenario 3 

0.21 -1.41 1.88 


them the most when making choices was the cost, followed by the broadleaf and mixed 

woodland (18%). The attribute that concerned respondents the least was rough grassland, 

chosen by only 3% of respondents as most important. 


personal or family enjoyment was the most indicated reason followed by environmental 

concerns and the enjoyment of future generations (see Fig. A5.2). Note that these 

percentages are approximate since responses could be coded under more than one 

category. 

eftec A- 45 

January 2006

19% 



11% 

15% 

5% 

3% 2% 

22% 

17% 

eftec A- 46 

January 2006 

6% 

Personal family 

enjoyment 

Other enjoyment 

altruism 





communities 



Warm glow 

Figure A5.2: Pie chart showing the break down of why respondents in the North West 

were willing to pay for alternative policies over the current policy. 

A5.2.3 Yorkshire and the Humber 

In the Yorkshire and the Humber region 312 respondents were interviewed. Of the total 

number of responses, 98 expressed a protest answer regarding the proposed project; these 

protest bids were removed from the sample. All respondents that displayed a genuine zero 

WTP by always choosing the current policy option (11%), and those that chose either 

alternative A or B at least once were considered in the analysis, giving a total number of 

1284 [(312-98) x 6)] observations for model estimation. 

Analysis of Yorkshire and the Humber data showed that the conditional logit model fulfils 

the IIA condition, and so is used in the analysis. Table A5.9 shows the conditional logit 

model that incorporates the socioeconomic information 6 . 

Overall the model is highly significant and shows a good fit to data (ρ 2 = 0.20). All the 

attributes save broadleaf and mixed woodland are significant and have the expected sign. 

Nevertheless, the low significance of the heather moorland and bog attribute and of the 

change from a “rapid decline” to a “no change” in the cultural heritage attribute suggests 

that for these attributes preferences cannot be accurately defined. The significance and 

positive sign of the constant term shows that respondents are, all else being equal, in 

favour of the payments to farmers for the “environmental services” they provide. 

6 The per capita income variable has been dropped from the model due to the high number of missing 

values. At the beginning we estimated a conditional logit model that included this variable, observing 

that it was significant. In the model finally chosen (without income) we observed that the education 

variable, that was not significant in the model specification that included the income, is significant. 

Hence it is likely that part of the information explained by the per capita income is explained by the 

education variable in the model without income, so that we do not loose a lot of information and we 

can use a greater number of observations for modelling.



Table A5.9: Conditional logit model; Maximum Likelihood Estimates 

Dependent variable: Choice 







Prob [chiSqd>value]=.0000 


Mean values 

K 2.915 0.722 4.036 0.000 

HMB 0.011 0.006 1.657 0.098 

RG 0.011 0.005 2.031 0.042 

BMW 0.005 0.006 0.871 0.384 

FB 0.001 0.001 2.036 0.042 

CH1 0.109 0.061 1.801 0.072 

CH2 0.423 0.066 6.430 0.000 

TAX -0.035 0.003 -11.784 0.000 

AGE -0.031 0.142 -0.216 0.829 

GENDER 0.231 0.154 1.502 0.133 

ENVIMP -0.599 0.124 -4.818 0.000 

VISFREQ -0.235 0.036 -6.551 0.000 

LIVING -0.006 0.007 -0.898 0.369 

REMAIN -0.196 0.116 -1.688 0.091 

MEMBER 0.437 0.275 1.590 0.112 

EDU 0.267 0.082 3.242 0.001 

EMPLOY 0.148 0.162 0.914 0.361 

RURAL 0.673 0.280 2.409 0.016 













ENVIMP: Ranking of environmental policy relative to other policies(1=very important; 4= 

not important) 

VISFREQ: Respondent’s frequency of visits to severely disadvantaged areas (1= every day; 

10 = never) 



ever) 

MEMBER: dummy = 1 if respondents’ belong to any “environmental” or trust organization 

and 0 otherwise 




Increasing the area of heather moorland and bog and rough grassland and the length of 

field boundaries are changes positively evaluated by respondents. As well, respondents 

receive utility from improvement in cultural heritage. Cost is highly significant and has a 

eftec A- 47 

January 2006



negative sign, showing that the higher the cost of a policy option, the less likely a given 

respondent is to choose it. 

Turning to the interpretation of individuals’ characteristics, it can be seen that the gender, 

age, number of years that respondents have been living in the area, whether the 

respondent belongs to an “environmental” organization and their working status did not 

influence the selection of the preferred alternative. Consistently, people who assigned a 

high importance to environmental policy relative to other policies were more likely to 

choose alternative A or B over the current policy. Along the same lines, people that visit 

SDAs more often are in favour of the payments to farmers. The more educated interviewees 

are the more likely they chose alternative A or B. Finally, rural dwellers are more willing to 

contribute for getting the environmental situations described in alternatives A and B. 

Table A5.10 shows the implicit prices and the respective 95% confidence intervals, 

calculated using the Krinsky and Robb (1986) procedure. Mean implicit prices for all 

attributes are positive, implying that respondents have positive WTP for increases in the 

quality or quantity of each attribute. One observation is noteworthy; the wide confidence 

intervals reveal that there is a low precision in the mean values: only three of the six 

implicit prices can be considered different from 0 at a 95% confidence level. 



95% lower 

bound 

Heather moorland and bog 0.30 -0.06 0.65 

Rough grassland 0.31 0.01 0.60 

Broadleaf and mixed woodlands 0.15 -0.16 0.48 

Field boundaries 0.04 0.01 0.08 




3.08 -0.24 6.71 

from “rapid decline” to “much better 11.93 8.47 15.44 


95% upper 

bound 







Scenario 


(£) 

95% lower 

bound 

95% upper 

bound 

Scenario 1 

18.64 12.28 25.56 

Scenario 2 

20.54 14.16 27.59 

Scenario 3 

-1.2 -2.78 0.63 

In the survey follow-up questions 47% of respondents declared that the attribute that 

concerned them the most when making choices was the cost, followed by cultural heritage. 

The attribute that concerned respondents the least was rough grassland, chosen by only 3% 

of respondents as most important. 


the need to preserve heritage and communities was the most important (see Fig. A5.3). 

eftec A- 48 

January 2006



These percentages are approximate since responses could be coded under more than one 

category. 

17% 

21% 

5% 2% 

9% 

1% 

17% 

14% 

14% 


enjoyment 


altruism 


future 

Personal financial 




communities 


concerns 


Warm glow 

Figure A5.3: Pie chart showing the break down of why respondents in Yorkshire and the 

Humber were willing to pay for alternative policies over the current policy. 

A5.2.4 South East 

In the South East region 345 respondents were interviewed. Of the total number of 



always choosing the current policy option (8%), and those that chose either alternative A or 

B at least once were considered in the analysis, giving a total number of 1524 [(345-91) x 


It is worth repeating that the scenario described to respondents in the South East region 

was different to the scenario described to respondents in other regions. Since there are no 

Severely Disadvantaged Areas in the South East region, respondents were instead asked for 

their preferences for the implementation of the policies in question in all the SDAs of 

England. This leads to two important differences in the valuation exercise. First, 

respondents are valuing the environmental policies for six regions instead of one; hence 

higher welfare estimates may be expected. Secondly, the areas of interest are much 

further away from respondents’ dwellings so that the “non-use” component of the value 

will be much more important in this region. The fact that in this region the use value has 

lower importance should reduce the WTP estimates. The final WTP will comprise elements 

of an increase due to the “global” valuation and a reduction due to the lesser actual use of 

the good. 

Analysis of South East data showed that the simple conditional logit model specification 


variables) suffer from IIA violations. Furthermore, the attribute only random parameters 

logit model did not converge. Therefore the random parameter logit model that includes 

the socioeconomic characteristics has been used. Coefficients of this model are shown in 

Table A5.12. 

eftec A- 49 

January 2006














Mean values 

K 2.828 0.641 4.412 0.000 

HMB 0.025 0.007 3.516 0.000 

RG 0.016 0.006 2.809 0.005 

BMW 0.038 0.007 5.557 0.000 

FB 0.002 0.001 2.698 0.007 

CH1 0.025 0.065 0.387 0.699 

CH2 0.493 0.071 6.907 0.000 

TAX -0.031 0.003 -11.336 0.000 

AGE -0.047 0.130 -0.359 0.720 

GENDER -0.122 0.164 -0.745 0.456 

ENVIMP -0.381 0.136 -2.790 0.005 

VISFREQ -0.250 0.046 -5.440 0.000 

LIVING -0.001 0.006 -0.152 0.879 

REMAIN -0.204 0.094 -2.164 0.031 

MEMBER 0.554 0.195 2.841 0.005 

EDU 0.286 0.067 4.294 0.000 

EMPLOY 0.089 0.171 0.524 0.600 

RURAL 1.481 0.232 6.380 0.000 


NsHMB 0.00002 0.00001 1.057 0.291 

NsRG 0.00002 0.00001 1.513 0.130 

NsBMW 0.00003 0.00001 2.126 0.034 

NsFB 0.000004 0.000002 2.504 0.012 

NsCH1 0.00030 0.00015 2.056 0.040 

NsCH2 0.00027 0.00015 1.784 0.075 




ENVIMP: Ranking of environmental policy relative to other policies(1=very important; 4= not 

important) 


never) 



ever) 


otherwise 




Overall the model is highly significant and shows a good fitting to data (ρ 2 = 0.18). Turning 

to the attribute coefficients, it can be seen that all are highly significant. Increasing the 

area of heather moorland and bog, rough grassland, broadleaf and mixed woodlands, the 

length of field boundaries and the improvement from “rapid decline” to “much better 

eftec A- 50 

January 2006



conservation” in cultural heritage all increase respondents’ utility. In addition, the 

significance and positive sign of the constant term shows that respondents are, all else 

being equal, in favour of the payments to farmers for the “environmental services” they 

provide. Cost is highly significant and has a negative sign, showing that the higher the cost 

of a policy option, the less likely a given respondent is to choose it. 

As in Yorkshire and the Humber, the per capita income variable has been dropped from the 

model since its inclusion would have caused the loss of an additional 22% of observations. 

Turning to the interpretation of individuals’ characteristics it can be seen that people who 

assigned a high importance to environmental policy relative to other policies are more 

likely to choose alternative A or B over the current policy. Along the same lines, people 

that visit SDAs more often are in favour of the payments to farmers. The same is true for 

people that belong to an environmental, heritage, recreation or farming organization. 

However, the longer respondents think they will remain in the region, the less likely they 

are to be willing to contribute to alternative policies. Finally, more educated people and 

rural dwellers prefer the “change scenario” policies than the current situation. 

Preferences for the heather moorland and bog and rough grassland attribute are 

homogeneous in the sample. The significance of the standard deviation terms indicates 

some heterogeneity in the sample. The standard deviation values are very small compared 

to their means, so the heterogeneity in this sample, although it exists for these attributes, 

is very low. 

In summary, the model suggests respondents value programs which result in an 

improvement in all landscape features. 


intervals, calculated using the Krinsky and Robb (1986) procedure. As expected, implicit 

prices are positive for all significant attributes. 

Table A5.13: Implicit prices and 95% confidence intervals. 


95% lower 

bound 



0.50 0.14 0.86 


1.21 0.81 1.66 


0.06 0.02 0.11 


from “rapid decline” to “no change” 0.81 -3.22 4.96 



conservation” 15.79 11.47 20.64 

95% upper 

bound 





eftec A- 51 

January 2006





Scenario 


(£) 

95% lower 

bound 

95% upper 

bound 

Scenario 1 

19.85 12.47 27.66 

Scenario 2 

25.4 17.72 34.17 

Scenario 3 

-0.89 -3.12 1.15 


them the most when making choices was the cost, followed by broadleaf and mixed 

woodland (26%) and cultural heritage (21%). The attribute that concerned respondents the 

least was rough grassland, chosen by only 3% of respondents as most important. 


environmental concerns followed by personal or family enjoyment and the enjoyment of 

future generations were the most commonly cited reasons (see Fig. A5.4). These 

percentages are approximate since responses could be coded under more than one 

category. 

18% 

10% 

16% 

7% 

13% 

9% 

3% 

11% 

13% 


enjoyment 


altruism 


future 




communities 


concerns 


Warm glow 

Figure A5.4: Pie chart showing the break down of why respondents in the South East 


A5.2.5 South West 

In the South West region 301 respondents were interviewed. Of the total number of 



always choosing the current policy option (8%), and those that chose either alternative A or 

B at least once were considered in the analysis, giving a total number of 1494 [(301-52) x 


In the South West, the sample was split into two groups to test for “procedural invariance” 

of CE estimates. In one half the highest value of increase in the area of broadleaf and 

mixed woodlands was 30%, whilst in the other, the maximum increase for the same 

eftec A- 52 

January 2006



attribute was 20%. In principle, this provides two different models that would allow a test 

of whether the results obtained by one model (say the model with the maximum increase of 

20%) could be replicated by the other (the one with a 30% increase). Unfortunately, the 

broadleaf and mixed woodland coefficient is no longer significant in the split models, so the 

test of procedural invariance cannot be taken; however, it was significant in the pooled 

model, as described below. Data were analysed jointly. 

Analysis of the South West data shows that the simple conditional logit model specification 





Number of obs.: 1494, skipped17 bad obs. 









Mean values 

K 1.019 0.155 6.571 0.000 

HMB 0.026 0.008 3.169 0.002 

RG -0.002 0.007 -0.256 0.798 

BMW 0.011 0.006 1.953 0.051 

FB -0.001 0.001 -1.164 0.245 

CH1 0.158 0.082 1.922 0.055 

CH2 0.222 0.099 2.234 0.026 

TAX -0.029 0.003 -9.490 0.000 


NsHMB 0.073 0.013 5.755 0.000 

NsRG 0.065 0.008 8.059 0.000 

NsBMW 0.033 0.012 2.800 0.005 

NsFB 0.008 0.001 5.986 0.000 

NsCH1 0.672 0.101 6.636 0.000 

NsCH2 1.014 0.115 8.798 0.000 











Overall the model is highly significant and the data fitting is moderate (ρ 2 = 0.15). Turning 

to the attribute coefficients, the change in the area of rough grassland and the change in 

the length of field boundaries did not affect respondents’ choices. The significance and 

positive sign of the constant term shows that respondents are, all else being equal, in 

favour of the payments to farmers for the “environmental services” they provide. 

eftec A- 53 

January 2006



Increasing the areas of heather moorland and bog is positively valued by respondents. The 

same is true for the cultural heritage attribute, so long as the change is large. Cost is highly 

significant and has a negative sign, showing that the higher the cost of a policy option, the 

less likely a given respondent is to choose it. 

Interestingly, all the standard deviation terms are significant, indicating preference 

heterogeneity does indeed exist. There is not a clear direction of preferences amongst 

respondents. For instance, from the mean and standard deviation values of the heather 

moorland and bog attribute it can be seen that 36% of respondents have negative 

preferences for increases in the area of that attribute. 

In summary, the model suggests respondents value programs which result in higher heather 

moorland and bog area, an increase in the woodland area, much better conservation of 

cultural heritage and the less these programs cost the better. But within the sample a high 

degree of heterogeneity exists. 



eftec A- 54 

January 2006





Weighting variable: None 










Mean values 

K 1.463 0.957 1.528 0.126 

HMB 0.030 0.009 3.306 0.001 

RG -0.002 0.007 -0.271 0.787 

BMW 0.009 0.006 1.435 0.151 

FB -0.001 0.001 -0.956 0.339 

CH1 0.187 0.083 2.248 0.025 

CH2 0.291 0.108 2.698 0.007 

TAX -0.029 0.003 -8.281 0.000 

AGE 0.155 0.188 0.829 0.407 

GENDER 0.903 0.240 3.761 0.000 

ENVIMP -0.255 0.204 -1.252 0.211 

VISFREQ -0.125 0.047 -2.696 0.007 

LIVING 0.003 0.008 0.358 0.720 

REMAIN -0.127 0.142 -0.899 0.369 

MEMBER -0.006 0.270 -0.023 0.981 

EDU 0.281 0.105 2.663 0.008 

EMPLOY -0.059 0.280 -0.211 0.833 

RURAL -0.394 0.261 -1.510 0.131 


NsHMB 0.062 0.014 4.548 0.000 

NsRG 0.049 0.009 5.203 0.000 

NsBMW 0.031 0.012 2.647 0.008 

NsFB 0.008 0.001 5.404 0.000 

NsCH1 0.471 0.131 3.604 0.000 

NsCH2 0.900 0.117 7.682 0.000 





important) 


never) 



ever) 


otherwise 






eftec A- 55 

January 2006



data in the socioeconomic characteristics. Again, the per capita income variable was 

dropped from analysis since it would have caused an additional loss of 21% of observations. 

The increase in model fit is very low (Δρ 2 = 0.02) given the addition of 11 parameters. 

The interpretation of the coefficients reveals some interesting differences in relation to the 

attributes only model. The constant is no longer significant; it means a part of the 

systematic unobserved variance that led respondents to choose a particular option is now 

explained by the socioeconomic characteristics. Secondly, the broadleaf and mixed 

woodland attribute is not significant. The reduction in the sample size of 24% probably 

caused the lack of significance. 

Turning to the interpretation of individuals’ characteristics, it can be seen that only 

respondents’ gender, their frequency of visit to the regions SDAs and their education are 

significant. In particular, men and more educated people were more likely to choose policy 

options A and B. As expected, people that visit SDAs more often are in favour of the 

payments to farmers. 


the effects of individual characteristics on choices, but the low increase in model fitting 

and the loss of the 24% of the sample observations makes choosing the attributes only 

model appropriate when estimating implicit prices and compensating surplus measures. 


intervals, calculated using the Krinsky and Robb (1986) procedure. Mean implicit price 

values for all significant attributes are positive implying that respondents have a positive 

willingness to pay (WTP) for increases in the quality or quantity of each attribute. The wide 

confidence intervals for the broadleaf and mixed woodland implicit price and for the 

qualitative change between “rapid decline” and “no change” in the cultural heritage 

attribute reveal that these implicit prices cannot be considered different from 0 at a 95% 

confidence level. 



95% lower 

bound 


Rough grassland -0.06 -0.56 0.39 

Broadleaf and mixed woodlands 0.39 -0.01 0.78 

Field boundaries -0.04 -0.11 0.02 




5.48 -0.11 11.59 



95% upper 

bound 





eftec A- 56 

January 2006





Scenario 


(£) 

95% lower 

bound 

95% upper 

bound 

Scenario 1 

20.59 9.28 32.83 

Scenario 2 

21.74 9.84 34.64 

Scenario 3 

-0.92 -3.76 2.2 


them the most when making choices was broadleaf and mixed woodland, closely followed 

by the cost (25%) and cultural heritage (24%). The attribute that concerned respondents the 

least was rough grassland, chosen by only 5% of respondents as most important. 


environmental concerns followed by the preservation of cultural heritage were the most 

important reasons (see Fig. A5.5). These percentages are approximate since responses 

could be coded under more than one category. 

18% 

8% 

17% 

6% 

11% 

13% 

4% 

10% 

13% 


enjoyment 


altruism 


future 

Personal financial 


Other financial 



communities 


concerns 


Warm glow 

Figure A5.5: Pie chart showing the break down of why respondents in the South West 


A5.2.6 North East 

In the North East region 300 respondents were interviewed. Of the total number of 

responses, 131 (44%) expressed a protest answer regarding the proposed project; these 

protest bids were removed from the sample. This proportion of protest answers is very 

high, and the resulting reduction of the sample size may influence results. All respondents 

that displayed a genuine zero WTP by always choosing the current policy option (11%), and 

those that chose either alternative A or B at least once were considered in the analysis, 

giving a total number of 1014 [(300-131) x 6)] observations for model estimation. 

Analysis of the North East data shows that the simple conditional logit model specification 



eftec A- 57 

January 2006














Mean values 

K 0.681 0.179 3.813 0.000 

HMB 0.005 0.010 0.463 0.644 

RG -0.001 0.009 -0.121 0.904 

BMW 0.016 0.012 1.242 0.214 

FB 0.001 0.001 0.772 0.440 

CH1 -0.044 0.101 -0.435 0.664 

CH2 0.171 0.126 1.356 0.175 

TAX -0.019 0.003 -5.419 0.000 


NsHMB 0.068 0.017 3.998 0.000 

NsRG 0.067 0.010 6.723 0.000 

NsBMW 0.103 0.014 7.596 0.000 

NsFB 0.007 0.002 4.185 0.000 

NsCH1 0.473 0.193 2.452 0.014 

NsCH2 0.982 0.136 7.235 0.000 











Although the model is globally significant the data fit is poor (ρ 2 = 0.13) and, apart from the 

tax attribute, none of the attributes is significant. The constant is positive and highly 

significant indicating that all else being equal, respondents (at least the 56% that did not 

protest) are in favour of the payments to farmers for the “environmental services” they 

provide. 

The high values of the standard deviations in relation to their mean provide a possible 

explanation of the observed lack of significance of attributes. From the mean and standard 

deviation values it is possible to calculate the percentage of respondent that hold negative 

or positive preferences for a specific attribute. In this sample the percentage of negative or 

positive preferences is very close to 50% for all attributes. If we consider the rough 

grasslands attribute for example, exactly half of respondents were observed to dislike it, 

half to like it. These “opposite” preferences offset each other giving an overall lack of 

significance for the attribute. In summary, the model suggests respondents hold 

heterogeneous preferences for all attributes and the heterogeneity is “equally” share 

between two halves. 

eftec A- 58 

January 2006

















Mean values 

K 0.248 1.238 0.200 0.841 

HMB 0.006 0.011 0.549 0.583 

RG 0.004 0.010 0.397 0.692 

BMW 0.005 0.014 0.353 0.724 

FB 0.000 0.001 0.289 0.772 

CH1 -0.079 0.107 -0.736 0.462 

CH2 0.139 0.138 1.009 0.313 

TAX -0.021 0.004 -5.273 0.000 

AGE 0.121 0.267 0.453 0.651 

GENDER 0.727 0.316 2.302 0.021 

ENVIMP -1.070 0.237 -4.516 0.000 

VISFREQ 0.085 0.067 1.274 0.203 

LIVING -0.021 0.012 -1.733 0.083 

REMAIN 0.064 0.197 0.326 0.744 

MEMBER 0.019 0.424 0.046 0.964 

EDU 0.275 0.149 1.839 0.066 

EMPLOY 1.046 0.328 3.189 0.001 

RURAL 2.862 0.613 4.666 0.000 


NsHMB 0.072 0.017 4.124 0.000 

NsRG 0.064 0.011 5.972 0.000 

NsBMW 0.097 0.016 6.062 0.000 

NsFB 0.007 0.002 3.771 0.000 

NsCH1 0.183 0.219 0.834 0.405 

NsCH2 1.034 0.152 6.818 0.000 





important) 


never) 



ever) 


otherwise 




eftec A- 59 

January 2006




estimated using 198 fewer observations than the attributes only model, due to missing data 

in the socioeconomic characteristics. Again, the per capita income variable was dropped 

from the analysis since it would have caused the loss of an additional 17% of observations. 

The interpretation of the model is fairly similar to that for the attributes only model, the 

exception being that the constant is no longer significant, showing that the socioeconomic 

variables now explain part of the variance that was not explained by the attributes in the 

attributes only model. 

Turning to the interpretation of individuals’ characteristics, it can be observed that men, 

more educated people, active workers and rural dwellers were more likely to choose policy 

options A and B. Again, people who visit the region more often are more in favour of the 

payments to farmers. 

Since neither model has significant attributes it is not logical to pursue welfare estimates, 

since no sound interpretation could be attained. 

Regarding the follow up questions, a total of 32% of respondents declared the attribute that 

concerned them the most when making choices was the broadleaf and mixed woodland. 

The cost was the second most important attribute for 26% of respondents. The attribute 

that concerned respondents the least was rough grassland, indicated by only 5% of 

respondents. 


the preservation of cultural heritage and environmental concerns were the most cited 

reasons (see Fig. A5.6). These percentages are approximate since responses could be coded 

under more than one category. 

17% 

9% 

18% 

6% 

15% 

11% 

3% 

11% 

10% 


enjoyment 


altruism 





communities 



Warm glow 

Figure A5.6: Pie chart showing the break down of why respondents in the North East 


A5.2.7 East Midlands 

In the East Midlands region 314 respondents were interviewed. Of the total number of 



eftec A- 60 

January 2006



always choosing the current policy option (13%), and those that chose either alternative A 



Analysis of East Midlands data shows that the simple conditional logit model specification 


variables) suffer from IIA violations. Furthermore, the attributes only RPL model did not 

converge. The same happens if a RPL model that includes the socioeconomic variables and 

does not include the per capita income variable is estimated. By including the per capita 

income variable, 600 observations are removed from the sample and, probably by chance, 

it is possible to fit a model to the remaining data. 

Table A5.21 presents a random parameter logit model that includes the socioeconomic and 

attitudinal characteristics of respondents. 

eftec A- 61 

January 2006






Number of observations: 4338 








Mean values 

K 2.225 0.819 2.718 0.007 

HMB 0.016 0.008 1.884 0.060 

RG 0.001 0.007 0.185 0.853 

BMW 0.015 0.007 1.946 0.052 

FB 0.001 0.001 0.989 0.323 

CH1 0.119 0.077 1.542 0.123 

CH2 0.338 0.087 3.865 0.000 

TAX -0.015 0.003 -4.453 0.000 

AGE -0.175 0.150 -1.166 0.244 

GENDER 0.261 0.194 1.346 0.178 

ENVIMP -0.794 0.148 -5.372 0.000 

VISFREQ -0.044 0.036 -1.239 0.215 

LIVING 0.009 0.007 1.245 0.213 

REMAIN -0.446 0.138 -3.222 0.001 

MEMBER -0.059 0.234 -0.251 0.802 

EDU 0.393 0.109 3.621 0.000 

EMPLOY 0.321 0.201 1.598 0.110 

RURAL 0.325 0.214 1.518 0.129 

INCOME 0.095 0.086 1.105 0.269 


NsHMB 0.000001 0.000017 0.077 0.939 

NsRG 0.000020 0.000012 1.764 0.078 

NsBMW 0.000008 0.000014 0.572 0.568 

NsFB 0.0000002 0.000002 0.099 0.921 

NsCH1 0.000056 0.000155 0.361 0.718 

NsCH2 0.000121 0.000155 0.780 0.436 





important) 

VISFREQ: Respondent’ frequency of visits to severely disadvantaged areas (1= every day; 10 = 

never) 



ever) 


otherwise 




eftec A- 62 

January 2006



Although the model is globally significant the data fitting is poor (ρ 2 = 0.11) and, as 

explained above, coefficients may not describe the weight that the attributes have in 

people’s utility. If the model is accepted, its coefficients suggest that respondents prefer 

increases in broadleaf and mixed woodland and large increases in cultural heritage. Cost is 

highly significant and has a negative sign, showing that the higher the cost of a policy 

option, the less likely a given respondent is to choose it. The significance and positive sign 

of the constant term shows that respondents are, all else being equal, in favour of the 

payments to farmers for “environmental services”. The small standard deviation terms 

reveal that preferences are homogeneous in the sample, except for the rough grassland 

attribute where a low degree of heterogeneity is observed. 

Table A5.22 describes the implicit prices for the East Midlands region. 



95% lower 

bound 

Heather moorland and bog 1.04 -0.03 2.31 

Rough grassland 0.08 -0.99 0.91 

Broadleaf and mixed woodlands 0.97 0.03 2.46 


0.06 -0.06 0.18 




7.92 -1.96 22.62 



95% upper 

bound 

Mean implicit price values for all significant attributes are positive implying that 

respondents have a positive WTP for increases in the quality or quantity of each attribute. 

The wide confidence intervals for the heather moorland and bog implicit price reveal that 

it cannot be considered different from 0 at a 95% confidence level. The implicit price for 

the broadleaf and mixed woodlands attributes, although positive, has a very wide 

confidence interval too. 







Scenario 


(£) 

95% lower 

bound 

95% upper 

bound 

Scenario 1 

41.81 22.27 81.34 

Scenario 2 

47.97 26.45 94.88 

Scenario 3 

-2.73 -9.06 3.83 

A total of 29% of respondents declared the attribute that concerned them the most to be 

cost, followed by the conservation of the cultural heritage, indicated by 25% of 

respondents. The attribute that concerned respondents the least was rough grassland, 

indicated by only 5% of respondents. 


personal family enjoyment, the preservation of cultural heritage and environmental 

eftec A- 63 

January 2006



concerns were the most indicated options (see Fig. A5.7). These percentages are 

approximate since responses could be coded under more than one category. 

16% 

18% 

5% 4% 

7% 

1% 

21% 

15% 

13% 


enjoyment 


altruism 


future 




communities 


concerns 


Warm glow 

Figure A5.7: Pie chart showing the break down of why respondents in the East Midlands 


A5.3 Contingent Valuation 

A5.3.1 Introduction 

Data were analyzed using two different approaches (parametric and non-parametric) to 

“double check” results. The main goal of a contingent valuation survey is to identify the 

mean WTP for a specific change in the current policy. Two policies were presented to 

respondents: the current policy at zero cost and an alternative policy at a range of six 

costs. The current policy used was the same as in the choice experiment part of the survey. 

The alternative policy represented the following change in attributes: a 5% increase in the 

area of heather moorland and bog; a 10% increase in rough grassland; a 20% increase in 

broadleaf and mixed woodland; an increase in field boundary improvement of 100 metres 

per kilometre; and a cultural heritage conservation level set to “much better conservation” 

rather than “rapid decline”. 

Respondents in each region were asked whether they were willing to pay the non-zero cost 

for the implementation of the alternative policy in the other five English GORs which 

contain Severely Disadvantaged Areas. The contingent valuation exercise was not carried 

out in the South East region survey. It should be emphasised that the expressed WTP refers 

to five regions where respondents do not live. Given the similarity of the valuation exercise 

in all regions, data from all regions are combined in this analysis. Respondents that stated a 

protest answer or said that they did not know if were willing to pay the proposed bid were 

excluded from analysis. 

A5.3.2 Non-parametric estimation of WTP 

As suggested by Bateman et al. (2002), the use of a parametric distribution to approximate 

the distribution of WTP in a sample represents a fairly large assumption, making the mean 

eftec A- 64 

January 2006



WTP particularly sensitive to the particular distributional assumption made by the analyst. 

Therefore, the authors advise the use of a non-parametric approach to provide an estimate 

of the mean WTP that can be taken as a lower bound for the statistic. The non-parametric 

technique provides a purely empirical approach to estimating the survivor function of WTP 

responses. Figure A5.8 shows the survivor function of the overall sample. 

Proportion 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 2 5 10 17 40 70 

WTP 

Figure A5.8: Non-parametric survivor function 

The survivor function indicates that the selected bids were too low. As can be observed in 

Figure A5.8, at the maximum bid (£70), 63% of the sample are still willing to pay. A possible 

reason for that is the fact that the CV question was not pilot-tested – it was added at the 

end of the study design using the same cost range as the choice experiment. 

Under these circumstances, the non-parametric approach provides a very conservative 

estimate of WTP, since it considers that 63% of the population is willing to pay a maximum 

of £70, when it is likely that a percentage of this 63% would be willing to pay more. 

Table A5.24 summarizes the mean WTP and the 95% confidence intervals estimated using 

the non-parametric methodology described in Bateman et al (2002). 

Table A5.24: non-parametric WTP mean estimation 

Statistics £ 95% lower 

bound(£) 

95% upper 

bound(£) 

Mean 49.27 47.56 50.99 

Std Error 0.88 

Respondents’ WTP for the alternative scenario is therefore approximately £50. The 

measure is quite precise, as can be deduced from the very tight confidence intervals. 

However, as explained above, this value should be considered a very conservative 

estimation of the welfare change induced by the implementation of the policy. 

A5.3.3 Parametric estimation of WTP 

Given the “incomplete” survivor function described above, it is very important to provide a 

parametric estimation of WTP. Amongst the possible parametric assumptions that can be 

made, the most used in the literature considers the sample WTP to have a normal or 

logistic distribution. The differences in the estimated WTP found using these two 

eftec A- 65 

January 2006



distributions are often negligible; however, the logistic distribution is frequently used given 

its analytical tractability, and is chosen in this analysis. 

A simple analysis was carried out, as the CV design did not allow splitting between 

respondents with zero WTP and those with positive WTP. Considering the low percentage 

of people that stated that they were not willing to pay (22.8%) this should not have a 

significant impact on results. Table A5.25 summarizes the mean WTP and 95% confidence 

intervals. 

Table A5.25: parametric WTP mean estimation 

Statistics £ 95% lower 

bound(£) 

95% upper 

bound(£) 

Mean 104.92 72.17 137.67 

Std Error 16.71 

On average, respondents valued the change from the current scenario to the change 

scenario at £105. As expected the mean WTP under the parametric approach is significantly 

higher than that estimated using the non-parametric approach, since the parametric 

distribution does not “cut” the WTP at £70. The right tail of the distribution attributes a 

positive probability to WTP greater than £70 to increase the likelihood of fitting the 

observed responses. 

A5.3.4 Reasons for contributing 

Finally, it is customary to apply tests of validity to CV data sets (Bateman et al., 2002). One 

such test that can be run for this data set is to check whether responses to the willingness 

to pay question are driven by some rational, consistent process. To test this, we estimate a 

logit model on WTP for the hypothetical improvements in landscape quality. A general 

model including a wide range of variables hypothesised a priori to determine respondents’ 

decisions was estimated. The variables initially included in the model were those used in 

the CE study. Next, we dropped all the variables that were not significant at the 10% level 

to obtain a final model (Table A5.26). This revealed significant correlations between the 

probability of positive WTP and: 1) region of residence; 2) importance of environmental 

policy; 3) the bid shown; 4) the frequency of visits to the Severely Disadvantaged Areas; 5) 

respondents’ expected residence in the area; 6) number of members in the household; and 

7) household per capita income. 

eftec A- 66 

January 2006



Table A5.26: Logit Model results for willingness to pay 

responses. 

Independent 

Variables a 

Coefficients Standard Signifi- 

error cance 

constant 4.619 .805 .000 

region SW -.537 .375 .152 

region YH -1.075 .350 .002 

region NE -1.360 .355 .000 

region NW -.914 .373 .014 

region EM -.626 .355 .078 

ENVIMP -.658 .138 .000 

BID -.015 .004 .000 

VISFREQ -.169 .038 .000 

REMAIN -.348 .112 .002 

FAMILY .254 .082 .002 

INCOME .356 .091 .000 

-2Log-Likelihood 739.863 

Significance level 

Percentage of 

0.000 

correct prediction 80 % 

Number of 

observations 820 (418 missing cases) 

a Dependent variable: probability of being willing to pay. 

ENVIMP: Ranking of environmental policy relative to other policies(1=very important; 4= not important) 

VISFREQ: Respondent’s frequency of visits to severely disadvantaged areas (1= every day; 10 = never) 

BID: bid shown to respondents 

REMAIN: time period respondents thing are staying in the area (1=less than 6 month; 5=for ever) 

FAMILY: number of people living in the household 



Regarding the interpretation of model coefficients, individuals living in the West Midlands 

region (used as reference for the region variable) were more willing to contribute to the 

change in policy than those living in Yorkshire and the Humber, the North East, the North 

West and East Midlands. Consistently, individuals who assigned a high importance to 

environmental policy in relation to others policies were more likely to contribute. Along the 

same lines, people that visited the region more often are in favour of the payments. 

Note that two of the variables – ENVIMP (importance of environmental policy to respondent) 

and VISFREQ (respondent’s frequency of visit to SDA) - have counter-intuitive rankings, i.e. 

a lower number implies that someone is more concerned about environmental policy, or 

that they visit SDAs more often. 

People with larger families are more willing to contribute to the proposed policy change. 

Reliably, higher income is also a sign of willingness to accept the payment. Unexpectedly, 

the negative sign of the coefficient of REMAIN shows that respondents who expect to 

remain in the region for longer are less likely to contribute to policies. Finally, the bid 

coefficient is highly significant and has a negative sign, showing that the higher the cost 

associated with a policy option, the less likely a given respondent is to choose that option. 

Overall, the model predicted correctly for 80% of observations and is significant at the 99% 

level of confidence. 

eftec A- 67 

January 2006



A5.4 Comparison of ELF WTP with Study WTP 

In Section 7.4.1 of the main report, the ELF model’s WTP figures are compared with those 

found in the study. However, this study values a small change in attributes, whereas the 

WTP values included in the ELF model are taken to value the entirety of the attribute 

within any given region. For this reason the per household WTP figures given in the ELF 

model for each attribute must be scaled down to make them comparable to WTP for a 1% 

change found in this study. This section derives an estimate for the ratio of total WTP to 

WTP for a 1% change, used to compare the two sets of results in Section 7.4.1. 

Importantly, the functional form of the marginal WTP is not known. Ordinarily it is 

reasonable to expect that marginal WTP with respect to the proportion of the attribute 

conserved would take a functional form similar to the dashed line in Figure A5.9. This 

suggests that WTP for additional conservation of an attribute declines as the abundance of 

the attribute is increased (i.e. diminishing marginal utility is displayed). However, since the 

parameters of such a function are unknown, it is instead assumed that that the marginal 

WTP function can be approximated by a linear function of the form given by the straight 

solid line. Willingness to pay for the attribute in its entirety – i.e. the integral between 0 

and 1 - is shown by the grey area. The WTP for a change from 100% to 90% is given by the 

area shaded with diagonal black lines. 

marginal WTP 

If the solid line function for marginal WTP (w) can be given as a function of abundance (Q): 

w = a + bQ 

0.1 

then the total WTP (W) to prevent a loss of a proportion x of the attribute is the integral of 

w between 1-x and 1: 

2 

⎡ Q ⎤ 

W = ⎢aQ 

+ b 

2 

⎥ 

⎣ ⎦ 

1 

1-x 

= 

0.3 

Abundance 

Figure A5.9: Assumed form of marginal WTP and comparison of different total 

WTP figures represented by integrals. 

(a - b)x 

bx 

+ 

2 

2 

0.5 

eftec A- 68 

January 2006 

0.7 

0.9



From this, the ratio r of the WTP to save the whole attribute (W1) and 1% of the attribute 

(W0.01) is given approximately as follows 7 : 

W1 

r = 

W 

0.01 

a − 0.5b 100(a − 0.5b) 

≈ 

≈ 

0.01a − 0.01b a − b 

i.e. a hundred times the ratio of the marginal WTP at 50% abundance and at 100% 

abundance. Note that r would only equal 100 if b = 0; i.e. if the marginal WTP was constant 

at no matter how great or small the abundance of the feature. 

From the WTP curves provided by the ELF model, it appears that for most attributes, WTP 

at 50% abundance is roughly twice WTP at 100% abundance. Therefore, it seems a 

reasonable approximation to divide ELF estimates by 200 for comparison purposes with this 

study’s estimates. 

A5.5 Theoretical Basis of Linear Time Treatment of Benefits 

This section presents the justification for treating the compensating surplus for each policy 

scenario to change linearly with time. 

The utility of a particular policy scenario is given by: 

Vi = C + ∑kβk Xik +S 

where Vi is utility, C is the constant, βk are coefficients, Xk are physical attribute levels. Xk 

is time-dependent, S is the sum of socioeconomic factors (treat as constant for present 

purposes). 

Utility changes with respect to time as follows: 

dV 

dt 

= 

∑ 

k 

dV 

dX 

ik 

dX 

dt 

ik 

= 

∑ 

k 

dX 

β k 

dt 

If attribute k changes linearly with time, then: 

dX k = ak 

+ b t and = bk 

dt 

Xk k 

where ak and bk are constants. 

ik 

If all attributes can be assumed to change linearly with time, then: 

dV 

dt 

∑ 

= k 

β kb 

k 

This is itself a constant; therefore utility, and compensating surplus measures, also change 

linearly with time. 

7 This derivation uses the simplifying assumption that the term in W0.01 incorporating 0.01 2 is small 

enough to be neglected. 

eftec A- 69 

January 2006



Annex 3 of Cumulus et al. (2005) indicates that all attributes are expected to change with 

time in a fashion which can reasonably be approximated as linear up to (but not beyond) 

2013, therefore a linear treatment of compensating surplus measures over time is justified. 

A5.6 Compensating Surplus Aggregation Estimates (Adjusted Variant) 

Table A5.27 is a version of Table 8.4 in the main report. The only difference is that the 

number of households used for each region is the total number of households in the GOR 

multiplied by the proportion of households who did not indicate a protest response in the 

choice experiment. This slightly modified set of aggregation figures is presented to give a 

lower bound estimate of the aggregate compensating surplus figures under consideration of 

the possibility that protest responses may have represented genuine zero WTP responses. 

Table A5.27: Adjusted compensating surplus estimates of the different policy options 

over the current policy. 

NW YH WM EM SW SE Total 

Annual per household compensating surplus (£ pHH) 

Scenario 1 

Scenario 2 

Scenario 3 

7.68 

(2.59 - 

13.33) 

9.17 

(3.60 - 

15.22) 

0.21 

(-1.41 - 

1.88) 

18.64 

(12.28 - 

25.56) 

20.54 

(14.16 - 

27.59) 

-1.20 

(-2.78 - 

0.63) 

7.44 

(0.39 - 

14.42) 

10.04 

(2.58 - 

17.51) 

-1.50 

(-3.36 - 

0.42) 

Figures in brackets are 95% confidence intervals. 

41.81 

(22.27 - 

81.34) 

47.97 

(26.45 - 

94.88) 

-2.73 

(-9.06 - 

3.83) 

20.59 

(9.28 - 

32.83) 

21.74 

(9.84 - 

34.64) 

-0.92 

(-3.76 - 

2.20) 

19.85 

(12.47 - 

27.66) 

25.40 

(17.72 - 

34.17) 

-0.89 

(-3.12 - 

1.15) 

Total no. 

households 

(millions) 2.81 2.07 2.15 1.73 2.09 3.29 

% protest 

bids 26 31 14 23 17 26 

No. 

households 

(adjusted) 2.07 1.42 1.85 1.33 1.73 2.42 

Total adjusted annual compensating surplus (£ million) 

Scenario 1 

15.91 

(5.37 - 

27.62) 

19.00 

(7.46 - 

26.47 

(17.44 - 

36.29) 

29.16 

(20.10 - 

13.73 

(0.72 - 

26.62) 

18.53 

(4.76 - 

55.52 

(29.57 – 

108.00) 

63.69 

(35.12 - 

35.62 

(16.06 - 

56.80) 

37.61 

(17.02 - 

48.08 

(30.20 – 

67.00) 

61.52 

(42.92 - 

195.33 

(99.35 - 

322.33) 

229.52 

(127.39 - 

Scenario 2 31.53) 39.17) 32.32) 125.98) 59.93) 82.77) 371.71) 

0.44 -1.70 (- -2.77 -3.62 -1.59 -2.16 -11.41 

(-2.92 - 3.95 - (-6.20 - (-12.03 - (-6.51 - (-7.56 - (-39.16 - 

Scenario 3 3.90) 0.89) 0.78) 5.09) 3.81) 2.79) 17.24) 

Total adjusted compensating surplus aggregated 2007-2013 (£ million) 

55.56 92.42 47.95 193.86 124.4 167.9 682.1 

(18.74 - (60.88 - (2.51 - (103.26 - (56.07 - (105.48 - (346.94 - 

Scenario 1 96.44) 126.73) 92.94) 377.15) 198.35) 233.96) 1125.57) 

66.34 101.84 64.71 222.42 131.35 214.84 801.51 

(26.05 - (70.21 - (16.63 - (122.64 - (59.45 - (149.88 - (444.86 - 

Scenario 2 110.12) 136.79) 112.86) 439.93) 209.28) 289.02) 1298.01) 

1.52 -5.95 -9.67 -12.66 -5.56 -7.53 -39.84 

(-10.20 - (-13.78 - (-21.66 - (-42.01 - (-22.72 - (-26.39 - (-136.76 

Scenario 3 13.6) 3.12) 2.71) 17.76) 13.29) 9.73) - 60.21) 

eftec A- 70 

January 2006



Assumptions made in time aggregation are the same as those in Section 8 of the main 

report: 

• Discount rate is 3.5% 

• 2007 is the base year – i.e. the discount factor in 2007 is equal to 1. 

• Physical changes can be acceptably approximated as occurring linearly 

References to Section 5 

Adamowicz W., Boxall P., Williams M. and Louviere J. (1998) “Stated preference 

approaches for measuring passive use values: choice experiments and contingent valuation” 

American Journal of Agricultural Economics, 80, 64-75. 

Allenby, G., Ginter, J., 1995. The effects of in-store displays and feature advertising on 

consideration sets. International Journal of Research in Marketing 12, 67–80. 

Bateman, I., Carson, R., Day, B., Hanemann, M., Hanley, N., Hett, T., Jones-Lee, M., 

Loomes, G., Mourato, S., Ozdemiroglu, E., Pearce, D., Sugden, R. and Swanson, J. (2002): 

“Economic Valuation with Stated Preference Techniques, A Manual”. Edward Elgar, 

Cheltenham, UK / Northampton, Ma, USA. 

Cumulus Consultants Ltd., Institute for European Environmental Policy and the Countryside 

and Countryside Research Unit (2005), “Assessment of the impact of CAP Reform and other 

key policies on upland farms and land use implications in both Severely Disadvantaged & 

Disadvantaged Areas of England”, report to the Department for Environment, Food and 

Rural Affairs 

Domencich, T. and McFadden, D. (1975): Urban travel demand: a behavioural approach. 

Amsterdam: North-Holland. 

Hanley, N., Adamowicz V. and Wright, R. (2005) “Price vector effects in choice 

experiments: an empirical testing”. Resource & Energy Economics, 27 (3), 2005: 227-234. 

Hausman, J., Wise, D., 1978. A conditional probit model for qualitative choice: discrete 

decisions recognizing interdependence and heterogeneous preferences. Econometrica 42, 

403–426. 

Hausman, J., McFadden, D., 1984. Specification tests for the multinomial logit model. 

Econometrica 52, 1219–1240. 

Hensher, D. and Green W (1999): Nested logit model estimation: clarifying the rules for 

model estimation, Institute of Transport Studies, University of Sydney. 

Krinsky, I. and Robb, A.L. “On Approximating the Statistical Properties of Elasticities”. 

Review of Economics and Statistics, 68(1986): 715-719. 

Luce, R.D., 1959. Individual Choice Behaviour: a Theoretical Analysis. Wiley, New York. 

Train, K., 1998. Recreation demand models with taste differences over people. Land 

Economics 74, 230–239. 

Train, K., 2003. Discrete Choice Models with Simulation. Cambridge University Press, 

Cambridge. 

eftec A- 71 

January 2006


Annex 6 – Valuation Workshop Protocol 


Knaresborough, 19 th October 2005 

Harrogate, 20 th October 2005 

Section A - Introduction 

This is not intended to be read out verbatim, but is written as suggested content 

• Introduction 

Good evening and welcome to the session today. Thank you for taking the time to 

join our discussion. 

My name is Tony, I work for SERS, a company that specializes in social and 

economic research. My colleague is Helen from eftec, an environmental 

consultancy. 

• Inform about purpose of focus group 

Part of a government funded research project we are currently working on. 

We are holding a series of discussions such as today’s to help us understand public 

attitudes towards environmental features. 

Basically we are here to learn from you and your discussion! 

• Instructions for the group 

Lots of issues to discuss, so we will have refreshments as we go along rather than 

take a break. 

We would like everyone to participate, with no-one dominating the discussion. We 

want to hear as many different things from as many of you as time allows. 

There really are no right or wrong answers, we are here to learn from your views 

and experiences. 

On the other hand, if you think your experience is just like everyone else’s that is 

still important information for us and we want to hear your story. There is always 

something unique to each person’s own experience/views. 

We are going to ask you to do a questionnaire and then talk about it afterwards. 

We will be on a first name basis tonight and in our reports no names will be 

attached to comments. You may be assured of complete confidentiality. 

We would like to record the session: it will help us a lot to register your views in an 

accurate way. 

Do you have any objections (to us recording the session)? 

However, you are assured that although we may quote what you say, your names 

will not be included anywhere in our reporting. 

As we are recording the session please try to speak one at a time and no side 

conversations between neighbours please! 

Please turn off your mobiles at this point. 

The discussion should last 1.5 hours. 

eftec A- 72 

January 2006



Opening / ice-breaker question: ask participants to briefly introduce themselves, 

etc. 

Section B - Questionnaire 

The first thing we would like you to do is fill in this questionnaire. You should have a 

questionnaire form and also a supplement booklet with some additional information. 

ASK RESPONDENTS TO FILL IN THE FIRST SECTION OF THE QUESTIONNAIRE 

“BASICS & ATTITUDES AND THEN WAIT. 

BASICS & ATTITUDES 

Question B1. Please tick the correct box for your age range and gender. 

Question B2. Where do you live? 

Question B3. What is the Occupation of the chief wage earner in your Household? 

Question B4. How important would you say that environmental policy is, in relation to 

other things that government is concerned with, such as law and order, or education? 

Please tick one. 

Very important 

Quite important 

Not all that important 

I really don’t care about the environment at all 

Question B5. What do you think should be the main concern for environmental policy in 

this country over the next 10 years? Please rank the options in this table from 1 (most 

important) to 4 (least important). 

Controlling air pollution 

Tackling climate change 

Protecting the countryside 

Protecting the quality of rivers, lakes and the sea 

Question B6. Do you ever visit the countryside for recreation, for work or for both? 

Please tick an option in this table. Short visits (e.g. to walk the dog), days out, 

weekends away or longer holidays and visiting friends and family all count as 

recreation. 

YES – RECREATION 

YES – WORK 

YES – BOTH 

NO, NEVER visit the countryside 

CHOICE EXPERIMENT VALUATION SCENARIO 

EXPLAIN THAT YOU ARE NOW GOING TO READ SOME INFORMATION OUT. 

eftec A- 73 

January 2006



This survey is mainly concerned with your opinions about hill farming areas in Yorkshire & 

Humber. The map on page 1 of your supplement shows your region. 

ASK PARTICIPANTS TO LOOK AT THE DETAILED MAP OF YORKSHIRE & HUMBER 

The green line shows the regional border and the pink shaded areas show the hill farming 

areas of interest. You can see that the main areas of concern in Yorkshire & Humber are 

the Yorkshire Dales and North Yorkshire Moors, but there are also other areas. 

We would like you to think in particular about the different landscape features in these 

areas, such as farm woodlands, hedgerows and moorland. These features are affected by 

the way in which farming is carried out. If there are fewer working hill farms in an area, or 

if they change their practices, then some of these landscape features may change. You may 

get more or less of them, or their quality might alter. 

In these areas, farmers receive special payments from the Government to make up for the 

fact that farming is more difficult, because of the steep ground, and because these areas 

are far away from the majority of customers. 

The Government may change how it pays farmers in these hilly areas. If this happened, the 

main aim would be to try and reduce the bad impacts of future changes in farming on the 

landscape, and to increase any good impacts. However, this policy change would come at a 

cost to people like you, either through higher national or local taxes, or even perhaps 

through charges on people visiting the areas. The government would like to know what 

people think of as good and bad impacts and whether the cost of the policy change is right. 

This is why we are conducting this survey. 

So what are these “Landscape Features” that I have been talking about? Well, the 

landscape features we are looking at in this survey are the following: 

ASK PARTICIPANTS TO LOOK AT TABLE 1 ON PAGE 2 OF THEIR SUPPLEMENT 

heather moorland and bog, rough grassland, broadleaf and mixed woodland, field 

boundaries and traditional farm buildings and farm practices. 

Please read Table 1 carefully - it will help you to make decisions in the next section of the 


eftec A- 74 

January 2006



TABLE 1 


These are areas of heather moorland in drier areas and on steeper slopes, but with 

less heather and more bog in wetter areas. Bogs contain peat, with bog mosses and 

sometimes bog pools. All of these areas are typically used for sheep grazing, and 

may contain many different kinds of birds, insects and plants. 


These areas can often look untidy where the soil is poor, but can be heavily grazed 

by sheep where soil is better. They may look a bit brown or pale at some times of the 

year, but at other times can look very green. Birds also like these areas. 


These woodlands usually consist of a mix of native tree species such as ash, oak 

and hazel. Unlike conifer plantations, these woodlands have an irregular shape when 

seen from a distance (so they don’t look like square blocks on the hillside!). Some of 

these woods are very old, others are more recently planted. 


These are the traditional stone walls (or dykes) and hedgerows seen across the 

upland landscape. Modern wire fences are NOT included in this feature, although 

they have often replaced, or run alongside, traditional stone walls and hedges. 

Traditional farm buildings and farming practices 

Here we mean the traditional farm buildings that can be seen in the uplands, and 

their associated barns and sheds. But we also include traditional farm practices such 

as shepherding. 

We would now like you to think about a number of possible options for future government 

policy in hill-farming areas. These options are shown in the “Choice Sets” which we’re 

going to look at. 

ASK PARTICIPANTS TO LOOK AT TABLE 2 ON PAGE 3 IN THEIR SUPPLEMENT. 

In each one, you will see there are three choices for you to make: the Current Policy, Policy 

Option A or Policy Option B. You’ll notice that if you choose the current policy then this 

comes at no extra cost to your household, but some landscape features will get worse in 

time according to predictions. Policy Option A and Policy Option B come at an additional 

cost to your household every year, but also give you some improvements in the landscape 

features of these hill-farming areas. 

eftec A- 75 

January 2006



TABLE 2 


policy 




Grassland 

Change in area of Broadleaf 

and mixed woodlands 







(-2%) 


(-10%) 


(+3%) 


100m is 

restored 

Policy 

Option A 


(+2%) 


(-2%) 


(+20%) 


200 m is 

restored 

Rapid decline no change 

Policy 

Option B 


(-2%) 


(-2%) 


(+10%) 

for every 1 km, 

50 m is 

restored 

Much better 

conservation 

£0 £20 £10 

As you can see in the “current policy” (POINT), the condition of many of the landscape 

features in Yorkshire & Humber is getting worse: the amounts of heather moorland and 

rough grassland are both falling, there is some restoration of field boundaries and there is a 

rapid decline in the condition of farm buildings and traditional farming practices. However, 

the area of woodland is increasing, and sticking with this option would not create any extra 

cost for your household. 

In Option A (POINT) the area of heather moorland increases, the area of rough grassland 

falls by less, and more field boundaries are restored. There is also more woodland 

compared to the current policy. However, your household would face an additional cost of 

£20 a year to pay for these landscape improvements. 

In Option B (POINT), the extra cost is lower - £10 per year - and you still get some 

landscape improvements over the current policy, but not as much as with Option A. 

So, which would you prefer? The current policy with no extra costs but some losses in most 

landscape features; Option A with an extra cost of £20 per year to you, but some 

improvements; or Option B, with fewer, but less expensive improvements? 

We are now going to give you six more of these choice sets. For each one we would like you 

to pick the option that you would prefer to see happen in the hill farm areas of Yorkshire & 

Humber. You’ll notice that sometimes in the sets you are shown that things get better, and 

sometimes things get worse. You might think that you do not have enough “expert 

knowledge” to make these kinds of choices, but we are interested in what ordinary people 

think about what the Government should be doing. 

eftec A- 76 

January 2006



Please do consider only the hill-farming areas, and only those areas in your region, 

Yorkshire & Humber, as the survey is being conducted in other regions separately. Each 

time, just think about what you would most like to happen. But remember that if you opt 

to pay extra by choosing Option A or B on any set, then this means your household would 

have less money available to spend on other things, or to save. Take your time, make your 

best choice for each of the six sets, and tick your choice in Question B7 of your 


There are no right or wrong answers. Please be honest if you would prefer not to pay the 

extra money. 

In each choice set, you will see that these landscape features can take a number of possible 

values. In the choice sets we show these as % changes over how much there is of each 

feature at the moment – for example, “a 2% loss”. You might like to know what these % 

changes mean for landscape features in Yorkshire & Humber. Table 3 in your supplement 

shows you this. You might find it helpful to think of “1 hectare” as a couple of football 

pitches. 

ASK PARTICIPANTS TO LOOK AT TABLE 3 ON PAGE 4 OF THEIR SUPPLMENT 

TABLE 3 

Heather 

moorland and 

bog 

Rough 

grassland 

Broadleaf and 

mixed 

woodlands 

Current policy: Effects of possible future policy changes 











A gain of 3% is 

equal to having an 

extra 600 hectares 

in addition to the 

current area of 





current total 
















You might like to keep this table visible as you go through your choices so that you can 

refer back to it. 

ASK PARTICIPANTS WHETHER ALL IS CLEAR AND ASK THEM TO FILL IN QUESTIONS 

B7 AND B8, THEN WAIT. 

Question B7. Please go through the six choice sets (on pages 5 to 10 in your 

supplement) and for each one tick which policy option you would prefer. 

eftec A- 77 

January 2006



Choice set 

number 

1 (page 5) 

2 

3 

4 

5 

6 (page 10) 

Current 

policy 

Policy 

Option A 

Policy 

Option B 

Question B8. In making your choices, which of these features concerned you the most? 





Field boundaries (hedges and stone walls) 

Farm buildings and traditional farm practices 

Cost 

[Now please look again at the table you filled in for Question B7. If all of your ticks are in 

the grey column, please skip Question B9 and answer Question B10. Otherwise please 

answer Question B9.] 

Question B9. Briefly, what were the main reasons you were willing to contribute to the 

funding of future policy options? 

Please go to Question B11. 

Question B10. Briefly, what were the main reasons you were not willing to contribute 

to the funding of future policy options? 

Please go to Question B11. 

Question B11. How easy or difficult did you find it to make your decisions about which 

policy option to choose? Please tick one. 

Very easy 

Fairly easy 

Neither easy nor difficult 

Fairly difficult 

Very difficult 

Don’t know 

CONTINGENT VALUATION SECTION 

The policy choices I showed you up to now were about the landscape features of the hillfarming 

areas in the Yorkshire & Humber region. For the final choice, please consider the 

landscape features of the hill farming areas in the rest of England. These are the shaded 

areas on the map of England. 

eftec A- 78 

January 2006



ASK PARTICIPANTS TO LOOK AT THE MAP OF ENGLAND ON PAGE 11 OF THEIR 

SUPPLEMENT. 

Question B12 shows the likely changes in other regions under the most likely government 

policy for hill farming. It shows the current policy contrasted with an alternative future 

policy option. 

ASK PARTICIPANTS TO ANSWER QUESTIONS B12 AND B13 AND THEN WAIT. 

Question B12. In addition to the amount you said you would (or would not) be willing to 

pay for alternative policies in Yorkshire & Humber, would you be willing to pay for the 

cost of an alternative policy in other regions of England? 

In the table below, please tick the grey box if you would prefer the alternative policy 

option at an annual cost to your household of £17, and the other blank box if you would 

prefer the current policy option at no additional cost. 

Change in area of Heather Moorland 

and Bog 

Change in area of Rough Grassland 

Change in area of Broadleaf and mixed 

woodlands 

Change in Restoration of Field 

Boundaries 

Change in Farm Buildings and 

Traditional Farm Practices 

Increase in Tax Payments by your 

household each year 

Please tick one 

Current policy 


(2% loss) 

Loss of 

39,500 ha 

(10% loss) 




restored 

Alternative policy 

option 

Gain of 

11,000 ha 



39,500 ha 



16,000 ha 



restored 

Rapid decline Much better 

£0 £17 

Question B13. What were the main reasons you were not willing to contribute to the 

funding of this future policy option? 

FOLLOW UP QUESTIONS 

EXPLAIN TO RESPONDENTS THAT THERE ARE JUST A FEW MORE BRIEF 

QUESTIONS TO FILL IN. 

Question B14. Going back again just to Yorkshire & Humber - do you ever visit any of 

the areas shaded pink on the map in the region? For recreation, for work or for both? 

Please tick an option in this table 

eftec A- 79 

January 2006



Short visits (e.g. to walk the dog), days out, weekends away or longer holidays and 

visiting friends and family all count as recreation. 

YES - RECREATION 

YES - WORK 

YES - BOTH 

NO, NEVER visit these areas 

Question B15. How often to you visit these areas in your region? Please tick one. 

Every day 

More than once a week 

Once a week 

More than once a month 

About once a month 

At least once every six months 

At least once a year 

Less than once a year 

Have visited at least once in past 

Never 

Don’t know 

Question B16. Including yourself, how many people in your household are (please fill in 

numbers): 

Over 60 years old 

Between 17 years and 60 

years old 

Between 5 and 16 years old 

Below 5 years old 

Question B17. Approximately, how long have you been living in Yorkshire & Humber? 

Question B18. Thinking ahead, which of these phrases best describes how long you 

think you will remain living in the Yorkshire & Humber region? Please tick one. 

Less than 6 months 

At least 1 year 

At least 5 years 

At least 10 years 

I have no intentions of moving out of 

this area 

Don’t know / Not sure 

eftec A- 80 

January 2006



Question B19. Are you or is anyone in your household a member of any of these 

organisations? Please tick any that apply. 

Royal Society for the Protection of Birds 

The Ramblers’ Association 

National Trust 

Friends of the Earth / Greenpeace 

A local wildlife trust or environmental organisation 

A local recreational club (e.g. angling or walking club) 

National Farmers’ Union 

Other environmental organisations 

Specify…………………………………………………….. 

Other farming organisations 

Specify……………………………………………………… 

Question B20. At what level did you complete your education? 

If still studying, which level best describes the highest level of education you have 

obtained until now? 


Primary 

O levels/ GCSE/ CSE/ School Cert./ Intermediate GNVQ / or 

equivalent 

A levels Advanced/ Vocational training 

(HNC/ HND) (BTEC) or equivalent / Advanced GNVQ 

Professional qualification of degree level 

College/ University/ First degree level 

Higher degree (MA, MSc, PhD, etc.) 

Question B21. What is your current work status? Please tick one. 

Self-employed 

Employed full-time 

(30 hours plus per week) 

Employed part-time 

(under 30 hours per week) 

Student 

Unemployed 

Looking after the home full-time / housewife 

Retired 

Unable to work due to sickness or disability 

eftec A- 81 

January 2006



Question B22. Could you estimate approximately your total household annual, monthly 

or weekly income before tax? Choose from one of the categories in this table. 

Remember to include all your sources of income, for instance, pensions, benefits, 

income from savings etc. Your answer is completely confidential. It will be used only 

for statistical analysis. 


Weekly gross Monthly gross Annual gross 

Up to £99 Up to £429 Up to £5,199 

£100-£199 £430-£869 £5,200-£10,399 

£200-£299 £870-£1,299 £10,400-£15,599 

£300-£399 £1,300-£1,699 £15,600-£20,799 

£400-£499 £1,700-£2,199 £20,800-£25,999 

£500-£599 £2,200-£2,599 £26,000-£31,199 

£600-£799 £2,600-£3,499 £31,200-£41,599 

£800-£999 £3,500-£4,299 £41,600-£51,999 

£1,000-£1,499 £4,300- £6,299 £52,000-£74,999 

£1,500-£1,999 £6,300-£8,299 £75,000-£99,999 

£2,000-£2,399 £8,300- 

£10,399 

£2,400 or £10,400 or 

more 

more 

£100,000- 

£124,999 

£125,000 or more 

Don’t know 

Don’t want to say 

Question B23. Finally, what did you think of this questionnaire? Please tick any that 

apply. 

Interesting 

Too long 

Difficult to understand 

Educational 

Unrealistic / not credible 

Other 

Specify…………………………………… 

………………..…………………………… 

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January 2006



Section C – Follow up 

Again not a verbatim Q&A session – but try to cover all topics 

EXPLAIN THAT WE WOULD NOW LIKE TO DISCUSS SOME OF THE ANSWERS THEY 

GAVE. 

C1. Before you were given this questionnaire, did you ever think about the landscape 

and environmental features of hill-farming areas as something you enjoy? 

C2. When you were going through the six choice sets and choosing which policy options 

to go for, what kind of factors affected your decisions? 

IF PEOPLE DON’T MENTION COST TRY TO PROMPT WHETHER THIS WAS AN 

IMPORTANT FACTOR. 

C3. Did you think that you were being asked to pay for hill farming areas in your own 

region or in the country as a whole? 

C4. I’d like to go back now to the first choice set and ask everyone in turn which policy 

option they chose and why. 

[THE ANSWER TO THE FOLLOWING QUESTION MIGHT ALREADY BE EVIDENT FROM 

THE PREVIOUS QUESTIONS ] 

C5. So which of the five things – heather moorland and bog, rough grassland, broadleaf 

and mixed woodland, field boundaries and traditional farm buildings an practices – do 

you think is most important and why? Which is least important? 

C6. I’d also like to go back to questions B9 and B10. Can I ask everyone who answered 

Question B9 what the reasons were that you gave for wanting to pay for alternative 

policies? 

HELEN TO TRY TO CODE ANSWERS USING TABLE GIVEN IN ORIGINAL 

QUESTIONNAIRE 

C7. And those who answered Question B10, what were the reasons you gave for not 

wanting to pay for alternative policies? 

HELEN TO TRY TO CODE ANSWERS USING TABLE GIVEN IN ORIGINAL 

QUESTIONNAIRE 

C8. We’re now going to give you a list of nine other environmental and landscape 

features associated with hill-farming areas. Please look at boxes 1 to 9 on pages12 to 13 

of your supplement. 

Are the descriptions clear? 

If some of these features had been included in the questionnaire (instead or as well as 

the others) do you think you would have been more or less willing to pay for their 

protection? 

C9. Are any of the fourteen features we’ve looked at together not important at all to 

you? 

Helen is now going to collect your questionnaires and check a couple of things. 

eftec A- 83 

January 2006



HELEN TO COLLECT QUESTIONNAIRES AND CHECK WHETHER (1) PARTICIPANT 

ANSWERS AS TO WHETHER THEY VISIT THE COUNTRYSIDE AND SDAS ARE 

CONSISTENT; AND (2) ANSWERS TO QUESTIONS B14 AND B15 ARE CONSISTENT. IF 

NOT WE NEED TO ASK INDIVIDUAL PARTICIPANTS WHY NOT. 

C10. This is the end of our discussion session. Now, as the final point, we would like 

you to look back at the choice cards and the choices you made in question B7. As a 

result of the discussions within the group, would you like to change any of the choices 

you made? If so, indicate what your new choice(s) will be. 

ASK IF THEY MADE ANY CHANGES AND ASK FOR THE REASONS. 

That’s it – thanks very much for your time. 

REMIND PARTICIPANTS TO COLLECT THEIR MONEY BEFORE THEY LEAVE. 

eftec A- 84 

January 2006


Annex 7 – Valuation Workshops – Results and Discussion 

Annex 7 - Valuation Workshops – Results and Discussion 

A7.1 Introduction 

In accordance with procedures used to investigate the validity of the study two valuation 

workshops were conducted. While workshops are somewhat constrained by a similar set of 

conditions to focus groups and other small sample exercises, they perform well as a tool to 

investigate choice processes and expression of opinion. In particular, workshops allow 

discourse, information processing and a period to reflect on choices given the inclusion of 

social (group) input. 

The study under scrutiny in these workshops included choices across putative policies, the 

analysis of which has its foundations in Random Utility Theory (RUT). One of the 

applications of RUT made in this study, and the most complex, is the choice experiment 

(CE). CE, through application of Lancastrian Theory (Lancaster, 1966) allows the analyst to 

assess a composite good through its several attributes, thus there is the assumption that 

some form of informed rational compensatory choice method is employed by respondents 

as they weigh the policy options described in terms of a bundle of attributes. Further 

assumptions are made regarding the theory of information processing and choice making 

outlined, for example, by Heiner (Heiner, 1983). As such the respondents must perceive 

relevance, realism and understand such issues as the scope of the proposals when making 

their choices. 

A7.2 Workshop methodology 

Although the study was concerned with public opinion on specifically rural regions, the 

SDAs, the spending on policies is a national issue of some relevance to all tax payers. 

Recognising this fact the study sample included both rural and urban residents. This division 

was replicated in the workshops; Knaresborough being the rural location and Harrogate 

representing an urban population. 

To replicate the ‘cold canvassing’ used in the study respondents were recruited on the day 

of the workshop, thereby preventing any significant information gathering or opinionforming. 

Efforts were made to simulate the sample frame of the study based on age, 

gender and social classification. 

Each workshop was conducted as a single session following the Protocol (see Annex 6). 

Following an introduction, respondents were asked to complete the questionnaire used in 

the study. The facilitator provided the same information used in the study to the group and 

asked each participant to complete the set of choice tasks as individuals. 

Upon completion a discussion, following the points in Section C of the Protocol, was 

initiated. Together these points examined the issues of relevance, choice strategy, scope, 

alternative attributes and their impact on choice, and respondents’ reasons for their 

choice. Finally, respondents were asked whether they might have altered their opinions 

(and consequently their policy selections) as a result of the discussion. Collectively, these 

discussion points: 

• investigated if the actual choice methods conformed to the assumptions in the analysis; 

• examined the possibility of a bias introduced through scope; and 

• permitted an assessment of the information provided in the study. 

This last issue would be made apparent by respondents changing their policy choices due to 

information obtained in the discussion. 

Probabilistic models used in the application of RUT require large sample sizes, which 

preclude comparing estimates from the workshop questionnaires with those in the main 

study. However, such comparisons are not necessary to the validation process; by 

considering the workshop participants as a representative sample of the main study then 

eftec A- 85 

January 2006



validation is made through comparison of their actual decision-making and information 

processing with underlying theory and assumptions. 

A7.3 Results 

A7.3.1 Attendees 

A total of nineteen persons attended the two workshops; seven in Knaresborough and 

twelve in Harrogate. 

Table A7.1: Valuation Workshop Participants and Logistics 

Groups Knaresborough Harrogate 

Number of participants 7 12 

Gender 3F 4M 4F 8M 

Age 18-34 18-70 

Socio-economic group BC1C2 ABC1C2 

Date 19/10/05 20/10/05 

Time 19:00 18:00 

A7.3.2 Questionnaire (Workshop Protocol Section B) 

In general, respondents were willing to pay for improvements in landscape attributes, with 

only one respondent indicating that he was not wiling to pay for any improvements. Ten out 

of seventeen respondents chose to pay for an alternative to the current policy in every 

choice set. Seven respondents indicated that they would be prepared to pay up to £70 for 

improvements. 

Although we cannot expect to replicate survey results proportionally with such a small 

sample, the respondents did have a range of views on key attitudinal questions, just as the 

survey respondents did. Responses to three questions are compared in Table A7.2. This is 

shown to demonstrate that the workshop respondents were not a select group with totally 

similar views and experiences. 

eftec A- 86 

January 2006



Table A7.2: Comparison of responses to select attitudinal questions 

between the workshop and survey respondents. 

Workshop 

response (%) 

Survey response 

(%) 

Question A1 – how important is environment policy? 

Very 37 45 

Quite 53 43 

Not all that 5 8 

Not at all 5 3 

Question B2 – which attribute was most important in making your 

choice? 

Heather moorland and bog 5 7 

Rough grassland 0 4 

Broadleaf and mixed 

woodland 42 22 

Field boundaries 16 7 

Cultural heritage 21 19 

Cost 16 39 

Question D1 – do you ever visit the SDAs in your region? 

Yes, for recreation 58 67 

Yes, for work 5 2 

Yes, for both 11 12 

No 26 17 

Question D6 – are you a member of an environmental, farming, 

heritage or recreational organization? 

Yes 29 24 

A7.3.3 Discussion (Workshop Protocol Section C) 

While not question and answer sessions, it is usual practice for focus groups to be guided by 

a set of topics for discussion. It is equally common for participants to move through the 

topics without prompting by the facilitator and to volunteer information which supports 

previous comments when discussing subsequent topics. Such natural conversation may not 

follow the order nor be as clearly delineated as the Protocol suggests. For clarity the topics 

and responses are reported below as they appeared in Section C of the protocol. 

Topic C1: Before you were given this questionnaire, did you ever think about the 

landscape and environmental features as something you enjoy? 

Respondents made a clear distinction between urban and rural areas, often considering the 

rural areas to be special places due to the cleaner environment and, for some, the place 

for recreation and relaxation. As such, there was significant evidence in their initial 

response that participants had thought about the landscape and environment, as something 

they were aware of but were less forthcoming on their relationship with it. Of all the 

discussion topics raised, the responses to this were the most dispersed over the session; 

participants tended to volunteer information in support of other responses. For example 

one participant supported his response to C6 by the happy memories he had of enjoying the 

countryside on trips with his father. While he did not at present go to the countryside, the 

impending birth of his first child had impressed on him the importance of preserving those 

areas for future generations’ enjoyment. 

Whilst all participants were at least aware of the landscape and environment there was a 

discernable correlation between use and enjoyment of those features; those who 

participated in activities in SDAs had a higher level of enjoyment and awareness. This was 

clearly demonstrated by one person who had recently taken up mountain biking. He 

mentioned that he was aware of, but knew little about the countryside prior to going 

cycling in the countryside. This abstract awareness was transformed by his experiences, he 

eftec A- 87 

January 2006



was now acutely aware that the enjoyment of his chosen activity is closely linked to the 

quality of the environment and landscape. Others who made use of the countryside offered 

similar observations. 

The opposite view was also expressed by one person in the Knaresborough group. He said 

that while he appreciated that others were interested and even enthusiastic about the 

landscape and environment, and that perhaps in the future he may change his views, it 

simply had no relevance to him. He did not go to the countryside for relaxation, and did not 

participate in any related activities; in fact it was little more that the “bit between towns” 

when he was travelling. Furthermore, while it may be more attractive than driving through 

streets, he felt he could not distinguish between a good and a bad landscape. 

In answer to this ambivalence, respondents raised an issue which touched on the issue of 

scope, a question also raised in the Harrogate group: isn’t the environment essential to us 

all, for our survival and well being? While both groups acknowledged, this they also 

demonstrated a sense of scale by acknowledging that they did not expect the proposed 

schemes to have far-reaching impacts - the effect would be mostly local. For the purposes 

of the validation, this confirms a sense of proportion and suggests no misunderstanding 

regarding the scale of impact which could engender bias due to scope. 

The majority of participants fit somewhere between the disinterest and the active 

participation in activities mentioned above. 

Topic C2: When you were completing the six choice sets what factors affected your 

decisions? 

One respondent in the Knaresborough group consistently chose the no change option; a 

second participant selected only two low cost alternative policy options (Cards B5 & B6), 

but mostly the current policy. The choices made by both were logical and explicable. 

The person who consistently chose no change worked for a contractor laying water supply 

pipes in the countryside. His experience of the countryside had led him to consider it as a 

vast area to which the loss of, for example, 2% of heather moorland and bog would be 

insignificant. By way of illustration, he stated that if he was given a map of the present 

situation and one of the putative future scenarios, the two would be indistinguishable. As 

the only reason for consistently selecting no change this would perhaps suggest a lack of 

relevance or credibility; however the individual also registered a protest against being 

taxed for what amounts to an indiscernible, irrelevant change. 

The second person who chose only low cost alternatives options said that the countryside 

had little relevance for him. 

All other participants decided that the policies were relevant and proceeded to use some 

form of compensatory choice; no respondents used random or heuristic selection. It may be 

worth noting that while it was attempted to replicate the survey conditions during the 

workshops there are a number of differences. Participants in the workshops, unlike 

respondents in the survey, had consented to a session lasting up to two hours and were 

seated in relatively comfortable conditions. As a result they may have had the opportunity 

to give greater consideration to the tasks than those respondents whose day-to-day 

activities were interrupted. The magnitude of this effect, if it occurs at all, is unclear. As 

shall be discussed below, further discussion and reflection had no impact on choice making 

strategy or selection suggesting that any difference is due to unavoidable differences in 

interview format. 

The simplest form of the choice was a summing of benefits associated with each policy: the 

better policy being the one with the most gains. Even this method had some variation, the 

most basic being any gain, regardless of whether it was of 5% or 10% in the area of 

grassland, for instance, was counted as +1, and losses were similarly treated. The best 

policy was the one with the greatest sum of positives. This choice strategy would suggest 

non-specific preferences for improvements as each of the attributes and levels were 

equally valued. These choices were driven by respondents accepting a general 

responsibility for the environment whilst maintaining ambivalence regarding the detail. 

eftec A- 88 

January 2006



Underlying this attitude were limited knowledge on the issues and trust in the 

administering authority and their advisors. Some comment was made on this matter: one 

respondent used the sum of benefits to make her choice but admitted she was unsure if it 

would have been the best landscape for farmers, wildlife, the environment or other things 

she did not know about. 

A more complex strategy took the levels of the attributes into account. To continue with 

the above example, a 5% gain scored +1, the 10% gain would score +2, and policy selection 

would be made by the same criteria of greatest equals preferred. Inherent in both the 

simple and more complex summing of benefits is the assumption of equal value for all 

attributes: restoring 200m per 1km of field boundaries has the same ‘value’ as the 

equivalent incremental change (1 level of the attribute) in rough grassland. Several of the 

people using this strategy appeared to graduate to it as they completed the tasks and 

became familiar with the levels of attributes. Two reasons were suggested by respondents; 

first, they were learning as they completed the tasks and, secondly, they used this to 

decide on policies which appeared to be of similar value using the simple summing above. 

Neither of these simple systems appears to have been consistently applied. The main 

variation was to apply existing knowledge and/or preferences for one or more attributes. 

For example, several participants expressed a preference for the broadleaf and mixed 

woodlands attribute. The reasons included that it was the best for the environment, it had 

financial as well as aesthetic values, and that it had more ecological functions (in the form 

of carbon sequestration and flood prevention) than the other attributes. Policy selection 

was made on one of two methods; either the woodlands carried more weight than the +1 of 

other attributes, or was used as an initial selection criteria. Initial selection would be made 

on the policy which gave the greatest gain of woodland. The initial choice may have been 

subsequently changed if the net losses of the selected policy were too great when 

compared with the lesser gains in forestry and greater gains in other attributes of the other 

policy option. 

A further modification to choice strategy, an application of logic, was used to ‘force’ 

decisions. Given an inability to decide using the above methods above, some logic was 

applied. For example if faced with similar gains in woodlands and a similar sum of gains 

across the four other environmental attributes, then a personal logic would be applied. It is 

important to note that it is an individual’s logic that is used. Applied to the example above 

that logic might suggest to one individual that field boundaries are less important to 

landscapes with woodland because there would be less livestock to contain in landscapes 

with more woodlands/less grazing land. To another individual the same reasons might 

suggest that traditional farm practices and buildings are less important because there is a 

reduction in farming land. Effectively, respondents appear to identify ‘critical attributes’ 

which maintain a consistently high value relative to other attributes; the remaining 

attributes have values which alter according to the make up of the bundle of attributes and 

a personal logic. 

While all of the above strategies were used at some point by most individuals they were not 

applied to every choice task. One respondent pointed out that some policy options were 

“obvious”. Using Card B1 to illustrate she pointed out that there was one small gain 

(woodland) and one loss (field boundaries) in Policy Option B. Selection of Option A would 

have been the obvious choice but for the tax increase of £70. Her options, as she perceived 

them were as follows: Option A is best but too expensive, Option B amounts to paying £10 

for, in total, no gain (one gain + one loss), the Current Policy is similar to B but costs 

nothing and while it has a lesser gain in woodland it also gets more field boundaries; the 

choice of Current Policy “was obvious”. In this example, the cost of the policy was shown 

to be a critical factor – but not because the respondent was not willing to pay in principle, 

but because the cost of the one policy option exceeded her personal threshold price, 

reducing the choice to a decision between the current policy and the affordable option. 

All of the respondents considered the cost of the policy in all choices. Cost was discussed 

both as the annual sum and the calculated equivalent weekly sum; in either form the 

greatest amount was consistently considered by many people to be ‘too much’. 

eftec A- 89 

January 2006



Most participants limited policy choice by setting a maximum amount they would be willing 

and able to pay. Various methods of setting this threshold were suggested based on relative 

prices (one smoker compared the cost of the policy with one packet of cigarettes per 

month), disposable income and perceived value for money. Individuals applied the price 

threshold in one of two forms: a ‘hard’ threshold above which the policy would be rejected 

without further consideration and a ‘soft’ threshold which was subject to some increase if 

the benefits of the policy were considered enough to justify the change. Notable points 

regarding the price vector were: 

1. the highest tax increase (£70) appeared in two choice tasks. Of the 38 opportunities 

to select the high cost policies (19 respondents x 2 opportunities) they were chosen 

on 8 occasions. This gives an acceptance rate of 21%. While a lower acceptance 

rate may indicate a value closer to the marginal WTP Hanley et al. (2000) suggest 

that the effect of a lower price vector is to enlarge confidence intervals and as a 

result the difference in estimated mean WTP of comparable vectors is rarely 

significant. 

2. the threshold WTP used by respondents varied across individuals; that is, an 

individuals’ marginal WTP was within the range of the price vector; 

3. the price was considered as an actual payment - there is no evidence of selecting 

best policy regardless of price; and 

4. there is no evidence of always selecting the least cost policy. 

Topic C3: Did you think you were being asked to pay for hill faming areas in your own 

region or in the country as a whole? 

The information provided in the survey instrument clearly specified the region of the CE 

exercise. All respondents confirmed they had considered only this region. Supporting this 

were some questions and comments from the participants. One noted that local people 

paying for their region made sense, however when asked to consider the national (CV) 

policy choice he noticed that there were few SDAs in the south and that a national payment 

amounted largely to a local payment (from his perspective) if the money was directed by 

the total area of SDA. Some others commented that they did not have any previous 

knowledge of designations and assumed, from the information they were given before the 

choice sets, that the policy was specific to the region. In short, respondents only 

considered the appropriate region. 

Topic C4: I’d like to go back to the first choice set and ask everyone in turn which policy 

option they chose and why 

As previously discussed, the choice strategies used by respondents were consistent (within 

the variations discussed), and as Card B1 contained a policy option with the highest tax 

level, for the majority of participants the outcome was largely a choice between the 

Current Policy and Policy Option B. The decision rested on whether the gain of 7% of 

woodland was worth a £10 tax increase plus 50m per km less field boundaries restored. 

The importance of the tax increase proposed in Option A was tested in two ways: obtaining 

participants’ suggestions for an acceptable value and an iterative bidding game starting at 

£70, and reducing by increments of £5. In both cases, respondents were asked to consider 

the choice task as it was presented and asked at what tax value they would definitely 

select Policy Option A. Both methods suggest an acceptable value for Policy Option A of 

between £20 and £55. 

Topic C5: Which of the five attributes do you think is the most important and why? 

In both groups only broad leafed woodland was suggested as a more important attribute 

due to the perception of its broader ecological function (i.e. carbon sequestration, oxygen 

exchange) than the other natural attributes (moorland and grassland). 

Interestingly, asking participants to consider the importance of individual attributes led to 

discussion in the Harrogate group on the scope of the study. Consideration ranged from the 

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potential function restored traditional buildings would serve other than the aesthetic, 

through questioning what sort of buildings farmers would prefer (modern and convenient 

versus traditional) to what function farmers perform in the countryside. The discussion was 

inconclusive and echoed contemporary debate; are farmers’ simply primary producers or 

guardians of the countryside? Should we import food and so aid development of other 

countries but in so doing increase food miles?, Should we remove farming subsidies 

altogether? 

Although the group did not achieve consensus (an unrealistic expectation!) the debate 

served to give context to the proposals. This caused one individual to note that he selected 

the options he would like to be implemented, but was sure they would never happen 

because he expected European policy to overrule whatever the public chose. 

In the Knaresborough group, the discussion also followed the pattern of expanding from 

discussion of the attributes to the wider context of the study. However the discussion was 

largely concerned with the funding of the policies. One participant questioned how much of 

the tax increase would actually be spent in the countryside. She was unsure of actual 

figures, but if every tax payer paid an additional £30 the total “would be hundreds of 

millions”. The group questioned: 

1. Do we as a country need to spend that much money on the countryside? 

2. Are there better things to spend it on? 

3. What proportion of the total will actually be spent on the countryside? 

4. How would the money be allocated to each of the attributes? 

Most members had assumed that the tax increase funded the proposed changes and had not 

considered that part of the tax may be used in administering the policy. In other words, the 

tax equated to the exact cost of the putative changes. However, without prompting the 

group concluded that it was reasonable and acceptable that the cost of the improvements 

included management. 

Topic C6/C7: What were the reasons you gave for being willing/not willing to pay in 

question B9/B10 (test for ability to code verbal responses) 

Only one respondent consistently chose the Current Policy option, as outlined above, due to 

lack of personal relevance and a protest against being taxed for an irrelevant, indiscernible 

change. 

All those participants selecting at least one paid-for option mentioned reasons which were 

able to be coded in the study schema. 

Topic C8/C9: See boxes 1 to 9 showing some alternative attributes. If some of these had 

been included do you think you would have been more or less willing to pay for their 

protection? 

Both groups had largely similar views on the alternative attributes. 

• Two of the alternative attributes, bracken and gorse, would not change WTP as 

they were assumed to be components of the attributes used in the study. 

• Improved grassland and set aside would likewise have no impact on WTP: the 

former it was assumed would be replaced by other features such as rough 

grassland, while the latter was thought to be one of the mechanisms for switching 

from the present to the proposed feature 

• Several respondents, approximately 20% of the total, would have increased their 

WTP for policies featuring hay meadows. Their beauty and ecological value were 

the main reasons given. 

• Only one participant would have increased her WTP for policies with coniferous 

woodlands; she liked their prominence in the landscape and their atmosphere for 

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walking in. The majority disliked them and would perhaps reduce WTP for polices 

which included completely coniferous forests - they would find mixed more 

acceptable, and favoured wholly broadleaf forests. 

• The attributes water quantity/flood prevention, water quality and greenhouse gas 

emissions would not have changed WTP for a number of reasons. First, all three 

attributes were thought to be the effects of the proposed policies. One respondent 

summarised his view that the actual survey was about landscape and environment, 

the effects of changes to which would impact on water quantity, water quality and 

greenhouse gasses. Landscape and environment are so closely linked that one is 

effectively the same as the other. Secondly, while respondents acknowledged the 

ecosystem services of the landscape, they were aware of the limited environmental 

effects of the proposed policies; the value of the environmental changes was 

limited by their scale. Finally, the issue of personal relevance was considered. Only 

one participant found personal relevance in flood control (she was considering 

buying a house near a canal), water quality was considered presently to be 

acceptable, and while greenhouse gases had relevance for almost everyone, the 

probable impact of the policies vis-à-vis the scale of the problem was not 

significant. 

Topic C10: As a result of the discussion would you change any of your policy choices? 

Despite interesting and informative discussions in which new information, alternative views 

and varied insights were offered all of the respondents stated that they would not alter any 

choices. None of the attributes dissected in the discussion were controversial enough to 

polarise opinion; a ‘better’ landscape/environment is simply that, no new information was 

given to disabuse participants of that belief. Likewise, the discussion was not sufficient to 

change the opinions of those who were not willing to pay. 

A7.4 Conclusion 

A broad spectrum of interest in the landscape/environment was represented in the groups, 

including: (1) those who were not willing to pay due to lack of personal relevance or 

protest; (2) a “realist” who chose the policies he would like to be implemented but did not 

believe they would be; (3) those with a passing interest and (4) those with an active 

interest in countryside-based activities. An equally diverse range of knowledge on the 

countryside and environment was evident. A range of incomes, ages and social categories 

were present; the groups were diverse and included, for example, an unemployed man 

living in a hostel, a chartered surveyor, a student and a retired couple in Harrogate. 

For the purpose of validation it is pleasing to note that there is evidence of logical, 

explicable behaviour which conforms to the assumptions of RUM. 

1. Those finding no relevance or realism in the study have bid appropriately and 

consistently for the no change option. 

2. There is no evidence of random choice by respondents. 

3. There is no evidence of heuristic choice. 

4. Respondents have considered all the attributes in the policy. This has been through 

the various strategies of ‘simple summing’ (all increases are considered with equal 

weight), ‘weighted summing’ (the level of the attribute is valued incrementally), 

‘personal weighting of attributes’ (a selected attribute has greater weight than 

others) and finally a logical deduction. The more complex strategies tended to be 

applied to the more difficult choices. 

5. Acceptance rates for the maximum tax (21%) are higher than the recommended 

10%. However this in not excessive and should not present any difficulties to model 

estimation. 

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6. Respondents considered appropriately the tax in policy choices; increases beyond 

their threshold WTP were rejected. 

7. Bidding games on Card B1 obtained a range of WTP values from £20 to £55 for the 

greatest improvements option suggesting there is no starting point bias or centring 

of WTP. 

8. The geographical limits of the study were maintained in policy decisions. 

9. There is no bias due to scope. The wider context of the national and global 

environment was discussed but was considered to be beyond the scale of this study, 

respondents did not change their choices as a result of the discussion. 

10. A broader ‘choice set’ than that presented in the study was discussed, for example 

the possibility of ending all agricultural subsidies. Respondents considered such 

matters to be beyond the scale of this study and did not change their choices as a 

result of the discussion: a clear demonstration that the necessary condition of 

Independence from Irrelevant Alternatives (IIA) demanded in the assumptions of 

RUT modelling has been fulfilled. 

11. Of the alternative attributes only hay meadows would have had any impact on WTP; 

the scale is not known but this affected four participants. All other attributes had 

little or no effect as they were viewed largely as components of the study 

attributes. 

12. Discussion, information and a period of reflection did not cause respondents to 

change their original policy selections. 

Information provided to respondents was generally sufficient to support informed decisionmaking. 

One exception was noted which may have a bearing on the both the CE and CV 

estimates. The northern regions have a relatively large proportion of SDA. This presents no 

issues for the CE choice tasks; however in the CV task respondents are asked to consider all 

SDAs in England. As there are fewer SDAs in the south of the country respondents queried 

how the funds would be distributed. “Is the north subsidising the south or vice-versa?” one 

participant asked. He went on to suggest that he may have changed his CE policy choices if 

this national split had been explained earlier. 

Finally, rightly or wrongly, it is commonly assumed that much of British agricultural policy 

is actually European Policy emanating from Brussels. To present putative changes in 

agricultural policy without any reference to Europe is an omission some respondents 

thought unrealistic. 

There is therefore a great deal of evidence in the behaviour of the participants which 

indicates the theoretical validity of this study. While the technical information was clearly 

sufficient to support the choice making of individuals, the lack of reference to European 

Regulations and the implied autonomy of the Government to implement agricultural policy 

annoyed at least one participant and are likely to be noted as a significant omission by 

more informed individuals. 

References 

Lancaster, K. (1966). A New Approach to Consumer Theory. Journal of Political Economy, 

74, 132-157. 

Heiner, R.A. (1983). The Origin of Predictable Behaviour. The American Economic Review. 

73(4), 560-595. 

Hanley, N., Adamowicz, W. and Wright, R.E. (2002). Price Vector Effects in Choice 

Experiments: An Empirical Test. Paper presented to World Congress of Environmental and 

Resource Economists, Monterey, CA, June 2002. 

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Annex 8 – Peer Reviewer Report 

Annex 8 - Peer Reviewer Report 

Comments on EFTEC (2005) “Economic Valuation of Environmental Impacts in the 

Severely Disadvantaged Areas: Draft Final Report to the Department for Environment, 

Food and Rural Affairs” 

Ian J. Bateman 

CSERGE 

School of Environmental Sciences 

University of East Anglia 

16 th November 2005 

Introduction 

• This document reports on a study to estimate the non-market benefits of changes in 

land use policy and practice in the Severely Disadvantaged Areas (SDA) of England. 

Specifically the study applies a stated preference approach to eliciting monetary 

measures of preferences for a range of policy options. The approach adopted for 

estimating these values is the choice experiment (CE) method as described by 

Adamowicz et al., (1994, 1998, 1999), Bateman, et al., (2002), Bennett and Blamey 

(2001), Holmes and Adamowicz (2003), Louviere (2001), Louviere and Hensher 

(1982), Louviere, et al., (2000) and others. This choice of methodology is, from an 

economic-theoretic and policy perspective, appropriate, being compatible with 

cost-benefit analysis (CBA), providing that the validity of the resultant valuation 

estimates can be robustly established. 

• Overall this is a high quality application addressing arguably one of the most 

difficult subjects for valuation. That degree of difficulty is reflected in a number of 

issues regarding which I comment below. 

• Furthermore, the level of reporting is generally good although latter sections were 

incomplete in the version sent to me. There are a few grammatical errors and odd 

changes of tense (particularly from past to present) which I will not dwell upon. 

Neither will I repeat the basic elements of the study, such as defining the good 

under consideration, as these are clearly stated in the report. Rather I will simply 

list my comments and concerns as follows using the ordering of the report as a 

template. 

Section 1: Introduction 

• An initial concern arises regarding a potential ‘adding-up’ problem. There are 

numerous SDAs across England and given the constraints of the study not all could 

be valued at once. This could cause a problem if (as seems likely) different areas 

are partial substitutes for each other. In such a case then values for individual 

areas cannot straightforwardly be added together as such a procedure would lead 

to an over-estimation of total values. It was not clear to me how the methodology 

developed would address this issue. 

Section 2: The Upland Attributes and Their Forecast Changes 

• An exhaustive list of potential land use attributes was considered. I am not an 

ecologist and cannot verify the accuracy of the various attribute descriptions. 

However, the estimated rates of land use change are in line with changes in general 

marginal land use during the specified period (1990-8). 

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• Four policy change scenarios are presented. These are developed from various 

permutations of Common Agricultural Policy (CAP) reform, Environmental 

Stewardship Scheme (ESS) implementation and differing levels of upland farming 

subsidy and consequent land use or abandonment. One could always imagine 

further permutations, but those chosen cover a good range of likely policy scenarios 

and consequent impacts. In theory this flexibility is enhanced by adoption of the 

chosen CE valuation methodology. This approach estimates the marginal value of 

each attribute (i.e. the value generated by a single unit change in that attribute) 

which should allow for further permutations to be examined (however, note 

subsequent comments on the implementation of the CE study). 

• Section 2 concludes by examining the impact of each scenario upon each of the 

highlighted land use attributes. Again, not being an ecologist, I am unable to verify 

the accuracy of these predictions but to the lay audience they seem credible. 

Section 3. Valuation Methodology 

• A variety of methods are employed to value changes to individual land use 

attributes. Individually these appear perfectly reasonable. However, as mentioned 

in relation to Section 1, my concern here is that there may well be substitution 

effects arising here. For example, a loss of set aside land might be either 

completely, partially or not at all compensated for by changes in the area of 

heather moorland. It is unclear to me how such substitution effects were allowed 

for. 

• The comments regarding methods are defensible. It is my own personal opinion that 

the doubtless advantages of the CE approach (such as the estimation of marginal 

attribute values mentioned above) have to some extent led practitioners to be less 

critical of the method than other approaches, such as contingent valuation (CV), 

which have been extensively researched and for which limitations are well known. 

My own research suggests that some of the elicitation anomalies documented in the 

CV transfer over into a CE context and that the latter has other foibles of its own. 

That does not in any way mean that I feel the choice of approach was incorrect. 

However, some recognition of limits might have been appreciated. 

• The section on benefits transfer is well informed and even handed. However, it 

should spell out the proposed methodology for adjusting values between sites. 

• The focus group exercises where intended to test out the robustness and clarity of 

the CE questionnaire. My one concern here is that the groups used seem highly 

weighted towards high prior experience of SDAs. This means that they did not 

inform the design with respect to how low experience respondents would cope with 

the questionnaire. 

• The sample size used was appropriate (1,800 local interviews plus 300 interviews 

with those who live far from SDAs). This is a strong point of the study. 

• The survey combines two forms of valuation exercise: a choice experiment 

valuation section for SDAs in the respondent’s own Government Office Region (GOR) 

and a CV question for SDAs in the rest of England. I do not understand the rationale 

for this simultaneous change in both valuation method and geographical scope of 

the good. Given that we know that different valuation methods are subject to 

certain measurement effects, such a simultaneous switch would appear to 

undermine the comparability of resultant value estimates. 

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• The CE uses a very interesting mix of attribute levels allowing the researchers to 

examine simultaneous gains and losses across a range of attribute levels (but see 

subsequent comments). 

• The combination of a relatively high number of attributes (six) each having a 

number of levels (either three or six) means that the total potential number of 

options (the ‘full factorial’) is large even given the substantial sample size 

employed. As a consequence the researchers adopt a ‘main effects’ design which 

effectively reduces the amount of information collected regarding the interaction 

of changes across levels (e.g. how a gain in say heather moorland is traded off 

against a loss in rough grassland). Given that interaction effects can be substantial 

this is a point of some concern and I would be interested in hearing the researchers 

views on the effects of this aspect of the design upon the applicability of resultant 

value estimates. 

• Given that we have a main effects design the decision to present respondents with 

just six out of the total main effects set of 18 choice questions was a good one and 

is to be applauded given the doubtless temptation to load respondents with more 

choice tasks and thereby generate substantial increases in response data. The 

danger of the latter strategy (avoided by the researchers) would be that where 

choices are cognitively demanding (see below) response fatigue sets in quickly 

meaning that answers are of variable certainty. 

• Arguably my main concern (and one which I highlighted in previous comments 

during the design stage) is in respect of the cognitive load imposed upon 

respondents by the complexity of choice tasks such as that illustrated on p.22. I 

still feel that these are very difficult questions for respondents to answer (I 

certainly struggled even with this one example). A number of related issues arise 

here: 

o Do respondents understand the various attributes which make up an option? 

o Do they understand the changes in attribute levels detailed in those 

options? 

o Do they have economic preferences regarding those options (do they care in 

a manner which relates to any formal economic value)? 

o Can respondents deal with the complexity of the choice questions? 

These questions are, I feel, only partially answered by the subsequent analysis of 

data. The danger is that, faced with such tasks, unless supportive answers to all of 

these questions can be established, then we may be concerned that respondents 

may have ‘constructed preferences’ (Clark, 1988; Hsee & Kunreuther, 2000; 

Kahneman, Schkade, & Sunstein, 1998; Loewenstein et al., 2001; Mitchell, 1989; 

Packard, 1957; Powell, 2001; Slovic, 1995; Slovic et al., 1991, 2004). Such 

preferences are malleable, being formed in part by the question frames used to 

elicit responses. As such they are not commensurate with standard economic 

preferences. However, I do wish to stress that there is a danger here of asking the 

researchers to pass tests which are more stringent that those normally required by 

policy makers. Very few valuation studies conduct any assessments of whether 

preferences are constructed or not. Furthermore, as discussed subsequently, the 

researchers do undertake conventional tests of theoretical consistency. Therefore 

Defra may wish to ignore concerns about preference construction on this occasion. 

• Comments regarding the difficulties of comparing the CV and CE exercises 

(discussed on p 23) are as outlined previously. 

• The researchers also conduct a number of valuation workshops. My concern here is 

that the value generated by such group exercises do not appear to be 

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commensurate with the individual willingness to pay (WTP) values which underpin 

conventional CBA. 

Section 4: Valuation of Landscape – Literature Review 

• This is a good review. My only comment is that it would be worth citing the 

excellent review by Munro and Hanley (1999) in respect of information effects in 

valuation studies. 

Section 5: Valuation of Attributes Not Included in the Choice Experiment 

• I have no familiarity with the Environmental Landscape Features (ELF) model. My 

main comment is that it needs to be established that values emanating from such a 

model are commensurate with incorporation with a CBA. Accepting that I am not 

familiar with the model, the resultant value estimates for hay meadows (p.29) 

seem high to me. I am unsure what the implications would be for a CBA but they 

would appear to be substantial. 

• The methodology adopted does not appear to be able to account for values derived 

from the interaction of land use types. Given that a diversity of land uses may well 

be considered to be a desirable feature of a landscape this seems an issue which 

should be considered in future research. 

Section 6: Stated Preference Survey: Summary and Results 

• Page 32 states “Question A1 asked whether respondents considered environmental 

policy to be important in relation to other areas of government expenditure.” This 

seems a leading question, likely to induce pro-environment biased answers 

(detailed on page 36) which should not be given much weight (they are susceptible 

to the type of focussing bias commented on by Daniel Kahneman in his Nobel Prize 

acceptance speech). However, it is not at all a central part of the study design. 

Section 7: Validity Testing 

• Generally the sampling looks much better than most valuation studies and as a 

result provides a much firmer basis for policy use than prior research. Perhaps as a 

result of the rigour of sampling and reporting it is easier to detect problems with 

that sample (p.35) such as the relatively high income of the NW sample (which is 

commented upon) and the unusually high educational attainment level of the WM 

sample (which is not investigated further). Item non-response for the income 

question is also high (p. 39) As I say, it would be churlish to criticise here as most 

other studies have done such a poor job of sampling that any inspection of 

representativeness is impossible. 

• The analysis of protest bidding is robust throughout however results indicate a high 

level of such protests. Indeed they are higher than those indicated on p.41 (where 

they range up to 44% of subsample size) as this only documents cases where 

protests occurred in both the CE and CV exercises. [Note: this is a 

misinterpretation by Professor Bateman: the table referred to details protest 

votes for the CE and CV exercises separately.] 

• In commenting upon the reasons for zero WTP responses the horizontal axis of the 

graph on p.43 appears to be incorrectly labelled. 

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• Tests of face validity (p.43) are somewhat mixed. In particular some 41% of 

respondents appear to have encountered some problem with the survey 

questionnaire (either “Too long”, “Difficult to understand” or “Not credible”). 

• In reporting WTP functions it would be useful to report some more raw data on 

choices with respect to different levels of attributes. 

• The construct validity WTP functions reported on page 45 are useful although I 

wondered why a pooled model was not reported using say a continuous variable 

defining distance to the relevant SDA being valued as an indicator of proximity (and 

distance decay on values). Nonetheless the reported analysis has been carried out 

to a typically high standard. There are a few results of concern. In particular a 

number of significant variables switch signs across subsamples (AGE, GENDER, 

VISFREQ (which is commented upon) and RURAL). Interestingly a number of the 

choice attributes are typically not significant predictors of choices. This is arguably 

a concern as it may reflect a lack of preference strength. It could well be that 

respondents either do not care about this issue or did not understand the 

complexity of the question. Either interpretation would raise questions regarding 

the validity of incorporating results within subsequent CBAs. 

• How was the possibility of status quo bias dealt with in the analysis? It does not 

appear to be a feature of the reported WTP functions. 

• The analysts are candid about problems such as the unexpected signs on the 

ENVIMP and VISFREQ variables. Nevertheless such findings do raise some concern 

here. Could the VISFREQ result be a product of the national sampling strategy. For 

example, I live a very long way from the south west of Cornwall. Yet it is my 

favourite area of the UK and I have a high WTP for its preservation. Because of this 

I would have a much higher probability of agreeing to be surveyed if I knew from 

the outset that the survey concerned Cornwall. Such a survey might possibly reveal 

an unexpected relation with visit frequency as my proximity means that I do not 

visit frequently despite my high valuation of the area. However, for such a result to 

arise the survey would have to be conducted somewhat improperly. Another 

possibility is that the high item non-response for income is contributing to this 

result. Yet a further possibility is that this reflects the omission of substitute 

availability from the analysis. If those who visit frequently are those who are more 

interested in outdoor pursuits and have as a consequence moved to areas of high 

substitute availability then such a finding may result. Finally, this anomaly might be 

alleviated by employing the pooled model with distance variable suggested above. 

The distance variable may supersede visitor frequency showing that this is actually 

the driving force behind the anomaly. 

• The CV analysis indicates that one third of responses were omitted. For some of 

these this will be due to item non-response, for others it will be as a result of 

protest bids. This should be clarified, but either way this is a high proportion. The 

finding that residents in the West Midlands area have the highest WTP does not 

seem to have an obvious explanation (although, as pointed out by the researchers, 

this may reflect the ENVIMP and VISFREQ anomaly which persists here). 

• Finally there are no clear tests for preference construction (although see my 

comments above regarding this). 

Section 8: Willingness to Pay Results, Aggregation, Policy Implications and Conclusions 

• A concern which I feel I should again reiterate is the lack of consideration of 

interaction and substitution effects. It is not clear to me that the findings allow us 

to understand how landscape attributes interact or how different land use scenarios 

might substitute for each other. Perhaps this is something which I have 

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misunderstood in the report in which case I would welcome guidance from the 

researchers. However, in the absence of such information it seems difficult to know 

how these results should be used in policy appraisal. I should note that this seems 

to be a general problem. In particular the Environment Agency PR04 exercise is 

clearly vulnerable to criticisms upon these grounds. 

• Because of the above issue and the switch in goods considered it seems very 

difficult to meaningfully compare the CE and CV values. The simultaneous switch of 

methods and scope of goods to my mind precludes such comparison. 

• The aggregation section is clearly still being written (this is acknowledged in the 

report) and so cannot really be commented upon. I feel that the researchers need 

to be very clear about the aggregation procedure adopted. The use of transfers and 

policy implications also needs to be clarified. 

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Adamowicz W.L., Boxall, P., Louviere, J.J., Swait, J. and Williams, M. (1999) Stated 

preference methods for valuing environmental amenities, in Bateman, I.J. and Willis, 

K.G. (eds.) Valuing Environmental Preferences: Theory and Practice of the Contingent 

Valuation Method in the US, EU and Developing Countries, Oxford University Press, 

Oxford, 460-479. 

Adamowicz, W., Boxall, P., Williams, M., Louviere, J. (1998) Stated preference approaches for 

measuring passive use values: choice experiments and contingent valuation, American 

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Adamowicz W.L., Louviere, J.J. and Williams, M. (1994) "Combining stated and revealed 

preference methods for valuing environmental amenities" Journal of Environmental 

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Bateman, I.J., Carson, R.T., Day, B., Hanemann, W.M., Hanley, N., Hett, T., Jones-Lee, M., 

Loomes, G., Mourato, S., Özdemiroğlu, E., Pearce, D.W., Sugden, R. and Swanson, J. 

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Bennett, J. and Blamey, R. (Eds). (2001) The Choice Modelling Approach to Environmental 

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Clark, E. (1988). The want makers: Inside the world of advertising. New York: Penguin 

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Holmes, T. and Adamowicz, W.L., (2003) Attribute-based methods, in Champ, P.A., Boyle, 

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Hsee, C. K., & Kunreuther, H. (2000). The affection effect in insurance decisions. Journal 

of Risk and Uncertainty, 20, 141-159. 

Kahneman, D., Schkade, D., & Sunstein, C. R. (1998). Shared outrage and erratic awards: 

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Psychological Bulletin, 127(2), 267-286. 

Louviere J (2001) "Choice experiments: an overview of concepts and issues" in Bennett J 

and Blamey R (eds.) The Choice Modelling Approach to Environmental Valuation. 

Cheltenham: Edward Elgar. 

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Louviere, J. and Hensher, D. (1982) ‘On the Design and Analysis of Simulated Choice or 

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