NCEE Working Paper
National Water Quality Values in
New Zealand: Policy-Relevant
Stated Preference Estimates
Patrick J. Walsh, Dennis Guignet, and Pamela
Booth
Working Paper 22-02
May, 2022
U.S. Environmental Protection Agency |L|f*pp gf
National Center for Environmental Economics livtt flr
https://www.epa.gov/environmental-economics env'ronmental^conomics
-------
National Water Quality Values in New
Zealand: Pol icy-Re levant Stated
Preference Estimates
Patrick J. Walsh^8, Dennis Guignetc, Pamela Booth8
Abstract:
Governments need tools to analyze trade-offs and inform freshwater policy. Although there is a large stated
preference (SP) literature valuing changes in freshwater quality, the estimates often cannot be transferred to policy
analyses. Obstacles to benefit transfer include (i) difficulties in scaling up local estimates to the national level, (ii) the
use of water quality attributes that cannot be linked to policy-relevant measures, and (iii) surveys with water
quality changes that don't represent realistic policy. Focusing on rivers and streams in New Zealand, a country that
has received international attention for efforts to protect its water resources, we develop and implement a
nationwide discrete choice SP study that can be more appropriately used in benefit transfer. The stated provision
mechanism and environmental commodity being valued are specified at the regional council-level, which is the
administrative unit for policy implementation. The survey is administered on a national scale, to just over 2,000
respondents. Therefore, our results can easily be applied to regional freshwater policies or scaled up to inform
federal actions. The discrete choice experiment attributes - nutrients, water clarity, and e. coli - were chosen
because they align with government policy levers and were found to be the most relevant and salient to the
general public. Estimation results suggest people are willing to pay for improvements in all three water quality
attributes with magnitudes that are roughly comparable to a recent Auckland referendum vote on a water quality
tax. We also find that willingness to pay varies across regions, types of recreation that a user engages in, and other
respondent characteristics, although notable unobserved heterogeneity remains unexplained. To illustrate the
utility of our study, we apply the results to a recent policy analyzed by New Zealand's Ministry for the
Environment and estimate nationwide annual benefits of NZ$115 million ($77 million USD).
Keywords: Water Quality, Benefit-Cost analysis, Stated Preference, Choice Experiment, Willingness to pay,
Valuation
JELCodes: Q25, Q51, Q58
DISCLAIMER
The views expressed in this paper are those of the author(s) and do not necessarily represent those of the U.S. Environmental Protection Agency
(EPA). In addition, although the research described in this paper may have been funded entirely or in part by the U.S EPA, it has not been
subjected to the Agency's required peer and policy review. No official Agency endorsement should be inferred.
Acknowledgements: We would like to thank Geoff Kerr, Ronlyn Duncan, and Chris Moore for comments on earlier versions of the manuscript.
This work was partially funded by Ministry of Business, Innovation and Employment funded programme "SmarterTargeting of Erosion Control
(contract C09X1804), as well as Manaaki Whenua-Landcare Research funding.
1
-------
I. Introduction1
New Zealand's freshwater is a critically important resource that fuels agriculture, recreation, cultural practices, and
various other activities and productive resources. New Zealand has had many national and regional efforts aimed
at improving water quality2 and received international attention in 2017 by declaring the Whanganui Rivera legal
person.3 However, the quality of New Zealand's freshwater waterbodies continue to decline: more than two-thirds
of rivers exceed the government's nitrogen or phosphorous limits (MFE and Stats NZ 2019), and in a recent
national survey the public ranked the condition of rivers and lakes the lowest among New Zealand's environmental
amenities (Hughey et al. 2016). To inform substantive change, the government needs tools to analyze trade-offs in
water quality policy options. Section 32 of New Zealand's Resource Management Act (1991) requires an
identification and assessment of the benefits and costs of environmental policies and rules, but existing literature is
not well-suited to analyze national policy, especially with water quality.
Although there are multiple studies on water quality valuation in New Zealand, including several stated preference
(SP) studies (see (Marsh and Mkwara 2013) and (Harris et al. 2016)), applying this existing literature to government
policies has been difficult. These difficulties arise from several issues, including scaling up local estimates to the
national level, studies using large water quality changes that do not represent actual policy changes, and the use of
subjective or aggregate water quality variables that cannot be linked to policy-relevant measures. These are
common issues found throughout the international literature on water quality valuation (Moran and Dann 2008,
Griffiths et al. 2012, Newbold et al. 2018).
We designed and implemented a national SP survey with explicit attention given to the use of the results for future
benefit transfer. Our discrete choice experiment utilizes three water quality parameters - nutrients, water clarity,
and e. coli levels—chosen to align with government policy levers and to be relevant and salient to the public. The
choice experiment presents policy changes at the regional council level, which corresponds to the administrative
unit for most environmental policies in New Zealand.4 The policy changes presented to respondents are also more
in-line with the outcomes of actual policies, as compared to many past studies that present unrealistically large
water quality changes in the environmental commodity (Newbold et al. 2018). Furthermore, the borders of New
Zealand's regional councils are based around watersheds and catchments, so there are less cross-boundary
pollution concerns compared to administrative units in other countries. The results of this study are particularly
useful for benefit cost analysis within New Zealand, and the methods provide a framework for studies in other
countries to better align with policy.
We find that people are willing to pay for improvements in all three water quality parameters, and identify
respondent characteristics that drive observed heterogeneity in willingness to pay (WTP). Accounting for such
heterogeneity allows the results to be tailored to sub-national areas in a benefit transfer, at the same time we do
find and control for significant unobserved heterogeneity in WTP. We apply our results to a recent water quality
policy proposed by New Zealand's Ministry for the Environment (MFE), to reduce sediment runoff (Neverman et
al., 2019). Benefits transfer based on our survey results suggests nationwide annual benefits of about NZ$144
million (2018, or approximately $99 million USD), and illuminates notable differences between regional councils.
We compare our results to a recent municipal vote on an Auckland property tax designed to raise over $500
million NZD in the next ten years for water quality improvements. The vote was successful, with the resulting tax
applied to commercial and residential buildings, which exemplifies the large values residents have for water
quality.
1 The author's affiliations are as follows: AUS EPA, National Center for Environmental Economics,
BManaaki Whenua-Landcare Research, New Zealand
¦-Appalachian State University, USA
Primary Contact: walsh.Datrick.igepa.gov
2 https://www.pce.parliament.nz/publications/managing-water-quality-examining-the-2014-national-policy-statement
3 https://www.nationalgecgraphic.com/culture/2019/04/maori-river-in-new-zealand-is-a-legal-person/
4 New Zealand is composed of 16 different regional council areas.
2
-------
II. Background
Freshwater resources are an integral part of the cultural heritage, economic development, and national character
of New Zealand (NZ) (Ambreyetal. 2017, Awatereetal. 2017). From a Maori world view, the separation of
Ranginui (sky father) and Papatuanuku (earth mother) produced freshwater, emphasizing both its importance and
connection to Maori people, who share a whakapapa (genealogy) with Ranginui and Papatuanuku.5 Given this
importance, there are several existing water quality valuation studies, but many are unpublished, in the grey
literature, or are government reports (Miller 2014, Phillips 2014, Tait et al. 2016)or consulting reports that do not
yield original estimates (Marsh and Mkwara 2013). Several studies focus on only one region of NZ (Tait et al. 2011,
Marsh and Phillips 2015). Transferring the results of these case studies to policies and populations in other areas or
at the national level is difficult and could result in large errors (Smith and Pattanayak2002).
The NZ-based SP literature has examined several different water quality indicators. Some of these indicators are
related to agricultural practices, such as riparian buffer restoration (Cullen et al. 2006) or nutrient leaching
(Baskaran et al. (2009), Takatsuka et al. (2009)). Tait et al. (2011) value the ecological condition of waterbodies
using poor, fair, and good quality categories. These categories are described by the type of weeds present, percent
algae cover, and the types of insects and fish species present. Swimming suitability indicators of waterbodies have
also been used (Marsh (2012), Miller (2014), Miller et al. (2015)) to represent recreation and health impacts.
Marsh and Phillips (2015) use several different indicators of water quality alongside a qualitative swimming
suitability measure, including ecological health, salmon and trout condition, and tributary water quality, presented
qualitatively as good, satisfactory, not satisfactory, or poor. Translating many these indicators and qualitative
categories to marginal changes, as predicted from policy projections, is difficult and generally inappropriate. Tait et
al. (2017) use qualitative indicators like poor, moderate, and good for water clarity and ecological quality. However,
they directly link each of their attributes to objective ranges in the underlying water quality parameters. For
instance, poor ecological quality is defined as a Macroinvertebrate Community Index score less than 80, and poor
clarity is defined as visibility of less than 1.1 meters (although it is not clear if those ranges were presented to
respondents).
Johnston et al. (2012) provide guidelines for including ecological content in SP surveys. They note that less
structured treatment of attributes can cause problems with subsequent welfare estimation as respondents'
internal conceptualization of the commodity may be different from that presented or intended by the researchers.
This can be a complicated balancing exercise with water quality because the commodity itself has multiple
dimensions that can be difficult to communicate to survey participants.
The size of the change presented to respondents creates another issue with using previous SP estimates for
benefit transfer. Miller etal. (2015), for example, have respondents compare policies that result in 0%, 20%, or
40% improvements in the percent of sites suitable for swimming. There are few plausible policies that could
improve water quality (or reduce nutrient inputs) by that large of a change. Large changes in the corresponding
water quality measures, on the order of 20% to 50%, are often posited in surveys (e.g. (Baskaran et al. 2009)). A
meta-analysis of 140 observations from 51 stated preference studies of water quality (USEPA 2015), where quality
was represented on a scale of 0-100 with 100 representing pristine waterbodies found that less than 10% of the
observations used water quality indices measures under 10 (Figure l).6
5 See httpsi//environment.govt.nz/assets/Pyblications/Files/oyr-freshwater-2020.pdf for additional detail.
6 The 0-100 scale is the water quality index, which has been used in the stated preference literature and EPA regulation.
3
-------
Figure 1: Distribution of Water Quality Index (WQ)) Changes in 51 Stated Preference Surveys (USEPA, 2015)
20 30
WQI Change
III. Survey Design and Implementation
We considered all of the aforementioned gaps when designing the SP survey instrument used in this study;
including: identifying water quality measures and changes that matter to the general public and that could be
accurately understood by respondents, are realistic, and that could be directly linked to objective policy-induced
changes. The ultimate commodity being valued in the SP survey is improvements in the quality of rivers and
streams in the regional council where a respondent resides. The survey was implemented in 2018 and 2019.There
are two versions of the survey, one for the North Island and one for the South Island of New Zealand. The surveys
are identical except for bar graphs illustrating current attribute levels for each region on that island.
The survey instrument was developed and refined using focus groups and cognitive interviews.7 Six focus groups
were conducted in total during May and June 2018 at three different locations: two focus groups each at two
urban locations (Auckland and Wellington) and two in a rural area (Hawke's Bay). Input from them was used to
refine the survey text and questions, identify relevant water quality attributes, and communication and
presentation. To further refine the survey instrument, ten cognitive interviews were conducted. Eight of the
cognitive interviews were in Wellington and two were held in the rural Wairarapa area.8
Depending on where a respondent lives, the survey begins with a map of the North or South Island that includes
the regions and the major rivers on that island. To emphasize consequentiality and credibility of the survey, the
instructions remind respondents' that their"... answers will help inform policy makers" and that the baseline data
are provided by the MFEand regional councils. Respondents are then asked questions on recreational use and
7 The consultancy firm Colmar Brunton was used to help organize and run the focus groups and cognitive interviews.
8 A diverse set of participants were recruited for the focus groups and cognitive interviews. For example, the participants in the cognitive
interviews had the following attributes-Gender: 4 male, 6 female; Age: 18-39 years (4), 40-59 years (5), 60 years and over (1); Ethnic groups: NZ
European (7), Maori (3); Educational history: School leavers / no qualifications (5), Tertiary-educated (5); Household income: up to $70,000 (3),
over $70,000 (7).
4
-------
visitation to rivers and streams in their region, followed by introductory text defining each water quality attribute
and figures of the current baseline levels in each region. Questions about awareness of the attributes are also
presented.
The policy scenarios, provision mechanism, and payment vehicle for public programs to improve water quality in a
respondent's region are then introduced. To minimize hypothetical bias and enforce consequentiality (Johnston et
al. 2017, Vossler and Zawojska 2020) respondents are reminded to act as though their household is actually facing
the costs presented and that their responses could influence future policies and programs, as well as costs to their
household. A generic regional policy is described as the provision mechanism. The payment vehicle is specified as a
permanent increase in a household's general cost of living. We provide examples of how a household's monthly
living costs may increase, including increases in home maintenance costs, utility bills, rent, and food and other
prices.
The survey then presents respondents an example choice question, followed by three separate discrete choice
questions. Each choice scenario includes a status quo option and two policy alternatives. In the status quo option
water quality attributes remain at their current levels, while in the two policy options there are improvements in
one or more of the water quality attributes, as well as an associated permanent increase in monthly living costs.
The final survey instrument includes three water quality parameters: water clarity, nutrients, and e. coli. These
parameters appear in several upcoming NZ freshwater policies and are well known to the public.9 Statistics NZ
includes these parameters in their list of central water quality tracking indicators (see
https://www.stats.govt.nz/indicators/). The representation of each parameter is also chosen to match policy-levers
and is informed by input from the focus groups and cognitive interviews. Water clarity is expressed as the average
visibility for rivers and streams in a respondent's region, and is measured as Secchi disk. Nutrients are measured as
the percent of rivers and streams in the region that have nutrient levels considered acceptable for aquatic life by
official nutrient limits.10 Similarly, E. coli is measured as the percent of rivers and streams in the region where
concentrations are low enough to be considered suitable for swimming, wading, and fishing.11
While the three water quality attributes described are correlated and the ecological endpoints that individuals
directly care about may relate to more than one attribute in some cases (Tait et al. 2011), distinctions are drawn in
the survey as to what each attribute primarily reflects. Excessive levels of nutrients are described as adversely
impacting aquatic ecosystems, although reduced aesthetics are also mentioned. Water clarity is described as how
"murky or cloudy" the water visually appears. E. coli is described in terms of how it affects the health of people
who swim, wade, or fish in the water. Based on the past literature and our own focus group and cognitive
interview findings, these categories reflect what the general public find to be the most relevant endpoints related
to surface water quality. An example choice question from the survey appears in Figure 2.
9 For additional context, see the National Policy Statement for Freshwater Management (NPSFM) https://www.mfe.govt.nz/fresh-
water/freshwater-acts-and-regulations/national-policy-statement-freshwater-management.
10 More information on New Zealand's nutrient limits can be found at the Statistics NZ page on river water quality:
https://www.stats.govt. nz/i nd icators/ri ver-water-qua 1 ity-n itrogen.
11 The Statistics NZ page on E. Coli can be found here: https://www.stats.govt.nz/indicators/river-water-quality-escherichia-coli
5
-------
Figure 2: Example Choice Question
Which outcome do you prefer for rivers and streams in your regional council area?
Outcomes by 2025
Outcome A
Outcome B
Outcome C
Nutrients
Increase in the percent of rivers
and streams with acceptable
levels.
For example, a change from
25% of rivers and streams to
27% is a change of +2
percentage points
No change
+ 5 percentage points
+ 1 percentage points
Water Clarity
Increase in average visibility in
rivers and streams
No change
+ 1 metre
+ 0.5 metre
E. coli
Increase in the percent of rivers
and streams suitable for
swimming, wading, and fishing.
For example, a change from
32% of rivers and streams to
35% is a change of+3
percentage points
No change
+ 6 percentage points
+ 8 percentage points
Permanent Increase in
the Cost of Living for
your Household
$0 per month
$6 per month
($72 per year)
$3 per month
($36 per year)
Your Choice
~
~
~
Please select your
preferred outcome
Outcome A
(No change)
Outcome B
Outcome C
The size of the water quality changes presented to respondents are based on the magnitude of changes in national
targets from the National Policy Statement for Freshwater Management (NPSFM; (MFE 2020), p. 64). Table 1
shows the national targets for improvements in primary contact suitability across different waterbody categories,
with red being the lowest quality rivers and blue being the highest quality rivers.12 For example, between 2.017 and
2030, the goal is to have a five percentage point reduction in rivers in the worst category (red) and a three
percentage point increase in rivers classified in the highest category (blue). These changes are smaller than the
scenarios presented in many previous SP surveys (US EPA 2015).
Table 1: National Targets for Improvements in Primary Contact Water Bodies
Waterbody quality categories 2017 to 2030 2031 to 2040 Total change in % of water bodies
Red (lowest quality) -5% -6% -11%
Orange -4% -4% -8%
® The full figure this is drawn from appears in Appendix C.
6
-------
Yellow
Green
Blue (highest quality)
3%
3%
3%
2%
3%
5%
5%
6%
8%
Each attribute and the posited changes in the attribute levels in the survey are presented in Table 2. A Bayesian
efficient design was developed for the choice questions using Ngene software (ChoiceMetrics 2018). Although
there is not much information on the priors for each coefficient, the sign of each parameter was informed by past
NZ-based literature (Marsh et al. 2011, Tait et al. 2011, Marsh and Phillips 2015, Tait et al. 2017). One advantage of
Bayesian efficient designs is that they are more robust than other designs to mis-specification of the priors
(ChoiceMetrics 2018).
Table 2: Changes in Survey Attribute Levels
Attribute
Metric
North Island
South Island
Change in % of rivers and
Nutrients
streams that are acceptable for
aquatic life
2, 4,8
2,4,8
Clarity
Change in average visibility
(meters)
0.1, 0.4, 0.8
0.1, 0.4, 1
E. Coli
Change in % rivers and streams
that are suitable for recreation
1, 5,7
1, 3,6
Cost
Permanent increase in monthly
cost of living ($NZ)
2, 6, 10, 14, 18, 20
2, 6, 10, 14, 18, 20
The SP survey module concludes with a series of questions to gauge the respondent's perceived consequentiality
of their responses, and flag individuals potentially exhibiting protest and warm-glow behaviors. Such questions are
used to screen the sample of respondents exhibiting potentially biasing behaviors and assess the robustness of our
results. The broader survey includes socio-economic questions to allow us to examine preference heterogeneity,
and possibly further tailor such heterogeneity when extrapolating benefit estimates to the broader population.
The survey was administered online by Horizons Research as a separate module in a broader survey on
environmental preferences in NZthat is implemented every few years by Lincoln University (Hugheyetal. 2019).
Horizon Research maintains an internet panel of approximately 7,000 people. The survey was open from March to
April of 2019, and 2,007 respondents participated in this effort. When compared to population data from the
Census, our survey sample overrepresents individuals 60-69 years of age, those with a tertiary education, and
urban populations, and under-represents individuals 18-19 years of age, people with only high school
qualifications, and rural populations.13
IV. Methodology
We employ a random utility model (RUM) framework to analyze the data from this discrete choice experiment. In
these models utility is divided into a deterministic component and a random component, represented by v{.) and
e, respectively. The utility that household i receives from alternative j is:
13 Details ofthe comparison to Census data by group can be found inAppendix2ofHugheyetal. (2019).
7
-------
Uy =^j>Ii-Cj) + ey
(1)
This specification assumes that the first component of utility is a function of the group of attributes defining each
alternative a;v along with numeraire consumption (/; — Cj), which is the difference between household income/;
and the cost of the alternative Cj.
In the empirical models, we also add a status quo constant {sqc{), which represents respondents' preferences for
or against the status quo option, irrespective of the attributes defining the alternative policy options. We allow for
unobserved heterogeneity in the sqq by estimating it as a normally distributed random parameter in a mixed logit
framework. In doing so, we accommodate respondents that have a bias towards or against the status quo (Moore
etal. 2018).
We assume a linear specification for v(.). RUMs are often estimated as conditional or mixed logit specifications
(Greene 2000, Haaband McConnell 2002). The conditional probability that household /'would choose alternative)
appears in equation (2).14
In this formulation, n refers to alternative options in a given choice occasion, D is an indicator variable denoting the
status quo alternative, /? is a vector of coefficients to be estimated, and <5 is the coefficient on the cost attribute. <5
can be interpreted as the negative of the marginal utility of income. We explore individual-level preference
heterogeneity in two ways. First, we include several interaction terms between the main choice attributes and
observed household characteristics, including household-specific socio-economic variables like income and
household size, recreational user-related variables, and baseline regional water quality. Second, we explore
possible unobserved preference heterogeneity by allowing /? to vary as a random coefficient across respondents
with each element of /? following an independent normal distribution. The cost parameter <5 is held fixed to ensure
MWTP has defined moments (Layton and Brown 2000, Revelt and Train 2001, Daly etal. 2012). The mixed logit
models and subsequent calculations are estimated using Stata statistical software (StataCorp 2021).
Welfare measures can be inferred from the estimated parameters. For example, under the linear specification in
equation (2), the vector of household marginal willingness to pay (MWTP) estimates can be calculated as:
p,U\a„,Cn) =
exp {sqciDj + fia + SC.)
(2)
exP<* A, + p an + 8Cn)
MWTP = -
(3)
<5
14 Subscript t denoting each choice occasion is omitted here for notational ease, but note that each respondent faces T=3 choice questions in
the empirical analysis. When estimating the individual-level parameters in the mixed logit models we account for the fact that each respondent
faces multiple choice questions, and allow the disturbances (£j) to be correlated across all alternatives and choice occasions an individual faces.
8
-------
Given a projected policy change in the attribute levels from the baseline of a°to a1, and based on our assumed
linear functional form, we can calculate the non-marginal welfare change for a household as
WTP = MWTP X (a1 - a0) (4)
Notice that we exclude the sqct estimates from our welfare calculations. This status quo term captures a
respondent's tendency to favor or disfavor the status quo option irrespective of the improvements and costs
defining the alternative policy options. The status quo term could therefore be capturing alternative, omitted
variable biases that would otherwise confound the welfare estimates (Moore et al., 2018). For example, sqct could
be capturing the "warm-glow" of environmental action, which is not directly related to the policy change or
attribute levels. Alternatively, this term could be capturing legitimate preferences for or against a policy and
therefore could be included it in welfare calculations. However, the implications of including sqct in welfare
calculations for benefit transfer is unclear, especially if the same primary study estimates are transferred to
numerous, iteratively implemented policies, as is often the case (Petrolia et al. 2021). To be conservative and
ensure that the welfare calculations are as unbiased as possible, we exclude sqctfrom the welfare calculations.
V. Data
Of the 2,007 respondents that took the survey, 1,736 completed all three choice questions (86%), 26 completed
two (1.3%), and 7 respondents completed only one (<1%). The remaining 238 respondents (12%) did not respond
to the choice questions and are excluded from the analysis. Among the 1,769 respondents that answered at least
one choice question, 73% are from the North Island (especially Auckland, Bay of Plenty, Waikato, and Wellington),
24% of the respondents are from the South Island (and in particular, Canterbury), and 3% did not provide their
region (Table 3).
Table 3. Sample size by regional council in the North and South Islands.
North Island
Unscreened
Fully screened
South Island
Unscreened
Fully screened
Auckland
509
413
Canterbury
238
178
Bay of Plenty
106
89
Marlborough
24
20
Gisborne
12
11
Nelson
27
23
Hawkes Bay
55
47
Otago
80
63
Manawatu-Wanganui
96
73
Southland
27
17
Northland
65
53
Tasman
16
15
Taranaki
37
34
West Coast
9
9
Waikato
128
102
Wellington
281
217
Total
1,289
1,039
Total
421
325
Note: Among the n=l,769respondents, 59 did not provide information on the region where they live and are excluded from the above table.
To reduce the potential influence of biasing behaviors sometimes associated with stated preference methods, we
screen the sample based on a combination of responses to the choice and debriefing questions. Based on the
criteria below we identify and flag respondents as potentially exhibiting the following behaviors:
• Consideration of other waters omitted from the choice experiment: Respondents who disagreed with the
statement that they only considered rivers and streams in their region.
9
-------
• Hypothetical bias due to warm-glow: Respondents who always chose the highest cost option in each
choice question they were presented, and who agreed with the statement that it is important to improve
water quality no matter how high the costs.
• Treated responses as nonconsequential: Respondents who disagreed with the statement that they made
their choices as if the presented water quality improvements or increased costs would actually be
experienced.
• Protest response: Respondents who always chose the status quo option, and who agreed with one of the
following statements: (i) that they value water quality improvements but their household should not have
to pay for it, or (ii) that they are against more regulations and government spending.
Table 4 shows how the sample size changes as we screen out respondents exhibiting responses that one would
expect to bias MWTP upwards (going from left to right), and that could bias MWTP downwards (going from top to
bottom). The diagonal displays the sample sizes as we treat potential upward and downward biases symmetrically
(Banzhaf et al. 2006, Moore et al. 2018). The upper left-corner shows the full sample size of 1,769 respondents
who answered at least one choice question and the bottom-right corner shows that 1,364 respondents remain
after fully screening out those who were flagged as exhibiting potentially biasing behaviors.
Table 4. Number of respondents in sample under alternative screening criteria.
Eliminate upward biasing behaviors
None Other waters Other waters and Warm-glow
"I
5 o
| fS
-8 |
None
1,769
1,525
1,480
Nonconsequential
1,612
1,444
1,405
g 8?
F
M _Q
Nonconsequential
1,564
1,403
1,364
LU
and Protest
When estimating the regression models, observations are weighted to account for differences in sampling
intensity, response rates, and sample screening across regions. We weight the observations in our regression
models to ensure that the sample appropriately represents the population across the regions, which in turn allows
interpretation of the estimates as national averages. The weight assigned to each respondent is the total
population in their council region divided by the region-specific sample size after screening.
The survey also included several questions about respondents' recreational activities in rivers and streams and
respondents' awareness of existing water quality levels in their region. Respondents were asked about activities
they did at rivers and streams in their regional council area in the last 12 months, and could choose multiple
options from the following categories:
• Swimming or wading
• Fishing
• Boating, including sailing, and motor boating
• Water skiing, jet skiing, or kayaking
• Actively viewing nature (for example: bird watching)
• Biking or walking on trails/paths alongside the water
• I didn't visit rivers or streams in my regional council area in the last 12 months
10
-------
The responses to these questions were aggregated into three user categories: Contact Users (including water
skiing, jet skiing, or kayaking, swimming or wading), non-Contact Users (including fishing, sailing, or motor boating),
and Passive Users (those actively viewing nature, biking, or walking). Respondents can fall into more than one user
category. After respondents were presented with baseline graphs and explanations of each water quality
parameter, they were asked if they were aware of the characteristics or impacts of nutrients and E. Coli, and
whether clarity levels met their expectations. Table 5 summarizes the percent of respondents that fall into the user
categories and percent of respondents who were aware of existing water quality levels.
There is some noticeable variation in how respondents use the rivers and streams in their regions (Table 5). For
example, Nelson and Marlborough are areas known for their beaches and coastal amenities and so it is no surprise
that a high proportion of respondents engage in water contact recreation in rivers and streams as well.
Respondents in the West Coast Region also had very high participation in recreation, although the number of
respondents there was small (see Table 3).
Table 5: Types of Users of Rivers and Streams and Percent Aware of Existing Water Quality Levels by Regional
Council
% Contact
% Non-
% Passive
% Aware
% Aware
% Aware E.
Regional Council
User
Contact User
User
Nutrients
Clarity
Coli
Auckland
39.8
47.7
43.8
56.1
67.8
49.6
Bay of Plenty
41.5
66
62.3
66.4
77.6
43
Canterbury
37.7
53.1
50
59.4
74.1
47.9
Gisborne
50
33.3
33.3
69.2
61.5
38.5
Hawke's Bay
56.7
60
60
72.7
89.1
49.1
Manawatu-
42.3
55.8
53.8
69.8
83.3
41.7
Whanganui
Marlborough
75
66.7
66.7
62.5
87.5
58.3
Nelson
75
50
41.7
55.6
70.4
44.4
Northland
44
56
52
69.2
72.3
46.2
Otago
56.1
53.7
51.2
73.8
81.3
55
Southland
42.9
50
42.9
59.3
70.4
29.6
Taranaki
43.8
25
18.8
60.5
81.6
23.7
Tasman
57.1
85.7
85.7
76.5
76.5
47.1
Waikato
40.6
55.1
53.6
62.8
70
47.7
Wellington
33.3
45.1
43.8
65.2
78.2
42.4
West Coast
100
100
100
100
88.9
77.8
VI. Results
Regression Results
Results from the econometric models estimated using the fully screened sample of respondents are presented in
Table 6. The first column shows the results from our base model that includes only the water quality attributes, the
cost parameter, and a status quo constant (SQC), with standard errors appearing in parentheses. In this model and
each of the subsequent variations, the coefficients corresponding to the water quality variables are treated as
random parameters. In models (2)-(5) additional variables are interacted with the water quality variables. The
coefficients on those interaction terms are held fixed. In essence, the interaction terms capture observed
heterogeneity by shifting the distributions of the random coefficients, which capture any unobserved
heterogeneity.
11
-------
The positive and statistically significant coefficients corresponding to the water quality attributes in Model (1)
suggest that respondents are more likely to choose an option with larger improvements in water clarity, and
higher proportions of waters meeting the government standards for nutrient and E. Coli levels. The coefficient
corresponding to the cost attribute is negative and significant, suggesting that respondents are less likely to choose
an option as costs increase, which is consistent with having a positive marginal utility of income. Finally, the SQC is
negative and statistically significant; suggesting a tendency for respondents, on average, to favor a policy option
irrespective of the improvements and costs defining that option. Such potentially biasing tendencies are controlled
for by the inclusion of the SQC and are not included in subsequent welfare calculations. The large and statistically
significant standard deviation estimate for the SQC suggests significant heterogeneity across respondents.
Similarly, the statistically significant standard deviation terms for the water quality attributes in model 1 suggest
significant unobserved heterogeneity in preferences for water quality across respondents. In the subsequent
models we add interaction terms to try and better explain some of this preference heterogeneity.
Model (2) adds interaction terms between each water quality attribute and (i) the corresponding region-specific
baseline level of that attribute and (ii) a measure of the quantity (total length) of rivers in the regional council area.
Both attributes were presented to respondents in the survey information (see Appendix). The nutrients
improvement interaction with river km is positive and significant while the clarity interaction with river km is
negative. The clarity result goes against initial expectations that WTP would increase with the quantity of waters
that experience an improvement but may reflect the importance of substitutes. Perhaps respondents do not care
about clarity improvements as much if they live in areas where there is an abundance of rivers to choose from. On
the other hand, this finding may also reflect differences in preferences between urban and rural areas. Two of the
three largest cities in New Zealand are in the Auckland and Wellington regions, which have comparatively low total
lengths of rivers (see Appendix). The positive coefficient corresponding to the nutrients and river km interaction
term provides some evidence of scope sensitivity - i.e., respondents WTP is increasing for improvements that
occur to a greater quantity of waters.
Models (3)—(5) include additional interactions with the user-related variables. Across these models, non-contact
users, or people that fish and boat, are willing to pay less for improvements in each water quality parameter.
Although excess nutrients are generally bad for aquatic environments (especially in large levels), some
fisherwomen and men may believe that more nutrients equal more fish. While some species do benefit from
additional nutrients, those benefits stop after a certain point (National Research Council 2000). The positive
coefficient estimates on E.Coli*Passive and Clarity*Passive suggest that passive users have a greater preference for
reductions in £ Coli contamination and improvements in clarity relative to nonusers(all else constant). These
results are not completely robust across models (3)-(5), however.
Model (4) includes interaction terms between each water quality attribute and (i) an indicator for achieving at least
a Bachelor's degree and (ii) with variables describing respondents' awareness of the negative effects of elevated
nutrient and E. Coli levels, and of current clarity levels.15 The results from the previous models are robust. We find,
a positive and significant interaction with Bachelors and Clarity, suggesting that more educated respondents value
improvements in clarity more. Otherwise, there is no evidence of preference heterogeneity with respect to
education.
The coefficients corresponding to the Nutrients*Aware and Clarity*Aware interaction terms are positive and
significant, while the E.Coli*Aware coefficient is significant and negative. These finding suggest that respondents
who are aware of the negative impacts of nutrients and whose priors for clarity matched current levels are willing
to pay more, while those informed of the negative effects of E. Coli are willing to pay less. The descriptive statistics
15 The nutrient and E. Coli awareness variables are based on a binary variable denoting whether respondents said they are aware of "the
negative effects that nutrients can have on aquatic plants and animals" and "the negative effects E. coli can have on the suitability of rivers and
streams for swimming, wading, and fishing." The awareness variable for clarity asks respondents how the provided average clarity level in their
region compares to their priors. In these regressions, a dummy is used for "about what I expected."
12
-------
(Table 5) show that awareness of E. Coli's negative effects was much lower than the other measures in every
region, with less than 50% in all but 3 regions.
Model (5) includes all the previous variables and an interaction term between a dummy variable denoting high
income earners and the cost parameter. That interaction term is insignificant (as was a low-income interaction in
an alternate model), indicating that the impact of policy cost does not vary across respondents of different income
levels. The results from model (5) are mostly consistent with the earlier models in terms of the signsand
significance of coefficients. However, the Clarity*Passive and Clarity*Base\'me variables are now significant at the
10% level.
At the bottom of the table, the estimated standard deviation terms remain statistically significant and similar in
magnitude across all the models suggesting that there is still unobserved preference heterogeneity across
respondents, despite our best efforts to identify and control for the sources of such heterogeneity. Comparisons of
the AIC and BIC criteria across all models support Model (5), the most complex model in terms of included
covariates, as the best overall model in fitting the data.16
Table 6: Econometric Coefficient Results using the Fully Screened Sample
Model
(1)
(2)
(3)
(4)
(5)
Cost
-0.0057***
-0.0054***
-0.0056***
-0.0055***
-0.0053***
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
Nutrients
0.1444***
0.0639
0.1412***
0.1028***
0.0498
(0.0264)
(0.0626)
(0.0305)
(0.0375)
(0.0656)
Clarity
0.6908***
0.8352***
0.6234***
0.6850***
0.7387**
(0.1720)
(0.2724)
(0.1922)
(0.2213)
(0.3117)
E Coli
0.1499***
0.1130***
0.1422***
0.0702**
0.0383
(0.0208)
(0.0361)
(0.0251)
(0.0338)
(0.0438)
Status Quo
-2.0161***
-1.8620***
-2.1118***
-2.0902***
-1.7709***
(0.3738)
(0.3789)
(0.3708)
(0.3735)
(0.3748)
Nutrients*Baseline
0.0009
0.0001
(0.0013)
(0.0013)
Clarity*Baseline
0.1423
0.1756*
(0.1076)
(0.1049)
E.Coli*Baseline
0.0014*
0.0013*
(0.0008)
(0.0008)
Nutrient*river(1000km)
0.0018**
0.0020**
(0.0008)
(0.0008)
Clarity* river(lOOOkm)
-0.0133***
-0.0122***
(0.0047)
(0.0045)
E.Coli*river(1000km)
-0.0001
-0.0001
(0.0008)
(0.0008)
Nutrients*Contact User
0.0017
0.0087
-0.0002
(0.0368)
(0.0359)
(0.0370)
Clarity*Contact User
-0.0563
-0.0219
-0.0473
(0.2186)
(0.2152)
(0.2201)
E.Coli*Contact User
-0.0404
-0.0319
-0.0319
(0.0326)
(0.0317)
(0.0334)
Nutrients*NonContact
-0.1044**
-0.1068**
-0.1006**
User
(0.0429)
(0.0419)
(0.0445)
Clarity*NonContact
-0.5757**
-0.4920*
-0.4929*
User
(0.2634)
(0.2609)
(0.2749)
Ecoli*NonContact
-0.0968***
-0.0934***
-0.1078***
User
(0.0367)
(0.0358)
(0.0387)
Nutrients*Passive User
0.0290
0.0118
0.0233
(0.0345)
(0.0347)
(0.0349)
16 Models with other interaction terms were also explored, including population, population density, North vs. South Island, and percent urban.
Those interactions were not significant and their inclusion did not affect the other model results.
13
-------
Clarity*Passive User
0.3230
0.3002
0.3617*
(0.1978)
(0.1978)
(0.2026)
E Coli*Passive User
0.0695**
0.0462
0.0540*
(0.0312)
(0.0305)
(0.0322)
Nutrients*Bachelors
0.0013
0.0226
(0.0330)
(0.0338)
Clarity*Bachelors
0.3543*
0.5160**
(0.1951)
(0.2066)
E Coli*Bachelors
0.0330
0.0470
(0.0295)
(0.0311)
Nutrients*Aware
0.0776**
0.0776**
(0.0338)
(0.0340)
Clarity*Aware
0.0919***
0.0881***
(0.0325)
(0.0341)
E Coli*Aware
-0.4183**
-0.4355**
(0.1877)
(0.1925)
Cost*High Income
0.0008
(0.0011)
S.D.
Nutrients
0.3234***
0.3206***
0.3102***
0.3080***
0.3047***
(0.0313)
(0.0310)
(0.0298)
(0.0295)
(0.0288)
Clarity
1.2142***
1.1609***
1.1416***
1.0815***
1.1541***
(0.2243)
(0.2434)
(0.2026)
(0.2071)
(0.2210)
E Coli
0.2499***
0.2559***
0.2122***
0.2131***
0.2354***
(0.0361)
(0.0356)
(0.0356)
(0.0336)
(0.0365)
Status Quo
3.7548***
3.7462***
4.0239***
3.951.8***
3.6753***
(0.3259)
(0.3177)
(0.3621)
(0.3294)
(0.2893)
Observations
12,219
12,219
12,219
12,177
11,835
AIC
18,942,062.5
18,884,903.0
18,870,771.2
18,701,703.0
18,167,150.4
BIC
18,942,129.2
18,885,014.1
18,870,904.6
18,701,880.7
18,167,379.2
Note: Standard errors appear in parentheses. ***, **, and * denote significance at the 99%, 95%, and 90% levels, respectively.
Willingness to Pay Estimates
To illustrate the practical implications of the econometric results, Table 7 contains the marginal WTP estimates for
the first (1) model specification. That model did not include interaction variables, so (assuming the weighted
sample of respondents is representative of the population) the calculated marginal WTP values represent national
household averages. Results indicate that people are willing to pay up to $25.30 annually ($NZ, 2018) for a one
percentage point increase in regional council rivers meeting nutrient standards and are thus considered acceptable
in terms of ecological health. Results also suggest that respondents hold an average annual marginal WTP (MWTP)
of $12.10 for a 10 cm increase in average river water clarity. Finally, we see a $26.25 annual MWTP for a one-
percentage point increase in the quantity of rivers within a region that meet E. Coli standards and are therefore
deemed safe for swimming.
Table 7: Marginal Willingness to Pay in NZDfor Model 1
Model 1
Nutrients MWTP (+1 percentage point)
25.30***
(7.02)
Clarity MWTP (+10 cm Secchi disk depth)
12.10**
(4.30)
E. Coli MWTP (+1 percentage point)
26.25***
(6.24)
Notes: *p<0.10, **p<0.05, *** p<0.01. Standard errors in parentheses.
14
-------
The other estimated models include several interaction terms with variables that allow the MWTP to vary across
regions. The region-specific values for baseline water quality and quantity levels can be plugged directly into the
parameterized model to predict region-specific MWTP estimates. In later models, regional population averages (or
proportions) based on the NZ census are entered in for the sociodemographic characteristics. Finally, sample
proportions of respondents in each region falling under the different user and awareness categories are used to
estimate the region-specific population percentages and are in turn plugged into the parameterized model.
The "average" MWTP values for each region and water quality attribute based on estimates from model (5)
appear in Figure 3 along with their 95% confidence intervals. For example, the circles in the first panel show that a
region-wide average one-percentage point improvement in rivers meeting the nutrient criteria is valued in the
range of (a statistically insignificant) $11.37 in Nelson to $39.60 in Canterbury. The second panel in Figure 3 shows
the MWTP values for a region-wide 10 cm increase in average clarity. These values range from $34.12 to $254.88.
The final graph in the figure depicts the MWTP estimates for a percentage point increase in regional waterbodies
meeting their E. Coli criteria, ranging from $11.60 to $30.07.
Overall, the results show that people are willing to pay positive amounts for improvements in water quality, on
average, with notable differences across regions. There are some statistically significant differences between
regions, such as between Canterbury's MWTP for nutrients and Marlborough and Nelson's MWTP.17 However,
there is no statistically significant difference between the MWTPs that are closer to the middle of the range, like
Auckland and Bay of Plenty. The MWTP in a region can also vary across the different water quality attributes.
Canterbury, for instance has the highest value for nutrients, but the lowest value for clarity.
Figure 3: Average MWTP Values for each Water Quality Parameter, Across Regions
Panel 1 - Nutrients
AucklandN -
BayofPlentyN -
CanterburyN -
GisborneN -
HawkesBayN -
HorizonsN -
MarlboroughN -
NelsonN -
NorthlandN -
OtagonN -
SouthlandN -
TaranakiN -
TasmanN -
WaikatoN -
WellingtonN -
WestCoastN -
-20 0 20 40 60
17 Based on a Wald Test that the two coefficients are equal.
15
-------
Panel 2-Clarity
AucklandC-
BayofPlentyC -
CanterburyC-
GisborneC-
HawkesBayC -
HorizonsC-
MarlboroughC-
NelsonC-
NorthlandC-
OtagonC-
SouthlandC-
TaranakiC-
TasmanC-
WaikatoC-
WellingtonC-
WestCoastC -
Panel 3 - E. Coli
AucklandEC-
BayofPlentyEC-
CanterburyEC-
GisborneEC-
HawkesBayEC-
HorizonsEC -
MarlboroughEC-
NelsonEC-
NorthlandEC-
OtagonEC-
SouthlandEC-
TaranakiEC-
TasmanEC -
WaikatoEC-
WellingtonEC-
WestCoastEC -
Notes: Horizontal lines in figure denote the 95% confidence intervals. The actual M'A/TP estimates and levels of statistical significance are
presented in the Appendix.
100
100
200
300
400
-i—
10
—I—
30
-n
50
20
40
16
-------
VII. Policy Illustration
To demonstrate how these values might be applied in a policy setting, we perform a benefit transfer on a
simulated national waterquality improvement that was previously modeled by the National Institute of Waterand
Atmospheric Research (NIWA) (Hicks etal. 2016, Hicks etal. 2019). Sediment was identified as a high priority
freshwater contaminant of to manage. The National Policy Statement for Freshwater Management (NPSFM) did
not previously have sediment as a target, so the Ministry for the Environment (MFE) was interested in identifying
the impact of proposed catchment sediment load limits.18 Catchment load limits could be achieved through land
use conversions (such as converting erodible pasture into forestry) and other erosion best management practices
aimed at reducing sediment from reaching waterbodies. Both in-stream sediment criteria and clarity criteria were
formulated that would meet nationwide "bottom lines" in each of these four primary state variables.19 We use
the NIWA modeling data on clarity that project feasible improvements in clarity as a result of catchment load limits.
The modeling identifies streams and rivers with a median clarity that is below the threshold and simulates the
potential improvement from the practices aimed to reduce catchment sediment loads.
To obtain the regional-council level changes for a benefit transfer, those water quality improvements are
then weighted by the length of each stream/river and added together to get the reach-weighted average clarity
change for each regional council area r, as in the following equation, where Nr denotes the total number of river
segments in region r.
Mean Clarity Changer = ( ^en9thir—clarity Changejr) (5)
1 \2iJi Lengthir }
A summary of these average clarity improvements appears in Figure 4. Most of the regional councils see a small
average change in clarity, of under 0.1 m, with the largest change in Waikato, at 0.154 m. These changes are
proportionally smaller than the changes desired by the national policy statement, pictured in Appendix C, with some
changes smaller than those presented in our choice experiment questions. This exercise further illustrates the
difficulty in achieving long-term goals for water quality.
18 The current version of the NPS FM can be found here: https://environroent.govt.nz/aets-and-regulations/national-poliey-
statements/national-poliq/-statement-freshwater-management/
19 Neverman etal (2019) used a combination of economic and environmental modeling to explore cost-effective ways to
achieve the suspended sediment concentration criteria.
17
-------
Figure 4: Projected Mean Regional Council Clarity Improvements (m)
0.18
0.16
-g" 0.14
1? 0.12
« 0.10
u
0.08
a>
g 0.06
TO
u 0.04
0.02
0.00
Although we have data on changes in clarity, we also need changes in nutrients and E. Coli. This is a common
problem with monetizing water quality policy: the need to convert between different parameters (Walsh and
Wheeler 2013, US EPA 2015). It is likely that the policies used to improve sediment or clarity will also improve E.
Coli and nutrients. For instance, to achieve sediment load targets, Neverman et al. (2019) simulate the impact of
whole farm planning and afforestation, which will also improve E. Coli and nutrient leaching to waterways.
To calculate the subsequent changes in E. Coli and nutrients associated with the clarity improvements, we use data
from NZ Statistics, who publish modeled segment-level data on several water quality parameters, resulting in
almost 600,000 observations for each parameter.20 For E. Coli, total nitrogen, and total phosphorous, we use a
regression to model the relationship between each indicator (WQ) and clarity, as shown in equation (6). Several
control variables are included in X: elevation and dummies for stream order and the dominant surrounding land
cover. The regression also includes regional council fixed effects, and the E. Coli regression includes dummies for
the baseline "letter grade" of the stream (Appendix C). Note that to properly model the ecological relationship
between these variables, a more in-depth approach should be used. However, for the purposes of this benefit
transfer, these regressions establish a reasonable relationship.
ln(I/K0 = /? In (Clarity) + SX + y + e (6)
Regression results appear in Appendix E, and exhibit significant negative relationships between the natural log of
clarity and each indicator. The estimated relationship with E. Coli is slightly lower than Davies-Colleyetal. (2018),
however they use a simple correlation coefficient of -0.54. Our estimated coefficients are used to translate the
change in clarity into the other indicators. Using the government thresholds referenced above, we can then
determine which changes result in a waterbody moving from exceeding the threshold to not exceeding. For
instance, with the E. Coli government criteria, river and stream segments are assigned a letter grade from A to E,
20 For instance, modeled E. Coli data for each river and stream segment can be downloaded from https://www.stats.govt.nz/indicators/river-
water-qualitv-escherichia-coli.
18
-------
with Dand E being unsafe for swimming.21 If the forecast E. Coli change bumps the waterbody from unsafe for
swimming to safe, it is counted as no longer exceeding. The TP and TN results are combined into a nutrients
indicator, so that if either exceeds its threshold the waterbody is still counted as exceeding. The projected increase
in rivers meeting the E.Coli and nutrient criteria appear in Figure 5. The values in Figure 4 and Figure 5 highlight the
difficulty in achieving meaningful water quality changes. Only 3-5 regional councils in each graph see water quality
changes that are within the scope of the attributes presented in our survey (see Table 2).
Figure 5: Projected Improvements in the Percent of Rivers Meeting E. coli and Nutrient Criteria
O
NO
¦ Nutrients
BE. Coli
In
fl n
.4?' i?
c?
XT
n
a
$¦
# ^ ^ ^ ^ ^ ^ xs-
-------
Table 8: Annual Household-Level Benefits from Policy Illustration (in NZD)
Parameter
Clarity
Nutrients
E. Coli
Model
(5)
(5)
(5)
Auckland
7.91**
48.79*
17.84**
Bay of Plenty
1.82**
1.20**
1.09***
Canterbury
1.48
8.55***
0.87**
Gisborne
5.24
19.23**
6.58*
Hawke's Bay
2.71**
9.68**
3.94***
Manawatu-Whanganui
9.93**
43.22***
24.00***
Marlborough
0.93**
0.73
0.32***
Nelson
0.47***
0.00
2.50***
Northland
4.20*
40.10**
19.06
Otago
1.52
11.84***
6.57***
Southland
6.11
49.63***
9.76**
Taranaki
3.59***
0.93**
0.48***
Tasman
1.36**
1.63
0.67***
Waikato
8.47
184.26***
37.44***
Wellington
4.58***
2.67**
5.32***
West Coast
1.86
2.57*
0.95**
Table 9: Regional Council-Level Annual Benefits (in NZD)
Clarity
Nutrients
E. Coli
Total
Auckland
4,475,665
27,603,716
10,092,862
42,172,243
Bay of Plenty
236,617
156,165
141,440
534,223
Canterbury
386,477
2,237,734
227,669
2,851,880
Gisborne
98,597
362,088
123,975
584,660
Hawke's Bay
183,148
653,555
266,049
1,102,752
Manawatu-Whanganui
1,032,266
4,492,181
2,494,089
8,018,536
Marlborough
21,343
16,639
7,353
45,335
Nelson
10,216
0
54,677
64,893
Northland
344,698
3,292,130
1,565,021
5,201,849
Otago
159,721
1,243,578
690,515
2,093,814
Southland
274,869
2,231,400
438,707
2,944,977
Taranaki
182,062
47,022
24,201
253,286
Tasman
32,735
39,226
16,075
88,037
Waikato
1,712,758
37,272,414
7,573,493
46,558,665
Wellington
942,873
549,718
1,094,999
2,587,589
West Coast
32,841
45,450
16,726
95,017
The estimated national annual benefits of this policy change is approximately $115 million (Table 9). To illustrate
the sensitivity of our overall estimates to model choices, Table 10 contains the total national benefits for each
model. The table shows the highest benefits for the more parsimonious models (1) and (2). Our preferred model,
the model that best fit the data based on BIC and AIC, yields total benefit estimates squarely in the middle of all our
specifications.
20
-------
Table 10: National Annual Benefits Across Models (in NZD)
Model
(1)
(2)
(3)
(4)
(5)
Clarity
1,405,058
1,516,302
9,404,867
8,732,744
10,126,886
E. Coli
36,064,345
38,449,151
26,674,132
25,025,484
24,827,850
Nutrients
99,082,639
103,796,435
70,258,975
72,620,167
80,243,018
Total
136,552,042
143,761,889
106,337,974
106,378,395
115,197,754
VIII. Discussion
Overall, our estimated choice model results show consistent positive values for several dimensions of water quality
and a benefit transfer exercise demonstrated substantial benefits from even small water quality improvements. It
is worth noting that although we calibrated the attribute levels in our choice sets with official government targets,
many of the regional council-level changes in our policy simulation were still below those levels. This further
highlights the difficulty in achieving policy-relevant changes in water quality in practice.
To further put these results into context, our estimates can be compared to recent New Zealand policy action on
water quality. Auckland Council recently implemented a vote on additional taxes to improve water quality in that
region.23 The goal of the additional taxes was to raise $400 million over 10 years through taxes on residential and
business properties, as summarized in Table 11. The funds would be used for new stormwater infrastructure and
other policies and programs dedicated to reducing wastewater, sediment, and other pollution.24 The vote passed
with approximately 65% of people voting for the rates. The vote presented residents with a choice of the status
quo versus a water quality tax where both water quality and household costs would increase. This revealed
preference setting overlaps with our SP discrete choice experiment. Many of the same water quality issues apply,
such as reduced beach closures, reduced septic tank overflows, reduced fecal contamination, reduced sediment
contamination, rehabilitation of urban and rural streams, and better stormwater infrastructure. In material
distributed from the council about the targeted rate, the Council noted that an average valued home would pay an
additional amount of $66 per year.25 That vote directly illustrates a positive WTP for water quality improvements in
the Auckland Region and reinforces the plausibility of our estimates. For example, our policy illustration suggested
the average Auckland resident is willing to pay NZ $74.53 ($49.94 USD) per year for moderate improvements in
clarity, nutrients, and E. Coli. Without details on projected water quality improvements to result from the Auckland
tax, we cannot carry out a formal test for convergent validity, but this comparison does lend credibility to our SP-
based estimates.
Table 11: Description of Auckland's Water Quality Targeted Rate (Tax)
Option
Description
Outcomes
Status Quo
Continue with existing plans for water quality
Reduce wastewater overflows in the
management under current budget.
Western Isthmus by 2028
Institute a water "Deliver the best water quality outcomes" Reduce wastewater overflows in the
quality property Leverage existing investments in stormwater Western Isthmus by 2028 by more than
tax and water quality management to achieve current plans
improved water quality outcomes in 10 years Reduce fecal contamination of waterways
Additional stormwater infrastructure in high-risk areas.
Rehabilitate urban and rural streams
23 More details about the targeted tax rate (Reti matawhaiti mo te whakapiki i te kounga o te wai) can be found here:
https://ourauckland.aucklandcouncil.govt.nz/media/Oivhgcxi/attachment-b-water-quality-targeted-rate.pdf and here:
https://www,a ucklandcou nci 1 .govt, nz/ ertvi ronment/1 ooki ng-after-a uckl ands-water/water-qual ity-ta rgeted-rate/Pages/defa ult. aspx
24 A video summary was also produced by the Council: https://youtu.be/y09ku68PwNl.
25 See https://ourayckland.aycklandcoyncil.govt.nz/media/Oiyhgcxi/attachment-b-water-qyality-targeted-rate.pdf
21
-------
Introduce septic tank monitoring
Total additional cost of $856 million. $452
million to be financed by the proposed
property tax
Reduce sediment runoff to the Kaipara
harbor.
Improved urban and rural stream
conditions.
IX. Conclusion
This paper reports the results of a choice experiment that focused on three water quality parameters: nutrients,
clarity, and E. coli. The choice experiment was administered to a national sample of respondents with the goal of
instituting a rigorous study aimed at future benefit transfer. Several aspects of our approach should serve as a
guide for future studies with a similar goal. First, the study used water quality measures that are not only salient
and understandable to respondents, but that can be directly linked to policy for analysis. This was done by focusing
on parameters that people care about, are relatively straightforward to communicate, and are relevant policy-
levers. Each of the three measures in our survey are targeted by the New Zealand government's water quality
goals. Our experimental design also posed water quality changes that are more in line with the size of
improvements experienced or projected from actual policy. Many previous studies use large changes in water
quality that would be difficult to achieve. Finally, the choice experiment focused on water quality improvements in
freshwater rivers and streams at the regional council level in New Zealand. Since regional councils are typically
responsible for implementing water quality policies passed by the central New Zealand government, this
represents a realistic management unit. Furthermore, New Zealand is unique in that its Regional Council borders
are aligned with catchment boundaries, so there is very little cross-border pollution.
Across several model specifications, we find significant and positive values for improvements in all three water
quality parameters. Our results also suggest that WTP varies with the types of recreation that a user engages in
and across regions, as well as education and existing knowledge about water quality. At the same time, there is
significant unobserved preference heterogeneity that is accounted for, but despite numerous attempts, remains
unexplained by our models.
The utility of the results are demonstrated using a policy simulation of water clarity improvements based on recent
government modeling (Hicks et al. 2019) aimed at achieving catchment-level sediment load targets. This exercise
highlighted the difficulty in specifying the size of the water quality changes on a survey, as several of the simulated
regional council-level improvements in water quality were lower than those in our survey. We estimate the
changes in clarity, E. coli, and nutrients associated with those sediment reductions across New Zealand and apply
our results in a benefit transfer exercise. The estimated annual average national benefits of a fully implemented
policy are approximately NZ $115 million ($77 million USD) using our preferred model. Although we do not have
estimates of the costs for those clarity changes, the monetized benefits should serve as a useful comparison. The
benefit transfer exercise we demonstrate is straightforward and should be applicable to many upcoming policy
proposals put forth by the central and regional council governments in New Zealand.
22
-------
Work Cited
Statistics New Zealand: 2006 census of populations and dwellings. Available: http://www.stats.govt.nz. Accessed
24th August, 2013.
Ambrey, C. L., C. M. Fleming and M. Manning (2017). "Valuing the state of water in New Zealand using the
experienced preference method" Australasian Journal of Environmental Management 24(4): 423-440.
Awatere, S., M. Robb, Y. Taura, K. Reihana, G. Harnsworth, J. Te Maru and E. Watene-Rawiri (2017). Wai Ora
Wai Maori - akaupapa Maori assessment tool. Manaaki Whenua Policy Brief. Hamilton, Landcare Research
Manaaki Whenua.
Banzhaf, H. S., D. Burtraw, D. Evans and A. Krupnick (2006). "Valuation of Natural Resource Improvements in
the Adirondacks." Land Economics 82(3): 445-464.
Baskaran, R.. R. Cullen and S. Colombo (2009). "Estimating values of environmental impacts of dairy farming in
New Zealand." New Zealand Journal of Agricultural Research 52(4): 377-389.
ChoiceMetrics (2018). Ngene 1.2.1 User Manual & Reference Guide. Australia
Cullen, R.. K. Hughey and G. Kerr (2006). "New Zealand freshw ater management and agricultural impacts."
Australian Journal of Agricultural and Resource Economics 50(3): 327-346.
Daly, A., S. Hess and K. Train (2012). "Assuring finite moments for willingness to pay in random coefficient
models." Transportation 39(1): 19-31.
Davies-Colley, R.. A. Valois and J. Milne (2018). "Faecal pollution and visual clarify in New Zealand rivers:
Correlation of key variables affecting swimming suitability." Journal of Water and Health 16: wh2018214.
Greene, W. H. (2000). Econometric Analysis. Saddle River, New Jersey, Prentice Hall.
Griffiths, C., H. Klemick, M. Massey, C. Moore, S. Newbold, D. Simpson, P. Walsh and W. Wheeler (2012).
"U.S. Environmental Protection Agency Valuation of Surface Water Qualify Improvements." Review of
Environmental Economics and Policy.
Haab, T. C. and K. E. McConnell (2002). Valuing Environmental and Natural Resources-The Econometrics of
Non-Market Valuation. Northampton, MA, Edward Elgar Publishers.
Harris, S., G. Kerr and G. J. Doole (2016). Economics of Fresh Waters. Advances in New Zealand Freshwater
Science. P. G. Jellyman, T. J. A. Davie, C. P. Pearson and J. S. Harding. Christehurch, New Zealand Freshwater
Sciences society and New Zealand Hydrological Society.
Hicks, M., M. Greenwood, J. Clapcott, R. J. Davies-Colley, J. R. Dymond, A. Hughes, U. Shankar and K. Walter
(2016). Sediment Attributes Stage 1, Report Prepared for the Ministry for the Environment. National Institute of
Water & Atmospheric Research. Christehurch, NZ. NIWA CLIENT REPORT No: CHC2016-058.
Hicks, M., Semadeni-Davies, A. Haddadchi, U. Shankar and D. Plew (2019). Updated sediment load estimator for
New Zealand. MFE18502. Christehurch, NZ, National Institute of Water & Atmospheric Research Ltd. Prepared
for Ministry for the Environment.
Hughey, K, G. Kerr and R. Cullen (2016). Public Perceptions ofNew Zealand's Environment: 2016.
http:/Avww.lincoln.ac.nz/Documents/LEaP/perceptions2016 fi wRes.pdf. Lincoln, Lincoln University.
Hughey, K, G. Kerr and R. Cullen (2019). Public Perceptions ofNew Zealand's Environment: 2019. EOS
Ecology and Lincoln University. Christehurch. ISSN 2230-4967.
Johnston, R. J., K. J. Boyle, W. Adamowicz, J. Bennett, R. Brouwer, T. A. Cameron, W. M. Hanemann, N.
Hanley, M. Ryan, R. Scarpa, R. Tourangeau and C. A. Vossler (2017). "Contemporary Guidance for Stated
Preference Studies." Journal of the Association of Environmental and Resource Economists 4(2): 319-405.
Johnston, R. J., E. T. Schultz, K. Segerson, E. Y. Besedin and M. Ramachandran (2012). "Enhancing the Content
Validity of Stated Preference Valuation: The Structure and Function of Ecological Indicators." Land Economics
88(1): 102-120.
Layton, D. F. and G. Brown (2000). "Heterogeneous Preferences Regarding Global Climate Change." The
Review of Economics and Statistics 82(4): 616-624.
Marsh, D. (2012). "Water resource management in New Zealand: Jobs or algal blooms?" Journal of
Environmental Management 109: 33-42.
Marsh, D. andL. Mkwara(2013). Review of Freshwater Non-Market Value Studies. Department of Economics,
University of Waikato, New Zealand.
Marsh, D., L. Mkwara and R. Scarpa (2011). "Do Respondents' Perceptions of the Status Quo Matter in Non-
Market Valuation with Choice Experiments? An Application to New Zealand Freshwater Streams." Sustainabilitv
3(9): 1593-1615.
23
-------
Marsh, D. and Y. Phillips (2015). "Combining Choice Analysis with Stakeholder Consultation to Assess
Management Options for New Zealand's Hurunui River." Water 7: 1649-1669.
MFE (2020). National Policy Statement for Freshwater Management. Ministry for the Environment. Wellington.
August 2020.
MFE and Stats NZ (2019). New Zealand's Environmental Reporting Series: Environment Aotearoa 2019.
Wellington. Available from www.mfe.govt.nz and www.stats.govt.nz.
Miller, S., P. Tait and C. Saunders (2015). "Estimating indigenous cultural values of freshwater: A choice
experiment approach to Maori values in New Zealand." Ecological Economics 118:207-214.
Miller, S. A. (2014). Assessing values for multiple and conflicting uses of freshwater in the Canterbury region.
PhD, Lincoln University.
Moore, C., D. Guignet, C. Dockins, K. B. Maguire andN. B. Simon (2018). "Valuing Ecological Improvements in
the Chesapeake Bay and the Importance of Ancillary Benefits." Journal of Benefit-Cost Analysis 9(1): 1-26.
Moran, D. and S. Dann (2008). "The economic value of water use: Implications for implementing the Water
Framework Directive in Scotland." Journal of Environmental Management 87(3): 484-496.
National Research Council (2000). What are the Effects ofNutrient Over-Enrichment? Clean Coastal Waters:
Understanding and Reducing the Effects of Nutrient Pollution. Washington, DC, National Academy Press.
Neverman, A., U. Djanibekov, T. Soliman, P. Walsh, R. Spiekermann and L. Basher (2019). Impact testing of a
proposed suspended sediment attribute: identifying erosion and sediment control mitigations to meet proposed
sediment attribute bottom lines and the costs and benefits of those mitigations. Ministry for the Environment.
Wellington, NZ. Contract Report: LC3574.
Newbold, S. C., P. J. Walsh, D. M. Massey and J. Hewitt (2018). "Using structural restrictions to achieve
theoretical consistency in benefit transfers." Environmental and Resource Economics 69(3): 529-553.
Petrolia, D. R.. D. Guignet, J. Whitehead, C. Kent, C. Caulder and K. Amon (2021). "Nonmarket Valuation in the
Environmental Protection Agency's Regulatory Process." Applied Economic Perspectives and Policy
forthcoming.
Phillips, Y. (2014). Non-market values for fresh water in the Waikato region: a combined revealed and stated
preference approach. W. R. Council. Hamilton.
Revelt, D. and K. E. Train (2001). Customer-specific taste parameters and mixed logit: Households' choice of
electricity supplier. Department of Economics. University of California Berkeley. Working paper No. E00-274.
Smith, V. K. and S. K. Pattanayak (2002). "Is Meta-Analysis a Noah's Ark for Non-Market Valuation?"
Environmental and Resource Economics 22(1): 271-296.
StataCorp (2021). Stata Statistical Software: Release 17. College Statin, Tx, StataCorp LLC.
Tait, P., R. Baskaran, R. Cullen and K. Bicknell (2011). "Valuation of agricultural impacts on rivers and streams
using choice modelling: A New Zealand case study." New Zealand Journal of Agricultural Research 54(3): 143-
154.
Tait, P., S. Miller, P. Rutherford and W. Abell (2017). Non-market valuation of improvements in freshwater
quality for New Zealand residents, from changes in stock exclusion policy. The Agribusiness and Economics
Research Unit (AERU) at Lincoln University Report Prepared for the Ministry for Primary Industries. MPI
Technical Paper No: 2017/08.
Tait, P., S. A. Miller, P. Rutherford and W. Abell (2016). Non-market valuation of improvements in freshwater
quality for New Zealand residents, from changes in stock exclusion policy. Agribusiness and Economics Research
Unit. Lincoln, Lincoln University.
Takatsuka, Y., R. Cullen, M. Wilson and S. Wratten (2009). "Using stated preference techniques to value four key
ecosystem services on New Zealand arable land." International Journal of Agricultural Sustainabilitv 7(4): 279-
291.
US EPA (2015). Benefit and Cost Analysis for the Effluent Limitations Guidelines and Standards for the Steam
Electric Power Generating Point Source Category. Office of Water.
https://www.epa.gov/sitBs/production/files/2015-10/docu.ments/steam-electric benefit-cost-analvsis 09-29-
2015.pdf. Washington, DC.
USEPA (2015). Peer Review Package for Meta-analysis of the Willingness-to-Pay for Water Quality
Improvements. US Environmental Protection Agency. Washington DC., Docket EPA-HQ-OW-2009-0819.
Materials from the Steam Electric Effluent Limitations Guidelines.
Vossler, C. A. and E. Zawojska (2020). "Behavioral Drivers or Economic Incentives? Toward a Better
Understanding of Elicitation Effects in Stated Preference Studies." Journal of the Association of Environmental
and Resource Economists 7(2): 279-303.
24
-------
Walsh, P. J. and W. Wheeler (2013). "Water Quality Indices and Benefit-Cost Analysis." Journal of Benefit-Cost
Analysis 4(1): 81-105.
25
-------
Appendix A
Appendix Figure Al: Baseline Graphs Presented to North Island Survey Respondents
Percent (%) of Rivers and Streams with Acceptable Nutrient Levels
1 2 3
Average visibility in river (m)
0 25 50 75 JQO
Percent (%) of Rivers and Streams with Acceptable E. Coli Levels
26
-------
Appendix Figure A2: Baseline Graphs Presented to South island Survey Respondents
Tasman
Nelson
Marlborough
West Coast
Southland
100%
0 25 50 75 100
Percent (%) of Rivers and Streams with Acceptable Nutrient Levels
5.3 meters
12 3 4
Average visibility in river(m)
Tasman
Nelson
Marlborough
West Coast
Southland
0 25 50 75
Percent (%} of Rivers and Streams with Acceptable E. Coli Levels
27
-------
Appendix Table Al: River Lengths and Baseline Levels
Region
River lengths
Baseline
Baseline
Baseline
(1000 km)
Nutrients
Clarity
E.Coli
Auckland
6.798
35
0.9
17
Bay of Plenty
19.283
45
2.9
57
Canterbury
70.41
58
3.3
41
Gisborne
12.655
62
0.6
9
Hawke's Bay
22.798
61
2.6
64
Manawatu-Whanganui
36.725
62
2.6
26
Marlborough
14.466
100
2.7
59
Nelson
0.598
92
4.9
56
Northland
18.558
50
1.4
3
Ota go
50.269
69
1.9
56
Southland
43.604
27
1.6
24
Taranaki
12.666
42
2.4
23
Tasman
14.311
79
5.3
58
Waikato
39.641
43
1.6
35
Wellington
12.786
52
2.7
64
West Coast
35.051
81
3.5
61
Notes: Data obtained from Land Air Water Aotearoa, at httos://www. la wa. org, nz/ LAW A is a collaboration between NZ's Central government
and local government.
28
-------
Appendix B
Appendix Table Bl: Average Marginal WTP values from Model (5)
Nutrients
Clarity
EColi
Auckland
22.13**
189.75**
26.98**
(10.93)
(87.03)
(10.72)
Bay of Plenty
23.19**
201.16**
35.86***
(11.38)
(86.19)
(12.42)
Canterbury
40.71**
78.17
26.52**
(16.21)
(66.10)
(12.50)
Gisborne
17.97*
171.52*
23.08**
(10.91)
(88.24)
(10.98)
Hawkes Bay
21.45*
186.58**
37.65***
(11.35)
(81.63)
(12.95)
Horizons
26.00**
154.15**
25.04**
(12.35)
(75.66)
(10.62)
Marlborough
6.28
221.76**
35.25***
(17.61)
(97.02)
(13.58)
Nelson
5.01
273.38**
36.25***
(15.93)
(121.34)
(13.22)
Northland
22.35**
174.87**
20.31*
(11.08)
(83.43)
(11.50)
Otago
29.75**
106.44
32.20***
(13.62)
(70.67)
(11.90)
Southland
37.90**
109.58
23.85**
(15.47)
(69.85)
(10.29)
Taranaki
23.25**
188.73**
26.20***
(10.47)
(80.52)
(10.07)
Tasman
12.83
256.58**
35.05***
(13.37)
(120.04)
(12.82)
Waikato
32.34**
131.47*
28.63***
(13.32)
(75.38)
(10.80)
Wellington
20.55*
214.82**
40.80***
(10.61)
(87.19)
(13.64)
West Coast
17.30
193.62*
30.51**
(15.57)
(99.65)
(13.86)
Observations
7014
7014
7014
Notes: *p<0.10, **p<0.05, ***p<0.01. Standard errors in parentheses.
29
-------
Appendix C
Appendix Figure CI: National Freshwater Targets for Primary Contact
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2017
National Target
2030
16%
15%
14%
42%
12%
18%
17%
45%
71% suitable
for primary contact
80% suitable
for primary contact
2040
8%
20%
20%
50%
90% suitable
for primary contact
Note: From the National Policy Statement on Freshwater Management, 2020httDs://environment.aovt.nz/assets/Publications/Files/national-
DoHcv-statement~for-freshwater-manaaement-2020.Ddf
30
-------
Appendix D - Full Example Survey Instrument
NORTH ISLAND RIVERS AND STREAMS
The next part of the survey is about the water quality of rivers and streams in the North Island, and
asks some questions about your experiences with these rivers and streams. Your answers will help
inform policymakers.
The information described in this survey was provided by the Ministry for the Environment and
regional councils. Please keep in mind this survey is only about flowing rivers and streams, so please
do not consider lakes or the sea when answering questions.
North Island Rivers
Tasman Region^, sQn
Region
( Marlborough Region ^
r L
Major rivers are coloured in dark blue, and Regional Council boundaries appear in grey.
31
-------
1. Over the last 12 months, have you done any of the following activities in or near rivers and
streams in the regional council area where you live? Tick all that apply
Swimming or wading
~
Fishing
~
Boating, including sailing, and motor boating
~
Water skiing, jet skiing, or kayaking
~
Actively viewing nature (for example: bird
watching)
~
Biking or walking on trails/paths alongside
the water
~
1 didn't visit rivers or streams in my regional
council area in the last 12 months
~
Other activity:
~
2. How much do you agree or disagree with each of the following statement?
Strongly Strongly
Disagree Agree
1 would be more likely to visit rivers and
streams in my regional council area if the
water quality was better.
1 2 3 4 5 6 7
32
-------
River and Stream Water Quality in the North Island
Some information appears below about three key things that affect water quality in rivers and
streams: nutrients, water clarity, and E. coli.
This survey will ask you to consider different programmes to improve water quality in your regional
council area. It's important to first read the information below because it will help you when
answering questions later.
Nutrients. Nitrogen and phosphorous are naturally occurring nutrients, but too much can lead
to excessive algae growth that harms underwater habitat, affecting fish, aquatic plants, and other
organisms. Sources of excessive nutrients include fertilizers, livestock manure, and wastewater
treatment plants. Regional councils set nutrient limits to reduce algae growth and protect aquatic
animals and plants. Waters with nutrient levels above these limits can look and smell bad, and/or
be unhealthy for aquatic animals and plant life.
How is it measured?
Regional councils report the percentage of rivers and streams meeting nutrient limits. The following figure
shows the percent of rivers and streams in each region that have acceptable nutrient levels. For example, 61%
of rivers and streams (or 6 out of 10) in Hawke's Bay have acceptable nutrient levels. A larger bar means that
rivers and streams are better for aquatic animals and plant life.
Rivers and Streams with Acceptable Nutrient Levels
0 25 50 75 100
Percent (%) of Rivers and Streams with Acceptable Nutrient Levels
Healthier Water for Aquatic Plants and Animals
:>
3. Before taking this survey, were you aware of the negative effects that nutrients can have on
aquatic plants and animals?
~ Yes
~ No
33
-------
Water clarity. Excessive pollution makes the water murky or cloudy, and may make the water
look less pleasing.
How is it measured?
Water clarity is measured by how far you can see in the water, in metres. Atone metre of clarity you can see
your feet if standing up to your waist in the water. The following figure shows average water clarity levels (in
metres) across different regions. A larger bar means that, on average, rivers and streams are clearer.
Water Clarity
-] r
1 2 3
Average visibility in river (m)
Clearer water
4. How does water clarity in your regional council area compare to your impressions before taking
this survey?
~ Lower (worse) than I expected
~ About what I expected
~ Higher than I expected
~ I had no expectations about water clarity
34
-------
E. COli are bacteria found in sewage and human and animal excrement. It is natural for rivers and
streams to have small amounts of E. coli, but too much can lead to a higher chance of getting sick
if you swim or wade in that water, or eat fish that live there. Regional councils set E. coli limits in
order to better ensure that rivers and streams are suitable for swimming, wading, and fishing.
How is it measured?
Regional councils report the percentage of rivers and streams that meet E. coli limits and are suitable for
swimming, wading, and fishing. The following figure shows the proportion of rivers and streams in each
regional council that meet E. coli limits. For example, 35% of rivers in Waikato are suitable for swimming,
wading, and fishing. A larger bar indicates more rivers and streams are safe to swim or fish in.
Rivers and Streams with Acceptable E. Coli Levels for Swimming, Wading, and Fishing
Northland
Wellington
0 25 50 75
Percent (%) of Rivers and Streams with Acceptable E. Coli Levels
Safer Water for Swimming, Wading: and Fishing
5. Before taking this survey, were you aware of the negative effects that E. coli can have on the
suitability of rivers and streams for swimming, wading, and fishing?
~ Yes
~ No
35
-------
Improvements in River and Stream Water Quality in your Regional Council Area
To achieve water quality goals in your region, your regional council and the central government would
need to implement and fund new and/or improved programmes to reduce water pollution and
improve water quality. If implemented, programme changes would be gradually phased in and be in
full effect by the year 2025.
Such programmes could, for example, require and/or fund:
• Planting natural vegetation in areas along rural and urban stream and river banks.
• More advanced water treatment technologies at sewage plants.
• Reduce the amount of paved surface when developing new residential or commercial areas,
to decrease stormwater runoff
• More environmentally friendly fertilizers for your garden and lawn care at home.
• Tree planting in urban or eroded areas.
• Programmes for farmers to better manage their soil or use some of their land to plant natural
vegetation.
The design of the programme can cause it to have different effects on nutrients, water clarity, and E.
coli.
36
-------
Funding Water Quality Programmes
If implemented, the changes required under such programmes would result in higher costs, and some
of these costs would be passed on to your household.
Costs to Your Household
Some of the basic things people spend money on would become slightly more expensive. For
example:
• Homeowners will experience increased requirements and maintenance costs for sewage and
septic systems,
• Homeowners and renters will get higher rates or costs on their sewage and water bills.
• Renters will experience increased rent.
• Prices for some products like food or other goods you buy will also increase, due to increased
costs to businesses as a result of the programmes.
Programmes to improve water quality, if implemented, would permanently increase the cost of
living for your household starting next month.
Even though the increase in the cost of living to your household would begin next month, it will
take some time for the programmes to be fully implemented. The improvements described would
be fully achieved by 2025.
37
-------
Deciding Future Outcomes
Each of the next few questions presents three different potential outcomes for the water quality of
rivers and streams in your regional council area water quality and costs to your household. Each
question asks you to choose the outcome you like the best. Your responses will guide future policy
decisions and programmes that would, if implemented, actually improve the quality of rivers and
streams in your regional council area. They would also increase costs to your household.
38
-------
When choosing the outcome you like best, please take time to consider both the benefits and the
costs to your household. Ask yourself if the outcomes for rivers and streams in your regional
council area are worth the additional cost to your household.
We urge you to respond as though costs for your household really would go up as described
under each outcome, and that the environmental improvements described (and only those
improvements) really would occur. Paying the costs means you would have less money to spend
on other things such as food, clothes, going on trips, and even towards resolving other
environmental problems you care about.
If you choose an outcome that results in a cost to your household, you would be making a
commitment to pay the additional cost every month from now on, so please choose carefully.
Remember that:
• The results of this survey will inform regional council and central government policymakers
about actual policies.
• Improvements in water quality apply only to flowing rivers and streams in your regional
council area.
• Improvements in water quality would be fully achieved by the year 2025.
• Your household costs would increase starting next month.
39
-------
Please study the table below.
Question 1.
Which outcome do you prefer for rivers and streams in your regional council area?
Outcomes by 2025
Outcome A
Outcome B
Outcome C
Nutrients
Increase in the percent of
rivers and streams with
acceptable levels.
For example, a change
No change
+ 5 percentage
points
+ 1 percentage points
from 25% of rivers and
streams to 27% is a
change of+2 percentage
points
Water Clarity
Increase in average
visibility in rivers and
No change
+ 1 metre
+ 0.5 metre
streams
E. coli
Increase in the percent of
rivers and streams
suitable for swimming.
wading, and fishing.
For example, a change
No change
+ 6 percentage
points
+ 8 percentage points
from 32% of rivers and
streams to 35% is a
change of+3 percentage
points
Permanent Increase in
the Cost of Living for your
Household
$0 per month
$6 per month
($72 per year)
$3 per month
($36 per year)
Your Choice
~
~
~
Please select your
Outcome A
Outcome B
Outcome C
preferred outcome
(No change)
40
-------
Thanks for answering the first question. You will next see two more questions about different
programmes. As you answer the next questions please remember:
• Each question presents a new set of outcomes.
• Consider each question separately. Do not compare across questions.
• Forget about the previous question, and now imagine that the listed outcomes in each of the next
questions are the only ones you can choose from.
41
-------
Please study the table below.
Question 2.
Which outcome do you prefer for rivers and streams in your regional council area?
Outcome by 2025
Outcome A
Outcome B
Outcome C
Nutrients
Increase in the percent of
rivers and streams with
acceptable levels.
For example, a change
No change
+ 5 percentage
points
+ 7 percentage points
from 25% of rivers and
streams to 27% is a
change of+2 percentage
points.
Water Clarity
Increase in average
visibility in rivers and
No change
+ 1 metre
+ 1.3 metre
streams
E. coli
Increase in the percent of
rivers and streams
suitable for swimming.
wading, and fishing.
For example, a change
No change
+ 8 percentage
points
+ 12 percentage points
from 32% of rivers and
streams to 35% is a
change of +3 percentage
points
Permanent Increase in
the Cost of Living for your
Household
$0 per month
$7 per month
($84 per year)
$20 per month
($240 per year)
Your Choice
~
~
~
Please select your
Outcome A
Outcome B
Outcome C
preferred outcome
(No change)
42
-------
Please study the table below.
Question 3.
Which outcome do you prefer for rivers and streams in your regional council area?
Outcomes by 2025
Outcome A
Outcome B
Outcome C
Nutrients
Increase in the percent of
rivers and streams with
acceptable levels.
For example, a change
No change
+ 15 percentage
points
+ 3 percentage points
from 25% of rivers and
streams to 27% is a
change in +2 percentage
points
Water Clarity
Increase in average
visibility in rivers and
No change
+ 1.6 metre
+ 0.5 metre
streams
E. coli
Increase in the percent of
rivers and streams
suitable for swimming.
wading, and fishing.
For example, a change
No change
+ 12 percentage
points
+ 4 percentage points
from 32% of rivers and
streams to 35% is a
change of+3 percentage
points
Permanent Increase in
$25 per month
($300 per year)
$5 per month
($60 per year)
the Cost of Living for your
Household
$0 per month
Your Choice
~
~
~
Please select your
Outcome A
Outcome B
Outcome C
preferred outcome
(No change)
43
-------
6. Thinking about how you decided which outcomes to choose in the previous questions, please
rate how much you agree or disagree with each of the following statements.
Strongly
Strongly
Disagree
Agree
1 made my choices as if the water quality
1
2
3
4
5
6
7
improvements described actually would be
achieved.
1 made my choices as if my household actually
1
2
3
4
5
6
7
would have to pay the additional monthly
costs.
When making my choices 1 only considered
1
2
3
4
5
6
7
flowing rivers and streams in my regional
council area.
It is important to improve waters in my
1
2
3
4
5
6
7
regional council area, no matter how high the
costs.
1 am against any more regulations and/or
1
2
3
4
5
6
7
government spending.
1 want better water quality, but my household
1
2
3
4
5
6
7
should not have to pay to fund it.
1 believe the data collected with this survey
1
2
3
4
5
6
7
will inform future policies to improve water
quality.
44
-------
Appendix E-Water Quality Regressions
ln(TP) ln(TN) ln(E. Coli)
ln(Clarity)
-0.5333***
-0.5054***
-0.2827***
(0.0015)
(0.0019)
(0.0019)
Landcover Data Missing
0.3365***
0.5668***
-0.4756***
(0.0548)
(0.0312)
(0.0270)
Landcover Native
-0.3093***
-0.5155***
-0.3219***
(0.0021)
(0.0027)
(0.0022)
Landcover Other
-0.0697***
-0.2025***
_0.m7***
(0.0094)
(0.0116)
(0.0115)
Landcover Pastoral
0.1299***
0.5798***
0.2622***
(0.0020)
(0.0026)
(0.0022)
Landcover Uiban
0.2945***
0.7816***
0.5169***
(0.0054)
(0.0066)
(0.0055)
Steam Oidei=2
-0.0892***
-0.0712***
0.0513***
(0.0013)
(0.0017)
(0.0015)
Steam Older =3
-0.2249***
-0.1685***
0.0595***
(0.0017)
(0.0021)
(0.0019)
Steam Older =4
-0.3954***
-0.2987***
0.0065**
(0.0024)
(0.0029)
(0.0027)
Steam Older =5
-0.5065***
-0.3921***
-0.0179***
(0.0036)
(0.0043)
(0.0039)
Steam Older =6
-0.6486***
-0.6413***
-0.1655***
(0.0050)
(0.0063)
(0.0054)
Steam Older =7
-0.8029***
-0.7170***
-0.2860***
(0.0065)
(0.0094)
(0.0095)
Steam Oidei=8
-1.1810***
-1.3324***
-0.4156***
(0.0167)
(0.0192)
(0.0160)
Elevation
-0.0010***
-0.0010***
-0.0017***
(0.0000)
(0.0000)
(0.0000)
nof swim=2
0.5045***
(0.0024)
nof swim=3
1.2165***
(0.0032)
nof swim=4
1.7509***
(0.0033)
nof swim=5
2.0637***
(0.0038)
Constant
3.7353***
6.2039***
3.3444***
(0.0023)
(0.0031)
(0.0043)
Observations
587,939
587,939
587,939
R2
0.7799
0.7790
0.9216
Notes-all models include regional council fixed effects. Standard errors appear in parentheses. Standard errors
appear in parentheses. ***, **, and * denote significance at the 99%, 95%, and 90% levels, respectively. The
omitted dominant landcover category is exotic forest.
45
------- |