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1
Accounting for Welfare Consistency and Institutional Effects on International Benefits
Transfer for Developing Countries, the Case of Air Quality Valuation.
Miranda Garces, Juan Francisco
Abstract
Considering the negative results of a recent study that test the adequacy of air valuation using
Meta-Analysis Benefits Transfer in developing countries, a research was conducted to assess
the effects on the transfer error of the satisfaction of welfare consistency criteria, the insertion
of good, study and institutional variables and the availability of access to primary studies.
The present study obtained transfer errors of 2 and 3 %, they are smaller than the errors in
similar studies and within the established parameters. It is concluded that the application of
the methodology in developing countries is valid and that the access to primary studies is
crucial for the feasibility of the methodology.
2
Keywords
Air valuation
Meta Analysis Benefits Transfer
Developing Countries
Transfer error
Highlights
Meta-Analysis benefits transfer can be conducted in developing countries with acceptable
levels of transfer errors (2-3%).
In developing countries of Asia, on average, WTP is higher, while in developing countries of
South America it is lower.
In developing countries, studies that used contingent valuation methodology report, on average,
lower values of WTP.
On average, the WTP for a 1% decrease in the concentration of pollutants in the air is 1,1
US$.
Abbreviations
EVRI Environmental Valuation Reference Inventory
MA BT Meta Analysis benefits transfer
OLS Ordinary Least squares
PM particulate matter
RED Review of externality data
3
WTP willingness to pay
1. Introduction
Meta-analysis benefits transfer MA BT is a non-marketable goods economic valuation
instrument appealing for its efficiency in the use of resources. In contrast with revealed and
stated preference methods, BT does not rely on primary information, reducing its budget
requirements. The methodology consists in the determination of an economic value in a
policy site based on the economic value previously estimated in one or several study sites.
First records of its application appear in the United States in the 70’s. From then on, several
authors have contributed to establish a protocol and define a theory for this methodology
(Champ, et al, 2003). Institutional efforts to facilitate its application resulted in the creation of
several databases that provided the inputs for the analysis. EVRI is one of them and it is an
initiative of Environment Canada. In the same fashion, RED is an infobase focused on the
European Union. Government agencies and private organizations make use of it and several
studies have been published valuing different environmental services (Bergstrom and Civita,
1999).
There are several studies that value air quality using benefits transfer between
developed countries (Rozan 2004, Ready et al., 2004, Smith & Huang, 1995), i.e., studies that
assess the applicability of benefits transfer between countries with income heterogeneity
(Chesnut et al. 1997, Czajkowski & Ščasný, 2010); and studies that transfer values between
developed countries. Nonetheless, there is still uncertainty in the reliability of the application
of the methodology in the last case, between developing countries. In a recent study,
Saldarriaga (2014) concluded MA BT is not a good option to value environmental services in
developing countries, thus, value transfer, which is a similar and less informational
demanding methodology, should instead be utilized. His conclusions are based on the
4
development of a thorough research that compared the results of a MA BT and a value
transfer. Although enough observations were compiled by the researcher, most of the
explanatory variables of the econometric model were not statistically significant and the
transfer error was deemed as high. Saldarriaga (2014) states that due to the lack of
accessibility to studies and considering that not every study reported information respect the
commodity being valued, income or demographic characteristics explains his results.
Considering the findings of Saldarriaga (2014), the present research is a second
attempt to test the validity of MA BT in developing countries. In opposition to Saldarriaga’s
study, it is found that the transfer error can be reduced considerably up to two percent (2)%
for within sample transfer, and three percent (3%) for out of sample transfer, and a MA
regression could be constructed including explanatory variables relative to the commodity
quality and quantity, study design, income and institutional variables. It is argued here that it
is important to increase the number of observations, and this is possible by pooling studies
that value Hicksian and Marshalian surplus. Further, it was found that a variable capturing
regional institutional effects was statistically significant. The present study agrees with
Saldarriaga’s remarks with respect to the challenging process of gathering data in developing
countries. The development of initiatives like EVRI or RED is important to satisfy the
informational requirements in developing countries. In the same fashion, it is important to
standardize the way information is reported in the studies to make them comparable.
5
2. Literature Review
The state of the art of the Meta-Analysis Benefits Transfer MA BT methodology is
discussed by several authors, among them, Bergstrom and Taylor (2006); Brouwer (2000);
and Ready and Navrud (2006). It is agreed that the methodology compiles and summarizes
the information with respect to the valuation of an environmental good or service conducted
by previous studies in different places. Once several observations are pooled, it is possible to
establish a causal relationship through a regression model. The dependent variable is the
valuation of the environmental good expressed in the willingness to pay WTP for it, while the
independent variables are the level of income of the individuals that demand the good, quality
and substitutes for the good or service, study and demographic characteristics.
Study characteristics refer to the methodologies used to value a non- marketable good.
There are two principal classes of methodologies: 1) stated; and 2) revealed preference
methods. Study characteristics refer too, to the elicitation techniques, e.g. open ended
questions, payment cards, iterative bidding, etc. In turn, demographic characteristics are the
age of the respondents, gender and educational level (Bergstrom and Taylor 2006).
MA BT analysis can be used to estimate the valuation of a particular environmental
good in a policy site based on the valuation obtained in several study sites. There is a
distinction between the policy site and the study site. The study site is a place, which may be
a city, a country or a specific region, where a primary study - an environmental valuation, has
taken place. On the other hand, the policy site is the place where the values will be
transferred (Bergstrom and Taylor 2006). The MA BT regression, mentioned above, captures
the effects of the explanatory variables on the WTP. It is through this way that the values are
transferred. Once the characteristics of the policy site are plugged in the regression, it is
possible to determine a range of values that the WTP can assume.
6
BT can be conducted using information of studies that has taken place in the same
country or it can be conducted using information of studies that taken place in different
countries. The last method is known as International Benefits Transfer. The said method is
subject to methodological challenges. As outlined by Ready and Navrud (2006), International
BT should take into account the following: currency conversion, differences in measurable
attributes of the users, measurement of wealth (income adjusted by taxes, subsidies, and
intergenerational transfers), differences in culture, extent of the market (local or national),
heterogeneity in purchasing power. It is also necessary to carry on validity tests.
Rosenberg and Stanley (2006) present three types of error in carrying out a BT analysis.
These are: generalization error, measurement error and publication bias.
Generalization Error
Generalization error refers to the similarity of the study site and the policy site.
Rosenberg and Stanley (2006) propose three dimensions to determine the similarity between
study sites and policy sites: 1) commodity, 2) market, and 3) welfare measure.
Commodity consistency occurs when the environmental services provided are similar
across places. Welfare consistency implies that the same welfare measure (Hicksian or
Marshalian surplus) is estimated in both sites. In this regard, Ayala et al. (2014) found that
studies of valuation using discrete choice experiments are not recommended to be included in
MA BT because there is commodity inconsistency and incommensurability across studies.
7
The analysis of the differences between study sites and policy sites is deepened by
Spash and Vatn (2006). They proposed the inclusion of institutional setting, geographic
location and environmental attitudes as explanatory variables in MA BT regression.
Measurement Error
Measurement error refers to the extent that research decisions and assumptions in
primary studies affect the MA BT. In this case, the authors suggest the use of studies with
representative and larger samples.
Publication Bias
Publication bias refers to the effects caused by the articles selection process on WTP.
Publishers may favour the publication of some kind of studies, for example, those that present
results that are statistically significant. An alternative is the inclusion of grey literature in the
meta-analysis. A review of BT studies published along 20 years, carried out by Kaul et al.
(2013), found that BT error is reduced for meta-analysis that compile observations from
studies that characterize the environmental good considering the quantity that is available of
the good rather than characterizing it based on its qualities. The BT error is reduced as well
for meta-analysis that compile observations from studies that valued the environmental goods
using contingent valuation methodologies. Finally, it was found that meta-analysis that
combined data for multiple studies reduced the transfer error.
Meta-analysis conducted in developing countries in which the study and policy sites
are located in developing countries face additional challenges. Saldarriaga (2014), who
carried out a MA BT of air quality valuation in developing countries, points out the reduced
8
accessibility to environmental valuation studies in developing countries, such as EVRI
infobase being available only for citizens of Australia, Canada, France, Mexico, New
Zealand, United Kingdom, and United States. EVRI is a benefit transfer infobase that eases
the process of compilation of information to carry out BT studies. Through this, results of
hundreds of valuation studies are summarized presenting relevant information for BT.
Saldariaga (2014), likewise found methodological difficulties. Specifically, meta-
analysis requires information relating to socioeconomic and geographic data of the
population surveyed. Nonetheless this information is not always reported. The definition of
the environmental good was found to be an important issue, as studies are not precise on the
change of air quality valuated. Saldarriaga concludes that the applicability of MA BT in
developing countries must be with caution as the results of his research suggested the
existence of errors in the prediction of economic valuations. Nonetheless, we propose the
inclusion of proxy variables to account for the lack of information reported in primary
studies. The number of environmental valuation studies is a constraint to the applicability of
the MA BT methodology as there would be no observations necessary to run the regression.
Other than Saldarriaga’s 2014 study, there is no other benefit transfer research for air
quality that focuses on the transfer from developing countries to developing countries.
Nonetheless, there are studies that transfer benefits from developed countries to developing
countries and from developed countries to developed countries.
Chesnut et al. (1997) and World Bank (2002) transferred values for air quality from
United States to Thailand, and from United States and Europe to Mexico, respectively. The
9
methodology used was Mean Unit Value Transfer and it corrects for the level of income
proportions between the place where the original research was carried out, the study site and
the place where the value will be transferred, the policy site. The values were positive and
smaller than those in the original studies. The methodology used relies on the strong
assumption that the study site is similar to the policy site, that they are perfect substitutes
(Rozan 2004). Validation of mean unit value transfer has been carried out several times.
Results show that the estimates could present a thirty percent (30%) error (Navrud 1998) or
lie in the confidence interval of estimates obtained in contingent valuation studies (Albertini
et al, 1997).
Not for a BT of air quality but for a BT of the value of the statistical life –VSL,
Milligan et al. (2014), found that the income elasticity difference across developed and
developing countries implies that meta-analysis that are carried out from and to developed
countries are more reliable, coinciding with the rules of consistency between study sites and
policy sites.
In the case of BT from and to developed countries, Rozan (2004) tested the benefit
function transfer methodology in two European countries for a homogenous environmental
good. To this, end four valuation studies were conducted. At first, a contingent valuation
study was carried out in each country. Thereafter, the values were transferred to the other
country. The results show that BT estimates lied outside the confidence interval of the values
obtained in first place with the contingent valuation methodology. The transfer of values for
same goods might not be a sufficient condition but maybe necessary along with other
conditions as suggested for International BT.
10
International Meta-analysis benefits transfer that valued environmental goods other
than air quality were reviewed to understand the challenges involved and advances done up to
date. Low reliability of MA BT methodology was found by Lindhjem and Navrud (2007), in
a study of stated preferences for the valuation of forest protection and multiple use forestry
plans in Norway, Sweden and Finland. The reliability was measured based on the transfer
error, a mean range from forty-seven percent (47%) to one hundred twenty six percent
(126%) was found. Variables included in the regression were study, payment vehicle, good
characteristics and country dummies. A within country MA BT showed the relevance of
market conditions and valuation procedures. In the analysis of air quality valuation carried
out by Smith and Huang (1995), in the United States, both variables were significant to
explain WTP.
3. Methods
3.1. Data collection
Following Bergstrom and Taylor (2006), a protocol to develop the meta-analysis
database was specified. The objective was to identify studies that valued air quality in
developing countries. The International Monetary Found (IMF) (2014) classification was
used to determine which countries were considered as developing.
To avoid publication bias, observations from grey literature were included and as
suggested by Bergstrom and Taylor (2006), commodity consistency was a selection criteria
for the study selection. In the final database, the ecosystem service valued, air quality, was
the same across observations. Air quality comprises a range of services: health, visibility and
non-use values. Some studies focused on the valuation of only one service i.e., health. They
11
were not considered and were only included studies that valued air quality holistically.
Differences arose with respect to the environmental stressors that affected air quality across
study sites, e.g. some studies focused their attention on PM2.5 concentration, others in PM10
or in S02. In order to account for this differences, information with respect to the stressors
was extracted from the primary studies.
Studies that used revealed preference or contingent valuation methodologies were
included but following Bergstrom and Taylor (2006), an interaction term between income
and the quantity of the good being valued was included to account for welfare consistency.
There were differences with respect to the concentration of pollutants in the air and the
changes being valued across studies, in the same way that there were differences in the time
frames considered for the valuation.
Studies were found in EVRI database, the Web of Science and using search engines
like Google or Duckduckgo.com. There was access to EVRI database since this research was
carried out in Australia and because Sydney University students have access to the website of
Science and several Journals. Thus, data gathering was less challenging than for Saldarriaga
(2014). In his study of MABT for developing countries, access to information was an issue as
there is no benefits transfer database in which researchers can rely while working on
developing countries.
Initially, fifteen (15) studies were identified, two (2) were not included because they
did not satisfy commodity consistency. Table 1 contains a list of the studies included in the
MA.
12
Table 1. List of Studies
# Authors Year Number of
Observations
WTP
2005 USD
1
Wang, X.J.,
W. Zhang,
Y. Li,
K.Z. Yang
M. Bai
2006 8
66.01
77.59
48.64
67.56
45.55
34.36
64.47
52.11
2 Rogat, J. 1994 1 76.38
3
Murty, M.N.,
S.C. Gulati
A. Banerjee
2003 4
10.86
5.95
1701.63
722.39
4 Rogat, J 1998 1 2648.33
5 Alsherfawi A., M. 2005 1 148.94
6 Yusuf, A
Resosudarmo, B 2006 2
311.42
937.66
7
Carriazo, F.
R. Ready
J. Shortle
2013 8
0.67
1.26
2.58
6.31
0.84
1.59
3.21
7.86
8 Wang, Y
Zhang, Y 2008 1 31.35
9
Lu, A.Y,
R.C. Bishop
M.P. Walsh
1996 6
47.65
44.53
25.17
30.10
206.16
123.72
10 Murty, M. N.
S. C. Gulati 2006 2
353.25
17.32
11 Abou-Ali, H
M. Belhaj 2004 2
17.53
27.24
12 Dziegielewska, D.A.P
R. Mendelsohn 2005 2
406.66
328.44
13
13 Wuang H
Whittington D 2000 2
20.36
91.52
The information extracted from the studies were: quality and quantity available of the
commodity, income of the respondents, study design and demographic variables. Bergstrom
and Taylor (2006), suggest to include information with respect to substitutes of the good
being valued. Nonetheless, no information was found in the studies reviewed. With respect to
the quantity of the environmental service, information on the concentration of environmental
stressors in the air was compiled. Not every study reported it and missing data was replaced
with pollution statistical information from official sources. Information was extracted with
respect to the hypothetical change in air quality established in the stated preference studies or
in the change in the air quality valued on studies using revealed preference methods. Not
every study presented information with respect to income and demographic characteristics.
Further, the information was not comparable among the studies that reported it, e.g. some
studies present the average age of the respondents while others report frequencies for broad
ranges of age (20-30, 30-50).
Studies were characterized with respect to the valuation method applied, stated or
revealed preference, and with respect to the elicitation and calculations methods.
Table 2 depicts a statistics summary of the variables included in the model. The
degrees of freedom imposed a restriction in the number of explanatory variables that could be
used. Nonetheless, the model was designed to satisfy the requirements of a MA BT.
14
Table 2. List of Variables
Variable Description Mean
( Std. Dev.) Min Max
Lwtp
Dependent variable: Natural logarithm of the
WTP for air quality accounting for purchasing
power parity in 2005 US$
3.71
(2.02)
-0.40183 7.881686
Socio Economic Characteristics
South America
1= if the study was carried out in South
America
0 = Otherwise
0.25
(0.44)
0 1
Asia 1= if the study was carried out in Asia
0 = Otherwise
0.6
(0.5)
0 1
GDP Gross domestic product per capita accounting
for purchasing power parity in 2005 US$
6273.45
(4607.74)
1980.979 15529.89
Environmental Service Characteristics
Pollutant
concentration
Quantity of pollutants concentration in a given
city measured in micrograms per cubic meter
(µg/m3).
122.79
(81.34)
20.13 319.84
Change in pollutants
Policy proposed percentage decrement in the
concentration of pollutants in the air, measured
in micrograms per cubic meter (µg/m3)
0.28
(0.23)
0.003127 0.5
Interaction term Interaction term between the variables gdp per
capita and change in pollutants
879644.4
(1147610)
45768.88 3521248
Square of Pollutants
concentration
Square of the variable pollutant concentration
variable.
21527.21
(25393.27)
405.2169 102297.6
Study Characteristics
Contingent
Valuation
1= If contingent valuation methodology was
used
0=Otherwise
0.6
(0.5)
0 1
Journal 1= If the study was published in a journal
0=Otherwise
0.2
(0.41)
0 1
Payment frequency 1= if the payment for air quality is annual
0 = Otherwise
0.275
(0.4522026)
0 1
The pollution concentration and the change in the concentration of the pollutant are
variables that capture the effects of commodity quantity on the WTP for it. A quadratic term
of the concentration of pollutants was included to account for non-linear relationships with
the explanatory variable. The journal and contingent valuation dummies are included to
capture the effects of the studies design on the WTP estimates. Two (2) dummy variables for
15
the locations of the study, i.e., South America and Asia, capture regional, social and
institutional, effects. GDP per capita is a variable that captures the effect of the level of
income in the WTP for the service.
The dependent variable, WTP, was expressed in different currencies across studies.
Therefore, it was necessary to express all values in terms of common currency. For practical
reasons, United States dollars were chosen. The purchasing power is different across
countries and therefore it was necessary to make adjustments to the WTP values. The
University of Pennsylvania publish regularly worldwide data of the exchange rate accounting
for purchasing power parity. Lastly, WTP values were estimated in different years and the
United States GDP deflator was used to bring all values to a common year. The year 2005
was chosen for practical reasons. All of these adjustments made the observations comparable
to each other.
The frequency of the payments for the environmental good differed across studies. To
capture this difference, a dummy variable, i.e., payment frequency, was included. The
dummy variable equals to one when the payment is annual.
3.2.Meta Regression Model
As there were extracted several observations per study, there was intra class
correlation (65,39%). Ordinary Least Squared – OLS clustered standard errors are
appropriate in this context to “generalize the White robust covariance matrix to allow for
clustering as well as heteroskedasticity” (Angrist and Prischke, 2008: 234). The model
results are presented in Table 3.
16
Table 3. Meta Regression Result (OLS clustered standard errors)
Lwtp Coefficient
(Std. Err.) P>|t|
Environmental Service Characteristics
Pollutants concentration -.0135738
(.0043521)
0.009
Square of Pollutants concentration .0001332
(.0000162)
0.000
Change in pollutants 10.25766
(1.356293)
0.000
Interaction term -7.58e-06
(6.57e-07)
0.000
Study Characteristics
Contingent Valuation -5.002581
(.6821238)
0.000
Journal 4.980275
(.3944114)
0.000
Payment frequency 1.106728
(.4236588)
0.023
Socio Economic Characteristics
South America -2.222535
(.3378793)
0.000
Asia 5.733213
(.6590886)
0.000
GDP .0017266
(.0001594)
0.000
F (10, 12) = 2918.74 P= 0,000
R-squared = 0,8336
Number of observations 40
Number of studies 13
The regression ran on a database of forty (40) observations from thirteen (13) studies.
The model explains 83.36% of the variation of the natural logarithm of willingness to pay.
The F test results show that altogether the explanatory variables are statistically significant at
the 95% confidence level.
17
To analyse the effect of pollutants concentration on the WTP for air quality, the
pollutants concentration and its quadratic term were included. Both variables are individually
and jointly significant at the 95% confidence level. An F test was performed to test joint
significance. The coefficient on the variable corresponding to the pollutants concentration is
negative and is equal to -0.014. The coefficient on the variable corresponding to the square of
the pollutants concentration is, in turn, positive and equal to 0.0001. This implies that the
willingness to pay is, in first place, decreasing with the levels of pollutants up to an inflection
point where it becomes increasing. Taking the derivative of the regression with respect to the
concentration of pollutants and equating to zero gives us the inflection point, 50.9 µg/m3. On
the average, individuals increase their willingness to pay with the pollutants concentration in
the air after the concentration level pass the level of 50.9 µg/m3.
The level of the percentage decrement in the concentration of pollutants given a
policy to improve air quality has a positive impact on the WTP. People are willing to pay
more for the implementation of policies that aim for higher levels of reduction in the
concentration of pollutants in the air. On the average, an additional decrement of the
concentration of pollutants drives individuals to pay one additional dollar.
The interaction term was included to account for welfare inconsistency among studies
using stated preference methods and revealed preference methods, as suggested by Bergstrom
and Taylor (2006). This implies the possibility to access a broader universe of observations
that eases the meta-analysis.
18
On the average, a contingent valuation study reports smaller values of willingness to
pay (US$148). In contrast, studies published on journals report, on average, higher values of
WTP (US$145). When the environmental service is paid annually, the values of WTP are
higher than if it is paid monthly or in just one initial payment (US$3).
3.3.Specification Tests
A Box Cox test was performed to determine the pertinence of the definition of the
functional form of the dependent variable. Two (2) models were compared: (1) where the
natural logarithm of the WTP was the dependent variable; and (2) where the WTP was the
dependent variable. Models differed in terms of goodness of fit and therefore it was
appropriate to take the logarithm of the dependent variable.
The F test shows that jointly all the explanatory variables are significant at the 95%
confidence level. All variables were individually significant at the 95% confidence level, too.
Residuals are distributed normally according to the results of the Shapiro-Wilk test.
The p value of the test was equal to 0.53. Therefore, the null hypothesis of normality of the
residuals was not rejected. Figure 1 depicts the graph of the distribution of the residuals. It
resembles a normal distribution.
19
Figure 1 Residuals histogram
3.4.Sensitivity Analysis
To analyse the sensitivity of the model, seven (7) additional regressions were run. An
observation was dropped each time a new regression was run. The observations were dropped
when a specific variable assumed values that lied away from the mean. It is observations that
could be suspected to have biased the estimators. In the first regression, the observation with
the highest WTP was dropped. In the second regression, the observation that corresponded to
the highest concentration of pollutants was dropped. In the third one, the observation with the
lowest concentrations of pollutants in the air was dropped. The forth and the fifth regressions
excluded observations that valued the lowest and the highest changes in the concentrations of
pollutants, respectively. The sixth regression excluded observations from Africa while the
last regression excluded observations from countries that had a GDP per capita higher than
US$14,000.
20
For each variable of the seven regressions, the coefficients and its standard errors are
presented in table 4. The marginal effect of every variable for every regression has the same
sign as the original regression. Nonetheless, the size of the effects is different. In the same
way, the change of the standard errors of some variables imply that some variables are not
significant in some scenarios. The biggest impact on the model is the exclusion of
observations with higher levels of income. This is probably because the number of
observations is smaller in this scenario. The variables that are sensible to the exclusions of
observations are the concentration of pollutants and the interaction term.
Table 4. Sensitivity Analysis
Coefficients
(Standard Errors)
Variable Regression
1
Regression
2
Regression
3
Regression
4
Regression
5
Regression
6
Regression
7
GDP 0.0017901
(0.0005738)
0.0018431
(0.000206)
0.0017743
(0.0001968)
0.0017791
(0.0002268)
0.0029117
(0.0004287)
0.0022613
(0.0002433)
0.0016107
(0.0004502)
Pollutants
concentration
-0.0128385
(0.005426)
-0.0078381
(0.006562)
-0.0206684
(0.0108967)
-0.0214131
(0.0167931)
-0.0067681
(0.0054897)
-0.0094003
(0.0050682)
-0.0150598
(0.0024943)
Change in pollutants
11.01347
(6.951542)
10.32311
(1.364558)
10.36149
(1.418758)
10.36902
(1.45021)
27.93347
(10.19551)
10.51552
(1.422379)
10.06807
(1.776093)
Interaction term
-0.00000771
(0.00000142)
-0.00000807
(0.000000842)
-0.00000779
(0.000000826)
-0.00000781
(0.000000963)
-0.0000104
(0.00000418)
-0.00000983
(0.00000103)
-0.00000632
(0.00000409)
Contingent
Valuation
-5.789331
(6.485542)
-5.169689
(0.7260705)
-5.035643
(0.6946796)
-5.044219
(0.7175267)
-20.93964
(5.03835)
-5.399
(0.7435943)
-5.013235
(0.6929589)
Journal 5.348004
(3.026145)
5.052221
(0.4096098)
5.10326
(0.4883127)
5.115798
(0.5635422)
7.691806
(1.794358)
5.590347
(0.3732838)
4.883939
(0.634746)
South
America
-2.474726
(1.996041)
-2.102768
(0.3800476)
-2.187495
(0.3632073)
-2.189033
(0.3641496)
-11.11957
(1.020119)
-1.407567
(0.3337651)
-2.248805
(0.342658)
Asia 5.638751
(0.8124445)
6.084544
(0.8909852)
5.614108
(0.6750904)
5.595189
(0.6500764)
0
(omitted)
8.106195
(0.8216582)
5.723817
(0.6488746)
Square of
Pollutants
concentration
0.0001327
(0.0000142)
0.0001218
(0.0000209)
0.0001662
(0.0000485)
0.0001699
(0.0000778)
0.0001459
(0.0000505)
0.000141
(0.0000177)
0.0001241
(0.0000424)
Payment
frequency
1.578408
(3.680917)
1.274657
(0.5120197)
1.459944
(0.5731667)
1.502485
(0.843686)
14.38757
(2.89019)
1.375504
(0.3580174)
0.9873544
(0.4922886)
Constant -5.838452
(1.863933)
-6.680285
(1.324746)
-5.671363
(0.9816437)
-5.664478
(0.9637913)
-4.391446
(1.54583)
-9.517725
(1.470556)
-5.226272
(1.932406)
Obs. 39 39 39 38 21 38 34
R2 0.813 0.8374 0.8234 0.8211 0.9458 0.8487 0.8518
3.5.Validity tests
21
Two validity tests were made, one to estimate the within sample transfer error and
another to estimate the out of sample transfer error. Transfer error is the proportion of the
difference of the observed and predicted WTP with respect to the observed WTP.
𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟 𝐸𝑟𝑟𝑜𝑟 =𝑊𝑇𝑃𝑂𝑏𝑠 − 𝑊𝑇𝑃𝑃𝑟𝑒𝑑
𝑊𝑇𝑃𝑂𝑏𝑠
Within sample transfer error compares the observed and predicted values for the
original database. Figure 2 below depicts the predicted and observed values of the WTP when
a within sample transfer was performed. In this case, the error amounted to two percent
(2%), a value well below the average transfer error for International Benefits Transfer
(Lindhjem and Navrud, 2007)
Figure 2
The out of sample transfer error is three percent (3%), higher than the within sample
transfer error but still below the average transfer error for international benefits transfer. To
estimate the out of sample transfer error, 40 regressions were ran, randomly dropping one
different observation each time. With the obtained regressions, WTP values were predicted
for each observation that was dropped. The results of the validity tests suggest that the MA
BT function is adequate to transfer WTP with acceptable levels of error.
-2
0
2
4
6
8
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Predicted vs. Observed WTPWithin Sample Transfer
Observed WTP Predicted WTP
22
Figure 3
4. Discussion and Conclusion
The findings of this research project confirm some of the conclusions presented by
Saldarriaga (2014). Accessibility to valuation studies is one of the determinants of a MA BT
results quality. Contrasting the present project data gathering processes with Saldarriaga’s, it
is evident that MA BT studies conducted in developing countries face challenges finding
previous economic valuation studies. Almost every developing nation has no access to the
biggest database (only Mexico has access to EVRI). It is important to mention that EVRI is
willing to broad the access of the infobase to new contributors of the project. In this way,
governments of developing countries have an opportunity to make use of the infobase if they
enter a contribution agreement.
There is neither an agreement nor standards with respect to the way in which the
results information should be presented in valuation studies. Heterogeneity in statistics
summaries of income and demographical variables make them non-contrastable and
therefore, it is information that cannot be included in a MA BT. Information with respect to
willingness to pay should include a clear definition of the good being valued; the payment
-2
0
2
4
6
8
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Predicted vs. Observed WTPOut of sample transfer
predicted observed
23
vehicle; the number of payments proposed by the researches; the change in the environmental
good of the service being valued; the time frame of the valuation, e.g. whether pollution
reduction will last for one year or whether they are permanent; and the initial and desired
states of the environmental service, e.g. the concentration of pollutants in the air before and
after the policy has taken place.
EVRI summarizes information of environmental valuation studies. The data presented
is pertinent and useful to carry out a MA BT. The list of variables compiled EVRI’s studies
summaries is a good reference of the information that should be presented by new
environmental valuation studies.
Once guaranteed the accessibility to valuation studies, the present research took some
strategies to solve the lack of information with respect to income and demographical
variables. The country GDP per capita is a statistically significant variable and was used
considering it reflects the average income level of the populations involved. To account for
the limited number of observations, studies that valued two different welfare measures were
pooled and the discrepancy was resolved including an interaction term between the pollutants
concentration in the air and the GDP per capita. This is because it was identified that the
difference between Marshallian and Hicksian surplus is a function of the quantity of the
commodity and the income (Bergstrom and Taylor 2006). The concentration of pollutants is
considered as a measure of the quantity of the environmental service that is being provided.
This is because, given a higher concentration of pollutants, there is less air quality and vice
versa.
24
Dummy variables for the regions of the places where the studies were conducted
captured institutional and social effects on the willingness to pay. Two dummy variables, one
for Asia and one for South America, where included in the model. Most of the studies took
place in those continents.
The strategies undertaken appear to be effective if the accuracy of the model is
measured by the within and the out of sample transfer errors, which are equal to two (2) and
three (3) percent, respectively. Transfer errors up to 20 or 40% are considered as acceptable
(Lindhjem and Navrud, 2007).
Saldararriaga (2014) present a transfer error equal to 31% and concludes the MA BT
is not an adequate methodology for developing countries. Nonetheless it is argued here that
accounting for welfare and commodity consistency and institutional and social differences
improves the explanatory power of the model. Therefore, contrary to Saldarriaga’s
conclusions, it is believed that MA BT for international transfer in developing countries is a
valid instrument if precautions are taken along the research process.
25
5. Reference
Alberini, A, Cropper, F, Tsu-Tan, A, Krupnick, L, Jin-Tan, D, Shaw & W, Harrington 1997,
‘Valuing Health effects of Air Pollution in developing countries: The case of Taiwan’.
Journal of Environmental Economics and Management 34, pp. 107-126.
Angrist, J & Pischke, J-S. (2008) Mostly Harmless Econometrics, Princeton University Press.
Ayala, A de, Mariel, P & Meyerhoff, J 2014, ‘Transferring landscape values using discrete
choice experiments: is meta-analysis an option?’, Economia Agraria y Recursos Naturales,
vol. 14, No. 1, pp. 103-128
Bergstrom, J & Civita, P 1999, ‘Status of Benefits Transfer in the United States and Canada:
A Review’, Canadian Journal of Agricultural Economics, vol. 47, pp. 79-87.
Bergstrom, J & Taylor, L 2006, ‘Using meta-analysis for benefits transfer: Theory and
practice’, Ecological Economics, vol. 60, pp. 351 – 360.
Brouwer, R 2000, Environmental value transfer: state of the art and future prospects,
Ecological Economics vol. 32 pp. 137–152
Champ, P., Boyle, K., & Brown, T., (2003) A Primer on Nonmarket Valuation. Kluwer
Academic Publishers, London
Chestnut, LG, Ostro, BD &Vichit-Vadakan, N 1997, Transferability of air pollution control
health benefits estimates from the United States to developing countries: Evidence from the
Bangkok study, American Journal of Agricultural Economics, Vol. 79 , No. 5, pp. 1630-1635
Czajkowski, M & Ščasný, M, 2010, Study on benefit transfer in an international setting. How
to improve welfare estimates in the case of the countries' income heterogeneity?, Ecological
Economics vol. 69 2409–2416
International Monetary Fund 2014, ‘World Economic Outlook—Recovery Strengthens,
Remains Uneven’, Washington
Kaul, S, Boyle, KJ, Kuminoff, NV, Parmeter, CF & Pope, JC 2013, ‘What can we learn from
benefit transfer errors? Evidence from 20 years of research on convergent validity’, Journal
of Environmental Economics and Management, Vol. 66, No. 1, pp. 90-104
Lindhjem, H & Navrud, S 2007, ‘How reliable are meta-analyses for international benefit
transfers?’, Ecological Economics, vol. 66, pp. 425 -435.
Milligan, C, Kopp, A, Dandah, S, Montufar, J 2014, ‘Value of a statistical life in road safety:
A benefit-transfer function with risk-analysis guidance based on developing country data’,
Accident Analysis and Prevention, Vol. 71, pp. 236-247
Navrud, S 1998, ‘Valuing Health impacts from Air Pollution in Europe : New empirical
evidence on Morbidity’, Discussion Paper #D-04/1998, The agricultural University of
Norway.
26
Ready, R & Navrud, S 2006, ‘International benefit transfer: Methods and validity tests’,
Ecological Economics, vol. 60, pp. 429-434.
Ready, R, Navrud, S, Day, B, Dubourg, R, Machado, F, Mourato, S, Spanninks, F, Vázquez,
M, 2004, ‘Benefit Transfer in Europe: How Reliable Are Transfers between Countries?’,
Environmental and Resource Economics, vol 29, no.1, pp. 67-82
Rosenberg, R & Stanley, T 2006, ‘Measurement, generalization, and publication: Sources of
error in benefit transfers and their management’. Ecological Economics, vol. 60, pp. 372-78.
Rozan, A 2004, ‘Benefit Transfer: A Comparison of WTP for Air Quality between France
and Germany’, Environmental and Resource Economics, Volume 29, no. 3, pp. 295-306
Saldarriaga, A 2014, ‘Benefit Transfer and the Economic Value of Air Quality Revisited’.
Revista Sociedad y Economía, num. pp. 207-223.
Smith, K & Huang, J 1995, ‘Can Markets Value Air Quality? A Meta-Analysis of Hedonic
Property Value Models’, Journal of Political Economy, Vol. 103, No. 1 pp. 209-227.
Spash, C & Vatn, A 2006, ‘Transferring environmental value estimates: Issues and
alternatives’, Ecological Economics, vol. 60, pp. 379-388.
Turner, R, Paavola J, Cooper, P, Farber, S, Jessamy, V & Georgiou, S 2003 ‘Valuing nature:
lessons learned and future research directions’, Ecological Economics, vol. 46, pp. 493-510
27
6. Appendices
Observations
Cod Good Stressor City province Country PPP GDP
per capita
2005 US$
Continent year of
data
WTP IN
2005 USD
Frequency baseline unit Academic/GreyLit porcentual
change
Number of
payments
CV/RP open ended elicitation
technique
Valuation Equation samplesize
CH-WX-
2006
Air Quality PM2.5 Dongcheng Beijing China 2709.402 Asia 1999 66.01114 annual 77.30395 ug/m3 Journal 0.5 5 Contingent
Valuation
open ended personal
interview
OLS 104
CH-WX-2006
Air Quality PM2.5 Xicheng Beijing China 2709.402 Asia 1999 77.59204 annual 74.62707 ug/m3 Journal 0.5 5 Contingent Valuation
open ended personal interview
OLS 141
CH-WX-
2006
Air Quality PM2.5 Chongwen Beijing China 2709.402 Asia 1999 48.63979 annual 77.30395 ug/m3 Journal 0.5 5 Contingent
Valuation
open ended personal
interview
OLS 5
CH-WX-2006
Air Quality PM2.5 Xuanwu Beijing China 2709.402 Asia 1999 67.55526 annual 74.62707 ug/m3 Journal 0.5 5 Contingent Valuation
open ended personal interview
OLS 41
CH-WX-
2006
Air Quality PM2.5 Chaoyang Beijing China 2709.402 Asia 1999 45.55155 annual 76.64865 ug/m3 Journal 0.5 5 Contingent
Valuation
open ended personal
interview
OLS 401
CH-WX-2006
Air Quality PM2.5 Fengtai Beijing China 2709.402 Asia 1999 34.35668 annual 74.45674 ug/m3 Journal 0.5 5 Contingent Valuation
open ended personal interview
OLS 239
CH-WX-
2006
Air Quality PM2.5 Shinjingshan Beijing China 2709.402 Asia 1999 64.46702 annual 73.44574 ug/m3 Journal 0.5 5 Contingent
Valuation
open ended personal
interview
OLS 70
CH-WX-2006
Air Quality PM2.5 Haidian Beijing China 2709.402 Asia 1999 52.11406 annual 61.65641 ug/m3 Journal 0.5 5 Contingent Valuation
open ended personal interview
OLS 371
CH-WX-
2006
Air Quality PM2.5 average Beijing China 2709.402 Asia 1999 55.2023 annual 121 ug/m3 Journal 0.5 5 Contingent
Valuation
open ended personal
interview
OLS 1371
STG-RJ-1994
Air Quality natural and human induced products
Santiago de Chile Santiago Metropolitan
Region
Chile 7330.611 South America
1994 76.37528 one time 100 ug/m3 Grey 0.5 1 Contingent Valuation
iterative biding personal interview
Probit/Tobit 455
DLH-MM-2003
Air Quality SPM Dehli Dehli India 1980.979 Asia 2002 10.86283 one time 235.9697 Igms/m3 Journal 0.004238 1 Revealed Preference
Hedonic Property personal interview
OLS 1250
DLH-MM-
2003
Air Quality SPM Kolkata West Bengal India 1980.979 Asia 2002 5.950794 one time 222.2515 Igms/m3 Journal 0.004499 1 Revealed
Preference
Hedonic Property personal
interview
OLS 1250
DLH-MM-2003
Air Quality SPM Dehli Dehli India 1980.979 Asia 2002 1701.632 one time 235.9697 Igms/m3 Journal 0.152434 1 Revealed Preference
Hedonic Property personal interview
OLS 1250
DLH-MM-
2003
Air Quality SPM Kolkata West Bengal India 1980.979 Asia 2002 722.3942 one time 222.2515 Igms/m3 Journal 0.100119 1 Revealed
Preference
Hedonic Property personal
interview
OLS 1250
STG-RJ-1998
Air Quality PM10 Santiago de Chile Santiago Metropolitan
Region
Chile 5974.219 South America
1991 2648.334 one time 100 ug/m3 Grey 0.5 1 Revealed Preference
Hedonic Property Secondary data Box Cox Transformation Method
992
SYR-AA-
2005
Built
Environment
TSP Damascus Damascus
Governorate
Syria 3485.661 Asia 2000 148.9373 one time 261.5 ug/m3 Journal 0.003824 1 Revealed
Preference
Hedonic Property personal
interview
log log 421
JKT-YA-
2006
Clean air SO2 Jakarta Jakarta Indonesia 3412.508 Asia 1997 311.4194 one time 22.88 ug/m3 Journal 0.043706 1 Revealed
Preference
Hedonic Property Secondary data Box Cox Transformation
Method
470
JKT-YA-
2006
Clean air THC Jakarta Jakarta Indonesia 3412.508 Asia 1997 937.6554 one time 319.84 ug/m3 Journal 0.003127 1 Revealed
Preference
Hedonic Property Secondary data Box Cox Transformation
Method
470
28
BGT-CF-
2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South
America
2005 0.669098 monthly 64.6 ug/m3 Journal 0.01548 300 Revealed
Preference
Hedonic Property Secondary data Heteroskedastic frontier 6544
BGT-CF-2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South America
2005 1.261727 monthly 56.3 ug/m3 Journal 0.017762 300 Revealed Preference
Hedonic Property Secondary data Heteroskedastic frontier 6544
BGT-CF-
2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South
America
2005 2.580805 monthly 50.2 ug/m3 Journal 0.01992 300 Revealed
Preference
Hedonic Property Secondary data Heteroskedastic frontier 6544
BGT-CF-2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South America
2005 6.308636 monthly 43 ug/m3 Journal 0.023256 300 Revealed Preference
Hedonic Property Secondary data Heteroskedastic frontier 6544
BGT-CF-
2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South
America
2005 0.841151 monthly 64.6 ug/m3 Journal 0.01548 300 Revealed
Preference
Hedonic Property Secondary data OLS 6544
BGT-CF-2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South America
2005 1.586717 monthly 56.3 ug/m3 Journal 0.017762 300 Revealed Preference
Hedonic Property Secondary data OLS 6544
BGT-CF-
2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South
America
2005 3.211669 monthly 50.2 ug/m3 Journal 0.01992 300 Revealed
Preference
Hedonic Property Secondary data OLS 6544
BGT-CF-2013
Air Quality PM10 Bogota Cundinamarca Colombia 6662.063 South America
2005 7.857119 monthly 43 ug/m3 Journal 0.023256 300 Revealed Preference
Hedonic Property Secondary data OLS 6544
JNN-WY-
2008
Air Quality PM10 Ji'nan Shangdong China 5249.988 Asia 2006 31.34966 yearly 117 ug/m3 Journal 0.15 25 Cntingent
Valuation
open ended scenario personal
interview
Probit model 1318.5
TPE-LA-1996
Air Quality TSP Taipei Taipei Taiwan 15529.89 Asia 1992 47.65232 monthly 226.74 ug/m3 Book 0.5 12 Stated Preference dichotomous choice experiment personal interview
Standard Statiscal Procedures
366
TPE-LA-
1996
Air Quality TSP Taipei Taipei Taiwan 15529.89 Asia 1992 44.52962 monthly 226.74 ug/m3 Book 0.5 12 Stated Preference iterative biding personal
interview
Standard Statiscal
Procedures
379
TPE-LA-1996
Air Quality TSP Taipei Taipei Taiwan 15529.89 Asia 1992 25.16892 monthly 226.74 ug/m3 Book 0.5 12 Stated Preference Payment Card mail Standard Statiscal Procedures
267
TPE-LA-
1996
Air Quality TSP Taipei Taipei Taiwan 15529.89 Asia 1992 30.10277 monthly 226.74 ug/m3 Book 0.5 12 Stated Preference Payment Card personal
interview
Standard Statiscal
Procedures
379
TPE-LA-
1996
Air Quality TSP Taipei Taipei Taiwan 15529.89 Asia 1992 206.1603 monthly 226.74 ug/m3 Book 0.5 12 Stated Preference dichotomous choice experiment Mail Logit Model 267
TPE-LA-
1996
Air Quality TSP Taipei Taipei Taiwan 15529.89 Asia 1992 123.7212 monthly 226.74 ug/m3 Book 0.5 12 Stated Preference dichotomous choice experiment personal
interview
Logit Model 366
HYD-MM-
2006
Air Quality RSOM, NOx, SO2 Hyderabad/
Secunderabad
Andhra
Pradesh
India 2249.864 Asia 2004 353.2462 annual 122 ug/m3 Grey 0.180328 25 Stated Preference Hedonic Property personal
interview
Box Cox Transformation
Method
1250
HYD-MM-
2006
Air Quality RSOM, NOx, SO2 Hyderabad/
Secunderabad
Andhra
Pradesh
India 2249.864 Asia 2004 17.32241 annual 122 ug/m3 Grey 0.008197 25 Stated Preference Hedonic Property personal
interview
Box Cox Transformation
Method
1250
CAI-AA-
2004
Air Quality PM10 Cairo Cairo Egypt 3403.13 Africa 1995 17.53105 monthly 135 ug/m3 Grey 0.5 12 Stated Preference iterative biding personal
interview
Logit Model 645
CAI-AA-
2004
Air Quality PM2.5 Rabat-Sale Rabat-Sale Morocco 2273.665 Africa 1995 27.23724 monthly 20.13 ug/m3 Grey 0.5 12 Stated Preference iterative biding personal
interview
Logit Model 382
POL-DD-
2005
Air Quality PM10 Poland Poland Poland 11486.02 Europe 2000 406.6618 one time 80 ug/m3 Journal 0.5 1 Stated Preference dichotomous choice experiment
(referendum)
personal
interview
Logit Model 528
POL-DD-
2005
Air Quality PM10 Poland Poland Poland 11486.02 Europe 2000 328.438 one time 80 ug/m3 Journal 0.25 1 Stated Preference dichotomous choice experiment
(referendum)
personal
interview
Logit Model 528
SOF-WH-
2000
Air Quality PM10 Sofia Sofia Bulgaria 6424.375 Europe 1995 20.36255 monthly 68 ug/m3 Grey 0.25 12 Stated Preference Payment Card Standard personal
interview
ordered probit 271
SOF-WH-
2000
Air Quality PM10 Sofia Sofia Bulgaria 6424.375 Europe 1995 91.51835 monthly 68 ug/m3 Grey 0.25 12 Stated Preference referendum personal
interview
Probit 243