accounting for welfare consistency and institutional effects on

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

[email protected]

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.

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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

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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

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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.

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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.

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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.

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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

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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

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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.

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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

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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


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


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


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


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


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


BGT-CF-

2013


America


Preference


BGT-CF-2013


2005 6.308636 monthly 43 ug/m3 Journal 0.023256 300 Revealed Preference


BGT-CF-

2013


America


Preference

Hedonic Property Secondary data OLS 6544

BGT-CF-2013


2005 1.586717 monthly 56.3 ug/m3 Journal 0.017762 300 Revealed Preference


BGT-CF-

2013


America


Preference


BGT-CF-2013


2005 7.857119 monthly 43 ug/m3 Journal 0.023256 300 Revealed Preference


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

accounting for welfare consistency and institutional effects on

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