Determinants of rural households’ willingness to pay for improved potable water supply in Central Rift Valley Ethiopia: contingent valuation method approach

Getinet, Shimeles; Mehare, Abule; Tazeze, Aemro Article Determinants of rural households’ willingness to pay for improved potable water supply in Central Rift Valley Ethiopia: contingent valuation method approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Getinet, Shimeles; Mehare, Abule; Tazeze, Aemro (2024) : Determinants of rural households’ willingness to pay for improved potable water supply in Central Rift Valley Ethiopia: contingent valuation method approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2024.2388233 This Version is available at: https://hdl.handle.net/10419/321567 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Determinants of rural households’ willingness to pay for improved potable water supply in Central Rift Valley Ethiopia: contingent valuation method approach Shimeles Getinet, Abule Mehare & Aemro Tazeze To cite this article: Shimeles Getinet, Abule Mehare & Aemro Tazeze (2024) Determinants of rural households’ willingness to pay for improved potable water supply in Central Rift Valley Ethiopia: contingent valuation method approach, Cogent Economics & Finance, 12:1, 2388233, DOI: 10.1080/23322039.2024.2388233 To link to this article: https://doi.org/10.1080/23322039.2024.2388233 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 14 Aug 2024. Submit your article to this journal Article views: 1059 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Determinants of rural households’willingness to pay for improved potable water supply in Central Rift Valley Ethiopia: contingent valuation method approach Shimeles Getinet a , Abule Mehare b and Aemro Tazeze c a College of Business and Economics, Haramaya University, Dire Dawa, Ethiopia; b Partnership and Communication, Ethiopian Economic Association, Addis Ababa, Ethiopia; c Department of Agricultural Economics, Bahar Dar University, Bahir Dar, Ethiopia ABSTRACT Access to improved water is a global issue aligned with sustainable economic development. Ethiopia plans to enhance access to safe water through low-cost tech and community mobilization. However, finance is crucial for rural water construction and rehab, and the price mechanisms through users’contributions can improve the cost recovery of rural water supply. Hence, the objective of this study is to investigate rural households’willingness to pay (WTP) for improved potable water supply using the contingent valuation method (CVM). Data collected from a randomly selected 272 sample households were analyzed using descriptive and econometrics analysis. The seemingly unrelated bivariate probit (SUBP) econometric model was used to calculate the mean WTP and identify the determinant factors. The results show that 70.96% of the households were willing to pay the initial bid. The results show that sex, annual farm income, off-farm income, the average time it takes to fetch water, use of water treatment and monthly water expense have a positive and significant effect. Yet, household size, perceptions of the quality and reliability of the existing water supply, and bid values have a negative and significant effect. The mean value for improved potable water supply was 1.80 ETB per 20 liters of Jerrican 1 . Rural households in the study area are willing to contribute up to 7.4% of their annual income. To ensure the financial sustainability and cost recovery of rural water supply, it may therefore be possible to intervene and adopt a new water price system. IMPACT STATEMENT The objective of this study was to estimate rural households’willingness to pay improved potable water supply using contingent valuation method. The study reveals the critical need for improved water supply in rural areas, highlighting the significant demand for safe and reliable water supply. It emphasizes the importance of community involvement in water management and provides a platform for collaborative solutions to address water supply challenges. This research provides valuable insights into the willingness of rural households to invest in improved potable water supply, indicating the potential for cost-recovery initiatives and community-driven solutions. The outcome of this study provides relevant information for making sound and wellinformed decisions and is essential for developing an optimal pricing strategy which helps to ensure financial sustainability of the rural water supply. It can serve as a baseline data to undertake an appropriate cost-benefit analysis and provide empirical evidence for further researcher on related topics. ARTICLE HISTORY Received 23 October 2023 Revised 9 July 2024 Accepted 31 July 2024 KEYWORDS Contingent valuation; Ethiopia; seemingly unrelated bivariate probit; water supply; willingness to pay SUBJECTS Agriculture & Environmental Sciences; Environmental Sciences; Environment & Economics; Environmental Economics; Environment & Society; Economics; Finance 1. Introduction Water is one of the most fundamental human necessities, and because it is necessary for practically all socioeconomic activity, it is also seen as a crucial element for permitting sustainable economic development (WWAP (UNESCO World Water Assessment Program), 2019). Since it is one of the CONTACT Shimeles Getinet [email protected] College of Business and Economics, Haramaya University, Dire Dawa, Ethiopia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2388233 https://doi.org/10.1080/23322039.2024.2388233 sustainable development goals (SDGs) of the 2030 Agenda, access to improved water continues to be a problem on a worldwide scale (Evan & Christian, 2018). Even though billions of people have had access to basic drinking water over the past 20 years, there is still insufficient improved water available. Almost 26% of the world’s population does not have access to properly managed drinking water as of 2020, with 83% of them residing in rural areas (WHO/UNICEF, 2021). Especially, most people without access to properly managed drinking water reside in rural areas in developing nations (Eludoyin & Olanrewaju, 2021). Lack of access to water has a range of detrimental repercussions, such as reduced output, low enrolment of girls and fatal infections associated with the use of water (Andres et al., 2018). Particularly, women and children suffer disproportionally in rural Sub-Saharan Africa since they are often in charge of collecting water. They travel long distances and spend hours each day collecting water, which creates a significant opportunity cost in terms of time that could have been used for work, education, or other income-generating activities that would have helped them diversify their sources of income and reduce their vulnerability to gender-based violence (Rolfe, 2019). Ethiopia has been referred to as the water tower of Africa because of the abundance of freshwater and groundwater resources that exist there. About 80% of the country’s potential groundwater serves the current water sources (FAO (Food & Agriculture Organization), 2018). Despite this potential, there has been a lack of access to safe drinking water in Ethiopia’s rural sector, where 77.8% of the population lives (Flerence, 2019). Access to basic drinking water is available to about 39.2% of the overall population, 29.9% of the rural population and 77.2% of the urban population. Also, in Ethiopia, half of the population now lives within 1.5 km of a source of clean water (WB (The World Bank), 2019). Ethiopia has improved water availability significantly, increasing it from 42.1% in 2015 to 49.6% in 2020 (WHO/ UNICEF, 2021). Even though access to improved water has significantly improved, some people are still getting their water from unimproved sources, thus there is still a need for more service. The main obstacles to accessing better drinking water in many parts of Ethiopia, especially in rural areas, included poor supply chains for obtaining spare parts, low levels of scheme functionality, insufficient financial allocation and inadequate sub-sector ability (Flerence, 2019). One of the biggest obstacles for the government in developing water supply projects, among others, is the lack of budgetary needs (Mahesh & Getu, 2018). Although the Ethiopian government frequently subsidies for water supply infrastructure, these funds are frequently insufficient to maintain the required infrastructure, guarantee water delivery and improve the quality of the water supply (MoWIE (Ministry of Water & Irrigation & Energy), 2019). Hence, it is necessary for all development agents involved to work together to mobilize funds and resources to build a water supply that is financially viable for everyone through the cost recovery supply. Studies have been carried out around the nation to evaluate households’willingness to pay (WTP) for improved water supply in both rural and urban areas using contingent valuation method (CVM), including ones by Bogale and Birhanu (2012), Lema and Beyene (2012), Kebede and Tariku (2016), Tenaw and Assfaw (2022), Eridadi et al. (2021) and Entele and Lee (2019). This suggests that CVM has been widely used in studies assessing household WTP for water supply and water quality enhancements. This method aids in forecasting household behavior in hypothetical improvement scenarios, consistent with economic theory. Again, the Double Bounded Dichotomous Choice (DBDC) elicitation format of the CVM reduces the respondent’s burden as helps to make decisions in a way that is similar to everyday market decisions individuals are facing (Freeman, 1993). The DBDC is also considered incentive-compatible and improves the statistical efficiency of the estimated mean and median WTP (Hanemann et al., 1991). Unlike the open-ended and bidding game elicitation format the result from DBDC is not affected by extreme values (outliers) and it results in a reliable response with a lower non-response rate. The DBDC reduces the visual complexity of choices and biased responses arising from uncertainty resulting from the use of the payment card elicitation method. Previous studies undertaken emphasize urban and semi-urban water supply and hence a limited number of studies (Bogale and Birhanu, 2012; Lema and Beyene 2012) undertaken in rural areas of the country results in a paucity of recent empirical evidence. Specifically, there is no empirical study that has estimated rural households’WTP for improved potable water supply in the study area. In addition, due to factors, such as availability of groundwater, alternative water sources and water supply-related factors, 2 S. GETINET ET AL. households located in different places are willing to pay different amount of money for improved water supply. Hence, the WTP differentials across different locations made it difficult to set a common or uniform fee or water tariff structure. The water resource management policy of the Ethiopian government also mandates that site-specific water tariff structures be determined based on local circumstances (MoWEI, 2001), necessitating location-specific estimations. Hence, a study is needed in the study area to estimate rural households’demand for improved potable water supply and identify the associated factors and provide up-to-date empirical evidence. Such estimation is crucial for formulating an effective pricing strategy and assisting with the project’s cost-benefit analysis, both of which are necessary to assure cost recovery and secure the financial sustainability of rural water supply. 2. Literature review 2.1. Economic valuation approaches The observable price and quantity-based demand estimating methodologies are not workable when commodities and services are not typically traded in the market. Many methods have been developed by economists to quantify the economic value or the intangible welfare impact of non-market goods and services. However, a number several have in common the use of market data and behavior to estimate the economic value of a related non-market welfare impact. There are two frequently used methods for valuing resources economically. These methods are revealed and stated preference approaches. The observed individual behavioral response to some market good treatments that are connected to the desired non-marketed benefit is what revealed preference techniques are based on. The main benefit of the revealed preference technique is its emphasis on actual options, which avoids potential issues with hypothetical replies. The revealed preference method includes the travel cost and hedonic pricing method (Robert, 2002). The travel cost method is more suitable for estimating recreational sites than estimating WTP for improved water services. Because households often use a variety of alternative sources to maintain a certain level of water quality and quantity. Measuring the time value of water transport from a particular location may not provide the complete picture (Francesco et al., 2004). The hedonic pricing strategy is based on what people want to acquire, not the actual items but rather the qualities or attributes they include (Blomquist & Worley, 1981). It is mostly employed in property pricing when a property’s price is decided by its unique features (Rosen, 1974). The stated preference approach is based on the technique of direct questioning how people would respond when asked directly about their preferences for goods and services in a hypothetical choice situation (Francesco et al., 2004). The stated preferred methods include choice experiment and CVM. The choice experiment method is rooted in the science of marketing and is an increasingly popular non-market pricing technique used in various economic sectors. A series of experimentally designed choice sets with different attributes are presented to each respondent, and the trade-offs that respondents make when choosing between a given choice set are quantified by using statistical techniques to estimate monetary value (Louviere et al., 2000). This method is useful because the goal is to choose the optimal combination of traits. This method has the major disadvantage of cognitive difficulty associated with multiple choices or complex ratings among packages with many attributes and levels (Adamowicz et al., 1998). When respondents are directly asked to indicate their WTP contingent on a carefully constructed hypothetical scenario and the details of the proposed intervention, the process is known as CVM (David et al., 2006). By using the CVM, it is possible to estimate the overall economic value of an environmental good or service, taking into account the quality of services that have not yet been received (Cerda et al., 2007). The CVM does not need to connect public products or services with an actual market transaction, in contrast to the travel cost and hedonic pricing methods, which demand an actual market transaction (Francesco et al., 2004). Some scholars have used the CVM to estimate the value of changes in water quality (Alberini & Cooper, 2000; Carson et al., 2001), and they highlighted that this approach has aided in predicting individual behavior in line with economic theory’s hypotheses. Most authors encourage COGENT ECONOMICS & FINANCE 3 using CVM because of its adaptability in determining the value of a variety of environmental products and services, and the National Oceanic and Atmospheric Administration (NOAA) panel has also recognized its importance. 2.2. Empirical literature review Several studies have been undertaken to estimate households’demand for improved potable water supply in developing countries including Ethiopia. A study by Dlamini et al. (2016), employed a CVM in semi-urban areas of Swaziland. Rahman et al. (2017) investigated households demand for improved water supply in semi-urban areas of Bangladesh using CVM. The CVM is also employed by Jianjun et al. (2016) to measure the demand for drinking water quality improvements in Songzi China. The results of these studies showed that households’socioeconomic and existing water supply characteristics plays a significant role in determining WTP. A study by Gossa (2019) and Tenaw and Assfaw (2022) employed the CVM to examine households’ WTP for improved urban water supply in Ethiopia. The findings of this study indicate that household income and perceptions of water availability and quality are significant factors. Kebede and Tariku (2016) estimated the demand for improved water supply and its determinants in Jigjiga town, Ethiopia. A similar study was conducted by Eridadi et al. (2021) in Sebeta town, Ethiopia, employing the CVM. The binary logistic model revealed that household socioeconomic and demographic characteristics are the main determinant factors. Entele and Lee (2019) examined the demand for fluoride-safe water service connections in the Rift Valley Region of Ethiopia. The result shows that water quality perceptions, dissatisfaction and number of children less than 5 years of age are found to have a significant influence. Bogale and Birhanu (2012) used a CVM to estimate the demand for improved water service provision and identify its determinants in Eastern Ethiopia. Lema and Beyene (2012) also employed a CVM and found that perception of the existing water supply, socioeconomic and condition of the existing water supply determines WTP. Generally, the above empirical evidence revealed that the CVM is the appropriate method for estimating households’demand for improved water supply. Although there are studies undertaken in rural areas of the country, studies focus on urban and semi-urban areas which indicates there are is a lack of recent empirical evidence. Furthermore, due to the water resource availability and water supply-related factors, households located in different places are willing to pay different amounts of money for improved water supply. Hence, a location-specific study is needed in the study area to estimate the demand for improved potable water supply and provide recent empirical evidence. The reviewed empirical literature also shows that the CVM is used with different elicitation formats. A study by Tenaw and Assfaw (2022), Kebede and Tariku (2016), and Eridadi et al. (2021), used a single bounded dichotomous choice format, while a study by Entele and Lee (2019) used an open-ended question format, where both of these elicitation formats have some methodological constraints. The former approach has limitations in terms of producing a statistically accurate estimate of mean WTP (Hanemann et al., 1991). Moreover, extreme values have an impact on the estimates found using the later approach. However, this study employed the DBDC elicitation format to overcome the limitations of the two methods. 3. Materials and methods 3.1. Description of the study area The study was conducted based on rural households in the Dugda districts of Oromia national regional state, Ethiopia (Figure 1). There are about 29,507 households in the district, of which, 21,202 households (71.85%) are living in rural areas and the remaining 8,305 households (28.15%) are urban dwellers (Dugda Woreda Office of Agriculture [DWOA], 2020). There are about 40 rural water supply sites that are distributed across the rural kebeles 2 in the district. There are four primary types of rural water supply systems in the district: hand-dug wells, dug wells, windmills and hand pump water supply systems. On 4 S. GETINET ET AL. average, the current rural water supply in the district serves only 47.17% of the total rural population (Dugda Woreda Water Resource & Energy Office [DWWREO]), 2020). 3.2. Sampling procedure and sample size A two-stage sampling procedure was employed to select sample respondents. In the first stage, rural kebeles in the district having a problem with access to water supply were identified and three kebeles were randomly selected. In the second stage, individual respondents from these kebeles were selected randomly. The probability proportional to sample size technique was used to determine the number of respondents from the three kebeles. For its simplicity once, the number of populations is known, the simplified formula given by Yamane (1967) was used to determine the required sample size of 274. Accordingly, the required sample size was calculated as follows: n¼N 1þNðe2Þ¼21, 202 1þ21, 202ð0:062Þ¼274 (1) where n¼sample size, N¼total number of rural households in the district and e¼level of precision. The precision level set at 6% is due to the homogeneity of the population in the study in terms of many attributes such as cultural, socioeconomic, institutional and livelihood strategies. 3.3. Elicitation format and survey design The DBDC format is the one that is recommended by the NOOA panel for its ease of use and resemblance to the day-to-day decision-making of individuals (Arrow et al., 1993). Due to its advantage in estimating a better confidence interval of the mean and median WTP, minimizing non-response and outliers, and controlling biases that arise during the CV study, the DBDC format followed by an openended was used in this study. The follow-up open-ended question is used to make a comparison in the mean WTP results obtained from the DBDC and open-ended elicitation format. As recommended by the NOAA guideline, a CV study should have a carefully designed survey questionnaire with a detailed description of the good under consideration (water supply improvement), hypothetical circumstances under which the good is made available to users, conditions for provision, description of a method of payment and questions that elicit WTP/WTA of the respondents for a proposed change and respondents socio-economic and other important issues (Arrow et al., 1993). Hence the CV survey questionnaire in this study is designed to have two main sections. The first section of the questionnaire includes general information which tries to gather information regarding the demographic and socio-economic condition Figure 1. The geographical location of the study area. Source: Own sketch from GIS. COGENT ECONOMICS & FINANCE 5 of the respondents. The second section includes questions regarding respondents’perceptions of the existing water supply, water use pattern, CV scenario and WTP for improved water supply. Before the survey data collection, enumerators’training and pre-tests were made. Enumerators were given training about the survey with special attention to the CV scenarios and elicitation method to avoid the potential biases that will arise from using CVM studies. A pre-test was made to get further information on the condition of the existing water supply, choose a payment vehicle, determine initial bid values and to further design a sound hypothetical market scenario. A pre-test of the questionnaire was undertaken with a total of 21 randomly selected households in the selected kebeles. It is feasible for the researcher to select a small number of respondents, which would facilitate obtaining detailed and reliable information on the current water supply conditions. The payment method of ‘cash payment on the spot’was chosen due to its familiarity, as it is widely used by the majority of households in the selected kebeles. The initial bid values were determined before the data collection using open-ended questions provided for a randomly selected household in the selected kebeles. Accordingly, seven households from each of the three kebeles were asked their maximum WTP for a 20-L jerrican of improved water supply. The most frequent values were taken as the initial bid to be used in the final survey. The most frequent value reflects what the majority of people would actually be willing to pay and a greater degree of public acceptability than taking an average value, which has been overly influenced by an outlier. Hence, an amount of 0.75ETB was selected as one of the initial bid values with its upper bid value of 1.5 ETB and lower bid value of 0.40 ETB. Again, the second bid amount of 2 ETB was selected as one of the initial bid values with its upper bid value of 4 ETB and lower bid value of 1 ETB. Finally, the amount of 1 ETB was selected as one of the third initial bid values with its upper bid value of 2 ETB and lower bid value of 0.50 ETB. The sets of the upper and lower bid values for each identified initial bid value were made by taking double and half of the initial bid value, respectively except for the lower limit of the initial bid 0.75ETB was rounded to 0.4ETB. These follow-up bid values (upper and lower) are taken as it is advisable to choose bid values that cover a relatively broad portion of the range for WTP (Creel & Loomis, 1997) and used by many previous CV studies. The use of a follow-up bid leads to a better confidence interval for the estimated mean WTP (Hanemann et al., 1991). Then, the predetermined set of bids was randomly and proportionally assigned to the respondents with the assumption to reduce the starting point bias that would arise in the CV survey. A follow-up question is asked based on the response to the initial bid offered. Hence, a second higher bid value is provided for the respondent if the answer to the first question is ‘yes’and a second lower bid value is presented if the answer to the first question is ‘no’. After this, the WTP survey question was ended by asking an open-ended follow-up question to state their maximum WTP for improved water supply. To estimate the mean WTP using a DBDC question starts by simply characterizing a household j’s unobserved true WTP as follows: WTP ij ¼liþeij (2) where WTP ij denotes households’jth WTP that is unobservable and i¼1, 2 represents the respondents’ response to the first and second questions (bids offered). l 1 and l 2 are the means of the first and second bid responses and e ij are unobservable random components. Proposing that lij ¼X0 ijbipermits both the means to be reliant upon the characteristics of the respondents (X’ ij ) and is assumed to depend on individual socioeconomic and demographic characteristics contained in the vector X i . In constructing the likelihood function from the DBDC question, there is a probability of observing the four possible responses from each of the two bids offered and the responses are (Yes-Yes, Yes-No, No-Yes and No-No). Accordingly, the four probabilities that household j answers to the initial bid offered,R 1 , and again to the follow-up bid offered (R 2 ), are given by: Pr yes,yes ðÞ ¼Pr WTP1j>R1,WTP2jR2  ¼Prðl1þe1j>R1,l2þe2jR2Þ Pr yes,no ðÞ ¼Pr WTP1jR1,WTP2j<R2  ¼Prðl1þe1jR1,l2þe2j<R2Þ 6 S. GETINET ET AL. Pr no,yes ðÞ ¼Pr WTP1j<R1,WTP2jR2  ¼Prðl1þe1j<R1,l2þe2jR2Þ Pr no,no ðÞ ¼Pr WTP1j<R1,WTP2j<R2  ¼Prðl1þe1j<R1,l2þe2j<R2Þ(3) From the above probabilities of the possible responses to the first and second dichotomous choice questions, households’jth contribution to the likelihood function can be derived following Haab and McConnel (2003). LiljR ðÞ ¼Pr l1þe1jR1,l2þe2j<R2  YN Pr l1þe1j>R1,l2þe2jR2  YY Pr l1þe1j<R1,l2þe2j<R2  NN Pr l1þe1j<R1,l2þe2jR2  NY (4) where YY takes the value 1 if the answer to both the initial and second follow-up bid is a Yes-Yes response, 0 otherwise; NY takes the value 1 if the answer to the first initial bid is No and answers Yes to the second followup question, 0 otherwise; YN takes the value 1 if the answer to the first initial bid is Yes and answers No to the second followup question, 0 otherwise; NN takes the value 1 if the answer to both the first initial bid and second follow-up question is a No- No response, and 0 otherwise. This type of responsive design is known as the bivariate discrete choice model. The random error term is assumed to be normally distributed with zero mean and a respective variance of r 11 and r 12 WTP 1j and WTP 2j , and a correlation coefficient of q.Then the WTP 1j and WTP 2j follow a bivariate normal distribution with mean l 1 and l 2 , variances r 1 and, r 2 and a correlation coefficient q. Given the design of the dichotomous choice responses to the bids offered, the normally distributed model is referred to as the bivariate probit model. The probabilities of all four possible response sequences are used to derive the likelihood function for the bivariate probit model. Pr l1þe1j>R1,l2þe2jR2  ¼Ue1e2− R1−l1 r1 ,− R2−l2 r2 ,q  Pr l1þe1jR1,l2þe2j<R2  ¼Ue1e2− R1−l1 r1 ,− R2−l2 r2 ,q  Pr l1þe1j<R1,l2þe2jR2  ¼Ue1e2− R1−l1 r1 ,− R2−l2 r2 ,q  Pr l1þe1j<R1,l2þe2j<R2  ¼Ue1e2− R1−l1 r1 ,− R2−l2 r2 ,q  (5) where Ue1e2(.) is the standardized bivariate normal cumulative distribution function with zero means, unit variances, and correlation coefficient q. Then the resulting likelihood function for a bivariate Probit model is given as follows: LjljR ðÞ ¼Ue1e2d1j R1−l1 r1  ,d2j R2−l2 r2  ,d1jd2jq ! (6) where Ue1e2(.) ¼the standardized bivariate normal cumulative distribution function. d1j¼R1 j−1and ¼R2 j−1 r1And r2¼standard deviation of errors q¼correlation coefficient The mean WTP from the bivariate Probit model will then be computed following running the regression of the dependent variable which is indicated by the yes/no indicator, on the independent variable consisting of the bid levels. The mean WTP from the bivariate Probit model will be computed depending on the normality assumption of WTP distribution (Haab & McConnell, 2003). COGENT ECONOMICS & FINANCE 7 Author contribution statement The authors confirm their contribution to the paper as follows: Shimeles Getinet: Study conception and design, data analysis and interpretation of results and draft manuscript preparation. Abule Mehare and Aemro Tazeze supervised the proposal development and findings of this study, critically revised the article for important intellectual content, and approved the final version for publication. All authors reviewed the results and approved the final version of the manuscript. Disclosure of interest The authors declare that they have no known competing interests that could have appeared to influence the result reported in this work. Funding The author disclosed receipt of the financial support for the research and this work was supported by the African Economic Research Consortium (AERC). About the authors Shimeles Getinet holds a Masters of Science degree in Agricultural and Applied Economics from Haramaya University, Ethiopia in collaboration with University of Pretoria, South Africa. Abule Mehare is a senior researcher and Director for partnership and communication at the Ethiopian Economics Association (EEA) in Ethiopia, boasting 15 years of teaching and research expertise. His research interest covers, welfare and human development, Gender and women economic empowerment, macroeconomics, price and market dynamics, and impact of microeconomic policies. His academic journey includes BA in Economics from Haramaya University, MSc in Agricultural and Applied Economics from University of Malawi and University of Pretoria, PhD in Agricultural and Resource Economics from Lilongwe University of Agriculture and Natural Resources (LUANAR), Bunda College, Malawi, under a regional DAAD_PhD program. Abule has a notable publication record, having authored over 32 journal articles, numerous proceedings, short communications, and policy briefs. Additionally, he has actively engaged in mainstream media briefings and dialogues. His research contributions extend to coordinating and successfully completing several national and international research projects. Aemro Tazeze was an Assistant Professor and Researcher at Haramaya University in Ethiopia and is currently working at Bahir Dar University. He holds a PhD in Agricultural Economics from Haramaya University. Aemro’s research interests include panel data analysis, impact evaluation, technology adoption, production economics, climate change, agricultural marketing, and their applications in development economics. His expertise and focus on these areas make him a valuable contributor to the field of agricultural economics and development. Data availability statement The data that support the findings of this study are available from the corresponding author, Shimeles Getinet upon reasonable request. References Adamowicz, W., Peter, B., Williams, M., & Louviere, J. (1998). 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